WorldWideScience

Sample records for learning resources dictionaries

  1. Greedy Deep Dictionary Learning

    OpenAIRE

    Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

    2016-01-01

    In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

  2. Robust Multimodal Dictionary Learning

    Science.gov (United States)

    Cao, Tian; Jojic, Vladimir; Modla, Shannon; Powell, Debbie; Czymmek, Kirk; Niethammer, Marc

    2014-01-01

    We propose a robust multimodal dictionary learning method for multimodal images. Joint dictionary learning for both modalities may be impaired by lack of correspondence between image modalities in training data, for example due to areas of low quality in one of the modalities. Dictionaries learned with such non-corresponding data will induce uncertainty about image representation. In this paper, we propose a probabilistic model that accounts for image areas that are poorly corresponding between the image modalities. We cast the problem of learning a dictionary in presence of problematic image patches as a likelihood maximization problem and solve it with a variant of the EM algorithm. Our algorithm iterates identification of poorly corresponding patches and re-finements of the dictionary. We tested our method on synthetic and real data. We show improvements in image prediction quality and alignment accuracy when using the method for multimodal image registration. PMID:24505674

  3. Weakly Supervised Dictionary Learning

    Science.gov (United States)

    You, Zeyu; Raich, Raviv; Fern, Xiaoli Z.; Kim, Jinsub

    2018-05-01

    We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis. Synthesis dictionary learning aims at jointly learning a dictionary and corresponding sparse coefficients to provide accurate data representation. This approach is useful for denoising and signal restoration, but may lead to sub-optimal classification performance. By contrast, analysis dictionary learning provides a transform that maps data to a sparse discriminative representation suitable for classification. We consider the problem of analysis dictionary learning for time-series data under a weak supervision setting in which signals are assigned with a global label instead of an instantaneous label signal. We propose a discriminative probabilistic model that incorporates both label information and sparsity constraints on the underlying latent instantaneous label signal using cardinality control. We present the expectation maximization (EM) procedure for maximum likelihood estimation (MLE) of the proposed model. To facilitate a computationally efficient E-step, we propose both a chain and a novel tree graph reformulation of the graphical model. The performance of the proposed model is demonstrated on both synthetic and real-world data.

  4. Dictionary learning in visual computing

    CERN Document Server

    Zhang, Qiang

    2015-01-01

    The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster c

  5. Learning multimodal dictionaries.

    Science.gov (United States)

    Monaci, Gianluca; Jost, Philippe; Vandergheynst, Pierre; Mailhé, Boris; Lesage, Sylvain; Gribonval, Rémi

    2007-09-01

    Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This paradigm can be also extremely useful in many signal processing and computer vision problems involving mutually related signals. The simultaneous processing of multimodal data can, in fact, reveal information that is otherwise hidden when considering the signals independently. However, in natural multimodal signals, the statistical dependencies between modalities are in general not obvious. Learning fundamental multimodal patterns could offer deep insight into the structure of such signals. In this paper, we present a novel model of multimodal signals based on their sparse decomposition over a dictionary of multimodal structures. An algorithm for iteratively learning multimodal generating functions that can be shifted at all positions in the signal is proposed, as well. The learning is defined in such a way that it can be accomplished by iteratively solving a generalized eigenvector problem, which makes the algorithm fast, flexible, and free of user-defined parameters. The proposed algorithm is applied to audiovisual sequences and it is able to discover underlying structures in the data. The detection of such audio-video patterns in audiovisual clips allows to effectively localize the sound source on the video in presence of substantial acoustic and visual distractors, outperforming state-of-the-art audiovisual localization algorithms.

  6. Tensor Dictionary Learning for Positive Definite Matrices.

    Science.gov (United States)

    Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikolaos

    2015-11-01

    Sparse models have proven to be extremely successful in image processing and computer vision. However, a majority of the effort has been focused on sparse representation of vectors and low-rank models for general matrices. The success of sparse modeling, along with popularity of region covariances, has inspired the development of sparse coding approaches for these positive definite descriptors. While in earlier work, the dictionary was formed from all, or a random subset of, the training signals, it is clearly advantageous to learn a concise dictionary from the entire training set. In this paper, we propose a novel approach for dictionary learning over positive definite matrices. The dictionary is learned by alternating minimization between sparse coding and dictionary update stages, and different atom update methods are described. A discriminative version of the dictionary learning approach is also proposed, which simultaneously learns dictionaries for different classes in classification or clustering. Experimental results demonstrate the advantage of learning dictionaries from data both from reconstruction and classification viewpoints. Finally, a software library is presented comprising C++ binaries for all the positive definite sparse coding and dictionary learning approaches presented here.

  7. Alternatively Constrained Dictionary Learning For Image Superresolution.

    Science.gov (United States)

    Lu, Xiaoqiang; Yuan, Yuan; Yan, Pingkun

    2014-03-01

    Dictionaries are crucial in sparse coding-based algorithm for image superresolution. Sparse coding is a typical unsupervised learning method to study the relationship between the patches of high-and low-resolution images. However, most of the sparse coding methods for image superresolution fail to simultaneously consider the geometrical structure of the dictionary and the corresponding coefficients, which may result in noticeable superresolution reconstruction artifacts. In other words, when a low-resolution image and its corresponding high-resolution image are represented in their feature spaces, the two sets of dictionaries and the obtained coefficients have intrinsic links, which has not yet been well studied. Motivated by the development on nonlocal self-similarity and manifold learning, a novel sparse coding method is reported to preserve the geometrical structure of the dictionary and the sparse coefficients of the data. Moreover, the proposed method can preserve the incoherence of dictionary entries and provide the sparse coefficients and learned dictionary from a new perspective, which have both reconstruction and discrimination properties to enhance the learning performance. Furthermore, to utilize the model of the proposed method more effectively for single-image superresolution, this paper also proposes a novel dictionary-pair learning method, which is named as two-stage dictionary training. Extensive experiments are carried out on a large set of images comparing with other popular algorithms for the same purpose, and the results clearly demonstrate the effectiveness of the proposed sparse representation model and the corresponding dictionary learning algorithm.

  8. Dictionary Usage in English Language Learning

    OpenAIRE

    Rohmatillah, Rohmatillah

    2016-01-01

    This article examined about the important of using dictionary in English language learning. We cannot deny in learning a foreign language, we need to consult a dictionary. It is supported by Laufer in Koca believes that when word looks familiar but the sentence in which it is found or its wider context makes no sense at all, the learner should be encouraged to consult a dictionary. Sometimes the learners are reluctant to find out the other meaning of word from dictionary, as a result the mea...

  9. A Weighted Block Dictionary Learning Algorithm for Classification

    OpenAIRE

    Shi, Zhongrong

    2016-01-01

    Discriminative dictionary learning, playing a critical role in sparse representation based classification, has led to state-of-the-art classification results. Among the existing discriminative dictionary learning methods, two different approaches, shared dictionary and class-specific dictionary, which associate each dictionary atom to all classes or a single class, have been studied. The shared dictionary is a compact method but with lack of discriminative information; the class-specific dict...

  10. Learning Dictionaries of Discriminative Image Patches

    DEFF Research Database (Denmark)

    Dahl, Anders Lindbjerg; Larsen, Rasmus

    2011-01-01

    using dictionaries of image patches with associated label data. The approach is based on ideas from sparse generative image models and texton based texture modeling. The intensity and label dictionaries are learned from training images with associated label information of (a subset) of the pixels based...... on a modified vector quantization approach. For new images the intensity dictionary is used to encode the image data and the label dictionary is used to build a segmentation of the image. We demonstrate the algorithm on composite and real texture images and show how successful training is possible even...

  11. Dictionary Networking in an LSP Learning Context

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2007-01-01

    text production, but discusses an individual dictionary for a particular function. It is shown that in a general context of learning accounting and its relevant LSP with a view to writing or translating financial reporting texts, the modern theory of dictionary functions provides a good theoretical...... and usage of a subject-field, particularly when they have to read, write or translate domain-specific texts. The modern theory of dictionary functions presented in Bergenholtz and Tarp (2002) opens up exciting new possibilities for theoretical and practical lexicography and encourages lexicographers......-lexicographic environment, i.e. what happens outside the dictionary when users write or translate texts, and relate these findings to the lexicographic environment represented by the theoretical basis and the dictionary itself. Nielsen (2006) gives a preliminary discussion of monolingual accounting dictionaries for EFL...

  12. Discriminative Bayesian Dictionary Learning for Classification.

    Science.gov (United States)

    Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

    2016-12-01

    We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

  13. Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization.

    Science.gov (United States)

    Gao, Shenghua; Tsang, Ivor Wai-Hung; Ma, Yi

    2014-02-01

    This paper targets fine-grained image categorization by learning a category-specific dictionary for each category and a shared dictionary for all the categories. Such category-specific dictionaries encode subtle visual differences among different categories, while the shared dictionary encodes common visual patterns among all the categories. To this end, we impose incoherence constraints among the different dictionaries in the objective of feature coding. In addition, to make the learnt dictionary stable, we also impose the constraint that each dictionary should be self-incoherent. Our proposed dictionary learning formulation not only applies to fine-grained classification, but also improves conventional basic-level object categorization and other tasks such as event recognition. Experimental results on five data sets show that our method can outperform the state-of-the-art fine-grained image categorization frameworks as well as sparse coding based dictionary learning frameworks. All these results demonstrate the effectiveness of our method.

  14. Seismic Signal Compression Using Nonparametric Bayesian Dictionary Learning via Clustering

    Directory of Open Access Journals (Sweden)

    Xin Tian

    2017-06-01

    Full Text Available We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

  15. Pronunciation dictionary development in resource-scarce environments

    CSIR Research Space (South Africa)

    Davel, M

    2009-09-01

    Full Text Available The deployment of speech technology systems in the developing world is often hampered by the lack of appropriate linguistic resources. A suitable pronunciation dictionary is one such resource that can be difficult to obtain for lesser...

  16. Hyperspectral Image Classification Using Discriminative Dictionary Learning

    International Nuclear Information System (INIS)

    Zongze, Y; Hao, S; Kefeng, J; Huanxin, Z

    2014-01-01

    The hyperspectral image (HSI) processing community has witnessed a surge of papers focusing on the utilization of sparse prior for effective HSI classification. In sparse representation based HSI classification, there are two phases: sparse coding with an over-complete dictionary and classification. In this paper, we first apply a novel fisher discriminative dictionary learning method, which capture the relative difference in different classes. The competitive selection strategy ensures that atoms in the resulting over-complete dictionary are the most discriminative. Secondly, motivated by the assumption that spatially adjacent samples are statistically related and even belong to the same materials (same class), we propose a majority voting scheme incorporating contextual information to predict the category label. Experiment results show that the proposed method can effectively strengthen relative discrimination of the constructed dictionary, and incorporating with the majority voting scheme achieve generally an improved prediction performance

  17. DOLPHIn—Dictionary Learning for Phase Retrieval

    Science.gov (United States)

    Tillmann, Andreas M.; Eldar, Yonina C.; Mairal, Julien

    2016-12-01

    We propose a new algorithm to learn a dictionary for reconstructing and sparsely encoding signals from measurements without phase. Specifically, we consider the task of estimating a two-dimensional image from squared-magnitude measurements of a complex-valued linear transformation of the original image. Several recent phase retrieval algorithms exploit underlying sparsity of the unknown signal in order to improve recovery performance. In this work, we consider such a sparse signal prior in the context of phase retrieval, when the sparsifying dictionary is not known in advance. Our algorithm jointly reconstructs the unknown signal - possibly corrupted by noise - and learns a dictionary such that each patch of the estimated image can be sparsely represented. Numerical experiments demonstrate that our approach can obtain significantly better reconstructions for phase retrieval problems with noise than methods that cannot exploit such "hidden" sparsity. Moreover, on the theoretical side, we provide a convergence result for our method.

  18. Coupled dictionary learning for joint MR image restoration and segmentation

    Science.gov (United States)

    Yang, Xuesong; Fan, Yong

    2018-03-01

    To achieve better segmentation of MR images, image restoration is typically used as a preprocessing step, especially for low-quality MR images. Recent studies have demonstrated that dictionary learning methods could achieve promising performance for both image restoration and image segmentation. These methods typically learn paired dictionaries of image patches from different sources and use a common sparse representation to characterize paired image patches, such as low-quality image patches and their corresponding high quality counterparts for the image restoration, and image patches and their corresponding segmentation labels for the image segmentation. Since learning these dictionaries jointly in a unified framework may improve the image restoration and segmentation simultaneously, we propose a coupled dictionary learning method to concurrently learn dictionaries for joint image restoration and image segmentation based on sparse representations in a multi-atlas image segmentation framework. Particularly, three dictionaries, including a dictionary of low quality image patches, a dictionary of high quality image patches, and a dictionary of segmentation label patches, are learned in a unified framework so that the learned dictionaries of image restoration and segmentation can benefit each other. Our method has been evaluated for segmenting the hippocampus in MR T1 images collected with scanners of different magnetic field strengths. The experimental results have demonstrated that our method achieved better image restoration and segmentation performance than state of the art dictionary learning and sparse representation based image restoration and image segmentation methods.

  19. Using Dictionary Pair Learning for Seizure Detection.

    Science.gov (United States)

    Ma, Xin; Yu, Nana; Zhou, Weidong

    2018-02-13

    Automatic seizure detection is extremely important in the monitoring and diagnosis of epilepsy. The paper presents a novel method based on dictionary pair learning (DPL) for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. First, for the EEG data, wavelet filtering and differential filtering are applied, and the kernel function is performed to make the signal linearly separable. In DPL, the synthesis dictionary and analysis dictionary are learned jointly from original training samples with alternating minimization method, and sparse coefficients are obtained by using of linear projection instead of costly [Formula: see text]-norm or [Formula: see text]-norm optimization. At last, the reconstructed residuals associated with seizure and nonseizure sub-dictionary pairs are calculated as the decision values, and the postprocessing is performed for improving the recognition rate and reducing the false detection rate of the system. A total of 530[Formula: see text]h from 20 patients with 81 seizures were used to evaluate the system. Our proposed method has achieved an average segment-based sensitivity of 93.39%, specificity of 98.51%, and event-based sensitivity of 96.36% with false detection rate of 0.236/h.

  20. Fast Low-Rank Shared Dictionary Learning for Image Classification.

    Science.gov (United States)

    Tiep Huu Vu; Monga, Vishal

    2017-11-01

    Despite the fact that different objects possess distinct class-specific features, they also usually share common patterns. This observation has been exploited partially in a recently proposed dictionary learning framework by separating the particularity and the commonality (COPAR). Inspired by this, we propose a novel method to explicitly and simultaneously learn a set of common patterns as well as class-specific features for classification with more intuitive constraints. Our dictionary learning framework is hence characterized by both a shared dictionary and particular (class-specific) dictionaries. For the shared dictionary, we enforce a low-rank constraint, i.e., claim that its spanning subspace should have low dimension and the coefficients corresponding to this dictionary should be similar. For the particular dictionaries, we impose on them the well-known constraints stated in the Fisher discrimination dictionary learning (FDDL). Furthermore, we develop new fast and accurate algorithms to solve the subproblems in the learning step, accelerating its convergence. The said algorithms could also be applied to FDDL and its extensions. The efficiencies of these algorithms are theoretically and experimentally verified by comparing their complexities and running time with those of other well-known dictionary learning methods. Experimental results on widely used image data sets establish the advantages of our method over the state-of-the-art dictionary learning methods.

  1. TWRS privatization support project waste characterization resource dictionary

    International Nuclear Information System (INIS)

    Patello, G.K.; Wiemers, K.D.

    1996-09-01

    A single estimate of waste characteristics for each underground storage tanks at the Hanford Site is not available. The information that is available was developed for specific programmatic objectives and varies in format and level of descriptive detail, depending on the intended application. This dictionary reflects an attempt to define what waste characterization information is available. It shows the relationship between the identified resource and the original data source and the inter-relationships among the resources; it also provides a brief description of each resource. Developed as a general dictionary for waste characterization information, this document is intended to make the user aware of potenially useful resources

  2. Polarimetric SAR image classification based on discriminative dictionary learning model

    Science.gov (United States)

    Sang, Cheng Wei; Sun, Hong

    2018-03-01

    Polarimetric SAR (PolSAR) image classification is one of the important applications of PolSAR remote sensing. It is a difficult high-dimension nonlinear mapping problem, the sparse representations based on learning overcomplete dictionary have shown great potential to solve such problem. The overcomplete dictionary plays an important role in PolSAR image classification, however for PolSAR image complex scenes, features shared by different classes will weaken the discrimination of learned dictionary, so as to degrade classification performance. In this paper, we propose a novel overcomplete dictionary learning model to enhance the discrimination of dictionary. The learned overcomplete dictionary by the proposed model is more discriminative and very suitable for PolSAR classification.

  3. Unsupervised behaviour-specific dictionary learning for abnormal event detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Liu, Weifeng; Olsen, Søren Ingvor

    2015-01-01

    the training data is only a small proportion of the surveillance data. Therefore, we propose behavior-specific dictionaries (BSD) through unsupervised learning, pursuing atoms from the same type of behavior to represent one behavior dictionary. To further improve the dictionary by introducing information from...... potential infrequent normal patterns, we refine the dictionary by searching ‘missed atoms’ that have compact coefficients. Experimental results show that our BSD algorithm outperforms state-of-the-art dictionaries in abnormal event detection on the public UCSD dataset. Moreover, BSD has less false alarms...

  4. The Role of Dictionaries in Language Learning.

    Science.gov (United States)

    White, Philip A.

    1997-01-01

    Examines assumptions about dictionaries, especially the bilingual dictionary, and suggests ways of integrating the monolingual dictionary into the second-language instructional process. Findings indicate that the monolingual dictionary can coexist with bilingual dictionaries within a foreign-language course if the latter are appropriately used as…

  5. An augmented Lagrangian multi-scale dictionary learning algorithm

    Directory of Open Access Journals (Sweden)

    Ye Meng

    2011-01-01

    Full Text Available Abstract Learning overcomplete dictionaries for sparse signal representation has become a hot topic fascinated by many researchers in the recent years, while most of the existing approaches have a serious problem that they always lead to local minima. In this article, we present a novel augmented Lagrangian multi-scale dictionary learning algorithm (ALM-DL, which is achieved by first recasting the constrained dictionary learning problem into an AL scheme, and then updating the dictionary after each inner iteration of the scheme during which majorization-minimization technique is employed for solving the inner subproblem. Refining the dictionary from low scale to high makes the proposed method less dependent on the initial dictionary hence avoiding local optima. Numerical tests for synthetic data and denoising applications on real images demonstrate the superior performance of the proposed approach.

  6. Weighted Discriminative Dictionary Learning based on Low-rank Representation

    International Nuclear Information System (INIS)

    Chang, Heyou; Zheng, Hao

    2017-01-01

    Low-rank representation has been widely used in the field of pattern classification, especially when both training and testing images are corrupted with large noise. Dictionary plays an important role in low-rank representation. With respect to the semantic dictionary, the optimal representation matrix should be block-diagonal. However, traditional low-rank representation based dictionary learning methods cannot effectively exploit the discriminative information between data and dictionary. To address this problem, this paper proposed weighted discriminative dictionary learning based on low-rank representation, where a weighted representation regularization term is constructed. The regularization associates label information of both training samples and dictionary atoms, and encourages to generate a discriminative representation with class-wise block-diagonal structure, which can further improve the classification performance where both training and testing images are corrupted with large noise. Experimental results demonstrate advantages of the proposed method over the state-of-the-art methods. (paper)

  7. Bayesian nonparametric dictionary learning for compressed sensing MRI.

    Science.gov (United States)

    Huang, Yue; Paisley, John; Lin, Qin; Ding, Xinghao; Fu, Xueyang; Zhang, Xiao-Ping

    2014-12-01

    We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRIs) from highly undersampled k -space data. We perform dictionary learning as part of the image reconstruction process. To this end, we use the beta process as a nonparametric dictionary learning prior for representing an image patch as a sparse combination of dictionary elements. The size of the dictionary and patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables. Dictionary learning is performed directly on the compressed image, and so is tailored to the MRI being considered. In addition, we investigate a total variation penalty term in combination with the dictionary learning model, and show how the denoising property of dictionary learning removes dependence on regularization parameters in the noisy setting. We derive a stochastic optimization algorithm based on Markov chain Monte Carlo for the Bayesian model, and use the alternating direction method of multipliers for efficiently performing total variation minimization. We present empirical results on several MRI, which show that the proposed regularization framework can improve reconstruction accuracy over other methods.

  8. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan

    2016-12-29

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  9. Multi-instance dictionary learning via multivariate performance measure optimization

    KAUST Repository

    Wang, Jim Jing-Yan; Tsang, Ivor Wai-Hung; Cui, Xuefeng; Lu, Zhiwu; Gao, Xin

    2016-01-01

    The multi-instance dictionary plays a critical role in multi-instance data representation. Meanwhile, different multi-instance learning applications are evaluated by specific multivariate performance measures. For example, multi-instance ranking reports the precision and recall. It is not difficult to see that to obtain different optimal performance measures, different dictionaries are needed. This observation motives us to learn performance-optimal dictionaries for this problem. In this paper, we propose a novel joint framework for learning the multi-instance dictionary and the classifier to optimize a given multivariate performance measure, such as the F1 score and precision at rank k. We propose to represent the bags as bag-level features via the bag-instance similarity, and learn a classifier in the bag-level feature space to optimize the given performance measure. We propose to minimize the upper bound of a multivariate loss corresponding to the performance measure, the complexity of the classifier, and the complexity of the dictionary, simultaneously, with regard to both the dictionary and the classifier parameters. In this way, the dictionary learning is regularized by the performance optimization, and a performance-optimal dictionary is obtained. We develop an iterative algorithm to solve this minimization problem efficiently using a cutting-plane algorithm and a coordinate descent method. Experiments on multi-instance benchmark data sets show its advantage over both traditional multi-instance learning and performance optimization methods.

  10. Weakly supervised visual dictionary learning by harnessing image attributes.

    Science.gov (United States)

    Gao, Yue; Ji, Rongrong; Liu, Wei; Dai, Qionghai; Hua, Gang

    2014-12-01

    Bag-of-features (BoFs) representation has been extensively applied to deal with various computer vision applications. To extract discriminative and descriptive BoF, one important step is to learn a good dictionary to minimize the quantization loss between local features and codewords. While most existing visual dictionary learning approaches are engaged with unsupervised feature quantization, the latest trend has turned to supervised learning by harnessing the semantic labels of images or regions. However, such labels are typically too expensive to acquire, which restricts the scalability of supervised dictionary learning approaches. In this paper, we propose to leverage image attributes to weakly supervise the dictionary learning procedure without requiring any actual labels. As a key contribution, our approach establishes a generative hidden Markov random field (HMRF), which models the quantized codewords as the observed states and the image attributes as the hidden states, respectively. Dictionary learning is then performed by supervised grouping the observed states, where the supervised information is stemmed from the hidden states of the HMRF. In such a way, the proposed dictionary learning approach incorporates the image attributes to learn a semantic-preserving BoF representation without any genuine supervision. Experiments in large-scale image retrieval and classification tasks corroborate that our approach significantly outperforms the state-of-the-art unsupervised dictionary learning approaches.

  11. Nonparametric, Coupled ,Bayesian ,Dictionary ,and Classifier Learning for Hyperspectral Classification.

    Science.gov (United States)

    Akhtar, Naveed; Mian, Ajmal

    2017-10-03

    We present a principled approach to learn a discriminative dictionary along a linear classifier for hyperspectral classification. Our approach places Gaussian Process priors over the dictionary to account for the relative smoothness of the natural spectra, whereas the classifier parameters are sampled from multivariate Gaussians. We employ two Beta-Bernoulli processes to jointly infer the dictionary and the classifier. These processes are coupled under the same sets of Bernoulli distributions. In our approach, these distributions signify the frequency of the dictionary atom usage in representing class-specific training spectra, which also makes the dictionary discriminative. Due to the coupling between the dictionary and the classifier, the popularity of the atoms for representing different classes gets encoded into the classifier. This helps in predicting the class labels of test spectra that are first represented over the dictionary by solving a simultaneous sparse optimization problem. The labels of the spectra are predicted by feeding the resulting representations to the classifier. Our approach exploits the nonparametric Bayesian framework to automatically infer the dictionary size--the key parameter in discriminative dictionary learning. Moreover, it also has the desirable property of adaptively learning the association between the dictionary atoms and the class labels by itself. We use Gibbs sampling to infer the posterior probability distributions over the dictionary and the classifier under the proposed model, for which, we derive analytical expressions. To establish the effectiveness of our approach, we test it on benchmark hyperspectral images. The classification performance is compared with the state-of-the-art dictionary learning-based classification methods.

  12. Active Discriminative Dictionary Learning for Weather Recognition

    Directory of Open Access Journals (Sweden)

    Caixia Zheng

    2016-01-01

    Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

  13. Scene recognition based on integrating active learning with dictionary learning

    Science.gov (United States)

    Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

    2018-04-01

    Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

  14. Personalized Age Progression with Bi-Level Aging Dictionary Learning.

    Science.gov (United States)

    Shu, Xiangbo; Tang, Jinhui; Li, Zechao; Lai, Hanjiang; Zhang, Liyan; Yan, Shuicheng

    2018-04-01

    Age progression is defined as aesthetically re-rendering the aging face at any future age for an individual face. In this work, we aim to automatically render aging faces in a personalized way. Basically, for each age group, we learn an aging dictionary to reveal its aging characteristics (e.g., wrinkles), where the dictionary bases corresponding to the same index yet from two neighboring aging dictionaries form a particular aging pattern cross these two age groups, and a linear combination of all these patterns expresses a particular personalized aging process. Moreover, two factors are taken into consideration in the dictionary learning process. First, beyond the aging dictionaries, each person may have extra personalized facial characteristics, e.g., mole, which are invariant in the aging process. Second, it is challenging or even impossible to collect faces of all age groups for a particular person, yet much easier and more practical to get face pairs from neighboring age groups. To this end, we propose a novel Bi-level Dictionary Learning based Personalized Age Progression (BDL-PAP) method. Here, bi-level dictionary learning is formulated to learn the aging dictionaries based on face pairs from neighboring age groups. Extensive experiments well demonstrate the advantages of the proposed BDL-PAP over other state-of-the-arts in term of personalized age progression, as well as the performance gain for cross-age face verification by synthesizing aging faces.

  15. Cross-View Action Recognition via Transferable Dictionary Learning.

    Science.gov (United States)

    Zheng, Jingjing; Jiang, Zhuolin; Chellappa, Rama

    2016-05-01

    Discriminative appearance features are effective for recognizing actions in a fixed view, but may not generalize well to a new view. In this paper, we present two effective approaches to learn dictionaries for robust action recognition across views. In the first approach, we learn a set of view-specific dictionaries where each dictionary corresponds to one camera view. These dictionaries are learned simultaneously from the sets of correspondence videos taken at different views with the aim of encouraging each video in the set to have the same sparse representation. In the second approach, we additionally learn a common dictionary shared by different views to model view-shared features. This approach represents the videos in each view using a view-specific dictionary and the common dictionary. More importantly, it encourages the set of videos taken from the different views of the same action to have the similar sparse representations. The learned common dictionary not only has the capability to represent actions from unseen views, but also makes our approach effective in a semi-supervised setting where no correspondence videos exist and only a few labeled videos exist in the target view. The extensive experiments using three public datasets demonstrate that the proposed approach outperforms recently developed approaches for cross-view action recognition.

  16. MR PROSTATE SEGMENTATION VIA DISTRIBUTED DISCRIMINATIVE DICTIONARY (DDD) LEARNING.

    Science.gov (United States)

    Guo, Yanrong; Zhan, Yiqiang; Gao, Yaozong; Jiang, Jianguo; Shen, Dinggang

    2013-01-01

    Segmenting prostate from MR images is important yet challenging. Due to non-Gaussian distribution of prostate appearances in MR images, the popular active appearance model (AAM) has its limited performance. Although the newly developed sparse dictionary learning method[1, 2] can model the image appearance in a non-parametric fashion, the learned dictionaries still lack the discriminative power between prostate and non-prostate tissues, which is critical for accurate prostate segmentation. In this paper, we propose to integrate deformable model with a novel learning scheme, namely the Distributed Discriminative Dictionary ( DDD ) learning, which can capture image appearance in a non-parametric and discriminative fashion. In particular, three strategies are designed to boost the tissue discriminative power of DDD. First , minimum Redundancy Maximum Relevance (mRMR) feature selection is performed to constrain the dictionary learning in a discriminative feature space. Second , linear discriminant analysis (LDA) is employed to assemble residuals from different dictionaries for optimal separation between prostate and non-prostate tissues. Third , instead of learning the global dictionaries, we learn a set of local dictionaries for the local regions (each with small appearance variations) along prostate boundary, thus achieving better tissue differentiation locally. In the application stage, DDDs will provide the appearance cues to robustly drive the deformable model onto the prostate boundary. Experiments on 50 MR prostate images show that our method can yield a Dice Ratio of 88% compared to the manual segmentations, and have 7% improvement over the conventional AAM.

  17. Comparing dictionary-induced vocabulary learning and inferencing ...

    African Journals Online (AJOL)

    This research examines dictionary-induced vocabulary learning and inferencing in the context of reading. One hundred and four intermediate English learners completed one of two word-focused tasks: reading comprehension and dictionary consultation, and reading comprehen-sion and inferencing. In addition to ...

  18. Image fusion via nonlocal sparse K-SVD dictionary learning.

    Science.gov (United States)

    Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang

    2016-03-01

    Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

  19. On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

    Science.gov (United States)

    Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

    2013-01-01

    Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

  20. Discriminative object tracking via sparse representation and online dictionary learning.

    Science.gov (United States)

    Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua

    2014-04-01

    We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

  1. Classification of Polarimetric SAR Data Using Dictionary Learning

    DEFF Research Database (Denmark)

    Vestergaard, Jacob Schack; Nielsen, Allan Aasbjerg; Dahl, Anders Lindbjerg

    2012-01-01

    This contribution deals with classification of multilook fully polarimetric synthetic aperture radar (SAR) data by learning a dictionary of crop types present in the Foulum test site. The Foulum test site contains a large number of agricultural fields, as well as lakes, forests, natural vegetation......, grasslands and urban areas, which make it ideally suited for evaluation of classification algorithms. Dictionary learning centers around building a collection of image patches typical for the classification problem at hand. This requires initial manual labeling of the classes present in the data and is thus...... a method for supervised classification. Sparse coding of these image patches aims to maintain a proficient number of typical patches and associated labels. Data is consecutively classified by a nearest neighbor search of the dictionary elements and labeled with probabilities of each class. Each dictionary...

  2. Using Different Types of Dictionaries for Improving EFL Reading Comprehension and Vocabulary Learning

    Science.gov (United States)

    Alharbi, Majed A.

    2016-01-01

    This study investigated the effects of monolingual book dictionaries, popup dictionaries, and type-in dictionaries on improving reading comprehension and vocabulary learning in an EFL program. An experimental design involving four groups and a post-test was chosen for the experiment: (1) pop-up dictionary (experimental group 1); (2) type-in…

  3. Supervised dictionary learning for inferring concurrent brain networks.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

    2015-10-01

    Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

  4. Concept dictionary creation and maintenance under resource constraints: lessons from the AMPATH Medical Record System.

    Science.gov (United States)

    Were, Martin C; Mamlin, Burke W; Tierney, William M; Wolfe, Ben; Biondich, Paul G

    2007-10-11

    The challenges of creating and maintaining concept dictionaries are compounded in resource-limited settings. Approaches to alleviate this burden need to be based on information derived in these settings. We created a concept dictionary and evaluated new concept proposals for an open source EMR in a resource-limited setting. Overall, 87% of the concepts in the initial dictionary were used. There were 5137 new concepts proposed, with 77% of these proposed only once. Further characterization of new concept proposals revealed that 41% were due to deficiency in the existing dictionary, and 19% were synonyms to existing concepts. 25% of the requests contained misspellings, 41% were complex terms, and 17% were ambiguous. Given the resource-intensive nature of dictionary creation and maintenance, there should be considerations for centralizing the concept dictionary service, using standards, prioritizing concept proposals, and redesigning the user-interface to reduce this burden in settings with limited resources.

  5. Convolutional Dictionary Learning: Acceleration and Convergence

    Science.gov (United States)

    Chun, Il Yong; Fessler, Jeffrey A.

    2018-04-01

    Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

  6. Regularized spherical polar fourier diffusion MRI with optimal dictionary learning.

    Science.gov (United States)

    Cheng, Jian; Jiang, Tianzi; Deriche, Rachid; Shen, Dinggang; Yap, Pew-Thian

    2013-01-01

    Compressed Sensing (CS) takes advantage of signal sparsity or compressibility and allows superb signal reconstruction from relatively few measurements. Based on CS theory, a suitable dictionary for sparse representation of the signal is required. In diffusion MRI (dMRI), CS methods proposed for reconstruction of diffusion-weighted signal and the Ensemble Average Propagator (EAP) utilize two kinds of Dictionary Learning (DL) methods: 1) Discrete Representation DL (DR-DL), and 2) Continuous Representation DL (CR-DL). DR-DL is susceptible to numerical inaccuracy owing to interpolation and regridding errors in a discretized q-space. In this paper, we propose a novel CR-DL approach, called Dictionary Learning - Spherical Polar Fourier Imaging (DL-SPFI) for effective compressed-sensing reconstruction of the q-space diffusion-weighted signal and the EAP. In DL-SPFI, a dictionary that sparsifies the signal is learned from the space of continuous Gaussian diffusion signals. The learned dictionary is then adaptively applied to different voxels using a weighted LASSO framework for robust signal reconstruction. Compared with the start-of-the-art CR-DL and DR-DL methods proposed by Merlet et al. and Bilgic et al., respectively, our work offers the following advantages. First, the learned dictionary is proved to be optimal for Gaussian diffusion signals. Second, to our knowledge, this is the first work to learn a voxel-adaptive dictionary. The importance of the adaptive dictionary in EAP reconstruction will be demonstrated theoretically and empirically. Third, optimization in DL-SPFI is only performed in a small subspace resided by the SPF coefficients, as opposed to the q-space approach utilized by Merlet et al. We experimentally evaluated DL-SPFI with respect to L1-norm regularized SPFI (L1-SPFI), which uses the original SPF basis, and the DR-DL method proposed by Bilgic et al. The experiment results on synthetic and real data indicate that the learned dictionary produces

  7. A dictionary learning approach for human sperm heads classification.

    Science.gov (United States)

    Shaker, Fariba; Monadjemi, S Amirhassan; Alirezaie, Javad; Naghsh-Nilchi, Ahmad Reza

    2017-12-01

    To diagnose infertility in men, semen analysis is conducted in which sperm morphology is one of the factors that are evaluated. Since manual assessment of sperm morphology is time-consuming and subjective, automatic classification methods are being developed. Automatic classification of sperm heads is a complicated task due to the intra-class differences and inter-class similarities of class objects. In this research, a Dictionary Learning (DL) technique is utilized to construct a dictionary of sperm head shapes. This dictionary is used to classify the sperm heads into four different classes. Square patches are extracted from the sperm head images. Columnized patches from each class of sperm are used to learn class-specific dictionaries. The patches from a test image are reconstructed using each class-specific dictionary and the overall reconstruction error for each class is used to select the best matching class. Average accuracy, precision, recall, and F-score are used to evaluate the classification method. The method is evaluated using two publicly available datasets of human sperm head shapes. The proposed DL based method achieved an average accuracy of 92.2% on the HuSHeM dataset, and an average recall of 62% on the SCIAN-MorphoSpermGS dataset. The results show a significant improvement compared to a previously published shape-feature-based method. We have achieved high-performance results. In addition, our proposed approach offers a more balanced classifier in which all four classes are recognized with high precision and recall. In this paper, we use a Dictionary Learning approach in classifying human sperm heads. It is shown that the Dictionary Learning method is far more effective in classifying human sperm heads than classifiers using shape-based features. Also, a dataset of human sperm head shapes is introduced to facilitate future research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

    Science.gov (United States)

    Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

    2017-04-01

    Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  9. Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Wei Zhou

    2017-01-01

    Full Text Available Recently, microaneurysm (MA detection has attracted a lot of attention in the medical image processing community. Since MAs can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL. The proposed method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art approaches and the experimental results validate the effectiveness of our algorithm.

  10. SDL: Saliency-Based Dictionary Learning Framework for Image Similarity.

    Science.gov (United States)

    Sarkar, Rituparna; Acton, Scott T

    2018-02-01

    In image classification, obtaining adequate data to learn a robust classifier has often proven to be difficult in several scenarios. Classification of histological tissue images for health care analysis is a notable application in this context due to the necessity of surgery, biopsy or autopsy. To adequately exploit limited training data in classification, we propose a saliency guided dictionary learning method and subsequently an image similarity technique for histo-pathological image classification. Salient object detection from images aids in the identification of discriminative image features. We leverage the saliency values for the local image regions to learn a dictionary and respective sparse codes for an image, such that the more salient features are reconstructed with smaller error. The dictionary learned from an image gives a compact representation of the image itself and is capable of representing images with similar content, with comparable sparse codes. We employ this idea to design a similarity measure between a pair of images, where local image features of one image, are encoded with the dictionary learned from the other and vice versa. To effectively utilize the learned dictionary, we take into account the contribution of each dictionary atom in the sparse codes to generate a global image representation for image comparison. The efficacy of the proposed method was evaluated using three tissue data sets that consist of mammalian kidney, lung and spleen tissue, breast cancer, and colon cancer tissue images. From the experiments, we observe that our methods outperform the state of the art with an increase of 14.2% in the average classification accuracy over all data sets.

  11. Classification of multiple sclerosis lesions using adaptive dictionary learning.

    Science.gov (United States)

    Deshpande, Hrishikesh; Maurel, Pierre; Barillot, Christian

    2015-12-01

    This paper presents a sparse representation and an adaptive dictionary learning based method for automated classification of multiple sclerosis (MS) lesions in magnetic resonance (MR) images. Manual delineation of MS lesions is a time-consuming task, requiring neuroradiology experts to analyze huge volume of MR data. This, in addition to the high intra- and inter-observer variability necessitates the requirement of automated MS lesion classification methods. Among many image representation models and classification methods that can be used for such purpose, we investigate the use of sparse modeling. In the recent years, sparse representation has evolved as a tool in modeling data using a few basis elements of an over-complete dictionary and has found applications in many image processing tasks including classification. We propose a supervised classification approach by learning dictionaries specific to the lesions and individual healthy brain tissues, which include white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The size of the dictionaries learned for each class plays a major role in data representation but it is an even more crucial element in the case of competitive classification. Our approach adapts the size of the dictionary for each class, depending on the complexity of the underlying data. The algorithm is validated using 52 multi-sequence MR images acquired from 13 MS patients. The results demonstrate the effectiveness of our approach in MS lesion classification. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. Customized Dictionary Learning for Subdatasets with Fine Granularity

    Directory of Open Access Journals (Sweden)

    Lei Ye

    2016-01-01

    Full Text Available Sparse models have a wide range of applications in machine learning and computer vision. Using a learned dictionary instead of an “off-the-shelf” one can dramatically improve performance on a particular dataset. However, learning a new one for each subdataset (subject with fine granularity may be unwarranted or impractical, due to restricted availability subdataset samples and tremendous numbers of subjects. To remedy this, we consider the dictionary customization problem, that is, specializing an existing global dictionary corresponding to the total dataset, with the aid of auxiliary samples obtained from the target subdataset. Inspired by observation and then deduced from theoretical analysis, a regularizer is employed penalizing the difference between the global and the customized dictionary. By minimizing the sum of reconstruction errors of the above regularizer under sparsity constraints, we exploit the characteristics of the target subdataset contained in the auxiliary samples while maintaining the basic sketches stored in the global dictionary. An efficient algorithm is presented and validated with experiments on real-world data.

  13. Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

    Science.gov (United States)

    Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

    2017-06-21

    In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

  14. Denoising of gravitational wave signals via dictionary learning algorithms

    Science.gov (United States)

    Torres-Forné, Alejandro; Marquina, Antonio; Font, José A.; Ibáñez, José M.

    2016-12-01

    Gravitational wave astronomy has become a reality after the historical detections accomplished during the first observing run of the two advanced LIGO detectors. In the following years, the number of detections is expected to increase significantly with the full commissioning of the advanced LIGO, advanced Virgo and KAGRA detectors. The development of sophisticated data analysis techniques to improve the opportunities of detection for low signal-to-noise-ratio events is, hence, a most crucial effort. In this paper, we present one such technique, dictionary-learning algorithms, which have been extensively developed in the last few years and successfully applied mostly in the context of image processing. However, to the best of our knowledge, such algorithms have not yet been employed to denoise gravitational wave signals. By building dictionaries from numerical relativity templates of both binary black holes mergers and bursts of rotational core collapse, we show how machine-learning algorithms based on dictionaries can also be successfully applied for gravitational wave denoising. We use a subset of signals from both catalogs, embedded in nonwhite Gaussian noise, to assess our techniques with a large sample of tests and to find the best model parameters. The application of our method to the actual signal GW150914 shows promising results. Dictionary-learning algorithms could be a complementary addition to the gravitational wave data analysis toolkit. They may be used to extract signals from noise and to infer physical parameters if the data are in good enough agreement with the morphology of the dictionary atoms.

  15. 3D dictionary learning based iterative cone beam CT reconstruction

    Directory of Open Access Journals (Sweden)

    Ti Bai

    2014-03-01

    Full Text Available Purpose: This work is to develop a 3D dictionary learning based cone beam CT (CBCT reconstruction algorithm on graphic processing units (GPU to improve the quality of sparse-view CBCT reconstruction with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms of 3 × 3 × 3 was trained from a large number of blocks extracted from a high quality volume image. On the basis, we utilized cholesky decomposition based orthogonal matching pursuit algorithm to find the sparse representation of each block. To accelerate the time-consuming sparse coding in the 3D case, we implemented the sparse coding in a parallel fashion by taking advantage of the tremendous computational power of GPU. Conjugate gradient least square algorithm was adopted to minimize the data fidelity term. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with tight frame (TF by performing reconstructions on a subset data of 121 projections. Results: Compared to TF based CBCT reconstruction that shows good overall performance, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, remove more streaking artifacts and also induce less blocky artifacts. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppress the noise, and hence to achieve high quality reconstruction under the case of sparse view. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential clinical application.-------------------------------Cite this article as: Bai T, Yan H, Shi F, Jia X, Lou Y, Xu Q, Jiang S, Mou X. 3D dictionary learning based iterative cone beam CT reconstruction. Int J Cancer Ther Oncol 2014; 2(2:020240. DOI: 10

  16. Tensor-Dictionary Learning with Deep Kruskal-Factor Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Stevens, Andrew J.; Pu, Yunchen; Sun, Yannan; Spell, Gregory; Carin, Lawrence

    2017-04-20

    We introduce new dictionary learning methods for tensor-variate data of any order. We represent each data item as a sum of Kruskal decomposed dictionary atoms within the framework of beta-process factor analysis (BPFA). Our model is nonparametric and can infer the tensor-rank of each dictionary atom. This Kruskal-Factor Analysis (KFA) is a natural generalization of BPFA. We also extend KFA to a deep convolutional setting and develop online learning methods. We test our approach on image processing and classification tasks achieving state of the art results for 2D & 3D inpainting and Caltech 101. The experiments also show that atom-rank impacts both overcompleteness and sparsity.

  17. Learning Words from Context and Dictionaries: An Experimental Comparison.

    Science.gov (United States)

    Fischer, Ute

    1994-01-01

    Investigated the independent and interactive effects of contextual and definitional information on vocabulary learning. German students of English received either a text with unfamiliar English words or their monolingual English dictionary entries. A third group received both. Information about word context is crucial to understanding meaning. (44…

  18. Pocket Electronic Dictionaries for Second Language Learning: Help or Hindrance?

    Science.gov (United States)

    Tang, Gloria M.

    1997-01-01

    Reports on the concerns of English-as-a-Second-Language (ESL) teachers in Canada regarding their students' use of pocket bilingual electronic dictionaries (EDs). The article highlights the ED's features, uses, and effectiveness as a tool for learning ESL at the secondary level and ESL students' perceptions of the ED's usefulness. (nine references)…

  19. Dynamic Textures Modeling via Joint Video Dictionary Learning.

    Science.gov (United States)

    Wei, Xian; Li, Yuanxiang; Shen, Hao; Chen, Fang; Kleinsteuber, Martin; Wang, Zhongfeng

    2017-04-06

    Video representation is an important and challenging task in the computer vision community. In this paper, we consider the problem of modeling and classifying video sequences of dynamic scenes which could be modeled in a dynamic textures (DT) framework. At first, we assume that image frames of a moving scene can be modeled as a Markov random process. We propose a sparse coding framework, named joint video dictionary learning (JVDL), to model a video adaptively. By treating the sparse coefficients of image frames over a learned dictionary as the underlying "states", we learn an efficient and robust linear transition matrix between two adjacent frames of sparse events in time series. Hence, a dynamic scene sequence is represented by an appropriate transition matrix associated with a dictionary. In order to ensure the stability of JVDL, we impose several constraints on such transition matrix and dictionary. The developed framework is able to capture the dynamics of a moving scene by exploring both sparse properties and the temporal correlations of consecutive video frames. Moreover, such learned JVDL parameters can be used for various DT applications, such as DT synthesis and recognition. Experimental results demonstrate the strong competitiveness of the proposed JVDL approach in comparison with state-of-the-art video representation methods. Especially, it performs significantly better in dealing with DT synthesis and recognition on heavily corrupted data.

  20. Generating a Spanish Affective Dictionary with Supervised Learning Techniques

    Science.gov (United States)

    Bermudez-Gonzalez, Daniel; Miranda-Jiménez, Sabino; García-Moreno, Raúl-Ulises; Calderón-Nepamuceno, Dora

    2016-01-01

    Nowadays, machine learning techniques are being used in several Natural Language Processing (NLP) tasks such as Opinion Mining (OM). OM is used to analyse and determine the affective orientation of texts. Usually, OM approaches use affective dictionaries in order to conduct sentiment analysis. These lexicons are labeled manually with affective…

  1. 3D Reconstruction of human bones based on dictionary learning.

    Science.gov (United States)

    Zhang, Binkai; Wang, Xiang; Liang, Xiao; Zheng, Jinjin

    2017-11-01

    An effective method for reconstructing a 3D model of human bones from computed tomography (CT) image data based on dictionary learning is proposed. In this study, the dictionary comprises the vertices of triangular meshes, and the sparse coefficient matrix indicates the connectivity information. For better reconstruction performance, we proposed a balance coefficient between the approximation and regularisation terms and a method for optimisation. Moreover, we applied a local updating strategy and a mesh-optimisation method to update the dictionary and the sparse matrix, respectively. The two updating steps are iterated alternately until the objective function converges. Thus, a reconstructed mesh could be obtained with high accuracy and regularisation. The experimental results show that the proposed method has the potential to obtain high precision and high-quality triangular meshes for rapid prototyping, medical diagnosis, and tissue engineering. Copyright © 2017 IPEM. Published by Elsevier Ltd. All rights reserved.

  2. Online Dictionary Learning Aided Target Recognition In Cognitive GPR

    OpenAIRE

    Giovanneschi, Fabio; Mishra, Kumar Vijay; Gonzalez-Huici, Maria Antonia; Eldar, Yonina C.; Ender, Joachim H. G.

    2017-01-01

    Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate the application of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel landmines. Our experimental results using real data from an L-band GPR for PMN/PMA2...

  3. Dictionary Pair Learning on Grassmann Manifolds for Image Denoising.

    Science.gov (United States)

    Zeng, Xianhua; Bian, Wei; Liu, Wei; Shen, Jialie; Tao, Dacheng

    2015-11-01

    Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary pair (i.e., the left and right dictionaries) by employing a subspace partition technique on the Grassmann manifold, wherein the refined dictionary pair is obtained through a sub-dictionary pair merging. The DPLG obtains a sparse representation by encoding each image patch only with the selected sub-dictionary pair. The non-zero elements of the sparse representation are further smoothed by the graph Laplacian operator to remove the noise. Consequently, the DPLG algorithm not only preserves the inherent 2D geometric structure of natural images but also performs manifold smoothing in the 2D sparse coding space. We demonstrate that the DPLG algorithm also improves the structural SIMilarity values of the perceptual visual quality for denoised images using the experimental evaluations on the benchmark images and Berkeley segmentation data sets. Moreover, the DPLG also produces the competitive peak signal-to-noise ratio values from popular image denoising algorithms.

  4. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

    Science.gov (United States)

    Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

    2016-09-22

    To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

  5. An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Donghao Wang

    2016-09-01

    Full Text Available To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

  6. Adaptive structured dictionary learning for image fusion based on group-sparse-representation

    Science.gov (United States)

    Yang, Jiajie; Sun, Bin; Luo, Chengwei; Wu, Yuzhong; Xu, Limei

    2018-04-01

    Dictionary learning is the key process of sparse representation which is one of the most widely used image representation theories in image fusion. The existing dictionary learning method does not use the group structure information and the sparse coefficients well. In this paper, we propose a new adaptive structured dictionary learning algorithm and a l1-norm maximum fusion rule that innovatively utilizes grouped sparse coefficients to merge the images. In the dictionary learning algorithm, we do not need prior knowledge about any group structure of the dictionary. By using the characteristics of the dictionary in expressing the signal, our algorithm can automatically find the desired potential structure information that hidden in the dictionary. The fusion rule takes the physical meaning of the group structure dictionary, and makes activity-level judgement on the structure information when the images are being merged. Therefore, the fused image can retain more significant information. Comparisons have been made with several state-of-the-art dictionary learning methods and fusion rules. The experimental results demonstrate that, the dictionary learning algorithm and the fusion rule both outperform others in terms of several objective evaluation metrics.

  7. Self-expressive Dictionary Learning for Dynamic 3D Reconstruction.

    Science.gov (United States)

    Zheng, Enliang; Ji, Dinghuang; Dunn, Enrique; Frahm, Jan-Michael

    2017-08-22

    We target the problem of sparse 3D reconstruction of dynamic objects observed by multiple unsynchronized video cameras with unknown temporal overlap. To this end, we develop a framework to recover the unknown structure without sequencing information across video sequences. Our proposed compressed sensing framework poses the estimation of 3D structure as the problem of dictionary learning, where the dictionary is defined as an aggregation of the temporally varying 3D structures. Given the smooth motion of dynamic objects, we observe any element in the dictionary can be well approximated by a sparse linear combination of other elements in the same dictionary (i.e. self-expression). Our formulation optimizes a biconvex cost function that leverages a compressed sensing formulation and enforces both structural dependency coherence across video streams, as well as motion smoothness across estimates from common video sources. We further analyze the reconstructability of our approach under different capture scenarios, and its comparison and relation to existing methods. Experimental results on large amounts of synthetic data as well as real imagery demonstrate the effectiveness of our approach.

  8. Implementation of dictionary pair learning algorithm for image quality improvement

    Science.gov (United States)

    Vimala, C.; Aruna Priya, P.

    2018-04-01

    This paper proposes an image denoising on dictionary pair learning algorithm. Visual information is transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmissions is often corrupted with noise. The received image needs processing before it can be used in applications. Image denoising involves the manipulation of the image data to produce a visually high quality image.

  9. Sparsity and Nullity: Paradigm for Analysis Dictionary Learning

    Science.gov (United States)

    2016-08-09

    applied mathematics , on account of its theoretical complexity, and its high relevance to big data problems. Dictionary learning has been one of the key...and hence rank(Ni ⊕ ni) = rank(Ni) + 1. There are two inherent difficulties in this formulation. First, ‖ · ‖0 is of combinatorial nature, hence the...from incomplete and inaccurate measurements, Communications on pure and applied mathematics , 59 (2006), pp. 1207–1223. [8] Thomas F Coleman and Alex

  10. Shape prior modeling using sparse representation and online dictionary learning.

    Science.gov (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N

    2012-01-01

    The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.

  11. Stochastic Learning of Multi-Instance Dictionary for Earth Mover's Distance based Histogram Comparison

    OpenAIRE

    Fan, Jihong; Liang, Ru-Ze

    2016-01-01

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover's distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stoc...

  12. Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

    Science.gov (United States)

    Li, Zheng-Zhou; Chen, Jing; Hou, Qian; Fu, Hong-Xia; Dai, Zhen; Jin, Gang; Li, Ru-Zhang; Liu, Chang-Ju

    2014-05-27

    It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

  13. Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online

    Directory of Open Access Journals (Sweden)

    Zheng-Zhou Li

    2014-05-01

    Full Text Available It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn’t be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.

  14. Sinogram denoising via simultaneous sparse representation in learned dictionaries

    International Nuclear Information System (INIS)

    Karimi, Davood; Ward, Rabab K

    2016-01-01

    Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster. (paper)

  15. Online multi-modal robust non-negative dictionary learning for visual tracking.

    Science.gov (United States)

    Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

    2015-01-01

    Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

  16. Learning Resources and MOOCs

    DEFF Research Database (Denmark)

    Christiansen, René Boyer

    MOOCs (Massive Open Online Courses) have become a serious player within the field of education and learning in the past few years. MOOC research is thus a new field but within the last 2-3 years, it has developed rapidly (Liyanagunawardena et al., 2013, Bayne & Ross, 2014). Much of this research...... has had an emphasis on learners and outcome as well as suitable business models. And even though the internet merely flows over with lists of MOOCs to attend (such as the list from “Top 5 onlinecolleges” which features a list of 99 MOOC environments) not much emphasis has been brought on the actual...... construction of learning resources within all these MOOCs – and what demands they lay on teachers competences and teachers skills....

  17. Tensor-Based Dictionary Learning for Spectral CT Reconstruction.

    Science.gov (United States)

    Zhang, Yanbo; Mou, Xuanqin; Wang, Ge; Yu, Hengyong

    2017-01-01

    Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods.

  18. Tensor-based Dictionary Learning for Spectral CT Reconstruction

    Science.gov (United States)

    Zhang, Yanbo; Wang, Ge

    2016-01-01

    Spectral computed tomography (CT) produces an energy-discriminative attenuation map of an object, extending a conventional image volume with a spectral dimension. In spectral CT, an image can be sparsely represented in each of multiple energy channels, and are highly correlated among energy channels. According to this characteristics, we propose a tensor-based dictionary learning method for spectral CT reconstruction. In our method, tensor patches are extracted from an image tensor, which is reconstructed using the filtered backprojection (FBP), to form a training dataset. With the Candecomp/Parafac decomposition, a tensor-based dictionary is trained, in which each atom is a rank-one tensor. Then, the trained dictionary is used to sparsely represent image tensor patches during an iterative reconstruction process, and the alternating minimization scheme is adapted for optimization. The effectiveness of our proposed method is validated with both numerically simulated and real preclinical mouse datasets. The results demonstrate that the proposed tensor-based method generally produces superior image quality, and leads to more accurate material decomposition than the currently popular popular methods. PMID:27541628

  19. Manifold optimization-based analysis dictionary learning with an ℓ1∕2-norm regularizer.

    Science.gov (United States)

    Li, Zhenni; Ding, Shuxue; Li, Yujie; Yang, Zuyuan; Xie, Shengli; Chen, Wuhui

    2018-02-01

    Recently there has been increasing attention towards analysis dictionary learning. In analysis dictionary learning, it is an open problem to obtain the strong sparsity-promoting solutions efficiently while simultaneously avoiding the trivial solutions of the dictionary. In this paper, to obtain the strong sparsity-promoting solutions, we employ the ℓ 1∕2 norm as a regularizer. The very recent study on ℓ 1∕2 norm regularization theory in compressive sensing shows that its solutions can give sparser results than using the ℓ 1 norm. We transform a complex nonconvex optimization into a number of one-dimensional minimization problems. Then the closed-form solutions can be obtained efficiently. To avoid trivial solutions, we apply manifold optimization to update the dictionary directly on the manifold satisfying the orthonormality constraint, so that the dictionary can avoid the trivial solutions well while simultaneously capturing the intrinsic properties of the dictionary. The experiments with synthetic and real-world data verify that the proposed algorithm for analysis dictionary learning can not only obtain strong sparsity-promoting solutions efficiently, but also learn more accurate dictionary in terms of dictionary recovery and image processing than the state-of-the-art algorithms. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

    Science.gov (United States)

    Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

    2017-09-12

    In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

  1. Dictionary learning for data recovery in positron emission tomography

    International Nuclear Information System (INIS)

    Valiollahzadeh, SeyyedMajid; Clark, John W Jr; Mawlawi, Osama

    2015-01-01

    Compressed sensing (CS) aims to recover images from fewer measurements than that governed by the Nyquist sampling theorem. Most CS methods use analytical predefined sparsifying domains such as total variation, wavelets, curvelets, and finite transforms to perform this task. In this study, we evaluated the use of dictionary learning (DL) as a sparsifying domain to reconstruct PET images from partially sampled data, and compared the results to the partially and fully sampled image (baseline).A CS model based on learning an adaptive dictionary over image patches was developed to recover missing observations in PET data acquisition. The recovery was done iteratively in two steps: a dictionary learning step and an image reconstruction step. Two experiments were performed to evaluate the proposed CS recovery algorithm: an IEC phantom study and five patient studies. In each case, 11% of the detectors of a GE PET/CT system were removed and the acquired sinogram data were recovered using the proposed DL algorithm. The recovered images (DL) as well as the partially sampled images (with detector gaps) for both experiments were then compared to the baseline. Comparisons were done by calculating RMSE, contrast recovery and SNR in ROIs drawn in the background, and spheres of the phantom as well as patient lesions.For the phantom experiment, the RMSE for the DL recovered images were 5.8% when compared with the baseline images while it was 17.5% for the partially sampled images. In the patients’ studies, RMSE for the DL recovered images were 3.8%, while it was 11.3% for the partially sampled images. Our proposed CS with DL is a good approach to recover partially sampled PET data. This approach has implications toward reducing scanner cost while maintaining accurate PET image quantification. (paper)

  2. Creating a medical dictionary using word alignment: The influence of sources and resources

    Directory of Open Access Journals (Sweden)

    Åhlfeldt Hans

    2007-11-01

    Full Text Available Abstract Background Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. Methods We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. Results The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English

  3. Creating a medical dictionary using word alignment: the influence of sources and resources.

    Science.gov (United States)

    Nyström, Mikael; Merkel, Magnus; Petersson, Håkan; Ahlfeldt, Hans

    2007-11-23

    Automatic word alignment of parallel texts with the same content in different languages is among other things used to generate dictionaries for new translations. The quality of the generated word alignment depends on the quality of the input resources. In this paper we report on automatic word alignment of the English and Swedish versions of the medical terminology systems ICD-10, ICF, NCSP, KSH97-P and parts of MeSH and how the terminology systems and type of resources influence the quality. We automatically word aligned the terminology systems using static resources, like dictionaries, statistical resources, like statistically derived dictionaries, and training resources, which were generated from manual word alignment. We varied which part of the terminology systems that we used to generate the resources, which parts that we word aligned and which types of resources we used in the alignment process to explore the influence the different terminology systems and resources have on the recall and precision. After the analysis, we used the best configuration of the automatic word alignment for generation of candidate term pairs. We then manually verified the candidate term pairs and included the correct pairs in an English-Swedish dictionary. The results indicate that more resources and resource types give better results but the size of the parts used to generate the resources only partly affects the quality. The most generally useful resources were generated from ICD-10 and resources generated from MeSH were not as general as other resources. Systematic inter-language differences in the structure of the terminology system rubrics make the rubrics harder to align. Manually created training resources give nearly as good results as a union of static resources, statistical resources and training resources and noticeably better results than a union of static resources and statistical resources. The verified English-Swedish dictionary contains 24,000 term pairs in base

  4. The Role of Electronic Pocket Dictionaries as an English Learning Tool among Chinese Students

    Science.gov (United States)

    Jian, Hua-Li; Sandnes, Frode Eika; Law, Kris M. Y.; Huang, Yo-Ping; Huang, Yueh-Min

    2009-01-01

    This study addressed the role of electronic pocket dictionaries as a language learning tool among university students in Hong Kong and Taiwan. The target groups included engineering and humanities students at both undergraduate and graduate level. Speed of reference was found to be the main motivator for using an electronic pocket dictionary.…

  5. Overcoming complexities: Damage detection using dictionary learning framework

    Science.gov (United States)

    Alguri, K. Supreet; Melville, Joseph; Deemer, Chris; Harley, Joel B.

    2018-04-01

    For in situ damage detection, guided wave structural health monitoring systems have been widely researched due to their ability to evaluate large areas and their ability detect many types of damage. These systems often evaluate structural health by recording initial baseline measurements from a pristine (i.e., undamaged) test structure and then comparing later measurements with that baseline. Yet, it is not always feasible to have a pristine baseline. As an alternative, substituting the baseline with data from a surrogate (nearly identical and pristine) structure is a logical option. While effective in some circumstance, surrogate data is often still a poor substitute for pristine baseline measurements due to minor differences between the structures. To overcome this challenge, we present a dictionary learning framework to adapt surrogate baseline data to better represent an undamaged test structure. We compare the performance of our framework with two other surrogate-based damage detection strategies: (1) using raw surrogate data for comparison and (2) using sparse wavenumber analysis, a precursor to our framework for improving the surrogate data. We apply our framework to guided wave data from two 108 mm by 108 mm aluminum plates. With 20 measurements, we show that our dictionary learning framework achieves a 98% accuracy, raw surrogate data achieves a 92% accuracy, and sparse wavenumber analysis achieves a 57% accuracy.

  6. The efficacy of dictionary use while reading for learning new words.

    Science.gov (United States)

    Hamilton, Harley

    2012-01-01

    The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online bilingual multimedia English-American Sign Language (ASL) dictionary (OBMEAD), (b) a paper English-ASL dictionary (PBEAD), and (c) an online monolingual English dictionary (OMED). It was found that for immediate recall of target words, the OBMEAD was superior to both the PBEAD and the OMED. For later recall, no significant difference appeared between the OBMEAD and the PBEAD. For both of these, recall was statistically superior to recall for words learned via the OMED.

  7. e-Learning Resource Brokers

    NARCIS (Netherlands)

    Retalis, Symeon; Papasalouros, Andreas; Avgeriou, Paris; Siassiakos, Kostas

    2004-01-01

    There is an exponentially increasing demand for provisioning of high-quality learning resources, which is not satisfied by current web technologies and systems. E-Learning Resource Brokers are a potential solution to this problem, as they represent the state-of-the-art in facilitating the exchange

  8. Exploiting Attribute Correlations: A Novel Trace Lasso-Based Weakly Supervised Dictionary Learning Method.

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2017-12-01

    It is now well established that sparse representation models are working effectively for many visual recognition tasks, and have pushed forward the success of dictionary learning therein. Recent studies over dictionary learning focus on learning discriminative atoms instead of purely reconstructive ones. However, the existence of intraclass diversities (i.e., data objects within the same category but exhibit large visual dissimilarities), and interclass similarities (i.e., data objects from distinct classes but share much visual similarities), makes it challenging to learn effective recognition models. To this end, a large number of labeled data objects are required to learn models which can effectively characterize these subtle differences. However, labeled data objects are always limited to access, committing it difficult to learn a monolithic dictionary that can be discriminative enough. To address the above limitations, in this paper, we propose a weakly-supervised dictionary learning method to automatically learn a discriminative dictionary by fully exploiting visual attribute correlations rather than label priors. In particular, the intrinsic attribute correlations are deployed as a critical cue to guide the process of object categorization, and then a set of subdictionaries are jointly learned with respect to each category. The resulting dictionary is highly discriminative and leads to intraclass diversity aware sparse representations. Extensive experiments on image classification and object recognition are conducted to show the effectiveness of our approach.

  9. Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems.

    Science.gov (United States)

    Ravishankar, Saiprasad; Nadakuditi, Raj Rao; Fessler, Jeffrey A

    2017-12-01

    The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step. This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns. The resulting block coordinate descent algorithms involve efficient closed-form solutions. Furthermore, we consider the problem of dictionary-blind image reconstruction, and propose novel and efficient algorithms for adaptive image reconstruction using block coordinate descent and sum of outer products methodologies. We provide a convergence study of the algorithms for dictionary learning and dictionary-blind image reconstruction. Our numerical experiments show the promising performance and speedups provided by the proposed methods over previous schemes in sparse data representation and compressed sensing-based image reconstruction.

  10. Reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning.

    Science.gov (United States)

    Song, Ying; Zhu, Zhen; Lu, Yang; Liu, Qiegen; Zhao, Jun

    2014-03-01

    To improve the magnetic resonance imaging (MRI) data acquisition speed while maintaining the reconstruction quality, a novel method is proposed for multislice MRI reconstruction from undersampled k-space data based on compressed-sensing theory using dictionary learning. There are two aspects to improve the reconstruction quality. One is that spatial correlation among slices is used by extending the atoms in dictionary learning from patches to blocks. The other is that the dictionary-learning scheme is used at two resolution levels; i.e., a low-resolution dictionary is used for sparse coding and a high-resolution dictionary is used for image updating. Numerical experiments are carried out on in vivo 3D MR images of brains and abdomens with a variety of undersampling schemes and ratios. The proposed method (dual-DLMRI) achieves better reconstruction quality than conventional reconstruction methods, with the peak signal-to-noise ratio being 7 dB higher. The advantages of the dual dictionaries are obvious compared with the single dictionary. Parameter variations ranging from 50% to 200% only bias the image quality within 15% in terms of the peak signal-to-noise ratio. Dual-DLMRI effectively uses the a priori information in the dual-dictionary scheme and provides dramatically improved reconstruction quality. Copyright © 2013 Wiley Periodicals, Inc.

  11. A tensor-based dictionary learning approach to tomographic image reconstruction

    DEFF Research Database (Denmark)

    Soltani, Sara; Kilmer, Misha E.; Hansen, Per Christian

    2016-01-01

    We consider tomographic reconstruction using priors in the form of a dictionary learned from training images. The reconstruction has two stages: first we construct a tensor dictionary prior from our training data, and then we pose the reconstruction problem in terms of recovering the expansion...... coefficients in that dictionary. Our approach differs from past approaches in that (a) we use a third-order tensor representation for our images and (b) we recast the reconstruction problem using the tensor formulation. The dictionary learning problem is presented as a non-negative tensor factorization problem...... with sparsity constraints. The reconstruction problem is formulated in a convex optimization framework by looking for a solution with a sparse representation in the tensor dictionary. Numerical results show that our tensor formulation leads to very sparse representations of both the training images...

  12. Recent research in data description of the measurement property resource on common data dictionary

    Science.gov (United States)

    Lu, Tielin; Fan, Zitian; Wang, Chunxi; Liu, Xiaojing; Wang, Shuo; Zhao, Hua

    2018-03-01

    A method for measurement equipment data description has been proposed based on the property resource analysis. The applications of common data dictionary (CDD) to devices and equipment is mainly used in digital factory to advance the management not only in the enterprise, also to the different enterprise in the same data environment. In this paper, we can make a brief of the data flow in the whole manufacture enterprise and the automatic trigger the process of the data exchange. Furthermore,the application of the data dictionary is available for the measurement and control equipment, which can also be used in other different industry in smart manufacture.

  13. Online English-English Learner Dictionaries Boost Word Learning

    Science.gov (United States)

    Nurmukhamedov, Ulugbek

    2012-01-01

    Learners of English might be familiar with several online monolingual dictionaries that are not necessarily the best choices for the English as Second/Foreign Language (ESL/EFL) context. Although these monolingual online dictionaries contain definitions, pronunciation guides, and other elements normally found in general-use dictionaries, they are…

  14. Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

    Science.gov (United States)

    Wu, Juan; Bai, Min

    2018-05-01

    We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

  15. Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

    Directory of Open Access Journals (Sweden)

    Li Jun-Bao

    2017-06-01

    Full Text Available Magnetic Resonance Super-resolution Imaging Measurement (MRIM is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

  16. Image Denoising Algorithm Combined with SGK Dictionary Learning and Principal Component Analysis Noise Estimation

    Directory of Open Access Journals (Sweden)

    Wenjing Zhao

    2018-01-01

    Full Text Available SGK (sequential generalization of K-means dictionary learning denoising algorithm has the characteristics of fast denoising speed and excellent denoising performance. However, the noise standard deviation must be known in advance when using SGK algorithm to process the image. This paper presents a denoising algorithm combined with SGK dictionary learning and the principal component analysis (PCA noise estimation. At first, the noise standard deviation of the image is estimated by using the PCA noise estimation algorithm. And then it is used for SGK dictionary learning algorithm. Experimental results show the following: (1 The SGK algorithm has the best denoising performance compared with the other three dictionary learning algorithms. (2 The SGK algorithm combined with PCA is superior to the SGK algorithm combined with other noise estimation algorithms. (3 Compared with the original SGK algorithm, the proposed algorithm has higher PSNR and better denoising performance.

  17. An Improved Sparse Representation over Learned Dictionary Method for Seizure Detection.

    Science.gov (United States)

    Li, Junhui; Zhou, Weidong; Yuan, Shasha; Zhang, Yanli; Li, Chengcheng; Wu, Qi

    2016-02-01

    Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific method is proposed for seizure detection in the long-term intracranial electroencephalogram (EEG) recordings. This seizure detection method is based on sparse representation with online dictionary learning and elastic net constraint. The online learned dictionary could sparsely represent the testing samples more accurately, and the elastic net constraint which combines the 11-norm and 12-norm not only makes the coefficients sparse but also avoids over-fitting problem. First, the EEG signals are preprocessed using wavelet filtering and differential filtering, and the kernel function is applied to make the samples closer to linearly separable. Then the dictionaries of seizure and nonseizure are respectively learned from original ictal and interictal training samples with online dictionary optimization algorithm to compose the training dictionary. After that, the test samples are sparsely coded over the learned dictionary and the residuals associated with ictal and interictal sub-dictionary are calculated, respectively. Eventually, the test samples are classified as two distinct categories, seizure or nonseizure, by comparing the reconstructed residuals. The average segment-based sensitivity of 95.45%, specificity of 99.08%, and event-based sensitivity of 94.44% with false detection rate of 0.23/h and average latency of -5.14 s have been achieved with our proposed method.

  18. Multi-level discriminative dictionary learning with application to large scale image classification.

    Science.gov (United States)

    Shen, Li; Sun, Gang; Huang, Qingming; Wang, Shuhui; Lin, Zhouchen; Wu, Enhua

    2015-10-01

    The sparse coding technique has shown flexibility and capability in image representation and analysis. It is a powerful tool in many visual applications. Some recent work has shown that incorporating the properties of task (such as discrimination for classification task) into dictionary learning is effective for improving the accuracy. However, the traditional supervised dictionary learning methods suffer from high computation complexity when dealing with large number of categories, making them less satisfactory in large scale applications. In this paper, we propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification. Our method takes advantage of hierarchical category correlation to encode multi-level discriminative information. Each internal node of the category hierarchy is associated with a discriminative dictionary and a classification model. The dictionaries at different layers are learnt to capture the information of different scales. Moreover, each node at lower layers also inherits the dictionary of its parent, so that the categories at lower layers can be described with multi-scale information. The learning of dictionaries and associated classification models is jointly conducted by minimizing an overall tree loss. The experimental results on challenging data sets demonstrate that our approach achieves excellent accuracy and competitive computation cost compared with other sparse coding methods for large scale image classification.

  19. Segmentation of MR images via discriminative dictionary learning and sparse coding: Application to hippocampus labeling

    OpenAIRE

    Tong, Tong; Wolz, Robin; Coupe, Pierrick; Hajnal, Joseph V.; Rueckert, Daniel

    2013-01-01

    International audience; We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labe...

  20. Sparse dictionary learning of resting state fMRI networks.

    Science.gov (United States)

    Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

    2012-07-02

    Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

  1. Designing Learning Resources in Synchronous Learning Environments

    DEFF Research Database (Denmark)

    Christiansen, Rene B

    2015-01-01

    Computer-mediated Communication (CMC) and synchronous learning environments offer new solutions for teachers and students that transcend the singular one-way transmission of content knowledge from teacher to student. CMC makes it possible not only to teach computer mediated but also to design...... and create new learning resources targeted to a specific group of learners. This paper addresses the possibilities of designing learning resources within synchronous learning environments. The empirical basis is a cross-country study involving students and teachers in primary schools in three Nordic...... Countries (Denmark, Sweden and Norway). On the basis of these empirical studies a set of design examples is drawn with the purpose of showing how the design fulfills the dual purpose of functioning as a remote, synchronous learning environment and - using the learning materials used and recordings...

  2. An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.

    Science.gov (United States)

    Zhang, Ye; Yu, Tenglong; Wang, Wenwu

    2014-01-01

    Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

  3. An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint

    Directory of Open Access Journals (Sweden)

    Ye Zhang

    2014-01-01

    Full Text Available Two common problems are often encountered in analysis dictionary learning (ADL algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high, as represented by the Analysis K-SVD (AK-SVD algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.

  4. Dictionary Learning Based on Nonnegative Matrix Factorization Using Parallel Coordinate Descent

    Directory of Open Access Journals (Sweden)

    Zunyi Tang

    2013-01-01

    Full Text Available Sparse representation of signals via an overcomplete dictionary has recently received much attention as it has produced promising results in various applications. Since the nonnegativities of the signals and the dictionary are required in some applications, for example, multispectral data analysis, the conventional dictionary learning methods imposed simply with nonnegativity may become inapplicable. In this paper, we propose a novel method for learning a nonnegative, overcomplete dictionary for such a case. This is accomplished by posing the sparse representation of nonnegative signals as a problem of nonnegative matrix factorization (NMF with a sparsity constraint. By employing the coordinate descent strategy for optimization and extending it to multivariable case for processing in parallel, we develop a so-called parallel coordinate descent dictionary learning (PCDDL algorithm, which is structured by iteratively solving the two optimal problems, the learning process of the dictionary and the estimating process of the coefficients for constructing the signals. Numerical experiments demonstrate that the proposed algorithm performs better than the conventional nonnegative K-SVD (NN-KSVD algorithm and several other algorithms for comparison. What is more, its computational consumption is remarkably lower than that of the compared algorithms.

  5. Joint seismic data denoising and interpolation with double-sparsity dictionary learning

    Science.gov (United States)

    Zhu, Lingchen; Liu, Entao; McClellan, James H.

    2017-08-01

    Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

  6. Electronic Dictionary as a Tool for Integration of Additional Learning Content

    Directory of Open Access Journals (Sweden)

    Stefka Kovacheva

    2015-12-01

    Full Text Available Electronic Dictionary as a Tool for Integration of Additional Learning Content This article discusses electronic dictionary as an element of the „Bulgarian cultural and historical heritage under the protection of UNESCO” database developed in IMI (BAS, that will be used to integrate additional learning content. The electronic dictionary is described as an easily accessible book of reference, offering information to the shape, meaning, usage and the origin of words in connection to the cultural-historical heritage sites in Bulgaria, protected by UNESCO. The dictionary targets 9–11 year old students from Bulgarian schools, who study the subjects “Man and Society” in 4th grade and “History and Civilization” in 5th grade.

  7. The Effectiveness of Using Contextual Clues, Dictionary Strategy and Computer Assisted Language Learning (Call In Learning Vocabulary

    Directory of Open Access Journals (Sweden)

    Zuraina Ali

    2013-07-01

    Full Text Available This study investigates the effectiveness of three vocabulary learning methods that are Contextual Clues, Dictionary Strategy, and Computer Assisted Language Learning (CALL in learning vocabulary among ESL learners. First, it aims at finding which of the vocabulary learning methods namely Dictionary Strategy, Contextual Clues, and CALL that may result in the highest number of words learnt in the immediate and delayed recall tests. Second, it compares the results of the Pre-test and the Delayed Recall Post-test to determine the differences of learning vocabulary using the methods. A quasi-experiment that tested the effectiveness of learning vocabulary using Dictionary Strategy, Contextual clues, and CALL involved 123 first year university students. Qualitative procedures included the collection of data from interviews which were conducted to triangulate the data obtain from the quantitative inquiries. Findings from the study using ANOVA revealed that there were significant differences when students were exposed to Dictionary Strategy, Contextual Clues and CALL in the immediate recall tests but not in the Delayed Recall Post-test. Also, there were significant differences when t test was used to compare the scores between the Pre-test and the Delayed Recall Post-test in using the three methods of vocabulary learning. Although many researchers have advocated the relative effectiveness of Dictionary Strategy, Contextual Clues, and CALL in learning vocabulary, the study however, is still paramount since there is no study has ever empirically investigated the relative efficacy of these three methods in a single study.

  8. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    International Nuclear Information System (INIS)

    Xu, Q; Han, H; Xing, L

    2016-01-01

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  9. SU-F-I-12: Region-Specific Dictionary Learning for Low-Dose X-Ray CT Reconstruction

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q; Han, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States)

    2016-06-15

    Purpose: Dictionary learning based method has attracted more and more attentions in low-dose CT due to the superior performance on suppressing noise and preserving structural details. Considering the structures and noise vary from region to region in one imaging object, we propose a region-specific dictionary learning method to improve the low-dose CT reconstruction. Methods: A set of normal-dose images was used for dictionary learning. Segmentations were performed on these images, so that the training patch sets corresponding to different regions can be extracted out. After that, region-specific dictionaries were learned from these training sets. For the low-dose CT reconstruction, a conventional reconstruction, such as filtered back-projection (FBP), was performed firstly, and then segmentation was followed to segment the image into different regions. Sparsity constraints of each region based on its dictionary were used as regularization terms. The regularization parameters were selected adaptively according to different regions. A low-dose human thorax dataset was used to evaluate the proposed method. The single dictionary based method was performed for comparison. Results: Since the lung region is very different from the other part of thorax, two dictionaries corresponding to lung region and the rest part of thorax respectively were learned to better express the structural details and avoid artifacts. With only one dictionary some artifact appeared in the body region caused by the spot atoms corresponding to the structures in the lung region. And also some structure in the lung regions cannot be recovered well by only one dictionary. The quantitative indices of the result by the proposed method were also improved a little compared to the single dictionary based method. Conclusion: Region-specific dictionary can make the dictionary more adaptive to different region characteristics, which is much desirable for enhancing the performance of dictionary learning

  10. Deformable segmentation via sparse representation and dictionary learning.

    Science.gov (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N

    2012-10-01

    "Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

  11. Resources and Resourcefulness in Language Teaching and Learning

    African Journals Online (AJOL)

    Attempts will be made in this paper to examine what we mean by language, language teaching and learning, resources and resourcefulness in language teaching and learning and the benefit of teachers being resourceful in language teaching and learning to both the learners, the teachers, the society and the nation at ...

  12. Group-sparse representation with dictionary learning for medical image denoising and fusion.

    Science.gov (United States)

    Li, Shutao; Yin, Haitao; Fang, Leyuan

    2012-12-01

    Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

  13. Travel time tomography with local image regularization by sparsity constrained dictionary learning

    Science.gov (United States)

    Bianco, M.; Gerstoft, P.

    2017-12-01

    We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

  14. Machinery vibration signal denoising based on learned dictionary and sparse representation

    International Nuclear Information System (INIS)

    Guo, Liang; Gao, Hongli; Li, Jun; Huang, Haifeng; Zhang, Xiaochen

    2015-01-01

    Mechanical vibration signal denoising has been an import problem for machine damage assessment and health monitoring. Wavelet transfer and sparse reconstruction are the powerful and practical methods. However, those methods are based on the fixed basis functions or atoms. In this paper, a novel method is presented. The atoms used to represent signals are learned from the raw signal. And in order to satisfy the requirements of real-time signal processing, an online dictionary learning algorithm is adopted. Orthogonal matching pursuit is applied to extract the most pursuit column in the dictionary. At last, denoised signal is calculated with the sparse vector and learned dictionary. A simulation signal and real bearing fault signal are utilized to evaluate the improved performance of the proposed method through the comparison with kinds of denoising algorithms. Then Its computing efficiency is demonstrated by an illustrative runtime example. The results show that the proposed method outperforms current algorithms with efficiency calculation. (paper)

  15. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction.

    Science.gov (United States)

    Lu, Hongyang; Wei, Jingbo; Liu, Qiegen; Wang, Yuhao; Deng, Xiaohua

    2016-01-01

    Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  16. A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

    Directory of Open Access Journals (Sweden)

    Hongyang Lu

    2016-01-01

    Full Text Available Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV approach and adaptive dictionary learning (DL. In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.

  17. Dictionary Learning on the Manifold of Square Root Densities and Application to Reconstruction of Diffusion Propagator Fields*

    Science.gov (United States)

    Sun, Jiaqi; Xie, Yuchen; Ye, Wenxing; Ho, Jeffrey; Entezari, Alireza; Blackband, Stephen J.

    2013-01-01

    In this paper, we present a novel dictionary learning framework for data lying on the manifold of square root densities and apply it to the reconstruction of diffusion propagator (DP) fields given a multi-shell diffusion MRI data set. Unlike most of the existing dictionary learning algorithms which rely on the assumption that the data points are vectors in some Euclidean space, our dictionary learning algorithm is designed to incorporate the intrinsic geometric structure of manifolds and performs better than traditional dictionary learning approaches when applied to data lying on the manifold of square root densities. Non-negativity as well as smoothness across the whole field of the reconstructed DPs is guaranteed in our approach. We demonstrate the advantage of our approach by comparing it with an existing dictionary based reconstruction method on synthetic and real multi-shell MRI data. PMID:24684004

  18. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    Science.gov (United States)

    Xue, Ming; Yang, Hua; Zheng, Shibao; Zhou, Yi; Yu, Zhenghua

    2014-01-01

    To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT) is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU) strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV) function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks. PMID:24549252

  19. Incremental Structured Dictionary Learning for Video Sensor-Based Object Tracking

    Directory of Open Access Journals (Sweden)

    Ming Xue

    2014-02-01

    Full Text Available To tackle robust object tracking for video sensor-based applications, an online discriminative algorithm based on incremental discriminative structured dictionary learning (IDSDL-VT is presented. In our framework, a discriminative dictionary combining both positive, negative and trivial patches is designed to sparsely represent the overlapped target patches. Then, a local update (LU strategy is proposed for sparse coefficient learning. To formulate the training and classification process, a multiple linear classifier group based on a K-combined voting (KCV function is proposed. As the dictionary evolves, the models are also trained to timely adapt the target appearance variation. Qualitative and quantitative evaluations on challenging image sequences compared with state-of-the-art algorithms demonstrate that the proposed tracking algorithm achieves a more favorable performance. We also illustrate its relay application in visual sensor networks.

  20. Building a RAPPOR with the Unknown: Privacy-Preserving Learning of Associations and Data Dictionaries

    Directory of Open Access Journals (Sweden)

    Fanti Giulia

    2016-07-01

    Full Text Available Techniques based on randomized response enable the collection of potentially sensitive data from clients in a privacy-preserving manner with strong local differential privacy guarantees. A recent such technology, RAPPOR [12], enables estimation of the marginal frequencies of a set of strings via privacy-preserving crowdsourcing. However, this original estimation process relies on a known dictionary of possible strings; in practice, this dictionary can be extremely large and/or unknown. In this paper, we propose a novel decoding algorithm for the RAPPOR mechanism that enables the estimation of “unknown unknowns,” i.e., strings we do not know we should be estimating. To enable learning without explicit dictionary knowledge, we develop methodology for estimating the joint distribution of multiple variables collected with RAPPOR. Our contributions are not RAPPOR-specific, and can be generalized to other local differential privacy mechanisms for learning distributions of string-valued random variables.

  1. Sign Inference for Dynamic Signed Networks via Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Yi Cen

    2013-01-01

    Full Text Available Mobile online social network (mOSN is a burgeoning research area. However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not. In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations. Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem. Specifically, motivated by the Singular Value Thresholding (SVT algorithm, a compact dictionary is selected from the observed dataset. Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem. Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.

  2. Abnormality detection of mammograms by discriminative dictionary learning on DSIFT descriptors.

    Science.gov (United States)

    Tavakoli, Nasrin; Karimi, Maryam; Nejati, Mansour; Karimi, Nader; Reza Soroushmehr, S M; Samavi, Shadrokh; Najarian, Kayvan

    2017-07-01

    Detection and classification of breast lesions using mammographic images are one of the most difficult studies in medical image processing. A number of learning and non-learning methods have been proposed for detecting and classifying these lesions. However, the accuracy of the detection/classification still needs improvement. In this paper we propose a powerful classification method based on sparse learning to diagnose breast cancer in mammograms. For this purpose, a supervised discriminative dictionary learning approach is applied on dense scale invariant feature transform (DSIFT) features. A linear classifier is also simultaneously learned with the dictionary which can effectively classify the sparse representations. Our experimental results show the superior performance of our method compared to existing approaches.

  3. Multi-Source Multi-Target Dictionary Learning for Prediction of Cognitive Decline.

    Science.gov (United States)

    Zhang, Jie; Li, Qingyang; Caselli, Richard J; Thompson, Paul M; Ye, Jieping; Wang, Yalin

    2017-06-01

    Alzheimer's Disease (AD) is the most common type of dementia. Identifying correct biomarkers may determine pre-symptomatic AD subjects and enable early intervention. Recently, Multi-task sparse feature learning has been successfully applied to many computer vision and biomedical informatics researches. It aims to improve the generalization performance by exploiting the shared features among different tasks. However, most of the existing algorithms are formulated as a supervised learning scheme. Its drawback is with either insufficient feature numbers or missing label information. To address these challenges, we formulate an unsupervised framework for multi-task sparse feature learning based on a novel dictionary learning algorithm. To solve the unsupervised learning problem, we propose a two-stage Multi-Source Multi-Target Dictionary Learning (MMDL) algorithm. In stage 1, we propose a multi-source dictionary learning method to utilize the common and individual sparse features in different time slots. In stage 2, supported by a rigorous theoretical analysis, we develop a multi-task learning method to solve the missing label problem. Empirical studies on an N = 3970 longitudinal brain image data set, which involves 2 sources and 5 targets, demonstrate the improved prediction accuracy and speed efficiency of MMDL in comparison with other state-of-the-art algorithms.

  4. Examining the Conditions of Using an On-Line Dictionary to Learn Words and Comprehend Texts

    Science.gov (United States)

    Dilenschneider, Robert Francis

    2018-01-01

    This study investigated three look-up conditions for language learners to learn unknown target words and comprehend a reading passage when their attention is transferred away to an on-line dictionary. The research questions focused on how each look-up condition impacted the recall and recognition of word forms, word meanings, and passage…

  5. A Latin Functionalist Dictionary as a Self-Learning Language Device: Previous Experiences to Digitalization

    Science.gov (United States)

    Márquez, Manuel; Chaves, Beatriz

    2016-01-01

    The application of a methodology based on S.C. Dik's Functionalist Grammar linguistic principles, which is addressed to the teaching of Latin to secondary students, has resulted in a quantitative improvement in students' acquisition process of knowledge. To do so, we have used a self-learning tool, an ad hoc dictionary, of which the use in…

  6. Comparing the Effect of Using Monolingual versus Bilingual Dictionary on Iranian Intermediate EFL Learners' Vocabulary Learning

    Science.gov (United States)

    Ahangari, Saeideh; Dogolsara, Shokoufeh Abbasi

    2015-01-01

    This study aimed to investigate the effect of using two types of dictionaries (monolingual and bilingual) on Iranian intermediate EFL learners' vocabulary learning. An OPT (Oxford placement test, 2001) was administered among 90 students 60 of whom were selected as the participants of this study. They were sophomore students studying English as a…

  7. English Digital Dictionaries as Valuable Blended Learning Tools for Palestinian College Students

    Science.gov (United States)

    Dwaik, Raghad A. A.

    2015-01-01

    Digital technology has become an indispensable aspect of foreign language learning around the globe especially in the case of college students who are often required to finish extensive reading assignments within a limited time period. Such pressure calls for the use of efficient tools such as digital dictionaries to help them achieve their…

  8. The Role of Consulting a Dictionary in Reading and Vocabulary Learning

    Directory of Open Access Journals (Sweden)

    Carol A. Fraser

    1999-12-01

    Full Text Available Abstract This article reviews recent research on consulting a dictionary in L2 reading and vocabulary learning. From the perspective of cognitive learning theory, the author re-evaluates the limited role that has often been accorded to dictionary consulting. It is noted that, among the three available lexical processing strategies (inferencing, consulting and ignoring, learners tend to use consulting infrequently and selectively and also to differ among each other in their strategy use. Consulting in combination with inferencing is shown to have the greatest positive effect on performance in L2 reading and vocabulary learning, although consulting is found to slow down task completion. Excerpts from think-aloud protocols illustrate the potential contribution of strategic dictionary use to the cognitive processes required for vocabulary acquisition: attention to form-meaning connections, rehearsal of words for storage in long-term memory and elaboration of associations with other knowledge. Among the pedagogical implications of these findings is the need for training in lexical processing strategies in order to help learners use the dictionary effectively and accurately in L2 reading comprehension and vocabulary learning.

  9. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    International Nuclear Information System (INIS)

    Guo, Yanrong; Shao, Yeqin; Gao, Yaozong; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

  10. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-01-01

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different

  11. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning.

    Science.gov (United States)

    Guo, Yanrong; Gao, Yaozong; Shao, Yeqin; Price, True; Oto, Aytekin; Shen, Dinggang

    2014-07-01

    Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the

  12. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanrong; Shao, Yeqin [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong; Price, True [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Oto, Aytekin [Department of Radiology, Section of Urology, University of Chicago, Illinois 60637 (United States); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-07-15

    Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on

  13. Sparse Coding and Dictionary Learning Based on the MDL Principle

    Science.gov (United States)

    2010-10-01

    average bits per pixel obtained was 4.08 bits per pixel ( bpp ), with p = 250 atoms in the final dictionary. We repeated this using `2 instead of Huber...loss, obtaining 4.12 bpp and p = 245. We now show example results obtained with our framework in two very different applications. In both cases we

  14. Managing Human Resource Learning for Innovation

    DEFF Research Database (Denmark)

    Nielsen, Peter

    Managing human resource learning for innovation develops a systemic understanding of building innovative capabilities. Building innovative capabilities require active creation, coordination and absorption of useful knowledge and thus a cohesive management approach to learning. Often learning...... in organizations and work is approached without considerations on how to integrate it in the management of human resources. The book investigates the empirical conditions for managing human resources learning for innovation. With focus on innovative performance the importance of modes of innovation, clues...

  15. Super resolution reconstruction of infrared images based on classified dictionary learning

    Science.gov (United States)

    Liu, Fei; Han, Pingli; Wang, Yi; Li, Xuan; Bai, Lu; Shao, Xiaopeng

    2018-05-01

    Infrared images always suffer from low-resolution problems resulting from limitations of imaging devices. An economical approach to combat this problem involves reconstructing high-resolution images by reasonable methods without updating devices. Inspired by compressed sensing theory, this study presents and demonstrates a Classified Dictionary Learning method to reconstruct high-resolution infrared images. It classifies features of the samples into several reasonable clusters and trained a dictionary pair for each cluster. The optimal pair of dictionaries is chosen for each image reconstruction and therefore, more satisfactory results is achieved without the increase in computational complexity and time cost. Experiments and results demonstrated that it is a viable method for infrared images reconstruction since it improves image resolution and recovers detailed information of targets.

  16. Sparse representation and dictionary learning penalized image reconstruction for positron emission tomography

    International Nuclear Information System (INIS)

    Chen, Shuhang; Liu, Huafeng; Shi, Pengcheng; Chen, Yunmei

    2015-01-01

    Accurate and robust reconstruction of the radioactivity concentration is of great importance in positron emission tomography (PET) imaging. Given the Poisson nature of photo-counting measurements, we present a reconstruction framework that integrates sparsity penalty on a dictionary into a maximum likelihood estimator. Patch-sparsity on a dictionary provides the regularization for our effort, and iterative procedures are used to solve the maximum likelihood function formulated on Poisson statistics. Specifically, in our formulation, a dictionary could be trained on CT images, to provide intrinsic anatomical structures for the reconstructed images, or adaptively learned from the noisy measurements of PET. Accuracy of the strategy with very promising application results from Monte-Carlo simulations, and real data are demonstrated. (paper)

  17. Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

    Science.gov (United States)

    Wang, Bigong; Li, Liang

    2015-01-01

    As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training. PMID:26089956

  18. Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction.

    Science.gov (United States)

    Wang, Bigong; Li, Liang

    2015-01-01

    As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.

  19. Paper, Electronic or Online? Different Dictionaries for Different Activities

    Science.gov (United States)

    Pasfield-Neofitou, Sarah

    2009-01-01

    Despite research suggesting that teachers highly influence their students' knowledge and use of language learning resources such as dictionaries (Loucky, 2005; Yamane, 2006), it appears that dictionary selection and use is considered something to be dealt with outside the classroom. As a result, many students receive too little advice to be able…

  20. Medical student use of digital learning resources.

    Science.gov (United States)

    Scott, Karen; Morris, Anne; Marais, Ben

    2018-02-01

    University students expect to use technology as part of their studies, yet health professional teachers can struggle with the change in student learning habits fuelled by technology. Our research aimed to document the learning habits of contemporary medical students during a clinical rotation by exploring the use of locally and externally developed digital and print self-directed learning resources, and study groups. We investigated the learning habits of final-stage medical students during their clinical paediatric rotation using mixed methods, involving learning analytics and a student questionnaire. Learning analytics tracked aggregate student usage statistics of locally produced e-learning resources on two learning management systems and mobile learning resources. The questionnaire recorded student-reported use of digital and print learning resources and study groups. The students made extensive use of digital self-directed learning resources, especially in the 2 weeks before the examination, which peaked the day before the written examination. All students used locally produced digital formative assessment, and most (74/98; 76%) also used digital resources developed by other institutions. Most reported finding locally produced e-learning resources beneficial for learning. In terms of traditional forms of self-directed learning, one-third (28/94; 30%) indicated that they never read the course textbook, and few students used face-to-face 39/98 (40%) or online 6/98 (6%) study groups. Learning analytics and student questionnaire data confirmed the extensive use of digital resources for self-directed learning. Through clarification of learning habits and experiences, we think teachers can help students to optimise effective learning strategies; however, the impact of contemporary learning habits on learning efficacy requires further evaluation. Health professional teachers can struggle with the change in student learning habits fuelled by technology. © 2017 John

  1. An efficient dictionary learning algorithm and its application to 3-D medical image denoising.

    Science.gov (United States)

    Li, Shutao; Fang, Leyuan; Yin, Haitao

    2012-02-01

    In this paper, we propose an efficient dictionary learning algorithm for sparse representation of given data and suggest a way to apply this algorithm to 3-D medical image denoising. Our learning approach is composed of two main parts: sparse coding and dictionary updating. On the sparse coding stage, an efficient algorithm named multiple clusters pursuit (MCP) is proposed. The MCP first applies a dictionary structuring strategy to cluster the atoms with high coherence together, and then employs a multiple-selection strategy to select several competitive atoms at each iteration. These two strategies can greatly reduce the computation complexity of the MCP and assist it to obtain better sparse solution. On the dictionary updating stage, the alternating optimization that efficiently approximates the singular value decomposition is introduced. Furthermore, in the 3-D medical image denoising application, a joint 3-D operation is proposed for taking the learning capabilities of the presented algorithm to simultaneously capture the correlations within each slice and correlations across the nearby slices, thereby obtaining better denoising results. The experiments on both synthetically generated data and real 3-D medical images demonstrate that the proposed approach has superior performance compared to some well-known methods. © 2011 IEEE

  2. A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources

    Science.gov (United States)

    Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

    2014-01-01

    Objective To create a sense inventory of abbreviations and acronyms from clinical texts. Methods The most frequently occurring abbreviations and acronyms from 352 267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). Results The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17 359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm (‘Sense Inventories’, website). Conclusions Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction. PMID:23813539

  3. A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources.

    Science.gov (United States)

    Moon, Sungrim; Pakhomov, Serguei; Liu, Nathan; Ryan, James O; Melton, Genevieve B

    2014-01-01

    To create a sense inventory of abbreviations and acronyms from clinical texts. The most frequently occurring abbreviations and acronyms from 352,267 dictated clinical notes were used to create a clinical sense inventory. Senses of each abbreviation and acronym were manually annotated from 500 random instances and lexically matched with long forms within the Unified Medical Language System (UMLS V.2011AB), Another Database of Abbreviations in Medline (ADAM), and Stedman's Dictionary, Medical Abbreviations, Acronyms & Symbols, 4th edition (Stedman's). Redundant long forms were merged after they were lexically normalized using Lexical Variant Generation (LVG). The clinical sense inventory was found to have skewed sense distributions, practice-specific senses, and incorrect uses. Of 440 abbreviations and acronyms analyzed in this study, 949 long forms were identified in clinical notes. This set was mapped to 17,359, 5233, and 4879 long forms in UMLS, ADAM, and Stedman's, respectively. After merging long forms, only 2.3% matched across all medical resources. The UMLS, ADAM, and Stedman's covered 5.7%, 8.4%, and 11% of the merged clinical long forms, respectively. The sense inventory of clinical abbreviations and acronyms and anonymized datasets generated from this study are available for public use at http://www.bmhi.umn.edu/ihi/research/nlpie/resources/index.htm ('Sense Inventories', website). Clinical sense inventories of abbreviations and acronyms created using clinical notes and medical dictionary resources demonstrate challenges with term coverage and resource integration. Further work is needed to help with standardizing abbreviations and acronyms in clinical care and biomedicine to facilitate automated processes such as text-mining and information extraction.

  4. Basis Expansion Approaches for Regularized Sequential Dictionary Learning Algorithms With Enforced Sparsity for fMRI Data Analysis.

    Science.gov (United States)

    Seghouane, Abd-Krim; Iqbal, Asif

    2017-09-01

    Sequential dictionary learning algorithms have been successfully applied to functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are, however, structured data matrices with the notions of temporal smoothness in the column direction. This prior information, which can be converted into a constraint of smoothness on the learned dictionary atoms, has seldomly been included in classical dictionary learning algorithms when applied to fMRI data analysis. In this paper, we tackle this problem by proposing two new sequential dictionary learning algorithms dedicated to fMRI data analysis by accounting for this prior information. These algorithms differ from the existing ones in their dictionary update stage. The steps of this stage are derived as a variant of the power method for computing the SVD. The proposed algorithms generate regularized dictionary atoms via the solution of a left regularized rank-one matrix approximation problem where temporal smoothness is enforced via regularization through basis expansion and sparse basis expansion in the dictionary update stage. Applications on synthetic data experiments and real fMRI data sets illustrating the performance of the proposed algorithms are provided.

  5. Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

    KAUST Repository

    Fan, Jihong

    2016-09-17

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However, up to now, there is no existing multi-instance dictionary learning methods designed for EMD-based histogram comparison. To fill this gap, we develop the first EMD-optimal dictionary learning method using stochastic optimization method. In the stochastic learning framework, we have one triplet of bags, including one basic bag, one positive bag, and one negative bag. These bags are mapped to histograms using a multi-instance dictionary. We argue that the EMD between the basic histogram and the positive histogram should be smaller than that between the basic histogram and the negative histogram. Base on this condition, we design a hinge loss. By minimizing this hinge loss and some regularization terms of the dictionary, we update the dictionary instances. The experiments over multi-instance retrieval applications shows its effectiveness when compared to other dictionary learning methods over the problems of medical image retrieval and natural language relation classification. © 2016 The Natural Computing Applications Forum

  6. Tensor-based Dictionary Learning for Dynamic Tomographic Reconstruction

    Science.gov (United States)

    Tan, Shengqi; Zhang, Yanbo; Wang, Ge; Mou, Xuanqin; Cao, Guohua; Wu, Zhifang; Yu, Hengyong

    2015-01-01

    In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. PMID:25779991

  7. Tensor-based dictionary learning for dynamic tomographic reconstruction

    International Nuclear Information System (INIS)

    Tan, Shengqi; Wu, Zhifang; Zhang, Yanbo; Mou, Xuanqin; Wang, Ge; Cao, Guohua; Yu, Hengyong

    2015-01-01

    In dynamic computed tomography (CT) reconstruction, the data acquisition speed limits the spatio-temporal resolution. Recently, compressed sensing theory has been instrumental in improving CT reconstruction from far few-view projections. In this paper, we present an adaptive method to train a tensor-based spatio-temporal dictionary for sparse representation of an image sequence during the reconstruction process. The correlations among atoms and across phases are considered to capture the characteristics of an object. The reconstruction problem is solved by the alternating direction method of multipliers. To recover fine or sharp structures such as edges, the nonlocal total variation is incorporated into the algorithmic framework. Preclinical examples including a sheep lung perfusion study and a dynamic mouse cardiac imaging demonstrate that the proposed approach outperforms the vectorized dictionary-based CT reconstruction in the case of few-view reconstruction. (paper)

  8. Low-Dose X-ray CT Reconstruction via Dictionary Learning

    Science.gov (United States)

    Xu, Qiong; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2013-01-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures. PMID:22542666

  9. Low-dose X-ray CT reconstruction via dictionary learning.

    Science.gov (United States)

    Xu, Qiong; Yu, Hengyong; Mou, Xuanqin; Zhang, Lei; Hsieh, Jiang; Wang, Ge

    2012-09-01

    Although diagnostic medical imaging provides enormous benefits in the early detection and accuracy diagnosis of various diseases, there are growing concerns on the potential side effect of radiation induced genetic, cancerous and other diseases. How to reduce radiation dose while maintaining the diagnostic performance is a major challenge in the computed tomography (CT) field. Inspired by the compressive sensing theory, the sparse constraint in terms of total variation (TV) minimization has already led to promising results for low-dose CT reconstruction. Compared to the discrete gradient transform used in the TV method, dictionary learning is proven to be an effective way for sparse representation. On the other hand, it is important to consider the statistical property of projection data in the low-dose CT case. Recently, we have developed a dictionary learning based approach for low-dose X-ray CT. In this paper, we present this method in detail and evaluate it in experiments. In our method, the sparse constraint in terms of a redundant dictionary is incorporated into an objective function in a statistical iterative reconstruction framework. The dictionary can be either predetermined before an image reconstruction task or adaptively defined during the reconstruction process. An alternating minimization scheme is developed to minimize the objective function. Our approach is evaluated with low-dose X-ray projections collected in animal and human CT studies, and the improvement associated with dictionary learning is quantified relative to filtered backprojection and TV-based reconstructions. The results show that the proposed approach might produce better images with lower noise and more detailed structural features in our selected cases. However, there is no proof that this is true for all kinds of structures.

  10. Accelerating the reconstruction of magnetic resonance imaging by three-dimensional dual-dictionary learning using CUDA.

    Science.gov (United States)

    Jiansen Li; Jianqi Sun; Ying Song; Yanran Xu; Jun Zhao

    2014-01-01

    An effective way to improve the data acquisition speed of magnetic resonance imaging (MRI) is using under-sampled k-space data, and dictionary learning method can be used to maintain the reconstruction quality. Three-dimensional dictionary trains the atoms in dictionary in the form of blocks, which can utilize the spatial correlation among slices. Dual-dictionary learning method includes a low-resolution dictionary and a high-resolution dictionary, for sparse coding and image updating respectively. However, the amount of data is huge for three-dimensional reconstruction, especially when the number of slices is large. Thus, the procedure is time-consuming. In this paper, we first utilize the NVIDIA Corporation's compute unified device architecture (CUDA) programming model to design the parallel algorithms on graphics processing unit (GPU) to accelerate the reconstruction procedure. The main optimizations operate in the dictionary learning algorithm and the image updating part, such as the orthogonal matching pursuit (OMP) algorithm and the k-singular value decomposition (K-SVD) algorithm. Then we develop another version of CUDA code with algorithmic optimization. Experimental results show that more than 324 times of speedup is achieved compared with the CPU-only codes when the number of MRI slices is 24.

  11. A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning.

    Science.gov (United States)

    Zhang, Shang; Dong, Yuhan; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

    2018-02-22

    The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.

  12. A Spectral Reconstruction Algorithm of Miniature Spectrometer Based on Sparse Optimization and Dictionary Learning

    Science.gov (United States)

    Zhang, Shang; Fu, Hongyan; Huang, Shao-Lun; Zhang, Lin

    2018-01-01

    The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer. PMID:29470406

  13. Discriminative Structured Dictionary Learning on Grassmann Manifolds and Its Application on Image Restoration.

    Science.gov (United States)

    Pan, Han; Jing, Zhongliang; Qiao, Lingfeng; Li, Minzhe

    2017-09-25

    Image restoration is a difficult and challenging problem in various imaging applications. However, despite of the benefits of a single overcomplete dictionary, there are still several challenges for capturing the geometric structure of image of interest. To more accurately represent the local structures of the underlying signals, we propose a new problem formulation for sparse representation with block-orthogonal constraint. There are three contributions. First, a framework for discriminative structured dictionary learning is proposed, which leads to a smooth manifold structure and quotient search spaces. Second, an alternating minimization scheme is proposed after taking both the cost function and the constraints into account. This is achieved by iteratively alternating between updating the block structure of the dictionary defined on Grassmann manifold and sparsifying the dictionary atoms automatically. Third, Riemannian conjugate gradient is considered to track local subspaces efficiently with a convergence guarantee. Extensive experiments on various datasets demonstrate that the proposed method outperforms the state-of-the-art methods on the removal of mixed Gaussian-impulse noise.

  14. Speckle Reduction on Ultrasound Liver Images Based on a Sparse Representation over a Learned Dictionary

    Directory of Open Access Journals (Sweden)

    Mohamed Yaseen Jabarulla

    2018-05-01

    Full Text Available Ultrasound images are corrupted with multiplicative noise known as speckle, which reduces the effectiveness of image processing and hampers interpretation. This paper proposes a multiplicative speckle suppression technique for ultrasound liver images, based on a new signal reconstruction model known as sparse representation (SR over dictionary learning. In the proposed technique, the non-uniform multiplicative signal is first converted into additive noise using an enhanced homomorphic filter. This is followed by pixel-based total variation (TV regularization and patch-based SR over a dictionary trained using K-singular value decomposition (KSVD. Finally, the split Bregman algorithm is used to solve the optimization problem and estimate the de-speckled image. The simulations performed on both synthetic and clinical ultrasound images for speckle reduction, the proposed technique achieved peak signal-to-noise ratios of 35.537 dB for the dictionary trained on noisy image patches and 35.033 dB for the dictionary trained using a set of reference ultrasound image patches. Further, the evaluation results show that the proposed method performs better than other state-of-the-art denoising algorithms in terms of both peak signal-to-noise ratio and subjective visual quality assessment.

  15. The effects of dictionary training on Turkish EFL students' reading comprehension and vocabulary learning

    OpenAIRE

    Altun, Arif

    1995-01-01

    Ankara : The Institute of Economic and Social Sciences of Bilkent Univ., 1995. Thesis (Master's) -- Bilkent University, 1995. Includes bibliographical references leaves 55-59 The present study investigated the effects of monolingual dictionary training on Turkish EFL students' reading comprehension and vocabulary learning. Thirty-seven intermediate-level Turkish EFL preparatory students in the Department of English Language Teaching at Mustafa Kemal University participated in this st...

  16. Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning.

    Science.gov (United States)

    Pant, Jeevan K; Krishnan, Sridhar

    2014-04-01

    A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

  17. Anomaly-sensitive dictionary learning for structural diagnostics from ultrasonic wavefields.

    Science.gov (United States)

    Druce, Jeffrey M; Haupt, Jarvis D; Gonella, Stefano

    2015-07-01

    This paper proposes a strategy for the detection and triangulation of localized anomalies, such as defects, inclusions, or damage zones, in solid and structural media. The method revolves around the construction of sparse representations of the structure's ultrasonic wavefield response, which are obtained by learning instructive dictionaries that form a suitable basis for the response data. The resulting sparse coding problem is cast as a modified dictionary learning task with additional spatial sparsity constraints enforced on the atoms of the learned dictionaries, which provide them with the ability to unveil anomalous regions in the physical domain. The proposed methodology is model-agnostic, i.e., it forsakes the need for a physical model and requires virtually no a priori knowledge of the material properties. This characteristic makes the approach especially powerful for anomaly identification in systems with unknown or highly heterogeneous property distribution, for which a material model is unsuitable or unreliable. The method is tested against synthetically generated data as well as experimental data acquired using a scanning laser Doppler vibrometer.

  18. Three-dimensional dictionary-learning reconstruction of (23)Na MRI data.

    Science.gov (United States)

    Behl, Nicolas G R; Gnahm, Christine; Bachert, Peter; Ladd, Mark E; Nagel, Armin M

    2016-04-01

    To reduce noise and artifacts in (23)Na MRI with a Compressed Sensing reconstruction and a learned dictionary as sparsifying transform. A three-dimensional dictionary-learning compressed sensing reconstruction algorithm (3D-DLCS) for the reconstruction of undersampled 3D radial (23)Na data is presented. The dictionary used as the sparsifying transform is learned with a K-singular-value-decomposition (K-SVD) algorithm. The reconstruction parameters are optimized on simulated data, and the quality of the reconstructions is assessed with peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The performance of the algorithm is evaluated in phantom and in vivo (23)Na MRI data of seven volunteers and compared with nonuniform fast Fourier transform (NUFFT) and other Compressed Sensing reconstructions. The reconstructions of simulated data have maximal PSNR and SSIM for an undersampling factor (USF) of 10 with numbers of averages equal to the USF. For 10-fold undersampling, the PSNR is increased by 5.1 dB compared with the NUFFT reconstruction, and the SSIM by 24%. These results are confirmed by phantom and in vivo (23)Na measurements in the volunteers that show markedly reduced noise and undersampling artifacts in the case of 3D-DLCS reconstructions. The 3D-DLCS algorithm enables precise reconstruction of undersampled (23)Na MRI data with markedly reduced noise and artifact levels compared with NUFFT reconstruction. Small structures are well preserved. © 2015 Wiley Periodicals, Inc.

  19. An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework

    Directory of Open Access Journals (Sweden)

    Guanqiu Qi

    2017-10-01

    Full Text Available Image fusion is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Medical image fusion, as an important image fusion application, can extract the details of multiple images from different imaging modalities and combine them into an image that contains complete and non-redundant information for increasing the accuracy of medical diagnosis and assessment. The quality of the fused image directly affects medical diagnosis and assessment. However, existing solutions have some drawbacks in contrast, sharpness, brightness, blur and details. This paper proposes an integrated dictionary-learning and entropy-based medical image-fusion framework that consists of three steps. First, the input image information is decomposed into low-frequency and high-frequency components by using a Gaussian filter. Second, low-frequency components are fused by weighted average algorithm and high-frequency components are fused by the dictionary-learning based algorithm. In the dictionary-learning process of high-frequency components, an entropy-based algorithm is used for informative blocks selection. Third, the fused low-frequency and high-frequency components are combined to obtain the final fusion results. The results and analyses of comparative experiments demonstrate that the proposed medical image fusion framework has better performance than existing solutions.

  20. Z-Index Parameterization for Volumetric CT Image Reconstruction via 3-D Dictionary Learning.

    Science.gov (United States)

    Bai, Ti; Yan, Hao; Jia, Xun; Jiang, Steve; Wang, Ge; Mou, Xuanqin

    2017-12-01

    Despite the rapid developments of X-ray cone-beam CT (CBCT), image noise still remains a major issue for the low dose CBCT. To suppress the noise effectively while retain the structures well for low dose CBCT image, in this paper, a sparse constraint based on the 3-D dictionary is incorporated into a regularized iterative reconstruction framework, defining the 3-D dictionary learning (3-DDL) method. In addition, by analyzing the sparsity level curve associated with different regularization parameters, a new adaptive parameter selection strategy is proposed to facilitate our 3-DDL method. To justify the proposed method, we first analyze the distributions of the representation coefficients associated with the 3-D dictionary and the conventional 2-D dictionary to compare their efficiencies in representing volumetric images. Then, multiple real data experiments are conducted for performance validation. Based on these results, we found: 1) the 3-D dictionary-based sparse coefficients have three orders narrower Laplacian distribution compared with the 2-D dictionary, suggesting the higher representation efficiencies of the 3-D dictionary; 2) the sparsity level curve demonstrates a clear Z-shape, and hence referred to as Z-curve, in this paper; 3) the parameter associated with the maximum curvature point of the Z-curve suggests a nice parameter choice, which could be adaptively located with the proposed Z-index parameterization (ZIP) method; 4) the proposed 3-DDL algorithm equipped with the ZIP method could deliver reconstructions with the lowest root mean squared errors and the highest structural similarity index compared with the competing methods; 5) similar noise performance as the regular dose FDK reconstruction regarding the standard deviation metric could be achieved with the proposed method using (1/2)/(1/4)/(1/8) dose level projections. The contrast-noise ratio is improved by ~2.5/3.5 times with respect to two different cases under the (1/8) dose level compared

  1. Segmentation of Thalamus from MR images via Task-Driven Dictionary Learning.

    Science.gov (United States)

    Liu, Luoluo; Glaister, Jeffrey; Sun, Xiaoxia; Carass, Aaron; Tran, Trac D; Prince, Jerry L

    2016-02-27

    Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.

  2. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning

    Science.gov (United States)

    Fang, Ruogu; Chen, Tsuhan; Sanelli, Pina C.

    2014-01-01

    Computed tomography perfusion (CTP) is an important functional imaging modality in the evaluation of cerebrovascular diseases, particularly in acute stroke and vasospasm. However, the post-processed parametric maps of blood flow tend to be noisy, especially in low-dose CTP, due to the noisy contrast enhancement profile and the oscillatory nature of the results generated by the current computational methods. In this paper, we propose a robust sparse perfusion deconvolution method (SPD) to estimate cerebral blood flow in CTP performed at low radiation dose. We first build a dictionary from high-dose perfusion maps using online dictionary learning and then perform deconvolution-based hemodynamic parameters estimation on the low-dose CTP data. Our method is validated on clinical data of patients with normal and pathological CBF maps. The results show that we achieve superior performance than existing methods, and potentially improve the differentiation between normal and ischemic tissue in the brain. PMID:23542422

  3. Integrative learning for practicing adaptive resource management

    Directory of Open Access Journals (Sweden)

    Craig A. McLoughlin

    2015-03-01

    Full Text Available Adaptive resource management is a learning-by-doing approach to natural resource management. Its effective practice involves the activation, completion, and regeneration of the "adaptive management cycle" while working toward achieving a flexible set of collaboratively identified objectives. This iterative process requires application of single-, double-, and triple-loop learning, to strategically modify inputs, outputs, assumptions, and hypotheses linked to improving policies, management strategies, and actions, along with transforming governance. Obtaining an appropriate balance between these three modes of learning has been difficult to achieve in practice and building capacity in this area can be achieved through an emphasis on reflexive learning, by employing adaptive feedback systems. A heuristic reflexive learning framework for adaptive resource management is presented in this manuscript. It is built on the conceptual pillars of the following: stakeholder driven adaptive feedback systems; strategic adaptive management (SAM; and hierarchy theory. The SAM Reflexive Learning Framework (SRLF emphasizes the types, roles, and transfer of information within a reflexive learning context. Its adaptive feedback systems enhance the facilitation of single-, double-, and triple-loop learning. Focus on the reflexive learning process is further fostered by streamlining objectives within and across all governance levels; incorporating multiple interlinked adaptive management cycles; having learning as an ongoing, nested process; recognizing when and where to employ the three-modes of learning; distinguishing initiating conditions for this learning; and contemplating practitioner mandates for this learning across governance levels. The SRLF is a key enabler for implementing the "adaptive management cycle," and thereby translating the theory of adaptive resource management into practice. It promotes the heuristics of adaptive management within a cohesive

  4. Extending an Afrikaans pronunciation dictionary using Dutch resources and P2P/GP2P

    CSIR Research Space (South Africa)

    Loots, L

    2010-11-01

    Full Text Available . This is compared to the more common approach of extending the Afrikaans dictionary by means of graphemeto-phoneme (G2P) conversion. The results indicate that the Afrikaans pronunciations obtained by P2P and GP2P from the Dutch dictionary are more accurate than...

  5. From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

    Science.gov (United States)

    Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

    2015-01-01

    This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

  6. JST Thesaurus Headwords and Synonyms: e‐learning [MeCab user dictionary for science technology term[Archive

    Lifescience Database Archive (English)

    Full Text Available MeCab user dictionary for science technology term e‐learning 名詞 一般 * * * * eラーニング e...ラーニング イーラーニング Thesaurus2015 200906043727726486 C EG01 UNKNOWN_2 e ‐ learning

  7. Natural-Annotation-based Unsupervised Construction of Korean-Chinese Domain Dictionary

    Science.gov (United States)

    Liu, Wuying; Wang, Lin

    2018-03-01

    The large-scale bilingual parallel resource is significant to statistical learning and deep learning in natural language processing. This paper addresses the automatic construction issue of the Korean-Chinese domain dictionary, and presents a novel unsupervised construction method based on the natural annotation in the raw corpus. We firstly extract all Korean-Chinese word pairs from Korean texts according to natural annotations, secondly transform the traditional Chinese characters into the simplified ones, and finally distill out a bilingual domain dictionary after retrieving the simplified Chinese words in an extra Chinese domain dictionary. The experimental results show that our method can automatically build multiple Korean-Chinese domain dictionaries efficiently.

  8. Learning a common dictionary for subject-transfer decoding with resting calibration.

    Science.gov (United States)

    Morioka, Hiroshi; Kanemura, Atsunori; Hirayama, Jun-ichiro; Shikauchi, Manabu; Ogawa, Takeshi; Ikeda, Shigeyuki; Kawanabe, Motoaki; Ishii, Shin

    2015-05-01

    Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments. Copyright © 2015 Elsevier Inc. All rights reserved.

  9. Seismic detection method for small-scale discontinuities based on dictionary learning and sparse representation

    Science.gov (United States)

    Yu, Caixia; Zhao, Jingtao; Wang, Yanfei

    2017-02-01

    Studying small-scale geologic discontinuities, such as faults, cavities and fractures, plays a vital role in analyzing the inner conditions of reservoirs, as these geologic structures and elements can provide storage spaces and migration pathways for petroleum. However, these geologic discontinuities have weak energy and are easily contaminated with noises, and therefore effectively extracting them from seismic data becomes a challenging problem. In this paper, a method for detecting small-scale discontinuities using dictionary learning and sparse representation is proposed that can dig up high-resolution information by sparse coding. A K-SVD (K-means clustering via Singular Value Decomposition) sparse representation model that contains two stage of iteration procedure: sparse coding and dictionary updating, is suggested for mathematically expressing these seismic small-scale discontinuities. Generally, the orthogonal matching pursuit (OMP) algorithm is employed for sparse coding. However, the method can only update one dictionary atom at one time. In order to improve calculation efficiency, a regularized version of OMP algorithm is presented for simultaneously updating a number of atoms at one time. Two numerical experiments demonstrate the validity of the developed method for clarifying and enhancing small-scale discontinuities. The field example of carbonate reservoirs further demonstrates its effectiveness in revealing masked tiny faults and small-scale cavities.

  10. SU-E-I-41: Dictionary Learning Based Quantitative Reconstruction for Low-Dose Dual-Energy CT (DECT)

    International Nuclear Information System (INIS)

    Xu, Q; Xing, L; Xiong, G; Elmore, K; Min, J

    2015-01-01

    Purpose: DECT collects two sets of projection data under higher and lower energies. With appropriates composition methods on linear attenuation coefficients, quantitative information about the object, such as density, can be obtained. In reality, one of the important problems in DECT is the radiation dose due to doubled scans. This work is aimed at establishing a dictionary learning based reconstruction framework for DECT for improved image quality while reducing the imaging dose. Methods: In our method, two dictionaries were learned respectively from the high-energy and lowenergy image datasets of similar objects under normal dose in advance. The linear attenuation coefficient was decomposed into two basis components with material based composition method. An iterative reconstruction framework was employed. Two basis components were alternately updated with DECT datasets and dictionary learning based sparse constraints. After one updating step under the dataset fidelity constraints, both high-energy and low-energy images can be obtained from the two basis components. Sparse constraints based on the learned dictionaries were applied to the high- and low-energy images to update the two basis components. The iterative calculation continues until a pre-set number of iteration was reached. Results: We evaluated the proposed dictionary learning method with dual energy images collected using a DECT scanner. We re-projected the projection data with added Poisson noise to reflect the low-dose situation. The results obtained by the proposed method were compared with that obtained using FBP based method and TV based method. It was found that the proposed approach yield better results than other methods with higher resolution and less noise. Conclusion: The use of dictionary learned from DECT images under normal dose is valuable and leads to improved results with much lower imaging dose

  11. SU-E-I-41: Dictionary Learning Based Quantitative Reconstruction for Low-Dose Dual-Energy CT (DECT)

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q [School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049 (China); Department of Radiation Oncology, Stanford University, Stanford, CA 94305 (United States); Xing, L [Department of Radiation Oncology, Stanford University, Stanford, CA 94305 (United States); Xiong, G; Elmore, K; Min, J [Dalio Institute of Cardiovascular Imaging, New York-Presbyterian Hospital and Weill Cornell Medical College, New York, NY (United States)

    2015-06-15

    Purpose: DECT collects two sets of projection data under higher and lower energies. With appropriates composition methods on linear attenuation coefficients, quantitative information about the object, such as density, can be obtained. In reality, one of the important problems in DECT is the radiation dose due to doubled scans. This work is aimed at establishing a dictionary learning based reconstruction framework for DECT for improved image quality while reducing the imaging dose. Methods: In our method, two dictionaries were learned respectively from the high-energy and lowenergy image datasets of similar objects under normal dose in advance. The linear attenuation coefficient was decomposed into two basis components with material based composition method. An iterative reconstruction framework was employed. Two basis components were alternately updated with DECT datasets and dictionary learning based sparse constraints. After one updating step under the dataset fidelity constraints, both high-energy and low-energy images can be obtained from the two basis components. Sparse constraints based on the learned dictionaries were applied to the high- and low-energy images to update the two basis components. The iterative calculation continues until a pre-set number of iteration was reached. Results: We evaluated the proposed dictionary learning method with dual energy images collected using a DECT scanner. We re-projected the projection data with added Poisson noise to reflect the low-dose situation. The results obtained by the proposed method were compared with that obtained using FBP based method and TV based method. It was found that the proposed approach yield better results than other methods with higher resolution and less noise. Conclusion: The use of dictionary learned from DECT images under normal dose is valuable and leads to improved results with much lower imaging dose.

  12. Resource Guide for Persons with Learning Impairments.

    Science.gov (United States)

    IBM, Atlanta, GA. National Support Center for Persons with Disabilities.

    The resource guide identifies products which assist learning disabled and mentally retarded individuals in accessing IBM (International Business Machine) Personal Computers or the IBM Personal System/2 family of products. An introduction provides a general overview of ways computers can help learning disabled or retarded persons. The document then…

  13. Efficient generation of pronunciation dictionaries: machine learning factors during bootstrapping

    CSIR Research Space (South Africa)

    Davel, MH

    2004-10-01

    Full Text Available The authors focus on factors related to the underlying rule-extraction algorithms, and demonstrate variants of the Dynamically Expanding Context algorithm, which are beneficial for this application. They show that continuous updating of the learned...

  14. A Study of Comparatively Low Achievement Students' Bilingualized Dictionary Use and Their English Learning

    Science.gov (United States)

    Chen, Szu-An

    2016-01-01

    This study investigates bilingualized dictionary use of Taiwanese university students. It aims to examine EFL learners' overall dictionary use behavior and their perspectives on book dictionary as well as the necessity of advance guidance in using dictionaries. Data was collected through questionnaires and analyzed by SPSS 15.0. Findings indicate…

  15. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    International Nuclear Information System (INIS)

    Valiollahzadeh, S; Clark, J; Mawlawi, O

    2014-01-01

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care

  16. MO-G-17A-05: PET Image Deblurring Using Adaptive Dictionary Learning

    Energy Technology Data Exchange (ETDEWEB)

    Valiollahzadeh, S [RICE University, Houston, Tx (United States); Clark, J [MD Anderson Cancer Ctr., Houston, TX (United States); Mawlawi, O

    2014-06-15

    Purpose: The aim of this work is to deblur PET images while suppressing Poisson noise effects using adaptive dictionary learning (DL) techniques. Methods: The model that relates a blurred and noisy PET image to the desired image is described as a linear transform y=Hm+n where m is the desired image, H is a blur kernel, n is Poisson noise and y is the blurred image. The approach we follow to recover m involves the sparse representation of y over a learned dictionary, since the image has lots of repeated patterns, edges, textures and smooth regions. The recovery is based on an optimization of a cost function having four major terms: adaptive dictionary learning term, sparsity term, regularization term, and MLEM Poisson noise estimation term. The optimization is solved by a variable splitting method that introduces additional variables. We simulated a 128×128 Hoffman brain PET image (baseline) with varying kernel types and sizes (Gaussian 9×9, σ=5.4mm; Uniform 5×5, σ=2.9mm) with additive Poisson noise (Blurred). Image recovery was performed once when the kernel type was included in the model optimization and once with the model blinded to kernel type. The recovered image was compared to the baseline as well as another recovery algorithm PIDSPLIT+ (Setzer et. al.) by calculating PSNR (Peak SNR) and normalized average differences in pixel intensities (NADPI) of line profiles across the images. Results: For known kernel types, the PSNR of the Gaussian (Uniform) was 28.73 (25.1) and 25.18 (23.4) for DL and PIDSPLIT+ respectively. For blinded deblurring the PSNRs were 25.32 and 22.86 for DL and PIDSPLIT+ respectively. NADPI between baseline and DL, and baseline and blurred for the Gaussian kernel was 2.5 and 10.8 respectively. Conclusion: PET image deblurring using dictionary learning seems to be a good approach to restore image resolution in presence of Poisson noise. GE Health Care.

  17. Subject-Specific Sparse Dictionary Learning for Atlas-Based Brain MRI Segmentation.

    Science.gov (United States)

    Roy, Snehashis; He, Qing; Sweeney, Elizabeth; Carass, Aaron; Reich, Daniel S; Prince, Jerry L; Pham, Dzung L

    2015-09-01

    Quantitative measurements from segmentations of human brain magnetic resonance (MR) images provide important biomarkers for normal aging and disease progression. In this paper, we propose a patch-based tissue classification method from MR images that uses a sparse dictionary learning approach and atlas priors. Training data for the method consists of an atlas MR image, prior information maps depicting where different tissues are expected to be located, and a hard segmentation. Unlike most atlas-based classification methods that require deformable registration of the atlas priors to the subject, only affine registration is required between the subject and training atlas. A subject-specific patch dictionary is created by learning relevant patches from the atlas. Then the subject patches are modeled as sparse combinations of learned atlas patches leading to tissue memberships at each voxel. The combination of prior information in an example-based framework enables us to distinguish tissues having similar intensities but different spatial locations. We demonstrate the efficacy of the approach on the application of whole-brain tissue segmentation in subjects with healthy anatomy and normal pressure hydrocephalus, as well as lesion segmentation in multiple sclerosis patients. For each application, quantitative comparisons are made against publicly available state-of-the art approaches.

  18. Cross-Modality Image Synthesis via Weakly Coupled and Geometry Co-Regularized Joint Dictionary Learning.

    Science.gov (United States)

    Huang, Yawen; Shao, Ling; Frangi, Alejandro F

    2018-03-01

    Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors, such as patient discomfort, increased cost, prolonged scanning time, and scanner unavailability. In additionally, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. In this paper, we propose a weakly coupled and geometry co-regularized joint dictionary learning method to address the problem of cross-modality synthesis while considering the fact that collecting the large amounts of training data is often impractical. Our learning stage requires only a few registered multi-modality image pairs as training data. To employ both paired images and a large set of unpaired data, a cross-modality image matching criterion is proposed. Then, we propose a unified model by integrating such a criterion into the joint dictionary learning and the observed common feature space for associating cross-modality data for the purpose of synthesis. Furthermore, two regularization terms are added to construct robust sparse representations. Our experimental results demonstrate superior performance of the proposed model over state-of-the-art methods.

  19. Metadata and Ontologies in Learning Resources Design

    Science.gov (United States)

    Vidal C., Christian; Segura Navarrete, Alejandra; Menéndez D., Víctor; Zapata Gonzalez, Alfredo; Prieto M., Manuel

    Resource design and development requires knowledge about educational goals, instructional context and information about learner's characteristics among other. An important information source about this knowledge are metadata. However, metadata by themselves do not foresee all necessary information related to resource design. Here we argue the need to use different data and knowledge models to improve understanding the complex processes related to e-learning resources and their management. This paper presents the use of semantic web technologies, as ontologies, supporting the search and selection of resources used in design. Classification is done, based on instructional criteria derived from a knowledge acquisition process, using information provided by IEEE-LOM metadata standard. The knowledge obtained is represented in an ontology using OWL and SWRL. In this work we give evidence of the implementation of a Learning Object Classifier based on ontology. We demonstrate that the use of ontologies can support the design activities in e-learning.

  20. Extended dynamic mode decomposition with dictionary learning: A data-driven adaptive spectral decomposition of the Koopman operator.

    Science.gov (United States)

    Li, Qianxiao; Dietrich, Felix; Bollt, Erik M; Kevrekidis, Ioannis G

    2017-10-01

    Numerical approximation methods for the Koopman operator have advanced considerably in the last few years. In particular, data-driven approaches such as dynamic mode decomposition (DMD) 51 and its generalization, the extended-DMD (EDMD), are becoming increasingly popular in practical applications. The EDMD improves upon the classical DMD by the inclusion of a flexible choice of dictionary of observables which spans a finite dimensional subspace on which the Koopman operator can be approximated. This enhances the accuracy of the solution reconstruction and broadens the applicability of the Koopman formalism. Although the convergence of the EDMD has been established, applying the method in practice requires a careful choice of the observables to improve convergence with just a finite number of terms. This is especially difficult for high dimensional and highly nonlinear systems. In this paper, we employ ideas from machine learning to improve upon the EDMD method. We develop an iterative approximation algorithm which couples the EDMD with a trainable dictionary represented by an artificial neural network. Using the Duffing oscillator and the Kuramoto Sivashinsky partical differential equation as examples, we show that our algorithm can effectively and efficiently adapt the trainable dictionary to the problem at hand to achieve good reconstruction accuracy without the need to choose a fixed dictionary a priori. Furthermore, to obtain a given accuracy, we require fewer dictionary terms than EDMD with fixed dictionaries. This alleviates an important shortcoming of the EDMD algorithm and enhances the applicability of the Koopman framework to practical problems.

  1. Heterogeneous iris image hallucination using sparse representation on a learned heterogeneous patch dictionary

    Science.gov (United States)

    Li, Yung-Hui; Zheng, Bo-Ren; Ji, Dai-Yan; Tien, Chung-Hao; Liu, Po-Tsun

    2014-09-01

    Cross sensor iris matching may seriously degrade the recognition performance because of the sensor mis-match problem of iris images between the enrollment and test stage. In this paper, we propose two novel patch-based heterogeneous dictionary learning method to attack this problem. The first method applies the latest sparse representation theory while the second method tries to learn the correspondence relationship through PCA in heterogeneous patch space. Both methods learn the basic atoms in iris textures across different image sensors and build connections between them. After such connections are built, at test stage, it is possible to hallucinate (synthesize) iris images across different sensors. By matching training images with hallucinated images, the recognition rate can be successfully enhanced. The experimental results showed the satisfied results both visually and in terms of recognition rate. Experimenting with an iris database consisting of 3015 images, we show that the EER is decreased 39.4% relatively by the proposed method.

  2. Queensland Museum Online Learning Resources

    Science.gov (United States)

    Bauer, Adriana

    2009-01-01

    This article evaluates three online educational resources on the Queensland Museum website in terms of their use of ICTs in science education; how they relate to the Queensland Middle School Science Curriculum and the Senior Biology, Marine Studies, Science 21 syllabuses; their visual appeal and level of student engagement; the appropriateness of…

  3. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Science.gov (United States)

    Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  4. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Directory of Open Access Journals (Sweden)

    Xin Tang

    Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

  5. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

    Science.gov (United States)

    Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  6. RoLo: A Dictionary Interface that Minimizes Extraneous Cognitive Load of Lookup and Supports Incidental and Incremental Learning of Vocabulary

    Science.gov (United States)

    Dang, Thanh-Dung; Chen, Gwo-Dong; Dang, Giao; Li, Liang-Yi; Nurkhamid

    2013-01-01

    Dictionary use can improve reading comprehension and incidental vocabulary learning. Nevertheless, great extraneous cognitive load imposed by the search process may reduce or even prevent the improvement. With the help of technology, dictionary users can now instantly access the meaning list of a searched word using a mouse click. However, they…

  7. Parametric dictionary learning for modeling EAP and ODF in diffusion MRI.

    Science.gov (United States)

    Merlet, Sylvain; Caruyer, Emmanuel; Deriche, Rachid

    2012-01-01

    In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the ensemble average propagator (EAP) and the orientation distribution function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features: the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

  8. Multi-View Multi-Instance Learning Based on Joint Sparse Representation and Multi-View Dictionary Learning.

    Science.gov (United States)

    Li, Bing; Yuan, Chunfeng; Xiong, Weihua; Hu, Weiming; Peng, Houwen; Ding, Xinmiao; Maybank, Steve

    2017-12-01

    In multi-instance learning (MIL), the relations among instances in a bag convey important contextual information in many applications. Previous studies on MIL either ignore such relations or simply model them with a fixed graph structure so that the overall performance inevitably degrades in complex environments. To address this problem, this paper proposes a novel multi-view multi-instance learning algorithm (MIL) that combines multiple context structures in a bag into a unified framework. The novel aspects are: (i) we propose a sparse -graph model that can generate different graphs with different parameters to represent various context relations in a bag, (ii) we propose a multi-view joint sparse representation that integrates these graphs into a unified framework for bag classification, and (iii) we propose a multi-view dictionary learning algorithm to obtain a multi-view graph dictionary that considers cues from all views simultaneously to improve the discrimination of the MIL. Experiments and analyses in many practical applications prove the effectiveness of the M IL.

  9. Multimodal Task-Driven Dictionary Learning for Image Classification

    Science.gov (United States)

    2015-12-18

    recognition, multi-view face recognition, multi-view action recognition, and multimodal biometric recognition. It is also shown that, compared to the...improvement in several multi-task learning applications such as target classification, biometric recognitions, and multiview face recognition [12], [14], [17...relevant samples from other modalities for a given unimodal query. However, α1 α2 …αS D1 … Index finger Thumb finger … Iris x1 x2 xS D2 DS … … … J o in

  10. Teaching Dictionary Skills through a Slang Dictionary.

    Science.gov (United States)

    Steed, Stanley M.

    A unit for teaching dictionary skills through the compilation of a slang dictionary was written with the purpose of providing an inductive learning situation. The students are to begin by defining slang usage and bringing in slang words and definitions on cards. Small groups are to be formed to evaluate the definitions and make additions. In…

  11. Change detection of medical images using dictionary learning techniques and principal component analysis.

    Science.gov (United States)

    Nika, Varvara; Babyn, Paul; Zhu, Hongmei

    2014-07-01

    Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of magnetic resonance imaging (MRI) scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are being used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. We present an improved version of the EigenBlockCD algorithm, named the EigenBlockCD-2. The EigenBlockCD-2 algorithm performs an initial global registration and identifies the changes between serial MR images of the brain. Blocks of pixels from a baseline scan are used to train local dictionaries to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between [Formula: see text] and [Formula: see text] norms as two possible similarity measures in the improved EigenBlockCD-2 algorithm. We show the advantages of the [Formula: see text] norm over the [Formula: see text] norm both theoretically and numerically. We also demonstrate the performance of the new EigenBlockCD-2 algorithm for detecting changes of MR images and compare our results with those provided in the recent literature. Experimental results with both simulated and real MRI scans show that our improved EigenBlockCD-2 algorithm outperforms the previous methods. It detects clinical changes while ignoring the changes due to the patient's position and other acquisition artifacts.

  12. Change detection of medical images using dictionary learning techniques and PCA

    Science.gov (United States)

    Nika, Varvara; Babyn, Paul; Zhu, Hongmei

    2014-03-01

    Automatic change detection methods for identifying the changes of serial MR images taken at different times are of great interest to radiologists. The majority of existing change detection methods in medical imaging, and those of brain images in particular, include many preprocessing steps and rely mostly on statistical analysis of MRI scans. Although most methods utilize registration software, tissue classification remains a difficult and overwhelming task. Recently, dictionary learning techniques are used in many areas of image processing, such as image surveillance, face recognition, remote sensing, and medical imaging. In this paper we present the Eigen-Block Change Detection algorithm (EigenBlockCD). It performs local registration and identifies the changes between consecutive MR images of the brain. Blocks of pixels from baseline scan are used to train local dictionaries that are then used to detect changes in the follow-up scan. We use PCA to reduce the dimensionality of the local dictionaries and the redundancy of data. Choosing the appropriate distance measure significantly affects the performance of our algorithm. We examine the differences between L1 and L2 norms as two possible similarity measures in the EigenBlockCD. We show the advantages of L2 norm over L1 norm theoretically and numerically. We also demonstrate the performance of the EigenBlockCD algorithm for detecting changes of MR images and compare our results with those provided in recent literature. Experimental results with both simulated and real MRI scans show that the EigenBlockCD outperforms the previous methods. It detects clinical changes while ignoring the changes due to patient's position and other acquisition artifacts.

  13. Desktop Publishing as a Learning Resources Service.

    Science.gov (United States)

    Drake, David

    In late 1988, Midland College in Texas implemented a desktop publishing service to produce instructional aids and reduce and complement the workload of the campus print shop. The desktop service was placed in the Media Services Department of the Learning Resource Center (LRC) for three reasons: the LRC was already established as a campus-wide…

  14. Introducing RFID at Middlesex University Learning Resources

    Science.gov (United States)

    Hopkinson, Alan; Chandrakar, Rajesh

    2006-01-01

    Purpose: To describe the first year of the implementation of radio frequency identification (RFID) in Middlesex University Learning Resources. Design/methodology/approach: The technology is explained in detail to set the scene. Information on the implementation is presented in chronological order. Findings: Problems which would generally be…

  15. Relationship between learning resources and student's academic ...

    African Journals Online (AJOL)

    The study investigated relationship between learning resources and student's academic achievement in science subjects in Taraba State Secondary Schools. A total of 35 science teachers and 18 science head of departments from 6 schools from three geopolitical zones of Taraba State were involved in the study.

  16. Enhanced Resource Descriptions Help Learning Matrix Users.

    Science.gov (United States)

    Roempler, Kimberly S.

    2003-01-01

    Describes the Learning Matrix digital library which focuses on improving the preparation of math and science teachers by supporting faculty who teach introductory math and science courses in two- and four-year colleges. Suggests it is a valuable resource for school library media specialists to support new science and math teachers. (LRW)

  17. Deep supervised dictionary learning for no-reference image quality assessment

    Science.gov (United States)

    Huang, Yuge; Liu, Xuesong; Tian, Xiang; Zhou, Fan; Chen, Yaowu; Jiang, Rongxin

    2018-03-01

    We propose a deep convolutional neural network (CNN) for general no-reference image quality assessment (NR-IQA), i.e., accurate prediction of image quality without a reference image. The proposed model consists of three components such as a local feature extractor that is a fully CNN, an encoding module with an inherent dictionary that aggregates local features to output a fixed-length global quality-aware image representation, and a regression module that maps the representation to an image quality score. Our model can be trained in an end-to-end manner, and all of the parameters, including the weights of the convolutional layers, the dictionary, and the regression weights, are simultaneously learned from the loss function. In addition, the model can predict quality scores for input images of arbitrary sizes in a single step. We tested our method on commonly used image quality databases and showed that its performance is comparable with that of state-of-the-art general-purpose NR-IQA algorithms.

  18. Sparse representations via learned dictionaries for x-ray angiogram image denoising

    Science.gov (United States)

    Shang, Jingfan; Huang, Zhenghua; Li, Qian; Zhang, Tianxu

    2018-03-01

    X-ray angiogram image denoising is always an active research topic in the field of computer vision. In particular, the denoising performance of many existing methods had been greatly improved by the widely use of nonlocal similar patches. However, the only nonlocal self-similar (NSS) patch-based methods can be still be improved and extended. In this paper, we propose an image denoising model based on the sparsity of the NSS patches to obtain high denoising performance and high-quality image. In order to represent the sparsely NSS patches in every location of the image well and solve the image denoising model more efficiently, we obtain dictionaries as a global image prior by the K-SVD algorithm over the processing image; Then the single and effectively alternating directions method of multipliers (ADMM) method is used to solve the image denoising model. The results of widely synthetic experiments demonstrate that, owing to learned dictionaries by K-SVD algorithm, a sparsely augmented lagrangian image denoising (SALID) model, which perform effectively, obtains a state-of-the-art denoising performance and better high-quality images. Moreover, we also give some denoising results of clinical X-ray angiogram images.

  19. Coupled Dictionary Learning for the Detail-Enhanced Synthesis of 3-D Facial Expressions.

    Science.gov (United States)

    Liang, Haoran; Liang, Ronghua; Song, Mingli; He, Xiaofei

    2016-04-01

    The desire to reconstruct 3-D face models with expressions from 2-D face images fosters increasing interest in addressing the problem of face modeling. This task is important and challenging in the field of computer animation. Facial contours and wrinkles are essential to generate a face with a certain expression; however, these details are generally ignored or are not seriously considered in previous studies on face model reconstruction. Thus, we employ coupled radius basis function networks to derive an intermediate 3-D face model from a single 2-D face image. To optimize the 3-D face model further through landmarks, a coupled dictionary that is related to 3-D face models and their corresponding 3-D landmarks is learned from the given training set through local coordinate coding. Another coupled dictionary is then constructed to bridge the 2-D and 3-D landmarks for the transfer of vertices on the face model. As a result, the final 3-D face can be generated with the appropriate expression. In the testing phase, the 2-D input faces are converted into 3-D models that display different expressions. Experimental results indicate that the proposed approach to facial expression synthesis can obtain model details more effectively than previous methods can.

  20. The Efficacy of Dictionary Use while Reading for Learning New Words

    Science.gov (United States)

    Hamilton, Harley

    2012-01-01

    The researcher investigated the use of three types of dictionaries while reading by high school students with severe to profound hearing loss. The objective of the study was to determine the effectiveness of each type of dictionary for acquiring the meanings of unknown vocabulary in text. The three types of dictionaries were (a) an online…

  1. Knot detection in X-ray images of wood planks using dictionary learning

    DEFF Research Database (Denmark)

    Hansson, Nils Mattias; Enescu, Alexandru; Brandt, Sami Sebastian

    2015-01-01

    This paper considers a novel application of x-ray imaging of planks, for the purpose of detecting knots in high quality furniture wood. X-ray imaging allows the detection of knots invisible from the surface to conventional cameras. Our approach is based on texture analysis, or more specifically, ......, discriminative dictionary learning. Experiments show that the knot detection and segmentation can be accurately performed by our approach. This is a promising result and can be directly applied in industrial processing of furniture wood.......This paper considers a novel application of x-ray imaging of planks, for the purpose of detecting knots in high quality furniture wood. X-ray imaging allows the detection of knots invisible from the surface to conventional cameras. Our approach is based on texture analysis, or more specifically...

  2. Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing

    International Nuclear Information System (INIS)

    Chen Yang; Shi Luyao; Shu Huazhong; Luo Limin; Coatrieux, Jean-Louis; Yin Xindao; Toumoulin, Christine

    2013-01-01

    In abdomen computed tomography (CT), repeated radiation exposures are often inevitable for cancer patients who receive surgery or radiotherapy guided by CT images. Low-dose scans should thus be considered in order to avoid the harm of accumulative x-ray radiation. This work is aimed at improving abdomen tumor CT images from low-dose scans by using a fast dictionary learning (DL) based processing. Stemming from sparse representation theory, the proposed patch-based DL approach allows effective suppression of both mottled noise and streak artifacts. The experiments carried out on clinical data show that the proposed method brings encouraging improvements in abdomen low-dose CT images with tumors. (paper)

  3. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    International Nuclear Information System (INIS)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K

    2014-01-01

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment

  4. TU-F-BRF-06: 3D Pancreas MRI Segmentation Using Dictionary Learning and Manifold Clustering

    Energy Technology Data Exchange (ETDEWEB)

    Gou, S; Rapacchi, S; Hu, P; Sheng, K [UCLA School of Medicine, Los Angeles, CA (United States)

    2014-06-15

    Purpose: The recent advent of MRI guided radiotherapy machines has lent an exciting platform for soft tissue target localization during treatment. However, tools to efficiently utilize MRI images for such purpose have not been developed. Specifically, to efficiently quantify the organ motion, we develop an automated segmentation method using dictionary learning and manifold clustering (DLMC). Methods: Fast 3D HASTE and VIBE MR images of 2 healthy volunteers and 3 patients were acquired. A bounding box was defined to include pancreas and surrounding normal organs including the liver, duodenum and stomach. The first slice of the MRI was used for dictionary learning based on mean-shift clustering and K-SVD sparse representation. Subsequent images were iteratively reconstructed until the error is less than a preset threshold. The preliminarily segmentation was subject to the constraints of manifold clustering. The segmentation results were compared with the mean shift merging (MSM), level set (LS) and manual segmentation methods. Results: DLMC resulted in consistently higher accuracy and robustness than comparing methods. Using manual contours as the ground truth, the mean Dices indices for all subjects are 0.54, 0.56 and 0.67 for MSM, LS and DLMC, respectively based on the HASTE image. The mean Dices indices are 0.70, 0.77 and 0.79 for the three methods based on VIBE images. DLMC is clearly more robust on the patients with the diseased pancreas while LS and MSM tend to over-segment the pancreas. DLMC also achieved higher sensitivity (0.80) and specificity (0.99) combining both imaging techniques. LS achieved equivalent sensitivity on VIBE images but was more computationally inefficient. Conclusion: We showed that pancreas and surrounding normal organs can be reliably segmented based on fast MRI using DLMC. This method will facilitate both planning volume definition and imaging guidance during treatment.

  5. Dictionary of Microscopy

    Science.gov (United States)

    Heath, Julian

    2005-10-01

    The past decade has seen huge advances in the application of microscopy in all areas of science. This welcome development in microscopy has been paralleled by an expansion of the vocabulary of technical terms used in microscopy: terms have been coined for new instruments and techniques and, as microscopes reach even higher resolution, the use of terms that relate to the optical and physical principles underpinning microscopy is now commonplace. The Dictionary of Microscopy was compiled to meet this challenge and provides concise definitions of over 2,500 terms used in the fields of light microscopy, electron microscopy, scanning probe microscopy, x-ray microscopy and related techniques. Written by Dr Julian P. Heath, Editor of Microscopy and Analysis, the dictionary is intended to provide easy navigation through the microscopy terminology and to be a first point of reference for definitions of new and established terms. The Dictionary of Microscopy is an essential, accessible resource for: students who are new to the field and are learning about microscopes equipment purchasers who want an explanation of the terms used in manufacturers' literature scientists who are considering using a new microscopical technique experienced microscopists as an aide mémoire or quick source of reference librarians, the press and marketing personnel who require definitions for technical reports.

  6. Learning Resource Centre in a Company

    Directory of Open Access Journals (Sweden)

    Aleksander Pokovec

    2001-12-01

    Full Text Available In the conditions of growing competition people are becoming the essential competitive advantage. Because of too much work, stress, too many responsibilities and other factors, employees are often unmotivated. Everyday self-study is the best way that leads to excellence. In order to enable self-study for all employees, the organisation should organise their own learning resource centre that includes: educational videoprogrammes, audio tapes, books and e-learning programmes. All educational programmes should cover business and personal topics.

  7. Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

    Science.gov (United States)

    Moody, Daniela; Wohlberg, Brendt

    2018-01-02

    An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

  8. MEAT: An Authoring Tool for Generating Adaptable Learning Resources

    Science.gov (United States)

    Kuo, Yen-Hung; Huang, Yueh-Min

    2009-01-01

    Mobile learning (m-learning) is a new trend in the e-learning field. The learning services in m-learning environments are supported by fundamental functions, especially the content and assessment services, which need an authoring tool to rapidly generate adaptable learning resources. To fulfill the imperious demand, this study proposes an…

  9. The First Steps to a New Comprehensive Slovenian-Hungarian Dictionary: The Analysis of Relevant Bilingual Resources

    Directory of Open Access Journals (Sweden)

    Júlia Bálint Čeh

    2018-01-01

    Full Text Available The paper presents the analysis of existing bilingual Slovenian-Hungarian dictionaries, which was made as part of the project aiming to design a concept for a new comprehensive Slovenian-Hungarian dictionary. First, a short historical overview of Slovenian-Hungarian lexicography is provided, including first collections of dialect vocabulary, glossaries, and collections and dictionaries of idioms. Then, an overview of Slovenian-Hungarian and Hungarian-Slovenian dictionaries is made, the first one being published in 1961. The paper then focuses on a comparison on three Slovenian-Hungarian dictionaries, which are currently used by majority of users, namely Slovenian-Hungarian part of the dictionary by Elizabeta Bernjak (1995, Slovenian-Hungarian dictionary by Jože Hradil (1996, and Slovenian-Hungarian part of the Hradil’s bidirectional dictionary. The dictionaries are compared in terms of size, headword list, coverage, headword presentation, grammar information, as well as in terms of other elements of dictionary microstructure such as translations and examples. The discussion section includes an analysis of the coverage offered by the dictionaries of the vocabulary compilled by teachers at bilingual schools in Prekmurje. The results indicate that the coverage of various levels of vocabulary, frequent or rare, is rather poor; as dictionaries are medium-sized and outdated, this is to be expected, however as the analysis shows, some basic concepts are also often not covered (e.g. research, death, allergy. The second part of the discussion is dedicated to the presentation of selected examples of good practice in bilingual lexicography, such as Comprehensive English-Slovenian dictionary Oxford-DZS as the first bilingual dictionary in Slovenia to use the corpus-based approach, as well as offer much more contextual information on the headwords. Also presented are English-Spanish online dictionaries by Oxford University Press and Collins, the focus

  10. Low dose CT reconstruction via L1 norm dictionary learning using alternating minimization algorithm and balancing principle.

    Science.gov (United States)

    Wu, Junfeng; Dai, Fang; Hu, Gang; Mou, Xuanqin

    2018-04-18

    Excessive radiation exposure in computed tomography (CT) scans increases the chance of developing cancer and has become a major clinical concern. Recently, statistical iterative reconstruction (SIR) with l0-norm dictionary learning regularization has been developed to reconstruct CT images from the low dose and few-view dataset in order to reduce radiation dose. Nonetheless, the sparse regularization term adopted in this approach is l0-norm, which cannot guarantee the global convergence of the proposed algorithm. To address this problem, in this study we introduced the l1-norm dictionary learning penalty into SIR framework for low dose CT image reconstruction, and developed an alternating minimization algorithm to minimize the associated objective function, which transforms CT image reconstruction problem into a sparse coding subproblem and an image updating subproblem. During the image updating process, an efficient model function approach based on balancing principle is applied to choose the regularization parameters. The proposed alternating minimization algorithm was evaluated first using real projection data of a sheep lung CT perfusion and then using numerical simulation based on sheep lung CT image and chest image. Both visual assessment and quantitative comparison using terms of root mean square error (RMSE) and structural similarity (SSIM) index demonstrated that the new image reconstruction algorithm yielded similar performance with l0-norm dictionary learning penalty and outperformed the conventional filtered backprojection (FBP) and total variation (TV) minimization algorithms.

  11. Constrained dictionary learning and probabilistic hypergraph ranking for person re-identification

    Science.gov (United States)

    He, You; Wu, Song; Pu, Nan; Qian, Li; Xiao, Guoqiang

    2018-04-01

    Person re-identification is a fundamental and inevitable task in public security. In this paper, we propose a novel framework to improve the performance of this task. First, two different types of descriptors are extracted to represent a pedestrian: (1) appearance-based superpixel features, which are constituted mainly by conventional color features and extracted from the supepixel rather than a whole picture and (2) due to the limitation of discrimination of appearance features, the deep features extracted by feature fusion Network are also used. Second, a view invariant subspace is learned by dictionary learning constrained by the minimum negative sample (termed as DL-cMN) to reduce the noise in appearance-based superpixel feature domain. Then, we use deep features and sparse codes transformed by appearancebased features to establish the hyperedges respectively by k-nearest neighbor, rather than jointing different features simply. Finally, a final ranking is performed by probabilistic hypergraph ranking algorithm. Extensive experiments on three challenging datasets (VIPeR, PRID450S and CUHK01) demonstrate the advantages and effectiveness of our proposed algorithm.

  12. Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Jingbo Wei

    2016-12-01

    Full Text Available Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

  13. Could a multimodal dictionary serve as a learning tool? An examination of the impact of technologically enhanced visual glosses on L2 text comprehension

    Directory of Open Access Journals (Sweden)

    Takeshi Sato

    2016-09-01

    Full Text Available This study examines the efficacy of a multimodal online bilingual dictionary based on cognitive linguistics in order to explore the advantages and limitations of explicit multimodal L2 vocabulary learning. Previous studies have examined the efficacy of the verbal and visual representation of words while reading L2 texts, concluding that it facilitates incidental word retention. This study explores other potentials of multimodal L2 vocabulary learning: explicit learning with a multimodal dictionary could enhance not only word retention, but also text comprehension; the dictionary could serve not only as a reference tool, but also as a learning tool; and technology-enhanced visual glosses could facilitate deeper text comprehension. To verify these claims, this study investigates the multimodal representations’ effects on Japanese students learning L2 locative prepositions by developing two online dictionaries, one with static pictures and one with animations. The findings show the advantage of such dictionaries in explicit learning; however, no significant differences are found between the two types of visual glosses, either in the vocabulary or in the listening tests. This study confirms the effectiveness of multimodal L2 materials, but also emphasizes the need for further research into making the technologically enhanced materials more effective.

  14. Empirical research on dictionary use in foreign-language learning: survey and discussion

    NARCIS (Netherlands)

    Hulstijn, J.H.; Atkins, B.T.S.; Atkins, B.T.S.

    1998-01-01

    This paper begins with a brief survey, in the form of a classified bibliography of research into dictionary use. A discussion follows of the type of research required in order to increase one's insight into the cognitive processes involved in using a dictionary; the principal factors which affect

  15. Dictionary Culture of University Students Learning English as a Foreign Language in Turkey

    Science.gov (United States)

    Baskin, Sami; Mumcu, Muhsin

    2018-01-01

    Dictionaries, one of the oldest tools of language education, have continued to be a part of education although information technologies and concept of education has changed over time. Until today, with the help of the developments in technology both types of dictionaries have increased, and usage areas have expanded. Therefore, it is possible to…

  16. The Efficacy of Dictionary Use while Reading for Learning New Words

    Science.gov (United States)

    Hamilton, Harley

    2012-01-01

    This paper describes a study investigating the use of three types of dictionaries by deaf (i.e., with severe to profound hearing loss) high school students while reading to determine the effectiveness of each type for acquiring the meanings of unknown vocabulary in text. The dictionary types used include an online bilingual multimedia English-ASL…

  17. Web Resources and Tools for Slovenian with a Focus on the Slovenian-English Language Infrastructure: Dictionaries in the Digital Age

    Directory of Open Access Journals (Sweden)

    Mojca Šorli

    2017-12-01

    Full Text Available The article begins with a presentation of a selection of electronic monolingual and bi/multilingual lexicographic resources and corpora available today to contemporary users of Slovene. The focus is on works combined with English and designed for translation purposes which provide information on the meaning of words and wider lexical units, i.e., e-dictionaries, lexical databases, web translation tools and various corpora. In a separate sub-section the most common translation technologies are presented, together with an evaluation of their role in the modern translation process. Sections 2 and 3 provide a brief outline of the changes that have affected classical dictionary planning, compilation and use in the new digital environment, as well as of the relationship between dictionaries and related resources, such as lexical databases. Some stereotypes regarding dictionary use are identified and, in conclusion, the existing corpus-based databases for the Slovenian-English pair are presented, with a view to determining priorities for the future interlingual infrastructure action plans in Slovenia.

  18. Learning Method, Facilities And Infrastructure, And Learning Resources In Basic Networking For Vocational School

    OpenAIRE

    Pamungkas, Bian Dwi

    2017-01-01

    This study aims to examine the contribution of learning methods on learning output, the contribution of facilities and infrastructure on output learning, the contribution of learning resources on learning output, and the contribution of learning methods, the facilities and infrastructure, and learning resources on learning output. The research design is descriptive causative, using a goal-oriented assessment approach in which the assessment focuses on assessing the achievement of a goal. The ...

  19. Noise-aware dictionary-learning-based sparse representation framework for detection and removal of single and combined noises from ECG signal.

    Science.gov (United States)

    Satija, Udit; Ramkumar, Barathram; Sabarimalai Manikandan, M

    2017-02-01

    Automatic electrocardiogram (ECG) signal enhancement has become a crucial pre-processing step in most ECG signal analysis applications. In this Letter, the authors propose an automated noise-aware dictionary learning-based generalised ECG signal enhancement framework which can automatically learn the dictionaries based on the ECG noise type for effective representation of ECG signal and noises, and can reduce the computational load of sparse representation-based ECG enhancement system. The proposed framework consists of noise detection and identification, noise-aware dictionary learning, sparse signal decomposition and reconstruction. The noise detection and identification is performed based on the moving average filter, first-order difference, and temporal features such as number of turning points, maximum absolute amplitude, zerocrossings, and autocorrelation features. The representation dictionary is learned based on the type of noise identified in the previous stage. The proposed framework is evaluated using noise-free and noisy ECG signals. Results demonstrate that the proposed method can significantly reduce computational load as compared with conventional dictionary learning-based ECG denoising approaches. Further, comparative results show that the method outperforms existing methods in automatically removing noises such as baseline wanders, power-line interference, muscle artefacts and their combinations without distorting the morphological content of local waves of ECG signal.

  20. Discovery and Use of Online Learning Resources: Case Study Findings

    Science.gov (United States)

    Recker, Mimi M.; Dorward, James; Nelson, Laurie Miller

    2004-01-01

    Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called "learning objects." Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article…

  1. Discovery and Use of Online Learning Resources: Case Study Findings

    OpenAIRE

    Laurie Miller Nelson; James Dorward; Mimi M. Recker

    2004-01-01

    Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called learning objects. Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article describes a case study of mathematics and science teachers practices and desires surrounding the discovery, selection, and use of digital library resources for...

  2. Response monitoring using quantitative ultrasound methods and supervised dictionary learning in locally advanced breast cancer

    Science.gov (United States)

    Gangeh, Mehrdad J.; Fung, Brandon; Tadayyon, Hadi; Tran, William T.; Czarnota, Gregory J.

    2016-03-01

    A non-invasive computer-aided-theragnosis (CAT) system was developed for the early assessment of responses to neoadjuvant chemotherapy in patients with locally advanced breast cancer. The CAT system was based on quantitative ultrasound spectroscopy methods comprising several modules including feature extraction, a metric to measure the dissimilarity between "pre-" and "mid-treatment" scans, and a supervised learning algorithm for the classification of patients to responders/non-responders. One major requirement for the successful design of a high-performance CAT system is to accurately measure the changes in parametric maps before treatment onset and during the course of treatment. To this end, a unified framework based on Hilbert-Schmidt independence criterion (HSIC) was used for the design of feature extraction from parametric maps and the dissimilarity measure between the "pre-" and "mid-treatment" scans. For the feature extraction, HSIC was used to design a supervised dictionary learning (SDL) method by maximizing the dependency between the scans taken from "pre-" and "mid-treatment" with "dummy labels" given to the scans. For the dissimilarity measure, an HSIC-based metric was employed to effectively measure the changes in parametric maps as an indication of treatment effectiveness. The HSIC-based feature extraction and dissimilarity measure used a kernel function to nonlinearly transform input vectors into a higher dimensional feature space and computed the population means in the new space, where enhanced group separability was ideally obtained. The results of the classification using the developed CAT system indicated an improvement of performance compared to a CAT system with basic features using histogram of intensity.

  3. Noise reduction by sparse representation in learned dictionaries for application to blind tip reconstruction problem

    International Nuclear Information System (INIS)

    Jóźwiak, Grzegorz

    2017-01-01

    Scanning probe microscopy (SPM) is a well known tool used for the investigation of phenomena in objects in the nanometer size range. However, quantitative results are limited by the size and the shape of the nanoprobe used in experiments. Blind tip reconstruction (BTR) is a very popular method used to reconstruct the upper boundary on the shape of the probe. This method is known to be very sensitive to all kinds of interference in the atomic force microscopy (AFM) image. Due to mathematical morphology calculus, the interference makes the BTR results biased rather than randomly disrupted. For this reason, the careful choice of methods used for image enhancement and denoising, as well as the shape of a calibration sample are very important. In the paper, the results of thorough investigations on the shape of a calibration standard are shown. A novel shape is proposed and a tool for the simulation of AFM images of this calibration standard was designed. It was shown that careful choice of the initial tip allows us to use images of hole structures to blindly reconstruct the shape of a probe. The simulator was used to test the impact of modern filtration algorithms on the BTR process. These techniques are based on sparse approximation with function dictionaries learned on the basis of an image itself. Various learning algorithms and parameters were tested to determine the optimal combination for sparse representation. It was observed that the strong reduction of noise does not guarantee strong reduction in reconstruction errors. It seems that further improvements will be possible by the combination of BTR and a noise reduction procedure. (paper)

  4. A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

    OpenAIRE

    Zhiqin Zhu; Guanqiu Qi; Yi Chai; Penghua Li

    2017-01-01

    In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different ...

  5. Towards the Sigma Online Learning Model for crowdsourced recommendations of good web-based learning resources

    OpenAIRE

    Aaberg, Robin Garen

    2016-01-01

    The web based learning resources is believed to be playing an active role in the learning environment of higher education today. This qualitative study is exploring how students at Bergen University College incorporate web-based learning resources in their learning activities. At the core of this research is the problem of retrieving good web-resources after their first discovery. Usefull and knowledge granting web-resources are discovered within a context of topics, objectives. It is here ar...

  6. TERRESTRIAL LASER SCANNER DATA DENOISING BY DICTIONARY LEARNING OF SPARSE CODING

    Directory of Open Access Journals (Sweden)

    E. Smigiel

    2013-07-01

    Full Text Available Point cloud processing is basically a signal processing issue. The huge amount of data which are collected with Terrestrial Laser Scanners or photogrammetry techniques faces the classical questions linked with signal or image processing. Among others, denoising and compression are questions which have to be addressed in this context. That is why, one has to turn attention to signal theory because it is susceptible to guide one's good practices or to inspire new ideas from the latest developments of this field. The literature have been showing for decades how strong and dynamic, the theoretical field is and how efficient the derived algorithms have become. For about ten years, a new technique has appeared: known as compressive sensing or compressive sampling, it is based first on sparsity which is an interesting characteristic of many natural signals. Based on this concept, many denoising and compression techniques have shown their efficiencies. Sparsity can also be seen as redundancy removal of natural signals. Taken along with incoherent measurements, compressive sensing has appeared and uses the idea that redundancy could be removed at the very early stage of sampling. Hence, instead of sampling the signal at high sampling rate and removing redundancy as a second stage, the acquisition stage itself may be run with redundancy removal. This paper gives some theoretical aspects of these ideas with first simple mathematics. Then, the idea of compressive sensing for a Terrestrial Laser Scanner is examined as a potential research question and finally, a denoising scheme based on a dictionary learning of sparse coding is experienced. Both the theoretical discussion and the obtained results show that it is worth staying close to signal processing theory and its community to take benefit of its latest developments.

  7. Radiation dose reduction with dictionary learning based processing for head CT

    International Nuclear Information System (INIS)

    Chen, Yang; Shi, Luyao; Hu, Yining; Luo, Limin; Yang, Jiang; Yin, Xindao; Coatrieux, Jean-Louis

    2014-01-01

    In CT, ionizing radiation exposure from the scan has attracted much concern from patients and doctors. This work is aimed at improving head CT images from low-dose scans by using a fast Dictionary learning (DL) based post-processing. Both Low-dose CT (LDCT) and Standard-dose CT (SDCT) nonenhanced head images were acquired in head examination from a multi-detector row Siemens Somatom Sensation 16 CT scanner. One hundred patients were involved in the experiments. Two groups of LDCT images were acquired with 50 % (LDCT50 %) and 25 % (LDCT25 %) tube current setting in SDCT. To give quantitative evaluation, Signal to noise ratio (SNR) and Contrast to noise ratio (CNR) were computed from the Hounsfield unit (HU) measurements of GM, WM and CSF tissues. A blinded qualitative analysis was also performed to assess the processed LDCT datasets. Fifty and seventy five percent dose reductions are obtained for the two LDCT groups (LDCT50 %, 1.15 ± 0.1 mSv; LDCT25 %, 0.58 ± 0.1 mSv; SDCT, 2.32 ± 0.1 mSv; P < 0.001). Significant SNR increase over the original LDCT images is observed in the processed LDCT images for all the GM, WM and CSF tissues. Significant GM–WM CNR enhancement is noted in the DL processed LDCT images. Higher SNR and CNR than the reference SDCT images can even be achieved in the processed LDCT50 % and LDCT25 % images. Blinded qualitative review validates the perceptual improvements brought by the proposed approach. Compared to the original LDCT images, the application of DL processing in head CT is associated with a significant improvement of image quality.

  8. Task-Driven Dictionary Learning Based on Mutual Information for Medical Image Classification.

    Science.gov (United States)

    Diamant, Idit; Klang, Eyal; Amitai, Michal; Konen, Eli; Goldberger, Jacob; Greenspan, Hayit

    2017-06-01

    We present a novel variant of the bag-of-visual-words (BoVW) method for automated medical image classification. Our approach improves the BoVW model by learning a task-driven dictionary of the most relevant visual words per task using a mutual information-based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. These maps demonstrate how the algorithm works and show the spatial layout of the most relevant words. We applied our algorithm to three different tasks: chest x-ray pathology identification (of four pathologies: cardiomegaly, enlarged mediastinum, right consolidation, and left consolidation), liver lesion classification into four categories in computed tomography (CT) images and benign/malignant clusters of microcalcifications (MCs) classification in breast mammograms. Validation was conducted on three datasets: 443 chest x-rays, 118 portal phase CT images of liver lesions, and 260 mammography MCs. The proposed method improves the classical BoVW method for all tested applications. For chest x-ray, area under curve of 0.876 was obtained for enlarged mediastinum identification compared to 0.855 using classical BoVW (with p-value 0.01). For MC classification, a significant improvement of 4% was achieved using our new approach (with p-value = 0.03). For liver lesion classification, an improvement of 6% in sensitivity and 2% in specificity were obtained (with p-value 0.001). We demonstrated that classification based on informative selected set of words results in significant improvement. Our new BoVW approach shows promising results in clinically important domains. Additionally, it can discover relevant parts of images for the task at hand without explicit annotations for training data. This can provide computer-aided support for medical experts in challenging image analysis tasks.

  9. Health professional learner attitudes and use of digital learning resources.

    Science.gov (United States)

    Maloney, Stephen; Chamberlain, Michael; Morrison, Shane; Kotsanas, George; Keating, Jennifer L; Ilic, Dragan

    2013-01-16

    Web-based digital repositories allow educational resources to be accessed efficiently and conveniently from diverse geographic locations, hold a variety of resource formats, enable interactive learning, and facilitate targeted access for the user. Unlike some other learning management systems (LMS), resources can be retrieved through search engines and meta-tagged labels, and content can be streamed, which is particularly useful for multimedia resources. The aim of this study was to examine usage and user experiences of an online learning repository (Physeek) in a population of physiotherapy students. The secondary aim of this project was to examine how students prefer to access resources and which resources they find most helpful. The following data were examined using an audit of the repository server: (1) number of online resources accessed per day in 2010, (2) number of each type of resource accessed, (3) number of resources accessed during business hours (9 am to 5 pm) and outside business hours (years 1-4), (4) session length of each log-on (years 1-4), and (5) video quality (bit rate) of each video accessed. An online questionnaire and 3 focus groups assessed student feedback and self-reported experiences of Physeek. Students preferred the support provided by Physeek to other sources of educational material primarily because of its efficiency. Peak usage commonly occurred at times of increased academic need (ie, examination times). Students perceived online repositories as a potential tool to support lifelong learning and health care delivery. The results of this study indicate that today's health professional students welcome the benefits of online learning resources because of their convenience and usability. This represents a transition away from traditional learning styles and toward technological learning support and may indicate a growing link between social immersions in Internet-based connections and learning styles. The true potential for Web

  10. LeadMine: a grammar and dictionary driven approach to entity recognition

    Science.gov (United States)

    2015-01-01

    Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

  11. Mobile authoring of open educational resources for authentic learning scenarios

    NARCIS (Netherlands)

    Tabuenca, Bernardo; Kalz, Marco; Ternier, Stefaan; Specht, Marcus

    2014-01-01

    The proliferation of smartphones in the last decade and the number of publications in the field of authoring systems for computer-assisted learning depict a scenario that needs to be explored in order to facilitate the scaffolding of learning activities across contexts. Learning resources are

  12. A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data.

    Science.gov (United States)

    Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

    2018-01-01

    The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks.

  13. A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data

    Science.gov (United States)

    Ge, Bao; Li, Xiang; Jiang, Xi; Sun, Yifei; Liu, Tianming

    2018-01-01

    The exponential growth of fMRI big data offers researchers an unprecedented opportunity to explore functional brain networks. However, this opportunity has not been fully explored yet due to the lack of effective and efficient tools for handling such fMRI big data. One major challenge is that computing capabilities still lag behind the growth of large-scale fMRI databases, e.g., it takes many days to perform dictionary learning and sparse coding of whole-brain fMRI data for an fMRI database of average size. Therefore, how to reduce the data size but without losing important information becomes a more and more pressing issue. To address this problem, we propose a signal sampling approach for significant fMRI data reduction before performing structurally-guided dictionary learning and sparse coding of whole brain's fMRI data. We compared the proposed structurally guided sampling method with no sampling, random sampling and uniform sampling schemes, and experiments on the Human Connectome Project (HCP) task fMRI data demonstrated that the proposed method can achieve more than 15 times speed-up without sacrificing the accuracy in identifying task-evoked functional brain networks. PMID:29706880

  14. SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

    Directory of Open Access Journals (Sweden)

    Shengli Song

    2016-08-01

    Full Text Available Automatic target recognition (ATR in synthetic aperture radar (SAR images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG-like feature for SAR ATR (called SAR-HOG. Then, we propose a supervised discriminative dictionary learning (SDDL method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR, in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC scenario “Training 17 ∘ —Testing 45 ∘ ”, the proposed method improves remarkably with respect to the previous works.

  15. Dynamic versus Static Dictionary with and without Printed Focal Words in e-Book Reading as Facilitator for Word Learning

    Science.gov (United States)

    Korat, Ofra; Levin, Iris; Ben-Shabt, Anat; Shneor, Dafna; Bokovza, Limor

    2014-01-01

    We investigated the extent to which a dictionary embedded in an e-book with static or dynamic visuals with and without printed focal words affects word learning. A pretest-posttest design was used to measure gains of expressive words' meaning and their spelling. The participants included 250 Hebrew-speaking second graders from…

  16. Could a Multimodal Dictionary Serve as a Learning Tool? An Examination of the Impact of Technologically Enhanced Visual Glosses on L2 Text Comprehension

    Science.gov (United States)

    Sato, Takeshi

    2016-01-01

    This study examines the efficacy of a multimodal online bilingual dictionary based on cognitive linguistics in order to explore the advantages and limitations of explicit multimodal L2 vocabulary learning. Previous studies have examined the efficacy of the verbal and visual representation of words while reading L2 texts, concluding that it…

  17. What can we learn from resource pulses?

    Science.gov (United States)

    Yang, Louie H; Bastow, Justin L; Spence, Kenneth O; Wright, Amber N

    2008-03-01

    An increasing number of studies in a wide range of natural systems have investigated how pulses of resource availability influence ecological processes at individual, population, and community levels. Taken together, these studies suggest that some common processes may underlie pulsed resource dynamics in a wide diversity of systems. Developing a common framework of terms and concepts for the study of resource pulses may facilitate greater synthesis among these apparently disparate systems. Here, we propose a general definition of the resource pulse concept, outline some common patterns in the causes and consequences of resource pulses, and suggest a few key questions for future investigations. We define resource pulses as episodes of increased resource availability in space and time that combine low frequency (rarity), large magnitude (intensity), and short duration (brevity), and emphasize the importance of considering resource pulses at spatial and temporal scales relevant to specific resource-onsumer interactions. Although resource pulses are uncommon events for consumers in specific systems, our review of the existing literature suggests that pulsed resource dynamics are actually widespread phenomena in nature. Resource pulses often result from climatic and environmental factors, processes of spatiotemporal accumulation and release, outbreak population dynamics, or a combination of these factors. These events can affect life history traits and behavior at the level of individual consumers, numerical responses at the population level, and indirect effects at the community level. Consumers show strategies for utilizing ephemeral resources opportunistically, reducing resource variability by averaging over larger spatial scales, and tolerating extended interpulse periods of reduced resource availability. Resource pulses can also create persistent effects in communities through several mechanisms. We suggest that the study of resource pulses provides opportunities

  18. WordEdge® A Career Mobility Guide to High Speed Dictionary-Based Electronic Learning and Testing

    Directory of Open Access Journals (Sweden)

    Robert Oliphant

    2009-01-01

    Full Text Available As Thomas Kuhn taught us, misery loves innovation even more than company. Small wonder our recession worriers — and who isn’t one these days, directly or indirectly? — are desperately looking for new and practical ways to increase their job mobility. Statistically considered, since most unskilled jobs are already filled, jobseekers from shrinking fields of employment are being advised to broaden their search to include entry level jobs in new high tech fields that are either stable or expanding, e.g., health care.Let’s grant that each high tech field has its own hands-on skills. But it’s also true that each field, e.g., plumbing, has its own high tech vocabulary which each candidate for employment is expected to know or learn, including correct pronunciation, very much like an aspiring restaurant server learning the complete menu by heart. Hence the desirability of acquiring preliminary mastery of an employment field’s high tech vocabulary well in ADVANCE of the first interview, not in a panicky last minute cram session. Until recently, the only way we could acquire a preliminary mastery of, say, health care terms was to take a course (inconvenient and expensive or to study a specific-field booklet (usually limited inscope. Today, however, our current partnership between print dictionaries and their electronic versions gives any job candidate quick access to an amazingly efficient learning tool for masteringa wide range of high tech vocabularies in current use. Here’s the why and how of our dictionary-based learning and testing route.

  19. Discovery and Use of Online Learning Resources: Case Study Findings

    Directory of Open Access Journals (Sweden)

    Laurie Miller Nelson

    2004-04-01

    Full Text Available Much recent research and funding have focused on building Internet-based repositories that contain collections of high-quality learning resources, often called ‘learning objects.’ Yet little is known about how non-specialist users, in particular teachers, find, access, and use digital learning resources. To address this gap, this article describes a case study of mathematics and science teachers’ practices and desires surrounding the discovery, selection, and use of digital library resources for instructional purposes. Findings suggest that the teacher participants used a broad range of search strategies in order to find resources that they deemed were age-appropriate, current, and accurate. They intended to include these resources with little modifications into planned instructional activities. The article concludes with a discussion of the implications of the findings for improving the design of educational digital library systems, including tools supporting resource reuse.

  20. A Geometric Dictionary Learning Based Approach for Fluorescence Spectroscopy Image Fusion

    Directory of Open Access Journals (Sweden)

    Zhiqin Zhu

    2017-02-01

    Full Text Available In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution.

  1. Online Dictionaries and the Teaching/Learning of English in the Expanding Circle

    Science.gov (United States)

    Fuertes-Olivera, Pedro A.; Cabello de Alba, Beatriz Perez

    2012-01-01

    This article follows current research on English for Specific Business Purposes, which focuses on the analysis of contextualized business genres and on identifying the strategies that can be associated with effective business communication (Nickerson, 2005). It explores whether free internet dictionaries can be used for promoting effective…

  2. Building a protein name dictionary from full text: a machine learning term extraction approach

    Directory of Open Access Journals (Sweden)

    Campagne Fabien

    2005-04-01

    Full Text Available Abstract Background The majority of information in the biological literature resides in full text articles, instead of abstracts. Yet, abstracts remain the focus of many publicly available literature data mining tools. Most literature mining tools rely on pre-existing lexicons of biological names, often extracted from curated gene or protein databases. This is a limitation, because such databases have low coverage of the many name variants which are used to refer to biological entities in the literature. Results We present an approach to recognize named entities in full text. The approach collects high frequency terms in an article, and uses support vector machines (SVM to identify biological entity names. It is also computationally efficient and robust to noise commonly found in full text material. We use the method to create a protein name dictionary from a set of 80,528 full text articles. Only 8.3% of the names in this dictionary match SwissProt description lines. We assess the quality of the dictionary by studying its protein name recognition performance in full text. Conclusion This dictionary term lookup method compares favourably to other published methods, supporting the significance of our direct extraction approach. The method is strong in recognizing name variants not found in SwissProt.

  3. Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

    Directory of Open Access Journals (Sweden)

    Jin Qi

    Full Text Available Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

  4. Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

    Science.gov (United States)

    Qi, Jin; Yang, Zhiyong

    2014-01-01

    Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

  5. Graded Lexicons: New Resources for Educational Purposes and Much More

    Science.gov (United States)

    Gala, Núria; Billami, Mokhtar B.; François, Thomas; Bernhard, Delphine

    2015-01-01

    Computational tools and resources play an important role for vocabulary acquisition. Although a large variety of dictionaries and learning games are available, few resources provide information about the complexity of a word, either for learning or for comprehension. The idea here is to use frequency counts combined with intralexical variables to…

  6. The fluidities of digital learning environments and resources

    DEFF Research Database (Denmark)

    Hansbøl, Mikala

    2012-01-01

    The research project “Educational cultures and serious games on a global market place” (2009-2011) dealt with the challenge of the digital learning environment and hence it’s educational development space always existing outside the present space and hence scope of activities. With a reference...... and establishments of the virtual universe called Mingoville.com, the research shows a need to include in researchers’ conceptualizations of digital learning environments and resources, their shifting materialities and platformations and hence emerging (often unpredictable) agencies and educational development...... spaces. Keywords: Fluidity, digital learning environment, digital learning resource, educational development space...

  7. TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

    International Nuclear Information System (INIS)

    Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T

    2016-01-01

    Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

  8. TH-CD-206-09: Learning-Based MRI-CT Prostate Registration Using Spare Patch-Deformation Dictionary

    Energy Technology Data Exchange (ETDEWEB)

    Yang, X; Jani, A; Rossi, P; Mao, H; Curran, W; Liu, T [Emory University, Atlanta, GA (United States)

    2016-06-15

    Purpose: To enable MRI-guided prostate radiotherapy, MRI-CT deformable registration is required to map the MRI-defined tumor and key organ contours onto the CT images. Due to the intrinsic differences in grey-level intensity characteristics between MRI and CT images, the integration of MRI into CT-based radiotherapy is very challenging. We are developing a learning-based registration approach to address this technical challenge. Methods: We propose to estimate the deformation between MRI and CT images in a patch-wise fashion by using the sparse representation technique. Specifically, we assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. We first extract a set of key points in the new CT image. Then, for each key point, we adaptively construct a coupled dictionary from the training MRI-CT images, where each coupled element includes both appearance and deformation of the same image patch. After calculating the sparse coefficients in representing the patch appearance of each key point based on the constructed dictionary, we can predict the deformation for this point by applying the same sparse coefficients to the respective deformations in the dictionary. Results: This registration technique was validated with 10 prostate-cancer patients’ data and its performance was compared with the commonly used free-form-deformation-based registration. Several landmarks in both images were identified to evaluate the accuracy of our approach. Overall, the averaged target registration error of the intensity-based registration and the proposed method was 3.8±0.4 mm and 1.9±0.3 mm, respectively. Conclusion: We have developed a novel prostate MR-CT registration approach based on patch-deformation dictionary, demonstrated its clinical feasibility, and validated its accuracy. This technique will either reduce or compensate for the effect of patient-specific treatment variation measured during the course of

  9. Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

    Directory of Open Access Journals (Sweden)

    Yuan Xu

    2015-01-01

    Full Text Available Very high resolution (VHR image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened, while they ignore the change pattern description (i.e., how the changes changed, which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique.

  10. Collaborative Learning in Practice: Examples from Natural Resource ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2010-12-01

    Dec 1, 2010 ... Case studies show how, through joint efforts with researchers and other actors, local ... address and learn from challenges in managing natural resources. ... health, and health systems research relevant to the emerging crisis.

  11. Parenting styles and learned resourcefulness of Turkish adolescents.

    Science.gov (United States)

    Türkel, Yeşim Deniz; Tezer, Esin

    2008-01-01

    This study investigated the differences among 834 high school students regarding learned resourcefulness in terms of perceived parenting style and gender. The data were gathered by administering the Parenting Style Inventory (PSI) and Rosenbaum's Self-Control Schedule (SCS). The results of ANOVA pertaining to the scores of learned resourcefulness yielded a significant main effect for parenting style groups. Neither the main effect for gender nor the gender and parenting style interaction effect was significant. The findings suggest that those who perceived their parents as authoritative had a relatively high level of learned resourcefulness as compared to those who perceived their parents as neglectful and authoritarian. Findings also indicated that those who perceived their parents as indulgent had a higher level of learned resourcefulness than those who perceived their parents as neglectful and authoritarian.

  12. CLOUD EDUCATIONAL RESOURCES FOR PHYSICS LEARNING RESEARCHES SUPPORT

    Directory of Open Access Journals (Sweden)

    Oleksandr V. Merzlykin

    2015-10-01

    Full Text Available The definition of cloud educational resource is given in paper. Its program and information components are characterized. The virtualization as the technological ground of transforming from traditional electronic educational resources to cloud ones is reviewed. Such levels of virtualization are described: data storage device virtualization (Data as Service, hardware virtualization (Hardware as Service, computer virtualization (Infrastructure as Service, software system virtualization (Platform as Service, «desktop» virtualization (Desktop as Service, software user interface virtualization (Software as Service. Possibilities of designing the cloud educational resources system for physics learning researches support taking into account standards of learning objects metadata (accessing via OAI-PMH protocol and standards of learning tools interoperability (LTI are shown. The example of integration cloud educational resources into Moodle learning management system with use of OAI-PMH and LTI is given.

  13. Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods.

    Science.gov (United States)

    Megjhani, Murad; Terilli, Kalijah; Frey, Hans-Peter; Velazquez, Angela G; Doyle, Kevin William; Connolly, Edward Sander; Roh, David Jinou; Agarwal, Sachin; Claassen, Jan; Elhadad, Noemie; Park, Soojin

    2018-01-01

    Accurate prediction of delayed cerebral ischemia (DCI) after subarachnoid hemorrhage (SAH) can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone. 488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt-Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation). Classifiers (partial least squares and linear and kernel support vector machines) were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset. The performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS): AUC 0.54. Combined demographics and grading scales (baseline features): AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset. Current DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

  14. Incorporating High-Frequency Physiologic Data Using Computational Dictionary Learning Improves Prediction of Delayed Cerebral Ischemia Compared to Existing Methods

    Directory of Open Access Journals (Sweden)

    Murad Megjhani

    2018-03-01

    Full Text Available PurposeAccurate prediction of delayed cerebral ischemia (DCI after subarachnoid hemorrhage (SAH can be critical for planning interventions to prevent poor neurological outcome. This paper presents a model using convolution dictionary learning to extract features from physiological data available from bedside monitors. We develop and validate a prediction model for DCI after SAH, demonstrating improved precision over standard methods alone.Methods488 consecutive SAH admissions from 2006 to 2014 to a tertiary care hospital were included. Models were trained on 80%, while 20% were set aside for validation testing. Modified Fisher Scale was considered the standard grading scale in clinical use; baseline features also analyzed included age, sex, Hunt–Hess, and Glasgow Coma Scales. An unsupervised approach using convolution dictionary learning was used to extract features from physiological time series (systolic blood pressure and diastolic blood pressure, heart rate, respiratory rate, and oxygen saturation. Classifiers (partial least squares and linear and kernel support vector machines were trained on feature subsets of the derivation dataset. Models were applied to the validation dataset.ResultsThe performances of the best classifiers on the validation dataset are reported by feature subset. Standard grading scale (mFS: AUC 0.54. Combined demographics and grading scales (baseline features: AUC 0.63. Kernel derived physiologic features: AUC 0.66. Combined baseline and physiologic features with redundant feature reduction: AUC 0.71 on derivation dataset and 0.78 on validation dataset.ConclusionCurrent DCI prediction tools rely on admission imaging and are advantageously simple to employ. However, using an agnostic and computationally inexpensive learning approach for high-frequency physiologic time series data, we demonstrated that we could incorporate individual physiologic data to achieve higher classification accuracy.

  15. Human resource management and learning for innovation: pharmaceuticals in Mexico

    OpenAIRE

    Santiago-Rodriguez, Fernando

    2010-01-01

    This paper investigates the influence of human resource management on learning from internal and external sources of knowledge. Learning for innovation is a key ingredient of catching-up processes. The analysis builds on survey data about pharmaceutical firms in Mexico. Results show that the influence of human resource management is contingent on the knowledge flows and innovation goals pursued by the firm. Practices such as training-- particularly from external partners; and remuneration for...

  16. eLearning resources to supplement postgraduate neurosurgery training.

    OpenAIRE

    Stienen, MN; Schaller, K; Cock, H; Lisnic, V; Regli, L; Thomson, S

    2017-01-01

    BACKGROUND: In an increasingly complex and competitive professional environment, improving methods to educate neurosurgical residents is key to ensure high-quality patient care. Electronic (e)Learning resources promise interactive knowledge acquisition. We set out to give a comprehensive overview on available eLearning resources that aim to improve postgraduate neurosurgical training and review the available literature. MATERIAL AND METHODS: A MEDLINE query was performed, using the search ter...

  17. Sustainable Development in the Engineering Curriculum: Teaching and Learning Resources

    OpenAIRE

    Penlington, Roger; Steiner, Simon

    2014-01-01

    This repository of teaching and learning resources is a companion to the 2nd edition of “An Introduction to Sustainable Development in the Engineering Curriculum”, by Roger Penlington and Simon Steiner, originally created by The Higher Education Academy Engineering Subject Centre, Loughborough University. \\ud The purpose of this collection of teaching and learning re-sources is to provide access, with a brief resumé, to materials in curricula reform, recognition awards, and university movemen...

  18. eLearning resources to supplement postgraduate neurosurgery training.

    Science.gov (United States)

    Stienen, Martin N; Schaller, Karl; Cock, Hannah; Lisnic, Vitalie; Regli, Luca; Thomson, Simon

    2017-02-01

    In an increasingly complex and competitive professional environment, improving methods to educate neurosurgical residents is key to ensure high-quality patient care. Electronic (e)Learning resources promise interactive knowledge acquisition. We set out to give a comprehensive overview on available eLearning resources that aim to improve postgraduate neurosurgical training and review the available literature. A MEDLINE query was performed, using the search term "electronic AND learning AND neurosurgery". Only peer-reviewed English-language articles on the use of any means of eLearning to improve theoretical knowledge in postgraduate neurosurgical training were included. Reference lists were crosschecked for further relevant articles. Captured parameters were the year, country of origin, method of eLearning reported, and type of article, as well as its conclusion. eLearning resources were additionally searched for using Google. Of n = 301 identified articles by the MEDLINE search, n = 43 articles were analysed in detail. Applying defined criteria, n = 28 articles were excluded and n = 15 included. Most articles were generated within this decade, with groups from the USA, the UK and India having a leadership role. The majority of articles reviewed existing eLearning resources, others reported on the concept, development and use of generated eLearning resources. There was no article that scientifically assessed the effectiveness of eLearning resources (against traditional learning methods) in terms of efficacy or costs. Only one article reported on satisfaction rates with an eLearning tool. All authors of articles dealing with eLearning and the use of new media in neurosurgery uniformly agreed on its great potential and increasing future use, but most also highlighted some weaknesses and possible dangers. This review found only a few articles dealing with the modern aspects of eLearning as an adjunct to postgraduate neurosurgery training. Comprehensive

  19. Motivational Factors in Self-Directed Informal Learning from Online Learning Resources

    Science.gov (United States)

    Song, Donggil; Bonk, Curtis J.

    2016-01-01

    Learning is becoming more self-directed and informal with the support of emerging technologies. A variety of online resources have promoted informal learning by allowing people to learn on demand and just when needed. It is significant to understand self-directed informal learners' motivational aspects, their learning goals, obstacles, and…

  20. Learning Resources Organization Using Ontological Framework

    Science.gov (United States)

    Gavrilova, Tatiana; Gorovoy, Vladimir; Petrashen, Elena

    The paper describes the ontological approach to the knowledge structuring for the e-learning portal design as it turns out to be efficient and relevant to current domain conditions. It is primarily based on the visual ontology-based description of the content of the learning materials and this helps to provide productive and personalized access to these materials. The experience of ontology developing for Knowledge Engineering coursetersburg State University is discussed and “OntolingeWiki” tool for creating ontology-based e-learning portals is described.

  1. Parenting Styles and Learned Resourcefulness of Turkish Adolescents

    Science.gov (United States)

    Turkel, Yesim Deniz; Tezer, Esin

    2008-01-01

    This study investigated the differences among 834 high school students regarding learned resourcefulness in terms of perceived parenting style and gender. The data were gathered by administering the Parenting Style Inventory (PSI) and Rosenbaum's Self-Control Schedule (SCS). The results of ANOVA pertaining to the scores of learned resourcefulness…

  2. Measuring Social Learning in Participatory Approaches to Natural Resource Management

    NARCIS (Netherlands)

    Wal, van der M.M.; Kraker, de J.; Offermans, A.; Kroeze, C.; Kirschner, P.; Ittersum, van M.K.

    2014-01-01

    The role of social learning as a governance mechanism in natural resource management has been frequently highlighted, but progress in finding evidence for this role and gaining insight into the conditions that promote it are hampered by the lack of operational definitions of social learning and

  3. Measuring social learning in participatory approaches to natural resource management.

    NARCIS (Netherlands)

    Van der Wal, Merel; De Kraker, Joop; Offermans, Astrid; Kroeze, Carolien; Kirschner, Paul A.; Van Ittersum, Martin

    2018-01-01

    The role of social learning as a governance mechanism in natural resource management has been frequently highlighted, but progress in finding evidence for this role and gaining insight into the conditions that promote it are hampered by the lack of operational definitions of social learning and

  4. FBRDLR: Fast blind reconstruction approach with dictionary learning regularization for infrared microscopy spectra

    Science.gov (United States)

    Liu, Tingting; Liu, Hai; Chen, Zengzhao; Chen, Yingying; Wang, Shengming; Liu, Zhi; Zhang, Hao

    2018-05-01

    Infrared (IR) spectra are the fingerprints of the molecules, and the spectral band location closely relates to the structure of a molecule. Thus, specimen identification can be performed based on IR spectroscopy. However, spectrally overlapping components prevent the specific identification of hyperfine molecular information of different substances. In this paper, we propose a fast blind reconstruction approach for IR spectra, which is based on sparse and redundant representations over a dictionary. The proposed method recovers the spectrum with the discrete wavelet transform dictionary on its content. The experimental results demonstrate that the proposed method is superior because of the better performance when compared with other state-of-the-art methods. The method the authors used remove the instrument aging issue to a large extent, thus leading the reconstruction IR spectra a more convenient tool for extracting features of an unknown material and interpreting it.

  5. A Remote Sensing Image Fusion Method based on adaptive dictionary learning

    Science.gov (United States)

    He, Tongdi; Che, Zongxi

    2018-01-01

    This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

  6. Video interviewing as a learning resource

    DEFF Research Database (Denmark)

    Hedemann, Lars; Søndergaard, Helle Alsted

    2011-01-01

    The present investigation was carried out as a pilot study, with the aim of obtaining exploratory insights into the field of learning, and more specifically, how the use of video technology can be used as a mean to excel the outcome of the learning process. The motivation behind the study has its...... basis in the management education literature, and thereby in the discussion of how to organize teaching, in order to equip students with improved skills in reflective realization. Following the notion that experience is the basis for knowledge, the study was set out to explore how students at higher...... education programmes, i.e. at MSc and MBA level, can benefit from utilizing video recorded interviews in their process of learning and reflection. On the basis of the study, it is suggested that video interviewing makes up an interesting alternative to other learning approaches such as Simulation...

  7. Students' Understanding of Dictionary Entries: A Study with Respect to Four Learners' Dictionaries.

    Science.gov (United States)

    Jana, Abhra; Amritavalli, Vijaya; Amritavalli, R.

    2003-01-01

    Investigates the effects of definitional information in the form of dictionary entries, on second language learners' vocabulary learning in an instructed setting. Indian students (Native Hindi speakers) of English received monolingual English dictionary entries of five previously unknown words from four different learner's dictionaries. Results…

  8. MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

    International Nuclear Information System (INIS)

    Xu, Q; Liu, H; Xing, L; Yu, H; Wang, G

    2016-01-01

    Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

  9. MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT

    Energy Technology Data Exchange (ETDEWEB)

    Xu, Q [Xi’an Jiaotong University, Xi’an (China); Stanford University School of Medicine, Stanford, CA (United States); Liu, H; Xing, L [Stanford University School of Medicine, Stanford, CA (United States); Yu, H [University of Massachusetts Lowell, Lowell, MA (United States); Wang, G [Rensselaer Polytechnic Instute., Troy, NY (United States)

    2016-06-15

    Purpose: Spectral CT enabled by an energy-resolved photon-counting detector outperforms conventional CT in terms of material discrimination, contrast resolution, etc. One reconstruction method for spectral CT is to generate a color image from a reconstructed component in each energy channel. However, given the radiation dose, the number of photons in each channel is limited, which will result in strong noise in each channel and affect the final color reconstruction. Here we propose a novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improve the reconstruction. Methods: The proposed method has two important features: (1) guarantee of the patch based sparsity in each energy channel, which is the result of the dictionary based sparse representation constraint; (2) the explicit consideration of the correlations among different energy channels, which is realized by patch-by-patch nuclear norm-based low-rank constraint. For each channel, the dictionary consists of two sub-dictionaries. One is learned from the average of the images in all energy channels, and the other is learned from the average of the images in all energy channels except the current channel. With average operation to reduce noise, these two dictionaries can effectively preserve the structural details and get rid of artifacts caused by noise. Combining them together can express all structural information in current channel. Results: Dictionary learning based methods can obtain better results than FBP and the TV-based method. With low-rank constraint, the image quality can be further improved in the channel with more noise. The final color result by the proposed method has the best visual quality. Conclusion: The proposed method can effectively improve the image quality of low-dose spectral

  10. Low-dose CT image reconstruction using gain intervention-based dictionary learning

    Science.gov (United States)

    Pathak, Yadunath; Arya, K. V.; Tiwari, Shailendra

    2018-05-01

    Computed tomography (CT) approach is extensively utilized in clinical diagnoses. However, X-ray residue in human body may introduce somatic damage such as cancer. Owing to radiation risk, research has focused on the radiation exposure distributed to patients through CT investigations. Therefore, low-dose CT has become a significant research area. Many researchers have proposed different low-dose CT reconstruction techniques. But, these techniques suffer from various issues such as over smoothing, artifacts, noise, etc. Therefore, in this paper, we have proposed a novel integrated low-dose CT reconstruction technique. The proposed technique utilizes global dictionary-based statistical iterative reconstruction (GDSIR) and adaptive dictionary-based statistical iterative reconstruction (ADSIR)-based reconstruction techniques. In case the dictionary (D) is predetermined, then GDSIR can be used and if D is adaptively defined then ADSIR is appropriate choice. The gain intervention-based filter is also used as a post-processing technique for removing the artifacts from low-dose CT reconstructed images. Experiments have been done by considering the proposed and other low-dose CT reconstruction techniques on well-known benchmark CT images. Extensive experiments have shown that the proposed technique outperforms the available approaches.

  11. Enhancement of snow cover change detection with sparse representation and dictionary learning

    Science.gov (United States)

    Varade, D.; Dikshit, O.

    2014-11-01

    Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

  12. Learning foreign languages in teletandem: Resources and strategies

    Directory of Open Access Journals (Sweden)

    João A. TELLES

    2015-12-01

    Full Text Available ABSTRACT Teletandem is a virtual, collaborative, and autonomous context in which two speakers of different languages use the text, voice, and webcam image resources of VOIP technology (Skype to help each other learn their native language (or language of proficiency. This paper focuses on learners' studying processes and their responses to teletandem. We collected quantitative and qualitative data from 134 university students through an online questionnaire. Results show the content of students' learning processes, resources, activities, and strategies. We conclude with a critical discussion of the results and raise pedagogical implications for the use o-f teletandem as a mode of online intercultural contact to learn foreign languages.

  13. Stressors, academic performance, and learned resourcefulness in baccalaureate nursing students.

    Science.gov (United States)

    Goff, Anne-Marie

    2011-01-01

    High stress levels in nursing students may affect memory, concentration, and problem-solving ability, and may lead to decreased learning, coping, academic performance, and retention. College students with higher levels of learned resourcefulness develop greater self-confidence, motivation, and academic persistence, and are less likely to become anxious, depressed, and frustrated, but no studies specifically involve nursing students. This explanatory correlational study used Gadzella's Student-life Stress Inventory (SSI) and Rosenbaum's Self Control Scale (SCS) to explore learned resourcefulness, stressors, and academic performance in 53 baccalaureate nursing students. High levels of personal and academic stressors were evident, but not significant predictors of academic performance (p = .90). Age was a significant predictor of academic performance (p = learned resourcefulness scores than females and Caucasians. Studies in larger, more diverse samples are necessary to validate these findings.

  14. Learning about water resource sharing through game play

    Science.gov (United States)

    Ewen, Tracy; Seibert, Jan

    2016-10-01

    Games are an optimal way to teach about water resource sharing, as they allow real-world scenarios to be enacted. Both students and professionals learning about water resource management can benefit from playing games, through the process of understanding both the complexity of sharing of resources between different groups and decision outcomes. Here we address how games can be used to teach about water resource sharing, through both playing and developing water games. An evaluation of using the web-based game Irrigania in the classroom setting, supported by feedback from several educators who have used Irrigania to teach about the sustainable use of water resources, and decision making, at university and high school levels, finds Irrigania to be an effective and easy tool to incorporate into a curriculum. The development of two water games in a course for masters students in geography is also presented as a way to teach and communicate about water resource sharing. Through game development, students learned soft skills, including critical thinking, problem solving, team work, and time management, and overall the process was found to be an effective way to learn about water resource decision outcomes. This paper concludes with a discussion of learning outcomes from both playing and developing water games.

  15. Personalized Resource Recommendations using Learning from Positive and Unlabeled Examples

    Directory of Open Access Journals (Sweden)

    Priyank Thakkar

    2016-08-01

    Full Text Available This paper proposes a novel approach for recommending social resources using learning from positive and unlabeled examples. Bookmarks submitted on social bookmarking system delicious1 and artists on online music system last.fm2 are considered as social resources. The foremost feature of this problem is that there are no labeled negative resources/examples available for learning a recommender/classifier. The memory based collaborative filtering has served as the most widely used algorithm for social resource recommendation. However, its predictions are based on some ad hoc heuristic rules and its success depends on the availability of a critical mass of users. This paper proposes model based two-step techniques to learn a classifier using positive and unlabeled examples to address personalized resource recommendations. In the first step of these techniques, naïve Bayes classifier is employed to identify reliable negative resources. In the second step, to generate effective resource recommender, classification and regression tree and least square support vector machine (LS-SVM are exercised. A direct method based on LS-SVM is also put forward to realize the recommendation task. LS-SVM is customized for learning from positive and unlabeled data. Furthermore, the impact of feature selection on our proposed techniques is also studied. Memory based collaborative filtering as well as our proposed techniques exploit usage data to generate personalized recommendations. Experimental results show that the proposed techniques outperform existing method appreciably.

  16. Learning about water resource sharing through game play

    Directory of Open Access Journals (Sweden)

    T. Ewen

    2016-10-01

    Full Text Available Games are an optimal way to teach about water resource sharing, as they allow real-world scenarios to be enacted. Both students and professionals learning about water resource management can benefit from playing games, through the process of understanding both the complexity of sharing of resources between different groups and decision outcomes. Here we address how games can be used to teach about water resource sharing, through both playing and developing water games. An evaluation of using the web-based game Irrigania in the classroom setting, supported by feedback from several educators who have used Irrigania to teach about the sustainable use of water resources, and decision making, at university and high school levels, finds Irrigania to be an effective and easy tool to incorporate into a curriculum. The development of two water games in a course for masters students in geography is also presented as a way to teach and communicate about water resource sharing. Through game development, students learned soft skills, including critical thinking, problem solving, team work, and time management, and overall the process was found to be an effective way to learn about water resource decision outcomes. This paper concludes with a discussion of learning outcomes from both playing and developing water games.

  17. NCI Dictionary of Genetics Terms

    Science.gov (United States)

    A dictionary of more than 150 genetics-related terms written for healthcare professionals. This resource was developed to support the comprehensive, evidence-based, peer-reviewed PDQ cancer genetics information summaries.

  18. Diagnostic imaging learning resources evaluated by students and recent graduates.

    Science.gov (United States)

    Alexander, Kate; Bélisle, Marilou; Dallaire, Sébastien; Fernandez, Nicolas; Doucet, Michèle

    2013-01-01

    Many learning resources can help students develop the problem-solving abilities and clinical skills required for diagnostic imaging. This study explored veterinary students' perceptions of the usefulness of a variety of learning resources. Perceived resource usefulness was measured for different levels of students and for academic versus clinical preparation. Third-year (n=139) and final (fifth) year (n=105) students and recent graduates (n=56) completed questionnaires on perceived usefulness of each resource. Resources were grouped for comparison: abstract/low complexity (e.g., notes, multimedia presentations), abstract/high complexity (e.g., Web-based and film case repositories), concrete/low complexity (e.g., large-group "clicker" workshops), and concrete/high complexity (e.g., small-group interpretation workshops). Lower-level students considered abstract/low-complexity resources more useful for academic preparation and concrete resources more useful for clinical preparation. Higher-level students/recent graduates also considered abstract/low-complexity resources more useful for academic preparation. For all levels, lecture notes were considered highly useful. Multimedia slideshows were an interactive complement to notes. The usefulness of a Web-based case repository was limited by accessibility problems and difficulty. Traditional abstract/low-complexity resources were considered useful for more levels and contexts than expected. Concrete/high-complexity resources need to better represent clinical practice to be considered more useful for clinical preparation.

  19. Learning Networks: connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning

    NARCIS (Netherlands)

    Koper, Rob; Sloep, Peter

    2003-01-01

    Koper, E.J.R., Sloep, P.B. (2002) Learning Networks connecting people, organizations, autonomous agents and learning resources to establish the emergence of effective lifelong learning. RTD Programma into Learning Technologies 2003-2008. More is different… Heerlen, Nederland: Open Universiteit

  20. Direct user guidance in e-dictionaries for text production and text ...

    African Journals Online (AJOL)

    This article introduces a prototype of a writing (and learning) assistant for verbal relative clauses of the African language Sepedi, accessible from within a dictionary or from a word processor. It is an example of how a user support tool for complicated grammatical structures in a scarcely resourced language can be compiled.

  1. Work Identification - Inhibition or Resource for Learning?

    DEFF Research Database (Denmark)

    Olesen, Henning Salling

    2006-01-01

    This chapter discusses ways in which the subjective identification with work influences one's motivation to engage in learning. The chapter argues that a life history approach, which takes into account the subjective as well as the objective aspects of work can help us to understand the life...... transition challenges faced by older workers. The chapter draws on research and experience of the life history project at Roskilde University...

  2. Big Rock Candy Mountain. Resources for Our Education. A Learning to Learn Catalog. Winter 1970.

    Science.gov (United States)

    Portola Inst., Inc., Menlo Park, CA.

    Imaginative learning resources of various types are reported in this catalog under the subject headings of process learning, education environments, classroom materials and methods, home learning, and self discovery. Books reviewed are on the subjects of superstition, Eastern religions, fairy tales, philosophy, creativity, poetry, child care,…

  3. Pupil Science Learning in Resource-Based e-Learning Environments

    Science.gov (United States)

    So, Wing-mui Winnie; Ching, Ngai-ying Fiona

    2011-01-01

    With the rapid expansion of broadband Internet connection and availability of high performance yet low priced computers, many countries around the world are advocating the adoption of e-learning, the use of computer technology to improve learning and teaching. The trend of e-learning has urged many teachers to incorporate online resources in their…

  4. Hupa Natural Resources Dictionary.

    Science.gov (United States)

    Bennett, Ruth, Ed.; And Others

    Created by children in grades 5-8 who were enrolled in a year-long Hupa language class, this computer-generated, bilingual book contains descriptions and illustrations of local animals, birds, and fish. The introduction explains that students worked on a Macintosh computer able to print the Unifon alphabet used in writing the Hupa language.…

  5. MANDARIN CHINESE DICTIONARY.

    Science.gov (United States)

    WANG, FRED FANGYU

    IN RESPONSE TO THE NEEDS OF THE GROWING NUMBER OF AMERICAN HIGH SCHOOL AND COLLEGE STUDENTS LEARNING CHINESE, SETON HALL UNIVERSITY UNDERTOOK A CONTRACT WITH THE U.S. OFFICE OF EDUCATION TO COMPILE A BILINGUAL POCKET-SIZE DICTIONARY FOR BEGINNING STUDENTS OF SPOKEN MANDARIN CHINESE. THE PRESENT WORK IS THE CHINESE TO ENGLISH SECTION IN PRELIMINARY…

  6. Dictionary criticism

    DEFF Research Database (Denmark)

    Nielsen, Sandro

    2018-01-01

    Dictionary criticism is part of the lexicographical universe and reviewing of electronic and printed dictionaries is not an exercise in linguistics or in subject fields but an exercise in lexicography. It does not follow from this that dictionary reviews should not be based on a linguistic approach......, but that the linguistic approach is only one of several approaches to dictionary reviewing. Similarly, the linguistic and factual competences of reviewers should not be relegated to an insignificant position in the review process. Moreover, reviewers should define the object of their reviews, the dictionary, as a complex...... information tool with several components and in terms of significant lexicographical features: lexicographical functions, data and structures. This emphasises the fact that dictionaries are much more than mere vessels of linguistic categories, namely lexicographical tools that have been developed to fulfil...

  7. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    International Nuclear Information System (INIS)

    Bai, T; Yan, H; Shi, F; Jia, X; Jiang, Steve B.; Lou, Y; Xu, Q; Mou, X

    2014-01-01

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  8. WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging

    Energy Technology Data Exchange (ETDEWEB)

    Bai, T [Xi' an Jiaotong University, Xi' an (China); UT Southwestern Medical Center, Dallas, TX (United States); Yan, H; Shi, F; Jia, X; Jiang, Steve B. [UT Southwestern Medical Center, Dallas, TX (United States); Lou, Y [University of California Irvine, Irvine, CA (United States); Xu, Q; Mou, X [Xi' an Jiaotong University, Xi' an (China)

    2014-06-15

    Purpose: To develop a 3D dictionary learning based statistical reconstruction algorithm on graphic processing units (GPU), to improve the quality of low-dose cone beam CT (CBCT) imaging with high efficiency. Methods: A 3D dictionary containing 256 small volumes (atoms) of 3x3x3 voxels was trained from a high quality volume image. During reconstruction, we utilized a Cholesky decomposition based orthogonal matching pursuit algorithm to find a sparse representation on this dictionary basis of each patch in the reconstructed image, in order to regularize the image quality. To accelerate the time-consuming sparse coding in the 3D case, we implemented our algorithm in a parallel fashion by taking advantage of the tremendous computational power of GPU. Evaluations are performed based on a head-neck patient case. FDK reconstruction with full dataset of 364 projections is used as the reference. We compared the proposed 3D dictionary learning based method with a tight frame (TF) based one using a subset data of 121 projections. The image qualities under different resolutions in z-direction, with or without statistical weighting are also studied. Results: Compared to the TF-based CBCT reconstruction, our experiments indicated that 3D dictionary learning based CBCT reconstruction is able to recover finer structures, to remove more streaking artifacts, and is less susceptible to blocky artifacts. It is also observed that statistical reconstruction approach is sensitive to inconsistency between the forward and backward projection operations in parallel computing. Using high a spatial resolution along z direction helps improving the algorithm robustness. Conclusion: 3D dictionary learning based CBCT reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction. The GPU realization of the whole algorithm offers a significant efficiency enhancement, making this algorithm more feasible for potential

  9. Educational resources and tools for robotic learning

    Directory of Open Access Journals (Sweden)

    Pablo Gil Vazquez

    2012-07-01

    Full Text Available Normal.dotm 0 0 1 139 795 Universidad de Salamanca 6 1 976 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper discusses different teaching experiences which aims are the learning robotics in the university. These experiences are reflected in the development of several robotics courses and subjects at the University of Alicante.  The authors have created various educational platforms or they have used tools of free distribution and open source for the implementation of these courses. The main objetive of these courses is to teach the design and implementation of robotic solutions to solve various problems not only such as the control, programming and handling of robot but also the assembly, building and programming of educational mini-robots. On the one hand, new teaching tools are used such as simulators and virtual labs which make flexible the learning of robot arms. On the other hand, competitions are used to motivate students because this way, the students put into action the skills learned through building and programming low-cost mini-robots.

  10. [Systems, boundaries and resources: the lexicographer Gerhard Wahrig (1923-1978) and the genesis of his project "dictionary as database"].

    Science.gov (United States)

    Wahrig-Burfeind, Renate; Wahrig, Bettina

    2014-09-01

    Gerhard Wahrig's private archive has recently been retrieved by the authors and their siblings. We undertake a first survey of the unpublished material and concentrate on those aspects of Wahrig's bio-ergography which stand in relation to his life project "dictionary as database", realised shortly before his death. We argue that this project was conceived in the 1950s, while Wahrig was writing and editing dictionaries and encyclopedias for the Bibliographisches Institut in Leipzig. Wahrig, who had been a wireless operator in WWII, was well informed about the development of computers in West Germany. He was influenced both by Ferdinand de Saussure and by the discussion on language and structure in the Soviet Union. When he crossed the German/German border in 1959, he experienced mechanisms of exclusion before he could establish himself in the West as a lexicographer. We argue that the transfer of symbolic and human capital was problematic due to the cultural differences between the two Germanies. In the 1970s, he became a professor of General and Applied Linguistics. The project of a "dictionary as database" was intended both as a basis for extensive empirical research on the semantic structure of natural languages and as a working tool for the average user of the German language. Due to his untimely death, he could not pursue his idea of exploring semantic networks.

  11. Resource-based learning strategies: implications for students and institutions

    Directory of Open Access Journals (Sweden)

    Malcolm Ryan

    1996-12-01

    Full Text Available In its strategic plan, the University of Greenwich envisages a significant shift to resource-based learning (RBL. Enterprise in Higher Education (EHE has funded five pilot RBL projects during the past year, including one in introductory economics. The project was managed by three lecturers in the School of Social Sciences, supported by an Academic Development Officer. Learning outcomes were completely revised, and a range of assessment strategies, including computer-based tests, was identified. A resources guide was produced which identified the materials and activities that would enable students to achieve the learning outcomes. A number of innovations were adopted, including: • computer-based curriculum delivery, assessment, and student evaluation of the course; • an open approach to assessment; • abolishing lectures in favour of a diverse range of teaching and learning activities.

  12. Teaching with technology: free Web resources for teaching and learning.

    Science.gov (United States)

    Wink, Diane M; Smith-Stoner, Marilyn

    2011-01-01

    In this bimonthly series, the department editor examines how nurse educators can use Internet and Web-based computer technologies such as search, communication, collaborative writing tools; social networking, and social bookmarking sites; virtual worlds; and Web-based teaching and learning programs. In this article, the department editor and her coauthor describe free Web-based resources that can be used to support teaching and learning.

  13. Sustainability Learning in Natural Resource Use and Management

    Directory of Open Access Journals (Sweden)

    J. David Tàbara

    2007-12-01

    Full Text Available We contribute to the normative discussion on sustainability learning and provide a theoretical integrative framework intended to underlie the main components and interrelations of what learning is required for social learning to become sustainability learning. We demonstrate how this framework has been operationalized in a participatory modeling interface to support processes of natural resource integrated assessment and management. The key modeling components of our view are: structure (S, energy and resources (E, information and knowledge (I, social-ecological change (C, and the size, thresholds, and connections of different social-ecological systems. Our approach attempts to overcome many of the cultural dualisms that exist in the way social and ecological systems are perceived and affect many of the most common definitions of sustainability. Our approach also emphasizes the issue of limits within a total social-ecological system and takes a multiscale, agent-based perspective. Sustainability learning is different from social learning insofar as not all of the outcomes of social learning processes necessarily improve what we consider as essential for the long-term sustainability of social-ecological systems, namely, the co-adaptive systemic capacity of agents to anticipate and deal with the unintended, undesired, and irreversible negative effects of development. Hence, the main difference of sustainability learning from social learning is the content of what is learned and the criteria used to assess such content; these are necessarily related to increasing the capacity of agents to manage, in an integrative and organic way, the total social-ecological system of which they form a part. The concept of sustainability learning and the SEIC social-ecological framework can be useful to assess and communicate the effectiveness of multiple agents to halt or reverse the destructive trends affecting the life-support systems upon which all humans

  14. Mediated learning in the workplace: student perspectives on knowledge resources.

    Science.gov (United States)

    Shanahan, Madeleine

    2015-01-01

    In contemporary clinical practice, student radiographers can use many types of knowledge resources to support their learning. These include workplace experts, digital and nondigital information sources (eg, journals, textbooks, and the Internet), and electronic communication tools such as e-mail and social media. Despite the range of knowledge tools available, there is little available data about radiography students' use of these resources during clinical placement. A 68-item questionnaire was distributed to 62 students enrolled in an Australian university undergraduate radiography program after they completed a clinical placement. Researchers used descriptive statistics to analyze student access to workplace experts and their use of digital and nondigital information sources and electronic communication tools. A 5-point Likert scale (1 = very important; 5 = not important) was used to assess the present importance and perceived future value of knowledge tools for workplace learning. Of the 53 students who completed and returned the questionnaire anonymously, most rely on the knowledge of practicing technologists and on print and electronic information sources to support their learning; some students also use electronic communication tools. Students perceive that these knowledge resources also will be important tools for their future learning as qualified health professionals. The findings from this study present baseline data regarding the value students attribute to multiple knowledge tools and regarding student access to and use of these tools during clinical placement. In addition, most students have access to multiple knowledge tools in the workplace and incorporate these tools simultaneously into their overall learning practice during clinical placement. Although a range of knowledge tools is used in the workplace to support learning among student radiographers, the quality of each tool should be critically analyzed before it is adopted in practice

  15. Fostering Environmental Knowledge and Action through Online Learning Resources

    DEFF Research Database (Denmark)

    Maier, Carmen Daniela

    2010-01-01

    In order to secure correct understanding of environmental issues, to promote behavioral change and to encourage environmental action, more and more educational practices support and provide environmental programs. This article explores the design of online learning resources created for teachers...... and students by the GreenLearning environmental education program. The topic is approached from a social semiotic perspective. I conduct a multimodal analysis of the knowledge processes and the knowledge selection types that characterize the GreenLearning environmental education program and its online...

  16. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  17. Use of Monolingual and Bilingual Dictionaries among Students of English

    Directory of Open Access Journals (Sweden)

    Monika Kavalir

    2010-12-01

    Full Text Available The study of dictionary use in 32 firstyear students of English at the University of Ljubljana in the academic year 2009/2010 shows that students use a variety of dictionaries with a slight preponderance of monolingual dictionaries over bilingual ones. The bilingual dictionaries listed do not include some of the most recent and most comprehensive dictionaries while some of the most frequently used resources are quite modest sized. The students are already predominantly users of electronic and online dictionaries with a lower frequency of printed resources – a trend which is only likely to accelerate with the advent of new bilingual online dictionaries. These results have practical relevance for teachers in all sectors, from primary and secondary schools to universities, as they point towards a need for additional training in the use of bilingual dictionaries. The transition from printed to electronic and online resources can also be expected to induce changes in EFL methodology at all levels.

  18. DELIVERing Library Resources to the Virtual Learning Environment

    Science.gov (United States)

    Secker, Jane

    2005-01-01

    Purpose: Examines a project to integrate digital libraries and virtual learning environments (VLE) focusing on requirements for online reading list systems. Design/methodology/approach: Conducted a user needs analysis using interviews and focus groups and evaluated three reading or resource list management systems. Findings: Provides a technical…

  19. Insurance dictionary

    International Nuclear Information System (INIS)

    Mueller-Lutz, H.L.

    1984-01-01

    Special technical terms used in the world of insurance can hardly be found in general dictionaries. This is a gap which the 'Insurance dictionary' now presented is designed to fill. In view of its supplementary function, the number of terms covered is limited to 1200. To make this dictionary especially convenient for ready reference, only the most commonly used translations are given for each key word in any of the four languages. This dictionary is subdivided into four parts, each containing the translation of the selected terms in the three other languages. To further facilitate the use of the booklet, paper of different colours was used for the printing of the German, English, French and Greek sections. The present volume was developed from a Swedish insurance dictionary (Fickordbok Foersaekring), published in 1967, which - with Swedish as the key language- offers English, French and German translations of the basic insurance terms. (orig./HP) [de

  20. Human resource recommendation algorithm based on ensemble learning and Spark

    Science.gov (United States)

    Cong, Zihan; Zhang, Xingming; Wang, Haoxiang; Xu, Hongjie

    2017-08-01

    Aiming at the problem of “information overload” in the human resources industry, this paper proposes a human resource recommendation algorithm based on Ensemble Learning. The algorithm considers the characteristics and behaviours of both job seeker and job features in the real business circumstance. Firstly, the algorithm uses two ensemble learning methods-Bagging and Boosting. The outputs from both learning methods are then merged to form user interest model. Based on user interest model, job recommendation can be extracted for users. The algorithm is implemented as a parallelized recommendation system on Spark. A set of experiments have been done and analysed. The proposed algorithm achieves significant improvement in accuracy, recall rate and coverage, compared with recommendation algorithms such as UserCF and ItemCF.

  1. Resource Letter ALIP-1: Active-Learning Instruction in Physics

    Science.gov (United States)

    Meltzer, David E.; Thornton, Ronald K.

    2012-06-01

    This Resource Letter provides a guide to the literature on research-based active-learning instruction in physics. These are instructional methods that are based on, assessed by, and validated through research on the teaching and learning of physics. They involve students in their own learning more deeply and more intensely than does traditional instruction, particularly during class time. The instructional methods and supporting body of research reviewed here offer potential for significantly improved learning in comparison to traditional lecture-based methods of college and university physics instruction. We begin with an introduction to the history of active learning in physics in the United States, and then discuss some methods for and outcomes of assessing pedagogical effectiveness. We enumerate and describe common characteristics of successful active-learning instructional strategies in physics. We then discuss a range of methods for introducing active-learning instruction in physics and provide references to those methods for which there is published documentation of student learning gains.

  2. Adoption of Technology and Augmentation of Resources for Teaching-Learning in Higher Education

    OpenAIRE

    P. M. Suresh Kumar

    2017-01-01

    Learner centred education through appropriate methodologies facilitates effective learning as teaching-learning modalities of higher education are considered to be relevant to the learner group. Curriculum delivery and pedagogy should incorporate multitude of learning experiences and innovative learning methodologies through adoption of technology. Plenty of resources external to the curriculum come into use, which offer valuable learning experiences. Augmentation of resources for teaching...

  3. Learning with Nature and Learning from Others: Nature as Setting and Resource for Early Childhood Education

    Science.gov (United States)

    MacQuarrie, Sarah; Nugent, Clare; Warden, Claire

    2015-01-01

    Nature-based learning is an increasingly popular type of early childhood education. Despite this, children's experiences--in particular, their form and function within different settings and how they are viewed by practitioners--are relatively unknown. Accordingly, the use of nature as a setting and a resource for learning was researched. A…

  4. Extendable supervised dictionary learning for exploring diverse and concurrent brain activities in task-based fMRI.

    Science.gov (United States)

    Zhao, Shijie; Han, Junwei; Hu, Xintao; Jiang, Xi; Lv, Jinglei; Zhang, Tuo; Zhang, Shu; Guo, Lei; Liu, Tianming

    2018-06-01

    Recently, a growing body of studies have demonstrated the simultaneous existence of diverse brain activities, e.g., task-evoked dominant response activities, delayed response activities and intrinsic brain activities, under specific task conditions. However, current dominant task-based functional magnetic resonance imaging (tfMRI) analysis approach, i.e., the general linear model (GLM), might have difficulty in discovering those diverse and concurrent brain responses sufficiently. This subtraction-based model-driven approach focuses on the brain activities evoked directly from the task paradigm, thus likely overlooks other possible concurrent brain activities evoked during the information processing. To deal with this problem, in this paper, we propose a novel hybrid framework, called extendable supervised dictionary learning (E-SDL), to explore diverse and concurrent brain activities under task conditions. A critical difference between E-SDL framework and previous methods is that we systematically extend the basic task paradigm regressor into meaningful regressor groups to account for possible regressor variation during the information processing procedure in the brain. Applications of the proposed framework on five independent and publicly available tfMRI datasets from human connectome project (HCP) simultaneously revealed more meaningful group-wise consistent task-evoked networks and common intrinsic connectivity networks (ICNs). These results demonstrate the advantage of the proposed framework in identifying the diversity of concurrent brain activities in tfMRI datasets.

  5. Learning with nature and learning from others: nature as setting and resource for early childhood education

    OpenAIRE

    MacQuarrie, Sarah; Nugent, Clare; Warden, Claire

    2015-01-01

    Nature-based learning is an increasingly popular type of early childhood education. Despite this, children's experiences-in particular, their form and function within different settings and how they are viewed by practitioners-are relatively unknown. Accordingly, the use of nature as a setting and a resource for learning was researched. A description and an emerging understanding of nature-based learning were obtained through the use of a group discussion and case studies. Practitioners' view...

  6. Evaluating Bilingual and Monolingual Dictionaries for L2 Learners.

    Science.gov (United States)

    Hunt, Alan

    1997-01-01

    A discussion of dictionaries and their use for second language (L2) learning suggests that lack of computerized modern language corpora can adversely affect bilingual dictionaries, commonly used by L2 learners, and shows how use of such corpora has benefitted two contemporary monolingual L2 learner dictionaries (1995 editions of the Longman…

  7. Effective Look-up Techniques to Approach a Monolingual Dictionary

    Directory of Open Access Journals (Sweden)

    Nauman Al Amin Ali El Sayed

    2013-05-01

    Full Text Available A dictionary is (a learning tool that can help the language learner in acquiring great knowledge of and about a foreign language. Almost all language learners buy or at least possess, at one time, a monolingual or bilingual dictionary, to which the learner may refer to look up the meaning of words. Unfortunately, using dictionary to look up the meaning of words seems to be the most important service, which a dictionary is expected to provide to language learners. In fact, a dictionary provides much data about language to its readers such as telling them about: the word spelling, phonology, phonetics, etymology, stylistics and definitions among other aspects. This paper sheds light on how the dictionary can teach its readers with special focus on monolingual dictionary. Hence, the discussion of this paper will centre on how dictionaries can teach students rather than on how students can learn from them.

  8. Bayesian Ising approximation for learning dictionaries of multispike timing patterns in premotor neurons

    Science.gov (United States)

    Hernandez Lahme, Damian; Sober, Samuel; Nemenman, Ilya

    Important questions in computational neuroscience are whether, how much, and how information is encoded in the precise timing of neural action potentials. We recently demonstrated that, in the premotor cortex during vocal control in songbirds, spike timing is far more informative about upcoming behavior than is spike rate (Tang et al, 2014). However, identification of complete dictionaries that relate spike timing patterns with the controled behavior remains an elusive problem. Here we present a computational approach to deciphering such codes for individual neurons in the songbird premotor area RA, an analog of mammalian primary motor cortex. Specifically, we analyze which multispike patterns of neural activity predict features of the upcoming vocalization, and hence are important codewords. We use a recently introduced Bayesian Ising Approximation, which properly accounts for the fact that many codewords overlap and hence are not independent. Our results show which complex, temporally precise multispike combinations are used by individual neurons to control acoustic features of the produced song, and that these code words are different across individual neurons and across different acoustic features. This work was supported, in part, by JSMF Grant 220020321, NSF Grant 1208126, NIH Grant NS084844 and NIH Grant 1 R01 EB022872.

  9. Simultaneous Semi-Coupled Dictionary Learning for Matching in Canonical Space.

    Science.gov (United States)

    Das, Nilotpal; Mandal, Devraj; Biswas, Soma

    2017-05-24

    Cross-modal recognition and matching with privileged information are important challenging problems in the field of computer vision. The cross-modal scenario deals with matching across different modalities and needs to take care of the large variations present across and within each modality. The privileged information scenario deals with the situation that all the information available during training may not be available during the testing stage and hence algorithms need to leverage the extra information from the training stage itself. We show that for multi-modal data, either one of the above situations may arise if one modality is absent during testing. Here, we propose a novel framework which can handle both these scenarios seamlessly with applications to matching multi-modal data. The proposed approach jointly uses data from the two modalities to build a canonical representation which encompasses information from both the modalities. We explore four different types of canonical representations for different types of data. The algorithm computes dictionaries and canonical representation for data from both the modalities such that the transformed sparse coefficients of both the modalities are equal to that of the canonical representation. The sparse coefficients are finally matched using Mahalanobis metric. Extensive experiments on different datasets, involving RGBD, text-image and audio-image data show the effectiveness of the proposed framework.

  10. Joint Dictionary Learning-Based Non-Negative Matrix Factorization for Voice Conversion to Improve Speech Intelligibility After Oral Surgery.

    Science.gov (United States)

    Fu, Szu-Wei; Li, Pei-Chun; Lai, Ying-Hui; Yang, Cheng-Chien; Hsieh, Li-Chun; Tsao, Yu

    2017-11-01

    Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient's speech may be distorted and difficult to understand. To overcome this problem, VC methods can be applied to convert the distorted speech such that it is clear and more intelligible. To design an effective VC method, two key points must be considered: 1) the amount of training data may be limited (because speaking for a long time is usually difficult for postoperative patients); 2) rapid conversion is desirable (for better communication). Methods: We propose a novel joint dictionary learning based non-negative matrix factorization (JD-NMF) algorithm. Compared to conventional VC techniques, JD-NMF can perform VC efficiently and effectively with only a small amount of training data. Results: The experimental results demonstrate that the proposed JD-NMF method not only achieves notably higher short-time objective intelligibility (STOI) scores (a standardized objective intelligibility evaluation metric) than those obtained using the original unconverted speech but is also significantly more efficient and effective than a conventional exemplar-based NMF VC method. Conclusion: The proposed JD-NMF method may outperform the state-of-the-art exemplar-based NMF VC method in terms of STOI scores under the desired scenario. Significance: We confirmed the advantages of the proposed joint training criterion for the NMF-based VC. Moreover, we verified that the proposed JD-NMF can effectively improve the speech intelligibility scores of oral surgery patients. Objective: This paper focuses on machine learning based voice conversion (VC) techniques for improving the speech intelligibility of surgical patients who have had parts of their articulators removed. Because of the removal of parts of the articulator, a patient

  11. Den Engelske Regnskabsordbog/English Dictionary of Accounting

    DEFF Research Database (Denmark)

    Nielsen, Sandro; Mourier, Lise; Bergenholtz, Henning

    The English Dictionary of Accounting contains about 5.600 accounting terms, both British, American and international (IFRS). The terms are defined and the dictionary gives language information about the terms. The dictionary can be used when writing and reading English accounting texts and when y...... want to learn more about accounting and financial reporting. The dictionary is designed for accountants, auditors, translators, students communication officers and others interested in financial reporting....

  12. Stochastic learning of multi-instance dictionary for earth mover’s distance-based histogram comparison

    KAUST Repository

    Fan, Jihong; Liang, Ru-Ze

    2016-01-01

    Dictionary plays an important role in multi-instance data representation. It maps bags of instances to histograms. Earth mover’s distance (EMD) is the most effective histogram distance metric for the application of multi-instance retrieval. However

  13. Electrochemical dictionary

    CERN Document Server

    Bard, Allen J; Scholz, Fritz 0

    2014-01-01

    This comprehensive dictionary includes some 3000 common terms in electrochemistry and energy research, and related fields. Offers clear, precise definitions, references and more than 600 illustrations. The new edition adds more than 300 new and revised terms.

  14. Dictionary Snakes

    DEFF Research Database (Denmark)

    Dahl, Anders Bjorholm; Dahl, Vedrana Andersen

    2014-01-01

    for image segmentation that operates without training data. Our method is based on a probabilistic dictionary of image patches coupled with a deformable model inspired by snakes and active contours without edges. We separate the image into two classes based on the information provided by the evolving curve......, which moves according to the probabilistic information obtained from the dictionary. Initially, the image patches are assigned to the nearest dictionary element, where the image is sampled at each pixel such that patches overlap. The curve divides the image into an inside and an outside region allowing...... us to estimate the pixel-wise probability of the dictionary elements. In each iteration we evolve the curve and update the probabilities, which merges similar texture patterns and pulls dissimilar patterns apart. We experimentally evaluate our approach, and show how textured objects are precisely...

  15. Linguistic and Cultural Strategies in ELT Dictionaries

    Science.gov (United States)

    Corrius, Montse; Pujol, Didac

    2010-01-01

    There are three main types of ELT dictionaries: monolingual, bilingual, and bilingualized. Each type of dictionary, while having its own advantages, also hinders the learning of English as a foreign language and culture in so far as it is written from a homogenizing (linguistic- and culture-centric) perspective. This paper presents a new type of…

  16. Online dictionaries

    DEFF Research Database (Denmark)

    Tarp, Sven

    2012-01-01

    This article initially provides a panoramic overview and a preliminary typologization of present and future online dictionaries based upon their application of the available technologies and suggests that the future of lexicography will be the development of highly sophisticated tools which may......, need, consultation, and data. The article then proceeds to the discussion of some advanced information science techniques that may contribute to the desired individualization. Upon this basis, it finally discusses the interaction between online dictionaries and external sources like the Internet...

  17. On development of “smart” dictionaries

    Directory of Open Access Journals (Sweden)

    Mark Kit

    2015-11-01

    Full Text Available On development of “smart” dictionaries The paper discusses the need for development of intelligent dictionaries that allow for two-way interaction with its users. Theoretical ground for such development is suggested. Practical implementation as LexSite lexical resource is shown, concepts for further improvement of the efficiency are proposed.

  18. Field – Football Expressions Dictionary: a lexicographic resource based on the theoretical-methodological approach of frame semantics and corpus linguistics

    Directory of Open Access Journals (Sweden)

    Rove Luiza de Oliveira Chishman

    2015-01-01

    Full Text Available The present article aims at problematizing the relevance of Frame Semantics (Fillmore, 1982 in the development of Field – Dictionary of Football Expressions – which the configuration allows the access to football language through expressions or through scenarios – or semantic frames. Frame Semantics, a theory developed in the realm of Cognitive Linguistics, is based on empirical data collected from the analysis of electronic corpora. The extraction of the data presented in this study was done with the Sketch Engine concordance, while their analysis was relegated to Frame Semantics. Among the results, it is possible to point out at the manner in which Fillmore´s theory contributes to the analysis of polysemy, presenting the different senses of a lexical unit considering different situations – or different frames – in which they appear. This article also emphasizes the pertinence of corpus linguistics and the processing of corpora as resources that allow the analysis of linguistic constructs present in the texts. It is also important to emphasize the applicability of Frame Semantics to a resource devoted to a non-specialized public, once the theory makes the contextualization of language possible through the everyday routine of the speakers.

  19. Adaptation of mathematical educational content in e-learning resources

    Directory of Open Access Journals (Sweden)

    Yuliya V. Vainshtein

    2017-01-01

    Full Text Available Modern trends in the world electronic educational system development determine the necessity of adaptive learning intellectual environments and resources’ development and implementation. An upcoming trend in improvement the quality of studying mathematical disciplines is the development and application of adaptive electronic educational resources. However, the development and application experience of adaptive technologies in higher education is currently extremely limited and does not imply the usage flexibility. Adaptive educational resources in the electronic environment are electronic educational resources that provide the student with a personal educational space, filled with educational content that “adapts” to the individual characteristics of the students and provides them with the necessary information.This article focuses on the mathematical educational content adaptation algorithms development and their implementation in the e-learning system. The peculiarity of the proposed algorithms is the possibility of their application and distribution for adaptive e-learning resources construction. The novelty of the proposed approach is the three-step content organization of the adaptive algorithms for the educational content: “introductory adaptation of content”, “the current adaptation of content”, “estimative and a corrective adaptation”. For each stage of the proposed system, mathematical algorithms for educational content adaptation in adaptive e-learning resources are presented.Due to the high level of abstraction and complexity perception of mathematical disciplines, educational content is represented in the various editions of presentation that correspond to the levels of assimilation of the course material. Adaptation consists in the selection of the optimal edition of the material that best matches the individual characteristics of the student. The introduction of a three-step content organization of the adaptive

  20. Learning on human resources management in the radiology residency program

    Energy Technology Data Exchange (ETDEWEB)

    Oliveira, Aparecido Ferreira de; Lederman, Henrique Manoel; Batista, Nildo Alves, E-mail: aparecidoliveira@ig.com.br [Universidade Federal de Sao Paulo (EPM/UNIFESP), Sao Paulo, SP (Brazil). Escola Paulista de Medicina

    2014-03-15

    Objective: to investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina - Universidade Federal de Sao Paulo, aiming at improving radiologists' education. Materials and methods: exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46), taped interviews (18), and categorization based on thematic analysis. Results: According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion: Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists. (author)

  1. Learning on human resources management in the radiology residency program

    International Nuclear Information System (INIS)

    Oliveira, Aparecido Ferreira de; Lederman, Henrique Manoel; Batista, Nildo Alves

    2014-01-01

    Objective: to investigate the process of learning on human resource management in the radiology residency program at Escola Paulista de Medicina - Universidade Federal de Sao Paulo, aiming at improving radiologists' education. Materials and methods: exploratory study with a quantitative and qualitative approach developed with the faculty staff, preceptors and residents of the program, utilizing a Likert questionnaire (46), taped interviews (18), and categorization based on thematic analysis. Results: According to 71% of the participants, residents have clarity about their role in the development of their activities, and 48% said that residents have no opportunity to learn how to manage their work in a multidisciplinary team. Conclusion: Isolation at medical records room, little interactivity between sectors with diversified and fixed activities, absence of a previous culture and lack of a training program on human resources management may interfere in the development of skills for the residents' practice. There is a need to review objectives of the medical residency in the field of radiology, incorporating, whenever possible, the commitment to the training of skills related to human resources management thus widening the scope of abilities of the future radiologists. (author)

  2. Internet accounting dictionaries

    DEFF Research Database (Denmark)

    Nielsen, Sandro; Mourier, Lise

    2005-01-01

    An examination of existing accounting dictionaries on the Internet reveals a general need for a new type of dictionary. In contrast to the dictionaries now accessible, the future accounting dictionaries should be designed as proper Internet dictionaries based on a functional approach so they can...

  3. Active Learning Techniques Applied to an Interdisciplinary Mineral Resources Course.

    Science.gov (United States)

    Aird, H. M.

    2015-12-01

    An interdisciplinary active learning course was introduced at the University of Puget Sound entitled 'Mineral Resources and the Environment'. Various formative assessment and active learning techniques that have been effective in other courses were adapted and implemented to improve student learning, increase retention and broaden knowledge and understanding of course material. This was an elective course targeted towards upper-level undergraduate geology and environmental majors. The course provided an introduction to the mineral resources industry, discussing geological, environmental, societal and economic aspects, legislation and the processes involved in exploration, extraction, processing, reclamation/remediation and recycling of products. Lectures and associated weekly labs were linked in subject matter; relevant readings from the recent scientific literature were assigned and discussed in the second lecture of the week. Peer-based learning was facilitated through weekly reading assignments with peer-led discussions and through group research projects, in addition to in-class exercises such as debates. Writing and research skills were developed through student groups designing, carrying out and reporting on their own semester-long research projects around the lasting effects of the historical Ruston Smelter on the biology and water systems of Tacoma. The writing of their mini grant proposals and final project reports was carried out in stages to allow for feedback before the deadline. Speakers from industry were invited to share their specialist knowledge as guest lecturers, and students were encouraged to interact with them, with a view to employment opportunities. Formative assessment techniques included jigsaw exercises, gallery walks, placemat surveys, think pair share and take-home point summaries. Summative assessment included discussion leadership, exams, homeworks, group projects, in-class exercises, field trips, and pre-discussion reading exercises

  4. An assessment of student experiences and learning based on a novel undergraduate e-learning resource.

    Science.gov (United States)

    Mehta, S; Clarke, F; Fleming, P S

    2016-08-12

    Purpose/objectives The aims of this study were to describe the development of a novel e-learning resource and to assess its impact on student learning experiences and orthodontic knowledge.Methods Thirty-two 4th year dental undergraduate students at Queen Mary University of London were randomly allocated to receive electronic access to e-learning material covering various undergraduate orthodontic topics over a 6-week period. Thirty-one control students were not given access during the study period. All students were asked to complete electronic quizzes both before (T0) and after (T1) the study period and a general questionnaire concerning familiarity with e-learning. The test group also completed a user satisfaction questionnaire at T1. Two focus groups were also undertaken to explore learners' experiences and suggestions in relation to the resource.Results The mean quiz result improved by 3.9% and 4.5% in the control and test groups, respectively. An independent t-test, however, demonstrated a lack of statistical significance in knowledge gain between control and test groups (P = 0.941). The qualitative feedback indicated that students believed that use of the resource enhanced knowledge and basic understanding with students expressing a wish to ingrain similar resources in other areas of undergraduate teaching.Conclusions Use of the novel orthodontic e-resource by 4th year undergraduate students over a 6-week period did not result in a significant improvement in subject knowledge. However, the e-learning has proven popular among undergraduates and the resources will continue to be refined.

  5. Dictionary Management

    DEFF Research Database (Denmark)

    Bergenholtz, Henning

    2018-01-01

    in different projects. The same steps can be applied to lexicographic projects. In this field, by looking at finished and not finished dictionary projects, we find that: many are started but never finished; and many are planned to be carried out within a certain time frame, but it takes much longer than...... anticipated until the project is completed with the publication of one or more dictionaries. The reason for that is normally an unrealistic and much too optimistic planning. But it is also due to a missing knowledge about management planning according to a relevant overall lexicographic theory....... There is a long tradition of understanding lexicography as the compiling of dictionaries, especially among British scholars. But there is also a tradition of focusing on theoretical lexicography, especially among German scholars. In this contribution, I consider lexicography a discipline with two legs: (1...

  6. Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

    Science.gov (United States)

    Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

    2016-01-01

    Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…

  7. A Sparse Dictionary Learning-Based Adaptive Patch Inpainting Method for Thick Clouds Removal from High-Spatial Resolution Remote Sensing Imagery.

    Science.gov (United States)

    Meng, Fan; Yang, Xiaomei; Zhou, Chenghu; Li, Zhi

    2017-09-15

    Cloud cover is inevitable in optical remote sensing (RS) imagery on account of the influence of observation conditions, which limits the availability of RS data. Therefore, it is of great significance to be able to reconstruct the cloud-contaminated ground information. This paper presents a sparse dictionary learning-based image inpainting method for adaptively recovering the missing information corrupted by thick clouds patch-by-patch. A feature dictionary was learned from exemplars in the cloud-free regions, which was later utilized to infer the missing patches via sparse representation. To maintain the coherence of structures, structure sparsity was brought in to encourage first filling-in of missing patches on image structures. The optimization model of patch inpainting was formulated under the adaptive neighborhood-consistency constraint, which was solved by a modified orthogonal matching pursuit (OMP) algorithm. In light of these ideas, the thick-cloud removal scheme was designed and applied to images with simulated and true clouds. Comparisons and experiments show that our method can not only keep structures and textures consistent with the surrounding ground information, but also yield rare smoothing effect and block effect, which is more suitable for the removal of clouds from high-spatial resolution RS imagery with salient structures and abundant textured features.

  8. Optional Anatomy and Physiology e-Learning Resources: Student Access, Learning Approaches, and Academic Outcomes

    Science.gov (United States)

    Guy, Richard,; Byrne, Bruce; Dobos, Marian

    2018-01-01

    Anatomy and physiology interactive video clips were introduced into a blended learning environment, as an optional resource, and were accessed by ~50% of the cohort. Student feedback indicated that clips were engaging, assisted understanding of course content, and provided lecture support. Students could also access two other optional online…

  9. "It's Just Reflex Now": German Language Learners' Use of Online Resources

    Science.gov (United States)

    Larson-Guenette, Julie

    2013-01-01

    This study examined how often and to what extent university learners of German use online resources (e.g., online dictionaries and translators) in relation to German coursework, their motivations for use, and their beliefs about online resources and language learning. Data for this study consisted of open-ended surveys ("n" = 71) and face-to-face…

  10. Parsing and Tagging of Binlingual Dictionary

    National Research Council Canada - National Science Library

    Ma, Huanfeng; Karagol-Ayan, Burcu; Doermann, David S; Oard, Doug; Wang, Jianqiang

    2003-01-01

    Bilingual dictionaries hold great potential as a source of lexical resources for training and testing automated systems for optical character recognition, machine translation, and cross-language information retrieval...

  11. Parsing and Tagging of Bilingual Dictionary

    National Research Council Canada - National Science Library

    Ma, Huanfeng; Karagol-Ayan, Burcu; Doermann, David S; Oard, Doug; Wang, Jianqiang

    2003-01-01

    Bilingual dictionaries hold great potential as a source of lexical resources for training and testing automated systems for optical character recognition, machine translation, and cross-language information retrieval...

  12. Image fusion using sparse overcomplete feature dictionaries

    Science.gov (United States)

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt

    2015-10-06

    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  13. Dictionaries for text production

    DEFF Research Database (Denmark)

    Fuertes-Olivera, Pedro; Bergenholtz, Henning

    2018-01-01

    Dictionaries for Text Production are information tools that are designed and constructed for helping users to produce (i.e. encode) texts, both oral and written texts. These can be broadly divided into two groups: (a) specialized text production dictionaries, i.e., dictionaries that only offer...... a small amount of lexicographic data, most or all of which are typically used in a production situation, e.g. synonym dictionaries, grammar and spelling dictionaries, collocation dictionaries, concept dictionaries such as the Longman Language Activator, which is advertised as the World’s First Production...... Dictionary; (b) general text production dictionaries, i.e., dictionaries that offer all or most of the lexicographic data that are typically used in a production situation. A review of existing production dictionaries reveals that there are many specialized text production dictionaries but only a few general...

  14. Effective Look-up Techniques to Approach a Monolingual Dictionary

    OpenAIRE

    Nauman Al Amin Ali El Sayed; Ahmed Gumaa Siddiek

    2013-01-01

    A dictionary is (a) learning tool that can help the language learner in acquiring great knowledge of and about a foreign language. Almost all language learners buy or at least possess, at one time, a monolingual or bilingual dictionary, to which the learner may refer to look up the meaning of words. Unfortunately, using dictionary to look up the meaning of words seems to be the most important service, which a dictionary is expected to provide to language learners. In fact, a dictionary provid...

  15. A Video Game for Learning Brain Evolution: A Resource or a Strategy?

    Science.gov (United States)

    Barbosa Gomez, Luisa Fernanda; Bohorquez Sotelo, Maria Cristina; Roja Higuera, Naydu Shirley; Rodriguez Mendoza, Brigitte Julieth

    2016-01-01

    Learning resources are part of the educational process of students. However, how video games act as learning resources in a population that has not selected the virtual formation as their main methodology? The aim of this study was to identify the influence of a video game in the learning process of brain evolution. For this purpose, the opinions…

  16. PBL, Hands-On/ Digital resources in Geology, (Teaching/ Learning)

    Science.gov (United States)

    Soares, Rosa; Santos, Cátia; Carvalho, Sara

    2015-04-01

    The present study reports the elaboration, application and evaluation of a problem-based learning (PBL) program that aims to evaluate the effectiveness in students learning the Rock Cycle theme. Prior research on both PBL and Rock Cycle was conducted within the context of science education so as to elaborate and construct the intervention program. Findings from these studies indicated both the PBL methodology and Rock Cycle as helpful for teachers and students. PBL methodology has been adopted in this study since it is logically incorporated in a constructivism philosophy application and it was expected that this approach would assist students towards achieving a specific set of competencies. PBL is a student-centered method based on the principle of using problems as the starting point for the acquisition of new knowledge. Problems are based on complex real-world situations. All information needed to solve the problem is initially not given. Students will identify, find, and use appropriate resources to complete the exercise. They work permanently in small groups, developing self-directed activities and increasing participation in discussions. Teacher based guidance allows students to be fully engaged in knowledge building. That way, the learning process is active, integrated, cumulative, and connected. Theme "Rock Cycle" was introduced using a problematic situation, which outlined the geological processes highlighted in "Foz do Douro" the next coastline of the school where the study was developed. The questions proposed by the students were solved, using strategies that involved the use of hands-on activities and virtual labs in Geology. The systematization of the selected theme was performed in a field excursion, implemented according to the organizational model of Nir Orion, to The "Foz do Douro" metamorphic complex. In the evaluation of the learning process, data were obtained on students' development of knowledge and competencies through the application of

  17. The gap between medical faculty's perceptions and use of e-learning resources.

    Science.gov (United States)

    Kim, Kyong-Jee; Kang, Youngjoon; Kim, Giwoon

    2017-01-01

    e-Learning resources have become increasingly popular in medical education; however, there has been scant research on faculty perceptions and use of these resources. To investigate medical faculty's use of e-learning resources and to draw on practical implications for fostering their use of such resources. Approximately 500 full-time faculty members in 35 medical schools across the nation in South Korea were invited to participate in a 30-item questionnaire on their perceptions and use of e-learning resources in medical education. The questionnaires were distributed in both online and paper formats. Descriptive analysis and reliability analysis were conducted of the data. Eighty faculty members from 28 medical schools returned the questionnaires. Twenty-two percent of respondents were female and 78% were male, and their rank, disciplines, and years of teaching experience all varied. Participants had positive perceptions of e-learning resources in terms of usefulness for student learning and usability; still, only 39% of them incorporated those resources in their teaching. The most frequently selected reasons for not using e-learning resources in their teaching were 'lack of resources relevant to my lectures,' 'lack of time to use them during lectures,' and 'was not aware of their availability.' Our study indicates a gap between medical faculty's positive perceptions of e-learning resources and their low use of such resources. Our findings highlight the needs for further study of individual and institutional barriers to faculty adoption of e-learning resources to bridge this gap.

  18. Compiling Dictionaries

    African Journals Online (AJOL)

    Information Technology

    This method results in a classified word list that can be efficiently ... standardized list of domains to classify multiple dictionaries opens up possibilities for cross-lin- .... part of speech, noun class, the plural form of each noun, and a simple gloss. ... But these mental links tend to cluster around a ... group, duet, trio, ensemble.

  19. Using Bilingual Dictionaries.

    Science.gov (United States)

    Thompson, Geoff

    1987-01-01

    Monolingual dictionaries have serious disadvantages in many language teaching situations; bilingual dictionaries are potentially more efficient and more motivating sources of information for language learners. (Author/CB)

  20. Exogenous and Endogenous Learning Resources in the Actiotope Model of Giftedness and Its Significance for Gifted Education

    Science.gov (United States)

    Ziegler, Albert; Chandler, Kimberley L.; Vialle, Wilma; Stoeger, Heidrun

    2017-01-01

    Based on the Actiotope Model of Giftedness, this article introduces a learning-resource-oriented approach for gifted education. It provides a comprehensive categorization of learning resources, including five exogenous learning resources termed "educational capital" and five endogenous learning resources termed "learning…

  1. Pocket dictionary of laboratory equipment

    International Nuclear Information System (INIS)

    Junge, H.D.

    1987-01-01

    This pocket dictionary contains the 2500 most common terms for scientific and technical equipment in chemical laboratories. It is a useful tool for those who are used to communicating in German and English, but have to learn the special terminology in this field. (orig.) [de

  2. An Electronic Dictionary and Translation System for Murrinh-Patha

    Science.gov (United States)

    Seiss, Melanie; Nordlinger, Rachel

    2012-01-01

    This paper presents an electronic dictionary and translation system for the Australian language Murrinh-Patha. Its complex verbal structure makes learning Murrinh-Patha very difficult. Design learning materials or a dictionary which is easy to understand and to use also presents a challenge. This paper discusses some of the difficulties posed by…

  3. Semi-Supervised Tripled Dictionary Learning for Standard-dose PET Image Prediction using Low-dose PET and Multimodal MRI

    Science.gov (United States)

    Wang, Yan; Ma, Guangkai; An, Le; Shi, Feng; Zhang, Pei; Lalush, David S.; Wu, Xi; Pu, Yifei; Zhou, Jiliu; Shen, Dinggang

    2017-01-01

    Objective To obtain high-quality positron emission tomography (PET) image with low-dose tracer injection, this study attempts to predict the standard-dose PET (S-PET) image from both its low-dose PET (L-PET) counterpart and corresponding magnetic resonance imaging (MRI). Methods It was achieved by patch-based sparse representation (SR), using the training samples with a complete set of MRI, L-PET and S-PET modalities for dictionary construction. However, the number of training samples with complete modalities is often limited. In practice, many samples generally have incomplete modalities (i.e., with one or two missing modalities) that thus cannot be used in the prediction process. In light of this, we develop a semi-supervised tripled dictionary learning (SSTDL) method for S-PET image prediction, which can utilize not only the samples with complete modalities (called complete samples) but also the samples with incomplete modalities (called incomplete samples), to take advantage of the large number of available training samples and thus further improve the prediction performance. Results Validation was done on a real human brain dataset consisting of 18 subjects, and the results show that our method is superior to the SR and other baseline methods. Conclusion This work proposed a new S-PET prediction method, which can significantly improve the PET image quality with low-dose injection. Significance The proposed method is favorable in clinical application since it can decrease the potential radiation risk for patients. PMID:27187939

  4. Learning Essential Terms and Concepts in Statistics and Accounting

    Science.gov (United States)

    Peters, Pam; Smith, Adam; Middledorp, Jenny; Karpin, Anne; Sin, Samantha; Kilgore, Alan

    2014-01-01

    This paper describes a terminological approach to the teaching and learning of fundamental concepts in foundation tertiary units in Statistics and Accounting, using an online dictionary-style resource (TermFinder) with customised "termbanks" for each discipline. Designed for independent learning, the termbanks support inquiring students…

  5. Cost-Benefit Analysis of Computer Resources for Machine Learning

    Science.gov (United States)

    Champion, Richard A.

    2007-01-01

    Machine learning describes pattern-recognition algorithms - in this case, probabilistic neural networks (PNNs). These can be computationally intensive, in part because of the nonlinear optimizer, a numerical process that calibrates the PNN by minimizing a sum of squared errors. This report suggests efficiencies that are expressed as cost and benefit. The cost is computer time needed to calibrate the PNN, and the benefit is goodness-of-fit, how well the PNN learns the pattern in the data. There may be a point of diminishing returns where a further expenditure of computer resources does not produce additional benefits. Sampling is suggested as a cost-reduction strategy. One consideration is how many points to select for calibration and another is the geometric distribution of the points. The data points may be nonuniformly distributed across space, so that sampling at some locations provides additional benefit while sampling at other locations does not. A stratified sampling strategy can be designed to select more points in regions where they reduce the calibration error and fewer points in regions where they do not. Goodness-of-fit tests ensure that the sampling does not introduce bias. This approach is illustrated by statistical experiments for computing correlations between measures of roadless area and population density for the San Francisco Bay Area. The alternative to training efficiencies is to rely on high-performance computer systems. These may require specialized programming and algorithms that are optimized for parallel performance.

  6. The Latent Structure of Dictionaries.

    Science.gov (United States)

    Vincent-Lamarre, Philippe; Massé, Alexandre Blondin; Lopes, Marcos; Lord, Mélanie; Marcotte, Odile; Harnad, Stevan

    2016-07-01

    How many words-and which ones-are sufficient to define all other words? When dictionaries are analyzed as directed graphs with links from defining words to defined words, they reveal a latent structure. Recursively removing all words that are reachable by definition but that do not define any further words reduces the dictionary to a Kernel of about 10% of its size. This is still not the smallest number of words that can define all the rest. About 75% of the Kernel turns out to be its Core, a "Strongly Connected Subset" of words with a definitional path to and from any pair of its words and no word's definition depending on a word outside the set. But the Core cannot define all the rest of the dictionary. The 25% of the Kernel surrounding the Core consists of small strongly connected subsets of words: the Satellites. The size of the smallest set of words that can define all the rest-the graph's "minimum feedback vertex set" or MinSet-is about 1% of the dictionary, about 15% of the Kernel, and part-Core/part-Satellite. But every dictionary has a huge number of MinSets. The Core words are learned earlier, more frequent, and less concrete than the Satellites, which are in turn learned earlier, more frequent, but more concrete than the rest of the Dictionary. In principle, only one MinSet's words would need to be grounded through the sensorimotor capacity to recognize and categorize their referents. In a dual-code sensorimotor/symbolic model of the mental lexicon, the symbolic code could do all the rest through recombinatory definition. Copyright © 2016 Cognitive Science Society, Inc.

  7. Dictionary of distances

    CERN Document Server

    Deza, Michel-Marie

    2006-01-01

    This book comes out of need and urgency (expressed especially in areas of Information Retrieval with respect to Image, Audio, Internet and Biology) to have a working tool to compare data.The book will provide powerful resource for all researchers using Mathematics as well as for mathematicians themselves. In the time when over-specialization and terminology fences isolate researchers, this Dictionary try to be ""centripedal"" and ""oikoumeni"", providing some access and altitude of vision but without taking the route of scientific vulgarisation. This attempted balance is the main philosophy

  8. Use of Online Learning Resources in the Development of Learning Environments at the Intersection of Formal and Informal Learning: The Student as Autonomous Designer

    Science.gov (United States)

    Lebenicnik, Maja; Pitt, Ian; Istenic Starcic, Andreja

    2015-01-01

    Learning resources that are used in the education of university students are often available online. The nature of new technologies causes an interweaving of formal and informal learning, with the result that a more active role is expected from students with regard to the use of ICT for their learning. The variety of online learning resources…

  9. Online Dictionaries and the Teaching/Learning of English in the Expanding Circle. A Statistical Approach to Term Extraction

    Directory of Open Access Journals (Sweden)

    Beatriz Pérez Cabello de Alba

    2012-06-01

    Full Text Available This article follows current research on English for Specific Business Purposes, which focuses on the analysis of contextualized business genres and on identifying the strategies that can be associated with effective business communication (Nickerson, 2005. It explores whether free internet dictionaries can be used for promoting effective business communication by presenting a detailed analysis of the definitions and encyclopedic information associated with three business terms that are retrieved from YourDictionary.com, and BusinessDictionary.com. Our results indicate that these two dictionary structures can be very effective for acquiring business knowledge in cognitive use situations. Hence, this paper makes a case for presenting free internet dictionaries as adequate tools for guiding instructors and learners of Business English into the new avenues of knowledge that are already forming, which are characterized by an interest in continuous retraining, especially in the field of ESP where both teachers and students have to deal with areas of knowledge with which they might not be very familiar.En el campo del Inglés para Finés Específicos la investigación actual está muy relacionada con el estudio del genre, y sus implicaciones retóricas, docentes y discursivas. Por ejemplo, Nickerson (2005 edita un número monográfico de English for Specific Purposes dedicado al Business English como lingua franca de la comunicación empresarial internacional. Todos los artículos de este número especial analizan los géneros prototípicos de este tipo de comunicación y dan por supuesto que tanto los profesores como los alumnos tienen un conocimiento suficiente de las principios conceptuales que subyacen en este discurso especializado. Desde nuestro punto de vista esta idea no se corresponde con la realidad: tanto los profesores como los alumnos de ingles empresarial tienen un conocimiento limitado de los principios teóricos y las pr

  10. Listening to Students: Customer Journey Mapping at Birmingham City University Library and Learning Resources

    Science.gov (United States)

    Andrews, Judith; Eade, Eleanor

    2013-01-01

    Birmingham City University's Library and Learning Resources' strategic aim is to improve student satisfaction. A key element is the achievement of the Customer Excellence Standard. An important component of the standard is the mapping of services to improve quality. Library and Learning Resources has developed a methodology to map these…

  11. Usability Testing of a Multimedia e-Learning Resource for Electrolyte and Acid-Base Disorders

    Science.gov (United States)

    Davids, Mogamat Razeen; Chikte, Usuf; Grimmer-Somers, Karen; Halperin, Mitchell L.

    2014-01-01

    The usability of computer interfaces may have a major influence on learning. Design approaches that optimize usability are commonplace in the software development industry but are seldom used in the development of e-learning resources, especially in medical education. We conducted a usability evaluation of a multimedia resource for teaching…

  12. Big Data X-Learning Resources Integration and Processing in Cloud Environments

    Directory of Open Access Journals (Sweden)

    Kong Xiangsheng

    2014-09-01

    Full Text Available The cloud computing platform has good flexibility characteristics, more and more learning systems are migrated to the cloud platform. Firstly, this paper describes different types of educational environments and the data they provide. Then, it proposes a kind of heterogeneous learning resources mining, integration and processing architecture. In order to integrate and process the different types of learning resources in different educational environments, this paper specifically proposes a novel solution and massive storage integration algorithm and conversion algorithm to the heterogeneous learning resources storage and management cloud environments.

  13. A Resource-Oriented Functional Approach to English Language Learning

    Science.gov (United States)

    Li, Jia

    2018-01-01

    This article reports on a case study that investigates the learning preferences and strategies of Chinese students learning English as a second language (ESL) in Canadian school settings. It focuses on the interaction between second language (L2) learning methods that the students have adopted from their previous learning experience in China and…

  14. Integrating gender into natural resources management projects: USAID lessons learned.

    Science.gov (United States)

    1998-01-01

    This article discusses USAID's lessons learned about integrating gender into natural resource management (NRM) projects in Peru, the Philippines, and Kenya. In Peru, USAID integrated women into a solid waste management project by lending money to invest in trash collection supplies. The loans allowed women to collect household waste, transfer it to a landfill, and provide additional sanitary disposal. The women were paid through direct fees from households and through service contracts with municipalities. In Mindanao, the Philippines, women were taught about the health impact of clean water and how to monitor water quality, including the monitoring of E. coli bacteria. Both men and women were taught soil conservation techniques for reducing the amount of silt running into the lake, which interferes with the generation of electricity and affects the health of everyone. The education helped women realize the importance of reducing silt and capitalized on their interest in protecting the health of their families. The women were thus willing to monitor the lake's water quality to determine if the conservation efforts were effective. In Kenya, USAID evaluated its Ecology, Community Organization, and Gender project in the Rift Valley, which helped resettle a landless community and helped with sustainable NRM. The evaluation revealed that women's relative bargaining power was less than men's. Organized capacity building that strengthened women's networks and improved their capacity to push issues onto the community agenda assured women a voice in setting the local NRM agenda.

  15. Learn-and-Adapt Stochastic Dual Gradients for Network Resource Allocation

    OpenAIRE

    Chen, Tianyi; Ling, Qing; Giannakis, Georgios B.

    2017-01-01

    Network resource allocation shows revived popularity in the era of data deluge and information explosion. Existing stochastic optimization approaches fall short in attaining a desirable cost-delay tradeoff. Recognizing the central role of Lagrange multipliers in network resource allocation, a novel learn-and-adapt stochastic dual gradient (LA-SDG) method is developed in this paper to learn the sample-optimal Lagrange multiplier from historical data, and accordingly adapt the upcoming resource...

  16. Studies of Sensitivity in the Dictionary Learning Approach to Computed Tomography: Simplifying the Reconstruction Problem, Rotation, and Scale

    DEFF Research Database (Denmark)

    Soltani, Sara

    investigate the sensitivity and robustness of the reconstruction to variations of the scale and orientation in the training images and we suggest algorithms to estimate the correct relative scale and orientation of the unknown image to the training images from the data....... formulation in [22] enforces that the solution is an exact representation by the dictionary; in this report, we investigate this requirement. Furthermore, the underlying assumption that the scale and orientation of the training images are consistent with the unknown image of interest may not be realistic. We...

  17. The Creation of Learner-Centred Dictionaries for Endangered Languages: A Rotuman Example

    Science.gov (United States)

    Vamarasi, M.

    2014-01-01

    This article examines the creation of dictionaries for endangered languages (ELs). Though each dictionary is uniquely prepared for its users, all dictionaries should be based on sound principles of vocabulary learning, including the importance of lexical chunks, as emphasised by Michael Lewis in his "Lexical Approach." Many of the…

  18. Facilitating Teachers' Reuse of Mobile Assisted Language Learning Resources Using Educational Metadata

    Science.gov (United States)

    Zervas, Panagiotis; Sampson, Demetrios G.

    2014-01-01

    Mobile assisted language learning (MALL) and open access repositories for language learning resources are both topics that have attracted the interest of researchers and practitioners in technology enhanced learning (TeL). Yet, there is limited experimental evidence about possible factors that can influence and potentially enhance reuse of MALL…

  19. The RISE Framework: Using Learning Analytics to Automatically Identify Open Educational Resources for Continuous Improvement

    Science.gov (United States)

    Bodily, Robert; Nyland, Rob; Wiley, David

    2017-01-01

    The RISE (Resource Inspection, Selection, and Enhancement) Framework is a framework supporting the continuous improvement of open educational resources (OER). The framework is an automated process that identifies learning resources that should be evaluated and either eliminated or improved. This is particularly useful in OER contexts where the…

  20. Dictionaries and distributions: Combining expert knowledge and large scale textual data content analysis : Distributed dictionary representation.

    Science.gov (United States)

    Garten, Justin; Hoover, Joe; Johnson, Kate M; Boghrati, Reihane; Iskiwitch, Carol; Dehghani, Morteza

    2018-02-01

    Theory-driven text analysis has made extensive use of psychological concept dictionaries, leading to a wide range of important results. These dictionaries have generally been applied through word count methods which have proven to be both simple and effective. In this paper, we introduce Distributed Dictionary Representations (DDR), a method that applies psychological dictionaries using semantic similarity rather than word counts. This allows for the measurement of the similarity between dictionaries and spans of text ranging from complete documents to individual words. We show how DDR enables dictionary authors to place greater emphasis on construct validity without sacrificing linguistic coverage. We further demonstrate the benefits of DDR on two real-world tasks and finally conduct an extensive study of the interaction between dictionary size and task performance. These studies allow us to examine how DDR and word count methods complement one another as tools for applying concept dictionaries and where each is best applied. Finally, we provide references to tools and resources to make this method both available and accessible to a broad psychological audience.

  1. Blended learning in dentistry: 3-D resources for inquiry-based learning

    Directory of Open Access Journals (Sweden)

    Susan Bridges

    2012-06-01

    Full Text Available Motivation is an important factor for inquiry-based learning, so creative design of learning resources and materials is critical to enhance students’ motivation and hence their cognition. Modern dentistry is moving towards “electronic patient records” for both clinical treatment and teaching. Study models have long been an essential part of dental records. Traditional plaster casts are, however, among the last type of clinical record in the dental field to be converted into digital media as virtual models. Advantages of virtual models include: simpler storage; reduced risk of damage, disappearance, or misplacement; simpler and effective measuring; and easy transferal to colleagues. In order to support student engagement with the rapidly changing world of digital dentistry, and in order to stimulate the students’ motivation and depth of inquiry, this project aims to introduce virtual models into a Bachelor and Dental Surgery (BDS curriculum. Under a “blended” e-learning philosophy, students are first introduced to the new software then 3-D models are incorporated into inquiry-based problems as stimulus materials. Face-to-face tutorials blend virtual model access via interactive whiteboards (IWBs. Students’ perceptions of virtual models including motivation and cognition as well as the virtual models’ functionality were rated after a workshop introducing virtual models and plaster models in parallel. Initial student feedback indicates that the 3-D models have been generally well accepted, which confirmed the functionality of the programme and the positive perception of virtual models for enhancing students’ learning motivation. Further investigation will be carried out to assess the impact of virtual models on students’ learning outcomes.

  2. Monolingual and Bilingual Learners' Dictionaries*

    Directory of Open Access Journals (Sweden)

    Rufus H. Gouws

    2011-10-01

    Full Text Available

    Abstract: When deciding on the best learners' dictionary for a specific user and a specificsituation of usage one often has to make a choice between a monolingual and a bilingual learners'dictionary. This article discusses some aspects of the user-driven approach so prevalent in moderndaylexicographic thought, focuses broadly on dictionary typology and takes a closer look at monolingualand bilingual learners' dictionaries. Some problems users experience when learning a newlanguage, e.g. language distortion and problems related to the phenomenon of false friends, especiallyin closely related languages, are mentioned. It is indicated that a typological hybrid dictionarycould assist certain users. The importance of an unambiguous identification of the relevantlexicographic functions is emphasised and the notions of function condensation and function mergingare introduced. It is shown that the typological choice should be determined by a function-basedapproach to dictionary usage.

    Keywords: BILINGUAL DICTIONARY, FALSE FRIENDS, FUNCTION CONDENSATION,FUNCTION MERGING, GENUINE PURPOSE, LEARNERS' DICTIONARY, LEXICOGRAPHICFUNCTIONS, MONOLINGUAL DICTIONARY, TEXT PRODUCTION, TEXT RECEPTION,TYPOLOGICAL HYBRID, TYPOLOGY.

    Opsomming: Eentalige en tweetalige aanleerderwoordeboeke. Wanneerbesluit moet word oor die beste aanleerderwoordeboek vir 'n spesifieke gebruiker en 'n spesifiekegebruiksituasie moet daar dikwels gekies word tussen 'n eentalige en 'n tweetalige aanleerderwoordeboek.Hierdie artikel bespreek bepaalde aspekte van die gebruikersgedrewe benaderingwat kenmerkend is van die moderne leksikografiese denke, fokus breedweg op woordeboektipologieen gee in meer besonderhede aandag aan sekere aspekte van eentalige en tweetalige aanleerderwoordeboeke.Bepaalde probleme wat gebruikers ervaar by die aanleer van 'n vreemde taal,bv. taalversteuring en probleme verwant aan die verskynsel van valse vriende, veral in nou verwantetale, kry aandag

  3. Ursula Wingate. The Effectiveness of Different Learner Dictionaries ...

    African Journals Online (AJOL)

    rbr

    Investigation into the Use of Dictionaries for Reading Comprehension by Inter- mediate Learners of German. 2002, X + 301 pp. ... empirical research conducted in dictionary use. Ursula Wingate is responsible for the ... Her expertise in applied linguistics and in learning strategies has been a particular asset in this research.

  4. The effectiveness of using a bilingualized dictionary for determining ...

    African Journals Online (AJOL)

    This article discusses the use of a bilingualized dictionary, namely Oxford Advanced Learner's English–Chinese Dictionary 8 (OALECD8), by advanced Hong Kong Cantonese ESL learn-ers in the determination of noun countability and associated article use. A homogenous group of 30 English majors in a local university ...

  5. A critical analysis of multilingual dictionaries | Prinsloo | Lexikos

    African Journals Online (AJOL)

    This article evaluates the lexicographic value of multilingual dictionaries. Dictionaries covering three or more languages spoken in South Africa are taken as a case in point. An attempt will be made to reflect on their merits and shortcomings as reference works and learning tools but the focus will be on presumed ...

  6. Evaluating Online Bilingual Dictionaries: The Case of Popular Free English-Polish Dictionaries

    Science.gov (United States)

    Lew, Robert; Szarowska, Agnieszka

    2017-01-01

    Language learners today exhibit a strong preference for free online resources. One problem with such resources is that their quality can vary dramatically. Building on related work on monolingual resources for English, we propose an evaluation framework for online bilingual dictionaries, designed to assess lexicographic quality in four major…

  7. Creating a Framework of a Resource-Based E-Learning Environment for Science Learning in Primary Classrooms

    Science.gov (United States)

    So, Winnie W. M.

    2012-01-01

    Advancements in information and communications technology and the rapid expansion of the Internet have changed the nature and the mode of the presentation and delivery of teaching and learning resources. This paper discusses the results of a study aimed at investigating how five teachers planned to integrate online resources in their teaching of…

  8. Development of Human Resources Using New Technologies in Long-Life Learning

    Directory of Open Access Journals (Sweden)

    Micu Bogdan Ghilic

    2011-01-01

    Full Text Available Information and communication technologies (ICT offer new opportunities to reinvent the education and to make people and makes learning more fun and contemporary but poses many problems to educational institutions. Implementation of ICT determines major structural changes in the organizations and mental switch from bureaucratic mentality to customer-oriented one. In this paper I try to evaluate methods of developing the lifelong learning programs, impact to human resources training and development and the impact of this process on educational institutions. E-learning usage in training the human resources can make a new step in development of the education institutions, human resources and companies.

  9. Dictionary Based Machine Translation from Kannada to Telugu

    Science.gov (United States)

    Sindhu, D. V.; Sagar, B. M.

    2017-08-01

    Machine Translation is a task of translating from one language to another language. For the languages with less linguistic resources like Kannada and Telugu Dictionary based approach is the best approach. This paper mainly focuses on Dictionary based machine translation for Kannada to Telugu. The proposed methodology uses dictionary for translating word by word without much correlation of semantics between them. The dictionary based machine translation process has the following sub process: Morph analyzer, dictionary, transliteration, transfer grammar and the morph generator. As a part of this work bilingual dictionary with 8000 entries is developed and the suffix mapping table at the tag level is built. This system is tested for the children stories. In near future this system can be further improved by defining transfer grammar rules.

  10. Can e-learning help you to connect compassionately? Commentary on a palliative care e-learning resource for India.

    Science.gov (United States)

    Datta, Soumitra Shankar; Agrawal, Sanjit

    2017-01-01

    e-learning resources need to be customised to the audience and learners to make them culturally relevant. The ' Palliative care e-learning resource for health care professionals in India' has been developed by the Karunashraya Hospice, Bengaluru in collaboration with the Cardiff Palliative Care Education Team, Wales to address the training needs of professionals in India. The resource, comprising over 20 modules, integrates psychological, social and medical care for patients requiring palliative care for cancer and other diseases. With increased internet usage, it would help in training a large number of professionals and volunteers in India who want to work in the field of palliative care.

  11. An International Survey of Veterinary Students to Assess Their Use of Online Learning Resources.

    Science.gov (United States)

    Gledhill, Laura; Dale, Vicki H M; Powney, Sonya; Gaitskell-Phillips, Gemma H L; Short, Nick R M

    Today's veterinary students have access to a wide range of online resources that support self-directed learning. To develop a benchmark of current global student practice in e-learning, this study measured self-reported access to, and use of, these resources by students internationally. An online survey was designed and promoted via veterinary student mailing lists and international organizations, resulting in 1,070 responses. Analysis of survey data indicated that students now use online resources in a wide range of ways to support their learning. Students reported that access to online veterinary learning resources was now integral to their studies. Almost all students reported using open educational resources (OERs). Ownership of smartphones was widespread, and the majority of respondents agreed that the use of mobile devices, or m-learning, was essential. Social media were highlighted as important for collaborating with peers and sharing knowledge. Constraints to e-learning principally related to poor or absent Internet access and limited institutional provision of computer facilities. There was significant geographical variation, with students from less developed countries disadvantaged by limited access to technology and networks. In conclusion, the survey provides an international benchmark on the range and diversity in terms of access to, and use of, online learning resources by veterinary students globally. It also highlights the inequalities of access among students in different parts of the world.

  12. Impact of e-resources on learning in biochemistry: first-year medical students’ perceptions

    Science.gov (United States)

    2012-01-01

    Background E-learning resources (e-resources) have been widely used to facilitate self-directed learning among medical students. The Department of Biochemistry at Christian Medical College (CMC), Vellore, India, has made available e-resources to first-year medical students to supplement conventional lecture-based teaching in the subject. This study was designed to assess students’ perceptions of the impact of these e-resources on various aspects of their learning in biochemistry. Methods Sixty first-year medical students were the subjects of this study. At the end of the one-year course in biochemistry, the students were administered a questionnaire that asked them to assess the impact of the e-resources on various aspects of their learning in biochemistry. Results Ninety-eight percent of students had used the e-resources provided to varying extents. Most of them found the e-resources provided useful and of a high quality. The majority of them used these resources to prepare for periodic formative and final summative assessments in the course. The use of these resources increased steadily as the academic year progressed. Students said that the extent to which they understood the subject (83%) and their ability to answer questions in assessments (86%) had improved as a result of using these resources. They also said that they found biochemistry interesting (73%) and felt motivated to study the subject (59%). Conclusions We found that first-year medical students extensively used the e-resources in biochemistry that were provided. They perceived that these resources had made a positive impact on various aspects of their learning in biochemistry. We conclude that e-resources are a useful supplement to conventional lecture-based teaching in the medical curriculum. PMID:22510159

  13. Impact of e-resources on learning in biochemistry: first-year medical students' perceptions.

    Science.gov (United States)

    Varghese, Joe; Faith, Minnie; Jacob, Molly

    2012-05-16

    E-learning resources (e-resources) have been widely used to facilitate self-directed learning among medical students. The Department of Biochemistry at Christian Medical College (CMC), Vellore, India, has made available e-resources to first-year medical students to supplement conventional lecture-based teaching in the subject. This study was designed to assess students' perceptions of the impact of these e-resources on various aspects of their learning in biochemistry. Sixty first-year medical students were the subjects of this study. At the end of the one-year course in biochemistry, the students were administered a questionnaire that asked them to assess the impact of the e-resources on various aspects of their learning in biochemistry. Ninety-eight percent of students had used the e-resources provided to varying extents. Most of them found the e-resources provided useful and of a high quality. The majority of them used these resources to prepare for periodic formative and final summative assessments in the course. The use of these resources increased steadily as the academic year progressed. Students said that the extent to which they understood the subject (83%) and their ability to answer questions in assessments (86%) had improved as a result of using these resources. They also said that they found biochemistry interesting (73%) and felt motivated to study the subject (59%). We found that first-year medical students extensively used the e-resources in biochemistry that were provided. They perceived that these resources had made a positive impact on various aspects of their learning in biochemistry. We conclude that e-resources are a useful supplement to conventional lecture-based teaching in the medical curriculum.

  14. An Investigation into Saudi Students' Knowledge of and Attitudes towards E-Resources on BBC Learning English

    Science.gov (United States)

    Alzahrani, Khalid Saleh

    2017-01-01

    The BBC Learning English website has become an important method of learning and studying English as a second language, a resource that enhances the importance of e-learning. The aim of the current research is to find Saudi students' knowledge of and attitude towards e-resources on BBC Learning English. The sample size was 28 participants (17 male…

  15. Measuring learning gain: Comparing anatomy drawing screencasts and paper-based resources.

    Science.gov (United States)

    Pickering, James D

    2017-07-01

    The use of technology-enhanced learning (TEL) resources is now a common tool across a variety of healthcare programs. Despite this popular approach to curriculum delivery there remains a paucity in empirical evidence that quantifies the change in learning gain. The aim of the study was to measure the changes in learning gain observed with anatomy drawing screencasts in comparison to a traditional paper-based resource. Learning gain is a widely used term to describe the tangible changes in learning outcomes that have been achieved after a specific intervention. In regard to this study, a cohort of Year 2 medical students voluntarily participated and were randomly assigned to either a screencast or textbook group to compare changes in learning gain across resource type. Using a pre-test/post-test protocol, and a range of statistical analyses, the learning gain was calculated at three test points: immediate post-test, 1-week post-test and 4-week post-test. Results at all test points revealed a significant increase in learning gain and large effect sizes for the screencast group compared to the textbook group. Possible reasons behind the difference in learning gain are explored by comparing the instructional design of both resources. Strengths and weaknesses of the study design are also considered. This work adds to the growing area of research that supports the effective design of TEL resources which are complimentary to the cognitive theory of multimedia learning to achieve both an effective and efficient learning resource for anatomical education. Anat Sci Educ 10: 307-316. © 2016 American Association of Anatomists. © 2016 American Association of Anatomists.

  16. Criteria and foundations for the implementation of the Learning Resource Centers

    OpenAIRE

    Raquel Zamora Fonseca

    2013-01-01

    Review the criteria and rationale basis for the implementation of research - library and learning resource centers. The analysis focused on the implementation of CRAIs in university libraries and organizational models that can take.

  17. Criteria and foundations for the implementation of the Learning Resource Centers

    Directory of Open Access Journals (Sweden)

    Raquel Zamora Fonseca

    2013-03-01

    Full Text Available Review the criteria and rationale basis for the implementation of research - library and learning resource centers. The analysis focused on the implementation of CRAIs in university libraries and organizational models that can take.

  18. Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

    OpenAIRE

    Vuorikari, Riina Hannuli

    2010-01-01

    Vuorikari, R. (2009). Tags and self-organisation: a metadata ecology for learning resources in a multilingual context. Doctoral thesis. November, 13, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC.

  19. Tags and self-organisation: a metadata ecology for learning resources in a multilingual context

    NARCIS (Netherlands)

    Vuorikari, Riina

    2009-01-01

    Vuorikari, R. (2009). Tags and self-organisation: a metadata ecology for learning resources in a multilingual context. Doctoral thesis. November, 13, 2009, Heerlen, The Netherlands: Open University of the Netherlands, CELSTEC.

  20. Using a Metro Map Metaphor for organizing Web-based learning resources

    DEFF Research Database (Denmark)

    Grønbæk, Kaj; Bang, Tove; Hansen, Per Steen

    2002-01-01

    This paper briefly describes the WebNize system and how it applies a Metro Map metaphor for organizing guided tours in Web based resources. Then, experiences in using the Metro Map based tours in a Knowledge Sharing project at the library at Aarhus School of Business (ASB) in Denmark, are discussed...... is to create models for Intelligent Knowledge Solutions that can contribute to form the learning environments of the School in the 21st century. The WebNize system is used for sharing of knowledge through metro maps for specific subject areas made available in the Learning Resource Centre at ASB. The metro....... The Library has been involved in establishing a Learning Resource Center (LRC). The LRC serves as an exploratorium for the development and the testing of new forms of communication and learning, at the same time as it integrates the information resources of the electronic research library. The objective...

  1. Chinese View of Learning and Implications for Developing Human Resources

    Science.gov (United States)

    Yang, Baiyin; Zheng, Wei; Li, Mingfei

    2006-01-01

    Chinese society has a unique view of teaching and learning that has evolved from its long history and is heavily embedded in its social and cultural roots. However, no systematic effort has been made to outline how cultural factors such as values and beliefs influence learning. This paper identifies traditional Chinese values and beliefs in…

  2. Electronic learning and open educational resources in the health ...

    African Journals Online (AJOL)

    All of the UG students viewed the TAH programme; 82% (130) of the KNUST students viewed the PCR animations. All students who viewed the programmes at both institutions indicated that the e-learning pro-grammes were “more effective” in comparison to other methods of learning. Conclusion: Computer ownership or ...

  3. Getting educated: e-learning resources in the design and execution of surgical trials.

    Science.gov (United States)

    Bains, Simrit

    2009-01-01

    An evidence-based approach to research, which includes important aspects such as critical appraisal, is essential for the effective conduct of clinical trials. Researchers who are interested in educating themselves about its principles in order to incorporate them into their trials face challenges when attempting to acquire this information from traditional learning sources. E-learning resources offer an intriguing possibility of overcoming the challenges posed by traditional learning, and show promise as a way to expand accessibility to quality education about evidence-based principles. An assessment of existing e-learning resources reveals positive educational avenues for researchers, although significant flaws exist. The Global EducatorTM by Global Research Solutions addresses many of these flaws and is an e-learning resource that combines convenience with comprehensiveness.

  4. Learning about Sex: Resource Guide for Sex Educators. Revised Edition

    Science.gov (United States)

    Huberman, Barbara

    2011-01-01

    Whether you are someone new to the field of sex education, trying to start a library or resource center on adolescent sexual health, or an old pro, this guide should give you a basic orientation to what's available to support your work. These resources are important to advancing positive attitudes toward adolescent sexual health and the author…

  5. Dictionary Visions, Research and Practice

    DEFF Research Database (Denmark)

    This book is about dictionaries and dictionary making. In six thematic sections it presents nineteen contributions covering a wide field within lexicography: Online Lexicography, Dictionary Structure, Phraseology in Dictionaries, LSP Lexicography, Dictionaries and the User, plus Etymology, History...... and Culture in Lexicography. Some chapters focus on theoretical aspects, others report on dictionary work in the making, and still others compare and analyze existing dictionaries. Common to all authors, however, is the concern for the dictionary user. Trivial as it may seem, the fact that dictionaries...

  6. The use of public health e-learning resources by pharmacists in Wales: a quantitative evaluation.

    Science.gov (United States)

    Evans, Andrew; Evans, Sian; Roberts, Debra

    2016-08-01

    The aim of this study was to examine how communicable disease e-learning resources were utilised by pharmacy professionals and to identify whether uptake of the resources was influenced by disease outbreaks. Retrospective analysis of routine data regarding the number of individuals completing e-learning resources and statutory notifications of communicable disease. A high proportion of pharmacy professionals in Wales (38.8%, n = 915/2357) accessed the resources; around one in six completed multiple resources (n = 156). The most commonly accessed were those where there had been a disease outbreak during the study period. There was a strong positive correlation between e-learning uptake and number of disease cases; this was observed both for measles and scarlet fever. Communicable disease e-learning appears to be an acceptable method for providing communicable disease information to pharmacy professionals. Study findings suggest that e-learning uptake is positively influenced by disease outbreaks this reflects well both on pharmacy professionals and on the e-learning resources themselves. © 2016 Royal Pharmaceutical Society.

  7. THE MIGHT OF RUSSIAN LANGUAGE ACCORDING TO SYNONYMIC DICTIONARY BY COMPUTER EVALUATION SYSTEM ASIS®

    Directory of Open Access Journals (Sweden)

    Vitaly N. Trishin

    2013-01-01

    Full Text Available The article describes electronic dictionary of synonyms in Russian language by ASIS® system (more than 500 000 words and collocations, 190 000 synonymic connections.The program can be used not just as a dictionary of synonyms and close meaning words, but also as spelling dictionary and definition dictionary of Russian language in order to check the orthography and define the meaning of unknown words. The dictionary is also designed to be an instrument of philological surveys and studies of the language trough the extensive query system on different characteristic of words (definition, composition, synonymy, etc.. Program’s lexical base includes words from dictionaries and guides in all subject areas - from astronomy to Japanese painting. Over compilation of dictionary developer used published dictionaries: spelling, synonymic, definition dictionaries, dictionary of collocations, dictionary of foreign words and etc. of 19-21 cc. Newspapers, magazines and web-resources were active used as well for appending the dictionary. This dictionary practically shows, that by the amount of words Russian language belongs with the most developed languages in the world, and by the scale and density of synonymic space, in the author’s opinion, it has no equal.

  8. Competitive debate classroom as a cooperative learning technique for the human resources subject

    Directory of Open Access Journals (Sweden)

    Guillermo A. SANCHEZ PRIETO

    2018-01-01

    Full Text Available The paper shows an academic debate model as a cooperative learning technique for teaching human resources at University. The general objective of this paper is to conclude if academic debate can be included in the category of cooperative learning. The Specific objective it is presenting a model to implement this technique. Thus the first part of the paper shows the concept of cooperative learning and its main characteristics. The second part presents the debate model believed to be labelled as cooperative learning. Last part concludes with the characteristics of the model that match different aspects or not of the cooperative learning.

  9. French Dictionaries. Series: Specialised Bibliographies.

    Science.gov (United States)

    Klaar, R. M.

    This is a list of French monolingual, French-English and English-French dictionaries available in December 1975. Dictionaries of etymology, phonetics, place names, proper names, and slang are included, as well as dictionaries for children and dictionaries of Belgian, Canadian, and Swiss French. Most other specialized dictionaries, encyclopedias,…

  10. Digital Cadavers: Online 2D Learning Resources Enhance Student Learning in Practical Head and Neck Anatomy within Dental Programs

    Directory of Open Access Journals (Sweden)

    Mahmoud M. Bakr

    2016-01-01

    Full Text Available Head and neck anatomy provides core concepts within preclinical dental curricula. Increased student numbers, reduced curricula time, and restricted access to laboratory-based human resources have increased technology enhanced learning approaches to support student learning. Potential advantages include cost-effectiveness, off-campus access, and self-directed review or mastery opportunities for students. This study investigated successful student learning within a first-year head and neck anatomy course at the School of Dentistry and Oral Health, Griffith University, Australia, taught by the same teaching team, between 2010 and 2015. Student learning success was compared, for cohorts before and after implementation of a supplementary, purpose-designed online digital library and quiz bank. Success of these online resources was confirmed using overall students’ performance within the course assessment tasks and Student Evaluation of Course surveys and online access data. Engagement with these supplementary 2D online resources, targeted at improving laboratory study, was positively evaluated by students (mean 85% and significantly increased their laboratory grades (mean difference 6%, P<0.027, despite being assessed using cadaveric resources. Written assessments in final exams were not significantly improved. Expanded use of supplementary online resources is planned to support student learning and success in head and neck anatomy, given the success of this intervention.

  11. Students' "Uses and Gratification Expectancy" Conceptual Framework in Relation to E-Learning Resources

    Science.gov (United States)

    Mondi, Makingu; Woods, Peter; Rafi, Ahmad

    2007-01-01

    This paper presents the systematic development of a "Uses and Gratification Expectancy" (UGE) conceptual framework which is able to predict students' "Perceived e-Learning Experience." It is argued that students' UGE as regards e-learning resources cannot be implicitly or explicitly explored without first examining underlying communication…

  12. Social Learning, Natural Resource Management, and Participatory Activities: A reflection on construct development and testing

    NARCIS (Netherlands)

    Rodela, R.

    2014-01-01

    This analysis reflects on the use of multidimensional constructs for the study of social learning in natural resource management. Insight from deliberative democracy and adult learning literature are used to ground the identified four dimensions (the moral dimension the cognitive dimension, the

  13. Enhancing Teaching and Learning Wi-Fi Networking Using Limited Resources to Undergraduates

    Science.gov (United States)

    Sarkar, Nurul I.

    2013-01-01

    Motivating students to learn Wi-Fi (wireless fidelity) wireless networking to undergraduate students is often difficult because many students find the subject rather technical and abstract when presented in traditional lecture format. This paper focuses on the teaching and learning aspects of Wi-Fi networking using limited hardware resources. It…

  14. Social Web Content Enhancement in a Distance Learning Environment: Intelligent Metadata Generation for Resources

    Science.gov (United States)

    García-Floriano, Andrés; Ferreira-Santiago, Angel; Yáñez-Márquez, Cornelio; Camacho-Nieto, Oscar; Aldape-Pérez, Mario; Villuendas-Rey, Yenny

    2017-01-01

    Social networking potentially offers improved distance learning environments by enabling the exchange of resources between learners. The existence of properly classified content results in an enhanced distance learning experience in which appropriate materials can be retrieved efficiently; however, for this to happen, metadata needs to be present.…

  15. Learning Agreements and Socially Responsible Approaches to Professional and Human Resource Development in the United Kingdom

    Science.gov (United States)

    Wallis, Emma

    2008-01-01

    This article draws upon original qualitative data to present an initial assessment of the significance of learning agreements for the development of socially responsible approaches to professional and human resource development within the workplace. The article suggests that the adoption of a partnership-based approach to learning is more…

  16. Online Dissection Audio-Visual Resources for Human Anatomy: Undergraduate Medical Students' Usage and Learning Outcomes

    Science.gov (United States)

    Choi-Lundberg, Derek L.; Cuellar, William A.; Williams, Anne-Marie M.

    2016-01-01

    In an attempt to improve undergraduate medical student preparation for and learning from dissection sessions, dissection audio-visual resources (DAVR) were developed. Data from e-learning management systems indicated DAVR were accessed by 28% ± 10 (mean ± SD for nine DAVR across three years) of students prior to the corresponding dissection…

  17. Students’ Use of Knowledge Resources in Environmental Interaction on an Outdoor Learning Trail

    NARCIS (Netherlands)

    Tan, Esther; So, Hyo-Jeong

    2016-01-01

    This study examined how students leveraged different types of knowledge resources on an outdoor learning trail. We positioned the learning trail as an integral part of the curriculum with a pre- and post-trail phase to scaffold and to support students’ meaning-making process. The study was conducted

  18. What Is the Impact of Online Resource Materials on Student Self-Learning Strategies?

    Science.gov (United States)

    Dowell, David John; Small, Felicity A.

    2011-01-01

    The purpose of this article is to examine how students are incorporating online resources into their self-regulated learning strategies. The process of developing these learning strategies and the importance of these strategies has been widely researched, but there has been little empirical research into how the students are affected by online…

  19. Drivers and Effects of Enterprise Resource Planning Post-Implementation Learning

    Science.gov (United States)

    Chang, Hsiu-Hua; Chou, Huey-Wen

    2011-01-01

    The use of enterprise resource planning (ERP) systems has grown enormously since 1990, but the failure to completely learn how to use them continues to produce disappointing results. Today's rapidly changing business environment and the integrative applications of ERP systems force users to continuously learn new skills after ERP implementation.…

  20. Researching into Learning Resources in Colleges and Universities. The Practical Research Series.

    Science.gov (United States)

    Higgins, Chris; Reading, Judy; Taylor, Paul

    This book examines issues and methods for conducting research into the educational resource environment in colleges and universities. That environment is defined as whatever is used to facilitate the learning process, including learning space, support staff, and teaching staff. Chapter 1 is an introduction to the series and lays out the process of…

  1. OER, Resources for Learning--Experiences from an OER Project in Sweden

    Science.gov (United States)

    Ossiannilsson, Ebba S. I.; Creelman, Alastair M.

    2012-01-01

    This article aims to share experience from a Swedish project on the introduction and implementation of Open Educational Resources (OER) in higher education with both national and international perspectives. The project, "OER--resources for learning", was part of the National Library of Sweden Open Access initiative and aimed at exploring, raising…

  2. Applying the Quadratic Usage Framework to Research on K-12 STEM Digital Learning Resources

    Science.gov (United States)

    Luetkemeyer, Jennifer R.

    2016-01-01

    Numerous policymakers have called for K-12 educators to increase their effectiveness by transforming science, technology, engineering, and mathematics (STEM) learning and teaching with digital resources and tools. In this study we outline the significance of studying pressing issues related to use of digital resources in the K-12 environment and…

  3. Housing Quality and Access to Material and Learning Resources within the Home Environment in Developing Countries

    Science.gov (United States)

    Bradley, Robert H.; Putnick, Diane L.

    2012-01-01

    This study examined home environment conditions (housing quality, material resources, formal and informal learning materials) and their relations with the Human Development Index (HDI) in 28 developing countries. Home environment conditions in these countries varied widely. The quality of housing and availability of material resources at home were…

  4. Fathers and Mothers of Children with Learning Disabilities: Links between Emotional and Coping Resources

    Science.gov (United States)

    Al-Yagon, Michal

    2015-01-01

    This study compared emotional and coping resources of two parent groups with children ages 8 to 12 years--children with learning disabilities (LD) versus with typical development--and explored how mothers' and fathers' emotional resources (low anxious/avoidant attachment, low negative affect, and high positive affect) may explain differences in…

  5. Leadership Learning Opportunities in Agriculture, Food, and Natural Resources Education: The Role of The Teacher

    Science.gov (United States)

    McKim, Aaron J.; Pauley, C. M.; Velez, Jonathan J.; Sorensen, Tyson J.

    2017-01-01

    Learning environments combining agriculture, food, natural resources, and leadership knowledge and skills are increasingly essential in preparing students for future success. School-based agricultural education offers a premier context in which to teach leadership within agriculture, food, and natural resources curriculum. However, providing…

  6. Learned Resourcefulness and the Long-Term Benefits of a Chronic Pain Management Program

    Science.gov (United States)

    Kennett, Deborah J.; O'Hagan, Fergal T.; Cezer, Diego

    2008-01-01

    A concurrent mixed methods approach was used to understand how learned resourcefulness empowers individuals. After completing Rosenbaum's Self-Control Schedule (SCS) measuring resourcefulness, 16 past clients of a multimodal pain clinic were interviewed about the kinds of pain-coping strategies they were practicing from the program. Constant…

  7. Learning about the Human Genome. Part 2: Resources for Science Educators. ERIC Digest.

    Science.gov (United States)

    Haury, David L.

    This ERIC Digest identifies how the human genome project fits into the "National Science Education Standards" and lists Human Genome Project Web sites found on the World Wide Web. It is a resource companion to "Learning about the Human Genome. Part 1: Challenge to Science Educators" (Haury 2001). The Web resources and…

  8. Application of ICT-based Learning Resources for University Inorganic Chemistry Course Training

    Directory of Open Access Journals (Sweden)

    Tatyana M. Derkach

    2013-01-01

    Full Text Available The article studies expediency and efficiency of various ICT-based learning resources use in university inorganic chemistry course training, detects difference of attitudes toward electronic resources between students and faculty members, which create the background for their efficiency loss

  9. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning

    Science.gov (United States)

    Yu, Hong Qing; Pedrinaci, C.; Dietze, S.; Domingue, J.

    2012-01-01

    Multimedia educational resources play an important role in education, particularly for distance learning environments. With the rapid growth of the multimedia web, large numbers of educational video resources are increasingly being created by several different organizations. It is crucial to explore, share, reuse, and link these educational…

  10. Dependent Narcissism, Organizational Learning, and Human Resource Development

    Science.gov (United States)

    Godkin, Lynn; Allcorn, Seth

    2009-01-01

    Narcissistic leadership can benefit organizational performance. Aberrant narcissism can destroy the psychosocial health of groups, limiting performance. This article examines Dependent Organizational Disorder, a common form of narcissism, which infects leadership, thwarts performance, and interrupts organizational learning. Dependent…

  11. Earth, Air, Fire, & Water: Resource Guide 6. The Arts and Learning, Interdisciplinary Resources for Education.

    Science.gov (United States)

    Lee, Ronald T., Ed.

    This resource guide is intended to aid practitioners in the design of new curriculum units or the enrichment of existing units by suggesting activities and resources in the topic areas of earth, air, fire, and water. Special projects and trips relating to these topic areas are proposed. A sample arts networking system used to integrate various…

  12. From Data to Dictionary

    DEFF Research Database (Denmark)

    Nielsen, Sandro; Almind, Richard

    2011-01-01

    definitions, thereby creating both monolingual and bilingual dictionaries. Users access the data through online dictionaries that allow them to make structured searches. The dictionaries mainly provide help in communicative situations such as understanding, producing and translating accounting texts, but also......Most online dictionaries are based on printed dictionaries or specially developed databases. However, these dictionaries do not fully satisfy the needs for help and knowledge users have, so a re-assessment of the practical and theoretical foundation is necessary. Based on the work on the Accounting...... help users acquire knowledge about general or specific accounting matters in cognitive user situations. This theoretical foundation allows lexicographers to develop dictionaries that search in structured data sets and then present data types selected because they provide help in specific situations...

  13. Model of e-learning with electronic educational resources of new generation

    OpenAIRE

    A. V. Loban; D. A. Lovtsov

    2017-01-01

    Purpose of the article: improving of scientific and methodical base of the theory of the е-learning of variability. Methods used: conceptual and logical modeling of the е-learning of variability process with electronic educational resource of new generation and system analysis of the interconnection of the studied subject area, methods, didactics approaches and information and communication technologies means. Results: the formalization complex model of the е-learning of variability with elec...

  14. Mobile Authoring of Open Educational Resources as Reusable Learning Objects

    Directory of Open Access Journals (Sweden)

    Dr Kinshuk

    2013-06-01

    Full Text Available E-learning technologies have allowed authoring and playback of standardized reusable learning objects (RLO for several years. Effective mobile learning requires similar functionality at both design time and runtime. Mobile devices can play RLO using applications like SMILE, mobile access to a learning management system (LMS, or other systems which deploy content to mobile learners (Castillo & Ayala, 2008; Chu, Hwang, & Tseng, 2010; Hsu & Chen, 2010; Nakabayashi, 2009; Zualkernan, Nikkhah, & Al-Sabah, 2009. However, implementations which author content in a mobile context do not typically permit reuse across multiple contexts due to a lack of standardization. Standards based (IMS and SCORM authoring implementations exist for non-mobile platforms (Gonzalez-Barbone & Anido-Rifon, 2008; Griffiths, Beauvoir, Liber, & Barrett-Baxendale, 2009; Téllez, 2010; Yang, Chiu, Tsai, & Wu, 2004. However, this paradigm precludes capturing learning where and when it occurs. Consequently, RLO authored for e-learning lack learner generated content, especially with timely, relevant, and location aware examples.

  15. CASE STUDY: Bhutan — Learning together to share resources in ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2010-12-14

    Dec 14, 2010 ... Shyly at first, because the participatory research methods were as new to them ... linked to other resource systems as well as to socioeconomic factors. .... use and maintenance of the forest by the entire watershed community.

  16. Controller resource management : what can we learn from aircrews?

    Science.gov (United States)

    1995-07-01

    This paper provides an overview of the scientific literature regarding Crew Resource Management (CRM). It responds to tasking from the Office of Air Traffic Program Management to conduct studies addressing the application of team training models such...

  17. Multimedia presentation as a form of E-learning resources in the educational process

    Directory of Open Access Journals (Sweden)

    Bizyaev АА

    2017-06-01

    Full Text Available The article describes the features of the use of multimedia presentations as an electronic learning resource in the educational process, reflecting resource requirements; pedagogical goals that may be achieved. Currently one of the main directions in the educational process is the effective use of teaching computers. Pressing issue implementation of information and communication technologies in education is to develop educational resources with the aim to increase the level and quality of education.

  18. Competition for resources can explain patterns of social and individual learning in nature.

    Science.gov (United States)

    Smolla, Marco; Gilman, R Tucker; Galla, Tobias; Shultz, Susanne

    2015-09-22

    In nature, animals often ignore socially available information despite the multiple theoretical benefits of social learning over individual trial-and-error learning. Using information filtered by others is quicker, more efficient and less risky than randomly sampling the environment. To explain the mix of social and individual learning used by animals in nature, most models penalize the quality of socially derived information as either out of date, of poor fidelity or costly to acquire. Competition for limited resources, a fundamental evolutionary force, provides a compelling, yet hitherto overlooked, explanation for the evolution of mixed-learning strategies. We present a novel model of social learning that incorporates competition and demonstrates that (i) social learning is favoured when competition is weak, but (ii) if competition is strong social learning is favoured only when resource quality is highly variable and there is low environmental turnover. The frequency of social learning in our model always evolves until it reduces the mean foraging success of the population. The results of our model are consistent with empirical studies showing that individuals rely less on social information where resources vary little in quality and where there is high within-patch competition. Our model provides a framework for understanding the evolution of social learning, a prerequisite for human cumulative culture. © 2015 The Author(s).

  19. Effective post-literacy learning: A question of a national human resource strategy

    Science.gov (United States)

    Ahmed, Manzoor

    1989-12-01

    Initial literacy courses must be followed by opportunities for consolidating the mechanics of literacy skills and practical application of three skills in life. Experience has shown that these `post-literacy' objectives can be achieved, not by a second stage of the literacy course, but by a range of opportunities for learning and application of learning through a network of continuing education opportunities geared to the diverse needs and circumstances of different categories of neo-literates. A taxonomy of learner categories and learning needs is seen as a basis for planning and supporting the network of post-literacy learning. Examples from China, India and Thailand demonstrate the importance of recognizing the continuity of literacy and post-literacy efforts, the need for commitment of resources for this continuum of learning, the role of an organizational structure to deal with this continuum in a coordinated way, and the value of a comprehensive range of learning opportunities for neo-literates. A necessary condition for success in building a network of continuing learning opportunities and contributing to the creation of a `learning society' is to make human resource development the core of national development. It is argued that the scope and dimensions of post-literacy continuing education are integrally linked with the goal of mass basic education and ultimately with the vision of a `learning society'. Such a vision can be a reality only with a serious human resource development focus in national development that will permit the necessary mobilization of resources, the coordination of sectors of government and society and the generation of popular enthusiasm. A radical or an incremental approach can be taken to move towards the primacy of a human resource strategy in national development. In either case, a functioning coordination and support mechanism has to be developed for the key elements of mass basic education including post-literacy learning.

  20. Dental and Medical Students' Use and Perceptions of Learning Resources in a Human Physiology Course.

    Science.gov (United States)

    Tain, Monica; Schwartzstein, Richard; Friedland, Bernard; Park, Sang E

    2017-09-01

    The aim of this study was to determine the use and perceived utility of various learning resources available during the first-year Integrated Human Physiology course at the dental and medical schools at Harvard University. Dental and medical students of the Class of 2018 were surveyed anonymously online in 2015 regarding their use of 29 learning resources in this combined course. The learning resources had been grouped into four categories to discern frequency of use and perceived usefulness among the categories. The survey was distributed to 169 students, and 73 responded for a response rate of 43.2%. There was no significant difference among the learning resource categories in frequency of use; however, there was a statistically significant difference among categories in students' perceptions of usefulness. No correlation was found between frequency of use and perceived usefulness of each category. Students seemingly were not choosing the most useful resources for them. These results suggest that, in the current educational environment, where new technologies and self-directed learning are highly sought after, there remains a need for instructor-guided learning.

  1. Efficient generation of pronunciation dictionaries: human factors factors during bootstrapping

    CSIR Research Space (South Africa)

    Davel, MH

    2004-10-01

    Full Text Available Bootstrapping techniques have significant potential for the efficient generation of linguistic resources such as electronic pronunciation dictionaries. The authors describe a system and an approach to bootstrapping for the development...

  2. Collocational Relations in Japanese Language Textbooks and Computer-Assisted Language Learning Resources

    Directory of Open Access Journals (Sweden)

    Irena SRDANOVIĆ

    2011-05-01

    Full Text Available In this paper, we explore presence of collocational relations in the computer-assisted language learning systems and other language resources for the Japanese language, on one side, and, in the Japanese language learning textbooks and wordlists, on the other side. After introducing how important it is to learn collocational relations in a foreign language, we examine their coverage in the various learners’ resources for the Japanese language. We particularly concentrate on a few collocations at the beginner’s level, where we demonstrate their treatment across various resources. A special attention is paid to what is referred to as unpredictable collocations, which have a bigger foreign language learning-burden than the predictable ones.

  3. Student perceptions on learning with online resources in a flipped mathematics classroom

    DEFF Research Database (Denmark)

    Triantafyllou, Eva; Timcenko, Olga

    2015-01-01

    This article discusses student perceptions of if and how online resources contribute to mathematics learning and motivation. It includes results from an online survey we conducted at the Media Technology department of Aalborg University, Copenhagen, Denmark. For this study, students were given...... links to various online resources (screencasts, online readings and quizzes, and lecture notes) for out-of-class preparation in a flipped classroom in mathematics. The survey results show support for student perceptions that online resources enhance learning, by providing visual and in depth...... explanations, and they can motivate students. However, students stated that they miss just-in-time explanations when learning with online resources and they questioned the quality and validity of some of them....

  4. Understanding Cognitive Load Using On-line Dictionaries

    OpenAIRE

    Robert F. , Dilenschneider

    2017-01-01

    Cognitive Load Theory may useful for language instructors to understand how the look up conditions ofusing an on-line dictionary might influence learning. This paper first reviews previous studies that haveinvestigated dictionary use for vocabulary acquisition and reading comprehension Second, it explainsthe various elements of Cognitive Load Theory. Third, it describes how Cognitive Load Theory appliesto language learners' to learn unknown words and comprehend texts Last, it discusses the pe...

  5. THE USE OF OPEN EDUCATIONAL RESOURCES IN ONLINE LEARNING: A Study of Students’ Perception

    Directory of Open Access Journals (Sweden)

    Meirani HARSASI

    2015-07-01

    Full Text Available Universitas Terbuka (UT is Indonesia’s higher education institution which implements distance education system. The term distance implies that learning is not performed face-to-face but there is geographically separation between students and teacher. Therefore, UT must provide many kinds of learning modes and learning support. To facilitate students in their learning process, UT provides an e-learning system named online tutorial. This tutorial is provided for all courses which are designed in 8 sessions of virtual class. Students can learn, discuss, and ask to the teacher via this mode of learning. As the development of methods in e-learning, the use of open educational resources (OER has increasing these days. Learning materials can be taken easily and freely from internet. UT also utilize OER in it’s learning process, especially in e-learning. The aim of this study was to collect data from students about their acceptance of integrating OER into e-learning. The use of OER is perceived by students as something interesting because it’s new for them and can help them to have a better understanding about a topic. The results also showed that video has found as the most interesting OER for students. Other results, limitation and suggestion from students about integrating OER into e-learning also will be discussed in this paper.

  6. EMPOWERING THE HUMAN RESOURCES AND THE ROLE OF DISTANCE LEARNING

    Directory of Open Access Journals (Sweden)

    Sukmaya LAMA

    2012-07-01

    Full Text Available As the world is invaded by technological inventions and wonders, life becoming more fast and crazy, yet there can be no doubt that the critical factor for the development of a nation or a state is its human resource. The productivity of a nation is influenced by the number of its skilled population. When we look into the problem of underdevelopment from human resource perspective we are bound to take a look at the educational scenario. In India, the higher education scenario has been very sickly, due to the pro profit policies, lack of infrastructure, entry of private players, etc. The growth of distance education phenomenon in India has no doubt brought a ray of hope. The present paper aims to look into the role of distance education in Assam and the potential it carries in building a huge wealth of human resources.

  7. The second generation of natural resource damage assessments: Lessons learned?

    International Nuclear Information System (INIS)

    Luthi, R.B.; Burlington, L.B.; Reinharz, E.; Shutler, S.K.

    1993-01-01

    The Damage Assessment Regulations Team (DART), under the Office of General Counsel of the National Oceanic and Atmospheric Administration (NOAA), has centered its efforts on developing natural resource damage assessment regulations for oil pollution in navigable waters. These procedures will likely lower the costs associated with damage assessments, encourage joint cooperative assessments and simplify most assessments. The DART team of NOAA is developing new regulations for the assessment of damages due to injuries related to oil spills under the Oil Pollution Act of 1990. These regulations will involve coordination, restoration, and economic valuation. Various methods are currently being developed to assess damages for injuries to natural resources. The proposed means include: compensation tables for spills under 50,000 gallons, Type A model, expedited damage assessment (EDA) procedures, and comprehensive procedures. They are being developed to provide trustees with a choice for assessing natural resource damages for each oil spill

  8. More technology, better learning resources, better learning? Lessons from adopting virtual microscopy in undergraduate medical education.

    Science.gov (United States)

    Helle, Laura; Nivala, Markus; Kronqvist, Pauliina

    2013-01-01

    The adoption of virtual microscopy at the University of Turku, Finland, created a unique real-world laboratory for exploring ways of reforming the learning environment. The purpose of this study was to evaluate the students' reactions and the impact of a set of measures designed to boost an experimental group's understanding of abnormal histology through an emphasis on knowledge of normal cells and tissues. The set of measures included (1) digital resources to review normal structures and an entrance examination for enforcement, (2) digital course slides highlighting normal and abnormal tissues, and (3) self-diagnostic quizzes. The performance of historical controls was used as a baseline, as previous students had never been exposed to the above-mentioned measures. The students' understanding of normal histology was assessed in the beginning of the module to determine the impact of the first set of measures, whereas that of abnormal histology was assessed at the end of the module to determine the impact of the whole set of measures. The students' reactions to the instructional measures were assessed by course evaluation data. Additionally, four students were interviewed. Results confirmed that the experimental group significantly outperformed the historical controls in understanding normal histology. The students held favorable opinions on the idea of emphasizing normal structures. However, with regards to abnormal histology, the historical controls outperformed the experimental group. In conclusion, allowing students access to high-quality digitized materials and boosting prerequisite skills are clearly not sufficient to boost final competence. Instead, the solution may lie in making students externally accountable for their learning throughout their training. Copyright © 2012 American Association of Anatomists.

  9. Work in the classrooms with European perspective: materials and resources for autonomus learning

    Directory of Open Access Journals (Sweden)

    Manuela RAPOSO RIVAS

    2011-04-01

    Full Text Available One of the key principles set forth in the bologna process is to focus teaching on students, by getting involved actively and independently in their learning process and in developing their skills. This requires the use of teaching and learning methods together with materials and resources to motivate and guide them. In this paper, we present three of them, from our experience in adapting the subject of «New Technologies Applied to education» to the ecTs system, which we have found useful for guiding and evaluating the learning process and promote the intended learning. These materials and resources are: «learning guides», the portfolio and the rubric.

  10. Managing Student Learning: Schools as Multipliers of Intangible Resources

    Science.gov (United States)

    Paletta, Angelo

    2011-01-01

    The conceptual categories that underlie the business analysis of intellectual capital are relevant to providing an explanation of school performance. By gathering data on student learning, this research provides empirical evidence for the use of school results as an accurate indicator of the effectiveness of the management of public education.…

  11. AN INCLUSIVE APPROACH TO ONLINE LEARNING ENVIRONMENTS: Models and Resources

    Directory of Open Access Journals (Sweden)

    Aline Germain-RUTHERFORD

    2008-04-01

    Full Text Available The impact of ever-increasing numbers of online courses on the demographic composition of classes has meant that the notions of diversity, multiculturality and globalization are now key aspects of curriculum planning. With the internationalization and globalization of education, and faced with rising needs for an increasingly educated and more adequately trained workforce, universities are offering more flexible programs, assisted by new educational and communications technologies. Faced with this diversity of populations and needs, many instructors are becoming aware of the importance of addressing the notions of multiculturality and interculturality in the design of online however this raises many questions. For example, how do we integrate and address this multicultural dimension in a distance education course aimed at students who live in diverse cultural environments? How do the challenges of intercultural communication in an online environment affect online teaching and learning? What are the characteristics of an online course that is inclusive of all types of diversity, and what are the guiding principles for designing such courses? We will attempt to answer some of these questions by first exploring the concepts of culture and learning cultures. This will help us to characterize the impact on online learning of particular cultural dimensions. We will then present and discuss different online instructional design models that are culturally inclusive, and conclude with the description of a mediated instructional training module on the management of the cultural dimension of online teaching and learning. This module is mainly addressed to teachers and designers of online courses.

  12. Digital Learning Resources and Ubiquitous Technologies in Education

    Science.gov (United States)

    Camilleri, Mark Anthony; Camilleri, Adriana Caterina

    2017-01-01

    This research explores the educators' attitudes and perceptions about their utilisation of digital learning technologies. The methodology integrates measures from "the pace of technological innovativeness" and the "technology acceptance model" to understand the rationale for further ICT investment in compulsory education. A…

  13. Information Resources Usage in Project Management Digital Learning System

    Science.gov (United States)

    Davidovitch, Nitza; Belichenko, Margarita; Kravchenko, Yurii

    2017-01-01

    The article combines a theoretical approach to structuring knowledge that is based on the integrated use of fuzzy semantic network theory predicates, Boolean functions, theory of complexity of network structures and some practical aspects to be considered in the distance learning at the university. The paper proposes a methodological approach that…

  14. Interaction and Technological Resources to Support Learning of Complex Numbers

    Directory of Open Access Journals (Sweden)

    Cassiano Scott Puhl

    2016-02-01

    Full Text Available This article presents a didactic proposal, a workshop for the introduction of the study of complex numbers. Unlike recurrent practices, the workshop began developing the geometric shape of the complex number, implicitly, through vectors. Eliminating student formal vision and algebraic, enriching the teaching practice. The main objective of the strategy was to build the concept of imaginary unit without causing a feeling of strangeness or insignificance of number. The theory of David Ausubel, meaningful learning, the workshop was based on a strategy developed to analyze the subsumers of students and develop a learning by subject. Combined with dynamic and interactive activities in the workshop, there is the use of a learning object (http://matematicacomplexa.meximas.com/. An environment created and basing on the theory of meaningful learning, making students reflect and interact in developed applications sometimes being challenged and other testing hypotheses and, above all, building knowledge. This proposal provided a rich environment for exchange of information between participants and deepening of ideas and concepts that served as subsumers. The result of the experience was very positive, as evidenced by the comments and data submitted by the participants, thus demonstrating that the objectives of this didactic proposal have been achieved.

  15. Women's Learning and Leadership Styles: Impact on Crew Resource Management.

    Science.gov (United States)

    Turney, Mary Ann

    With an increasing number of women becoming members of flight crews, the leadership styles of men and women are at issue. A study explored three basic questions: (1) How do male and female learning and leadership styles differ? (2) What barriers to gender integration and crew teamwork are perceived by pilot crew members? and (3) What…

  16. Science Learning via Multimedia Portal Resources: The Scottish Case

    Science.gov (United States)

    Elliot, Dely; Wilson, Delia; Boyle, Stephen

    2014-01-01

    Scotland's rich heritage in the field of science and engineering and recent curricular developments led to major investment in education to equip pupils with improved scientific knowledge and skills. However, due to its abstract and conceptual nature, learning science can be challenging. Literature supports the role of multimedia technology in…

  17. Empowering the Human Resources and the Role of Distance Learning

    Science.gov (United States)

    Lama, Sukmaya; Kashyap, Mridusmita

    2012-01-01

    As the world is invaded by technological inventions and wonders, life becoming more fast and crazy, yet there can be no doubt that the critical factor for the development of a nation or a state is its human resource. The productivity of a nation is influenced by the number of its skilled population. When we look into the problem of…

  18. in_focus - Comangement of Natural Resources: Local Learning for ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    The developing world's poorest people live in marginal, often harsh rural environments. ... Co-Management of Natural Resources in Canada: A Review of Concepts and Case Studies ... He holds a doctorate in city and regional planning from the University of ... funding for the Climate and Development Knowledge Network.

  19. Managing human resources in the nuclear power industry: Lessons learned

    International Nuclear Information System (INIS)

    2003-08-01

    This report is intended for senior and middle level managers in nuclear operating organizations. Its objectives are to facilitate the recognition of priority issues with respect to managing human resources, and to provide pragmatic ideas regarding improvements. The human resource issues addressed in this report, if not managed effectively, can result in significant performance problems at nuclear power plants. About 10 years ago the IAEA initiated an effort to identify such management issues and to find effective practices to deal with them. This information was provided in IAEA Technical Reports Series No. 369, Management for Excellence in Nuclear Power Plant Performance - A Manual (1994). This report builds upon the information in the subject manual. In the past 10 years there have been significant changes in the nuclear power industry resulting primarily from more competitive energy markets and privatization of nuclear power plant operating organizations. In general, the industry has responded positively to these changes, as indicated by IAEA/WANO performance indicators that show both improved operational and safety performance. This report provides examples of approaches to managing human resources that have been effective in responding to these changes. This report was produced through a series of meetings, where meeting participants were asked to share information regarding effective practices in their organizations with respect to managing human resources. The information provided through these meetings was supplemented with good practices in this area identified through IAEA Operational Safety Review Teams (OSARTs) conducted during the past 10 years

  20. From Training to Learning in Enterprise Resource Planning Systems

    Science.gov (United States)

    Kerr, Don; Murray, Peter A.; Burgess, Kevin

    2012-01-01

    The information systems' literature outlines how training is a critical factor to successful Enterprise Resource Planning (ERP) implementations. Yet, types of training are not discussed in the literature and there is little indication if existing training is effective and whether relevant contextual factors have been considered. Without…

  1. Investigating Student Use and Value of E-Learning Resources to Develop Academic Writing within the Discipline of Environmental Science

    Science.gov (United States)

    Taffs, Kathryn H.; Holt, Julienne I.

    2013-01-01

    The use of information and communication technologies (ICTs) in higher education to support student learning is expanding. However, student usage has been low and the value of e-learning resources has been under investigation. We reflect on best practices for pedagogical design of e-learning resources to support academic writing in environmental…

  2. The SMAP Dictionary Management System

    Science.gov (United States)

    Smith, Kevin A.; Swan, Christoper A.

    2014-01-01

    The Soil Moisture Active Passive (SMAP) Dictionary Management System is a web-based tool to develop and store a mission dictionary. A mission dictionary defines the interface between a ground system and a spacecraft. In recent years, mission dictionaries have grown in size and scope, making it difficult for engineers across multiple disciplines to coordinate the dictionary development effort. The Dictionary Management Systemaddresses these issues by placing all dictionary information in one place, taking advantage of the efficiencies inherent in co-locating what were once disparate dictionary development efforts.

  3. E-learning in medical education in resource constrained low- and middle-income countries.

    Science.gov (United States)

    Frehywot, Seble; Vovides, Yianna; Talib, Zohray; Mikhail, Nadia; Ross, Heather; Wohltjen, Hannah; Bedada, Selam; Korhumel, Kristine; Koumare, Abdel Karim; Scott, James

    2013-02-04

    In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators "AND" and "OR" to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. E-learning in medical education is a means to an end

  4. E-learning in medical education in resource constrained low- and middle-income countries

    Science.gov (United States)

    2013-01-01

    Background In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC), and presents the spectrum of tools and strategies used. Methods Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators “AND” and “OR” to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Results Of the relevant articles found, most referred to e-learning in Brazil (14 articles), India (14), Egypt (10) and South Africa (10). While e-learning has been used by a variety of health workers in LMICs, the majority (58%) reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles) of which computer-assisted learning (CAL) comprised the majority (45 articles). Other approaches included simulations and the use of multimedia software (20 articles), web-based learning (14 articles), and eTutor/eMentor programs (3 articles). Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities. Conclusions E-learning

  5. E-learning in medical education in resource constrained low- and middle-income countries

    Directory of Open Access Journals (Sweden)

    Frehywot Seble

    2013-02-01

    Full Text Available Abstract Background In the face of severe faculty shortages in resource-constrained countries, medical schools look to e-learning for improved access to medical education. This paper summarizes the literature on e-learning in low- and middle-income countries (LMIC, and presents the spectrum of tools and strategies used. Methods Researchers reviewed literature using terms related to e-learning and pre-service education of health professionals in LMIC. Search terms were connected using the Boolean Operators “AND” and “OR” to capture all relevant article suggestions. Using standard decision criteria, reviewers narrowed the article suggestions to a final 124 relevant articles. Results Of the relevant articles found, most referred to e-learning in Brazil (14 articles, India (14, Egypt (10 and South Africa (10. While e-learning has been used by a variety of health workers in LMICs, the majority (58% reported on physician training, while 24% focused on nursing, pharmacy and dentistry training. Although reasons for investing in e-learning varied, expanded access to education was at the core of e-learning implementation which included providing supplementary tools to support faculty in their teaching, expanding the pool of faculty by connecting to partner and/or community teaching sites, and sharing of digital resources for use by students. E-learning in medical education takes many forms. Blended learning approaches were the most common methodology presented (49 articles of which computer-assisted learning (CAL comprised the majority (45 articles. Other approaches included simulations and the use of multimedia software (20 articles, web-based learning (14 articles, and eTutor/eMentor programs (3 articles. Of the 69 articles that evaluated the effectiveness of e-learning tools, 35 studies compared outcomes between e-learning and other approaches, while 34 studies qualitatively analyzed student and faculty attitudes toward e-learning modalities

  6. Introducing the ICF: the development of an online resource to support learning, teaching and curriculum design.

    Science.gov (United States)

    Jones, Lester E

    2011-03-01

    The International Classification of Functioning, Disability and Health (ICF) was adopted as one of the key models to support early health professional learning across a suite of new preregistration health science courses. It was decided that an online resource should be developed to enable students, course designers and teaching staff, across all disciplines, to have access to the same definitions, government policies and other supporting information on disability. As part of the comprehensive curriculum review, enquiry-based learning was adopted as the educational approach. Enquiry-based learning promotes deeper learning by encouraging students to engage in authentic challenges. As such, it was important that the online resource was not merely a site for accessing content, but enabled students to make decisions about where else to explore for credible information about the ICF. The selection of a host location that all students and staff could access meant that the resource could not be located in the existing online learning management system. Construction using software being trialled by the library at La Trobe University allowed for the required access, as well as alignment with an enquiry-based learning approach. Consultation for the content of the online resource included formal and informal working groups on curriculum review. The published version included resources from the World Health Organization, examples of research completed within different disciplines, a test of knowledge and a preformatted search page. The format of the online resource allows for updating of information, and feedback on the utilisation of the software has been used to enhance the student experience. The key issues for the development of this online resource were accessibility for students and staff, alignment with the adopted educational approach, consultation with all disciplines, and ease of modification of information and format once published. Copyright © 2010 Chartered

  7. Automation of information decision support to improve e-learning resources quality

    Directory of Open Access Journals (Sweden)

    A.L. Danchenko

    2013-06-01

    Full Text Available Purpose. In conditions of active development of e-learning the high quality of e-learning resources is very important. Providing the high quality of e-learning resources in situation with mass higher education and rapid obsolescence of information requires the automation of information decision support for improving the quality of e-learning resources by development of decision support system. Methodology. The problem is solved by methods of artificial intelligence. The knowledge base of information structure of decision support system that is based on frame model of knowledge representation and inference production rules are developed. Findings. According to the results of the analysis of life cycle processes and requirements to the e-learning resources quality the information model of the structure of the knowledge base of the decision support system, the inference rules for the automatically generating of recommendations and the software implementation are developed. Practical value. It is established that the basic requirements for quality are performance, validity, reliability and manufacturability. It is shown that the using of a software implementation of decision support system for researched courses gives a growth of the quality according to the complex quality criteria. The information structure of a knowledge base system to support decision-making and rules of inference can be used by methodologists and content developers of learning systems.

  8. An evaluation of learning resources in the teaching of formal philosophical methods

    Directory of Open Access Journals (Sweden)

    Susan A.J. Stuart

    2003-12-01

    Full Text Available In any discipline, across a wide variety of subjects, there are numerous learning resources available to students. For many students the resources that will be most beneficial to them are quickly apparent but, because of the nature of philosophy and the philosophical method, it is not immediately clear which resources will be most valuable to students for whom the development of critical thinking skills is crucial. If we are to support these students effectively in their learning we must establish what these resources are how we can continue to maintain and improve them, and how we can encourage students to make good use of them. In this paper we describe and assess our evaluation of the use made by students of learning resources in the context of learning logic and in developing their critical thinking skills. We also assess the use of a new resource, electronic handsets, the purpose of which is to encourage students to respond to questions in lectures and to gain feedback about how they are progressing with the material.

  9. A review of assertions about the processes and outcomes of social learning in natural resource management.

    Science.gov (United States)

    Cundill, G; Rodela, R

    2012-12-30

    Social learning has become a central theme in natural resource management. This growing interest is underpinned by a number of assertions about the outcomes of social learning, and about the processes that support these outcomes. Yet researchers and practitioners who seek to engage with social learning through the natural resource management literature often become disorientated by the myriad processes and outcomes that are identified. We trace the roots of current assertions about the processes and outcomes of social learning in natural resource management, and assess the extent to which there is an emerging consensus on these assertions. Results suggest that, on the one hand, social learning is described as taking place through deliberative interactions amongst multiple stakeholders. During these interactions, it is argued that participants learn to work together and build relationships that allow for collective action. On the other hand, social learning is described as occurring through deliberate experimentation and reflective practice. During these iterative cycles of action, monitoring and reflection, participants learn how to cope with uncertainty when managing complex systems. Both of these processes, and their associated outcomes, are referred to as social learning. Where, therefore, should researchers and practitioners focus their attention? Results suggest that there is an emerging consensus that processes that support social learning involve sustained interaction between stakeholders, on-going deliberation and the sharing of knowledge in a trusting environment. There is also an emerging consensus that the key outcome of such learning is improved decision making underpinned by a growing awareness of human-environment interactions, better relationships and improved problem-solving capacities for participants. Copyright © 2012 Elsevier Ltd. All rights reserved.

  10. Culinary Arts Dictionary 1. Project HIRE.

    Science.gov (United States)

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this first of three picture dictionary booklets in the Culinary Arts series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  11. Culinary Arts Dictionary 3. Project HIRE.

    Science.gov (United States)

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this third of three picture dictionary booklets in the Culinary Arts series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  12. Culinary Arts Dictionary 2. Project HIRE.

    Science.gov (United States)

    Gardner, David C.; And Others

    Designed as supplemental material to on-going instruction in the vocational program, this second of three picture dictionary booklets in the Culinary Arts series is intended to assist the learning handicapped student to master the core vocabulary taught in the trade. Intended for individual or small group instruction with minimal supervision, this…

  13. Mobilizing Learning Resources in a Transnational Classroom: Translocal and Digital Resources in a Community Technology Center

    Science.gov (United States)

    Noguerón-Liu, Silvia

    2014-01-01

    Drawing from transnational and activity theory frameworks, this study analyzes the ways translocal flows shape learning in a community technology center serving adult immigrants in the US Southwest. It also explores students' constructions of the transnational nature of the courses they took, where they had access to both online and face-to-face…

  14. Planning for partnerships: Maximizing surge capacity resources through service learning.

    Science.gov (United States)

    Adams, Lavonne M; Reams, Paula K; Canclini, Sharon B

    2015-01-01

    Infectious disease outbreaks and natural or human-caused disasters can strain the community's surge capacity through sudden demand on healthcare activities. Collaborative partnerships between communities and schools of nursing have the potential to maximize resource availability to meet community needs following a disaster. This article explores how communities can work with schools of nursing to enhance surge capacity through systems thinking, integrated planning, and cooperative efforts.

  15. Towards a supervised rescoring system for unstructured data bases used to build specialized dictionaries

    Directory of Open Access Journals (Sweden)

    Antonio Rico-Sulayes

    2014-12-01

    Full Text Available This article proposes the architecture for a system that uses previously learned weights to sort query results from unstructured data bases when building specialized dictionaries. A common resource in the construction of dictionaries, unstructured data bases have been especially useful in providing information about lexical items frequencies and examples in use. However, when building specialized dictionaries, whose selection of lexical items does not rely on frequency, the use of these data bases gets restricted to a simple provider of examples. Even in this task, the information unstructured data bases provide may not be very useful when looking for specialized uses of lexical items with various meanings and very long lists of results. In the face of this problem, long lists of hits can be rescored based on a supervised learning model that relies on previously helpful results. The allocation of a vast set of high quality training data for this rescoring system is reported here. Finally, the architecture of sucha system,an unprecedented tool in specialized lexicography, is proposed.

  16. A novel structured dictionary for fast processing of 3D medical images, with application to computed tomography restoration and denoising

    Science.gov (United States)

    Karimi, Davood; Ward, Rabab K.

    2016-03-01

    Sparse representation of signals in learned overcomplete dictionaries has proven to be a powerful tool with applications in denoising, restoration, compression, reconstruction, and more. Recent research has shown that learned overcomplete dictionaries can lead to better results than analytical dictionaries such as wavelets in almost all image processing applications. However, a major disadvantage of these dictionaries is that their learning and usage is very computationally intensive. In particular, finding the sparse representation of a signal in these dictionaries requires solving an optimization problem that leads to very long computational times, especially in 3D image processing. Moreover, the sparse representation found by greedy algorithms is usually sub-optimal. In this paper, we propose a novel two-level dictionary structure that improves the performance and the speed of standard greedy sparse coding methods. The first (i.e., the top) level in our dictionary is a fixed orthonormal basis, whereas the second level includes the atoms that are learned from the training data. We explain how such a dictionary can be learned from the training data and how the sparse representation of a new signal in this dictionary can be computed. As an application, we use the proposed dictionary structure for removing the noise and artifacts in 3D computed tomography (CT) images. Our experiments with real CT images show that the proposed method achieves results that are comparable with standard dictionary-based methods while substantially reducing the computational time.

  17. JaSlo: Integration of a Japanese-Slovene Bilingual Dictionary with a Corpus Search System

    Directory of Open Access Journals (Sweden)

    Kristina HMELJAK SANGAWA

    2012-12-01

    Full Text Available The paper presents a set of integrated on-line language resources targeted at Japanese language learners, primarily those whose mother tongue is Slovene. The resources consist of the on-line Japanese-Slovene learners’ dictionary jaSlo and two corpora, a 1 million word Japanese-Slovene parallel corpus and a 300 million word corpus of web pages, where each word and sentence is marked by its difficulty level; this corpus is furthermore available as a set of five distinct corpora, each one containing sentences of the particular level. The corpora are available for exploration through NoSketch Engine, the open source version of the commercial state-of-the-art corpus analysis software Sketch Engine. The dictionary is available for Web searching, and dictionary entries have direct links to examples from the corpora, thus offering a wider picture of a possible translations in concrete contextualised examples, and b monolingual Japanese usage examples of different difficulty levels to support language learning.

  18. Few-view image reconstruction with dual dictionaries

    International Nuclear Information System (INIS)

    Lu Yang; Zhao Jun; Wang Ge

    2012-01-01

    In this paper, we formulate the problem of computed tomography (CT) under sparsity and few-view constraints, and propose a novel algorithm for image reconstruction from few-view data utilizing the simultaneous algebraic reconstruction technique (SART) coupled with dictionary learning, sparse representation and total variation (TV) minimization on two interconnected levels. The main feature of our algorithm is the use of two dictionaries: a transitional dictionary for atom matching and a global dictionary for image updating. The atoms in the global and transitional dictionaries represent the image patches from high-quality and low-quality CT images, respectively. Experiments with simulated and real projections were performed to evaluate and validate the proposed algorithm. The results reconstructed using the proposed approach are significantly better than those using either SART or SART–TV. (paper)

  19. Open Educational Resources and Informational Ecosystems: «Edutags» as a connector for open learning

    Directory of Open Access Journals (Sweden)

    Michael Kerres

    2014-10-01

    Full Text Available Teaching and learning in school essentially relies on analogous and digital media, artefacts and tools of all kinds. They are supported and provided by various players. The role of these players for providing learning infrastructures and the interaction between them are discussed in the following paper. Increasingly, Open Educational Resources (OER become available and the question arises how the interaction between these players is impacted. On the one hand, some players implement closed informational ecosystems that might provide a rich and coherent environment for learning, but also lock the users into a defined and often restricted environment. On the other hand, other players are interested in developing an infrastructure that supports open learning without the boundaries of closed informational ecosystems. Such open informational ecosystems must provide interconnections to numerous, in principal, unlimited number of platforms for learning contents. In the context of the project «Edutags» a reference platform is being implemented by way in which the contents of various providers are being connected and enriched through user-generated tags, commentaries and evaluations. The discussion points out that such an independent reference platform, operated separately from content platforms, must be considered as an important element in an open and truly distributed infrastructure for learning resources. Hence, we do not only need open educational resources to support open learning, we also need to establish an open informational ecosystem that supports such approaches.

  20. Use of Online Learning Resources in the Development of Learning Environments at the Intersection of Formal and Informal Learning: The Student as Autonomous Designer

    Directory of Open Access Journals (Sweden)

    Maja Lebeničnik

    2015-06-01

    Full Text Available Learning resources that are used in the education of university students are often available online. The nature of new technologies causes an interweaving of formal and informal learning, with the result that a more active role is expected from students with regard to the use of ICT for their learning. The variety of online learning resources (learning content and learning tools facilitates informed use and enables students to create the learning environment that is most appropriate for their personal learning needs and preferences. In contemporary society, the creation of an inclusive learning environment supported by ICT is pervasive. The model of Universal Design for Learning is becoming increasingly significant in responding to the need for inclusive learning environments. In this article, we categorize different online learning activities into the principles of Universal Design for Learning. This study examines ICT use among university students (N = 138, comparing student teachers with students in other study programs. The findings indicate that among all students, activities with lower demands for engagement are most common. Some differences were observed between student teachers and students from other programs. Student teachers were more likely than their peers to perform certain activities aimed at meeting diverse learner needs, but the percentage of students performing more advanced activities was higher for students in other study programs than for student teachers. The categorization of activities revealed that student teachers are less likely to undertake activities that involve interaction with others. Among the sample of student teachers, we found that personal innovativeness is correlated with diversity of activities in only one category. The results show that student teachers should be encouraged to perform more advanced activities, especially activities involving interaction with others, collaborative learning and use of ICT to

  1. A holistic model for evaluating the impact of individual technology-enhanced learning resources.

    Science.gov (United States)

    Pickering, James D; Joynes, Viktoria C T

    2016-12-01

    The use of technology within education has now crossed the Rubicon; student expectations, the increasing availability of both hardware and software and the push to fully blended learning environments mean that educational institutions cannot afford to turn their backs on technology-enhanced learning (TEL). The ability to meaningfully evaluate the impact of TEL resources nevertheless remains problematic. This paper aims to establish a robust means of evaluating individual resources and meaningfully measure their impact upon learning within the context of the program in which they are used. Based upon the experience of developing and evaluating a range of mobile and desktop based TEL resources, this paper outlines a new four-stage evaluation process, taking into account learner satisfaction, learner gain, and the impact of a resource on both the individual and the institution in which it has been adapted. A new multi-level model of TEL resource evaluation is proposed, which includes a preliminary evaluation of need, learner satisfaction and gain, learner impact and institutional impact. Each of these levels are discussed in detail, and in relation to existing TEL evaluation frameworks. This paper details a holistic, meaningful evaluation model for individual TEL resources within the specific context in which they are used. It is proposed that this model is adopted to ensure that TEL resources are evaluated in a more meaningful and robust manner than is currently undertaken.

  2. Reflections of health care professionals on e-learning resources for patient safety.

    Science.gov (United States)

    Walsh, Kieran

    2018-01-01

    There is a paucity of evidence on how health care professionals view e-learning as a means of education to achieve safer health care. To address this gap, the reflections of health care professionals who used the resources on BMJ Learning were captured and analyzed. Key themes emerged from the analysis. Health care professionals are keen to put their e-learning into action to achieve safer health care and to learn how to follow guidelines that will help them achieve safer health care. Learners wanted their learning to remain grounded in reality. Finally, many commented that it was difficult for their individual learning to have a real impact when the culture of the organization did not change.

  3. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data

    International Nuclear Information System (INIS)

    Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

    2015-01-01

    Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy. (paper)

  4. Sparsity-promoting orthogonal dictionary updating for image reconstruction from highly undersampled magnetic resonance data.

    Science.gov (United States)

    Huang, Jinhong; Guo, Li; Feng, Qianjin; Chen, Wufan; Feng, Yanqiu

    2015-07-21

    Image reconstruction from undersampled k-space data accelerates magnetic resonance imaging (MRI) by exploiting image sparseness in certain transform domains. Employing image patch representation over a learned dictionary has the advantage of being adaptive to local image structures and thus can better sparsify images than using fixed transforms (e.g. wavelets and total variations). Dictionary learning methods have recently been introduced to MRI reconstruction, and these methods demonstrate significantly reduced reconstruction errors compared to sparse MRI reconstruction using fixed transforms. However, the synthesis sparse coding problem in dictionary learning is NP-hard and computationally expensive. In this paper, we present a novel sparsity-promoting orthogonal dictionary updating method for efficient image reconstruction from highly undersampled MRI data. The orthogonality imposed on the learned dictionary enables the minimization problem in the reconstruction to be solved by an efficient optimization algorithm which alternately updates representation coefficients, orthogonal dictionary, and missing k-space data. Moreover, both sparsity level and sparse representation contribution using updated dictionaries gradually increase during iterations to recover more details, assuming the progressively improved quality of the dictionary. Simulation and real data experimental results both demonstrate that the proposed method is approximately 10 to 100 times faster than the K-SVD-based dictionary learning MRI method and simultaneously improves reconstruction accuracy.

  5. Which Dictionary? A Review of the Leading Learners' Dictionaries.

    Science.gov (United States)

    Nesi, Hilary

    Three major dictionaries designed for learners of English as a second language are reviewed, their elements and approaches compared and evaluated, their usefulness for different learners discussed, and recommendations for future dictionary improvement made. The dictionaries in question are the "Oxford Advanced Learner's Dictionary," the…

  6. Session: What can we learn from developed wind resource areas

    Energy Technology Data Exchange (ETDEWEB)

    Thelander, Carl; Erickson, Wally

    2004-09-01

    This session at the Wind Energy and Birds/Bats workshop was composed of two parts intended to examine what existing science tells us about wind turbine impacts at existing wind project sites. Part one dealt with the Altamont Wind Resource area, one of the older wind projects in the US, with a paper presented by Carl Thelander titled ''Bird Fatalities in the Altamont Pass Wind Resource Area: A Case Study, Part 1''. Questions addressed by the presenter included: how is avian habitat affected at Altamont and do birds avoid turbine sites; are birds being attracted to turbine strings; what factors contribute to direct impacts on birds by wind turbines at Altamont; how do use, behavior, avoidance and other factors affect risk to avian species, and particularly impacts those species listed as threatened, endangered, or of conservation concern, and other state listed species. The second part dealt with direct impacts to birds at new generation wind plants outside of California, examining such is sues as mortality, avoidance, direct habitat impacts from terrestrial wind projects, species and numbers killed per turbine rates/MW generated, impacts to listed threatened and endangered species, to USFWS Birds of Conservation Concern, and to state listed species. This session focused on newer wind project sites with a paper titled ''Bird Fatality and Risk at New Generation Wind Projects'' by Wally Erickson. Each paper was followed by a discussion/question and answer period.

  7. A machine learning approach for predicting the relationship between energy resources and economic development

    Science.gov (United States)

    Cogoljević, Dušan; Alizamir, Meysam; Piljan, Ivan; Piljan, Tatjana; Prljić, Katarina; Zimonjić, Stefan

    2018-04-01

    The linkage between energy resources and economic development is a topic of great interest. Research in this area is also motivated by contemporary concerns about global climate change, carbon emissions fluctuating crude oil prices, and the security of energy supply. The purpose of this research is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources. Our results indicate that GDP predictive accuracy can be improved slightly by applying a machine learning approach.

  8. Effect of improving the usability of an e-learning resource: a randomized trial.

    Science.gov (United States)

    Davids, Mogamat Razeen; Chikte, Usuf M E; Halperin, Mitchell L

    2014-06-01

    Optimizing the usability of e-learning materials is necessary to reduce extraneous cognitive load and maximize their potential educational impact. However, this is often neglected, especially when time and other resources are limited. We conducted a randomized trial to investigate whether a usability evaluation of our multimedia e-learning resource, followed by fixing of all problems identified, would translate into improvements in usability parameters and learning by medical residents. Two iterations of our e-learning resource [version 1 (V1) and version 2 (V2)] were compared. V1 was the first fully functional version and V2 was the revised version after all identified usability problems were addressed. Residents in internal medicine and anesthesiology were randomly assigned to one of the versions. Usability was evaluated by having participants complete a user satisfaction questionnaire and by recording and analyzing their interactions with the application. The effect on learning was assessed by questions designed to test the retention and transfer of knowledge. Participants reported high levels of satisfaction with both versions, with good ratings on the System Usability Scale and adjective rating scale. In contrast, analysis of video recordings revealed significant differences in the occurrence of serious usability problems between the two versions, in particular in the interactive HandsOn case with its treatment simulation, where there was a median of five serious problem instances (range: 0-50) recorded per participant for V1 and zero instances (range: 0-1) for V2 (P e-learning resource resulted in significant improvements in usability. This is likely to translate into improved motivation and willingness to engage with the learning material. In this population of relatively high-knowledge participants, learning scores were similar across the two versions. Copyright © 2014 The American Physiological Society.

  9. Plasticity in learning causes immediate and trans-generational changes in allocation of resources.

    Science.gov (United States)

    Snell-Rood, Emilie C; Davidowitz, Goggy; Papaj, Daniel R

    2013-08-01

    Plasticity in the development and expression of behavior may allow organisms to cope with novel and rapidly changing environments. However, plasticity itself may depend on the developmental experiences of an individual. For instance, individuals reared in complex, enriched environments develop enhanced cognitive abilities as a result of increased synaptic connections and neurogenesis. This suggests that costs associated with behavioral plasticity-in particular, increased investment in "self" at the expense of reproduction-may also be flexible. Using butterflies as a system, this work tests whether allocation of resources changes as a result of experiences in "difficult" environments that require more investment in learning. We contrast allocation of resources among butterflies with experience in environments that vary in the need for learning. Butterflies with experience searching for novel (i.e., red) hosts, or searching in complex non-host environments, allocate more resources (protein and carbohydrate reserves) to their own flight muscle. In addition, butterflies with experience in these more difficult environments allocate more resources per individual offspring (i.e., egg size and/or lipid reserves). This results in a mother's experience having significant effects on the growth of her offspring (i.e., dry mass and wing length). A separate study showed this re-allocation of resources comes at the expense of lifetime fecundity. These results suggest that investment in learning, and associated changes in life history, can be adjusted depending on an individual's current need, and their offspring's future needs, for learning.

  10. Request Stream Control for the Access to Broadband Multimedia Educational Resources in the Distance Learning System

    Directory of Open Access Journals (Sweden)

    Irina Pavlovna Bolodurina

    2013-10-01

    Full Text Available This article presents a model of queuing system for broadband multimedia educational resources, as well as a model of access to a hybrid cloud system storage. These models are used to enhance the efficiency of computing resources in a distance learning system. An additional OpenStack control module has been developed to achieve the distribution of request streams and balance the load between cloud nodes.

  11. Development of inquiry-based learning activities integrated with the local learning resource to promote learning achievement and analytical thinking ability of Mathayomsuksa 3 student

    Science.gov (United States)

    Sukji, Paweena; Wichaidit, Pacharee Rompayom; Wichaidit, Sittichai

    2018-01-01

    The objectives of this study were to: 1) compare learning achievement and analytical thinking ability of Mathayomsuksa 3 students before and after learning through inquiry-based learning activities integrated with the local learning resource, and 2) compare average post-test score of learning achievement and analytical thinking ability to its cutting score. The target of this study was 23 Mathayomsuksa 3 students who were studying in the second semester of 2016 academic year from Banchatfang School, Chainat Province. Research instruments composed of: 1) 6 lesson plans of Environment and Natural Resources, 2) the learning achievement test, and 3) analytical thinking ability test. The results showed that 1) student' learning achievement and analytical thinking ability after learning were higher than that of before at the level of .05 statistical significance, and 2) average posttest score of student' learning achievement and analytical thinking ability were higher than its cutting score at the level of .05 statistical significance. The implication of this research is for science teachers and curriculum developers to design inquiry activities that relate to student's context.

  12. Do Dictionaries Help Students Write?

    Science.gov (United States)

    Nesi, Hilary

    Examples are given of real lexical errors made by learner writers, and consideration is given to the way in which three learners' dictionaries could deal with the lexical items that were misused. The dictionaries were the "Oxford Advanced Learner's Dictionary," the "Longman Dictionary of Contemporary English," and the "Chambers Universal Learners'…

  13. The ABCs of Data Dictionaries

    Science.gov (United States)

    Gould, Tate; Nicholas, Amy; Blandford, William; Ruggiero, Tony; Peters, Mary; Thayer, Sara

    2014-01-01

    This overview of the basic components of a data dictionary is designed to educate and inform IDEA Part C and Part B 619 state staff about the purpose and benefits of having up-to-date data dictionaries for their data systems. This report discusses the following topics: (1) What Is a Data Dictionary?; (2) Why Is a Data Dictionary Needed and How Can…

  14. Macmillan English Dictionary: The End of Print?

    Directory of Open Access Journals (Sweden)

    Michael Rundell

    2014-12-01

    Full Text Available This paper reports on the Macmillan English Dictionary (MED and its transition from printed book to digital-only resource. The background to this decision is explained in terms of changes both in technology and in dictionary-users’ behaviour: was this move inevitable, and will other dictionary publishers follow (sooner or later? The possible downsides of abandoning print are discussed, alongside the advantages of digital media. As well as offering great opportunities (many still unexplored, being online also creates new demands. With easy access to numerous free reference sites, users searching for lexical information have a huge variety of options. Consequently, publishers are under pressure to continually broaden the range of content they supply, to improve the quality of the design and “user experience”, and above all to stay abreast of language change. And, it will be shown, there is much more to keeping a dictionary up to date than simply adding new words as they emerge. The imperative of moving to digital has generated a good deal of turbulence in the world of dictionary publishing (especially for commercial publishers who cannot run at a loss, and there is considerable uncertainty around the long-term survival of “the dictionary” as the autonomous object we are all familiar with. But humans’ communicative needs should ensure a continued demand for high-quality lexical data – even if this data is delivered and accessed in new and different ways.

  15. Video Productions as Learning Resources in Students’ Knowledge Building in the Ubiquitous Society

    DEFF Research Database (Denmark)

    Buhl, Mie; Andreasen, Lars Birch; Ørngreen, Rikke

    productions developed by the students themselves. This is investigated from a theoretical as well as an empirical perspective, building on the authors’ experiences from researching and teaching dealing with production of video in learning situations, with different learning objectives and didactic designs...... in mind. The paper will present an overview of the state-of-the art of research on using video productions as learning resources, followed by discussions of our own research results and practices. From the overview and the discussions concepts are defined and research questions formed, based...... on a multimodal perspective on teaching and educational design. We conclude by arguing where and why there is a need for more knowledge....

  16. Adaptive Greedy Dictionary Selection for Web Media Summarization.

    Science.gov (United States)

    Cong, Yang; Liu, Ji; Sun, Gan; You, Quanzeng; Li, Yuncheng; Luo, Jiebo

    2017-01-01

    Initializing an effective dictionary is an indispensable step for sparse representation. In this paper, we focus on the dictionary selection problem with the objective to select a compact subset of basis from original training data instead of learning a new dictionary matrix as dictionary learning models do. We first design a new dictionary selection model via l 2,0 norm. For model optimization, we propose two methods: one is the standard forward-backward greedy algorithm, which is not suitable for large-scale problems; the other is based on the gradient cues at each forward iteration and speeds up the process dramatically. In comparison with the state-of-the-art dictionary selection models, our model is not only more effective and efficient, but also can control the sparsity. To evaluate the performance of our new model, we select two practical web media summarization problems: 1) we build a new data set consisting of around 500 users, 3000 albums, and 1 million images, and achieve effective assisted albuming based on our model and 2) by formulating the video summarization problem as a dictionary selection issue, we employ our model to extract keyframes from a video sequence in a more flexible way. Generally, our model outperforms the state-of-the-art methods in both these two tasks.

  17. A note on resource allocation scheduling with group technology and learning effects on a single machine

    Science.gov (United States)

    Lu, Yuan-Yuan; Wang, Ji-Bo; Ji, Ping; He, Hongyu

    2017-09-01

    In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions.

  18. Topological structure of dictionary graphs

    International Nuclear Information System (INIS)

    Fuks, Henryk; Krzeminski, Mark

    2009-01-01

    We investigate the topological structure of the subgraphs of dictionary graphs constructed from WordNet and Moby thesaurus data. In the process of learning a foreign language, the learner knows only a subset of all words of the language, corresponding to a subgraph of a dictionary graph. When this subgraph grows with time, its topological properties change. We introduce the notion of the pseudocore and argue that the growth of the vocabulary roughly follows decreasing pseudocore numbers-that is, one first learns words with a high pseudocore number followed by smaller pseudocores. We also propose an alternative strategy for vocabulary growth, involving decreasing core numbers as opposed to pseudocore numbers. We find that as the core or pseudocore grows in size, the clustering coefficient first decreases, then reaches a minimum and starts increasing again. The minimum occurs when the vocabulary reaches a size between 10 3 and 10 4 . A simple model exhibiting similar behavior is proposed. The model is based on a generalized geometric random graph. Possible implications for language learning are discussed.

  19. Assessment for Complex Learning Resources: Development and Validation of an Integrated Model

    Directory of Open Access Journals (Sweden)

    Gudrun Wesiak

    2013-01-01

    Full Text Available Today’s e-learning systems meet the challenge to provide interactive, personalized environments that support self-regulated learning as well as social collaboration and simulation. At the same time assessment procedures have to be adapted to the new learning environments by moving from isolated summative assessments to integrated assessment forms. Therefore, learning experiences enriched with complex didactic resources - such as virtualized collaborations and serious games - have emerged. In this extension of [1] an integrated model for e-assessment (IMA is outlined, which incorporates complex learning resources and assessment forms as main components for the development of an enriched learning experience. For a validation the IMA was presented to a group of experts from the fields of cognitive science, pedagogy, and e-learning. The findings from the validation lead to several refinements of the model, which mainly concern the component forms of assessment and the integration of social aspects. Both aspects are accounted for in the revised model, the former by providing a detailed sub-model for assessment forms.

  20. Using mobile technologies to give health students access to learning resources in the UK community setting.

    Science.gov (United States)

    Walton, Graham; Childs, Susan; Blenkinsopp, Elizabeth

    2005-12-01

    This article describes a project which explored the potential for mobile technologies to give health students in the community access to learning resources. The purpose included the need to identify possible barriers students could face in using mobile technologies. Another focus was to assess the students perceptions of the importance of being able to access learning resources in the community. This 1-year project used two main approaches for data collection. A review of the literature on mobile technologies in the health context was conducted. This was used in a systematic way to identify key issues and trends. The literature review was used to inform the design and production of a questionnaire. This was distributed to and completed by a group of community health students at Northumbria University, UK. The questionnaire was piloted and there was a 100% completion rate with 49 returned forms. The literature review indicated that most mobile technology applications were occurring in the US. At the time of the review the most prevalent mobile technologies were PDAs, laptops, WAP phones and portable radios with use being concentrated around doctors in the acute sector. A range of advantages and disadvantages to the technology were discovered. Mobile technologies were mainly being used for clinical rather than learning applications. The students showed a low level of awareness of the technology but placed great importance to accessing learning resources from the community. Significant development and changes are taking place in mobile technologies. Since the data collection for this work was completed in 2004 podcasting and videocasting have become significant in mobile learning for health professionals. Librarians will need to address the relevance and implications of m-learning for their practice. Care and consideration needs to be given on the time and resources librarians allocate for the necessary development work around mobile technologies. Collaboration and

  1. Dictionary of nuclear power

    International Nuclear Information System (INIS)

    Koelzer, W.

    2012-04-01

    The actualized version (April 2012) of the dictionary on nuclear power includes all actualizations and new inputs since the last version of 2001. The original publication dates from 1980. The dictionary includes definitions, terms, measuring units and helpful information on the actual knowledge concerning nuclear power, nuclear facilities, and radiation protection.

  2. Dictionary as Database.

    Science.gov (United States)

    Painter, Derrick

    1996-01-01

    Discussion of dictionaries as databases focuses on the digitizing of The Oxford English dictionary (OED) and the use of Standard Generalized Mark-Up Language (SGML). Topics include the creation of a consortium to digitize the OED, document structure, relational databases, text forms, sequence, and discourse. (LRW)

  3. Dictionary of Multicultural Education.

    Science.gov (United States)

    Grant, Carl A., Ed.; Ladson-Billings, Gloria, Ed.

    The focus of this dictionary is the meanings and perspectives of various terms that are used in multicultural education. Contributors have often addressed the literal meanings of words and terms as well as contextual meanings and examples that helped create those meanings. Like other dictionaries, this one is arranged alphabetically, but it goes…

  4. Dictionary of Marketing Terms.

    Science.gov (United States)

    Everhardt, Richard M.

    A listing of words and definitions compiled from more than 10 college and high school textbooks are presented in this dictionary of marketing terms. Over 1,200 entries of terms used in retailing, wholesaling, economics, and investments are included. This dictionary was designed to aid both instructors and students to better understand the…

  5. A model of positive and negative learning : Learning demands and resources, learning engagement, critical thinking, and fake news detection

    NARCIS (Netherlands)

    Dormann, Christian; Demerouti, Eva; Bakker, Arnold; Zlatkin-Troitschanskaia, O.; Wittum, G.; Dengel, A.

    2018-01-01

    This chapter proposes a model of positive and negative learning (PNL model). We use the term negative learning when stress among students occurs, and when knowledge and abilities are not properly developed. We use the term positive learning if motivation is high and active learning occurs. The PNL

  6. Sophisticated Online Learning Scheme for Green Resource Allocation in 5G Heterogeneous Cloud Radio Access Networks

    KAUST Repository

    Alqerm, Ismail

    2018-01-23

    5G is the upcoming evolution for the current cellular networks that aims at satisfying the future demand for data services. Heterogeneous cloud radio access networks (H-CRANs) are envisioned as a new trend of 5G that exploits the advantages of heterogeneous and cloud radio access networks to enhance spectral and energy efficiency. Remote radio heads (RRHs) are small cells utilized to provide high data rates for users with high quality of service (QoS) requirements, while high power macro base station (BS) is deployed for coverage maintenance and low QoS users service. Inter-tier interference between macro BSs and RRHs and energy efficiency are critical challenges that accompany resource allocation in H-CRANs. Therefore, we propose an efficient resource allocation scheme using online learning, which mitigates interference and maximizes energy efficiency while maintaining QoS requirements for all users. The resource allocation includes resource blocks (RBs) and power. The proposed scheme is implemented using two approaches: centralized, where the resource allocation is processed at a controller integrated with the baseband processing unit and decentralized, where macro BSs cooperate to achieve optimal resource allocation strategy. To foster the performance of such sophisticated scheme with a model free learning, we consider users\\' priority in RB allocation and compact state representation learning methodology to improve the speed of convergence and account for the curse of dimensionality during the learning process. The proposed scheme including both approaches is implemented using software defined radios testbed. The obtained results and simulation results confirm that the proposed resource allocation solution in H-CRANs increases the energy efficiency significantly and maintains users\\' QoS.

  7. Identifying and evaluating electronic learning resources for use in adult-gerontology nurse practitioner education.

    Science.gov (United States)

    Thompson, Hilaire J; Belza, Basia; Baker, Margaret; Christianson, Phyllis; Doorenbos, Ardith; Nguyen, Huong

    2014-01-01

    Enhancing existing curricula to meet newly published adult-gerontology advanced practice registered nurse (APRN) competencies in an efficient manner presents a challenge to nurse educators. Incorporating shared, published electronic learning resources (ELRs) in existing or new courses may be appropriate in order to assist students in achieving competencies. The purposes of this project were to (a) identify relevant available ELR for use in enhancing geriatric APRN education and (b) to evaluate the educational utility of identified ELRs based on established criteria. A multilevel search strategy was used. Two independent team members reviewed identified ELR against established criteria to ensure utility. Only resources meeting all criteria were retained. Resources were found for each of the competency areas and included formats such as podcasts, Web casts, case studies, and teaching videos. In many cases, resources were identified using supplemental strategies and not through traditional search or search of existing geriatric repositories. Resources identified have been useful to advanced practice educators in improving lecture and seminar content in a particular topic area and providing students and preceptors with additional self-learning resources. Addressing sustainability within geriatric APRN education is critical for sharing of best practices among educators and for sustainability of teaching and related resources. © 2014.

  8. Model of e-learning with electronic educational resources of new generation

    Directory of Open Access Journals (Sweden)

    A. V. Loban

    2017-01-01

    Full Text Available Purpose of the article: improving of scientific and methodical base of the theory of the е-learning of variability. Methods used: conceptual and logical modeling of the е-learning of variability process with electronic educational resource of new generation and system analysis of the interconnection of the studied subject area, methods, didactics approaches and information and communication technologies means. Results: the formalization complex model of the е-learning of variability with electronic educational resource of new generation is developed, conditionally decomposed into three basic components: the formalization model of the course in the form of the thesaurusclassifier (“Author of e-resource”, the model of learning as management (“Coordination. Consultation. Control”, the learning model with the thesaurus-classifier (“Student”. Model “Author of e-resource” allows the student to achieve completeness, high degree of didactic elaboration and structuring of the studied material in triples of variants: modules of education information, practical task and control tasks; the result of the student’s (author’s of e-resource activity is the thesaurus-classifier. Model of learning as management is based on the principle of personal orientation of learning in computer environment and determines the logic of interaction between the lecturer and the student when determining the triple of variants individually for each student; organization of a dialogue between the lecturer and the student for consulting purposes; personal control of the student’s success (report generation and iterative search for the concept of the class assignment in the thesaurus-classifier before acquiring the required level of training. Model “Student” makes it possible to concretize the learning tasks in relation to the personality of the student and to the training level achieved; the assumption of the lecturer about the level of training of a

  9. A Learning Perspective On The Role Of Natural Resources In Economic Development

    DEFF Research Database (Denmark)

    Andersen, Allan Dahl

    2011-01-01

    Natural resource-based industries are in economics often is understood as being unable to stimulate growth and development. The latter point has been put forward in the form of the ‘resource curse’ and is epitomised by inter alia Reinert (2007) who sees natural resource-based industries...... as detrimental to growth and development. Still, it will be argued here that Reinert’s approach is unsuitable for grasping the full role of natural resources in economic development because important aspects of industrial dynamics are ignored. In pursuit of the latter research aim two topics in economic research...... will be integrated: (i) the area of learning, innovation, capability building and economic development; (ii) with the area of natural resources and economic development. Such integration will be a contribution to both topics. Hence, this paper seeks to address the question: how can we understand the role of natural...

  10. Community knowledge and sustainable natural resources management: learning from the Monpa of Arunachal Pradesh

    Directory of Open Access Journals (Sweden)

    Ranjay K. Singh

    2006-04-01

    Full Text Available Community knowledge and local institutions play a significant role in sustainable comanagement, use and conservation of natural resources. Looking to the importance of these resources, a project, funded by the National Innovation Foundation (NIF, Ahmedabad, India was implemented to document the community knowledge associated with agriculture and natural resources in few selected Monpa tribe dominating villages of West Kameng and Tawang Districts of Arunachal Pradesh, India. Dynamics of various indigenous practices, gender role, culture and informal rural social institutions, cultural edges significantly contribute in managing and using the natural resources sustainably. Experiential learning and location specific knowledge play a pivotal role in ecosystem sustainability. Study also indicates the synergistic relation existing between local knowledge and ecological edges, thereby helping in sustaining livelihood in high altitude. Indigenous resource management systems are not mere traditions but adaptive responses that have evolved over time.

  11. Selection and Use of Online Learning Resources by First-Year Medical Students: Cross-Sectional Study.

    Science.gov (United States)

    Judd, Terry; Elliott, Kristine

    2017-10-02

    Medical students have access to a wide range of learning resources, many of which have been specifically developed for or identified and recommended to them by curriculum developers or teaching staff. There is an expectation that students will access and use these resources to support their self-directed learning. However, medical educators lack detailed and reliable data about which of these resources students use to support their learning and how this use relates to key learning events or activities. The purpose of this study was to comprehensively document first-year medical student selection and use of online learning resources to support their bioscience learning within a case-based curriculum and assess these data in relation to our expectations of student learning resource requirements and use. Study data were drawn from 2 sources: a survey of student learning resource selection and use (2013 cohort; n=326) and access logs from the medical school learning platform (2012 cohort; n=337). The paper-based survey, which was distributed to all first-year students, was designed to assess the frequency and types of online learning resources accessed by students and included items about their perceptions of the usefulness, quality, and reliability of various resource types and sources. Of 237 surveys returned, 118 complete responses were analyzed (36.2% response rate). Usage logs from the learning platform for an entire semester were processed to provide estimates of first-year student resource use on an individual and cohort-wide basis according to method of access, resource type, and learning event. According to the survey data, students accessed learning resources via the learning platform several times per week on average, slightly more often than they did for resources from other online sources. Google and Wikipedia were the most frequently used nonuniversity sites, while scholarly information sites (eg, online journals and scholarly databases) were accessed

  12. Investigation of blended learning video resources to teach health students clinical skills: An integrative review.

    Science.gov (United States)

    Coyne, Elisabeth; Rands, Hazel; Frommolt, Valda; Kain, Victoria; Plugge, Melanie; Mitchell, Marion

    2018-04-01

    The aim of this review is to inform future educational strategies by synthesising research related to blended learning resources using simulation videos to teach clinical skills for health students. An integrative review methodology was used to allow for the combination of diverse research methods to better understand the research topic. This review was guided by the framework described by Whittemore and Knafl (2005), DATA SOURCES: Systematic search of the following databases was conducted in consultation with a librarian using the following databases: SCOPUS, MEDLINE, COCHRANE, PsycINFO databases. Keywords and MeSH terms: clinical skills, nursing, health, student, blended learning, video, simulation and teaching. Data extracted from the studies included author, year, aims, design, sample, skill taught, outcome measures and findings. After screening the articles, extracting project data and completing summary tables, critical appraisal of the projects was completed using the Mixed Methods Appraisal Tool (MMAT). Ten articles met all the inclusion criteria and were included in this review. The MMAT scores varied from 50% to 100%. Thematic analysis was undertaken and we identified the following three themes: linking theory to practice, autonomy of learning and challenges of developing a blended learning model. Blended learning allowed for different student learning styles, repeated viewing, and enabled links between theory and practice. The video presentation needed to be realistic and culturally appropriate and this required both time and resources to create. A blended learning model, which incorporates video-assisted online resources, may be a useful tool to teach clinical skills to students of health including nursing. Blended learning not only increases students' knowledge and skills, but is often preferred by students due to its flexibility. Copyright © 2018 Elsevier Ltd. All rights reserved.

  13. Automated Library Networking in American Public Community College Learning Resources Centers.

    Science.gov (United States)

    Miah, Adbul J.

    1994-01-01

    Discusses the need for community colleges to assess their participation in automated library networking systems (ALNs). Presents results of questionnaires sent to 253 community college learning resource center directors to determine their use of ALNs. Reviews benefits of automation and ALN activities, planning and communications, institution size,…

  14. Planning for the Digital Classroom and Distributed Learning: Policies and Planning for Online Instructional Resources

    Science.gov (United States)

    McGee, Patricia; Diaz, Veronica

    2005-01-01

    In an era of state budget cuts and a tight economy, distributed learning is often seen as a way to address the needs of colleges and universities looking for additional revenue sources. Likewise, budding virtual universities, consortia, and corporate partnerships are now providing new ways for institutions to share resources across campuses. The…

  15. Exploring the Learning Problems and Resource Usage of Undergraduate Industrial Design Students in Design Studio Courses

    Science.gov (United States)

    Chen, Wenzhi

    2016-01-01

    Design is a powerful weapon for modern companies so it is important to have excellent designers in the industry. The purpose of this study is to explore the learning problems and the resources that students use to overcome problems in undergraduate industrial design studio courses. A survey with open-type questions was conducted to collect data.…

  16. Introduction to the papers of TWG16: Learning Mathematics with Technology and Other Resources

    NARCIS (Netherlands)

    Drijvers, P.H.M.; Faggiano, Eleonora; Geraniou, Eirini; Weigand, Hans-Georg

    2017-01-01

    The use of technology and other resources for mathematical learning is a current issue in the field of mathematics education and lags behind the rapid advances in Information and Communication Technology. Technological developments offer opportunities, which are not straightforward to exploit in

  17. Technological Change in the Workplace: A Statewide Survey of Community College Library and Learning Resources Personnel.

    Science.gov (United States)

    Poole, Carolyn E.; Denny, Emmett

    2001-01-01

    Discussion of the effects of technostress on library personnel focuses on an investigation that examined how employees in Florida community college libraries and learning resources centers are dealing with technological change in their work environment. Considers implications for planning and implementing technological change and includes…

  18. An Assessment of Resource Availability for Problem Based Learning in a Ghanaian University Setting

    Science.gov (United States)

    Okyere, Gabriel Asare; Tawiah, Richard; Lamptey, Richard Bruce; Oduro, William; Thompson, Michael

    2017-01-01

    Purpose: The purpose of this paper is to assess the differences pertaining to the resources presently accessible for problem-based learning (PBL) among six colleges of Kwame Nkrumah University of Science and Technology (KNUST) in Ghana. Design/methodology/approach: Data for the study are the cross-sectional type drawn from 1,020 students. Poisson…

  19. Empowering Teachers to Author Multimedia Learning Resources That Support Students' Critical Thinking

    Science.gov (United States)

    Holley, Debbie; Boyle, Tom

    2012-01-01

    Students studying Marketing, Fashion, Public Relations, Advertising and similar subjects need to develop a "critical eye" in relation to images, media and digital technologies. This project aims to empower teachers to develop multimedia learning resources that would support students engaging in this essential activity. Developing such…

  20. Fostering postgraduate student engagement: online resources supporting self-directed learning in a diverse cohort

    Directory of Open Access Journals (Sweden)

    Luciane V. Mello

    2016-03-01

    Full Text Available The research question for this study was: ‘Can the provision of online resources help to engage and motivate students to become self-directed learners?’ This study presents the results of an action research project to answer this question for a postgraduate module at a research-intensive university in the United Kingdom. The analysis of results from the study was conducted dividing the students according to their programme degree – Masters or PhD – and according to their language skills. The study indicated that the online resources embedded in the module were consistently used, and that the measures put in place to support self-directed learning (SDL were both perceived and valued by the students, irrespective of their programme or native language. Nevertheless, a difference was observed in how students viewed SDL: doctoral students seemed to prefer the approach and were more receptive to it than students pursuing their Masters degree. Some students reported that the SDL activity helped them to achieve more independence than did traditional approaches to teaching. Students who engaged with the online resources were rewarded with higher marks and claimed that they were all the more motivated within the module. Despite the different learning experiences of the diverse cohort, the study found that the blended nature of the course and its resources in support of SDL created a learning environment which positively affected student learning.