WorldWideScience

Sample records for supervised learning system

  1. ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

    National Research Council Canada - National Science Library

    Hughey, Jacob J; Hastie, Trevor; Butte, Atul J

    2016-01-01

    Numerous biological systems oscillate over time or space. Despite these oscillators' importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning...

  2. Supervised learning with decision tree-based methods in computational and systems biology.

    Science.gov (United States)

    Geurts, Pierre; Irrthum, Alexandre; Wehenkel, Louis

    2009-12-01

    At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

  3. Inductive Supervised Quantum Learning

    Science.gov (United States)

    Monràs, Alex; Sentís, Gael; Wittek, Peter

    2017-05-01

    In supervised learning, an inductive learning algorithm extracts general rules from observed training instances, then the rules are applied to test instances. We show that this splitting of training and application arises naturally, in the classical setting, from a simple independence requirement with a physical interpretation of being nonsignaling. Thus, two seemingly different definitions of inductive learning happen to coincide. This follows from the properties of classical information that break down in the quantum setup. We prove a quantum de Finetti theorem for quantum channels, which shows that in the quantum case, the equivalence holds in the asymptotic setting, that is, for large numbers of test instances. This reveals a natural analogy between classical learning protocols and their quantum counterparts, justifying a similar treatment, and allowing us to inquire about standard elements in computational learning theory, such as structural risk minimization and sample complexity.

  4. Using Supervised Learning Techniques for Diagnosis of Dynamic Systems

    Science.gov (United States)

    2002-05-04

    classification systems [11]. Neural network techniques have recently been applied in diverse fields, as 1 INTRODUCTION medicine [12] or power supply [13]. Machine...partiality financed by the Comisi6n 0.99 0.98 1.02 OK OK OK Interministerial de Ciencia y Tecnologia (DP12000-0666-C02-02) 1 1.02 1.02 OK OK OK and the

  5. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... investigates learning opportunities in supervision with multiple supervisors. This was investigated through observations and recording of supervision, and subsequent analysis of transcripts. The analyses used different perspectives on learning; learning as participation, positioning theory and variation theory....... The research illuminates how learning opportunities are created in the interaction through the scientific discussions. It also shows how multiple supervisors can contribute to supervision by providing new perspectives and opinions that have a potential for creating new understandings. The combination...

  6. Knowledge Work Supervision: Transforming School Systems into High Performing Learning Organizations.

    Science.gov (United States)

    Duffy, Francis M.

    1997-01-01

    This article describes a new supervision model conceived to help a school system redesign its anatomy (structures), physiology (flow of information and webs of relationships), and psychology (beliefs and values). The new paradigm (Knowledge Work Supervision) was constructed by reviewing the practices of several interrelated areas: sociotechnical…

  7. Towards designing an email classification system using multi-view based semi-supervised learning

    NARCIS (Netherlands)

    Li, Wenjuan; Meng, Weizhi; Tan, Zhiyuan; Xiang, Yang

    2014-01-01

    The goal of email classification is to classify user emails into spam and legitimate ones. Many supervised learning algorithms have been invented in this domain to accomplish the task, and these algorithms require a large number of labeled training data. However, data labeling is a labor intensive t

  8. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  9. Supervision Learning as Conceptual Threshold Crossing: When Supervision Gets "Medieval"

    Science.gov (United States)

    Carter, Susan

    2016-01-01

    This article presumes that supervision is a category of teaching, and that we all "learn" how to teach better. So it enquires into what novice supervisors need to learn. An anonymised digital questionnaire sought data from supervisors [n226] on their experiences of supervision to find out what was difficult, and supervisor interviews…

  10. Supervised Dictionary Learning

    CERN Document Server

    Mairal, Julien; Ponce, Jean; Sapiro, Guillermo; Zisserman, Andrew

    2008-01-01

    It is now well established that sparse signal models are well suited to restoration tasks and can effectively be learned from audio, image, and video data. Recent research has been aimed at learning discriminative sparse models instead of purely reconstructive ones. This paper proposes a new step in that direction, with a novel sparse representation for signals belonging to different classes in terms of a shared dictionary and multiple class-decision functions. The linear variant of the proposed model admits a simple probabilistic interpretation, while its most general variant admits an interpretation in terms of kernels. An optimization framework for learning all the components of the proposed model is presented, along with experimental results on standard handwritten digit and texture classification tasks.

  11. Supervised Dictionary Learning

    Science.gov (United States)

    2008-11-01

    recently led to state-of-the-art results for numerous low-level image processing tasks such as denoising [2], show- ing that sparse models are well... denoising via sparse and redundant representations over learned dictio- naries. IEEE Trans. IP, 54(12), 2006. [3] K. Huang and S. Aviyente. Sparse...2006. [19] M. Aharon, M. Elad, and A. M. Bruckstein. The K- SVD : An algorithm for designing of overcomplete dictionaries for sparse representations

  12. Implementability of Instructional Supervision as a Contemporary Educational Supervision Model in Turkish Education System

    OpenAIRE

    2012-01-01

    In this study, implementability of instructional supervision as one of contemporary educational supervision models in Turkish Education System was evaluated. Instructional supervision which aims to develop instructional processes and increase the quality of student learning based on observation of classroom activities requires collaboration among supervisors and teachers. In this literature review, significant problems have been detected due to structural organization, structural and control-...

  13. Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

    OpenAIRE

    Kurth, Thorsten; Zhang, Jian; Satish, Nadathur; Mitliagkas, Ioannis; Racah, Evan; Patwary, Mostofa Ali; Malas, Tareq; Sundaram, Narayanan; Bhimji, Wahid; Smorkalov, Mikhail; Deslippe, Jack; Shiryaev, Mikhail; Sridharan, Srinivas; Prabhat; Dubey, Pradeep

    2017-01-01

    This paper presents the first, 15-PetaFLOP Deep Learning system for solving scientific pattern classification problems on contemporary HPC architectures. We develop supervised convolutional architectures for discriminating signals in high-energy physics data as well as semi-supervised architectures for localizing and classifying extreme weather in climate data. Our Intelcaffe-based implementation obtains $\\sim$2TFLOP/s on a single Cori Phase-II Xeon-Phi node. We use a hybrid strategy employin...

  14. Multi-Instance Learning from Supervised View

    Institute of Scientific and Technical Information of China (English)

    Zhi-Hua Zhou

    2006-01-01

    In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances,and the task is to predict the labels of unseen bags. This paper studies multi-instance learning from the view of supervised learning. First, by analyzing some representative learning algorithms, this paper shows that multi-instance learners can be derived from supervised learners by shifting their focuses from the discrimination on the instances to the discrimination on the bags. Second, considering that ensemble learning paradigms can effectively enhance supervised learners, this paper proposes to build multi-instance ensembles to solve multi-instance problems. Experiments on a real-world benchmark test show that ensemble learning paradigms can significantly enhance multi-instance learners.

  15. Physical Realization of a Supervised Learning System Built with Organic Memristive Synapses

    Science.gov (United States)

    Lin, Yu-Pu; Bennett, Christopher H.; Cabaret, Théo; Vodenicarevic, Damir; Chabi, Djaafar; Querlioz, Damien; Jousselme, Bruno; Derycke, Vincent; Klein, Jacques-Olivier

    2016-09-01

    Multiple modern applications of electronics call for inexpensive chips that can perform complex operations on natural data with limited energy. A vision for accomplishing this is implementing hardware neural networks, which fuse computation and memory, with low cost organic electronics. A challenge, however, is the implementation of synapses (analog memories) composed of such materials. In this work, we introduce robust, fastly programmable, nonvolatile organic memristive nanodevices based on electrografted redox complexes that implement synapses thanks to a wide range of accessible intermediate conductivity states. We demonstrate experimentally an elementary neural network, capable of learning functions, which combines four pairs of organic memristors as synapses and conventional electronics as neurons. Our architecture is highly resilient to issues caused by imperfect devices. It tolerates inter-device variability and an adaptable learning rule offers immunity against asymmetries in device switching. Highly compliant with conventional fabrication processes, the system can be extended to larger computing systems capable of complex cognitive tasks, as demonstrated in complementary simulations.

  16. Incremental Supervised Subspace Learning for Face Recognition

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    Subspace learning algorithms have been well studied in face recognition. Among them, linear discriminant analysis (LDA) is one of the most widely used supervised subspace learning method. Due to the difficulty of designing an incremental solution of the eigen decomposition on the product of matrices, there is little work for computing LDA incrementally. To avoid this limitation, an incremental supervised subspace learning (ISSL) algorithm was proposed, which incrementally learns an adaptive subspace by optimizing the maximum margin criterion (MMC). With the dynamically added face images, ISSL can effectively constrain the computational cost. Feasibility of the new algorithm has been successfully tested on different face data sets.

  17. Supervised Learning in Multilayer Spiking Neural Networks

    CERN Document Server

    Sporea, Ioana

    2012-01-01

    The current article introduces a supervised learning algorithm for multilayer spiking neural networks. The algorithm presented here overcomes some limitations of existing learning algorithms as it can be applied to neurons firing multiple spikes and it can in principle be applied to any linearisable neuron model. The algorithm is applied successfully to various benchmarks, such as the XOR problem and the Iris data set, as well as complex classifications problems. The simulations also show the flexibility of this supervised learning algorithm which permits different encodings of the spike timing patterns, including precise spike trains encoding.

  18. Learning Dynamics in Doctoral Supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie

    This doctoral research explores doctoral supervision within life science research in a Danish university. From one angle it investigates doctoral students’ experiences with strengthening the relationship with their supervisors through a structured meeting with the supervisor, prepared as part...... of an introduction course for new doctoral students. This study showed how the course provides an effective way build supervisee agency and strengthening supervisory relationships through clarification and alignment of expectations and sharing goals about doctoral studies. From the other angle the research...

  19. Action learning in undergraduate engineering thesis supervision

    Directory of Open Access Journals (Sweden)

    Brad Stappenbelt

    2017-03-01

    Full Text Available In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative, investigated the influence of the action learning environment on student approaches to learning and any accompanying academic, learning and personal benefits realised. The influence of preferred learning styles on set function and student adoption of the action learning process were also examined. The action learning environment implemented had a measurable significant positive effect on student academic performance, their ability to cope with the stresses associated with conducting a research thesis, the depth of learning, the development of autonomous learners and student perception of the research thesis experience. The present study acts as an addendum to a smaller scale implementation of this action learning approach, applied to supervision of third and fourth year research projects and theses, published in 2010.

  20. Performance Monitoring Applied to System Supervision

    Directory of Open Access Journals (Sweden)

    Bertille Somon

    2017-07-01

    Full Text Available Nowadays, automation is present in every aspect of our daily life and has some benefits. Nonetheless, empirical data suggest that traditional automation has many negative performance and safety consequences as it changed task performers into task supervisors. In this context, we propose to use recent insights into the anatomical and neurophysiological substrates of action monitoring in humans, to help further characterize performance monitoring during system supervision. Error monitoring is critical for humans to learn from the consequences of their actions. A wide variety of studies have shown that the error monitoring system is involved not only in our own errors, but also in the errors of others. We hypothesize that the neurobiological correlates of the self-performance monitoring activity can be applied to system supervision. At a larger scale, a better understanding of system supervision may allow its negative effects to be anticipated or even countered. This review is divided into three main parts. First, we assess the neurophysiological correlates of self-performance monitoring and their characteristics during error execution. Then, we extend these results to include performance monitoring and error observation of others or of systems. Finally, we provide further directions in the study of system supervision and assess the limits preventing us from studying a well-known phenomenon: the Out-Of-the-Loop (OOL performance problem.

  1. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    set of demands to the design lecturer. On one hand she is the facilitator of the learning process, where the students are in charge of their own projects, and where learning happens through the students’ own experiences, successes and mistakes and on the other hand she is a supervisor, who uses her...... experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  2. Balancing Design Project Supervision and Learning Facilitation

    DEFF Research Database (Denmark)

    Nielsen, Louise Møller

    2012-01-01

    experiences and expertise to guide the students’ decisions in relation to the design project. This paper focuses on project supervision in the context of design education – and more specifically on how this supervision is unfolded in a Problem Based Learning culture. The paper explores the supervisor......’s balance between the roles: 1) Design Project Supervisor – and 2) Learning Facilitator – with the aim to understand when to apply the different roles, and what to be aware of when doing so. This paper represents the first pilot-study of a larger research effort. It is based on a Lego Serious Play workshop......In design there is a long tradition for apprenticeship, as well as tradition for learning through design projects. Today many design educations are positioned within the University context, and have to be aligned with the learning culture and structure, which they represent. This raises a specific...

  3. Equality of Opportunity in Supervised Learning

    OpenAIRE

    Hardt, Moritz; Price, Eric; Srebro, Nathan

    2016-01-01

    We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to...

  4. Missing Data Imputation for Supervised Learning

    OpenAIRE

    Poulos, Jason; Valle, Rafael

    2016-01-01

    This paper compares methods for imputing missing categorical data for supervised learning tasks. The ability of researchers to accurately fit a model and yield unbiased estimates may be compromised by missing data, which are prevalent in survey-based social science research. We experiment on two machine learning benchmark datasets with missing categorical data, comparing classifiers trained on non-imputed (i.e., one-hot encoded) or imputed data with different degrees of missing-data perturbat...

  5. The Supervised Learning Gaussian Mixture Model

    Institute of Scientific and Technical Information of China (English)

    马继涌; 高文

    1998-01-01

    The traditional Gaussian Mixture Model(GMM)for pattern recognition is an unsupervised learning method.The parameters in the model are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes,hence,its recognition accuracy is not ideal sometimes.This paper introduces an approach for estimating the parameters in GMM in a supervising way.The Supervised Learning Gaussian Mixture Model(SLGMM)improves the recognition accuracy of the GMM.An experimental example has shown its effectiveness.The experimental results have shown that the recognition accuracy derived by the approach is higher than those obtained by the Vector Quantization(VQ)approach,the Radial Basis Function (RBF) network model,the Learning Vector Quantization (LVQ) approach and the GMM.In addition,the training time of the approach is less than that of Multilayer Perceptrom(MLP).

  6. Opportunities to Learn Scientific Thinking in Joint Doctoral Supervision

    Science.gov (United States)

    Kobayashi, Sofie; Grout, Brian W.; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervision. While joint supervision has become widely…

  7. Self-Supervised Dynamical Systems

    Science.gov (United States)

    Zak, Michail

    2003-01-01

    Some progress has been made in a continuing effort to develop mathematical models of the behaviors of multi-agent systems known in biology, economics, and sociology (e.g., systems ranging from single or a few biomolecules to many interacting higher organisms). Living systems can be characterized by nonlinear evolution of probability distributions over different possible choices of the next steps in their motions. One of the main challenges in mathematical modeling of living systems is to distinguish between random walks of purely physical origin (for instance, Brownian motions) and those of biological origin. Following a line of reasoning from prior research, it has been assumed, in the present development, that a biological random walk can be represented by a nonlinear mathematical model that represents coupled mental and motor dynamics incorporating the psychological concept of reflection or self-image. The nonlinear dynamics impart the lifelike ability to behave in ways and to exhibit patterns that depart from thermodynamic equilibrium. Reflection or self-image has traditionally been recognized as a basic element of intelligence. The nonlinear mathematical models of the present development are denoted self-supervised dynamical systems. They include (1) equations of classical dynamics, including random components caused by uncertainties in initial conditions and by Langevin forces, coupled with (2) the corresponding Liouville or Fokker-Planck equations that describe the evolutions of probability densities that represent the uncertainties. The coupling is effected by fictitious information-based forces, denoted supervising forces, composed of probability densities and functionals thereof. The equations of classical mechanics represent motor dynamics that is, dynamics in the traditional sense, signifying Newton s equations of motion. The evolution of the probability densities represents mental dynamics or self-image. Then the interaction between the physical and

  8. Combining Unsupervised and Supervised Learning for Discovering Disease Subclasses

    OpenAIRE

    Tucker, A; Bosoni, P; Bellazzi, R.; Nihtyanova, S; Denton, C.

    2016-01-01

    Diseases are often umbrella terms for many subcategories of disease. The identification of these subcategories is vital if we are to develop personalised treatments that are better focussed on individual patients. In this short paper, we explore the use of a combination of unsupervised learning to identify potential subclasses, and supervised learning to build models for better predicting a number of different health outcomes for patients that suffer from systemic sclerosis, a rare chronic co...

  9. Graph-based semi-supervised learning

    CERN Document Server

    Subramanya, Amarnag

    2014-01-01

    While labeled data is expensive to prepare, ever increasing amounts of unlabeled data is becoming widely available. In order to adapt to this phenomenon, several semi-supervised learning (SSL) algorithms, which learn from labeled as well as unlabeled data, have been developed. In a separate line of work, researchers have started to realize that graphs provide a natural way to represent data in a variety of domains. Graph-based SSL algorithms, which bring together these two lines of work, have been shown to outperform the state-of-the-art in many applications in speech processing, computer visi

  10. Very Short Literature Survey From Supervised Learning To Surrogate Modeling

    CERN Document Server

    Brusan, Altay

    2012-01-01

    The past century was era of linear systems. Either systems (especially industrial ones) were simple (quasi)linear or linear approximations were accurate enough. In addition, just at the ending decades of the century profusion of computing devices were available, before then due to lack of computational resources it was not easy to evaluate available nonlinear system studies. At the moment both these two conditions changed, systems are highly complex and also pervasive amount of computation strength is cheap and easy to achieve. For recent era, a new branch of supervised learning well known as surrogate modeling (meta-modeling, surface modeling) has been devised which aimed at answering new needs of modeling realm. This short literature survey is on to introduce surrogate modeling to whom is familiar with the concepts of supervised learning. Necessity, challenges and visions of the topic are considered.

  11. Semi-supervised learning for ordinal Kernel Discriminant Analysis.

    Science.gov (United States)

    Pérez-Ortiz, M; Gutiérrez, P A; Carbonero-Ruz, M; Hervás-Martínez, C

    2016-12-01

    Ordinal classification considers those classification problems where the labels of the variable to predict follow a given order. Naturally, labelled data is scarce or difficult to obtain in this type of problems because, in many cases, ordinal labels are given by a user or expert (e.g. in recommendation systems). Firstly, this paper develops a new strategy for ordinal classification where both labelled and unlabelled data are used in the model construction step (a scheme which is referred to as semi-supervised learning). More specifically, the ordinal version of kernel discriminant learning is extended for this setting considering the neighbourhood information of unlabelled data, which is proposed to be computed in the feature space induced by the kernel function. Secondly, a new method for semi-supervised kernel learning is devised in the context of ordinal classification, which is combined with our developed classification strategy to optimise the kernel parameters. The experiments conducted compare 6 different approaches for semi-supervised learning in the context of ordinal classification in a battery of 30 datasets, showing (1) the good synergy of the ordinal version of discriminant analysis and the use of unlabelled data and (2) the advantage of computing distances in the feature space induced by the kernel function.

  12. The Cryogenic Supervision System in NSRRC

    CERN Document Server

    Li, Hsing-Chieh; Chiou, Wen-Song; Hsiao, Feng-Zone; Tsai, Zong-Da

    2005-01-01

    The helium cryogenic system in NSRRC is a fully automatic PLC system using the Siemens SIMATIC 300 controller. Modularization in both hardware and software makes it easy in the program reading, the system modification and the problem debug. Based on the Laview program we had developed a supervision system taking advantage of the Internet technology to get system's real-time information in any place. The functions of this supervision system include the real-time data accessing with more than 300 digital/analog signals, the data restore, the history trend display, and the human machine interface. The data is accessed via a Profibus line connecting the PLC system and the supervision system with a maximum baud rate 1.5 Mbit/s. Due to this supervision system, it is easy to master the status of the cryogenic system within a short time and diagnose the problem.

  13. Genetic classification of populations using supervised learning.

    LENUS (Irish Health Repository)

    Bridges, Michael

    2011-01-01

    There are many instances in genetics in which we wish to determine whether two candidate populations are distinguishable on the basis of their genetic structure. Examples include populations which are geographically separated, case-control studies and quality control (when participants in a study have been genotyped at different laboratories). This latter application is of particular importance in the era of large scale genome wide association studies, when collections of individuals genotyped at different locations are being merged to provide increased power. The traditional method for detecting structure within a population is some form of exploratory technique such as principal components analysis. Such methods, which do not utilise our prior knowledge of the membership of the candidate populations. are termed unsupervised. Supervised methods, on the other hand are able to utilise this prior knowledge when it is available.In this paper we demonstrate that in such cases modern supervised approaches are a more appropriate tool for detecting genetic differences between populations. We apply two such methods, (neural networks and support vector machines) to the classification of three populations (two from Scotland and one from Bulgaria). The sensitivity exhibited by both these methods is considerably higher than that attained by principal components analysis and in fact comfortably exceeds a recently conjectured theoretical limit on the sensitivity of unsupervised methods. In particular, our methods can distinguish between the two Scottish populations, where principal components analysis cannot. We suggest, on the basis of our results that a supervised learning approach should be the method of choice when classifying individuals into pre-defined populations, particularly in quality control for large scale genome wide association studies.

  14. Semi-supervised Learning with Deep Generative Models

    NARCIS (Netherlands)

    Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M.

    2014-01-01

    The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis. We revisit the approach to semi-supervised learning with generative models and

  15. The Learning Alliance: Ethics in Doctoral Supervision

    Science.gov (United States)

    Halse, Christine; Bansel, Peter

    2012-01-01

    This paper is concerned with the ethics of relationships in doctoral supervision. We give an overview of four paradigms of doctoral supervision that have endured over the past 25 years and elucidate some of their strengths and limitations, contextualise them historically and consider their implications for doctoral supervision in the contemporary…

  16. A new supervised learning algorithm for spiking neurons.

    Science.gov (United States)

    Xu, Yan; Zeng, Xiaoqin; Zhong, Shuiming

    2013-06-01

    The purpose of supervised learning with temporal encoding for spiking neurons is to make the neurons emit a specific spike train encoded by the precise firing times of spikes. If only running time is considered, the supervised learning for a spiking neuron is equivalent to distinguishing the times of desired output spikes and the other time during the running process of the neuron through adjusting synaptic weights, which can be regarded as a classification problem. Based on this idea, this letter proposes a new supervised learning method for spiking neurons with temporal encoding; it first transforms the supervised learning into a classification problem and then solves the problem by using the perceptron learning rule. The experiment results show that the proposed method has higher learning accuracy and efficiency over the existing learning methods, so it is more powerful for solving complex and real-time problems.

  17. Integrating the Supervised Information into Unsupervised Learning

    Directory of Open Access Journals (Sweden)

    Ping Ling

    2013-01-01

    Full Text Available This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

  18. Supervised Speech Separation Based on Deep Learning: An Overview

    OpenAIRE

    Wang, DeLiang; Chen, Jitong

    2017-01-01

    Speech separation is the task of separating target speech from background interference. Traditionally, speech separation is studied as a signal processing problem. A more recent approach formulates speech separation as a supervised learning problem, where the discriminative patterns of speech, speakers, and background noise are learned from training data. Over the past decade, many supervised separation algorithms have been put forward. In particular, the recent introduction of deep learning ...

  19. 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.

  20. Opportunities to learn scientific thinking in joint doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Grout, Brian William Wilson; Rump, Camilla Østerberg

    2015-01-01

    Research into doctoral supervision has increased rapidly over the last decades, yet our understanding of how doctoral students learn scientific thinking from supervision is limited. Most studies are based on interviews with little work being reported that is based on observation of actual supervi...

  1. Subsampled Hessian Newton Methods for Supervised Learning.

    Science.gov (United States)

    Wang, Chien-Chih; Huang, Chun-Heng; Lin, Chih-Jen

    2015-08-01

    Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification.

  2. Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification

    Directory of Open Access Journals (Sweden)

    R. Sathya

    2013-02-01

    Full Text Available This paper presents a comparative account of unsupervised and supervised learning models and their pattern classification evaluations as applied to the higher education scenario. Classification plays a vital role in machine based learning algorithms and in the present study, we found that, though the error back-propagation learning algorithm as provided by supervised learning model is very efficient for a number of non-linear real-time problems, KSOM of unsupervised learning model, offers efficient solution and classification in the present study.

  3. QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms

    Directory of Open Access Journals (Sweden)

    Ardjan Zwartjes

    2016-10-01

    Full Text Available In this work, we introduce QUEST (QUantile Estimation after Supervised Training, an adaptive classification algorithm for Wireless Sensor Networks (WSNs that eliminates the necessity for online supervised learning. Online processing is important for many sensor network applications. Transmitting raw sensor data puts high demands on the battery, reducing network life time. By merely transmitting partial results or classifications based on the sampled data, the amount of traffic on the network can be significantly reduced. Such classifications can be made by learning based algorithms using sampled data. An important issue, however, is the training phase of these learning based algorithms. Training a deployed sensor network requires a lot of communication and an impractical amount of human involvement. QUEST is a hybrid algorithm that combines supervised learning in a controlled environment with unsupervised learning on the location of deployment. Using the SITEX02 dataset, we demonstrate that the presented solution works with a performance penalty of less than 10% in 90% of the tests. Under some circumstances, it even outperforms a network of classifiers completely trained with supervised learning. As a result, the need for on-site supervised learning and communication for training is completely eliminated by our solution.

  4. Document Classification Using Expectation Maximization with Semi Supervised Learning

    CERN Document Server

    Nigam, Bhawna; Salve, Sonal; Vamney, Swati

    2011-01-01

    As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is "Dynamically Generation of New Class". The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.

  5. Path Control Experiment of Mobile Robot Based on Supervised Learning

    Directory of Open Access Journals (Sweden)

    Gao Chi

    2013-07-01

    Full Text Available To solve the weak capacity and low control accuracy of the robots which adapt to the complex working conditions, proposed that a path control method based on the driving experience and supervised learning. According to the slope road geometry characteristics, established the modeling study due to ramp pavement path control method and the control structure based on monitoring and self-learning. Made use of the Global Navigation Satellite System did the experiment. The test data illustrates that when the running speed is not greater than 5 m / s, the straight-line trajectory path transverse vertical deviation within 士20cm ,which proved that the control method has a high feasibility. 

  6. Detection of money laundering groups using supervised learning in networks

    CERN Document Server

    Savage, David; Chou, Pauline; Zhang, Xiuzhen; Yu, Xinghuo

    2016-01-01

    Money laundering is a major global problem, enabling criminal organisations to hide their ill-gotten gains and to finance further operations. Prevention of money laundering is seen as a high priority by many governments, however detection of money laundering without prior knowledge of predicate crimes remains a significant challenge. Previous detection systems have tended to focus on individuals, considering transaction histories and applying anomaly detection to identify suspicious behaviour. However, money laundering involves groups of collaborating individuals, and evidence of money laundering may only be apparent when the collective behaviour of these groups is considered. In this paper we describe a detection system that is capable of analysing group behaviour, using a combination of network analysis and supervised learning. This system is designed for real-world application and operates on networks consisting of millions of interacting parties. Evaluation of the system using real-world data indicates th...

  7. Biomedical data analysis by supervised manifold learning.

    Science.gov (United States)

    Alvarez-Meza, A M; Daza-Santacoloma, G; Castellanos-Dominguez, G

    2012-01-01

    Biomedical data analysis is usually carried out by assuming that the information structure embedded into the biomedical recordings is linear, but that statement actually does not corresponds to the real behavior of the extracted features. In order to improve the accuracy of an automatic system to diagnostic support, and to reduce the computational complexity of the employed classifiers, we propose a nonlinear dimensionality reduction methodology based on manifold learning with multiple kernel representations, which learns the underlying data structure of biomedical information. Moreover, our approach can be used as a tool that allows the specialist to do a visual analysis and interpretation about the studied variables describing the health condition. Obtained results show how our approach maps the original high dimensional features into an embedding space where simple and straightforward classification strategies achieve a suitable system performance.

  8. Enhancing Adult Learning in Clinical Supervision

    Science.gov (United States)

    Goldman, Stuart

    2011-01-01

    Objective/Background: For decades, across almost every training site, clinical supervision has been considered "central to the development of skills" in psychiatry. The crucial supervisor/supervisee relationship has been described extensively in the literature, most often framed as a clinical apprenticeship of the novice to the master craftsman.…

  9. 监督学习的发展动态%Current Directions in Supervised Learning Research

    Institute of Scientific and Technical Information of China (English)

    蒋艳凰; 周海芳; 杨学军

    2003-01-01

    Supervised learning is very important in machine learning area. It has been making great progress in manydirections. This article summarizes three of these directions ,which are the hot problems in supervised learning field.These three directions are (a) improving classification accuracy by learning ensembles of classifiers, (b) methods forscaling up supervised learning algorithm, (c) extracting understandable rules from classifiers.

  10. An efficient flow-based botnet detection using supervised machine learning

    DEFF Research Database (Denmark)

    Stevanovic, Matija; Pedersen, Jens Myrup

    2014-01-01

    Botnet detection represents one of the most crucial prerequisites of successful botnet neutralization. This paper explores how accurate and timely detection can be achieved by using supervised machine learning as the tool of inferring about malicious botnet traffic. In order to do so, the paper...... introduces a novel flow-based detection system that relies on supervised machine learning for identifying botnet network traffic. For use in the system we consider eight highly regarded machine learning algorithms, indicating the best performing one. Furthermore, the paper evaluates how much traffic needs...... to accurately and timely detect botnet traffic using purely flow-based traffic analysis and supervised machine learning. Additionally, the results show that in order to achieve accurate detection traffic flows need to be monitored for only a limited time period and number of packets per flow. This indicates...

  11. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that pre-specified target region. Locally-biased problems...... Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes...

  12. Action Learning in Undergraduate Engineering Thesis Supervision

    Science.gov (United States)

    Stappenbelt, Brad

    2017-01-01

    In the present action learning implementation, twelve action learning sets were conducted over eight years. The action learning sets consisted of students involved in undergraduate engineering research thesis work. The concurrent study accompanying this initiative investigated the influence of the action learning environment on student approaches…

  13. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    Deep generative models based upon continuous variational distributions parameterized by deep networks give state-of-the-art performance. In this paper we propose a framework for extending the latent representation with extra auxiliary variables in order to make the variational distribution more...... expressive for semi-supervised learning. By utilizing the stochasticity of the auxiliary variable we demonstrate how to train discriminative classifiers resulting in state-of-the-art performance within semi-supervised learning exemplified by an 0.96% error on MNIST using 100 labeled data points. Furthermore...

  14. Pulsar Search Using Supervised Machine Learning

    Science.gov (United States)

    Ford, John M.

    2017-05-01

    Pulsars are rapidly rotating neutron stars which emit a strong beam of energy through mechanisms that are not entirely clear to physicists. These very dense stars are used by astrophysicists to study many basic physical phenomena, such as the behavior of plasmas in extremely dense environments, behavior of pulsar-black hole pairs, and tests of general relativity. Many of these tasks require a large ensemble of pulsars to provide enough statistical information to answer the scientific questions posed by physicists. In order to provide more pulsars to study, there are several large-scale pulsar surveys underway, which are generating a huge backlog of unprocessed data. Searching for pulsars is a very labor-intensive process, currently requiring skilled people to examine and interpret plots of data output by analysis programs. An automated system for screening the plots will speed up the search for pulsars by a very large factor. Research to date on using machine learning and pattern recognition has not yielded a completely satisfactory system, as systems with the desired near 100% recall have false positive rates that are higher than desired, causing more manual labor in the classification of pulsars. This work proposed to research, identify, propose and develop methods to overcome the barriers to building an improved classification system with a false positive rate of less than 1% and a recall of near 100% that will be useful for the current and next generation of large pulsar surveys. The results show that it is possible to generate classifiers that perform as needed from the available training data. While a false positive rate of 1% was not reached, recall of over 99% was achieved with a false positive rate of less than 2%. Methods of mitigating the imbalanced training and test data were explored and found to be highly effective in enhancing classification accuracy.

  15. Semi-supervised Eigenvectors for Locally-biased Learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2012-01-01

    of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities. In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph...

  16. SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS

    Data.gov (United States)

    National Aeronautics and Space Administration — SPATIALLY ADAPTIVE SEMI-SUPERVISED LEARNING WITH GAUSSIAN PROCESSES FOR HYPERSPECTRAL DATA ANALYSIS GOO JUN * AND JOYDEEP GHOSH* Abstract. A semi-supervised learning...

  17. SLEAS: Supervised Learning using Entropy as Attribute Selection Measure

    Directory of Open Access Journals (Sweden)

    Kishor Kumar Reddy C

    2014-10-01

    Full Text Available There is embryonic importance in scaling up the broadly used decision tree learning algorithms to huge datasets. Even though abundant diverse methodologies have been proposed, a fast tree growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential to a greater extent. This paper aims at improving the performance of the SLIQ (Supervised Learning in Quest decision tree algorithm for classification in data mining. In the present research, we adopted entropy as attribute selection measure, which overcomes the problems facing with Gini Index. Classification accuracy of the proposed supervised learning using entropy as attribute selection measure (SLEAS algorithm is compared with the existing SLIQ algorithm using twelve datasets taken from UCI Machine Learning Repository, and the results yields that the SLEAS outperforms when compared with SLIQ decision tree. Further, error rate is also computed and the results clearly show that the SLEAS algorithm is giving less error rate when compared with SLIQ decision tree.

  18. Transfer learning improves supervised image segmentation across imaging protocols

    DEFF Research Database (Denmark)

    van Opbroek, Annegreet; Ikram, M. Arfan; Vernooij, Meike W.;

    2015-01-01

    well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore......The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform...... may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data...

  19. A review of supervised machine learning applied to ageing research.

    Science.gov (United States)

    Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

    2017-04-01

    Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

  20. Effects of coaching supervision, mentoring supervision and abusive supervision on talent development among trainee doctors in public hospitals: moderating role of clinical learning environment.

    Science.gov (United States)

    Subramaniam, Anusuiya; Silong, Abu Daud; Uli, Jegak; Ismail, Ismi Arif

    2015-08-13

    Effective talent development requires robust supervision. However, the effects of supervisory styles (coaching, mentoring and abusive supervision) on talent development and the moderating effects of clinical learning environment in the relationship between supervisory styles and talent development among public hospital trainee doctors have not been thoroughly researched. In this study, we aim to achieve the following, (1) identify the extent to which supervisory styles (coaching, mentoring and abusive supervision) can facilitate talent development among trainee doctors in public hospital and (2) examine whether coaching, mentoring and abusive supervision are moderated by clinical learning environment in predicting talent development among trainee doctors in public hospital. A questionnaire-based critical survey was conducted among trainee doctors undergoing housemanship at six public hospitals in the Klang Valley, Malaysia. Prior permission was obtained from the Ministry of Health Malaysia to conduct the research in the identified public hospitals. The survey yielded 355 responses. The results were analysed using SPSS 20.0 and SEM with AMOS 20.0. The findings of this research indicate that coaching and mentoring supervision are positively associated with talent development, and that there is no significant relationship between abusive supervision and talent development. The findings also support the moderating role of clinical learning environment on the relationships between coaching supervision-talent development, mentoring supervision-talent development and abusive supervision-talent development among public hospital trainee doctors. Overall, the proposed model indicates a 26 % variance in talent development. This study provides an improved understanding on the role of the supervisory styles (coaching and mentoring supervision) on facilitating talent development among public hospital trainee doctors. Furthermore, this study extends the literature to better

  1. Enhancing fieldwork learning using blended learning, GIS and remote supervision

    Science.gov (United States)

    Marra, Wouter A.; Alberti, Koko; Karssenberg, Derek

    2015-04-01

    Fieldwork is an important part of education in geosciences and essential to put theoretical knowledge into an authentic context. Fieldwork as teaching tool can take place in various forms, such as field-tutorial, excursion, or supervised research. Current challenges with fieldwork in education are to incorporate state-of-the art methods for digital data collection, on-site GIS-analysis and providing high-quality feedback to large groups of students in the field. We present a case on first-year earth-sciences fieldwork with approximately 80 students in the French Alps focused on geological and geomorphological mapping. Here, students work in couples and each couple maps their own fieldwork area to reconstruct the formative history. We present several major improvements for this fieldwork using a blended-learning approach, relying on open source software only. An important enhancement to the French Alps fieldwork is improving students' preparation. In a GIS environment, students explore their fieldwork areas using existing remote sensing data, a digital elevation model and derivatives to formulate testable hypotheses before the actual fieldwork. The advantage of this is that the students already know their area when arriving in the field, have started to apply the empirical cycle prior to their field visit, and are therefore eager to investigate their own research questions. During the fieldwork, students store and analyze their field observations in the same GIS environment. This enables them to get a better overview of their own collected data, and to integrate existing data sources also used in the preparation phase. This results in a quicker and enhanced understanding by the students. To enable remote access to observational data collected by students, the students synchronize their data daily with a webserver running a web map application. Supervisors can review students' progress remotely, examine and evaluate their observations in a GIS, and provide

  2. Collaborative Supervised Learning for Sensor Networks

    Science.gov (United States)

    Wagstaff, Kiri L.; Rebbapragada, Umaa; Lane, Terran

    2011-01-01

    Collaboration methods for distributed machine-learning algorithms involve the specification of communication protocols for the learners, which can query other learners and/or broadcast their findings preemptively. Each learner incorporates information from its neighbors into its own training set, and they are thereby able to bootstrap each other to higher performance. Each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. After being seeded with an initial labeled training set, each learner proceeds to learn in an iterative fashion. New data is collected and classified. The learner can then either broadcast its most confident classifications for use by other learners, or can query neighbors for their classifications of its least confident items. As such, collaborative learning combines elements of both passive (broadcast) and active (query) learning. It also uses ideas from ensemble learning to combine the multiple responses to a given query into a single useful label. This approach has been evaluated against current non-collaborative alternatives, including training a single classifier and deploying it at all nodes with no further learning possible, and permitting learners to learn from their own most confident judgments, absent interaction with their neighbors. On several data sets, it has been consistently found that active collaboration is the best strategy for a distributed learner network. The main advantages include the ability for learning to take place autonomously by collaboration rather than by requiring intervention from an oracle (usually human), and also the ability to learn in a distributed environment, permitting decisions to be made in situ and to yield faster response time.

  3. Multivariate Statistics and Supervised Learning for Predictive Detection of Unintentional Islanding in Grid-Tied Solar PV Systems

    Directory of Open Access Journals (Sweden)

    Shashank Vyas

    2016-01-01

    Full Text Available Integration of solar photovoltaic (PV generation with power distribution networks leads to many operational challenges and complexities. Unintentional islanding is one of them which is of rising concern given the steady increase in grid-connected PV power. This paper builds up on an exploratory study of unintentional islanding on a modeled radial feeder having large PV penetration. Dynamic simulations, also run in real time, resulted in exploration of unique potential causes of creation of accidental islands. The resulting voltage and current data underwent dimensionality reduction using principal component analysis (PCA which formed the basis for the application of Q statistic control charts for detecting the anomalous currents that could island the system. For reducing the false alarm rate of anomaly detection, Kullback-Leibler (K-L divergence was applied on the principal component projections which concluded that Q statistic based approach alone is not reliable for detection of the symptoms liable to cause unintentional islanding. The obtained data was labeled and a K-nearest neighbor (K-NN binomial classifier was then trained for identification and classification of potential islanding precursors from other power system transients. The three-phase short-circuit fault case was successfully identified as statistically different from islanding symptoms.

  4. Facial nerve image enhancement from CBCT using supervised learning technique.

    Science.gov (United States)

    Ping Lu; Barazzetti, Livia; Chandran, Vimal; Gavaghan, Kate; Weber, Stefan; Gerber, Nicolas; Reyes, Mauricio

    2015-08-01

    Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

  5. Modeling Time Series Data for Supervised Learning

    Science.gov (United States)

    Baydogan, Mustafa Gokce

    2012-01-01

    Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

  6. Supervised Learning with Complex-valued Neural Networks

    CERN Document Server

    Suresh, Sundaram; Savitha, Ramasamy

    2013-01-01

    Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks.  Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computati...

  7. Using Supervised Deep Learning for Human Age Estimation Problem

    Science.gov (United States)

    Drobnyh, K. A.; Polovinkin, A. N.

    2017-05-01

    Automatic facial age estimation is a challenging task upcoming in recent years. In this paper, we propose using the supervised deep learning features to improve an accuracy of the existing age estimation algorithms. There are many approaches solving the problem, an active appearance model and the bio-inspired features are two of them which showed the best accuracy. For experiments we chose popular publicly available FG-NET database, which contains 1002 images with a broad variety of light, pose, and expression. LOPO (leave-one-person-out) method was used to estimate the accuracy. Experiments demonstrated that adding supervised deep learning features has improved accuracy for some basic models. For example, adding the features to an active appearance model gave the 4% gain (the error decreased from 4.59 to 4.41).

  8. Semi-supervised Learning with Density Based Distances

    CERN Document Server

    Bijral, Avleen S; Srebro, Nathan

    2012-01-01

    We present a simple, yet effective, approach to Semi-Supervised Learning. Our approach is based on estimating density-based distances (DBD) using a shortest path calculation on a graph. These Graph-DBD estimates can then be used in any distance-based supervised learning method, such as Nearest Neighbor methods and SVMs with RBF kernels. In order to apply the method to very large data sets, we also present a novel algorithm which integrates nearest neighbor computations into the shortest path search and can find exact shortest paths even in extremely large dense graphs. Significant runtime improvement over the commonly used Laplacian regularization method is then shown on a large scale dataset.

  9. Rapid analysis of microbial systems using vibrational spectroscopy and supervised learning methods: application to the discrimination between methicillin-resistant and methicillin-susceptible Staphy

    Science.gov (United States)

    Goodacre, Royston; Rooney, Paul J.; Kell, Douglas B.

    1998-04-01

    FTIR spectra were obtained from 15 methicillin-resistant and 22 methicillin-susceptible Staphylococcus aureus strains using our DRASTIC approach. Cluster analysis showed that the major source of variation between the IR spectra was not due to their resistance or susceptibility to methicillin; indeed early studies suing pyrolysis mass spectrometry had shown that this unsupervised analysis gave information on the phage group of the bacteria. By contrast, artificial neural networks, based on a supervised learning, could be trained to recognize those aspects of the IR spectra which differentiated methicillin-resistant from methicillin- susceptible strains. These results give the first demonstration that the combination of FTIR with neural networks can provide a very rapid and accurate antibiotic susceptibility testing technique.

  10. Nonlinear system identification by Gustafson-Kessel fuzzy clustering and supervised local model network learning for the drug absorption spectra process.

    Science.gov (United States)

    Teslic, Luka; Hartmann, Benjamin; Nelles, Oliver; Skrjanc, Igor

    2011-12-01

    This paper deals with the problem of fuzzy nonlinear model identification in the framework of a local model network (LMN). A new iterative identification approach is proposed, where supervised and unsupervised learning are combined to optimize the structure of the LMN. For the purpose of fitting the cluster-centers to the process nonlinearity, the Gustafsson-Kessel (GK) fuzzy clustering, i.e., unsupervised learning, is applied. In combination with the LMN learning procedure, a new incremental method to define the number and the initial locations of the cluster centers for the GK clustering algorithm is proposed. Each data cluster corresponds to a local region of the process and is modeled with a local linear model. Since the validity functions are calculated from the fuzzy covariance matrices of the clusters, they are highly adaptable and thus the process can be described with a very sparse amount of local models, i.e., with a parsimonious LMN model. The proposed method for constructing the LMN is finally tested on a drug absorption spectral process and compared to two other methods, namely, Lolimot and Hilomot. The comparison between the experimental results when using each method shows the usefulness of the proposed identification algorithm.

  11. Supervised learning algorithms for visual object categorization

    NARCIS (Netherlands)

    bin Abdullah, A.

    2010-01-01

    This thesis presents novel techniques for image recognition systems for better understanding image content. More specifically, it looks at the algorithmic aspects and experimental verification to demonstrate the capability of the proposed algorithms. These techniques aim to improve the three major

  12. Facilitating the Learning Process in Design-Based Learning Practices: An Investigation of Teachers' Actions in Supervising Students

    Science.gov (United States)

    Gómez Puente, S. M.; van Eijck, M.; Jochems, W.

    2013-01-01

    Background: In research on design-based learning (DBL), inadequate attention is paid to the role the teacher plays in supervising students in gathering and applying knowledge to design artifacts, systems, and innovative solutions in higher education. Purpose: In this study, we examine whether teacher actions we previously identified in the DBL…

  13. Semi-supervised Learning for Photometric Supernova Classification

    CERN Document Server

    Richards, Joseph W; Freeman, Peter E; Schafer, Chad M; Poznanski, Dovi

    2011-01-01

    We present a semi-supervised method for photometric supernova typing. Our approach is to first use the nonlinear dimension reduction technique diffusion map to detect structure in a database of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template based methods. Applied to supernova data simulated by Kessler et al. (2010b) to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 96% Type Ia purity and 86% Type Ia efficiency on the spectroscopic sample, but only 56% Type Ia purity and 48% efficiency on the photometric sample due to their spectroscopic followup strategy. To improve the performance on the photometric sample...

  14. Baccalaureate nursing students' perceptions of learning and supervision in the clinical environment.

    Science.gov (United States)

    Dimitriadou, Maria; Papastavrou, Evridiki; Efstathiou, Georgios; Theodorou, Mamas

    2015-06-01

    This study is an exploration of nursing students' experiences within the clinical learning environment (CLE) and supervision provided in hospital settings. A total of 357 second-year nurse students from all universities in Cyprus participated in the study. Data were collected using the Clinical Learning Environment, Supervision and Nurse Teacher instrument. The dimension "supervisory relationship (mentor)", as well as the frequency of individualized supervision meetings, were found to be important variables in the students' clinical learning. However, no statistically-significant connection was established between successful mentor relationship and team supervision. The majority of students valued their mentor's supervision more highly than a nurse teacher's supervision toward the fulfillment of learning outcomes. The dimensions "premises of nursing care" and "premises of learning" were highly correlated, indicating that a key component of a quality clinical learning environment is the quality of care delivered. The results suggest the need to modify educational strategies that foster desirable learning for students in response to workplace demands.

  15. Function approximation using combined unsupervised and supervised learning.

    Science.gov (United States)

    Andras, Peter

    2014-03-01

    Function approximation is one of the core tasks that are solved using neural networks in the context of many engineering problems. However, good approximation results need good sampling of the data space, which usually requires exponentially increasing volume of data as the dimensionality of the data increases. At the same time, often the high-dimensional data is arranged around a much lower dimensional manifold. Here we propose the breaking of the function approximation task for high-dimensional data into two steps: (1) the mapping of the high-dimensional data onto a lower dimensional space corresponding to the manifold on which the data resides and (2) the approximation of the function using the mapped lower dimensional data. We use over-complete self-organizing maps (SOMs) for the mapping through unsupervised learning, and single hidden layer neural networks for the function approximation through supervised learning. We also extend the two-step procedure by considering support vector machines and Bayesian SOMs for the determination of the best parameters for the nonlinear neurons in the hidden layer of the neural networks used for the function approximation. We compare the approximation performance of the proposed neural networks using a set of functions and show that indeed the neural networks using combined unsupervised and supervised learning outperform in most cases the neural networks that learn the function approximation using the original high-dimensional data.

  16. Robust head pose estimation via supervised manifold learning.

    Science.gov (United States)

    Wang, Chao; Song, Xubo

    2014-05-01

    Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with the pose being the only variable, the face images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background clutter, facial expression, and illumination. To address the problem, we propose to incorporate supervised information (pose angles of training samples) into the process of manifold learning. The process has three stages: neighborhood construction, graph weight computation and projection learning. For the first two stages, we redefine inter-point distance for neighborhood construction as well as graph weight by constraining them with the pose angle information. For Stage 3, we present a supervised neighborhood-based linear feature transformation algorithm to keep the data points with similar pose angles close together but the data points with dissimilar pose angles far apart. The experimental results show that our method has higher estimation accuracy than the other state-of-art algorithms and is robust to identity and illumination variations.

  17. Semi-Supervised Learning Based on Manifold in BCI

    Institute of Scientific and Technical Information of China (English)

    Ji-Ying Zhong; Xu Lei; De-Zhong Yao

    2009-01-01

    A Laplacian support vector machine (LapSVM) algorithm,a semi-supervised learning based on manifold,is introduced to brain-computer interface (BCI) to raise the classification precision and reduce the subjects' training complexity.The data are collected from three subjects in a three-task mental imagery experiment.LapSVM and transductive SVM (TSVM) are trained with a few labeled samples and a large number of unlabeled samples.The results confirm that LapSVM has a much better classification than TSVM.

  18. Generalization of Supervised Learning for Binary Mask Estimation

    DEFF Research Database (Denmark)

    May, Tobias; Gerkmann, Timo

    2014-01-01

    This paper addresses the problem of speech segregation by es- timating the ideal binary mask (IBM) from noisy speech. Two methods will be compared, one supervised learning approach that incorporates a priori knowledge about the feature distri- bution observed during training. The second method...... solely relies on a frame-based speech presence probability (SPP) es- timation, and therefore, does not depend on the acoustic con- dition seen during training. We investigate the influence of mismatches between the acoustic conditions used for training and testing on the IBM estimation performance...

  19. Learning outcomes using video in supervision and peer feedback during clinical skills training

    DEFF Research Database (Denmark)

    Lauridsen, Henrik Hein; Toftgård, Rie Castella; Nørgaard, Cita

    supervision of clinical skills (formative assessment). Demonstrations of these principles will be presented as video podcasts during the session. The learning outcomes of video supervision and peer-feedback were assessed in an online questionnaire survey. Results Results of the supervision showed large self...

  20. 77 FR 21099 - Public Water System Supervision Program Approval for the State of Ohio

    Science.gov (United States)

    2012-04-09

    ... AGENCY Public Water System Supervision Program Approval for the State of Ohio AGENCY: Environmental... has tentatively approved three revisions to the State of Ohio's public water system supervision... of Ohio's public water system supervision program, thereby giving Ohio EPA primary...

  1. 77 FR 76034 - Public Water System Supervision Program Approval for the State of Ohio

    Science.gov (United States)

    2012-12-26

    ... AGENCY Public Water System Supervision Program Approval for the State of Ohio AGENCY: Environmental... has tentatively approved revisions to the State of Ohio's public water system supervision program... public water system supervision program, thereby giving Ohio EPA primary enforcement responsibility...

  2. 78 FR 14791 - Public Water System Supervision Program Approval for the State of Indiana

    Science.gov (United States)

    2013-03-07

    ... AGENCY Public Water System Supervision Program Approval for the State of Indiana AGENCY: Environmental... has tentatively approved three revisions to the State of Indiana's public water system supervision... the State of Indiana's public water system supervision program, thereby giving IDEM...

  3. SUPERVISED LEARNING METHODS FOR BANGLA WEB DOCUMENT CATEGORIZATION

    Directory of Open Access Journals (Sweden)

    Ashis Kumar Mandal

    2014-09-01

    Full Text Available This paper explores the use of machine learning approaches, or more specifically, four supervised learning Methods, namely Decision Tree(C 4.5, K-Nearest Neighbour (KNN, Naïve Bays (NB, and Support Vector Machine (SVM for categorization of Bangla web documents. This is a task of automatically sorting a set of documents into categories from a predefined set. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been conducted on Bangla language text categorization. Hence, we attempt to analyze the efficiency of those four methods for categorization of Bangla documents. In order to validate, Bangla corpus from various websites has been developed and used as examples for the experiment. For Bangla, empirical results support that all four methods produce satisfactory performance with SVM attaining good result in terms of high dimensional and relatively noisy document feature vectors.

  4. Mining visual collocation patterns via self-supervised subspace learning.

    Science.gov (United States)

    Yuan, Junsong; Wu, Ying

    2012-04-01

    Traditional text data mining techniques are not directly applicable to image data which contain spatial information and are characterized by high-dimensional visual features. It is not a trivial task to discover meaningful visual patterns from images because the content variations and spatial dependence in visual data greatly challenge most existing data mining methods. This paper presents a novel approach to coping with these difficulties for mining visual collocation patterns. Specifically, the novelty of this work lies in the following new contributions: 1) a principled solution to the discovery of visual collocation patterns based on frequent itemset mining and 2) a self-supervised subspace learning method to refine the visual codebook by feeding back discovered patterns via subspace learning. The experimental results show that our method can discover semantically meaningful patterns efficiently and effectively.

  5. Prototype Vector Machine for Large Scale Semi-Supervised Learning

    Energy Technology Data Exchange (ETDEWEB)

    Zhang, Kai; Kwok, James T.; Parvin, Bahram

    2009-04-29

    Practicaldataminingrarelyfalls exactlyinto the supervisedlearning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computationalintensivenessofgraph-based SSLarises largely from the manifold or graph regularization, which in turn lead to large models that are dificult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highlyscalable,graph-based algorithm for large-scale SSL. Our key innovation is the use of"prototypes vectors" for effcient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.

  6. Multicultural supervision: lessons learned about an ongoing struggle.

    Science.gov (United States)

    Christiansen, Abigail Tolhurst; Thomas, Volker; Kafescioglu, Nilufer; Karakurt, Gunnur; Lowe, Walter; Smith, William; Wittenborn, Andrea

    2011-01-01

    This article examines the experiences of seven diverse therapists in a supervision course as they wrestled with the real-world application of multicultural supervision. Existing literature on multicultural supervision does not address the difficulties that arise in addressing multicultural issues in the context of the supervision relationship. The experiences of six supervisory candidates and one mentoring supervisor in addressing multicultural issues in supervision are explored. Guidelines for conversations regarding multicultural issues are provided.

  7. Descriptor Learning via Supervised Manifold Regularization for Multioutput Regression.

    Science.gov (United States)

    Zhen, Xiantong; Yu, Mengyang; Islam, Ali; Bhaduri, Mousumi; Chan, Ian; Li, Shuo

    2016-06-08

    Multioutput regression has recently shown great ability to solve challenging problems in both computer vision and medical image analysis. However, due to the huge image variability and ambiguity, it is fundamentally challenging to handle the highly complex input-target relationship of multioutput regression, especially with indiscriminate high-dimensional representations. In this paper, we propose a novel supervised descriptor learning (SDL) algorithm for multioutput regression, which can establish discriminative and compact feature representations to improve the multivariate estimation performance. The SDL is formulated as generalized low-rank approximations of matrices with a supervised manifold regularization. The SDL is able to simultaneously extract discriminative features closely related to multivariate targets and remove irrelevant and redundant information by transforming raw features into a new low-dimensional space aligned to targets. The achieved discriminative while compact descriptor largely reduces the variability and ambiguity for multioutput regression, which enables more accurate and efficient multivariate estimation. We conduct extensive evaluation of the proposed SDL on both synthetic data and real-world multioutput regression tasks for both computer vision and medical image analysis. Experimental results have shown that the proposed SDL can achieve high multivariate estimation accuracy on all tasks and largely outperforms the algorithms in the state of the arts. Our method establishes a novel SDL framework for multioutput regression, which can be widely used to boost the performance in different applications.

  8. Supervised learning of semantic classes for image annotation and retrieval.

    Science.gov (United States)

    Carneiro, Gustavo; Chan, Antoni B; Moreno, Pedro J; Vasconcelos, Nuno

    2007-03-01

    A probabilistic formulation for semantic image annotation and retrieval is proposed. Annotation and retrieval are posed as classification problems where each class is defined as the group of database images labeled with a common semantic label. It is shown that, by establishing this one-to-one correspondence between semantic labels and semantic classes, a minimum probability of error annotation and retrieval are feasible with algorithms that are 1) conceptually simple, 2) computationally efficient, and 3) do not require prior semantic segmentation of training images. In particular, images are represented as bags of localized feature vectors, a mixture density estimated for each image, and the mixtures associated with all images annotated with a common semantic label pooled into a density estimate for the corresponding semantic class. This pooling is justified by a multiple instance learning argument and performed efficiently with a hierarchical extension of expectation-maximization. The benefits of the supervised formulation over the more complex, and currently popular, joint modeling of semantic label and visual feature distributions are illustrated through theoretical arguments and extensive experiments. The supervised formulation is shown to achieve higher accuracy than various previously published methods at a fraction of their computational cost. Finally, the proposed method is shown to be fairly robust to parameter tuning.

  9. An AdaBoost algorithm for multiclass semi-supervised learning

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; Afsarmanesh, H.; Zaki, M.J.; Siebes, A.; Yu, J.X.; Goethals, B.; Webb, G.; Wu, X.

    2012-01-01

    We present an algorithm for multiclass Semi-Supervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semi-supervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems

  10. Complex networks and banking systems supervision

    Science.gov (United States)

    Papadimitriou, Theophilos; Gogas, Periklis; Tabak, Benjamin M.

    2013-10-01

    Comprehensive and thorough supervision of all banking institutions under a Central Bank’s regulatory control has become necessary as recent banking crises show. Promptly identifying bank distress and contagion issues is of great importance to the regulators. This paper proposes a methodology that can be used additionally to the standard methods of bank supervision or the new ones proposed to be implemented. By this, one can reveal the degree of banks’ connectedness and thus identify “core” instead of just “big” banks. Core banks are central in the network in the sense that they are shown to be crucial for network supervision. Core banks can be used as gauges of bank distress over a sub-network and promptly raise a red flag so that the central bank can effectively and swiftly focus on the corresponding neighborhood of financial institutions. In this paper we demonstrate the proposed scheme using as an example the asset returns variable. The method may and should be used with alternative variables as well.

  11. Phenotype classification of zebrafish embryos by supervised learning.

    Directory of Open Access Journals (Sweden)

    Nathalie Jeanray

    Full Text Available Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  12. Unsupervised/supervised learning concept for 24-hour load forecasting

    Energy Technology Data Exchange (ETDEWEB)

    Djukanovic, M. (Electrical Engineering Inst. ' Nikola Tesla' , Belgrade (Yugoslavia)); Babic, B. (Electrical Power Industry of Serbia, Belgrade (Yugoslavia)); Sobajic, D.J.; Pao, Y.-H. (Case Western Reserve Univ., Cleveland, OH (United States). Dept. of Electrical Engineering and Computer Science)

    1993-07-01

    An application of artificial neural networks in short-term load forecasting is described. An algorithm using an unsupervised/supervised learning concept and historical relationship between the load and temperature for a given season, day type and hour of the day to forecast hourly electric load with a lead time of 24 hours is proposed. An additional approach using functional link net, temperature variables, average load and last one-hour load of previous day is introduced and compared with the ANN model with one hidden layer load forecast. In spite of limited available weather variables (maximum, minimum and average temperature for the day) quite acceptable results have been achieved. The 24-hour-ahead forecast errors (absolute average) ranged from 2.78% for Saturdays and 3.12% for working days to 3.54% for Sundays. (Author)

  13. Online Semi-Supervised Learning on Quantized Graphs

    CERN Document Server

    Valko, Michal; Huang, Ling; Ting, Daniel

    2012-01-01

    In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empirically and theoretically, that good behavior can be achieved by collapsing nearby points into a set of local "representative points" that minimize distortion. Moreover, we regularize the harmonic solution to achieve better stability properties. We apply our algorithm to face recognition and optical character recognition applications to show that we can take advantage of the manifold structure to outperform the previous methods. Unlike previous heuristic approaches, we show that our method yields provable performance bounds.

  14. Using Supervised Learning to Improve Monte Carlo Integral Estimation

    CERN Document Server

    Tracey, Brendan; Alonso, Juan J

    2011-01-01

    Monte Carlo (MC) techniques are often used to estimate integrals of a multivariate function using randomly generated samples of the function. In light of the increasing interest in uncertainty quantification and robust design applications in aerospace engineering, the calculation of expected values of such functions (e.g. performance measures) becomes important. However, MC techniques often suffer from high variance and slow convergence as the number of samples increases. In this paper we present Stacked Monte Carlo (StackMC), a new method for post-processing an existing set of MC samples to improve the associated integral estimate. StackMC is based on the supervised learning techniques of fitting functions and cross validation. It should reduce the variance of any type of Monte Carlo integral estimate (simple sampling, importance sampling, quasi-Monte Carlo, MCMC, etc.) without adding bias. We report on an extensive set of experiments confirming that the StackMC estimate of an integral is more accurate than ...

  15. Phenotype classification of zebrafish embryos by supervised learning.

    Science.gov (United States)

    Jeanray, Nathalie; Marée, Raphaël; Pruvot, Benoist; Stern, Olivier; Geurts, Pierre; Wehenkel, Louis; Muller, Marc

    2015-01-01

    Zebrafish is increasingly used to assess biological properties of chemical substances and thus is becoming a specific tool for toxicological and pharmacological studies. The effects of chemical substances on embryo survival and development are generally evaluated manually through microscopic observation by an expert and documented by several typical photographs. Here, we present a methodology to automatically classify brightfield images of wildtype zebrafish embryos according to their defects by using an image analysis approach based on supervised machine learning. We show that, compared to manual classification, automatic classification results in 90 to 100% agreement with consensus voting of biological experts in nine out of eleven considered defects in 3 days old zebrafish larvae. Automation of the analysis and classification of zebrafish embryo pictures reduces the workload and time required for the biological expert and increases the reproducibility and objectivity of this classification.

  16. Clinical learning environment, supervision and nurse teacher evaluation scale: psychometric evaluation of the Swedish version.

    Science.gov (United States)

    Johansson, Unn-Britt; Kaila, Päivi; Ahlner-Elmqvist, Marianne; Leksell, Janeth; Isoaho, Hannu; Saarikoski, Mikko

    2010-09-01

    This article is a report of the development and psychometric testing of the Swedish version of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale. To achieve quality assurance, collaboration between the healthcare and nursing systems is a pre-requisite. Therefore, it is important to develop a tool that can measure the quality of clinical education. The Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale is a previously validated instrument, currently used in several universities across Europe. The instrument has been suggested for use as part of quality assessment and evaluation of nursing education. The scale was translated into Swedish from the English version. Data were collected between March 2008 and May 2009 among nursing students from three university colleges, with 324 students completing the questionnaire. Exploratory factor analysis was performed on the 34-item scale to determine construct validity and Cronbach's alpha was used to measure the internal consistency. The five sub-dimensions identified in the original scale were replicated in the exploratory factor analysis. The five factors had explanation percentages of 60.2%, which is deemed sufficient. Cronbach's alpha coefficient for the total scale was 0.95, and varied between 0.96 and 0.75 within the five sub-dimensions. The Swedish version of Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale has satisfactory psychometric properties and could be a useful quality instrument in nursing education. However, further investigation is required to develop and evaluate the questionnaire.

  17. 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.

  18. I’m just thinking - How learning opportunities are created in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Berge, Maria; Grout, Brian William Wilson;

    With this paper we aim to contribute towards an understanding of learning dynamics in doctoral supervision by analysing how learning opportunities are created in the interaction. We analyse interaction between supervisors and doctoral students using the notion of experiencing variation as a key...... for learning. Earlier research into doctoral supervision has been rather vague on how doctoral students learn to carry out research. Empirically, we have based the study on four cases each with one doctoral student and their supervisors. The supervision sessions were captured on video and audio to provide...

  19. New supervised learning theory applied to cerebellar modeling for suppression of variability of saccade end points.

    Science.gov (United States)

    Fujita, Masahiko

    2013-06-01

    A new supervised learning theory is proposed for a hierarchical neural network with a single hidden layer of threshold units, which can approximate any continuous transformation, and applied to a cerebellar function to suppress the end-point variability of saccades. In motor systems, feedback control can reduce noise effects if the noise is added in a pathway from a motor center to a peripheral effector; however, it cannot reduce noise effects if the noise is generated in the motor center itself: a new control scheme is necessary for such noise. The cerebellar cortex is well known as a supervised learning system, and a novel theory of cerebellar cortical function developed in this study can explain the capability of the cerebellum to feedforwardly reduce noise effects, such as end-point variability of saccades. This theory assumes that a Golgi-granule cell system can encode the strength of a mossy fiber input as the state of neuronal activity of parallel fibers. By combining these parallel fiber signals with appropriate connection weights to produce a Purkinje cell output, an arbitrary continuous input-output relationship can be obtained. By incorporating such flexible computation and learning ability in a process of saccadic gain adaptation, a new control scheme in which the cerebellar cortex feedforwardly suppresses the end-point variability when it detects a variation in saccadic commands can be devised. Computer simulation confirmed the efficiency of such learning and showed a reduction in the variability of saccadic end points, similar to results obtained from experimental data.

  20. Supervised Filter Learning for Representation Based Face Recognition.

    Directory of Open Access Journals (Sweden)

    Chao Bi

    Full Text Available Representation based classification methods, such as Sparse Representation Classification (SRC and Linear Regression Classification (LRC have been developed for face recognition problem successfully. However, most of these methods use the original face images without any preprocessing for recognition. Thus, their performances may be affected by some problematic factors (such as illumination and expression variances in the face images. In order to overcome this limitation, a novel supervised filter learning algorithm is proposed for representation based face recognition in this paper. The underlying idea of our algorithm is to learn a filter so that the within-class representation residuals of the faces' Local Binary Pattern (LBP features are minimized and the between-class representation residuals of the faces' LBP features are maximized. Therefore, the LBP features of filtered face images are more discriminative for representation based classifiers. Furthermore, we also extend our algorithm for heterogeneous face recognition problem. Extensive experiments are carried out on five databases and the experimental results verify the efficacy of the proposed algorithm.

  1. How Supervisor Experience Influences Trust, Supervision, and Trainee Learning: A Qualitative Study.

    Science.gov (United States)

    Sheu, Leslie; Kogan, Jennifer R; Hauer, Karen E

    2017-09-01

    Appropriate trust and supervision facilitate trainees' growth toward unsupervised practice. The authors investigated how supervisor experience influences trust, supervision, and subsequently trainee learning. In a two-phase qualitative inductive content analysis, phase one entailed reviewing 44 internal medicine resident and attending supervisor interviews from two institutions (July 2013 to September 2014) for themes on how supervisor experience influences trust and supervision. Three supervisor exemplars (early, developing, experienced) were developed and shared in phase two focus groups at a single institution, wherein 23 trainees validated the exemplars and discussed how each impacted learning (November 2015). Phase one: Four domains of trust and supervision varying with experience emerged: data, approach, perspective, clinical. Early supervisors were detail oriented and determined trust depending on task completion (data), were rule based (approach), drew on their experiences as trainees to guide supervision (perspective), and felt less confident clinically compared with more experienced supervisors (clinical). Experienced supervisors determined trust holistically (data), checked key aspects of patient care selectively and covertly (approach), reflected on individual experiences supervising (perspective), and felt comfortable managing clinical problems and gauging trainee abilities (clinical). Phase two: Trainees felt the exemplars reflected their experiences, described their preferences and learning needs shifting over time, and emphasized the importance of supervisor flexibility to match their learning needs. With experience, supervisors differ in their approach to trust and supervision. Supervisors need to trust themselves before being able to trust others. Trainees perceive these differences and seek supervision approaches that align with their learning needs.

  2. SPAM CLASSIFICATION BASED ON SUPERVISED LEARNING USING MACHINE LEARNING TECHNIQUES

    Directory of Open Access Journals (Sweden)

    T. Hamsapriya

    2011-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Spam emails are invading users without their consent and filling their mail boxes. They consume more network capacity as well as time in checking and deleting spam mails. The vast majority of Internet users are outspoken in their disdain for spam, although enough of them respond to commercial offers that spam remains a viable source of income to spammers. While most of the users want to do right think to avoid and get rid of spam, they need clear and simple guidelines on how to behave. In spite of all the measures taken to eliminate spam, they are not yet eradicated. Also when the counter measures are over sensitive, even legitimate emails will be eliminated. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifier-related issues. In recent days, Machine learning for spam classification is an important research issue. The effectiveness of the proposed work is explores and identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysis among the algorithms has also been presented.

  3. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  4. Just How Much Can School Pupils Learn from School Gardening? A Study of Two Supervised Agricultural Experience Approaches in Uganda

    Science.gov (United States)

    Okiror, John James; Matsiko, Biryabaho Frank; Oonyu, Joseph

    2011-01-01

    School systems in Africa are short of skills that link well with rural communities, yet arguments to vocationalize curricula remain mixed and school agriculture lacks the supervised practical component. This study, conducted in eight primary (elementary) schools in Uganda, sought to compare the learning achievement of pupils taught using…

  5. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  6. The Practice of Supervision for Professional Learning: The Example of Future Forensic Specialists

    Science.gov (United States)

    Köpsén, Susanne; Nyström, Sofia

    2015-01-01

    Supervision intended to support learning is of great interest in professional knowledge development. No single definition governs the implementation and enactment of supervision because of different conditions, intentions, and pedagogical approaches. Uncertainty exists at a time when knowledge and methods are undergoing constant development. This…

  7. Influencing Factors and Path Choice of Rebuilding Rural Financial Supervision System

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    The necessity of rebuilding Chinese rural financial supervision system is expounded. Rebuilding rural financial supervision system is conducive to normalizing rural financial order and forming the benign competitive situation; to clarifying the role played by rural financial organization and providing better financial services for "three agriculture";to forming safe rural financial environment to promote the development of rural economy and national economy. The restricting factors of rebuilding Chinese rural financial supervision system are analyzed. The major reasons are that rural financial legislation is relatively backward and the supervision department lacks the necessary legal basis. The administrative supervision is insufficient, which lead to the mal-position, administrative offside and vacancy of rural financial supervision;rural financial organization mechanism is unreasonable and the internal supervision mechanism is relatively bad;supervision organization of rural financial industry is imperfect and the function is imperfect; public’s supervision awareness is weak and social supervision mechanism develops slowly. The paths for rebuilding Chinese rural financial supervision system are put forward including accelerating rural financial legislation to provide perfect legal evidence for rural financial supervision system; clarifying the position on governmental functions and fully displaying the leading role of rural financial supervision; establishing specialized rural financial supervision organization to perfect its supervision functions; vigorously cultivating people’s supervision awareness to promote the development of social supervision mechanism.

  8. Semi-Supervised Learning for Classification of Protein Sequence Data

    Directory of Open Access Journals (Sweden)

    Brian R. King

    2008-01-01

    Full Text Available Protein sequence data continue to become available at an exponential rate. Annotation of functional and structural attributes of these data lags far behind, with only a small fraction of the data understood and labeled by experimental methods. Classification methods that are based on semi-supervised learning can increase the overall accuracy of classifying partly labeled data in many domains, but very few methods exist that have shown their effect on protein sequence classification. We show how proven methods from text classification can be applied to protein sequence data, as we consider both existing and novel extensions to the basic methods, and demonstrate restrictions and differences that must be considered. We demonstrate comparative results against the transductive support vector machine, and show superior results on the most difficult classification problems. Our results show that large repositories of unlabeled protein sequence data can indeed be used to improve predictive performance, particularly in situations where there are fewer labeled protein sequences available, and/or the data are highly unbalanced in nature.

  9. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities...... this paper focuses on the methodological advantages and potential criticism of combining theories. Learning in doctoral education, as in classroom learning, can be analysed from different perspectives. Zembylas (2005) suggests three perspectives with the aim of linking the cognitive and the emotional...

  10. Efficient supervised learning in networks with binary synapses

    CERN Document Server

    Baldassi, Carlo; Brunel, Nicolas; Zecchina, Riccardo

    2007-01-01

    Recent experimental studies indicate that synaptic changes induced by neuronal activity are discrete jumps between a small number of stable states. Learning in systems with discrete synapses is known to be a computationally hard problem. Here, we study a neurobiologically plausible on-line learning algorithm that derives from Belief Propagation algorithms. We show that it performs remarkably well in a model neuron with binary synapses, and a finite number of `hidden' states per synapse, that has to learn a random classification task. Such system is able to learn a number of associations close to the theoretical limit, in time which is sublinear in system size. This is to our knowledge the first on-line algorithm that is able to achieve efficiently a finite number of patterns learned per binary synapse. Furthermore, we show that performance is optimal for a finite number of hidden states which becomes very small for sparse coding. The algorithm is similar to the standard `perceptron' learning algorithm, with a...

  11. Semi-Supervised Learning Techniques in AO Applications: A Novel Approach To Drift Counteraction

    Science.gov (United States)

    De Vito, S.; Fattoruso, G.; Pardo, M.; Tortorella, F.; Di Francia, G.

    2011-11-01

    In this work we proposed and tested the use of SSL techniques in the AO domain. The SSL characteristics have been exploited to reduce the need for costly supervised samples and the effects of time dependant drift of state-of-the-art statistical learning approaches. For this purpose, an on-field recorded one year long atmospheric pollution dataset has been used. The semi-supervised approach benefitted from the use of updated unlabeled samples, adapting its knowledge to the slowly changing drift effects. We expect that semi-supervised learning can provide significant advantages to the performance of sensor fusion subsystems in artificial olfaction exhibiting an interesting drift counteraction effect.

  12. Virtual Calibration of Cosmic Ray Sensor: Using Supervised Ensemble Machine Learning

    Directory of Open Access Journals (Sweden)

    Ritaban Dutta

    2013-09-01

    Full Text Available In this paper an ensemble of supervised machine learning methods has been investigated to virtually and dynamically calibrate the cosmic ray sensors measuring area wise bulk soil moisture. Main focus of this study was to find an alternative to the currently available field calibration method; based on expensive and time consuming soil sample collection methodology. Data from the Australian Water Availability Project (AWAP database was used as independent soil moisture ground truth and results were compared against the conventionally estimated soil moisture using a Hydroinnova CRS-1000 cosmic ray probe deployed in Tullochgorum, Australia. Prediction performance of a complementary ensemble of four supervised estimators, namely Sugano type Adaptive Neuro-Fuzzy Inference System (S-ANFIS, Cascade Forward Neural Network (CFNN, Elman Neural Network (ENN and Learning Vector Quantization Neural Network (LVQN was evaluated using training and testing paradigms. An AWAP trained ensemble of four estimators was able to predict bulk soil moisture directly from cosmic ray neutron counts with 94.4% as best accuracy. The ensemble approach outperformed the individual performances from these networks. This result proved that an ensemble machine learning based paradigm could be a valuable alternative data driven calibration method for cosmic ray sensors against the current expensive and hydrological assumption based field calibration method.

  13. Towards harmonized seismic analysis across Europe using supervised machine learning approaches

    Science.gov (United States)

    Zaccarelli, Riccardo; Bindi, Dino; Cotton, Fabrice; Strollo, Angelo

    2017-04-01

    In the framework of the Thematic Core Services for Seismology of EPOS-IP (European Plate Observing System-Implementation Phase), a service for disseminating a regionalized logic-tree of ground motions models for Europe is under development. While for the Mediterranean area the large availability of strong motion data qualified and disseminated through the Engineering Strong Motion database (ESM-EPOS), supports the development of both selection criteria and ground motion models, for the low-to-moderate seismic regions of continental Europe the development of ad-hoc models using weak motion recordings of moderate earthquakes is unavoidable. Aim of this work is to present a platform for creating application-oriented earthquake databases by retrieving information from EIDA (European Integrated Data Archive) and applying supervised learning models for earthquake records selection and processing suitable for any specific application of interest. Supervised learning models, i.e. the task of inferring a function from labelled training data, have been extensively used in several fields such as spam detection, speech and image recognition and in general pattern recognition. Their suitability to detect anomalies and perform a semi- to fully- automated filtering on large waveform data set easing the effort of (or replacing) human expertise is therefore straightforward. Being supervised learning algorithms capable of learning from a relatively small training set to predict and categorize unseen data, its advantage when processing large amount of data is crucial. Moreover, their intrinsic ability to make data driven predictions makes them suitable (and preferable) in those cases where explicit algorithms for detection might be unfeasible or too heuristic. In this study, we consider relatively simple statistical classifiers (e.g., Naive Bayes, Logistic Regression, Random Forest, SVMs) where label are assigned to waveform data based on "recognized classes" needed for our use case

  14. Supervised learning of short and high-dimensional temporal sequences for life science measurements

    CERN Document Server

    Schleif, F -M; Hammer, B

    2011-01-01

    The analysis of physiological processes over time are often given by spectrometric or gene expression profiles over time with only few time points but a large number of measured variables. The analysis of such temporal sequences is challenging and only few methods have been proposed. The information can be encoded time independent, by means of classical expression differences for a single time point or in expression profiles over time. Available methods are limited to unsupervised and semi-supervised settings. The predictive variables can be identified only by means of wrapper or post-processing techniques. This is complicated due to the small number of samples for such studies. Here, we present a supervised learning approach, termed Supervised Topographic Mapping Through Time (SGTM-TT). It learns a supervised mapping of the temporal sequences onto a low dimensional grid. We utilize a hidden markov model (HMM) to account for the time domain and relevance learning to identify the relevant feature dimensions mo...

  15. Supervising System Stress in Multiple Markets

    Directory of Open Access Journals (Sweden)

    Mikhail V. Oet

    2015-09-01

    Full Text Available This paper develops an extended financial stress measure that considers the supervisory objective of identifying risks to the stability of the financial system. The measure provides a continuous and bounded signal of financial stress using daily public market data. Broad coverage of material financial system markets over time is achieved by leveraging dynamic credit weights. We consider how this measure can be used to monitor, analyze, and alert financial system stress.

  16. DATA VERIFICATION IN ISSUE SUPERVISING SYSTEMS

    Directory of Open Access Journals (Sweden)

    R. S. Katerinenko

    2013-01-01

    Full Text Available The paper proposes a method of data verification in issues tracking systems by means of production rules. This model makes it possible to formulate declaratively conditions that the information containment should comply with and apply reasoning procedures. Practical application of proposed verification system in a real software development project is described.

  17. Combining theories to reach multi-faceted insights into learning opportunities in doctoral supervision

    DEFF Research Database (Denmark)

    Kobayashi, Sofie; Rump, Camilla Østerberg

    in science learning; conceptual change, socio-constructivism and post-structuralism. In the present study we employ variation theory (Marton & Tsui, 2004) to study the individual acquisition perspective, what Zembylas terms conceptual change. As for the post-structural perspective we employ positioning......The aim of this paper is to illustrate how theories can be combined to explore opportunities for learning in doctoral supervision. While our earlier research into learning dynamics in doctoral supervision in life science research (Kobayashi, 2014) has focused on illustrating learning opportunities......-another when intertwining the analyses to get a multi-faceted insight into the phenomenon of learning to be a life science researcher. The data was derived from four observations of supervision of doctoral students in life science, each with a doctoral student and two supervisors. The storylines hypothesized...

  18. Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Chihyun Park

    Full Text Available BACKGROUND: The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. RESULTS: In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. CONCLUSIONS: The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

  19. 77 FR 12580 - Public Water System Supervision Program Revision for the State of Colorado

    Science.gov (United States)

    2012-03-01

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Colorado AGENCY: Environmental... the state of Colorado has revised its Public Water System Supervision (PWSS) Program by...

  20. 77 FR 12581 - Public Water System Supervision Program Revision for the State of Montana

    Science.gov (United States)

    2012-03-01

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Montana AGENCY: Environmental... the state of Montana has revised its Public Water System Supervision (PWSS) Program by...

  1. 77 FR 15367 - Public Water System Supervision Program Approval for the State of Minnesota

    Science.gov (United States)

    2012-03-15

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Approval for the State of Minnesota AGENCY: Environmental... of Minnesota is revising its approved public water system supervision program for four major...

  2. 75 FR 80493 - Public Water System Supervision Program Approval for the State of Wisconsin

    Science.gov (United States)

    2010-12-22

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Approval for the State of Wisconsin AGENCY: Environmental... of Wisconsin submitted a primacy application for its approved Public Water System Supervision...

  3. 75 FR 69434 - Public Water System Supervision Program Revision for the State of Montana

    Science.gov (United States)

    2010-11-12

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Montana AGENCY: Environmental... the State of Montana has revised its Public Water System Supervision (PWSS) Primacy Program...

  4. Modeling Multiple Annotator Expertise in the Semi-Supervised Learning Scenario

    CERN Document Server

    Yan, Yan; Fung, Glenn; Dy, Jennifer

    2012-01-01

    Learning algorithms normally assume that there is at most one annotation or label per data point. However, in some scenarios, such as medical diagnosis and on-line collaboration,multiple annotations may be available. In either case, obtaining labels for data points can be expensive and time-consuming (in some circumstances ground-truth may not exist). Semi-supervised learning approaches have shown that utilizing the unlabeled data is often beneficial in these cases. This paper presents a probabilistic semi-supervised model and algorithm that allows for learning from both unlabeled and labeled data in the presence of multiple annotators. We assume that it is known what annotator labeled which data points. The proposed approach produces annotator models that allow us to provide (1) estimates of the true label and (2) annotator variable expertise for both labeled and unlabeled data. We provide numerical comparisons under various scenarios and with respect to standard semi-supervised learning. Experiments showed ...

  5. 77 FR 64336 - Public Water System Supervision Program Revision for the State of Florida

    Science.gov (United States)

    2012-10-19

    ... AGENCY Public Water System Supervision Program Revision for the State of Florida AGENCY: Environmental... of Florida is revising its Public Water System Supervision Program by adopting the Lead and Copper... Florida's Public Water System Supervision Program. DATES: Any interested person may request a...

  6. 77 FR 44238 - Public Water System Supervision Program Revision for the State of Alabama

    Science.gov (United States)

    2012-07-27

    ... AGENCY Public Water System Supervision Program Revision for the State of Alabama AGENCY: Environmental... of Alabama is revising its approved Public Water System Supervision Program. Alabama has adopted the... State of Alabama's Public Water System Supervision Program. DATES: Any interested person may request...

  7. Multiclass Semi-Supervised Boosting and Similarity Learning

    NARCIS (Netherlands)

    Tanha, J.; Saberian, M.J.; van Someren, M.; Xiong, H.; Karypis, G.; Thuraisingham, B.; Cook, D.; Wu, X.

    2013-01-01

    In this paper, we consider the multiclass semi-supervised classification problem. A boosting algorithm is proposed to solve the multiclass problem directly. The proposed multiclass approach uses a new multiclass loss function, which includes two terms. The first term is the cost of the multiclass ma

  8. Learning to Teach: Teaching Internships in Counselor Education and Supervision

    Science.gov (United States)

    Hunt, Brandon; Gilmore, Genevieve Weber

    2011-01-01

    In an effort to ensure the efficacy of preparing emerging counselors in the field, CACREP standards require that by 2013 all core faculty at accredited universities have a doctorate in Counselor Education and Supervision. However, literature suggests that a disparity may exist in the preparation of counselor educators and the actual…

  9. Predicting incomplete gene microarray data with the use of supervised learning algorithms

    CSIR Research Space (South Africa)

    Twala, B

    2010-10-01

    Full Text Available of many well-established supervised learning (SL) algorithms in an attempt to provide more accurate and automatic diagnosis class (cancer/non cancer) prediction. Virtually all research on SL addresses the task of learning to classify complete domain...

  10. 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.

  11. A comparative evaluation of supervised and unsupervised representation learning approaches for anaplastic medulloblastoma differentiation

    Science.gov (United States)

    Cruz-Roa, Angel; Arevalo, John; Basavanhally, Ajay; Madabhushi, Anant; González, Fabio

    2015-01-01

    Learning data representations directly from the data itself is an approach that has shown great success in different pattern recognition problems, outperforming state-of-the-art feature extraction schemes for different tasks in computer vision, speech recognition and natural language processing. Representation learning applies unsupervised and supervised machine learning methods to large amounts of data to find building-blocks that better represent the information in it. Digitized histopathology images represents a very good testbed for representation learning since it involves large amounts of high complex, visual data. This paper presents a comparative evaluation of different supervised and unsupervised representation learning architectures to specifically address open questions on what type of learning architectures (deep or shallow), type of learning (unsupervised or supervised) is optimal. In this paper we limit ourselves to addressing these questions in the context of distinguishing between anaplastic and non-anaplastic medulloblastomas from routine haematoxylin and eosin stained images. The unsupervised approaches evaluated were sparse autoencoders and topographic reconstruct independent component analysis, and the supervised approach was convolutional neural networks. Experimental results show that shallow architectures with more neurons are better than deeper architectures without taking into account local space invariances and that topographic constraints provide useful invariant features in scale and rotations for efficient tumor differentiation.

  12. Innovation of Supervision System for Quality and Safety of Edible Agricultural Products

    Institute of Scientific and Technical Information of China (English)

    Xingxing; MEI; Zhongchao; FENG

    2014-01-01

    This paper elaborated multidimensional characteristics of quality and safety of agricultural products,introduced current situation of quality and safety supervision of edible agricultural products in China,analyzed existing problems of quality and safety supervision system and corresponding reasons,and finally came up with recommendations for innovation of supervision system for quality and safety of agricultural products.

  13. The Process of Supervision in the Turkish Educational System: Purpose, Structure, Operation

    Science.gov (United States)

    Memduhoglu, Hasan Basri; Aydin, Inayet; Yilmaz, Kursad; Gungor, Sabri; Oguz, Ebru

    2007-01-01

    The aim of this study is to provide information on the purposes, structure and operation of the process of supervision in the Turkish educational system. In this paper, the historical development of supervision services in the Turkish educational system, as well as the purposes and principles of educational supervision in Turkey and the structure…

  14. Research on effectiveness of coal mine safety supervision system reform on three types of collieries in China

    Institute of Scientific and Technical Information of China (English)

    Quanlong Liu; Xinchun Li; Fuyuan Guan

    2014-01-01

    Coal mine safety supervision system plays an important role in the coal mine safety management in China. However, the current supervision system is established on the basis of learning the advanced experience from other developed countries. It needs to be further improved according to national conditions. Therefore, the effectiveness of coal mine safety supervision system reform on three types of collieries are assessed by using time series analysis method based on comparative analysis of the supervision system before and after the reform in this paper. The regression results show that the structural reform is not conductive to the improvement of coal mine safety situation in the short term, but conductive significantly in the long term. Specifically, the effects in township coal mines are more significant than state-owned key coal mines in the long run, but negative effects also exist in the short term. The negative effects in state-owned key coal mines are non-significant compared with township coal mines. Moreover, the regression results are analyzed from the aspects of the closure policy of illegal small township coal mines at the end of 1998 and shortage of the new supervision system. Finally, the suggestions on improving the new supervision system are put forward based on the above analysis.

  15. Semi-supervised prediction of gene regulatory networks using machine learning algorithms

    Indian Academy of Sciences (India)

    Nihir Patel; T L Wang

    2015-10-01

    Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

  16. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned...

  17. Customers Behavior Modeling by Semi-Supervised Learning in Customer Relationship Management

    CERN Document Server

    Emtiyaz, Siavash; 10.4156/AISS.vol3.issue9.31

    2012-01-01

    Leveraging the power of increasing amounts of data to analyze customer base for attracting and retaining the most valuable customers is a major problem facing companies in this information age. Data mining technologies extract hidden information and knowledge from large data stored in databases or data warehouses, thereby supporting the corporate decision making process. CRM uses data mining (one of the elements of CRM) techniques to interact with customers. This study investigates the use of a technique, semi-supervised learning, for the management and analysis of customer-related data warehouse and information. The idea of semi-supervised learning is to learn not only from the labeled training data, but to exploit also the structural information in additionally available unlabeled data. The proposed semi-supervised method is a model by means of a feed-forward neural network trained by a back propagation algorithm (multi-layer perceptron) in order to predict the category of an unknown customer (potential cus...

  18. Supervised Machine Learning Methods Applied to Predict Ligand- Binding Affinity.

    Science.gov (United States)

    Heck, Gabriela S; Pintro, Val O; Pereira, Richard R; de Ávila, Mauricio B; Levin, Nayara M B; de Azevedo, Walter F

    2017-01-01

    Calculation of ligand-binding affinity is an open problem in computational medicinal chemistry. The ability to computationally predict affinities has a beneficial impact in the early stages of drug development, since it allows a mathematical model to assess protein-ligand interactions. Due to the availability of structural and binding information, machine learning methods have been applied to generate scoring functions with good predictive power. Our goal here is to review recent developments in the application of machine learning methods to predict ligand-binding affinity. We focus our review on the application of computational methods to predict binding affinity for protein targets. In addition, we also describe the major available databases for experimental binding constants and protein structures. Furthermore, we explain the most successful methods to evaluate the predictive power of scoring functions. Association of structural information with ligand-binding affinity makes it possible to generate scoring functions targeted to a specific biological system. Through regression analysis, this data can be used as a base to generate mathematical models to predict ligandbinding affinities, such as inhibition constant, dissociation constant and binding energy. Experimental biophysical techniques were able to determine the structures of over 120,000 macromolecules. Considering also the evolution of binding affinity information, we may say that we have a promising scenario for development of scoring functions, making use of machine learning techniques. Recent developments in this area indicate that building scoring functions targeted to the biological systems of interest shows superior predictive performance, when compared with other approaches. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

  19. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    2012-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  20. Contributions to unsupervised and supervised learning with applications in digital image processing

    OpenAIRE

    González Acuña, Ana Isabel

    2014-01-01

    311 p. : il. [EN]This Thesis covers a broad period of research activities with a commonthread: learning processes and its application to image processing. The twomain categories of learning algorithms, supervised and unsupervised, have beentouched across these years. The main body of initial works was devoted tounsupervised learning neural architectures, specially the Self Organizing Map.Our aim was to study its convergence properties from empirical and analyticalviewpoints.From the digita...

  1. Determining effects of non-synonymous SNPs on protein-protein interactions using supervised and semi-supervised learning.

    Directory of Open Access Journals (Sweden)

    Nan Zhao

    2014-05-01

    Full Text Available Single nucleotide polymorphisms (SNPs are among the most common types of genetic variation in complex genetic disorders. A growing number of studies link the functional role of SNPs with the networks and pathways mediated by the disease-associated genes. For example, many non-synonymous missense SNPs (nsSNPs have been found near or inside the protein-protein interaction (PPI interfaces. Determining whether such nsSNP will disrupt or preserve a PPI is a challenging task to address, both experimentally and computationally. Here, we present this task as three related classification problems, and develop a new computational method, called the SNP-IN tool (non-synonymous SNP INteraction effect predictor. Our method predicts the effects of nsSNPs on PPIs, given the interaction's structure. It leverages supervised and semi-supervised feature-based classifiers, including our new Random Forest self-learning protocol. The classifiers are trained based on a dataset of comprehensive mutagenesis studies for 151 PPI complexes, with experimentally determined binding affinities of the mutant and wild-type interactions. Three classification problems were considered: (1 a 2-class problem (strengthening/weakening PPI mutations, (2 another 2-class problem (mutations that disrupt/preserve a PPI, and (3 a 3-class classification (detrimental/neutral/beneficial mutation effects. In total, 11 different supervised and semi-supervised classifiers were trained and assessed resulting in a promising performance, with the weighted f-measure ranging from 0.87 for Problem 1 to 0.70 for the most challenging Problem 3. By integrating prediction results of the 2-class classifiers into the 3-class classifier, we further improved its performance for Problem 3. To demonstrate the utility of SNP-IN tool, it was applied to study the nsSNP-induced rewiring of two disease-centered networks. The accurate and balanced performance of SNP-IN tool makes it readily available to study the

  2. Semi-supervised learning and domain adaptation in natural language processing

    CERN Document Server

    Søgaard, Anders

    2013-01-01

    This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias.This book is intended to be both

  3. Using distant supervised learning to identify protein subcellular localizations from full-text scientific articles.

    Science.gov (United States)

    Zheng, Wu; Blake, Catherine

    2015-10-01

    Databases of curated biomedical knowledge, such as the protein-locations reflected in the UniProtKB database, provide an accurate and useful resource to researchers and decision makers. Our goal is to augment the manual efforts currently used to curate knowledge bases with automated approaches that leverage the increased availability of full-text scientific articles. This paper describes experiments that use distant supervised learning to identify protein subcellular localizations, which are important to understand protein function and to identify candidate drug targets. Experiments consider Swiss-Prot, the manually annotated subset of the UniProtKB protein knowledge base, and 43,000 full-text articles from the Journal of Biological Chemistry that contain just under 11.5 million sentences. The system achieves 0.81 precision and 0.49 recall at sentence level and an accuracy of 57% on held-out instances in a test set. Moreover, the approach identifies 8210 instances that are not in the UniProtKB knowledge base. Manual inspection of the 50 most likely relations showed that 41 (82%) were valid. These results have immediate benefit to researchers interested in protein function, and suggest that distant supervision should be explored to complement other manual data curation efforts.

  4. Data driven information system for supervision of judicial open

    Directory of Open Access Journals (Sweden)

    Ming LI

    2016-08-01

    Full Text Available Aiming at the four outstanding problems of informationized supervision for judicial publicity, the judicial public data is classified based on data driven to form the finally valuable data. Then, the functional structure, technical structure and business structure of the data processing system are put forward, including data collection module, data reduction module, data analysis module, data application module and data security module, etc. The development of the data processing system based on these structures can effectively reduce work intensity of judicial open iformation management, summarize the work state, find the problems, and promote the level of judicial publicity.

  5. Restricted Boltzmann machines based oversampling and semi-supervised learning for false positive reduction in breast CAD.

    Science.gov (United States)

    Cao, Peng; Liu, Xiaoli; Bao, Hang; Yang, Jinzhu; Zhao, Dazhe

    2015-01-01

    The false-positive reduction (FPR) is a crucial step in the computer aided detection system for the breast. The issues of imbalanced data distribution and the limitation of labeled samples complicate the classification procedure. To overcome these challenges, we propose oversampling and semi-supervised learning methods based on the restricted Boltzmann machines (RBMs) to solve the classification of imbalanced data with a few labeled samples. To evaluate the proposed method, we conducted a comprehensive performance study and compared its results with the commonly used techniques. Experiments on benchmark dataset of DDSM demonstrate the effectiveness of the RBMs based oversampling and semi-supervised learning method in terms of geometric mean (G-mean) for false positive reduction in Breast CAD.

  6. Network traffic identification system based on supervised machine learning%基于有督导机器学习的网络流量识别系统

    Institute of Scientific and Technical Information of China (English)

    邢玉凤; 毛艳琼

    2015-01-01

    针对真实网络环境中存在大量干扰噪声和野值样本等严重影响最小二乘支持向量机算法的性能等问题,提出一种结合协同量子粒子群优化算法和最小二乘支持向量机的网络流量识别系统.将网络流量分为12个类型,并进行数据采集.使用采集的数据对网络流量识别系统进行训练和性能测试.为研究提出的基于CQPSO-LSSVM算法的性能,将其与基于CQPSO-LSSVM算法和基于PSO-LSSVM算法进行对比,结果表明基于CQPSO-LSSVM算法具有更快的识别速度以及更好的识别准确率,避免了出现陷入局部最优解的情况发生.%In the real network environment,a large number of interference noise and outlier samples are existed,which se-riously affect on the performance of the least square support vector machine(LSSVM)algorithm. A network traffic identification system combining cooperative quantum particle swarm optimization (CQPSO) algorithm with LSSVM is proposed. The network traffic is divided into 12 types,in which the data of network traffic are collected. The network traffic identification system is con-ducted with training and performance test by the collected data. To study the performance of the CQPSO-LSSVM based algo-rithm,the CQPSO-LSSVM based algorithm is compared with the PSO-LSSVM based algorithm. The comparison results show that the CQPSO-LSSVM based algorithm has faster identification speed and better identification accuracy,which can avoid the occur-rence that the system is caught in local optimal solution.

  7. A semi-supervised learning framework for biomedical event extraction based on hidden topics.

    Science.gov (United States)

    Zhou, Deyu; Zhong, Dayou

    2015-05-01

    Scientists have devoted decades of efforts to understanding the interaction between proteins or RNA production. The information might empower the current knowledge on drug reactions or the development of certain diseases. Nevertheless, due to the lack of explicit structure, literature in life science, one of the most important sources of this information, prevents computer-based systems from accessing. Therefore, biomedical event extraction, automatically acquiring knowledge of molecular events in research articles, has attracted community-wide efforts recently. Most approaches are based on statistical models, requiring large-scale annotated corpora to precisely estimate models' parameters. However, it is usually difficult to obtain in practice. Therefore, employing un-annotated data based on semi-supervised learning for biomedical event extraction is a feasible solution and attracts more interests. In this paper, a semi-supervised learning framework based on hidden topics for biomedical event extraction is presented. In this framework, sentences in the un-annotated corpus are elaborately and automatically assigned with event annotations based on their distances to these sentences in the annotated corpus. More specifically, not only the structures of the sentences, but also the hidden topics embedded in the sentences are used for describing the distance. The sentences and newly assigned event annotations, together with the annotated corpus, are employed for training. Experiments were conducted on the multi-level event extraction corpus, a golden standard corpus. Experimental results show that more than 2.2% improvement on F-score on biomedical event extraction is achieved by the proposed framework when compared to the state-of-the-art approach. The results suggest that by incorporating un-annotated data, the proposed framework indeed improves the performance of the state-of-the-art event extraction system and the similarity between sentences might be precisely

  8. Supervised Learning of Logical Operations in Layered Spiking Neural Networks with Spike Train Encoding

    CERN Document Server

    Grüning, André

    2011-01-01

    Few algorithms for supervised training of spiking neural networks exist that can deal with patterns of multiple spikes, and their computational properties are largely unexplored. We demonstrate in a set of simulations that the ReSuMe learning algorithm can be successfully applied to layered neural networks. Input and output patterns are encoded as spike trains of multiple precisely timed spikes, and the network learns to transform the input trains into target output trains. This is done by combining the ReSuMe learning algorithm with multiplicative scaling of the connections of downstream neurons. We show in particular that layered networks with one hidden layer can learn the basic logical operations, including Exclusive-Or, while networks without hidden layer cannot, mirroring an analogous result for layered networks of rate neurons. While supervised learning in spiking neural networks is not yet fit for technical purposes, exploring computational properties of spiking neural networks advances our understand...

  9. Out-of-Sample Generalizations for Supervised Manifold Learning for Classification

    Science.gov (United States)

    Vural, Elif; Guillemot, Christine

    2016-03-01

    Supervised manifold learning methods for data classification map data samples residing in a high-dimensional ambient space to a lower-dimensional domain in a structure-preserving way, while enhancing the separation between different classes in the learned embedding. Most nonlinear supervised manifold learning methods compute the embedding of the manifolds only at the initially available training points, while the generalization of the embedding to novel points, known as the out-of-sample extension problem in manifold learning, becomes especially important in classification applications. In this work, we propose a semi-supervised method for building an interpolation function that provides an out-of-sample extension for general supervised manifold learning algorithms studied in the context of classification. The proposed algorithm computes a radial basis function (RBF) interpolator that minimizes an objective function consisting of the total embedding error of unlabeled test samples, defined as their distance to the embeddings of the manifolds of their own class, as well as a regularization term that controls the smoothness of the interpolation function in a direction-dependent way. The class labels of test data and the interpolation function parameters are estimated jointly with a progressive procedure. Experimental results on face and object images demonstrate the potential of the proposed out-of-sample extension algorithm for the classification of manifold-modeled data sets.

  10. Fall detection using supervised machine learning algorithms: A comparative study

    KAUST Repository

    Zerrouki, Nabil

    2017-01-05

    Fall incidents are considered as the leading cause of disability and even mortality among older adults. To address this problem, fall detection and prevention fields receive a lot of intention over the past years and attracted many researcher efforts. We present in the current study an overall performance comparison between fall detection systems using the most popular machine learning approaches which are: Naïve Bayes, K nearest neighbor, neural network, and support vector machine. The analysis of the classification power associated to these most widely utilized algorithms is conducted on two fall detection databases namely FDD and URFD. Since the performance of the classification algorithm is inherently dependent on the features, we extracted and used the same features for all classifiers. The classification evaluation is conducted using different state of the art statistical measures such as the overall accuracy, the F-measure coefficient, and the area under ROC curve (AUC) value.

  11. Expert Students in Social Learning Management Systems

    Science.gov (United States)

    Avogadro, Paolo; Calegari, Silvia; Dominoni, Matteo Alessandro

    2016-01-01

    Purpose: A social learning management system (social LMS) is a tool which favors social interactions and allows scholastic institutions to supervise and guide the learning process. The inclusion of the social feature to a "normal" LMS leads to the creation of educational social networks (EduSN), where the students interact and learn. The…

  12. 借鉴国外经验,完善我国食品安全监管体系%To Improve Our Country's Food Safety Supervision System by Learning Experiences Abroad

    Institute of Scientific and Technical Information of China (English)

    胡琼天

    2012-01-01

    Since "Food Safety Law" was published in 2009,food safety problems have still been occurring frequently,which reveals the defects of supervision system,such as the shortage of risk analysis preceding procedure,market interest conflict supervision,disorderly food safety standards.By researching into various models of U.S.,European Union,Canada,Australia,Japan and South Korea,this paper holds that we should construct a three-dimensional supervision system of government who domi-nates,enterprises who discipline themselves and public actively participate,so that we can effectively implement supervision of food safety.The absence of any party will but increase the risk of food safety accidents.%2009年《食品安全法》颁布以来,食品安全问题频发,并暴露出监管体系存在的风险分析前置程序缺位、市场利益关系冲击监管、食品安全标准杂乱等缺陷。在系统研究美国模式、欧盟模式、加拿大模式、澳大利亚模式、日本模式、韩国模式的基础上,我国应当构建以政府主导、企业自检自律和公众积极参与的三维一体监管模式,才能有效实施食品安全监管。任何一方的缺位,都将徒增食品安全事故发生的风险。

  13. Evaluation of Four Supervised Learning Methods for Benthic Habitat Mapping Using Backscatter from Multi-Beam Sonar

    Directory of Open Access Journals (Sweden)

    Jacquomo Monk

    2012-11-01

    Full Text Available An understanding of the distribution and extent of marine habitats is essential for the implementation of ecosystem-based management strategies. Historically this had been difficult in marine environments until the advancement of acoustic sensors. This study demonstrates the applicability of supervised learning techniques for benthic habitat characterization using angular backscatter response data. With the advancement of multibeam echo-sounder (MBES technology, full coverage datasets of physical structure over vast regions of the seafloor are now achievable. Supervised learning methods typically applied to terrestrial remote sensing provide a cost-effective approach for habitat characterization in marine systems. However the comparison of the relative performance of different classifiers using acoustic data is limited. Characterization of acoustic backscatter data from MBES using four different supervised learning methods to generate benthic habitat maps is presented. Maximum Likelihood Classifier (MLC, Quick, Unbiased, Efficient Statistical Tree (QUEST, Random Forest (RF and Support Vector Machine (SVM were evaluated to classify angular backscatter response into habitat classes using training data acquired from underwater video observations. Results for biota classifications indicated that SVM and RF produced the highest accuracies, followed by QUEST and MLC, respectively. The most important backscatter data were from the moderate incidence angles between 30° and 50°. This study presents initial results for understanding how acoustic backscatter from MBES can be optimized for the characterization of marine benthic biological habitats.

  14. Re/Learning Student Teaching Supervision: A Co/Autoethnographic Self-Study

    Science.gov (United States)

    Butler, Brandon M.; Diacopoulos, Mark M.

    2016-01-01

    This article documents the critical friendship of an experienced teacher educator and a doctoral student through our joint exploration of student teaching supervision. By adopting a co/autoethnographic approach, we learned from biographical and contemporaneous critical incidents that informed short- and long-term practices. In particular, we…

  15. Undergraduate Internship Supervision in Psychology Departments: Use of Experiential Learning Best Practices

    Science.gov (United States)

    Bailey, Sarah F.; Barber, Larissa K.; Nelson, Videl L.

    2017-01-01

    This study examined trends in how psychology internships are supervised compared to current experiential learning best practices in the literature. We sent a brief online survey to relevant contact persons for colleges/universities with psychology departments throughout the United States (n = 149 responded). Overall, the majority of institutions…

  16. Multiclass semi-supervised learning for animal behavior recognition from accelerometer data

    NARCIS (Netherlands)

    Tanha, J.; van Someren, M.; de Bakker, M.; Bouten, W.; Shamoun-Baranes, J.; Afsarmanesh, H.

    2012-01-01

    In this paper we present a new Multiclass semi-supervised learning algorithm that uses a base classifier in combination with a similarity function applied to all data to find a classifier that maximizes the margin and consistency over all data. A novel multiclass loss function is presented and used

  17. Social media research: The application of supervised machine learning in organizational communication research

    NARCIS (Netherlands)

    van Zoonen, W.; van der Meer, T.G.L.A.

    2016-01-01

    Despite the online availability of data, analysis of this information in academic research is arduous. This article explores the application of supervised machine learning (SML) to overcome challenges associated with online data analysis. In SML classifiers are used to categorize and code binary dat

  18. Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning.

    Science.gov (United States)

    Adal, Kedir M; Sidibé, Désiré; Ali, Sharib; Chaum, Edward; Karnowski, Thomas P; Mériaudeau, Fabrice

    2014-04-01

    Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.

  19. Supervised learning for neural manifold using spatiotemporal brain activity

    Science.gov (United States)

    Kuo, Po-Chih; Chen, Yong-Sheng; Chen, Li-Fen

    2015-12-01

    Objective. Determining the means by which perceived stimuli are compactly represented in the human brain is a difficult task. This study aimed to develop techniques for the construction of the neural manifold as a representation of visual stimuli. Approach. We propose a supervised locally linear embedding method to construct the embedded manifold from brain activity, taking into account similarities between corresponding stimuli. In our experiments, photographic portraits were used as visual stimuli and brain activity was calculated from magnetoencephalographic data using a source localization method. Main results. The results of 10 × 10-fold cross-validation revealed a strong correlation between manifolds of brain activity and the orientation of faces in the presented images, suggesting that high-level information related to image content can be revealed in the brain responses represented in the manifold. Significance. Our experiments demonstrate that the proposed method is applicable to investigation into the inherent patterns of brain activity.

  20. Cost-conscious comparison of supervised learning algorithms over multiple data sets

    OpenAIRE

    Ulaş, Aydın; Yıldız, Olcay Taner; Alpaydın, Ahmet İbrahim Ethem

    2012-01-01

    In the literature, there exist statistical tests to compare supervised learning algorithms on multiple data sets in terms of accuracy but they do not always generate an ordering. We propose Multi(2)Test, a generalization of our previous work, for ordering multiple learning algorithms on multiple data sets from "best" to "worst" where our goodness measure is composed of a prior cost term additional to generalization error. Our simulations show that Multi2Test generates orderings using pairwise...

  1. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  2. Problems of Rural Food Safety and Strategies of Constructing Supervision System

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    This paper expounds the practical necessity of constructing diversified rural food safety supervision system as follows: it is the necessary requirements of guaranteeing people’s health and life safety; it is an important component of governmental function of social management and the logical extension of administrative responsibilities; it is the basis of maintaining order of rural society and constructing harmonious society. The main problems existing in the supervision of rural food safety are analyzed as follows: first, the legislative work of rural food safety lags behind to some extent; second, the supervision of governmental departments on rural food safety is insufficient; third, the industrial supervision mechanism of rural food security is not perfect; fourth, the role of rural social organizations in supervising food safety is limited; fifth, the farmers’ awareness of food safety supervision is not strong. Based on these problems, the targeted strategies of constructing diversified rural food safety supervision system are put forward as follows: accelerate the legislation of rural food safety, and ensure that there are laws to go by; give play to the dominant role of government, and strengthen administrative supervision on rural food safety; perfect industrial convention of rural food safety, and improve industrial supervision mechanism; actively support the fostering of social organizations, and give play to the role of supervision of organizations; cultivate correct concept of rights and obligations of farmers, and form awareness of food safety supervision.

  3. Supervised orthogonal discriminant subspace projects learning for face recognition.

    Science.gov (United States)

    Chen, Yu; Xu, Xiao-Hong

    2014-02-01

    In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.

  4. Developing a practice of supervision in university as a collective learning process

    DEFF Research Database (Denmark)

    Lund, Birthe; Jensen, Annie Aarup

    2009-01-01

    of the framework surrounding the supervision process, both as regards the students and the teachers; to de-privatize the problems encountered by the individual teacher during the supervision; to ensure that students would be able to graduate within the timeframe of the education (the institutional economic......The point of departure of the paper is a university pedagogical course established with the purpose of strengthening the university teachers’ competence regarding the supervision of students working on their master’s thesis. The purpose of the course is furthermore to ensure the improvement...... of creating a transformation in the sense that it may change from being a top-down project (instigated by the Faculty) and develop into being a bottom-up project. It may hold the potential for developing collective learning processes assuming that good structures and frameworks can be created, as well...

  5. Gene classification using parameter-free semi-supervised manifold learning.

    Science.gov (United States)

    Huang, Hong; Feng, Hailiang

    2012-01-01

    A new manifold learning method, called parameter-free semi-supervised local Fisher discriminant analysis (pSELF), is proposed to map the gene expression data into a low-dimensional space for tumor classification. Motivated by the fact that semi-supervised and parameter-free are two desirable and promising characteristics for dimension reduction, a new difference-based optimization objective function with unlabeled samples has been designed. The proposed method preserves the global structure of unlabeled samples in addition to separating labeled samples in different classes from each other. The semi-supervised method has an analytic form of the globally optimal solution, which can be computed efficiently by eigen decomposition. Experimental results on synthetic data and SRBCT, DLBCL, and Brain Tumor gene expression data sets demonstrate the effectiveness of the proposed method.

  6. Emotional Literacy Support Assistants' Views on Supervision Provided by Educational Psychologists: What EPs Can Learn from Group Supervision

    Science.gov (United States)

    Osborne, Cara; Burton, Sheila

    2014-01-01

    The Educational Psychology Service in this study has responsibility for providing group supervision to Emotional Literacy Support Assistants (ELSAs) working in schools. To date, little research has examined this type of inter-professional supervision arrangement. The current study used a questionnaire to examine ELSAs' views on the supervision…

  7. Online semi-supervised learning: algorithm and application in metagenomics

    NARCIS (Netherlands)

    S. Imangaliyev; B. Keijser; W. Crielaard; E. Tsivtsivadze

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key role in metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm an

  8. Online Semi-Supervised Learning: Algorithm and Application in Metagenomics

    NARCIS (Netherlands)

    Imangaliyev, S.; Keijser, B.J.F.; Crielaard, W.; Tsivtsivadze, E.

    2013-01-01

    As the amount of metagenomic data grows rapidly, online statistical learning algorithms are poised to play key rolein metagenome analysis tasks. Frequently, data are only partially labeled, namely dataset contains partial information about the problem of interest. This work presents an algorithm and

  9. 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…

  10. Extended apprenticeship learning in doctoral training and supervision - moving beyond 'cookbook recipes'

    DEFF Research Database (Denmark)

    Tanggaard, Lene; Wegener, Charlotte

    An apprenticeship perspective on learning in academia sheds light on the potential for mutual learning and production, and also reveals the diverse range of learning resources beyond the formal novice-–expert relationship. Although apprenticeship is a well-known concept in educational research......, in this case apprenticeship offers an innovative perspective on future practice and research in academia allowing more students access to high high-quality research training and giving supervisors a chance to combine their own research with their supervision obligations....

  11. 78 FR 38714 - Public Water System Supervision Program Approval for the State of Illinois

    Science.gov (United States)

    2013-06-27

    ... AGENCY Public Water System Supervision Program Approval for the State of Illinois AGENCY: Environmental... of Illinois is revising its approved public water system supervision program for the Ground Water Rule, the Arsenic Rule and the ] new Public Water System Definition. EPA has determined that...

  12. 75 FR 23264 - Public Water System Supervision Program Revision for the State of Alabama

    Science.gov (United States)

    2010-05-03

    ... AGENCY Public Water System Supervision Program Revision for the State of Alabama AGENCY: Environmental... of Alabama is revising its approved Public Water System Supervision Program. Alabama has adopted the..., EPA is tentatively approving this revision to the State of Alabama's Public Water System...

  13. Recent advances on techniques and theories of feedforward networks with supervised learning

    Science.gov (United States)

    Xu, Lei; Klasa, Stan

    1992-07-01

    The rediscovery and popularization of the back propagation training technique for multilayer perceptrons as well as the invention of the Boltzmann Machine learning algorithm has given a new boost to the study of supervised learning networks. In recent years, besides the widely spread applications and the various further improvements of the classical back propagation technique, many new supervised learning models, techniques as well as theories, have also been proposed in a vast number of publications. This paper tries to give a rather systematical review on the recent advances on supervised learning techniques and theories for static feedforward networks. We summarize a great number of developments into four aspects: (1) Various improvements and variants made on the classical back propagation techniques for multilayer (static) perceptron nets, for speeding up training, avoiding local minima, increasing the generalization ability, as well as for many other interesting purposes. (2) A number of other learning methods for training multilayer (static) perceptron, such as derivative estimation by perturbation, direct weight update by perturbation, genetic algorithms, recursive least square estimate and extended Kalman filter, linear programming, the policy of fixing one layer while updating another, constructing networks by converting decision tree classifiers, and others. (3) Various other feedforward models which are also able to implement function approximation, probability density estimation and classification, including various models of basis function expansion (e.g., radial basis functions, restricted coulomb energy, multivariate adaptive regression splines, trigonometric and polynomial bases, projection pursuit, basis function tree, and may others), and several other supervised learning models. (4) Models with complex structures, e.g., modular architecture, hierarchy architecture, and others. (5) A number of theoretical issues involving the universal

  14. Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection

    DEFF Research Database (Denmark)

    Alstrøm, Tommy Sonne

    of sensors, as the sensors are designed to provide robust and reliable measurements. That means, the sensors are designed to have repeated measurement clusters. Sensor fusion is presented for the sensor based on chemoselective compounds. An array of color changing compounds are handled and in unity they make......This Ph.D. thesis titled “Assessing Miniaturized Sensor Performance using Supervised Learning, with Application to Drug and Explosive Detection” is a part of the strategic research project “Miniaturized sensors for explosives detection in air” funded by the Danish Agency for Science and Technology...... before the sensor responses can be applied to supervised learning algorithms. The technologies used for sensing consist of Calorimetry, Cantilevers, Chemoselective compounds, Quartz Crystal Microbalance and Surface Enhanced Raman Scattering. Each of the sensors have their own strength and weaknesses...

  15. Weakly supervised learning of a classifier for unusual event detection.

    Science.gov (United States)

    Jäger, Mark; Knoll, Christian; Hamprecht, Fred A

    2008-09-01

    In this paper, we present an automatic classification framework combining appearance based features and hidden Markov models (HMM) to detect unusual events in image sequences. One characteristic of the classification task is that anomalies are rare. This reflects the situation in the quality control of industrial processes, where error events are scarce by nature. As an additional restriction, class labels are only available for the complete image sequence, since frame-wise manual scanning of the recorded sequences for anomalies is too expensive and should, therefore, be avoided. The proposed framework reduces the feature space dimension of the image sequences by employing subspace methods and encodes characteristic temporal dynamics using continuous hidden Markov models (CHMMs). The applied learning procedure is as follows. 1) A generative model for the regular sequences is trained (one-class learning). 2) The regular sequence model (RSM) is used to locate potentially unusual segments within error sequences by means of a change detection algorithm (outlier detection). 3) Unusual segments are used to expand the RSM to an error sequence model (ESM). The complexity of the ESM is controlled by means of the Bayesian Information Criterion (BIC). The likelihood ratio of the data given the ESM and the RSM is used for the classification decision. This ratio is close to one for sequences without error events and increases for sequences containing error events. Experimental results are presented for image sequences recorded from industrial laser welding processes. We demonstrate that the learning procedure can significantly reduce the user interaction and that sequences with error events can be found with a small false positive rate. It has also been shown that a modeling of the temporal dynamics is necessary to reach these low error rates.

  16. Lightning location system supervising Swedish power transmission network

    Science.gov (United States)

    Melin, Stefan A.

    1991-01-01

    For electric utilities, the ability to prevent or minimize lightning damage on personnel and power systems is of great importance. Therefore, the Swedish State Power Board, has been using data since 1983 from a nationwide lightning location system (LLS) for accurately locating lightning ground strikes. Lightning data is distributed and presented on color graphic displays at regional power network control centers as well as at the national power system control center for optimal data use. The main objectives for use of LLS data are: supervising the power system for optimal and safe use of the transmission and generating capacity during periods of thunderstorms; warning service to maintenance and service crews at power line and substations to end operations hazardous when lightning; rapid positioning of emergency crews to locate network damage at areas of detected lightning; and post analysis of power outages and transmission faults in relation to lightning, using archived lightning data for determination of appropriate design and insulation levels of equipment. Staff have found LLS data useful and economically justified since the availability of power system has increased as well as level of personnel safety.

  17. Integrating learning assessment and supervision in a competency framework for clinical workplace education.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2015-02-01

    Although competency-based education is well established in health care education, research shows that the competencies do not always match the reality of clinical workplaces. Therefore, there is a need to design feasible and evidence-based competency frameworks that fit the workplace reality. This theoretical paper outlines a competency-based framework, designed to facilitate learning, assessment and supervision in clinical workplace education. Integration is the cornerstone of this holistic competency framework.

  18. Hierarchical Wireless Multimedia Sensor Networks for Collaborative Hybrid Semi-Supervised Classifier Learning

    Directory of Open Access Journals (Sweden)

    Liang Ding

    2007-11-01

    Full Text Available Wireless multimedia sensor networks (WMSN have recently emerged as one ofthe most important technologies, driven by the powerful multimedia signal acquisition andprocessing abilities. Target classification is an important research issue addressed in WMSN,which has strict requirement in robustness, quickness and accuracy. This paper proposes acollaborative semi-supervised classifier learning algorithm to achieve durative onlinelearning for support vector machine (SVM based robust target classification. The proposedalgorithm incrementally carries out the semi-supervised classifier learning process inhierarchical WMSN, with the collaboration of multiple sensor nodes in a hybrid computingparadigm. For decreasing the energy consumption and improving the performance, somemetrics are introduced to evaluate the effectiveness of the samples in specific sensor nodes,and a sensor node selection strategy is also proposed to reduce the impact of inevitablemissing detection and false detection. With the ant optimization routing, the learningprocess is implemented with the selected sensor nodes, which can decrease the energyconsumption. Experimental results demonstrate that the collaborative hybrid semi-supervised classifier learning algorithm can effectively implement target classification inhierarchical WMSN. It has outstanding performance in terms of energy efficiency and timecost, which verifies the effectiveness of the sensor nodes selection and ant optimizationrouting.

  19. Systemic-Developmental Supervision: Clinical Supervisory Approach for Family Counseling Student Interns

    Science.gov (United States)

    Carlson, Ryan G.; Lambie, Glenn W.

    2012-01-01

    Supervision models for marriage and family counseling student interns primarily focus on the use of traditional systemic techniques. In addition, a supervisee's level of development may not be considered when utilizing systemic tools. Furthermore, the supervisory relationship has been identified as a significant indicator of quality supervision,…

  20. 76 FR 57740 - Program Requirement Revisions Related to the Public Water System Supervision Programs for the...

    Science.gov (United States)

    2011-09-16

    ... AGENCY Program Requirement Revisions Related to the Public Water System Supervision Programs for the... the process of revising their respective approved Public Water System Supervision (PWSS) programs to meet the requirements of the Safe Drinking Water Act (SDWA). The State of Rhode Island has...

  1. 78 FR 67361 - Public Water System Supervision Program Revision for the Commonwealth of Kentucky

    Science.gov (United States)

    2013-11-12

    ... AGENCY Public Water System Supervision Program Revision for the Commonwealth of Kentucky AGENCY: U.S... that the Commonwealth of Kentucky is revising its approved Public Water System Supervision Program... corresponding federal regulations. Therefore, the EPA is tentatively approving this revision to the Commonwealth...

  2. 77 FR 23246 - Public Water System Supervision Program Revision for the Commonwealth of Kentucky

    Science.gov (United States)

    2012-04-18

    ... AGENCY Public Water System Supervision Program Revision for the Commonwealth of Kentucky AGENCY... that the Commonwealth of Kentucky is revising its approved Public Water System Supervision Program... regulations. Therefore, the EPA is tentatively approving this revision to the Commonwealth of Kentucky's...

  3. 76 FR 69734 - Public Water System Supervision Program Revision for the State of New Mexico

    Science.gov (United States)

    2011-11-09

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of New Mexico AGENCY: Environmental... of New Mexico is revising its approved Public Water System Supervision Program. New Mexico...

  4. 75 FR 69436 - Public Water System Supervision Program Revision for the State of South Dakota

    Science.gov (United States)

    2010-11-12

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of South Dakota AGENCY... hereby given that the State of South Dakota has revised its Public Water System Supervision...

  5. 78 FR 73858 - Public Water System Supervision Program Revision for the State of Oklahoma

    Science.gov (United States)

    2013-12-09

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Oklahoma AGENCY: United States... that the State of Oklahoma is revising its approved Public Water System Supervision Program....

  6. 77 FR 12582 - Public Water System Supervision Program Revision for the State of North Dakota

    Science.gov (United States)

    2012-03-01

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of North Dakota AGENCY... hereby given that the state of North Dakota has revised its Public Water System Supervision...

  7. 75 FR 9895 - Public Water System Supervision Program Revision for the State of Oklahoma

    Science.gov (United States)

    2010-03-04

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Oklahoma AGENCY: United States... the State of Oklahoma is revising its approved Public Water System Supervision Program adopting...

  8. 78 FR 9047 - Public Water System Supervision Program Revision for the State of Texas

    Science.gov (United States)

    2013-02-07

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Texas AGENCY: United States... that the State of Texas is revising its approved Public Water System Supervision Program. Texas...

  9. 76 FR 7845 - Public Water System Supervision Program Revision for the State of Utah

    Science.gov (United States)

    2011-02-11

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Utah AGENCY: Environmental... the State of Utah has revised its Public Water System Supervision (PWSS) Program by adopting...

  10. 76 FR 45794 - Public Water System Supervision Program Revision for the State of Louisiana

    Science.gov (United States)

    2011-08-01

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Louisiana AGENCY: United States... the State of Louisiana is revising its approved Public Water System Supervision Program, by...

  11. 77 FR 35676 - Public Water System Supervision Program Revision for the State of Texas

    Science.gov (United States)

    2012-06-14

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of Texas AGENCY: United States... that the State of Texas is revising its approved Public Water System Supervision Program. Texas...

  12. 75 FR 69662 - Public Water System Supervision Program Revision for the State of Colorado

    Science.gov (United States)

    2010-11-15

    ... [Federal Register Volume 75, Number 219 (Monday, November 15, 2010)] [Notices] [Page 69662] [FR Doc No: 2010-28497] ENVIRONMENTAL PROTECTION AGENCY [FRL-9225-3] Public Water System Supervision... Public Water System Supervision (PWSS) Primacy Program by adopting Federal regulations for the Lead...

  13. 75 FR 69435 - Public Water System Supervision Program Revision for the State of North Dakota

    Science.gov (United States)

    2010-11-12

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Revision for the State of North Dakota AGENCY... hereby given that the State of North Dakota has revised its Public Water System Supervision...

  14. Clinical learning environment and supervision of international nursing students: A cross-sectional study.

    Science.gov (United States)

    Mikkonen, Kristina; Elo, Satu; Miettunen, Jouko; Saarikoski, Mikko; Kääriäinen, Maria

    2017-05-01

    Previously, it has been shown that the clinical learning environment causes challenges for international nursing students, but there is a lack of empirical evidence relating to the background factors explaining and influencing the outcomes. To describe international and national students' perceptions of their clinical learning environment and supervision, and explain the related background factors. An explorative cross-sectional design was used in a study conducted in eight universities of applied sciences in Finland during September 2015-May 2016. All nursing students studying English language degree programs were invited to answer a self-administered questionnaire based on both the clinical learning environment, supervision and nurse teacher scale and Cultural and Linguistic Diversity scale with additional background questions. Participants (n=329) included international (n=231) and Finnish (n=98) nursing students. Binary logistic regression was used to identify background factors relating to the clinical learning environment and supervision. International students at a beginner level in Finnish perceived the pedagogical atmosphere as worse than native speakers. In comparison to native speakers, these international students generally needed greater support from the nurse teacher at their university. Students at an intermediate level in Finnish reported two times fewer negative encounters in cultural diversity at their clinical placement than the beginners. To facilitate a successful learning experience, international nursing students require a sufficient level of competence in the native language when conducting clinical placements. Educational interventions in language education are required to test causal effects on students' success in the clinical learning environment. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. Semi-supervised learning for detecting text-lines in noisy document images

    Science.gov (United States)

    Liu, Zongyi; Zhou, Hanning

    2010-01-01

    Document layout analysis is a key step in document image understanding with wide applications in document digitization and reformatting. Identifying correct layout from noisy scanned images is especially challenging. In this paper, we introduce a semi-supervised learning framework to detect text-lines from noisy document images. Our framework consists of three steps. The first step is the initial segmentation that extracts text-lines and images using simple morphological operations. The second step is a grouping-based layout analysis that identifies text-lines, image zones, column separator and vertical border noise. It is able to efficiently remove the vertical border noises from multi-column pages. The third step is an online classifier that is trained with the high confidence line detection results from Step Two, and filters out noise from low confidence lines. The classifier effectively removes speckle noises embedded inside the content zones. We compare the performance of our algorithm to the state-of-the-art work in the field on the UW-III database. We choose the results reported by the Image Understanding Pattern Recognition Research (IUPR) and Scansoft Omnipage SDK 15.5. We evaluate the performances at both the page frame level and the text-line level. The result shows that our system has much lower false-alarm rate, while maintains similar content detection rate. In addition, we also show that our online training model generalizes better than algorithms depending on offline training.

  16. An Efficient Refining Of Cbir through Supervised Learning Approach

    Directory of Open Access Journals (Sweden)

    R. Bindhu

    2014-03-01

    Full Text Available CBIR(Content Based Image Retrieval technique has its own importance in medical field to store, manage, and retrieve data images based on user query. Here we propose a framework based on design and development of a multi-tier Content-Based Image Retrieval system for MRI brain images utilizing a reference database that contains both normal and tumor brain images under the category which it falls(i.e. normal, benign or malignant tumor ,with their identity number, which are mostly difficult to classify and discriminate. The features of the image are extracted using gray level co-occurrence matrix (GLCM technique and a subset of features is selected using Differential Evolution Feature Selection (DEFS technique. The selected features are sent through the classifier (SVM. Searching is done by means of matching the image features such as texture, shape, or different combinations of them. SVM (Support Vector machine classifier followed by KNN (K-nearest neighbor for CBIR using texture and shape feature.This CBIR system enables both multi-image query and slide-level image retrieval in order to protect semantic consistency among the retrieved images. The performance of the system is tested on the dataset by several MRI brain images of various categories, and the features of the image in the dataset matching more accurately of the features of query images are listed as retrieved images with their identification number for better accuracy.

  17. Conducting Supervised Experiential Learning/Field Experiences for Students' Development and Career Reinforcement.

    Science.gov (United States)

    Leventhal, Jerome I.

    A major problem in the educational system of the United States is that a great number of students and graduates lack a career objective, and, therefore, many workers are unhappy. Offering a variety of supervised field experiences, paid or unpaid, in which students see workers in their occupations will help students identify career choices.…

  18. Automated labeling of cancer textures in larynx histopathology slides using quasi-supervised learning.

    Science.gov (United States)

    Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

    2014-12-01

    To evaluate the performance of a quasi-supervised statistical learning algorithm, operating on datasets having normal and neoplastic tissues, to identify larynx squamous cell carcinomas. Furthermore, cancer texture separability measures against normal tissues are to be developed and compared either for colorectal or larynx tissues. Light microscopic digital images from histopathological sections were obtained from laryngectomy materials including squamous cell carcinoma and nonneoplastic regions. The texture features were calculated by using co-occurrence matrices and local histograms. The texture features were input to the quasi-supervised learning algorithm. Larynx regions containing squamous cell carcinomas were accurately identified, having false and true positive rates up to 21% and 87%, respectively. Larynx squamous cell carcinoma versus normal tissue texture separability measures were higher than colorectal adenocarcinoma versus normal textures for the colorectal database. Furthermore, the resultant labeling performances for all larynx datasets are higher than or equal to that of colorectal datasets. The results in larynx datasets, in comparison with the former colorectal study, suggested that quasi-supervised texture classification is to be a helpful method in histopathological image classification and analysis.

  19. Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

    Science.gov (United States)

    Bajracharya, Max; Howard, Andrew B.; Matthies, Larry H.

    2009-01-01

    Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically.

  20. Neural Gen Feature Selection for Supervised Learning Classifier

    Directory of Open Access Journals (Sweden)

    Mohammed Hasan Abdulameer

    2014-04-01

    Full Text Available Face recognition has recently received significant attention, especially during the past few years. Many face recognition techniques were developed such as PSO-SVM and LDA-SVM However, inefficient features in the face recognition may lead to inadequate in the recognition results. Hence, a new face recognition system based on Genetic Algorithm and FFBNN technique is proposed. Our proposed face recognition system initially performs the feature extraction and these optimal features are promoted to the recognition process. In the feature extraction, the optimal features are extracted from the face image database by Genetic Algorithm (GA with FFBNN and the computed optimal features are given to the FFBNN technique to carry out the training and testing process. The optimal features from the feature database are fed to the FFBNN for accomplishing the training process. The well trained FFBNN with the optimal features provide the recognition result. The optimal features in FFBNN by GA efficiently perform the face recognition process. The human face dataset called YALE is utilized to analyze the performance of our proposed GA-FFNN technique and also this GA-FFBNN is compared with standard SVM and PSO-SVM techniques.

  1. Musical Instrument Classification Based on Nonlinear Recurrence Analysis and Supervised Learning

    Directory of Open Access Journals (Sweden)

    R.Rui

    2013-04-01

    Full Text Available In this paper, the phase space reconstruction of time series produced by different instruments is discussed based on the nonlinear dynamic theory. The dense ratio, a novel quantitative recurrence parameter, is proposed to describe the difference of wind instruments, stringed instruments and keyboard instruments in the phase space by analyzing the recursive property of every instrument. Furthermore, a novel supervised learning algorithm for automatic classification of individual musical instrument signals is addressed deriving from the idea of supervised non-negative matrix factorization (NMF algorithm. In our approach, the orthogonal basis matrix could be obtained without updating the matrix iteratively, which NMF is unable to do. The experimental results indicate that the accuracy of the proposed method is improved by 3% comparing with the conventional features in the individual instrument classification.

  2. Semi-supervised Learning for Classification of Polarimetric SAR Images Based on SVM-Wishart

    Directory of Open Access Journals (Sweden)

    Hua Wen-qiang

    2015-02-01

    Full Text Available In this study, we propose a new semi-supervised classification method for Polarimetric SAR (PolSAR images, aiming at handling the issue that the number of train set is small. First, considering the scattering characters of PolSAR data, this method extracts multiple scattering features using target decomposition approach. Then, a semi-supervised learning model is established based on a co-training framework and Support Vector Machine (SVM. Both labeled and unlabeled data are utilized in this model to obtain high classification accuracy. Third, a recovery scheme based on the Wishart classifier is proposed to improve the classification performance. From the experiments conducted in this study, it is evident that the proposed method performs more effectively compared with other traditional methods when the number of train set is small.

  3. Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning.

    Science.gov (United States)

    Vamsikrishna, K M; Dogra, Debi Prosad; Desarkar, Maunendra Sankar

    2016-05-01

    Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts.

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

    Science.gov (United States)

    Wu, Lin; Wang, Yang; Pan, Shirui

    2016-10-04

    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.

  5. Enhanced low-rank representation via sparse manifold adaption for semi-supervised learning.

    Science.gov (United States)

    Peng, Yong; Lu, Bao-Liang; Wang, Suhang

    2015-05-01

    Constructing an informative and discriminative graph plays an important role in various pattern recognition tasks such as clustering and classification. Among the existing graph-based learning models, low-rank representation (LRR) is a very competitive one, which has been extensively employed in spectral clustering and semi-supervised learning (SSL). In SSL, the graph is composed of both labeled and unlabeled samples, where the edge weights are calculated based on the LRR coefficients. However, most of existing LRR related approaches fail to consider the geometrical structure of data, which has been shown beneficial for discriminative tasks. In this paper, we propose an enhanced LRR via sparse manifold adaption, termed manifold low-rank representation (MLRR), to learn low-rank data representation. MLRR can explicitly take the data local manifold structure into consideration, which can be identified by the geometric sparsity idea; specifically, the local tangent space of each data point was sought by solving a sparse representation objective. Therefore, the graph to depict the relationship of data points can be built once the manifold information is obtained. We incorporate a regularizer into LRR to make the learned coefficients preserve the geometric constraints revealed in the data space. As a result, MLRR combines both the global information emphasized by low-rank property and the local information emphasized by the identified manifold structure. Extensive experimental results on semi-supervised classification tasks demonstrate that MLRR is an excellent method in comparison with several state-of-the-art graph construction approaches.

  6. SU-E-J-107: Supervised Learning Model of Aligned Collagen for Human Breast Carcinoma Prognosis

    Energy Technology Data Exchange (ETDEWEB)

    Bredfeldt, J; Liu, Y; Conklin, M; Keely, P; Eliceiri, K; Mackie, T [University of Wisconsin, Madison, WI (United States)

    2014-06-01

    Purpose: Our goal is to develop and apply a set of optical and computational tools to enable large-scale investigations of the interaction between collagen and tumor cells. Methods: We have built a novel imaging system for automating the capture of whole-slide second harmonic generation (SHG) images of collagen in registry with bright field (BF) images of hematoxylin and eosin stained tissue. To analyze our images, we have integrated a suite of supervised learning tools that semi-automatically model and score collagen interactions with tumor cells via a variety of metrics, a method we call Electronic Tumor Associated Collagen Signatures (eTACS). This group of tools first segments regions of epithelial cells and collagen fibers from BF and SHG images respectively. We then associate fibers with groups of epithelial cells and finally compute features based on the angle of interaction and density of the collagen surrounding the epithelial cell clusters. These features are then processed with a support vector machine to separate cancer patients into high and low risk groups. Results: We validated our model by showing that eTACS produces classifications that have statistically significant correlation with manual classifications. In addition, our system generated classification scores that accurately predicted breast cancer patient survival in a cohort of 196 patients. Feature rank analysis revealed that TACS positive fibers are more well aligned with each other, generally lower density, and terminate within or near groups of epithelial cells. Conclusion: We are working to apply our model to predict survival in larger cohorts of breast cancer patients with a diversity of breast cancer types, predict response to treatments such as COX2 inhibitors, and to study collagen architecture changes in other cancer types. In the future, our system may be used to provide metastatic potential information to cancer patients to augment existing clinical assays.

  7. 78 FR 18336 - Public Water System Supervision Program Approval for the State of Michigan

    Science.gov (United States)

    2013-03-26

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Approval for the State of Michigan AGENCY: Environmental... has tentatively approved five revisions to the State of Michigan's public water system...

  8. 77 FR 8865 - Public Water System Supervision Program Approval for the State of Illinois; Tentative Approval

    Science.gov (United States)

    2012-02-15

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Approval for the State of Illinois; Tentative Approval... State of Illinois submitted a primacy application for its approved Public Water System...

  9. Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning

    KAUST Repository

    Mousa, Mustafa

    2014-04-01

    This article describes a machine learning approach to water level estimation in a dual ultrasonic/passive infrared urban flood sensor system. We first show that an ultrasonic rangefinder alone is unable to accurately measure the level of water on a road due to thermal effects. Using additional passive infrared sensors, we show that ground temperature and local sensor temperature measurements are sufficient to correct the rangefinder readings and improve the flood detection performance. Since floods occur very rarely, we use a supervised learning approach to estimate the correction to the ultrasonic rangefinder caused by temperature fluctuations. Preliminary data shows that water level can be estimated with an absolute error of less than 2 cm. © 2014 IEEE.

  10. DL-ReSuMe: A Delay Learning-Based Remote Supervised Method for Spiking Neurons.

    Science.gov (United States)

    Taherkhani, Aboozar; Belatreche, Ammar; Li, Yuhua; Maguire, Liam P

    2015-12-01

    Recent research has shown the potential capability of spiking neural networks (SNNs) to model complex information processing in the brain. There is biological evidence to prove the use of the precise timing of spikes for information coding. However, the exact learning mechanism in which the neuron is trained to fire at precise times remains an open problem. The majority of the existing learning methods for SNNs are based on weight adjustment. However, there is also biological evidence that the synaptic delay is not constant. In this paper, a learning method for spiking neurons, called delay learning remote supervised method (DL-ReSuMe), is proposed to merge the delay shift approach and ReSuMe-based weight adjustment to enhance the learning performance. DL-ReSuMe uses more biologically plausible properties, such as delay learning, and needs less weight adjustment than ReSuMe. Simulation results have shown that the proposed DL-ReSuMe approach achieves learning accuracy and learning speed improvements compared with ReSuMe.

  11. The researches of GPS/GSM supervise and control system of vehicle

    Institute of Scientific and Technical Information of China (English)

    Ji,Changpeng; Bao,Jian; Liu,Jianhui

    2003-01-01

    the treatise introduced the GPS supervision and con-trol system, analyzed the features of the VHF/UHF single chan-nel call web, composite groups mobile communication web whichare regularity used in current supervision and control system ofvehicle, and several traffics that the GSM provided, therebybrought forward the project of utilizing GSM-short message toaccomplish the GPS supervision and control system of vehicle ofthe ore deposit of yuanbaoshan, designed the system structureand the method of organizing web, put emphasis on how theGSM-short message technology was used in the supervise andcontrol system,including system constitution, channel using mode,calling mode, synchrony' s mode, data transmitted mode, etc.

  12. Test-retest reliability of the Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale.

    Science.gov (United States)

    Gustafsson, Margareta; Blomberg, Karin; Holmefur, Marie

    2015-07-01

    The Clinical Learning Environment, Supervision and Nurse Teacher (CLES + T) scale evaluates the student nurses' perception of the learning environment and supervision within the clinical placement. It has never been tested in a replication study. The aim of the present study was to evaluate the test-retest reliability of the CLES + T scale. The CLES + T scale was administered twice to a group of 42 student nurses, with a one-week interval. Test-retest reliability was determined by calculations of Intraclass Correlation Coefficients (ICCs) and weighted Kappa coefficients. Standard Error of Measurements (SEM) and Smallest Detectable Difference (SDD) determined the precision of individual scores. Bland-Altman plots were created for analyses of systematic differences between the test occasions. The results of the study showed that the stability over time was good to excellent (ICC 0.88-0.96) in the sub-dimensions "Supervisory relationship", "Pedagogical atmosphere on the ward" and "Role of the nurse teacher". Measurements of "Premises of nursing on the ward" and "Leadership style of the manager" had lower but still acceptable stability (ICC 0.70-0.75). No systematic differences occurred between the test occasions. This study supports the usefulness of the CLES + T scale as a reliable measure of the student nurses' perception of the learning environment within the clinical placement at a hospital.

  13. Identification of Village Building via Google Earth Images and Supervised Machine Learning Methods

    Directory of Open Access Journals (Sweden)

    Zhiling Guo

    2016-03-01

    Full Text Available In this study, a method based on supervised machine learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine learning methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine learning methods for classification: adaptive boosting (AdaBoost and convolutional neural networks (CNN. To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.

  14. Principles and models of a co-operative systems of a supervision aid; SCAS: principes et modeles d`un systeme cooperatif d`assistance a la supervision

    Energy Technology Data Exchange (ETDEWEB)

    Penalva, J.M. [CEA Centre d`Etudes de la Vallee du Rhone, 30 - Marcoule (France). Dept. d`Exploitation du Retraitement et de Demantelement; Cases, E. [CEA Centre d`Etudes de la Vallee du Rhone, 30 - Marcoule (France). Dept. d`Exploitation du Retraitement et de Demantelement]|[Paris-6 Univ., 75 (France); Brezillon, P. [Paris-6 Univ., 75 (France); Minault, S.

    1994-12-31

    This paper presents the functioning principles and the necessary models for a cooperative system of supervision aid (SCAS) used for a high-automated workshop. A meta-system of supervision is made up of the operator and the SCAS. The SCAS can operate under 2 different modes: wakefulness and cooperation. On the first one the behaviours of the process and the operator is observed and analysed. On the second one, it helps to solve the problems occurred by the operator. (TEC). 3 refs.

  15. The Transnational Comparison of Systemic Risk Supervision and Its Learning Experience%系统性风险监管的跨国比较与经验借鉴

    Institute of Scientific and Technical Information of China (English)

    麦强盛

    2012-01-01

    American subprime mortgage crisis gradually evolved into the global financial crisis. The systemic risk regulation lessons in the United States was bitter, whereby, the defect in the regulation theory and practice was one of important reasons. By contrast, the successful experience in Chinese systemic risk regulation is that the financial system enhances the ability to withstand risks by implementing macro prudential supervision;ensures the stable operation of the financial system making use of inverse cycle strengths the government intervention in time for avoiding crisis philosophy and principles with Chinese characteristics, sumsup promote the healthy development of the national economy. regulation; warns in time to take early measures; infection. In short, China must adhere to regulation the supervisory experience conscientiously to better%美国次贷危机,逐步演变为席卷全球的金融危机。就其中的系统性风险监管而言,美国系统性风险监管的教训惨痛,其监管理论和做法的缺陷是重要原因。中国系统性风险监管的成功经验在于,力行宏观审慎监管,使金融体系增强风险抵御能力;实施逆周期调控,保证金融体系稳健运行;适时风险警示,提早采取应对措施;政府及时干预,避免危机传染。中国必须坚持有中国特色的监管理念与原则,认真总结系统性风险监管经验,更好地促进国民经济健康发展。

  16. A Situational Analysis of Educational Supervision in the Turkish Educational System

    Directory of Open Access Journals (Sweden)

    Tuncay Yavuz Ozdemir

    2015-11-01

    Full Text Available The purpose of this study is to conduct a situational analysis on the educational supervisions carried out within the Turkish educational system. Content analysis was used in this study, which is one of the qualitative research methods. An interview form was prepared by the researchers in accordance with the study purpose and expert opinion sought to ensure content and face validity. Findings of the study show that; supervision is necessary for an increase in educational quality, sustainability of educational worker development, determination and elimination of possible deficiencies, to ensure not falling behind developments in the educational system, and to collaborate within the school. It has been determined that the agents who carry out the supervision should have professional competencies, should be able to enter into effective communication, should be able to spare enough time for supervision and follow the principles of equality. In addition, the importance of effective and sufficient guidance and parental participation was highlighted. In a change to the Turkish educational system in 2014, the authority and responsibility of supervision was assigned to the school principals. Because it would decrease the psychological pressure that supervision imposes on educational workers, and enable a positive atmosphere for communication over a prolonged period, this change was believed to be beneficial overall. According to another standpoint, because school principals lack professional competencies regarding educational supervision and do not follow the principles of equality, this change instead was believed to be unfavorable.

  17. Multi-Modal Curriculum Learning for Semi-Supervised Image Classification.

    Science.gov (United States)

    Gong, Chen; Tao, Dacheng; Maybank, Stephen J; Liu, Wei; Kang, Guoliang; Yang, Jie

    2016-07-01

    Semi-supervised image classification aims to classify a large quantity of unlabeled images by typically harnessing scarce labeled images. Existing semi-supervised methods often suffer from inadequate classification accuracy when encountering difficult yet critical images, such as outliers, because they treat all unlabeled images equally and conduct classifications in an imperfectly ordered sequence. In this paper, we employ the curriculum learning methodology by investigating the difficulty of classifying every unlabeled image. The reliability and the discriminability of these unlabeled images are particularly investigated for evaluating their difficulty. As a result, an optimized image sequence is generated during the iterative propagations, and the unlabeled images are logically classified from simple to difficult. Furthermore, since images are usually characterized by multiple visual feature descriptors, we associate each kind of features with a teacher, and design a multi-modal curriculum learning (MMCL) strategy to integrate the information from different feature modalities. In each propagation, each teacher analyzes the difficulties of the currently unlabeled images from its own modality viewpoint. A consensus is subsequently reached among all the teachers, determining the currently simplest images (i.e., a curriculum), which are to be reliably classified by the multi-modal learner. This well-organized propagation process leveraging multiple teachers and one learner enables our MMCL to outperform five state-of-the-art methods on eight popular image data sets.

  18. A supervised machine learning estimator for the non-linear matter power spectrum - SEMPS

    CERN Document Server

    Mohammed, Irshad

    2015-01-01

    In this article, we argue that models based on machine learning (ML) can be very effective in estimating the non-linear matter power spectrum ($P(k)$). We employ the prediction ability of the supervised ML algorithms to build an estimator for the $P(k)$. The estimator is trained on a set of cosmological models, and redshifts for which the $P(k)$ is known, and it learns to predict $P(k)$ for any other set. We review three ML algorithms -- Random Forest, Gradient Boosting Machines, and K-Nearest Neighbours -- and investigate their prime parameters to optimize the prediction accuracy of the estimator. We also compute an optimal size of the training set, which is realistic enough, and still yields high accuracy. We find that, employing the optimal values of the internal parameters, a set of $50-100$ cosmological models is enough to train the estimator that can predict the $P(k)$ for a wide range of cosmological models, and redshifts. Using this configuration, we build a blackbox -- Supervised Estimator for Matter...

  19. AcceleRater: a web application for supervised learning of behavioral modes from acceleration measurements.

    Science.gov (United States)

    Resheff, Yehezkel S; Rotics, Shay; Harel, Roi; Spiegel, Orr; Nathan, Ran

    2014-01-01

    The study of animal movement is experiencing rapid progress in recent years, forcefully driven by technological advancement. Biologgers with Acceleration (ACC) recordings are becoming increasingly popular in the fields of animal behavior and movement ecology, for estimating energy expenditure and identifying behavior, with prospects for other potential uses as well. Supervised learning of behavioral modes from acceleration data has shown promising results in many species, and for a diverse range of behaviors. However, broad implementation of this technique in movement ecology research has been limited due to technical difficulties and complicated analysis, deterring many practitioners from applying this approach. This highlights the need to develop a broadly applicable tool for classifying behavior from acceleration data. Here we present a free-access python-based web application called AcceleRater, for rapidly training, visualizing and using models for supervised learning of behavioral modes from ACC measurements. We introduce AcceleRater, and illustrate its successful application for classifying vulture behavioral modes from acceleration data obtained from free-ranging vultures. The seven models offered in the AcceleRater application achieved overall accuracy of between 77.68% (Decision Tree) and 84.84% (Artificial Neural Network), with a mean overall accuracy of 81.51% and standard deviation of 3.95%. Notably, variation in performance was larger between behavioral modes than between models. AcceleRater provides the means to identify animal behavior, offering a user-friendly tool for ACC-based behavioral annotation, which will be dynamically upgraded and maintained.

  20. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based...

  1. Semi-supervised eigenvectors for large-scale locally-biased learning

    DEFF Research Database (Denmark)

    Hansen, Toke Jansen; Mahoney, Michael W.

    2014-01-01

    -based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. In this paper, we address this issue by providing......In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks nearby that prespecified target region. For example, one might...... be interested in the clustering structure of a data graph near a prespecified seed set of nodes, or one might be interested in finding partitions in an image that are near a prespecified ground truth set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector...

  2. The Consolidation on Banking Supervision in the Context of a Pan European Banking System

    Directory of Open Access Journals (Sweden)

    Teodora Barbu

    2007-03-01

    Full Text Available The diversity of national banking systems in the European banking system and the absence of consolidated supervision creates the premises for a series of interrogations whose essence is the same: Is it possible to discuss about a Pan European Banking System? The starting point in answering this question was the efforts to create a single banking market, which took place in 1973-1999, and the impact of integration on the European Banking Industry. Among the most representative aspects, it must be emphasized the necessity of consolidating banking supervision at an European level, considering that the International Banking Community studies the problematic of banking regulations at a global level. The two dimensions of the prudential and European bank supervision device – the geographic and the institutional – demand the creation of a structural reform in order to ensure the functioning of a Pan European system of banking supervision and regulations. The considerations on the Consolidation of European Banking Supervision draws into discussion the Financial Supervision Authority which has generalized as an applicable model in numerous European countries and has been mentioned as an alternative of Pan European banking supervision. In the process of the integration of the banking sector, the Basel II Accord represents an opportunity in reaching a convergence of national regulations and practices in matters of risk management, considering that these actions are in line with the preoccupations of realizing a Pan European banking system. Thus, the creation of Pan European banking system involves actions in more directions: legal, institutional, operational meant to ensure the consolidation of banking supervision.

  3. Learning a Markov Logic network for supervised gene regulatory network inference.

    Science.gov (United States)

    Brouard, Céline; Vrain, Christel; Dubois, Julie; Castel, David; Debily, Marie-Anne; d'Alché-Buc, Florence

    2013-09-12

    Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. We propose to learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network. Finally our main result concerns a third test that measures the ability of the method to predict regulations with a new set of genes. As expected, MLN, when provided with only numerical discretized gene expression data, does not perform as well as a pairwise SVM in terms of AUPR. However, when a more complete description of gene properties is provided by heterogeneous sources, MLN achieves the same performance as a black-box model such as a

  4. 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.

  5. Semi-supervised analysis of human brain tumours from partially labeled MRS information, using manifold learning models.

    Science.gov (United States)

    Cruz-Barbosa, Raúl; Vellido, Alfredo

    2011-02-01

    Medical diagnosis can often be understood as a classification problem. In oncology, this typically involves differentiating between tumour types and grades, or some type of discrete outcome prediction. From the viewpoint of computer-based medical decision support, this classification requires the availability of accurate diagnoses of past cases as training target examples. The availability of such labeled databases is scarce in most areas of oncology, and especially so in neuro-oncology. In such context, semi-supervised learning oriented towards classification can be a sensible data modeling choice. In this study, semi-supervised variants of Generative Topographic Mapping, a model of the manifold learning family, are applied to two neuro-oncology problems: the diagnostic discrimination between different brain tumour pathologies, and the prediction of outcomes for a specific type of aggressive brain tumours. Their performance compared favorably with those of the alternative Laplacian Eigenmaps and Semi-Supervised SVM for Manifold Learning models in most of the experiments.

  6. Clinical supervision.

    Science.gov (United States)

    Goorapah, D

    1997-05-01

    The introduction of clinical supervision to a wider sphere of nursing is being considered from a professional and organizational point of view. Positive views are being expressed about adopting this concept, although there are indications to suggest that there are also strong reservations. This paper examines the potential for its success amidst the scepticism that exists. One important question raised is whether clinical supervision will replace or run alongside other support systems.

  7. Hearing in a shoe-box : binaural source position and wall absorption estimation using virtually supervised learning

    OpenAIRE

    Kataria, Saurabh; Gaultier, Clément; Deleforge, Antoine

    2016-01-01

    This paper introduces a new framework for supervised sound source localization referred to as virtually-supervised learning. An acoustic shoe-box room simulator is used to generate a large number of binaural single-source audio scenes. These scenes are used to build a dataset of spatial binaural features annotated with acoustic properties such as the 3D source position and the walls' absorption coefficients. A probabilis-tic high-to low-dimensional regression framework is used to learn a mapp...

  8. A new semi-supervised classification strategy combining active learning and spectral unmixing of hyperspectral data

    Science.gov (United States)

    Sun, Yanli; Zhang, Xia; Plaza, Antonio; Li, Jun; Dópido, Inmaculada; Liu, Yi

    2016-10-01

    Hyperspectral remote sensing allows for the detailed analysis of the surface of the Earth by providing high-dimensional images with hundreds of spectral bands. Hyperspectral image classification plays a significant role in hyperspectral image analysis and has been a very active research area in the last few years. In the context of hyperspectral image classification, supervised techniques (which have achieved wide acceptance) must address a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. Semi-supervised learning offers an effective solution that can take advantage of both unlabeled and a small amount of labeled samples. Spectral unmixing is another widely used technique in hyperspectral image analysis, developed to retrieve pure spectral components and determine their abundance fractions in mixed pixels. In this work, we propose a method to perform semi-supervised hyperspectral image classification by combining the information retrieved with spectral unmixing and classification. Two kinds of samples that are highly mixed in nature are automatically selected, aiming at finding the most informative unlabeled samples. One kind is given by the samples minimizing the distance between the first two most probable classes by calculating the difference between the two highest abundances. Another kind is given by the samples minimizing the distance between the most probable class and the least probable class, obtained by calculating the difference between the highest and lowest abundances. The effectiveness of the proposed method is evaluated using a real hyperspectral data set collected by the airborne visible infrared imaging spectrometer (AVIRIS) over the Indian Pines region in Northwestern Indiana. In the

  9. Literature mining of protein-residue associations with graph rules learned through distant supervision

    Directory of Open Access Journals (Sweden)

    Ravikumar KE

    2012-10-01

    Full Text Available Abstract Background We propose a method for automatic extraction of protein-specific residue mentions from the biomedical literature. The method searches text for mentions of amino acids at specific sequence positions and attempts to correctly associate each mention with a protein also named in the text. The methods presented in this work will enable improved protein functional site extraction from articles, ultimately supporting protein function prediction. Our method made use of linguistic patterns for identifying the amino acid residue mentions in text. Further, we applied an automated graph-based method to learn syntactic patterns corresponding to protein-residue pairs mentioned in the text. We finally present an approach to automated construction of relevant training and test data using the distant supervision model. Results The performance of the method was assessed by extracting protein-residue relations from a new automatically generated test set of sentences containing high confidence examples found using distant supervision. It achieved a F-measure of 0.84 on automatically created silver corpus and 0.79 on a manually annotated gold data set for this task, outperforming previous methods. Conclusions The primary contributions of this work are to (1 demonstrate the effectiveness of distant supervision for automatic creation of training data for protein-residue relation extraction, substantially reducing the effort and time involved in manual annotation of a data set and (2 show that the graph-based relation extraction approach we used generalizes well to the problem of protein-residue association extraction. This work paves the way towards effective extraction of protein functional residues from the literature.

  10. Supervised learning classification models for prediction of plant virus encoded RNA silencing suppressors.

    Directory of Open Access Journals (Sweden)

    Zeenia Jagga

    Full Text Available Viral encoded RNA silencing suppressor proteins interfere with the host RNA silencing machinery, facilitating viral infection by evading host immunity. In plant hosts, the viral proteins have several basic science implications and biotechnology applications. However in silico identification of these proteins is limited by their high sequence diversity. In this study we developed supervised learning based classification models for plant viral RNA silencing suppressor proteins in plant viruses. We developed four classifiers based on supervised learning algorithms: J48, Random Forest, LibSVM and Naïve Bayes algorithms, with enriched model learning by correlation based feature selection. Structural and physicochemical features calculated for experimentally verified primary protein sequences were used to train the classifiers. The training features include amino acid composition; auto correlation coefficients; composition, transition, and distribution of various physicochemical properties; and pseudo amino acid composition. Performance analysis of predictive models based on 10 fold cross-validation and independent data testing revealed that the Random Forest based model was the best and achieved 86.11% overall accuracy and 86.22% balanced accuracy with a remarkably high area under the Receivers Operating Characteristic curve of 0.95 to predict viral RNA silencing suppressor proteins. The prediction models for plant viral RNA silencing suppressors can potentially aid identification of novel viral RNA silencing suppressors, which will provide valuable insights into the mechanism of RNA silencing and could be further explored as potential targets for designing novel antiviral therapeutics. Also, the key subset of identified optimal features may help in determining compositional patterns in the viral proteins which are important determinants for RNA silencing suppressor activities. The best prediction model developed in the study is available as a

  11. Networked control and supervision system based on LonWorks fieldbus and Intranet/Internet

    Institute of Scientific and Technical Information of China (English)

    WU Min; ZHAO Hong; LIU Guo-ping; SHE Jin-hua

    2007-01-01

    A networked control and supervision system (NCSS) based on LonWorks fieldbus and Intranet/Internet was designed,which was composed of the universal intelligent control nodes (ICNs), the visual control and supervision configuration platforms (VCCP and VSCP) and an Intranet/Internet-based remote supervision platform (RSP). The ICNs were connected to field devices,such as sensors, actuators and controllers. The VCCP and VSCP were implemented by means of a graphical programming environment and network management so as to simplify the tasks of programming and maintaining the ICNs. The RSP was employed to perform the remote supervision function, which was based on a three-layer browser/server(B/S) structure mode. The validity of the NCSS was demonstrated by laboratory experiments.

  12. Supervised practice in occupational therapy in a psychosocial care center: Challenges for the assistance and the teaching and learning process

    Directory of Open Access Journals (Sweden)

    Milton Carlos Mariotti

    2014-09-01

    Full Text Available The psychiatric reform in Brazil has replaced the hospital-centered model by the reintegration of users to their respective communities. The Center of Psychosocial Care (CAPS has been the main equipment in that scope. Objectives: To report the development of Supervised Practice in Occupational Therapy in a CAPS II unit in Curitiba, Parana state, Brazil. Methods: This is an experience report. It features the training field and describes the stages of the teaching and learning process which involved institutional observation, reporting and intervention proposal, collecting data about the users’ profile and attendances. The work focused the non-intensive users because they are close to hospital discharge. Results: We found that users of the non-intensive system, rather than crave the discharge, would like to return to the semi-intensive or intensive systems, aiming to regain sickness and transportation benefits, which are lost as users make progress. This fact denotes great contradictions in the system. We also attended intensive and semi-intensive systems users. Conclusions: The students’ learning included aspects such as direct contact with the institutional reality; knowledge about the health system, its limitations and contradictions; approach to users, their families, realities, socioeconomic conditions, desires, aspirations, or lack thereof; difficulties in engaging in meaningful occupations in their territories, limitations, and social stigma; working with frustrations, reflecting about ways to change the reality; in addition to expanded clinical practice, participating in the discussions and formulation of public policies on mental healthcare and social control.

  13. Supervised neural network modeling: an empirical investigation into learning from imbalanced data with labeling errors.

    Science.gov (United States)

    Khoshgoftaar, Taghi M; Van Hulse, Jason; Napolitano, Amri

    2010-05-01

    Neural network algorithms such as multilayer perceptrons (MLPs) and radial basis function networks (RBFNets) have been used to construct learners which exhibit strong predictive performance. Two data related issues that can have a detrimental impact on supervised learning initiatives are class imbalance and labeling errors (or class noise). Imbalanced data can make it more difficult for the neural network learning algorithms to distinguish between examples of the various classes, and class noise can lead to the formulation of incorrect hypotheses. Both class imbalance and labeling errors are pervasive problems encountered in a wide variety of application domains. Many studies have been performed to investigate these problems in isolation, but few have focused on their combined effects. This study presents a comprehensive empirical investigation using neural network algorithms to learn from imbalanced data with labeling errors. In particular, the first component of our study investigates the impact of class noise and class imbalance on two common neural network learning algorithms, while the second component considers the ability of data sampling (which is commonly used to address the issue of class imbalance) to improve their performances. Our results, for which over two million models were trained and evaluated, show that conclusions drawn using the more commonly studied C4.5 classifier may not apply when using neural networks.

  14. SimNest: Social Media Nested Epidemic Simulation via Online Semi-supervised Deep Learning.

    Science.gov (United States)

    Zhao, Liang; Chen, Jiangzhuo; Chen, Feng; Wang, Wei; Lu, Chang-Tien; Ramakrishnan, Naren

    2015-11-01

    Infectious disease epidemics such as influenza and Ebola pose a serious threat to global public health. It is crucial to characterize the disease and the evolution of the ongoing epidemic efficiently and accurately. Computational epidemiology can model the disease progress and underlying contact network, but suffers from the lack of real-time and fine-grained surveillance data. Social media, on the other hand, provides timely and detailed disease surveillance, but is insensible to the underlying contact network and disease model. This paper proposes a novel semi-supervised deep learning framework that integrates the strengths of computational epidemiology and social media mining techniques. Specifically, this framework learns the social media users' health states and intervention actions in real time, which are regularized by the underlying disease model and contact network. Conversely, the learned knowledge from social media can be fed into computational epidemic model to improve the efficiency and accuracy of disease diffusion modeling. We propose an online optimization algorithm to substantialize the above interactive learning process iteratively to achieve a consistent stage of the integration. The extensive experimental results demonstrated that our approach can effectively characterize the spatio-temporal disease diffusion, outperforming competing methods by a substantial margin on multiple metrics.

  15. Semi-supervised learning of causal relations in biomedical scientific discourse

    Science.gov (United States)

    2014-01-01

    Background The increasing number of daily published articles in the biomedical domain has become too large for humans to handle on their own. As a result, bio-text mining technologies have been developed to improve their workload by automatically analysing the text and extracting important knowledge. Specific bio-entities, bio-events between these and facts can now be recognised with sufficient accuracy and are widely used by biomedical researchers. However, understanding how the extracted facts are connected in text is an extremely difficult task, which cannot be easily tackled by machinery. Results In this article, we describe our method to recognise causal triggers and their arguments in biomedical scientific discourse. We introduce new features and show that a self-learning approach improves the performance obtained by supervised machine learners to 83.47% for causal triggers. Furthermore, the spans of causal arguments can be recognised to a slightly higher level that by using supervised or rule-based methods that have been employed before. Conclusion Exploiting the large amount of unlabelled data that is already available can help improve the performance of recognising causal discourse relations in the biomedical domain. This improvement will further benefit the development of multiple tasks, such as hypothesis generation for experimental laboratories, contradiction detection, and the creation of causal networks. PMID:25559746

  16. A Novel Semi-Supervised Electronic Nose Learning Technique: M-Training

    Directory of Open Access Journals (Sweden)

    Pengfei Jia

    2016-03-01

    Full Text Available When an electronic nose (E-nose is used to distinguish different kinds of gases, the label information of the target gas could be lost due to some fault of the operators or some other reason, although this is not expected. Another fact is that the cost of getting the labeled samples is usually higher than for unlabeled ones. In most cases, the classification accuracy of an E-nose trained using labeled samples is higher than that of the E-nose trained by unlabeled ones, so gases without label information should not be used to train an E-nose, however, this wastes resources and can even delay the progress of research. In this work a novel multi-class semi-supervised learning technique called M-training is proposed to train E-noses with both labeled and unlabeled samples. We employ M-training to train the E-nose which is used to distinguish three indoor pollutant gases (benzene, toluene and formaldehyde. Data processing results prove that the classification accuracy of E-nose trained by semi-supervised techniques (tri-training and M-training is higher than that of an E-nose trained only with labeled samples, and the performance of M-training is better than that of tri-training because more base classifiers can be employed by M-training.

  17. Non-Supervised Learning for Spread Spectrum Signal Pseudo-Noise Sequence Acquisition

    Institute of Scientific and Technical Information of China (English)

    Hao Cheng; Na Yu,; Tai-Jun Wang

    2015-01-01

    Abstract¾An idea of estimating the direct sequence spread spectrum (DSSS) signal pseudo-noise (PN) sequence is presented. Without the apriority knowledge about the DSSS signal in the non-cooperation condition, we propose a self-organizing feature map (SOFM) neural network algorithm to detect and identify the PN sequence. A non-supervised learning algorithm is proposed according the Kohonen rule in SOFM. The blind algorithm can also estimate the PN sequence in a low signal-to-noise (SNR) and computer simulation demonstrates that the algorithm is effective. Compared with the traditional correlation algorithm based on slip-correlation, the proposed algorithm’s bit error rate (BER) and complexity are lower.

  18. Classification of Autism Spectrum Disorder Using Supervised Learning of Brain Connectivity Measures Extracted from Synchrostates

    CERN Document Server

    Jamal, Wasifa; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-01-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave ...

  19. Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

    CERN Document Server

    Lu, Zhiwu; Peng, Yuxin

    2011-01-01

    This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal mul...

  20. Anxiety, supervision and a space for thinking: some narcissistic perils for clinical psychologists in learning psychotherapy.

    Science.gov (United States)

    Mollon, P

    1989-06-01

    The process of learning psychotherapy involves narcissistic dangers--there may be injuries to self-esteem and self-image, especially when working with certain kinds of disturbed and hostile patients. Some patients will unconsciously recreate, in the transference, representations of early damaging experiences with parents, but now reversed with the therapist as the victim. It is vital for the trainee to be helped to understand these powerful interactional pressures. There are aspects of the professional culture and ideals of clinical psychologists (and possibly of some psychiatrists and social workers as well) which may make them particularly vulnerable in work with the hostile patient. It is argued that the function of supervision is not to teach a technique directly, but to create a 'space for thinking'--a kind of thinking which is more akin to maternal reverie, as described by Bion, than problem solving.

  1. How to guide group to create learning-type project supervision department%如何带领团队创建学习型项目部

    Institute of Scientific and Technical Information of China (English)

    高春玉

    2011-01-01

    阐述了在工作中学习的重要性,介绍了如何创建学习型项目部的方法,并从三个方面加以分析,以建立和完善学习体制,有效地提高监理人员自身素质。%This paper expounds the significance of study in work, introduces methods of how to creating learning-type project supervision department, and makes an analysis from three aspects, with a view to establish and improve learning system and to effectively improve supervisors' quality.

  2. Technological process supervising using vision systems cooperating with the LabVIEW vision builder

    Science.gov (United States)

    Hryniewicz, P.; Banaś, W.; Gwiazda, A.; Foit, K.; Sękala, A.; Kost, G.

    2015-11-01

    One of the most important tasks in the production process is to supervise its proper functioning. Lack of required supervision over the production process can lead to incorrect manufacturing of the final element, through the production line downtime and hence to financial losses. The worst result is the damage of the equipment involved in the manufacturing process. Engineers supervise the production flow correctness use the great range of sensors supporting the supervising of a manufacturing element. Vision systems are one of sensors families. In recent years, thanks to the accelerated development of electronics as well as the easier access to electronic products and attractive prices, they become the cheap and universal type of sensors. These sensors detect practically all objects, regardless of their shape or even the state of matter. The only problem is considered with transparent or mirror objects, detected from the wrong angle. Integrating the vision system with the LabVIEW Vision and the LabVIEW Vision Builder it is possible to determine not only at what position is the given element but also to set its reorientation relative to any point in an analyzed space. The paper presents an example of automated inspection. The paper presents an example of automated inspection of the manufacturing process in a production workcell using the vision supervising system. The aim of the work is to elaborate the vision system that could integrate different applications and devices used in different production systems to control the manufacturing process.

  3. Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

    Directory of Open Access Journals (Sweden)

    Ivana Šemanjski

    2015-12-01

    Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

  4. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents…

  5. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  6. Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

    NARCIS (Netherlands)

    Burscher, B.; Odijk, D.; Vliegenthart, R.; de Rijke, M.; de Vreese, C.H.

    2014-01-01

    We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation

  7. Entry-Level Technical Skills That Teachers Expected Students to Learn through Supervised Agricultural Experiences (SAEs): A Modified Delphi Study

    Science.gov (United States)

    Ramsey, Jon W.; Edwards, M. Craig

    2012-01-01

    Supervised experiences are designed to provide opportunities for the hands-on learning of skills and practices that lead to successful personal growth and future employment in an agricultural career (Talbert, Vaughn, Croom, & Lee, 2007). In the Annual Report for Agricultural Education (2005-2006), it was stated that 91% of the respondents (i.e.,…

  8. Collective Academic Supervision: A Model for Participation and Learning in Higher Education

    Science.gov (United States)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    Supervision of graduate students is a core activity in higher education. Previous research on graduate supervision focuses on individual and relational aspects of the supervisory relationship rather than collective, pedagogical and methodological aspects of the supervision process. In presenting a collective model we have developed for academic…

  9. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    Science.gov (United States)

    Alberti, Koko; Marra, Wouter; Baarsma, Rein; Karssenberg, Derek

    2016-04-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their supervisors. We developed an online application for remote supervision of students on fieldwork. In our fieldworkonline webapp, which is accessible through a web browser, students can upload their field data in the form of a spreadsheet with coordinates (in a system of choice) and data-fields. Field data can be any combination of quantitative or qualitative data, and can contain references to photos or other documents uploaded to the app. The student's data is converted to a map with data-points that contain all the data-fields and links to photos and documents associated with that location. Supervisors can review the data of their students and provide feedback on observations, or geo-referenced feedback on the map. Similarly, students can ask geo-referenced questions to their supervisors. Furthermore, supervisors can choose different basemaps or upload their own. Fieldwork online is a useful tool for supervising students at a distant location in the field and is most suitable for first-order feedback on students' observations, can be used to guide students to interesting locations, and allows for short discussions on phenomena observed in the field. We seek user that like to use this system, we are able to provide support and add new features if needed. The website is built and controlled using Flask, an open-source Python Framework. The maps are generated and controlled using MapServer and OpenLayers, and the database is built in PostgreSQL with PostGIS support. Fieldworkonline and all tools used to create it are open-source. Experience fieldworkonline at our demo during this session, or online at fieldworkonline.geo.uu.nl (username: EGU2016, password: Vienna).

  10. A High Accuracy Method for Semi-supervised Information Extraction

    Energy Technology Data Exchange (ETDEWEB)

    Tratz, Stephen C.; Sanfilippo, Antonio P.

    2007-04-22

    Customization to specific domains of dis-course and/or user requirements is one of the greatest challenges for today’s Information Extraction (IE) systems. While demonstrably effective, both rule-based and supervised machine learning approaches to IE customization pose too high a burden on the user. Semi-supervised learning approaches may in principle offer a more resource effective solution but are still insufficiently accurate to grant realistic application. We demonstrate that this limitation can be overcome by integrating fully-supervised learning techniques within a semi-supervised IE approach, without increasing resource requirements.

  11. THE COMPARISON OF BANKING SUPERVISION MODEL IN INDONESIA, UNITED KINGDOM, SOUTH KOREA AS EFORTS TO IMPROVE INDONESIAN SUPERVISION SYSTEM

    Directory of Open Access Journals (Sweden)

    Sulistyandari

    2015-05-01

    Full Text Available This study aims to revise banking supervision by conducting comparative studies research model of banking supervision in Indonesia, the UK, South Korea and the aspirations of the respondents (Bank, OJK, theorist in Central Java on efforts to improve banking supervision is now done in Indonesia. The results show Indonesian comparison with the UK and South Korea gives the idea that the OJK in charge of education and consumer protection to enhance its role as practiced by the FCA in the UK, and the LPS assignments need to be expanded in order to ensure that all consumers of financial institutions as was done by the FSCS in the UK and KDIC in South Korea. Aspirations of the people of the regulation and supervision of banking include aspects of regulatory, law enforcement, infrastructure, community (the Bank and culture.

  12. Visual Recognition by Learning From Web Data via Weakly Supervised Domain Generalization.

    Science.gov (United States)

    Niu, Li; Li, Wen; Xu, Dong; Cai, Jianfei

    2016-06-01

    In this paper, a weakly supervised domain generalization (WSDG) method is proposed for real-world visual recognition tasks, in which we train classifiers by using Web data (\\eg, Web images and Web videos) with noisy labels. In particular, two challenging problems need to be solved when learning robust classifiers, in which the first issue is to cope with the label noise of training Web data from the source domain, while the second issue is to enhance the generalization capability of learned classifiers to an arbitrary target domain. In order to handle the first problem, the training samples within each category are partitioned into clusters, where we use one bag to denote each cluster and instances to denote the samples in each cluster. Then, we identify a proportion of good training samples in each bag and train robust classifiers by using the good training samples, which leads to a multi-instance learning (MIL) problem. In order to handle the second problem, we assume that the training samples possibly form a set of hidden domains, with each hidden domain associated with a distinctive data distribution. Then, for each category and each hidden latent domain, we propose to learn one classifier by extending our MIL formulation, which leads to our WSDG approach. In the testing stage, our approach can obtain better generalization capability by effectively integrating multiple classifiers from different latent domains in each category. Moreover, our WSDG approach is further extended to utilize additional textual descriptions associated with Web data as privileged information (PI), although testing data do not have such PI. Extensive experiments on three benchmark data sets indicate that our newly proposed methods are effective for real-world visual recognition tasks by learning from Web data.

  13. Whither Supervision?

    Directory of Open Access Journals (Sweden)

    Duncan Waite

    2006-11-01

    Full Text Available This paper inquires if the school supervision is in decadence. Dr. Waite responds that the answer will depend on which perspective you look at it. Dr. Waite suggests taking in consideration three elements that are related: the field itself, the expert in the field (the professor, the theorist, the student and the administrator, and the context. When these three elements are revised, it emphasizes that there is not a consensus about the field of supervision, but there are coincidences related to its importance and that it is related to the improvement of the practice of the students in the school for their benefit. Dr. Waite suggests that the practice on this field is not always in harmony with what the theorists affirm. When referring to the supervisor or the skilled person, the author indicates that his or her perspective depends on his or her epistemological believes or in the way he or she conceives the learning; that is why supervision can be understood in different ways. About the context, Waite suggests that there have to be taken in consideration the social or external forces that influent the people and the society, because through them the education is affected. Dr. Waite concludes that the way to understand the supervision depends on the performer’s perspective. He responds to the initial question saying that the supervision authorities, the knowledge on this field, the performers, and its practice, are maybe spread but not extinct because the supervision will always be part of the great enterprise that we called education.

  14. Real-Time System Supervision for the LHC Beam Loss Monitoring System at CERN

    CERN Document Server

    Zamantzas, C; Effinger, E; Emery, J; Jackson, S

    2014-01-01

    The strategy for machine protection and quench prevention of the Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) is mainly based on the Beam Loss Monitoring (BLM) system. The LHC BLM system is one of the most complex and large instrumentation systems deployed in the LHC. In addition to protecting the collider, the system also needs to provide a means of diagnosing machine faults and deliver feedback of the losses to the control room as well as to several systems for their setup and analysis. In order to augment the dependability of the system several layers of supervision has been implemented internally and externally to the system. This paper describes the different methods employed to achieve the expected availability and system fault detection.

  15. Theory and realization of supervision and control system for tunneling machine

    Energy Technology Data Exchange (ETDEWEB)

    Wei, J.; Song, D.; Chen, N. [Zhejiang University, Hangzhou (China). State Key Laboratory of Fluid Power Transmission and Control

    2004-07-01

    A visualized supervision and control system for a shield tunnel boring machine was developed, following a request of the upper workstation. The hardware design and software structure are described. Specially, logical judgment of working state and static and dynamic prediction model of earth surface level variation were given and analyzed. The solutions to avoid land surface sedimentation accruing are presented: ensuring the working state of the programs precisely at any time according to running state of the tunneling machine using logical judgement formula; forecasting the quantities of the surface sedimentation comparatively exactly by evaluating on line and visualized supervision means. It is testified that the system can supervise multi-subjects can forecast exactly and can realize visualized evaluation and prediction on-line. 8 refs., 9 figs.

  16. Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm

    Directory of Open Access Journals (Sweden)

    Ricardo Andres Pizarro

    2016-12-01

    Full Text Available High-resolution three-dimensional magnetic resonance imaging (3D-MRI is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM algorithm in the quality assessment of structural brain images, using global and region of interest (ROI automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

  17. The effects of supervised learning on event-related potential correlates of music-syntactic processing.

    Science.gov (United States)

    Guo, Shuang; Koelsch, Stefan

    2015-11-11

    Humans process music even without conscious effort according to implicit knowledge about syntactic regularities. Whether such automatic and implicit processing is modulated by veridical knowledge has remained unknown in previous neurophysiological studies. This study investigates this issue by testing whether the acquisition of veridical knowledge of a music-syntactic irregularity (acquired through supervised learning) modulates early, partly automatic, music-syntactic processes (as reflected in the early right anterior negativity, ERAN), and/or late controlled processes (as reflected in the late positive component, LPC). Excerpts of piano sonatas with syntactically regular and less regular chords were presented repeatedly (10 times) to non-musicians and amateur musicians. Participants were informed by a cue as to whether the following excerpt contained a regular or less regular chord. Results showed that the repeated exposure to several presentations of regular and less regular excerpts did not influence the ERAN elicited by less regular chords. By contrast, amplitudes of the LPC (as well as of the P3a evoked by less regular chords) decreased systematically across learning trials. These results reveal that late controlled, but not early (partly automatic), neural mechanisms of music-syntactic processing are modulated by repeated exposure to a musical piece. This article is part of a Special Issue entitled SI: Prediction and Attention. Copyright © 2015 Elsevier B.V. All rights reserved.

  18. Semi-supervised manifold learning with affinity regularization for Alzheimer's disease identification using positron emission tomography imaging.

    Science.gov (United States)

    Lu, Shen; Xia, Yong; Cai, Tom Weidong; Feng, David Dagan

    2015-01-01

    Dementia, Alzheimer's disease (AD) in particular is a global problem and big threat to the aging population. An image based computer-aided dementia diagnosis method is needed to providing doctors help during medical image examination. Many machine learning based dementia classification methods using medical imaging have been proposed and most of them achieve accurate results. However, most of these methods make use of supervised learning requiring fully labeled image dataset, which usually is not practical in real clinical environment. Using large amount of unlabeled images can improve the dementia classification performance. In this study we propose a new semi-supervised dementia classification method based on random manifold learning with affinity regularization. Three groups of spatial features are extracted from positron emission tomography (PET) images to construct an unsupervised random forest which is then used to regularize the manifold learning objective function. The proposed method, stat-of-the-art Laplacian support vector machine (LapSVM) and supervised SVM are applied to classify AD and normal controls (NC). The experiment results show that learning with unlabeled images indeed improves the classification performance. And our method outperforms LapSVM on the same dataset.

  19. Gaia eclipsing binary and multiple systems. Supervised classification and self-organizing maps

    Science.gov (United States)

    Süveges, M.; Barblan, F.; Lecoeur-Taïbi, I.; Prša, A.; Holl, B.; Eyer, L.; Kochoska, A.; Mowlavi, N.; Rimoldini, L.

    2017-07-01

    Context. Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with reliable assessment of its uncertainty. This study investigates the potential of a few machine-learning methods for the automated analysis of eclipsing binaries in the data of such surveys. Aims: We aim to aid the extraction of samples of eclipsing binaries from such databases and to provide basic information about the objects. We intend to estimate class labels according to two different, well-known classification systems, one based on the light curve morphology (EA/EB/EW classes) and the other based on the physical characteristics of the binary system (system morphology classes; detached through overcontact systems). Furthermore, we explore low-dimensional surfaces along which the light curves of eclipsing binaries are concentrated, and consider their use in the characterization of the binary systems and in the exploration of biases of the full unknown Gaia data with respect to the training sets. Methods: We have explored the performance of principal component analysis (PCA), linear discriminant analysis (LDA), Random Forest classification and self-organizing maps (SOM) for the above aims. We pre-processed the photometric time series by combining a double Gaussian profile fit and a constrained smoothing spline, in order to de-noise and interpolate the observed light curves. We achieved further denoising, and selected the most important variability elements from the light curves using PCA. Supervised classification was performed using Random Forest and LDA based on the PC decomposition, while SOM gives a continuous 2-dimensional manifold of the light curves arranged by a few important features. We estimated the uncertainty of the supervised methods due to the specific finite training set using ensembles of models constructed

  20. A Semi-Supervised Learning Approach to Enhance Health Care Community–Based Question Answering: A Case Study in Alcoholism

    Science.gov (United States)

    Klabjan, Diego; Jonnalagadda, Siddhartha Reddy

    2016-01-01

    Background Community-based question answering (CQA) sites play an important role in addressing health information needs. However, a significant number of posted questions remain unanswered. Automatically answering the posted questions can provide a useful source of information for Web-based health communities. Objective In this study, we developed an algorithm to automatically answer health-related questions based on past questions and answers (QA). We also aimed to understand information embedded within Web-based health content that are good features in identifying valid answers. Methods Our proposed algorithm uses information retrieval techniques to identify candidate answers from resolved QA. To rank these candidates, we implemented a semi-supervised leaning algorithm that extracts the best answer to a question. We assessed this approach on a curated corpus from Yahoo! Answers and compared against a rule-based string similarity baseline. Results On our dataset, the semi-supervised learning algorithm has an accuracy of 86.2%. Unified medical language system–based (health related) features used in the model enhance the algorithm’s performance by proximately 8%. A reasonably high rate of accuracy is obtained given that the data are considerably noisy. Important features distinguishing a valid answer from an invalid answer include text length, number of stop words contained in a test question, a distance between the test question and other questions in the corpus, and a number of overlapping health-related terms between questions. Conclusions Overall, our automated QA system based on historical QA pairs is shown to be effective according to the dataset in this case study. It is developed for general use in the health care domain, which can also be applied to other CQA sites. PMID:27485666

  1. 78 FR 2993 - Public Water System Supervision Program Approval for the State of Ohio

    Science.gov (United States)

    2013-01-15

    ... From the Federal Register Online via the Government Publishing Office ENVIRONMENTAL PROTECTION AGENCY Public Water System Supervision Program Approval for the State of Ohio Correction In notice document 2012-30953, appearing on pages 76034-76035 in the issue of Wednesday, December 26, 2012, make...

  2. 77 FR 36274 - Public Water System Supervision Program Revision for the State of Alabama

    Science.gov (United States)

    2012-06-18

    ...Notice is hereby given that the State of Alabama is revising its approved Public Water System Supervision Program. Alabama has adopted the following rules: Long Term 1 Enhanced Surface Water Treatment Rule, Long Term 2 Enhanced Surface Water Treatment Rule, and Stage 2 Disinfection/Disinfection Byproducts Rule. EPA has determined that Alabama's rules are no less stringent than the......

  3. Clinical supervision by consultants in teaching hospitals.

    Science.gov (United States)

    Hore, Craig T; Lancashire, William; Fassett, Robert G

    2009-08-17

    Clinical supervision is a vital part of postgraduate medical education. Without it, trainees may not learn effectively from their experiences; this may lead to acceptance by registrars and junior doctors of lower standards of care. Currently, supervision is provided by consultants to registrars and junior doctors, and by registrars to junior doctors. Evidence suggests that the clinical supervision provided to postgraduate doctors is inadequate. Registrars and juniors doctors have the right to expect supervision in the workplace. Impediments to the provision of clinical supervision include competing demands of hospital service provision on trainees and supervisors, lack of clarity of job descriptions, private versus public commitments of supervisors and lack of interest. Supervisors should be trained in the process of supervision and provided with the time and resources to conduct it. Those being supervised should be provided with clear expectations of the process. We need to create and develop systems, environments and cultures that support high standards of conduct and effective clinical supervision. These systems must ensure the right to supervision, feedback, support, decent working conditions and respect for both trainees and their supervisors.

  4. Application of graph-based semi-supervised learning for development of cyber COP and network intrusion detection

    Science.gov (United States)

    Levchuk, Georgiy; Colonna-Romano, John; Eslami, Mohammed

    2017-05-01

    The United States increasingly relies on cyber-physical systems to conduct military and commercial operations. Attacks on these systems have increased dramatically around the globe. The attackers constantly change their methods, making state-of-the-art commercial and military intrusion detection systems ineffective. In this paper, we present a model to identify functional behavior of network devices from netflow traces. Our model includes two innovations. First, we define novel features for a host IP using detection of application graph patterns in IP's host graph constructed from 5-min aggregated packet flows. Second, we present the first application, to the best of our knowledge, of Graph Semi-Supervised Learning (GSSL) to the space of IP behavior classification. Using a cyber-attack dataset collected from NetFlow packet traces, we show that GSSL trained with only 20% of the data achieves higher attack detection rates than Support Vector Machines (SVM) and Naïve Bayes (NB) classifiers trained with 80% of data points. We also show how to improve detection quality by filtering out web browsing data, and conclude with discussion of future research directions.

  5. 17 CFR 240.17i-4 - Internal risk management control system requirements for supervised investment bank holding...

    Science.gov (United States)

    2010-04-01

    ... Supervised Investment Bank Holding Company Rules § 240.17i-4 Internal risk management control system...) As part of its internal risk management control system, a supervised investment bank holding company... 17 Commodity and Securities Exchanges 3 2010-04-01 2010-04-01 false Internal risk...

  6. Supervision--growing and building a sustainable general practice supervisor system.

    Science.gov (United States)

    Thomson, Jennifer S; Anderson, Katrina J; Mara, Paul R; Stevenson, Alexander D

    2011-06-06

    This article explores various models and ideas for future sustainable general practice vocational training supervision in Australia. The general practitioner supervisor in the clinical practice setting is currently central to training the future general practice workforce. Finding ways to recruit, retain and motivate both new and experienced GP teachers is discussed, as is the creation of career paths for such teachers. Some of the newer methods of practice-based teaching are considered for further development, including vertically integrated teaching, e-learning, wave consulting and teaching on the run, teaching teams and remote teaching. Approaches to supporting and resourcing teaching and the required infrastructure are also considered. Further research into sustaining the practice-based general practice supervision model will be required.

  7. A framework to facilitate self-directed learning, assessment and supervision in midwifery practice: a qualitative study of supervisors' perceptions.

    Science.gov (United States)

    Embo, M; Driessen, E; Valcke, M; van der Vleuten, C P M

    2014-08-01

    Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) as a framework to facilitate self-directed learning. In the present study, we sought clinical supervisors' perceptions of the usefulness of MAFI. Interviews with fifteen clinical supervisors were audio taped, transcribed verbatim and analysed thematically using Atlas-Ti software for qualitative data analysis. Four themes emerged from the analysis. (1) The competency-based educational structure promotes the setting of realistic learning outcomes and a focus on competency development, (2) instructing students to write reflections facilitates student-centred supervision, (3) creating a feedback culture is necessary to achieve continuity in supervision and (4) integrating feedback and assessment might facilitate competency development under the condition that evidence is discussed during assessment meetings. Supervisors stressed the need for direct observation, and instruction how to facilitate a self-directed learning process. The MAFI appears to be a useful framework to promote self-directed learning in clinical practice. The effect can be advanced by creating a feedback and assessment culture where learners and supervisors share the responsibility for developing self-directed learning. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Automatic learning rate adjustment for self-supervising autonomous robot control

    Science.gov (United States)

    Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

    1992-01-01

    Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

  9. Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

    Science.gov (United States)

    Jamal, Wasifa; Das, Saptarshi; Oprescu, Ioana-Anastasia; Maharatna, Koushik; Apicella, Fabio; Sicca, Federico

    2014-08-01

    Objective. The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. Approach. Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. Main results. The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. Significance. The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.

  10. A Model for Detecting Tor Encrypted Traffic using Supervised Machine Learning

    Directory of Open Access Journals (Sweden)

    Alaeddin Almubayed

    2015-06-01

    Full Text Available Tor is the low-latency anonymity tool and one of the prevalent used open source anonymity tools for anonymizing TCP traffic on the Internet used by around 500,000 people every day. Tor protects user's privacy against surveillance and censorship by making it extremely difficult for an observer to correlate visited websites in the Internet with the real physical-world identity. Tor accomplished that by ensuring adequate protection of Tor traffic against traffic analysis and feature extraction techniques. Further, Tor ensures anti-website fingerprinting by implementing different defences like TLS encryption, padding, and packet relaying. However, in this paper, an analysis has been performed against Tor from a local observer in order to bypass Tor protections; the method consists of a feature extraction from a local network dataset. Analysis shows that it's still possible for a local observer to fingerprint top monitored sites on Alexa and Tor traffic can be classified amongst other HTTPS traffic in the network despite the use of Tor's protections. In the experiment, several supervised machine-learning algorithms have been employed. The attack assumes a local observer sitting on a local network fingerprinting top 100 sites on Alexa; results gave an improvement amongst previous results by achieving an accuracy of 99.64% and 0.01% false positive.

  11. An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jingsha He

    2017-03-01

    Full Text Available In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people’s daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users’ privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL, to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.

  12. How to measure metallicity from five-band photometry with supervised machine learning algorithms

    CERN Document Server

    Acquaviva, Viviana

    2015-01-01

    We demonstrate that it is possible to measure metallicity from the SDSS five-band photometry to better than 0.1 dex using supervised machine learning algorithms. Using spectroscopic estimates of metallicity as ground truth, we build, optimize and train several estimators to predict metallicity. We use the observed photometry, as well as derived quantities such as stellar mass and photometric redshift, as features, and we build two sample data sets at median redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3 respectively. We find that ensemble methods, such as Random Forests of Trees and Extremely Randomized Trees, and Support Vector Machines all perform comparably well and can measure metallicity with a Root Mean Square Error (RMSE) of 0.081 and 0.090 for the two data sets when all objects are included. The fraction of outliers (objects for which the difference between true and predicted metallicity is larger than 0.2 dex) is only 2.2 and 3.9% respectively, and the RMSE decreases to 0.0...

  13. Supervised Learning Detection of Sixty Non-transiting Hot Jupiter Candidates

    Science.gov (United States)

    Millholland, Sarah; Laughlin, Gregory

    2017-09-01

    The optical full-phase photometric variations of a short-period planet provide a unique view of the planet’s atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small to study them as an aggregate population, motivating an extension of the search to non-transiting planets. Here we present an algorithm for the detection of non-transiting short-period giant planets in the Kepler field. The procedure uses the phase curves themselves as evidence for the planets’ existence. We employ a supervised learning algorithm to recognize the salient time-dependent properties of synthetic phase curves; we then search for detections of signals that match these properties. After demonstrating the algorithm’s capabilities, we classify 142,630 FGK Kepler stars without confirmed planets or Kepler Objects of Interest, and for each one, we assign a probability of a phase curve of a non-transiting planet being present. We identify 60 high-probability non-transiting hot Jupiter candidates. We also derive constraints on the candidates’ albedos and offsets of the phase curve maxima. These targets are strong candidates for follow-up radial velocity confirmation and characterization. Once confirmed, the atmospheric information content in the phase curves may be studied in yet greater detail.

  14. Distributed multisensory integration in a recurrent network model through supervised learning

    Science.gov (United States)

    Wang, He; Wong, K. Y. Michael

    Sensory integration between different modalities has been extensively studied. It is suggested that the brain integrates signals from different modalities in a Bayesian optimal way. However, how the Bayesian rule is implemented in a neural network remains under debate. In this work we propose a biologically plausible recurrent network model, which can perform Bayesian multisensory integration after trained by supervised learning. Our model is composed of two modules, each for one modality. We assume that each module is a recurrent network, whose activity represents the posterior distribution of each stimulus. The feedforward input on each module is the likelihood of each modality. Two modules are integrated through cross-links, which are feedforward connections from the other modality, and reciprocal connections, which are recurrent connections between different modules. By stochastic gradient descent, we successfully trained the feedforward and recurrent coupling matrices simultaneously, both of which resembles the Mexican-hat. We also find that there are more than one set of coupling matrices that can approximate the Bayesian theorem well. Specifically, reciprocal connections and cross-links will compensate each other if one of them is removed. Even though trained with two inputs, the network's performance with only one input is in good accordance with what is predicted by the Bayesian theorem.

  15. Supporting and Supervising Teachers Working With Adults Learning English. CAELA Network Brief

    Science.gov (United States)

    Young, Sarah

    2009-01-01

    This brief provides an overview of the knowledge and skills that administrators need in order to support and supervise teachers of adult English language learners. It begins with a review of resources and literature related to teacher supervision in general and to adult ESL education. It continues with information on the background and…

  16. Understanding Trust as an Essential Element of Trainee Supervision and Learning in the Workplace

    Science.gov (United States)

    Hauer, Karen E.; ten Cate, Olle; Boscardin, Christy; Irby, David M.; Iobst, William; O'Sullivan, Patricia S.

    2014-01-01

    Clinical supervision requires that supervisors make decisions about how much independence to allow their trainees for patient care tasks. The simultaneous goals of ensuring quality patient care and affording trainees appropriate and progressively greater responsibility require that the supervising physician trusts the trainee. Trust allows the…

  17. Enhancing the Doctoral Journey: The Role of Group Supervision in Supporting Collaborative Learning and Creativity

    Science.gov (United States)

    Fenge, Lee-Ann

    2012-01-01

    This article explores the role of group supervision within doctoral education, offering an exploration of the experience of group supervision processes through a small-scale study evaluating both student and staff experience across three cohorts of one professional doctorate programme. There has been very little research to date exploring…

  18. Is Direct Supervision in Clinical Education for Athletic Training Students Always Necessary to Enhance Student Learning?

    Science.gov (United States)

    Scriber, Kent; Trowbridge, Cindy

    2009-01-01

    Objective: To present an alternative model of supervision within clinical education experiences. Background: Several years ago direct supervision was defined more clearly in the accreditation standards for athletic training education programs (ATEPs). Currently, athletic training students may not gain any clinical experience without their clinical…

  19. Clinical group supervision in yoga therapy: model effects, and lessons learned.

    Science.gov (United States)

    Forbes, Bo; Volpe Horii, Cassandra; Earls, Bethany; Mashek, Stephanie; Akhtar, Fiona

    2012-01-01

    Clinical supervision is an integral component of therapist training and professional development because of its capacity for fostering knowledge, self-awareness, and clinical acumen. Individual supervision is part of many yoga therapy training programs and is referenced in the IAYT Standards as "mentoring." Group supervision is not typically used in the training of yoga therapists. We propose that group supervision effectively supports the growth and development of yoga therapists-in-training. We present a model of group supervision for yoga therapist trainees developed by the New England School of Integrative Yoga Therapeutics™ (The NESIYT Model) that includes the background, structure, format, and development of our inaugural 18-month supervision group. Pre-and post-supervision surveys and analyzed case notes, which captured key didactic and process themes, are discussed. Clinical issues, such as boundaries, performance anxiety, sense of self efficacy, the therapeutic alliance, transference and counter transference, pacing of yoga therapy sessions, evaluation of client progress, and adjunct therapist interaction are reviewed. The timing and sequence of didactic and process themes and benefits for yoga therapist trainees' professional development, are discussed. The NESIYT group supervision model is offered as an effective blueprint for yoga therapy training programs.

  20. Classification and Diagnostic Output Prediction of Cancer Using Gene Expression Profiling and Supervised Machine Learning Algorithms

    DEFF Research Database (Denmark)

    Yoo, C.; Gernaey, Krist

    2008-01-01

    In this paper, a new supervised clustering and classification method is proposed. First, the application of discriminant partial least squares (DPLS) for the selection of a minimum number of key genes is applied on a gene expression microarray data set. Second, supervised hierarchical clustering ...

  1. Material classification and automatic content enrichment of images using supervised learning and knowledge bases

    Science.gov (United States)

    Mallepudi, Sri Abhishikth; Calix, Ricardo A.; Knapp, Gerald M.

    2011-02-01

    In recent years there has been a rapid increase in the size of video and image databases. Effective searching and retrieving of images from these databases is a significant current research area. In particular, there is a growing interest in query capabilities based on semantic image features such as objects, locations, and materials, known as content-based image retrieval. This study investigated mechanisms for identifying materials present in an image. These capabilities provide additional information impacting conditional probabilities about images (e.g. objects made of steel are more likely to be buildings). These capabilities are useful in Building Information Modeling (BIM) and in automatic enrichment of images. I2T methodologies are a way to enrich an image by generating text descriptions based on image analysis. In this work, a learning model is trained to detect certain materials in images. To train the model, an image dataset was constructed containing single material images of bricks, cloth, grass, sand, stones, and wood. For generalization purposes, an additional set of 50 images containing multiple materials (some not used in training) was constructed. Two different supervised learning classification models were investigated: a single multi-class SVM classifier, and multiple binary SVM classifiers (one per material). Image features included Gabor filter parameters for texture, and color histogram data for RGB components. All classification accuracy scores using the SVM-based method were above 85%. The second model helped in gathering more information from the images since it assigned multiple classes to the images. A framework for the I2T methodology is presented.

  2. Web Design Based on Integrated and Supervision Control System in City Rail Transit

    Science.gov (United States)

    Li, Xiaojuan; Xing, Yu; Zheng, Hengchao

    This paper presents a method of setting up WEB System to Integrated Supervision Control System for the requirements of city Rail Transit. First, basic platform and software/hardware architecture of WEB System are discussed. Then the function module, data flow and communication mechanisms are described and a design based on technologies of SVG and Ajax is proposed and the WEB video release function and system security are described. This design makes it possible that important information of Integrated Supervision Control System can be browsed and queried in external Web pages. Watching Real-time images of all cameras in internal network of Rail Transit is possible, which is providing remote viewing and management functions for metro managers.

  3. Assessing Electronic Cigarette-Related Tweets for Sentiment and Content Using Supervised Machine Learning.

    Science.gov (United States)

    Cole-Lewis, Heather; Varghese, Arun; Sanders, Amy; Schwarz, Mary; Pugatch, Jillian; Augustson, Erik

    2015-08-25

    Electronic cigarettes (e-cigarettes) continue to be a growing topic among social media users, especially on Twitter. The ability to analyze conversations about e-cigarettes in real-time can provide important insight into trends in the public's knowledge, attitudes, and beliefs surrounding e-cigarettes, and subsequently guide public health interventions. Our aim was to establish a supervised machine learning algorithm to build predictive classification models that assess Twitter data for a range of factors related to e-cigarettes. Manual content analysis was conducted for 17,098 tweets. These tweets were coded for five categories: e-cigarette relevance, sentiment, user description, genre, and theme. Machine learning classification models were then built for each of these five categories, and word groupings (n-grams) were used to define the feature space for each classifier. Predictive performance scores for classification models indicated that the models correctly labeled the tweets with the appropriate variables between 68.40% and 99.34% of the time, and the percentage of maximum possible improvement over a random baseline that was achieved by the classification models ranged from 41.59% to 80.62%. Classifiers with the highest performance scores that also achieved the highest percentage of the maximum possible improvement over a random baseline were Policy/Government (performance: 0.94; % improvement: 80.62%), Relevance (performance: 0.94; % improvement: 75.26%), Ad or Promotion (performance: 0.89; % improvement: 72.69%), and Marketing (performance: 0.91; % improvement: 72.56%). The most appropriate word-grouping unit (n-gram) was 1 for the majority of classifiers. Performance continued to marginally increase with the size of the training dataset of manually annotated data, but eventually leveled off. Even at low dataset sizes of 4000 observations, performance characteristics were fairly sound. Social media outlets like Twitter can uncover real-time snapshots of

  4. Attend in groups: a weakly-supervised deep learning framework for learning from web data

    OpenAIRE

    Zhuang, Bohan; Liu, Lingqiao; Li, Yao; Shen, Chunhua; Reid, Ian

    2016-01-01

    Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervis...

  5. A new microcontroller supervised thermoelectric renal hypothermia system.

    Science.gov (United States)

    Işik, Hakan

    2005-10-01

    In the present study, a thermoelectric system controlled by a microcontroller is developed to induce renal hypothermia. Temperature value was managed by 8-byte microcontroller, PIC16F877, and was programmed using microcontroller MPASM package. In order to ensure hypothermia in the kidney 1-4 modules and sensors perceiving temperature of the area can be selected. Temperature values are arranged proportionately for the selected area and the determined temperature values can be monitored from an Liquid Crystal Display (LCD) screen. The temperature range of the system is between -50 and +50 degrees C. Renal hypothermia system was tried under in vivo conditions on the kidney of a dog.

  6. Real-time supervision of building HVAC system performance

    Energy Technology Data Exchange (ETDEWEB)

    Djuric, Natasa

    2008-07-01

    This thesis presents techniques for improving building HVAC system performance in existing buildings generated using simulation-based tools and real data. Therefore, one of the aims has been to research the needs and possibilities to assess and improve building HVAC system performance. In addition, this thesis aims at an advanced utilization of building energy management system (BEMS) and the provision of useful information to building operators using simulation tools. Buildings are becoming more complex systems with many elements, while BEMS provide many data about the building systems. There are, however, many faults and issues in building performance, but there are legislative and cost-benefit forces induced by energy savings. Therefore, both BEMS and the computer-based tools have to be utilized more efficiently to improve building performance. The thesis consists of four main parts that can be read separately. The first part explains the term commissioning and the commissioning tool work principal based on literature reviews. The second part presents practical experiences and issues introduced through the work on this study. The third part deals with the computer-based tools application in design and operation. This part is divided into two chapters. The first deals with improvement in the design, and the second deals with the improvement in the control strategies. The last part of the thesis gives several rules for fault diagnosis developed using simulation tools. In addition, this part aims at the practical explanation of the faults in the building HVAC systems. The practical background for the thesis was obtained though two surveys. The first survey was carried out with the aim to find the commissioning targets in Norwegian building facilities. In that way, an overview of the most typical buildings, HVAC equipment, and their related problems was obtained. An on-site survey was carried out on an example building, which was beneficial for introducing the

  7. Real-time supervision of building HVAC system performance

    Energy Technology Data Exchange (ETDEWEB)

    Djuric, Natasa

    2008-07-01

    This thesis presents techniques for improving building HVAC system performance in existing buildings generated using simulation-based tools and real data. Therefore, one of the aims has been to research the needs and possibilities to assess and improve building HVAC system performance. In addition, this thesis aims at an advanced utilization of building energy management system (BEMS) and the provision of useful information to building operators using simulation tools. Buildings are becoming more complex systems with many elements, while BEMS provide many data about the building systems. There are, however, many faults and issues in building performance, but there are legislative and cost-benefit forces induced by energy savings. Therefore, both BEMS and the computer-based tools have to be utilized more efficiently to improve building performance. The thesis consists of four main parts that can be read separately. The first part explains the term commissioning and the commissioning tool work principal based on literature reviews. The second part presents practical experiences and issues introduced through the work on this study. The third part deals with the computer-based tools application in design and operation. This part is divided into two chapters. The first deals with improvement in the design, and the second deals with the improvement in the control strategies. The last part of the thesis gives several rules for fault diagnosis developed using simulation tools. In addition, this part aims at the practical explanation of the faults in the building HVAC systems. The practical background for the thesis was obtained though two surveys. The first survey was carried out with the aim to find the commissioning targets in Norwegian building facilities. In that way, an overview of the most typical buildings, HVAC equipment, and their related problems was obtained. An on-site survey was carried out on an example building, which was beneficial for introducing the

  8. Learning fuzzy logic control system

    Science.gov (United States)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  9. Supervision functions - Secure operation of sustainable power systems

    DEFF Research Database (Denmark)

    Morais, Hugo; Zhang, Xinxin; Lind, Morten

    2013-01-01

    The globalization of use of Distributed Generation (DG) and other distributed energy resources in recent years have strongly influenced the power systems operation changes. The growing use of new technologies such as Phasor Measurements Units (PMUs) increases the possibilities and the efficiency ...

  10. Kollegial supervision

    DEFF Research Database (Denmark)

    Andersen, Ole Dibbern; Petersson, Erling

    Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution......Publikationen belyser, hvordan kollegial supervision i en kan organiseres i en uddannelsesinstitution...

  11. VDES J2325-5229 a z=2.7 gravitationally lensed quasar discovered using morphology independent supervised machine learning

    CERN Document Server

    Ostrovski, Fernanda; Connolly, Andrew J; Lemon, Cameron A; Auger, Matthew W; Banerji, Manda; Hung, Johnathan M; Koposov, Sergey E; Lidman, Christopher E; Reed, Sophie L; Allam, Sahar; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Rosell, Aurelio Carnero; Kind, Matias Carrasco; Carretero, Jorge; Cunha, Carlos E; da Costa, Luiz N; Desai, Shantanu; Diehl, H Thomas; Dietrich, Jörg P; Evrard, August E; Finley, David A; Flaugher, Brenna; Fosalba, Pablo; Frieman, Josh; Gerdes, David W; Goldstein, Daniel A; Gruen, Daniel; Gruendl, Robert A; Gutierrez, Gaston; Honscheid, Klaus; James, David J; Kuehn, Kyler; Kuropatkin, Nikolay; Lima, Marcos; Lin, Huan; Maia, Marcio A G; Marshall, Jennifer L; Martini, Paul; Melchior, Peter; Miquel, Ramon; Ogando, Ricardo; Malagón, Andrés Plazas; Reil, Kevin; Romer, Kathy; Sanchez, Eusebio; Santiago, Basilio; Scarpine, Vic; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, Flavia; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Tucker, Douglas L; Walker, Alistair R

    2016-01-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift $z_{s}=2.74$ and image separation of $2.9"$ lensed by a foreground $z_{l}=0.40$ elliptical galaxy. Since the images of gravitationally lensed quasars are the superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and $gi$ multicolour photometric observations from the Dark Energy Survey (DES), near IR $JK$ photometry from the VISTA Hemisphere Survey (VHS) and WISE mid IR photometry, we have identified a candidate system with two catalogue components with $i_{AB}=18.61$ and $i_{AB}=20.44$ comprised of an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as...

  12. Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

    Science.gov (United States)

    Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

    2017-04-01

    Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

  13. Investigating the control of climatic oscillations over global terrestrial evaporation using a simple supervised learning method

    Science.gov (United States)

    Martens, Brecht; Miralles, Diego; Waegeman, Willem; Dorigo, Wouter; Verhoest, Niko

    2017-04-01

    Intra-annual and multi-decadal variations in the Earth's climate are to a large extent driven by periodic oscillations in the coupled state of atmosphere and ocean. These oscillations alter not only the climate in nearby regions, but also have an important impact on the local climate in remote areas, a phenomenon that is often referred to as 'teleconnection'. Because changes in local climate immediately impact terrestrial ecosystems through a series of complex processes and feedbacks, ocean-atmospheric teleconnections are expected to influence land evaporation - i.e. the return flux of water from land to atmosphere. In this presentation, the effects of these intra-annual and multi-decadal climate oscillations on global terrestrial evaporation are analysed. To this end, we use satellite observations of different essential climate variables in combination with a simple supervised learning method, the lasso regression. A total of sixteen Climate Oscillation Indices (COIs) - which are routinely used to diagnose the major ocean-atmospheric oscillations - are selected. Multi-decadal data of terrestrial evaporation are retrieved from the Global Land Evaporation Amsterdam Model (GLEAM, www.gleam.eu). Using the lasso regression, it is shown that more than 30% of the inter-annual variations in terrestrial evaporation can be explained by ocean-atmospheric oscillations. In addition, the impact in different regions across the globe can typically be attributed to a small subset of the sixteen COIs. For instance, the dynamics in terrestrial evaporation over Australia are substantially impacted by both the El Niño Southern Oscillation (here diagnosed using the Southern Oscillation Index, SOI) and the Indian Ocean Dipole Oscillation (here diagnosed using the Indian Dipole Mode Index, DMI). Subsequently, using the same learning method but regressing terrestrial evaporation to its local climatic drivers (air temperature, precipitation, radiation), allows us to discern through which

  14. 78 FR 47697 - Public Water System Supervision Program Revision for the State of Louisiana

    Science.gov (United States)

    2013-08-06

    ...Notice is hereby given that the State of Louisiana is revising its approved Public Water System Supervision Program. Louisiana has adopted three EPA drinking water rules, namely the: 1) Long Term 2 Enhanced Surface Water Treatment Rule (LT2), 2) the Stage 2 Disinfectants and Disinfection Byproducts Rule (DBP2), and 3) the Lead and Copper Rule Short-Term Revisions and Clarifications (LCR). EPA......

  15. 77 FR 58132 - Public Water System Supervision Program Revision for the State of Colorado

    Science.gov (United States)

    2012-09-19

    ...In accordance with the provisions of Section 1413 of the Safe Drinking Water Act (SDWA), 42 U.S.C. 300g-2, public notice is hereby given that the state of Colorado has revised its Public Water System Supervision (PWSS) Program by adopting regulations for the Long Term 2 Enhanced Surface Water Treatment Rule and the Stage 2 Disinfectants and Disinfection Byproducts Rule that correspond to the......

  16. 77 FR 58132 - Public Water System Supervision Program Revision for the State of Utah

    Science.gov (United States)

    2012-09-19

    ...In accordance with the provisions of Section 1413 of the Safe Drinking Water Act (SDWA), 42 U.S.C. 300g-2, public notice is hereby given that the state of Utah has revised its Public Water System Supervision (PWSS) Program by adopting regulations for the Lead and Copper Short Term Revisions, Long Term 1 Enhanced Surface Water Treatment Rule, the Long Term 2 Enhanced Surface Water Treatment......

  17. Including Pressure Measurements in Supervision of Energy Efficiency of Wastewater Pump Systems

    DEFF Research Database (Denmark)

    Larsen, Torben; Arensman, Mareike; Nerup-Jensen, Ole

    2016-01-01

    Wastewater pump systems decompose relatively rapidly compared to other pump systems because of the demanding properties of the pump medium. Only a systematic maintenance of the systems can prevent a significant and continuous decrease of the energy consumption per unit volume pumped (the specific...... energy). This article presents a method for a continuous supervision of the performance of both the pump and the pipeline in order to maintain the initial specific energy consumption as close as possible to the original value from when the system was commissioned. The method is based on pressure...... measurements only. The flow is determined indirectly from pressure fluctuations during pump run-up....

  18. Dynamic Interactive Learning Systems

    Science.gov (United States)

    Sabry, Khaled; Barker, Jeff

    2009-01-01

    This paper reviews and discusses the notions of interactivity and dynamicity of learning systems in relation to information technologies and design principles that can contribute to interactive and dynamic learning. It explores the concept of dynamic interactive learning systems based on the emerging generation of information as part of a…

  19. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  20. Anticipatory Driving for a Robot-Car Based on Supervised Learning

    DEFF Research Database (Denmark)

    Markelic, I.; Kulvicius, Tomas; Tamosiunaite, M.

    2009-01-01

    Using look ahead information and plan making improves hu- man driving. We therefore propose that also autonomously driving systems should dispose over such abilities. We adapt a machine learning approach, where the system, a car-like robot, is trained by an experienced driver by correlating visual...... adapt a two-level ap- proach, where the result of the database is combined with an additional reactive controller for robust behavior. Concerning velocity control this paper makes a novel contribution which is the ability of the system to react adequatly to upcoming curves...

  1. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Benien, Parul; Ozcan, Aydogan

    2017-06-01

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of 0.8 cm2 and weighs only 180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved a

  2. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    Directory of Open Access Journals (Sweden)

    Ceylan Koydemir Hatice

    2017-06-01

    Full Text Available Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond

  3. Comparison of supervised machine learning algorithms for waterborne pathogen detection using mobile phone fluorescence microscopy

    KAUST Repository

    Ceylan Koydemir, Hatice

    2017-06-14

    Giardia lamblia is a waterborne parasite that affects millions of people every year worldwide, causing a diarrheal illness known as giardiasis. Timely detection of the presence of the cysts of this parasite in drinking water is important to prevent the spread of the disease, especially in resource-limited settings. Here we provide extended experimental testing and evaluation of the performance and repeatability of a field-portable and cost-effective microscopy platform for automated detection and counting of Giardia cysts in water samples, including tap water, non-potable water, and pond water. This compact platform is based on our previous work, and is composed of a smartphone-based fluorescence microscope, a disposable sample processing cassette, and a custom-developed smartphone application. Our mobile phone microscope has a large field of view of ~0.8 cm2 and weighs only ~180 g, excluding the phone. A custom-developed smartphone application provides a user-friendly graphical interface, guiding the users to capture a fluorescence image of the sample filter membrane and analyze it automatically at our servers using an image processing algorithm and training data, consisting of >30,000 images of cysts and >100,000 images of other fluorescent particles that are captured, including, e.g. dust. The total time that it takes from sample preparation to automated cyst counting is less than an hour for each 10 ml of water sample that is tested. We compared the sensitivity and the specificity of our platform using multiple supervised classification models, including support vector machines and nearest neighbors, and demonstrated that a bootstrap aggregating (i.e. bagging) approach using raw image file format provides the best performance for automated detection of Giardia cysts. We evaluated the performance of this machine learning enabled pathogen detection device with water samples taken from different sources (e.g. tap water, non-potable water, pond water) and achieved

  4. A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

    Science.gov (United States)

    Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

    2014-01-01

    Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

  5. Adaptive Learning Management System

    Directory of Open Access Journals (Sweden)

    Violeta Moisa

    2013-06-01

    Full Text Available This article is an introduction to a new model for an adaptive Learning Management System. It presents the current e-learning standards and describes the elements that can be used to create the system: the sequencing control modes, sequencing rules, navigation controls, learning records and learning record stores. The model is based on artificial intelligent algorithms that analyze the data captured for each user and creates an adaptive navigation path through the learning content of the system, allowing each user to experience the content in different ways

  6. Don't Leave Teaching to Chance: Learning Objectives for Psychodynamic Psychotherapy Supervision

    Science.gov (United States)

    Rojas, Alicia; Arbuckle, Melissa; Cabaniss, Deborah

    2010-01-01

    Objective: The way in which the competencies for psychodynamic psychotherapy specified by the Psychiatry Residency Review Committee of the Accreditation Council for Graduate Medical Education translate into the day-to-day work of individual supervision remains unstudied and unspecified. The authors hypothesized that despite the existence of…

  7. Pre-trained Convolutional Networks and generative statiscial models: a study in semi-supervised learning

    OpenAIRE

    John Michael Salgado Cebola

    2016-01-01

    Comparative study between the performance of Convolutional Networks using pretrained models and statistical generative models on tasks of image classification in semi-supervised enviroments.Study of multiple ensembles using these techniques and generated data from estimated pdfs.Pretrained Convents, LDA, pLSA, Fisher Vectors, Sparse-coded SPMs, TSVMs being the key models worked upon.

  8. Fieldwork online: a GIS-based electronic learning environment for supervising fieldwork

    NARCIS (Netherlands)

    Alberti, K.; Marra, W.A.; Baarsma, R.J.; Karssenberg, D.J.

    2016-01-01

    Fieldwork comes in many forms: individual research projects in unique places, large groups of students on organized fieldtrips, and everything in between those extremes. Supervising students in often distant places can be a logistical challenge and requires a significant time investment of their

  9. Enabling Connections in Postgraduate Supervision for an Applied eLearning Professional Development Programme

    Science.gov (United States)

    Donnelly, Roisin

    2013-01-01

    This article describes the practice of postgraduate supervision on a blended professional development programme for academics, and discusses how connectivism has been a useful lens to explore a complex form of instruction. By examining the processes by which supervisors and their students on a two-year part-time masters in Applied eLearning…

  10. An Early Historical Examination of the Educational Intent of Supervised Agricultural Experiences (SAEs) and Project-Based Learning in Agricultural Education

    Science.gov (United States)

    Smith, Kasee L.; Rayfield, John

    2016-01-01

    Project-based learning has been a component of agricultural education since its inception. In light of the current call for additional emphasis of the Supervised Agricultural Experience (SAE) component of agricultural education, there is a need to revisit the roots of project-based learning. This early historical research study was conducted to…

  11. Supervising and Controlling Unmanned Systems: A Multi-Phase Study with Subject Matter Experts.

    Science.gov (United States)

    Porat, Talya; Oron-Gilad, Tal; Rottem-Hovev, Michal; Silbiger, Jacob

    2016-01-01

    Proliferation in the use of Unmanned Aerial Systems (UASs) in civil and military operations has presented a multitude of human factors challenges; from how to bridge the gap between demand and availability of trained operators, to how to organize and present data in meaningful ways. Utilizing the Design Research Methodology (DRM), a series of closely related studies with subject matter experts (SMEs) demonstrate how the focus of research gradually shifted from "how many systems can a single operator control" to "how to distribute missions among operators and systems in an efficient way". The first set of studies aimed to explore the modal number, i.e., how many systems can a single operator supervise and control. It was found that an experienced operator can supervise up to 15 UASs efficiently using moderate levels of automation, and control (mission and payload management) up to three systems. Once this limit was reached, a single operator's performance was compared to a team controlling the same number of systems. In general, teams led to better performances. Hence, shifting design efforts toward developing tools that support teamwork environments of multiple operators with multiple UASs (MOMU). In MOMU settings, when the tasks are similar or when areas of interest overlap, one operator seems to have an advantage over a team who needs to collaborate and coordinate. However, in all other cases, a team was advantageous over a single operator. Other findings and implications, as well as future directions for research are discussed.

  12. Clinical supervision in a community setting.

    Science.gov (United States)

    Evans, Carol; Marcroft, Emma

    Clinical supervision is a formal process of professional support, reflection and learning that contributes to individual development. First Community Health and Care is committed to providing clinical supervision to nurses and allied healthcare professionals to support the provision and maintenance of high-quality care. In 2012, we developed new guidelines for nurses and AHPs on supervision, incorporating a clinical supervision framework. This offers a range of options to staff so supervision accommodates variations in work settings and individual learning needs and styles.

  13. Regulation and Supervision of The Global Financial System. A Proposal for Institutional Reform

    NARCIS (Netherlands)

    Denters, H.M.G.

    2009-01-01

    nternational financial markets are supervised primarily by national authorities. However, national authorities are inherently incapable to regulate and supervise seamless globalised financial markets. To the extent international regulators exist, they constitute a disorderly patchwork of

  14. An Industrial Control System for the Supervision of the CERN Electrical Distribution Network

    CERN Document Server

    Poulsen, S

    1999-01-01

    CERN operates a large distribution network for the supply of electricity to the particle accelerators, experiments and the associated infrastructure. The distribution network operates on voltage levels from 400 V to 400 kV with a total yearly consumption of near to 1000 GWh. In the past, the laboratory has developed an in-house control system for this network, using the technologies applied to the accelerator control system. However, CERN is now working on a project to purchase, configure and install an industrial Electrical Network Supervisor (ENS). This is a state-of-the-art industrial control system completely developed and supported by an external contractor. The system - based on a scalable and distributed architecture - will allow the installation to be performed gradually, and will be tested while the existing system is fully operational. Ultimately, the complete electrical distribution network will be supervised with this new system, the maintenance and further development of which will be the complet...

  15. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2016-01-01

    Background and objective: This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. Methods: The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). Results: AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. Conclusion: The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions. PMID:27802228

  16. Development of a Late-Life Dementia Prediction Index with Supervised Machine Learning in the Population-Based CAIDE Study.

    Science.gov (United States)

    Pekkala, Timo; Hall, Anette; Lötjönen, Jyrki; Mattila, Jussi; Soininen, Hilkka; Ngandu, Tiia; Laatikainen, Tiina; Kivipelto, Miia; Solomon, Alina

    2017-01-01

    This study aimed to develop a late-life dementia prediction model using a novel validated supervised machine learning method, the Disease State Index (DSI), in the Finnish population-based CAIDE study. The CAIDE study was based on previous population-based midlife surveys. CAIDE participants were re-examined twice in late-life, and the first late-life re-examination was used as baseline for the present study. The main study population included 709 cognitively normal subjects at first re-examination who returned to the second re-examination up to 10 years later (incident dementia n = 39). An extended population (n = 1009, incident dementia 151) included non-participants/non-survivors (national registers data). DSI was used to develop a dementia index based on first re-examination assessments. Performance in predicting dementia was assessed as area under the ROC curve (AUC). AUCs for DSI were 0.79 and 0.75 for main and extended populations. Included predictors were cognition, vascular factors, age, subjective memory complaints, and APOE genotype. The supervised machine learning method performed well in identifying comprehensive profiles for predicting dementia development up to 10 years later. DSI could thus be useful for identifying individuals who are most at risk and may benefit from dementia prevention interventions.

  17. Supervised pixel classification using a feature space derived from an artificial visual system

    Science.gov (United States)

    Baxter, Lisa C.; Coggins, James M.

    1991-01-01

    Image segmentation involves labelling pixels according to their membership in image regions. This requires the understanding of what a region is. Using supervised pixel classification, the paper investigates how groups of pixels labelled manually according to perceived image semantics map onto the feature space created by an Artificial Visual System. Multiscale structure of regions are investigated and it is shown that pixels form clusters based on their geometric roles in the image intensity function, not by image semantics. A tentative abstract definition of a 'region' is proposed based on this behavior.

  18. Recommender Systems for Learning

    CERN Document Server

    Manouselis, Nikos; Verbert, Katrien; Duval, Erik

    2013-01-01

    Technology enhanced learning (TEL) aims to design, develop and test sociotechnical innovations that will support and enhance learning practices of both individuals and organisations. It is therefore an application domain that generally covers technologies that support all forms of teaching and learning activities. Since information retrieval (in terms of searching for relevant learning resources to support teachers or learners) is a pivotal activity in TEL, the deployment of recommender systems has attracted increased interest. This brief attempts to provide an introduction to recommender systems for TEL settings, as well as to highlight their particularities compared to recommender systems for other application domains.

  19. System Supervision of Chinese Professional Football%论中国职业足球的体制性监管

    Institute of Scientific and Technical Information of China (English)

    刘霞

    2012-01-01

    现有足球体制监管的失范是导致职业足球领域丑闻不断的根源。从保证职业足球市场监管的专业化,确保足协对联赛监督机制的独立性,完善外部监督、保持监督的公开性和透明性等三方面,提出了完善我国职业足球体制性监管的对策。%The inadequate system supervision of Chinese football is the root of the scandals in the Chinese football field.The paper proposes to improve the system supervision of the Chinese professional football by guaranteeing the specific system supervision of the Chinese professional football,realizing the independent supervision of the league by the Football Association,improving the external supervision and ensuring the openness and transparency of the supervision.

  20. Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques

    Science.gov (United States)

    Cheng, Hsu-Yung; Lin, Chih-Lung

    2017-01-01

    Cloud detection is important for providing necessary information such as cloud cover in many applications. Existing cloud detection methods include red-to-blue ratio thresholding and other classification-based techniques. In this paper, we propose to perform cloud detection using supervised learning techniques with multi-resolution features. One of the major contributions of this work is that the features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We consider classifiers including random forest, support vector machine, and Bayesian classifier. To take advantage of the clues provided by multiple classifiers and various levels of patch sizes, we employ a voting scheme to combine the results to further increase the detection accuracy. In the experiments, we have shown that the proposed method can distinguish cloud and non-cloud pixels more accurately compared with existing works.

  1. Cavity contour segmentation in chest radiographs using supervised learning and dynamic programming

    Energy Technology Data Exchange (ETDEWEB)

    Maduskar, Pragnya, E-mail: pragnya.maduskar@radboudumc.nl; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van [Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Jong, Pim A. de [Department of Radiology, University Medical Center Utrecht, 3584 CX (Netherlands); Peters-Bax, Liesbeth [Department of Radiology, Radboud University Medical Center, Nijmegen, 6525 GA (Netherlands); Dawson, Rodney [University of Cape Town Lung Institute, Cape Town 7700 (South Africa); Ayles, Helen [Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London WC1E 7HT (United Kingdom)

    2014-07-15

    Purpose: Efficacy of tuberculosis (TB) treatment is often monitored using chest radiography. Monitoring size of cavities in pulmonary tuberculosis is important as the size predicts severity of the disease and its persistence under therapy predicts relapse. The authors present a method for automatic cavity segmentation in chest radiographs. Methods: A two stage method is proposed to segment the cavity borders, given a user defined seed point close to the center of the cavity. First, a supervised learning approach is employed to train a pixel classifier using texture and radial features to identify the border pixels of the cavity. A likelihood value of belonging to the cavity border is assigned to each pixel by the classifier. The authors experimented with four different classifiers:k-nearest neighbor (kNN), linear discriminant analysis (LDA), GentleBoost (GB), and random forest (RF). Next, the constructed likelihood map was used as an input cost image in the polar transformed image space for dynamic programming to trace the optimal maximum cost path. This constructed path corresponds to the segmented cavity contour in image space. Results: The method was evaluated on 100 chest radiographs (CXRs) containing 126 cavities. The reference segmentation was manually delineated by an experienced chest radiologist. An independent observer (a chest radiologist) also delineated all cavities to estimate interobserver variability. Jaccard overlap measure Ω was computed between the reference segmentation and the automatic segmentation; and between the reference segmentation and the independent observer's segmentation for all cavities. A median overlap Ω of 0.81 (0.76 ± 0.16), and 0.85 (0.82 ± 0.11) was achieved between the reference segmentation and the automatic segmentation, and between the segmentations by the two radiologists, respectively. The best reported mean contour distance and Hausdorff distance between the reference and the automatic segmentation were

  2. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.

    Science.gov (United States)

    Premaladha, J; Ravichandran, K S

    2016-04-01

    Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.

  3. Learning Content Management Systems

    Directory of Open Access Journals (Sweden)

    Tache JURUBESCU

    2008-01-01

    Full Text Available The paper explains the evolution of e-Learning and related concepts and tools and its connection with other concepts such as Knowledge Management, Human Resources Management, Enterprise Resource Planning, and Information Technology. The paper also distinguished Learning Content Management Systems from Learning Management Systems and Content Management Systems used for general web-based content. The newest Learning Content Management System, very expensive and yet very little implemented is one of the best tools that helps us to cope with the realities of the 21st Century in what learning concerns. The debates over how beneficial one or another system is for an organization, can be driven by costs involved, efficiency envisaged, and availability of the product on the market.

  4. THE IMPACT OF THE FINANCIAL CRISIS ON THE THEORY AND PRACTICE OF FINANCIAL SYSTEM SUPERVISION

    Directory of Open Access Journals (Sweden)

    Roxana Heteș

    2013-04-01

    Full Text Available The recent global financial crisis has reopened the debate about macroeconomic policies’ objectives, but also the need and extent of state involvement in the functioning of the economy, either directly or indirectly. This has exposed some weaknesses in the system of regulation and supervision of the financial system and the its architecture, especially in the treatment of systemic risks and vulnerabilities, but also the financial implications of the globalization process. The global nature of financial crisis highlighted the fact that, although integrated financial markets offer a number of significant benefits, risks involved are not negligible. Therefore, to ensure the financial stability of an increasingly integrated landscape there was felt the need for reform of the financial system architecture, both nationally and internationally.

  5. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care.

    Science.gov (United States)

    Öhman, Eva; Alinaghizadeh, Hassan; Kaila, Päivi; Hult, Håkan; Nilsson, Gunnar H; Salminen, Helena

    2016-12-01

    Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES) was originally developed for evaluation of nursing students' clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students' perceptions of their learning environment in primary health care. In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach's alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. CLES, in its adapted form, appears to be a valid instrument to evaluate medical students' perceptions of

  6. Adaptation and validation of the instrument Clinical Learning Environment and Supervision for medical students in primary health care

    Directory of Open Access Journals (Sweden)

    Eva Öhman

    2016-12-01

    Full Text Available Abstract Background Clinical learning takes place in complex socio-cultural environments that are workplaces for the staff and learning places for the students. In the clinical context, the students learn by active participation and in interaction with the rest of the community at the workplace. Clinical learning occurs outside the university, therefore is it important for both the university and the student that the student is given opportunities to evaluate the clinical placements with an instrument that allows evaluation from many perspectives. The instrument Clinical Learning Environment and Supervision (CLES was originally developed for evaluation of nursing students’ clinical learning environment. The aim of this study was to adapt and validate the CLES instrument to measure medical students’ perceptions of their learning environment in primary health care. Methods In the adaptation process the face validity was tested by an expert panel of primary care physicians, who were also active clinical supervisors. The adapted CLES instrument with 25 items and six background questions was sent electronically to 1,256 medical students from one university. Answers from 394 students were eligible for inclusion. Exploratory factor analysis based on principal component methods followed by oblique rotation was used to confirm the adequate number of factors in the data. Construct validity was assessed by factor analysis. Confirmatory factor analysis was used to confirm the dimensions of CLES instrument. Results The construct validity showed a clearly indicated four-factor model. The cumulative variance explanation was 0.65, and the overall Cronbach’s alpha was 0.95. All items loaded similarly with the dimensions in the non-adapted CLES except for one item that loaded to another dimension. The CLES instrument in its adapted form had high construct validity and high reliability and internal consistency. Conclusion CLES, in its adapted form, appears

  7. The collaborative model of fieldwork education: a blueprint for group supervision of students.

    Science.gov (United States)

    Hanson, Debra J; DeIuliis, Elizabeth D

    2015-04-01

    Historically, occupational therapists have used a traditional one-to-one approach to supervision on fieldwork. Due to the impact of managed care on health-care delivery systems, a dramatic increase in the number of students needing fieldwork placement, and the advantages of group learning, the collaborative supervision model has evolved as a strong alternative to an apprenticeship supervision approach. This article builds on the available research to address barriers to model use, applying theoretical foundations of collaborative supervision to practical considerations for academic fieldwork coordinators and fieldwork educators as they prepare for participation in group supervision of occupational therapy and occupational therapy assistant students on level II fieldwork.

  8. Remote Video Supervision in Adapted Physical Education

    Science.gov (United States)

    Kelly, Luke; Bishop, Jason

    2013-01-01

    Supervision for beginning adapted physical education (APE) teachers and inservice general physical education teachers who are learning to work with students with disabilities poses a number of challenges. The purpose of this article is to describe a project aimed at developing a remote video system that could be used by a university supervisor to…

  9. Development and psychometric testing of the Clinical Learning Environment, Supervision and Nurse Teacher evaluation scale (CLES+T): the Spanish version.

    Science.gov (United States)

    Vizcaya-Moreno, M Flores; Pérez-Cañaveras, Rosa M; De Juan, Joaquín; Saarikoski, Mikko

    2015-01-01

    The Clinical Learning Environment, Supervision and Nurse Teacher scale is a reliable and valid instrument to evaluate the quality of the clinical learning process in international nursing education contexts. This paper reports the development and psychometric testing of the Spanish version of the Clinical Learning Environment, Supervision and Nurse Teacher scale. Cross-sectional validation study of the scale. 10 public and private hospitals in the Alicante area, and the Faculty of Health Sciences (University of Alicante, Spain). 370 student nurses on clinical placement (January 2011-March 2012). The Clinical Learning Environment, Supervision and Nurse Teacher scale was translated using the modified direct translation method. Statistical analyses were performed using PASW Statistics 18 and AMOS 18.0.0 software. A multivariate analysis was conducted in order to assess construct validity. Cronbach's alpha coefficient was used to evaluate instrument reliability. An exploratory factorial analysis identified the five dimensions from the original version, and explained 66.4% of the variance. Confirmatory factor analysis supported the factor structure of the Spanish version of the instrument. Cronbach's alpha coefficient for the scale was .95, ranging from .80 to .97 for the subscales. This version of the Clinical Learning Environment, Supervision and Nurse Teacher scale instrument showed acceptable psychometric properties for use as an assessment scale in Spanish-speaking countries. Copyright © 2014 Elsevier Ltd. All rights reserved.

  10. Learning in the Absence of Direct Supervision: Person-Dependent Scaffolding

    Science.gov (United States)

    Palesy, Debra

    2017-01-01

    Contemporary accounts of learning emphasise the importance of immediate social partners such as teachers and co-workers. Yet, much of our learning for work occurs without such experts. This paper provides an understanding of how and why new home care workers use scaffolding to learn and enact safe manual handling techniques in their workplaces,…

  11. Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning

    Institute of Scientific and Technical Information of China (English)

    周竞; 朱山风; 黄晓地; 张彦春

    2015-01-01

    Time series clustering is widely applied in various areas. Existing researches focus mainly on distance measures between two time series, such as dynamic time warping (DTW) based methods, edit-distance based methods, and shapelets-based methods. In this work, we experimentally demonstrate, for the first time, that no single distance measure performs significantly better than others on clustering datasets of time series where spectral clustering is used. As such, a question arises as to how to choose an appropriate measure for a given dataset of time series. To answer this question, we propose an integration scheme that incorporates multiple distance measures using semi-supervised clustering. Our approach is able to integrate all the measures by extracting valuable underlying information for the clustering. To the best of our knowledge, this work demonstrates for the first time that the semi-supervised clustering method based on constraints is able to enhance time series clustering by combining multiple distance measures. Having tested on clustering various time series datasets, we show that our method outperforms individual measures, as well as typical integration approaches.

  12. Social Network Analysis and Big Data tools applied to the Systemic Risk supervision

    Directory of Open Access Journals (Sweden)

    Mari-Carmen Mochón

    2016-03-01

    Full Text Available After the financial crisis initiated in 2008, international market supervisors of the G20 agreed to reinforce their systemic risk supervisory duties. For this purpose, several regulatory reporting obligations were imposed to the market participants. As a consequence, millions of trade details are now available to National Competent Authorities on a daily basis. Traditional monitoring tools may not be capable of analyzing such volumes of data and extracting the relevant information, in order to identify the potential risks hidden behind the market. Big Data solutions currently applied to the Social Network Analysis (SNA, can be successfully applied the systemic risk supervision. This case of study proposes how relations established between the financial market participants could be analyzed, in order to identify risk of propagation and market behavior, without the necessity of expensive and demanding technical architectures.

  13. Algorithm of Supervised Learning on Outlier Manifold%有监督的噪音流形学习算法

    Institute of Scientific and Technical Information of China (English)

    黄添强; 李凯; 郑之

    2011-01-01

    流形学习算法是维度约简与数据可视化领域的重要工具,提高算法的效率与健壮性对其实际应用有积极意义.经典的流形学习算法普遍的对噪音点较为敏感,现有的改进算法尚存在不足.本文提出一种基于监督学习与核函数的健壮流形学习算法,把核方法与监督学习引入降维过程,利用已知标签数据信息与核函数特性,使得同类样本变得紧密,不同类样本变成分散,提高后续分类任务的效果,降低算法对流形上噪音的敏感性.在UCI数据与白血病拉曼光谱数据上的实验表明本文改进的算法具有更高的抗噪性.%Manifold learning algorithm is an important tool in the field of dimension reduction and data visualization. Improving the algorithm's efficiency and robustness is of positive significance to its practical application. Classical manifold learning algorithm is sensitive to noise points,and its improved algorithms have been imperfect. This paper presents a robust manifold learning algorithm based on supervised learning and kernel function. It introduces nuclear methods and supervised learning into the dimensionality reduction ,and takes full advantage of the label of some data and the property of kernel function. The proposed algorithm can make close and same types of samples and distribute different types of samples,thus to improves the effect of the classification task and reduce the noise sensitivity of outliers on manifold. The experiments on the UCI data and Raman data of leukemia reveal that the algorithm has better noise immunity.

  14. Enhancing the Standard of Teaching and Learning in the 21st Century via Qualitative School-Based Supervision in Secondary Schools in Abuja Municipal Area Council (AMAC)

    Science.gov (United States)

    Ebele, Uju F.; Olofu, Paul A.

    2017-01-01

    The study focused on enhancing the standard of teaching and learning in the 21st century via qualitative school-based supervision in secondary schools in Abuja municipal area council. To guide the study, two null hypotheses were formulated. A descriptive survey research design was adopted. The sample of the study constituted of 270 secondary…

  15. Exploration of joint redundancy but not task space variability facilitates supervised motor learning.

    Science.gov (United States)

    Singh, Puneet; Jana, Sumitash; Ghosal, Ashitava; Murthy, Aditya

    2016-12-13

    The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

  16. Reflections on Doctoral Supervision: Drawing from the Experiences of Students with Additional Learning Needs in Two Universities

    Science.gov (United States)

    Collins, Bethan

    2015-01-01

    Supervision is an essential part of doctoral study, consisting of relationship and process aspects, underpinned by a range of values. To date there has been limited research specifically about disabled doctoral students' experiences of supervision. This paper draws on qualitative, narrative interviews about doctoral supervision with disabled…

  17. Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats.

    Science.gov (United States)

    Awaysheh, Abdullah; Wilcke, Jeffrey; Elvinger, François; Rees, Loren; Fan, Weiguo; Zimmerman, Kurt L

    2016-11-01

    Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p machine learning provided a method for distinguishing between ALA-IBD, ALA-normal, and IBD-normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats. © 2016 The Author(s).

  18. Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization

    CERN Document Server

    Garcia-Cardona, Cristina; Percus, Allon G

    2013-01-01

    We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes. Motivated by total variation techniques, the method involves minimizing an energy functional made up of three terms. The first two terms promote a stepwise continuous classification function with sharp transitions between classes, while preserving symmetry among the class labels. The third term is a data fidelity term, allowing us to incorporate prior information into the model in a semi-supervised framework. The performance of the algorithm on synthetic data, as well as on the COIL and MNIST benchmark datasets, is competitive with state-of-the-art graph-based multiclass segmentation methods.

  19. Using group supervision and social annotation systems to support students’ academic writing

    Directory of Open Access Journals (Sweden)

    Daniel Pargman

    2013-01-01

    Full Text Available In this best practice paper, we present how we have used a Social annotation system (SAS in a bachelor’s thesis course in media technology to support students’ academic writing. In the paper, we reflect on both technical and social practices with using SAS. Despite limited instructional support and despite the fact that different groups used SAS in different ways, there have been a high completion rate, good quality of the theses and satisfied students. The combination of group supervision and the use of SAS has been successful, especially when taking into consideration that this was the first year we broadly introduced SAS in the bachelor’s thesis course. 

  20. Student experiences in learning person-centred care of patients with Alzheimer's disease as perceived by nursing students and supervising nurses.

    Science.gov (United States)

    Skaalvik, Mari W; Normann, Hans Ketil; Henriksen, Nils

    2010-09-01

    The aims and objectives of this paper are to illuminate and discuss the experiences and perceptions of nursing students and supervising nurses regarding the students' learning of person- centred care of patients with Alzheimer's disease in a teaching nursing home. This information is then used to develop recommendations as to how student learning could be improved. The clinical experiences of nursing students are an important part of learning person-centred care. Caring for patients with Alzheimer's disease may cause frustration, sadness, fear and empathy. Person-centred care can be learned in clinical practice. A qualitative study. The study was performed in 2006 using field work with field notes and qualitative interviews with seven-fifth-semester nursing students and six supervising nurses. This study determined the variation in the perceptions of nursing students and supervising nurses with regards to the students' expertise in caring for patients with Alzheimer's disease. The nursing students experienced limited learning regarding person-centred approaches in caring for patients with Alzheimer's disease. However, the supervising nurses perceived the teaching nursing home as a site representing multiple learning opportunities in this area. Nursing students perceived limited learning outcomes because they did not observe or experience systematic person-centred approaches in caring for patients with Alzheimer's disease. It is important that measures of quality improvements in the care of patients with Alzheimer's disease are communicated and demonstrated for nursing students working in clinical practices in a teaching nursing home. Introduction of person-centred approaches is vital regarding learning outcomes for nursing students caring for patients with Alzheimer's disease. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

  1. Dolanan Dance Learning on Supervising Pre-Service Teachers during Teaching Practicum Program

    Directory of Open Access Journals (Sweden)

    Nilam Cahyaningrum

    2015-01-01

    Full Text Available Taman Kanak- kanak Mekarsari (Mekarsari Kindergarten is a school that choses dolanan anak dance lesson which is taught using demonstration methods. This study aims to find, understand, and describe the process and learning outcomes of dolanan anak dance in Mekarsari Kindergarten, Kandeman District of Batang. This study uses qualitative research methods with a phenomenological approach to research sites in Mekarsari Kindergarten, Kandeman District of Batang. Data collection techniques used were observation, interview techniques, and technical documentation. Data analysis were using data reduction, data presentation, drawing conclusions, and verification. The validity test were using triangulation of data sources, techniques, and time. Dolanan anak dance learning in Mekarsari Kindergarten consists of several components, namely teaching and learning activities, goals, teachers, students, materials, methods, media, tools and learning resources, and evaluation. Dolanan dance learning was using demonstration method implemented through three stages: pre-development activities, core activities, and closing activities. The learning outcomes of dolanan anak dance learning in Mekarsari kindergarten were categorized into three aspects, namely cognitive, affective, and psychomotor. Cognitive aspects can be seen from the students’ ability to remember, memorize and understand the dance. Affective aspects include familiar levels, namely learning to know friends and dance movements, respond the movements amomg friends, and appreciate the teacher’s explanation given to each student. Psychomotor aspects can be seen from the students’ ability to imitate the dance movements, use the concept of doing the movements and precision of movements, weave movement and exercise appropriately.

  2. Supervised Expert System for Wearable MEMS Accelerometer-Based Fall Detector

    Directory of Open Access Journals (Sweden)

    Gabriele Rescio

    2013-01-01

    Full Text Available Falling is one of the main causes of trauma, disability, and death among older people. Inertial sensors-based devices are able to detect falls in controlled environments. Often this kind of solution presents poor performances in real conditions. The aim of this work is the development of a computationally low-cost algorithm for feature extraction and the implementation of a machine-learning scheme for people fall detection, by using a triaxial MEMS wearable wireless accelerometer. The proposed approach allows to generalize the detection of fall events in several practical conditions. It appears invariant to the age, weight, height of people, and to the relative positioning area (even in the upper part of the waist, overcoming the drawbacks of well-known threshold-based approaches in which several parameters need to be manually estimated according to the specific features of the end user. In order to limit the workload, the specific study on posture analysis has been avoided, and a polynomial kernel function is used while maintaining high performances in terms of specificity and sensitivity. The supervised clustering step is achieved by implementing an one-class support vector machine classifier in a stand-alone PC.

  3. Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques

    Science.gov (United States)

    Omenzetter, Piotr; de Lautour, Oliver R.

    2010-04-01

    Developed for studying long, periodic records of various measured quantities, time series analysis methods are inherently suited and offer interesting possibilities for Structural Health Monitoring (SHM) applications. However, their use in SHM can still be regarded as an emerging application and deserves more studies. In this research, Autoregressive (AR) models were used to fit experimental acceleration time histories from two experimental structural systems, a 3- storey bookshelf-type laboratory structure and the ASCE Phase II SHM Benchmark Structure, in healthy and several damaged states. The coefficients of the AR models were chosen as damage sensitive features. Preliminary visual inspection of the large, multidimensional sets of AR coefficients to check the presence of clusters corresponding to different damage severities was achieved using Sammon mapping - an efficient nonlinear data compression technique. Systematic classification of damage into states based on the analysis of the AR coefficients was achieved using two supervised classification techniques: Nearest Neighbor Classification (NNC) and Learning Vector Quantization (LVQ), and one unsupervised technique: Self-organizing Maps (SOM). This paper discusses the performance of AR coefficients as damage sensitive features and compares the efficiency of the three classification techniques using experimental data.

  4. Scada Systems – Control, Supervision and Data Acquisition for the Power Plants Settled on a Stream (Part 1

    Directory of Open Access Journals (Sweden)

    Cosmin Ursoniu

    2015-09-01

    Full Text Available Scada (supervisory control and data acquisition is a complex system that supervises and control an industrial process and performs several functions. A human machine interface will also be presented and how the process in a power plant is controlled and supervised through it by the operator. The main screen will be described (which is a global view of the hydro unit and what the operator can see and what he can press to control the power plants process also a few more screens will be presented for auxiliary installations and it will be described what the operator can see and what he can do to control the installation.

  5. Scada Systems – Control, Supervision and Data Acquisition for the Power Plants Settled on a Stream (Part 2

    Directory of Open Access Journals (Sweden)

    Cosmin Ursoniu

    2015-09-01

    Full Text Available Scada (supervisory control and data acquisition is a complex system that supervises and controls an industrial process and performs several functions. A human machine interface will also be presented and how the process in a power plant is controlled and supervised through it by the operator. The main screen will be described (which is a global view of the hydro unit and what the operator can see and what he can press to control the power plants process also a few more screens will be presented for auxiliary installations and it will be described what the operator can see and what he can do to control the installation.

  6. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers

    Energy Technology Data Exchange (ETDEWEB)

    Kilgour, Andrew J, E-mail: akilgour@csu.edu.au [Charles Sturt University, Wagga Wagga, NSW (Australia); Kilgour, Peter W [Avondale College of Higher Education, Cooranbong, NSW (Australia); Gerzina, Tania [Dental Educational Research, Faculty of Dentistry, Jaw Function and Orofacial Pain Research Unit, Westmead Centre for Oral Health, C24- Westmead Hospital, The University of Sydney, Sydney, NSW, 2006 (Australia); Christian, Beverly [Avondale College of Higher Education, Cooranbong, NSW (Australia); Charles Sturt University, Wagga Wagga, NSW (Australia)

    2014-02-15

    Introduction: Professional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. Methods: WIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students’ work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors’ reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. Results: Benefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different

  7. Assessment of work-integrated learning: comparison of the usage of a grading rubric by supervising radiographers and teachers.

    Science.gov (United States)

    Kilgour, Andrew J; Kilgour, Peter W; Gerzina, Tania; Christian, Beverly

    2014-02-01

    IntroductionProfessional work-integrated learning (WIL) that integrates the academic experience with off-campus professional experience placements is an integral part of many tertiary courses. Issues with the reliability and validity of assessment grades in these placements suggest that there is a need to strengthen the level of academic rigour of placements in these programmes. This study aims to compare the attitudes to the usage of assessment rubrics of radiographers supervising medical imaging students and teachers supervising pre-service teachers. MethodsWIL placement assessment practices in two programmes, pre-service teacher training (Avondale College of Higher Education, NSW) and medical diagnostic radiography (Faculty of Health Sciences, University of Sydney, NSW), were compared with a view to comparing assessment strategies across these two different educational domains. Educators (course coordinators) responsible for teaching professional development placements of teacher trainees and diagnostic radiography students developed a standards-based grading rubric designed to guide assessment of students' work during WIL placement by assessors. After ∼12 months of implementation of the rubrics, assessors' reaction to the effectiveness and usefulness of the grading rubric was determined using a specially created survey form. Data were collected over the period from March to June 2011. Quantitative and qualitative data found that assessors in both programmes considered the grading rubric to be a vital tool in the assessment process, though teacher supervisors were more positive about the benefits of its use than the radiographer supervisors. ResultsBenefits of the grading rubric included accuracy and consistency of grading, ability to identify specific areas of desired development and facilitation of the provision of supervisor feedback. The use of assessment grading rubrics is of benefit to assessors in WIL placements from two very different teaching

  8. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Directory of Open Access Journals (Sweden)

    Chao Wei

    Full Text Available Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  9. Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation.

    Science.gov (United States)

    Wei, Chao; Luo, Senlin; Ma, Xincheng; Ren, Hao; Zhang, Ji; Pan, Limin

    2016-01-01

    Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.

  10. Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms.

    Science.gov (United States)

    Jagga, Zeenia; Gupta, Dinesh

    2014-01-01

    Clear-cell Renal Cell Carcinoma (ccRCC) is the most- prevalent, chemotherapy resistant and lethal adult kidney cancer. There is a need for novel diagnostic and prognostic biomarkers for ccRCC, due to its heterogeneous molecular profiles and asymptomatic early stage. This study aims to develop classification models to distinguish early stage and late stage of ccRCC based on gene expression profiles. We employed supervised learning algorithms- J48, Random Forest, SMO and Naïve Bayes; with enriched model learning by fast correlation based feature selection to develop classification models trained on sequencing based gene expression data of RNAseq experiments, obtained from The Cancer Genome Atlas. Different models developed in the study were evaluated on the basis of 10 fold cross validations and independent dataset testing. Random Forest based prediction model performed best amongst the models developed in the study, with a sensitivity of 89%, accuracy of 77% and area under Receivers Operating Curve of 0.8. We anticipate that the prioritized subset of 62 genes and prediction models developed in this study will aid experimental oncologists to expedite understanding of the molecular mechanisms of stage progression and discovery of prognostic factors for ccRCC tumors.

  11. 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.

  12. [Application of globe geographical positioning with wireless communication monitoring and supervision system in field survey on the endemic of schistosomiasis].

    Science.gov (United States)

    Yu, Qing; Bao, Zi-ping; Cao, Chun-li; Zhu, Hong-qing; Guo, Jia-gang

    2007-09-01

    To evaluate the practical value and the advantages of globe geographical positioning with wireless communication monitoring and supervision system in the field survey. Spots which were randomly sampled by the National Ministry of Health for the investigation were chosen in the endemic areas for schistosomiasis in Jiangsu, Jiangxi, Anhui, Hunan, Hubei, Sichuan and Yunnan provinces. Portable GPS CEC9680 was used for collecting relevant waypoints and track, recording on-the-spot geographical positions. The positioning data package was sent back synchronously in the form of short message of SMS to the monitoring service center, and the moving routes of the terminal receiver monitored were displayed on the GIS map to achieve real-time supervision and staff scheduling. With globe geographical positioning with wireless communication monitoring and supervision system, accurate positioning of 12 spots in the provinces of Jiangsu and 3 trial spots for schistosomiasis control with comprehensive treatment designated by the State Council has been established with real-time communicating recording, and monitoring systems. The globe geographical positioning with wireless communication monitoring and supervision system has provided a technical platform for the survey of schistosomiasis and other infectious diseases.

  13. [Medical and hygienic aspects of instrumental supervision system over nuclear materials and radioactive substances transport on Russian Federation territory].

    Science.gov (United States)

    Grabskiĭ, Iu V; Gavrish, N N; Shevchenko, G T; Viaz'min, S O; Pertsev, V S; Kirillov, V F; Tsov'ianov, A G

    2014-01-01

    Hygienic evaluation of radiation situation in operation of mobile and stationery elements within a project of national system for instrumental supervision over nuclear materials and radioactive substances transport, created with a Global initiative against nuclear terrorism. Levels of exposure to ionizing radiation of the screening complexes appeared to match requirements on radiation safety for service personnel and general population.

  14. On Training Targets for Supervised Speech Separation

    OpenAIRE

    Wang, Yuxuan; Narayanan, Arun; Wang, DeLiang

    2014-01-01

    Formulation of speech separation as a supervised learning problem has shown considerable promise. In its simplest form, a supervised learning algorithm, typically a deep neural network, is trained to learn a mapping from noisy features to a time-frequency representation of the target of interest. Traditionally, the ideal binary mask (IBM) is used as the target because of its simplicity and large speech intelligibility gains. The supervised learning framework, however, is not restricted to the...

  15. Částečně řízené učení algoritmů strojového učení (semi-supervised learning)

    OpenAIRE

    Burda, Karel

    2014-01-01

    The final thesis summarizes in its theoretical part basic knowledge of machine learning algorithms that involves supervised, semi-supervised, and unsupervised learning. Experiments with textual data in natural spoken language involving different machine learning methods and parameterization are carried out in its practical part. Conclusions made in the thesis may be of use to individuals that are at least slightly interested in this domain.

  16. Evaluating students' perception of their clinical placements - testing the clinical learning environment and supervision and nurse teacher scale (CLES + T scale) in Germany.

    Science.gov (United States)

    Bergjan, Manuela; Hertel, Frank

    2013-11-01

    Clinical nursing education in Germany has not received attention in nursing science and practice for a long time, as it often seems to be a more or less "formalized appendix" of nursing education. Several development projects of clinical education taking place are mainly focused on the qualification of clinical preceptors. However, the clinical context and its influence on learning processes have still not been sufficiently investigated. The aim of this study was the testing of a German version of the clinical learning environment and supervision and nurse teacher scale (CLES + T scale). The sample of the pilot study consists of first-, second- and third-year student nurses (n=240) of a university nursing school from January to March 2011. Psychometric testing of the instrument is carried out by selected methods of classical testing theories using SPPS 19. The results show transferability of all subcategories of the CLES + T scale in the non-academic nursing education system of a university hospital in Germany, without the teacher scale. The strongest factor is "supervisory relationship". The German version of the CLES + T scale may help to evaluate and compare traditional and new models in clinical nursing education.

  17. Semi-Supervised Multi-View Learning in Big Data%半监督多视图学习在大数据分析中的应用探讨

    Institute of Scientific and Technical Information of China (English)

    蓝超; 饶泓; 浣军

    2015-01-01

    半监督多视图学习是机器学习领域一种极具潜力的大数据处理和分析方法,该方法能有效处理异构和半监督数据,并能方便地在线化和并行化,适合处理海量数据.该方法在大数据时代的应用前景值得研究人员和业界关注.指出未来需要通过引入其他领域新的研究技术和成果,不断丰富和完善半监督多视图学习的理论体系和算法设计,并在实验和实践中不断检验和探索.%This paper introduces a promising machine-learning paradigm cal ed semi-supervised multi-view learning. With this paradigm, information is extracted from heterogeneous and semi-supervised data sets. Lately, multi-view learning has been scaled up online and through paral elization to deal with emerging big data chal enges. Due to its successful application in many research domains and the fact that it has been explored and used by leading companies, multi-view learning may have a future in the big-data era as a major data analytic technique. New research techniques should be introduced into this area to improve the theoretical system and algorithm design of semi-supervised multi-view learning.

  18. Collective academic supervision

    DEFF Research Database (Denmark)

    Nordentoft, Helle Merete; Thomsen, Rie; Wichmann-Hansen, Gitte

    2013-01-01

    are interconnected. Collective Academic Supervision provides possibilities for systematic interaction between individual master students in their writing process. In this process they learn core academic competencies, such as the ability to assess theoretical and practical problems in their practice and present them...

  19. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  20. Supervision of Teachers Based on Adjusted Arithmetic Learning in Special Education

    Science.gov (United States)

    Eriksson, Gota

    2008-01-01

    This article reports on 20 children's learning in arithmetic after teaching was adjusted to their conceptual development. The report covers periods from three months up to three terms in an ongoing intervention study of teachers and children in schools for the intellectually disabled and of remedial teaching in regular schools. The researcher…

  1. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    CERN Document Server

    Möller, A; Leloup, C; Neveu, J; Palanque-Delabrouille, N; Rich, J; Carlberg, R; Lidman, C; Pritchet, C

    2016-01-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts ($0.2learning classification. We study the performance of different algorithms such as Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia sa...

  2. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-12-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  3. Supervised Learning Approach for Spam Classification Analysis using Data Mining Tools

    Directory of Open Access Journals (Sweden)

    R.Deepa Lakshmi

    2010-11-01

    Full Text Available E-mail is one of the most popular and frequently used ways of communication due to its worldwide accessibility, relatively fast message transfer, and low sending cost. The flaws in the e-mail protocols and the increasing amount of electronic business and financial transactions directly contribute to the increase in e-mail-based threats. Email spam is one of the major problems of the today’s Internet, bringing financial damage to companies and annoying individual users. Among the approaches developed to stop spam, filtering is the one of the most important technique. Many researches in spam filtering have been centered on the more sophisticated classifierrelated issues. In recent days, Machine learning for spamclassification is an important research issue. This paper exploresand identifies the use of different learning algorithms for classifying spam messages from e-mail. A comparative analysisamong the algorithms has also been presented.

  4. Manifold regularized multitask learning for semi-supervised multilabel image classification.

    Science.gov (United States)

    Luo, Yong; Tao, Dacheng; Geng, Bo; Xu, Chao; Maybank, Stephen J

    2013-02-01

    It is a significant challenge to classify images with multiple labels by using only a small number of labeled samples. One option is to learn a binary classifier for each label and use manifold regularization to improve the classification performance by exploring the underlying geometric structure of the data distribution. However, such an approach does not perform well in practice when images from multiple concepts are represented by high-dimensional visual features. Thus, manifold regularization is insufficient to control the model complexity. In this paper, we propose a manifold regularized multitask learning (MRMTL) algorithm. MRMTL learns a discriminative subspace shared by multiple classification tasks by exploiting the common structure of these tasks. It effectively controls the model complexity because different tasks limit one another's search volume, and the manifold regularization ensures that the functions in the shared hypothesis space are smooth along the data manifold. We conduct extensive experiments, on the PASCAL VOC'07 dataset with 20 classes and the MIR dataset with 38 classes, by comparing MRMTL with popular image classification algorithms. The results suggest that MRMTL is effective for image classification.

  5. A MACROPRUDENTIAL SUPERVISION MODEL. EMPIRICAL EVIDENCE FROM THE CENTRAL AND EASTERN EUROPEAN BANKING SYSTEM

    Directory of Open Access Journals (Sweden)

    Trenca Ioan

    2013-07-01

    Full Text Available One of the positive effects of the financial crises is the increasing concern of the supervisors regarding the financial system’s stability. There is a need to strengthen the links between different financial components of the financial system and the macroeconomic environment. Banking systems that have an adequate capitalization and liquidity level may face easier economic and financial shocks. The purpose of this empirical study is to identify the main determinants of the banking system’s stability and soundness in the Central and Eastern Europe countries. We asses the impact of different macroeconomic variables on the quality of capital and liquidity conditions and examine the behaviour of these financial stability indicators, by analyzing a sample of 10 banking systems during 2000-2011. The availability of banking capital signals the banking system’s resiliency to shocks. Capital adequacy ratio is the main indicator used to assess the banking fragility. One of the causes of the 2008-2009 financial crisis was the lack of liquidity in the banking system which led to the collapse of several banking institutions and macroeconomic imbalances. Given the importance of liquidity for the banking system, we propose several models in order to determine the macroeconomic variables that have a significant influence on the liquid reserves to total assets ratio. We found evidence that GDP growth, inflation, domestic credit to private sector, as well as the money and quasi money aggregate indicator have significant impact on the banking stability. The empirical regression confirms the high level of interdependence of the real sector with the financial-banking sector. Also, they prove the necessity for an effective macro prudential supervision at country level which enables the supervisory authorities to have an adequate control over the macro prudential indicators and to take appropriate decisions at the right time.

  6. The perceptions of nurses in a district health system in KwaZulu-Natal of their supervision, self-esteem and job satisfaction

    Directory of Open Access Journals (Sweden)

    L.R. Uys

    2004-09-01

    Full Text Available Supervision has been identified as a major issue in quality of care. Although increasing attention is being given to supervision in the District Health System, there have been no studies describing the current situation. This article describes a survey done in two health districts in KwaZulu-Natal involving 319 nurses from all types of government health care settings.

  7. Anti-crisis Regulation, Banking Supervision and Monetary Policy in the Mechanism of Ensuring Financial Stability of the Banking System

    OpenAIRE

    Dovgan Zhanna N.

    2013-01-01

    The article develops a scheme of correlations of subjects of anti-crisis regulation and tools of support of financial stability of the banking system of Ukraine (FSBS). It analyses instruments of influence of the bodies of state regulation on financial stability of the banking system of Ukraine. It develops proposals with respect to improvement of the banking supervision in the context of ensuring FSBS of Ukraine. It studies interdependence between the type of the mode of national regulatory ...

  8. 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

    2017-06-09

    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.

  9. Automated cell analysis tool for a genome-wide RNAi screen with support vector machine based supervised learning

    Science.gov (United States)

    Remmele, Steffen; Ritzerfeld, Julia; Nickel, Walter; Hesser, Jürgen

    2011-03-01

    RNAi-based high-throughput microscopy screens have become an important tool in biological sciences in order to decrypt mostly unknown biological functions of human genes. However, manual analysis is impossible for such screens since the amount of image data sets can often be in the hundred thousands. Reliable automated tools are thus required to analyse the fluorescence microscopy image data sets usually containing two or more reaction channels. The herein presented image analysis tool is designed to analyse an RNAi screen investigating the intracellular trafficking and targeting of acylated Src kinases. In this specific screen, a data set consists of three reaction channels and the investigated cells can appear in different phenotypes. The main issue of the image processing task is an automatic cell segmentation which has to be robust and accurate for all different phenotypes and a successive phenotype classification. The cell segmentation is done in two steps by segmenting the cell nuclei first and then using a classifier-enhanced region growing on basis of the cell nuclei to segment the cells. The classification of the cells is realized by a support vector machine which has to be trained manually using supervised learning. Furthermore, the tool is brightness invariant allowing different staining quality and it provides a quality control that copes with typical defects during preparation and acquisition. A first version of the tool has already been successfully applied for an RNAi-screen containing three hundred thousand image data sets and the SVM extended version is designed for additional screens.

  10. A spatio-temporal latent atlas for semi-supervised learning of fetal brain segmentations and morphological age estimation.

    Science.gov (United States)

    Dittrich, Eva; Riklin Raviv, Tammy; Kasprian, Gregor; Donner, René; Brugger, Peter C; Prayer, Daniela; Langs, Georg

    2014-01-01

    Prenatal neuroimaging requires reference models that reflect the normal spectrum of fetal brain development, and summarize observations from a representative sample of individuals. Collecting a sufficiently large data set of manually annotated data to construct a comprehensive in vivo atlas of rapidly developing structures is challenging but necessary for large population studies and clinical application. We propose a method for the semi-supervised learning of a spatio-temporal latent atlas of fetal brain development, and corresponding segmentations of emerging cerebral structures, such as the ventricles or cortex. The atlas is based on the annotation of a few examples, and a large number of imaging data without annotation. It models the morphological and developmental variability across the population. Furthermore, it serves as basis for the estimation of a structures' morphological age, and its deviation from the nominal gestational age during the assessment of pathologies. Experimental results covering the gestational period of 20-30 gestational weeks demonstrate segmentation accuracy achievable with minimal annotation, and precision of morphological age estimation. Age estimation results on fetuses suffering from lissencephaly demonstrate that they detect significant differences in the age offset compared to a control group. Copyright © 2013. Published by Elsevier B.V.

  11. Predicting the Ecological Quality Status of Marine Environments from eDNA Metabarcoding Data Using Supervised Machine Learning.

    Science.gov (United States)

    Cordier, Tristan; Esling, Philippe; Lejzerowicz, Franck; Visco, Joana; Ouadahi, Amine; Martins, Catarina; Cedhagen, Tomas; Pawlowski, Jan

    2017-08-15

    Monitoring biodiversity is essential to assess the impacts of increasing anthropogenic activities in marine environments. Traditionally, marine biomonitoring involves the sorting and morphological identification of benthic macro-invertebrates, which is time-consuming and taxonomic-expertise demanding. High-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) represents a promising alternative for benthic monitoring. However, an important fraction of eDNA sequences remains unassigned or belong to taxa of unknown ecology, which prevent their use for assessing the ecological quality status. Here, we show that supervised machine learning (SML) can be used to build robust predictive models for benthic monitoring, regardless of the taxonomic assignment of eDNA sequences. We tested three SML approaches to assess the environmental impact of marine aquaculture using benthic foraminifera eDNA, a group of unicellular eukaryotes known to be good bioindicators, as features to infer macro-invertebrates based biotic indices. We found similar ecological status as obtained from macro-invertebrates inventories. We argue that SML approaches could overcome and even bypass the cost and time-demanding morpho-taxonomic approaches in future biomonitoring.

  12. Translation and validation of the clinical learning environment, supervision and nurse teacher scale (CLES + T) in Croatian language.

    Science.gov (United States)

    Lovrić, Robert; Piškorjanac, Silvija; Pekić, Vlasta; Vujanić, Jasenka; Ratković, Karolina Kramarić; Luketić, Suzana; Plužarić, Jadranka; Matijašić-Bodalec, Dubravka; Barać, Ivana; Žvanut, Boštjan

    2016-07-01

    Clinical practice is essential to nursing education as it provides experience with patients and work environments that prepare students for future work as nurses. The aim of this study was to translate the "Clinical Learning Environment, Supervision and Nurse Teacher" questionnaire in Croatian language and test its validity and reliability in practice. The study was performed at the Faculty of medicine, Josip Juraj Strossmayer University of Osijek, Croatia in April 2014. The translated questionnaire was submitted to 136 nursing students: 20 males and 116 females. Our results reflected a slightly different factor structure, consisting of four factors. All translated items of the original constructs "Supervisory relationship", "Role of nurse teacher" and "Leadership style of the ward manager" loaded on factor 1. Items of "Pedagogical atmosphere on the ward" are distributed on two factors (3 and 4). The items of "Premises of nursing on the ward" loaded on factor 2. Three items were identified as problematic and iteratively removed from the analysis. The translated version of the aforementioned questionnaire has properties suitable for the evaluation of clinical practice for nursing students within a Croatian context and reflects the specifics of the nursing clinical education in this country.

  13. A neuron model with trainable activation function (TAF) and its MFNN supervised learning

    Institute of Scientific and Technical Information of China (English)

    吴佑寿; 赵明生

    2001-01-01

    This paper addresses a new kind of neuron model, which has trainable activation function (TAF) in addition to only trainable weights in the conventional M-P model. The final neuron activation function can be derived from a primitive neuron activation function by training. The BP like learning algorithm has been presented for MFNN constructed by neurons of TAF model. Several simulation examples are given to show the network capacity and performance advantages of the new MFNN in comparison with that of conventional sigmoid MFNN.

  14. Unsupervised Labeling Of Data For Supervised Learning And Its Application To Medical Claims Prediction

    Directory of Open Access Journals (Sweden)

    Che Ngufor

    2013-01-01

    Full Text Available The task identifying changes and irregularities in medical insurance claim pay-ments is a difficult process of which the traditional practice involves queryinghistorical claims databases and flagging potential claims as normal or abnor-mal. Because what is considered as normal payment is usually unknown andmay change over time, abnormal payments often pass undetected; only to bediscovered when the payment period has passed.This paper presents the problem of on-line unsupervised learning from datastreams when the distribution that generates the data changes or drifts overtime. Automated algorithms for detecting drifting concepts in a probabilitydistribution of the data are presented. The idea behind the presented driftdetection methods is to transform the distribution of the data within a slidingwindow into a more convenient distribution. Then, a test statistics p-value ata given significance level can be used to infer the drift rate, adjust the windowsize and decide on the status of the drift. The detected concepts drifts areused to label the data, for subsequent learning of classification models by asupervised learner. The algorithms were tested on several synthetic and realmedical claims data sets.

  15. Photometric classification of type Ia supernovae in the SuperNova Legacy Survey with supervised learning

    Science.gov (United States)

    Möller, A.; Ruhlmann-Kleider, V.; Leloup, C.; Neveu, J.; Palanque-Delabrouille, N.; Rich, J.; Carlberg, R.; Lidman, C.; Pritchet, C.

    2016-12-01

    In the era of large astronomical surveys, photometric classification of supernovae (SNe) has become an important research field due to limited spectroscopic resources for candidate follow-up and classification. In this work, we present a method to photometrically classify type Ia supernovae based on machine learning with redshifts that are derived from the SN light-curves. This method is implemented on real data from the SNLS deferred pipeline, a purely photometric pipeline that identifies SNe Ia at high-redshifts (0.2 Random Forest and Boosted Decision Trees. We evaluate the performance using SN simulations and real data from the first 3 years of the Supernova Legacy Survey (SNLS), which contains large spectroscopically and photometrically classified type Ia samples. Using the Area Under the Curve (AUC) metric, where perfect classification is given by 1, we find that our best-performing classifier (Extreme Gradient Boosting Decision Tree) has an AUC of 0.98.We show that it is possible to obtain a large photometrically selected type Ia SN sample with an estimated contamination of less than 5%. When applied to data from the first three years of SNLS, we obtain 529 events. We investigate the differences between classifying simulated SNe, and real SN survey data. In particular, we find that applying a thorough set of selection cuts to the SN sample is essential for good classification. This work demonstrates for the first time the feasibility of machine learning classification in a high-z SN survey with application to real SN data.

  16. Supervised machine learning on a network scale: application to seismic event classification and detection

    Science.gov (United States)

    Reynen, Andrew; Audet, Pascal

    2017-09-01

    A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

  17. Impact of corpus domain for sentiment classification: An evaluation study using supervised machine learning techniques

    Science.gov (United States)

    Karsi, Redouane; Zaim, Mounia; El Alami, Jamila

    2017-07-01

    Thanks to the development of the internet, a large community now has the possibility to communicate and express its opinions and preferences through multiple media such as blogs, forums, social networks and e-commerce sites. Today, it becomes clearer that opinions published on the web are a very valuable source for decision-making, so a rapidly growing field of research called “sentiment analysis” is born to address the problem of automatically determining the polarity (Positive, negative, neutral,…) of textual opinions. People expressing themselves in a particular domain often use specific domain language expressions, thus, building a classifier, which performs well in different domains is a challenging problem. The purpose of this paper is to evaluate the impact of domain for sentiment classification when using machine learning techniques. In our study three popular machine learning techniques: Support Vector Machines (SVM), Naive Bayes and K nearest neighbors(KNN) were applied on datasets collected from different domains. Experimental results show that Support Vector Machines outperforms other classifiers in all domains, since it achieved at least 74.75% accuracy with a standard deviation of 4,08.

  18. Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

    Energy Technology Data Exchange (ETDEWEB)

    Park, Sang Hyun [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Gao, Yaozong, E-mail: yzgao@cs.unc.edu [Department of Computer Science, Department of Radiology, and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 (United States); Shi, Yinghuan, E-mail: syh@nju.edu.cn [State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023 (China); Shen, Dinggang, E-mail: dgshen@med.unc.edu [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-713 (Korea, Republic of)

    2014-11-01

    Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to

  19. 高校学生管理工作的辩证思考%Dialectical thought about the supervision of students in institutions of higher learning

    Institute of Scientific and Technical Information of China (English)

    李宜祥; 邢大伟; 沈广元

    2001-01-01

    针对强化素质教育问题,研究了高校学生管理工作,论述了学生管理与自身建设、行为管理与思想疏导、理性说服与人情感化、群体教育与个体工作的辩证关系,提出加强自我修养、强化思想疏导、加大感情投入、做好个体工作,是新形势下做好学生管理工作的重要手段.%In accordance with the development of quality education thispaper deals with the supervision of students in institutions of higher learning and discusses the dialectical relations between the supervision of students and colleges and universities′ self reconstruction,the supervision of students′ behaviour and ideological mediation,rational persuasion and human feeling change by persuasion ,groups education and individual education,expounds important measures to improve the supervision of students such as raise teachers′ self quality,strengthening thought mediation,giving more affection to the work and neglecting no student.

  20. Application of supervised machine learning algorithms for the classification of regulatory RNA riboswitches.

    Science.gov (United States)

    Singh, Swadha; Singh, Raghvendra

    2016-04-03

    Riboswitches, the small structured RNA elements, were discovered about a decade ago. It has been the subject of intense interest to identify riboswitches, understand their mechanisms of action and use them in genetic engineering. The accumulation of genome and transcriptome sequence data and comparative genomics provide unprecedented opportunities to identify riboswitches in the genome. In the present study, we have evaluated the following six machine learning algorithms for their efficiency to classify riboswitches: J48, BayesNet, Naïve Bayes, Multilayer Perceptron, sequential minimal optimization, hidden Markov model (HMM). For determining effective classifier, the algorithms were compared on the statistical measures of specificity, sensitivity, accuracy, F-measure and receiver operating characteristic (ROC) plot analysis. The classifier Multilayer Perceptron achieved the best performance, with the highest specificity, sensitivity, F-score and accuracy, and with the largest area under the ROC curve, whereas HMM was the poorest performer. At present, the available tools for the prediction and classification of riboswitches are based on covariance model, support vector machine and HMM. The present study determines Multilayer Perceptron as a better classifier for the genome-wide riboswitch searches.

  1. A survey of supervised machine learning models for mobile-phone based pathogen identification and classification

    Science.gov (United States)

    Ceylan Koydemir, Hatice; Feng, Steve; Liang, Kyle; Nadkarni, Rohan; Tseng, Derek; Benien, Parul; Ozcan, Aydogan

    2017-03-01

    Giardia lamblia causes a disease known as giardiasis, which results in diarrhea, abdominal cramps, and bloating. Although conventional pathogen detection methods used in water analysis laboratories offer high sensitivity and specificity, they are time consuming, and need experts to operate bulky equipment and analyze the samples. Here we present a field-portable and cost-effective smartphone-based waterborne pathogen detection platform that can automatically classify Giardia cysts using machine learning. Our platform enables the detection and quantification of Giardia cysts in one hour, including sample collection, labeling, filtration, and automated counting steps. We evaluated the performance of three prototypes using Giardia-spiked water samples from different sources (e.g., reagent-grade, tap, non-potable, and pond water samples). We populated a training database with >30,000 cysts and estimated our detection sensitivity and specificity using 20 different classifier models, including decision trees, nearest neighbor classifiers, support vector machines (SVMs), and ensemble classifiers, and compared their speed of training and classification, as well as predicted accuracies. Among them, cubic SVM, medium Gaussian SVM, and bagged-trees were the most promising classifier types with accuracies of 94.1%, 94.2%, and 95%, respectively; we selected the latter as our preferred classifier for the detection and enumeration of Giardia cysts that are imaged using our mobile-phone fluorescence microscope. Without the need for any experts or microbiologists, this field-portable pathogen detection platform can present a useful tool for water quality monitoring in resource-limited-settings.

  2. Mapping of riparian invasive species with supervised classification of Unmanned Aerial System (UAS) imagery

    Science.gov (United States)

    Michez, Adrien; Piégay, Hervé; Jonathan, Lisein; Claessens, Hugues; Lejeune, Philippe

    2016-02-01

    Riparian zones are key landscape features, representing the interface between terrestrial and aquatic ecosystems. Although they have been influenced by human activities for centuries, their degradation has increased during the 20th century. Concomitant with (or as consequences of) these disturbances, the invasion of exotic species has increased throughout the world's riparian zones. In our study, we propose a easily reproducible methodological framework to map three riparian invasive taxa using Unmanned Aerial Systems (UAS) imagery: Impatiens glandulifera Royle, Heracleum mantegazzianum Sommier and Levier, and Japanese knotweed (Fallopia sachalinensis (F. Schmidt Petrop.), Fallopia japonica (Houtt.) and hybrids). Based on visible and near-infrared UAS orthophoto, we derived simple spectral and texture image metrics computed at various scales of image segmentation (10, 30, 45, 60 using eCognition software). Supervised classification based on the random forests algorithm was used to identify the most relevant variable (or combination of variables) derived from UAS imagery for mapping riparian invasive plant species. The models were built using 20% of the dataset, the rest of the dataset being used as a test set (80%). Except for H. mantegazzianum, the best results in terms of global accuracy were achieved with the finest scale of analysis (segmentation scale parameter = 10). The best values of overall accuracies reached 72%, 68%, and 97% for I. glandulifera, Japanese knotweed, and H. mantegazzianum respectively. In terms of selected metrics, simple spectral metrics (layer mean/camera brightness) were the most used. Our results also confirm the added value of texture metrics (GLCM derivatives) for mapping riparian invasive species. The results obtained for I. glandulifera and Japanese knotweed do not reach sufficient accuracies for operational applications. However, the results achieved for H. mantegazzianum are encouraging. The high accuracies values combined to

  3. Supervised Learning of Two-Layer Perceptron under the Existence of External Noise — Learning Curve of Boolean Functions of Two Variables in Tree-Like Architecture —

    Science.gov (United States)

    Uezu, Tatsuya; Kiyokawa, Shuji

    2016-06-01

    We investigate the supervised batch learning of Boolean functions expressed by a two-layer perceptron with a tree-like structure. We adopt continuous weights (spherical model) and the Gibbs algorithm. We study the Parity and And machines and two types of noise, input and output noise, together with the noiseless case. We assume that only the teacher suffers from noise. By using the replica method, we derive the saddle point equations for order parameters under the replica symmetric (RS) ansatz. We study the critical value αC of the loading rate α above which the learning phase exists for cases with and without noise. We find that αC is nonzero for the Parity machine, while it is zero for the And machine. We derive the exponents barβ of order parameters expressed as (α - α C)bar{β} when α is near to αC. Furthermore, in the Parity machine, when noise exists, we find a spin glass solution, in which the overlap between the teacher and student vectors is zero but that between student vectors is nonzero. We perform Markov chain Monte Carlo simulations by simulated annealing and also by exchange Monte Carlo simulations in both machines. In the Parity machine, we study the de Almeida-Thouless stability, and by comparing theoretical and numerical results, we find that there exist parameter regions where the RS solution is unstable, and that the spin glass solution is metastable or unstable. We also study asymptotic learning behavior for large α and derive the exponents hat{β } of order parameters expressed as α - hat{β } when α is large in both machines. By simulated annealing simulations, we confirm these results and conclude that learning takes place for the input noise case with any noise amplitude and for the output noise case when the probability that the teacher's output is reversed is less than one-half.

  4. The Team-Based Internal Supervision System Development for the Primary Schools under the Office of the Basic Education Commission

    Science.gov (United States)

    Tubsuli, Nattapong; Julsuwan, Suwat; Tesaputa, Kowat

    2017-01-01

    Internal supervision in the school is currently experiencing various problems. Supervision preparation problems are related to: lacking of supervision plan, lacking of holistic and systematic planning, and lacking of analysis in current conditions or requirements. While supervision operational problems are included: lacking of supervision…

  5. Self-Supervised Learning to Visually Detect Terrain Surfaces for Autonomous Robots Operating in Forested Terrain

    Science.gov (United States)

    2012-01-01

    classified. Stereo algorithms can generate 3D point clouds at relatively high frequency (sev- eral hertz). However, the resulting depth map is typically...10.1002/rob 280 • Journal of Field Robotics—2012 (a) (b) (c) (d) Figure 1. Experimental robot platform, (a) lateral view and (b) top view. (c) Perception ... monocular road detection in desert terrain. In Proceedings of robotics: Science and systems, Philadelphia, USA. Elmqvist, M. (2002). Ground surface

  6. VDES J2325-5229 a z = 2.7 gravitationally lensed quasar discovered using morphology-independent supervised machine learning

    Science.gov (United States)

    Ostrovski, Fernanda; McMahon, Richard G.; Connolly, Andrew J.; Lemon, Cameron A.; Auger, Matthew W.; Banerji, Manda; Hung, Johnathan M.; Koposov, Sergey E.; Lidman, Christopher E.; Reed, Sophie L.; Allam, Sahar; Benoit-Lévy, Aurélien; Bertin, Emmanuel; Brooks, David; Buckley-Geer, Elizabeth; Carnero Rosell, Aurelio; Carrasco Kind, Matias; Carretero, Jorge; Cunha, Carlos E.; da Costa, Luiz N.; Desai, Shantanu; Diehl, H. Thomas; Dietrich, Jörg P.; Evrard, August E.; Finley, David A.; Flaugher, Brenna; Fosalba, Pablo; Frieman, Josh; Gerdes, David W.; Goldstein, Daniel A.; Gruen, Daniel; Gruendl, Robert A.; Gutierrez, Gaston; Honscheid, Klaus; James, David J.; Kuehn, Kyler; Kuropatkin, Nikolay; Lima, Marcos; Lin, Huan; Maia, Marcio A. G.; Marshall, Jennifer L.; Martini, Paul; Melchior, Peter; Miquel, Ramon; Ogando, Ricardo; Plazas Malagón, Andrés; Reil, Kevin; Romer, Kathy; Sanchez, Eusebio; Santiago, Basilio; Scarpine, Vic; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, Flavia; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Tucker, Douglas L.; Walker, Alistair R.

    2017-03-01

    We present the discovery and preliminary characterization of a gravitationally lensed quasar with a source redshift zs = 2.74 and image separation of 2.9 arcsec lensed by a foreground zl = 0.40 elliptical galaxy. Since optical observations of gravitationally lensed quasars show the lens system as a superposition of multiple point sources and a foreground lensing galaxy, we have developed a morphology-independent multi-wavelength approach to the photometric selection of lensed quasar candidates based on Gaussian Mixture Models (GMM) supervised machine learning. Using this technique and gi multicolour photometric observations from the Dark Energy Survey (DES), near-IR JK photometry from the VISTA Hemisphere Survey (VHS) and WISE mid-IR photometry, we have identified a candidate system with two catalogue components with iAB = 18.61 and iAB = 20.44 comprising an elliptical galaxy and two blue point sources. Spectroscopic follow-up with NTT and the use of an archival AAT spectrum show that the point sources can be identified as a lensed quasar with an emission line redshift of z = 2.739 ± 0.003 and a foreground early-type galaxy with z = 0.400 ± 0.002. We model the system as a single isothermal ellipsoid and find the Einstein radius θE ∼ 1.47 arcsec, enclosed mass Menc ∼ 4 × 1011 M⊙ and a time delay of ∼52 d. The relatively wide separation, month scale time delay duration and high redshift make this an ideal system for constraining the expansion rate beyond a redshift of 1.

  7. 论我国食品安全监管体制的完善%Analysis on Improvement of Food Safety Supervision System

    Institute of Scientific and Technical Information of China (English)

    陈蓉

    2011-01-01

    Increasing serious food security issues have roused people' s extreme concern. The high frequency of the problem indicates that China's food safety supervision and enforcement system still have many loopholes. The main problem is that ex ante supervision is not scientific, process supervision is not effective, and ex post supervision is not timeliness and lacks of traceability. We think it is essential to build up reasonable food security standard, and regulate food access standard in ex ante supervision; we should improve food production process supervision, and the level of food safety inspection in process supervision, as well as establish government liability mechanism, strengthen law enforcement on illegal behaviors, and improve society supervision mechanism in ex post supervision, in order to supervise food security effectively.%食品安全问题的日益严重,引发国人的极度关心。问题的频发表明我国食品安全监管执法体制还存在不少漏洞。其问题主要是:事前监管缺乏科学性、事中监管缺乏有效性、事后监管缺乏及时性、可追溯性。因此食品安全监管事前必须要建立合理的食品安全标准,严格食品行业准人标准;事中应完善食品生产经营过程监管,提高食品安全检验水平;事后要建立政府问责机制,加大对食品生产者违法行为的惩罚力度,完善社会监督机制。

  8. How to Build a Supervised Autonomous System for Robot-Enhanced Therapy for Children with Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Esteban Pablo G.

    2017-04-01

    Full Text Available Robot-Assisted Therapy (RAT has successfully been used to improve social skills in children with autism spectrum disorders (ASD through remote control of the robot in so-called Wizard of Oz (WoZ paradigms.However, there is a need to increase the autonomy of the robot both to lighten the burden on human therapists (who have to remain in control and, importantly, supervise the robot and to provide a consistent therapeutic experience. This paper seeks to provide insight into increasing the autonomy level of social robots in therapy to move beyond WoZ. With the final aim of improved human-human social interaction for the children, this multidisciplinary research seeks to facilitate the use of social robots as tools in clinical situations by addressing the challenge of increasing robot autonomy.We introduce the clinical framework in which the developments are tested, alongside initial data obtained from patients in a first phase of the project using a WoZ set-up mimicking the targeted supervised-autonomy behaviour. We further describe the implemented system architecture capable of providing the robot with supervised autonomy.

  9. Semi-supervised Phonetic Category Learning: Does Word-level Information Enhance the Efficacy of Distributional Learning?

    Directory of Open Access Journals (Sweden)

    Till Poppels

    2014-08-01

    Full Text Available To test whether word-level information facilitates the learning of phonetic categories, 40 adult native English speakers were exposed to a bimodal distribution of vowels embedded in non-words. Half of the subjects received phonetic categories aligned with lexical categories, while the other half received no such cue. It was hypothesized that the subjects exposed to lexically-informative training stimuli that were aligned with the target categories would outperform the control subjects on a perceptual categorization task after training. While the results revealed no such group differences, the data indicated that many subjects used the relevant dimension for categorization before having received any training. Implications regarding experimental design and suggestions for future research based on the results are discussed.

  10. Learning Systems: Architecture and Intervention.

    Science.gov (United States)

    Buxton, Bill

    1984-01-01

    Discusses tasks, resources, and feedback, the three basic elements in the architecture of learning systems; analyzes these elements in relation to on-the-job learning; and discusses interventions to improve these processes and provide a positive contribution to the productivity of the organization by helping people learn. (MBR)

  11. Status Quo of Drug Supervision in China(Part Ⅱ)

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    @@ Ⅲ. Policies and Measures Concerning Drug Safety SupervisionBased on its national conditions and learning from international advanced ex-perience, China has formulated policies and measures concerning the improve-merit of drug safety, effectiveness and quality control, and established a regula-tory system covering research, produc-tion, distribution and use of drugs.

  12. Data integration modeling applied to drill hole planning through semi-supervised learning: A case study from the Dalli Cu-Au porphyry deposit in the central Iran

    Science.gov (United States)

    Fatehi, Moslem; Asadi, Hooshang H.

    2017-04-01

    In this study, the application of a transductive support vector machine (TSVM), an innovative semi-supervised learning algorithm, has been proposed for mapping the potential drill targets at a detailed exploration stage. The semi-supervised learning method is a hybrid of supervised and unsupervised learning approach that simultaneously uses both training and non-training data to design a classifier. By using the TSVM algorithm, exploration layers at the Dalli porphyry Cu-Au deposit in the central Iran were integrated to locate the boundary of the Cu-Au mineralization for further drilling. By applying this algorithm on the non-training (unlabeled) and limited training (labeled) Dalli exploration data, the study area was classified in two domains of Cu-Au ore and waste. Then, the results were validated by the earlier block models created, using the available borehole and trench data. In addition to TSVM, the support vector machine (SVM) algorithm was also implemented on the study area for comparison. Thirty percent of the labeled exploration data was used to evaluate the performance of these two algorithms. The results revealed 87 percent correct recognition accuracy for the TSVM algorithm and 82 percent for the SVM algorithm. The deepest inclined borehole, recently drilled in the western part of the Dalli deposit, indicated that the boundary of Cu-Au mineralization, as identified by the TSVM algorithm, was only 15 m off from the actual boundary intersected by this borehole. According to the results of the TSVM algorithm, six new boreholes were suggested for further drilling at the Dalli deposit. This study showed that the TSVM algorithm could be a useful tool for enhancing the mineralization zones and consequently, ensuring a more accurate drill hole planning.

  13. 局部学习半监督多类分类机%Local learning semi-supervised multi-class classifier

    Institute of Scientific and Technical Information of China (English)

    吕佳; 邓乃扬; 田英杰; 邵元海; 杨新民

    2013-01-01

    半监督多类分类问题是机器学习和模式识别领域中的一个研究热点,目前大多数多类分类算法是将问题分解成若干个二类分类问题来求解.提出两种类标号表示方法来避免多个二类分类问题的求解,一种是单位圆类标号表示方法,一种是二进制序列类标号表示方法,并利用局部学习在二类分类问题中的良好学习特性,提出基于局部学习的半监督多类分类机.实验结果证明采用了基于局部学习的半监督多类分类机错分率更小,稳定性更高.%Semi-supervised multi-class classification problem opens research focuses in machine learning and pattern recognition, currently it is decomposed into a set of binary classification problems. Two kinds of class label presentation methods that one was class label presentation method of unit disc and the other was that of binary string were proposed for fear that multiple binary classification problems were solved. Besides, local learning has the good feature in semi-supervised binary classification problem. On the basis of it, local learning semi-supervised multi-class classifier was presented in this paper. The effectiveness of the algorithms was confirmed with experiments on benchmark datasets compared to other related algorithms.

  14. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

    Directory of Open Access Journals (Sweden)

    Jinping Liu

    2016-06-01

    Full Text Available The topic of online product quality inspection (OPQI with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs, e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF, which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines.

  15. Quality-Related Monitoring and Grading of Granulated Products by Weibull-Distribution Modeling of Visual Images with Semi-Supervised Learning

    Science.gov (United States)

    Liu, Jinping; Tang, Zhaohui; Xu, Pengfei; Liu, Wenzhong; Zhang, Jin; Zhu, Jianyong

    2016-01-01

    The topic of online product quality inspection (OPQI) with smart visual sensors is attracting increasing interest in both the academic and industrial communities on account of the natural connection between the visual appearance of products with their underlying qualities. Visual images captured from granulated products (GPs), e.g., cereal products, fabric textiles, are comprised of a large number of independent particles or stochastically stacking locally homogeneous fragments, whose analysis and understanding remains challenging. A method of image statistical modeling-based OPQI for GP quality grading and monitoring by a Weibull distribution(WD) model with a semi-supervised learning classifier is presented. WD-model parameters (WD-MPs) of GP images’ spatial structures, obtained with omnidirectional Gaussian derivative filtering (OGDF), which were demonstrated theoretically to obey a specific WD model of integral form, were extracted as the visual features. Then, a co-training-style semi-supervised classifier algorithm, named COSC-Boosting, was exploited for semi-supervised GP quality grading, by integrating two independent classifiers with complementary nature in the face of scarce labeled samples. Effectiveness of the proposed OPQI method was verified and compared in the field of automated rice quality grading with commonly-used methods and showed superior performance, which lays a foundation for the quality control of GP on assembly lines. PMID:27367703

  16. Modeling learning technology systems as business systems

    NARCIS (Netherlands)

    Avgeriou, Paris; Retalis, Symeon; Papaspyrou, Nikolaos

    2003-01-01

    The design of Learning Technology Systems, and the Software Systems that support them, is largely conducted on an intuitive, ad hoc basis, thus resulting in inefficient systems that defectively support the learning process. There is now justifiable, increasing effort in formalizing the engineering o

  17. Modeling learning technology systems as business systems

    NARCIS (Netherlands)

    Avgeriou, Paris; Retalis, Symeon; Papaspyrou, Nikolaos

    2003-01-01

    The design of Learning Technology Systems, and the Software Systems that support them, is largely conducted on an intuitive, ad hoc basis, thus resulting in inefficient systems that defectively support the learning process. There is now justifiable, increasing effort in formalizing the engineering

  18. 基于半监督流形学习的人脸识别方法%Face Recognition Based on Semi-supervised Manifold Learning

    Institute of Scientific and Technical Information of China (English)

    黄鸿; 李见为; 冯海亮

    2008-01-01

    如何有效地将流形学习(Manifold learning,ML)和半监督学习(Semi-supervised learning,SSL)方法进行结合是近年来模式识别和机器学习领域研究的热点问题.提出一种基于半监督流形学习(Semi-supervised manifold learning,SSML)的人脸识别方法,它在部分有标签信息的人脸数据的情况下,通过利用人脸数据本身的非线性流形结构信息和部分标签信息来调整点与点之间的距离形成距离矩阵,而后基于被调整的距离矩阵进行线性近邻重建来实现维数约简,提取低维鉴别特征用于人脸识别.基于公开的人脸数据库上的实验结果表明,该方法能有效地提高人脸识别的性能.

  19. Microprocessor supervised stability control system for the united power system of Middle Volga in fault conditions

    Energy Technology Data Exchange (ETDEWEB)

    Berdnikov, V.I.; Birgel, E.R.; Kovalev, V.D.; Kuznestov, A.N.

    1994-12-31

    The development of the 500 kV UPS of Middle Volga, the complication of its configuration and operating conditions particularly in connection with concentration of the generating power at Balakovo NPS have aggravated the problem of stability of the Middle Volga UPS when high power is transmitted along the 500 kV transient system. In this case the necessity for improving control actions` dosage accuracy has also appeared. This work discusses solution to the above mentioned issue. (author) 3 figs.

  20. Anatomy of power system blackouts and preventive strategies by rational supervision and control of protection systems

    Energy Technology Data Exchange (ETDEWEB)

    Phadke, A.G. [Virginia Polytechnic Institute and State Univ., Blacksburg, VA (United States); Horowitz, S.H.; Thorp, J.S.

    1995-01-01

    This report establishes the concept of hidden failures in relays and associated devices used for the protection of electric power systems. A hidden failure is a defect such as a component failure, inappropriate setting or incorrect external connection that remains undetected until some other system event causes the hidden failure to initiate a cascading outage. Associated with the study of hidden failures, this report examines the impact such defects might have by defining regions of vulnerability. A region of vulnerability is the area of the system in which a hidden failure will be activated. To determine such areas we have established criteria associated with load flows and steady-state stability, such as lack of convergence, and employed a technique known as importance sampling in which the simulation is done with the probabilities altered so that the rare event happens more frequently. Our purpose is to provide a framework for further research into relay vulnerability, possibly using adaptive techniques to eliminate hidden defects. We believe control strategies can be developed to prevent cascading normal operations from developing into severe outages by extending the present criteria using steady-state stability and load flow studies into the area of transient stability, and further research into importance sampling would provide significant benefits in evaluating corrective actions.

  1. 48 CFR 836.572 - Government supervision.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Government supervision. 836.572 Section 836.572 Federal Acquisition Regulations System DEPARTMENT OF VETERANS AFFAIRS SPECIAL... supervision. The contracting officer shall insert the clause at 852.236-78, Government supervision,...

  2. The Learning Process of Supervisees Who Engage in the Reflecting Team Model within Group Supervision: A Grounded Theory Inquiry

    Science.gov (United States)

    Pender, Rebecca Lynn

    2012-01-01

    In recent years, counselor educators have begun to incorporate the use of the reflecting team process with the training of counselors. Specifically, the reflecting team has been used in didactic courses (Cox, 2003; Landis & Young, 1994; Harrawood, Wilde & Parmanand, 2011) and in supervision (Cox, 1997; Prest, Darden, & Keller, 1990;…

  3. Implications of resolved hypoxemia on the utility of desaturation alerts sent from an anesthesia decision support system to supervising anesthesiologists.

    Science.gov (United States)

    Epstein, Richard H; Dexter, Franklin

    2012-10-01

    Hypoxemia (oxygen saturation anesthesia in operating room settings. Alarm management functionality can be added to decision support systems (DSS) to send text alerts about vital signs outside specified thresholds, using data in anesthesia information management systems. We considered enhancing our DSS to send hypoxemia alerts to the text pagers of supervising anesthesiologists. As part of a voluntary application for an investigative device exemption from our IRB to implement such functionality, we evaluated the maximum potential utility of such an alert system. Pulse oximetry values (Spo(2)) were extracted from our anesthesia information management systems for all cases performed in our main operating rooms and ambulatory surgical center between September 1, 2011, and February 4, 2012 (n = 16,870). Hypoxemic episodes (Spo(2) anesthesia care provider to initiate treatment promptly, to interpret or correct artifacts, and to make it easier to call for assistance via a rapid communication system.

  4. A Study on Supervision System of Crimes against Food Safety in China%我国食品安全犯罪监管体制研究

    Institute of Scientific and Technical Information of China (English)

    董士昙

    2012-01-01

    我国目前实行的是“以分段监管为主,以品种监管为辅”的多头监管体制。这种看似“分工明确,环环相扣”的监管模式,由于职权配置不合理、监管主体职责不清等原因,实践中导致职权交叉重复和出现大量的监管真空。这种监管体制已成为食品安全犯罪事件频发的元凶。如果强行将之前形成的多部门监管体制全部推倒重建为单一部门监管体制,可以避免分段监管的诸多弊端,但势必涉及政府有关部门利益的冲突和食品安全法律体系的重构,改革将面临重重阻力并因此付出巨大成本。因此,与我国当前的行政体制以及市场发育状况相适应,减少现有监管部门,建立以“两部门监管为主,其他部门监管为辅,并以刚性协调部门相配合”的监管模式,是目前最理想的选择,应作为监管体制改革与创新的方向。%At present, we carry out multiple supervision system called "section supervision as principal, supplemented with varieties supervision" in China. This supervision mode with characteristic of seemingly clear - cut division of labor and interlocking connection leads to overlapping functions and a large quantity of empty space of supervision in practice for unreasonable functional allocation and unclear duty of supervision subjects. This supervision system has become the primary culprit of frequently occurred crime cases against food safety. If previously formed multi -section supervision system was forcibly overthrew to reconstruct single -section supervision system, a lot of disadvantages arising from section supervision would be avoided, but interests of related governmental departments would be certainly trapped into conflicts and legal system concerning food safety would be reconstructed, meanwhile reform would face stiff headwinds and thus bring a heavy cost. Therefore, it is an optimal choice at present and should be regarded as

  5. A Viable Individualized Learning System

    Science.gov (United States)

    Rubillo, James M.

    1977-01-01

    An individualized learning system for college algebra was devised and tested. Results indicated that the individualized system was at least as effective as traditional approaches, and superior with respect to student attitudes toward the course. (SD)

  6. A Viable Individualized Learning System

    Science.gov (United States)

    Rubillo, James M.

    1977-01-01

    An individualized learning system for college algebra was devised and tested. Results indicated that the individualized system was at least as effective as traditional approaches, and superior with respect to student attitudes toward the course. (SD)

  7. Clinical supervision for clinical psychology students in Uganda: an initial qualitative exploration.

    Science.gov (United States)

    Hall, Jennifer; Kasujja, Rosco; Oakes, Peter

    2015-01-01

    Burn out in clinical psychologists working in low income countries has been reported. Clinical supervisory structures do not yet exist in Uganda. A way to decrease levels of burn out and increase quality of care for people with mental illness is through clinical supervision. The aim of this study was to explore the initial experiences of supervision for clinical psychology students in Uganda to ascertain whether or not clinical supervision is culturally appropriate, and what aspects of supervision had been helpful and unhelpful. A qualitative design with thematic analysis was utilized. A focus group was held with 12 second year clinical psychology students to ask their experiences of receiving supervision. Data analysis created five themes. Firstly, the negative emotions that resulted from the training processed were discussed, and how supervision helped and did not help the students to manage these. Secondly, the students voiced that supervision helped them to learn through observational experiences, co-therapist roles and parallel processes within the supervisory relationship. Thirdly, supervision had taught the clinical psychology students their role as a clinical psychology student, how to act within the Ugandan mental health system and skills to conduct therapy. Fourthly, suggestions for the future of supervision were given, with the students requesting for it to start earlier in the training, for supervisors who can meet with the students on a regular basis to be selected and for the training the students receive at university to match the skills required on their placements, with a request for more practical techniques rather than theory. The final theme related to left over miscellaneous data, such as the students agreeing with each other. The students stated that supervision was helpful overall, implying that clinical supervision is culturally appropriate for clinical psychology students in Uganda. Suggestions for future supervision were given. In order to

  8. Intra-individual gait patterns across different time-scales as revealed by means of a supervised learning model using kernel-based discriminant regression.

    Science.gov (United States)

    Horst, Fabian; Eekhoff, Alexander; Newell, Karl M; Schöllhorn, Wolfgang I

    2017-01-01

    Traditionally, gait analysis has been centered on the idea of average behavior and normality. On one hand, clinical diagnoses and therapeutic interventions typically assume that average gait patterns remain constant over time. On the other hand, it is well known that all our movements are accompanied by a certain amount of variability, which does not allow us to make two identical steps. The purpose of this study was to examine changes in the intra-individual gait patterns across different time-scales (i.e., tens-of-mins, tens-of-hours). Nine healthy subjects performed 15 gait trials at a self-selected speed on 6 sessions within one day (duration between two subsequent sessions from 10 to 90 mins). For each trial, time-continuous ground reaction forces and lower body joint angles were measured. A supervised learning model using a kernel-based discriminant regression was applied for classifying sessions within individual gait patterns. Discernable characteristics of intra-individual gait patterns could be distinguished between repeated sessions by classification rates of 67.8 ± 8.8% and 86.3 ± 7.9% for the six-session-classification of ground reaction forces and lower body joint angles, respectively. Furthermore, the one-on-one-classification showed that increasing classification rates go along with increasing time durations between two sessions and indicate that changes of gait patterns appear at different time-scales. Discernable characteristics between repeated sessions indicate continuous intrinsic changes in intra-individual gait patterns and suggest a predominant role of deterministic processes in human motor control and learning. Natural changes of gait patterns without any externally induced injury or intervention may reflect continuous adaptations of the motor system over several time-scales. Accordingly, the modelling of walking by means of average gait patterns that are assumed to be near constant over time needs to be reconsidered in the context of

  9. Configuration and supervision of advanced distributed data acquisition and processing systems for long pulse experiments using JINI technology

    Energy Technology Data Exchange (ETDEWEB)

    Gonzalez, Joaquin; Ruiz, Mariano [Grupo de Investigacion en Instrumentacion y Acustica Aplicada, Universidad Politecnica de Madrid (UPM), Ctra. Valencia Km-7, 28031, Madrid (Spain); Barrera, Eduardo [Grupo de Investigacion en Instrumentacion y Acustica Aplicada, Universidad Politecnica de Madrid (UPM), Ctra. Valencia Km-7, 28031, Madrid (Spain)], E-mail: eduardo.barrera@upm.es; Lopez, Juan Manuel; de Arcas, Guillermo [Grupo de Investigacion en Instrumentacion y Acustica Aplicada, Universidad Politecnica de Madrid (UPM), Ctra. Valencia Km-7, 28031, Madrid (Spain); Vega, Jesus [Asociacion EURATOM/CIEMAT para Fusion, Avda. Complutense 22, 28040, Madrid (Spain)

    2009-06-15

    The development of tools for managing the capabilities and functionalities of distributed data acquisition systems is essential in long pulse fusion experiments. The intelligent test and measurement system (ITMS) developed by UPM and CIEMAT is a technology that permits implementation of a scalable data acquisition and processing system based on PXI or CompactPCI hardware. Several applications based on JINI technology have been developed to enable use of this platform for extensive implementation of distributed data acquisition and processing systems. JINI provides a framework for developing service-oriented, distributed applications. The applications are based on the paradigm of a JINI federation that supports mechanisms for publication, discovering, subscription, and links to remote services. The model we implemented in the ITMS platform included services in the system CPU (SCPU) and peripheral CPUs (PCPUs). The resulting system demonstrated the following capabilities: (1) setup of the data acquisition and processing to apply to the signals, (2) information about the evolution of the data acquisition, (3) information about the applied data processing and (4) detection and distribution of the events detected by the ITMS software applications. With this approach, software applications running on the ITMS platform can be understood, from the perspective of their implementation details, as a set of dynamic, accessible, and transparent services. The search for services is performed using the publication and subscription mechanisms of the JINI specification. The configuration and supervision applications were developed using remotely accessible (LAN or WAN) objects. The consequence of this approach is a hardware and software architecture that provides a transparent model of remote configuration and supervision, and thereby a means to simplify the implementation of a distributed data acquisition system with scalable and dynamic local processing capability developed in a

  10. 一种用于半监督学习的核优化设计%A Kernel Optimization Design for Semi-supervised Learning

    Institute of Scientific and Technical Information of China (English)

    崔鹏

    2013-01-01

    Semi-supervised learning aims to obtain good performance and learning ability under lacking of some information on training examples.We proposed a semi-supervised learning framework based on optimizing kernel,which project data into high dimensional feature space and equal to linear classification.In kernel design,we applied spectral feature decomposition to unsupervised kernel design,and found optimal kernel by minimizing learning bound.With experimental results,we demonstrated our theory by comparison of different kernel approaches.%半监督学习研究主要关注当训练数据的部分信息缺失的情况下,如何获得具有良好性能和推广能力的学习机器。本文我们提出了一种基于核优化的半监督学习框架,将数据嵌入到高维特征空间,从而与线性分类器等价。在核的设计上,采用了基于谱分解的无监督核设计,提出了学习边界,通过最小化边界来获得最优核表示。通过实验,对不同的核方法进行了比较,证明了我们结论的正确性。

  11. 基于半监督的SVM迁移学习文本分类算法%Semi-Supervised Transfer Learning Text Classiifcation Algorithms Based on SVM

    Institute of Scientific and Technical Information of China (English)

    谭建平; 刘波; 肖燕珊

    2016-01-01

    随着互联网的快速发展,文本信息量巨大,大规模的文本处理已经成为一个挑战。文本处理的一个重要技术便是分类,基于SVM的传统文本分类算法已经无法满足快速的文本增长分类。于是如何利用过时的历史文本数据(源任务数据)进行迁移来帮助新产生文本数据进行分类显得异常重要。文章提出了基于半监督的SVM迁移学习算法(Semi-supervised TL_SVM)来对文本进行分类。首先,在半监督SVM的模型中引入迁移学习,构建分类模型。其次,采用交互迭代的方法对目标方程求解,最终得到面向目标领域的分类器。实验验证了基于半监督的SVM迁移学习分类器具有比传统分类器更高的精确度。%With the rapid development of the Internet, texts contain a huge amount of information and the large-scale text processing has become a challenge. An important technical of the text processing is classiifcation, the traditional text categorization algorithm based on SVM has been unable to meet the rapid growth of text classiifcation. So how to utilize the source tasks data to help build a transfer learning classiifer for the target task is especially important. Semi-supervised TL_SVM algorithms is proposed to text classiifcation. First, semi-supervised SVM model combines transfer learning to build the model of classiifcation. Second, we utilize the iterative algorithm to solve the optimization function and obtain the transfer classiifer for the target task. Experiments have shown that our Semi-supervised-based transfer SVM can obtain higher accuracy compared with the traditional method.

  12. CLASSIFICATION OF LEARNING MANAGEMENT SYSTEMS

    Directory of Open Access Journals (Sweden)

    Yu. B. Popova

    2016-01-01

    Full Text Available Using of information technologies and, in particular, learning management systems, increases opportunities of teachers and students in reaching their goals in education. Such systems provide learning content, help organize and monitor training, collect progress statistics and take into account the individual characteristics of each user. Currently, there is a huge inventory of both paid and free systems are physically located both on college servers and in the cloud, offering different features sets of different licensing scheme and the cost. This creates the problem of choosing the best system. This problem is partly due to the lack of comprehensive classification of such systems. Analysis of more than 30 of the most common now automated learning management systems has shown that a classification of such systems should be carried out according to certain criteria, under which the same type of system can be considered. As classification features offered by the author are: cost, functionality, modularity, keeping the customer’s requirements, the integration of content, the physical location of a system, adaptability training. Considering the learning management system within these classifications and taking into account the current trends of their development, it is possible to identify the main requirements to them: functionality, reliability, ease of use, low cost, support for SCORM standard or Tin Can API, modularity and adaptability. According to the requirements at the Software Department of FITR BNTU under the guidance of the author since 2009 take place the development, the use and continuous improvement of their own learning management system.

  13. Randomized controlled trial of supervised patient self-testing of warfarin therapy using an internet-based expert system.

    Science.gov (United States)

    Ryan, F; Byrne, S; O'Shea, S

    2009-08-01

    Increased frequency of prothrombin time testing, facilitated by patient self-testing (PST) of the International Normalized Ratio (INR) can improve the clinical outcomes of oral anticoagulation therapy (OAT). However, oversight of this type of management is often difficult and time-consuming for healthcare professionals. This study reports the first randomized controlled trial of an automated direct-to-patient expert system, enabling remote and effective management of patients on OAT. A prospective, randomized controlled cross-over study was performed to test the hypothesis that supervised PST using an internet-based, direct-to-patient expert system could provide improved anticoagulation control as compared with that provided by an anticoagulation management service (AMS). During the 6 months of supervised PST, patients measured their INR at home using a portable meter and entered this result, along with other information, onto the internet web page. Patients received instant feedback from the system as to what dose to take and when the next test was due. During the routine care arm, patients attended the AMS at least every 4-6 weeks and were dosed by the anticoagulation pharmacist or physician. The primary outcome variable was the difference in the time in therapeutic range (TTR) between both arms. One hundred and sixty-two patients were enrolled (male 61.6%, mean age 58.7 years), and 132 patients (81.5%) completed both arms. TTR was significantly higher during PST management than during AMS management (median TTR 74% vs 58.6%; z=5.67, P expert system for the management of PST improves the control of OAT as compared with AMS management.

  14. Mood Extraction Using Facial Features to Improve Learning Curves of Students in E-Learning Systems

    Directory of Open Access Journals (Sweden)

    Abdulkareem Al-Alwani

    2016-11-01

    Full Text Available Students’ interest and involvement during class lectures is imperative for grasping concepts and significantly improves academic performance of the students. Direct supervision of lectures by instructors is the main reason behind student attentiveness in class. Still, there is sufficient percentage of students who even under direct supervision tend to lose concentration. Considering the e-learning environment, this problem is aggravated due to absence of any human supervision. This calls for an approach to assess and identify lapses of attention by a student in an e-learning session. This study is carried out to improve student’s involvement in e-learning platforms by using their facial feature to extract mood patterns. Analyzing themoods based on emotional states of a student during an online lecture can provide interesting results which can be readily used to improvethe efficacy of content delivery in an e-learning platform. A survey is carried out among instructors involved in e-learning to identify most probable facial features that represent the facial expressions or mood patterns of a student. A neural network approach is used to train the system using facial feature sets to predict specific facial expressions. Moreover, a data association based algorithm specifically for extracting information on emotional states by correlating multiple sets of facial features is also proposed. This framework showed promising results in inciting student’s interest by varying the content being delivered.Different combinations of inter-related facial expressions for specific time frames were used to estimate mood patterns and subsequently level of involvement of a student in an e-learning environment.The results achieved during the course of research showed that mood patterns of a student provide a good correlation with his interest or involvement during online lectures and can be used to vary the content to improve students’ involvement in the e-learning

  15. Incremental Image Classification Method Based on Semi-Supervised Learning%基于半监督学习的增量图像分类方法

    Institute of Scientific and Technical Information of China (English)

    梁鹏; 黎绍发; 覃姜维; 罗剑高

    2012-01-01

    In order to use large numbers of unlabeled images effectively, an image classification method is proposed based on semi-supervised learning. The proposed method bridges a large amount of unlabeled images and limited numbers of labeled images by exploiting the common topics. The classification accuracy is improved by using the must-link constraint and cannot-link constraint of labeled images. The experimental results on Caltech-101 and 7-classes image dataset demonstrate that the classification accuracy improves about 10% by the proposed method. Furthermore, due to the present semi-supervised image classification methods lacking of incremental learning ability, an incremental implementation of our method is proposed. Comparing with non-incremental learning model in literature, the incrementallearning method improves the computation efficiency of nearly 90%.%为有效使用大量未标注的图像进行分类,提出一种基于半监督学习的图像分类方法.通过共同的隐含话题桥接少量已标注的图像和大量未标注的图像,利用已标注图像的Must-link约束和Cannot-link约束提高未标注图像分类的精度.实验结果表明,该方法有效提高Caltech-101数据集和7类图像集约10%的分类精度.此外,针对目前绝大部分半监督图像分类方法不具备增量学习能力这一缺点,提出该方法的增量学习模型.实验结果表明,增量学习模型相比无增量学习模型提高近90%的计算效率.

  16. Out-of-Sample Extrapolation utilizing Semi-Supervised Manifold Learning (OSE-SSL): Content Based Image Retrieval for Histopathology Images.

    Science.gov (United States)

    Sparks, Rachel; Madabhushi, Anant

    2016-06-06

    Content-based image retrieval (CBIR) retrieves database images most similar to the query image by (1) extracting quantitative image descriptors and (2) calculating similarity between database and query image descriptors. Recently, manifold learning (ML) has been used to perform CBIR in a low dimensional representation of the high dimensional image descriptor space to avoid the curse of dimensionality. ML schemes are computationally expensive, requiring an eigenvalue decomposition (EVD) for every new query image to learn its low dimensional representation. We present out-of-sample extrapolation utilizing semi-supervised ML (OSE-SSL) to learn the low dimensional representation without recomputing the EVD for each query image. OSE-SSL incorporates semantic information, partial class label, into a ML scheme such that the low dimensional representation co-localizes semantically similar images. In the context of prostate histopathology, gland morphology is an integral component of the Gleason score which enables discrimination between prostate cancer aggressiveness. Images are represented by shape features extracted from the prostate gland. CBIR with OSE-SSL for prostate histology obtained from 58 patient studies, yielded an area under the precision recall curve (AUPRC) of 0.53 ± 0.03 comparatively a CBIR with Principal Component Analysis (PCA) to learn a low dimensional space yielded an AUPRC of 0.44 ± 0.01.

  17. Integrating Learning Styles into Adaptive E-Learning System

    Science.gov (United States)

    Truong, Huong May

    2015-01-01

    This paper provides an overview and update on my PhD research project which focuses on integrating learning styles into adaptive e-learning system. The project, firstly, aims to develop a system to classify students' learning styles through their online learning behaviour. This will be followed by a study on the complex relationship between…

  18. Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

    CERN Document Server

    Taillandier, Patrick

    2012-01-01

    Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.

  19. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  20. 环境监管物联网技术架构及应用体系研究%Environmental Supervision Iot Technology Architecture and Application System Research

    Institute of Scientific and Technical Information of China (English)

    游波; 甄欣

    2015-01-01

    物联网是支撑环境管理由粗放向精细、被动向主动转变的重要技术手段,本文以重庆市环境监管物联网为例,阐述了环境监管物联网建设的必要性、可行性,提出了环境监管物联网"一网一库一平台"的技术架构及融合监管网格化、PPP模式等要素的应用体系,对环保物联网技术与管理实际融合进行了探索.%the Internet of things is the important technical method to support environmental management change from extensive to elaborate, passive to take the initiative. This paper is to the Chongqing environmental supervision iot as an example, expounds the necessity and feasibility of environmental supervision iot construction, puts forward environmental supervision iot "a network,a library,a platform"technology architecture and application system of fusion grid supervision, PPP pattern and other factors.Environmental supervision iot technology and management of practical fusion are explored.

  1. 台湾食品安全监管法律制度及其借鉴意义%Taiwan Food Safety Supervision Legal System and Reference Significance

    Institute of Scientific and Technical Information of China (English)

    毕博

    2013-01-01

    Recently, food safety problems frequently emerged in mainland China exposed the defects of food supervision legal system, which need to be solved urgently. Taiwans food safety regulatory system is relatively perfect compared with Mainland, containing sophisticated regulators and legal system, strong social organization supervision, and severe illegal punishment measures. Nowadays, food safety supervision system in mainland China lacks of perfect food safety certification and legal system. We should draw lessons from Taiwan food safety supervision system, and improve food safety supervision of the mainland legal system by perfecting food safety legislation system, food safety certification and legal system, giving full play to the role of food industry association, and making severe illegal punishment measures.%我国大陆近来频发的食品安全问题暴露出食品监管法律制度的缺陷,亟待解决.台湾地区的食品安全监管制度相对大陆来说比较完善,有比较健全的监管机构和法律制度、强有力的社会团体监督以及严厉的违法处罚措施.目前,我国大陆食品安全监管缺乏完善的食品安全认证和法律制度,应该借鉴台湾食品安全的监管经验,通过完善食品安全立法制度和法律制度,发挥食品行业协会的作用,以及制定严格的违法处罚措施来健全大陆的食品安全监管法律体系.

  2. 48 CFR 52.247-12 - Supervision, Labor, or Materials.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 2 2010-10-01 2010-10-01 false Supervision, Labor, or....247-12 Supervision, Labor, or Materials. As prescribed in 47.207-5(b), insert a clause substantially... when the contractor is required to furnish supervision, labor, or materials: Supervision, Labor,...

  3. Fuzzy self-learning control for magnetic servo system

    Science.gov (United States)

    Tarn, J. H.; Kuo, L. T.; Juang, K. Y.; Lin, C. E.

    1994-01-01

    It is known that an effective control system is the key condition for successful implementation of high-performance magnetic servo systems. Major issues to design such control systems are nonlinearity; unmodeled dynamics, such as secondary effects for copper resistance, stray fields, and saturation; and that disturbance rejection for the load effect reacts directly on the servo system without transmission elements. One typical approach to design control systems under these conditions is a special type of nonlinear feedback called gain scheduling. It accommodates linear regulators whose parameters are changed as a function of operating conditions in a preprogrammed way. In this paper, an on-line learning fuzzy control strategy is proposed. To inherit the wealth of linear control design, the relations between linear feedback and fuzzy logic controllers have been established. The exercise of engineering axioms of linear control design is thus transformed into tuning of appropriate fuzzy parameters. Furthermore, fuzzy logic control brings the domain of candidate control laws from linear into nonlinear, and brings new prospects into design of the local controllers. On the other hand, a self-learning scheme is utilized to automatically tune the fuzzy rule base. It is based on network learning infrastructure; statistical approximation to assign credit; animal learning method to update the reinforcement map with a fast learning rate; and temporal difference predictive scheme to optimize the control laws. Different from supervised and statistical unsupervised learning schemes, the proposed method learns on-line from past experience and information from the process and forms a rule base of an FLC system from randomly assigned initial control rules.

  4. Research of food-safety supervision systems of developed countries%发达国家食品安全监管体系研究

    Institute of Scientific and Technical Information of China (English)

    何猛

    2012-01-01

    In recent years,major developed countries have constructed an efficient food-safety supervision system according to the high standards of food-safety requirements.The common characteristics of the food-safety supervision system are regarding risk analysis as notion and "From farmland to dining table".For drawing on the experience of food-safety supervision systems in developed countries,this paper studies food-safety supervision systems in Japan/America and the European Union and draws on the experiences of these countries.%近年来,主要发达国家围绕着高标准的食品安全要求,逐步建立起统一高效的食品安全监管体系。这些国家的食品安全监管体系的共同特点是以风险分析为理念和"从农田到餐桌"。为了更好地借鉴发达国家食品安全监管体系的经验,本文对日本、美国和欧盟的食品安全监管体系进行了剖析,并对其有益经验进行了借鉴。

  5. 中英教育督导制度的比较及原因探析%Sino-British education supervision system comparison and the reason analysis

    Institute of Scientific and Technical Information of China (English)

    廖启良; 芦雷

    2012-01-01

    to build and strengthen education supervision system on education development and the improvement of the quality of education has an important role. Strengthen the education supervision system construction has become the world today education management modernization the important standard. Study English education supervision system of the same point, differences and reasons for the differences, to perfect our education supervision system has the important enlightenment function.%建立和加强教育督导制度对教育的发展和教育质量的提高具有重要的作用。加强教育督导制度建设已成为当今世界教育管理现代化的重要标志。研究中英教育督导制度的相通点、不同点以及产生差异的原因,对于完善我国的教育督导制度有着重要的启示作用。

  6. Introduction of imported food safety supervision system in New Zealand%新西兰进口食品监管机制介绍

    Institute of Scientific and Technical Information of China (English)

    刘良; 刘环; 仇华磊; 贝君; 张锡全; 焦阳; 张雷; 张伟

    2015-01-01

    New Zealand is one of a major exporter of food and primary agricultural products. It applies stringent Market Access and supervision measures against imported food and agricultural products to protect domestic eco-system from foreign species. The paper provided an in-depth introduction of requirements for importing food and importer, importing process, as well as monitoring measures as following: relevant regulations of imported foods, requirements of environment contaminants, toxicants, pesticide residues, microbes in food, supervision measures applied to prescribed foods exporters, and the frequency of sampling and inspection of prescribed foods taken at borders with “Switching Rule”, which was classified as 3 different types: the tightened level, the normal level, and the reduced level. This paper would be help for Chinese government and enterprises to learn New Zealand imported food safety control system.%新西兰作为初级农牧产品和食品出口大国,为严格控制外来生物物种入侵,保护生态安全,对食品农产品的进口采取了严格的准入和监管制度。本文介绍了新西兰对进口食品的要求,对进口商的要求,以及进口流程和监管措施,特别是新西兰进口高风险食品的监管控制措施,包括:进口食品法规要求;进口食品的污染物和天然毒素限量、农药残留和化学污染物限量和微生物限量要求;高风险食品的进口国家或地区的企业准入和许可、入境前的许可证办理;高风险食品的入境口岸控制,实施加严检验、正常水平、减少水平3种动态“转换”抽样频率;进口食品的检验监督控制处理措施。旨在帮助我国政府相关部门和企业全面、深入了解新西兰进口食品检验监管制度。

  7. A framework to facilitate self-directed learning, assessment and supervision in midwifery practice: A qualitative study of supervisors' perceptions

    NARCIS (Netherlands)

    Embo, M.; Driessen, E.; Valcke, M.; Vleuten, C.P.M. van der

    2014-01-01

    BACKGROUND: Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) a

  8. A framework to facilitate self-directed learning, assessment and supervision in midwifery practice: A qualitative study of supervisors' perceptions

    NARCIS (Netherlands)

    Embo, M.; Driessen, E.; Valcke, M.; Vleuten, C.P.M. van der

    2014-01-01

    BACKGROUND: Self-directed learning is an educational concept that has received increasing attention. The recent workplace literature, however, reports problems with the facilitation of self-directed learning in clinical practice. We developed the Midwifery Assessment and Feedback Instrument (MAFI) a

  9. Learning an Interactive Segmentation System

    CERN Document Server

    Nickisch, Hannes; Rother, Carsten

    2009-01-01

    Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.

  10. Social software: E-learning beyond learning management systems

    DEFF Research Database (Denmark)

    Dalsgaard, Christian

    2006-01-01

    activities of students. The article suggests a limitation of the use of learning management systems to cover only administrative issues. Further, it is argued that students' self-governed learning processes are supported by providing students with personal tools and engaging them in different kinds of social......The article argues that it is necessary to move e-learning beyond learning management systems and engage students in an active use of the web as a resource for their self-governed, problem-based and collaborative activities. The purpose of the article is to discuss the potential of social software...... to move e-learning beyond learning management systems. An approach to use of social software in support of a social constructivist approach to e-learning is presented, and it is argued that learning management systems do not support a social constructivist approach which emphasizes self-governed learning...

  11. Social software: E-learning beyond learning management systems

    DEFF Research Database (Denmark)

    Dalsgaard, Christian

    2006-01-01

    The article argues that it is necessary to move e-learning beyond learning management systems and engage students in an active use of the web as a resource for their self-governed, problem-based and collaborative activities. The purpose of the article is to discuss the potential of social software...... to move e-learning beyond learning management systems. An approach to use of social software in support of a social constructivist approach to e-learning is presented, and it is argued that learning management systems do not support a social constructivist approach which emphasizes self-governed learning...... activities of students. The article suggests a limitation of the use of learning management systems to cover only administrative issues. Further, it is argued that students' self-governed learning processes are supported by providing students with personal tools and engaging them in different kinds of social...

  12. Implementing a system of structured clinical supervision with a group of DipHE(nursing) RMN students.

    Science.gov (United States)

    Markham, V; Turner, P

    1998-01-01

    Clinical supervision is to become an integral part of mental health nursing, and the United Kingdom Central Council for Nursing, Midwifery & Health Visiting has recommended that it be incorporated in pre-registration education. This paper describes teachers' experiences of delivering a programme of clinical supervision education within the mental health branch of a diploma in nursing course. It outlines the implementation and evaluation of the programme, including discussion of the process and difficulties encountered. The programme appears to have provided a positive first experience for the students and to have given them the enthusiasm to adopt clinical supervision as part of their future roles as qualified practitioners.

  13. Quality Supervision System of Developed Countries and the Enlightenments%发达国家质量监管体系及对我国的启示

    Institute of Scientific and Technical Information of China (English)

    王艳红

    2012-01-01

    Chinese product quality law has been implemented for nearly twenty years in our country,but there are still ninny prob- lems in China's product quality status.Exploring the reasons,China has not yet established a set of perfect quality supervision sys- tem.This paper analyzes the quality supervision system,policies and measures,in developed countries,represented by United States, Germany and Japan,and puts forward legal proposes to improve the quality supervision system in our country,to perfect the legal system of the quality supervision,and to unit the administrative law enforcement system,industry self-regulation,public opinion,mass participation in a combination of "Four in One" quality supervision system.%我国产品质量法的实施已近二十年了,但我国产品质量状况仍然存在诸多问题。究其原因,与我国目前没有建立起一套完善的质量监管体系不无关系。本文分析了美国、德国和日本等发达国家产品质量监管体制、政策及措施,并以此为启示,提出了构建行政执法、行业自律、舆论监督、群众参与相结合"四位一体"的质量监管体系的立法建议。

  14. Adaptive Device Context Based Mobile Learning Systems

    Science.gov (United States)

    Pu, Haitao; Lin, Jinjiao; Song, Yanwei; Liu, Fasheng

    2011-01-01

    Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for…

  15. Adaptive Device Context Based Mobile Learning Systems

    Science.gov (United States)

    Pu, Haitao; Lin, Jinjiao; Song, Yanwei; Liu, Fasheng

    2011-01-01

    Mobile learning is e-learning delivered through mobile computing devices, which represents the next stage of computer-aided, multi-media based learning. Therefore, mobile learning is transforming the way of traditional education. However, as most current e-learning systems and their contents are not suitable for mobile devices, an approach for…

  16. Establishment of the Supervision Systems of Equine Animal Competitions in Beijing%北京市马属动物赛事监管制度的建立

    Institute of Scientific and Technical Information of China (English)

    谢磊; 吉鸿武; 刘建华; 孙向华; 王昀丹; 焦振明; 于宗振

    2015-01-01

    Through supervising important equestrian sports in recent years and combining with the implementation of‘Animal Epidemic Prevention Regulations of Beijing’,Beijing has gradually formed eight supervision systems for Equine Animal Exhibition & Performance & Competition,that is‘Event Report’,‘application for quarantine inspection and checking’,‘archives recording’,‘inspection and auditing for epidemic prevention conditions’,‘quarantine and observation’,‘mid-term supervision during the events’,‘emergency preparedness’ and‘publicity and informing’. The above supervision systems will ensure the safety and disease free status of local equine animals.%通过对近年来重大马术活动的监管,结合《北京市动物防疫条例》的实施,北京市逐步形成马属动物展览演出比赛的“赛事报告、报检报验、档案记录、防疫条件审查、隔离观察、赛中监管、应急预备和宣传告知”八大监管制度,确保了北京市马属动物无疫安全。

  17. 地理信息系统在财政投资项目监管中的应用%Application of Geographic Information System in Financial Supervision

    Institute of Scientific and Technical Information of China (English)

    张国合; 谢孟利; 翟娅娟; 王石岩

    2013-01-01

    针对目前省级财政投资项目难于监管的特点,重点研究了空间信息技术应用于财政投资监管的问题,建立了财政投资监管模式,依托于省级地理信息公共服务平台,研发了财政投资监管地理信息系统,实现了财政投资项目的空间化管理,使财政投资监管的业务工作更加直观、便捷和高效。%Currently, provincial finance investment projects are difficult to supervise, after discussing the problems of space information technology in the financial investment supervision, this article build up a spatial management mode. Based on the provincial geographic information public service platform, we developed a geographical information system of supervision of financial investment. The system realizes the spatial management of financial investments, and makes the work of supervision of the financial investment become more intuitive, convenient, and efficient.

  18. Automatic Supervision And Fault Detection In PV System By Wireless Sensors With Interfacing By Labview Program

    Directory of Open Access Journals (Sweden)

    Yousra M Abbas

    2015-08-01

    Full Text Available In this work a wireless monitoring system are designed for automatic detection localization fault in photovoltaic system. In order to avoid the use of modeling and simulation of the PV system we detected the fault by monitoring the output of each individual photovoltaic panel connected in the system by Arduino and transmit this data wirelessly to laptop then interface it by LabVIEW program which made comparison between this data and the measured data taking from reference module at the same condition. The proposed method is very simple but effective detecting and diagnosing the main faults of a PV system and was experimentally validated and has demonstrated its effectiveness in the detection and diagnosing of main faults present in the DC side of PV system.

  19. GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data.

    Science.gov (United States)

    Rue-Albrecht, Kévin; McGettigan, Paul A; Hernández, Belinda; Nalpas, Nicolas C; Magee, David A; Parnell, Andrew C; Gordon, Stephen V; MacHugh, David E

    2016-03-11

    Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.

  20. A Generalized Image Scene Decomposition-Based System for Supervised Classification of Very High Resolution Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    ZhiYong Lv

    2016-09-01

    Full Text Available Very high resolution (VHR remote sensing images are widely used for land cover classification. However, to the best of our knowledge, few approaches have been shown to improve classification accuracies through image scene decomposition. In this paper, a simple yet powerful observational scene scale decomposition (OSSD-based system is proposed for the classification of VHR images. Different from the traditional methods, the OSSD-based system aims to improve the classification performance by decomposing the complexity of an image’s content. First, an image scene is divided into sub-image blocks through segmentation to decompose the image content. Subsequently, each sub-image block is classified respectively, or each block is processed firstly through an image filter or spectral–spatial feature extraction method, and then each processed segment is taken as the feature input of a classifier. Finally, classified sub-maps are fused together for accuracy evaluation. The effectiveness of our proposed approach was investigated through experiments performed on different images with different supervised classifiers, namely, support vector machine, k-nearest neighbor, naive Bayes classifier, and maximum likelihood classifier. Compared with the accuracy achieved without OSSD processing, the accuracy of each classifier improved significantly, and our proposed approach shows outstanding performance in terms of classification accuracy.

  1. Body-monitoring and health supervision by means of optical fiber-based sensing systems in medical textiles.

    Science.gov (United States)

    Quandt, Brit M; Scherer, Lukas J; Boesel, Luciano F; Wolf, Martin; Bona, Gian-Luca; Rossi, René M

    2015-02-18

    Long-term monitoring with optical fibers has moved into the focus of attention due to the applicability for medical measurements. Within this Review, setups of flexible, unobtrusive body-monitoring systems based on optical fibers and the respective measured vital parameters are in focus. Optical principles are discussed as well as the interaction of light with tissue. Optical fiber-based sensors that are already used in first trials are primarily selected for the section on possible applications. These medical textiles include the supervision of respiration, cardiac output, blood pressure, blood flow and its saturation with hemoglobin as well as oxygen, pressure, shear stress, mobility, gait, temperature, and electrolyte balance. The implementation of these sensor concepts prompts the development of wearable smart textiles. Thus, current sensing techniques and possibilities within photonic textiles are reviewed leading to multiparameter designs. Evaluation of these designs should show the great potential of optical fibers for the introduction into textiles especially due to the benefit of immunity to electromagnetic radiation. Still, further improvement of the signal-to-noise ratio is often necessary to develop a commercial monitoring system.

  2. Contextualizing Learning Scenarios According to Different Learning Management Systems

    Science.gov (United States)

    Drira, R.; Laroussi, M.; Le Pallec, X.; Warin, B.

    2012-01-01

    In this paper, we first demonstrate that an instructional design process of Technology Enhanced Learning (TEL) systems based on a Model Driven Approach (MDA) addresses the limits of Learning Technology Standards (LTS), such as SCORM and IMS-LD. Although these standards ensure the interoperability of TEL systems across different Learning Management…

  3. System safety management lessons learned

    Energy Technology Data Exchange (ETDEWEB)

    Piatt, J.A.

    1989-05-01

    The Assistant Secretary of the Army for Research, Development and Acquisition directed the Army Safety Center to provide an audit of the causes of accidents and safety of use restrictions on recently fielded systems by tracking residual hazards back through the acquisition process. The objective was to develop ''lessons learned'' that could be applied to the acquisition process to minimize mishaps in fielded systems. System safety management lessons learned are defined as Army practices or policies, derived from past successes and failures, that are expected to be effective in eliminating or reducing specific systemic causes of residual hazards. They are broadly applicable and supportive of the Army structure and acquisition objectives. 29 refs., 7 figs.

  4. PNNL: A Supervised Maximum Entropy Approach to Word Sense Disambiguation

    Energy Technology Data Exchange (ETDEWEB)

    Tratz, Stephen C.; Sanfilippo, Antonio P.; Gregory, Michelle L.; Chappell, Alan R.; Posse, Christian; Whitney, Paul D.

    2007-06-23

    In this paper, we described the PNNL Word Sense Disambiguation system as applied to the English All-Word task in Se-mEval 2007. We use a supervised learning approach, employing a large number of features and using Information Gain for dimension reduction. Our Maximum Entropy approach combined with a rich set of features produced results that are significantly better than baseline and are the highest F-score for the fined-grained English All-Words subtask.

  5. Stereoscopic Augmented Reality System for Supervised Training on Minimal Invasive Surgery Robots

    DEFF Research Database (Denmark)

    Matu, Florin-Octavian; Thøgersen, Mikkel; Galsgaard, Bo

    2014-01-01

    Training in the use of robot-assisted surgery systems is necessary before a surgeon is able to perform procedures using these systems because the setup is very different from manual procedures. In addition, surgery robots are highly expensive to both acquire and maintain --- thereby entailing...... the need for efficient training. When training with the robot, the communication between the trainer and the trainee is limited, since the trainee often cannot see the trainer. To overcome this issue, this paper proposes an Augmented Reality (AR) system where the trainer is controlling two virtual robotic...

  6. Learning Management Systems and Comparison of Open Source Learning Management Systems and Proprietary Learning Management Systems

    Directory of Open Access Journals (Sweden)

    Yücel Yılmaz

    2016-04-01

    Full Text Available The concept of learning has been increasingly gaining importance for individuals, businesses and communities in the age of information. On the other hand, developments in information and communication technologies take effect in the field of learning activities. With these technologies, barriers of time and space against the learning activities largely disappear and these technologies make it easier to carry out these activities more effectively. There remain a lot of questions regarding selection of learning management system (LMS to be used for the management of e-learning processes by all organizations conducing educational practices including universities, companies, non-profit organizations, etc. The main questions are as follows: Shall we choose open source LMS or commercial LMS? Can the selected LMS meet existing needs and future potential needs for the organization? What are the possibilities of technical support in the management of LMS? What kind of problems may be experienced in the use of LMS and how can these problems be solved? How much effective can officials in the organization be in the management of LMS? In this study, primarily e-learning and the concept of LMS will be discussed, and in the next section, as for answers to these questions, open source LMSs and centrally developed LMSs will be examined and their advantages and disadvantages relative to each other will be discussed.

  7. Researching online supervision

    DEFF Research Database (Denmark)

    Smedegaard Ernst Bengtsen, Søren; Mathiasen, Helle

    2014-01-01

    , or a poor substitution of such. This one-sidedness on the conceptual level makes it challenging to empirically study the deeper implications digital tools have for the supervisory dialogue. Drawing on phenomenology and systems theory we argue that we need new concepts in qualitative methodology that allow...... us to research the digital tools on their own premises as autonomous things in themselves, possessing an ontological creativity of their own. In order for qualitative research to match the ontological nature of digital tools we conclude the article by formulating three criteria of a ‘torn......’ methodology that makes room for new approaches to researching online supervision at the university....

  8. Researching online supervision

    DEFF Research Database (Denmark)

    Bengtsen, Søren S. E.; Mathiasen, Helle

    2014-01-01

    us to research the digital tools on their own premises as autonomous things in themselves, possessing an ontological creativity of their own. In order for qualitative research to match the ontological nature of digital tools we conclude the article by formulating three criteria of a ‘torn......’ methodology that makes room for new approaches to researching online supervision at the university......., or a poor substitution of such. This one-sidedness on the conceptual level makes it challenging to empirically study the deeper implications digital tools have for the supervisory dialogue. Drawing on phenomenology and systems theory we argue that we need new concepts in qualitative methodology that allow...

  9. Insulation measurement and supervision in live AC and DC unearthed systems

    CERN Document Server

    Olszowiec, Piotr

    2013-01-01

    Low voltage unearthed (IT) AC and DC systems are commonly applied for supply of power and control circuits in industry, transportation, medical objects etc. The main reasons for their use are high reliability and numerous advantages offered by isolating them against ground. Insulation level is a decisive factor for networks operational reliability and safety. Insufficient insulation-to-ground resistance can cause various disturbances. Though ground faults in IT systems do not make networks operation impossible, they may cause severe problems with their safe functioning. In this book the most important issues concerning normal operation and ground fault phenomena are described in concise form. Numerous methods of insulation resistance and capacitance measurement in live circuits are presented. Important other procedures of  these parameters determination based on measurement and calculation are explained and reviews of selected insulation resistance measurement devices as well as earth fault locating systems ...

  10. Insulation measurement and supervision in live AC and DC unearthed systems

    CERN Document Server

    Olszowiec, Piotr

    2014-01-01

    Low voltage unearthed (IT) AC and DC systems are commonly applied for supply of power and control circuits in industry, transportation, medical objects etc. The main reasons for their use are high reliability and numerous advantages offered by isolating them against ground. Insulation level is a decisive factor for networks operational reliability and safety. Insufficient insulation-to-ground resistance can cause various disturbances. Though ground faults in IT systems do not make networks operation impossible, they may cause severe problems with their safe functioning. In this book the most important issues concerning normal operation and ground fault phenomena are described in concise form. Numerous methods of insulation resistance and capacitance measurement in live circuits are presented. Important other procedures of  these parameters determination based on measurement and calculation are explained and reviews of selected insulation resistance measurement devices as well as earth fault locating systems ...

  11. Logic Learning Machine and standard supervised methods for Hodgkin's lymphoma prognosis using gene expression data and clinical variables.

    Science.gov (United States)

    Parodi, Stefano; Manneschi, Chiara; Verda, Damiano; Ferrari, Enrico; Muselli, Marco

    2016-06-27

    This study evaluates the performance of a set of machine learning techniques in predicting the prognosis of Hodgkin's lymphoma using clinical factors and gene expression data. Analysed samples from 130 Hodgkin's lymphoma patients included a small set of clinical variables and more than 54,000 gene features. Machine learning classifiers included three black-box algorithms (k-nearest neighbour, Artificial Neural Network, and Support Vector Machine) and two methods based on intelligible rules (Decision Tree and the innovative Logic Learning Machine method). Support Vector Machine clearly outperformed any of the other methods. Among the two rule-based algorithms, Logic Learning Machine performed better and identified a set of simple intelligible rules based on a combination of clinical variables and gene expressions. Decision Tree identified a non-coding gene (XIST) involved in the early phases of X chromosome inactivation that was overexpressed in females and in non-relapsed patients. XIST expression might be responsible for the better prognosis of female Hodgkin's lymphoma patients.

  12. Considering Alternate Futures to Classify Off-Task Behavior as Emotion Self-Regulation: A Supervised Learning Approach

    Science.gov (United States)

    Sabourin, Jennifer L.; Rowe, Jonathan P.; Mott, Bradford W.; Lester, James C.

    2013-01-01

    Over the past decade, there has been growing interest in real-time assessment of student engagement and motivation during interactions with educational software. Detecting symptoms of disengagement, such as off-task behavior, has shown considerable promise for understanding students' motivational characteristics during learning. In this paper, we…

  13. 一种结合半监督Boosting方法的迁移学习算法%Transfer Learning via Semi-supervised Boosting Method

    Institute of Scientific and Technical Information of China (English)

    洪佳明; 陈炳超; 印鉴

    2011-01-01

    迁移学习是数据挖掘中的一个研究方向,试图重用相关领域的数据样本,将相关领域的知识”迁移”到新领域中帮助训练.当前,基于实例的迁移学习算法容易产生过度拟合的问题,不能充分利用相关领域中的有用数据,为了避免这个问题,通过引入目标领域的无标记样本参与训练,利用半监督Boosting方法,提出一种新的迁移学习算法,能够对样本的相关性进行更好的判断,减少选择性偏差的影响,在大量文本数据集上的实验表明了新算法的有效性.%Transfer learning aims at reusing existing instances from other related domains to help learning models for the target domain. Existing algorithms in instance-transfer learning might easily suffer from the problem of overfitting. To address this problem, we propose to incorporate additional unlabeled instances from the target domain, so that more domain knowledge can be brought into the training process. Specifically, under the generalized framework of boosting methods, we show that a semi-supervised boosting method can be applied to help re-weighting the source domain instances, making the final classifiers less sensitive to the small amount of labeled instances in the target domain. Extensive experiments confirm the efficiency of the new algorithm.

  14. Supervision Software for the Integration of the Beam Interlock System with the CERN Accelerator Complex

    CERN Document Server

    Audrain, M; Dragu, M; Fuchsberger, K; Garnier, JC; Gorzawski, AA; Koza, M; Krol, K; Moscatelli, A; Puccio, B; Stamos, K; Zerlauth, M

    2014-01-01

    The Accelerator complex at the European Organisation for Nuclear Research (CERN) is composed of many systems which are required to function in a valid state to ensure safe beam operation. One key component of machine protection, the Beam Interlock System (BIS), was designed to interface critical systems around the accelerator chain, provide fast and reliable transmission of beam dump requests and trigger beam extraction in case of malfunctioning of equipment systems or beam losses. Numerous upgrades of accelerator and controls components during the Long Shutdown 1 (LS1) are followed by subsequent software updates that need to be thoroughly validated before the restart of beam operation in 2015. In parallel, the ongoing deployments of the BIS hardware in the PS booster (PSB) and the future LINAC4 give rise to new requirements for the related controls and monitoring software due to their fast cycle times. This paper describes the current status and ongoing work as well as the long-term vision for the integratio...

  15. Stereoscopic Augmented Reality System for Supervised Training on Minimal Invasive Surgery Robots

    DEFF Research Database (Denmark)

    Matu, Florin-Octavian; Thøgersen, Mikkel; Galsgaard, Bo

    2014-01-01

    the need for efficient training. When training with the robot, the communication between the trainer and the trainee is limited, since the trainee often cannot see the trainer. To overcome this issue, this paper proposes an Augmented Reality (AR) system where the trainer is controlling two virtual robotic...

  16. Using open source software for the supervision and management of the water resources system of Athens

    Science.gov (United States)

    Kozanis, S.; Christofides, A.; Efstratiadis, A.; Koukouvinos, A.; Karavokiros, G.; Mamassis, N.; Koutsoyiannis, D.; Nikolopoulos, D.

    2012-04-01

    The water supply of Athens, Greece, is implemented through a complex water resource system, extending over an area of around 4 000 km2 and including surface water and groundwater resources. It incorporates four reservoirs, 350 km of main aqueducts, 15 pumping stations, more than 100 boreholes and 5 small hydropower plants. The system is run by the Athens Water Supply and Sewerage Company (EYDAP) Over more than 10 years we have developed, information technology tools such as GIS, database and decision support systems, to assist the management of the system. Among the software components, "Enhydris", a web application for the visualization and management of geographical and hydrometeorological data, and "Hydrognomon", a data analysis and processing tool, are now free software. Enhydris is entirely based on free software technologies such as Python, Django, PostgreSQL, and JQuery. We also created http://openmeteo.org/, a web site hosting our free software products as well as a free database system devoted to the dissemination of free data. In particular, "Enhydris" is used for the management of the hydrometeorological stations and the major hydraulic structures (aqueducts, reservoirs, boreholes, etc.), as well as for the retrieval of time series, online graphs etc. For the specific needs of EYDAP, additional GIS functionality was introduced for the display and monitoring of the water supply network. This functionality is also implemented as free software and can be reused in similar projects. Except for "Hydrognomon" and "Enhydris", we have developed a number of advanced modeling applications, which are also generic-purpose tools that have been used for a long time to provide decision support for the water resource system of Athens. These are "Hydronomeas", which optimizes the operation of complex water resource systems, based on a stochastic simulation framework, "Castalia", which implements the generation of synthetic time series, and "Hydrogeios", which employs

  17. Supervised Multi-Manifold Learning Algorithm Based on ISOMAP%基于等距映射的监督多流形学习算法

    Institute of Scientific and Technical Information of China (English)

    邵超; 万春红

    2014-01-01

    The existing supervised multi-manifold learning algorithms adjust the distances between data points according to their class labels, and hence the multiple manifolds can be classified successfully. However, the poor generalization ability of these algorithms results in unfaithful display of the intrinsic geometric structure of some manifolds. A supervised multi-manifold learning algorithm based on Isometric mapping ( ISOMAP) is proposed. The shortest path algorithm suitable for the multi-manifold structure is used to compute the shortest path distances which can effectively approximate the corresponding geodesic distances even in the multi-manifold structure. Then, Sammon mapping is used to further preserve shorter distances in the low-dimensional embedding space. Consequently, the intrinsic geometric structure of each manifold can be faithfully displayed. Moreover, the manifolds of new data points can be precisely judged based on the similarities between neighboring local tangent spaces according to the local Euclidean nature of the manifold, and thus the proposed algorithm obtains a good generalization ability. The effectiveness of the proposed algorithm is verified by experimental results.%目前的监督多流形学习算法大多数都根据数据的类别标记对彼此间的距离进行调整,能较好实现多流形的分类,但难以成功展现各流形的内在几何结构,泛化能力也较差,因此文中提出一种基于等距映射的监督多流形学习算法。该算法采用适合于多流形的最短路径算法,得到在多流形下依然能正确逼近相应测地距离的最短路径距离,并采用Sammon映射以更好地保持短距离,最终可成功展现各流形的内在几何结构。此外,该算法根据邻近局部切空间的相似性可准确判定新数据点所在的流形,从而具有较强的泛化能力。该算法的有效性可通过实验结果得以证实。

  18. Supervision as Metaphor

    Science.gov (United States)

    Lee, Alison; Green, Bill

    2009-01-01

    This article takes up the question of the language within which discussion of research degree supervision is couched and framed, and the consequences of such framings for supervision as a field of pedagogical practice. It examines the proliferation and intensity of metaphor, allegory and allusion in the language of candidature and supervision,…

  19. A Supervision of Solidarity

    Science.gov (United States)

    Reynolds, Vikki

    2010-01-01

    This article illustrates an approach to therapeutic supervision informed by a philosophy of solidarity and social justice activism. Called a "Supervision of Solidarity", this approach addresses the particular challenges in the supervision of therapists who work alongside clients who are subjected to social injustice and extreme marginalization. It…

  20. Flexible Ubiquitous Learning Management System Adapted to Learning Context

    Science.gov (United States)

    Jeong, Ji-Seong; Kim, Mihye; Park, Chan; Yoo, Jae-Soo; Yoo, Kwan-Hee

    This paper proposes a u-learning management system (ULMS) appropriate to the ubiquitous learning environment, with emphasis on the significance of context awareness and adaptation in learning. The proposed system supports the basic functions of an e-learning management system and incorporates a number of tools and additional features to provide a more customized learning service. The proposed system automatically corresponds to various forms of user terminal without modifying the existing system. The functions, formats, and course learning activities of the system are dynamically and adaptively constructed at runtime according to user terminals, course types, pedagogical goals as well as student characteristics and learning context. A prototype for university use has been implemented to demonstrate and evaluate the proposed approach. We regard the proposed ULMS as an ideal u-learning system because it can not only lead students into continuous and mobile 'anytime, anywhere' learning using any kind of terminal, but can also foster enhanced self-directed learning through the establishment of an adaptive learning environment.

  1. A mobile phone based alarm system for supervising vital parameters in free moving rats

    Directory of Open Access Journals (Sweden)

    Kellermann Kristine

    2012-02-01

    Full Text Available Abstract Background Study protocols involving experimental animals often require the monitoring of different parameters not only in anesthetized, but also in free moving animals. Most animal research involves small rodents, in which continuously monitoring parameters such as temperature and heart rate is very stressful for the awake animals or simply not possible. Aim of the underlying study was to monitor heart rate, temperature and activity and to assess inflammation in the heart, lungs, liver and kidney in the early postoperative phase after experimental cardiopulmonary bypass involving 45 min of deep hypothermic circulatory arrest in rats. Besides continuous monitoring of heart rate, temperature and behavioural activity, the main focus was on avoiding uncontrolled death of an animal in the early postoperative phase in order to harvest relevant organs before autolysis would render them unsuitable for the assessment of inflammation. Findings We therefore set up a telemetry-based system (Data Science International, DSI™ that continuously monitored the rat's temperature, heart rate and activity in their cages. The data collection using telemetry was combined with an analysis software (Microsoft excel™, a webmail application (GMX and a text message-service. Whenever an animal's heart rate dropped below the pre-defined threshold of 150 beats per minute (bpm, a notification in the form of a text message was automatically sent to the experimenter's mobile phone. With a positive predictive value of 93.1% and a negative predictive value of 90.5%, the designed surveillance and alarm system proved a reliable and inexpensive tool to avoid uncontrolled death in order to minimize suffering and harvest relevant organs before autolysis would set in. Conclusions This combination of a telemetry-based system and software tools provided us with a reliable notification system of imminent death. The system's high positive predictive value helped to avoid

  2. Supervised Sequence Labelling with Recurrent Neural Networks

    CERN Document Server

    Graves, Alex

    2012-01-01

    Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.    The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional...

  3. The Supervision and Control System of the Common Import & Export Goods

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    @@ The meaning of common import and export The common import and export refers to the Customs clearance system in which the goods can be used and sold within the territory through direct import or put into free circulation by transporting out of the territory after the import and export duty has been fully paid in the entry and exit part and all Customs formalities has been gone through.

  4. Free Open Source Software: FOSS Based e-learning, Mobile Learning Systems Together with Blended Learning System

    Directory of Open Access Journals (Sweden)

    Kohei Arai

    2013-11-01

    Full Text Available Free Open Source Software: FOSS based e-learning system is proposed together with blended learning and mobile learning. Mashup search engine for e-learning contents search and content adaptation from e-learning to mobile learning content are also implemented. Through implementation of the proposed system, it is found that the system does work well for improvement of learning efficiency.

  5. 上司不当督导与下属绩效:反馈寻求行为和学习目标定向的作用%Abusive Supervision and Employee' Performance: Mechanisms of FSB and Learning Goral Orientation

    Institute of Scientific and Technical Information of China (English)

    申传刚; 马红宇; 杨璟; 刘腾飞

    2012-01-01

    本研究从下属反馈管理行为的视角来探索领导与下属的社会交换过程.具体为探讨下属的反馈寻求行为在上司不当督导与下属绩效之间的中介作用,下属的学习目标定向对上述过程中的调节作用.通过问卷法获得306名下属与上司的对偶数据,基于层级回归和Bootstrap分析的结果表明:上司不当督导不仅直接影响下属的绩效,还能通过抑制下属的反馈寻求行为间接地影响员工的绩效;下属的学习目标定向调节着上司不当督导与下属的反馈寻求行为的关系,当下属的学习目标定向越低,上司不当督导对反馈寻求行为的抑制作用更加明显.%The literature on abusive supervision has consistently demonstrated the negative relationship between member perception of supervisor's abusive behavior and member performance. The process through which relationship supervisor's abusive behavior influences subordinates' performance, however, is still not fully understood. The present study provides a mechanism for the process. Specifically, we predict that the feedback seeking behavior (FSB) of members mediates these relationships, and learning goal orientation moderates the relationship between abusive supervision and FSB.In order to avoid the common method variance problem, two sources of survey were administrated. Data was from a total of 306 matched supervisor-subordinate dyads in 7 enterprises located in Hubei, Zhejiang, Xiamen. Two structured questionnaires were employed as the research instrument for this study. One consisted of three scales designed to measure abusive supervision, FSB and learning goal orientation. Among the major measures, the 15-items abusive supervision was adopted from Tepper (2000); FSB was measured via 6 items that was adopted from Saori Yanagizawa (2008); the five item learning goal orientation scale was adopted from Vandewalle & Cummings (1997). We used a scale adopted from Tusi et al. (1997) in the other

  6. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M; Wallace, David J; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R; Hravnak, Marilyn

    2016-07-01

    The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability. Observational cohort study. Twenty-four-bed trauma step-down unit. Two thousand one hundred fifty-three patients. Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time. The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development. Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).

  7. Good supervision and PBL

    DEFF Research Database (Denmark)

    Otrel-Cass, Kathrin

    This field study was conducted at the Faculty of Social Sciences at Aalborg University with the intention to investigate how students reflect on their experiences with supervision in a PBL environment. The overall aim of this study was to inform about the continued work in strengthening supervision...... at this faculty. This particular study invited Master level students to discuss: • How a typical supervision process proceeds • How they experienced and what they expected of PBL in the supervision process • What makes a good supervision process...

  8. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

    Directory of Open Access Journals (Sweden)

    Luis D Lledó

    Full Text Available This paper presents the application of an Adaptive Resonance Theory (ART based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  9. Supervised and dynamic neuro-fuzzy systems to classify physiological responses in robot-assisted neurorehabilitation.

    Science.gov (United States)

    Lledó, Luis D; Badesa, Francisco J; Almonacid, Miguel; Cano-Izquierdo, José M; Sabater-Navarro, José M; Fernández, Eduardo; Garcia-Aracil, Nicolás

    2015-01-01

    This paper presents the application of an Adaptive Resonance Theory (ART) based on neural networks combined with Fuzzy Logic systems to classify physiological reactions of subjects performing robot-assisted rehabilitation therapies. First, the theoretical background of a neuro-fuzzy classifier called S-dFasArt is presented. Then, the methodology and experimental protocols to perform a robot-assisted neurorehabilitation task are described. Our results show that the combination of the dynamic nature of S-dFasArt classifier with a supervisory module are very robust and suggest that this methodology could be very useful to take into account emotional states in robot-assisted environments and help to enhance and better understand human-robot interactions.

  10. Lack of supervision? The building manager`s task of supervising facility management systems; Ein Stiefkind der Planung? Objektueberwachung (Fachbauleitung) der technischen Gebaeudeausruestung

    Energy Technology Data Exchange (ETDEWEB)

    Bernhard, M. [IMB-Ingenieurteam Manfred Bernhard GmbH, Muehlheim am Main (Germany). Fachbauleitung Technische Gebaeudeausruestung

    1998-12-31

    The technical comfort requirements of new buildings and the resulting density of techncal facilities makes the task of builder-owners, architects and building managers increasingly difficult. Reasons for this are the lack of space available for the technical systems and the increasingly short construction times. (orig.) [Deutsch] Die Aufgabe des Fachbauleiters ist es, Fachkompetenz mit Auftraggeberfunktion zu praktizieren und dafuer zu sorgen, dass im Sinne des Auftraggebers der gesetzte Termin- und Finanzrahmen bei gleichzeitig hohem Qualitaetsanspruch eingehalten wird. Die technische Komfortausstattung von Grossobjekten und die daraus resultierende hohe Installationsdichte stellt immer groessere Anforderungen bei der Auftragsabwicklung an Auftraggeber, Architekten und planende Fachingenieure. Die Gruende hierfuer liegen u.a. in dem meist sehr begrenzten Platzangebot fuer die Technik und vor allem in den immer kuerzeren Bauzeiten der Objekte. (orig.)

  11. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  12. 构建中国特色权力制约监督机制:原则、方向与重点%Establishment of a Power Restriction and Supervision System with Chinese Characteristics: Principles, Directions and Focuses

    Institute of Scientific and Technical Information of China (English)

    郑东风

    2011-01-01

    The latest fruit of Marxism and its localization in China is the ideological basis to constitute a power restriction and supervision system with Chinese characteristics. It is a major subject and an urgent mission faced by the Chinese government and Communist Party of China to further improve the power supervision and restriction system, which is of vital importance to enhance the ability of fighting against corruption, guarding against risks and refusing deterioration. To realize the aim, we should stick to Chinese basic national conditions, learn from the pertinent achievement by other countries, adapt to the requirements and tendencies associated with economic, social, scientific and technological development. We should emphasize the reduction of resources directly controlled by power, optimize power allocation structure, regulate power use by laws, facilitate public and democratic power supervision, and transplant moral principles into power exertion. All these measures can serve as the practical way to reach the goal. We should also reinforce the supervision and restriction of chief leaders, make full use of the platform of internet power supervision, promote the ability of science and technology to prevent risks, focus on system construction, and improve the administrative accountability system. These aspects constitute decisive links need to be broken through if a power restriction and supervision system with Chinese characteristics is to be established.%马克思主义及其中国化的最新成果是构建中国特色权力制约监督机制的思想基础。进一步加大对权力的制约监督是党和国家面临的重大课题和紧迫任务,对于推进反腐倡廉建设、提高党抵御风险和拒腐防变的能力有着十分重要的意义。健全中国特色的权力制约监督机制,必须坚持立足中国的基本国情,兼收并蓄当今世界各国权力制约监督机制的有益成果,顺应经济、社会、科技发展的要求和

  13. Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy.

    Science.gov (United States)

    Gibbons, Chris; Richards, Suzanne; Valderas, Jose Maria; Campbell, John

    2017-03-15

    Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor's activity for the purposes of quality assurance, safety, and continuing professional development. The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors' professional performance in the United Kingdom. We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians' colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to "popular" (recall=.97), "innovator" (recall=.98), and "respected" (recall=.87) codes and was lower for the "interpersonal" (recall=.80) and "professional" (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as "respected," "professional," and "interpersonal" related to higher doctor scores on the GMC-CQ compared with comments that were not classified (Pdoctors who were rated as popular or innovative and those who were not rated at all (P>.05). Machine learning algorithms can classify open-text feedback

  14. Instructional Leadership and Supervision in Special Language Programs.

    Science.gov (United States)

    Florez-Tighe, Viola

    A recent review of English as a Second Language (ESL) research revealed an increase in studies dealing with instructional approaches, language learning theories, ESL curriculum, and learning-aid study strategies; however, supervision of teaching in ESL programs was mentioned only occasionally. Supervision, when properly practiced, can provide a…

  15. 建立多方协同监督的科技项目监理体系%The Establishment of Multi-party Collaborative Supervision System of Science and Technology Project

    Institute of Scientific and Technical Information of China (English)

    韩靓; 林祥

    2013-01-01

    The biggest drawbacks of China's scientific and technological projects implementation is the lack of monitoring and e-valuation. Firstly, this paper argues that the relevant laws and regulations should be developed to protect the standardization and insti-tutionalization of monitoring and evaluation of science and technology project. Secondly, this paper builts up a multi-party collaborative supervision system of science and technology project, consists of the government, the independent third-party project management com-pany, the masses and project undertaking unit. The aim is to realize the diversification of supervision subject and the legalization of su-pervision mechanism. We can learn from foreign experience to optimize the government oversight way, including the introduction of modern project management model, the establishment of project evaluation mechanism and the establishment of invited supervisors to strengthen technology supervision of the administrative organs. The third-party supervision model has obvious advantages, but the gov-ernment should check the composition and qualification of supervision company strictly and avoid the assimilation phenomenon of super-vision company.%  目前我国科技项目实施中最大的弊端就是缺乏监督和评估。本文认为,首先,应该制定、完善相关法律法规,以保障科技项目监督和评估的规范化和制度化,实现监理机制法制化;其次,从优化政府科技行政监督方式、引入项目第三方监理方式和科技项目承担单位配合几方面,构建起由政府、独立第三方监理公司、群众和项目承担单位多方协同监督的科技项目监理体系,逐步实现监理主体多元化。其中,在优化政府科技监督方式方面可以借鉴国外经验,引入现代项目管理模式,加强项目实施的全过程管理;建立项目评估机制,强化项目管理的监督;设立“科技特邀监督员”,加强对科

  16. Target Localization in Wireless Sensor Networks Using Online Semi-Supervised Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Jaehyun Yoo

    2015-05-01

    Full Text Available Machine learning has been successfully used for target localization in wireless sensor networks (WSNs due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR. The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods.

  17. La vigilanza sul sistema finanziario: obiettivi, assetti e approcci (Supervision of the financial system: objectives, structures and approaches

    Directory of Open Access Journals (Sweden)

    Mario Sarcinelli

    2009-12-01

    be a carrier. The fall of some banking institutions and the concern to match the conditions of international competition have led to the formulation of a standard such as the coefficients of capital that Basel II is extending beyond credit risk and joining the other pillars of surveillance and discipline market. More attention has been paid recently to the organizational forms of supervision of the financial system, which explains the interest for the reasons and experiences related to the single authority on financial markets. Between the requirements of supervisory agencies to achieve the goal of stability and / or efficiency there is the independence, which can not be separated, however, by a process of reporting and achieving a political balance that respects the mandate given to the Agency. In a Europe that is struggling to progress along a quasi-federalism, in order to extract all the benefits of the unification of the power of money and the creation of the euro, there is an increasing need to forge ahead on the path of unification of the market financial and therefore have at least one co-ordination of financial supervisory structures of the member states, possibly connected by an instance centralized at EU level. The efficiency of a micro-supervision should not be assessed only by reference to the safety and soundness of individual institutions, but also its impact on the funding of businesses. Deregulation, free movement of capital, financial innovation and technological helped to exacerbate cyclical trends in the real economy, which increasingly emphasizes the importance of systemic risk, brings up financial instability and the question is whether macro-prudential supervision approach is not the answer you need. The American events that have highlighted the failure of that system of corporate governance had a following in Europe and particularly in Italy with the Parmalat scandal. The comparison between the reaction received by the U.S. Congress and

  18. An Imbalanced Learning based MDR-TB Early Warning System.

    Science.gov (United States)

    Li, Sheng; Tang, Bo; He, Haibo

    2016-07-01

    As a man-made disease, multidrug-resistant tuberculosis (MDR-TB) is mainly caused by improper treatment programs and poor patient supervision, most of which could be prevented. According to the daily treatment and inspection records of tuberculosis (TB) cases, this study focuses on establishing a warning system which could early evaluate the risk of TB patients converting to MDR-TB using machine learning methods. Different imbalanced sampling strategies and classification methods were compared due to the disparity between the number of TB cases and MDR-TB cases in historical data. The final results show that the relative optimal predictions results can be obtained by adopting CART-USBagg classification model in the first 90 days of half of a standardized treatment process.

  19. Using Optimal Ratio Mask as Training Target for Supervised Speech Separation

    OpenAIRE

    Xia, Shasha; Li, Hao; ZHANG Xueliang

    2017-01-01

    Supervised speech separation uses supervised learning algorithms to learn a mapping from an input noisy signal to an output target. With the fast development of deep learning, supervised separation has become the most important direction in speech separation area in recent years. For the supervised algorithm, training target has a significant impact on the performance. Ideal ratio mask is a commonly used training target, which can improve the speech intelligibility and quality of the separate...

  20. Remote Supervision Information System for Testing Organization%检验检测机构远程监管信息系统

    Institute of Scientific and Technical Information of China (English)

    裘锋

    2014-01-01

    从需求分析、系统设计、系统实现3个方面介绍检验检测机构远程监管信息系统的开发过程,并介绍Ajax、NPOI在系统中的应用与实现。%This article describes the development process of a remote supervision information system for testing organization from demand analysis , system design and system implementation , and introduces the application and implementation of Ajax and NPOI in this system .

  1. 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

    CERN Document Server

    Mirabal, N; Ferrara, E C; Gonthier, P L; Harding, A K; Sánchez-Conde, M A; Thompson, D J

    2016-01-01

    Nearly 1/3 of the sources listed in the Third Fermi Large Area Telescope (LAT) catalog (3FGL) remain unassociated. It is possible that predicted and even unanticipated gamma-ray source classes are present in these data waiting to be discovered. Taking advantage of the excellent spectral capabilities achieved by the Fermi LAT, we use machine learning classifiers (Random Forest and XGBoost) to pinpoint potentially novel source classes in the unassociated 3FGL sample outside the Galactic plane. Here we report a total of 34 high-confidence Galactic candidates at |b| > 5 degrees. The currently favored standard astrophysical interpretations for these objects are pulsars or low-luminosity globular clusters hosting millisecond pulsars (MSPs). Yet, these objects could also be interpreted as dark matter annihilation taking place in ultra-faint dwarf galaxies or dark matter subhalos. Unfortunately, Fermi LAT spectra are not sufficient to break degeneracies between the different scenarios. Careful visual inspection of ar...

  2. Learning Visual Representations for Perception-Action Systems

    DEFF Research Database (Denmark)

    Piater, Justus; Jodogne, Sebastien; Detry, Renaud;

    2011-01-01

    We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily complex, we argue that in many cases it is advantageous to link them rather directly to action via learned mappings. These a......We discuss vision as a sensory modality for systems that effect actions in response to perceptions. While the internal representations informed by vision may be arbitrarily complex, we argue that in many cases it is advantageous to link them rather directly to action via learned mappings....... These arguments are illustrated by two examples of our own work. First, our RLVC algorithm performs reinforcement learning directly on the visual input space. To make this very large space manageable, RLVC interleaves the reinforcement learner with a supervised classification algorithm that seeks to split...... perceptual states so as to reduce perceptual aliasing. This results in an adaptive discretization of the perceptual space based on the presence or absence of visual features. Its extension RLJC also handles continuous action spaces. In contrast to the minimalistic visual representations produced by RLVC...

  3. Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multi-signal Vital Sign Monitoring Data

    Science.gov (United States)

    Chen, Lujie; Dubrawski, Artur; Wang, Donghan; Fiterau, Madalina; Guillame-Bert, Mathieu; Bose, Eliezer; Kaynar, Ata M.; Wallace, David J.; Guttendorf, Jane; Clermont, Gilles; Pinsky, Michael R.; Hravnak, Marilyn

    2015-01-01

    OBJECTIVE Use machine-learning (ML) algorithms to classify alerts as real or artifacts in online noninvasive vital sign (VS) data streams to reduce alarm fatigue and missed true instability. METHODS Using a 24-bed trauma step-down unit’s non-invasive VS monitoring data (heart rate [HR], respiratory rate [RR], peripheral oximetry [SpO2]) recorded at 1/20Hz, and noninvasive oscillometric blood pressure [BP] less frequently, we partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were VS deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained ML algorithms. The best model was evaluated on alerts in the test set to enact online alert classification as signals evolve over time. MAIN RESULTS The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve (AUC) performance of 0.79 (95% CI 0.67-0.93) for SpO2 at the instant the VS first crossed threshold and increased to 0.87 (95% CI 0.71-0.95) at 3 minutes into the alerting period. BP AUC started at 0.77 (95%CI 0.64-0.95) and increased to 0.87 (95% CI 0.71-0.98), while RR AUC started at 0.85 (95%CI 0.77-0.95) and increased to 0.97 (95% CI 0.94–1.00). HR alerts were too few for model development. CONCLUSIONS ML models can discern clinically relevant SpO2, BP and RR alerts from artifacts in an online monitoring dataset (AUC>0.87). PMID:26992068

  4. 28 CFR 802.28 - Exemption of the Court Services and Offender Supervision Agency System-limited access.

    Science.gov (United States)

    2010-07-01

    ...) Supervision & Management Automated Record Tracking (SMART) (CSOSA-11). (v) Recidivism Tracking Database (CSOSA... personnel are able to formulate decisions and policies with regard to offenders, to prevent disclosure of... or compromise law enforcement such as: the destruction of documentary evidence; improper...

  5. Geographical provenancing of purple grape juices from different farming systems by proton transfer reaction mass spectrometry using supervised statistical techniques

    NARCIS (Netherlands)

    Granato, Daniel; Koot, Alex; Ruth, van S.M.

    2015-01-01

    BACKGROUND: Organic, biodynamic and conventional purple grape juices (PGJ; n = 79) produced in Brazil and Europe were characterized by volatile organic compounds (m/z 20-160) measured by proton transfer reaction mass spectrometry (PTR-MS), and classification models were built using supervised sta

  6. Research Drug Supervision and Administrationbased on Cyber-physical System%基于信息物理融合的药品监管系统研究

    Institute of Scientific and Technical Information of China (English)

    谭颖; 李海庆; 谭睿; 沈小涛; 吴兵

    2015-01-01

    根据国家对药品安全管理的要求,提出一种将信息物理融合技术引入药品监管的方法。药品监管一般包括药械物流动态追踪、应急管理和药品温湿度管理与控制等功能,根据药品监管的工作流程,结合药品本身的特殊性,构建了具有相关功能的药品监管信息物理融合系统模型,通过分析该系统体系架构,对其主要功能进行了实现。该系统的实现可以有效提高药品监管的安全性,促进药品监管向信息化方向的发展。%Drug safety is one of the most important work which is beneficial to the people’s livelihood. How to make informatization combined with drug safety is valuable to research.To meet the demands for drug safety administration in China,in this paper,the idea to introduce cyber-physical system into drug supervision and administration system is presented.Aimed at the process involved in drug supervision and administration,and based on the special characteristics of drug,the multi-functional drug supervision and administration system model based on the cyber-physical system is constructed,which combines functions such as dynamic tracking of drug & instrument logistics,emergency administration,and administration and control of drug temperature and humidity.The framework of the cyber-physical system for drug supervi-sion and administration is analyzed,and the main functions are realized.With the realization of cyber-physi-cal system,drug safety administration will be improved,and the development of informationalization in drug supervision and administration will be promoted.

  7. Learning in a Simple Motor System

    Science.gov (United States)

    Broussard, Dianne M.; Kassardjian, Charles D.

    2004-01-01

    Motor learning is a very basic, essential form of learning that appears to share common mechanisms across different motor systems. We evaluate and compare a few conceptual models for learning in a relatively simple neural system, the vestibulo-ocular reflex (VOR) of vertebrates. We also compare the different animal models that have been used to…

  8. Improving the fire supervision and management system%完善消防监督管理制度的思考

    Institute of Scientific and Technical Information of China (English)

    李佳

    2012-01-01

      The fire supervision management is important of fire control work, related to the social and economic development and the social stability. At present our country fire supervision and management system is not perfect. This paper considers that we should set up the relevant fire laws and regulations system, strengthen supervision of fire team construction, to promote science and technology application and education and so on to improve.%  消防监督管理是消防工作的重中之重,关系到社会经济的发展及社会的稳定。目前我国消防监督管理制度不够不完善。本文认为应当从建立健全相关的消防法律法规体系、加大消防监督队伍建设、提升科技应用和教育培训水平等方面予以完善

  9. 48 CFR 852.236-78 - Government supervision.

    Science.gov (United States)

    2010-10-01

    ... 48 Federal Acquisition Regulations System 5 2010-10-01 2010-10-01 false Government supervision. 852.236-78 Section 852.236-78 Federal Acquisition Regulations System DEPARTMENT OF VETERANS AFFAIRS... Government supervision. As prescribed in 836.572, insert the following clause: Government Supervision...

  10. 检察机关办理自侦案件接受监督制约制度的完善%Improvement of the system that the prosecution transact self-detecting cases to accept the supervision and restraint

    Institute of Scientific and Technical Information of China (English)

    潘凌; 徐艳

    2009-01-01

    The prosecutions, as the legal supervision organs, in order to ensure the quality and results of self-detecting cases, are necessary to constantly improve the system of accepting supervision and restraint, and establish multi-level and multi-link in the supervision system.%检察机关作为法律监督机关,为确保自行侦查案件质量和办案效果,就必须不断完善接受监督制约制度,建立多层次多环节的监督体系.

  11. Micro Learning: A Modernized Education System

    OpenAIRE

    Omer Jomah; Amamer Khalil Masoud; Xavier Patrick Kishore; Sagaya Aurelia

    2016-01-01

    Learning is an understanding of how the human brain is wired to learning rather than to an approach or a system. It is one of the best and most frequent approaches for the 21st century learners. Micro learning is more interesting due to its way of teaching and learning the content in a small, very specific burst. Here the learners decide what and when to learn. Content, time, curriculum, form, process, mediality, and learning type are the dimensions of micro learning. Our paper will discuss a...

  12. IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING

    Data.gov (United States)

    National Aeronautics and Space Administration — IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG Abstract. Active learning has been successfully applied to many natural language...

  13. Comparison of Three Supervised Learning Methods for Digital Soil Mapping: Application to a Complex Terrain in the Ecuadorian Andes

    Directory of Open Access Journals (Sweden)

    Martin Hitziger

    2014-01-01

    Full Text Available A digital soil mapping approach is applied to a complex, mountainous terrain in the Ecuadorian Andes. Relief features are derived from a digital elevation model and used as predictors for topsoil texture classes sand, silt, and clay. The performance of three statistical learning methods is compared: linear regression, random forest, and stochastic gradient boosting of regression trees. In linear regression, a stepwise backward variable selection procedure is applied and overfitting is controlled by minimizing Mallow’s Cp. For random forest and boosting, the effect of predictor selection and tuning procedures is assessed. 100-fold repetitions of a 5-fold cross-validation of the selected modelling procedures are employed for validation, uncertainty assessment, and method comparison. Absolute assessment of model performance is achieved by comparing the prediction error of the selected method and the mean. Boosting performs best, providing predictions that are reliably better than the mean. The median reduction of the root mean square error is around 5%. Elevation is the most important predictor. All models clearly distinguish ridges and slopes. The predicted texture patterns are interpreted as result of catena sequences (eluviation of fine particles on slope shoulders and landslides (mixing up mineral soil horizons on slopes.

  14. 金融监管体系演进轨迹:国际经验及启示%Evolution of Financial Supervision System,Intemational Experiences and Inspiration

    Institute of Scientific and Technical Information of China (English)

    孔萌萌

    2011-01-01

    金融监管主要有统一监管、分业监管、不完全统一监管三种模式。发达国家和新兴市场国家都特别注重健全和完善本国金融监管体系,并依据经济社会发展的需求,对金融监管体系进行适应性变革。从长远看,中国应当走金融统一监管或综合监管之路,变分业监管为统一监管,建立统一监管、分工协作、伞形管理的金融监管体系。加强宏观金融审慎监管、理顺协调沟通机制、处理好金融创新与金融监管的关系、保护金融消费者和投资者的权益、加强国际合作是全球金融监管体系的改革趋向。%Financial supervision could be divided into three modes that are unified supervision, augmented supervision and uncomplete unified supervision. Developed and emerging market countries all emphasize on improving and perfecting national financial supervision system. They often implement reform to adapt to the economic and social development. In long term, the financial supervision in China should be unified or comprehensive supervision, which requires us to change augmented to unified supervision, establish supervision system on the basis of unified supervision, division and coordination of work, management of umbrella-type. Government should strengthen prudent financial supervision,arrange the coordinational and communication mechanism, deal well with financial innovation and supervision,protect the rights of financial consumers and investors, strenghen innternational cooperation, which are the reforming trend of financial supervision system.

  15. Information Construction of Military Supervision System for Psychological Service%军队心理服务督导信息化体系设计

    Institute of Scientific and Technical Information of China (English)

    吕锐; 冯杰

    2013-01-01

    Psychological service has become more and more important in the army , and plays an active role in multiple military actions .Current psychological service personnel in our army consist of a variety of backgrounds and education levels , who need qualified training and supervision urgently to improve their expertise and perform -ance abilities .Information construction of military supervision system for psychological service could solve the prob-lem of supervision resources insufficient and scattered .The system would be featured in overall -planning , system-ization , economy , stability and openness .A website would be built for military psychological service personnel , prospective psychological service personnel and general users that are interested in mental health .The website should consist of two functional blocks:a central block includes supervision services and training resources , and an extended block includes blogs and forums .The website would act as a platform for supervision and training for psy-chological services , and communication and popularization of mental health knowledge .%我军心理服务日益受到重视,在多样化军事行动中均起到积极作用。我军心理服务人员目前专业水平参差不齐,急需督导培训,提高专业水平和服务质量。军队心理服务督导体系的信息化建设,可以解决督导资源稀少且分布不均的问题,具有统筹性、系统化、经济性、稳定性、开放性等特点。网站将面向军队心理服务人员、潜在心理服务人员和对心理健康感兴趣的一般用户,由两个模块构成:一是包括督导服务、培训资源等在内的核心功能模块;二是包括空间分享、交流论坛等在内的扩展功能模块,主要实现心理服务的督导、培训功能,和心理健康知识普及交流功能。

  16. 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

    Science.gov (United States)

    Mirabal, N.; Charles, E.; Ferrara, E. C.; Gonthier, P. L.; Harding, A. K.; Sánchez-Conde, M. A.; Thompson, D. J.

    2016-07-01

    Nearly one-third of the sources listed in the Third Fermi Large Area Telescope (LAT) catalog (3FGL) remain unassociated. It is possible that predicted and even unanticipated gamma-ray source classes are present in these data waiting to be discovered. Taking advantage of the excellent spectral capabilities achieved by the Fermi LAT, we use machine-learning classifiers (Random Forest and XGBoost) to pinpoint potentially novel source classes in the unassociated 3FGL sample outside the Galactic plane. Here we report a total of 34 high-confidence Galactic candidates at | b| ≥slant 5^\\circ . The currently favored standard astrophysical interpretations for these objects are pulsars or low-luminosity globular clusters hosting millisecond pulsars (MSPs). Yet these objects could also be interpreted as dark matter annihilation taking place in ultra-faint dwarf galaxies or dark matter subhalos. Unfortunately, Fermi LAT spectra are not sufficient to break degeneracies between the different scenarios. Careful visual inspection of archival optical images reveals no obvious evidence for low-luminosity globular clusters or ultra-faint dwarf galaxies inside the 95% error ellipses. If these are pulsars, this would bring the total number of MSPs at | b| ≥slant 5^\\circ to 106, down to an energy flux ≈4.0 × 10-12 erg cm-2 s-1 between 100 MeV and 100 GeV. We find this number to be in excellent agreement with predictions from a new population synthesis of MSPs that predicts 100-126 high-latitude 3FGL MSPs depending on the choice of high-energy emission model. If, however, these are dark matter substructures, we can place upper limits on the number of Galactic subhalos surviving today and on dark matter annihilation cross sections. These limits are beginning to approach the canonical thermal relic cross section for dark matter particle masses below ˜100 GeV in the bottom quark (b\\bar{b}) annihilation channel.

  17. Critical Points in Distance Learning System

    Directory of Open Access Journals (Sweden)

    Airina Savickaitė

    2013-08-01

    Full Text Available Purpose – This article presents the results of distance learning system analysis, i.e. the critical elements of the distance learning system. The critical points of distance learning are a part of distance education online environment interactivity/community process model. The most important is the fact that the critical point is associated with distance learning participants. Design/methodology/approach – Comparative review of articles and analysis of distance learning module. Findings – A modern man is a lifelong learner and distance learning is a way to be a modern person. The focus on a learner and feedback is the most important thing of learning distance system. Also, attention should be paid to the lecture-appropriate knowledge and ability to convey information. Distance system adaptation is the way to improve the learner’s learning outcomes. Research limitations/implications – Different learning disciplines and learning methods may have different critical points. Practical implications – The information of analysis could be important for both lecturers and students, who studies distance education systems. There are familiar critical points which may deteriorate the quality of learning. Originality/value – The study sought to develop remote systems for applications in order to improve the quality of knowledge. Keywords: distance learning, process model, critical points. Research type: review of literature and general overview.

  18. Web Based Design of Long-Distance Supervising System for VRFF%基于Web的VRFF远程监测系统设计

    Institute of Scientific and Technical Information of China (English)

    杜秀霞; 李平康

    2009-01-01

    The design and realization of the website project based on the output data of the vacuum rabble foundry furnace(VRFF) system is introducted. The technology of ASP(active server pages), the language of HTML and the real time database are used to construct the supervising website.The real time data is transferred from DB(database of LiKong) to the database of the web by compiling SQL(Structured Query Language) and setting the LiKong's.And the homepage of supervising to refresh the web to receive the dynamic data are discussed. Through setting the IIS of Windows XP and using TCP/IP, the website is published in LAN, which can actualize the function of long-distance supervising, user checking and common browser exploring. A whole long distance supervising website is designed and the applications is successfully realized.%介绍了利用力控工控组态软件进行真空搅拌铸造炉(VRFF)[BFQB]远程计算机控制的设计与实现.探讨了利用ASP技术及HTML语言编写小型监测网站的技术,包括对远程计算机与真空搅拌铸造炉测控系统的实时数据交换,利用IIS的设置及TCP/IP协议实现远程访问等WEB工程应用技术.给出了通过主页登陆,用户核实等功能,构成一个完整安全的工业远程监测系统的实现实例.

  19. On Improvement of Legislation System of Supervision Law in China%中国监督法立法体系的完善

    Institute of Scientific and Technical Information of China (English)

    张义清

    2012-01-01

    Legislation Supervision Law should be system of the Supervision Law is supposed regarded as its core. Related supporting to be based on Constitution, and the specific regulations and norms should be compatible and coordinated. However, looking back to the implementation of Supervision Law in last five years in China, the fact shows that improvement of legislation system still has a long way to go due to intrinsically rough text, ambiguous provisions and out - of - order supporting regulations and norms. To this end, legislation system of the Supervision Law should be improved by gradually revising the textual provisions of the Supervision Law, constantly improving related legislation, moderately speeding up the pace of local supporting legislation, timely organizing relevant normative documents and establishing and developing review procedures for the constitutionality and legitimacy of normative documents.%监督法的立法体系应当以宪法为依据,以单行的《监督法》为核心。相关的配套规范亦应当协调一致。然而,我国《监督法》施行五年多以来的实际情况表明,由于该法文本自身的粗糙、相关规定的模糊、配套规范的无序,该立法体系的完善依然任重道远。为此,必须逐步地修正《监督法》的文本规定,不断地完善相关立法,适度地加快地方配套立法的步伐,及时地开展相关规范性文件的清理工作,建立和健全规范性文件"合宪性"与"合法性"审查的程序机制,以此促进我国监督法立法体系的完善。

  20. Authoring Systems Delivering Reusable Learning Objects

    Directory of Open Access Journals (Sweden)

    George Nicola Sammour

    2009-10-01

    Full Text Available A three layer e-learning course development model has been defined based on a conceptual model of learning content object. It starts by decomposing the learning content into small chunks which are initially placed in a hierarchic structure of units and blocks. The raw content components, being the atomic learning objects (ALO, were linked to the blocks and are structured in the database. We set forward a dynamic generation of LO's using re-usable e-learning raw materials or ALO’s In that view we need a LO authoring/ assembling system fitting the requirements of interoperability and reusability and starting from selecting the raw learning content from the learning materials content database. In practice authoring systems are used to develop e-learning courses. The company EDUWEST has developed an authoring system that is database based and will be SCORM compliant in the near future.