IG-KNN UNTUK PREDIKSI CUSTOMER CHURN TELEKOMUNIKASI

Directory of Open Access Journals (Sweden)

Muhammad Arifin

2015-04-01

Full Text Available ABSTRAK IG-KNN merupakan gabungan dari algotitma pemilihan fitur information gain dengan algoritma klasifikasi KNN, kedua algoritma ini diharapkan dapat meningkatkan akurasi dalam memprediksi customer churn telekomunikasi. Prediksi customer churn telekomunikasi merupakan kebutuhan yang sangat penting bagi kelangsungan hidup perusahaan telekomunikasi, dimana dengan banyaknya pelanggang yang meninggalkan perusaan maka perusahaan berpeluang untuk merugi. Mendeteksi pelanggan yang berpeluang meninggalkan perusahaan sejak dini perusahaan akan mendapatkan keuntungan 10 kali, karena biaya untuk mempertahankan pelanggan lebih murah 10 kali lipat dibanding dengan mecari pelanggan baru. Berdasarkan hasil penelitian ini prediksi customer churn telekomunikasi dengan menggunakan IG-KNN menunjukkan akurasi yang lebih baik meski dengan nilai k yang berbeda- beda bila dibandingkan dengan prediksi customer churn telekomunikasi dengan menggunkan KNN tanpa fitur seleksi Information Gain, adapun peningkatan akurasi dari k1 sampai dengan k11 sebesar 1,7%. Kata kunci: information gain, KNN, customer churn telekomunikasi.

Different Apple Varieties Classification Using kNN and MLP Algorithms

OpenAIRE

Sabancı, Kadir

2016-01-01

In this study, three different apple varieties grown in Karaman provinceare classified using kNN and MLP algorithms. 90 apples in total, 30 GoldenDelicious, 30 Granny Smith and 30 Starking Delicious have been used in thestudy. DFK 23U445 USB 3.0 (with Fujinon C Mount Lens) industrial camera hasbeen used to capture apple images. 4 size properties (diameter, area, perimeterand fullness) and 3 color properties (red, green, blue) have been decided usingimage processing techniques through analyzin...

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

Science.gov (United States)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

Supervised Self-Organizing Classification of Superresolution ISAR Images: An Anechoic Chamber Experiment

Directory of Open Access Journals (Sweden)

Radoi Emanuel

2006-01-01

Full Text Available The problem of the automatic classification of superresolution ISAR images is addressed in the paper. We describe an anechoic chamber experiment involving ten-scale-reduced aircraft models. The radar images of these targets are reconstructed using MUSIC-2D (multiple signal classification method coupled with two additional processing steps: phase unwrapping and symmetry enhancement. A feature vector is then proposed including Fourier descriptors and moment invariants, which are calculated from the target shape and the scattering center distribution extracted from each reconstructed image. The classification is finally performed by a new self-organizing neural network called SART (supervised ART, which is compared to two standard classifiers, MLP (multilayer perceptron and fuzzy KNN ( nearest neighbors. While the classification accuracy is similar, SART is shown to outperform the two other classifiers in terms of training speed and classification speed, especially for large databases. It is also easier to use since it does not require any input parameter related to its structure.

Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

Science.gov (United States)

Balouchestani, Mohammadreza; Krishnan, Sridhar

2014-01-01

Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

KNN BASED CLASSIFICATION OF DIGITAL MODULATED SIGNALS

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Sajjad Ahmed Ghauri

2016-11-01

Full Text Available Demodulation process without the knowledge of modulation scheme requires Automatic Modulation Classification (AMC. When receiver has limited information about received signal then AMC become essential process. AMC finds important place in the field many civil and military fields such as modern electronic warfare, interfering source recognition, frequency management, link adaptation etc. In this paper we explore the use of K-nearest neighbor (KNN for modulation classification with different distance measurement methods. Five modulation schemes are used for classification purpose which is Binary Phase Shift Keying (BPSK, Quadrature Phase Shift Keying (QPSK, Quadrature Amplitude Modulation (QAM, 16-QAM and 64-QAM. Higher order cummulants (HOC are used as an input feature set to the classifier. Simulation results shows that proposed classification method provides better results for the considered modulation formats.

Regular graph construction for semi-supervised learning

International Nuclear Information System (INIS)

Vega-Oliveros, Didier A; Berton, Lilian; Eberle, Andre Mantini; Lopes, Alneu de Andrade; Zhao, Liang

2014-01-01

Semi-supervised learning (SSL) stands out for using a small amount of labeled points for data clustering and classification. In this scenario graph-based methods allow the analysis of local and global characteristics of the available data by identifying classes or groups regardless data distribution and representing submanifold in Euclidean space. Most of methods used in literature for SSL classification do not worry about graph construction. However, regular graphs can obtain better classification accuracy compared to traditional methods such as k-nearest neighbor (kNN), since kNN benefits the generation of hubs and it is not appropriate for high-dimensionality data. Nevertheless, methods commonly used for generating regular graphs have high computational cost. We tackle this problem introducing an alternative method for generation of regular graphs with better runtime performance compared to methods usually find in the area. Our technique is based on the preferential selection of vertices according some topological measures, like closeness, generating at the end of the process a regular graph. Experiments using the global and local consistency method for label propagation show that our method provides better or equal classification rate in comparison with kNN

Assessment of various supervised learning algorithms using different performance metrics

Science.gov (United States)

Susheel Kumar, S. M.; Laxkar, Deepak; Adhikari, Sourav; Vijayarajan, V.

2017-11-01

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.

Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting

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Fei Wang

2017-12-01

Full Text Available Accurate solar photovoltaic (PV power forecasting is an essential tool for mitigating the negative effects caused by the uncertainty of PV output power in systems with high penetration levels of solar PV generation. Weather classification based modeling is an effective way to increase the accuracy of day-ahead short-term (DAST solar PV power forecasting because PV output power is strongly dependent on the specific weather conditions in a given time period. However, the accuracy of daily weather classification relies on both the applied classifiers and the training data. This paper aims to reveal how these two factors impact the classification performance and to delineate the relation between classification accuracy and sample dataset scale. Two commonly used classification methods, K-nearest neighbors (KNN and support vector machines (SVM are applied to classify the daily local weather types for DAST solar PV power forecasting using the operation data from a grid-connected PV plant in Hohhot, Inner Mongolia, China. We assessed the performance of SVM and KNN approaches, and then investigated the influences of sample scale, the number of categories, and the data distribution in different categories on the daily weather classification results. The simulation results illustrate that SVM performs well with small sample scale, while KNN is more sensitive to the length of the training dataset and can achieve higher accuracy than SVM with sufficient samples.

Frog sound identification using extended k-nearest neighbor classifier

Science.gov (United States)

Mukahar, Nordiana; Affendi Rosdi, Bakhtiar; Athiar Ramli, Dzati; Jaafar, Haryati

2017-09-01

Frog sound identification based on the vocalization becomes important for biological research and environmental monitoring. As a result, different types of feature extractions and classifiers have been employed to evaluate the accuracy of frog sound identification. This paper presents a frog sound identification with Extended k-Nearest Neighbor (EKNN) classifier. The EKNN classifier integrates the nearest neighbors and mutual sharing of neighborhood concepts, with the aims of improving the classification performance. It makes a prediction based on who are the nearest neighbors of the testing sample and who consider the testing sample as their nearest neighbors. In order to evaluate the classification performance in frog sound identification, the EKNN classifier is compared with competing classifier, k -Nearest Neighbor (KNN), Fuzzy k -Nearest Neighbor (FKNN) k - General Nearest Neighbor (KGNN)and Mutual k -Nearest Neighbor (MKNN) on the recorded sounds of 15 frog species obtained in Malaysia forest. The recorded sounds have been segmented using Short Time Energy and Short Time Average Zero Crossing Rate (STE+STAZCR), sinusoidal modeling (SM), manual and the combination of Energy (E) and Zero Crossing Rate (ZCR) (E+ZCR) while the features are extracted by Mel Frequency Cepstrum Coefficient (MFCC). The experimental results have shown that the EKNCN classifier exhibits the best performance in terms of accuracy compared to the competing classifiers, KNN, FKNN, GKNN and MKNN for all cases.

Data characteristics that determine classifier performance

CSIR Research Space (South Africa)

Van der Walt, Christiaan M

2006-11-01

Full Text Available available at [11]. The kNN uses a LinearNN nearest neighbour search algorithm with an Euclidean distance metric [8]. The optimal k value is determined by performing 10-fold cross-validation. An optimal k value between 1 and 10 is used for Experiments 1... classifiers. 10-fold cross-validation is used to evaluate and compare the performance of the classifiers on the different data sets. 3.1. Artificial data generation Multivariate Gaussian distributions are used to generate artificial data sets. We use d...

A kNN method that uses a non-natural evolutionary algorithm for ...

African Journals Online (AJOL)

We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

Ensemble Clustering Classification Applied to Competing SVM and One-Class Classifiers Exemplified by Plant MicroRNAs Data

Directory of Open Access Journals (Sweden)

Yousef Malik

2016-12-01

Full Text Available The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how they co-cluster. This distance is then used within the framework of the kNN algorithm to define a classifier named ensemble clustering kNN classifier (EC-kNN. In many instances in our experiments we achieved highest accuracy while SVM failed to perform as well. In this study, we compare the performance of a two-class classifier using EC-kNN with different one-class and two-class classifiers. The comparison was applied to seven different plant microRNA species considering eight feature selection methods. In this study, the averaged results show that EC-kNN outperforms all other methods employed here and previously published results for the same data. In conclusion, this study shows that the chosen classifier shows high performance when the distance metric is carefully chosen.

Growth of KNN thin films for non-linear optical applications

International Nuclear Information System (INIS)

Sharma, Shweta; Gupta, Reema; Gupta, Vinay; Tomar, Monika

2018-01-01

Two-wave mixing is a remarkable area of research in the field of non-linear optics, finding various applications in the development of opto-electronic devices, photorefractive waveguides, real time holography, etc. Non-linear optical properties of ferroelectric potassium sodium niobate (KNN) thin films have been interrogated using two-wave mixing phenomenon. Regarding this, a-axis oriented K 0.35 Na (1-0.35) NbO 3 thin films were successfully grown on epitaxial matched (100) SrTiO 3 substrate using pulsed laser deposition (PLD) technique. The uniformly distributed Au micro-discs of 200 μm diameter were integrated with KNN/STO thin film to study the plasmonic enhancement in the optical response. Beam amplification has been observed as a result of the two-wave mixing. This is due to the alignment of ferroelectric domains in KNN films and the excitement of plasmons at the metal-dielectric (Au-KNN) interface. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Growth of KNN thin films for non-linear optical applications

Energy Technology Data Exchange (ETDEWEB)

Sharma, Shweta; Gupta, Reema; Gupta, Vinay [Department of Physics and Astrophysics, University of Delhi (India); Tomar, Monika [Department of Physics, Miranda House University of Delhi (India)

2018-02-15

Two-wave mixing is a remarkable area of research in the field of non-linear optics, finding various applications in the development of opto-electronic devices, photorefractive waveguides, real time holography, etc. Non-linear optical properties of ferroelectric potassium sodium niobate (KNN) thin films have been interrogated using two-wave mixing phenomenon. Regarding this, a-axis oriented K{sub 0.35}Na{sub (1-0.35)}NbO{sub 3} thin films were successfully grown on epitaxial matched (100) SrTiO{sub 3} substrate using pulsed laser deposition (PLD) technique. The uniformly distributed Au micro-discs of 200 μm diameter were integrated with KNN/STO thin film to study the plasmonic enhancement in the optical response. Beam amplification has been observed as a result of the two-wave mixing. This is due to the alignment of ferroelectric domains in KNN films and the excitement of plasmons at the metal-dielectric (Au-KNN) interface. (copyright 2017 WILEY-VCH Verlag GmbH and Co. KGaA, Weinheim)

Optimistic semi-supervised least squares classification

DEFF Research Database (Denmark)

Krijthe, Jesse H.; Loog, Marco

2017-01-01

The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to semi-supervised learning applied to the least squares classifier. We show that a soft-label and a hard-label variant ...

Efficient and Flexible KNN Query Processing in Real-Life Road Networks

DEFF Research Database (Denmark)

Lu, Yang; Bui, Bin; Zhao, Jiakui

2008-01-01

are included into the RNG index, which enables the index to support both distance-based and time-based KNN queries and continuous KNN queries. Our work extends previous ones by taking into account more practical scenarios, such as complexities in real-life road networks and time-based KNN queries. Extensive......Along with the developments of mobile services, effectively modeling road networks and efficiently indexing and querying network constrained objects has become a challenging problem. In this paper, we first introduce a road network model which captures real-life road networks better than previous...

Evaluation of normalization methods for cDNA microarray data by k-NN classification

Energy Technology Data Exchange (ETDEWEB)

Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J

2004-12-17

Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Directory of Open Access Journals (Sweden)

A. Moosavian

2013-01-01

Full Text Available Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC engine based on power spectral density (PSD technique and two classifiers, namely, K-nearest neighbor (KNN and artificial neural network (ANN. Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine.

PENERAPAN K-OPTIMAL PADA ALGORITMA KNN UNTUK PREDIKSI KELULUSAN TEPAT WAKTU MAHASISWA PROGRAM STUDI ILMU KOMPUTER FMIPA UNLAM BERDASARKAN IP SAMPAI DENGAN SEMESTER 4

Directory of Open Access Journals (Sweden)

Mutiara Ayu Banjarsari

2016-04-01

Full Text Available The data pile on a database of academic information systems at Computer Science Program of Mathematic and Natural Science Faculty of Lambung Mangkurat University is not fully utilized, although it can provide new information that has not been known before. Data mining techniques can be used to predict the timely graduation of students. The k-Nearest Nieghbor, a method to classify objects based on training data located closest to the object, was used in this study. Selection of the value of k in kNN algorithm became important because it would affect the performance of the algorithm kNN, therefore it was necessary to know how the value of k and the level of accuracy. The k-Fold Cross Validation method and Accuracy Test was used to determine the value of k-Optimal. The result showed that the value of k = 5 was defined as k-Optimal which was then be applied in the kNN algorithm for prediction of timely graduation of students based on the Grade Point Average up to 4th semester. Keywords: kNN, k-Optimal, Classification, Data mining, k-Fold Cross Validation method Tumpukan data pada database sistem informasi akademik Program Studi Ilmu Komputer FMIPA Unlam belum dimanfaatkan secara maksimal, padahal dari data tersebut dapat memberikan sebuah informasi baru yang belum diketahui sebelumnya. Teknik data mining dapat digunakan untuk memprediksi kelulusan tepat waktu mahasiswa. Penelitian menggunakan metode k-Nearest Nieghbor yang merupakan sebuah metode untuk melakukan klasifikasi terhadap objek berdasarkan data training yang jaraknya paling dekat dengan objek tersebut. Pemilihan nilai k pada algoritma kNN menjadi hal yang penting karena akan mempengaruhi kinerja dari algoritma kNN, oleh karena itu perlu diketahui berapa nilai k dan tingkat akurasinya. Metode k-Fold Cross Validation dan Uji Akurasi digunakan untuk mengetahui nilai k-Optimal. Hasil yang didapat adalah nilai k=5 dengan tingkat akurasi sebesar 80.00% yang ditetapkan sebagai k-Optimal. Nilai k

Secure kNN Computation and Integrity Assurance of Data Outsourcing in the Cloud

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Jun Hong

2017-01-01

Full Text Available As cloud computing has been popularized massively and rapidly, individuals and enterprises prefer outsourcing their databases to the cloud service provider (CSP to save the expenditure for managing and maintaining the data. The outsourced databases are hosted, and query services are offered to clients by the CSP, whereas the CSP is not fully trusted. Consequently, the security shall be violated by multiple factors. Data privacy and query integrity are perceived as two major factors obstructing enterprises from outsourcing their databases. A novel scheme is proposed in this paper to effectuate k-nearest neighbors (kNN query and kNN query authentication on an encrypted outsourced spatial database. An asymmetric scalar-product-preserving encryption scheme is elucidated, in which data points and query points are encrypted with diverse encryption keys, and the CSP can determine the distance relation between encrypted data points and query points. Furthermore, the similarity search tree is extended to build a novel verifiable SS-tree that supports efficient kNN query and kNN query verification. It is indicated from the security analysis and experiment results that our scheme not only maintains the confidentiality of outsourced confidential data and query points but also has a lower kNN query processing and verification overhead than the MR-tree.

Analisis Perbandingan KNN dengan SVM untuk Klasifikasi Penyakit Diabetes Retinopati berdasarkan Citra Eksudat dan Mikroaneurisma

Directory of Open Access Journals (Sweden)

SUCI AULIA

2015-01-01

Full Text Available ABSTRAK Penelitian mengenai pengklasifikasian tingkat keparahan penyakit Diabetes Retinopati berbasis image processing masih hangat dibicarakan, citra yang biasa digunakan untuk mendeteksi jenis penyakit ini adalah citra optik disk, mikroaneurisma, eksudat, dan hemorrhages yang berasal dari citra fundus. Pada penelitian ini telah dilakukan perbandingan algoritma SVM dengan KNN untuk klasifikasi penyakit diabetes retinopati (mild, moderate, severe berdasarkan citra eksudat dan microaneurisma. Untuk proses ekstraksi ciri digunakan metode wavelet  pada masing-masing kedua metode tersebut. Pada penelitian ini digunakan 160 data uji, masing-masing 40 citra untuk kelas normal, kelas mild, kelas moderate, kelas saviere. Tingkat akurasi yang diperoleh dengan menggunakan metode KNN lebih tinggi dibandingkan SVM, yaitu 65 % dan 62%. Klasifikasi dengan algoritma KNN diperoleh hasil terbaik dengan parameter K=9 cityblock. Sedangkan klasifikasi dengan metode SVM diperoleh hasil terbaik dengan parameter One Agains All. Kata kunci: Diabetic Retinopathy, KNN , SVM, Wavelet.   ABSTRACT Research based on severity classification of the disease diabetic retinopathy by using image processing method is still hotly debated, the image is used to detect the type of this disease is an optical image of the disk, microaneurysm, exudates, and bleeding of the image of the fundus. This study was performed to compare SVM method with KNN method for classification of diabetic retinopathy disease (mild, moderate, severe based on exudate and microaneurysm image. For feature extraction uses wavelet method, and each of the two methods. This study made use of 160 test data, each of 40 images for normal class, mild class, moderate class, severe class. The accuracy obtained by KNN higher than SVM, with 65% and 62%. KNN classification method achieved the best results with the parameters K = 9, cityblock. While the classification with SVM method obtained the best results with

CONTROLLED CONDENSATION IN K-NN AND ITS APPLICATION FOR REAL TIME COLOR IDENTIFICATION

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Carmen Villar Patiño

2017-04-01

Full Text Available k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging.

PHYSICAL AND ELECTRICAL PROPERTIES ENHANCEMENT OF RARE-EARTH DOPED-POTASSIUM SODIUM NIOBATE (KNN: A REVIEW

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Akmal Mat Harttat Maziati

2015-06-01

Full Text Available Alkaline niobate mainly potassium sodium niobate, (KxNa1-x NbO3 (abreviated as KNN has long attracted attention as piezoelectric materials as its high Curie temperature (Tc and piezoelectric properties. The volatility of alkaline element (K, Na is, however detrimental to the stoichiometry of KNN, contributing to the failure to achieve high-density structure and lead to the formation of intrinsic defects. By partially doping of several rare-earth elements, the inherent defects could be improved significantly. Therefore, considerable attempts have been made to develop doped-KNN based ceramic materials with high electrical properties. In this paper, these research activities are reviewed, including dopants type and doping role in KNN perovskite structure.

Improving GPU-accelerated adaptive IDW interpolation algorithm using fast kNN search.

Science.gov (United States)

Mei, Gang; Xu, Nengxiong; Xu, Liangliang

2016-01-01

This paper presents an efficient parallel Adaptive Inverse Distance Weighting (AIDW) interpolation algorithm on modern Graphics Processing Unit (GPU). The presented algorithm is an improvement of our previous GPU-accelerated AIDW algorithm by adopting fast k-nearest neighbors (kNN) search. In AIDW, it needs to find several nearest neighboring data points for each interpolated point to adaptively determine the power parameter; and then the desired prediction value of the interpolated point is obtained by weighted interpolating using the power parameter. In this work, we develop a fast kNN search approach based on the space-partitioning data structure, even grid, to improve the previous GPU-accelerated AIDW algorithm. The improved algorithm is composed of the stages of kNN search and weighted interpolating. To evaluate the performance of the improved algorithm, we perform five groups of experimental tests. The experimental results indicate: (1) the improved algorithm can achieve a speedup of up to 1017 over the corresponding serial algorithm; (2) the improved algorithm is at least two times faster than our previous GPU-accelerated AIDW algorithm; and (3) the utilization of fast kNN search can significantly improve the computational efficiency of the entire GPU-accelerated AIDW algorithm.

Just-in-time adaptive classifiers-part II: designing the classifier.

Science.gov (United States)

Alippi, Cesare; Roveri, Manuel

2008-12-01

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on k-nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. k-NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity k and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tackled in a companion paper) followed by an adaptive management of the knowledge base (KB) of the classifier to cope with the process change. The novelty of the proposed approach resides in the general framework supporting the real-time update of the KB of the classification system in response to novel information coming from the process both in stationary conditions (accuracy improvement) and in nonstationary ones (process tracking) and in providing a suitable estimate of k. It is shown that the classification system grants consistency once the change targets the process generating the data in a new stationary state, as it is the case in many real applications.

A Supervised Multiclass Classifier for an Autocoding System

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Yukako Toko

2017-11-01

Full Text Available Classification is often required in various contexts, including in the field of official statistics. In the previous study, we have developed a multiclass classifier that can classify short text descriptions with high accuracy. The algorithm borrows the concept of the naïve Bayes classifier and is so simple that its structure is easily understandable. The proposed classifier has the following two advantages. First, the processing times for both learning and classifying are extremely practical. Second, the proposed classifier yields high-accuracy results for a large portion of a dataset. We have previously developed an autocoding system for the Family Income and Expenditure Survey in Japan that has a better performing classifier. While the original system was developed in Perl in order to improve the efficiency of the coding process of short Japanese texts, the proposed system is implemented in the R programming language in order to explore versatility and is modified to make the system easily applicable to English text descriptions, in consideration of the increasing number of R users in the field of official statistics. We are planning to publish the proposed classifier as an R-package. The proposed classifier would be generally applicable to other classification tasks including coding activities in the field of official statistics, and it would contribute greatly to improving their efficiency.

Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree.

Science.gov (United States)

Özdemir, Merve Erkınay; Telatar, Ziya; Eroğul, Osman; Tunca, Yusuf

2018-05-01

Dysmorphic syndromes have different facial malformations. These malformations are significant to an early diagnosis of dysmorphic syndromes and contain distinctive information for face recognition. In this study we define the certain features of each syndrome by considering facial malformations and classify Fragile X, Hurler, Prader Willi, Down, Wolf Hirschhorn syndromes and healthy groups automatically. The reference points are marked on the face images and ratios between the points' distances are taken into consideration as features. We suggest a neural network based hierarchical decision tree structure in order to classify the syndrome types. We also implement k-nearest neighbor (k-NN) and artificial neural network (ANN) classifiers to compare classification accuracy with our hierarchical decision tree. The classification accuracy is 50, 73 and 86.7% with k-NN, ANN and hierarchical decision tree methods, respectively. Then, the same images are shown to a clinical expert who achieve a recognition rate of 46.7%. We develop an efficient system to recognize different syndrome types automatically in a simple, non-invasive imaging data, which is independent from the patient's age, sex and race at high accuracy. The promising results indicate that our method can be used for pre-diagnosis of the dysmorphic syndromes by clinical experts.

A SURVEY OF SEMI-SUPERVISED LEARNING

OpenAIRE

Amrita Sadarangani *, Dr. Anjali Jivani

2016-01-01

Semi Supervised Learning involves using both labeled and unlabeled data to train a classifier or for clustering. Semi supervised learning finds usage in many applications, since labeled data can be hard to find in many cases. Currently, a lot of research is being conducted in this area. This paper discusses the different algorithms of semi supervised learning and then their advantages and limitations are compared. The differences between supervised classification and semi-supervised classific...

Surveying alignment-free features for Ortholog detection in related yeast proteomes by using supervised big data classifiers.

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Galpert, Deborah; Fernández, Alberto; Herrera, Francisco; Antunes, Agostinho; Molina-Ruiz, Reinaldo; Agüero-Chapin, Guillermin

2018-05-03

The development of new ortholog detection algorithms and the improvement of existing ones are of major importance in functional genomics. We have previously introduced a successful supervised pairwise ortholog classification approach implemented in a big data platform that considered several pairwise protein features and the low ortholog pair ratios found between two annotated proteomes (Galpert, D et al., BioMed Research International, 2015). The supervised models were built and tested using a Saccharomycete yeast benchmark dataset proposed by Salichos and Rokas (2011). Despite several pairwise protein features being combined in a supervised big data approach; they all, to some extent were alignment-based features and the proposed algorithms were evaluated on a unique test set. Here, we aim to evaluate the impact of alignment-free features on the performance of supervised models implemented in the Spark big data platform for pairwise ortholog detection in several related yeast proteomes. The Spark Random Forest and Decision Trees with oversampling and undersampling techniques, and built with only alignment-based similarity measures or combined with several alignment-free pairwise protein features showed the highest classification performance for ortholog detection in three yeast proteome pairs. Although such supervised approaches outperformed traditional methods, there were no significant differences between the exclusive use of alignment-based similarity measures and their combination with alignment-free features, even within the twilight zone of the studied proteomes. Just when alignment-based and alignment-free features were combined in Spark Decision Trees with imbalance management, a higher success rate (98.71%) within the twilight zone could be achieved for a yeast proteome pair that underwent a whole genome duplication. The feature selection study showed that alignment-based features were top-ranked for the best classifiers while the runners-up were

A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

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Abdullah M. Iliyasu

2017-12-01

Full Text Available A quantum hybrid (QH intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN algorithm (known simply as the Q-Fuzzy approach is proposed for efficient feature selection and classification of cells in cervical smeared (CS images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach. In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

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Blanco, A.; Rodriguez, R.; Martinez-Maranon, I.

2014-03-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity.

An ensemble of dissimilarity based classifiers for Mackerel gender determination

International Nuclear Information System (INIS)

Blanco, A; Rodriguez, R; Martinez-Maranon, I

2014-01-01

Mackerel is an infravalored fish captured by European fishing vessels. A manner to add value to this specie can be achieved by trying to classify it attending to its sex. Colour measurements were performed on Mackerel females and males (fresh and defrozen) extracted gonads to obtain differences between sexes. Several linear and non linear classifiers such as Support Vector Machines (SVM), k Nearest Neighbors (k-NN) or Diagonal Linear Discriminant Analysis (DLDA) can been applied to this problem. However, theyare usually based on Euclidean distances that fail to reflect accurately the sample proximities. Classifiers based on non-Euclidean dissimilarities misclassify a different set of patterns. We combine different kind of dissimilarity based classifiers. The diversity is induced considering a set of complementary dissimilarities for each model. The experimental results suggest that our algorithm helps to improve classifiers based on a single dissimilarity

Short Term Prediction of Freeway Exiting Volume Based on SVM and KNN

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Xiang Wang

2015-09-01

The model results indicate that the proposed algorithm is feasible and accurate. The Mean Absolute Percentage Error is under 10%. When comparing with the results of single KNN or SVM method, the results show that the combination of KNN and SVM can improve the reliability of the prediction significantly. The proposed method can be implemented in the on-line application of exiting volume prediction, which is able to consider different vehicle types.

Supervised Learning for Visual Pattern Classification

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Zheng, Nanning; Xue, Jianru

This chapter presents an overview of the topics and major ideas of supervised learning for visual pattern classification. Two prevalent algorithms, i.e., the support vector machine (SVM) and the boosting algorithm, are briefly introduced. SVMs and boosting algorithms are two hot topics of recent research in supervised learning. SVMs improve the generalization of the learning machine by implementing the rule of structural risk minimization (SRM). It exhibits good generalization even when little training data are available for machine training. The boosting algorithm can boost a weak classifier to a strong classifier by means of the so-called classifier combination. This algorithm provides a general way for producing a classifier with high generalization capability from a great number of weak classifiers.

Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.

Science.gov (United States)

Sigdel, Madhav; Dinç, İmren; Dinç, Semih; Sigdel, Madhu S; Pusey, Marc L; Aygün, Ramazan S

2014-03-01

In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.

Self-Organization in Aggregating Robot Swarms: A DW-KNN Topological Approach

KAUST Repository

Khaldi, Belkacem

2018-02-02

In certain swarm applications, where the inter-agent distance is not the only factor in the collective behaviours of the swarm, additional properties such as density could have a crucial effect. In this paper, we propose applying a Distance-Weighted K-Nearest Neighbouring (DW-KNN) topology to the behaviour of robot swarms performing self-organized aggregation, in combination with a virtual physics approach to keep the robots together. A distance-weighted function based on a Smoothed Particle Hydrodynamic (SPH) interpolation approach, which is used to evaluate the robot density in the swarm, is applied as the key factor for identifying the K-nearest neighbours taken into account when aggregating the robots. The intra virtual physical connectivity among these neighbours is achieved using a virtual viscoelastic-based proximity model. With the ARGoS based-simulator, we model and evaluate the proposed approach, showing various self-organized aggregations performed by a swarm of N foot-bot robots. Also, we compared the aggregation quality of DW-KNN aggregation approach to that of the conventional KNN approach and found better performance.

Classifier Directed Data Hybridization for Geographic Sample Supervised Segment Generation

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Christoff Fourie

2014-11-01

Full Text Available Quality segment generation is a well-known challenge and research objective within Geographic Object-based Image Analysis (GEOBIA. Although methodological avenues within GEOBIA are diverse, segmentation commonly plays a central role in most approaches, influencing and being influenced by surrounding processes. A general approach using supervised quality measures, specifically user provided reference segments, suggest casting the parameters of a given segmentation algorithm as a multidimensional search problem. In such a sample supervised segment generation approach, spatial metrics observing the user provided reference segments may drive the search process. The search is commonly performed by metaheuristics. A novel sample supervised segment generation approach is presented in this work, where the spectral content of provided reference segments is queried. A one-class classification process using spectral information from inside the provided reference segments is used to generate a probability image, which in turn is employed to direct a hybridization of the original input imagery. Segmentation is performed on such a hybrid image. These processes are adjustable, interdependent and form a part of the search problem. Results are presented detailing the performances of four method variants compared to the generic sample supervised segment generation approach, under various conditions in terms of resultant segment quality, required computing time and search process characteristics. Multiple metrics, metaheuristics and segmentation algorithms are tested with this approach. Using the spectral data contained within user provided reference segments to tailor the output generally improves the results in the investigated problem contexts, but at the expense of additional required computing time.

Semi-supervised sparse coding

KAUST Repository

Wang, Jim Jing-Yan; Gao, Xin

2014-01-01

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.

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.

Physical Human Activity Recognition Using Wearable Sensors

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Ferhat Attal

2015-12-01

Full Text Available This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle. Three main steps describe the activity recognition process: sensors’ placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN, Support Vector Machines (SVM, Gaussian Mixture Models (GMM, and Random Forest (RF as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM and Hidden Markov Model (HMM, are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Physical Human Activity Recognition Using Wearable Sensors.

Science.gov (United States)

Attal, Ferhat; Mohammed, Samer; Dedabrishvili, Mariam; Chamroukhi, Faicel; Oukhellou, Latifa; Amirat, Yacine

2015-12-11

This paper presents a review of different classification techniques used to recognize human activities from wearable inertial sensor data. Three inertial sensor units were used in this study and were worn by healthy subjects at key points of upper/lower body limbs (chest, right thigh and left ankle). Three main steps describe the activity recognition process: sensors' placement, data pre-processing and data classification. Four supervised classification techniques namely, k-Nearest Neighbor (k-NN), Support Vector Machines (SVM), Gaussian Mixture Models (GMM), and Random Forest (RF) as well as three unsupervised classification techniques namely, k-Means, Gaussian mixture models (GMM) and Hidden Markov Model (HMM), are compared in terms of correct classification rate, F-measure, recall, precision, and specificity. Raw data and extracted features are used separately as inputs of each classifier. The feature selection is performed using a wrapper approach based on the RF algorithm. Based on our experiments, the results obtained show that the k-NN classifier provides the best performance compared to other supervised classification algorithms, whereas the HMM classifier is the one that gives the best results among unsupervised classification algorithms. This comparison highlights which approach gives better performance in both supervised and unsupervised contexts. It should be noted that the obtained results are limited to the context of this study, which concerns the classification of the main daily living human activities using three wearable accelerometers placed at the chest, right shank and left ankle of the subject.

Lead-free piezoelectric KNN-BZ-BNT films with a vertical morphotropic phase boundary

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Wen Chen

2015-07-01

Full Text Available The lead-free piezoelectric 0.915K0.5Na0.5NbO3-0.075BaZrO3-0.01Bi0.5Na0.5TiO3 (0.915KNN-0.075BZ-0.01BNT films were prepared by a chemical solution deposition method. The films possess a pure rhomobohedral perovskite phase and a dense surface without crack. The temperature-dependent dielectric properties of the specimens manifest that only phase transition from ferroelectric to paraelectric phase occurred and the Curie temperature is 217 oC. The temperature stability of ferroelectric phase was also supported by the stable piezoelectric properties of the films. These results suggest that the slope of the morphotropic phase boundary (MPB for the solid solution formed with the KNN and BZ in the films should be vertical. The voltage-induced polarization switching, and a distinct piezo-response suggested that the 0.915 KNN-0.075BZ-0.01BNT films show good piezoelectric properties.

Examining the Capability of Supervised Machine Learning Classifiers in Extracting Flooded Areas from Landsat TM Imagery: A Case Study from a Mediterranean Flood

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Gareth Ireland

2015-03-01

Full Text Available This study explored the capability of Support Vector Machines (SVMs and regularised kernel Fisher’s discriminant analysis (rkFDA machine learning supervised classifiers in extracting flooded area from optical Landsat TM imagery. The ability of both techniques was evaluated using a case study of a riverine flood event in 2010 in a heterogeneous Mediterranean region, for which TM imagery acquired shortly after the flood event was available. For the two classifiers, both linear and non-linear (kernel versions were utilised in their implementation. The ability of the different classifiers to map the flooded area extent was assessed on the basis of classification accuracy assessment metrics. Results showed that rkFDA outperformed SVMs in terms of accurate flooded pixels detection, also producing fewer missed detections of the flooded area. Yet, SVMs showed less false flooded area detections. Overall, the non-linear rkFDA classification method was the more accurate of the two techniques (OA = 96.23%, K = 0.877. Both methods outperformed the standard Normalized Difference Water Index (NDWI thresholding (OA = 94.63, K = 0.818 by roughly 0.06 K points. Although overall accuracy results for the rkFDA and SVMs classifications only showed a somewhat minor improvement on the overall accuracy exhibited by the NDWI thresholding, notably both classifiers considerably outperformed the thresholding algorithm in other specific accuracy measures (e.g. producer accuracy for the “not flooded” class was ~10.5% less accurate for the NDWI thresholding algorithm in comparison to the classifiers, and average per-class accuracy was ~5% less accurate than the machine learning models. This study provides evidence of the successful application of supervised machine learning for classifying flooded areas in Landsat imagery, where few studies so far exist in this direction. Considering that Landsat data is open access and has global coverage, the results of this study

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms

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Shengyun Liang

2015-11-01

Full Text Available The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers participated in functional movement tests, including walking and sit-to-stand (STS. A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN, pseudo nearest neighbor (PNN, local mean pseudo nearest neighbor (LMPNN classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population.

Processing and characterizations of BNT-KNN ceramics for actuator applications

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Mallam Chandrasekhar

2016-06-01

Full Text Available BNT-KNN powder (with composition 0.93Bi0.5Na0.5TiO3–0.07K0.5Na0.5NbO3 was synthesized as a single perovskite phase by conventional solid state reaction route and dense ceramics were obtained by sintering of powder compacts at 1100 °C for 4 h. Dielectric study confirmed relaxor behaviour, whereas the microstructure study showed sharp cornered cubic like grains with an average grain size ∼1.15 µm. The saturated polarization vs. electric field (P-E hysteresis loops confirmed the ferroelectric (FE nature while the butterfly shaped strain vs. electric field (S-E loops suggested the piezoelectric nature of the BNT-KNN ceramic samples. Maximum electric field induced strain of ∼0.62% suggested the usefulness of this system for actuator applications.

Comparison of several chemometric methods of libraries and classifiers for the analysis of expired drugs based on Raman spectra.

Science.gov (United States)

Gao, Qun; Liu, Yan; Li, Hao; Chen, Hui; Chai, Yifeng; Lu, Feng

2014-06-01

Some expired drugs are difficult to detect by conventional means. If they are repackaged and sold back into market, they will constitute a new public health challenge. For the detection of repackaged expired drugs within specification, paracetamol tablet from a manufacturer was used as a model drug in this study for comparison of Raman spectra-based library verification and classification methods. Raman spectra of different batches of paracetamol tablets were collected and a library including standard spectra of unexpired batches of tablets was established. The Raman spectrum of each sample was identified by cosine and correlation with the standard spectrum. The average HQI of the suspicious samples and the standard spectrum were calculated. The optimum threshold values were 0.997 and 0.998 respectively as a result of ROC and four evaluations, for which the accuracy was up to 97%. Three supervised classifiers, PLS-DA, SVM and k-NN, were chosen to establish two-class classification models and compared subsequently. They were used to establish a classification of expired batches and an unexpired batch, and predict the suspect samples. The average accuracy was 90.12%, 96.80% and 89.37% respectively. Different pre-processing techniques were tried to find that first derivative was optimal for methods of libraries and max-min normalization was optimal for that of classifiers. The results obtained from these studies indicated both libraries and classifier methods could detect the expired drugs effectively, and they should be used complementarily in the fast-screening. Copyright © 2014 Elsevier B.V. All rights reserved.

Nanoscale characterization and local piezoelectric properties of lead-free KNN-LT-LS thin films

Energy Technology Data Exchange (ETDEWEB)

Abazari, M; Safari, A [Glenn Howatt Electroceramics Laboratories, Department of Materials Science and Engineering, Rutgers-The state University of New Jersey, Piscataway, NJ 08854 (United States); Choi, T; Cheong, S-W [Rutgers Center for Emergent Materials, Department of Physics and Astronomy, Rutgers-The state University of New Jersey, Piscataway, NJ 08854 (United States)

2010-01-20

We report the observation of domain structure and piezoelectric properties of pure and Mn-doped (K{sub 0.44},Na{sub 0.52},Li{sub 0.04})(Nb{sub 0.84},Ta{sub 0.1},Sb{sub 0.06})O{sub 3} (KNN-LT-LS) thin films on SrTiO{sub 3} substrates. It is revealed that, using piezoresponse force microscopy, ferroelectric domain structure in such 500 nm thin films comprised of primarily 180{sup 0} domains. This was in accordance with the tetragonal structure of the films, confirmed by relative permittivity measurements and x-ray diffraction patterns. Effective piezoelectric coefficient (d{sub 33}) of the films were calculated using piezoelectric displacement curves and shown to be {approx}53 pm V{sup -1} for pure KNN-LT-LS thin films. This value is among the highest values reported for an epitaxial lead-free thin film and shows a great potential for KNN-LT-LS to serve as an alternative to PZT thin films in future applications.

Nanoscale characterization and local piezoelectric properties of lead-free KNN-LT-LS thin films

Science.gov (United States)

Abazari, M.; Choi, T.; Cheong, S.-W.; Safari, A.

2010-01-01

We report the observation of domain structure and piezoelectric properties of pure and Mn-doped (K0.44,Na0.52,Li0.04)(Nb0.84,Ta0.1,Sb0.06)O3 (KNN-LT-LS) thin films on SrTiO3 substrates. It is revealed that, using piezoresponse force microscopy, ferroelectric domain structure in such 500 nm thin films comprised of primarily 180° domains. This was in accordance with the tetragonal structure of the films, confirmed by relative permittivity measurements and x-ray diffraction patterns. Effective piezoelectric coefficient (d33) of the films were calculated using piezoelectric displacement curves and shown to be ~53 pm V-1 for pure KNN-LT-LS thin films. This value is among the highest values reported for an epitaxial lead-free thin film and shows a great potential for KNN-LT-LS to serve as an alternative to PZT thin films in future applications.

A systematic comparison of supervised classifiers.

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Diego Raphael Amancio

Full Text Available Pattern recognition has been employed in a myriad of industrial, commercial and academic applications. Many techniques have been devised to tackle such a diversity of applications. Despite the long tradition of pattern recognition research, there is no technique that yields the best classification in all scenarios. Therefore, as many techniques as possible should be considered in high accuracy applications. Typical related works either focus on the performance of a given algorithm or compare various classification methods. In many occasions, however, researchers who are not experts in the field of machine learning have to deal with practical classification tasks without an in-depth knowledge about the underlying parameters. Actually, the adequate choice of classifiers and parameters in such practical circumstances constitutes a long-standing problem and is one of the subjects of the current paper. We carried out a performance study of nine well-known classifiers implemented in the Weka framework and compared the influence of the parameter configurations on the accuracy. The default configuration of parameters in Weka was found to provide near optimal performance for most cases, not including methods such as the support vector machine (SVM. In addition, the k-nearest neighbor method frequently allowed the best accuracy. In certain conditions, it was possible to improve the quality of SVM by more than 20% with respect to their default parameter configuration.

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

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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

Determining Effects of Non-synonymous SNPs on Protein-Protein Interactions using Supervised and Semi-supervised Learning

Science.gov (United States)

Zhao, Nan; Han, Jing Ginger; Shyu, Chi-Ren; Korkin, Dmitry

2014-01-01

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 rewiring of

Weakly supervised semantic segmentation using fore-background priors

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Han, Zheng; Xiao, Zhitao; Yu, Mingjun

2017-07-01

Weakly-supervised semantic segmentation is a challenge in the field of computer vision. Most previous works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels, thus result in expensive computation. In this study, we tackle this problem from a different perspective. We proposed a novel semantic segmentation algorithm based on background priors, which avoids the construction of a huge graph in whole training dataset. Specifically, a random forest classifier is obtained using weakly supervised training data .Then semantic texton forest (STF) feature is extracted from image superpixels. Finally, a CRF based optimization algorithm is proposed. The unary potential of CRF derived from the outputting probability of random forest classifier and the robust saliency map as background prior. Experiments on the MSRC21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms. Furthermore, the use of efficient decision forests classifier and parallel computing of saliency map significantly accelerates the implementation.

Quasi-supervised scoring of human sleep in polysomnograms using augmented input variables.

Science.gov (United States)

Yaghouby, Farid; Sunderam, Sridhar

2015-04-01

The limitations of manual sleep scoring make computerized methods highly desirable. Scoring errors can arise from human rater uncertainty or inter-rater variability. Sleep scoring algorithms either come as supervised classifiers that need scored samples of each state to be trained, or as unsupervised classifiers that use heuristics or structural clues in unscored data to define states. We propose a quasi-supervised classifier that models observations in an unsupervised manner but mimics a human rater wherever training scores are available. EEG, EMG, and EOG features were extracted in 30s epochs from human-scored polysomnograms recorded from 42 healthy human subjects (18-79 years) and archived in an anonymized, publicly accessible database. Hypnograms were modified so that: 1. Some states are scored but not others; 2. Samples of all states are scored but not for transitional epochs; and 3. Two raters with 67% agreement are simulated. A framework for quasi-supervised classification was devised in which unsupervised statistical models-specifically Gaussian mixtures and hidden Markov models--are estimated from unlabeled training data, but the training samples are augmented with variables whose values depend on available scores. Classifiers were fitted to signal features incorporating partial scores, and used to predict scores for complete recordings. Performance was assessed using Cohen's Κ statistic. The quasi-supervised classifier performed significantly better than an unsupervised model and sometimes as well as a completely supervised model despite receiving only partial scores. The quasi-supervised algorithm addresses the need for classifiers that mimic scoring patterns of human raters while compensating for their limitations. Copyright © 2015 Elsevier Ltd. All rights reserved.

An improved method of early diagnosis of smoking-induced respiratory changes using machine learning algorithms.

Science.gov (United States)

Amaral, Jorge L M; Lopes, Agnaldo J; Jansen, José M; Faria, Alvaro C D; Melo, Pedro L

2013-12-01

The purpose of this study was to develop an automatic classifier to increase the accuracy of the forced oscillation technique (FOT) for diagnosing early respiratory abnormalities in smoking patients. The data consisted of FOT parameters obtained from 56 volunteers, 28 healthy and 28 smokers with low tobacco consumption. Many supervised learning techniques were investigated, including logistic linear classifiers, k nearest neighbor (KNN), neural networks and support vector machines (SVM). To evaluate performance, the ROC curve of the most accurate parameter was established as baseline. To determine the best input features and classifier parameters, we used genetic algorithms and a 10-fold cross-validation using the average area under the ROC curve (AUC). In the first experiment, the original FOT parameters were used as input. We observed a significant improvement in accuracy (KNN=0.89 and SVM=0.87) compared with the baseline (0.77). The second experiment performed a feature selection on the original FOT parameters. This selection did not cause any significant improvement in accuracy, but it was useful in identifying more adequate FOT parameters. In the third experiment, we performed a feature selection on the cross products of the FOT parameters. This selection resulted in a further increase in AUC (KNN=SVM=0.91), which allows for high diagnostic accuracy. In conclusion, machine learning classifiers can help identify early smoking-induced respiratory alterations. The use of FOT cross products and the search for the best features and classifier parameters can markedly improve the performance of machine learning classifiers. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Combining different views of mammographic texture resemblance (MTR) marker of breast cancer risk

DEFF Research Database (Denmark)

Sun, S.; Karemore, Gopal; Chernoff, Konstantin

the subsequent 4 years whereas 245 cases had a diagnosis 2-4 years post mammography. We employed the MTR supervised texture learning framework to perform risk evaluation from a single mammography view. In the framework 20,000 pixels were sampled and classified by a kNN pixel classifier. A feature selection step......PURPOSE Mammographic density is a well established breast cancer risk factor. Texture analysis in terms of the Mammographoc Texture Resemblance (MTR) marker has recently shown to add to risk segregation. Hitherto only single view MTR analysis has been performed. Standard mammography examinations...

Multi-feature classifiers for burst detection in single EEG channels from preterm infants

Science.gov (United States)

Navarro, X.; Porée, F.; Kuchenbuch, M.; Chavez, M.; Beuchée, Alain; Carrault, G.

2017-08-01

Objective. The study of electroencephalographic (EEG) bursts in preterm infants provides valuable information about maturation or prognostication after perinatal asphyxia. Over the last two decades, a number of works proposed algorithms to automatically detect EEG bursts in preterm infants, but they were designed for populations under 35 weeks of post menstrual age (PMA). However, as the brain activity evolves rapidly during postnatal life, these solutions might be under-performing with increasing PMA. In this work we focused on preterm infants reaching term ages (PMA  ⩾36 weeks) using multi-feature classification on a single EEG channel. Approach. Five EEG burst detectors relying on different machine learning approaches were compared: logistic regression (LR), linear discriminant analysis (LDA), k-nearest neighbors (kNN), support vector machines (SVM) and thresholding (Th). Classifiers were trained by visually labeled EEG recordings from 14 very preterm infants (born after 28 weeks of gestation) with 36-41 weeks PMA. Main results. The most performing classifiers reached about 95% accuracy (kNN, SVM and LR) whereas Th obtained 84%. Compared to human-automatic agreements, LR provided the highest scores (Cohen’s kappa  =  0.71) using only three EEG features. Applying this classifier in an unlabeled database of 21 infants  ⩾36 weeks PMA, we found that long EEG bursts and short inter-burst periods are characteristic of infants with the highest PMA and weights. Significance. In view of these results, LR-based burst detection could be a suitable tool to study maturation in monitoring or portable devices using a single EEG channel.

Multi-site Stochastic Simulation of Daily Streamflow with Markov Chain and KNN Algorithm

Science.gov (United States)

Mathai, J.; Mujumdar, P.

2017-12-01

A key focus of this study is to develop a method which is physically consistent with the hydrologic processes that can capture short-term characteristics of daily hydrograph as well as the correlation of streamflow in temporal and spatial domains. In complex water resource systems, flow fluctuations at small time intervals require that discretisation be done at small time scales such as daily scales. Also, simultaneous generation of synthetic flows at different sites in the same basin are required. We propose a method to equip water managers with a streamflow generator within a stochastic streamflow simulation framework. The motivation for the proposed method is to generate sequences that extend beyond the variability represented in the historical record of streamflow time series. The method has two steps: In step 1, daily flow is generated independently at each station by a two-state Markov chain, with rising limb increments randomly sampled from a Gamma distribution and the falling limb modelled as exponential recession and in step 2, the streamflow generated in step 1 is input to a nonparametric K-nearest neighbor (KNN) time series bootstrap resampler. The KNN model, being data driven, does not require assumptions on the dependence structure of the time series. A major limitation of KNN based streamflow generators is that they do not produce new values, but merely reshuffle the historical data to generate realistic streamflow sequences. However, daily flow generated using the Markov chain approach is capable of generating a rich variety of streamflow sequences. Furthermore, the rising and falling limbs of daily hydrograph represent different physical processes, and hence they need to be modelled individually. Thus, our method combines the strengths of the two approaches. We show the utility of the method and improvement over the traditional KNN by simulating daily streamflow sequences at 7 locations in the Godavari River basin in India.

Classifying galaxy spectra at 0.5 < z < 1 with self-organizing maps

Science.gov (United States)

Rahmani, S.; Teimoorinia, H.; Barmby, P.

2018-05-01

The spectrum of a galaxy contains information about its physical properties. Classifying spectra using templates helps elucidate the nature of a galaxy's energy sources. In this paper, we investigate the use of self-organizing maps in classifying galaxy spectra against templates. We trained semi-supervised self-organizing map networks using a set of templates covering the wavelength range from far ultraviolet to near infrared. The trained networks were used to classify the spectra of a sample of 142 galaxies with 0.5 K-means clustering, a supervised neural network, and chi-squared minimization. Spectra corresponding to quiescent galaxies were more likely to be classified similarly by all methods while starburst spectra showed more variability. Compared to classification using chi-squared minimization or the supervised neural network, the galaxies classed together by the self-organizing map had more similar spectra. The class ordering provided by the one-dimensional self-organizing maps corresponds to an ordering in physical properties, a potentially important feature for the exploration of large datasets.

Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers.

Science.gov (United States)

Siuly; Yin, Xiaoxia; Hadjiloucas, Sillas; Zhang, Yanchun

2016-04-01

This work provides a performance comparison of four different machine learning classifiers: multinomial logistic regression with ridge estimators (MLR) classifier, k-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) as applied to terahertz (THz) transient time domain sequences associated with pixelated images of different powder samples. The six substances considered, although have similar optical properties, their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties. As scattering can be unquantifiable in many spectroscopic experiments, classification solely on differences in complex insertion loss can be inconclusive. The problem is addressed using two-dimensional (2-D) cross-correlations between background and sample interferograms, these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers. A cross-validation procedure is adopted to assess the performance of the classifiers. Firstly the measurements related to samples that had thicknesses of 2mm were classified, then samples at thicknesses of 4mm, and after that 3mm were classified and the success rate and consistency of each classifier was recorded. In addition, mixtures having thicknesses of 2 and 4mm as well as mixtures of 2, 3 and 4mm were presented simultaneously to all classifiers. This approach provided further cross-validation of the classification consistency of each algorithm. The results confirm the superiority in classification accuracy and robustness of the MLR (least accuracy 88.24%) and KNN (least accuracy 90.19%) algorithms which consistently outperformed the SVM (least accuracy 74.51%) and NB (least accuracy 56.86%) classifiers for the same number of feature vectors across all studies

A Supervised Classification Algorithm for Note Onset Detection

Directory of Open Access Journals (Sweden)

Douglas Eck

2007-01-01

Full Text Available This paper presents a novel approach to detecting onsets in music audio files. We use a supervised learning algorithm to classify spectrogram frames extracted from digital audio as being onsets or nononsets. Frames classified as onsets are then treated with a simple peak-picking algorithm based on a moving average. We present two versions of this approach. The first version uses a single neural network classifier. The second version combines the predictions of several networks trained using different hyperparameters. We describe the details of the algorithm and summarize the performance of both variants on several datasets. We also examine our choice of hyperparameters by describing results of cross-validation experiments done on a custom dataset. We conclude that a supervised learning approach to note onset detection performs well and warrants further investigation.

Arabic Supervised Learning Method Using N-Gram

Science.gov (United States)

Sanan, Majed; Rammal, Mahmoud; Zreik, Khaldoun

2008-01-01

Purpose: Recently, classification of Arabic documents is a real problem for juridical centers. In this case, some of the Lebanese official journal documents are classified, and the center has to classify new documents based on these documents. This paper aims to study and explain the useful application of supervised learning method on Arabic texts…

A CLASSIFIER SYSTEM USING SMOOTH GRAPH COLORING

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JORGE FLORES CRUZ

2017-01-01

Full Text Available Unsupervised classifiers allow clustering methods with less or no human intervention. Therefore it is desirable to group the set of items with less data processing. This paper proposes an unsupervised classifier system using the model of soft graph coloring. This method was tested with some classic instances in the literature and the results obtained were compared with classifications made with human intervention, yielding as good or better results than supervised classifiers, sometimes providing alternative classifications that considers additional information that humans did not considered.

Role of sintering time, crystalline phases and symmetry in the piezoelectric properties of lead-free KNN-modified ceramics

International Nuclear Information System (INIS)

Rubio-Marcos, F.; Marchet, P.; Merle-Mejean, T.; Fernandez, J.F.

2010-01-01

Lead-free KNN-modified piezoceramics of the system (Li,Na,K)(Nb,Ta,Sb)O 3 were prepared by conventional solid-state sintering. The X-ray diffraction patterns revealed a perovskite phase, together with some minor secondary phase, which was assigned to K 3 LiNb 6 O 17 , tetragonal tungsten-bronze (TTB). A structural evolution toward a pure tetragonal structure with the increasing sintering time was observed, associated with the decrease of TTB phase. A correlation between higher tetragonality and higher piezoelectric response was clearly evidenced. Contrary to the case of the LiTaO 3 modified KNN, very large abnormal grains with TTB structure were not detected. As a consequence, the simultaneous modification by tantalum and antimony seems to induce during sintering a different behaviour from the one of LiTaO 3 modified KNN.

Role of sintering time, crystalline phases and symmetry in the piezoelectric properties of lead-free KNN-modified ceramics

Energy Technology Data Exchange (ETDEWEB)

Rubio-Marcos, F., E-mail: frmarcos@icv.csic.es [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain); Marchet, P.; Merle-Mejean, T. [SPCTS, UMR 6638 CNRS, Universite de Limoges, 123, Av. A. Thomas, 87060 Limoges (France); Fernandez, J.F. [Electroceramic Department, Instituto de Ceramica y Vidrio, CSIC, Kelsen 5, 28049 Madrid (Spain)

2010-09-01

Lead-free KNN-modified piezoceramics of the system (Li,Na,K)(Nb,Ta,Sb)O{sub 3} were prepared by conventional solid-state sintering. The X-ray diffraction patterns revealed a perovskite phase, together with some minor secondary phase, which was assigned to K{sub 3}LiNb{sub 6}O{sub 17}, tetragonal tungsten-bronze (TTB). A structural evolution toward a pure tetragonal structure with the increasing sintering time was observed, associated with the decrease of TTB phase. A correlation between higher tetragonality and higher piezoelectric response was clearly evidenced. Contrary to the case of the LiTaO{sub 3} modified KNN, very large abnormal grains with TTB structure were not detected. As a consequence, the simultaneous modification by tantalum and antimony seems to induce during sintering a different behaviour from the one of LiTaO{sub 3} modified KNN.

Performance of some supervised and unsupervised multivariate techniques for grouping authentic and unauthentic Viagra and Cialis

Directory of Open Access Journals (Sweden)

Michel J. Anzanello

2014-09-01

Full Text Available A typical application of multivariate techniques in forensic analysis consists of discriminating between authentic and unauthentic samples of seized drugs, in addition to finding similar properties in the unauthentic samples. In this paper, the performance of several methods belonging to two different classes of multivariate techniques–supervised and unsupervised techniques–were compared. The supervised techniques (ST are the k-Nearest Neighbor (KNN, Support Vector Machine (SVM, Probabilistic Neural Networks (PNN and Linear Discriminant Analysis (LDA; the unsupervised techniques are the k-Means CA and the Fuzzy C-Means (FCM. The methods are applied to Infrared Spectroscopy by Fourier Transform (FTIR from authentic and unauthentic Cialis and Viagra. The FTIR data are also transformed by Principal Components Analysis (PCA and kernel functions aimed at improving the grouping performance. ST proved to be a more reasonable choice when the analysis is conducted on the original data, while the UT led to better results when applied to transformed data.

GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

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Ahmed Shamsul Arefin

Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

Personalised news filtering and recommendation system using Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model

Science.gov (United States)

Adeniyi, D. A.; Wei, Z.; Yang, Y.

2017-10-01

Recommendation problem has been extensively studied by researchers in the field of data mining, database and information retrieval. This study presents the design and realisation of an automated, personalised news recommendations system based on Chi-square statistics-based K-nearest neighbour (χ2SB-KNN) model. The proposed χ2SB-KNN model has the potential to overcome computational complexity and information overloading problems, reduces runtime and speeds up execution process through the use of critical value of χ2 distribution. The proposed recommendation engine can alleviate scalability challenges through combined online pattern discovery and pattern matching for real-time recommendations. This work also showcases the development of a novel method of feature selection referred to as Data Discretisation-Based feature selection method. This is used for selecting the best features for the proposed χ2SB-KNN algorithm at the preprocessing stage of the classification procedures. The implementation of the proposed χ2SB-KNN model is achieved through the use of a developed in-house Java program on an experimental website called OUC newsreaders' website. Finally, we compared the performance of our system with two baseline methods which are traditional Euclidean distance K-nearest neighbour and Naive Bayesian techniques. The result shows a significant improvement of our method over the baseline methods studied.

Coincidence Detection Using Spiking Neurons with Application to Face Recognition

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Fadhlan Kamaruzaman

2015-01-01

Full Text Available We elucidate the practical implementation of Spiking Neural Network (SNN as local ensembles of classifiers. Synaptic time constant τs is used as learning parameter in representing the variations learned from a set of training data at classifier level. This classifier uses coincidence detection (CD strategy trained in supervised manner using a novel supervised learning method called τs Prediction which adjusts the precise timing of output spikes towards the desired spike timing through iterative adaptation of τs. This paper also discusses the approximation of spike timing in Spike Response Model (SRM for the purpose of coincidence detection. This process significantly speeds up the whole process of learning and classification. Performance evaluations with face datasets such as AR, FERET, JAFFE, and CK+ datasets show that the proposed method delivers better face classification performance than the network trained with Supervised Synaptic-Time Dependent Plasticity (STDP. We also found that the proposed method delivers better classification accuracy than k nearest neighbor, ensembles of kNN, and Support Vector Machines. Evaluation on several types of spike codings also reveals that latency coding delivers the best result for face classification as well as for classification of other multivariate datasets.

Efficient Multi-Concept Visual Classifier Adaptation in Changing Environments

Science.gov (United States)

2016-09-01

sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming , and...performed as a necessary but time- consuming step to train su- pervised classifiers. U nsupervised o r s elf-supervised a pproaches h ave b een used to...time- consuming labeling pro- cess. However, the lack of human supervision has limited most of this work to binary classification (e.g., traversability

Just-in-time classifiers for recurrent concepts.

Science.gov (United States)

Alippi, Cesare; Boracchi, Giacomo; Roveri, Manuel

2013-04-01

Just-in-time (JIT) classifiers operate in evolving environments by classifying instances and reacting to concept drift. In stationary conditions, a JIT classifier improves its accuracy over time by exploiting additional supervised information coming from the field. In nonstationary conditions, however, the classifier reacts as soon as concept drift is detected; the current classification setup is discarded and a suitable one activated to keep the accuracy high. We present a novel generation of JIT classifiers able to deal with recurrent concept drift by means of a practical formalization of the concept representation and the definition of a set of operators working on such representations. The concept-drift detection activity, which is crucial in promptly reacting to changes exactly when needed, is advanced by considering change-detection tests monitoring both inputs and classes distributions.

On the evaluation of the fidelity of supervised classifiers in the prediction of chimeric RNAs.

Science.gov (United States)

Beaumeunier, Sacha; Audoux, Jérôme; Boureux, Anthony; Ruffle, Florence; Commes, Thérèse; Philippe, Nicolas; Alves, Ronnie

2016-01-01

High-throughput sequencing technology and bioinformatics have identified chimeric RNAs (chRNAs), raising the possibility of chRNAs expressing particularly in diseases can be used as potential biomarkers in both diagnosis and prognosis. The task of discriminating true chRNAs from the false ones poses an interesting Machine Learning (ML) challenge. First of all, the sequencing data may contain false reads due to technical artifacts and during the analysis process, bioinformatics tools may generate false positives due to methodological biases. Moreover, if we succeed to have a proper set of observations (enough sequencing data) about true chRNAs, chances are that the devised model can not be able to generalize beyond it. Like any other machine learning problem, the first big issue is finding the good data to build models. As far as we were concerned, there is no common benchmark data available for chRNAs detection. The definition of a classification baseline is lacking in the related literature too. In this work we are moving towards benchmark data and an evaluation of the fidelity of supervised classifiers in the prediction of chRNAs. We proposed a modelization strategy that can be used to increase the tools performances in context of chRNA classification based on a simulated data generator, that permit to continuously integrate new complex chimeric events. The pipeline incorporated a genome mutation process and simulated RNA-seq data. The reads within distinct depth were aligned and analysed by CRAC that integrates genomic location and local coverage, allowing biological predictions at the read scale. Additionally, these reads were functionally annotated and aggregated to form chRNAs events, making it possible to evaluate ML methods (classifiers) performance in both levels of reads and events. Ensemble learning strategies demonstrated to be more robust to this classification problem, providing an average AUC performance of 95 % (ACC=94 %, Kappa=0.87 %). The

Optical and Piezoelectric Study of KNN Solid Solutions Co-Doped with La-Mn and Eu-Fe

Directory of Open Access Journals (Sweden)

Jesús-Alejandro Peña-Jiménez

2016-09-01

Full Text Available The solid-state method was used to synthesize single phase potassium-sodium niobate (KNN co-doped with the La3+–Mn4+ and Eu3+–Fe3+ ion pairs. Structural determination of all studied solid solutions was accomplished by XRD and Rietveld refinement method. Electron paramagnetic resonance (EPR studies were performed to determine the oxidation state of paramagnetic centers. Optical spectroscopy measurements, excitation, emission and decay lifetime were carried out for each solid solution. The present study reveals that doping KNN with La3+–Mn4+ and Eu3+–Fe3+ at concentrations of 0.5 mol % and 1 mol %, respectively, improves the ferroelectric and piezoelectric behavior and induce the generation of optical properties in the material for potential applications.

Transfer learning improves supervised image segmentation across imaging protocols.

Science.gov (United States)

van Opbroek, Annegreet; Ikram, M Arfan; Vernooij, Meike W; de Bruijne, Marleen

2015-05-01

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 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 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 with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

Using supervised machine learning to code policy issues: Can classifiers generalize across contexts?

NARCIS (Netherlands)

Burscher, B.; Vliegenthart, R.; de Vreese, C.H.

2015-01-01

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles

An empirical study of ensemble-based semi-supervised learning approaches for imbalanced splice site datasets.

Science.gov (United States)

Stanescu, Ana; Caragea, Doina

2015-01-01

Recent biochemical advances have led to inexpensive, time-efficient production of massive volumes of raw genomic data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data. The process of labeling data can be expensive, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on the problem of predicting splice sites in a genome using semi-supervised learning approaches. This is a challenging problem, due to the highly imbalanced distribution of the data, i.e., small number of splice sites as compared to the number of non-splice sites. To address this challenge, we propose to use ensembles of semi-supervised classifiers, specifically self-training and co-training classifiers. Our experiments on five highly imbalanced splice site datasets, with positive to negative ratios of 1-to-99, showed that the ensemble-based semi-supervised approaches represent a good choice, even when the amount of labeled data consists of less than 1% of all training data. In particular, we found that ensembles of co-training and self-training classifiers that dynamically balance the set of labeled instances during the semi-supervised iterations show improvements over the corresponding supervised ensemble baselines. In the presence of limited amounts of labeled data, ensemble-based semi-supervised approaches can successfully leverage the unlabeled data to enhance supervised ensembles learned from highly imbalanced data distributions. Given that such distributions are common for many biological sequence classification problems, our work can be seen as a stepping stone towards more sophisticated ensemble-based approaches to biological sequence annotation in a semi-supervised framework.

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

Optimizing area under the ROC curve using semi-supervised learning.

Science.gov (United States)

Wang, Shijun; Li, Diana; Petrick, Nicholas; Sahiner, Berkman; Linguraru, Marius George; Summers, Ronald M

2015-01-01

Receiver operating characteristic (ROC) analysis is a standard methodology to evaluate the performance of a binary classification system. The area under the ROC curve (AUC) is a performance metric that summarizes how well a classifier separates two classes. Traditional AUC optimization techniques are supervised learning methods that utilize only labeled data (i.e., the true class is known for all data) to train the classifiers. In this work, inspired by semi-supervised and transductive learning, we propose two new AUC optimization algorithms hereby referred to as semi-supervised learning receiver operating characteristic (SSLROC) algorithms, which utilize unlabeled test samples in classifier training to maximize AUC. Unlabeled samples are incorporated into the AUC optimization process, and their ranking relationships to labeled positive and negative training samples are considered as optimization constraints. The introduced test samples will cause the learned decision boundary in a multidimensional feature space to adapt not only to the distribution of labeled training data, but also to the distribution of unlabeled test data. We formulate the semi-supervised AUC optimization problem as a semi-definite programming problem based on the margin maximization theory. The proposed methods SSLROC1 (1-norm) and SSLROC2 (2-norm) were evaluated using 34 (determined by power analysis) randomly selected datasets from the University of California, Irvine machine learning repository. Wilcoxon signed rank tests showed that the proposed methods achieved significant improvement compared with state-of-the-art methods. The proposed methods were also applied to a CT colonography dataset for colonic polyp classification and showed promising results.

Effects of doping on ferroelectric properties and leakage current behavior of KNN-LT-LS thin films on SrTiO3 substrate

Science.gov (United States)

Abazari, M.; Safari, A.

2009-05-01

We report the effects of Ba, Ti, and Mn dopants on ferroelectric polarization and leakage current of (K0.44Na0.52Li0.04)(Nb0.84Ta0.1Sb0.06)O3 (KNN-LT-LS) thin films deposited by pulsed laser deposition. It is shown that donor dopants such as Ba2+, which increased the resistivity in bulk KNN-LT-LS, had an opposite effect in the thin film. Ti4+ as an acceptor B-site dopant reduces the leakage current by an order of magnitude, while the polarization values showed a slight degradation. Mn4+, however, was found to effectively suppress the leakage current by over two orders of magnitude while enhancing the polarization, with 15 and 23 μC/cm2 remanent and saturated polarization, whose values are ˜70% and 82% of the reported values for bulk composition. This phenomenon has been associated with the dual effect of Mn4+ in KNN-LT-LS thin film, by substituting both A- and B-site cations. A detailed description on how each dopant affects the concentrations of vacancies in the lattice is presented. Mn-doped KNN-LT-LS thin films are shown to be a promising candidate for lead-free thin films and applications.

Generative Adversarial Networks-Based Semi-Supervised Learning for Hyperspectral Image Classification

Directory of Open Access Journals (Sweden)

Zhi He

2017-10-01

Full Text Available Classification of hyperspectral image (HSI is an important research topic in the remote sensing community. Significant efforts (e.g., deep learning have been concentrated on this task. However, it is still an open issue to classify the high-dimensional HSI with a limited number of training samples. In this paper, we propose a semi-supervised HSI classification method inspired by the generative adversarial networks (GANs. Unlike the supervised methods, the proposed HSI classification method is semi-supervised, which can make full use of the limited labeled samples as well as the sufficient unlabeled samples. Core ideas of the proposed method are twofold. First, the three-dimensional bilateral filter (3DBF is adopted to extract the spectral-spatial features by naturally treating the HSI as a volumetric dataset. The spatial information is integrated into the extracted features by 3DBF, which is propitious to the subsequent classification step. Second, GANs are trained on the spectral-spatial features for semi-supervised learning. A GAN contains two neural networks (i.e., generator and discriminator trained in opposition to one another. The semi-supervised learning is achieved by adding samples from the generator to the features and increasing the dimension of the classifier output. Experimental results obtained on three benchmark HSI datasets have confirmed the effectiveness of the proposed method , especially with a limited number of labeled samples.

Defending Malicious Script Attacks Using Machine Learning Classifiers

Directory of Open Access Journals (Sweden)

Nayeem Khan

2017-01-01

Full Text Available The web application has become a primary target for cyber criminals by injecting malware especially JavaScript to perform malicious activities for impersonation. Thus, it becomes an imperative to detect such malicious code in real time before any malicious activity is performed. This study proposes an efficient method of detecting previously unknown malicious java scripts using an interceptor at the client side by classifying the key features of the malicious code. Feature subset was obtained by using wrapper method for dimensionality reduction. Supervised machine learning classifiers were used on the dataset for achieving high accuracy. Experimental results show that our method can efficiently classify malicious code from benign code with promising results.

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...

Automated labelling of cancer textures in colorectal histopathology slides using quasi-supervised learning.

Science.gov (United States)

Onder, Devrim; Sarioglu, Sulen; Karacali, Bilge

2013-04-01

Quasi-supervised learning is a statistical learning algorithm that contrasts two datasets by computing estimate for the posterior probability of each sample in either dataset. This method has not been applied to histopathological images before. The purpose of this study is to evaluate the performance of the method to identify colorectal tissues with or without adenocarcinoma. Light microscopic digital images from histopathological sections were obtained from 30 colorectal radical surgery materials including adenocarcinoma and non-neoplastic regions. The texture features were extracted by using local histograms and co-occurrence matrices. The quasi-supervised learning algorithm operates on two datasets, one containing samples of normal tissues labelled only indirectly, and the other containing an unlabeled collection of samples of both normal and cancer tissues. As such, the algorithm eliminates the need for manually labelled samples of normal and cancer tissues for conventional supervised learning and significantly reduces the expert intervention. Several texture feature vector datasets corresponding to different extraction parameters were tested within the proposed framework. The Independent Component Analysis dimensionality reduction approach was also identified as the one improving the labelling performance evaluated in this series. In this series, the proposed method was applied to the dataset of 22,080 vectors with reduced dimensionality 119 from 132. Regions containing cancer tissue could be identified accurately having false and true positive rates up to 19% and 88% respectively without using manually labelled ground-truth datasets in a quasi-supervised strategy. The resulting labelling performances were compared to that of a conventional powerful supervised classifier using manually labelled ground-truth data. The supervised classifier results were calculated as 3.5% and 95% for the same case. The results in this series in comparison with the benchmark

Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN classification method

Directory of Open Access Journals (Sweden)

D.A. Adeniyi

2016-01-01

Full Text Available The major problem of many on-line web sites is the presentation of many choices to the client at a time; this usually results to strenuous and time consuming task in finding the right product or information on the site. In this work, we present a study of automatic web usage data mining and recommendation system based on current user behavior through his/her click stream data on the newly developed Really Simple Syndication (RSS reader website, in order to provide relevant information to the individual without explicitly asking for it. The K-Nearest-Neighbor (KNN classification method has been trained to be used on-line and in Real-Time to identify clients/visitors click stream data, matching it to a particular user group and recommend a tailored browsing option that meet the need of the specific user at a particular time. To achieve this, web users RSS address file was extracted, cleansed, formatted and grouped into meaningful session and data mart was developed. Our result shows that the K-Nearest Neighbor classifier is transparent, consistent, straightforward, simple to understand, high tendency to possess desirable qualities and easy to implement than most other machine learning techniques specifically when there is little or no prior knowledge about data distribution.

Supervised Gaussian mixture model based remote sensing image ...

African Journals Online (AJOL)

Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...

An Effective Big Data Supervised Imbalanced Classification Approach for Ortholog Detection in Related Yeast Species

Directory of Open Access Journals (Sweden)

Deborah Galpert

2015-01-01

Full Text Available Orthology detection requires more effective scaling algorithms. In this paper, a set of gene pair features based on similarity measures (alignment scores, sequence length, gene membership to conserved regions, and physicochemical profiles are combined in a supervised pairwise ortholog detection approach to improve effectiveness considering low ortholog ratios in relation to the possible pairwise comparison between two genomes. In this scenario, big data supervised classifiers managing imbalance between ortholog and nonortholog pair classes allow for an effective scaling solution built from two genomes and extended to other genome pairs. The supervised approach was compared with RBH, RSD, and OMA algorithms by using the following yeast genome pairs: Saccharomyces cerevisiae-Kluyveromyces lactis, Saccharomyces cerevisiae-Candida glabrata, and Saccharomyces cerevisiae-Schizosaccharomyces pombe as benchmark datasets. Because of the large amount of imbalanced data, the building and testing of the supervised model were only possible by using big data supervised classifiers managing imbalance. Evaluation metrics taking low ortholog ratios into account were applied. From the effectiveness perspective, MapReduce Random Oversampling combined with Spark SVM outperformed RBH, RSD, and OMA, probably because of the consideration of gene pair features beyond alignment similarities combined with the advances in big data supervised classification.

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions.

Science.gov (United States)

Liang, Zhaohui; Huang, Jimmy Xiangji; Zeng, Xing; Zhang, Gang

2016-08-10

Genomic variations are associated with the metabolism and the occurrence of adverse reactions of many therapeutic agents. The polymorphisms on over 2000 locations of cytochrome P450 enzymes (CYP) due to many factors such as ethnicity, mutations, and inheritance attribute to the diversity of response and side effects of various drugs. The associations of the single nucleotide polymorphisms (SNPs), the internal pharmacokinetic patterns and the vulnerability of specific adverse reactions become one of the research interests of pharmacogenomics. The conventional genomewide association studies (GWAS) mainly focuses on the relation of single or multiple SNPs to a specific risk factors which are a one-to-many relation. However, there are no robust methods to establish a many-to-many network which can combine the direct and indirect associations between multiple SNPs and a serial of events (e.g. adverse reactions, metabolic patterns, prognostic factors etc.). In this paper, we present a novel deep learning model based on generative stochastic networks and hidden Markov chain to classify the observed samples with SNPs on five loci of two genes (CYP2D6 and CYP1A2) respectively to the vulnerable population of 14 types of adverse reactions. A supervised deep learning model is proposed in this study. The revised generative stochastic networks (GSN) model with transited by the hidden Markov chain is used. The data of the training set are collected from clinical observation. The training set is composed of 83 observations of blood samples with the genotypes respectively on CYP2D6*2, *10, *14 and CYP1A2*1C, *1 F. The samples are genotyped by the polymerase chain reaction (PCR) method. A hidden Markov chain is used as the transition operator to simulate the probabilistic distribution. The model can perform learning at lower cost compared to the conventional maximal likelihood method because the transition distribution is conditional on the previous state of the hidden Markov

Arabic Text Categorization Using Improved k-Nearest neighbour Algorithm

Directory of Open Access Journals (Sweden)

Wail Hamood KHALED

2014-10-01

Full Text Available The quantity of text information published in Arabic language on the net requires the implementation of effective techniques for the extraction and classifying of relevant information contained in large corpus of texts. In this paper we presented an implementation of an enhanced k-NN Arabic text classifier. We apply the traditional k-NN and Naive Bayes from Weka Toolkit for comparison purpose. Our proposed modified k-NN algorithm features an improved decision rule to skip the classes that are less similar and identify the right class from k nearest neighbours which increases the accuracy. The study evaluates the improved decision rule technique using the standard of recall, precision and f-measure as the basis of comparison. We concluded that the effectiveness of the proposed classifier is promising and outperforms the classical k-NN classifier.

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.

Reinforcement Learning Based Artificial Immune Classifier

Directory of Open Access Journals (Sweden)

Mehmet Karakose

2013-01-01

Full Text Available One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This approach uses reinforcement learning to find better antibody with immune operators. The proposed new approach has many contributions according to other methods in the literature such as effectiveness, less memory cell, high accuracy, speed, and data adaptability. The performance of the proposed approach is demonstrated by simulation and experimental results using real data in Matlab and FPGA. Some benchmark data and remote image data are used for experimental results. The comparative results with supervised/unsupervised based artificial immune system, negative selection classifier, and resource limited artificial immune classifier are given to demonstrate the effectiveness of the proposed new method.

Evaluation of three classifiers in mapping forest stand types using ...

African Journals Online (AJOL)

EJIRO

applied for classification of the image. Supervised classification technique using maximum likelihood algorithm is the most commonly and widely used method for land cover classification (Jia and Richards, 2006). In Australia, the maximum likelihood classifier was effectively used to map different forest stand types with high.

QUEST: Eliminating Online Supervised Learning for Efficient Classification Algorithms.

Science.gov (United States)

Zwartjes, Ardjan; Havinga, Paul J M; Smit, Gerard J M; Hurink, Johann L

2016-10-01

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.

Supervised machine learning and active learning in classification of radiology reports.

Science.gov (United States)

Nguyen, Dung H M; Patrick, Jon D

2014-01-01

This paper presents an automated system for classifying the results of imaging examinations (CT, MRI, positron emission tomography) into reportable and non-reportable cancer cases. This system is part of an industrial-strength processing pipeline built to extract content from radiology reports for use in the Victorian Cancer Registry. In addition to traditional supervised learning methods such as conditional random fields and support vector machines, active learning (AL) approaches were investigated to optimize training production and further improve classification performance. The project involved two pilot sites in Victoria, Australia (Lake Imaging (Ballarat) and Peter MacCallum Cancer Centre (Melbourne)) and, in collaboration with the NSW Central Registry, one pilot site at Westmead Hospital (Sydney). The reportability classifier performance achieved 98.25% sensitivity and 96.14% specificity on the cancer registry's held-out test set. Up to 92% of training data needed for supervised machine learning can be saved by AL. AL is a promising method for optimizing the supervised training production used in classification of radiology reports. When an AL strategy is applied during the data selection process, the cost of manual classification can be reduced significantly. The most important practical application of the reportability classifier is that it can dramatically reduce human effort in identifying relevant reports from the large imaging pool for further investigation of cancer. The classifier is built on a large real-world dataset and can achieve high performance in filtering relevant reports to support cancer registries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Classification of gene expression data: A hubness-aware semi-supervised approach.

Science.gov (United States)

Buza, Krisztian

2016-04-01

Classification of gene expression data is the common denominator of various biomedical recognition tasks. However, obtaining class labels for large training samples may be difficult or even impossible in many cases. Therefore, semi-supervised classification techniques are required as semi-supervised classifiers take advantage of unlabeled data. Gene expression data is high-dimensional which gives rise to the phenomena known under the umbrella of the curse of dimensionality, one of its recently explored aspects being the presence of hubs or hubness for short. Therefore, hubness-aware classifiers have been developed recently, such as Naive Hubness-Bayesian k-Nearest Neighbor (NHBNN). In this paper, we propose a semi-supervised extension of NHBNN which follows the self-training schema. As one of the core components of self-training is the certainty score, we propose a new hubness-aware certainty score. We performed experiments on publicly available gene expression data. These experiments show that the proposed classifier outperforms its competitors. We investigated the impact of each of the components (classification algorithm, semi-supervised technique, hubness-aware certainty score) separately and showed that each of these components are relevant to the performance of the proposed approach. Our results imply that our approach may increase classification accuracy and reduce computational costs (i.e., runtime). Based on the promising results presented in the paper, we envision that hubness-aware techniques will be used in various other biomedical machine learning tasks. In order to accelerate this process, we made an implementation of hubness-aware machine learning techniques publicly available in the PyHubs software package (http://www.biointelligence.hu/pyhubs) implemented in Python, one of the most popular programming languages of data science. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

Energy Technology Data Exchange (ETDEWEB)

Baraldi, Piero, E-mail: piero.baraldi@polimi.i [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Razavi-Far, Roozbeh [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Zio, Enrico [Dipartimento di Energia - Sezione Ingegneria Nucleare, Politecnico di Milano, via Ponzio 34/3, 20133 Milano (Italy); Ecole Centrale Paris-Supelec, Paris (France)

2011-04-15

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Classifier-ensemble incremental-learning procedure for nuclear transient identification at different operational conditions

International Nuclear Information System (INIS)

Baraldi, Piero; Razavi-Far, Roozbeh; Zio, Enrico

2011-01-01

An important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels.

Improving the potential of pixel-based supervised classification in ...

African Journals Online (AJOL)

The goal of this paper was to describe the impact of various parameters when applying a supervised Maximum Likelihood Classifier (MLC) to SPOT 5 image analysis in a remote savanna biome. Pair separation indicators and probability thresholds were used to analyse the effect of training area size and heterogeneity as ...

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

KAUST Repository

Wang, Jingbin; Zhou, Yihua; Duan, Kanghong; Wang, Jim Jing-Yan; Bensmail, Halima

2015-01-01

. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor

Pervasive Sound Sensing: A Weakly Supervised Training Approach.

Science.gov (United States)

Kelly, Daniel; Caulfield, Brian

2016-01-01

Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smartphone sensors in the context of human behavior sensing. We postulate that, in order for microphones to be useful in behavior sensing applications, the analysis techniques must be flexible and allow easy modification of the types of sounds to be sensed. A simplification of the training data collection process could allow a more flexible sound classification framework. We hypothesize that detailed training, a prerequisite for the majority of sound sensing techniques, is not necessary and that a significantly less detailed and time consuming data collection process can be carried out, allowing even a nonexpert to conduct the collection, labeling, and training process. To test this hypothesis, we implement a diverse density-based multiple instance learning framework, to identify a target sound, and a bag trimming algorithm, which, using the target sound, automatically segments weakly labeled sound clips to construct an accurate training set. Experiments reveal that our hypothesis is a valid one and results show that classifiers, trained using the automatically segmented training sets, were able to accurately classify unseen sound samples with accuracies comparable to supervised classifiers, achieving an average F -measure of 0.969 and 0.87 for two weakly supervised datasets.

A Comparison of the Spatial Linear Model to Nearest Neighbor (k-NN) Methods for Forestry Applications

Science.gov (United States)

Jay M. Ver Hoef; Hailemariam Temesgen; Sergio Gómez

2013-01-01

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically,...

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.

Classification of Pulse Waveforms Using Edit Distance with Real Penalty

Directory of Open Access Journals (Sweden)

Zhang Dongyu

2010-01-01

Full Text Available Abstract Advances in sensor and signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis (TCPD. Because of the inevitable intraclass variation of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. In this paper, by referring to the edit distance with real penalty (ERP and the recent progress in -nearest neighbors (KNN classifiers, we propose two novel ERP-based KNN classifiers. Taking advantage of the metric property of ERP, we first develop an ERP-induced inner product and a Gaussian ERP kernel, then embed them into difference-weighted KNN classifiers, and finally develop two novel classifiers for pulse waveform classification. The experimental results show that the proposed classifiers are effective for accurate classification of pulse waveform.

Application of SVM classifier in thermographic image classification for early detection of breast cancer

Science.gov (United States)

Oleszkiewicz, Witold; Cichosz, Paweł; Jagodziński, Dariusz; Matysiewicz, Mateusz; Neumann, Łukasz; Nowak, Robert M.; Okuniewski, Rafał

2016-09-01

This article presents the application of machine learning algorithms for early detection of breast cancer on the basis of thermographic images. Supervised learning model: Support vector machine (SVM) and Sequential Minimal Optimization algorithm (SMO) for the training of SVM classifier were implemented. The SVM classifier was included in a client-server application which enables to create a training set of examinations and to apply classifiers (including SVM) for the diagnosis and early detection of the breast cancer. The sensitivity and specificity of SVM classifier were calculated based on the thermographic images from studies. Furthermore, the heuristic method for SVM's parameters tuning was proposed.

Development of a skateboarding trick classifier using accelerometry and machine learning

Directory of Open Access Journals (Sweden)

Nicholas Kluge Corrêa

Full Text Available Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN. Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE. The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation. Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.

A New Method to Improve the Electrical Properties of KNN-based Ceramics: Tailoring Phase Fraction

KAUST Repository

Lv, Xiang; Wu, Jiagang; Zhu, Jianguo; Xiao, Dingquan; Zhang, Xixiang

2017-01-01

Although both the phase type and fraction of multi-phase coexistence can affect the electrical properties of (K,Na)NbO3 (KNN)-based ceramics, effects of phase fraction on their electrical properties were few concerned. In this work, through changing the calcination temperature of CaZrO3 powders, we successfully developed the 0.96K0.5Na0.5Nb0.96Sb0.04O3-0.01CaZrO3-0.03Bi0.5Na0.5HfO3 ceramics containing a wide rhombohedral-tetragonal (R-T) phase coexistence with the variations of T (or R) phase fractions. It was found that higher T phase fraction can warrant a larger piezoelectric constant (d33) and d33 also showed a linear variation with respect to tetragonality ratio (c/a). More importantly, a number of domain patterns were observed due to high T phase fraction and large c/a ratio, greatly benefiting the piezoelectricity. In addition, the improved ferroelectric fatigue behavior and thermal stability were also shown in the ceramics containing high T phase fraction. Therefore, this work can bring a new viewpoint into the physical mechanism of KNN-based ceramics behind R-T phase coexistence.

A New Method to Improve the Electrical Properties of KNN-based Ceramics: Tailoring Phase Fraction

KAUST Repository

Lv, Xiang

2017-08-18

Although both the phase type and fraction of multi-phase coexistence can affect the electrical properties of (K,Na)NbO3 (KNN)-based ceramics, effects of phase fraction on their electrical properties were few concerned. In this work, through changing the calcination temperature of CaZrO3 powders, we successfully developed the 0.96K0.5Na0.5Nb0.96Sb0.04O3-0.01CaZrO3-0.03Bi0.5Na0.5HfO3 ceramics containing a wide rhombohedral-tetragonal (R-T) phase coexistence with the variations of T (or R) phase fractions. It was found that higher T phase fraction can warrant a larger piezoelectric constant (d33) and d33 also showed a linear variation with respect to tetragonality ratio (c/a). More importantly, a number of domain patterns were observed due to high T phase fraction and large c/a ratio, greatly benefiting the piezoelectricity. In addition, the improved ferroelectric fatigue behavior and thermal stability were also shown in the ceramics containing high T phase fraction. Therefore, this work can bring a new viewpoint into the physical mechanism of KNN-based ceramics behind R-T phase coexistence.

An ensemble self-training protein interaction article classifier.

Science.gov (United States)

Chen, Yifei; Hou, Ping; Manderick, Bernard

2014-01-01

Protein-protein interaction (PPI) is essential to understand the fundamental processes governing cell biology. The mining and curation of PPI knowledge are critical for analyzing proteomics data. Hence it is desired to classify articles PPI-related or not automatically. In order to build interaction article classification systems, an annotated corpus is needed. However, it is usually the case that only a small number of labeled articles can be obtained manually. Meanwhile, a large number of unlabeled articles are available. By combining ensemble learning and semi-supervised self-training, an ensemble self-training interaction classifier called EST_IACer is designed to classify PPI-related articles based on a small number of labeled articles and a large number of unlabeled articles. A biological background based feature weighting strategy is extended using the category information from both labeled and unlabeled data. Moreover, a heuristic constraint is put forward to select optimal instances from unlabeled data to improve the performance further. Experiment results show that the EST_IACer can classify the PPI related articles effectively and efficiently.

Automatic Classification Using Supervised Learning in a Medical Document Filtering Application.

Science.gov (United States)

Mostafa, J.; Lam, W.

2000-01-01

Presents a multilevel model of the information filtering process that permits document classification. Evaluates a document classification approach based on a supervised learning algorithm, measures the accuracy of the algorithm in a neural network that was trained to classify medical documents on cell biology, and discusses filtering…

A review of supervised object-based land-cover image classification

Science.gov (United States)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data

Directory of Open Access Journals (Sweden)

Hongchao Song

2017-01-01

Full Text Available Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observation and suffer from the curse of dimensionality in high-dimensional space; for example, the distances between any pair of samples are similar and each sample may perform like an outlier. In this paper, we propose a hybrid semi-supervised anomaly detection model for high-dimensional data that consists of two parts: a deep autoencoder (DAE and an ensemble k-nearest neighbor graphs- (K-NNG- based anomaly detector. Benefiting from the ability of nonlinear mapping, the DAE is first trained to learn the intrinsic features of a high-dimensional dataset to represent the high-dimensional data in a more compact subspace. Several nonparametric KNN-based anomaly detectors are then built from different subsets that are randomly sampled from the whole dataset. The final prediction is made by all the anomaly detectors. The performance of the proposed method is evaluated on several real-life datasets, and the results confirm that the proposed hybrid model improves the detection accuracy and reduces the computational complexity.

Optimization of internet content filtering-Combined with KNN and OCAT algorithms

Science.gov (United States)

Guo, Tianze; Wu, Lingjing; Liu, Jiaming

2018-04-01

The face of the status quo that rampant illegal content in the Internet, the result of traditional way to filter information, keyword recognition and manual screening, is getting worse. Based on this, this paper uses OCAT algorithm nested by KNN classification algorithm to construct a corpus training library that can dynamically learn and update, which can be improved on the filter corpus for constantly updated illegal content of the network, including text and pictures, and thus can better filter and investigate illegal content and its source. After that, the research direction will focus on the simplified updating of recognition and comparison algorithms and the optimization of the corpus learning ability in order to improve the efficiency of filtering, save time and resources.

Semi-supervised adaptation in ssvep-based brain-computer interface using tri-training

DEFF Research Database (Denmark)

Bender, Thomas; Kjaer, Troels W.; Thomsen, Carsten E.

2013-01-01

This paper presents a novel and computationally simple tri-training based semi-supervised steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI). It is implemented with autocorrelation-based features and a Naïve-Bayes classifier (NBC). The system uses nine characters...

Texture-based analysis of COPD

DEFF Research Database (Denmark)

Sørensen, Lauge; Nielsen, Mads; Lo, Pechin Chien Pau

2012-01-01

This study presents a fully automatic, data-driven approach for texture-based quantitative analysis of chronic obstructive pulmonary disease (COPD) in pulmonary computed tomography (CT) images. The approach uses supervised learning where the class labels are, in contrast to previous work, based...... on measured lung function instead of on manually annotated regions of interest (ROIs). A quantitative measure of COPD is obtained by fusing COPD probabilities computed in ROIs within the lung fields where the individual ROI probabilities are computed using a k nearest neighbor (kNN ) classifier. The distance...... and subsequently applied to classify 200 independent images from the same screening trial. The texture-based measure was significantly better at discriminating between subjects with and without COPD than were the two most common quantitative measures of COPD in the literature, which are based on density...

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

with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two MRI brain-segmentation tasks with multi-site data: white matter, gray matter, and CSF segmentation; and white-matter- /MS-lesion segmentation......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...... 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...

Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers.

Directory of Open Access Journals (Sweden)

Muhammad Ahmad

Full Text Available Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF, in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN. The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.

Human Activity Recognition by Combining a Small Number of Classifiers.

Science.gov (United States)

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

A supervised learning rule for classification of spatiotemporal spike patterns.

Science.gov (United States)

Lilin Guo; Zhenzhong Wang; Adjouadi, Malek

2016-08-01

This study introduces a novel supervised algorithm for spiking neurons that take into consideration synapse delays and axonal delays associated with weights. It can be utilized for both classification and association and uses several biologically influenced properties, such as axonal and synaptic delays. This algorithm also takes into consideration spike-timing-dependent plasticity as in Remote Supervised Method (ReSuMe). This paper focuses on the classification aspect alone. Spiked neurons trained according to this proposed learning rule are capable of classifying different categories by the associated sequences of precisely timed spikes. Simulation results have shown that the proposed learning method greatly improves classification accuracy when compared to the Spike Pattern Association Neuron (SPAN) and the Tempotron learning rule.

A Supervised Machine Learning Study of Online Discussion Forums about Type-2 Diabetes

DEFF Research Database (Denmark)

Reichert, Jonathan-Raphael; Kristensen, Klaus Langholz; Mukkamala, Raghava Rao

2017-01-01

supervised machine learning techniques to analyze the online conversations. In order to analyse these online textual conversations, we have chosen four domain specific models (Emotions, Sentiment, Personality Traits and Patient Journey). As part of text classification, we employed the ensemble learning...... method by using 5 different supervised machine learning algorithms to build a set of text classifiers by using the voting method to predict most probable label for a given textual conversation from the online discussion forums. Our findings show that there is a high amount of trust expressed by a subset...

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

International Nuclear Information System (INIS)

Maduskar, Pragnya; Hogeweg, Laurens; Sánchez, Clara I.; Ginneken, Bram van; Jong, Pim A. de; Peters-Bax, Liesbeth; Dawson, Rodney; Ayles, Helen

2014-01-01

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

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

Automated lesion detection on MRI scans using combined unsupervised and supervised methods

International Nuclear Information System (INIS)

Guo, Dazhou; Fridriksson, Julius; Fillmore, Paul; Rorden, Christopher; Yu, Hongkai; Zheng, Kang; Wang, Song

2015-01-01

Accurate and precise detection of brain lesions on MR images (MRI) is paramount for accurately relating lesion location to impaired behavior. In this paper, we present a novel method to automatically detect brain lesions from a T1-weighted 3D MRI. The proposed method combines the advantages of both unsupervised and supervised methods. First, unsupervised methods perform a unified segmentation normalization to warp images from the native space into a standard space and to generate probability maps for different tissue types, e.g., gray matter, white matter and fluid. This allows us to construct an initial lesion probability map by comparing the normalized MRI to healthy control subjects. Then, we perform non-rigid and reversible atlas-based registration to refine the probability maps of gray matter, white matter, external CSF, ventricle, and lesions. These probability maps are combined with the normalized MRI to construct three types of features, with which we use supervised methods to train three support vector machine (SVM) classifiers for a combined classifier. Finally, the combined classifier is used to accomplish lesion detection. We tested this method using T1-weighted MRIs from 60 in-house stroke patients. Using leave-one-out cross validation, the proposed method can achieve an average Dice coefficient of 73.1 % when compared to lesion maps hand-delineated by trained neurologists. Furthermore, we tested the proposed method on the T1-weighted MRIs in the MICCAI BRATS 2012 dataset. The proposed method can achieve an average Dice coefficient of 66.5 % in comparison to the expert annotated tumor maps provided in MICCAI BRATS 2012 dataset. In addition, on these two test datasets, the proposed method shows competitive performance to three state-of-the-art methods, including Stamatakis et al., Seghier et al., and Sanjuan et al. In this paper, we introduced a novel automated procedure for lesion detection from T1-weighted MRIs by combining both an unsupervised and a

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

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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.

Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

Science.gov (United States)

Rajagopal, Rekha; Ranganathan, Vidhyapriya

2018-06-05

Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

Pixel Classification of SAR ice images using ANFIS-PSO Classifier

Directory of Open Access Journals (Sweden)

G. Vasumathi

2016-12-01

Full Text Available Synthetic Aperture Radar (SAR is playing a vital role in taking extremely high resolution radar images. It is greatly used to monitor the ice covered ocean regions. Sea monitoring is important for various purposes which includes global climate systems and ship navigation. Classification on the ice infested area gives important features which will be further useful for various monitoring process around the ice regions. Main objective of this paper is to classify the SAR ice image that helps in identifying the regions around the ice infested areas. In this paper three stages are considered in classification of SAR ice images. It starts with preprocessing in which the speckled SAR ice images are denoised using various speckle removal filters; comparison is made on all these filters to find the best filter in speckle removal. Second stage includes segmentation in which different regions are segmented using K-means and watershed segmentation algorithms; comparison is made between these two algorithms to find the best in segmenting SAR ice images. The last stage includes pixel based classification which identifies and classifies the segmented regions using various supervised learning classifiers. The algorithms includes Back propagation neural networks (BPN, Fuzzy Classifier, Adaptive Neuro Fuzzy Inference Classifier (ANFIS classifier and proposed ANFIS with Particle Swarm Optimization (PSO classifier; comparison is made on all these classifiers to propose which classifier is best suitable for classifying the SAR ice image. Various evaluation metrics are performed separately at all these three stages.

Classifying Transition Behaviour in Postural Activity Monitoring

Directory of Open Access Journals (Sweden)

James BRUSEY

2009-10-01

Full Text Available A few accelerometers positioned on different parts of the body can be used to accurately classify steady state behaviour, such as walking, running, or sitting. Such systems are usually built using supervised learning approaches. Transitions between postures are, however, difficult to deal with using posture classification systems proposed to date, since there is no label set for intermediary postures and also the exact point at which the transition occurs can sometimes be hard to pinpoint. The usual bypass when using supervised learning to train such systems is to discard a section of the dataset around each transition. This leads to poorer classification performance when the systems are deployed out of the laboratory and used on-line, particularly if the regimes monitored involve fast paced activity changes. Time-based filtering that takes advantage of sequential patterns is a potential mechanism to improve posture classification accuracy in such real-life applications. Also, such filtering should reduce the number of event messages needed to be sent across a wireless network to track posture remotely, hence extending the system’s life. To support time-based filtering, understanding transitions, which are the major event generators in a classification system, is a key. This work examines three approaches to post-process the output of a posture classifier using time-based filtering: a naïve voting scheme, an exponentially weighted voting scheme, and a Bayes filter. Best performance is obtained from the exponentially weighted voting scheme although it is suspected that a more sophisticated treatment of the Bayes filter might yield better results.

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.

GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting

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Lintao Yang

2018-01-01

Full Text Available With the development of smart power grids, communication network technology and sensor technology, there has been an exponential growth in complex electricity load data. Irregular electricity load fluctuations caused by the weather and holiday factors disrupt the daily operation of the power companies. To deal with these challenges, this paper investigates a day-ahead electricity peak load interval forecasting problem. It transforms the conventional continuous forecasting problem into a novel interval forecasting problem, and then further converts the interval forecasting problem into the classification forecasting problem. In addition, an indicator system influencing the electricity load is established from three dimensions, namely the load series, calendar data, and weather data. A semi-supervised feature selection algorithm is proposed to address an electricity load classification forecasting issue based on the group method of data handling (GMDH technology. The proposed algorithm consists of three main stages: (1 training the basic classifier; (2 selectively marking the most suitable samples from the unclassified label data, and adding them to an initial training set; and (3 training the classification models on the final training set and classifying the test samples. An empirical analysis of electricity load dataset from four Chinese cities is conducted. Results show that the proposed model can address the electricity load classification forecasting problem more efficiently and effectively than the FW-Semi FS (forward semi-supervised feature selection and GMDH-U (GMDH-based semi-supervised feature selection for customer classification models.

Semi-Supervised Classification for Fault Diagnosis in Nuclear Power Plants

International Nuclear Information System (INIS)

Ma, Jian Ping; Jiang, Jin

2014-01-01

Pattern classification methods have become important tools for fault diagnosis in industrial systems. However, it is normally difficult to obtain reliable labeled data to train a supervised pattern classification model for applications in a nuclear power plant (NPP). However, unlabeled data easily become available through increased deployment of supervisory, control, and data acquisition (SCADA) systems. In this paper, a fault diagnosis scheme based on semi-supervised classification (SSC) method is developed with specific applications for NPP. In this scheme, newly measured plant data are treated as unlabeled data. They are integrated with selected labeled data to train a SSC model which is then used to estimate labels of the new data. Compared to exclusive supervised approaches, the proposed scheme requires significantly less number of labeled data to train a classifier. Furthermore, it is shown that higher degree of uncertainties in the labeled data can be tolerated. The developed scheme has been validated using the data generated from a desktop NPP simulator and also from a physical NPP simulator using a graph-based SSC algorithm. Two case studies have been used in the validation process. In the first case study, three faults have been simulated on the desktop simulator. These faults have all been classified successfully with only four labeled data points per fault case. In the second case, six types of fault are simulated on the physical NPP simulator. All faults have been successfully diagnosed. The results have demonstrated that SSC is a promising tool for fault diagnosis

Supervised Quality Assessment Of Medical Image Registration: Application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.; Ginneken, B. van; Murphy, K.; Pluim, J.P.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

Supervised quality assessment of medical image registration : application to intra-patient CT lung registration

NARCIS (Netherlands)

Muenzing, S.E.A.; Ginneken, van B.; Murphy, K.; Pluim, J.P.W.

2012-01-01

A novel method for automatic quality assessment of medical image registration is presented. The method is based on supervised learning of local alignment patterns, which are captured by statistical image features at distinctive landmark points. A two-stage classifier cascade, employing an optimal

A semi-supervised classification algorithm using the TAD-derived background as training data

Science.gov (United States)

Fan, Lei; Ambeau, Brittany; Messinger, David W.

2013-05-01

In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

Extracting microRNA-gene relations from biomedical literature using distant supervision.

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Andre Lamurias

Full Text Available Many biomedical relation extraction approaches are based on supervised machine learning, requiring an annotated corpus. Distant supervision aims at training a classifier by combining a knowledge base with a corpus, reducing the amount of manual effort necessary. This is particularly useful for biomedicine because many databases and ontologies have been made available for many biological processes, while the availability of annotated corpora is still limited. We studied the extraction of microRNA-gene relations from text. MicroRNA regulation is an important biological process due to its close association with human diseases. The proposed method, IBRel, is based on distantly supervised multi-instance learning. We evaluated IBRel on three datasets, and the results were compared with a co-occurrence approach as well as a supervised machine learning algorithm. While supervised learning outperformed on two of those datasets, IBRel obtained an F-score 28.3 percentage points higher on the dataset for which there was no training set developed specifically. To demonstrate the applicability of IBRel, we used it to extract 27 miRNA-gene relations from recently published papers about cystic fibrosis. Our results demonstrate that our method can be successfully used to extract relations from literature about a biological process without an annotated corpus. The source code and data used in this study are available at https://github.com/AndreLamurias/IBRel.

Pathological Brain Detection Using Weiner Filtering, 2D-Discrete Wavelet Transform, Probabilistic PCA, and Random Subspace Ensemble Classifier

Directory of Open Access Journals (Sweden)

Debesh Jha

2017-01-01

Full Text Available Accurate diagnosis of pathological brain images is important for patient care, particularly in the early phase of the disease. Although numerous studies have used machine-learning techniques for the computer-aided diagnosis (CAD of pathological brain, previous methods encountered challenges in terms of the diagnostic efficiency owing to deficiencies in the choice of proper filtering techniques, neuroimaging biomarkers, and limited learning models. Magnetic resonance imaging (MRI is capable of providing enhanced information regarding the soft tissues, and therefore MR images are included in the proposed approach. In this study, we propose a new model that includes Wiener filtering for noise reduction, 2D-discrete wavelet transform (2D-DWT for feature extraction, probabilistic principal component analysis (PPCA for dimensionality reduction, and a random subspace ensemble (RSE classifier along with the K-nearest neighbors (KNN algorithm as a base classifier to classify brain images as pathological or normal ones. The proposed methods provide a significant improvement in classification results when compared to other studies. Based on 5×5 cross-validation (CV, the proposed method outperforms 21 state-of-the-art algorithms in terms of classification accuracy, sensitivity, and specificity for all four datasets used in the study.

Data Stream Classification Based on the Gamma Classifier

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Abril Valeria Uriarte-Arcia

2015-01-01

Full Text Available The ever increasing data generation confronts us with the problem of handling online massive amounts of information. One of the biggest challenges is how to extract valuable information from these massive continuous data streams during single scanning. In a data stream context, data arrive continuously at high speed; therefore the algorithms developed to address this context must be efficient regarding memory and time management and capable of detecting changes over time in the underlying distribution that generated the data. This work describes a novel method for the task of pattern classification over a continuous data stream based on an associative model. The proposed method is based on the Gamma classifier, which is inspired by the Alpha-Beta associative memories, which are both supervised pattern recognition models. The proposed method is capable of handling the space and time constrain inherent to data stream scenarios. The Data Streaming Gamma classifier (DS-Gamma classifier implements a sliding window approach to provide concept drift detection and a forgetting mechanism. In order to test the classifier, several experiments were performed using different data stream scenarios with real and synthetic data streams. The experimental results show that the method exhibits competitive performance when compared to other state-of-the-art algorithms.

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.

Detecting Dutch political tweets : A classifier based on voting system using supervised learning

NARCIS (Netherlands)

de Mello Araújo, Eric Fernandes; Ebbelaar, Dave

The task of classifying political tweets has been shown to be very difficult, with controversial results in many works and with non-replicable methods. Most of the works with this goal use rule-based methods to identify political tweets. We propose here two methods, being one rule-based approach,

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

2016-06-29

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.

Effectiveness of Group Supervision versus Combined Group and Individual Supervision.

Science.gov (United States)

Ray, Dee; Altekruse, Michael

2000-01-01

Investigates the effectiveness of different types of supervision (large group, small group, combined group, individual supervision) with counseling students (N=64). Analyses revealed that all supervision formats resulted in similar progress in counselor effectiveness and counselor development. Participants voiced a preference for individual…

Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

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Turky N. Alotaiby

2017-01-01

Full Text Available Epilepsy is a neurological disorder that affects millions of people worldwide. Monitoring the brain activities and identifying the seizure source which starts with spike detection are important steps for epilepsy treatment. Magnetoencephalography (MEG is an emerging epileptic diagnostic tool with high-density sensors; this makes manual analysis a challenging task due to the vast amount of MEG data. This paper explores the use of eight statistical features and genetic programing (GP with the K-nearest neighbor (KNN for interictal spike detection. The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. The effectiveness of the proposed approach has been evaluated using real MEG data obtained from 28 epileptic patients. It has achieved a 91.75% average sensitivity and 92.99% average specificity.

A Topic Model Approach to Representing and Classifying Football Plays

KAUST Repository

Varadarajan, Jagannadan

2013-09-09

We address the problem of modeling and classifying American Football offense teams’ plays in video, a challenging example of group activity analysis. Automatic play classification will allow coaches to infer patterns and tendencies of opponents more ef- ficiently, resulting in better strategy planning in a game. We define a football play as a unique combination of player trajectories. To this end, we develop a framework that uses player trajectories as inputs to MedLDA, a supervised topic model. The joint maximiza- tion of both likelihood and inter-class margins of MedLDA in learning the topics allows us to learn semantically meaningful play type templates, as well as, classify different play types with 70% average accuracy. Furthermore, this method is extended to analyze individual player roles in classifying each play type. We validate our method on a large dataset comprising 271 play clips from real-world football games, which will be made publicly available for future comparisons.

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...

Highly textured KNN-based piezoelectric ceramics by conventional sintering

International Nuclear Information System (INIS)

Zapata, Angelica Maria Mazuera; Silva Junior, Paulo Sergio da; Zambrano, Michel Venet

2016-01-01

Full text: Texturing in ferroelectric ceramics has played an important role in the enhancement of their piezoelectric properties. Common methods for ceramic texturing are hot pressing and template grain ground; nevertheless, the needed facilities to apply hot pressing and the processing of single crystal make the texture of ceramics expensive and very difficult. In this study, a novel method was investigated to obtain highly textured lead-free ceramics. A (K 0.5 Na 0.5 ) 0.97 Li 0. 0 3 Nb 0.8 Ta 0. 2 matrix (KNLNT), with CuO excess was sintered between 1070 and 1110 °C following a solid state reaction procedure. The CuO excess promotes liquid phase formation and a partial melting of the material. XRD patterns showed the intensity of (100) family peaks became much stronger with the increasing of sintering temperature and CuO. In addition, Lotgering factor was calculated and exhibited a texture degree between 40 % and 70 % for sintered samples having 13 and 16 wt. % CuO, respectively. These, highly textured ceramics, with adequate cut, can be used as substitutes single crystals for texturing of KNN-based lead-free ceramics. (author)

Semi-Supervised Bayesian Classification of Materials with Impact-Echo Signals

Directory of Open Access Journals (Sweden)

Jorge Igual

2015-05-01

Full Text Available The detection and identification of internal defects in a material require the use of some technology that translates the hidden interior damages into observable signals with different signature-defect correspondences. We apply impact-echo techniques for this purpose. The materials are classified according to their defective status (homogeneous, one defect or multiple defects and kind of defect (hole or crack, passing through or not. Every specimen is impacted by a hammer, and the spectrum of the propagated wave is recorded. This spectrum is the input data to a Bayesian classifier that is based on the modeling of the conditional probabilities with a mixture of Gaussians. The parameters of the Gaussian mixtures and the class probabilities are estimated using an extended expectation-maximization algorithm. The advantage of our proposal is that it is flexible, since it obtains good results for a wide range of models even under little supervision; e.g., it obtains a harmonic average of precision and recall value of 92.38% given only a 10% supervision ratio. We test the method with real specimens made of aluminum alloy. The results show that the algorithm works very well. This technique could be applied in many industrial problems, such as the optimization of the marble cutting process.

Entropy based classifier for cross-domain opinion mining

Directory of Open Access Journals (Sweden)

Jyoti S. Deshmukh

2018-01-01

Full Text Available In recent years, the growth of social network has increased the interest of people in analyzing reviews and opinions for products before they buy them. Consequently, this has given rise to the domain adaptation as a prominent area of research in sentiment analysis. A classifier trained from one domain often gives poor results on data from another domain. Expression of sentiment is different in every domain. The labeling cost of each domain separately is very high as well as time consuming. Therefore, this study has proposed an approach that extracts and classifies opinion words from one domain called source domain and predicts opinion words of another domain called target domain using a semi-supervised approach, which combines modified maximum entropy and bipartite graph clustering. A comparison of opinion classification on reviews on four different product domains is presented. The results demonstrate that the proposed method performs relatively well in comparison to the other methods. Comparison of SentiWordNet of domain-specific and domain-independent words reveals that on an average 72.6% and 88.4% words, respectively, are correctly classified.

Legislation and supervision

International Nuclear Information System (INIS)

1998-01-01

In this part next aspects are described: (1) Legislative and supervision-related framework (reviews of structure of supervisory bodies; legislation; state supervision in the nuclear safety area, and state supervision in the area of health protection against radiation are given); (2) Operator's responsibility

Maximum margin semi-supervised learning with irrelevant data.

Science.gov (United States)

Yang, Haiqin; Huang, Kaizhu; King, Irwin; Lyu, Michael R

2015-10-01

Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of the targeted labeled data. In this paper, we address a different, yet formidable scenario in semi-supervised classification, where the unlabeled data may contain irrelevant data to the labeled data. To tackle this problem, we develop a maximum margin model, named tri-class support vector machine (3C-SVM), to utilize the available training data, while seeking a hyperplane for separating the targeted data well. Our 3C-SVM exhibits several characteristics and advantages. First, it does not need any prior knowledge and explicit assumption on the data relatedness. On the contrary, it can relieve the effect of irrelevant unlabeled data based on the logistic principle and maximum entropy principle. That is, 3C-SVM approaches an ideal classifier. This classifier relies heavily on labeled data and is confident on the relevant data lying far away from the decision hyperplane, while maximally ignoring the irrelevant data, which are hardly distinguished. Second, theoretical analysis is provided to prove that in what condition, the irrelevant data can help to seek the hyperplane. Third, 3C-SVM is a generalized model that unifies several popular maximum margin models, including standard SVMs, Semi-supervised SVMs (S(3)VMs), and SVMs learned from the universum (U-SVMs) as its special cases. More importantly, we deploy a concave-convex produce to solve the proposed 3C-SVM, transforming the original mixed integer programming, to a semi-definite programming relaxation, and finally to a sequence of quadratic programming subproblems, which yields the same worst case time complexity as that of S(3)VMs. Finally, we demonstrate the effectiveness and efficiency of our proposed 3C-SVM through systematical experimental comparisons. Copyright �

Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy

Science.gov (United States)

2017-01-01

Background 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. Objective 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. Methods 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. Results 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 (P.05). Conclusions Machine learning algorithms can classify open-text feedback

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 (P.05). Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high

Bias and Stability of Single Variable Classifiers for Feature Ranking and Selection.

Science.gov (United States)

Fakhraei, Shobeir; Soltanian-Zadeh, Hamid; Fotouhi, Farshad

2014-11-01

Feature rankings are often used for supervised dimension reduction especially when discriminating power of each feature is of interest, dimensionality of dataset is extremely high, or computational power is limited to perform more complicated methods. In practice, it is recommended to start dimension reduction via simple methods such as feature rankings before applying more complex approaches. Single Variable Classifier (SVC) ranking is a feature ranking based on the predictive performance of a classifier built using only a single feature. While benefiting from capabilities of classifiers, this ranking method is not as computationally intensive as wrappers. In this paper, we report the results of an extensive study on the bias and stability of such feature ranking method. We study whether the classifiers influence the SVC rankings or the discriminative power of features themselves has a dominant impact on the final rankings. We show the common intuition of using the same classifier for feature ranking and final classification does not always result in the best prediction performance. We then study if heterogeneous classifiers ensemble approaches provide more unbiased rankings and if they improve final classification performance. Furthermore, we calculate an empirical prediction performance loss for using the same classifier in SVC feature ranking and final classification from the optimal choices.

Fast and robust segmentation of white blood cell images by self-supervised learning.

Science.gov (United States)

Zheng, Xin; Wang, Yong; Wang, Guoyou; Liu, Jianguo

2018-04-01

A fast and accurate white blood cell (WBC) segmentation remains a challenging task, as different WBCs vary significantly in color and shape due to cell type differences, staining technique variations and the adhesion between the WBC and red blood cells. In this paper, a self-supervised learning approach, consisting of unsupervised initial segmentation and supervised segmentation refinement, is presented. The first module extracts the overall foreground region from the cell image by K-means clustering, and then generates a coarse WBC region by touching-cell splitting based on concavity analysis. The second module further uses the coarse segmentation result of the first module as automatic labels to actively train a support vector machine (SVM) classifier. Then, the trained SVM classifier is further used to classify each pixel of the image and achieve a more accurate segmentation result. To improve its segmentation accuracy, median color features representing the topological structure and a new weak edge enhancement operator (WEEO) handling fuzzy boundary are introduced. To further reduce its time cost, an efficient cluster sampling strategy is also proposed. We tested the proposed approach with two blood cell image datasets obtained under various imaging and staining conditions. The experiment results show that our approach has a superior performance of accuracy and time cost on both datasets. Copyright © 2018 Elsevier Ltd. All rights reserved.

Collective academic supervision

DEFF Research Database (Denmark)

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

2013-01-01

Supervision of students is a core activity in higher education. Previous research on student supervision in higher education focus on individual and relational aspects in the supervisory relationship rather than collective, pedagogical and methodical aspects of the planning of the supervision...... process. This article fills these gaps by discussing potentials and challenges in “Collective Academic Supervision”, a model for supervision at the Master of Education in Guidance at Aarhus University in Denmark. The pedagogical rationale behind the model is that students’ participation and learning...

Use of classifier to determine coffee harvest time by detachment force

Directory of Open Access Journals (Sweden)

Murilo M. de Barros

Full Text Available ABSTRACT Coffee quality is an essential aspect to increase its commercial value and for the Brazilian coffee business to remain prominent in the world market. Fruit maturity stage at harvest is an important factor that affects the quality and commercial value of the product. Therefore, the objective of this study was to develop a classifier using neural networks to distinguish green coffee fruits from mature coffee fruits, based on the detachment force. Fruit detachment force and the percentage value of the maturity stage were measured during a 75-day harvest window. Collections were carried out biweekly, resulting in five different moments within the harvest period. A classifier was developed using neural networks to distinguish green fruits from mature fruits in the harvest period analyzed. The results show that, in the first half of June, the supervised classified had the highest success percentage in differentiating green fruits from mature fruits, and this period was considered as ideal for a selective harvest under these experimental conditions.

Social constructionism and supervision: experiences of AAMFT supervisors and supervised therapists.

Science.gov (United States)

Hair, Heather J; Fine, Marshall

2012-10-01

A phenomenological research process was used to investigate the supervision experience for supervisors and therapists when supervisors use a social constructionist perspective. Participants of the one-to-one interviews were six AAMFT Approved Supervisors and six therapists providing counseling to individuals, couples and families. The findings suggest supervisors were committed to their self-identified supervision philosophy and intentionally sought out congruence between epistemology and practice. The shared experience of therapists indicates they associated desirable supervision experiences with their supervisors' social constructionist perspective. Our findings also indicated that supervisors' and therapists' understanding of social constructionism included the more controversial concepts of agency and extra-discursiveness. This research has taken an empirical step in the direction of understanding what the social constructionist supervision experience is like for supervisors and therapists. Our findings suggest a linkage between epistemology and supervision practice and a satisfaction with the supervision process. © 2012 American Association for Marriage and Family Therapy.

Security system signal supervision

International Nuclear Information System (INIS)

Chritton, M.R.; Matter, J.C.

1991-09-01

This purpose of this NUREG is to present technical information that should be useful to NRC licensees for understanding and applying line supervision techniques to security communication links. A review of security communication links is followed by detailed discussions of link physical protection and DC/AC static supervision and dynamic supervision techniques. Material is also presented on security for atmospheric transmission and video line supervision. A glossary of security communication line supervision terms is appended. 16 figs

Man-machine supervision; Supervision homme-machine

Energy Technology Data Exchange (ETDEWEB)

Montmain, J. [CEA Valrho, Dir. de l' Energie Nucleaire (DEN), 30 - Marcoule (France)

2005-05-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

Semi-Supervised Multi-View Ensemble Learning Based On Extracting Cross-View Correlation

Directory of Open Access Journals (Sweden)

ZALL, R.

2016-05-01

Full Text Available Correlated information between different views incorporate useful for learning in multi view data. Canonical correlation analysis (CCA plays important role to extract these information. However, CCA only extracts the correlated information between paired data and cannot preserve correlated information between within-class samples. In this paper, we propose a two-view semi-supervised learning method called semi-supervised random correlation ensemble base on spectral clustering (SS_RCE. SS_RCE uses a multi-view method based on spectral clustering which takes advantage of discriminative information in multiple views to estimate labeling information of unlabeled samples. In order to enhance discriminative power of CCA features, we incorporate the labeling information of both unlabeled and labeled samples into CCA. Then, we use random correlation between within-class samples from cross view to extract diverse correlated features for training component classifiers. Furthermore, we extend a general model namely SSMV_RCE to construct ensemble method to tackle semi-supervised learning in the presence of multiple views. Finally, we compare the proposed methods with existing multi-view feature extraction methods using multi-view semi-supervised ensembles. Experimental results on various multi-view data sets are presented to demonstrate the effectiveness of the proposed methods.

[Quantitative classification in catering trade and countermeasures of supervision and management in Hunan Province].

Science.gov (United States)

Liu, Xiulan; Chen, Lizhang; He, Xiang

2012-02-01

To analyze the status quo of quantitative classification in Hunan Province catering industry, and to discuss the countermeasures in-depth. According to relevant laws and regulations, and after referring to Daily supervision and quantitative scoring sheet and consulting experts, a checklist of key supervision indicators was made. The implementation of quantitative classification in 10 cities in Hunan Province was studied, and the status quo was analyzed. All the 390 catering units implemented quantitative classified management. The larger the catering enterprise, the higher level of quantitative classification. In addition to cafeterias, the smaller the catering units, the higher point of deduction, and snack bars and beverage stores were the highest. For those quantified and classified as C and D, the point of deduction was higher in the procurement and storage of raw materials, operation processing and other aspects. The quantitative classification of Hunan Province has relatively wide coverage. There are hidden risks in food security in small catering units, snack bars, and beverage stores. The food hygienic condition of Hunan Province needs to be improved.

Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

Science.gov (United States)

Salman, S. S.; Abbas, W. A.

2018-05-01

The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

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...

Short text sentiment classification based on feature extension and ensemble classifier

Science.gov (United States)

Liu, Yang; Zhu, Xie

2018-05-01

With the rapid development of Internet social media, excavating the emotional tendencies of the short text information from the Internet, the acquisition of useful information has attracted the attention of researchers. At present, the commonly used can be attributed to the rule-based classification and statistical machine learning classification methods. Although micro-blog sentiment analysis has made good progress, there still exist some shortcomings such as not highly accurate enough and strong dependence from sentiment classification effect. Aiming at the characteristics of Chinese short texts, such as less information, sparse features, and diverse expressions, this paper considers expanding the original text by mining related semantic information from the reviews, forwarding and other related information. First, this paper uses Word2vec to compute word similarity to extend the feature words. And then uses an ensemble classifier composed of SVM, KNN and HMM to analyze the emotion of the short text of micro-blog. The experimental results show that the proposed method can make good use of the comment forwarding information to extend the original features. Compared with the traditional method, the accuracy, recall and F1 value obtained by this method have been improved.

Analyzing the defect structure of CuO-doped PZT and KNN piezoelectrics from electron paramagnetic resonance.

Science.gov (United States)

Jakes, Peter; Kungl, Hans; Schierholz, Roland; Eichel, Rüdiger-A

2014-09-01

The defect structure for copper-doped sodium potassium niobate (KNN) ferroelectrics has been analyzed with respect to its defect structure. In particular, the interplay between the mutually compensating dimeric (Cu(Nb)'''-V(O)··) and trimeric (V(O)··-Cu(Nb)'''-V(O)··)· defect complexes with 180° and non-180° domain walls has been analyzed and compared to the effects from (Cu'' - V(O)··)(x)× dipoles in CuO-doped lead zirconate titanate (PZT). Attempts are made to relate the rearrangement of defect complexes to macroscopic electromechanical properties.

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.

Reflecting reflection in supervision

DEFF Research Database (Denmark)

Lystbæk, Christian Tang

associated with reflection and an exploration of alternative conceptions that view reflection within the context of settings which have a more group- and team-based orientation. Drawing on an action research project on health care supervision, the paper questions whether we should reject earlier views...... of reflection, rehabilitate them in order to capture broader connotations or move to new ways of regarding reflection that are more in keeping with not only reflective but also emotive, normative and formative views on supervision. The paper presents a critical perspective on supervision that challenge...... the current reflective paradigm I supervision and relate this to emotive, normative and formative views supervision. The paper is relevant for Nordic educational research into the supervision and guidance...

Resistance to group clinical supervision

DEFF Research Database (Denmark)

Buus, Niels; Delgado, Cynthia; Traynor, Michael

2018-01-01

This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has traditiona......This present study is a report of an interview study exploring personal views on participating in group clinical supervision among mental health nursing staff members who do not participate in supervision. There is a paucity of empirical research on resistance to supervision, which has...... traditionally been theorized as a supervisee's maladaptive coping with anxiety in the supervision process. The aim of the present study was to examine resistance to group clinical supervision by interviewing nurses who did not participate in supervision. In 2015, we conducted semistructured interviews with 24...... Danish mental health nursing staff members who had been observed not to participate in supervision in two periods of 3 months. Interviews were audio-recorded and subjected to discourse analysis. We constructed two discursive positions taken by the informants: (i) 'forced non-participation', where...

Semi-supervised learning via regularized boosting working on multiple semi-supervised assumptions.

Science.gov (United States)

Chen, Ke; Wang, Shihai

2011-01-01

Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learning with various strategies. To our knowledge, however, none of them takes all three semi-supervised assumptions, i.e., smoothness, cluster, and manifold assumptions, together into account during boosting learning. In this paper, we propose a novel cost functional consisting of the margin cost on labeled data and the regularization penalty on unlabeled data based on three fundamental semi-supervised assumptions. Thus, minimizing our proposed cost functional with a greedy yet stagewise functional optimization procedure leads to a generic boosting framework for semi-supervised learning. Extensive experiments demonstrate that our algorithm yields favorite results for benchmark and real-world classification tasks in comparison to state-of-the-art semi-supervised learning algorithms, including newly developed boosting algorithms. Finally, we discuss relevant issues and relate our algorithm to the previous work.

Optimal preventive bank supervision

OpenAIRE

Belhaj, Mohamed; Klimenko, Nataliya

2012-01-01

Early regulator interventions into problem banks is one of the key suggestions of Basel Committee on Banking Supervision. However, no guidance is given on their design. To fill this gap, we outline an incentive-based preventive supervision strategy that eliminates bad asset management in banks. Two supervision techniques are combined: temporary regulatory administration and random audits. Our design ensures good management without excessive supervision costs, through a gradual adjustment of...

Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.

Science.gov (United States)

Matsubara, Takashi

2017-01-01

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.

Supervised Learning

Science.gov (United States)

Rokach, Lior; Maimon, Oded

This chapter summarizes the fundamental aspects of supervised methods. The chapter provides an overview of concepts from various interrelated fields used in subsequent chapters. It presents basic definitions and arguments from the supervised machine learning literature and considers various issues, such as performance evaluation techniques and challenges for data mining tasks.

Psykoterapi og supervision

DEFF Research Database (Denmark)

Jacobsen, Claus Haugaard

2014-01-01

Kapitlet beskriver supervisionen funktioner i forhold til psykoterapi. Supervision af psykoterapi henviser i almindelighed til, at en psykoterapeut konsulterer en ofte mere erfaren kollega (supervisor) med henblik på drøftelse af et konkret igangværende psykoterapeutisk behandlingsforløb. Formålet...... er at fremme denne fagpersons (psykoterapeutens) faglige udvikling samt sikre kvaliteten af behandlingen.kan defineres som i. Der redegøres for, hvorfor supervision er vigtig del af psykoterapeutens profession samt vises, hvorledes supervision foruden den faglige udvikling også er vigtigt redskab i...... psykoterapiens kvalitetssikring. Efter at have drøftet nogle etiske forhold ved supervision, fremlægges endelig nogle få forskningsresultater vedr. psykoterapisupervision af danske psykologer....

Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm.

Science.gov (United States)

Manganaro, Alberto; Pizzo, Fabiola; Lombardo, Anna; Pogliaghi, Alberto; Benfenati, Emilio

2016-02-01

The ability of a substance to resist degradation and persist in the environment needs to be readily identified in order to protect the environment and human health. Many regulations require the assessment of persistence for substances commonly manufactured and marketed. Besides laboratory-based testing methods, in silico tools may be used to obtain a computational prediction of persistence. We present a new program to develop k-Nearest Neighbor (k-NN) models. The k-NN algorithm is a similarity-based approach that predicts the property of a substance in relation to the experimental data for its most similar compounds. We employed this software to identify persistence in the sediment compartment. Data on half-life (HL) in sediment were obtained from different sources and, after careful data pruning the final dataset, containing 297 organic compounds, was divided into four experimental classes. We developed several models giving satisfactory performances, considering that both the training and test set accuracy ranged between 0.90 and 0.96. We finally selected one model which will be made available in the near future in the freely available software platform VEGA. This model offers a valuable in silico tool that may be really useful for fast and inexpensive screening. Copyright © 2015 Elsevier Ltd. All rights reserved.

Non supervised classification of vegetable covers on digital images of remote sensors: Landsat - ETM+

International Nuclear Information System (INIS)

Arango Gutierrez, Mauricio; Branch Bedoya, John William; Botero Fernandez, Veronica

2005-01-01

The plant species diversity in Colombia and the lack of inventory of them suggests the need for a process that facilitates the work of investigators in these disciplines. Remote satellite sensors such as landsat ETM+ and non-supervised artificial intelligence techniques, such as self-organizing maps - SOM, could provide viable alternatives for advancing in the rapid obtaining of information related to zones with different vegetative covers in the national geography. The zone proposed for the study case was classified in a supervised form by the method of maximum likelihood by another investigation in forest sciences and eight types of vegetative covers were discriminated. This information served as a base line to evaluate the performance of the non-supervised sort keys isodata and SOM. However, the information that the images provided had to first be purified according to the criteria of use and data quality, so that adequate information for these non-supervised methods were used. For this, several concepts were used; such as, image statistics, spectral behavior of the vegetative communities, sensor characteristics and the average divergence that allowed to define the best bands and their combinations. Principal component analysis was applied to these to reduce to the number of data while conserving a large percentage of the information. The non-supervised techniques were applied to these purified data, modifying some parameters that could yield a better convergence of the methods. The results obtained were compared with the supervised classification via confusion matrices and it was concluded that there was not a good convergence of non-supervised classification methods with this process for the case of vegetative covers

Rethinking Educational Supervision

OpenAIRE

Burhanettin DÖNMEZ; Kadir BEYCİOĞLU

2009-01-01

The history of educational (school) supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in...

New Informative Features for Fault Diagnosis of Industrial Systems by Supervised Classification

OpenAIRE

Verron , Sylvain; Tiplica , Teodor; Kobi , Abdessamad

2009-01-01

International audience; The purpose of this article is to present a method for industrial process diagnosis. We are interested in fault diagnosis considered as a supervised classication task. The interest of the proposed method is to take into account new features (and so new informations) in the classifier. These new features are probabilities extracted from a Bayesian network comparing the faulty observations to the normal operating conditions. The performances of this method are evaluated ...

MULTIPERIOD BANKING SUPERVISION

OpenAIRE

KARL-THEODOR EISELE; PHILIPPE ARTZNER

2013-01-01

This paper is based on a general method for multiperiod prudential supervision of companies submitted to hedgeable and non-hedgeable risks. Having treated the case of insurance in an earlier paper, we now consider a quantitative approach to supervision of commercial banks. The various elements under supervision are the bank’s current amount of tradeable assets, the deposit amount, and four flow processes: future trading risk exposures, deposit flows, flows of loan repayments and of deposit re...

Semi-supervised vibration-based classification and condition monitoring of compressors

Science.gov (United States)

Potočnik, Primož; Govekar, Edvard

2017-09-01

Semi-supervised vibration-based classification and condition monitoring of the reciprocating compressors installed in refrigeration appliances is proposed in this paper. The method addresses the problem of industrial condition monitoring where prior class definitions are often not available or difficult to obtain from local experts. The proposed method combines feature extraction, principal component analysis, and statistical analysis for the extraction of initial class representatives, and compares the capability of various classification methods, including discriminant analysis (DA), neural networks (NN), support vector machines (SVM), and extreme learning machines (ELM). The use of the method is demonstrated on a case study which was based on industrially acquired vibration measurements of reciprocating compressors during the production of refrigeration appliances. The paper presents a comparative qualitative analysis of the applied classifiers, confirming the good performance of several nonlinear classifiers. If the model parameters are properly selected, then very good classification performance can be obtained from NN trained by Bayesian regularization, SVM and ELM classifiers. The method can be effectively applied for the industrial condition monitoring of compressors.

Applying active learning to supervised word sense disambiguation in MEDLINE.

Science.gov (United States)

Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

2013-01-01

This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models.

Instance Selection for Classifier Performance Estimation in Meta Learning

Directory of Open Access Journals (Sweden)

Marcin Blachnik

2017-11-01

Full Text Available Building an accurate prediction model is challenging and requires appropriate model selection. This process is very time consuming but can be accelerated with meta-learning–automatic model recommendation by estimating the performances of given prediction models without training them. Meta-learning utilizes metadata extracted from the dataset to effectively estimate the accuracy of the model in question. To achieve that goal, metadata descriptors must be gathered efficiently and must be informative to allow the precise estimation of prediction accuracy. In this paper, a new type of metadata descriptors is analyzed. These descriptors are based on the compression level obtained from the instance selection methods at the data-preprocessing stage. To verify their suitability, two types of experiments on real-world datasets have been conducted. In the first one, 11 instance selection methods were examined in order to validate the compression–accuracy relation for three classifiers: k-nearest neighbors (kNN, support vector machine (SVM, and random forest. From this analysis, two methods are recommended (instance-based learning type 2 (IB2, and edited nearest neighbor (ENN which are then compared with the state-of-the-art metaset descriptors. The obtained results confirm that the two suggested compression-based meta-features help to predict accuracy of the base model much more accurately than the state-of-the-art solution.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data

Directory of Open Access Journals (Sweden)

Evangelos Stromatias

2017-06-01

Full Text Available This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77% and Poker-DVS (100% real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

Science.gov (United States)

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

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…

Predicting protein subnuclear location with optimized evidence-theoretic K-nearest classifier and pseudo amino acid composition

International Nuclear Information System (INIS)

Shen Hongbin; Chou Kuochen

2005-01-01

The nucleus is the brain of eukaryotic cells that guides the life processes of the cell by issuing key instructions. For in-depth understanding of the biochemical process of the nucleus, the knowledge of localization of nuclear proteins is very important. With the avalanche of protein sequences generated in the post-genomic era, it is highly desired to develop an automated method for fast annotating the subnuclear locations for numerous newly found nuclear protein sequences so as to be able to timely utilize them for basic research and drug discovery. In view of this, a novel approach is developed for predicting the protein subnuclear location. It is featured by introducing a powerful classifier, the optimized evidence-theoretic K-nearest classifier, and using the pseudo amino acid composition [K.C. Chou, PROTEINS: Structure, Function, and Genetics, 43 (2001) 246], which can incorporate a considerable amount of sequence-order effects, to represent protein samples. As a demonstration, identifications were performed for 370 nuclear proteins among the following 9 subnuclear locations: (1) Cajal body, (2) chromatin, (3) heterochromatin, (4) nuclear diffuse, (5) nuclear pore, (6) nuclear speckle, (7) nucleolus, (8) PcG body, and (9) PML body. The overall success rates thus obtained by both the re-substitution test and jackknife cross-validation test are significantly higher than those by existing classifiers on the same working dataset. It is anticipated that the powerful approach may also become a useful high throughput vehicle to bridge the huge gap occurring in the post-genomic era between the number of gene sequences in databases and the number of gene products that have been functionally characterized. The OET-KNN classifier will be available at www.pami.sjtu.edu.cn/people/hbshen

WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia

OpenAIRE

Mukherjee, Subhabrata; Bhattacharyya, Pushpak

2012-01-01

This paper describes a weakly supervised system for sentiment analysis in the movie review domain. The objective is to classify a movie review into a polarity class, positive or negative, based on those sentences bearing opinion on the movie alone. The irrelevant text, not directly related to the reviewer opinion on the movie, is left out of analysis. Wikipedia incorporates the world knowledge of movie-specific features in the system which is used to obtain an extractive summary of the review...

Genetic classification of populations using supervised learning.

Directory of Open Access Journals (Sweden)

Michael Bridges

2011-05-01

Full Text Available 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.

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.

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.

A National Survey of School Counselor Supervision Practices: Administrative, Clinical, Peer, and Technology Mediated Supervision

Science.gov (United States)

Perera-Diltz, Dilani M.; Mason, Kimberly L.

2012-01-01

Supervision is vital for personal and professional development of counselors. Practicing school counselors (n = 1557) across the nation were surveyed to explore current supervision practices. Results indicated that 41.1% of school counselors provide supervision. Although 89% receive some type of supervision, only 10.3% of school counselors receive…

Ensemble learning with trees and rules: supervised, semi-supervised, unsupervised

Science.gov (United States)

In this article, we propose several new approaches for post processing a large ensemble of conjunctive rules for supervised and semi-supervised learning problems. We show with various examples that for high dimensional regression problems the models constructed by the post processing the rules with ...

Adequate supervision for children and adolescents.

Science.gov (United States)

Anderst, James; Moffatt, Mary

2014-11-01

Primary care providers (PCPs) have the opportunity to improve child health and well-being by addressing supervision issues before an injury or exposure has occurred and/or after an injury or exposure has occurred. Appropriate anticipatory guidance on supervision at well-child visits can improve supervision of children, and may prevent future harm. Adequate supervision varies based on the child's development and maturity, and the risks in the child's environment. Consideration should be given to issues as wide ranging as swimming pools, falls, dating violence, and social media. By considering the likelihood of harm and the severity of the potential harm, caregivers may provide adequate supervision by minimizing risks to the child while still allowing the child to take "small" risks as needed for healthy development. Caregivers should initially focus on direct (visual, auditory, and proximity) supervision of the young child. Gradually, supervision needs to be adjusted as the child develops, emphasizing a safe environment and safe social interactions, with graduated independence. PCPs may foster adequate supervision by providing concrete guidance to caregivers. In addition to preventing injury, supervision includes fostering a safe, stable, and nurturing relationship with every child. PCPs should be familiar with age/developmentally based supervision risks, adequate supervision based on those risks, characteristics of neglectful supervision based on age/development, and ways to encourage appropriate supervision throughout childhood. Copyright 2014, SLACK Incorporated.

Group supervision for general practitioners

DEFF Research Database (Denmark)

Galina Nielsen, Helena; Sofie Davidsen, Annette; Dalsted, Rikke

2013-01-01

AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long-establish......AIM: Group supervision is a sparsely researched method for professional development in general practice. The aim of this study was to explore general practitioners' (GPs') experiences of the benefits of group supervision for improving the treatment of mental disorders. METHODS: One long...... considered important prerequisites for disclosing and discussing professional problems. CONCLUSION: The results of this study indicate that participation in a supervision group can be beneficial for maintaining and developing GPs' skills in dealing with patients with mental health problems. Group supervision...... influenced other areas of GPs' professional lives as well. However, more studies are needed to assess the impact of supervision groups....

Evaluering af kollegial supervision

DEFF Research Database (Denmark)

Petersen, Anne Line Bjerre Folsgaard; Bager, Lene Tortzen; Jørgensen, Mette Eg

2015-01-01

Videoen er en evaluering af arbejdet med en metodisk tilgang til kollegial supervision på VIA Ergoterapeutuddannelsen gennem et par år. Evalueringen sætter fokus på selve metoden, der er anvendt til kollegial supervision. Derudover er der fokus på erfaringer og udbytte af at arbejde systematisk med...... kollegial supervision blandt undervisere på VIA Ergoterapeutuddannelsen....

Classification of JERS-1 Image Mosaic of Central Africa Using A Supervised Multiscale Classifier of Texture Features

Science.gov (United States)

Saatchi, Sassan; DeGrandi, Franco; Simard, Marc; Podest, Erika

1999-01-01

In this paper, a multiscale approach is introduced to classify the Japanese Research Satellite-1 (JERS-1) mosaic image over the Central African rainforest. A series of texture maps are generated from the 100 m mosaic image at various scales. Using a quadtree model and relating classes at each scale by a Markovian relationship, the multiscale images are classified from course to finer scale. The results are verified at various scales and the evolution of classification is monitored by calculating the error at each stage.

Rethinking Educational Supervision

Directory of Open Access Journals (Sweden)

Burhanettin DÖNMEZ

2009-08-01

Full Text Available The history of educational (school supervision has been influenced by the history of the interaction of intellectual movements in politics, society, philosophy and industrial movements. The purpose of this conceptual and theoretical study is to have a brief look at the concept of educational supervision with related historical developments in the field. The paper also intends to see the terms and issues critically, and to conceptualize some issues associated with educational supervision in practice. In the paper, the issues are discussed and a number of suggestions are addressed for debate.

Social construction : discursive perspective towards supervision

OpenAIRE

Naujanienė, Rasa

2010-01-01

The aim of publication is to discuss the development of supervision theory in relation with social and social work theory and practice. Main focus in the analysis is done to social constructionist ideas and its’ relevance to supervision practice. The development of supervision is related with supervision practice. Starting in 19th century supervision from giving practical advices supervision came to 21st century as dialog based on critical and philosophical reflection. Different theory and pr...

Public Supervision over Private Relationships : Towards European Supervision Private Law?

NARCIS (Netherlands)

Cherednychenko, O.O.

2014-01-01

The rise of public supervision over private relationships in many areas of private law has led to the development of what, in the author’s view, could be called ‘European supervision private law’. This emerging body of law forms part of European regulatory private law and is made up of

Forskellighed i supervision

DEFF Research Database (Denmark)

Petersen, Birgitte; Beck, Emma

2009-01-01

Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008......Indtryk og tendenser fra den anden danske konference om supervision, som blev holdt på Københavns Universitet i oktober 2008...

[Terahertz Spectroscopic Identification with Deep Belief Network].

Science.gov (United States)

Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

2015-12-01

Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

Supervision af psykoterapi

DEFF Research Database (Denmark)

SUPERVISION AF PSYKOTERAPI indtager en central position i uddannelsen og udviklingen af psykoterapeuter. Trods flere lighedspunkter med psykoterapi, undervisning og konsultation er psykoterapisupervision et selvstændigt virksomhedsområde. Supervisor må foruden at være en trænet psykoterapeut kende...... supervisionens rammer og indplacering i forhold til organisation og samfund. En række kapitler drejer sig om supervisors opgaver, roller og kontrolfunktion, supervision set fra supervisandens perspektiv samt betragtninger over relationer og processer i supervision. Der drøftes fordele og ulemper ved de...... forskellige måder, hvorpå en sag kan fremlægges. Bogens første del afsluttes med refleksioner over de etiske aspekter ved psykoterapisupervision. Bogens anden del handler om de særlige forhold, der gør sig gældende ved supervision af en række specialiserede behandlingsformer eller af psykoterapi med bestemte...

Mercury⊕: An evidential reasoning image classifier

Science.gov (United States)

Peddle, Derek R.

1995-12-01

MERCURY⊕ is a multisource evidential reasoning classification software system based on the Dempster-Shafer theory of evidence. The design and implementation of this software package is described for improving the classification and analysis of multisource digital image data necessary for addressing advanced environmental and geoscience applications. In the remote-sensing context, the approach provides a more appropriate framework for classifying modern, multisource, and ancillary data sets which may contain a large number of disparate variables with different statistical properties, scales of measurement, and levels of error which cannot be handled using conventional Bayesian approaches. The software uses a nonparametric, supervised approach to classification, and provides a more objective and flexible interface to the evidential reasoning framework using a frequency-based method for computing support values from training data. The MERCURY⊕ software package has been implemented efficiently in the C programming language, with extensive use made of dynamic memory allocation procedures and compound linked list and hash-table data structures to optimize the storage and retrieval of evidence in a Knowledge Look-up Table. The software is complete with a full user interface and runs under Unix, Ultrix, VAX/VMS, MS-DOS, and Apple Macintosh operating system. An example of classifying alpine land cover and permafrost active layer depth in northern Canada is presented to illustrate the use and application of these ideas.

Researching online supervision

DEFF Research Database (Denmark)

Bengtsen, Søren S. E.; Mathiasen, Helle

2014-01-01

Online supervision and the use of digital media in supervisory dialogues is a fast increasing practice in higher education today. However, the concepts in our pedagogical repertoire often reflect the digital tools used for supervision purposes as either a prolongation of the face-to-face contact...

Implementation of Instructional Supervision in Secondary School ...

African Journals Online (AJOL)

Science, Technology and Arts Research Journal ... Supervision is critical in the development of any educational program in both developed and ... Clinical Supervision, Collegial Supervision, Self-directive supervision, Informal Supervision etc.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning

Directory of Open Access Journals (Sweden)

Victoria Plaza-Leiva

2017-03-01

Full Text Available Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM, Gaussian processes (GP, and Gaussian mixture models (GMM. A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl. Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Voxel-Based Neighborhood for Spatial Shape Pattern Classification of Lidar Point Clouds with Supervised Learning.

Science.gov (United States)

Plaza-Leiva, Victoria; Gomez-Ruiz, Jose Antonio; Mandow, Anthony; García-Cerezo, Alfonso

2017-03-15

Improving the effectiveness of spatial shape features classification from 3D lidar data is very relevant because it is largely used as a fundamental step towards higher level scene understanding challenges of autonomous vehicles and terrestrial robots. In this sense, computing neighborhood for points in dense scans becomes a costly process for both training and classification. This paper proposes a new general framework for implementing and comparing different supervised learning classifiers with a simple voxel-based neighborhood computation where points in each non-overlapping voxel in a regular grid are assigned to the same class by considering features within a support region defined by the voxel itself. The contribution provides offline training and online classification procedures as well as five alternative feature vector definitions based on principal component analysis for scatter, tubular and planar shapes. Moreover, the feasibility of this approach is evaluated by implementing a neural network (NN) method previously proposed by the authors as well as three other supervised learning classifiers found in scene processing methods: support vector machines (SVM), Gaussian processes (GP), and Gaussian mixture models (GMM). A comparative performance analysis is presented using real point clouds from both natural and urban environments and two different 3D rangefinders (a tilting Hokuyo UTM-30LX and a Riegl). Classification performance metrics and processing time measurements confirm the benefits of the NN classifier and the feasibility of voxel-based neighborhood.

Applying active learning to supervised word sense disambiguation in MEDLINE

Science.gov (United States)

Chen, Yukun; Cao, Hongxin; Mei, Qiaozhu; Zheng, Kai; Xu, Hua

2013-01-01

Objectives This study was to assess whether active learning strategies can be integrated with supervised word sense disambiguation (WSD) methods, thus reducing the number of annotated samples, while keeping or improving the quality of disambiguation models. Methods We developed support vector machine (SVM) classifiers to disambiguate 197 ambiguous terms and abbreviations in the MSH WSD collection. Three different uncertainty sampling-based active learning algorithms were implemented with the SVM classifiers and were compared with a passive learner (PL) based on random sampling. For each ambiguous term and each learning algorithm, a learning curve that plots the accuracy computed from the test set as a function of the number of annotated samples used in the model was generated. The area under the learning curve (ALC) was used as the primary metric for evaluation. Results Our experiments demonstrated that active learners (ALs) significantly outperformed the PL, showing better performance for 177 out of 197 (89.8%) WSD tasks. Further analysis showed that to achieve an average accuracy of 90%, the PL needed 38 annotated samples, while the ALs needed only 24, a 37% reduction in annotation effort. Moreover, we analyzed cases where active learning algorithms did not achieve superior performance and identified three causes: (1) poor models in the early learning stage; (2) easy WSD cases; and (3) difficult WSD cases, which provide useful insight for future improvements. Conclusions This study demonstrated that integrating active learning strategies with supervised WSD methods could effectively reduce annotation cost and improve the disambiguation models. PMID:23364851

Supervised Variational Relevance Learning, An Analytic Geometric Feature Selection with Applications to Omic Datasets.

Science.gov (United States)

Boareto, Marcelo; Cesar, Jonatas; Leite, Vitor B P; Caticha, Nestor

2015-01-01

We introduce Supervised Variational Relevance Learning (Suvrel), a variational method to determine metric tensors to define distance based similarity in pattern classification, inspired in relevance learning. The variational method is applied to a cost function that penalizes large intraclass distances and favors small interclass distances. We find analytically the metric tensor that minimizes the cost function. Preprocessing the patterns by doing linear transformations using the metric tensor yields a dataset which can be more efficiently classified. We test our methods using publicly available datasets, for some standard classifiers. Among these datasets, two were tested by the MAQC-II project and, even without the use of further preprocessing, our results improve on their performance.

20 CFR 656.21 - Supervised recruitment.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 656.21 Section 656.21... Supervised recruitment. (a) Supervised recruitment. Where the Certifying Officer determines it appropriate, post-filing supervised recruitment may be required of the employer for the pending application or...

Supervision Duty of School Principals

Directory of Open Access Journals (Sweden)

Kürşat YILMAZ

2009-04-01

Full Text Available Supervision by school administrators is becoming more and more important. The change in the roles ofschool administrators has a great effect on that increase. At present, school administrators are consideredmore than as technical directors, but as instructional leaders. This increased the importance of schooladministrators’ expected supervision acts. In this respect, the aim of this study is to make a conceptualanalysis about school administrators’ supervision duties. For this reason, a literature review related withsupervision and contemporary supervision approaches was done, and the official documents concerningsupervision were examined. As a result, it can be said that school administrators’ supervision duties havebecome very important. And these duties must certainly be carried out by school administrators.

Moment constrained semi-supervised LDA

DEFF Research Database (Denmark)

Loog, Marco

2012-01-01

This BNAIC compressed contribution provides a summary of the work originally presented at the First IAPR Workshop on Partially Supervised Learning and published in [5]. It outlines the idea behind supervised and semi-supervised learning and highlights the major shortcoming of many current methods...

Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.

Science.gov (United States)

Velickovski, Filip; Ceccaroni, Luigi; Marti, Robert; Burgos, Felip; Gistau, Concepcion; Alsina-Restoy, Xavier; Roca, Josep

2018-01-01

Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus, a need to expand the availability of high-quality spirometry assessment beyond specialist pulmonary centres has arisen. In this paper, we propose a method to select and optimise a classifier using supervised learning techniques by learning from previously classified forced spirometry tests from a group of experts. Such a method is able to take into account the shape of the curve as an expert would during visual inspection. We evaluated the final classifier on a dataset put aside for evaluation yielding an area under the receiver operating characteristic curve of 0.88 and specificities of 0.91 and 0.86 for sensitivities of 0.60 and 0.82. Furthermore, other specificities and sensitivities along the receiver operating characteristic curve were close to the level of the experts when compared against each-other, and better than an earlier rules-based method assessed on the same dataset. We foresee key benefits in raising diagnostic quality, saving time, reducing cost, and also improving remote care and monitoring services for patients with chronic respiratory diseases in the future if a clinical decision support system with the encapsulated classifier is to be integrated into the work-flow of forced spirometry testing.

Postprocessing of Accidental Scenarios by Semi-Supervised Self-Organizing Maps

Directory of Open Access Journals (Sweden)

Francesco Di Maio

2017-01-01

Full Text Available Integrated Deterministic and Probabilistic Safety Analysis (IDPSA of dynamic systems calls for the development of efficient methods for accidental scenarios generation. The necessary consideration of failure events timing and sequencing along the scenarios requires the number of scenarios to be generated to increase with respect to conventional PSA. Consequently, their postprocessing for retrieving safety relevant information regarding the system behavior is challenged because of the large amount of generated scenarios that makes the computational cost for scenario postprocessing enormous and the retrieved information difficult to interpret. In the context of IDPSA, the interpretation consists in the classification of the generated scenarios as safe, failed, Near Misses (NMs, and Prime Implicants (PIs. To address this issue, in this paper we propose the use of an ensemble of Semi-Supervised Self-Organizing Maps (SSSOMs whose outcomes are combined by a locally weighted aggregation according to two strategies: a locally weighted aggregation and a decision tree based aggregation. In the former, we resort to the Local Fusion (LF principle for accounting the classification reliability of the different SSSOM classifiers, whereas in the latter we build a classification scheme to select the appropriate classifier (or ensemble of classifiers, for the type of scenario to be classified. The two strategies are applied for the postprocessing of the accidental scenarios of a dynamic U-Tube Steam Generator (UTSG.

Obtención de polvos cerámicos de BNKT-KNN por el método Pechini

Directory of Open Access Journals (Sweden)

Yasnó, J. P.

2013-10-01

Full Text Available Pechini method was used in order to obtain fine ceramic and single-phase powders for a lead-free ferroelectric system 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3]or BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. This method allowed obtaining powders with 100 % perovskite phase, which was confirmed by X-ray diffraction, for this particular system in all the studied stoichiometries using temperature as low as 600 ºC. The effects on the bonds present in the structure due to variation of the stoichiometry, Na-K, were determined using infrared spectroscopy, FT-IR. Irregular nanoparticles were observed by scanning electron microscopy.El método Pechini fue utilizado para obtener polvos cerámicos finos y monofásicos del sistema ferroeléctrico libre de plomo 0,97[(Bi1/2Na1/21-x(Bi1/2K1/2xTiO3]-0,03[(Na1/2K1/2NbO3] ó BNKT-KNN (x = 0.00, 0.18, 0.21, 0.24, 0.27. Este método permitió la obtención de polvos con 100 % de fase perovskita, para el sistema de interés en todas las estequiometrias estudiadas, a una temperatura tan baja como 600 ºC, lo que fue confirmado por difracción de rayos X. Por medio de espectroscopia infrarroja, FT-IR, se pudo determinar cómo afecta la variación de la estequiometria, Na-K, los enlaces presentes en la estructura. Mediante microscopia electrónica de barrido se observaron partículas nanométricas irregulares.

Asco 2044 nuclear power plant: supervision; Central nuclear Asco 2044: supervision

Energy Technology Data Exchange (ETDEWEB)

Sabartes, J.

2010-07-01

Good supervision constitutes an efficient barrier to avoid the errors caused by inadequate work practices. In this sense, it is necessary to strengthen supervision to make sure that the work is carried out with adequate human performance, tending to avoid error ande provinding safety quality and efficiency at work. (Author).

Whither Supervision?

OpenAIRE

Duncan Waite

2006-01-01

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...

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

Advanced Music Therapy Supervision Training

DEFF Research Database (Denmark)

Pedersen, Inge Nygaard

2009-01-01

supervision training excerpts live in the workshop will be offered. The workshop will include demonstrating a variety of supervision methods and techniques used in A) post graduate music therapy training programs b) a variety of work contexts such as psychiatry and somatic music psychotherapy. The workshop......The presentation will illustrate training models in supervision for experienced music therapists where transference/counter transference issues are in focus. Musical, verbal and body related tools will be illustrated from supervision practice by the presenters. A possibility to experience small...

Human semi-supervised learning.

Science.gov (United States)

Gibson, Bryan R; Rogers, Timothy T; Zhu, Xiaojin

2013-01-01

Most empirical work in human categorization has studied learning in either fully supervised or fully unsupervised scenarios. Most real-world learning scenarios, however, are semi-supervised: Learners receive a great deal of unlabeled information from the world, coupled with occasional experiences in which items are directly labeled by a knowledgeable source. A large body of work in machine learning has investigated how learning can exploit both labeled and unlabeled data provided to a learner. Using equivalences between models found in human categorization and machine learning research, we explain how these semi-supervised techniques can be applied to human learning. A series of experiments are described which show that semi-supervised learning models prove useful for explaining human behavior when exposed to both labeled and unlabeled data. We then discuss some machine learning models that do not have familiar human categorization counterparts. Finally, we discuss some challenges yet to be addressed in the use of semi-supervised models for modeling human categorization. Copyright © 2013 Cognitive Science Society, Inc.

CATEGORIZATION OF GELAM, ACACIA AND TUALANG HONEY ODORPROFILE USING K-NEAREST NEIGHBORS

Directory of Open Access Journals (Sweden)

Nurdiyana Zahed

2018-02-01

Full Text Available Honey authenticity refer to honey types is of great importance issue and interest in agriculture. In current research, several documents of specific types of honey have their own usage in medical field. However, it is quite challenging task to classify different types of honey by simply using our naked eye. This work demostrated a successful an electronic nose (E-nose application as an instrument for identifying odor profile pattern of three common honey in Malaysia (Gelam, Acacia and Tualang honey. The applied E-nose has produced signal for odor measurement in form of numeric resistance (Ω. The data reading have been pre-processed using normalization technique for standardized scale of unique features. Mean features is extracted and boxplot used as the statistical tool to present the data pattern according to three types of honey. Mean features that have been extracted were employed into K-Nearest Neighbors classifier as an input features and evaluated using several splitting ratio. Excellent results were obtained by showing 100% rate of accuracy, sensitivity and specificity of classification from KNN using weigh (k=1, ratio 90:10 and Euclidean distance. The findings confirmed the ability of KNN classifier as intelligent classification to classify different honey types from E-nose calibration. Outperform of other classifier, KNN required less parameter optimization and achieved promising result.

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective.

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Sang-Bing Tsai

Full Text Available Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established.

Using a Mixed Model to Explore Evaluation Criteria for Bank Supervision: A Banking Supervision Law Perspective

Science.gov (United States)

Tsai, Sang-Bing; Chen, Kuan-Yu; Zhao, Hongrui; Wei, Yu-Min; Wang, Cheng-Kuang; Zheng, Yuxiang; Chang, Li-Chung; Wang, Jiangtao

2016-01-01

Financial supervision means that monetary authorities have the power to supervise and manage financial institutions according to laws. Monetary authorities have this power because of the requirements of improving financial services, protecting the rights of depositors, adapting to industrial development, ensuring financial fair trade, and maintaining stable financial order. To establish evaluation criteria for bank supervision in China, this study integrated fuzzy theory and the decision making trial and evaluation laboratory (DEMATEL) and proposes a fuzzy-DEMATEL model. First, fuzzy theory was applied to examine bank supervision criteria and analyze fuzzy semantics. Second, the fuzzy-DEMATEL model was used to calculate the degree to which financial supervision criteria mutually influenced one another and their causal relationship. Finally, an evaluation criteria model for evaluating bank and financial supervision was established. PMID:27992449

Effective Sequential Classifier Training for SVM-Based Multitemporal Remote Sensing Image Classification

Science.gov (United States)

Guo, Yiqing; Jia, Xiuping; Paull, David

2018-06-01

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study, a SVM-based Sequential Classifier Training (SCT-SVM) approach is proposed for multitemporal remote sensing image classification. The approach leverages the classifiers of previous images to reduce the required number of training samples for the classifier training of an incoming image. For each incoming image, a rough classifier is firstly predicted based on the temporal trend of a set of previous classifiers. The predicted classifier is then fine-tuned into a more accurate position with current training samples. This approach can be applied progressively to sequential image data, with only a small number of training samples being required from each image. Experiments were conducted with Sentinel-2A multitemporal data over an agricultural area in Australia. Results showed that the proposed SCT-SVM achieved better classification accuracies compared with two state-of-the-art model transfer algorithms. When training data are insufficient, the overall classification accuracy of the incoming image was improved from 76.18% to 94.02% with the proposed SCT-SVM, compared with those obtained without the assistance from previous images. These results demonstrate that the leverage of a priori information from previous images can provide advantageous assistance for later images in multitemporal image classification.

Supervision in banking industry

OpenAIRE

Šmída, David

2012-01-01

The aim of submitted thesis Supervision in banking is to define the nature and the importance of banking supervision, to justify its existence and to analyze the applicable mechanisms while the system of banking regulation and supervision in this thesis is primarily examined in the European context, with a focus on the Czech Republic. The thesis is divided into five main chapters. The first chapter is devoted to the financial system and the importance of banks in this system, it defines the c...

17 CFR 166.3 - Supervision.

Science.gov (United States)

2010-04-01

... 17 Commodity and Securities Exchanges 1 2010-04-01 2010-04-01 false Supervision. 166.3 Section 166.3 Commodity and Securities Exchanges COMMODITY FUTURES TRADING COMMISSION CUSTOMER PROTECTION RULES § 166.3 Supervision. Each Commission registrant, except an associated person who has no supervisory duties, must diligently supervise the handling b...

28 CFR 810.1 - Supervision contact requirements.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Supervision contact requirements. 810.1 Section 810.1 Judicial Administration COURT SERVICES AND OFFENDER SUPERVISION AGENCY FOR THE DISTRICT OF COLUMBIA COMMUNITY SUPERVISION: ADMINISTRATIVE SANCTIONS § 810.1 Supervision contact requirements. If you are an offender under supervision by th...

Mentoring, coaching and supervision

OpenAIRE

McMahon, Samantha; Dyer, Mary; Barker, Catherine

2016-01-01

This chapter considers the purpose of coaching, mentoring and supervision in early childhood eduaction and care. It examines a number of different approaches and considers the key skills required for effective coaching, mentoring and supervision.

Empirical study of supervised gene screening

Directory of Open Access Journals (Sweden)

Ma Shuangge

2006-12-01

Full Text Available Abstract Background Microarray studies provide a way of linking variations of phenotypes with their genetic causations. Constructing predictive models using high dimensional microarray measurements usually consists of three steps: (1 unsupervised gene screening; (2 supervised gene screening; and (3 statistical model building. Supervised gene screening based on marginal gene ranking is commonly used to reduce the number of genes in the model building. Various simple statistics, such as t-statistic or signal to noise ratio, have been used to rank genes in the supervised screening. Despite of its extensive usage, statistical study of supervised gene screening remains scarce. Our study is partly motivated by the differences in gene discovery results caused by using different supervised gene screening methods. Results We investigate concordance and reproducibility of supervised gene screening based on eight commonly used marginal statistics. Concordance is assessed by the relative fractions of overlaps between top ranked genes screened using different marginal statistics. We propose a Bootstrap Reproducibility Index, which measures reproducibility of individual genes under the supervised screening. Empirical studies are based on four public microarray data. We consider the cases where the top 20%, 40% and 60% genes are screened. Conclusion From a gene discovery point of view, the effect of supervised gene screening based on different marginal statistics cannot be ignored. Empirical studies show that (1 genes passed different supervised screenings may be considerably different; (2 concordance may vary, depending on the underlying data structure and percentage of selected genes; (3 evaluated with the Bootstrap Reproducibility Index, genes passed supervised screenings are only moderately reproducible; and (4 concordance cannot be improved by supervised screening based on reproducibility.

32 CFR 727.11 - Supervision.

Science.gov (United States)

2010-07-01

... 32 National Defense 5 2010-07-01 2010-07-01 false Supervision. 727.11 Section 727.11 National Defense Department of Defense (Continued) DEPARTMENT OF THE NAVY PERSONNEL LEGAL ASSISTANCE § 727.11 Supervision. The Judge Advocate General will exercise supervision over all legal assistance activities in the Department of the Navy. Subject to the...

Neural-network classifiers for automatic real-world aerial image recognition

Science.gov (United States)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

The vision in supervision: transference-countertransference dynamics and disclosure in the supervision relationship.

Science.gov (United States)

Coburn, W J

1997-01-01

The centrality of the supervision experience in the development of the supervisee's personal and professional capacities is addressed. The supervision relationship and process are explored in light of the potential effects of transference-countertransference configurations of supervisor and supervisee. Parallels between supervision and treatment are highlighted. The importance of developing and utilizing the capacity for reflectivity is reviewed, as is the impact of supervisee nondisclosure to supervisor. The direct use of countertransference experiences in the context of supervision is explored, and the centrality of self-disclosure is highlighted. It is recommended that supervisor and supervisee remain receptive to exploring these experiences in the service of developing a shared subjective sense of the patient, of increasing the supervisee's capacity to treat his or her patient, and of providing the supervisee with a novel, growth-enhancing relationship.

Supervised Learning Applied to Air Traffic Trajectory Classification

Science.gov (United States)

Bosson, Christabelle; Nikoleris, Tasos

2018-01-01

Given the recent increase of interest in introducing new vehicle types and missions into the National Airspace System, a transition towards a more autonomous air traffic control system is required in order to enable and handle increased density and complexity. This paper presents an exploratory effort of the needed autonomous capabilities by exploring supervised learning techniques in the context of aircraft trajectories. In particular, it focuses on the application of machine learning algorithms and neural network models to a runway recognition trajectory-classification study. It investigates the applicability and effectiveness of various classifiers using datasets containing trajectory records for a month of air traffic. A feature importance and sensitivity analysis are conducted to challenge the chosen time-based datasets and the ten selected features. The study demonstrates that classification accuracy levels of 90% and above can be reached in less than 40 seconds of training for most machine learning classifiers when one track data point, described by the ten selected features at a particular time step, per trajectory is used as input. It also shows that neural network models can achieve similar accuracy levels but at higher training time costs.

Advanced recognition of explosives in traces on polymer surfaces using LIBS and supervised learning classifiers.

Science.gov (United States)

Serrano, Jorge; Moros, Javier; Sánchez, Carlos; Macías, Jorge; Laserna, J Javier

2014-01-02

The large similarity existing in the spectral emissions collected from organic compounds by laser-induced breakdown spectroscopy (LIBS) is a limiting factor for the use of this technology in the real world. Specifically, among the most ambitious challenges of today's LIBS involves the recognition of an organic residue when neglected on the surface of an object of identical nature. Under these circumstances, the development of an efficient algorithm to disclose the minute differences within this highly complex spectral information is crucial for a realistic application of LIBS in countering explosive threats. An approach cemented on scatter plots of characteristic emission features has been developed to identify organic explosives when located on polymeric surfaces (teflon, nylon and polyethylene). By using selected spectral variables, the approach allows to design a concise classifier for alerting when one of four explosives (DNT, TNT, RDX and PETN) is present on the surface of the polymer. Ordinary products (butter, fuel oil, hand cream, olive oil and motor oil) cause no confusion in the decisions taken by the classifier. With rates of false negatives and false positives below 5%, results demonstrate that the classification algorithm enables to label residues according to their harmful nature in the most demanding scenario for a LIBS sensor. Copyright © 2013 Elsevier B.V. All rights reserved.

Supervision som undervisningsform i voksenspecialundervisningen

DEFF Research Database (Denmark)

Kristensen, René

2000-01-01

Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne.......Supervision som undervisningsform i voksenspecialundervisningen. Procesarbejde i undervisning af voksne....

Optimal Detection Range of RFID Tag for RFID-based Positioning System Using the k-NN Algorithm

Directory of Open Access Journals (Sweden)

Joon Heo

2009-06-01

Full Text Available Positioning technology to track a moving object is an important and essential component of ubiquitous computing environments and applications. An RFID-based positioning system using the k-nearest neighbor (k-NN algorithm can determine the position of a moving reader from observed reference data. In this study, the optimal detection range of an RFID-based positioning system was determined on the principle that tag spacing can be derived from the detection range. It was assumed that reference tags without signal strength information are regularly distributed in 1-, 2- and 3-dimensional spaces. The optimal detection range was determined, through analytical and numerical approaches, to be 125% of the tag-spacing distance in 1-dimensional space. Through numerical approaches, the range was 134% in 2-dimensional space, 143% in 3-dimensional space.

Multi combined Adlerian supervision in Counseling

OpenAIRE

Gungor, Abdi

2017-01-01

For counselor professional and counselor education, supervision is an important process, in which more experienced professional helps and guides less experienced professional. To provide an effective and beneficial supervision, various therapy, development, or process based approaches and models have been developed. In addition, different eclectic models integrating more than one model have been developed. In this paper, as a supervision model, multi combined Adlerian supervision model is pro...

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition.

Science.gov (United States)

Fong, Simon; Song, Wei; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2017-02-27

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called 'shadow features' are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Directory of Open Access Journals (Sweden)

Simon Fong

2017-02-01

Full Text Available In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

Nursing supervision for care comprehensiveness

Directory of Open Access Journals (Sweden)

Lucieli Dias Pedreschi Chaves

Full Text Available ABSTRACT Objective: To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. Method: A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Results: Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Final considerations: Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks.

Learning Dynamics in Doctoral Supervision

DEFF Research Database (Denmark)

Kobayashi, Sofie

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...... of different theoretical frameworks from the perspectives of learning as individual acquisition and a sociocultural perspective on learning contributed to a nuanced illustration of the otherwise implicit practices of supervision....

Supervision and group dynamics

DEFF Research Database (Denmark)

Hansen, Søren; Jensen, Lars Peter

2004-01-01

 An important aspect of the problem based and project organized study at Aalborg University is the supervision of the project groups. At the basic education (first year) it is stated in the curriculum that part of the supervisors' job is to deal with group dynamics. This is due to the experience...... that many students are having difficulties with practical issues such as collaboration, communication, and project management. Most supervisors either ignore this demand, because they do not find it important or they find it frustrating, because they do not know, how to supervise group dynamics...... as well as at Aalborg University. The first visible result has been participating supervisors telling us that the course has inspired them to try supervising group dynamics in the future. This paper will explore some aspects of supervising group dynamics as well as, how to develop the Aalborg model...

Exploring Land Use and Land Cover of Geotagged Social-Sensing Images Using Naive Bayes Classifier

Directory of Open Access Journals (Sweden)

Asamaporn Sitthi

2016-09-01

Full Text Available Online social media crowdsourced photos contain a vast amount of visual information about the physical properties and characteristics of the earth’s surface. Flickr is an important online social media platform for users seeking this information. Each day, users generate crowdsourced geotagged digital imagery containing an immense amount of information. In this paper, geotagged Flickr images are used for automatic extraction of low-level land use/land cover (LULC features. The proposed method uses a naive Bayes classifier with color, shape, and color index descriptors. The classified images are mapped using a majority filtering approach. The classifier performance in overall accuracy, kappa coefficient, precision, recall, and f-measure was 87.94%, 82.89%, 88.20%, 87.90%, and 88%, respectively. Labeled-crowdsourced images were filtered into a spatial tile of a 30 m × 30 m resolution using the majority voting method to reduce geolocation uncertainty from the crowdsourced data. These tile datasets were used as training and validation samples to classify Landsat TM5 images. The supervised maximum likelihood method was used for the LULC classification. The results show that the geotagged Flickr images can classify LULC types with reasonable accuracy and that the proposed approach improves LULC classification efficiency if a sufficient spatial distribution of crowdsourced data exists.

Conditional High-Order Boltzmann Machines for Supervised Relation Learning.

Science.gov (United States)

Huang, Yan; Wang, Wei; Wang, Liang; Tan, Tieniu

2017-09-01

Relation learning is a fundamental problem in many vision tasks. Recently, high-order Boltzmann machine and its variants have shown their great potentials in learning various types of data relation in a range of tasks. But most of these models are learned in an unsupervised way, i.e., without using relation class labels, which are not very discriminative for some challenging tasks, e.g., face verification. In this paper, with the goal to perform supervised relation learning, we introduce relation class labels into conventional high-order multiplicative interactions with pairwise input samples, and propose a conditional high-order Boltzmann Machine (CHBM), which can learn to classify the data relation in a binary classification way. To be able to deal with more complex data relation, we develop two improved variants of CHBM: 1) latent CHBM, which jointly performs relation feature learning and classification, by using a set of latent variables to block the pathway from pairwise input samples to output relation labels and 2) gated CHBM, which untangles factors of variation in data relation, by exploiting a set of latent variables to multiplicatively gate the classification of CHBM. To reduce the large number of model parameters generated by the multiplicative interactions, we approximately factorize high-order parameter tensors into multiple matrices. Then, we develop efficient supervised learning algorithms, by first pretraining the models using joint likelihood to provide good parameter initialization, and then finetuning them using conditional likelihood to enhance the discriminant ability. We apply the proposed models to a series of tasks including invariant recognition, face verification, and action similarity labeling. Experimental results demonstrate that by exploiting supervised relation labels, our models can greatly improve the performance.

Clinical Supervision in Denmark

DEFF Research Database (Denmark)

Jacobsen, Claus Haugaard

2011-01-01

Core Questionnaire (DPCCQ) has only few questions on supervision. To rectify this limitation, a recent Danish version of the DPCCQ included two new sections on supervision, one focusing on supervisees and another on supervisors and their supervisory training. This paper presents our initial findings...

Networks of Professional Supervision

Science.gov (United States)

Annan, Jean; Ryba, Ken

2013-01-01

An ecological analysis of the supervisory activity of 31 New Zealand school psychologists examined simultaneously the theories of school psychology, supervision practices, and the contextual qualities that mediated participants' supervisory actions. The findings indicated that the school psychologists worked to achieve the supervision goals of…

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed

2018-04-08

Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

SemiBoost: boosting for semi-supervised learning.

Science.gov (United States)

Mallapragada, Pavan Kumar; Jin, Rong; Jain, Anil K; Liu, Yi

2009-11-01

Semi-supervised learning has attracted a significant amount of attention in pattern recognition and machine learning. Most previous studies have focused on designing special algorithms to effectively exploit the unlabeled data in conjunction with labeled data. Our goal is to improve the classification accuracy of any given supervised learning algorithm by using the available unlabeled examples. We call this as the Semi-supervised improvement problem, to distinguish the proposed approach from the existing approaches. We design a metasemi-supervised learning algorithm that wraps around the underlying supervised algorithm and improves its performance using unlabeled data. This problem is particularly important when we need to train a supervised learning algorithm with a limited number of labeled examples and a multitude of unlabeled examples. We present a boosting framework for semi-supervised learning, termed as SemiBoost. The key advantages of the proposed semi-supervised learning approach are: 1) performance improvement of any supervised learning algorithm with a multitude of unlabeled data, 2) efficient computation by the iterative boosting algorithm, and 3) exploiting both manifold and cluster assumption in training classification models. An empirical study on 16 different data sets and text categorization demonstrates that the proposed framework improves the performance of several commonly used supervised learning algorithms, given a large number of unlabeled examples. We also show that the performance of the proposed algorithm, SemiBoost, is comparable to the state-of-the-art semi-supervised learning algorithms.

Coupled dimensionality reduction and classification for supervised and semi-supervised multilabel learning.

Science.gov (United States)

Gönen, Mehmet

2014-03-01

Coupled training of dimensionality reduction and classification is proposed previously to improve the prediction performance for single-label problems. Following this line of research, in this paper, we first introduce a novel Bayesian method that combines linear dimensionality reduction with linear binary classification for supervised multilabel learning and present a deterministic variational approximation algorithm to learn the proposed probabilistic model. We then extend the proposed method to find intrinsic dimensionality of the projected subspace using automatic relevance determination and to handle semi-supervised learning using a low-density assumption. We perform supervised learning experiments on four benchmark multilabel learning data sets by comparing our method with baseline linear dimensionality reduction algorithms. These experiments show that the proposed approach achieves good performance values in terms of hamming loss, average AUC, macro F 1 , and micro F 1 on held-out test data. The low-dimensional embeddings obtained by our method are also very useful for exploratory data analysis. We also show the effectiveness of our approach in finding intrinsic subspace dimensionality and semi-supervised learning tasks.

A new feature constituting approach to detection of vocal fold pathology

Science.gov (United States)

Hariharan, M.; Polat, Kemal; Yaacob, Sazali

2014-08-01

In the last two decades, non-invasive methods through acoustic analysis of voice signal have been proved to be excellent and reliable tool to diagnose vocal fold pathologies. This paper proposes a new feature vector based on the wavelet packet transform and singular value decomposition for the detection of vocal fold pathology. k-means clustering based feature weighting is proposed to increase the distinguishing performance of the proposed features. In this work, two databases Massachusetts Eye and Ear Infirmary (MEEI) voice disorders database and MAPACI speech pathology database are used. Four different supervised classifiers such as k-nearest neighbour (k-NN), least-square support vector machine, probabilistic neural network and general regression neural network are employed for testing the proposed features. The experimental results uncover that the proposed features give very promising classification accuracy of 100% for both MEEI database and MAPACI speech pathology database.

Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

Science.gov (United States)

Vasuki, Perumal; Roomi, S. Mohamed Mansoor

2013-01-01

Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

Educational Supervision Appropriate for Psychiatry Trainee's Needs

Science.gov (United States)

Rele, Kiran; Tarrant, C. Jane

2010-01-01

Objective: The authors studied the regularity and content of supervision sessions in one of the U.K. postgraduate psychiatric training schemes (Mid-Trent). Methods: A questionnaire sent to psychiatry trainees assessed the timing and duration of supervision, content and protection of supervision time, and overall quality of supervision. The authors…

Tværfaglig supervision

DEFF Research Database (Denmark)

Tværfaglig supervision dækker over supervision af forskellige faggrupper. Det er en kompleks disciplin der stiller store krav tl supervisor. Bogens første del præsenterer fire faglige supervisionsmodeller: En almen, en psykodynamisk, en kognitiv adfærdsterapeutisk og en narrativ. Anden del...

Methods of Feminist Family Therapy Supervision.

Science.gov (United States)

Prouty, Anne M.; Thomas, Volker; Johnson, Scott; Long, Janie K.

2001-01-01

Presents three supervision methods which emerged from a qualitative study of the experiences of feminist family therapy supervisors and the therapists they supervised: the supervision contract, collaborative methods, and hierarchical methods. Provides a description of the participants' experiences of these methods and discusses their fit with…

New approach to detect and classify stroke in skull CT images via analysis of brain tissue densities.

Science.gov (United States)

Rebouças Filho, Pedro P; Sarmento, Róger Moura; Holanda, Gabriel Bandeira; de Alencar Lima, Daniel

2017-09-01

Cerebral vascular accident (CVA), also known as stroke, is an important health problem worldwide and it affects 16 million people worldwide every year. About 30% of those that have a stroke die and 40% remain with serious physical limitations. However, recovery in the damaged region is possible if treatment is performed immediately. In the case of a stroke, Computed Tomography (CT) is the most appropriate technique to confirm the occurrence and to investigate its extent and severity. Stroke is an emergency problem for which early identification and measures are difficult; however, computer-aided diagnoses (CAD) can play an important role in obtaining information imperceptible to the human eye. Thus, this work proposes a new method for extracting features based on radiological density patterns of the brain, called Analysis of Brain Tissue Density (ABTD). The proposed method is a specific approach applied to CT images to identify and classify the occurrence of stroke diseases. The evaluation of the results of the ABTD extractor proposed in this paper were compared with extractors already established in the literature, such as features from Gray-Level Co-Occurrence Matrix (GLCM), Local binary patterns (LBP), Central Moments (CM), Statistical Moments (SM), Hu's Moment (HM) and Zernike's Moments (ZM). Using a database of 420 CT images of the skull, each extractor was applied with the classifiers such as MLP, SVM, kNN, OPF and Bayesian to classify if a CT image represented a healthy brain or one with an ischemic or hemorrhagic stroke. ABTD had the shortest extraction time and the highest average accuracy (99.30%) when combined with OPF using the Euclidean distance. Also, the average accuracy values for all classifiers were higher than 95%. The relevance of the results demonstrated that the ABTD method is a useful algorithm to extract features that can potentially be integrated with CAD systems to assist in stroke diagnosis. Copyright © 2017 Elsevier B.V. All rights

28 CFR 2.91 - Supervision responsibility.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision responsibility. 2.91 Section 2.91 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Code: Prisoners and Parolees § 2.91 Supervision responsibility. (a) Pursuan...

Closing the loop: from paper to protein annotation using supervised Gene Ontology classification.

Science.gov (United States)

Gobeill, Julien; Pasche, Emilie; Vishnyakova, Dina; Ruch, Patrick

2014-01-01

Gene function curation of the literature with Gene Ontology (GO) concepts is one particularly time-consuming task in genomics, and the help from bioinformatics is highly requested to keep up with the flow of publications. In 2004, the first BioCreative challenge already designed a task of automatic GO concepts assignment from a full text. At this time, results were judged far from reaching the performances required by real curation workflows. In particular, supervised approaches produced the most disappointing results because of lack of training data. Ten years later, the available curation data have massively grown. In 2013, the BioCreative IV GO task revisited the automatic GO assignment task. For this issue, we investigated the power of our supervised classifier, GOCat. GOCat computes similarities between an input text and already curated instances contained in a knowledge base to infer GO concepts. The subtask A consisted in selecting GO evidence sentences for a relevant gene in a full text. For this, we designed a state-of-the-art supervised statistical approach, using a naïve Bayes classifier and the official training set, and obtained fair results. The subtask B consisted in predicting GO concepts from the previous output. For this, we applied GOCat and reached leading results, up to 65% for hierarchical recall in the top 20 outputted concepts. Contrary to previous competitions, machine learning has this time outperformed standard dictionary-based approaches. Thanks to BioCreative IV, we were able to design a complete workflow for curation: given a gene name and a full text, this system is able to select evidence sentences for curation and to deliver highly relevant GO concepts. Contrary to previous competitions, machine learning this time outperformed dictionary-based systems. Observed performances are sufficient for being used in a real semiautomatic curation workflow. GOCat is available at http://eagl.unige.ch/GOCat/. http://eagl.unige.ch/GOCat4FT/. �

20 CFR 655.30 - Supervised recruitment.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Supervised recruitment. 655.30 Section 655.30... Workers) § 655.30 Supervised recruitment. (a) Supervised recruitment. Where an employer is found to have... failed to adequately conduct recruitment activities or failed in any obligation of this part, the CO may...

Man-machine supervision

International Nuclear Information System (INIS)

Montmain, J.

2005-01-01

Today's complexity of systems where man is involved has led to the development of more and more sophisticated information processing systems where decision making has become more and more difficult. The operator task has moved from operation to supervision and the production tool has become indissociable from its numerical instrumentation and control system. The integration of more and more numerous and sophisticated control indicators in the control room does not necessary fulfill the expectations of the operation team. It is preferable to develop cooperative information systems which are real situation understanding aids. The stake is not the automation of operators' cognitive tasks but the supply of a reasoning help. One of the challenges of interactive information systems is the selection, organisation and dynamical display of information. The efficiency of the whole man-machine system depends on the communication interface efficiency. This article presents the principles and specificities of man-machine supervision systems: 1 - principle: operator's role in control room, operator and automation, monitoring and diagnosis, characteristics of useful models for supervision; 2 - qualitative reasoning: origin, trends, evolutions; 3 - causal reasoning: causality, causal graph representation, causal and diagnostic graph; 4 - multi-points of view reasoning: multi flow modeling method, Sagace method; 5 - approximate reasoning: the symbolic numerical interface, the multi-criteria decision; 6 - example of application: supervision in a spent-fuel reprocessing facility. (J.S.)

Evolution in banking supervision

OpenAIRE

Edward J. Stevens

2000-01-01

Banking supervision must keep pace with technical innovations in the banking industry. The international Basel Committee on Banking Supervision currently is reviewing public comments on its proposed new method for judging whether a bank maintains enough capital to absorb unexpected losses. This Economic Commentary explains how existing standards became obsolete and describes the new plan.

Projected estimators for robust semi-supervised classification

DEFF Research Database (Denmark)

Krijthe, Jesse H.; Loog, Marco

2017-01-01

For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the procedure...... specifically, we prove that, measured on the labeled and unlabeled training data, this semi-supervised procedure never gives a lower quadratic loss than the supervised alternative. To our knowledge this is the first approach that offers such strong, albeit conservative, guarantees for improvement over...... the supervised solution. The characteristics of our approach are explicated using benchmark datasets to further understand the similarities and differences between the quadratic loss criterion used in the theoretical results and the classification accuracy typically considered in practice....

An Accurate CT Saturation Classification Using a Deep Learning Approach Based on Unsupervised Feature Extraction and Supervised Fine-Tuning Strategy

Directory of Open Access Journals (Sweden)

Muhammad Ali

2017-11-01

Full Text Available Current transformer (CT saturation is one of the significant problems for protection engineers. If CT saturation is not tackled properly, it can cause a disastrous effect on the stability of the power system, and may even create a complete blackout. To cope with CT saturation properly, an accurate detection or classification should be preceded. Recently, deep learning (DL methods have brought a subversive revolution in the field of artificial intelligence (AI. This paper presents a new DL classification method based on unsupervised feature extraction and supervised fine-tuning strategy to classify the saturated and unsaturated regions in case of CT saturation. In other words, if protection system is subjected to a CT saturation, proposed method will correctly classify the different levels of saturation with a high accuracy. Traditional AI methods are mostly based on supervised learning and rely heavily on human crafted features. This paper contributes to an unsupervised feature extraction, using autoencoders and deep neural networks (DNNs to extract features automatically without prior knowledge of optimal features. To validate the effectiveness of proposed method, a variety of simulation tests are conducted, and classification results are analyzed using standard classification metrics. Simulation results confirm that proposed method classifies the different levels of CT saturation with a remarkable accuracy and has unique feature extraction capabilities. Lastly, we provided a potential future research direction to conclude this paper.

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...

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.

Current Risk Management Practices in Psychotherapy Supervision.

Science.gov (United States)

Mehrtens, Ilayna K; Crapanzano, Kathleen; Tynes, L Lee

2017-12-01

Psychotherapy competence is a core skill for psychiatry residents, and psychotherapy supervision is a time-honored approach to teaching this skill. To explore the current supervision practices of psychiatry training programs, a 24-item questionnaire was sent to all program directors of Accreditation Council for Graduate Medical Education (ACGME)-approved adult psychiatry programs. The questionnaire included items regarding adherence to recently proposed therapy supervision practices aimed at reducing potential liability risk. The results suggested that current therapy supervision practices do not include sufficient management of the potential liability involved in therapy supervision. Better protections for patients, residents, supervisors and the institutions would be possible with improved credentialing practices and better documentation of informed consent and supervision policies and procedures. © 2017 American Academy of Psychiatry and the Law.

A Study of Supervision of China's Commercial Banks from the Perspective of the Trinity-Characteristics of Bank Supervision System

Institute of Scientific and Technical Information of China (English)

LV Jianglin; HUANG Guang

2015-01-01

Based on the theoretical analysis,this paper applies the entropy method to establish a comprehensive index system for the evaluation of the overall level of risk control and comprehensive efficiency of the supervision of China's commercial banks.Considering the trinity-characteristics of bank supervision system consisting of the People's Bank of China(PBC),the CBRC and the financial offices of local governments,the following conclusions have been drawn:the amount of penalties on banking illegal transactions is not correlated with the supervision efficiency of China's commercial banks;the capital adequacy ratio,the loan to deposit ratio,the percentage point of the non-performing loan rate of urban commercial banks higher than that of the national joint-stock banks are negatively correlated with the supervision efficiency of China 's commercial banks;the total asset variation of the PBC and the different loan balance in local and foreign currency of the banks are positively correlated with the supervision efficiency of China's commercial banks,but the effect is minor.Therefore,China should give the capital adequacy ratio a full play in the bank supervision,accelerate the construction of supervision information system and improve the supervision function of the local governments.

Distance-Based Image Classification: Generalizing to New Classes at Near Zero Cost

NARCIS (Netherlands)

Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.

2013-01-01

We study large-scale image classification methods that can incorporate new classes and training images continuously over time at negligible cost. To this end, we consider two distance-based classifiers, the k-nearest neighbor (k-NN) and nearest class mean (NCM) classifiers, and introduce a new

Alignment of Custom Standards by Machine Learning Algorithms

Directory of Open Access Journals (Sweden)

Adela Sirbu

2010-09-01

Full Text Available Building an efficient model for automatic alignment of terminologies would bring a significant improvement to the information retrieval process. We have developed and compared two machine learning based algorithms whose aim is to align 2 custom standards built on a 3 level taxonomy, using kNN and SVM classifiers that work on a vector representation consisting of several similarity measures. The weights utilized by the kNN were optimized with an evolutionary algorithm, while the SVM classifier's hyper-parameters were optimized with a grid search algorithm. The database used for train was semi automatically obtained by using the Coma++ tool. The performance of our aligners is shown by the results obtained on the test set.

Supervision Experiences of Professional Counselors Providing Crisis Counseling

Science.gov (United States)

Dupre, Madeleine; Echterling, Lennis G.; Meixner, Cara; Anderson, Robin; Kielty, Michele

2014-01-01

In this phenomenological study, the authors explored supervision experiences of 13 licensed professional counselors in situations requiring crisis counseling. Five themes concerning crisis and supervision were identified from individual interviews. Findings support intensive, immediate crisis supervision and postlicensure clinical supervision.

Intelligent multivariate process supervision

International Nuclear Information System (INIS)

Visuri, Pertti.

1986-01-01

This thesis addresses the difficulties encountered in managing large amounts of data in supervisory control of complex systems. Some previous alarm and disturbance analysis concepts are reviewed and a method for improving the supervision of complex systems is presented. The method, called multivariate supervision, is based on adding low level intelligence to the process control system. By using several measured variables linked together by means of deductive logic, the system can take into account the overall state of the supervised system. Thus, it can present to the operators fewer messages with higher information content than the conventional control systems which are based on independent processing of each variable. In addition, the multivariate method contains a special information presentation concept for improving the man-machine interface. (author)

On psychoanalytic supervision as signature pedagogy.

Science.gov (United States)

Watkins, C Edward

2014-04-01

What is signature pedagogy in psychoanalytic education? This paper examines that question, considering why psychoanalytic supervision best deserves that designation. In focusing on supervision as signature pedagogy, I accentuate its role in building psychoanalytic habits of mind, habits of hand, and habits of heart, and transforming theory and self-knowledge into practical product. Other facets of supervision as signature pedagogy addressed in this paper include its features of engagement, uncertainty, formation, and pervasiveness, as well as levels of surface, deep, and implicit structure. Epistemological, ontological, and axiological in nature, psychoanalytic supervision engages trainees in learning to do, think, and value what psychoanalytic practitioners in the field do, think, and value: It is, most fundamentally, professional preparation for competent, "good work." In this paper, effort is made to shine a light on and celebrate the pivotal role of supervision in "making" or developing budding psychoanalysts and psychoanalytic psychotherapists. Now over a century old, psychoanalytic supervision remains unparalleled in (1) connecting and integrating conceptualization and practice, (2) transforming psychoanalytic theory and self-knowledge into an informed analyzing instrument, and (3) teaching, transmitting, and perpetuating the traditions, practice, and culture of psychoanalytic treatment.

Abusive Supervision and Subordinate Performance : Instrumentality Considerations in the Emergence and Consequences of Abusive Supervision

NARCIS (Netherlands)

Walter, Frank; Lam, Catherine K.; van der Vegt, Geert; Huang, X.; Miao, Q.

Drawing from moral exclusion theory, this article examines outcome dependence and interpersonal liking as key boundary conditions for the linkage between perceived subordinate performance and abusive supervision. Moreover, it investigates the role of abusive supervision for subordinates' subsequent,

Supervision of radiation environment management of nuclear facilities

International Nuclear Information System (INIS)

Luo Mingyan

2013-01-01

Through literature and documents, the basis, content and implementation of the supervision of radiation environment management of nuclear facilities were defined. Such supervision was extensive and complicated with various tasks and overlapping duties, and had large social impact. Therefore, it was recommend to make further research on this supervision should be done, clarify and specify responsibilities of the executor of the supervision so as to achieve institutionalization, standardization and routinization of the supervision. (author)

Skærpet bevidsthed om supervision

DEFF Research Database (Denmark)

Pedersen, Inge Nygaard

2002-01-01

This article presents a historical survey of the initiatives which have taken place in european music therapy towards developing a deeper consciousness about supervision. Supervision as a disciplin in music therapy training, as a maintenance of music therapy profession and as a postgraduate...... training for examined music therapists. Definitions are presented and methods developed by working groups in european music therapy supervision are presented....

Human Supervision of Multiple Autonomous Vehicles

Science.gov (United States)

2013-03-22

AFRL-RH-WP-TR-2013-0143 HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES Heath A. Ruff Ball...REPORT TYPE Interim 3. DATES COVERED (From – To) 09-16-08 – 03-22-13 4. TITLE AND SUBTITLE HUMAN SUPERVISION OF MULTIPLE AUTONOMOUS VEHICLES 5a...Supervision of Multiple Autonomous Vehicles To support the vision of a system that enables a single operator to control multiple next-generation

Providing effective supervision in clinical neuropsychology.

Science.gov (United States)

Stucky, Kirk J; Bush, Shane; Donders, Jacobus

2010-01-01

A specialty like clinical neuropsychology is shaped by its selection of trainees, educational standards, expected competencies, and the structure of its training programs. The development of individual competency in this specialty is dependent to a considerable degree on the provision of competent supervision to its trainees. In clinical neuropsychology, as in other areas of professional health-service psychology, supervision is the most frequently used method for teaching a variety of skills, including assessment, report writing, differential diagnosis, and treatment. Although much has been written about the provision of quality supervision in clinical and counseling psychology, very little published guidance is available regarding the teaching and provision of supervision in clinical neuropsychology. The primary focus of this article is to provide a framework and guidance for the development of suggested competency standards for training of neuropsychological supervisors, particularly at the residency level. In this paper we outline important components of supervision for neuropsychology trainees and suggest ways in which clinicians can prepare for supervisory roles. Similar to Falender and Shafranske (2004), we propose a competency-based approach to supervision that advocates for a science-informed, formalized, and objective process that clearly delineates the competencies required for good supervisory practice. As much as possible, supervisory competencies are related to foundational and functional competencies in professional psychology, as well as recent legislative initiatives mandating training in supervision. It is our hope that this article will foster further discussion regarding this complex topic, and eventually enhance training in clinical neuropsychology.

BRONCHIAL ASTHMA SUPERVISION AMONG TEENAGERS

Directory of Open Access Journals (Sweden)

N.M. Nenasheva

2008-01-01

Full Text Available The article highlights the results of the act test based bronchial asthma supervision evaluation among teenagers and defines the interrelation of the objective and subjective asthma supervision parameters. The researchers examined 214 male teenagers aged from 16 to 18, suffering from the bronchial asthma, who were sent to the allergy department to verify the diagnosis. Bronchial asthma supervision evaluation was assisted by the act test. The research has showed that over a half (56% of teenagers, suffering from mild bronchial asthma, mention its un control course, do not receive any adequate pharmacotherapy and are consequently a risk group in terms of the bronchial asthma exacerbation. Act test results correlate with the functional indices (fev1, as well as with the degree of the bronchial hyperresponsiveness, which is one of the markers of an allergic inflammation in the lower respiratory passages.Key words: bronchial asthma supervision, act test, teenagers.

Multicultural Supervision: What Difference Does Difference Make?

Science.gov (United States)

Eklund, Katie; Aros-O'Malley, Megan; Murrieta, Imelda

2014-01-01

Multicultural sensitivity and competency represent critical components to contemporary practice and supervision in school psychology. Internship and supervision experiences are a capstone experience for many new school psychologists; however, few receive formal training and supervision in multicultural competencies. As an increased number of…

Projected estimators for robust semi-supervised classification

NARCIS (Netherlands)

Krijthe, J.H.; Loog, M.

2017-01-01

For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification using the well-known quadratic surrogate loss function. Unlike other approaches to semi-supervised learning, the

28 CFR 2.207 - Supervision reports to Commission.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision reports to Commission. 2.207 Section 2.207 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Supervised Releasees § 2.207 Supervision reports to Commission. A...

Postgraduate research supervision in a socially distributed ...

African Journals Online (AJOL)

Postgraduate supervision is a higher education practice with a long history. Through the conventional "apprenticeship" model postgraduate supervision has served as an important vehicle of intellectual inheritance between generations. However, this model of supervision has come under scrutiny as a consequence of the ...

Supervision Experiences of New Professional School Counselors

Science.gov (United States)

Bultsma, Shawn A.

2012-01-01

This qualitative study examined the supervision experiences of 11 new professional school counselors. They reported that their supervision experiences were most often administrative in nature; reports of clinical and developmental supervision were limited to participants whose supervisors were licensed as professional counselors. In addition,…

Online supervision at the university

DEFF Research Database (Denmark)

Bengtsen, Søren Smedegaard; Jensen, Gry Sandholm

2015-01-01

supervision proves unhelpful when trying to understand how online supervision and feedback is a pedagogical phenomenon in its own right, and irreducible to the face-to-face context. Secondly we show that not enough attention has been given to the way different digital tools and platforms influence...... pedagogy we forge a new concept of “format supervision” that enables supervisors to understand and reflect their supervision practice, not as caught in the physical-virtual divide, but as a choice between face-to-face and online formats that each conditions the supervisory dialogue in their own particular...

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. © 2011 American Association for Marriage and Family Therapy.

Optimum supervision intervals and order of supervision in nuclear reactor protective systems

International Nuclear Information System (INIS)

Kontoleon, J.M.

1978-01-01

The optimum inspection strategy of an m-out-of-n:G nuclear reactor protective system with nonidentical units is analyzed. A 2-out-of-4:G system is used to formulate a multi-variable optimization problem to determine (a) the optimum order of supervision of the units and (b) the optimum supervision intervals between units. The case of systems with identical units is a special case of the above. Numerical results are derived using a computer algorithm

Safety supervision on high-pressure gas regulations

International Nuclear Information System (INIS)

Lee, Won Il

1991-01-01

The first part lists the regulation on safety supervision of high-pressure gas, enforcement ordinance on high-pressure gas safety supervision and enforcement regulations about high-pressure gas safety supervision. The second part indicates safety regulations on liquefied petroleum gas and business, enforcement ordinance of safety on liquefied petroleum gas and business, enforcement regulation of safety supervision over liquefied petroleum gas and business. The third part lists regulation on gas business, enforcement ordinance and enforcement regulations on gas business. Each part has theory and explanation for questions.

Nuclear safety culture and nuclear safety supervision

International Nuclear Information System (INIS)

Chai Jianshe

2013-01-01

In this paper, the author reviews systematically and summarizes up the development process and stage characteristics of nuclear safety culture, analysis the connotation and characteristics of nuclear safety culture, sums up the achievements of our country's nuclear safety supervision, dissects the challenges and problems of nuclear safety supervision. This thesis focused on the relationship between nuclear safety culture and nuclear safety supervision, they are essential differences, but there is a close relationship. Nuclear safety supervision needs to introduce some concepts of nuclear safety culture, lays emphasis on humanistic care and improves its level and efficiency. Nuclear safety supervision authorities must strengthen nuclear safety culture training, conduct the development of nuclear safety culture, make sure that nuclear safety culture can play significant roles. (author)

A numeric comparison of variable selection algorithms for supervised learning

International Nuclear Information System (INIS)

Palombo, G.; Narsky, I.

2009-01-01

Datasets in modern High Energy Physics (HEP) experiments are often described by dozens or even hundreds of input variables. Reducing a full variable set to a subset that most completely represents information about data is therefore an important task in analysis of HEP data. We compare various variable selection algorithms for supervised learning using several datasets such as, for instance, imaging gamma-ray Cherenkov telescope (MAGIC) data found at the UCI repository. We use classifiers and variable selection methods implemented in the statistical package StatPatternRecognition (SPR), a free open-source C++ package developed in the HEP community ( (http://sourceforge.net/projects/statpatrec/)). For each dataset, we select a powerful classifier and estimate its learning accuracy on variable subsets obtained by various selection algorithms. When possible, we also estimate the CPU time needed for the variable subset selection. The results of this analysis are compared with those published previously for these datasets using other statistical packages such as R and Weka. We show that the most accurate, yet slowest, method is a wrapper algorithm known as generalized sequential forward selection ('Add N Remove R') implemented in SPR.

Exploring paraprofessional and classroom factors affecting teacher supervision.

Science.gov (United States)

Irvin, Dwight W; Ingram, Paul; Huffman, Jonathan; Mason, Rose; Wills, Howard

2018-02-01

Paraprofessionals serve a primary role in supporting students with disabilities in the classroom, which necessitates teachers' supervision as a means to improve their practice. Yet, little is known regarding what factors affect teacher supervision. We sought to identify how paraprofessional competence and classroom type affected the levels of teacher direction. We administered an adapted version of the Paraprofessional Needs, Knowledge & Tasks Survey and the Survey for Teachers Supervising Paraprofessionals to teachers supervising paraprofessionals in elementary schools. Structural Equation Modeling was used to examine the link between paraprofessional competence and classroom factors affecting the level of teacher supervision. Our results indicated that when teachers perceived paraprofessionals as being more skilled, they provided more supervision, and when more supervision was provided the less they thought paraprofessionals should be doing their assigned tasks. Additionally, paraprofessionals working in classrooms with more students with mild disabilities received less supervision than paraprofessionals working in classrooms with more students with moderate-to-severe disabilities. Those paraprofessionals in classrooms serving mostly children with mild disabilities were also perceived as having lower levels of skill competence than those serving in classrooms with students with more moderate-to-severe disabilities. By understanding the factors that affect teacher supervision, policy and professional development opportunities can be refined/developed to better support both supervising teachers and paraprofessionals and, in turn, improve the outcomes of children with disabilities. Copyright © 2017 Elsevier Ltd. All rights reserved.

Abusive Supervision Scale Development in Indonesia

OpenAIRE

Wulani, Fenika; Purwanto, Bernadinus M; Handoko, Hani

2014-01-01

The purpose of this study was to develop a scale of abusive supervision in Indonesia. The study was conducted with a different context and scale development method from Tepper’s (2000) abusive supervision scale. The abusive supervision scale from Tepper (2000) was developed in the U.S., which has a cultural orientation of low power distance. The current study was conducted in Indonesia, which has a high power distance. This study used interview procedures to obtain information about superviso...

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.

Intuitive expertise in ICT graduate supervision

Directory of Open Access Journals (Sweden)

Jill Jameson

2002-12-01

Full Text Available Intuitive expertise in the application of advanced interdisciplinary facilitation is the subject of this personal reflection on the graduate supervisory style of Professor David Squires in computers in education. This single-case reflective study examines the characteristics of effective supervision observed during masters and doctoral supervision at King's College in the years 1990-9. Interdisciplinarity in ICT graduate studies particularly requires a fluency of supervisory expertise in enabling supervisees to combine multiple complex perspectives from a number of fields of knowledge. Intuitive combinatory aspects of supervision are highlighted in this reflection on the role carried out by an academic expert in facilitating student success. This is examined from a perspective incorporating affective as well as intellectual elements, informed by characteristics identified in professional sports and performing arts coaching/mentoring. Key characteristics comprising a model of intuitive expertise in ICT graduate supervision were outlined. The resultant portrait aims to complement existing literature on graduate supervision, with reference to the field of ICTI computers in education relating to student hypermedia composition.

46 CFR 131.420 - Manning and supervision.

Science.gov (United States)

2010-10-01

... 46 Shipping 4 2010-10-01 2010-10-01 false Manning and supervision. 131.420 Section 131.420 Shipping COAST GUARD, DEPARTMENT OF HOMELAND SECURITY (CONTINUED) OFFSHORE SUPPLY VESSELS OPERATIONS Sufficiency and Supervision of Crew of Survival Craft § 131.420 Manning and supervision. (a) There must be enough trained persons aboard each survival craf...

32 CFR 631.3 - Supervision.

Science.gov (United States)

2010-07-01

... 32 National Defense 4 2010-07-01 2010-07-01 true Supervision. 631.3 Section 631.3 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY (CONTINUED) LAW ENFORCEMENT AND CRIMINAL INVESTIGATIONS ARMED FORCES DISCIPLINARY CONTROL BOARDS AND OFF-INSTALLATION LIAISON AND OPERATIONS General § 631.3 Supervision. The following will...

Supervised retinal vessel segmentation from color fundus images based on matched filtering and AdaBoost classifier.

Directory of Open Access Journals (Sweden)

Nogol Memari

Full Text Available The structure and appearance of the blood vessel network in retinal fundus images is an essential part of diagnosing various problems associated with the eyes, such as diabetes and hypertension. In this paper, an automatic retinal vessel segmentation method utilizing matched filter techniques coupled with an AdaBoost classifier is proposed. The fundus image is enhanced using morphological operations, the contrast is increased using contrast limited adaptive histogram equalization (CLAHE method and the inhomogeneity is corrected using Retinex approach. Then, the blood vessels are enhanced using a combination of B-COSFIRE and Frangi matched filters. From this preprocessed image, different statistical features are computed on a pixel-wise basis and used in an AdaBoost classifier to extract the blood vessel network inside the image. Finally, the segmented images are postprocessed to remove the misclassified pixels and regions. The proposed method was validated using publicly accessible Digital Retinal Images for Vessel Extraction (DRIVE, Structured Analysis of the Retina (STARE and Child Heart and Health Study in England (CHASE_DB1 datasets commonly used for determining the accuracy of retinal vessel segmentation methods. The accuracy of the proposed segmentation method was comparable to other state of the art methods while being very close to the manual segmentation provided by the second human observer with an average accuracy of 0.972, 0.951 and 0.948 in DRIVE, STARE and CHASE_DB1 datasets, respectively.

Online supervision at the university - A comparative study of supervision on student assignments face-to-face and online

Directory of Open Access Journals (Sweden)

Søren Smedegaard Bengtsen

2015-09-01

Full Text Available Through an empirical study of supervision on student assignments at the university across face-to-face and online settings, we show firstly the limiting implications of traditional dichotomies between face-to-face and online supervision. Secondly we show that more attention must be given to the way different digital tools influence the supervisory dialogue. These findings illustrate a form of ‘torn pedagogy’; that online tools and platforms destabilize and tear traditional understandings of supervision pedagogy apart. Also we forge a new concept of “format supervision” that enables supervisors to understand and reflect their supervision practice as a deliberate choice between face-to-face and online formats.

Online supervision at the university - A comparative study of supervision on student assignments face-to-face and online

Directory of Open Access Journals (Sweden)

Søren Smedegaard Bengtsen

2015-02-01

Full Text Available Through an empirical study of supervision on student assignments at the university across face-to-face and online settings, we show firstly the limiting implications of traditional dichotomies between face-to-face and online supervision. Secondly we show that more attention must be given to the way different digital tools influence the supervisory dialogue. These findings illustrate a form of ‘torn pedagogy’; that online tools and platforms destabilize and tear traditional understandings of supervision pedagogy apart. Also we forge a new concept of “format supervision” that enables supervisors to understand and reflect their supervision practice as a deliberate choice between face-to-face and online formats.

Combination of supervised and semi-supervised regression models for improved unbiased estimation

DEFF Research Database (Denmark)

Arenas-Garía, Jeronimo; Moriana-Varo, Carlos; Larsen, Jan

2010-01-01

In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised and semisupervi......In this paper we investigate the steady-state performance of semisupervised regression models adjusted using a modified RLS-like algorithm, identifying the situations where the new algorithm is expected to outperform standard RLS. By using an adaptive combination of the supervised...

Supervision in social work NGOs in Bihor County

Directory of Open Access Journals (Sweden)

Cristiana Marcela MARC

2012-01-01

Full Text Available This paper presents a qualitative research which aims at analyzing supervision in the social services provided by NGOs in Bihor County. We used the method of sociological investigation by means of interview and data collection was accomplished through the technique of individual semi-structured interview. The obtained responses demonstrate that individual supervision was mostly used and in most cases the professional supervisor was from outside the organization. The respondents considered that supervision reduces professional stress. The main problems encountered in the implementation of supervision are the lack of financial resources and the association of supervision with bureaucratic control.

Arrangement and Applying of Movement Patterns in the Cerebellum Based on Semi-supervised Learning.

Science.gov (United States)

Solouki, Saeed; Pooyan, Mohammad

2016-06-01

Biological control systems have long been studied as a possible inspiration for the construction of robotic controllers. The cerebellum is known to be involved in the production and learning of smooth, coordinated movements. Therefore, highly regular structure of the cerebellum has been in the core of attention in theoretical and computational modeling. However, most of these models reflect some special features of the cerebellum without regarding the whole motor command computational process. In this paper, we try to make a logical relation between the most significant models of the cerebellum and introduce a new learning strategy to arrange the movement patterns: cerebellar modular arrangement and applying of movement patterns based on semi-supervised learning (CMAPS). We assume here the cerebellum like a big archive of patterns that has an efficient organization to classify and recall them. The main idea is to achieve an optimal use of memory locations by more than just a supervised learning and classification algorithm. Surely, more experimental and physiological researches are needed to confirm our hypothesis.

Medical supervision of radiation workers

International Nuclear Information System (INIS)

Santani, S.B.; Nandakumar, A.N.; Subramanian, G.

1982-01-01

The basic elements of an occupational medical supervision programme for radiation workers are very much the same as those relevant to other professions with some additional special features. This paper cites examples from literature and recommends measures such as spot checks and continuance of medical supervision even after a radiation worker leaves this profession. (author)

A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network

Directory of Open Access Journals (Sweden)

Han Kyungsook

2010-06-01

Full Text Available Abstract Background Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited for annotating gene functions and dissecting specific pathway structures. However, our understanding is rather limited to the relationship between double concurrent perturbation and various higher level phenotypic changes, e.g. those in cells, tissues or organs. Modifier screens, such as synthetic genetic arrays (SGA can help us to understand the phenotype caused by combined gene mutations. Unfortunately, exhaustive tests on all possible combined mutations in any genome are vulnerable to combinatorial explosion and are infeasible either technically or financially. Therefore, an accurate computational approach to predict genetic interaction is highly desirable, and such methods have the potential of alleviating the bottleneck on experiment design. Results In this work, we introduce a computational systems biology approach for the accurate prediction of pairwise synthetic genetic interactions (SGI. First, a high-coverage and high-precision functional gene network (FGN is constructed by integrating protein-protein interaction (PPI, protein complex and gene expression data; then, a graph-based semi-supervised learning (SSL classifier is utilized to identify SGI, where the topological properties of protein pairs in weighted FGN is used as input features of the classifier. We compare the proposed SSL method with the state-of-the-art supervised classifier, the support vector machines (SVM, on a benchmark dataset in S. cerevisiae to validate our method's ability to distinguish synthetic genetic interactions from non-interaction gene pairs. Experimental results show that the proposed method can accurately predict genetic interactions in S. cerevisiae (with a sensitivity of 92% and specificity of 91%. Noticeably, the SSL method is more efficient than SVM, especially for

Cultural Humility in Psychotherapy Supervision.

Science.gov (United States)

Hook, Joshua N; Watkins, C Edward; Davis, Don E; Owen, Jesse; Van Tongeren, Daryl R; Ramos, Marciana J

2016-01-01

As a core component of multicultural orientation, cultural humility can be considered an important attitude for clinical supervisees to adopt and practically implement. How can cultural humility be most meaningfully incorporated in supervision? In what ways can supervisors stimulate the development of a culturally humble attitude in our supervisees? We consider those questions in this paper and present a model for addressing cultural humility in clinical supervision. The primary focus is given to two areas: (a) modeling and teaching of cultural humility through interpersonal interactions in supervision, and (b) teaching cultural humility through outside activities and experiences. Two case studies illustrating the model are presented, and a research agenda for work in this area is outlined.

OPTIMAL REPRESENTATION OF MER SIGNALS APPLIED TO THE IDENTIFICATION OF BRAIN STRUCTURES DURING DEEP BRAIN STIMULATION

Directory of Open Access Journals (Sweden)

Hernán Darío Vargas Cardona

2015-07-01

Full Text Available Identification of brain signals from microelectrode recordings (MER is a key procedure during deep brain stimulation (DBS applied in Parkinson’s disease patients. The main purpose of this research work is to identify with high accuracy a brain structure called subthalamic nucleus (STN, since it is the target structure where the DBS achieves the best therapeutic results. To do this, we present an approach for optimal representation of MER signals through method of frames. We obtain coefficients that minimize the Euclidean norm of order two. From optimal coefficients, we extract some features from signals combining the wavelet packet and cosine dictionaries. For a comparison frame with the state of the art, we also process the signals using the discrete wavelet transform (DWT with several mother functions. We validate the proposed methodology in a real data base. We employ simple supervised machine learning algorithms, as the K-Nearest Neighbors classifier (K-NN, a linear Bayesian classifier (LDC and a quadratic Bayesian classifier (QDC. Classification results obtained with the proposed method improves significantly the performance of the DWT. We achieve a positive identification of the STN superior to 97,6%. Identification outcomes achieved by the MOF are highly accurate, as we can potentially get a false positive rate of less than 2% during the DBS.

Childhood unintentional injuries: Supervision and first aid provided

Directory of Open Access Journals (Sweden)

Annabel Jonkheijm

2013-01-01

Full Text Available Background : The aim of this study was to investigate the circumstances surrounding unintentional injuries of children and the appropriateness of the first aid provided by caregivers. Materials and Methods : This prospective study included children with aged range 0-12 years, who presented with an unintentional injury at the Trauma Unit of a hospital in Cape Town, South Africa, over a 3 month period. Caregivers were interviewed about the circumstances of the injury and the first aid provided. Experts classified the first aid as appropriate, appropriate but incomplete, or inappropriate. Results: A total of 313 children were included with a median age of 3.75 years. The most common causes of injury were falls (39.6%, n = 124, burns (23.9%, n = 75 and motor vehicle crashes (10.5%, n = 33. More than a quarter of the children (27.2%, n = 81 had been left under the supervision of another child below the age of 12. When the injury occurred, 7.1% (n = 22 of the children were unattended. First aid was provided in 43.1% (n = 134 of the cases. More than half of these interventions (53%, n = 72 were inappropriate or appropriate but incomplete. Conclusions: Especially young children are at risk for unintentional injuries. Lack of appropriate supervision increases this risk. Prevention education of parents and children may help to protect children from injuries. First-aid training should also be more accessible to civilians as both the providing of as well as the quality of first-aid provided lacked in the majority of cases.

Supervised Cross-Modal Factor Analysis for Multiple Modal Data Classification

KAUST Repository

Wang, Jingbin

2015-10-09

In this paper we study the problem of learning from multiple modal data for purpose of document classification. In this problem, each document is composed two different modals of data, i.e., An image and a text. Cross-modal factor analysis (CFA) has been proposed to project the two different modals of data to a shared data space, so that the classification of a image or a text can be performed directly in this space. A disadvantage of CFA is that it has ignored the supervision information. In this paper, we improve CFA by incorporating the supervision information to represent and classify both image and text modals of documents. We project both image and text data to a shared data space by factor analysis, and then train a class label predictor in the shared space to use the class label information. The factor analysis parameter and the predictor parameter are learned jointly by solving one single objective function. With this objective function, we minimize the distance between the projections of image and text of the same document, and the classification error of the projection measured by hinge loss function. The objective function is optimized by an alternate optimization strategy in an iterative algorithm. Experiments in two different multiple modal document data sets show the advantage of the proposed algorithm over other CFA methods.

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

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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.

Assessment of Counselors' Supervision Processes

Science.gov (United States)

Ünal, Ali; Sürücü, Abdullah; Yavuz, Mustafa

2013-01-01

The aim of this study is to investigate elementary and high school counselors' supervision processes and efficiency of their supervision. The interview method was used as it was thought to be better for realizing the aim of the study. The study group was composed of ten counselors who were chosen through purposeful sampling method. Data were…

Supervised learning with decision margins in pools of spiking neurons.

Science.gov (United States)

Le Mouel, Charlotte; Harris, Kenneth D; Yger, Pierre

2014-10-01

Learning to categorise sensory inputs by generalising from a few examples whose category is precisely known is a crucial step for the brain to produce appropriate behavioural responses. At the neuronal level, this may be performed by adaptation of synaptic weights under the influence of a training signal, in order to group spiking patterns impinging on the neuron. Here we describe a framework that allows spiking neurons to perform such "supervised learning", using principles similar to the Support Vector Machine, a well-established and robust classifier. Using a hinge-loss error function, we show that requesting a margin similar to that of the SVM improves performance on linearly non-separable problems. Moreover, we show that using pools of neurons to discriminate categories can also increase the performance by sharing the load among neurons.

Abusive Supervision Scale Development in Indonesia

Directory of Open Access Journals (Sweden)

Fenika Wulani

2014-02-01

Full Text Available The purpose of this study was to develop a scale of abusive supervision in Indonesia. The study was conducted with a different context and scale development method from Tepper’s (2000 abusive supervision scale. The abusive supervision scale from Tepper (2000 was developed in the U.S., which has a cultural orientation of low power distance. The current study was conducted in Indonesia, which has a high power distance. This study used interview procedures to obtain information about supervisor’s abusive behavior, and it was also assessed by experts. The results of this study indicated that abusive supervision was a 3-dimensional construct. There were anger-active abuse (6 items, humiliation-active abuse (4 items, and passive abuse (15 items. These scales have internal reliabilities of 0.947, 0.922, and 0.845, in sequence.

Zooniverse: Combining Human and Machine Classifiers for the Big Survey Era

Science.gov (United States)

Fortson, Lucy; Wright, Darryl; Beck, Melanie; Lintott, Chris; Scarlata, Claudia; Dickinson, Hugh; Trouille, Laura; Willi, Marco; Laraia, Michael; Boyer, Amy; Veldhuis, Marten; Zooniverse

2018-01-01

Many analyses of astronomical data sets, ranging from morphological classification of galaxies to identification of supernova candidates, have relied on humans to classify data into distinct categories. Crowdsourced galaxy classifications via the Galaxy Zoo project provided a solution that scaled visual classification for extant surveys by harnessing the combined power of thousands of volunteers. However, the much larger data sets anticipated from upcoming surveys will require a different approach. Automated classifiers using supervised machine learning have improved considerably over the past decade but their increasing sophistication comes at the expense of needing ever more training data. Crowdsourced classification by human volunteers is a critical technique for obtaining these training data. But several improvements can be made on this zeroth order solution. Efficiency gains can be achieved by implementing a “cascade filtering” approach whereby the task structure is reduced to a set of binary questions that are more suited to simpler machines while demanding lower cognitive loads for humans.Intelligent subject retirement based on quantitative metrics of volunteer skill and subject label reliability also leads to dramatic improvements in efficiency. We note that human and machine classifiers may retire subjects differently leading to trade-offs in performance space. Drawing on work with several Zooniverse projects including Galaxy Zoo and Supernova Hunter, we will present recent findings from experiments that combine cohorts of human and machine classifiers. We show that the most efficient system results when appropriate subsets of the data are intelligently assigned to each group according to their particular capabilities.With sufficient online training, simple machines can quickly classify “easy” subjects, leaving more difficult (and discovery-oriented) tasks for volunteers. We also find humans achieve higher classification purity while samples

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…

Framing doctoral supervision as formative assessment

DEFF Research Database (Denmark)

Kobayashi, Sofie

Doctoral supervision has been described through a number of models useful for understanding different aspects of supervision. None of these are all-encompassing, but each emphasizes a particular perspective, like the relationship, personal vs. structural support, process vs. product orientation. ...

Re-thinking reflection in supervision

DEFF Research Database (Denmark)

Lystbæk, Christian Tang

The paper presents a socio-cultural perspective on supervision in professional education, which challenges the current reflective paradigm and move the debate on reflection in supervision in professional education and learning towards a recognition of context, power dynamics and ideological...... of reflective practice has been formalized by regulatory bodies as a way to develop the professionalism of both individual professional practitioners as students through continuing professional developmental processes. Consequently, reflection is often used as a `tool´ for personal and professional development...... al., 2010). This conceptual paper presents a critical, socio-cultural perspective on the current paradigm or dogma of reflective practice within supervision in professional education and learning. The purpose I to challenge the dogma and critically to analyze and move the debate on reflection...

The efficiency of government supervision

International Nuclear Information System (INIS)

Paetzold, H.

1992-01-01

In 1970, fires as events initiating plant failure were included in the accident analyses of nuclear power plant design concepts. In the meantime, they have been expressed in more precise terms and incorporated into the bodies of nuclear technical rules and regulations. Following a suggestion by the Baden-Wuerttemberg State Ministry for the Environment, the efficiency of government supervision has been examined for the example of fire protection measures or the site of Phillipsburg with one BWR and one PWR plant in operation. The result of the examination indicated that pragmatic approaches and the establishment of key areas of supervision could further enhance the efficiency of government supervision under Section 19 of the German Atomic Energy Act and achieve improvements in plant safety. (orig.) [de

Organization and competences of nuclear supervision in Poland

International Nuclear Information System (INIS)

Sowinski, M.

1989-01-01

Organization and tasks of nuclear supervision are presented. All supervised nuclear installations are listed. The rights of the president of the National Atomic Energy Agency and the chief inspector of nuclear supervision are given. Licensing and cooperation with the IAEA are described. (A.S.)

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances. PMID:27627768

Semi-Supervised Active Learning for Sound Classification in Hybrid Learning Environments.

Science.gov (United States)

Han, Wenjing; Coutinho, Eduardo; Ruan, Huabin; Li, Haifeng; Schuller, Björn; Yu, Xiaojie; Zhu, Xuan

2016-01-01

Coping with scarcity of labeled data is a common problem in sound classification tasks. Approaches for classifying sounds are commonly based on supervised learning algorithms, which require labeled data which is often scarce and leads to models that do not generalize well. In this paper, we make an efficient combination of confidence-based Active Learning and Self-Training with the aim of minimizing the need for human annotation for sound classification model training. The proposed method pre-processes the instances that are ready for labeling by calculating their classifier confidence scores, and then delivers the candidates with lower scores to human annotators, and those with high scores are automatically labeled by the machine. We demonstrate the feasibility and efficacy of this method in two practical scenarios: pool-based and stream-based processing. Extensive experimental results indicate that our approach requires significantly less labeled instances to reach the same performance in both scenarios compared to Passive Learning, Active Learning and Self-Training. A reduction of 52.2% in human labeled instances is achieved in both of the pool-based and stream-based scenarios on a sound classification task considering 16,930 sound instances.

Doctoral Dissertation Supervision: Identification and Evaluation of Models

Directory of Open Access Journals (Sweden)

Ngozi Agu

2014-01-01

Full Text Available Doctoral research supervision is one of the major avenues for sustaining students’ satisfaction with the programme, preparing students to be independent researchers and effectively initiating students into the academic community. This work reports doctoral students’ evaluation of their various supervision models, their satisfaction with these supervision models, and development of research-related skills. The study used a descriptive research design and was guided by three research questions and two hypotheses. A sample of 310 Ph.D. candidates drawn from a federal university in Eastern part of Nigeria was used for this study. The data generated through the questionnaire was analyzed using descriptive statistics and t-tests. Results show that face-to-face interactive model was not only the most frequently used, but also the most widely adopted in doctoral thesis supervision while ICT-based models were rarely used. Students supervised under face-to-face interactive model reported being more satisfied with dissertation supervision than those operating under face-to-face noninteractive model. However, students supervised under these two models did not differ significantly in their perceived development in research-related skills.

Effective use of technology in clinical supervision

Directory of Open Access Journals (Sweden)

Priya Martin

2017-06-01

Full Text Available Clinical supervision is integral to continuing professional development of health professionals. With advances in technology, clinical supervision too can be undertaken using mediums such as videoconference, email and teleconference. This mode of clinical supervision is termed as telesupervision. While telesupervision could be useful in any context, its value is amplified for health professionals working in rural and remote areas where access to supervisors within the local work environment is often diminished. While telesupervision offers innovative means to undertake clinical supervision, there remain gaps in the literature in terms of its parameters of use in clinical practice. This article outlines ten evidence-informed, practical tips stemming from a review of the literature that will enable health care stakeholders to use technology effectively and efficiently while undertaking clinical supervision. By highlighting the “how to” aspect, telesupervision can be delivered in the right way, to the right health professional, at the right time.

Application of Contingency Theories to the Supervision of Student Teachers.

Science.gov (United States)

Phelps, Julia D.

1985-01-01

This article examines selected approaches to student teacher supervision within the context of contingency theory. These include authentic supervision, developmental supervision, and supervision based on the student's level of maturity. (MT)

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.

Supervision and inspection plans of plants activities; Plan de inspeccion y supervision de actividades en planta

Energy Technology Data Exchange (ETDEWEB)

Feijoo, J. P.

2009-07-01

Any idea of hierarchization between supervisor and supervised in inspection and supervision activities should necessarily be dismissed, and the independence of the supervisor when executing has tasks should be guaranteed. The inspection and supervision program enable the detection and resolution of materials and human problems alike. In addition, they are a solution to anticipate potential problems in the future, which results in a very significant reduction of industrial accidents and human errors, as well as better use and upkeep of equipment. With these programs we improve our management and our work, and without a doubt they help to strengthen the safety culture in Cofrentes Nuclear Power Plant. (Author)

Educational Technology and Distance Supervision in Counselor Education

Science.gov (United States)

Carlisle, Robert Milton; Hays, Danica G.; Pribesh, Shana L.; Wood, Chris T.

2017-01-01

The authors used a nonexperimental descriptive design to examine the prevalence of distance supervision in counselor education programs, educational technology used in supervision, training on technology in supervision, and participants' (N = 673) perceptions of legal and ethical compliance. Program policies are recommended to guide the training…

State Radiation Protection Supervision and Control

International Nuclear Information System (INIS)

2003-01-01

Radiation Protection Centre is carrying state supervision and control of radiation protection. The main objective of state supervision and control of radiation protection is assessing how licensees comply with requirements of the appropriate legislation and enforcement. Summary of inspections conducted in 2002 is presented

State Radiation Protection Supervision and Control

CERN Document Server

2002-01-01

Radiation Protection Centre is carrying state supervision and control of radiation protection. The main objective of state supervision and control of radiation protection is assessing how licensees comply with requirements of the appropriate legislation and enforcement. Summary of inspections conducted in 2002 is presented.

[Possibilities of supervision in medical practice].

Science.gov (United States)

Lönnqvist, Jouko

2014-01-01

In supervision, a doctor examines in interaction with the supervisor her/his work, work role and collaborative relationships with the aim to develop herself/himself and the associated work community. In clinical supervision, a doctor's way of acting in interactive relationships with the patients is examined through patient cases, based on the doctor's own experience. Supervision can be used to strengthen the physician identity, clarify the work role, assimilate and delve into clinical work, support professional development and working career, manage one's own work and coping at work, develop collaboration and team work, and support the work of medical directors.

Semi-supervised and unsupervised extreme learning machines.

Science.gov (United States)

Huang, Gao; Song, Shiji; Gupta, Jatinder N D; Wu, Cheng

2014-12-01

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Is it possible to strengthen psychiatric nursing staff's clinical supervision?

DEFF Research Database (Denmark)

Gonge, Henrik; Buus, Niels

2015-01-01

AIM: To test the effects of a meta-supervision intervention in terms of participation, effectiveness and benefits of clinical supervision of psychiatric nursing staff. BACKGROUND: Clinical supervision is regarded as a central component in developing mental health nursing practices, but the evidence...... an intervention group (n = 40) receiving the meta-supervision in addition to attending usual supervision or to a control group (n = 43) attending usual supervision. METHODS: Self-reported questionnaire measures of clinical supervision effectiveness and benefits were collected at base line in January 2012...... and at follow-up completed in February 2013. In addition, a prospective registration of clinical supervision participation was carried out over 3 months subsequent to the intervention. RESULTS: The main result was that it was possible to motivate staff in the intervention group to participate significantly more...

Classifying Microorganisms

DEFF Research Database (Denmark)

Sommerlund, Julie

2006-01-01

This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological characteris......This paper describes the coexistence of two systems for classifying organisms and species: a dominant genetic system and an older naturalist system. The former classifies species and traces their evolution on the basis of genetic characteristics, while the latter employs physiological...... characteristics. The coexistence of the classification systems does not lead to a conflict between them. Rather, the systems seem to co-exist in different configurations, through which they are complementary, contradictory and inclusive in different situations-sometimes simultaneously. The systems come...

Multiplicity in supervision relationships: A factor in improving ...

African Journals Online (AJOL)

Supervision has been identified as an important factor in the success of postgraduate students, even as the most significant variable and a large number of studies have been conducted to identify factors that contribute to supervision success. However the dependent variable in these studies – supervision success – has ...

28 CFR 2.94 - Supervision reports to Commission.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Supervision reports to Commission. 2.94 Section 2.94 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Code: Prisoners and Parolees § 2.94 Supervision reports to Commissio...

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 CATEGORIES OF CONTRACTING CONSTRUCTION AND ARCHITECT-ENGINEER CONTRACTS Contract Clauses 836.572 Government supervision. The contracting officer shal...

A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.

Directory of Open Access Journals (Sweden)

Fei Gao

Full Text Available For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic data selection mechanism. In this paper, inspired by human learning process, a novel classification algorithm based on incremental semi-supervised support vector machine (SVM is proposed. Through the analysis of prediction confidence of samples and data distribution in a changing environment, a "soft-start" approach, a data selection mechanism and a data cleaning mechanism are designed, which complete the construction of our incremental semi-supervised learning system. Noticeably, with the ingenious design procedure of our proposed algorithm, the computation complexity is reduced effectively. In addition, for the possible appearance of some new labeled samples in the learning process, a detailed analysis is also carried out. The results show that our algorithm does not rely on the model of sample distribution, has an extremely low rate of introducing wrong semi-labeled samples and can effectively make use of the unlabeled samples to enrich the knowledge system of classifier and improve the accuracy rate. Moreover, our method also has outstanding generalization performance and the ability to overcome the concept drift in a changing environment.

New developments in technology-assisted supervision and training: a practical overview.

Science.gov (United States)

Rousmaniere, Tony; Abbass, Allan; Frederickson, Jon

2014-11-01

Clinical supervision and training are now widely available online. In this article, three of the most accessible and widely adopted new developments in clinical supervision and training technology are described: Videoconference supervision, cloud-based file sharing software, and clinical outcome tracking software. Partial transcripts from two online supervision sessions are provided as examples of videoconference-based supervision. The benefits and limitations of technology in supervision and training are discussed, with an emphasis on supervision process, ethics, privacy, and security. Recommendations for supervision practice are made, including methods to enhance experiential learning, the supervisory working alliance, and online security. © 2014 Wiley Periodicals, Inc.

Predicting Classifier Performance with Limited Training Data: Applications to Computer-Aided Diagnosis in Breast and Prostate Cancer

Science.gov (United States)

Basavanhally, Ajay; Viswanath, Satish; Madabhushi, Anant

2015-01-01

Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets. PMID:25993029

Teacher Supervision Practices and Principals' Characteristics

Science.gov (United States)

April, Daniel; Bouchamma, Yamina

2015-01-01

A questionnaire was used to determine the individual and collective teacher supervision practices of school principals and vice-principals in Québec (n = 39) who participated in a research-action study on pedagogical supervision. These practices were then analyzed in terms of the principals' sociodemographic and socioprofessional characteristics…

32 CFR 552.65 - Command supervision.

Science.gov (United States)

2010-07-01

... 32 National Defense 3 2010-07-01 2010-07-01 true Command supervision. 552.65 Section 552.65 National Defense Department of Defense (Continued) DEPARTMENT OF THE ARMY MILITARY RESERVATIONS AND NATIONAL CEMETERIES REGULATIONS AFFECTING MILITARY RESERVATIONS Solicitation on Military Reservations § 552.65 Command supervision. (a) All insurance...

Ethics in education supervision

Directory of Open Access Journals (Sweden)

Fatma ÖZMEN

2008-06-01

Full Text Available Supervision in education plays a crucial role in attaining educational goals. In addition to determining the present situation, it has a theoretical and practical function regarding the actions to be taken in general and the achievement of teacher development in particular to meet the educational goals in the most effective way. For the education supervisors to act ethically in their tasks while achieving this vital mission shall facilitate them to build up trust, to enhance the level of collaboration and sharing, thus it shall contribute to organizational effectiveness. Ethics is an essential component of educational supervision. Yet, it demonstrates rather vague quality due to the conditions, persons, and situations. Therefore, it is a difficult process to develop the ethical standards in institutions. This study aims to clarify the concept of ethics, to bring up its importance, and to make recommendations for more effective supervisions from the aspect of ethics, based on the literature review, some research results, and sample cases reported by teachers and supervisors.

Clinical Supervision of International Supervisees: Suggestions for Multicultural Supervision

Science.gov (United States)

Lee, Ahram

2018-01-01

An increase of international students in various settings has been noted in a range of disciplines including counseling and other mental health professions. The author examined the literature on international counseling students related to their experiences in counseling training, particularly in supervision. From the counseling literature, five…

Who attends clinical supervision? The uptake of clinical supervision by hospital nurses.

Science.gov (United States)

Koivu, Aija; Hyrkäs, Kristiina; Saarinen, Pirjo Irmeli

2011-01-01

The aim of the present study was to identify which nurses decide to participate in clinical supervision (CS) when it is provided for all nursing staff. Clinical supervision is available today for health care providers in many organisations. However, regardless of evidence showing the benefits of CS, some providers decide not to participate in the sessions. A baseline survey on work and health issues was conducted in 2003 with a 3-year follow-up of the uptake of CS by the respondents. Background characteristics and perceptions of work and health were compared between medical and surgical nurses who had undertaken CS (n=124) and their peers who decided not to undertake it (n=204). Differences in the perceptions of work and dimensions of burnout were found between the two groups. Nurses attracted to CS form a distinctive group in the unit, standing out as self-confident, committed and competent professionals supported by empowering and fair leadership. Facilitating clinical supervision for committed and innovative nurses may be seen as part of the empowering leadership of the nurse manager. © 2010 The Authors. Journal compilation © 2010 Blackwell Publishing Ltd.

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

Defeating abusive supervision: Training supervisors to support subordinates.

Science.gov (United States)

Gonzalez-Morales, M Gloria; Kernan, Mary C; Becker, Thomas E; Eisenberger, Robert

2018-04-01

Although much is known about the antecedents and consequences of abusive supervision, scant attention has been paid to investigating procedures to reduce its frequency. We conducted a quasiexperiment to examine the effects of supervisor support training on subordinate perceptions of abusive supervision and supervisor support. Supervisors (n = 23) in 4 restaurants were trained in 4 supportive supervision strategies (benevolence, sincerity, fairness, and experiential processing) during 4 2-hr sessions over a period of 2 months. We compared perceived supervisor support and abusive supervision before and 9 months after training for 208 employees whose supervisors received support training and 241 employees in 4 similar control restaurants. Compared to employees in the control restaurants, employees whose supervisors received the support training reported higher levels of perceived supervisor support and less abusive supervision. These findings suggest that a relatively brief training program can help managers become more supportive and less abusive. Theoretical and practical implications for effectively managing abusive supervision are discussed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Improving supervision: a team approach.

Science.gov (United States)

1993-01-01

This issue of "The Family Planning Manager" outlines an interactive team supervision strategy as a means of improving family planning service quality and enabling staff to perform to their maximum potential. Such an approach to supervision requires a shift from a monitoring to a facilitative role. Because supervisory visits to the field are infrequent, the regional supervisor, clinic manager, and staff should form a team to share ongoing supervisory responsibilities. The team approach removes individual blame and builds consensus. An effective team is characterized by shared leadership roles, concrete work problems, mutual accountability, an emphasis on achieving team objectives, and problem resolution within the group. The team supervision process includes the following steps: prepare a visit plan and schedule; meet with the clinic manager and staff to explain how the visit will be conducted; supervise key activity areas (clinical, management, and personnel); conduct a problem-solving team meeting; conduct a debriefing meeting with the clinic manager; and prepare a report on the visit, including recommendations and follow-up plans. In Guatemala's Family Planning Unit, teams identify problem areas on the basis of agreement that a problem exists, belief that the problem can be solved with available resources, and individual willingness to accept responsibility for the specific actions identified to correct the problem.

Asco 2044 nuclear power plant: supervision

International Nuclear Information System (INIS)

Sabartes, J.

2010-01-01

Good supervision constitutes an efficient barrier to avoid the errors caused by inadequate work practices. In this sense, it is necessary to strengthen supervision to make sure that the work is carried out with adequate human performance, tending to avoid error and providing safety quality and efficiency at work. (Author).

28 CFR 551.32 - Staff supervision.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Staff supervision. 551.32 Section 551.32 Judicial Administration BUREAU OF PRISONS, DEPARTMENT OF JUSTICE INSTITUTIONAL MANAGEMENT MISCELLANEOUS Inmate Organizations § 551.32 Staff supervision. (a) The Warden shall appoint a staff member as the institution's Inmate Organization Manager (IO...

Detecting android malicious apps and categorizing benign apps with ensemble of classifiers

KAUST Repository

Wang, Wei

2017-01-17

Android platform has dominated the markets of smart mobile devices in recent years. The number of Android applications (apps) has seen a massive surge. Unsurprisingly, Android platform has also become the primary target of attackers. The management of the explosively expansive app markets has thus become an important issue. On the one hand, it requires effectively detecting malicious applications (malapps) in order to keep the malapps out of the app market. On the other hand, it needs to automatically categorize a big number of benign apps so as to ease the management, such as correcting an app’s category falsely designated by the app developer. In this work, we propose a framework to effectively and efficiently manage a big app market in terms of detecting malapps and categorizing benign apps. We extract 11 types of static features from each app to characterize the behaviors of the app, and employ the ensemble of multiple classifiers, namely, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Classification and Regression Tree (CART) and Random Forest (RF), to detect malapps and to categorize benign apps. An alarm will be triggered if an app is identified as malicious. Otherwise, the benign app will be identified as a specific category. We evaluate the framework on a large app set consisting of 107,327 benign apps as well as 8,701 malapps. The experimental results show that our method achieves the accuracy of 99.39% in the detection of malapps and achieves the best accuracy of 82.93% in the categorization of benign apps.

Development of the Artistic Supervision Model Scale (ASMS)

Science.gov (United States)

Kapusuzoglu, Saduman; Dilekci, Umit

2017-01-01

The purpose of the study is to develop the Artistic Supervision Model Scale in accordance with the perception of inspectors and the elementary and secondary school teachers on artistic supervision. The lack of a measuring instrument related to the model of artistic supervision in the field of literature reveals the necessity of such study. 290…

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

OpenAIRE

Sulistyandari; Arief Suryono

2015-01-01

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...

Multi-level deep supervised networks for retinal vessel segmentation.

Science.gov (United States)

Mo, Juan; Zhang, Lei

2017-12-01

Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

Self-supervised Traversability Assessment in Field Environments with Lidar and Camera

DEFF Research Database (Denmark)

Hansen, Mikkel Kragh; Underwood, James; Karstoft, Henrik

, the visual classifier detects non-traversable image patches as outliers from a Gaussian Mixture Model that maintains the appearance of only traversable ground. Results Our method is evaluated using a diverse dataset of agricultural fields and orchards gathered with a perception research robot developed......Introduction The application of robotic automation within agriculture is increasing. There is a high demand for fully autonomous robots that are both efficient, reliable and affordable. In order to ensure safety, autonomous agricultural vehicles must perceive the environment and detect potential...... obstacles and threats across a variety of environmental conditions. In this paper, a self-supervised framework is proposed, combining laser range sensing from a lidar with images from a monocular camera to reliably assess terrain traversability/navigability. Methods The method uses a near-to-far approach...

19 CFR 111.28 - Responsible supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Responsible supervision. 111.28 Section 111.28 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY CUSTOMS BROKERS Duties and Responsibilities of Customs Brokers § 111.28 Responsible supervision. (a) General. Every individual broker...

Weakly supervised classification in high energy physics

Energy Technology Data Exchange (ETDEWEB)

Dery, Lucio Mwinmaarong [Physics Department, Stanford University,Stanford, CA, 94305 (United States); Nachman, Benjamin [Physics Division, Lawrence Berkeley National Laboratory,1 Cyclotron Rd, Berkeley, CA, 94720 (United States); Rubbo, Francesco; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA, 94025 (United States)

2017-05-29

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Weakly supervised classification in high energy physics

International Nuclear Information System (INIS)

Dery, Lucio Mwinmaarong; Nachman, Benjamin; Rubbo, Francesco; Schwartzman, Ariel

2017-01-01

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics — quark versus gluon tagging — we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Doctoral Supervision in Virtual Spaces: A Review of Research of Web-Based Tools to Develop Collaborative Supervision

Science.gov (United States)

Maor, Dorit; Ensor, Jason D.; Fraser, Barry J.

2016-01-01

Supervision of doctoral students needs to be improved to increase completion rates, reduce attrition rates (estimated to be at 25% or more) and improve quality of research. The current literature review aimed to explore the contribution that technology can make to higher degree research supervision. The articles selected included empirical studies…

PERBANDINGAN K-NEAREST NEIGHBOR DAN NAIVE BAYES UNTUK KLASIFIKASI TANAH LAYAK TANAM POHON JATI

Directory of Open Access Journals (Sweden)

Didik Srianto

2016-10-01

Full Text Available Data mining adalah proses menganalisa data dari perspektif yang berbeda dan menyimpulkannya menjadi informasi-informasi penting yang dapat dipakai untuk meningkatkan keuntungan, memperkecil biaya pengeluaran, atau bahkan keduanya. Secara teknis, data mining dapat disebut sebagai proses untuk menemukan korelasi atau pola dari ratusan atau ribuan field dari sebuah relasional database yang besar. Pada perum perhutani KPH SEMARANG saat ini masih menggunakan cara manual untuk menentukan jenis tanaman (jati / non jati. K-Nearest Neighbour atau k-NN merupakan algoritma data mining yang dapat digunakan untuk proses klasifikasi dan regresi. Naive bayes Classifier merupakan suatu teknik yang dapat digunakan untuk teknik klasifikasi. Pada penelitian ini k-NN dan Naive Bayes akan digunakan untuk mengklasifikasi data pohon jati dari perum perhutani KPH SEMARANG. Yang mana hasil klasifikasi dari k-NN dan Naive Bayes akan dibandingkan hasilnya. Pengujian dilakukan menggunakan software RapidMiner. Setelah dilakukan pengujian k-NN dianggap lebih baik dari Naife Bayes dengan akurasi 96.66% dan 82.63. Kata kunci -k-NN,Klasifikasi,Naive Bayes,Penanaman Pohon Jati

The Comparison of Banking Supervision Model in Indonesia, United Kingdom, South Korea as Eforts to Improve Indonesian Supervision System

OpenAIRE

Sulistyandari, Sulistyandari

2015-01-01

This study aims to improve banking supervision by conductingcomparative 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...

19 CFR 146.3 - Customs supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Customs supervision. 146.3 Section 146.3 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) FOREIGN TRADE ZONES General Provisions § 146.3 Customs supervision. (a) Assignment of Customs officers. Customs officers will be...

36 CFR 25.3 - Supervision; suspensions.

Science.gov (United States)

2010-07-01

... 36 Parks, Forests, and Public Property 1 2010-07-01 2010-07-01 false Supervision; suspensions. 25.3 Section 25.3 Parks, Forests, and Public Property NATIONAL PARK SERVICE, DEPARTMENT OF THE INTERIOR NATIONAL MILITARY PARKS; LICENSED GUIDE SERVICE REGULATIONS § 25.3 Supervision; suspensions. (a) The guide service will operate under the direction...

The effectiveness of banking supervision

OpenAIRE

Davis, EP; Obasi, U

2009-01-01

Banking supervision is an essential aspect of modern financial systems, seeking crucially to monitor risk-taking by banks so as to protect depositors, the government safety net and the economy as a whole against systemic bank failure and its consequences. In this context, this paper seeks to explore the relationship between risk indicators for individual banks and the different approaches to banking supervision adopted around the world. This is the first work to make use of the currently avai...

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

Science.gov (United States)

Xingquan Zhu

2011-12-01

Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

Counseling Supervision within a Feminist Framework: Guidelines for Intervention

Science.gov (United States)

Degges-White, Suzanne E.; Colon, Bonnie R.; Borzumato-Gainey, Christine

2013-01-01

Feminist supervision is based on the principles of feminist theory. Goals include sharing responsibility for the supervision process, empowering the supervisee, attending to the contextual assumptions about clients, and analyzing gender roles. This article explores feminist supervision and guidelines for providing counseling supervision…

Nurses’ perceptions on nursing supervision in Primary Health Care

Directory of Open Access Journals (Sweden)

Beatriz Francisco Farah

2016-01-01

Full Text Available Objective: to understand the perceptions of nurses on nursing supervision in the work process. Methods: this is a qualitative research, with a semi-structured interview, performed with 16 nurses. Data analysis was performed through content analysis. Results: two meanings topics emerged from the speeches of the participants: Nurses´ activities in Primary Health Care Units and Nurses´ perceptions about nursing supervision. In the first category, the actions listed were filling out forms and reports under the supervision of the nursing service. In the second category, supervision was perceived as a function of management and follow-up of the activities planned by the team, in opposition to the classical supervision concept, which is inspecting. Conclusion: nursing supervision has been configured for primary care nurses as an administrative function that involves planning, organization, coordination, evaluation, follow-up and support for the health team.

21 CFR 640.62 - Medical supervision.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 7 2010-04-01 2010-04-01 false Medical supervision. 640.62 Section 640.62 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) BIOLOGICS ADDITIONAL STANDARDS FOR HUMAN BLOOD AND BLOOD PRODUCTS Source Plasma § 640.62 Medical supervision. A qualified licensed physician shall be on the...

19 CFR 19.34 - Customs supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Customs supervision. 19.34 Section 19.34 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY CUSTOMS WAREHOUSES, CONTAINER STATIONS AND CONTROL OF MERCHANDISE THEREIN Space Bonded for the Storage of Wheat § 19.34 Customs supervision. Port...

40 CFR 35.935-8 - Supervision.

Science.gov (United States)

2010-07-01

... 40 Protection of Environment 1 2010-07-01 2010-07-01 false Supervision. 35.935-8 Section 35.935-8 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY GRANTS AND OTHER FEDERAL ASSISTANCE STATE AND LOCAL ASSISTANCE Grants for Construction of Treatment Works-Clean Water Act § 35.935-8 Supervision. In the case of any project involving Step 3,...

Risk-oriented banking supervision: understanding change of course

Directory of Open Access Journals (Sweden)

Vlasov K. A.

2017-11-01

Full Text Available in this article the existing model of national bank supervision, its substantial party are considered. By means of the legal analysis and comparative jurisprudence foreign models of bank supervision, the international standards of the «soft» right are investigated, the analysis of an opportunity and positive sides of change of approach of the operating bank supervision to substantial (risk-focused is made.

Active relearning for robust supervised classification of pulmonary emphysema

Science.gov (United States)

Raghunath, Sushravya; Rajagopalan, Srinivasan; Karwoski, Ronald A.; Bartholmai, Brian J.; Robb, Richard A.

2012-03-01

Radiologists are adept at recognizing the appearance of lung parenchymal abnormalities in CT scans. However, the inconsistent differential diagnosis, due to subjective aggregation, mandates supervised classification. Towards optimizing Emphysema classification, we introduce a physician-in-the-loop feedback approach in order to minimize uncertainty in the selected training samples. Using multi-view inductive learning with the training samples, an ensemble of Support Vector Machine (SVM) models, each based on a specific pair-wise dissimilarity metric, was constructed in less than six seconds. In the active relearning phase, the ensemble-expert label conflicts were resolved by an expert. This just-in-time feedback with unoptimized SVMs yielded 15% increase in classification accuracy and 25% reduction in the number of support vectors. The generality of relearning was assessed in the optimized parameter space of six different classifiers across seven dissimilarity metrics. The resultant average accuracy improved to 21%. The co-operative feedback method proposed here could enhance both diagnostic and staging throughput efficiency in chest radiology practice.

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.

Group Supervision in Psychotherapy. Main Findings from a Swedish Research Project on Psychotherapy Supervision in a Group Format

Science.gov (United States)

Ogren, Marie-Louise; Sundin, Eva C.

2009-01-01

Psychotherapy supervision is considered crucial for psychotherapists in training. During the last decades, group supervision has been a frequently used format in many countries. Until recently, very few studies had evaluated the small-group format for training of beginner psychotherapists and psychotherapy supervisors. This article aims to…

Safe semi-supervised learning based on weighted likelihood.

Science.gov (United States)

Kawakita, Masanori; Takeuchi, Jun'ichi

2014-05-01

We are interested in developing a safe semi-supervised learning that works in any situation. Semi-supervised learning postulates that n(') unlabeled data are available in addition to n labeled data. However, almost all of the previous semi-supervised methods require additional assumptions (not only unlabeled data) to make improvements on supervised learning. If such assumptions are not met, then the methods possibly perform worse than supervised learning. Sokolovska, Cappé, and Yvon (2008) proposed a semi-supervised method based on a weighted likelihood approach. They proved that this method asymptotically never performs worse than supervised learning (i.e., it is safe) without any assumption. Their method is attractive because it is easy to implement and is potentially general. Moreover, it is deeply related to a certain statistical paradox. However, the method of Sokolovska et al. (2008) assumes a very limited situation, i.e., classification, discrete covariates, n(')→∞ and a maximum likelihood estimator. In this paper, we extend their method by modifying the weight. We prove that our proposal is safe in a significantly wide range of situations as long as n≤n('). Further, we give a geometrical interpretation of the proof of safety through the relationship with the above-mentioned statistical paradox. Finally, we show that the above proposal is asymptotically safe even when n(')

Supervised Classification High-Resolution Remote-Sensing Image Based on Interval Type-2 Fuzzy Membership Function

Directory of Open Access Journals (Sweden)

Chunyan Wang

2018-05-01

Full Text Available Because of the degradation of classification accuracy that is caused by the uncertainty of pixel class and classification decisions of high-resolution remote-sensing images, we proposed a supervised classification method that is based on an interval type-2 fuzzy membership function for high-resolution remote-sensing images. We analyze the data features of a high-resolution remote-sensing image and construct a type-1 membership function model in a homogenous region by supervised sampling in order to characterize the uncertainty of the pixel class. On the basis of the fuzzy membership function model in the homogeneous region and in accordance with the 3σ criterion of normal distribution, we proposed a method for modeling three types of interval type-2 membership functions and analyze the different types of functions to improve the uncertainty of pixel class expressed by the type-1 fuzzy membership function and to enhance the accuracy of classification decision. According to the principle that importance will increase with a decrease in the distance between the original, upper, and lower fuzzy membership of the training data and the corresponding frequency value in the histogram, we use the weighted average sum of three types of fuzzy membership as the new fuzzy membership of the pixel to be classified and then integrated into the neighborhood pixel relations, constructing a classification decision model. We use the proposed method to classify real high-resolution remote-sensing images and synthetic images. Additionally, we qualitatively and quantitatively evaluate the test results. The results show that a higher classification accuracy can be achieved with the proposed algorithm.

Active link selection for efficient semi-supervised community detection

Science.gov (United States)

Yang, Liang; Jin, Di; Wang, Xiao; Cao, Xiaochun

2015-01-01

Several semi-supervised community detection algorithms have been proposed recently to improve the performance of traditional topology-based methods. However, most of them focus on how to integrate supervised information with topology information; few of them pay attention to which information is critical for performance improvement. This leads to large amounts of demand for supervised information, which is expensive or difficult to obtain in most fields. For this problem we propose an active link selection framework, that is we actively select the most uncertain and informative links for human labeling for the efficient utilization of the supervised information. We also disconnect the most likely inter-community edges to further improve the efficiency. Our main idea is that, by connecting uncertain nodes to their community hubs and disconnecting the inter-community edges, one can sharpen the block structure of adjacency matrix more efficiently than randomly labeling links as the existing methods did. Experiments on both synthetic and real networks demonstrate that our new approach significantly outperforms the existing methods in terms of the efficiency of using supervised information. It needs ~13% of the supervised information to achieve a performance similar to that of the original semi-supervised approaches. PMID:25761385

An Approach to Supervision for Doctoral and Entry-Level Group Counseling Students

Science.gov (United States)

Walsh, Robyn; Bambacus, Elizabeth; Gibson, Donna

2017-01-01

The purpose of this article is to provide a supervision approach to experiential groups that replaces professors with doctoral students in the chain of supervision, enlists a faculty member to provide supervision of supervision to the doctoral students, and translates supervision theory to meet the unique needs of group counseling supervision.…

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.

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

KAUST Repository

Escorcia, Victor

2018-04-05

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.

Guess Where? Actor-Supervision for Spatiotemporal Action Localization

KAUST Repository

Escorcia, Victor; Dao, Cuong D.; Jain, Mihir; Ghanem, Bernard; Snoek, Cees

2018-01-01

This paper addresses the problem of spatiotemporal localization of actions in videos. Compared to leading approaches, which all learn to localize based on carefully annotated boxes on training video frames, we adhere to a weakly-supervised solution that only requires a video class label. We introduce an actor-supervised architecture that exploits the inherent compositionality of actions in terms of actor transformations, to localize actions. We make two contributions. First, we propose actor proposals derived from a detector for human and non-human actors intended for images, which is linked over time by Siamese similarity matching to account for actor deformations. Second, we propose an actor-based attention mechanism that enables the localization of the actions from action class labels and actor proposals and is end-to-end trainable. Experiments on three human and non-human action datasets show actor supervision is state-of-the-art for weakly-supervised action localization and is even competitive to some fully-supervised alternatives.

Robust Semi-Supervised Manifold Learning Algorithm for Classification

Directory of Open Access Journals (Sweden)

Mingxia Chen

2018-01-01

Full Text Available In the recent years, manifold learning methods have been widely used in data classification to tackle the curse of dimensionality problem, since they can discover the potential intrinsic low-dimensional structures of the high-dimensional data. Given partially labeled data, the semi-supervised manifold learning algorithms are proposed to predict the labels of the unlabeled points, taking into account label information. However, these semi-supervised manifold learning algorithms are not robust against noisy points, especially when the labeled data contain noise. In this paper, we propose a framework for robust semi-supervised manifold learning (RSSML to address this problem. The noisy levels of the labeled points are firstly predicted, and then a regularization term is constructed to reduce the impact of labeled points containing noise. A new robust semi-supervised optimization model is proposed by adding the regularization term to the traditional semi-supervised optimization model. Numerical experiments are given to show the improvement and efficiency of RSSML on noisy data sets.

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…

Accurate Traffic Flow Prediction in Heterogeneous Vehicular Networks in an Intelligent Transport System Using a Supervised Non-Parametric Classifier

Directory of Open Access Journals (Sweden)

Hesham El-Sayed

2018-05-01

Full Text Available Heterogeneous vehicular networks (HETVNETs evolve from vehicular ad hoc networks (VANETs, which allow vehicles to always be connected so as to obtain safety services within intelligent transportation systems (ITSs. The services and data provided by HETVNETs should be neither interrupted nor delayed. Therefore, Quality of Service (QoS improvement of HETVNETs is one of the topics attracting the attention of researchers and the manufacturing community. Several methodologies and frameworks have been devised by researchers to address QoS-prediction service issues. In this paper, to improve QoS, we evaluate various traffic characteristics of HETVNETs and propose a new supervised learning model to capture knowledge on all possible traffic patterns. This model is a refinement of support vector machine (SVM kernels with a radial basis function (RBF. The proposed model produces better results than SVMs, and outperforms other prediction methods used in a traffic context, as it has lower computational complexity and higher prediction accuracy.

Is supervision necessary? Examining the effects of internet-based CBT training with and without supervision.

Science.gov (United States)

Rakovshik, Sarah G; McManus, Freda; Vazquez-Montes, Maria; Muse, Kate; Ougrin, Dennis

2016-03-01

To investigate the effect of Internet-based training (IBT), with and without supervision, on therapists' (N = 61) cognitive-behavioral therapy (CBT) skills in routine clinical practice. Participants were randomized into 3 conditions: (1) Internet-based training with use of a consultation worksheet (IBT-CW); (2) Internet-based training with CBT supervision via Skype (IBT-S); and (3) "delayed-training" controls (DTs), who did not receive the training until all data collection was completed. The IBT participants received access to training over a period of 3 months. CBT skills were evaluated at pre-, mid- and posttraining/wait using assessor competence ratings of recorded therapy sessions. Hierarchical linear analysis revealed that the IBT-S participants had significantly greater CBT competence at posttraining than did IBT-CW and DT participants at both the mid- and posttraining/wait assessment points. There were no significant differences between IBT-CW and the delayed (no)-training DTs. IBT programs that include supervision may be a scalable and effective method of disseminating CBT into routine clinical practice, particularly for populations without ready access to more-traditional "live" methods of training. There was no evidence for a significant effect of IBT without supervision over a nontraining control, suggesting that merely providing access to IBT programs may not be an effective method of disseminating CBT to routine clinical practice. (c) 2016 APA, all rights reserved).

Ethical Issues in the Conduct of Supervision.

Science.gov (United States)

Sherry, Patrick

1991-01-01

Uses American Psychological Association code of ethics to understand ethical issues present in the conduct of supervision. Discusses ethical issues of responsibility, client and supervisee welfare, confidentiality, competency, moral and legal standards, public statements, and professional relationships in relation to supervision. (Author/NB)

Challenges for Better thesis supervision.

Science.gov (United States)

Ghadirian, Laleh; Sayarifard, Azadeh; Majdzadeh, Reza; Rajabi, Fatemeh; Yunesian, Masoud

2014-01-01

Conduction of thesis by the students is one of their major academic activities. Thesis quality and acquired experiences are highly dependent on the supervision. Our study is aimed at identifing the challenges in thesis supervision from both students and faculty members point of view. This study was conducted using individual in-depth interviews and Focus Group Discussions (FGD). The participants were 43 students and faculty members selected by purposive sampling. It was carried out in Tehran University of Medical Sciences in 2012. Data analysis was done concurrently with data gathering using content analysis method. Our data analysis resulted in 162 codes, 17 subcategories and 4 major categories, "supervisory knowledge and skills", "atmosphere", "bylaws and regulations relating to supervision" and "monitoring and evaluation". This study showed that more attention and planning in needed for modifying related rules and regulations, qualitative and quantitative improvement in mentorship training, research atmosphere improvement and effective monitoring and evaluation in supervisory area.

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...... supervision. While joint supervision has become widely used, its learning dynamics remains under-researched and this paper aims to address these gaps in research by exploring learning opportunities in doctoral supervision with two supervisors. The study explores how the tensions in scientific discussion...... between supervisors can become learning opportunities. We combine two different theoretical perspectives, using participation and positioning theory as a sociocultural perspective and variation theory as an individual constructivist perspective on learning. Based on our analysis of a complex episode we...

Supervision as transformative leadership in the context of university ...

African Journals Online (AJOL)

This article discusses different models of supervision and promotion of Masters', Doctoral and PhD students. It argues that leadership is inherent in and underpins any model of supervision or promotion of students. The article advances a view that supervision and promotion of the said students should be transformative ...

18 CFR 367.80 - Supervision and engineering.

Science.gov (United States)

2010-04-01

... engineering. 367.80 Section 367.80 Conservation of Power and Water Resources FEDERAL ENERGY REGULATORY... ACT Operating Expense Instructions § 367.80 Supervision and engineering. (a) The supervision and engineering includible in the operating expense accounts must consist of the pay and expenses of...

Wellness Model of Supervision: A Comparative Analysis

Science.gov (United States)

Lenz, A. Stephen; Sangganjanavanich, Varunee Faii; Balkin, Richard S.; Oliver, Marvarene; Smith, Robert L.

2012-01-01

This quasi-experimental study compared the effectiveness of the Wellness Model of Supervision (WELMS; Lenz & Smith, 2010) with alternative supervision models for developing wellness constructs, total personal wellness, and helping skills among counselors-in-training. Participants were 32 master's-level counseling students completing their…

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

State Supervision and Control of Radiation Protection

CERN Document Server

2001-01-01

Radiation Protection Centre is carrying state supervision and control of radiation protection. The main objective of state supervision and control of radiation protection is assessing how licensees comply with requirements of the appropriate legislation and enforcement. Summary of inspections conducted in 1999-2001 is presented.

ECB Banking Supervision and beyond

OpenAIRE

Lannoo, Karel

2014-01-01

With publication of the results of its Comprehensive Assessment at the end of October 2014, the European Central Bank has set the standard for its new mandate as supervisor. But this was only the beginning. The heavy work started in early November, with the day-to-day supervision of the 120 most significant banks in the eurozone under the Single Supervisory Mechanism. The centralisation of the supervision in the eurozone will pose a number of challenges for the ECB in the coming months and ye...

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.

Radiation supervision - NPPs A-1, V-1, V-2

International Nuclear Information System (INIS)

2000-01-01

In this leaflet the radiation supervision of the nuclear power plants A-1, V-1, V-2 is presented. Off-site radiation supervision laboratory is a part of monitoring scheme of the NPPs. More than 1150 samples are taken from the environment annually. The tele-dosimetric system was constructed to improve the quality of the Bohunice NPPs operation impacts supervision. It has been running in a continuous operation from 1992 and providing supervision of the nuclear power plant off-site area within 25 kilometres. The tele-dosimetric system is described

nRC: non-coding RNA Classifier based on structural features.

Science.gov (United States)

Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

2017-01-01

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

Science.gov (United States)

Pasquet-Itam, J.; Pasquet, J.

2018-04-01

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope. A table of the candidates is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/611/A97

The LHC string2 supervision system

CERN Document Server

Mayya, Y S; Sicard, Claude Henri

2002-01-01

This paper describes the implementation of the supervision system for the LHC Prototype Full-Cell also known as String 2. The supervision application is based on a commercial package targeted to industrial controls, but because of the complexity and the specifics of such a system, integration with custom components is necessary in order to merge the industrial requirements with the specificity of the accelerator controls.

18 CFR 367.9110 - Account 911, Supervision.

Science.gov (United States)

2010-04-01

... account must include the cost of labor and expenses incurred in the general direction and supervision of sales activities, except merchandising. Direct supervision of a specific activity, such as demonstrating, selling, or advertising, must be charged to the account wherein the costs of such activity are included...

Theory of Multiple Intelligences at Teacher Supervision

Directory of Open Access Journals (Sweden)

İzzet Döş

2012-07-01

Full Text Available This study aims to determine views of teachers and supervisors related to the multiple intelligences in students’ learning that they took into consideration in the evaluation of teachers during lesson supervision. The study was conducted with 5 supervisors who work at Kahramanmaraş provincial directorate of national education and 10 teachers who work at primary schools in the centre of Kahramanmaraş in 2011-2012 year. Data was gathered with the help of interview form consisting of five open-ended questions. In the analysis of the data content analysis which is one of the qualitative research methods. According to the results of the analysis, it has been found that usage of multiple intelligences theory in the evaluation students’ learning during supervision enabled them to evaluate students’ learning in a more detailed way. It also made it possible for the supervisors to examine supervision evaluations at different levels. It was also mentioned that supervisions made according to multiple intelligence theory has some limitations.

An intelligent fault diagnosis method of rolling bearings based on regularized kernel Marginal Fisher analysis

International Nuclear Information System (INIS)

Jiang Li; Shi Tielin; Xuan Jianping

2012-01-01

Generally, the vibration signals of fault bearings are non-stationary and highly nonlinear under complicated operating conditions. Thus, it's a big challenge to extract optimal features for improving classification and simultaneously decreasing feature dimension. Kernel Marginal Fisher analysis (KMFA) is a novel supervised manifold learning algorithm for feature extraction and dimensionality reduction. In order to avoid the small sample size problem in KMFA, we propose regularized KMFA (RKMFA). A simple and efficient intelligent fault diagnosis method based on RKMFA is put forward and applied to fault recognition of rolling bearings. So as to directly excavate nonlinear features from the original high-dimensional vibration signals, RKMFA constructs two graphs describing the intra-class compactness and the inter-class separability, by combining traditional manifold learning algorithm with fisher criteria. Therefore, the optimal low-dimensional features are obtained for better classification and finally fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories of bearings. The experimental results demonstrate that the proposed approach improves the fault classification performance and outperforms the other conventional approaches.

Kontraktetablering i supervision

DEFF Research Database (Denmark)

Mortensen, Karen Vibeke; Jacobsen, Claus Haugaard

2007-01-01

Kapitlet behandler kontraktetablering i supervision, et element, der ofte er blevet negligeret eller endog helt forbigået ved indledningen af supervisionsforløb. Sikre aftaler om emner som tid, sted, procedurer for fremlæggelse, fortrolighed, ansvarsfordeling og evaluering skaber imidlertid trygh...

Automated diagnosis of dry eye using infrared thermography images

Science.gov (United States)

Acharya, U. Rajendra; Tan, Jen Hong; Koh, Joel E. W.; Sudarshan, Vidya K.; Yeo, Sharon; Too, Cheah Loon; Chua, Chua Kuang; Ng, E. Y. K.; Tong, Louis

2015-07-01

Dry Eye (DE) is a condition of either decreased tear production or increased tear film evaporation. Prolonged DE damages the cornea causing the corneal scarring, thinning and perforation. There is no single uniform diagnosis test available to date; combinations of diagnostic tests are to be performed to diagnose DE. The current diagnostic methods available are subjective, uncomfortable and invasive. Hence in this paper, we have developed an efficient, fast and non-invasive technique for the automated identification of normal and DE classes using infrared thermography images. The features are extracted from nonlinear method called Higher Order Spectra (HOS). Features are ranked using t-test ranking strategy. These ranked features are fed to various classifiers namely, K-Nearest Neighbor (KNN), Nave Bayesian Classifier (NBC), Decision Tree (DT), Probabilistic Neural Network (PNN), and Support Vector Machine (SVM) to select the best classifier using minimum number of features. Our proposed system is able to identify the DE and normal classes automatically with classification accuracy of 99.8%, sensitivity of 99.8%, and specificity if 99.8% for left eye using PNN and KNN classifiers. And we have reported classification accuracy of 99.8%, sensitivity of 99.9%, and specificity if 99.4% for right eye using SVM classifier with polynomial order 2 kernel.

Supervised hub-detection for brain connectivity

DEFF Research Database (Denmark)

Kasenburg, Niklas; Liptrot, Matthew George; Reislev, Nina Linde

2016-01-01

, but can smooth discriminative signals in the population, degrading predictive performance. We present a novel hub-detection optimized for supervised learning that both clusters network nodes based on population level variation in connectivity and also takes the learning problem into account. The found......A structural brain network consists of physical connections between brain regions. Brain network analysis aims to find features associated with a parameter of interest through supervised prediction models such as regression. Unsupervised preprocessing steps like clustering are often applied...... hubs are a low-dimensional representation of the network and are chosen based on predictive performance as features for a linear regression. We apply our method to the problem of finding age-related changes in structural connectivity. We compare our supervised hub-detection (SHD) to an unsupervised hub...

Structural Evolution of the R-T Phase Boundary in KNN-Based Ceramics

KAUST Repository

Lv, Xiang

2017-10-04

Although a rhombohedral-tetragonal (R-T) phase boundary is known to substantially enhance the piezoelectric properties of potassium-sodium niobate ceramics, the structural evolution of the R-T phase boundary itself is still unclear. In this work, the structural evolution of R-T phase boundary from -150 °C to 200 °C is investigated in (0.99-x)K0.5Na0.5Nb1-ySbyO3-0.01CaSnO3-xBi0.5K0.5HfO3 (where x=0~0.05 with y=0.035, and y=0~0.07 with x=0.03) ceramics. Through temperature-dependent powder X-ray diffraction (XRD) patterns and Raman spectra, the structural evolution was determined to be Rhombohedral (R, <-125 °C) → Rhombohedral+Orthorhombic (R+O, -125 °C to 0 °C) → Rhombohedral+Tetragonal (R+T, 0 °C to 150 °C) → dominating Tetragonal (T, 200 °C to Curie temperature (TC)) → Cubic (C, >TC). In addition, the enhanced electrical properties (e.g., a direct piezoelectric coefficient (d33) of ~450±5 pC/N, a conversion piezoelectric coefficient (d33*) of ~580±5 pm/V, an electromechanical coupling factor (kp) of ~0.50±0.02, and TC~250 °C), fatigue-free behavior, and good thermal stability were exhibited by the ceramics possessing the R-T phase boundary. This work improves understanding of the physical mechanism behind the R-T phase boundary in KNN-based ceramics and is an important step towards their adoption in practical applications. This article is protected by copyright. All rights reserved.

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.

Principals Performance of Supervision of Instructions in Public ...

African Journals Online (AJOL)

Data were collected from a sample of 604 out of 1640 teachers using stratified ... supervision of instructions in the school since effective supervision improves ... and reduces incidence of students' involvement in examination malpractices.

Supervision and inspection plans of plants activities

International Nuclear Information System (INIS)

Feijoo, J. P.

2009-01-01

Any idea of hierarchization between supervisor and supervised in inspection and supervision activities should necessarily be dismissed, and the independence of the supervisor when executing has tasks should be guaranteed. The inspection and supervision program enable the detection and resolution of materials and human problems alike. In addition, they are a solution to anticipate potential problems in the future, which results in a very significant reduction of industrial accidents and human errors, as well as better use and upkeep of equipment. With these programs we improve our management and our work, and without a doubt they help to strengthen the safety culture in Cofrentes Nuclear Power Plant. (Author)

Development of well construction and workover supervising in Russian Federation

International Nuclear Information System (INIS)

Sizov, A; Boyarko, G; Shenderova, I

2014-01-01

Despite long history of drilling supervising it still has a number of uncertainties. The period of rapid rise in supervising development at the beginning of the 90's changed in the 2000's. The necessity in the development of this sphere is obvious. The author describes the history of supervising, period of its market condition adaptation. The research also gives principles methods of supervising development and first steps for its position improvement

Applying Services Marketing Principles to Postgraduate Supervision

Science.gov (United States)

Dann, Stephen

2008-01-01

Purpose: The paper aims to describe the application of two key service quality frameworks for improving the delivery of postgraduate research supervision. The services quality frameworks are used to identify key areas of overlap between services marketing practice and postgraduate supervision that can be used by the supervisor to improve research…

School Counselor Perceptions of Administrative Supervision Practices

Science.gov (United States)

Eddings, Geoffrey Creighton

2012-01-01

This study examined the perceptions of school counselors regarding administrative supervision practices in K-12 public schools in South Carolina. Specifically, the goal was to gain insight into how school counselors view current building-level supervision practices in relation to Pajak's Twelve Dimensions of Supervisory Practice, as well as how…

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 CLAUSES AND FORMS SOLICITATION PROVISIONS AND CONTRACT CLAUSES Texts of Provisions and Clauses 852.236-78 Government supervision. As prescribed in...

28 CFR 810.3 - Consequences of violating the conditions of supervision.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 2 2010-07-01 2010-07-01 false Consequences of violating the conditions of supervision. 810.3 Section 810.3 Judicial Administration COURT SERVICES AND OFFENDER SUPERVISION AGENCY FOR THE DISTRICT OF COLUMBIA COMMUNITY SUPERVISION: ADMINISTRATIVE SANCTIONS § 810.3 Consequences of violating the conditions of supervision. ...

Clinical supervision reflected in a Danish DPCCQ sample

DEFF Research Database (Denmark)

Nielsen, Jan; Jacobsen, Claus Haugaard

Core Questionnaire (DPCCQ) has only few questions on supervision. To rectify this limitation, a recent Danish version of the DPCCQ included two new sections on supervision, one focusing on supervisees and another on supervisors and their supervisory training. This paper presents our initial findings...... on giving and receiving clinical supervision as reported by therapists in Denmark. Method: Currently, the Danish sample consists of 350 clinical psychologist doing psychotherapy who completed DPCCQ. Data are currently being prepared for statistical analysis. Results: This paper will focus primarily...... on describing the amount and type of supervision received and given by the sample. Findings from these descriptive statistics will be compared within the sample across demographic parameters such as age and sex, and professional characteristics such as career level, theoretical preferences, type of clients...

A comparison of supervised classification methods for the prediction of substrate type using multibeam acoustic and legacy grain-size data.

Directory of Open Access Journals (Sweden)

David Stephens

Full Text Available Detailed seabed substrate maps are increasingly in demand for effective planning and management of marine ecosystems and resources. It has become common to use remotely sensed multibeam echosounder data in the form of bathymetry and acoustic backscatter in conjunction with ground-truth sampling data to inform the mapping of seabed substrates. Whilst, until recently, such data sets have typically been classified by expert interpretation, it is now obvious that more objective, faster and repeatable methods of seabed classification are required. This study compares the performances of a range of supervised classification techniques for predicting substrate type from multibeam echosounder data. The study area is located in the North Sea, off the north-east coast of England. A total of 258 ground-truth samples were classified into four substrate classes. Multibeam bathymetry and backscatter data, and a range of secondary features derived from these datasets were used in this study. Six supervised classification techniques were tested: Classification Trees, Support Vector Machines, k-Nearest Neighbour, Neural Networks, Random Forest and Naive Bayes. Each classifier was trained multiple times using different input features, including i the two primary features of bathymetry and backscatter, ii a subset of the features chosen by a feature selection process and iii all of the input features. The predictive performances of the models were validated using a separate test set of ground-truth samples. The statistical significance of model performances relative to a simple baseline model (Nearest Neighbour predictions on bathymetry and backscatter were tested to assess the benefits of using more sophisticated approaches. The best performing models were tree based methods and Naive Bayes which achieved accuracies of around 0.8 and kappa coefficients of up to 0.5 on the test set. The models that used all input features didn't generally perform well

Caregivers' satisfaction and supervision of primary health care ...

African Journals Online (AJOL)

Caregivers' satisfaction and supervision of primary health care services in Nnewi, ... made in the reduction of childhood health indicators in the previous decade, ... supervision of PHCs should also improve the quality of child health services.

Exploring Organizational Barriers to Strengthening Clinical Supervision of Psychiatric Nursing Staff

DEFF Research Database (Denmark)

Gonge, Henrik; Buus, Niels

2016-01-01

This article reports findings from a longitudinal controlled intervention study of 115 psychiatric nursing staff. The twofold objective of the study was: (a) To test whether the intervention could increase clinical supervision participation and effectiveness of existing supervision practices, and...... in the experienced effectiveness of supervision. It is concluded that organizational support is an imperative for implementation of clinical supervision......., and (b) To explore organizational constraints to implementation of these strengthened practices. Questionnaire responses and registration of participation in clinical supervision were registered prior and subsequent to the intervention consisting of an action learning oriented reflection on staff......'s existing clinical supervision practices. Major organizational changes in the intervention group during the study period obstructed the implementation of strengthened clinical supervision practices, but offered an opportunity for studying the influences of organizational constraints. The main findings were...

Supervised Convolutional Sparse Coding

KAUST Repository

Affara, Lama Ahmed; Ghanem, Bernard; Wonka, Peter

2018-01-01

coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements

Institutional Arrangement of Financial Markets Supervision: The Case of the Czech Republic

OpenAIRE

Musílek, Petr

2008-01-01

The paper deals with institutional arrangement of financial supervision in the Czech Republic. Financial markets are composed of partial financial segments specialized in individual types of financial instruments and individual customer groups. Financial institutions gradually transform into financial supermarkets. There are several models of institutional arrangement of financial supervision (integrated financial supervision model, sectional financial supervision model, financial supervision...

Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets.

Science.gov (United States)

McAllister, Patrick; Zheng, Huiru; Bond, Raymond; Moorhead, Anne

2018-04-01

Obesity is increasing worldwide and can cause many chronic conditions such as type-2 diabetes, heart disease, sleep apnea, and some cancers. Monitoring dietary intake through food logging is a key method to maintain a healthy lifestyle to prevent and manage obesity. Computer vision methods have been applied to food logging to automate image classification for monitoring dietary intake. In this work we applied pretrained ResNet-152 and GoogleNet convolutional neural networks (CNNs), initially trained using ImageNet Large Scale Visual Recognition Challenge (ILSVRC) dataset with MatConvNet package, to extract features from food image datasets; Food 5K, Food-11, RawFooT-DB, and Food-101. Deep features were extracted from CNNs and used to train machine learning classifiers including artificial neural network (ANN), support vector machine (SVM), Random Forest, and Naive Bayes. Results show that using ResNet-152 deep features with SVM with RBF kernel can accurately detect food items with 99.4% accuracy using Food-5K validation food image dataset and 98.8% with Food-5K evaluation dataset using ANN, SVM-RBF, and Random Forest classifiers. Trained with ResNet-152 features, ANN can achieve 91.34%, 99.28% when applied to Food-11 and RawFooT-DB food image datasets respectively and SVM with RBF kernel can achieve 64.98% with Food-101 image dataset. From this research it is clear that using deep CNN features can be used efficiently for diverse food item image classification. The work presented in this research shows that pretrained ResNet-152 features provide sufficient generalisation power when applied to a range of food image classification tasks. Copyright © 2018 Elsevier Ltd. All rights reserved.

The technical supervision interface

CERN Document Server

Sollander, P

1998-01-01

The Technical Control Room (TCR) is currently using 30 different applications for the remote supervision of the technical infrastructure at CERN. These applications have all been developed with the CERN made Uniform Man Machine Interface (UMMI) tools built in 1990. However, the visualization technology has evolved phenomenally since 1990, the Technical Data Server (TDS) has radically changed our control system architecture, and the standardization and the maintenance of the UMMI applications have become important issues as their number increases. The Technical Supervision Interface is intended to replace the UMMI and solve the above problems. Using a standard WWW-browser for the display, it will be inherently multi-platform and hence available for control room operators, equipment specialists and on-call personnel.

Generating prior probabilities for classifiers of brain tumours using belief networks

Directory of Open Access Journals (Sweden)

Arvanitis Theodoros N

2007-09-01

Full Text Available Abstract Background Numerous methods for classifying brain tumours based on magnetic resonance spectra and imaging have been presented in the last 15 years. Generally, these methods use supervised machine learning to develop a classifier from a database of cases for which the diagnosis is already known. However, little has been published on developing classifiers based on mixed modalities, e.g. combining imaging information with spectroscopy. In this work a method of generating probabilities of tumour class from anatomical location is presented. Methods The method of "belief networks" is introduced as a means of generating probabilities that a tumour is any given type. The belief networks are constructed using a database of paediatric tumour cases consisting of data collected over five decades; the problems associated with using this data are discussed. To verify the usefulness of the networks, an application of the method is presented in which prior probabilities were generated and combined with a classification of tumours based solely on MRS data. Results Belief networks were constructed from a database of over 1300 cases. These can be used to generate a probability that a tumour is any given type. Networks are presented for astrocytoma grades I and II, astrocytoma grades III and IV, ependymoma, pineoblastoma, primitive neuroectodermal tumour (PNET, germinoma, medulloblastoma, craniopharyngioma and a group representing rare tumours, "other". Using the network to generate prior probabilities for classification improves the accuracy when compared with generating prior probabilities based on class prevalence. Conclusion Bayesian belief networks are a simple way of using discrete clinical information to generate probabilities usable in classification. The belief network method can be robust to incomplete datasets. Inclusion of a priori knowledge is an effective way of improving classification of brain tumours by non-invasive methods.

19 CFR 19.38 - Supervision of exportation.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 1 2010-04-01 2010-04-01 false Supervision of exportation. 19.38 Section 19.38 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY CUSTOMS WAREHOUSES, CONTAINER STATIONS AND CONTROL OF MERCHANDISE THEREIN Duty-Free Stores § 19.38 Supervision of exportation. (a) Sales...

Supervised learning for the automated transcription of spacer classification from spoligotype films

Directory of Open Access Journals (Sweden)

Abernethy Neil

2009-08-01

Full Text Available Abstract Background Molecular genotyping of bacteria has revolutionized the study of tuberculosis epidemiology, yet these established laboratory techniques typically require subjective and laborious interpretation by trained professionals. In the context of a Tuberculosis Case Contact study in The Gambia we used a reverse hybridization laboratory assay called spoligotype analysis. To facilitate processing of spoligotype images we have developed tools and algorithms to automate the classification and transcription of these data directly to a database while allowing for manual editing. Results Features extracted from each of the 1849 spots on a spoligo film were classified using two supervised learning algorithms. A graphical user interface allows manual editing of the classification, before export to a database. The application was tested on ten films of differing quality and the results of the best classifier were compared to expert manual classification, giving a median correct classification rate of 98.1% (inter quartile range: 97.1% to 99.2%, with an automated processing time of less than 1 minute per film. Conclusion The software implementation offers considerable time savings over manual processing whilst allowing expert editing of the automated classification. The automatic upload of the classification to a database reduces the chances of transcription errors.

Discriminative semi-supervised feature selection via manifold regularization.

Science.gov (United States)

Xu, Zenglin; King, Irwin; Lyu, Michael Rung-Tsong; Jin, Rong

2010-07-01

Feature selection has attracted a huge amount of interest in both research and application communities of data mining. We consider the problem of semi-supervised feature selection, where we are given a small amount of labeled examples and a large amount of unlabeled examples. Since a small number of labeled samples are usually insufficient for identifying the relevant features, the critical problem arising from semi-supervised feature selection is how to take advantage of the information underneath the unlabeled data. To address this problem, we propose a novel discriminative semi-supervised feature selection method based on the idea of manifold regularization. The proposed approach selects features through maximizing the classification margin between different classes and simultaneously exploiting the geometry of the probability distribution that generates both labeled and unlabeled data. In comparison with previous semi-supervised feature selection algorithms, our proposed semi-supervised feature selection method is an embedded feature selection method and is able to find more discriminative features. We formulate the proposed feature selection method into a convex-concave optimization problem, where the saddle point corresponds to the optimal solution. To find the optimal solution, the level method, a fairly recent optimization method, is employed. We also present a theoretic proof of the convergence rate for the application of the level method to our problem. Empirical evaluation on several benchmark data sets demonstrates the effectiveness of the proposed semi-supervised feature selection method.

Evaluation Of Loan Disbursement And Repayment Of Supervised ...

African Journals Online (AJOL)

Evaluation Of Loan Disbursement And Repayment Of Supervised Credit ... bank as regard to loan supervision was scored low as a result of low rate of loan recovery, ... strategy to recover outstanding debts and reduce interest charge on loans.

Constrained Deep Weak Supervision for Histopathology Image Segmentation.

Science.gov (United States)

Jia, Zhipeng; Huang, Xingyi; Chang, Eric I-Chao; Xu, Yan

2017-11-01

In this paper, we develop a new weakly supervised learning algorithm to learn to segment cancerous regions in histopathology images. This paper is under a multiple instance learning (MIL) framework with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: 1) we build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCNs) in which image-to-image weakly-supervised learning is performed; 2) we develop a DWS formulation to exploit multi-scale learning under weak supervision within FCNs; and 3) constraints about positive instances are introduced in our approach to effectively explore additional weakly supervised information that is easy to obtain and enjoy a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates the state-of-the-art results on large-scale histopathology image data sets and can be applied to various applications in medical imaging beyond histopathology images, such as MRI, CT, and ultrasound images.

Supervising undergraduate research: a collective approach utilising groupwork and peer support.

Science.gov (United States)

Baker, Mary-Jane; Cluett, Elizabeth; Ireland, Lorraine; Reading, Sheila; Rourke, Susan

2014-04-01

Nursing education now requires graduate entry for professional registration. The challenge is to ensure that students develop independence and team working in a resource effective manner. The dissertation is one opportunity for this. To evaluate changing from individual dissertation supervision to group peer supervision. Group supervision was implemented for one cohort. Dissertation outcomes were compared with two previous cohorts. Student evaluative data was assessed. Group supervision did not adversely affect dissertation outcomes (p=0.85). 88% of students reported peer supervision to be helpful, with themes being 'support and sharing', and 'progress and moving forward'. Peer group support provided consistent supervision harnessing the energy and resources of the students and Faculty, without adversely affecting outcomes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Contrast-based fully automatic segmentation of white matter hyperintensities: method and validation.

Directory of Open Access Journals (Sweden)

Thomas Samaille

Full Text Available White matter hyperintensities (WMH on T2 or FLAIR sequences have been commonly observed on MR images of elderly people. They have been associated with various disorders and have been shown to be a strong risk factor for stroke and dementia. WMH studies usually required visual evaluation of WMH load or time-consuming manual delineation. This paper introduced WHASA (White matter Hyperintensities Automated Segmentation Algorithm, a new method for automatically segmenting WMH from FLAIR and T1 images in multicentre studies. Contrary to previous approaches that were based on intensities, this method relied on contrast: non linear diffusion filtering alternated with watershed segmentation to obtain piecewise constant images with increased contrast between WMH and surroundings tissues. WMH were then selected based on subject dependant automatically computed threshold and anatomical information. WHASA was evaluated on 67 patients from two studies, acquired on six different MRI scanners and displaying a wide range of lesion load. Accuracy of the segmentation was assessed through volume and spatial agreement measures with respect to manual segmentation; an intraclass correlation coefficient (ICC of 0.96 and a mean similarity index (SI of 0.72 were obtained. WHASA was compared to four other approaches: Freesurfer and a thresholding approach as unsupervised methods; k-nearest neighbours (kNN and support vector machines (SVM as supervised ones. For these latter, influence of the training set was also investigated. WHASA clearly outperformed both unsupervised methods, while performing at least as good as supervised approaches (ICC range: 0.87-0.91 for kNN; 0.89-0.94 for SVM. Mean SI: 0.63-0.71 for kNN, 0.67-0.72 for SVM, and did not need any training set.

A Delphi Study and Initial Validation of Counselor Supervision Competencies

Science.gov (United States)

Neuer Colburn, Anita A.; Grothaus, Tim; Hays, Danica G.; Milliken, Tammi

2016-01-01

The authors addressed the lack of supervision training standards for doctoral counseling graduates by developing and validating an initial list of supervision competencies. They used content analysis, Delphi polling, and content validity methods to generate a list, vetted by 2 different panels of supervision experts, of 33 competencies grouped…

7 CFR 550.32 - Project supervision and responsibilities.

Science.gov (United States)

2010-01-01

... Management of Agreements Program Management § 550.32 Project supervision and responsibilities. (a) The... with a project plan for use for external peer review. ... 7 Agriculture 6 2010-01-01 2010-01-01 false Project supervision and responsibilities. 550.32...

Ship-Iceberg Discrimination in Sentinel-2 Multispectral Imagery by Supervised Classification

Directory of Open Access Journals (Sweden)

Peder Heiselberg

2017-11-01

Full Text Available The European Space Agency Sentinel-2 satellites provide multispectral images with pixel sizes down to 10 m. This high resolution allows for fast and frequent detection, classification and discrimination of various objects in the sea, which is relevant in general and specifically for the vast Arctic environment. We analyze several sets of multispectral image data from Denmark and Greenland fall and winter, and describe a supervised search and classification algorithm based on physical parameters that successfully finds and classifies all objects in the sea with reflectance above a threshold. It discriminates between objects like ships, islands, wakes, and icebergs, ice floes, and clouds with accuracy better than 90%. Pan-sharpening the infrared bands leads to classification and discrimination of ice floes and clouds better than 95%. For complex images with abundant ice floes or clouds, however, the false alarm rate dominates for small non-sailing boats.

Enhanced manifold regularization for semi-supervised classification.

Science.gov (United States)

Gan, Haitao; Luo, Zhizeng; Fan, Yingle; Sang, Nong

2016-06-01

Manifold regularization (MR) has become one of the most widely used approaches in the semi-supervised learning field. It has shown superiority by exploiting the local manifold structure of both labeled and unlabeled data. The manifold structure is modeled by constructing a Laplacian graph and then incorporated in learning through a smoothness regularization term. Hence the labels of labeled and unlabeled data vary smoothly along the geodesics on the manifold. However, MR has ignored the discriminative ability of the labeled and unlabeled data. To address the problem, we propose an enhanced MR framework for semi-supervised classification in which the local discriminative information of the labeled and unlabeled data is explicitly exploited. To make full use of labeled data, we firstly employ a semi-supervised clustering method to discover the underlying data space structure of the whole dataset. Then we construct a local discrimination graph to model the discriminative information of labeled and unlabeled data according to the discovered intrinsic structure. Therefore, the data points that may be from different clusters, though similar on the manifold, are enforced far away from each other. Finally, the discrimination graph is incorporated into the MR framework. In particular, we utilize semi-supervised fuzzy c-means and Laplacian regularized Kernel minimum squared error for semi-supervised clustering and classification, respectively. Experimental results on several benchmark datasets and face recognition demonstrate the effectiveness of our proposed method.

Hybrid classifiers methods of data, knowledge, and classifier combination

CERN Document Server

Wozniak, Michal

2014-01-01

This book delivers a definite and compact knowledge on how hybridization can help improving the quality of computer classification systems. In order to make readers clearly realize the knowledge of hybridization, this book primarily focuses on introducing the different levels of hybridization and illuminating what problems we will face with as dealing with such projects. In the first instance the data and knowledge incorporated in hybridization were the action points, and then a still growing up area of classifier systems known as combined classifiers was considered. This book comprises the aforementioned state-of-the-art topics and the latest research results of the author and his team from Department of Systems and Computer Networks, Wroclaw University of Technology, including as classifier based on feature space splitting, one-class classification, imbalance data, and data stream classification.

The Relationships between Doctoral Students’ Perceptions of Supervision and Burnout

Directory of Open Access Journals (Sweden)

Solveig Cornér

2017-06-01

Full Text Available Aim/Purpose: Both the quality and the quantity of doctoral supervision have been identified as central determinants of the doctoral journey. However, there is a gap in our understanding of how supervision activities are associated with lack of wellbeing, such as burnout, and also to completion of the studies among doctoral students. Background:\tThe study explored doctoral students’ perceptions of different aspects of supervision including the primary sources, frequency, expressed satisfaction and their interrelation with experienced stress, exhaustion and cynicism. Methodology: Altogether 248 doctoral students from three Finnish universities representing social sciences, arts and humanities, and natural and life sciences responded to an adapted version of a Doctoral Experience Survey. A combination of several measures was used to investigate the students’ experiences of supervision and burnout. Contribution:\tThe results showed that students benefit from having several and different kinds of supervision activities. Various sources contribute not only to experiences of the doctoral journey and burnout, but also to the completion of the studies. Findings: Experienced lack of satisfaction with supervision and equality within the researcher community and a low frequency of supervision were related to experiences of burnout. Experiences of burnout were connected to students’ attrition intentions. Attrition intentions were related to source of supervision, the form of thesis, and inadequate supervision frequency. Frequency was related to both experience of burnout and likelihood of attrition. Recommendations for Practitioners: A recommendation developed from this research is to assist doctoral students with sufficient support, especially equality within the scholarly community and frequency of supervision. Further, greater emphasis could be put on group supervision and other collective forms of supervision. It is important that doctoral

Optimization of safety production supervision mode of coalmining enterprises

Energy Technology Data Exchange (ETDEWEB)

Zhou, M.; Xiao, Z. [China University of Mining and Technology, Xuzhou (China). School of Management

2005-12-01

In view of the fact that safety production supervision of coal mines in China features low efficacy, this paper applies principles of cybernetics to simulate the dynamic process of safety supervision, and proposes that institutional variables be controlled to support intermediate goals, which in turn contribute to the ultimate safety production objective. Rather than focussing all attention on safety issues of working faces, supervising departments of coalmines are advised to pay much more attention to institutional factors that may impact people's attitude and behavior, which are responsible for most coalmine accidents. It is believed that such a shift of attention can effectively reduce coalmining production accidents and greatly enhance supervision efficacy. 8 refs., 5 figs.

Preparing supervisors to provide safeguarding supervision for healthcare staff.

Science.gov (United States)

Smikle, Marcia

2017-11-28

This paper outlines why experienced supervisors at a London healthcare provider received skills training so they could offer safeguarding supervision to front-line colleagues with case management responsibilities for vulnerable children and young people. It examines how supervisors use the main functions of supervision and a cycle of reflection in clinical practice with supervisees. As well as the professional issues encountered by supervisors in relation to the benefits, the challenges of providing supervision and the action required to make safeguarding supervision a part of the organisational culture are also explored. ©2017 RCN Publishing Company Ltd. All rights reserved. Not to be copied, transmitted or recorded in any way, in whole or part, without prior permission of the publishers.

28 CFR 2.206 - Travel approval and transfers of supervision.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Travel approval and transfers of supervision. 2.206 Section 2.206 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Supervised Releasees § 2.206 Travel approval and...

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.

Alternative approaches to postgraduate supervision: A planning tool ...

African Journals Online (AJOL)

Increased demands on academics due to the changing work and higher educational environments challenge traditional approaches to postgraduate supervision. Supervisors often tend to follow the apprenticeship approach uncritically. Supervisors therefore need to be aware of alternative approaches to supervision and of ...

Supervision is also about Addressing the Group Dynamics

DEFF Research Database (Denmark)

Jensen, Lars Peter; Hansen, S.

2003-01-01

that many students are having difficulties with practical issues such as collaboration, communication, and project management. Most supervisors either ignore this demand, because they do not find it important or they find it frustrating, because they do not know, how to supervise group dynamics......An important aspect of the problem based and project organized study at Aalborg University is the supervision of the project groups. At the basic education (first year) it is stated in the curriculum that part of the supervisors' job is to deal with group dynamics. This is due to the experience...... as well as at Aalborg University. The first visible result has been participating supervisors telling us that the course has inspired them to try supervising group dynamics in the future. This paper will explore some aspects of supervising group dynamics as well as, how to develop the Aalborg model...

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.

Nursing supervision for care comprehensiveness.

Science.gov (United States)

Chaves, Lucieli Dias Pedreschi; Mininel, Vivian Aline; Silva, Jaqueline Alcântara Marcelino da; Alves, Larissa Roberta; Silva, Maria Ferreira da; Camelo, Silvia Helena Henriques

2017-01-01

To reflect on nursing supervision as a management tool for care comprehensiveness by nurses, considering its potential and limits in the current scenario. A reflective study based on discourse about nursing supervision, presenting theoretical and practical concepts and approaches. Limits on the exercise of supervision are related to the organization of healthcare services based on the functional and clinical model of care, in addition to possible gaps in the nurse training process and work overload. Regarding the potential, researchers emphasize that supervision is a tool for coordinating care and management actions, which may favor care comprehensiveness, and stimulate positive attitudes toward cooperation and contribution within teams, co-responsibility, and educational development at work. Nursing supervision may help enhance care comprehensiveness by implying continuous reflection on including the dynamics of the healthcare work process and user needs in care networks. refletir a supervisão de enfermagem como instrumento gerencial do enfermeiro para integralidade do cuidado, considerando suas potencialidades e limitações no cenário atual. estudo reflexivo baseado na formulação discursiva sobre a supervisão de enfermagem, apresentando conceitos e enfoques teóricos e/ou práticos. limitações no exercício da supervisão estão relacionadas à organização dos serviços de saúde embasada no modelo funcional e clínico de atenção, assim como possíveis lacunas no processo de formação do enfermeiro e sobrecarga de trabalho. Quanto às potencialidades, destaca-se a supervisão como instrumento de articulação de ações assistenciais e gerenciais, que pode favorecer integralidade da atenção, estimular atitudes de cooperação e colaboração em equipe, além da corresponsabilização e promoção da educação no trabalho. supervisão de enfermagem pode contribuir para fortalecimento da integralidade do cuidado, pressupondo reflexão cont

To Be or Not to Be: Community Supervision Deja Vu

Science.gov (United States)

Taxman, Faye S.

2008-01-01

Supervision is an undervalued part of the correctional services. Over the last three decades, innovations have focused on increasing the number of contacts between the offender and the supervision employee, to little avail. A new generation of innovations is occurring in the supervision field that is directed at changing the interaction between…

Building an Arabic Sentiment Lexicon Using Semi-supervised Learning

Directory of Open Access Journals (Sweden)

Fawaz H.H. Mahyoub

2014-12-01

Full Text Available Sentiment analysis is the process of determining a predefined sentiment from text written in a natural language with respect to the entity to which it is referring. A number of lexical resources are available to facilitate this task in English. One such resource is the SentiWordNet, which assigns sentiment scores to words found in the English WordNet. In this paper, we present an Arabic sentiment lexicon that assigns sentiment scores to the words found in the Arabic WordNet. Starting from a small seed list of positive and negative words, we used semi-supervised learning to propagate the scores in the Arabic WordNet by exploiting the synset relations. Our algorithm assigned a positive sentiment score to more than 800, a negative score to more than 600 and a neutral score to more than 6000 words in the Arabic WordNet. The lexicon was evaluated by incorporating it into a machine learning-based classifier. The experiments were conducted on several Arabic sentiment corpora, and we were able to achieve a 96% classification accuracy.

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.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Energy Technology Data Exchange (ETDEWEB)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A. [School of Mechatronic Engineering, Universiti Malaysia Perlis, Kampus Pauh Putra, 02600 Arau, Perlis (Malaysia); Omar, O. [Malaysian Agriculture Research and Development Institute (MARDI), Persiaran MARDI-UPM, 43400 Serdang, Selangor (Malaysia)

2015-05-15

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

Science.gov (United States)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-05-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC-MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties.

Classification of Malaysia aromatic rice using multivariate statistical analysis

International Nuclear Information System (INIS)

Abdullah, A. H.; Adom, A. H.; Shakaff, A. Y. Md; Masnan, M. J.; Zakaria, A.; Rahim, N. A.; Omar, O.

2015-01-01

Aromatic rice (Oryza sativa L.) is considered as the best quality premium rice. The varieties are preferred by consumers because of its preference criteria such as shape, colour, distinctive aroma and flavour. The price of aromatic rice is higher than ordinary rice due to its special needed growth condition for instance specific climate and soil. Presently, the aromatic rice quality is identified by using its key elements and isotopic variables. The rice can also be classified via Gas Chromatography Mass Spectrometry (GC-MS) or human sensory panels. However, the uses of human sensory panels have significant drawbacks such as lengthy training time, and prone to fatigue as the number of sample increased and inconsistent. The GC–MS analysis techniques on the other hand, require detailed procedures, lengthy analysis and quite costly. This paper presents the application of in-house developed Electronic Nose (e-nose) to classify new aromatic rice varieties. The e-nose is used to classify the variety of aromatic rice based on the samples odour. The samples were taken from the variety of rice. The instrument utilizes multivariate statistical data analysis, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and K-Nearest Neighbours (KNN) to classify the unknown rice samples. The Leave-One-Out (LOO) validation approach is applied to evaluate the ability of KNN to perform recognition and classification of the unspecified samples. The visual observation of the PCA and LDA plots of the rice proves that the instrument was able to separate the samples into different clusters accordingly. The results of LDA and KNN with low misclassification error support the above findings and we may conclude that the e-nose is successfully applied to the classification of the aromatic rice varieties

Handwritten Digit Recognition using Edit Distance-Based KNN

OpenAIRE

Bernard , Marc; Fromont , Elisa; Habrard , Amaury; Sebban , Marc

2012-01-01

We discuss the student project given for the last 5 years to the 1st year Master Students which follow the Machine Learning lecture at the University Jean Monnet in Saint Etienne, France. The goal of this project is to develop a GUI that can recognize digits and/or letters drawn manually. The system is based on a string representation of the dig- its using Freeman codes and on the use of an edit-distance-based K-Nearest Neighbors classifier. In addition to the machine learning knowledge about...

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

Directory of Open Access Journals (Sweden)

Muhammad Bilal

2016-07-01

Full Text Available Sentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.. To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA. Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure.

The reflective meta-dialogue in psycho-dynamic supervision

DEFF Research Database (Denmark)

Frølund, Lone; Nielsen, Jan

2009-01-01

the therapeutic practice in the supervision. The mutual relations and processes between therapeutic practice and supervision will be illustrated by the so-called mirror axes, which play an important part in the transformation from learning to integrated experience. We will focus on the relationship...

EEM{sup TM} wireless supervision

Energy Technology Data Exchange (ETDEWEB)

Bilic, H. [Ericsson-Nikola Tesla d.d. Zagreb (Croatia)

2000-07-01

By adding the GSM network to the communication level of Energy Management systems, energy operating centres (EOC) can offer wireless access to the supervised equipment. Furthermore EOC can profit from rapid service development in the GSM networks. With implementation of GPRS to the GSM network EOC can instantly offer wireless access to external IP based networks such as Internet and corporate Intranets. The author describes architecture and key characteristic of Ericsson EnergyMaster{sup TM} (EEM{sup TM}) system for Energy Management, how and where to implement wireless supervision, wireless access to IP addresses and also how to implement new services provided by the GSM network. (orig.)

Shame, the scourge of supervision

Directory of Open Access Journals (Sweden)

Valérie Perret

2017-07-01

• How can the supervisor deal with it? My motivation in writing this article is born from my personal experience with shame. It inhibited my thinking, my spontaneity, my creativity, and therefore limited my personal and professional development. Freeing myself allowed me to recover liberty, energy and legitimacy. I gained in professional competence and assertiveness within my practice as supervisor. My purpose in writing this article is that we, as supervisors, reflect together on how we look at the process of shame in our supervision sessions.  Citation - APA format: Perret, V. (2017. Shame, the scourge of supervision. International Journal of Transactional Analysis Research & Practice, 8(2, 41-48.

Semi-supervised Learning for Phenotyping Tasks.

Science.gov (United States)

Dligach, Dmitriy; Miller, Timothy; Savova, Guergana K

2015-01-01

Supervised learning is the dominant approach to automatic electronic health records-based phenotyping, but it is expensive due to the cost of manual chart review. Semi-supervised learning takes advantage of both scarce labeled and plentiful unlabeled data. In this work, we study a family of semi-supervised learning algorithms based on Expectation Maximization (EM) in the context of several phenotyping tasks. We first experiment with the basic EM algorithm. When the modeling assumptions are violated, basic EM leads to inaccurate parameter estimation. Augmented EM attenuates this shortcoming by introducing a weighting factor that downweights the unlabeled data. Cross-validation does not always lead to the best setting of the weighting factor and other heuristic methods may be preferred. We show that accurate phenotyping models can be trained with only a few hundred labeled (and a large number of unlabeled) examples, potentially providing substantial savings in the amount of the required manual chart review.

The supervisions in the field develop nuclear professionals

International Nuclear Information System (INIS)

Fernandez de la Casa, M.; Buedo, J. L.; Gonzalez, F.

2015-01-01

In 2011 Cofrentes Nuclear Power Plants began a training program for improving the supervision of managers in the field: the effort done not only has improved the quality of supervisions but also has defined a way to reinforce behavior expectations of Cofrentes Nuclear Power Plant. (Author)

77 FR 32881 - Supervised Securities Holding Company Registration

Science.gov (United States)

2012-06-04

...), The Report of Foreign Banking Organizations (FR Y-7), The Consolidated Financial Statements for Bank... Y-9ES), The Supplement to the Consolidated Financial Statements for Bank Holding Companies (FR Y-9CS... comprehensive consolidated supervision by a foreign regulator, a nonbank financial company supervised by the...

Supervision og de tre k´er

DEFF Research Database (Denmark)

Schilling, Benedicte; Jacobsen, Claus Haugaard; Nielsen, Jan

2010-01-01

Kontrol, kontrakt og kontekst er supervisionens tre k'er. Men hvad er supervision i det hele taget for en størrelse, der spillerså central en rolle for den psykologfaglige profession?......Kontrol, kontrakt og kontekst er supervisionens tre k'er. Men hvad er supervision i det hele taget for en størrelse, der spillerså central en rolle for den psykologfaglige profession?...

Supervising away from home: clinical, cultural and professional challenges.

Science.gov (United States)

Abramovitch, Henry; Wiener, Jan

2017-02-01

This paper explores some challenges of supervising clinical work of trainees, known as 'routers', who live in countries with diverse cultural, social and political traditions, and the analysts who travel to supervise them. It is written as an evolving dialogue between the authors, who explore together the effects of their own culture of origin, and in particular the legacy and values of their own training institutes on the styles and models of analytic supervision. Their dialogue is framed around the meaning of home and experiences of homesickness for analysts working away from home in an interactive field of strangeness in countries where analytical psychology is a relatively new discipline. The authors outline the findings from their own qualitative survey, where other supervisors working abroad, and those they have supervised, describe their experiences and their encounters with difference. The dialogue ends with both authors discussing what they have learned about teaching and supervising abroad, the implications for more flexible use of Jungian concepts, and how such visits have changed their clinical practice in their home countries. © 2017, The Society of Analytical Psychology.

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.

The Kokeshi Doll: A Tool for Family Supervision.

Science.gov (United States)

Sampson, Dick T.

1996-01-01

Claims that the use of Kokeshi dolls--a small limbless cylindrical wooden doll from Japan--allows counseling supervisees to focus on conceptualizations, personalization, and process skills. Uses a case study to illustrate how these dolls can enhance supervision, allowing trainees to become actively involved in the supervision process. (RJM)

The Views of Educational Supervisors on Clinical Supervision

Science.gov (United States)

Kayikçi, Kemal; Yilmaz, Ozan; Sahin, Ahmet

2017-01-01

Contemporary educational supervision expresses democratic and leadership focused supervisory approach which consists of collaboration, trust, sharing and improving. The aims of the study are to investigate the answer of how current teacher supervision in Turkey is conducted according to the views of educational supervisors, and to unearth what the…

Cliché, Gossip, and Anecdote as Supervision Training

Science.gov (United States)

Grealy, Liam

2016-01-01

This article expands on a co-authored project with Timothy Laurie on the practices and ethics of higher degree research (HDR) supervision (or advising): "What does good HDR supervision look like?" in contemporary universities. It connects that project with scholarship on the relevance of "common sense" to questions of…

Online Lab Books for Supervision of Project Students

Science.gov (United States)

Badge, J. L.; Badge, R. M.

2009-01-01

In this article, the authors report a case study where Blackboard's wiki function was used to create electronic lab books for the supervision of undergraduate students completing laboratory based research projects. This successful experiment in supervision using electronic notebooks provided a searchable record of student work and a permanent…

Professional Disclosure Statements and Formal Plans for Supervision: Two Strategies for Minimizing the Risk of Ethical Conflicts in Post-Master's Supervision.

Science.gov (United States)

Cobia, Debra C.; Boes, Susan R.

2000-01-01

Discusses ethical conflicts related to issues of informed consent, due process, competence, confidentiality, and dual relationships in supervision. Proposes two strategies as ways to minimize the potential for ethical conflict in post-master's supervision: the use of professional disclosure statements by supervisors and the development of formal…

Study and development of equipment supervision technique system and its management software for nuclear electricity production

International Nuclear Information System (INIS)

Zhang Liying; Zou Pingguo; Zhu Chenghu; Lu Haoliang; Wu Jie

2008-01-01

The equipment supervision technique system, which standardized the behavior of supervision organizations in planning and implementing of equipment supervision, is built up based on equipment supervision technique documents, such as Quality Supervision Classifications, Special Supervision Plans and Supervision Guides. Furthermore, based on the research, the equipment supervision management information system is developed by Object Oriented Programming, which consists of supervision information, supervision technique, supervision implementation, quality statistics and analysis module. (authors)

Application of texture analysis method for mammogram density classification

Science.gov (United States)

Nithya, R.; Santhi, B.

2017-07-01

Mammographic density is considered a major risk factor for developing breast cancer. This paper proposes an automated approach to classify breast tissue types in digital mammogram. The main objective of the proposed Computer-Aided Diagnosis (CAD) system is to investigate various feature extraction methods and classifiers to improve the diagnostic accuracy in mammogram density classification. Texture analysis methods are used to extract the features from the mammogram. Texture features are extracted by using histogram, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Difference Matrix (GLDM), Local Binary Pattern (LBP), Entropy, Discrete Wavelet Transform (DWT), Wavelet Packet Transform (WPT), Gabor transform and trace transform. These extracted features are selected using Analysis of Variance (ANOVA). The features selected by ANOVA are fed into the classifiers to characterize the mammogram into two-class (fatty/dense) and three-class (fatty/glandular/dense) breast density classification. This work has been carried out by using the mini-Mammographic Image Analysis Society (MIAS) database. Five classifiers are employed namely, Artificial Neural Network (ANN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). Experimental results show that ANN provides better performance than LDA, NB, KNN and SVM classifiers. The proposed methodology has achieved 97.5% accuracy for three-class and 99.37% for two-class density classification.

Psychiatric nursing menbers' reflections on participating in group-based clinical supervision

DEFF Research Database (Denmark)

Buus, Niels; Angel, Sanne; Traynor, Michael

2011-01-01

This paper is a report of an interview study exploring psychiatric hospital nursing staff members' reflections on participating in supervision. Clinical supervision is a pedagogical process designed to direct, develop, and support clinical nurses. Participation rates in clinical supervision...... they influence participation rates. Twenty-two psychiatric hospital nursing staff members were interviewed with a semistructured interview guide. Interview transcripts were interpreted by means of Ricoeur's hermeneutic method. The respondents understood clinical supervision to be beneficial, but with very...

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

KAUST Repository

Fernandes, José Antonio

2013-02-01

A multi-species approach to fisheries management requires taking into account the interactions between species in order to improve recruitment forecasting of the fish species. Recent advances in Bayesian networks direct the learning of models with several interrelated variables to be forecasted simultaneously. These models are known as multi-dimensional Bayesian network classifiers (MDBNs). Pre-processing steps are critical for the posterior learning of the model in these kinds of domains. Therefore, in the present study, a set of \\'state-of-the-art\\' uni-dimensional pre-processing methods, within the categories of missing data imputation, feature discretization and feature subset selection, are adapted to be used with MDBNs. A framework that includes the proposed multi-dimensional supervised pre-processing methods, coupled with a MDBN classifier, is tested with synthetic datasets and the real domain of fish recruitment forecasting. The correctly forecasting of three fish species (anchovy, sardine and hake) simultaneously is doubled (from 17.3% to 29.5%) using the multi-dimensional approach in comparison to mono-species models. The probability assessments also show high improvement reducing the average error (estimated by means of Brier score) from 0.35 to 0.27. Finally, these differences are superior to the forecasting of species by pairs. © 2012 Elsevier Ltd.

28 CFR 2.95 - Early termination from supervision.

Science.gov (United States)

2010-07-01

... 28 Judicial Administration 1 2010-07-01 2010-07-01 false Early termination from supervision. 2.95 Section 2.95 Judicial Administration DEPARTMENT OF JUSTICE PAROLE, RELEASE, SUPERVISION AND RECOMMITMENT OF PRISONERS, YOUTH OFFENDERS, AND JUVENILE DELINQUENTS District of Columbia Code: Prisoners and Parolees § 2.95 Early termination from...

Gender Effects on Managing and Supervising Salespersons: A ...

African Journals Online (AJOL)

This study investigates student perceptions of the gender effect on managing and supervising efforts. Based on 385 surveys, the results for the entire sample of students, as well as for male and female samples, showed the existence of a significant gender effect for some aspects of managing and supervising. Also ...

Supervision af psykologkandidater i privat praksis

DEFF Research Database (Denmark)

Petersen, Birgitte

et litteratur review over relevante temaer i supervisionslitteraturen samt overvejelser om læring i supervision. Afhandlingens empiriske resultater vil blive belyst og diskuteret med udgangspunkt i tilsvarende fænomener i supervisionslitteraturen. Resultaterne af undersøgelsen viser, at der er en...... række vigtige elementer ved supervision, der skal være opfyldt, hvis den skal opleves som udviklende og lærerig af praksiskandidaterne. Det er elementer som kontraktetablering, rådgivning og teoretisk refleksion, en tydelig teoretisk referenceramme samt støtte og anerkendelse fra supervisor. Det...

Declarative modeling for process supervision

International Nuclear Information System (INIS)

Leyval, L.

1989-01-01

Our work is a contribution to computer aided supervision of continuous processes. It is inspired by an area of Artificial Intelligence: qualitative physics. Here, supervision is based on a model which continuously provides operators with a synthetic view of the process; but this model is founded on general principles of control theory rather than on physics. It involves concepts such as high gain or small time response. It helps in linking temporally the evolution of various variables. Moreover, the model provides predictions of the future behaviour of the process, which allows action advice and alarm filtering. This should greatly reduce the famous cognitive overload associated to any complex and dangerous evolution of the process

Supervision of tunnelling constructions and software used for their evaluation

Science.gov (United States)

Caravanas, Aristotelis; Hilar, Matous

2017-09-01

Supervision is a common instrument for controlling constructions of tunnels. In order to suit relevant project’s purposes a supervision procedure is modified by local conditions, habits, codes and ways of allocating of a particular tunnelling project. The duties of tunnel supervision are specified in an agreement with the client and they can include a wide range of activities. On large scale tunnelling projects the supervision tasks are performed by a high number of people of different professions. Teamwork, smooth communication and coordination are required in order to successfully fulfil supervision tasks. The efficiency and quality of tunnel supervision work are enhanced when specialized software applications are used. Such applications should allow on-line data management and the prompt evaluation, reporting and sharing of relevant construction information and other aspects. The client is provided with an as-built database that contains all the relevant information related to a construction process, which is a valuable tool for the claim management as well as for the evaluation of structure defects that can occur in the future. As a result, the level of risks related to tunnel constructions is decreased.

Developing a manual for strengthening mental health nurses' clinical supervision

DEFF Research Database (Denmark)

Buus, Niels; Cassedy, Paul; Gonge, Henrik

2013-01-01

In this article, we report findings from a study aimed at developing the content and implementation of a manual for a research-based intervention on clinical supervision of mental health nursing staff. The intervention was designed to strengthen already existing supervision practices through...... educational preparation for supervision and systematic reflection on supervision. The intervention consists of three sessions and was implemented on two groups of mental health hospital staff. We present an outline of the manual and explain how the trial sessions made us adjust the preliminary manual....... The effects of implementing the manual will subsequently be analysed in an independent randomised controlled trial....

Model for investigating the benefits of clinical supervision in psychiatric nursing

DEFF Research Database (Denmark)

Gonge, Henrik; Buus, Niels

2011-01-01

with the effectiveness of clinical supervision, as measured by the Manchester Clinical Supervision Scale (MCSS). Furthermore, MCSS scores were associated with benefits, such as increased job satisfaction, vitality, rational coping and less stress, emotional exhaustion, and depersonalization. Multivariate analyses......The objective of this study was to test a model for analysing the possible benefits of clinical supervision. The model suggested a pathway from participation to effectiveness to benefits of clinical supervision, and included possible influences of individual and workplace factors. The study sample...

The ViewPoint radioprotection supervision workstation; Poste de supervision radioprotection viewpoint

Energy Technology Data Exchange (ETDEWEB)

Gaultier, E. [APVL Ingenierie- 6, bd Nobel - Equatop La Rabelais - 37540 Saint Cyr sur Loire (France)

2009-07-01

The author briefly presents the ViewPoint supervision global solution which incorporates audio and video advanced technologies to manage radioprotection operational measurements. Data can be transmitted by-wire or wireless. It can integrate a large number of radioprotection measurement instruments, such as a belt for the monitoring of physiological parameters (body temperature, breathing rhythm, body posture)

19 CFR 191.44 - Destruction under Customs supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Destruction under Customs supervision. 191.44 Section 191.44 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) DRAWBACK Rejected Merchandise § 191.44 Destruction under Customs supervision. A claimant may destroy merchandise an...

19 CFR 191.37 - Destruction under Customs supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Destruction under Customs supervision. 191.37 Section 191.37 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) DRAWBACK Unused Merchandise Drawback § 191.37 Destruction under Customs supervision. A claimant may destroy...

A Good Supervisor--Ten Facts of Caring Supervision

Science.gov (United States)

Määttä, Kaarina

2015-01-01

This article describes the elements of caring supervision of doctoral theses. The purpose was to describe the best practices as well as challenges of supervision especially from the supervisor's perspective. The analysis is based on the author's extensive experience as a supervisor and related data obtained for research and developmental purposes.…

19 CFR 191.25 - Destruction under Customs supervision.

Science.gov (United States)

2010-04-01

... 19 Customs Duties 2 2010-04-01 2010-04-01 false Destruction under Customs supervision. 191.25 Section 191.25 Customs Duties U.S. CUSTOMS AND BORDER PROTECTION, DEPARTMENT OF HOMELAND SECURITY; DEPARTMENT OF THE TREASURY (CONTINUED) DRAWBACK Manufacturing Drawback § 191.25 Destruction under Customs supervision. A claimant may destroy merchandise...

33 CFR 326.4 - Supervision of authorized activities.

Science.gov (United States)

2010-07-01

... 33 Navigation and Navigable Waters 3 2010-07-01 2010-07-01 false Supervision of authorized activities. 326.4 Section 326.4 Navigation and Navigable Waters CORPS OF ENGINEERS, DEPARTMENT OF THE ARMY, DEPARTMENT OF DEFENSE ENFORCEMENT § 326.4 Supervision of authorized activities. (a) Inspections. District engineers will, at their discretion, take...

Experiences of Supervision at Practice Placement Sites

Directory of Open Access Journals (Sweden)

Lesley Diack

2014-01-01

Full Text Available Background. Whilst placement supervision and clinical education programmes are of significant value in shaping the behaviours of undergraduate healthcare students, appropriate provisions which are efficacious to the learner are somewhat lacking, particularly for students studying on UK MPharm programmes. Objectives. To explore and explain the value of placement supervision to the personal development and employability of undergraduate pharmacy students. Methods. Students participated in a week long community pharmacy pilot programme, a result of a collaborative effort between the School of Pharmacy and Life Sciences and a small consortium of community pharmacies. Students and stakeholders were asked to evaluate their experiences via separate questionnaires which had been developed to elicit views and attitudes. Key Findings. Feedback from students and stakeholders towards the experience was overwhelmingly positive with multiple benefits being reported. Of particular prominence was the emphasis in student feedback on the value of placement supervision to their professional and personal development. Findings were indicative of a development in clinical practice proficiencies, core skills, and improvement in decision-making practice. Conclusions. The benefits of clinical supervision to the professional and personal development of MPharm students are well documented, although attracting professional pharmacy supervisors is proving a problematic task for educational providers in the UK.

A model for dealing with parallel processes in supervision

Directory of Open Access Journals (Sweden)

Lilja Cajvert

2011-03-01

Supervision in social work is essential for successful outcomes when working with clients. In social work, unconscious difficulties may arise and similar difficulties may occur in supervision as parallel processes. In this article, the development of a practice-based model of supervision to deal with parallel processes in supervision is described. The model has six phases. In the first phase, the focus is on the supervisor’s inner world, his/her own reflections and observations. In the second phase, the supervision situation is “frozen”, and the supervisees are invited to join the supervisor in taking a meta-perspective on the current situation of supervision. The focus in the third phase is on the inner world of all the group members as well as the visualization and identification of reflections and feelings that arose during the supervision process. Phase four focuses on the supervisee who presented a case, and in phase five the focus shifts to the common understanding and theorization of the supervision process as well as the definition and identification of possible parallel processes. In the final phase, the supervisee, with the assistance of the supervisor and other members of the group, develops a solution and determines how to proceed with the client in treatment. This article uses phenomenological concepts to provide a theoretical framework for the supervision model. Phenomenological reduction is an important approach to examine and to externalize and visualize the inner words of the supervisor and supervisees. Een model voor het hanteren van parallelle processen tijdens supervisie Om succesvol te zijn in de hulpverlening aan cliënten, is supervisie cruciaal in het sociaal werk. Tijdens de hulpverlening kunnen impliciete moeilijkheden de kop opsteken en soortgelijke moeilijkheden duiken soms ook op tijdens supervisie. Dit worden parallelle processen genoemd. Dit artikel beschrijft een op praktijkervaringen gebaseerd model om dergelijke parallelle

QUEST : Eliminating online supervised learning for efficient classification algorithms

NARCIS (Netherlands)