Search results for: training time

Authors: Ghazy M.R. Assassa, Mona F. M. Mursi, Hatim A. Aboalsamh

Abstract:

Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent.

Keywords: Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).

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Authors: Pedro Barbosa Cabral, Neuza Pedro, Ana Mafalda Gonçalves

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The purpose of this research is the analysis of the impact of ICT-related training in the adoption of a learning management systems (LMS) for teaching practicesby faculties in a higher education institution. Based on comparative analyses the impact will be obtained by the number of LMS courses created and managed by participants in ICT for teaching workshops and those who have not attended to any workshops. Involving near 1320 LMS courses and 265 faculties, the results evidence that(i) faculties who have not attend any workshop present a larger distribution of empty courses and (ii) faculties who have attended three or more workshops managed a higher distribution of courses with a considerable level of use intensity, when compared to the others groups. These findings supportthe idea that faculty training is a crucial factor in the process of LMS integration in higher education institutions and that faculties who have been enrolled in three or more workshops develop a higher level of technical and pedagogical proficiency in LMS.

Keywords: Higher Education, Faculty Training, LMS, Comparative Analyses

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Authors: Donald C. Lantu, Henndy Ginting, M. Yorga Permana, Dany M. A. Ramdlany

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We constructed a training program for top-talents of a Bank with Scharmer Theory-U as the model. In this training program, we implemented the action learning perspective, as it is claimed to be the most effective one currently available. In the process, participants were encouraged to be more involved, especially compared to traditional lecturing. The goal of this study is to assess the effectiveness of this particular training. The program consists of six days non-residential workshop within two months. Between each workshop, the participants were involved in the works of action learning group. They were challenged by dealing with the real problem related to their tasks at work. The participants of the program were 30 best talents who were chosen according to their yearly performance. Using paired difference statistical test in the behavioral assessment, we found that the training was not effective to increase participants’ leadership competencies. For the future development program, we suggested to modify the goals of the program toward the next stage of development.

Keywords: Action learning, behaviour, leadership development, Theory-U.

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Authors: Dhananjoy Shaw, Seema Sharma (Kaushik)

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Anthropometry helps in associating the physical properties of an individual with their racial, cultural, and psychological attributes. Numerous research studies have included different skeletal diameters as a variable. However, most of the studies suggest their inclusion describing specific characteristics/traits of the body. However, there seems to be a scarcity of literature related to the effect of any kind of longitudinal physical training on human skeletal diameters. Hence, the present investigation was conducted to study the variability of covariance of selected skeletal diameters of females in a longitudinal physical training programme. The sample for the study was 78 college going students of the University of Delhi, classified equally in three groups, i.e. viz. (a) Progressive load of training or conditioning group coded as PLT; (b) Constant load of training or non-conditioning group coded as CLT; and (c) No-load or control or sedentary group coded as NL. Collectively, mean age of the sample was 19.54±1.79 years. The randomly selected samples were given maximum consideration to maintain their homogeneity. The variables included biacromial diameter, biiliocristal diameter, bitrochantaerion diameter, humeral bicondylar, femoral bicondylar, wrist diameter, ankle diameter, and foot breadth. Multi-group repeated measure design was adopted for the experimentation. Each group was measured four times after completion of each of the three meso-cycles of six-weeks duration. The measurements were taken following the standard landmarks and procedures. Mean, standard deviation, analysis of co-variance and its post-hoc analysis were computed to analyze the data statistically. The study concluded that both the progressive and constant load of physical training bring changes in the selected skeletal diameters of females. It also reflected the increase due to growth also along with training.

Keywords: Longitudinal, physical training, skeletal diameters, step progression load.

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Authors: Saptiadi Nugroho, A. Nur Muhamad Afif

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Understanding factors that influence employee performance is very important. By finding the significant factors, organization could intervene to improve the employee performance that simultaneously will affect organization itself. In this research, four aspects consist of PCCD training, education level, corrective action, and work location were tested to identify their influence on employee performance. By using correlation analysis and T-Test, it was found that employee performance significantly influenced by PCCD training, work location, and corrective action. Meanwhile the education level did not influence employee performance.

Keywords: Training, employee development, performance management system.

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Hsi-Ya Chang

Abstract:

The rapid improvement of the microprocessor and network has made it possible for the PC cluster to compete with conventional supercomputers. Lots of high throughput type of applications can be satisfied by using the current desktop PCs, especially for those in PC classrooms, and leave the supercomputers for the demands from large scale high performance parallel computations. This paper presents our development on enabling an automated deployment mechanism for cluster computing to utilize the computing power of PCs such as reside in PC classroom. After well deployment, these PCs can be transformed into a pre-configured cluster computing resource immediately without touching the existing education/training environment installed on these PCs. Thus, the training activities will not be affected by this additional activity to harvest idle computing cycles. The time and manpower required to build and manage a computing platform in geographically distributed PC classrooms also can be reduced by this development.

Keywords: PC cluster, automated deployment, cluster computing, PC classroom.

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Authors: Shuen-Tai Wang, Fang-An Kuo, Chau-Yi Chou, Yu-Bin Fang

Abstract:

2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn  features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.

Keywords: Artificial Intelligence, machine learning, deep learning, convolutional neural networks.

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Authors: Alhaji M. H. Conteh, Xiangbin Yan, M. E. S. Mvodo

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This study endeavors to evaluate the effects of farmers’ training program on the adoption of improved farming practices, the output of rice farming, and the income as well as the profit from rice farming by employing an ex-post non-experimental data in Sierra Leone. It was established that participating in farmers’ training program increased the possibility of adoption of the improved farming activities that were implemented in the study area. Through the training program also, the proceeds from rice production was also established to have increased considerably. These results were in line with the assumption that one of the main constraints on the growth in agricultural output particularly rice cultivation in most African states is the lack of efficient extension programs.

Keywords: Dissemination of information, improved farming practices, rice ecologies, Sierra Leone.

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Authors: Golnaz Rezai, Zainalabidin Mohamed, Mad Nasir Shamsudin

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The Malaysian government is promoting entrepreneurship development skills amongst farmers through informal courses. These courses will concentrate on teaching managerial skills as inevitable means for small farms to succeed by making farmers more creative and innovative. Therefore it is important to assess the effect of informal agri-entrepreneurial training in developing entrepreneurship among the farmers in Malaysia. Seven hundred and ninety six farmers (796) farmers were interviewed via structured questionnaire to define their opinion on whether the current informal educational and training establishments are sufficient to teach and develop entrepreneurial skills. Factor analysis and logic regression analysis were used to determine the motivating factors and predict their impact on the development of entrepreneurial skills. The result from the factor analysis led us to investigate the association between these factors and farmers- opinions about the development of entrepreneurial skills and traits through participating in informal entrepreneurship training or education. The outcome has shown us that the importance of informal training to promote entrepreneurship among farmers is crucial. The training should be intensified to encourage farmers to not only focus on the modern technologies but also on the fundamental changes in their attitude towards agriculture as a business. DOA: KMO: Kaiser- Meyer- Olkin Test MOA: Ministry of Agriculture NMP: Ninth Malaysia Plan NAP: Third National Agricultural Policy (2000-2010)

Keywords: Entrepreneurial skills, farmers, informal education, Malaysia

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Authors: Sorin Valcan, Mihail Găianu

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Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need of labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to a ground truth data generation algorithm for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual labels adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: Labeling automation, infrared camera, driver monitoring, eye detection, Convolutional Neural Networks.

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Authors: S. Khoshemehry, M. J. Pourvaghar

Abstract:

Inflammatory markers are known as the main predictors of cardiovascular diseases. This study aimed at determining the effect of 8 weeks of interval endurance training on hs-CRP level and some anthropometric parameters in overweight men. Following the call for participation in research project in Kashan, 73 volunteers participated in it and constituted the statistical population of the study. Then, 28 overweight young men from the age of 22 to 25 years old were randomly assigned into two groups of experimental and control group (n=14). Anthropometric and the blood sample was collected before and after the termination of the program for measuring hs-CRP. The interval endurance program was performed at 60 to 75% of maximum heart rate in 2 sessions per week for 8 weeks. Kolmogorov-Smirnov test was used to test whether two samples come from the same distribution and T-test was used to assess the difference of two groups which were statistically significant at the level of 0.05. The result indicated that there was a significant difference between the hs-RP, weight, BMI and W/H ratio of overweight men in posttest in the exercise group (P≤0.05) but not in the control group. Interval endurance training program causes decrease in hs-CRP level and anthropometric parameters.

Keywords: Interval endurance training program, hs-CRP, overweight, anthropometric.

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Authors: Fauquet-Alekhine Philippe

Abstract:

Information and Communication Technologies (ICT) are increasing in importance everyday, especially since the 90’s (last decade of birth for the Millennials generation). While social interactions involving the Millennials generation have been studied, a lack of investigation remains regarding the use of the ICT by this generation as well as the impact on outcomes in education and professional training. Observing and interviewing students preparing a MSc, we aimed at characterizing the interaction students-ICT during the courses. We found that up to 50% of the students (mainly female) could use ICT during courses at a rate of 0.84 occurrence/minutes for some of them, and they thought this involvement did not disturb learning, even was helpful. As recent researches show that multitasking leads people think they are much better than they actually are, further observations with assessments are needed to conclude whether or not the use ICT by students during the courses is a real strength.

Keywords: Education, ICT, generational effect, training.

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Authors: Rumyana Peytcheva-Forsyth, Blagovesna Yovkova

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This paper presents the outcomes of a qualitative study which aims to investigate the pedagogical potentials of serious games in the preparation of future teachers. The authors discuss the existing problems and barriers associated with the organization of teaching practices in Bulgaria as part of the pre-service teacher training, as well as the attitudes and perceptions of the interviewed academics, teachers and trainees concerning the integration of serious games in the teaching practicum. The study outcomes strongly confirm the positive attitudes of the respondents to the introduction of virtual learning environments for the development of professional skills of future teachers as a supplement to the traditional forms of education. Through the inclusion of serious games it is expected to improve the quality of practical training of pre-service teachers as they overcome many of the problems identified in the existing teaching practices. The outcomes of the study will inform the design of the educational simulation software which is part of the project SimAula Tomorrow's Teachers Training.

Keywords: pre-service teacher training, serious games, virtual practicum, simulations

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Authors: Jahan Zeb, Muhammad Younus Javed, Usman Qayyum

Abstract:

This research paper deals with the implementation of face recognition using neural network (recognition classifier) on low-resolution images. The proposed system contains two parts, preprocessing and face classification. The preprocessing part converts original images into blurry image using average filter and equalizes the histogram of those image (lighting normalization). The bi-cubic interpolation function is applied onto equalized image to get resized image. The resized image is actually low-resolution image providing faster processing for training and testing. The preprocessed image becomes the input to neural network classifier, which uses back-propagation algorithm to recognize the familiar faces. The crux of proposed algorithm is its beauty to use single neural network as classifier, which produces straightforward approach towards face recognition. The single neural network consists of three layers with Log sigmoid, Hyperbolic tangent sigmoid and Linear transfer function respectively. The training function, which is incorporated in our work, is Gradient descent with momentum (adaptive learning rate) back propagation. The proposed algorithm was trained on ORL (Olivetti Research Laboratory) database with 5 training images. The empirical results provide the accuracy of 94.50%, 93.00% and 90.25% for 20, 30 and 40 subjects respectively, with time delay of 0.0934 sec per image.

Keywords: Average filtering, Bicubic Interpolation, Neurons, vectorization.

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Authors: Constantin Pehoiu

Abstract:

The scientific perspective, the practice area of physical education and sports activities improve power capacity in all its forms of expression, being a generator of the research topics. Today theories that strength training athletes and slow down development progress will affect the strength and flexibility are discredited. On the other hand there are sectors and / or samples whose results are sports of the way higher manifestation of power as a result of the composition of the force and velocity, being based in this respect on the systematic and continuous development of both bio-motric capacities said. Training of force for children was and is controversial. Teama de accidentări sau a stopării premature a procesului de creştere a făcut ca în trecut copiii să fie ţinuţi departe de lucrul cu diferite greutăţi.Fear of injury or premature stop the growth process in the past made the children to be kept away from working with different weights. Recent studies have shown that the risk of accidents is relatively small and the strength training can help prevent them. For example, most accidents occur at the level of athletics ligaments and tendons. From this point of view, it can be said that a progressive intervention of force training, optimal design, will help enhancing their process, such as athlete much better prepared to meet training requests and competitions. Preparation of force provides a solid basis for further phases in the highest performance.

Keywords: Detente, education, effort will, power.

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Authors: Hussain Syed Asad, Richard Kwok Kit Yuen, Gongsheng Huang

Abstract:

Real-time optimization has been considered an effective approach for improving energy efficient operation of heating, ventilation, and air-conditioning (HVAC) systems. In model-based real-time optimization, model mismatches cannot be avoided. When model mismatches are significant, the performance of the real-time optimization will be impaired and hence the expected energy saving will be reduced. In this paper, the model mismatches for chiller plant on real-time optimization are considered. In the real-time optimization of the chiller plant, simplified semi-physical or grey box model of chiller is always used, which should be identified using available operation data. To overcome the model mismatches associated with the chiller model, hybrid Genetic Algorithms (HGAs) method is used for online real-time training of the chiller model. HGAs combines Genetic Algorithms (GAs) method (for global search) and traditional optimization method (i.e. faster and more efficient for local search) to avoid conventional hit and trial process of GAs. The identification of model parameters is synthesized as an optimization problem; and the objective function is the Least Square Error between the output from the model and the actual output from the chiller plant. A case study is used to illustrate the implementation of the proposed method. It has been shown that the proposed approach is able to provide reliability in decision making, enhance the robustness of the real-time optimization strategy and improve on energy performance.

Keywords: Energy performance, hybrid adaptive modeling, hybrid genetic algorithms, real-time optimization, heating, ventilation, and air-conditioning.

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Authors: К. R. Aida–Zade, C. Ardil, S. S. Rustamov

Abstract:

Statement of the automatic speech recognition problem, the assignment of speech recognition and the application fields are shown in the paper. At the same time as Azerbaijan speech, the establishment principles of speech recognition system and the problems arising in the system are investigated. The computing algorithms of speech features, being the main part of speech recognition system, are analyzed. From this point of view, the determination algorithms of Mel Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC) coefficients expressing the basic speech features are developed. Combined use of cepstrals of MFCC and LPC in speech recognition system is suggested to improve the reliability of speech recognition system. To this end, the recognition system is divided into MFCC and LPC-based recognition subsystems. The training and recognition processes are realized in both subsystems separately, and recognition system gets the decision being the same results of each subsystems. This results in decrease of error rate during recognition. The training and recognition processes are realized by artificial neural networks in the automatic speech recognition system. The neural networks are trained by the conjugate gradient method. In the paper the problems observed by the number of speech features at training the neural networks of MFCC and LPC-based speech recognition subsystems are investigated. The variety of results of neural networks trained from different initial points in training process is analyzed. Methodology of combined use of neural networks trained from different initial points in speech recognition system is suggested to improve the reliability of recognition system and increase the recognition quality, and obtained practical results are shown.

Keywords: Speech recognition, cepstral analysis, Voice activation detection algorithm, Mel Frequency Cepstral Coefficients, features of speech, Cepstral Mean Subtraction, neural networks, Linear Predictive Coding.

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Authors: Thi Nguyen, Lee Gordon-Brown, Jim Peterson, Peter Wheeler

Abstract:

An additive fuzzy system comprising m rules with n inputs and p outputs in each rule has at least t m(2n + 2 p + 1) parameters needing to be tuned. The system consists of a large number of if-then fuzzy rules and takes a long time to tune its parameters especially in the case of a large amount of training data samples. In this paper, a new learning strategy is investigated to cope with this obstacle. Parameters that tend toward constant values at the learning process are initially fixed and they are not tuned till the end of the learning time. Experiments based on applications of the additive fuzzy system in function approximation demonstrate that the proposed approach reduces the learning time and hence improves convergence speed considerably.

Keywords: Additive fuzzy system, improving convergence, parameter learning process, unsupervised learning.

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Authors: Lee Ock-Sun

Abstract:

The effective development of a geoscience education and training program takes account of the rapidly changing environment in the geoscience market, includes information about resource-rich countries which have international education demands. In this paper, we introduce the geoscience program run by the International School for Geoscience Resources at the Korea Institute of Geoscience and Mineral Resources (IS-Geo of KIGAM), and show its remarkable performance. To further effective geoscience program planning and operation, we present recommendations for strategic management for customer-oriented operation with a more favorable program format and advanced training aids. Above all, the IS-Geo of KIGAM should continue improve through ‘plan-do-see-feedback’ activities based on the recommendations.

Keywords: Demand survey, geoscience program, program performance, strategic management.

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Authors: Zabre Eric, Román Rafael

Abstract:

This article presents the evolution and technological changes implemented on the full scale simulators developed by the Simulation Department of the Instituto de Investigaciones Eléctricas1 (Mexican Electric Research Institute) and located at different training centers around the Mexican territory, and allows US to know the last updates, basically from the input/output view point, of the current simulators at some facilities of the electrical sector as well as the compatible industry of the electrical manufactures and industries such as Comision Federal de Electricidad (CFE*, The utility Mexican company). Tendencies of these developments and impact within the operators- scope are also presented.

Keywords: Control room, communication protocol, instructor console, modeling, controller, training simulator

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Authors: André M. Senna, Mary L. G. S. Senna, Rosa M. Machado-de-Sena

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One of the great challenges presented in educational practice is how to ensure the students not only acquire knowledge of training courses throughout their academic life, but also how to apply it in their current professional activities. Consequently, aiming to incite changes in the education system of healthcare professionals noticed the inadequacy of the training providers to solve the social problems related to health, the education related to these procedures should initiate in the earliest years of process. Following that idea, there is another question that needs an answer: If the change in the education should start sooner, in the period of basic training of healthcare professionals, what guidelines should a permanent education program incorporate to promote changes in an already established system? For this reason, the objective of this paper is to present different views of the teaching-learning process, with the purpose of better understanding the behavior adopted by healthcare professionals, through bibliographic study. The conclusion was that more than imparting knowledge to the individual, a larger approach is necessary on permanent education programs concerning the performance of professional health services in order to foment significant changes in education.

Keywords: Health education, continuing education, training, behavior.

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Authors: Aleksandra Zysk, Pawel Badura

Abstract:

Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: Classification, singing, spectral analysis, vocal emission, vocal register.

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Authors: Tin Hninn Hninn Maung

Abstract:

This paper introduces a hand gesture recognition system to recognize real time gesture in unstrained environments. Efforts should be made to adapt computers to our natural means of communication: Speech and body language. A simple and fast algorithm using orientation histograms will be developed. It will recognize a subset of MAL static hand gestures. A pattern recognition system will be using a transforrn that converts an image into a feature vector, which will be compared with the feature vectors of a training set of gestures. The final system will be Perceptron implementation in MATLAB. This paper includes experiments of 33 hand postures and discusses the results. Experiments shows that the system can achieve a 90% recognition average rate and is suitable for real time applications.

Keywords: Hand gesture recognition, Orientation Histogram, Myanmar Alphabet Language, Perceptronnetwork, MATLAB.

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Authors: Guillermo Romero-Jiménez, Luis A. Jiménez-Fraustro, Mayolo Salinas-Camacho, Heriberto Avalos-Valenzuela

Abstract:

A geothermal power plant multiple simulator for operators training is presented. The simulator is designed to be installed in a wireless local area network and has a capacity to train one to six operators simultaneously, each one with an independent simulation session. The sessions must be supervised only by one instructor. The main parts of this multiple simulator are: instructor and operator-s stations. On the instructor station, the instructor controls the simulation sessions, establishes training exercises and supervises each power plant operator in individual way. This station is hosted in a Main Personal Computer (NS) and its main functions are: to set initial conditions, snapshots, malfunctions or faults, monitoring trends, and process and soft-panel diagrams. On the other hand the operators carry out their actions over the power plant simulated on the operator-s stations; each one is also hosted in a PC. The main software of instructor and operator-s stations are executed on the same NS and displayed in PCs through graphical Interactive Process Diagrams (IDP). The geothermal multiple simulator has been installed in the Geothermal Simulation Training Center (GSTC) of the Comisi├│n Federal de Electricidad, (Federal Commission of Electricity, CFE), Mexico, and is being utilized as a part of the training courses for geothermal power plant operators.

Keywords: Geothermal power plant, multiple simulator, training operator.

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Authors: Francisco Miranda

Abstract:

The optimization problem using time scales is studied. Time scale is a model of time. The language of time scales seems to be an ideal tool to unify the continuous-time and the discrete-time theories. In this work we present necessary conditions for a solution of an optimization problem on time scales. To obtain that result we use properties and results of the partial diamond-alpha derivatives for continuous-multivariable functions. These results are also presented here.

Keywords: Lagrange multipliers, mathematical programming, optimization problem, time scales.

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Authors: Hassan Ezzaidi, Jean Rouat

Abstract:

Recently many research has been conducted to retrieve pertinent parameters and adequate models for automatic music genre classification. In this paper, two measures based upon information theory concepts are investigated for mapping the features space to decision space. A Gaussian Mixture Model (GMM) is used as a baseline and reference system. Various strategies are proposed for training and testing sessions with matched or mismatched conditions, long training and long testing, long training and short testing. For all experiments, the file sections used for testing are never been used during training. With matched conditions all examined measures yield the best and similar scores (almost 100%). With mismatched conditions, the proposed measures yield better scores than the GMM baseline system, especially for the short testing case. It is also observed that the average discrimination information measure is most appropriate for music category classifications and on the other hand the divergence measure is more suitable for music subcategory classifications.

Keywords: Audio feature, information measures, music genre.

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Authors: M. Nemissi, H. Seridi, H. Akdag

Abstract:

This paper presents and evaluates a new classification method that aims to improve classifiers performances and speed up their training process. The proposed approach, called labeled classification, seeks to improve convergence of the BP (Back propagation) algorithm through the addition of an extra feature (labels) to all training examples. To classify every new example, tests will be carried out each label. The simplicity of implementation is the main advantage of this approach because no modifications are required in the training algorithms. Therefore, it can be used with others techniques of acceleration and stabilization. In this work, two models of the labeled classification are proposed: the LMLP (Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro Fuzzy Classifier). These models are tested using Iris, wine, texture and human thigh databases to evaluate their performances.

Keywords: Artificial neural networks, Fusion of neural networkfuzzysystems, Learning theory, Pattern recognition.

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Authors: Beata Wentura-Dudek

Abstract:

In the United States in 2007-2010 before the crisis, the US labour market policy focused mainly on providing residents with unemployment insurance, after the recession this policy changed. The aim of the article was to present quantitative research presenting the most effective labor market instruments contributing to reducing unemployment during the crisis in the USA. The article presents research based on the analysis of available documents and statistical data. The results of the conducted research show that the most effective forms of counteracting unemployment at that time were: direct job creation, job search assistance, subsidized employment, training and employment promotion using new technologies, including social media.

Keywords: United States, financial crisis, unemployment, employment promotion, social media, job creation, training, labour market, employment agencies, lifelong learning, job search assistance, subsidized employment, companies, tax.

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Authors: Ana Mercedes Vernia Carrasco

Abstract:

The objective of this work is to reflect through the theoretical framework on the access to the university for the formation of a degree in a teacher of primary education. The University Access Tests in Spain evaluate a series of skills and competencies in writing, which leave aside the sample of another set of skills and tools that this type of test cannot evaluate. In the last years, a very much diversified student body has arrived in the classrooms of the universities. Nowadays, talking about special education means attending to the changes that are being experienced in this area. At present, the educational model focuses on the reinforcement by the educational institutions so that they form the students according to their personal characteristics and that it is not the students that must adapt to the system. A bibliographic review plus some years of experience in training for the future teacher allows us to make an initial assessment about the lack of rigor in the tests of access to the university. In conclusion, we can say that, although we are not a specialist in the type of Special Educational Needs that can manifest the students, therefore, we understand that teacher today needs training and support to develop their teaching with the best quality possible. These teacher and student needs also imply more institutional support.

Keywords: Teacher training, special educational needs, music education.

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

Abstract:

Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: Stacking, multi-layers, ensemble, multi-class.

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