Search results for: Learning Tool

Authors: Shih-Wen Hsiao, Rong-Qi Chen

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In the field of fashion design, 3D Mannequin is a kind of assisting tool which could rapidly realize the design concepts. While the concept of 3D Mannequin is applied to the computer added fashion design, it will connect with the development and the application of design platform and system. Thus, the situation mentioned above revealed a truth that it is very critical to develop a module of 3D Mannequin which would correspond with the necessity of fashion design. This research proposes a concrete plan that developing and constructing a system of 3D Mannequin with Kinect. In the content, ergonomic measurements of objective human features could be attained real-time through the implement with depth camera of Kinect, and then the mesh morphing can be implemented through transformed the locations of the control-points on the model by inputting those ergonomic data to get an exclusive 3D mannequin model. In the proposed methodology, after the scanned points from the Kinect are revised for accuracy and smoothening, a complete human feature would be reconstructed by the ICP algorithm with the method of image processing. Also, the objective human feature could be recognized to analyze and get real measurements. Furthermore, the data of ergonomic measurements could be applied to shape morphing for the division of 3D Mannequin reconstructed by feature curves. Due to a standardized and customer-oriented 3D Mannequin would be generated by the implement of subdivision, the research could be applied to the fashion design or the presentation and display of 3D virtual clothes. In order to examine the practicality of research structure, a system of 3D Mannequin would be constructed with JAVA program in this study. Through the revision of experiments the practicability-contained research result would come out.

Keywords: 3D Mannequin, kinect scanner, interactive closest point, shape morphing, subdivision.

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Authors: T. Delval, C. Sauvage, Q. Jullien, R. Viano, T. Diallo, B. Collignan, G. Picinbono

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In the context of the international spread of COVID-19, the Centre Scientifique et Technique du Bâtiment (CSTB) has led a joint research with the French government authorities Hauts-de-Seine department, to analyse the risk in school spaces according to their configuration, ventilation system and spatial segmentation strategy. This paper describes the main results of this joint research. A multidisciplinary team involving experts in indoor air quality/ventilation, pedestrian movements and IT domains was established to develop a COVID risk analysis tool based on Building Information Model. The work started with specific analysis on two pilot schools in order to provide for the local administration specifications to minimize the spread of the virus. Different recommendations were published to optimize/validate the use of ventilation systems and the strategy of student occupancy and student flow segmentation within the building. This COVID expertise has been digitized in order to manage a quick risk analysis on the entire building that could be used by the public administration through an easy user interface implemented in a free BIM Management software. One of the most interesting results is to enable a dynamic comparison of different ventilation system scenarios and space occupation strategy inside the BIM model. This concurrent engineering approach provides users with the optimal solution according to both ventilation and pedestrian flow expertise.

Keywords: BIM, knowledge management, system expert, risk management, indoor ventilation, pedestrian movement, integrated design.

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Authors: Iwan Nugroho, Purnawan D. Negara

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This paper is aimed to study the roles of leadership and innovation in the development of local people based ecotourism services. The survey is conducted in Candirejo village, Borobudur District, Magelang Regency. The study of a descriptive approach is employed to identify people's behavior in ecotourism services. The results showed that ecotourism services have developed and provided benefits to the people. The roles of leadership and innovation interact positively with a cooperative to organize an ecotourism services management. The leadership is able to identify substances, to do the vision and missions of environmental and cultural conservation. The innovation provides alternative development efforts and increases the added value of ecotourism. The cooperative management was able to support a process to realize the goals of ecotourism, to build participation and communication, and to perform organizational learning. The phenomenon of the leadership in the Candirejo ecotourism enriches the studies of the ecotourism management. During this time, the ecotourism management is always associated with the standard management of national park. The ecotourism management of Candirejo is considered successful even outside the national park management.

Keywords: Borobudur, Candirejo, ecotourism, inovation, Leadership.

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Authors: Salaheddine Bendak, Sara S. Alnaqbi

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The seat belt is a vital tool in improving traffic safety conditions and minimising injuries due to traffic accidents. Most developing countries are facing a big problems associated with the human and financial losses due to traffic accidents. One way to minimise these losses is the use of seat belts by passengers both in the front and rear seats of a vehicle; however, at the same time, close to nothing is known about the rates of seat belt utilisation among rear seat passengers in many developing countries. Therefore, there is a need to estimate these rates in order to know the extent of this problem and how people interact with traffic safety measures like seat belts and find demographic characteristics that contribute to wearing or non-wearing of seat belts with the aim of finding solutions to improve wearing rates. In this paper, an observational study was done to gather data on restraints use in motor vehicle rear seats in eight observational stations in a rapidly developing country, the United Arab Emirates (UAE), and estimate a use rate for the whole country. Also, a questionnaire was used in order to study demographic characteristics affecting the wearing of seatbelts in rear seats. Results of the observational study showed that the overall wearing/usage rate was 12.3%, which is considered very low when compared to other countries. Survey results show that single, male, less educated passengers from Arab and South Asian backgrounds use seat belts reportedly less than others. Finally, solutions are put forward to improve this wearing rate based on the results of this study.

Keywords: Seat belts, traffic crashes, United Arab Emirates, rear seats.

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Authors: Yu Zhang, Pedro Desouza

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Since big data has become substantially more accessible and manageable due to the development of powerful tools for dealing with unstructured data, people are eager to mine information from social media resources that could not be handled in the past. Sentiment analysis, as a novel branch of text mining, has in the last decade become increasingly important in marketing analysis, customer risk prediction and other fields. Scientists and researchers have undertaken significant work in creating and improving their sentiment models. In this paper, we present a concept of selecting appropriate classifiers based on the features and qualities of data sources by comparing the performances of five classifiers with three popular social media data sources: Twitter, Amazon Customer Reviews, and Movie Reviews. We introduced a couple of innovative models that outperform traditional sentiment classifiers for these data sources, and provide insights on how to further improve the predictive power of sentiment analysis. The modeling and testing work was done in R and Greenplum in-database analytic tools.

Keywords: Sentiment Analysis, Social Media, Twitter, Amazon, Data Mining, Machine Learning, Text Mining.

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Authors: Aki Sugano, Mika Ohta, Mineko Ikegami, Kenji Miura, Sayo Tsukamoto, Akihiro Ichinose, Toshiko Ohshima, Eiichi Maeda, Masako Matsuura, Yutaka Takao

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Along with the advances in medicine, providing medical information to individual patient is becoming more important. In Japan such information via Braille is hardly provided to blind and partially sighted people. Thus we are researching and developing a Web-based automatic translation program “eBraille" to translate Japanese text into Japanese Braille. First we analyzed the Japanese transcription rules to implement them on our program. We then added medical words to the dictionary of the program to improve its translation accuracy for medical text. Finally we examined the efficacy of statistical learning models (SLMs) for further increase of word segmentation accuracy in braille translation. As a result, eBraille had the highest translation accuracy in the comparison with other translation programs, improved the accuracy for medical text and is utilized to make hospital brochures in braille for outpatients and inpatients.

Keywords: Automatic Braille translation, Medical text, Partially sighted people.

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Authors: Manash Pratim Sarma, Kandarpa Kumar Sarma

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A set of Artificial Neural Network (ANN) based methods for the design of an effective system of speech recognition of numerals of Assamese language captured under varied recording conditions and moods is presented here. The work is related to the formulation of several ANN models configured to use Linear Predictive Code (LPC), Principal Component Analysis (PCA) and other features to tackle mood and gender variations uttering numbers as part of an Automatic Speech Recognition (ASR) system in Assamese. The ANN models are designed using a combination of Self Organizing Map (SOM) and Multi Layer Perceptron (MLP) constituting a Learning Vector Quantization (LVQ) block trained in a cooperative environment to handle male and female speech samples of numerals of Assamese- a language spoken by a sizable population in the North-Eastern part of India. The work provides a comparative evaluation of several such combinations while subjected to handle speech samples with gender based differences captured by a microphone in four different conditions viz. noiseless, noise mixed, stressed and stress-free.

Keywords: Assamese, Recognition, LPC, Spectral, ANN.

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Authors: Ines Hamdi, Mohamed Ben Ahmed

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The understanding of the system level of biological behavior and phenomenon variously needs some elements such as gene sequence, protein structure, gene functions and metabolic pathways. Challenging problems are representing, learning and reasoning about these biochemical reactions, gene and protein structure, genotype and relation between the phenotype, and expression system on those interactions. The goal of our work is to understand the behaviors of the interactions networks and to model their evolution in time and in space. We propose in this study an ontological meta-model for the knowledge representation of the genetic regulatory networks. Ontology in artificial intelligence means the fundamental categories and relations that provide a framework for knowledge models. Domain ontology's are now commonly used to enable heterogeneous information resources, such as knowledge-based systems, to communicate with each other. The interest of our model is to represent the spatial, temporal and spatio-temporal knowledge. We validated our propositions in the genetic regulatory network of the Aarbidosis thaliana flower

Keywords: Ontological model, spatio-temporal modeling, Genetic Regulatory Networks (GRNs), knowledge representation.

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: Automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection.

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Authors: Dinesh Khandujaa, Pardeep Kumar Sharma

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Education, as the most important resource in any country, has multiplying effects on all facets of development in a society. The new social realities, particularly the interplay between democratization of education; unprecedented developments in IT sector; emergence of knowledge society, liberalization of economy and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for socio-economic development of a society. Unfortunately in India there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to sustainable growth of women entrepreneurship in India.

Keywords: Women Empowerment, Entrepreneurship, Education System, Women Entrepreneurship, Sustainable Development.

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Authors: Pedro Pablo González Pérez, Hiram I. Beltrán, Arturo Rojo-Domínguez, Máximo EduardoSánchez Gutiérrez

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Multi-agent system approach has proven to be an effective and appropriate abstraction level to construct whole models of a diversity of biological problems, integrating aspects which can be found both in "micro" and "macro" approaches when modeling this type of phenomena. Taking into account these considerations, this paper presents the important computational characteristics to be gathered into a novel bioinformatics framework built upon a multiagent architecture. The version of the tool presented herein allows studying and exploring complex problems belonging principally to structural biology, such as protein folding. The bioinformatics framework is used as a virtual laboratory to explore a minimalist model of protein folding as a test case. In order to show the laboratory concept of the platform as well as its flexibility and adaptability, we studied the folding of two particular sequences, one of 45-mer and another of 64-mer, both described by an HP model (only hydrophobic and polar residues) and coarse grained 2D-square lattice. According to the discussion section of this piece of work, these two sequences were chosen as breaking points towards the platform, in order to determine the tools to be created or improved in such a way to overcome the needs of a particular computation and analysis of a given tough sequence. The backwards philosophy herein is that the continuous studying of sequences provides itself important points to be added into the platform, to any time improve its efficiency, as is demonstrated herein.

Keywords: multi-agent systems, blackboard-based agent architecture, bioinformatics framework, virtual laboratory, protein folding.

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Authors: Mohamed Abdallah, Mostafa Warith, Roberto Narbaitz, Emil Petriu, Kevin Kennedy

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Heterogeneity of solid waste characteristics as well as the complex processes taking place within the landfill ecosystem motivated the implementation of soft computing methodologies such as artificial neural networks (ANN), fuzzy logic (FL), and their combination. The present work uses a hybrid ANN-FL model that employs knowledge-based FL to describe the process qualitatively and implements the learning algorithm of ANN to optimize model parameters. The model was developed to simulate and predict the landfill gas production at a given time based on operational parameters. The experimental data used were compiled from lab-scale experiment that involved various operating scenarios. The developed model was validated and statistically analyzed using F-test, linear regression between actual and predicted data, and mean squared error measures. Overall, the simulated landfill gas production rates demonstrated reasonable agreement with actual data. The discussion focused on the effect of the size of training datasets and number of training epochs.

Keywords: Adaptive neural fuzzy inference system (ANFIS), gas production, landfill

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Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: Creativity, education, expertise, technology.

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Authors: Charawee Butbumrung

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This paper aimed to study the factors that relate to working behavior of employees at Pakkred Municipality, Nonthaburi Province. A questionnaire was utilized as the tool in collecting information. Descriptive statistics included frequency, percentage, mean and standard deviation. Independent- sample t- test, analysis of variance and Pearson Correlation were also used. The findings of this research revealed that the majority of the respondents were female, between 25- 35 years old, married, with a Bachelor degree. The average monthly salary of respondents was between 8,001- 12,000 Baht, and having about 4-7 years of working experience. Regarding the overall working motivation factors, the findings showed that interrelationship, respect, and acceptance were ranked as highly important factors, whereas motivation, remunerations & welfare, career growth, and working conditions were ranked as moderately important factors. Also, overall working behavior was ranked as high. The hypotheses testing revealed that different genders had a different working behavior and had a different way of working as a team, which was significant at the 0.05 confidence level, Moreover, there was a difference among employees with different monthly salary in working behavior, problem- solving and decision making, which all were significant at the 0.05 confidence level. Employees with different years of working experience were found to have work working behavior both individual and as a team at the statistical significance level of 0.01 and 0.05. The result of testing the relationship between motivation in overall working revealed that interrelationship, respect and acceptance from others, career growth, and working conditions related to working behavior at a moderate level, while motivation in performing duties and remunerations and welfares related to working behavior towards the same direction at a low level, with a statistical significance of 0.01.

Keywords: Employees of Pakkred Municipality, Factors, Nonthaburi Province, Working Behavior.

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

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The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: Coupled Markov random field, environment, object-based analysis, Polarimetric SAR images.

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: Anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines.

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Authors: N. Cetinkaya, N. H. Ozdemir

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The aim of the study was to investigate the effect of Saccharomyces cerevisiae (SC) live yeast culture on microbial protein supply to small intestine in Kivircik male yearlings when fed with different ratio of forage and concentrate diets. Four Kivircik male yearlings with permanent rumen canula were used in the experiment. The treatments were allocated to a 4x4 Latin square design. Diet I consisted of 70% alfalfa hay and 30% concentrate, Diet II consisted of 30% alfalfa hay and 70% concentrate, Diet I and II were supplemented with a SC. Daily urine was collected and stored at -20°C until analysis. Calorimetric methods were used for the determination of urinary allantoin and creatinine levels. The estimated microbial N supply to small intestine for Diets I, I+SC, II and II+SC were 2.51, 2.64, 2.95 and 3.43 g N/d respectively. Supplementation of Diets I and II with SC significantly affected the allantoin levels in μmol/W0.75 (p<0.05). Mean creatinine values in μmol/W0.75 and allantoin:creatinine ratios were not significantly different among diets. In conclusion, supplementation with SC live yeast culture had a significant effect on urinary allantoin excretion and microbial protein supply to small intestine in Kivircik yearlings fed with high concentrate Diet II (P<0.05). Hence urinary allantoin excretion may be used as a tool for estimating microbial protein supply in Kivircık yearlings. However, further studies are necessary to understand the metabolism of Saccharomyces cerevisiae live yeast culture with different forage:concentrate ratio in Kıvırcık Yearlings.

Keywords: Allantoin, creatinine, Kivircik yearling, microbial nitrogen, Saccharomyces cerevisiae.

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Authors: C. Adam, G. Henri, T. Levent, J-B Mauro, A-L Mayet

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Energy Efficiency Management is the heart of a worldwide problem. The capability of a multi-agent system as a technology to manage the micro-grid operation has already been proved. This paper deals with the implementation of a decisional pattern applied to a multi-agent system which provides intelligence to a distributed local energy network considered at local consumer level. Development of multi-agent application involves agent specifications, analysis, design, and realization. Furthermore, it can be implemented by following several decisional patterns. The purpose of present article is to suggest a new approach for a decisional pattern involving a multi-agent system to control a distributed local energy network in a decentralized competitive system. The proposed solution is the result of a dichotomous approach based on environment observation. It uses an iterative process to solve automatic learning problems and converges monotonically very fast to system attracting operation point.

Keywords: Energy Efficiency Management, Distributed Smart- Grid, Multi-Agent System, Decisional Decentralized Competitive System.

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: Memory, planning navigational ability, virtual reality.

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Authors: R. Sullivan, T. Holden, G. Tremberger, Jr, E. Cheung, C. Branch, J. Burrero, G. Surpris, S. Quintana, A. Rameau, N. Gadura, H. Yao, R. Subramaniam, P. Schneider, S. A. Rotenberg, P. Marchese, A. Flamhlolz, D. Lieberman, T. Cheung

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Migration in breast cancer cell wound healing assay had been studied using image fractal dimension analysis. The migration of MDA-MB-231 cells (highly motile) in a wound healing assay was captured using time-lapse phase contrast video microscopy and compared to MDA-MB-468 cell migration (moderately motile). The Higuchi fractal method was used to compute the fractal dimension of the image intensity fluctuation along a single pixel width region parallel to the wound. The near-wound region fractal dimension was found to decrease three times faster in the MDA-MB- 231 cells initially as compared to the less cancerous MDA-MB-468 cells. The inner region fractal dimension was found to be fairly constant for both cell types in time and suggests a wound influence range of about 15 cell layer. The box-counting fractal dimension method was also used to study region of interest (ROI). The MDAMB- 468 ROI area fractal dimension was found to decrease continuously up to 7 hours. The MDA-MB-231 ROI area fractal dimension was found to increase and is consistent with the behavior of a HGF-treated MDA-MB-231 wound healing assay posted in the public domain. A fractal dimension based capacity index has been formulated to quantify the invasiveness of the MDA-MB-231 cells in the perpendicular-to-wound direction. Our results suggest that image intensity fluctuation fractal dimension analysis can be used as a tool to quantify cell migration in terms of cancer severity and treatment responses.

Keywords: Higuchi fractal dimension, box-counting fractal dimension, cancer cell migration, wound healing.

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Authors: Nor Aziati Abdul Hamid, Juhana Salim

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The main objective of this paper is to contribute the existing knowledge transfer and IT Outsourcing literature specifically in the context of Malaysia by reviewing the current practices of e-government IT outsourcing in Malaysia including the issues and challenges faced by the public agencies in transferring the knowledge during the engagement. This paper discusses various factors and different theoretical model of knowledge transfer starting from the traditional model to the recent model suggested by the scholars. The present paper attempts to align organizational knowledge from the knowledge-based view (KBV) and organizational learning (OL) lens. This review could help shape the direction of both future theoretical and empirical studies on inter-firm knowledge transfer specifically on how KBV and OL perspectives could play significant role in explaining the complex relationships between the client and vendor in inter-firm knowledge transfer and the role of organizational management information system and Transactive Memory System (TMS) to facilitate the organizational knowledge transferring process. Conclusion is drawn and further research is suggested.

Keywords: E-government, IT Outsourcing, Knowledge Management, Knowledge Transfer

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Authors: Farhad Asadi, Mohammad Javad Mollakazemi

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In this paper, Bayesian online inference in models of data series are constructed by change-points algorithm, which separated the observed time series into independent series and study the change and variation of the regime of the data with related statistical characteristics. variation of statistical characteristics of time series data often represent separated phenomena in the some dynamical system, like a change in state of brain dynamical reflected in EEG signal data measurement or a change in important regime of data in many dynamical system. In this paper, prediction algorithm for studying change point location in some time series data is simulated. It is verified that pattern of proposed distribution of data has important factor on simpler and smother fluctuation of hazard rate parameter and also for better identification of change point locations. Finally, the conditions of how the time series distribution effect on factors in this approach are explained and validated with different time series databases for some dynamical system.

Keywords: Time series, fluctuation in statistical characteristics, optimal learning.

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Authors: Parvinder S. Sandhu, Sunil Khullar, Satpreet Singh, Simranjit K. Bains, Manpreet Kaur, Gurvinder Singh

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Fault-proneness of a software module is the probability that the module contains faults. To predict faultproneness of modules different techniques have been proposed which includes statistical methods, machine learning techniques, neural network techniques and clustering techniques. The aim of proposed study is to explore whether metrics available in the early lifecycle (i.e. requirement metrics), metrics available in the late lifecycle (i.e. code metrics) and metrics available in the early lifecycle (i.e. requirement metrics) combined with metrics available in the late lifecycle (i.e. code metrics) can be used to identify fault prone modules using Genetic Algorithm technique. This approach has been tested with real time defect C Programming language datasets of NASA software projects. The results show that the fusion of requirement and code metric is the best prediction model for detecting the faults as compared with commonly used code based model.

Keywords: Genetic Algorithm, Fault Proneness, Software Faultand Software Quality.

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Authors: Chao-Linag Hsieh, Zheng-Wei Ye, Chen-Kang Huang, Yeun-Chung Lee, Chih-Hong Sun, Tzai-Hung Wen, Jehn-Yih Juang, Joe-Air Jiang

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Air pollution and climate warming become more and more intensified in many areas, especially in urban areas. Environmental parameters are critical information to air pollution and weather monitoring. Thus, it is necessary to develop a suitable air pollution and weather monitoring system for urban areas. In this study, a vehicle monitoring system (VMS) based on the IoT technique is developed. Cars are selected as the research tool because it can reach a greater number of streets to collect data. The VMS can monitor different environmental parameters, including ambient temperature and humidity, and air quality parameters, including PM2.5, NO₂, CO, and O₃. The VMS can provide other information, including GPS signals and the vibration information through driving a car on the street. Different sensor modules are used to measure the parameters and collect the measured data and transmit them to a cloud server through the LoRa protocol. A user interface is used to show the sensing data storing at the cloud server. To examine the performance of the system, a researcher drove a Nissan x-trail 1998 to the area close to the Da’an District office in Taipei to collect monitoring data. The collected data are instantly shown on the user interface. The four kinds of information are provided by the interface: GPS positions, weather parameters, vehicle information, and air quality information. With the VMS, users can obtain the information regarding air quality and weather conditions when they drive their car to an urban area. Also, government agencies can make decisions on traffic planning based on the information provided by the proposed VMS.

Keywords: Vehicle, monitoring system, LoRa, smart city.

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Authors: Elham Serkani, Hossein Gharaee Garakani, Naser Mohammadzadeh, Elaheh Vaezpour

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Intrusion detection systems (IDS) are the main components of network security. These systems analyze the network events for intrusion detection. The design of an IDS is through the training of normal traffic data or attack. The methods of machine learning are the best ways to design IDSs. In the method presented in this article, the pruning algorithm of C5.0 decision tree is being used to reduce the features of traffic data used and training IDS by the least square vector algorithm (LS-SVM). Then, the remaining features are arranged according to the predictor importance criterion. The least important features are eliminated in the order. The remaining features of this stage, which have created the highest level of accuracy in LS-SVM, are selected as the final features. The features obtained, compared to other similar articles which have examined the selected features in the least squared support vector machine model, are better in the accuracy, true positive rate, and false positive. The results are tested by the UNSW-NB15 dataset.

Keywords: Intrusion detection system, decision tree, support vector machine, feature selection.

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Authors: Hui Fang Huang Su, Leanne Lai, Pei-Fen Li, Mei-Hwei Ho, Yu-Wen Chiu

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This study aimed to provide a practical, systematic, and comprehensive intervention for children with Autism Spectrum Disorder (ASD). A pilot study of quasi-experimental pre-post intervention with control group design was conducted to evaluate if the mathematical intervention (Project MIND - Math Is Not Difficult) increases the math comprehension of children with ASD Children with ASD in the primary grades (K-1, 2) participated in math interventions to enhance their math comprehension and cognitive ability. The Bracken basic concept scale was used to evaluate subjects’ language skills, cognitive development, and school readiness. The study found that our systemic interventions of Project MIND significantly improved the mathematical and cognitive abilities in children with autism. The results of this study may lead to a major change in effective and adequate health care services for children with ASD and their families. All statistical analyses were performed with the IBM SPSS Statistics Version 25 for Windows. The significant level was set at 0.05 P-value.

Keywords: Young Children, Autism, Mathematics, Curriculum, teaching and learning, children with special needs, Project MIND.

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Authors: Yahia Salah, Med Lassaad Kaddachi, Rached Tourki

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This paper presents the design, implementation and evaluation of a micro-network, or Network-on-Chip (NoC), based on a generic pipeline router architecture. The router is designed to efficiently support traffic generated by multimedia applications on embedded multi-core systems. It employs a simplest routing mechanism and implements the round-robin scheduling strategy to resolve output port contentions and minimize latency. A virtual channel flow control is applied to avoid the head-of-line blocking problem and enhance performance in the NoC. The hardware design of the router architecture has been implemented at the register transfer level; its functionality is evaluated in the case of the two dimensional Mesh/Torus topology, and performance results are derived from ModelSim simulator and Xilinx ISE 9.2i synthesis tool. An example of a multi-core image processing system utilizing the NoC structure has been implemented and validated to demonstrate the capability of the proposed micro-network architecture. To reduce complexity of the image compression and decompression architecture, the system use image processing algorithm based on classical discrete cosine transform with an efficient zonal processing approach. The experimental results have confirmed that both the proposed image compression scheme and NoC architecture can achieve a reasonable image quality with lower processing time.

Keywords: Generic Pipeline Network-on-Chip Router Architecture, JPEG Image Compression, FPGA Hardware Implementation, Performance Evaluation.

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

Abstract:

Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: Meta-regression analysis, social networking site use, academic performance, multitasking, motivation.

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Authors: Dev Ras Pandey, Bharat Mishra, S. K. Tripathi

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Cloud computing is ready to transform the structure of businesses and learning through supplying the real-time applications and provide an immediate help for small to medium sized businesses. The ability to run a hypervisor inside a virtual machine is important feature of virtualization and it is called nested virtualization. In today’s growing field of information technology, many of the virtualization models are available, that provide a convenient approach to implement, but decision for a single model selection is difficult. This paper explains the applications of operating system based virtualization in cloud computing with an appropriate/suitable model with their different specifications and user’s requirements. In the present paper, most popular models are selected, and the selection was based on container and hypervisor based virtualization. Selected models were compared with a wide range of user’s requirements as number of CPUs, memory size, nested virtualization supports, live migration and commercial supports, etc. and we identified a most suitable model of virtualization.

Keywords: Virtualization, OS based virtualization, container and hypervisor based virtualization.

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Authors: S. M. Biondi, V. Catania, R. Di Natale, A. R. Intilisano, D. Panno

Abstract:

One of the major goals of Spoken Dialog Systems (SDS) is to understand what the user utters. In the SDS domain, the Spoken Language Understanding (SLU) Module classifies user utterances by means of a pre-definite conceptual knowledge. The SLU module is able to recognize only the meaning previously included in its knowledge base. Due the vastity of that knowledge, the information storing is a very expensive process. Updating and managing the knowledge base are time-consuming and error-prone processes because of the rapidly growing number of entities like proper nouns and domain-specific nouns. This paper proposes a solution to the problem of Name Entity Recognition (NER) applied to a SDS domain. The proposed solution attempts to automatically recognize the meaning associated with an utterance by using the PANKOW (Pattern based Annotation through Knowledge On the Web) method at runtime. The method being proposed extracts information from the Web to increase the SLU knowledge module and reduces the development effort. In particular, the Google Search Engine is used to extract information from the Facebook social network.

Keywords: Spoken Dialog System, Spoken Language Understanding, Web Semantic, Name Entity Recognition.

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