Search results for: work based learning

Authors: Hongyan Dai

Abstract:

This work presents a matched field processing (MFP) algorithm based on Dopplerlet transform for estimating the motion parameters of a sound source moving along a straight line and with a constant speed by using a piecewise strategy, which can significantly reduce the computational burden. Monte Carlo simulation results and an experimental result are presented to verify the effectiveness of the algorithm advocated.

Keywords: matched field processing; Dopplerlet transform; motion parameter estimation; piecewise strategy

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Authors: K. Freedman

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In the present study, a support vector machine (SVM) learning approach to character recognition is proposed. Simple feature detectors, similar to those found in the human visual system, were used in the SVM classifier. Alphabetic characters were rotated to 8 different angles and using the proposed cognitive model, all characters were recognized with 100% accuracy and specificity. These same results were found in psychiatric studies of human character recognition.

Keywords: Character recognition, cognitive model, support vector machine learning.

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Authors: Blesson Varghese, Gerard T. McKee

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The work reported in this paper is motivated by the fact that there is a need to apply autonomic computing concepts to parallel computing systems. Advancing on prior work based on intelligent cores [36], a swarm-array computing approach, this paper focuses on 'Intelligent agents' another swarm-array computing approach in which the task to be executed on a parallel computing core is considered as a swarm of autonomous agents. A task is carried to a computing core by carrier agents and is seamlessly transferred between cores in the event of a predicted failure, thereby achieving self-ware objectives of autonomic computing. The feasibility of the proposed swarm-array computing approach is validated on a multi-agent simulator.

Keywords: Autonomic computing, intelligent agents, swarm-array computing.

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Authors: Linda H. Meyer, Carol S. Sternberger

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Self-efficacy, self-reliance, and motivation were examined in a quasi-experimental study with 178 sophomore university students. Participants used an interactive cardiovascular anatomy and physiology CD-ROM, and completed a 15-item questionnaire. Reliability of the questionnaire was established using Cronbach-s alpha. Post-tests and course grades were examined using a t-test, demonstrating no significance. Results of an item-to-item analysis of the questionnaire showed overall satisfaction with the teaching methodology and varied results for self-efficacy, selfreliance, and motivation. Kendall-s Tau was calculated for all items in the questionnaire.

Keywords: Asynchronous learning environments, motivation, self-efficacy, self-reliance.

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Authors: S. Odeh, E. Abdelghani

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Collaborative working environments for distance education can be considered as a more generic form of contemporary remote labs. At present, the majority of existing real laboratories are not constructed to allow the involved participants to collaborate in real time. To make this revolutionary learning environment possible we must allow the different users to carry out an experiment simultaneously. In recent times, multi-user environments are successfully applied in many applications such as air traffic control systems, team-oriented military systems, chat-text tools, multi-player games etc. Thus, understanding the ideas and techniques behind these systems could be of great importance in the contribution of ideas to our e-learning environment for collaborative working. In this investigation, collaborative working environments from theoretical and practical perspectives are considered in order to build an effective collaborative real laboratory, which allows two students or more to conduct remote experiments at the same time as a team. In order to achieve this goal, we have implemented distributed system architecture, enabling students to obtain an automated help by either a human tutor or a rule-based e-tutor.

Keywords: Collaboration environment, e-tutor, multi-user environments, socio-technical system.

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Authors: Rohan Putatunda, Aryya Gangopadhyay

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Acoustic sensors, if embedded in smart street lights, can help in capturing the activities (car honking, sirens, events, traffic, etc.) in cities. Needless to say, the acoustic data from such scenarios are complex due to multiple audio streams originating from different events, and when decomposed to independent signals, the amount of retrieved data volume is small in quantity which is inadequate to train deep neural networks. So, in this paper, we address the two challenges: a) separating the mixed signals, and b) developing an efficient acoustic classifier under data paucity. So, to address these challenges, we propose an architecture with supervised deep learning, where the initial captured mixed acoustics data are analyzed with Fast Fourier Transformation (FFT), followed by filtering the noise from the signal, and then decomposed to independent signals by fast independent component analysis (Fast ICA). To address the challenge of data paucity, we propose a multi feature-based deep neural network with high performance that is reflected in our experiments when compared to the conventional convolutional neural network (CNN) and multi-layer perceptron (MLP).

Keywords: FFT, ICA, vehicle classification, multi-feature DNN, CNN, MLP.

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Authors: Khaled Elbehiery, Hussam Elbehiery

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The success of education is dependent on evolution and adaptation, while the traditional system has worked before, one type of education evolved with the digital age is virtual education that has influenced efficiency in today’s learning environments. Virtual learning has indeed proved its efficiency to overcome the drawbacks of the physical environment such as time, facilities, location, etc., but despite what it had accomplished, the educational system over all is not adequate for being a productive system yet. Earning a degree is not anymore enough to obtain a career job; it is simply missing the skills and creativity. There are always two sides of a coin; a college degree or a specialized certificate, each has its own merits, but having both can put you on a successful IT career path. For many of job-seeking individuals across world to have a clear meaningful goal for work and education and positively contribute the community, a productive correlation and cooperation among employers, universities alongside with the individual technical skills is a must for generations to come. Fortunately, the proposed research “Entrepreneur Universal Education System” is an evolution to meet the needs of both employers and students, in addition to gaining vital and real-world experience in the chosen fields is easier than ever. The new vision is to empower the education to improve organizations’ needs which means improving the world as its primary goal, adopting universal skills of effective thinking, effective action, effective relationships, preparing the students through real-world accomplishment and encouraging them to better serve their organization and their communities faster and more efficiently.

Keywords: Virtual education, academic degree, certificates, internship, amazon web services, Microsoft Azure, Google cloud platform, hybrid models.

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Authors: J. P. Dubois, O. M. Abdul-Latif

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Support Vector Machine (SVM) is a statistical learning tool that was initially developed by Vapnik in 1979 and later developed to a more complex concept of structural risk minimization (SRM). SVM is playing an increasing role in applications to detection problems in various engineering problems, notably in statistical signal processing, pattern recognition, image analysis, and communication systems. In this paper, SVM was applied to the detection of medical ultrasound images in the presence of partially developed speckle noise. The simulation was done for single look and multi-look speckle models to give a complete overlook and insight to the new proposed model of the SVM-based detector. The structure of the SVM was derived and applied to clinical ultrasound images and its performance in terms of the mean square error (MSE) metric was calculated. We showed that the SVM-detected ultrasound images have a very low MSE and are of good quality. The quality of the processed speckled images improved for the multi-look model. Furthermore, the contrast of the SVM detected images was higher than that of the original non-noisy images, indicating that the SVM approach increased the distance between the pixel reflectivity levels (detection hypotheses) in the original images.

Keywords: LS-SVM, medical ultrasound imaging, partially developed speckle, multi-look model.

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Authors: Rajesh Kumar, Puneet Aggarwal

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In context of understanding problems faced by undergraduate students while carrying out laboratory experiments dealing with high voltages, it was found that most of the students are hesitant to work directly on machine. The reason is that error in the circuitry might lead to deterioration of machine and laboratory instruments. So, it has become inevitable to include modern pedagogic techniques for undergraduate students, which would help them to first carry out experiment in virtual system and then to work on live circuit. Further advantages include that students can try out their intuitive ideas and perform in virtual environment, hence leading to new research and innovations. In this paper, virtual environment used is of MATLAB/Simulink for three-phase induction machines. The performance analysis of three-phase induction machine is carried out using virtual environment which includes Direct Current (DC) Test, No-Load Test, and Block Rotor Test along with speed torque characteristics for different rotor resistances and input voltage, respectively. Further, this paper carries out computer aided teaching of basic Voltage Source Inverter (VSI) drive circuitry. Hence, this paper gave undergraduates a clearer view of experiments performed on virtual machine (No-Load test, Block Rotor test and DC test, respectively). After successful implementation of basic tests, VSI circuitry is implemented, and related harmonic distortion (THD) and Fast Fourier Transform (FFT) of current and voltage waveform are studied.

Keywords: Block rotor test, DC test, no-load test, virtual environment, VSI.

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Authors: S. Prem Kumar, H. Thameemul Ansari, V. Saminadan

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A smart grid is an emerging technology in the power delivery system which provides an intelligent, self-recovery and homeostatic grid in delivering power to the users. Smart grid communication network provides transmission capacity for information transformation within the connected nodes in the network, in favor of functional and operational needs. In the electric grids communication network delay is based on choosing the appropriate technology and the types of devices enforced. In distinction, the combination of IEEE 802.16 based WiMAX and IEEE 802.11 based WiFi technologies provides improved coverage and gives low delay performances to meet the smart grid needs. By incorporating this method in Wide Area Monitoring System (WAMS) and Advanced Metering Infrastructure (AMI) the performance of the smart grid will be considerably improved. This work deals with the implementation of WiMAX-WLAN integrated network architecture for WAMS and AMI in the smart grid.

Keywords: WiMAX, WLAN, WAMS, Smart Grid, HetNet, AMI.

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Authors: M. R. Homaeinezhad, E. Tavakkoli, M. Habibi, S. A. Atyabi, A. Ghaffari

Abstract:

This paper describes a new supervised fusion (hybrid) electrocardiogram (ECG) classification solution consisting of a new QRS complex geometrical feature extraction as well as a new version of the learning vector quantization (LVQ) classification algorithm aimed for overcoming the stability-plasticity dilemma. Toward this objective, after detection and delineation of the major events of ECG signal via an appropriate algorithm, each QRS region and also its corresponding discrete wavelet transform (DWT) are supposed as virtual images and each of them is divided into eight polar sectors. Then, the curve length of each excerpted segment is calculated and is used as the element of the feature space. To increase the robustness of the proposed classification algorithm versus noise, artifacts and arrhythmic outliers, a fusion structure consisting of five different classifiers namely as Support Vector Machine (SVM), Modified Learning Vector Quantization (MLVQ) and three Multi Layer Perceptron-Back Propagation (MLP–BP) neural networks with different topologies were designed and implemented. The new proposed algorithm was applied to all 48 MIT–BIH Arrhythmia Database records (within–record analysis) and the discrimination power of the classifier in isolation of different beat types of each record was assessed and as the result, the average accuracy value Acc=98.51% was obtained. Also, the proposed method was applied to 6 number of arrhythmias (Normal, LBBB, RBBB, PVC, APB, PB) belonging to 20 different records of the aforementioned database (between– record analysis) and the average value of Acc=95.6% was achieved. To evaluate performance quality of the new proposed hybrid learning machine, the obtained results were compared with similar peer– reviewed studies in this area.

Keywords: Feature Extraction, Curve Length Method, SupportVector Machine, Learning Vector Quantization, Multi Layer Perceptron, Fusion (Hybrid) Classification, Arrhythmia Classification, Supervised Learning Machine.

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Authors: Nagore Guerra Bilbao, Clemente Lobato Fraile

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This paper outlines the design and development of the MENDEPRO questionnaire, designed to analyze mentoring performance within a professional development process carried out with professors at the University of the Basque Country, Spain. The study took into account the international research carried out over the past two decades into teachers' professional development, and was also based on a thorough review of the most common instruments used to identify and analyze mentoring styles, many of which fail to provide sufficient psychometric guarantees. The present study aimed to gather empirical data in order to verify the metric quality of the questionnaire developed. To this end, the process followed to validate the theoretical construct was as follows: The formulation of the items and indicators in accordance with the study variables; the analysis of the validity and reliability of the initial questionnaire; the review of the second version of the questionnaire and the definitive measurement instrument. Content was validated through the formal agreement and consensus of 12 university professor training experts. A reduced sample of professors who had participated in a lifelong learning program was then selected for a trial evaluation of the instrument developed. After the trial, 18 items were removed from the initial questionnaire. The final version of the instrument, comprising 33 items, was then administered to a sample group of 99 participants. The results revealed a five-dimensional structure matching theoretical expectations. Also, the reliability data for both the instrument as a whole (.98) and its various dimensions (between .91 and .97) were very high. The questionnaire was thus found to have satisfactory psychometric properties and can therefore be considered apt for studying the performance of mentoring in both induction programs for young professors and lifelong learning programs for senior faculty members.

Keywords: Higher education, mentoring, professional development, university teachers.

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Authors: L. R. Valencia Pérez, J. M. Peña Aguilar, A. Lamadrid Álvarez, A. Pastrana Palma, H. F. Valencia Pérez, M. Vivanco Vargas

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This work proposes a Cooperation-Competitive (Coopetitive) approach that allows coordinated work among the Secretary of Public Education (SEP), the Autonomous University of Querétaro (UAQ) and government funds from National Council for Science and Technology (CONACYT) or some other international organizations. To work on an overall knowledge transfer strategy with e-learning over the Cloud, where experts in junior high and high school education, working in multidisciplinary teams, perform analysis, evaluation, design, production, validation and knowledge transfer at large scale using a Cloud Computing platform. Allowing teachers and students to have all the information required to ensure a homologated nationally knowledge of topics such as mathematics, statistics, chemistry, history, ethics, civism, etc. This work will start with a pilot test in Spanish and initially in two regional dialects Otomí and Náhuatl. Otomí has more than 285,000 speaking indigenes in Queretaro and Mexico´s central region. Náhuatl is number one indigenous dialect spoken in Mexico with more than 1,550,000 indigenes. The phase one of the project takes into account negotiations with indigenous tribes from different regions, and the Information and Communication technologies to deliver the knowledge to the indigenous schools in their native dialect. The methodology includes the following main milestones: Identification of the indigenous areas where Otomí and Náhuatl are the spoken dialects, research with the SEP the location of actual indigenous schools, analysis and inventory or current schools conditions, negotiation with tribe chiefs, analysis of the technological communication requirements to reach the indigenous communities, identification and inventory of local teachers technology knowledge, selection of a pilot topic, analysis of actual student competence with traditional education system, identification of local translators, design of the e-learning platform, design of the multimedia resources and storage strategy for “Cloud Computing”, translation of the topic to both dialects, Indigenous teachers training, pilot test, course release, project follow up, analysis of student requirements for the new technological platform, definition of a new and improved proposal with greater reach in topics and regions. Importance of phase one of the project is multiple, it includes the proposal of a working technological scheme, focusing in the cultural impact in Mexico so that indigenous tribes can improve their knowledge about new forms of crop improvement, home storage technologies, proven home remedies for common diseases, ways of preparing foods containing major nutrients, disclose strengths and weaknesses of each region, communicating through cloud computing platforms offering regional products and opening communication spaces for inter-indigenous cultural exchange.

Keywords: Mexicans indigenous tribes, education, knowledge transfer, cloud computing, Otomí, Náhuatl, language.

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Authors: Shaira Ismail

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The empirical studies on High Performance Work Systems (HPWSs) and their impacts on firm performance have remarkably little in the developing countries. This paper reviews literatures on the HPWSs practices in different work settings, Western and Asian countries. A review on the empirical research leads to a conclusion that, country differences influence the Human Resource Management (HRM) practices. It is anticipated that there are similarities and differences in the extent of implementation of HPWSs practices by the Malaysian manufacturing firms due to the organizational contextual factors and, the HPWSs have a significant impact on firms- better performance amongst MNCs and local firms.

Keywords: Firm Performance, High Performance Work Systems (HPWSs), Human Resource Management (HRM), Multinational Corporations (MNCs).

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Authors: B. Arun Kumar, T. Ravichandran

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Workflow scheduling is an important part of cloud computing and based on different criteria it decides cost, execution time, and performances. A cloud workflow system is a platform service facilitating automation of distributed applications based on new cloud infrastructure. An aspect which differentiates cloud workflow system from others is market-oriented business model, an innovation which challenges conventional workflow scheduling strategies. Time and Cost optimization algorithm for scheduling Hybrid Clouds (TCHC) algorithm decides which resource should be chartered from public providers is combined with a new De-De algorithm considering that every instance of single and multiple workflows work without deadlocks. To offset this, two new concepts - De-De Dodging Algorithm and Priority Based Decisive Algorithm - combine with conventional deadlock avoidance issues by proposing one algorithm that maximizes active (not just allocated) resource use and reduces Makespan.

Keywords: Workflow Scheduling, cloud workflow, TCHC algorithm, De-De Dodging Algorithm, Priority Based Decisive Algorithm (PBD), Makespan.

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: Artificial neural networks, fuel consumption, machine learning, regression, statistical tests.

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Authors: Paul Lajbcygier, Seng Lee

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Co-integration models the long-term, equilibrium relationship of two or more related financial variables. Even if cointegration is found, in the short run, there may be deviations from the long run equilibrium relationship. The aim of this work is to forecast these deviations using neural networks and create a trading strategy based on them. A case study is used: co-integration residuals from Australian Bank Bill futures are forecast and traded using various exogenous input variables combined with neural networks. The choice of the optimal exogenous input variables chosen for each neural network, undertaken in previous work [1], is validated by comparing the forecasts and corresponding profitability of each, using a trading strategy.

Keywords: Artificial neural networks, co-integration, forecasting, trading rule.

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Authors: Payam Mousavi, Andrew L. Stewart, Huiwen You, Aryeh F. G. Fayerman

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We present a decision-support tool to assist an operator in the detection and tracking of a suspect vehicle traveling to an unknown target destination. Multiple data sources, such as traffic cameras, traffic information, weather, etc., are integrated and processed in real-time to infer a suspect’s intended destination chosen from a list of pre-determined high-value targets. Previously, we presented our work in the detection and tracking of vehicles using traffic and airborne cameras. Here, we focus on the fusion and processing of that information to predict a suspect’s behavior. The network of cameras is represented by a directional graph, where the edges correspond to direct road connections between the nodes and the edge weights are proportional to the average time it takes to travel from one node to another. For our experiments, we construct our graph based on the greater Los Angeles subset of the Caltrans’s “Performance Measurement System” (PeMS) dataset. We propose a Bayesian approach where a posterior probability for each target is continuously updated based on detections of the suspect in the live video feeds. Additionally, we introduce the concept of ‘soft interventions’, inspired by the field of Causal Inference. Soft interventions are herein defined as interventions that do not immediately interfere with the suspect’s movements; rather, a soft intervention may induce the suspect into making a new decision, ultimately making their intent more transparent. For example, a soft intervention could be temporarily closing a road a few blocks from the suspect’s current location, which may require the suspect to change their current course. The objective of these interventions is to gain the maximum amount of information about the suspect’s intent in the shortest possible time. Our system currently operates in a human-on-the-loop mode where at each step, a set of recommendations are presented to the operator to aid in decision-making. In principle, the system could operate autonomously, only prompting the operator for critical decisions, allowing the system to significantly scale up to larger areas and multiple suspects. Once the intended target is identified with sufficient confidence, the vehicle is reported to the authorities to take further action. Other recommendations include a selection of road closures, i.e., soft interventions, or to continue monitoring. We evaluate the performance of the proposed system using simulated scenarios where the suspect, starting at random locations, takes a noisy shortest path to their intended target. In all scenarios, the suspect’s intended target is unknown to our system. The decision thresholds are selected to maximize the chances of determining the suspect’s intended target in the minimum amount of time and with the smallest number of interventions. We conclude by discussing the limitations of our current approach to motivate a machine learning approach, based on reinforcement learning in order to relax some of the current limiting assumptions.

Keywords: Autonomous surveillance, Bayesian reasoning, decision-support, interventions, patterns-of-life, predictive analytics, predictive insights.

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Authors: Delfim Soares Jr.

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In this work, a new family of time marching procedures based on Green’s function matrices is presented. The formulation is based on the development of new recurrence relationships, which employ time integral terms to treat initial condition values. These integral terms are numerically evaluated taking into account Newton-Cotes formulas. The Green’s matrices of the model are also numerically computed, taking into account the generalized-α method and subcycling techniques. As it is discussed and illustrated along the text, the proposed procedure is efficient and accurate, providing a very attractive time marching technique. 

Keywords: Dynamics, Time-Marching, Green’s Function, Generalized-α Method, Subcycling.

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Authors: K. A. Mistry, I. B. Patel, A. H. Prajapati

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In the present work, the alloy of Bismuth-lead is prepared on the basis of percentage of molecular weight 9:1, 5:5 and 1:9 ratios and grown by Zone- Refining Technique under a vacuum atmosphere. The EDAX of these samples are done and the results are reported. Micro hardness test has been used as an alternative test for measuring material’s tensile properties. The effect of temperature and load on the hardness of the grown alloy has been studied. Further the comparative studies of work hardening coefficients are reported.

Keywords: EDAX, hardening coefficient, Micro hardness, Bi-Pb alloy.

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Authors: Shigeaki Sakurai, Ryohei Orihara

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This paper proposes a new method for analyzing textual data. The method deals with items of textual data, where each item is described based on various viewpoints. The method acquires 2- class classification models of the viewpoints by applying an inductive learning method to items with multiple viewpoints. The method infers whether the viewpoints are assigned to the new items or not by using the models. The method extracts expressions from the new items classified into the viewpoints and extracts characteristic expressions corresponding to the viewpoints by comparing the frequency of expressions among the viewpoints. This paper also applies the method to questionnaire data given by guests at a hotel and verifies its effect through numerical experiments.

Keywords: Text mining, Multiple viewpoints, Differential analysis, Questionnaire data

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Authors: Rauno Pääkkönen, Fabriziomaria Gobba, Leena Korpinen

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The objective of the study is to assess the implementation of LED lighting into forest machine work in the dark. In addition, the paper includes a wide variety of important and relevant safety and health parameters. In modern, computerized work in the cab of forest machines, artificial illumination is a demanding task when performing duties, such as the visual inspections of wood and computer calculations. We interviewed entrepreneurs and gathered the following as the most pertinent themes: (1) safety, (2) practical problems, and (3) work with LED lighting. The most important comments were in regards to the practical problems of LED lighting. We found indications of technical problems in implementing LED lighting, like snow and dirt on the surfaces of lamps that dim the emission of light. Moreover, service work in the dark forest is dangerous and increases the risks of on-site accidents. We also concluded that the amount of blue light to the eyes should be assessed, especially, when the drivers are working in a semi-dark cab.

Keywords: Forest machines, health, LED, safety.

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Authors: Kiranmai S.Rai

Abstract:

The neurogenic potential of many herbal extracts used in Indian medicine is hitherto unknown. Extracts derived from Clitoria ternatea Linn have been used in Indian Ayurvedic system of medicine as an ingredient of “Medhya rasayana", consumed for improving memory and longevity in humans and also in treatment of various neurological disorders. Our earlier experimental studies with oral intubation of Clitoria ternatea aqueous root extract (CTR) had shown significant enhancement of learning and memory in postnatal and young adult Wistar rats. The present study was designed to elucidate the in vitro effects of 200ng/ml of CTR on proliferation, differentiation and growth of anterior subventricular zone neural stem cells (aSVZ NSC-s) derived from prenatal and postnatal rat pups. Results show significant increase in proliferation and growth of neurospheres and increase in the yield of differentiated neurons of aSVZ neural precursor cells (aSVZNPC-s) at 7 days in vitro when treated with 200ng/ml of CTR as compared to age matched control. Results indicate that CTR has growth promoting neurogenic effect on aSVZ neural stem cells and their survival similar to neurotrophic factors like Survivin, Neuregulin 1, FGF-2, BDNF possibly the basis for enhanced learning and memory.

Keywords: Anterior subventricular zone (aSVZ) neural stemcell, Clitoria ternatea, Learning and memory, Neurogenesis.

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Authors: Aya Salama

Abstract:

Digital Twin has emerged as a compelling research area, capturing the attention of scholars over the past decade. It finds applications across diverse fields, including smart manufacturing and healthcare, offering significant time and cost savings. Notably, it often intersects with other cutting-edge technologies such as Data Mining, Artificial Intelligence, and Machine Learning. However, the concept of a Human Digital Twin (HDT) is still in its infancy and requires further demonstration of its practicality. HDT takes the notion of Digital Twin a step further by extending it to living entities, notably humans, who are vastly different from inanimate physical objects. The primary objective of this research was to create an HDT capable of automating real-time human responses by simulating human behavior. To achieve this, the study delved into various areas, including clustering, supervised classification, topic extraction, and sentiment analysis. The paper successfully demonstrated the feasibility of HDT for generating personalized responses in social messaging applications. Notably, the proposed approach achieved an overall accuracy of 63%, a highly promising result that could pave the way for further exploration of the HDT concept. The methodology employed Random Forest for clustering the question database and matching new questions, while K-nearest neighbor was utilized for sentiment analysis.

Keywords: Human Digital twin, sentiment analysis, topic extraction, supervised machine learning, unsupervised machine learning, classification and clustering.

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Authors: Chandrakala Varatharajoo, Adelina Binti Asmawi, Nabeel Abdallah Mohammad Abedalaziz

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This study investigated the impact of inflectional and derivational morphemic analysis awareness on ESL secondary school students’ vocabulary learning strategy. The quasi-experimental study was conducted with 106 low proficiency secondary school students in two experimental groups (inflectional and derivational) and one control group. The students’ vocabulary acquisition was assessed through two measures: Morphemic Analysis Test and Vocabulary- Morphemic Test in the pretest and posttest before and after an intervention programme. Results of ANCOVA revealed that both the experimental groups achieved a significant score in Morphemic Analysis Test and Vocabulary-Morphemic Test. However, the inflectional group obtained a fairly higher score than the derivational group. Thus, the results indicated that ESL low proficiency secondary school students performed better on inflectional morphemic awareness as compared to derivatives. The results also showed that the awareness of inflectional morphology contributed more on the vocabulary acquisition. Importantly, learning inflectional morphology can help ESL low proficiency secondary school students to develop both morphemic awareness and vocabulary gain. Theoretically, these findings show that not all morphemes are equally useful to students for their language development. Practically, these findings indicate that morphological instruction should at least be included in remediation and instructional efforts with struggling learners across all grade levels, allowing them to focus on meaning within the word before they attempt the text in large for better comprehension. Also, by methodologically, by conducting individualized intervention and assessment this study provided fresh empirical evidence to support the existing literature on morphemic analysis awareness and vocabulary learning strategy. Thus, a major pedagogical implication of the study is that morphemic analysis awareness strategy is a definite boon for ESL secondary school students in learning English vocabulary.

Keywords: ESL, instruction, morphemic analysis, vocabulary.

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Authors: Khin May Win, Nan Sai Moon Kham

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Identification of cancer genes that might anticipate the clinical behaviors from different types of cancer disease is challenging due to the huge number of genes and small number of patients samples. The new method is being proposed based on supervised learning of classification like support vector machines (SVMs).A new solution is described by the introduction of the Maximized Margin (MM) in the subset criterion, which permits to get near the least generalization error rate. In class prediction problem, gene selection is essential to improve the accuracy and to identify genes for cancer disease. The performance of the new method was evaluated with real-world data experiment. It can give the better accuracy for classification.

Keywords: Microarray data, feature selection, recursive featureelimination, support vector machines.

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Authors: Ta-Min Hung, Tien-Lung Sun

Abstract:

Thanks to VR technology advanced, there are many researches had used VR technology to develop a training system. Using VR characteristics can simulate many kinds of situations to reach our training-s goal. However, a good training system not only considers real simulation but also considers learner-s learning motivation. So, there are many researches started to conduct game-s features into VR training system. We typically called this is a serious game. It is using game-s features to engage learner-s learning motivation. However, VR or Serious game has another important advantage. That is simulating feature. Using this feature can create any kinds of pressured environments. Because in the real environment may happen any emergent situations. So, increasing the trainees- pressure is more important when they are training. Most pervious researches are investigated serious game-s applications and learning performance. Seldom researches investigated how to increase the learner-s mental workload when they are training. So, in our study, we will introduce a real case study and create two types training environments. Comparing the learner-s mental workload between VR training and serious game.

Keywords: Intrinsic Motivation, Mental Workload, VR Training, Serious Game

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Authors: Talal Alsulaiman, Khaldoun Khashanah

Abstract:

In this paper, we provided a literature survey on the artificial stock problem (ASM). The paper began by exploring the complexity of the stock market and the needs for ASM. ASM aims to investigate the link between individual behaviors (micro level) and financial market dynamics (macro level). The variety of patterns at the macro level is a function of the AFM complexity. The financial market system is a complex system where the relationship between the micro and macro level cannot be captured analytically. Computational approaches, such as simulation, are expected to comprehend this connection. Agent-based simulation is a simulation technique commonly used to build AFMs. The paper proceeds by discussing the components of the ASM. We consider the roles of behavioral finance (BF) alongside the traditionally risk-averse assumption in the construction of agent’s attributes. Also, the influence of social networks in the developing of agents interactions is addressed. Network topologies such as a small world, distance-based, and scale-free networks may be utilized to outline economic collaborations. In addition, the primary methods for developing agents learning and adaptive abilities have been summarized. These incorporated approach such as Genetic Algorithm, Genetic Programming, Artificial neural network and Reinforcement Learning. In addition, the most common statistical properties (the stylized facts) of stock that are used for calibration and validation of ASM are discussed. Besides, we have reviewed the major related previous studies and categorize the utilized approaches as a part of these studies. Finally, research directions and potential research questions are argued. The research directions of ASM may focus on the macro level by analyzing the market dynamic or on the micro level by investigating the wealth distributions of the agents.

Keywords: Artificial stock markets, agent based simulation, bounded rationality, behavioral finance, artificial neural network, interaction, scale-free networks.

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Authors: Dante Jose R. Amisola, Glenford M. Prospero

Abstract:

'Driving What’s Next' is a strong campaign of the new administration of De La Salle Lipa in promoting social innovation in quality education. The new leadership directs social innovation in quality education in the institutional directions and initiatives to address real-world challenges with real-world solutions. This research under study aims to qualify the commitment of the institution to extend the Lasallian quality human and Christian education to all, as expressed in the Institution’s new mission-vision statement. The Classic Grounded Theory methodology is employed in the process of generating concepts in reference to the documents, a series of meetings, focus group discussions and other related activities that account for the conceptualization and formulation of the new mission-vision along with the new education innovation framework. Notably, Driving What’s Next is the emergent theory that encapsulates the commitment of giving quality human and Christian education to all. It directs the new leadership in driving social innovation in quality education initiatives. Correspondingly, Driving What’s Next is continually resolved through four interrelated strategies also termed as the institution's four strategic directions, namely: (1) driving social innovation in quality education, (2) embracing our shared humanity and championing social inclusion and justice initiatives, (3) creating sustainable futures and (4) engaging diverse stakeholders in our shared mission. Significantly, the four strategic directions capture and integrate the 17 UN sustainable development goals, making the innovative curriculum locally and globally relevant. To conclude, the main concern of the new administration and how it is continually resolved, provide meaningful and fun learning experiences and promote a new way of learning in the light of the 21^st century skills among the members of the academic community including stakeholders and extended communities at large, which are defined as: learning together and by association (collaboration), learning through engagement (communication), learning by design (creativity) and learning with social impact (critical thinking).

Keywords: De La Salle Lipa, Driving What’s Next, social innovation in quality education, DLSL mission - vision, strategic directions.

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Authors: J. H. Choi, S. J. Kim, M. S. Jung, S. J. Kim, S. J. Joo, S. C. Kim

Abstract:

There are a many of needs for the development of SiC-based hydrogen sensor for harsh environment applications. We fabricated and investigated Pd/Ta2O5/SiC-based hydrogen sensors with MOS capacitor structure for high temperature process monitoring and leak detection applications in such automotive, chemical and petroleum industries as well as direct monitoring of combustion processes. In this work, we used silicon carbide (SiC) as a substrate to replace silicon which operating temperatures are limited to below 200°C. Tantalum oxide was investigated as dielectric layer which has high permeability for hydrogen gas and high dielectric permittivity, compared with silicon dioxide or silicon nitride. Then, electrical response properties, such as I-V curve and dependence of capacitance on hydrogen concentrations were analyzed in the temperature ranges of room temperature to 500°C for performance evaluation of the sensor.

Keywords: High temperature, hydrogen sensor, SiC, Ta2O5 dielectric layer.

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