Search results for: Task experience.

Authors: A. Nielen, S. Mütze-Niewöhner, C. M. Schlick

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In view of growing competition in the service sector, services are as much in need of modeling, analysis and improvement as business or working processes. Graphical process models are important means to capture process-related know-how for an effective management of the service process. In this contribution, a human performance analysis of process model development paying special attention to model development time and the working method was conducted. It was found that modelers with higher application experience need significantly less time for mental activities than modelers with lower application experience, spend more time on labeling graphical elements, and achieved higher process model quality in terms of activity label quality.

Keywords: Model quality, predetermined motion time system, process modeling, working method.

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Authors: Ruibiao J. Guo, Brad Cain, Pierre Meunier

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The Integrated Performance Modelling Environment (IPME) is a powerful simulation engine for task simulation and performance analysis. However, it has no high level cognition such as memory and reasoning for complex simulation. This article introduces a knowledge representation and reasoning scheme that can accommodate uncertainty in simulations of military personnel with IPME. This approach demonstrates how advanced reasoning models that support similarity-based associative process, rule-based abstract process, multiple reasoning methods and real-time interaction can be integrated with conventional task network modelling to provide greater functionality and flexibility when modelling operator performance.

Keywords: Computer-Generated Forces, Human Behaviour Representation, IPME, Modelling and Simulation, Uncertainty Reasoning

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Authors: Andreas Ahrens, Ojaras Purvinis Jelena Zāšcerinska

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A successful shopping experience without overcrowded shops and long waiting times undoubtedly leads to the release of happiness hormones and is generally considered as the goal of any optimization. Factors influencing the shopping experience can be divided into internal and external ones. External factors are related e. g. to the arrival of the customers to the shop whereas internal factors are linked with the service process itself when checking out (waiting in the queue to the cash register and the scanning of the goods as well as the payment process itself) or any other non-expected delay when changing the status from a visitor to a buyer by choosing goods or items. This paper divides the customer-server interaction in five phases starting with the customer arrival at the shop, the selection of goods, the buyer waiting in the queue to the cash register, the payment process and ending with the customer or buyer departure. Our simulation results show how five phases are intertwined and influence the overall shopping experience. Parameters for measuring the shopping experience based on a burstiness level in each of the five phases of the customer-server interaction are estimated.

Keywords: Customers’ burstiness, cash register, customers’ waiting time, gap distribution function.

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Authors: D. Reich, R. Stark

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To provide reliable and valid findings when evaluating innovative in-car devices in the automotive context highly realistic driving environments are recommended. Nowadays, in-car devices are mostly evaluated due to driving simulator studies followed by real car driving experiments. Driving simulators are characterized by high internal validity, but weak regarding ecological validity. Real car driving experiments are ecologically valid, but difficult to standardize, more time-robbing and costly. One economizing suggestion is to implement more immersive driving environments when applying driving simulator studies. This paper presents research comparing non-immersive standard PC conditions with mobile and highly immersive Oculus Rift conditions while performing the Lane Change Task (LCT). Subjective data with twenty participants show advantages regarding presence and immersion experience when performing the LCT with the Oculus Rift, but affect adversely cognitive workload and simulator sickness, compared to non-immersive PC condition.

Keywords: LCT, immersion, oculus rift, presence, situation awareness.

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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: Yalçın Ozdemir

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The purpose of this study was to examine to what extend classroom management efficacy, marital status, gender, and teaching experience predict burnout among primary school teachers. Participants of this study were 523 (345 female, 178 male) teachers who completed inventories. The results of multiple regression analysis indicated that three dimensions of teacher burnout (Emotional Exhaustion, Depersonalization, Personal Accomplishment) were affected differently from four predictor variables. Findings indicated that for the emotional exhaustion, classroom management efficacy, marital status and teaching experience; for depersonalization dimension, classroom management efficacy and marital status and finally for the personal accomplishment dimension, classroom management efficacy, gender, and teaching experience were significant predictors.

Keywords: Classroom management efficacy, teacher burnout.

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Authors: Eirini Petratou, Pezirkianidis Christos, Anastassios Stalikas

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Nostalgia is characterized as a mental state of human’s emotional longing for the past that activates both positive and negative emotions. The bittersweet emotions that are activated by nostalgia aid psychological functions to humans and are depended on the type of stimuli that evoke nostalgia but also on the nostalgia activation context. In general, despite that nostalgia can be activated and experienced by all people; however, it differs both in terms of nostalgia experience but also nostalgia frequency. As a matter of fact, nostalgia experience along with nostalgia frequency differs according to the level of the nostalgia proneness. People with high nostalgia proneness tend to experience nostalgia more intensely and frequently than people with low nostalgia proneness. Nostalgia proneness is considered as a basic individual difference that affects the experience of nostalgia, and it can be measured by the Southampton Nostalgia Scale (SNS); a psychometric instrument that measures human’s nostalgia proneness consisting of seven questions that assess a person’s attitude towards nostalgia, the degree of experience or tendency to nostalgic feelings and the nostalgia frequency. In the current study, we translated, validated and calibrated the SNS in Greek population (N = 267). For the calibration process, we used several scales relevant to positive dimensions, such as life satisfaction, positive and negative emotions, time perspective and wellbeing. A confirmatory factor analysis revealed the factors that provide a good Southampton Nostalgia Proneness model fit for young adult Greek population.

Keywords: Nostalgia proneness, nostalgia, psychometric instruments, positive emotions.

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Authors: G. Nur

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Monitored 3-Dimensional (3D) video experience can be utilized as “feedback information” to fine tune the service parameters for providing a better service to the demanding 3D service customers. The 3D video experience which includes both video quality and depth perception is influenced by several contextual and content related factors (e.g., ambient illumination condition, content characteristics, etc) due to the complex nature of the 3D video. Therefore, effective factors on this experience should be utilized while assessing it. In this paper, structural information of the depth map sequences of the 3D video is considered as content related factor effective on the depth perception assessment. Cartoon-like filter is utilized to abstract the significant depth levels in the depth map sequences to determine the structural information. Moreover, subjective experiments are conducted using 3D videos associated with cartoon-like depth map sequences to investigate the effectiveness of ambient illumination condition, which is a contextual factor, on depth perception. Using the knowledge gained through this study, 3D video experience metrics can be developed to deliver better service to the 3D video service users. 

Keywords: 3D Video, Ambient Illumination, Cartoon Effect, Depth Perception.

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Authors: Tamara Tallman

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Collaboration is a powerful tool for professional development and central for creating opportunities for teachers to reflect on their practice. However, school districts continue to have difficulty both implementing and sustaining collaboration. The purpose of this research was to investigate the experience of the teacher in a creative, instructional collaboration. The teachers in this study found that teacher-initiated collaboration offered them trust and they were more open with their partners. An interpretative phenomenological analysis was used for this study as it told the story of the teacher’s experience. Interpretative Phenomenological Analysis was chosen for this study to capture the complex and contextual nature of the teacher experience from a creative, instructional collaborative experience. This study sought to answer the question of how teachers in a private, faith-based school experience collaboration. In particular, the researcher engaged the study’s participants in interviews where they shared their unique perspectives on their experiences in relation to this phenomenon. Through the use of interpretative phenomenological analysis, the researcher interpreted the experiences of each participant in an attempt to gain deeper insight into how teachers made sense of their understanding of collaboration. In addition to the researcher’s interpreting the meaning of this construct for each research participant, this study gave a voice to the individual experiences and positionality of each participant at the research site. Moreover, the key findings presented in this study shed light on how teachers within this particular context participated in and made sense of their experience of creating an instructional collaborative. The research presented the findings that speak to the meaning that each research participant experienced in their relation to participating in building a collaborative culture and its effect on professional and personal growth. The researcher provided recommendations for future practice and research possibilities. The research findings demonstrated the unique experiences of each participant as well as a connection to the literature within the field of teacher professional development. The results also supported the claim that teacher collaboration can facilitate school reform. Participating teachers felt less isolation and developed more teacher knowledge.

Keywords: Collaboration, professional development, teacher, growth.

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Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

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In order to support the continued growth, critical latency of ‎IoT ‎applications and ‎various obstacles of traditional data centers, ‎Mobile Edge ‎Computing (MEC) has ‎emerged as a promising solution that extends the cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other making Task Offloading (TO), ‎Resource Allocation (RA) and Service Placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP and RA recent Multi-‎Objective Optimization (MOO) approaches used in edge computing environments, particularly Artificial Intelligent (AI) ones, to satisfy various objectives, constraints and dynamic conditions related to IoT applications‎.

Keywords: Mobile Edge Computing, Multi-Objective Optimization, Artificial Intelligence ‎Approaches, Task Offloading, Resource Allocation, Service Placement‎.

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Authors: Sandra Baßler, Peter Dünow, Mathias Marquardt

Abstract:

A Fourier series based learning control (FSBLC) algorithm for tracking trajectories of mechanical systems with unknown nonlinearities is presented. Two processes are introduced to which the FSBLC with PD controller is applied. One is a simplified service robot capable of climbing stairs due to special wheels and the other is a propeller driven pendulum with nearly the same requirements on control. Additionally to the investigation of learning the feed forward for the desired trajectories some considerations on the implementation of such an algorithm on low cost microcontroller hardware are made. Simulations of the service robot as well as practical experiments on the pendulum show the capability of the used FSBLC algorithm to perform the task of improving control behavior for repetitive task of such mechanical systems.

Keywords: Climbing stairs, FSBLC, ILC, Service robot.

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Authors: L. C. Moreira, M. Kauffman

Abstract:

This paper outlines the results of an experimental research on deploying an emerging augmented reality (AR) system for real-time task assistance (or work instructions) of highly customised and high-risk manual operations. The focus is on human operators’ training effectiveness and performance and the aim is to test if such technologies can support enhancing the knowledge retention levels and accuracy of task execution to improve health and safety (H&S). An AR enhanced assembly method is proposed and experimentally tested using a real industrial process as case study for electric vehicles’ (EV) battery module assembly. The experimental results revealed that the proposed method improved the training practices and performance through increases in the knowledge retention levels from 40% to 84%, and accuracy of task execution from 20% to 71%, when compared to the traditional paper-based method. The results of this research validate and demonstrate how emerging technologies are advancing the choice for manual, hybrid or fully automated processes by promoting the XR-assisted processes, and the connected worker (a vision for Industry 4 and 5.0), and supporting manufacturing become more resilient in times of constant market changes.

Keywords: Augmented reality, extended reality, connected worker, XR-assisted operator, manual assembly 4.0, industry 5.0, smart training, battery assembly.

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Authors: Caihong Sun, Xizi Zhang

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Rating prediction is an important problem for recommender systems. The task is to predict the rating for an item that a user would give. Most of the existing algorithms for the task ignore the effect of negative ratings rated by users on items, but the negative ratings have a significant impact on users’ purchasing decisions in practice. In this paper, we present a rating prediction algorithm based on factorization machines that consider the effect of negative ratings inspired by Loss Aversion theory. The aim of this paper is to develop a concave and a convex negative disgust function to evaluate the negative ratings respectively. Experiments are conducted on MovieLens dataset. The experimental results demonstrate the effectiveness of the proposed methods by comparing with other four the state-of-the-art approaches. The negative ratings showed much importance in the accuracy of ratings predictions.

Keywords: Factorization machines, feature engineering, negative ratings, recommendation systems.

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Authors: Anod H. Alhazmi, Hanaa A. Yamani

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The lack of digital transformation professionals could prevent Saudi Arabia’s universities from providing digital services. The task of understanding what digital skills are needed within an organization, measuring the existing skills, and developing or attracting talents is a complex task. This paper provides a comprehensive analysis of the digital transformation skills needed in the organizations who seek digital transformation and identifies the skills and competencies framework DigSC built on Skills Framework for the Informational Age (SFIA) framework that is adopted by the Ministry of Communications and Information Technology (MCIT) in Saudi Arabia. The framework adopted identifies the main digital transformation skills clusters, categories and levels of responsibilities for each job description to fill the gap between this requirement and the digital skills supplied by the Umm Alqura University (UQU).

Keywords: Competencies, digital transformation, framework, skills, Umm Alqura University.

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Authors: Jihong Wang

Abstract:

Little research has examined working memory capacity (WMC) in signed language interpreters and deaf signers. This paper presents the findings of a study that investigated WMC in professional Australian Sign Language (Auslan)/English interpreters and deaf signers. Thirty-one professional Auslan/English interpreters (14 hearing native signers and 17 hearing non-native signers) completed an English listening span task and then an Auslan working memory span task, which tested their English WMC and their Auslan WMC, respectively. Moreover, 26 deaf signers (6 deaf native signers and 20 deaf non-native signers) completed the Auslan working memory span task. The results revealed a non-significant difference between the hearing native signers and the hearing non-native signers in their English WMC, and a non-significant difference between the hearing native signers and the hearing non-native signers in their Auslan WMC. Moreover, the results yielded a non-significant difference between the hearing native signers- English WMC and their Auslan WMC, and a non-significant difference between the hearing non-native signers- English WMC and their Auslan WMC. Furthermore, a non-significant difference was found between the deaf native signers and the deaf non-native signers in their Auslan WMC.

Keywords: deaf signers, signed language interpreters, working memory capacity

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Authors: Jana Puchyova

Abstract:

In recent years a number of applications with multirobot systems (MRS) is growing in various areas. But their design is in practice often difficult and algorithms are proposed for the theoretical background and do not consider errors and noise in real conditions, so they are not usable in real environment. These errors are visible also in task of target localization enough, when robots try to find and estimate the position of the target by the sensors. Localization of target is possible also with one robot but as it was examined target finding and localization with group of mobile robots can estimate the target position more accurately and faster. The accuracy of target position estimation is made by cooperation of MRS and particle filtering. Advantage of usage the MRS with particle filtering was tested on task of fixed target localization by group of mobile robots.

Keywords: Multi-robot system, particle filter, position estimation, target localization.

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Authors: K. Okubo, Y. Kurihara, T. Kaburagi, K. Watanabe

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Many of the ever-growing elderly population require exercise, such as running, for health management. One important element of a runner’s training is the choice of shoes for exercise; shoes are important because they provide the interface between the feet and road. When we purchase shoes, we may instinctively choose a pair after trying on many different pairs of shoes. Selecting the shoes instinctively may work, but it does not guarantee a suitable fit for running activities. Therefore, if we could select suitable shoes for each runner from the viewpoint of brain activities, it would be helpful for validating shoe selection. In this paper, we describe how brain activities show different characteristics during particular task, corresponding to different properties of shoes. Using five subjects, we performed a verification experiment, applying weight, softness, and flexibility as shoe properties. In order to affect the shoe property’s differences to the brain, subjects run for 10 min. Before and after running, subjects conducted a paced auditory serial addition task (PASAT) as the particular task; and the subjects’ brain activities during the PASAT are evaluated based on oxyhemoglobin and deoxyhemoglobin relative concentration changes, measured by near-infrared spectroscopy (NIRS). When the brain works actively, oxihemoglobin and deoxyhemoglobin concentration drastically changes; therefore, we calculate the maximum values of concentration changes. In order to normalize relative concentration changes after running, the maximum value are divided by before running maximum value as evaluation parameters. The classification of the groups of shoes is expressed on a self-organizing map (SOM). As a result, deoxyhemoglobin can make clusters for two of the three types of shoes.

Keywords: Brain activities, NIRS, PASAT, running shoes.

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Prevent abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep Long Short-Term Memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviours far from the expected one, depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behaviour analysis, LSTM

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Authors: Sarah M. Oteifa, Lobna A. Sherif, Yasser M. Mostafa

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Visually impaired people, in their daily lives, face struggles and spatial barriers because the built environment is often designed with an extreme focus on the visual element, causing what is called architectural visual bias or ocularcentrism. The aim of the study is to holistically understand the world of the visually impaired as an attempt to extract the qualities of space that accommodate their needs, and to show the importance of multi-sensory, holistic designs for the blind. Within the framework of existential phenomenology, common themes are reached through "intersubjectivity": experience descriptions by blind people and blind architects, observation of how blind children learn to perceive their surrounding environment, and a personal lived blind-folded experience are analyzed. The extracted themes show how visually impaired people filter out and prioritize tactile (active, passive and dynamic touch), acoustic and olfactory spatial qualities respectively, and how this happened during the personal lived blind folded experience. The themes clarify that haptic and aural inclusive designs are essential to create environments suitable for the visually impaired to empower them towards an independent, safe and efficient life.

Keywords: Visually impaired, architecture, multi-sensory design, architectural ocularcentrism.

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Authors: Rhoann Kerh, Chen-Fu Chien, Kuo-Yi Lin

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In the era of rapidly increasing notebook market, consumer electronics manufacturers are facing a highly dynamic and competitive environment. In particular, the product appearance is the first part for user to distinguish the product from the product of other brands. Notebook product should differ in its appearance to engage users and contribute to the user experience (UX). The UX evaluates various product concepts to find the design for user needs; in addition, help the designer to further understand the product appearance preference of different market segment. However, few studies have been done for exploring the relationship between consumer background and the reaction of product appearance. This study aims to propose a data mining framework to capture the user’s information and the important relation between product appearance factors. The proposed framework consists of problem definition and structuring, data preparation, rules generation, and results evaluation and interpretation. An empirical study has been done in Taiwan that recruited 168 subjects from different background to experience the appearance performance of 11 different portable computers. The results assist the designers to develop product strategies based on the characteristics of consumers and the product concept that related to the UX, which help to launch the products to the right customers and increase the market shares. The results have shown the practical feasibility of the proposed framework.

Keywords: Consumers Decision Making, Product Design, Rough Set Theory, User Experience.

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Authors: Svitov David, Alyamkin Sergey

Abstract:

The usage of convolutional neural networks (CNNs) in conjunction with the margin-based softmax approach demonstrates the state-of-the-art performance for the face recognition problem. Recently, lightweight neural network models trained with the margin-based softmax have been introduced for the face identification task for edge devices. In this paper, we propose a distillation method for lightweight neural network architectures that outperforms other known methods for the face recognition task on LFW, AgeDB-30 and Megaface datasets. The idea of the proposed method is to use class centers from the teacher network for the student network. Then the student network is trained to get the same angles between the class centers and face embeddings predicted by the teacher network.

Keywords: ArcFace, distillation, face recognition, margin-based softmax.

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Authors: Shu-Yin Yu

Abstract:

With the advancement of artificial intelligence technology, most companies are aware of the profound potential of artificial intelligence in commercial marketing. Man-machine dialogue has become the latest trend in marketing customer service. However, chatbots are often considered to be lack of intelligent or unfriendly conversion, which instead reduces the communication effect of chatbots. To ensure that chatbots represent the brand image and provide a good user experience, companies and users attach great importance. In this study, customer service chatbot was used as the research sample. The research variables are based on the theory of artificial intelligence emotions, integrating the technology acceptance model and innovation diffusion theory, and the three aspects of pleasure, arousal, and dominance of the human-machine PAD (Pleasure, Arousal and Dominance) dimension. The results show that most of the participants have a higher acceptance of innovative technologies and are high pleasure and arousal in the user experience. Participants still have traditional gender (female) service stereotypes about customer service chatbots. Users who have high trust in using chatbots can easily enhance brand acceptance and easily accept brand messages, extend the trust of chatbots to trust in the brand, and develop a positive attitude towards the brand.

Keywords: Brand attitude, chatbot, emotional interaction, user experience.

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Authors: Adnan Ahmad, Yusri Kamin, Asnol Dahar Minghat, Mohd. Khir Nordin, Dayana Farzeha, Ahmad Nabil

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The purpose of this study is to identify the teaching method practices of the practical work subject in Vocational Secondary School. This study examined the practice of Vocational Teaching Method in Automotive Practical Work. The quantitative method used the sets of the questionnaire. 283 students and 63 teachers involved from ten VSS involved in this research. Research finding showed in conducting the introduction session teachers prefer used the demonstration method and questioning technique. While in deliver the content of practical task, teachers applied group monitoring and problem solving approach. To conclude the task of automotive practical work, teachers choose re-explain and report writing to make sure students really understand all the process of teaching. VTM-APW also involved the competency-based concept to embed in the model. Derived from factors investigated, research produced the combination of elements in teaching skills and vocational skills which could be used as the best teaching method in automotive practical work for school level. As conclusion this study has concluded that the VTM-APW model is able to apply in teaching to make an improvement with current practices in Vocational Secondary School. Hence, teachers are suggested to use this method to enhance student's knowledge in Automotive and teachers will deliver skills to the current and future workforce relevant with the required competency skilled in workplace.

Keywords: Vocational Teaching Method, Practical Task, Teacher Preferences, Student Preferences.

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Authors: K. B. Manwade, G. A. Patil

Abstract:

Mobile agent has motivated the creation of a new methodology for parallel computing. We introduce a methodology for the creation of parallel applications on the network. The proposed Mobile-Agent parallel processing framework uses multiple Javamobile Agents. Each mobile agent can travel to the specified machine in the network to perform its tasks. We also introduce the concept of master agent, which is Java object capable of implementing a particular task of the target application. Master agent is dynamically assigns the task to mobile agents. We have developed and tested a prototype application: Mobile Agent Based Parallel Computing. Boosted by the inherited benefits of using Java and Mobile Agents, our proposed methodology breaks the barriers between the environments, and could potentially exploit in a parallel manner all the available computational resources on the network. This paper elaborates performance issues of a mobile agent for parallel computing.

Keywords: Parallel Computing, Mobile Agent.

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Authors: Shireen Simon

Abstract:

Cyberbullying among schoolchildren and teenagers became a hot issue discussed by Malaysian society. Cyberbullying is a new age of bullying because it uses the modern digital technology intentionally to hurt and degrade someone in the cyber world. Cyberbullying is a problem affecting many teenagers as they embrace online communication and interaction whereby virtual world with no borders. By adopting a qualitative approach, this study has captured 8 cyberbullied victims’ school experience. Even years after leaving school, these 8 cyberbullied victims remember how it feels to be bullied in the cyber world. The principal investigator also tries to identify the possibility factors that contribute to cyberbullying among these 8 victims. The result shows that these victims were bullied differently in cyber world. This study not just primarily focuses on cyberbullying issues among schoolchildren and teenagers; it also addresses the motives and causes of cyberbullying. Lastly, this article will be served as guidance for school teachers, parents and teenagers to prepare to tackle cyberbullying together. Cyberbullying is no laughing matter in our community, and it is time to spread the seeds of peace inspires others to do the same.

Keywords: Cyberbullying, cyber victimization, internet, school experience, teenagers.

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Authors: Robin Lok-Wang

Abstract:

The center/unit for industry engagement and collaboration, as well as Internship, plays a significant role at a university. In general, the Center serves as the official interface between industry and the school or department to cultivate students’ early exposure to professional experience. The missions of the Center are not limited to provide a communication channel and collaborative platform for the industries and the university but also to assist students to build their career paths early while still at the university. In recent years, a cooperative education experience (commonly known as a co-op) has been strongly advocated for students to make the school-to-work transition. The nature of the co-op program is not only consistent with the internships/final year design projects, but it is also more industrial-oriented with academic support from faculty at the university. The purpose of this paper is to describe an approach to how cooperative education experience can be integrated into the engineering curriculum. It provides a mutual understanding and exchange of ideas for the approach between the university and industry. A suggested format in terms of timeline, duration, selection of candidates, students, and companies’ expectations for the co-op program is described. Also, feedback from employers/industries shows that a longer-term co-op program is well suited for students compared with a short-term internship. To this end, it provides an insight into collaboration and/or partnership between the university and the industries to prepare professional work-ready graduates.

Keywords: Cooperative education, internship, industry collaboration, engineering curriculum.

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: Outlier detection, generative adversary networks, semi-supervised learning.

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Authors: G. Lingam, N. Lingam, K. Raghuwaiya

Abstract:

The reported study focuses on pre-service teachers’ professional development during the teaching practice. The cohort studied comprised participants in their final year in the Bachelor of Arts and Bachelor of Science with Graduate Certificate in Education programmes of a university in Fiji. Analysis of the data obtained using a survey questionnaire indicates that overall, the pre-service teachers were satisfied with the practicum experience. This is assumed to demonstrate that the practicum experience contributed well towards their professional preparation for work expected of them in Fiji secondary schools. Participants also identified some concerns as needing attention. To conclude, the paper provides suggestions for improving the preparation of teachers by strengthening the identified areas of the practicum offered by the university. The study has implications for other teacher education providers in small developing island states and even beyond for the purpose of enhancing learning in student teachers’ for future work.

Keywords: Pre-service, teacher education, practicum, teachers’ world of work, student teachers.

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Authors: Salima Behdenna, Fatiha Barigou, Ghalem Belalem

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Due to the quick increase in the volume of Arabic opinions posted on various social media, Arabic sentiment analysis has become one of the most important areas of research. Compared to English, there is very little works on Arabic sentiment analysis, in particular aspect-based sentiment analysis (ABSA). In ABSA, aspect extraction is the most important task. In this paper, we propose a semantic ABSA approach for standard Arabic reviews to extract explicit aspect terms and identify the polarity of the extracted aspects. The proposed approach was evaluated using HAAD datasets. Experiments showed that the proposed approach achieved a good level of performance compared with baseline results. The F-measure was improved by 19% for the aspect term extraction tasks and 55% aspect term polarity task.

Keywords: Sentiment analysis, opinion mining, Arabic, aspect level, opinion, polarity.

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Authors: R.Ramli

Abstract:

This paper discusses ways to foster cooperative learning through the integration of online communication technology. While the education experts believe constructivism produces a more positive learning experience, the educators are still facing problems in getting students to participate due to numerous reasons such as shy personality, language and cultural barriers. This paper will look into the factors that lead to lack of participations among students and how technology can be implemented to overcome these issues.

Keywords: cooperative learning, encouraging class participation, education, online discussion

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