Search results for: Pedagogy Learning Module

Authors: Ioannis Pratikakis, Anna Karahaliou, Katerina Vassiou, Vassilis Virvilis, Dimitrios Kosmopoulos, Stavros Perantonis

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In this paper, a Web-based e-Training platform that is dedicated to multimodal breast imaging is presented. The assets of this platform are summarised in (i) the efficient representation of the curriculum flow that will permit efficient training; (ii) efficient tagging of multimodal content appropriate for the completion of realistic cases and (iii) ubiquitous accessibility and platform independence via a web-based approach.

Keywords: Breast imaging, e-Training, web-based learning.

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Authors: A. Karaali

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This paper will seek to clarify important key terms such as home schooling and home education as well as the legalities attached to such terms. It will reflect on the recent proposed changes to terminology in NSW, Australia. The various pedagogical approaches to home education will be explored including their prominence in the Australian context. There is a strong focus on literature from Australia. The historical background of home education in Australia will be explained as well as the difference between distance education and home education. The future of home education in Australia will be discussed.

Keywords: Alternative education, e-learning, home education, home schooling, online resources, technology.

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Authors: Yasar Guneri Sahin, Mehmet Cudi Okur

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In this presentation, we discuss the use of information technologies in the area of special education for teaching individuals with learning disabilities. Application software which was developed for this purpose is used to demonstrate the applicability of a database integrated information processing system to alleviate the burden of educators. The software allows the preparation of individualized education programs based on the predefined objectives, goals and behaviors.

Keywords: Special education, disabled individual, informationtechnology, individual education programs.

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Authors: Ebipatei Victoria Tunyan, T. A. Cao, Cheol Young Ock

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Detecting subjectively biased statements is a vital task. This is because this kind of bias, when present in the text or other forms of information dissemination media such as news, social media, scientific texts, and encyclopedias, can weaken trust in the information and stir conflicts amongst consumers. Subjective bias detection is also critical for many Natural Language Processing (NLP) tasks like sentiment analysis, opinion identification, and bias neutralization. Having a system that can adequately detect subjectivity in text will boost research in the above-mentioned areas significantly. It can also come in handy for platforms like Wikipedia, where the use of neutral language is of importance. The goal of this work is to identify the subjectively biased language in text on a sentence level. With machine learning, we can solve complex AI problems, making it a good fit for the problem of subjective bias detection. A key step in this approach is to train a classifier based on BERT (Bidirectional Encoder Representations from Transformers) as upstream model. BERT by itself can be used as a classifier; however, in this study, we use BERT as data preprocessor as well as an embedding generator for a Bi-LSTM (Bidirectional Long Short-Term Memory) network incorporated with attention mechanism. This approach produces a deeper and better classifier. We evaluate the effectiveness of our model using the Wiki Neutrality Corpus (WNC), which was compiled from Wikipedia edits that removed various biased instances from sentences as a benchmark dataset, with which we also compare our model to existing approaches. Experimental analysis indicates an improved performance, as our model achieved state-of-the-art accuracy in detecting subjective bias. This study focuses on the English language, but the model can be fine-tuned to accommodate other languages.

Keywords: Subjective bias detection, machine learning, BERT–BiLSTM–Attention, text classification, natural language processing.

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Authors: Mousavian, M. Moghimi Mofrad, M. H. Vakili, D. Ashouri, R. Alizadeh

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The aim of this research is to use artificial neural networks computing technology for estimating the net heating value (NHV) of crude oil by its Properties. The approach is based on training the neural network simulator uses back-propagation as the learning algorithm for a predefined range of analytically generated well test response. The network with 8 neurons in one hidden layer was selected and prediction of this network has been good agreement with experimental data.

Keywords: Neural Network, Net Heating Value, Crude Oil, Experimental, Modeling.

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Authors: A. Breda, C. Cruz

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The Japanese art of origami provides a rich context for designing exploratory mathematical activities for children and young people. By folding a simple sheet of paper, fascinating and surprising planar and spatial configurations emerge. Equally surprising is the unfolding process, which also produces striking patterns. The procedure of folding, unfolding, and folding again allows the exploration of interesting geometric patterns. When adequately and systematically done, we may deduce some of the mathematical rules ruling origami. As the child/youth folds the sheet of paper repeatedly, he can physically observe how the forms he obtains are transformed and how they relate to the pattern of the corresponding unfolding, creating space for the understanding/discovery of mathematical principles regulating the folding-unfolding process. As part of a 2023 Summer Academy organized by a Portuguese university, a session entitled “Folding, Thinking and Generalizing” took place. 23 students attended the session, all enrolled in the 2nd cycle of Portuguese Basic Education and aged between 10 and 12 years old. The main focus of this session was to foster the development of critical cognitive and socio-emotional skills among these young learners, using origami. These skills included creativity, critical analysis, mathematical reasoning, collaboration, and communication. Employing a qualitative, descriptive, and interpretative analysis of data, collected during the session through field notes and students’ written productions, our findings reveal that structured origami-based activities not only promote student engagement with mathematical concepts in a playful and interactive but also facilitate the development of socio-emotional skills, which include collaboration and effective communication between participants. This research highlights the value of integrating origami into educational practices, highlighting its role in supporting comprehensive cognitive and emotional learning experiences.

Keywords: Active learning, hands-on activities, origami, creativity, critical thinking.

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Authors: Sreejith Jayachandran, Mojtaba Ghodsi, Morteza Mohammadzaheri

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The modern busy world is running behind new embedded technologies based on computers and software meanwhile some people are unable to monitor their health condition and regular medical check-ups. Some of them postpone medical check-ups due to a lack of time and convenience while others skip these regular evaluations and medical examinations due to huge medical bills and hospital expenses. In this research, we present a device in the telemonitoring system capable of monitoring, checking, and evaluating the health status of the human body remotely through the internet for the needs of all kinds of people. The remote health monitoring device is a microcontroller-based embedded unit. The various types of sensors in this device are connected to the human body, and with the help of an Arduino UNO board, the required analogue data are collected from the sensors. The microcontroller on the Arduino board processes the analogue data collected in this way into digital data and transfers that information to the cloud and stores it there; the processed digital data are then instantly displayed through the LCD attached to the machine. By accessing the cloud storage with a username and password, the concerned person’s health care teams/doctors, and other health staff can collect these data for the assessment and follow-up of that patient. Besides that, the family members/guardians can use and evaluate these data for awareness of the patient's current health status. Moreover, the system is connected to a GPS module. In emergencies, the concerned team can be positioning the patient or the person with this device. The setup continuously evaluates and transfers the data to the cloud and also the user can prefix a normal value range for the evaluation. For example, the blood pressure normal value is universally prefixed between 80/120 mmHg. Similarly, the Remote Health Monitoring System (RHMS) is also allowed to fix the range of values referred to as normal coefficients. This IoT-based miniature system 11×10×10 cm3 with a low weight of 500 gr only consumes 10 mW. This smart monitoring system is manufactured for 100 GBP (British Pound Sterling), and can facilitate the communication between patients and health systems, but also it can be employed for numerous other uses including communication sectors in the aerospace and transportation systems.

Keywords: Embedded Technology, Telemonitoring system, Microcontroller, Arduino UNO, Cloud storage, GPS, RHMS, Remote Health Monitoring System, Alert system.

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Authors: Itgel Bayarsaikhan, Jimin Lee, Sejong Oh

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Classification is one of the primary themes in computational biology. The accuracy of classification strongly depends on quality of a dataset, and we need some method to evaluate this quality. In this paper, we propose a new graphical analysis method using 'Membership-Deviation Graph (MDG)' for analyzing quality of a dataset. MDG represents degree of membership and deviations for instances of a class in the dataset. The result of MDG analysis is used for understanding specific feature and for selecting best feature for classification.

Keywords: feature, classification, machine learning algorithm.

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Authors: Mikael Soini, Kari Björn

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Metropolia University of Applied Sciences (MUAS) Information and Communication Technology (ICT) Degree Programme provides full-time Bachelor-level undergraduate studies. ICT Degree Programme has seven different major options; this paper focuses on Health Technology. In Health Technology, a significant curriculum change in 2014 enabled transition from fragmented curriculum including dozens of courses to a new integrated curriculum built around three 30 ECTS themes. This paper focuses especially on the second theme called Customer Oriented Software Applications. From students’ point of view, the goal of this theme is to get familiar with existing health related ICT solutions and systems, understand business around health technology, recognize social and healthcare operating principles and services, and identify customers and users and their special needs and perspectives. This also acts as a background for health related web application development. Built web application is tested, developed and evaluated with real users utilizing versatile user centred development methods. This paper presents experiences obtained from the first implementation of Customer Oriented Software Applications theme. Student feedback was gathered with two questionnaires, one in the middle of the theme and other at the end of the theme. Questionnaires had qualitative and quantitative parts. Similar questionnaire was implemented in the first theme; this paper evaluates how the theme-based integrated curriculum has progressed in Health Technology major by comparing results between theme 1 and 2. In general, students were satisfied for the implementation, timing and synchronization of the courses, and the amount of work. However there is still room for development. Student feedback and teachers’ observations have been and will be used to develop the content and operating principles of the themes and whole curriculum.

Keywords: Engineering education, integrated and theme-based curriculum, learning experience, student centred learning.

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Authors: Monjed Ezzeddinne, Nicolas Lefebvre, Régis Lengellé

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This paper presents a new version of the SVM mixture algorithm initially proposed by Kwok for classification and regression problems. For both cases, a slight modification of the mixture model leads to a standard SVM training problem, to the existence of an exact solution and allows the direct use of well known decomposition and working set selection algorithms. Only the regression case is considered in this paper but classification has been addressed in a very similar way. This method has been successfully applied to engine pollutants emission modeling.

Keywords: Identification, Learning systems, Mixture ofExperts, Support Vector Machines.

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Authors: Fatema Akram, Mohammad G. Rasul, M. Masud K. Khan, M. Sharif I. I. Amir

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Australia is a country of some 7,700 million square kilometers with a population of about 22.6 million. At present water security is a major challenge for Australia. In some areas the use of water resources is approaching and in some parts it is exceeding the limits of sustainability. A focal point of proposed national water conservation programs is the recycling of both urban stormwater and treated wastewater. But till now it is not widely practiced in Australia, and particularly stormwater is neglected. In Australia, only 4% of stormwater and rainwater is recycled, whereas less than 1% of reclaimed wastewater is reused within urban areas. Therefore, accurately monitoring, assessing and predicting the availability, quality and use of this precious resource are required for better management. As stormwater is usually of better quality than untreated sewage or industrial discharge, it has better public acceptance for recycling and reuse, particularly for non-potable use such as irrigation, watering lawns, gardens, etc. Existing stormwater recycling practice is far behind of research and no robust technologies developed for this purpose. Therefore, there is a clear need for using modern technologies for assessing feasibility of stormwater harvesting and reuse. Numerical modeling has, in recent times, become a popular tool for doing this job. It includes complex hydrological and hydraulic processes of the study area. The hydrologic model computes stormwater quantity to design the system components, and the hydraulic model helps to route the flow through stormwater infrastructures. Nowadays water quality module is incorporated with these models. Integration of Geographic Information System (GIS) with these models provides extra advantage of managing spatial information. However for the overall management of a stormwater harvesting project, Decision Support System (DSS) plays an important role incorporating database with model and GIS for the proper management of temporal information. Additionally DSS includes evaluation tools and Graphical user interface. This research aims to critically review and discuss all the aspects of stormwater harvesting and reuse such as available guidelines of stormwater harvesting and reuse, public acceptance of water reuse, the scopes and recommendation for future studies. In addition to these, this paper identifies, understand and address the importance of modern technologies capable of proper management of stormwater harvesting and reuse.

Keywords: Stormwater Management, Stormwater Harvesting and Reuse, Numerical Modeling, Geographic Information System (GIS), Decision Support System (DSS), Database.

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Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

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Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: Human Motion Recognition, Motion representation, Laban Movement Analysis, Discrete Hidden Markov Model.

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Authors: Roland Lang, Dietmar Bruckner, Rosemarie Velik, Tobias Deutsch

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Modern building automation needs to deal with very different types of demands, depending on the use of a building and the persons acting in it. To meet the requirements of situation awareness in modern building automation, scenario recognition becomes more and more important in order to detect sequences of events and to react to them properly. We present two concepts of scenario recognition and their implementation, one based on predefined templates and the other applying an unsupervised learning algorithm using statistical methods. Implemented applications will be described and their advantages and disadvantages will be outlined.

Keywords: Building automation, ubiquitous computing, scenariorecognition, surveillance system.

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Authors: Ahmed A. A. Radwan, Bahgat A. Abdel Latef, Abdel Mgeid A. Ali, Osman A. Sadek

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This study investigates the use of genetic algorithms in information retrieval. The method is shown to be applicable to three well-known documents collections, where more relevant documents are presented to users in the genetic modification. In this paper we present a new fitness function for approximate information retrieval which is very fast and very flexible, than cosine similarity fitness function.

Keywords: Cosine similarity, Fitness function, Genetic Algorithm, Information Retrieval, Query learning.

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Authors: Shaun Cox, Michael Oakes, Stefan Wermter, Maurice Hawthorne

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We report on the results of a pilot study in which a data-mining tool was developed for mining audiology records. The records were heterogeneous in that they contained numeric, category and textual data. The tools developed are designed to observe associations between any field in the records and any other field. The techniques employed were the statistical chi-squared test, and the use of self-organizing maps, an unsupervised neural learning approach.

Keywords: Audiology, data mining, chi-squared, self-organizing maps

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Authors: Bremananth R, Radhika.V, Thenmozhi.S

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Sequences of execution of algorithms in an interactive manner using multimedia tools are employed in this paper. It helps to realize the concept of fundamentals of algorithms such as searching and sorting method in a simple manner. Visualization gains more attention than theoretical study and it is an easy way of learning process. We propose methods for finding runtime sequence of each algorithm in an interactive way and aims to overcome the drawbacks of the existing character systems. System illustrates each and every step clearly using text and animation. Comparisons of its time complexity have been carried out and results show that our approach provides better perceptive of algorithms.

Keywords: Algorithms, Searching, Sorting, Visualization.

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Authors: Talal Alsulaiman, Khaldoun Khashanah

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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: Zeynep Orhan, Zeynep Altan

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This paper summarizes the results of some experiments for finding the effective features for disambiguation of Turkish verbs. Word sense disambiguation is a current area of investigation in which verbs have the dominant role. Generally verbs have more senses than the other types of words in the average and detecting these features for verbs may lead to some improvements for other word types. In this paper we have considered only the syntactical features that can be obtained from the corpus and tested by using some famous machine learning algorithms.

Keywords: Word sense disambiguation, feature selection.

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Authors: Takaharu Miyoshi, Tadahiko Higuchi

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Education supported by mobile computers has been widely done for some time. Teachers have attempted to use mobile computers and to find concrete subjects for student-s fieldwork training in college education. The purpose of this research is to develop software for Personal Digital Assistant (PDA) to conduct fieldwork in our campus, and to report a fieldwork class using PDAs in the curriculum of the Department of Regional Environment Studies.

Keywords: Development of software for PDA, fieldwork training, computer supported education, experiential learning.

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Authors: Volker Koch

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This paper deals with the education of purchasing professionals in Austria. In this education, equivalent and measurable criteria are collected in order to create a comparison. The comparison shows the problem. To make the aforementioned comparison possible, methodologies such as KODE-Competence Atlas or presentations in a matrix form are used. The result shows the content taught and whether there are any similarities or interesting differences in the current Austrian purchasers’ formations. Purchasing professionals learning competencies are also illustrated in the study result.

Keywords: Competencies, education, purchasing professional, technological-oriented.

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Authors: M. Amanipour, J. Towfighi, E. Ganji Babakhani, M. Heidari

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Cylindrical alumina microfiltration membrane (GMITM Corporation, inside diameter=9 mm, outside diameter=13 mm, length= 50 mm) with an average pore size of 0.5 micrometer and porosity of about 0.35 was used as the support for membrane reactor. This support was soaked in boehmite sols, and the mean particle size was adjusted in the range of 50 to 500 nm by carefully controlling hydrolysis time, and calcined at 650 °C for two hours. This process was repeated with different boehmite solutions in order to achieve an intermediate layer with an average pore size of about 50 nm. The resulting substrate was then coated with a thin and dense layer of silica by counter current chemical vapour deposition (CVD) method. A boehmite sol with 10 wt.% of nickel which was prepared by a standard procedure was used to make the catalytic layer. BET, SEM, and XRD analysis were used to characterize this layer. The catalytic membrane reactor was placed in an experimental setup to evaluate the permeation and hydrogen separation performance for a steam reforming reaction. The setup consisted of a tubular module in which the membrane was fixed, and the reforming reaction occurred at the inner side of the membrane. Methane stream, diluted with nitrogen, and deionized water with a steam to carbon (S/C) ratio of 3.0 entered the reactor after the reactor was heated up to 500 °C with a specified rate of 2 °C/ min and the catalytic layer was reduced at presence of hydrogen for 2.5 hours. Nitrogen flow was used as sweep gas through the outer side of the reactor. Any liquid produced was trapped and separated at reactor exit by a cold trap, and the produced gases were analyzed by an on-line gas chromatograph (Agilent 7890A) to measure total CH₄ conversion and H₂ permeation. BET analysis indicated uniform size distribution for catalyst with average pore size of 280 nm and average surface area of 275 m².g^-1. Single-component permeation tests were carried out for hydrogen, methane, and carbon dioxide at temperature range of 500-800 °C, and the results showed almost the same permeance and hydrogen selectivity values for hydrogen as the composite membrane without catalytic layer. Performance of the catalytic membrane was evaluated by applying membranes as a membrane reactor for methane steam reforming reaction at gas hourly space velocity (GHSV) of 10,000 h⁻¹ and 2 bar. CH₄ conversion increased from 50% to 85% with increasing reaction temperature from 600 °C to 750 °C, which is sufficiently above equilibrium curve at reaction conditions, but slightly lower than membrane reactor with packed nickel catalytic bed because of its higher surface area compared to the catalytic layer.

Keywords: Catalytic membrane, hydrogen, methane steam reforming, permeance.

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Authors: Azelin Mohamed Noor, Abd Mutalib Embong, Muhamad Ridhuan Tony Lim Abdullah

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After the Terengganu state government decided to give a boost in teaching and learning through the allocation of free ebooks to all Primary five and six students; it was time to examine the presence of e-books in the classrooms. A survey was conducted on 101 students to determine how they felt about using the e-book and their experiences. It was discovered that a majority of these students liked using the e-book. However, although they had little problems using the e-book and the e-book helped to lighten the schoolbags, these new-age textbooks were not fully utilized. It is implied that perhaps the school administrators, teachers and students may not be able to overcome the unfamiliar characteristics of the e-book and its limitations.

Keywords: E-books, students, classroom, limitations

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Authors: Yi Yang, Jiasong Sun

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Almost every aspect of our daily lives is now intertwined with some degree of Human-Computer Interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology and more. The HCI courses in the Department of Electronics at Tsinghua University, known as the Media and Cognition course, is constantly updated to reflect the most advanced technological advances, such as virtual reality, augmented reality and artificial intelligence-based interaction. For more than a decade, this course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which has gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. The latest version of the HCI course practices a semantic communication platform based on AI-generated techniques. We explored a student-centered model and proposed an interest-based teaching method. Students are no longer just recipients of knowledge, but become active participants in the learning process driven by personal interests, thereby encouraging students to take responsibility for their own education. One of the latest results of this teaching approach in the course "Media and Cognition" is a student proposal to develop a semantic communication platform rooted in artificial intelligence generative technologies. The platform solves a key challenge in communications technology: the ability to preserve visual signals. The interest-based approach emphasizes personal curiosity and active participation, and the proposal of an artificial intelligence-generated semantic communication platform is an example and successful result of how students can exert greater creativity when they have the power to control their own learning.

Keywords: Human-computer interaction, media and cognition course, semantic communication, retain ability, prompts.

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Authors: Elham Shirvani-Ghadikolaei

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In this paper, we examine the connection between the human mind and body within the framework of Merleau-Ponty's phenomenology. We study the role of this connection in designing more efficient learning environments, alongside the findings in physical recognition and educational neuroscience. Our research shows the interplay between the mind and the body in the external world and discusses its implications. Based on these observations, we make suggestions as to how the educational system can benefit from taking into account the interaction between the mind and the body in educational affairs.

Keywords: Educational neurosciences, embodied cognition, pedagogical neurosciences, phenomenology.

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Authors: Ricardo Avila, Gabriel Lopes, Vania Vidal, Jose Macedo

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This article offers a approach to the automatic discovery of semantic concepts and links in the domain of Oil Exploration and Production (E&P). Machine learning methods combined with textual pre-processing techniques were used to detect local patterns in texts and, thus, generate new concepts and new semantic links. Even using more specific vocabularies within the oil domain, our approach has achieved satisfactory results, suggesting that the proposal can be applied in other domains and languages, requiring only minor adjustments.

Keywords: Semantic links, data mining, linked data, SKOS.

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Authors: Hassan Haghighi, Seyedeh Zahra Hosseini, Seyedeh Elahe Jalambadani

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ERP systems are often supposed to be implemented and deployed in multi-national companies. On the other hand, an ERP developer may plan to market and sale its product in various countries. Therefore, an EPR system should have the ability to communicate with its users, who usually have different languages and cultures, in a suitable way. EPR support of Multilanguage capability is a solution to achieve this objective. In this paper, an agent oriented architecture including several independent but cooperative agents has been suggested that helps to implement Multilanguage EPR systems.

Keywords: enterprise resource planning, Multilanguage, software architecture, agent oriented architecture, intelligence, learning, translation.

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Authors: Essam Almuhsin, Ben Soh, Alice Li, Azmat Ullah

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It is widely accepted that university students must acquire new knowledge, skills, awareness, and understanding to increase opportunities for professional and personal growth. The study reveals a significant increase in users engaging in e-coaching activities and a growing need for it during the COVID-19 pandemic. The paper proposes an e-coaching methodology for higher education in Saudi Arabia to address the need for effective coaching in the current online learning environment.

Keywords: Role of e-coaching, e-coaching in higher education, Saudi higher education environment, e-coaching methodology, importance of e-coaching.

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Authors: Mohamed Akrout, Douglas Tweed

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The great success of deep learning relies on efficient optimizers, which are the algorithms that decide how to adjust network weights and biases based on gradient information. One of the most effective and widely used optimizers in recent years has been the method of adaptive moments, or Adam, but the mathematical reasons behind its effectiveness are still unclear. Attempts to analyse its behaviour have remained incomplete, in part because they hinge on a conjecture which has never been proven, regarding ratios of powers of the first and second moments of the gradient. Here we show that this conjecture is in fact false, but that a modified version of it is true, and can take its place in analyses of Adam.

Keywords: Adam optimizer, Bock’s conjecture, stochastic optimization, average regret.

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Authors: S. B. Tende, H. L. Abubakar

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It is acknowledged that small and medium enterprises (SMEs) may encounter different ethical issues and pressures that could affect the way in which they strategize or make decisions concerning the outcome of their business. Therefore, this research aimed at assessing entrepreneurial ethics in the business of SMEs in Nigeria. Secondary data were adopted as source of corpus for the analysis. The findings conclude that a sound entrepreneurial ethics system has a significant effect on the level of performance of SMEs in Nigeria. The Nigerian Government needs to provide both guiding and physical structures; as well as learning systems that could inculcate these entrepreneurial ethics.

Keywords: Entrepreneurial ethics, culture, performance, SME.

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Authors: Ehrenstorfer Barbara, Peherstorfer Tanja, Nový Jan

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Urbanization and regionalization are two different approaches when it comes to economical structures and development, infrastructure and mobility, quality of life and living, education, social cohesion and many other topics. At first glance, the structures associated with urbanization and regionalization seems to be contradicting. This paper discusses possibilities of transfer and cooperation between rural and urban structures. An empirical investigation contributed to reveal scenarios of supposable forms of exchange and cooperation of remote rural areas and big cities.

Keywords: Learning Regions, Quality of Life and Living, Regional and Rural Development, Social Innovation.

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