Search results for: Learning Management Tool

Authors: Ahmed Abbas Helmy, M. R. M. Rizk, Mohamed El-Sayed

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A model predictive controller based on recursive learning is proposed. In this SISO adaptive controller, a model is automatically updated using simple recursive equations. The identified models are then stored in the memory to be re-used in the future. The decision for model update is taken based on a new control performance index. The new controller allows the use of simple linear model predictive controllers in the control of nonlinear time varying processes.

Keywords: Adaptive control, model predictive control, dynamic matrix control, online model identification

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Authors: Alessandra Agostini, Roberto Boselli, Flavio De Paoli, Riccardo Dondi

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The work presented in this paper focus on Knowledge Management services enabling CSCW (Computer Supported Cooperative Work) applications to provide an appropriate adaptation to the user and the situation in which the user is working. In this paper, we explain how a knowledge management system can be designed to support users in different situations exploiting contextual data, users' preferences, and profiles of involved artifacts (e.g., documents, multimedia files, mockups...). The presented work roots in the experience we had in the MILK project and early steps made in the MAIS project.

Keywords: Information Management Systems, InformationRetrieval, Knowledge Management, Mobile CommunicationSystems.

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Authors: Ilkka Niskanen, Jarmo Kalaoja, Julia Kantorovitch, Toni Piirainen

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Ontologies are broadly used in the context of networked home environments. With ontologies it is possible to define and store context information, as well as to model different kinds of physical environments. Ontologies are central to networked home environments as they carry the meaning. However, ontologies and the OWL language is complex. Several ontology visualization approaches have been developed to enhance the understanding of ontologies. The domain of networked home environments sets some special requirements for the ontology visualization approach. The visualization tool presented here, visualizes ontologies in a domain-specific way. It represents effectively the physical structures and spatial relationships of networked home environments. In addition, it provides extensive interaction possibilities for editing and manipulating the visualization. The tool shortens the gap from beginner to intermediate OWL ontology reader by visualizing instances in their actual locations and making OWL ontologies more interesting and concrete, and above all easier to comprehend.

Keywords: Ontologies, visualization, interaction.

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Authors: Rodrigo Pacheco-Ruiz, Jonathan Adams, Felix Pedrotti

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This paper introduces the applicability of underwater photogrammetric survey within challenging conditions as the main tool to enhance and enrich the process of documenting archaeological excavation through the creation of 4D models. Photogrammetry was being attempted on underwater archaeological sites at least as early as the 1970s’ and today the production of traditional 3D models is becoming a common practice within the discipline. Photogrammetry underwater is more often implemented to record exposed underwater archaeological remains and less so as a dynamic interpretative tool.  Therefore, it tends to be applied in bright environments and when underwater visibility is > 1m, reducing its implementation on most submerged archaeological sites in more turbid conditions. Recent years have seen significant development of better digital photographic sensors and the improvement of optical technology, ideal for darker environments. Such developments, in tandem with powerful processing computing systems, have allowed underwater photogrammetry to be used by this research as a standard recording and interpretative tool. Using multi-source photogrammetry (5, GoPro5 Hero Black cameras) this paper presents the accumulation of daily (4D) underwater surveys carried out in the Early Bronze Age (3,300 BC) to Late Ottoman (17th Century AD) archaeological site of Ropotamo in the Bulgarian Black Sea under challenging conditions (< 0.5m visibility). It proves that underwater photogrammetry can and should be used as one of the main recording methods even in low light and poor underwater conditions as a way to better understand the complexity of the underwater archaeological record.

Keywords: 4D modelling, Black Sea, maritime archaeology, underwater photogrammetry, Bronze Age, low visibility.

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Authors: V. Lendel, M. Varmus

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The aim of the paper is based on detailed analysis of literary sources and carried out research to develop a model development and implementation of innovation strategy in the business. The paper brings the main results of the authors conducted research on a sample of 462 respondents that shows the current situation in the Slovak enterprises in the use of innovation strategy. Carried out research and analysis provided the base for a model development and implementation of innovation strategy in the business, which is in the paper in detail, step by step explained with emphasis on the implementation process. Implementing the innovation strategy is described a separate model. Paper contains recommendations for successful implementation of innovation strategy in the business. These recommendations should serve mainly business managers as valuable tool in implementing the innovation strategy.

Keywords: innovation, innovation strategy, innovative management, innovative potential

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Authors: Chia-Jou Hsieh, Feng-Chuan Pan

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Oral health is particular important to the hospitalized patients with chronic schizophrenia for an extreme high potential of the respiratory infections. Due to the degeneration of physical capability, patients of this kind typically fall dependent in the activity of daily living (ADL). A very high percentage of patients had dental problems of which mostly could be easily avoid by easy regular tooth brushing. Purpose of the project is to develop a mechanism in helping the schizophrenia patients in rebuilding a tooth-cleaning habit. The project observed and evaluated the tooth-cleaning behavior of 100 male patients in a psychiatric hospital, and found the majority of them ignored such an activity in a three-month period of time. In the meantime, the primary care-givers were not aware or not convinced the importance of such a need of dental hygiene, and thus few if any tooth cleaning training or knowledge on dental hygiene were given to the patients. The project then developed a program based on the numerous observations and discussions. The improvement program included patients- group education, care-givers- training, and a tool-kit for tooth-brush holding was erected. The project launched with some incentive package. The outcomes were encouraging with 87% of the patients had rebuilt their tooth-brushing habits against previous 22%, and the tooth cleaning kits were 100% kept against 22% in the past. This project had significantly improved the oral health of the patients. The project, included the procedure and the tool-kit holder specific for this purpose, was a good examples for psychiatric hospitals.

Keywords: Schizophrenia, dental hygiene, tool-kit holder, health education

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Authors: A. Pedram, M. R. Jamali, T. Pedram, S. M. Fakhraie, C. Lucas

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Local Linear Neuro-Fuzzy Models (LLNFM) like other neuro- fuzzy systems are adaptive networks and provide robust learning capabilities and are widely utilized in various applications such as pattern recognition, system identification, image processing and prediction. Local linear model tree (LOLIMOT) is a type of Takagi-Sugeno-Kang neuro fuzzy algorithm which has proven its efficiency compared with other neuro fuzzy networks in learning the nonlinear systems and pattern recognition. In this paper, a dedicated reconfigurable and parallel processing hardware for LOLIMOT algorithm and its applications are presented. This hardware realizes on-chip learning which gives it the capability to work as a standalone device in a system. The synthesis results on FPGA platforms show its potential to improve the speed at least 250 of times faster than software implemented algorithms.

Keywords: LOLIMOT, hardware, neurofuzzy systems, reconfigurable, parallel.

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Authors: Sevgi Özlem Bulu, Arif Furkan Mendi, Tolga Erol, İzzet Gökhan Özbilgin

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The rapid progress of technology in today's competitive conditions has also accelerated companies' technology development activities. As a result, companies are paying more attention to R&D studies and are beginning to allocate a larger share to R&D projects. A more systematic, comprehensive, target-oriented implementation of R&D studies is crucial for the company to achieve successful results. As a consequence, Technology Roadmap (TRM) is gaining importance as a management tool. It has critical prospects for achieving medium and long term success as it contains decisions about past business, future plans, technological infrastructure. When studies on TRM are examined, projects to be placed on the roadmap are selected by many different methods. Generally preferred methods are based on multi-criteria decision making methods. Management of selected projects becomes an important point after the selection phase of the projects. At this stage, TRM are used. TRM can be created in many different ways so that each institution can prepare its own Technology Roadmap according to their strategic plan. Depending on the intended use, there can be TRM with different layers at different sizes. In the evaluation phase of the R&D projects and in the creation of the TRM, HAVELSAN, Turkey's largest defense company in the software field, carries out this process with great care and diligence. At the beginning, suggested R&D projects are evaluated by the Technology Management Board (TMB) of HAVELSAN in accordance with the company's resources, objectives, and targets. These projects are presented to the TMB periodically for evaluation within the framework of certain criteria by board members. After the necessary steps have been passed, the approved projects are added to the time-based TRM, which is composed of four layers as market, product, project and technology. The use of a four-layered roadmap provides a clearer understanding and visualization of company strategy and objectives. This study demonstrates the benefits of using TRM, four-layered Technology Roadmapping and the possibilities for the institutions in the defense industry.

Keywords: Project selection, R&D in defense industry, R&D project selection, technology roadmapping.

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Authors: Yukinori Taniguchi, Kazuyoshi Kurita, Kohei Mizuta, Keigo Nishitani, Ryuichi Fukuda

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Tungsten carbide is widely used as a tool material in metal manufacturing process. Since tungsten is typical rare metal, establishment of recycle process of tungsten carbide tools and restore into cemented carbide material bring great impact to metal manufacturing industry. Recently, recycle process of tungsten carbide has been developed and established gradually. However, the demands for quality of cemented carbide tool are quite severe because hardness, toughness, anti-wear ability, heat resistance, fatigue strength and so on should be guaranteed for precision machining and tool life. Currently, it is hard to restore the recycled tungsten carbide powder entirely as raw material for new processed cemented carbide tool. In this study, to suggest positive use of recycled tungsten carbide powder, we have tried to fabricate a carbon based sintered steel which shows reinforced mechanical properties with recycled tungsten carbide powder. We have made set of newly designed sintered steels. Compression test of sintered specimen in density ratio of 0.85 (which means 15% porosity inside) has been conducted. As results, at least 1.7 times higher in nominal strength in the amount of 7.0 wt.% was shown in recycled WC powder. The strength reached to over 600 MPa for the Fe-WC-Co-Cu sintered alloy. Wear test has been conducted by using ball-on-disk type friction tester using 5 mm diameter ball with normal force of 2 N in the dry conditions. Wear amount after 1,000 m running distance shows that about 1.5 times longer life was shown in designed sintered alloy. Since results of tensile test showed that same tendency in previous testing, it is concluded that designed sintered alloy can be used for several mechanical parts with special strength and anti-wear ability in relatively low cost due to recycled tungsten carbide powder.

Keywords: Tungsten carbide, recycle process, compression test, powder metallurgy, anti-wear ability.

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Authors: T. R. Alencar, P. T. Leite

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Several optimization algorithms specifically applied to the problem of Operation Planning of Hydrothermal Power Systems have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. Thus, this paper presents the development of a computational tool for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique, Genetic Algorithms and programming language Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: Energy, Optimization, Hydrothermal Power Systemsand Genetic Algorithms

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Authors: H. Soleimanimehr, M. J. Nategh , S. Amini

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Development of artificial neural network (ANN) for prediction of aluminum workpieces' surface roughness in ultrasonicvibration assisted turning (UAT) has been the subject of the present study. Tool wear as the main cause of surface roughness was also investigated. ANN was trained through experimental data obtained on the basis of full factorial design of experiments. Various influential machining parameters were taken into consideration. It was illustrated that a multilayer perceptron neural network could efficiently model the surface roughness as the response of the network, with an error less than ten percent. The performance of the trained network was verified by further experiments. The results of UAT were compared with the results of conventional turning experiments carried out with similar machining parameters except for the vibration amplitude whence considerable reduction was observed in the built-up edge and the surface roughness.

Keywords: Aluminum, Artificial Neural Network (ANN), BuiltupEdge, Surface Roughness, Tool Wear, Ultrasonic VibrationAssisted Turning (UAT).

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Authors: B. Stroetmann, A. Aisenbrey

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The Siemens Healthcare Sector is one of the world's largest suppliers to the healthcare industry and a trendsetter in medical imaging and therapy, laboratory diagnostics, medical information technology, and hearing aids. Siemens offers its customers products and solutions for the entire range of patient care from a single source – from prevention and early detection to diagnosis, and on to treatment and aftercare. By optimizing clinical workflows for the most common diseases, Siemens also makes healthcare faster, better, and more cost effective. The optimization of clinical workflows requires a multidisciplinary focus and a collaborative approach of e.g. medical advisors, researchers and scientists as well as healthcare economists. This new form of collaboration brings together experts with deep technical experience, physicians with specialized medical knowledge as well as people with comprehensive knowledge about health economics. As Charles Darwin is often quoted as saying, “It is neither the strongest of the species that survive, nor the most intelligent, but the one most responsive to change," We believe that those who can successfully manage this change will emerge as winners, with valuable competitive advantage. Current medical information and knowledge are some of the core assets in the healthcare industry. The main issue is to connect knowledge holders and knowledge recipients from various disciplines efficiently in order to spread and distribute knowledge.

Keywords: Business Excellence, Clinical Knowledge, Knowledge Management, Knowledge Services, Learning Organizations, Trust.

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Authors: Çiğdem Bakır, Mecit Yüzkat

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With the increasing emphasis on water quality worldwide, the search for and expanding the market for new and intelligent monitoring systems has increased. The current method is the laboratory process, where samples are taken from bodies of water, and tests are carried out in laboratories. This method is time-consuming, a waste of manpower and uneconomical. To solve this problem, we used machine learning methods to detect water pollution in our study. We created decision trees with the Orange3 software used in the study and tried to determine all the factors that cause water pollution. An automatic prediction model based on water quality was developed by taking many model inputs such as water temperature, pH, transparency, conductivity, dissolved oxygen, and ammonia nitrogen with machine learning methods. The proposed approach consists of three stages: Preprocessing of the data used, feature detection and classification. We tried to determine the success of our study with different accuracy metrics and the results were presented comparatively. In addition, we achieved approximately 98% success with the decision tree.

Keywords: Decision tree, water quality, water pollution, machine learning.

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Authors: Yishu Gong, Liangliang Yang, Jianyu Zhang, Zhengyu Chen, Sihong He, Xusheng Zhang, Wei Zhang

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide and is characterized by cognitive decline and behavioral changes. People living with Alzheimer’s disease often find it hard to complete routine tasks. However, there are limited objective assessments that aim to quantify the difficulty of certain tasks for AD patients compared to non-AD people. In this study, we propose to use speech emotion recognition (SER), especially the frustration level as a potential biomarker for quantifying the difficulty patients experience when describing a picture. We build an SER model using data from the IEMOCAP dataset and apply the model to the DementiaBank data to detect the AD/non-AD group difference and perform longitudinal analysis to track the AD disease progression. Our results show that the frustration level detected from the SER model can possibly be used as a cost-effective tool for objective tracking of AD progression in addition to the Mini-Mental State Examination (MMSE) score.

Keywords: Alzheimer’s disease, Speech Emotion Recognition, longitudinal biomarker, machine learning.

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Authors: Ahmed Shuhaiber

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The explosion of the World Wide Web and the electronic trend of university teaching have transformed the learning style to become more learner-centered, which has popularized the digital delivery of mediated lectures as an alternative or an adjunct to traditional lectures. Despite its potential and popularity, virtual lectures have not been adopted yet in Jordanian universities. This research aimed to fill this gap by studying the factors that influence students’ willingness to accept virtual lectures in one Jordanian University. A quantitative approach was followed, by obtaining 216 survey responses and statistically applying the UTAUT model with some modifications. Results revealed that performance expectancy, effort expectancy, social influences, and self-efficacy could significantly influence students’ attitudes towards virtual lectures. Additionally, Facilitating conditions and attitudes towards virtual lectures were found with significant influence on students’ intention to take virtual lectures. Research implications and future work were specified afterwards.

Keywords: E-Learning, Student willingness, UTAUT, Virtual Lectures, Web-based learning systems.

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Authors: Ahmet Ok, Banu Yücel Toy

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Promoting critical thinking (CT) in an educational setting has been appraised in order to enhance learning and intellectual skills. In this study, a pedagogical course in a vocational teacher education program in Turkey was designed by integrating CT skill-based strategies/activities into the course content and CT skills were means leading to intended course objectives. The purpose of the study was to evaluate the importance of the course objectives, the attainment of the objectives, and the effectiveness of teachinglearning strategies/activities from prospective teachers- points of view. The results revealed that although the students mostly considered the course objectives important, they did not feel competent in the attainment of all objectives especially in those related to the main topic of Learning and those requiring higher order thinking skills. On the other hand, the students considered the course activities effective for learning and for the development of thinking skills, especially, in interpreting, comparing, questioning, contrasting, and forming relationships.

Keywords: Critical thinking, critical thinking-based instruction, higher order thinking skills, teacher education

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Authors: N. Ismail, O. Thammajinda, U. Thongpanya

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Games-based learning (GBL) has become increasingly important in teaching and learning. This paper explains the first two phases (analysis and design) of a GBL development project, ending up with a prototype design based on students’ and teachers’ perceptions. The two phases are part of a full cycle GBL project aiming to help secondary school students in Thailand in their study of Comprehensive Sex Education (CSE). In the course of the study, we invited 1,152 students to complete questionnaires and interviewed 12 secondary school teachers in focus groups. This paper found that GBL can serve students in their learning about CSE, enabling them to gain understanding of their sexuality, develop skills, including critical thinking skills and interact with others (peers, teachers, etc.) in a safe environment. The objectives of this paper are to outline the development of GBL variables from the research question(s) into the developers’ flow chart, to be responsive to the GBL beneficiaries’ preferences and expectations, and to help in answering the research questions. This paper details the steps applied to generate GBL variables that can feed into a game flow chart to develop a GBL prototype. In our approach, we detailed two models: (1) Game Elements Model (GEM) and (2) Game Object Model (GOM). There are three outcomes of this research – first, to achieve the objectives and benefits of GBL in learning, game design has to start with the research question(s) and the challenges to be resolved as research outcomes. Second, aligning the educational aims with engaging GBL end users (students) within the data collection phase to inform the game prototype with the game variables is essential to address the answer/solution to the research question(s). Third, for efficient GBL to bridge the gap between pedagogy and technology and in order to answer the research questions via technology (i.e. GBL) and to minimise the isolation between the pedagogists “P” and technologist “T”, several meetings and discussions need to take place within the team.

Keywords: Games-based learning, design, engagement, pedagogy, preferences, prototype, variables.

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Authors: Jānis Zuters

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There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capability in certain cases. The new model requires additional computational resources to compare to the classic model, nevertheless the loss in efficiency isn-t regarded to be significant.

Keywords: Learning algorithm, multi-layer perceptron, topology.

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Authors: M. Farzadkia, S. Jorfi

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Hospital waste is a category of waste consisting of infectious and non-infectious waste, which pose environmental and health risks. Therefore, special planning and management is required, due to the potential hazards of them. The lack of valid and comprehensive information regarding the generation and management of hospital waste in Iran is one of the most important problems in this field. This research aimed to evaluate hospital waste management efficiency in Karaj city, Iran. The four greatest hospitals in Karaj city had been selected in this cross-sectional study. Site observations and interviews with employees were implemented. The data was gathered based on the hospital waste management questionnaire which was designed by World Health Organization for developing countries. Collected Data had been analyzed using SPSS software. The average of solid waste which was generated per bed was 2.78 kg, which included 90% of domestic waste and 10% of infectious waste. Based on the quantitative analysis of general and infectious waste in these hospitals, the highest contributors of general waste were consisting of food waste (37.39%), while textile (28.06%) were the highest contributors of the infectious waste. According to the information contained in the questionnaires, the main defects of waste management in these hospitals were; inadequate staff in waste management sector, poorly disinfection of solid waste containers and temporary storage locations, and a lack of proper infectious waste treatment. According to the results of this research, waste management in these hospitals were far from optimum conditions. In order to improve the existing conditions, mentioned problems must be solved quickly, and planning for continuous monitoring in the waste management field in these hospitals should be established.

Keywords: Waste management, hospital wastes, solid wastes, Iran.

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Authors: Zurinahni Zainol, Rosalina Abdul Salam, Rosni Abdullah, Nur'Aini, Wahidah Husain

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The size, complexity and number of databases used for protein information have caused bioinformatics to lag behind in adapting to the need to handle this distributed information. Integrating all the information from different databases into one database is a challenging problem. Our main research is to develop a tool which can be used to access and manipulate protein information from difference databases. In our approach, we have integrated difference databases such as Swiss-prot, PDB, Interpro, and EMBL and transformed these databases in flat file format into relational form using XML and Bioperl. As a result, we showed this tool can search different sizes of protein information stored in relational database and the result can be retrieved faster compared to flat file database. A web based user interface is provided to allow user to access or search for protein information in the local database.

Keywords: Protein sequence database, relational database, integrated database.

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Authors: Haifeng Wang, Haili Zhang

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Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.

Keywords: Computational social science, movie preference, machine learning, SVM.

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Authors: Muhammad Ridhuan Tony Lim Abdullah, Zulqarnain Abu Bakar, Razol Mahari Ali, Ibrahima Faye, Hilmi Hasan

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This paper describes a research project on Year 3 primary school students in Malaysia in their use of computer-based video game to enhance learning of multiplication facts (tables) in the Mathematics subject. This study attempts to investigate whether video games could actually contribute to positive effect on children-s learning or otherwise. In conducting this study, the researchers assume a neutral stand in the investigation as an unbiased outcome of the study would render reliable response to the impact of video games in education which would contribute to the literature of technology-based education as well as impact to the pedagogical aspect of formal education. In order to conduct the study, a subject (Mathematics) with a specific topic area in the subject (multiplication facts) is chosen. The study adopts a causal-comparative research to investigate the impact of the inclusion of a computer-based video game designed to teach multiplication facts to primary level students. Sample size is 100 students divided into two i.e., A: conventional group and B conventional group aided by video games. The conventional group (A) would be taught multiplication facts (timetables) and skills conventionally. The other group (B) underwent the same lessons but with supplementary activity: a computer-based video game on multiplication which is called Timez-Attack. Analysis of marks accrued from pre-test will be compared to post- test using comparisons of means, t tests, and ANOVA tests to investigate the impact of computer games as an added learning activity. The findings revealed that video games as a supplementary activity to classroom learning brings significant and positive effect on students- retention and mastery of multiplication tables as compared to students who rely only upon formal classroom instructions.

Keywords: Technology for education, Gaming for education, Computer-based video games, Cognitive learning

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Authors: C. W. Kan

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Cotton fibre is a commonly-used natural fibre because of its good fibre strength, high moisture absorption behaviour and minimal static problems. However, one of the main drawbacks of cotton fibre is wrinkling after washing, which is recently overcome by wrinkle-resistant treatment. 1,2,3,4-butanetetracarboxylic acid (BTCA) could improve the wrinkle-resistant properties of cotton fibre. Although the BTCA process is an effective method for wrinkle resistant application of cotton fabrics, reduced fabric strength was observed after treatment. Therefore, this paper would explore the use of atmospheric pressure plasma treatment under different discharge powers as a pretreatment process to enhance the application of BTCA process on cotton fabric without generating adverse effect. The aim of this study is to provide learning information to the users to know how the atmospheric pressure plasma treatment can be incorporated in textile finishing process with positive impact.

Keywords: Learning materials, atmospheric pressure plasma treatment, cotton, wrinkle-resistant, BTCA.

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Authors: Philippe Fauquet-Alekhine

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For professions of high risk industries, simulation training has always been thought in terms of high degree of fidelity regarding the real operational situation. Due to the recent progress, this way of training is changing, modifying the human-computer and software interactions: the interactions between trainees during simulation training session tend to become virtual, transforming the social-based embodiness (the way subjects integrate social skills for interpersonal relationship with co-workers). On the basis of the analysis of eight different profession trainings, a categorization of interactions has help to produce an analytical tool, the social interactions table. This tool may be very valuable to point out the changes of social interactions when the training sessions are skipping from a high fidelity simulator to a virtual simulator. In this case, it helps the designers of professional training to analyze and to assess the consequences of the potential lack the social-based embodiness.

Keywords: Interface, interaction, simulator, virtual training.

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Authors: Reza Sigari Tabrizi, Yeap Peik Foong, Nazli Ebrahimi

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The Knowledge Management (KM) Criteria is an essential foundation to evaluate KM outcomes. Different sets of criteria were developed and tailored by many researchers to determine the results of KM initiatives. However, literature review has emphasized on incomplete set of criteria for evaluating KM outcomes. Hence, this paper tried to address the problem of determining the criteria for measuring knowledge management outcomes among different types of Malaysian organizations. Successively, this paper was assumed to develop widely accepted criteria to measure success of knowledge management efforts for Malaysian organizations. Our analysis approach was based on the ANOVA procedure to compare a set of criteria among different types of organizations. This set of criteria was exploited from literature review. It is hoped that this study provides a better picture for different types of Malaysian organizations to establish a comprehensive set of criteria due to measure results of KM programs.

Keywords: KM Criteria, Knowledge Management, KMOutcomes, ANOVA

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Authors: S. B. Kotsiantis, P. E. Pintelas

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Bagging and boosting are among the most popular resampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noisefree data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 subclassifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.

Keywords: data mining, machine learning, pattern recognition.

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Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: Compliance Course, Corporate Training, Learner Behaviours, xAPI.

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Authors: Yannan Wu, An Chen, Yan Zhao

Abstract:

Termination Mechanism is an indispensible part of the emergency management mechanism. Despite of its importance in both theory and practice, it is almost a brand new field for researching. The concept of termination mechanism is proposed firstly in this paper, and the design and implementation which are helpful to guarantee the effect and integrity of emergency management are discussed secondly. Starting with introduction of the problems caused by absent termination and incorrect termination, the essence of termination mechanism is analyzed, a model based on Optimal Stopping Theory is constructed and the termination index is given. The model could be applied to find the best termination time point.. Termination decision should not only be concerned in termination stage, but also in the whole emergency management process, which makes it a dynamic decision making process. Besides, the main subjects and the procedure of termination are illustrated after the termination time point is given. Some future works are discussed lastly.

Keywords: Emergency management, Termination Mechanism, Optimal Termination Model, Decision Making, Optimal StoppingTheory.

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Authors: Adel Aloraini

Abstract:

Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Keywords: Causal interactions , banks, feature selection, regularizere,

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Authors: Κonstantinos Kalovrektis, Chryssanthi Palazi

Abstract:

Schools today face ever-increasing demands in their attempts to ensure that students are well equipped to enter the workforce and navigate a complex world. Research indicates that computer technology can help support learning, implementation of various experiments or learning games, and that it is especially useful in developing the higher-order skills of critical thinking, observation, comprehension, implementation, comparison, analysis and active attention to activities such as research, field work, simulations and scientific inquiry. The ICT in education supports the learning procedure by enabling it to be more flexible and effective, create a rich and attractive training environment and equip the students with knowledge and potential useful for the competitive social environment in which they live. This paper presents the design, the development, and the results of the evaluation analysis of an interactive educational game which using real electric vehicles - toys (material) on a toy race track. When the game starts each student selects a specific vehicle toy. Then students are answering questionnaires in the computer. The vehicles' speed is related to the percentage of right answers in a multiple choice questionnaire (software). Every question has its own significant value depending of the different level of questionnaire. Via the developed software, each right or wrong answers in questionnaire increase or decrease the real time speed of their vehicle toys. Moreover the rate of vehicle's speed increase or decrease depends on the difficulty level of each question. The aim of the work is to attract the student’s interest in a learning process and also to improve their scores. The developed real time game was tested using independent populations of students of age groups: 8-10, 11-14, 15-18 years. Standard educational and statistical analysis tools were used for the evaluation analysis of the game. Results reveal that students using the developed real time control game scored much higher (60%) than students using a traditional simulation game on the same questionnaire. Results further indicate that student's interest in repeating the developed real time control gaming was far higher (70%) than the interest of students using a traditional simulation game.

Keywords: Real time game, sensor, learning games, LabVIEW

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