Search results for: intellectually engaging learning environments

Authors: Eric Filiol

Abstract:

This paper presents a protocol aiming at proving that an encryption system contains structural weaknesses without disclosing any information on those weaknesses. A verifier can check in a polynomial time that a given property of the cipher system output has been effectively realized. This property has been chosen by the prover in such a way that it cannot been achieved by known attacks or exhaustive search but only if the prover indeed knows some undisclosed weaknesses that may effectively endanger the cryptosystem security. This protocol has been denoted zero-knowledge-like proof of cryptanalysis. In this paper, we apply this protocol to the Bluetooth core encryption algorithm E0, used in many mobile environments and thus we suggest that its security can seriously be put into question.

Keywords: Bluetooth encryption, Bluetooth security, Bluetoothprotocol, Stream cipher, Zero-knowledge, Cryptanalysis

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Authors: Y. A. Adla, R. Soubra, M. Kasab, M. O. Diab, A. Chkeir

Abstract:

Over the past several years, researchers have shown a great interest in assessing the mobility of elderly people to measure their functional status. Usually, such an assessment is done by conducting tests that require the subject to walk a certain distance, turn around, and finally sit back down. Consequently, this study aims to provide an at home monitoring system to assess the patient’s status continuously. Thus, we proposed a technique to automatically detect when a subject sits down while walking at home. In this study, we utilized a Doppler radar system to capture the motion of the subjects. More than 20 features were extracted from the radar signals out of which 11 were chosen based on their Intraclass Correlation Coefficient (ICC > 0.75). Accordingly, the sequential floating forward selection wrapper was applied to further narrow down the final feature vector. Finally, five features were introduced to the Linear Discriminant Analysis classifier and an accuracy of 93.75% was achieved as well as a precision and recall of 95% and 90% respectively.

Keywords: Doppler radar system, stand-to-sit phase, TUG test, machine learning, classification

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Authors: M. Benattou, K. Tamine

Abstract:

This paper presents a distributed intrusion detection system IDS, based on the concept of specialized distributed agents community representing agents with the same purpose for detecting distributed attacks. The semantic of intrusion events occurring in a predetermined network has been defined. The correlation rules referring the process which our proposed IDS combines the captured events that is distributed both spatially and temporally. And then the proposed IDS tries to extract significant and broad patterns for set of well-known attacks. The primary goal of our work is to provide intrusion detection and real-time prevention capability against insider attacks in distributed and fully automated environments.

Keywords: Mobile agent, specialized agent, interpreter agent, event rules, correlation.

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Authors: J.Zouhair, D.Ellison

Abstract:

This paper includes a review of three physics simulation packages that can be used to provide researchers with a virtual ground for modeling, implementing and simulating complex models, as well as testing their control methods with less cost and time of development. The inverted pendulum model was used as a test bed for comparing ODE, DANCE and Webots, while Linear State Feedback was used to control its behavior. The packages were compared with respect to model creation, solving systems of differential equation, data storage, setting system variables, control the experiment and ease of use. The purpose of this paper is to give an overview about our experience with these environments and to demonstrate some of the benefits and drawbacks involved in practice for each package.

Keywords: DANCE, Inverted Pendulum, ODE, Simulation Packages, Webots.

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Authors: Khalid Ghoulam, Belaid Bouikhalene, Zakaria Harmouch, Hicham Mouncif

Abstract:

The goal of engineering education is to prepare students to cope with problems of real devices and systems. Usually there are not enough devices or time for conducting experiments in a real lab. Other factors that prevent the use of lab devices directly by students are inaccessible or dangerous phenomena, or polluting chemical reactions. The technology brings additional strategies of learning and teaching, there are two types of online labs, virtual and remote labs RL. We present an example of a successful development and deployment of a remote lab in the field of engineering education, integrated in the Moodle platform, using very low-coast, high documented devices and free software. The remote lab is user friendly for both teachers and students. Our web 2.0 based user interface would attract and motivate students, as well as solving the problem of larger classes and expensive lab devices.

Keywords: Remote lab, online learning, Moodle, Arduino, SMSU, lab experimentation, engineering education, online engineering education.

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Authors: R. Sridevi, G. Jagajothi

Abstract:

Artificial Immune Systems (AIS), inspired by the human immune system, are algorithms and mechanisms which are self-adaptive and self-learning classifiers capable of recognizing and classifying by learning, long-term memory and association. Unlike other human system inspired techniques like genetic algorithms and neural networks, AIS includes a range of algorithms modeling on different immune mechanism of the body. In this paper, a mechanism of a human immune system based on apoptosis is adopted to build an Intrusion Detection System (IDS) to protect computer networks. Features are selected from network traffic using Fisher Score. Based on the selected features, the record/connection is classified as either an attack or normal traffic by the proposed methodology. Simulation results demonstrates that the proposed AIS based on apoptosis performs better than existing AIS for intrusion detection.

Keywords: Apoptosis, Artificial Immune System (AIS), Fisher Score, KDD dataset, Network intrusion detection.

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Authors: Revanth Sai Kosaraju, Michael Ramscar, Melody Dye

Abstract:

Accounts of language acquisition differ significantly in their treatment of the role of prediction in language learning. In particular, nativist accounts posit that probabilistic learning about words and word sequences has little to do with how children come to use language. The accuracy of this claim was examined by testing whether distributional probabilities and frequency contributed to how well 3-4 year olds repeat simple word chunks. Corresponding chunks were the same length, expressed similar content, and were all grammatically acceptable, yet the results of the study showed marked differences in performance when overall distributional frequency varied. It was found that a distributional model of language predicted the empirical findings better than a number of other models, replicating earlier findings and showing that children attend to distributional probabilities in an adult corpus. This suggested that language is more prediction-and-error based, rather than on abstract rules which nativist camps suggest.

Keywords: Abstractness, child psychology, language acquisition, prediction and error.

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Authors: Bharatendra Rai

Abstract:

Predictive data analysis and modeling involving machine learning techniques become challenging in presence of too many explanatory variables or features. Presence of too many features in machine learning is known to not only cause algorithms to slow down, but they can also lead to decrease in model prediction accuracy. This study involves housing dataset with 79 quantitative and qualitative features that describe various aspects people consider while buying a new house. Boruta algorithm that supports feature selection using a wrapper approach build around random forest is used in this study. This feature selection process leads to 49 confirmed features which are then used for developing predictive random forest models. The study also explores five different data partitioning ratios and their impact on model accuracy are captured using coefficient of determination (r-square) and root mean square error (rsme).

Keywords: Housing data, feature selection, random forest, Boruta algorithm, root mean square error.

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Authors: Pablo Zoghbi-Manrique-de-Lara, Miguel A. Suárez-Acosta

Abstract:

The goal of the present paper is to model two classic lines of research in which employees starred, organizational justice and citizenship behavior (OCB), but that have never been studied together when targeting customers. The suggestion is made that a hotel’s fair treatment (in terms of distributive, procedural, and interactional justice) toward customers will be appreciated by the employees, who will reciprocate in kind by favoring the hotel with increased customer-oriented behaviors (COBs). Data were collected from 204 employees at eight upscale hotels in the Canary Islands (Spain). Unlike in the case of perceptions of distributive justice, results of structural equation modeling demonstrate that employees substantively react to interactional and procedural justice toward guests by engaging in customer-oriented behaviors (COBs). The findings offer new reasons why employees decide to engage in COBs, and they highlight potentially beneficial effects of fair treatment toward guests bring to hospitality through promoting COBs.

Keywords: Hotel guests’ (mis) treatment, customer-oriented behaviors, employee citizenship, organizational justice, third-party observers, third-party intervention.

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Authors: Rahib Hidayat Abiyev

Abstract:

This paper presents the development of recurrent neural network based fuzzy inference system for identification and control of dynamic nonlinear plant. The structure and algorithms of fuzzy system based on recurrent neural network are described. To train unknown parameters of the system the supervised learning algorithm is used. As a result of learning, the rules of neuro-fuzzy system are formed. The neuro-fuzzy system is used for the identification and control of nonlinear dynamic plant. The simulation results of identification and control systems based on recurrent neuro-fuzzy network are compared with the simulation results of other neural systems. It is found that the recurrent neuro-fuzzy based system has better performance than the others.

Keywords: Fuzzy logic, neural network, neuro-fuzzy system, control system.

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Authors: Chonmapat Torasa

Abstract:

This paper presents the performance of electricity generation and consumption from solar generator installed at Rajabhat Suan Sunandha’s learning center in Samutsongkram. The result from the experiment showed that solar cell began to work and distribute the current into the system when the solar energy intensity was 340 w/m2, starting from 8:00 am to 4:00 pm (duration of 8 hours). The highest intensity read during the experiment was 1,051.64w/m2. The solar power was 38.74kWh/day. The electromotive force from solar cell averagely was 93.6V. However, when connecting solar cell with the battery charge controller system, the voltage was dropped to 69.07V. After evaluating the power distribution ability and electricity load of tested solar cell, the result showed that it could generate power to 11 units of 36-watt fluorescent lamp bulbs, which was altogether 396W. In the meantime, the AC to DC power converter generated 3.55A to the load, and gave 781VA.

Keywords: Solar Cell, Solar-cell power generating system.

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Authors: Siyao Zhu, Yifang Xu

Abstract:

After natural and man-made disasters, robots can bypass the danger, expedite the search, and acquire unprecedented situational awareness to design rescue plans. Brain-computer interface is a promising option to overcome the limitations of tedious manual control and operation of robots in the urgent search-and-rescue tasks. This study aims to test the feasibility of using electroencephalography (EEG) signals to decode human intentions and detect the level of consensus on robot-provided information. EEG signals were classified using machine-learning and deep-learning methods to discriminate search intentions and agreement perceptions. The results show that the average classification accuracy for intention recognition and consensus assessment is 67% and 72%, respectively, proving the potential of incorporating recognizable users’ bioelectrical responses into advanced robot-assisted systems for emergency response.

Keywords: Consensus assessment, electroencephalogram, EEG, emergency response, human-robot collaboration, intention recognition, search and rescue.

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Authors: Quratulain N. Rajput, Sajjad Haider

Abstract:

This paper applies Bayesian Networks to support information extraction from unstructured, ungrammatical, and incoherent data sources for semantic annotation. A tool has been developed that combines ontologies, machine learning, and information extraction and probabilistic reasoning techniques to support the extraction process. Data acquisition is performed with the aid of knowledge specified in the form of ontology. Due to the variable size of information available on different data sources, it is often the case that the extracted data contains missing values for certain variables of interest. It is desirable in such situations to predict the missing values. The methodology, presented in this paper, first learns a Bayesian network from the training data and then uses it to predict missing data and to resolve conflicts. Experiments have been conducted to analyze the performance of the presented methodology. The results look promising as the methodology achieves high degree of precision and recall for information extraction and reasonably good accuracy for predicting missing values.

Keywords: Information Extraction, Bayesian Network, ontology, Machine Learning

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Authors: Abdelbasit Gadour

Abstract:

A great number of children in mainstream schools across Libya is currently living with emotional, behavioural difficulties. This study aims to explore teachers’ perceptions of children’s emotional and behavioural difficulties (EBD) and their attributions of the causes of EBD. The relevance of this area of study to current educational practice is illustrated in the fact that primary school teachers in Libya find classroom behaviour problems one of the major difficulties they face. The information presented in this study was gathered from 182 teachers that responded back to the survey, of whom, 27 teachers were later interviewed. In general, teachers’ perceptions of EBD reflect personal experience, training, and attitudes. Teachers appear from this study to use words such as indifferent, frightened, withdrawn, aggressive, disobedient, hyperactive, less ambitious, lacking concentration, and academically weak to describe pupils with EBD. The implications of this study are envisaged as being extremely important to support teachers addressing children’s EBD and shed light on the contributing factors to EBD for a successful teaching-learning process in Libyan primary schools.

Keywords: Teachers, children, learning, emotional and behaviour difficulties.

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Authors: Akella Amarendra Babu, Rama Devi Yellasiri, Natukula Sainath

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Where human beings can easily learn and adopt pronunciation variations, machines need training before put into use. Also humans keep minimum vocabulary and their pronunciation variations are stored in front-end of their memory for ready reference, while machines keep the entire pronunciation dictionary for ready reference. Supervised methods are used for preparation of pronunciation dictionaries which take large amounts of manual effort, cost, time and are not suitable for real time use. This paper presents an unsupervised adaptation model for building agile and dynamic pronunciation dictionaries online. These methods mimic human approach in learning the new pronunciations in real time. A new algorithm for measuring sound distances called Dynamic Phone Warping is presented and tested. Performance of the system is measured using an adaptation model and the precision metrics is found to be better than 86 percent.

Keywords: Pronunciation variations, dynamic programming, machine learning, natural language processing.

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Authors: Yun-Husan Huang

Abstract:

Many English for Specific Purposes (ESP) textbooks are distributed globally as the content development is often obliged to compromises between commercial and pedagogical demands. Therefore, the issue of regional application and usefulness of globally published ESP textbooks has received much debate. For ESP instructors, textbook selection is definitely a priority consideration for curriculum design. An appropriate ESP textbook can facilitate teaching and learning, while an inappropriate one may cause a disaster for both teachers and students. This study aims to investigate the regional application and usefulness of an ESP textbook for information technology (IT). Participants were 51 sophomores majoring in Applied Informatics and Multimedia at a university in Taiwan. As they were non-English majors, their English proficiency was mostly at elementary and elementary-to-intermediate levels. This course was offered for two semesters. The textbook selected was Oxford English for Information Technology. At class end, the students were required to complete a survey comprising five choices of Very Easy, Easy, Neutral, Difficult, and Very Difficult for each item. Based on the content design of the textbook, the survey investigated how the students viewed the difficulty of grammar, listening, speaking, reading, and writing materials of the textbook. In terms of difficulty, results reveal that only 22% of them found the grammar section difficult and very difficult. For listening, 71% responded difficult and very difficult. For general reading, 55% responded difficult and very difficult. For speaking, 56% responded difficult and very difficult. For writing, 78% responded difficult and very difficult. For advanced reading, 90% reported difficult and very difficult. These results indicate that, except the grammar section, more than half of the students found the textbook contents difficult in terms of listening, speaking, reading, and writing materials. Such contradictory results between the easy grammar section and the difficult four language skills sections imply that the textbook designers do not well understand the English learning background of regional ESP learners. For the participants, the learning contents of the grammar section were the general grammar level of junior high school, while the learning contents of the four language skills sections were more of the levels of college English majors. Implications from the findings are obtained for instructors and textbook designers. First of all, existing ESP textbooks for IT are few and thus textbook selections for instructors are insufficient. Second, existing globally published textbooks for IT cannot be applied to learners of all English proficiency levels, especially the low level. With limited textbook selections, third, instructors should modify the selected textbook contents or supplement extra ESP materials to meet the proficiency level of target learners. Fourth, local ESP publishers should collaborate with local ESP instructors who understand best the learning background of their students in order to develop appropriate ESP textbooks for local learners. Even though the instructor reduced learning contents and simplified tests in curriculum design, in conclusion, the students still found difficult. This implies that in addition to the instructor’s professional experience, there is a need to understand the usefulness of the textbook from learner perspectives.

Keywords: ESP textbooks, ESP materials, ESP textbook design, learner perspectives on ESP textbooks.

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Authors: Ahmad Rouhani, Masoud Jabbari, Sima Honarmand

Abstract:

This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technical and economic. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations.

Keywords: Hybrid energy system, optimum sizing, power management, TLBO.

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Authors: Pierre Parrend, Stephane Frenot

Abstract:

Providing Services at Home has become over the last few years a very dynamic and promising technological domain. It is likely to enable wide dissemination of secure and automated living environments. We propose a methodology for identifying threats to Services at Home Delivery systems, as well as a threat analysis of a multi-provider Home Gateway architecture. This methodology is based on a dichotomous positive/preventive study of the target system: it aims at identifying both what the system must do, and what it must not do. This approach completes existing methods with a synthetic view of potential security flaws, thus enabling suitable measures to be taken into account. Security implications of the evolution of a given system become easier to deal with. A prototype is built based on the conclusions of this analysis.

Keywords: Security requirements, Connected Home, OSGi, Sofware Components.

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: Deep learning, long-short-term memory, energy, renewable energy load forecasting.

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Authors: R. Sharma, M. Gopal

Abstract:

This paper presents a new problem solving approach that is able to generate optimal policy solution for finite-state stochastic sequential decision-making problems with high data efficiency. The proposed algorithm iteratively builds and improves an approximate Markov Decision Process (MDP) model along with cost-to-go value approximates by generating finite length trajectories through the state-space. The approach creates a synergy between an approximate evolving model and approximate cost-to-go values to produce a sequence of improving policies finally converging to the optimal policy through an intelligent and structured search of the policy space. The approach modifies the policy update step of the policy iteration so as to result in a speedy and stable convergence to the optimal policy. We apply the algorithm to a non-holonomic mobile robot control problem and compare its performance with other Reinforcement Learning (RL) approaches, e.g., a) Q-learning, b) Watkins Q(λ), c) SARSA(λ).

Keywords: Markov Decision Process (MDP), Mobile robot, Policy iteration, Simulation.

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Authors: Cristina-Maria Păcurar

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The paper presents an original Python-based application that outlines the advantages of combining some elementary notions of mathematics with the study of a programming language. The application support refers to some of the first lessons of analytic geometry, meaning conics and quadrics and their reduction to a standard form, as well as some related notions. The chosen programming language is Python, not only for its closer to an everyday language syntax – and therefore, enhanced readability – but also for its highly reusable code, which is of utmost importance for a mathematician that is accustomed to exploit already known and used problems to solve new ones. The purpose of this paper is, on one hand, to support the idea that one of the most appropriate means to initiate one into programming is throughout mathematics, and reciprocal, one of the most facile and handy ways to assimilate some basic knowledge in the study of mathematics is to apply them in a personal project. On the other hand, besides being a mean of learning both programming and analytic geometry, the application subject to this paper is itself a useful tool for it can be seen as an independent original Python package for analytic geometry.

Keywords: Analytic geometry, conics, Python programming language, quadrics.

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Authors: M. Daskolia, C. Kynigos, N. Yiannoutsou

Abstract:

Teaching and learning about sustainability is a pedagogical endeavour with various innate difficulties and increased demands. Higher education has a dual role to play in addressing this challenge: to identify and explore innovative approaches and tools for addressing the complex and value-laden nature of sustainability in more meaningful ways, and to help teachers to integrate these approaches into their practice through appropriate professional development programs. The study reported here was designed and carried out within the context of a Masters course in Environmental Education. Eight teachers were collaboratively engaged in reconstructing a digital game microworld which was deliberately designed by the researchers to be questioned and evoke critical discussion on the idea of ‘sustainable city’. The study was based on the design-based research method. The findings indicate that the teachers’ involvement in processes of co-constructing the microworld initiated discussion and reflection upon the concepts of sustainability and sustainable lifestyles.

Keywords: sustainability, sustainable lifestyles, constructionism, environmental education, digital games, teacher training

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Authors: Syed Muhammad Aqil Burney, Tahseen Ahmed Jilani, C. Ardil

Abstract:

Using neural network we try to model the unknown function f for given input-output data pairs. The connection strength of each neuron is updated through learning. Repeated simulations of crisp neural network produce different values of weight factors that are directly affected by the change of different parameters. We propose the idea that for each neuron in the network, we can obtain quasi-fuzzy weight sets (QFWS) using repeated simulation of the crisp neural network. Such type of fuzzy weight functions may be applied where we have multivariate crisp input that needs to be adjusted after iterative learning, like claim amount distribution analysis. As real data is subjected to noise and uncertainty, therefore, QFWS may be helpful in the simplification of such complex problems. Secondly, these QFWS provide good initial solution for training of fuzzy neural networks with reduced computational complexity.

Keywords: Crisp neural networks, fuzzy systems, extraction of logical rules, quasi-fuzzy numbers.

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Authors: S. Chee Choy, Pauline Swee Choo Goh, Yow Lin Liew

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The purpose of this study is to evaluate the English version and a Malay translation of the 21-item Learner Awareness Questionnaire for its application to assess student learning in higher education. The Learner Awareness Questionnaire, originally written in English, is a quantitative measure of how and why students learn. The questionnaire gives an indication of the process and motives to learn using four scales: survival, establishing stability, approval and loving to learn. Data in the present study came from 680 university students enrolled in various programmes in Malaysia. The Malay version of the questionnaire supported a similar four factor structure and internal consistency to the English version. The four factors of the Malay version also showed moderate to strong correlations with those of the English versions. The results suggest that the Malay version of the questionnaire is similar to the English version. However, further refinement to the questions is needed to strengthen the correlations between the two questionnaires.

Keywords: Student learning, learner awareness, instrument validation.

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Authors: Arun Prasad Jaganathan

Abstract:

In today's dynamic industrial world, traditional task allocation methods often fall short in adapting to evolving operational conditions. This paper presents an AI-enabled task assistance and management system designed to overcome the limitations of conventional approaches. By using artificial intelligence (AI) and machine learning (ML), the system intelligently interprets user instructions, analyzes tasks, and allocates resources based on real-time data and environmental factors. Additionally, geolocation tracking enables proactive identification of potential delays, ensuring timely interventions. With its transparent reporting mechanisms, the system provides stakeholders with clear insights into task progress, fostering accountability and informed decision-making. The paper presents a comprehensive overview of the system architecture, algorithm, and implementation, highlighting its potential to revolutionize task management across diverse industries.

Keywords: Artificial intelligence, machine learning, task allocation, operational efficiency, resource optimization.

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Authors: Gheorghe Galben, Daniel N. Aloi

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Recently, the Spherical Motion Models (SMM-s) have been introduced [1]. These new models have been developed for 3D local landmark-base Autonomous Navigation (AN). This paper is revealing new arguments and experimental results to support the SMM-s characteristics. The accuracy and the robustness in performing a specific task are the main concerns of the new investigations. To analyze their performances of the SMM-s, the most powerful tools of estimation theory, the extended Kalman filter (EKF) and unscented Kalman filter (UKF), which give the best estimations in noisy environments, have been employed. The Monte Carlo validation implementations used to test the stability and robustness of the models have been employed as well.

Keywords: Autonomous navigation, extended kalman filter, unscented kalman filter, localization algorithms.

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Authors: J. C. T. Benavente, J. C. E. Ferreira

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The rapid development of manufacturing and information systems has caused significant changes in manufacturing environments in recent decades. Mass production has given way to flexible manufacturing systems, in which an important characteristic is customized or "on demand" production. In this scenario, the seamless and without gaps information flow becomes a key factor for success of enterprises. In this paper we present a framework to support the mapping of features into machining workingsteps compliant with the ISO 14649 standard (known as STEP-NC). The system determines how the features can be made with the available manufacturing resources. Examples of the mapping method are presented for features such as a pocket with a general surface.

Keywords: Features, ISO 14649 standard, STEP-NC, mapping, machining workingsteps.

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Authors: Towards Better Quality in Healthcare, Operations Management: A Developmental Literature Review

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This work presents the various perspectives, dimensions, components and definitions given to quality in the operations management (OM) and healthcare services (HCS) literature in time, highlighting gaps and learning opportunities between the two disciplines through a thorough search into their rich and distinct body of knowledge. Greater and new insights about the general nature of quality are obtained with findings such as in OM, quality has been approached in six fairly distinct paradigms (excellence, value, conformity to specifications, attributes, satisfaction and meeting or exceeding customer expectations), whereas in HCS, two approaches are prominent (Donabedian’s structure, process and outcomes model and Lohr and Schroeder’s circumscribed definition). The two disciplines views on quality seem to have progressed much in parallel with little cross-learning from each other. This work then proposes an encompassing definition of quality as a lever and suggests further research and development avenues for a better use of the concept of quality by academics and practitioners alike toward the goals of greater organizational performance and improved management in healthcare and possibly other service domains.

Keywords: Healthcare, management, operations, quality, services.

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Authors: Phalaunnaphat Siriwongs

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The classroom of the 21st century is an ever changing forum for new and innovative thoughts and ideas. With increasing technology and opportunity, students have rapid access to information that only decades ago would have taken weeks to obtain. Unfortunately, new techniques and technology are not the cure for the fundamental problems that have plagued the classroom ever since education was established. Class size has been an issue long debated in academia. While it is difficult to pin point an exact number, it is clear that in this case more does not mean better. By looking into the success and pitfalls of classroom size the true advantages of smaller classes will become clear. Previously, one class was comprised of 50 students. Being seventeen and eighteen- year- old students, sometimes it was quite difficult for them to stay focused. To help them understand and gain much knowledge, a researcher introduced “The Theory of Multiple Intelligence” and this, in fact, enabled students to learn according to their own learning preferences no matter how they were being taught. In this lesson, the researcher designed a cycle of learning activities involving all intelligences so that everyone had equal opportunities to learn.

Keywords: Multiple intelligences, role play, performance assessment, formative assessment.

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Authors: Xiaohua Liu, Juan F. Beltran, Nishant Mohanchandra, Godfried T. Toussaint

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Support vector machines (SVMs) are considered to be the best machine learning algorithms for minimizing the predictive probability of misclassification. However, their drawback is that for large data sets the computation of the optimal decision boundary is a time consuming function of the size of the training set. Hence several methods have been proposed to speed up the SVM algorithm. Here three methods used to speed up the computation of the SVM classifiers are compared experimentally using a musical genre classification problem. The simplest method pre-selects a random sample of the data before the application of the SVM algorithm. Two additional methods use proximity graphs to pre-select data that are near the decision boundary. One uses k-Nearest Neighbor graphs and the other Relative Neighborhood Graphs to accomplish the task.

Keywords: Machine learning, data mining, support vector machines, proximity graphs, relative-neighborhood graphs, k-nearestneighbor graphs, random sampling, training data condensation.

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