Search results for: Attention Multiple Instance Learning

Authors: M. H. Imam, Imran A. Tasadduq, Abdul-Rahim Ahmad, Fahd M. Aldosari

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Learning outcomes of a course (CLOs) and the abilities at the time of graduation referred to as Student Outcomes (SOs) are required to be assessed for ABET accreditation. A question in an assessment must target a CLO as well as an SO and must represent a required level of competence. This paper presents the idea of an Expert System (ES) to select a proper question to satisfy ABET accreditation requirements. For ES implementation, seven attributes of a question are considered including the learning outcomes and Bloom’s Taxonomy level. A database contains all the data about a course including course content topics, course learning outcomes and the CLO-SO relationship matrix. The knowledge base of the presented ES contains a pool of questions each with tags of the specified attributes. Questions and the attributes represent expert opinions. With implicit rule base the inference engine finds the best possible question satisfying the required attributes. It is shown that the novel idea of such an ES can be implemented and applied to a course with success. An application example is presented to demonstrate the working of the proposed ES.

Keywords: Expert system, student outcomes, course learning outcomes, question attributes.

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Authors: Liping Li, Wenan Tan, Na Wang

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With the rapid development of information technology, project management has gained more and more attention recently. Based on CDIO, this paper proposes some teaching reform ideas for software project management curriculum. We first change from Teacher-centered classroom to Student-centered and adopt project-driven, scenario animation show, teaching rhythms, case study and team work practice to improve students' learning enthusiasm. Results showed these attempts have been well received and very effective; as well, students prefer to learn with this curriculum more than before the reform.

Keywords: CDIO, teaching reform, engineering education, project-driven, scenario animation simulation.

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: Intrusion prevention, network security, optimal policy, Q-learning.

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Authors: Karen Armstrong

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Online discussions are an important component of both blended and online courses. This paper examines the varieties of online discussions and the perils, pitfalls and possibilities of this rather new technological tool for enhanced learning. The discussion begins with possible perils and pitfalls inherent in this educational tool and moves to a consideration of the advantages of the varieties of online discussions feasible for use in teacher education programs.

Keywords: online discussions, computer-mediatedcommunication (CMC), computer-supported collaborative learning(CSCL), e-learning, teacher education

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Authors: Serena Alvino, Paola Forcheri, Maria Grazia Ierardi, Luigi Sarti

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One of the main advantages of the LO paradigm is to allow the availability of good quality, shareable learning material through the Web. The effectiveness of the retrieval process requires a formal description of the resources (metadata) that closely fits the user-s search criteria; in spite of the huge international efforts in this field, educational metadata schemata often fail to fulfil this requirement. This work aims to improve the situation, by the definition of a metadata model capturing specific didactic features of shareable learning resources. It classifies LOs into “teacher-oriented" and “student-oriented" categories, in order to describe the role a LO is to play when it is integrated into the educational process. This article describes the model and a first experimental validation process that has been carried out in a controlled environment.

Keywords: Learning object, pedagogical metadata, experimental validation.

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Authors: An Chen, Huilin Ye

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Mining sequential patterns from large customer transaction databases has been recognized as a key research topic in database systems. However, the previous works more focused on mining sequential patterns at a single concept level. In this study, we introduced concept hierarchies into this problem and present several algorithms for discovering multiple-level sequential patterns based on the hierarchies. An experiment was conducted to assess the performance of the proposed algorithms. The performances of the algorithms were measured by the relative time spent on completing the mining tasks on two different datasets. The experimental results showed that the performance depends on the characteristics of the datasets and the pre-defined threshold of minimal support for each level of the concept hierarchy. Based on the experimental results, some suggestions were also given for how to select appropriate algorithm for a certain datasets.

Keywords: Data Mining, Multiple-Level Sequential Pattern, Concept Hierarchy, Customer Transaction Database.

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Authors: Thapanee Seechaliao

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This research paper presents guiding on how to design social media into higher education courses. The research methodology used a survey approach. The research instrument was a questionnaire about guiding on how to design social media into higher education courses. Thirty-one lecturers completed the questionnaire. The data were scored by frequency and percentage. The research results were the lecturers’ opinions concerning the designing social media into higher education courses as follows: 1) Lecturers deem that the most suitable learning theory is Collaborative Learning. 2) Lecturers consider that the most important learning and innovation Skill in the 21st century is communication and collaboration skills. 3) Lecturers think that the most suitable evaluation technique is authentic assessment. 4) Lecturers consider that the most appropriate portion used as blended learning should be 70% in the classroom setting and 30% online.

Keywords: Instructional design, social media, courses, higher education.

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Authors: M. Mazhar Manzoor, Nasar.A, A. Sattar

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In the open space of decision support system the mental impression of a manager-s decision has been the subject of large importance than the ordinary famous one, when helped by decision support system. Much of this study is an attempt to realize the relation of decision support system usage and decision outcomes that governs the system. For example, several researchers have proposed so many different models to analyze the linkage between decision support system processes and results of decision making. This study draws the important relation of manager-s mental approach with the use of decision support system. The findings of this paper are theoretical attempts to provide Decision Support System (DSS) in a way to exhibit and promote the learning in semi structured area. The proposed model shows the points of one-s learning improvements and maintains a theoretical approach in order to explore the DSS contribution in enhancing the decision forming and governing the system.

Keywords: Decision Support System , Learning Organization,

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Authors: N. Parimala, P. Pahwa

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OLAP uses multidimensional structures, to provide access to data for analysis. Traditionally, OLAP operations are more focused on retrieving data from a single data mart. An exception is the drill across operator. This, however, is restricted to retrieving facts on common dimensions of the multiple data marts. Our concern is to define further operations while retrieving data from multiple data marts. Towards this, we have defined six operations which coalesce data marts. While doing so we consider the common as well as the non-common dimensions of the data marts.

Keywords: Data warehouse, Dimension, OLAP, Star Schema.

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Authors: A. O. Oladele

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The purpose of this study was to find out the effectiveness of neurological impress method and repeated reading technique on reading fluency of children with learning disabilities. Thirty primary four pupils in three public primary schools participated in the study. There were two experimental groups and a control. This research employed a 3 by 2 factorial matrix and the participants were taught for one session. Two hypotheses were formulated to guide the research. T-test was used to analyse the data gathered, and data analysis revealed that pupils exposed to the two treatment strategies had improvement in their reading fluency. It was recommended that the two strategies used in the study can be used to intervene in reading fluency problems in children with learning disabilities.

Keywords: Learning disabilities, neurological impress method, repeated reading, reading fluency.

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Authors: Saher Ali Al-Sabbah, See Ching Mey, Ong Saw Lan

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This article addresses the procedures to validate the Arabic version of Multiple Intelligence Development Assessment Scale (MIDAS). The content validity was examined based on the experts- judgments on the MIDAS-s items in the Arabic version. The content of eleven items in the Arabic version of MIDAS was modified to match the Arabic context. Then a translation from original English version of MIDAS into Arabic language was performed. The reliability of the Arabic MIDAS was calculated based on test and retest method and found to be 0.85 for the overall MIDAS and for the different subscales ranging between 0.78 - 0.87. The examination of construct validity for the overall Arabic MIDAS and its subscales was established by using Winsteps program version 6 based on Rasch model in order to fit the items into the Arabic context. The findings indicated that, the eight subscales in Arabic version of MIDAS scale have a unidimensionality, and the total number of kept items in the overall scale is 108 items.

Keywords: Rasch-Model, validation, multiple intelligence, and MIDAS scale.

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Authors: Hadi Firouzi, Majid Nili Ahmadabadi, Babak N. Araabi

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Conceptualization strengthens intelligent systems in generalization skill, effective knowledge representation, real-time inference, and managing uncertain and indefinite situations in addition to facilitating knowledge communication for learning agents situated in real world. Concept learning introduces a way of abstraction by which the continuous state is formed as entities called concepts which are connected to the action space and thus, they illustrate somehow the complex action space. Of computational concept learning approaches, action-based conceptualization is favored because of its simplicity and mirror neuron foundations in neuroscience. In this paper, a new biologically inspired concept learning approach based on the probabilistic framework is proposed. This approach exploits and extends the mirror neuron-s role in conceptualization for a reinforcement learning agent in nondeterministic environments. In the proposed method, instead of building a huge numerical knowledge, the concepts are learnt gradually from rewards through interaction with the environment. Moreover the probabilistic formation of the concepts is employed to deal with uncertain and dynamic nature of real problems in addition to the ability of generalization. These characteristics as a whole distinguish the proposed learning algorithm from both a pure classification algorithm and typical reinforcement learning. Simulation results show advantages of the proposed framework in terms of convergence speed as well as generalization and asymptotic behavior because of utilizing both success and failures attempts through received rewards. Experimental results, on the other hand, show the applicability and effectiveness of the proposed method in continuous and noisy environments for a real robotic task such as maze as well as the benefits of implementing an incremental learning scenario in artificial agents.

Keywords: Concept learning, probabilistic decision making, reinforcement learning.

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Authors: Somayeh Alizadeh, Mehdi Ghazanfari, Mohammad Fathian

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Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show relations between essential components. In some FCM, there are more nodes, which related to each other and more nodes means more complex in system behaviors and analysis. In this paper, a novel learning method used to construct FCMs based on historical data and by using data mining and DEMATEL method, a new method defined to reduce nodes number. This method cluster nodes in FCM based on their cause and effect behaviors.

Keywords: Clustering, Data Mining, Fuzzy Cognitive Map(FCM), Learning.

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Authors: Muhammed Yousoof, Mohd Sapiyan, Khaja Kamaluddin

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Learning programming is difficult for many learners. Some researches have found that the main difficulty relates to cognitive load. Cognitive overload happens in programming due to the nature of the subject which is intrinisicly over-bearing on the working memory. It happens due to the complexity of the subject itself. The problem is made worse by the poor instructional design methodology used in the teaching and learning process. Various efforts have been proposed to reduce the cognitive load, e.g. visualization softwares, part-program method etc. Use of many computer based systems have also been tried to tackle the problem. However, little success has been made to alleviate the problem. More has to be done to overcome this hurdle. This research attempts at understanding how cognitive load can be managed so as to reduce the problem of overloading. We propose a mechanism to measure the cognitive load during pre instruction, post instruction and in instructional stages of learning. This mechanism is used to help the instruction. As the load changes the instruction is made to adapt itself to ensure cognitive viability. This mechanism could be incorporated as a sub domain in the student model of various computer based instructional systems to facilitate the learning of programming.

Keywords: Cognitive load, Working memory, Cognitive Loadmeasurement.

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Authors: Khaddouja Boujenfa, Nadia Essoussi, Mohamed Limam

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Multiple sequence alignment is a fundamental part in many bioinformatics applications such as phylogenetic analysis. Many alignment methods have been proposed. Each method gives a different result for the same data set, and consequently generates a different phylogenetic tree. Hence, the chosen alignment method affects the resulting tree. However in the literature, there is no evaluation of multiple alignment methods based on the comparison of their phylogenetic trees. This work evaluates the following eight aligners: ClustalX, T-Coffee, SAGA, MUSCLE, MAFFT, DIALIGN, ProbCons and Align-m, based on their phylogenetic trees (test trees) produced on a given data set. The Neighbor-Joining method is used to estimate trees. Three criteria, namely, the dNNI, the dRF and the Id_Tree are established to test the ability of different alignment methods to produce closer test tree compared to the reference one (true tree). Results show that the method which produces the most accurate alignment gives the nearest test tree to the reference tree. MUSCLE outperforms all aligners with respect to the three criteria and for all datasets, performing particularly better when sequence identities are within 10-20%. It is followed by T-Coffee at lower sequence identity (<10%), Align-m at 20-30% identity, and ClustalX and ProbCons at 30-50% identity. Also, it is noticed that when sequence identities are higher (>30%), trees scores of all methods become similar.

Keywords: Multiple alignment methods, phylogenetic trees, Neighbor-Joining method, Robinson-Foulds distance.

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Authors: Xiaoquan Ding, Jianmin Hao, Changwen Liu

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This paper is devoted to a delayed periodic predatorprey system with non-monotonic numerical response on time scales. With the help of a continuation theorem based on coincidence degree theory, we establish easily verifiable criteria for the existence of multiple periodic solutions. As corollaries, some applications are listed. In particular, our results improve and generalize some known ones.

Keywords: Predator-prey system, periodic solution, time scale, delay, coincidence degree.

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Authors: Wen-Chao Tang, Tang-Yi Liu, Ming Gao, Gang Xu, Hua-Yuan Yang

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This study developed a method for teaching acupuncture manipulation in small groups optimized with the theory of motor learning. Sixty acupuncture students and their teacher participated in our research. Motion videos were recorded of their manipulations using the lifting-thrusting method. These videos were analyzed using Simi Motion software to acquire the movement parameters of the thumb tip. The parameter velocity curves along Y axis was used to generate small teaching groups clustered by a self-organized map (SOM) and K-means. Ten groups were generated. All the targeted instruction based on the comparative results groups as well as the videos of teacher and student was provided to the members of each group respectively. According to the theory and research of motor learning, the factors or technologies such as video instruction, observational learning, external focus and summary feedback were integrated into this teaching method. Such efforts were desired to improve and enhance the effectiveness of current acupuncture teaching methods in limited classroom teaching time and extracurricular training.

Keywords: Acupuncture, group teaching, video instruction, observational learning, external focus, summary feedback.

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Authors: Francisca Grimón, Marylin Giugni, Josep Monguet F., Joaquín Fernández, Luis León G.

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In blended learning environments, the Internet can be combined with other technologies. The aim of this research was to design, introduce and validate a model to support synchronous and asynchronous activities by managing content domains in an Adaptive Hypermedia System (AHS). The application is based on information recovery techniques, clustering algorithms and adaptation rules to adjust the user's model to contents and objects of study. This system was applied to blended learning in higher education. The research strategy used was the case study method. Empirical studies were carried out on courses at two universities to validate the model. The results of this research show that the model had a positive effect on the learning process. The students indicated that the synchronous and asynchronous scenario is a good option, as it involves a combination of work with the lecturer and the AHS. In addition, they gave positive ratings to the system and stated that the contents were adapted to each user profile.

Keywords: Blended Learning, System Adaptive, Model, Clustering Algorithms.

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Authors: Rosaline Govender

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This research study is an exploration of the selfdirected professional development of teachers who teach in public schools in an era of democracy and educational change in South Africa. Amidst an ever-changing educational system, the teachers in this study position themselves as self-directed teacher-learners where they adopt particular learning practices which enable change within the broader discourses of public schooling. Life-story interviews were used to enter into the private and public spaces of five teachers which offer glimpses of how particular systems shaped their identities, and how the meanings of self-directed teacher-learner shaped their learning practices. Through the Multidimensional Framework of Analysis and Interpretation the teachers’ stories were analysed through three lenses: restorying the field texts - the self through story; the teacher-learner in relation to social contexts, and practices of self-directed learning. This study shows that as teacherlearners learn for change through self-directed learning practices, they develop their agency as transformative intellectuals, which is necessary for the reworking of South African public schools.

Keywords: Professional development, professionality, professionalism, self-directed learning.

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Authors: Estevan B. Costa, Armando M. Toda, Marcell A. A. Mesquita, Jacques D. Brancher

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The advances in technology in the last five years allowed an improvement in the educational area, as the increasing in the development of educational software. One of the techniques that emerged in this lapse is called Gamification, which is the utilization of video game mechanics outside its bounds. Recent studies involving this technique provided positive results in the application of these concepts in many areas as marketing, health and education. In the last area there are studies that covers from elementary to higher education, with many variations to adequate to the educators methodologies. Among higher education, focusing on IT courses, data structures are an important subject taught in many of these courses, as they are base for many systems. Based on the exposed this paper exposes the development of an interactive web learning environment, called DSLEP (Data Structure Learning Platform), to aid students in higher education IT courses. The system includes basic concepts seen on this subject such as stacks, queues, lists, arrays, trees and was implemented to ease the insertion of new structures. It was also implemented with gamification concepts, such as points, levels, and leader boards, to engage students in the search for knowledge and stimulate self-learning.

Keywords: Gamification, Interactive learning environment, Data structures, e-learning.

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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: Glenda A. Gunter

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Current research has explored the impact of instructional immediacy, defined as those behaviors that help build close relationships or feelings of closeness, both on cognition and motivation in the traditional classroom and online classroom; however, online courses continue to suffer from higher dropout rates. Based on Albert Bandura-s Social Cognitive Theory, four primary relationships or interactions in an online course will be explored in light of how they can provide immediacy thereby reducing student attrition and improving cognitive learning. The four relationships are teacher-student, student-student, and student-content, and studentcomputer. Results of a study conducted with inservice teachers completing a 14-week online professional development technology course will be examined to demonstrate immediacy strategies that improve cognitive learning and reduce student attrition. Results of the study reveal that students can be motivated through various interactions and instructional immediacy behaviors which lead to higher completion rates, improved self-efficacy, and cognitive learning.

Keywords: Distance Learning, Self-Efficacy, Instructional immediacy, Student achievement.

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Authors: K. Chandra Sekaran

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This paper describes a paradigmatic approach to develop architecture of secure systems by describing the requirements from four different points of view: that of the owner, the administrator, the user, and the network. Deriving requirements and developing architecture implies the joint elicitation and describing the problem and the structure of the solution. The view points proposed in this paper are those we consider as requirements towards their contributions as major parties in the design, implementation, usage and maintenance of secure systems. The dramatic growth of the technology of Internet and the applications deployed in World Wide Web have lead to the situation where the security has become a very important concern in the development of secure systems. Many security approaches are currently being used in organizations. In spite of the widespread use of many different security solutions, the security remains a problem. It is argued that the approach that is described in this paper for the development of secure architecture is practical by all means. The models representing these multiple points of view are termed the requirements model (views of owner and administrator) and the operations model (views of user and network). In this paper, this multiple view paradigm is explained by first describing the specific requirements and or characteristics of secure systems (particularly in the domain of networks) and the secure architecture / system development methodology.

Keywords: Multiple view paradigms, requirements model, operations model, secure system, owner, administrator, user, network.

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Authors: Andrey V. Timofeev

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, Classifiers Ensembles, LPBoost, C-OTDR systems, ν-OTDR systems.

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Authors: Jean-Alain Grunchec, Jules Hernández-Sánchez, Sara Knott

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Some meta-schedulers query the information system of individual supercomputers in order to submit jobs to the least busy supercomputer on a computational Grid. However, this information can become outdated by the time a job starts due to changes in scheduling priorities. The MSR scheme is based on Multiple Simultaneous Requests and can take advantage of opportunities resulting from these priorities changes. This paper presents the SWARM meta-scheduler, which can speed up the execution of large sets of tasks by minimizing the job queuing time through the submission of multiple requests. Performance tests have shown that this new meta-scheduler is faster than an implementation of the MSR scheme and the gLite meta-scheduler. SWARM has been used through the GridQTL project beta-testing portal during the past year. Statistics are provided for this usage and demonstrate its capacity to achieve reliably a substantial reduction of the execution time in production conditions.

Keywords: Grid computing, multiple simultaneous requests, fault tolerance, GridQTL.

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Authors: Laura Zizka, Gaby Probst

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The COVID-19 pandemic has affected the way Higher Education Institutions (HEIs) have given their courses. From emergency remote where all students and faculty were immediately confined to home teaching and learning, the continuing evolving sanitary situation obliged HEIs to adopt other methods of teaching and learning from blended courses that included both synchronous and asynchronous courses and activities to HyFlex models where some students were on campus while others followed the course simultaneously online. Each semester brought new challenges for HEIs and, subsequently, additional emotional reactions. This paper investigates the affective side of teaching and learning in various online modalities and its toll on students and faculty members over the past three semesters. The findings confirm that students and faculty who have more self-efficacy, flexibility, and resilience reported positive emotions and embraced the opportunities that these past semesters have offered. While HEIs have begun a new semester in an attempt to return to ‘normal’ face-to-face courses, this paper posits that there are lessons to be learned from these past three semesters. The opportunities that arose from the challenge of the pandemic should be considered when moving forward by focusing on a greater emphasis on the affective aspect of teaching and learning in HEIs worldwide. 

Keywords: affective teaching and learning, engagement, interaction, motivation, social presence

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Authors: S. Alfalah, J. Falah, T. Alfalah, M. Elfalah, O. Falah

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The field of education is an ever-evolving area constantly enriched by newly discovered techniques provided by active research in all areas of technologies. The recent years have witnessed the introduction of a number of promising technologies and applications to enhance the teaching and learning experience. Virtual Reality (VR) applications are considered one of the evolving methods that have contributed to enhancing education in many fields. VR creates an artificial environment, using computer hardware and software, which is similar to the real world. This simulation provides a solution to improve the delivery of materials, which facilitates the teaching process by providing a useful aid to instructors, and enhances the learning experience by providing a beneficial learning aid. In order to assure future utilization of such systems, students’ perceptions were examined toward utilizing VR as an educational tool in the Faculty of Information Technology (IT) in The University of Jordan. A questionnaire was administered to IT undergraduates investigating students’ opinions about the potential opportunities that VR technology could offer and its implications as learning and teaching aid. The results confirmed the end users’ willingness to adopt VR systems as a learning aid. The result of this research forms a solid base for investing in a VR system for IT education.

Keywords: Education, information, technology, virtual reality.

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Authors: Ibrahim S AlNomay, Alaa Jaber, Ghada AlNasser

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A learning management system (commonly abbreviated as LMS) is a software application for the administration, documentation, tracking, and reporting of training programs, classroom and online events, e-learning programs, and training content (Ellis 2009). (Hall 2003) defines an LMS as \"software that automates the administration of training events. All Learning Management Systems manage the log-in of registered users, manage course catalogs, record data from learners, and provide reports to management\". Evidence of the worldwide spread of e-learning in recent years is easy to obtain. In April 2003, no fewer than 66,000 fully online courses and 1,200 complete online programs were listed on the TeleCampus portal from TeleEducation (Paulsen 2003). In the report \" The US market in the Self-paced eLearning Products and Services:2010-2015 Forecast and Analysis\" The number of student taken classes exclusively online will be nearly equal (1% less) to the number taken classes exclusively in physical campuses. Number of student taken online course will increase from 1.37 million in 2010 to 3.86 million in 2015 in USA. In another report by The Sloan Consortium three-quarters of institutions report that the economic downturn has increased demand for online courses and programs.

Keywords: LMS, Interactive Materials, Exam Centers, Learning Outcomes

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Authors: Jan Pomikalek, Radim Rehurek

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Text categorization (the assignment of texts in natural language into predefined categories) is an important and extensively studied problem in Machine Learning. Currently, popular techniques developed to deal with this task include many preprocessing and learning algorithms, many of which in turn require tuning nontrivial internal parameters. Although partial studies are available, many authors fail to report values of the parameters they use in their experiments, or reasons why these values were used instead of others. The goal of this work then is to create a more thorough comparison of preprocessing parameters and their mutual influence, and report interesting observations and results.

Keywords: Text categorization, machine learning, electronic documents, classification.

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Authors: Ali Sarosh, Dong Yun-Feng, Muhammad Umer

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Fully reusable spaceplanes do not exist as yet. This implies that design-qualification for optimized highly-integrated forebody-inlet configuration of booster-stage vehicle cannot be based on archival data of other spaceplanes. Therefore, this paper proposes a novel TIPSO-SVM expert system methodology. A non-trivial problem related to optimization and classification of hypersonic forebody-inlet configuration in conjunction with mass-model of the two-stage-to-orbit (TSTO) vehicle is solved. The hybrid-heuristic machine learning methodology is based on two-step improved particle swarm optimizer (TIPSO) algorithm and two-step support vector machine (SVM) data classification method. The efficacy of method is tested by first evolving an optimal configuration for hypersonic compression system using TIPSO algorithm; thereafter, classifying the results using two-step SVM method. In the first step extensive but non-classified mass-model training data for multiple optimized configurations is segregated and pre-classified for learning of SVM algorithm. In second step the TIPSO optimized mass-model data is classified using the SVM classification. Results showed remarkable improvement in configuration and mass-model along with sizing parameters.

Keywords: TIPSO-SVM expert system, TIPSO algorithm, two-step SVM method, aerothermodynamics, mass-modeling, TSTO vehicle.

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