Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
The skyline operator for selection of virtual machines in mobile computing
Статья научная
The article provides a solution to the problem of placing mobile users’ queries (tasks or software applications) on a balanced virtual machine (VMs) developed on cloudlets placed near base stations of the Wireless Metropolitan Area Networks (WMAN) taking into account their technical capabilities. For this purpose, hierarchically structured architecture and algorithm based on cloudlets are proposed for the selection of virtual machines that provide the requirements (solution time and cost) to the solution of the user’s task. An approach to the optimal VM selection is proposed for the solution of Bi-Criteria selection out of set of VMs based on Skyline operator.
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Throughput and Delay Analysis of Database Replication Algorithm
Статья научная
Recently, (PDDRA) a Pre-fetching based dynamic data replication algorithm has been published. In our previous work, modifications to the algorithm have been suggested to minimize the delay in data replication. In this paper a mathematical framework is presented to evaluate mean waiting time before data can be replicated on the requested site. The idea is further investigated and simulation results are presented to estimate the throughput and average delay.
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Статья научная
An early prediction of students' academic performance helps to identify at-risk students and enables management to take corrective actions to prevent them from going astray. Most of the research works in this field have used supervised machine learning approaches to their crafted datasets having numerous attributes or features. Since these datasets are not publicly available, it is hard to understand and compare the significance of the chosen features and the efficacy of the different machine learning models employed in the classification task. In this work, we analyzed 27 research papers published in the last ten tears (2011- 2021) that used machine learning models for predicting students' performance. We identify the most frequently used features in the private datasets, their interrelationships, and abstraction levels. We also explored three popular public datasets and performed statistical analysis like the Chi-square test and Person's correlation on its features. A minimal set of essential features is prepared by fusing the frequent features and the statistically significant features. We propose an algorithm for selecting a minimal set of features from any dataset with a given set of features. We compared the performance of different machine learning models on the three public datasets in two experimental setups- one with the complete feature set and the other with a minimal set of features. Compared to using the complete feature set, it is observed that most supervised models perform nearly identically and, in some cases, even better with the reduced feature set. The proposed method is capable of identifying the most essential feature set from any new dataset for predicting students' performance.
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Towards Improving Recommender System: A Social Trust-Aware Approach
Статья научная
Recommender systems have shown great potential to help users find interesting and relevant Web service (WS) from within large registers. However, with the proliferation of WSs, recommendation becomes a very difficult task. Social computing seems offering innovative solutions to overcome those shortcomings. Social computing is at the crossroad of computer sciences and social sciences disciplines by looking into ways of improving application design and development using elements that people encounter daily such as social networks, trust, reputation, and recommendation. In this paper, we propose a social trust-aware system for recommending Web services (WSs) based on social qualities of WSs that they exhibit towards peers at run-time, and trustworthiness of the users who provide feedback on their overall experience using WSs. A set of experiments to assess the fairness and accuracy of the proposed system are reported in the paper, showing promising results and demonstrating that our service recommendation method significantly outperforms conventional similarity-based and trust-based service recommendation methods.
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Towards MORK: Model for Representing Knowledge
Статья научная
Smart world needs intelligent system for effective and timely decision making. This is achieved only through a knowledge based system with functional knowledge representation units. In this paper, two models are proposed for representing knowledge. This process involves in getting the data and placing the information in the correct location. Logical notations are used for taking the clauses and graph is used for putting the entities. In Model one, the data is translated into logical statements using predicate logics, later the knowledge is stored in conceptual graph and retrieved. Whereas in Model two, the given information is translated using First Order Logic (FOL), by applying description logic concept rules are defined and as a result reasoning is done. Storage is done by using concept-relation graph. The main aims of our models are to have easy and simple access over the information. These models return the required exact answer, for the higher order query posted by the end user to the intelligent system.
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Towards Qualitative Computer Science Education: Engendering Effective Teaching Methods
Статья научная
An investigation into the teaching method(s) that can effectively yield qualitative computer science education in Basic Schools becomes necessary due to the Nigerian government policy on education. The government’s policy stipulates that every graduate of Basic Schools or UBE (Universal Basic education) should be computer literate. This policy intends to ensure her citizens are ICT (Information and Communication Technology) compliant. The foregoing thus necessitatesthe production of highly qualified manpower―grounded in computer knowledge―to implement the computer science education strand of the UBE curriculum. Accordingly, this research investigates the opinion of computer teacher-trainees on the teaching methods used while on training. Some of the teacher-trainees―that taught computer study while on teaching practice―were systematically sampled using “Purposive” sampling technique. The results show consensus in male and female teacher-trainees’ views; both gender agreed that all the teaching methods used, while on training, will engender effective teaching of computer study. On the whole, the mean performance ratings of male teacher-trainees were found to be higher than that of females. However, this is not in accord with the target set by Universal Basic Education Commission which intends to eliminate gender disparity in the UBE programme. The results thussuggestthe need for further investigation using larger sample.
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Towards Semantics-Aware Recommender System: A LOD-Based Approach
Статья научная
Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.
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Towards to an Bio-inspired Orchestration of Mobile Learning Activities
Статья научная
This paper presents a new approach to a recommendation of learning activities adapted to the spatial and temporal context of each mobile learner. Indeed, the path traveled by the user during a field trip can be guided using the technique of passive collaborative filtering. This recommendation is based on the ACO (Ant Colony Optimization) algorithm, which represents a good model for swarm intelligence. For this reason, the structure of our mobile scenario is described as a graph where POIs (Point Of Interest) are represented by nodes and the arcs indicate the probability of moving between them. This recommendation system allows the orchestration of mobile learning according to the geographical location of learners and the historical of their activities. Our contribution is devised in three parts: (1) the creation of a mobile learning scenario based on POIs, (2) the adaptation of the ACO algorithm for the orchestration of paths taken by learners, and (3) the development of a recommender system that helps learners to better choose their paths during the field trip.
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Статья научная
Automated Vehicular System has become a necessity in the current technological revolution. Real Traffic sign detection and recognition is a vital part of that system that will find roadside traffic signs to warn the automated system or driver beforehand of the physical conditions of roads. Mostly, researchers based on Traffic sign detection face problems such as locating the sign, classifying it and distinguishing one sign from another. The most common approach for locating and detecting traffic signs is the color information extraction method. The accuracy of color information extraction is dependent upon the selection of a proper color space and its capability to be robust enough to provide color analysis data. Techniques ranging from template matching to critical Machine Learning algorithms are used in the recognition process. The main purpose of this research is to give a review based on methods and framework of Traffic Sign Detection and Recognition solution and discuss also the current challenges of the whole solution.
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Training Program Supporting Language Acquisition
Статья научная
This study was conducted to evaluate the effects of a parenting training program that supports language acquisition in early childhood. To reveal such an effect, an experimental research design including pre-test-post-test and retention test was applied respectively. Experimental and control groups were formed with the parents of 4-6 years old children attending pre-school education institutions. To assess the language development levels of the children, Peabody Picture-Vocabulary Test (PPVT) was applied during the pre-test phase; after the parental training, a post-test was applied; and a year later, a retention test was implemented alternately. Parents in the experimental group evaluated the program after the Parental Support Program (PSP). The personal characteristics of the study group and the opinions of the parents evaluating the training have been shown by using the frequency and percentages. Whether PPVT and PSP scores differ according to socio-demographic variables was analyzed by t-tests. In the end, there was a significant increase in the results of the post-test and retention test performed after parent training that supports language acquisition. This increase has been found to be significantly higher than the PPVT scores of children in the control group. Thus, we have determined that the parents have a positive attitude towards the training program. The results of the study also reveal that parenting training that supports children's language acquisition has a positive effect on children's language development.
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Traveling Transportation Problem Optimization by Adaptive Current Search Method
Статья научная
The adaptive current search (ACS) is one of the novel metaheuristic optimization search techniques proposed for solving the combinatorial optimization problems. This paper aimed to present the application of the ACS to optimize the real-world traveling transportation problems (TTP) of a specific car factory. The total distance of the selected TTP is performed as the objective function to be minimized in order to decrease the vehicle’s energy. To perform its effectiveness, four real-world TTP problems are conducted. Results obtained by the ACS are compared with those obtained by genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS can provide very satisfactory solutions superior to other algorithms. The minimum total distance and the minimum vehicle’s energy of all TTP problems can be achieved by the ACS with the distant error of no longer than 3.05%.
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Tuning Schema Matching Systems using Parallel Genetic Algorithms on GPU
Статья научная
Most recent schema matching systems combine multiple components, each of which employs a particular matching technique with several knobs. The multi-component nature has brought a tuning problem, that is to determine which components to execute and how to adjust the knobs (e.g., thresholds, weights, etc.) of these components for domain users. In this paper, we present an approach to automatically tune schema matching systems using genetic algorithms. We match a given schema S against generated matching scenarios, for which the ground truth matches are known, and find a configuration that effectively improves the performance of matching S against real schemas. To search the huge space of configuration candidates efficiently, we adopt genetic algorithms (GAs) during the tuning process. To promote the performance of our approach, we implement parallel genetic algorithms on graphic processing units (GPUs) based on NVIDIA’s Compute Unified Device Architecture (CUDA). Experiments over four real-world domains with two main matching systems demonstrate that our approach provides more qualified matches over different domains.
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Twitter benchmark dataset for arabic sentiment analysis
Статья научная
Sentiment classification is the most rising research areas of sentiment analysis and text mining, especially with the massive amount of opinions available on social media. Recent results and efforts have demonstrated that there is no single strategy can mutually accomplish the best prediction performance on various datasets. There is a lack of existing researches to Arabic sentiment analysis compared to English sentiment analysis, because of the unique nature and difficulty of the Arabic language which leads to shortage in Arabic dataset used in sentiment analysis. An Arabic benchmark dataset is proposed in this paper for sentiment analysis showing the gathering methodology of the most recent tweets in different Arabic dialects. This dataset includes more than 151,000 different opinions in variant Arabic dialects which labeled into two balanced classes, namely, positive and negative. Different machine learning algorithms are applied on this dataset including the ridge regression which gives the highest accuracy of 99.90%.
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Two Way Question Classification in Higher Education Domain
Статья научная
Question classification plays vital role in Question Answering (QA) systems. The task of classifying a question to appropriate class is performed to predict the question type of the natural language question. In this paper, initially we have presented a brief overview of classification approaches adapted by different question answering systems so far and then propose a two-way question classification approach for higher education domain which not only identifies focus word and question class but also reduces answer search space within corpus comprise of question-answer pair, adding to the classification accuracy. For precise semantic interpretation of domain keywords, a domain specific dictionary is constructed which primarily have four domain word type. Classified features are built upon domain attributes in the form of constraints. The experiment proved the efficiency for restricted domain, even though we used quite simplistic approach.
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Two-Level Alloyed Branch Predictor based on Genetic Algorithm for Deep Pipelining Processors
Статья научная
To gain improved performance in multiple issue superscalar processors, the increment in instruction fetch and issue rate is pretty necessary. Evasion of control hazard is a primary source to get peak instruction level parallelism in superscalar processors. Conditional branch prediction can help in improving the performance of processors only when these predictors are equipped with algorithms to give higher accuracy. The Increment in single miss-prediction rate can cause wastage of more than 20% of the instructions cycles, which leads us to an exploration of new techniques and algorithms that increase the accuracy of branch prediction. Alloying is a way to exploit the local and global history of different predictors in the same structure and sometimes also called hybrid branch prediction. In this paper, we aim to design a more accurate and robust two-level alloyed predictor, whose behavior is more dynamic on changing branch direction.
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Статья научная
In this paper, using fractional differential and integral operators, constructed are two-dimensional mathematical models of viscoelastic deformation, which are characterized by memory effects, spatial non-locality, and self-organization. The fractal rheological models by Maxwell, Kelvin and Voigt, their structural properties and the influence of the fractional integro-differential operator on the process of viscoelasticity are investigated. Using the Laplace transform method and taking into account the properties of the fractional differential apparatus, analytical relations are obtained in the integral form for describing the stresses of generalized two-dimensional fractional-differential rheological models by Maxwell, Kelvin, and Voigt. Since the fractional-differential parameters of fractal models allow describing deformation-relaxation processes more perfectly than traditional methods, algorithmic aspects of identification of structural and fractal parameters of models are presented in the work. Explicit expressions have been obtained to describe the deformation process for one-dimensional fractional-differential models by Voigt, Kelvin, and Maxwell. The results of identification of structural and fractal parameters of the Maxwell and Voigt models are presented. The estimates of the accuracy of the obtained identification results were found using the statistical criterion based on the correlation coefficient. The influence of fractional-differential parameters on deformation-relaxation processes is investigated.
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Статья научная
In the present paper a new cryptographic method called UES Version-III has been introduced. Nath et al recently developed few efficient encryption methods such as UES version-I, Modified UES-I, UES version-II, TTJSA, DJMNA Nath et. al showed that TTJSA and DJMNA is most suitable methods to encrypt password or any small message. The name of the present method is Ultra Encryption Standard Version-III. It is a Symmetric key Cryptosystem which includes multiple encryption, bit-wise randomization, new advanced bit-wise encryption technique with feedback. In this paper, the authors have performed encryption entirely at the bit-level to achieve greater strength of encryption. In the result section the authors have shown the spectral analysis of encrypted text as well as plain text. The spectral analysis shows that UES-III is free from standard cryptography attack such as brute force attack, known plain text attack and differential attack.
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Статья научная
In the present paper a new combined cryptographic method called Modified UES Version-I has been introduced. Nath et al. have already developed several symmetric key methods. It combines three different methods namely, Generalized Modified Vernam Cipher method, Permutation method and Columnar Transposition method. Nath et al recently developed few efficient combined encryption methods such as TTJSA, DJMNA where they have used generalized MSA method, NJJSAA method and DJSA methods. Each of the methods can be applied independently to encrypt any message. Nath et. al showed that TTJSA and DJMNA is most suitable methods to encrypt password or any small message. The name of this method is Ultra Encryption Standard modified (UES) version-I since it is based on UES version-I developed by Roy et. al. In this method an encryption key pad in Vernam Cipher Method also the feedback has been used which is considered to make the encryption process stronger. Modified UES Version-I may be applied to encrypt data in any office, corporate sectors etc. The method is most suitable to encrypt any type of file such as text, audio, video, image and databases etc.
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Undergraduate Program in Network Engineering and Security – A Feasibility Study
Статья научная
In this article, a feasibility study for initiating a new undergraduate program in network engineering and security is presented. The study was based on surveying and analyzing the current and projected future market demand for specialized network engineering graduates. The results of the study concluded that the demand for such a specialty in the work place is rapidly growing as the networking and telecommunication technologies are becoming essential and integral parts of about any organization around the world. As a result of the study, a pioneering program of network engineering and security was established at the Jordan University of Science and Technology.
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Understandings of Graduate Students on Nature of Science
Статья научная
Knowing about nature of science (NOS) and its products is a basic requirement of all graduate students and researchers due to being both members of society and experts on different scientific disciplines. As the first step, determining NOS understandings of graduate students has importance to go further in developing current situation. Therefore, this study aimed to determine NOS understandings of graduate students from different disciplines. The study included seven graduate students who were enrolled in universities as researchers. As the data collection way, face-to-face interview was utilized. The data of the study was analyzed by assigning the participants to four categories; expert, naive, mixed and not applicable. The results showed that majority of the participants were expert on social and cultural embeddedness of science and role of creativity and imagination in science while majority of the participants were naive on the aspects of “hierarchy between theories and laws”. Majority of them had mixed understandings on the aspects of existence of only one method in science, subjectivity, tentativeness. Interestingly, all of the participants were naive in terms of definition of science. The results and implications of the study will be discussed..
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