Статьи журнала - International Journal of Modern Education and Computer Science
Все статьи: 1064
A Criterion for Hurwitz Polynomials and its Applications
Статья научная
We present a new criterion to determine the stability of polynomial with real coefficients. Combing with the existing results of the real and negative roots discrimination, we deduced the explicit conditions of stability for any real polynomial with a degree no more than four. Meanwhile, we discussed the problem of controls system stability and inertia of Bezout matrix as the applications of the criterion. A necessary and sufficient condition to determine the stability of the characteristic polynomial of the continuous time control systems was proposed. And also, we discussed a pathological case of the bilinear transformation, which can convert the stability analysis of a given discrete time system to the corresponding continuous time system, and brought forward an alternative one.
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A Design of Learning Management System for Electronic Secretary Based on Ubiquitous Learning
Статья научная
The proposed ubiquitous computing creates a new calculation model. Ubiquitous learning based on ubiquitous computing is the trend of current study theory. This paper introduces the concept of ubiquitous learning, analyzes and summarizes its thoughts and main characteristics, constructs the model of a ubiquitous learning environment. With the guidance of constructed ubiquitous learning environment model, we design the management system for electronic course-lattice, and use the metaphor of secretary naming the management system as electronic secretary.
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A Driving Behavior Retrieval Application for Vehicle Surveillance System
Статья научная
Vehicle surveillance system provides a large range of informational services for the driver and administrator such as multiview road and driver surveillance videos from multiple cameras mounted on the vehicle, video shots monitoring driving behavior and highlighting the traffic conditions on the roads. How to retrieval driver’s specific behavior, such as ignoring pedestrian, operating infotainment, near collision or running the red light, is difficult in large scale driving data. Annotation and retrieving of these video streams has an important role on visual aids for safety and driving behavior assessment. In a vehicle surveillance system, video as a primary data source requires effective ways of retrieving the desired clip data from a database. And data from naturalistic studies allow for an unparalleled breadth and depth of driver behavior analysis that goes beyond the quantification and description of driver distraction into a deeper understanding of how drivers interact with their vehicles. To do so, a model that classifies vehicle video data on the basis of traffic information and its semantic properties which were described by driver’s eye gaze orientation was developed in this paper. The vehicle data from OBD and sensors is also used to annotate the video. Then the annotated video data based on the model is organized and streamed by retrieval platform and adaptive streaming method. The experimental results show that this model is a good example for evidence-based traffic instruction programs and driving behavior assessment.
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A Dynamic Probabilistic Broadcasting Scheme based on Cross-Layer design for MANETs
Статья научная
Broadcasting plays a fundamental role in transmitting a message from the sender to the rest of the network nodes in Mobile Ad hoc Networks (MANETs). The blind flooding scheme causes a broadcast storm problem, which leads to significant network performance degradation. In order to solve the problem, a dynamic probabilistic broadcasting scheme cross-layer design for MANETs (DPBSC) is proposed. DPBSC adopts the cross-layer design, which lets routing layer share the received signal power information at MAC layer while still maintaining separation between the two layers. The additional transmission range that can benefit from rebroadcast is calculated according to the received signal power, which is applied to dynamically adjust the rebroadcast probability. DPBSC reduces the redundant retransmission and the chance of the contention and collision in the networks. Simulation results reveal that the DPBSC achieves better performance in terms of the saved-rebroadcast, the average packet drop fraction, the average number of collisions and average end-to-end delay at expense of the throughput, which is respectively compared with the blind flooding and fixed probabilistic flooding applied at the routing layer while IEEE 802.11 at the MAC layer.
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A Face Recognition System by Embedded Hidden Markov Model and Discriminating Set Approach
Статья научная
Different approaches have been proposed over the last few years for improving holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. There has been also some research about the combination of different recognition methods, both at the feature and score levels. Embedded hidden Markov model (E-HHM) has been widely used in pattern recognition. The performance of Face recognition by E-HMM heavily depends on the choice of model parameters. In this paper, we propose a discriminating set of multi E-HMMs based face recognition algorithm. Experimental results illustrate that compared with the conventional HMM based face recognition algorithm the proposed method obtain better recognition accuracies and higher generalization ability.
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Статья научная
State-of-the-art architectures of convolutional neural networks (CNN) are widely used by authors for facial expression recognition (FER). There are many variants of these models with positive results in studies for FER and successful applications, some well-known models are VGG, ResNet, Xception, EfficientNet, DenseNet. However, these models have considerable complexity for some real-world applications with limitations of computational resources. This paper proposes a lightweight CNN model based on a modern architecture of dense-connectivity with moderate complexity but still ensures quality and efficiency for facial expression recognition. Then, it is designed to be integrated into learning management systems (LMS) for recording and evaluation of online learning activities. The proposed model is to run experiments on some popular datasets for testing and evaluation, the results show that the model is effective and can be used in practice.
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A Frame of Intrusion Detection Learning System Utilizing Radial Basis Function
Статья научная
The process of monitoring the events that occur in a computer system or network and analyzing them for signs of intrusion is known as Intrusion Detection System (IDS). Detection ability of most of the IDS are limited to known attack patterns; hence new signatures for novel attacks can be troublesome, time consuming and has high false alarm rate. To achieve this, system was trained and tested with known and unknown patterns with the help of Radial Basis Functions (RBF). KDD 99 IDE (Knowledge Discovery in Databases Intrusion Detection Evaluation) data set was used for training and testing. The IDS is supposed to distinguish normal traffic from intrusions and to classify them into four classes: DoS, probe, R2L and U2R. The dataset is quite unbalanced, with 79% of the traffic belonging to the DoS category, 19% is normal traffic and less than 2% constitute the other three categories. The usefulness of the data set used for experimental evaluation has been demonstrated. The different metrics available for the evaluation of IDS were also introduced. Experimental evaluations were shown that the proposed methods were having the capacity of detecting a significant percentage of rate and new attacks.
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A Framework for Adaptation of Virtual Data Enumeration for Enhancing Census – Tanzania Case Study
Статья научная
Population census is an enormous and challenging national exercise with many stakeholders whose participation is required at all levels of government or public administration. The problem of high cost in conducting the traditional census process imposes extra and unaffordable cost in most of the developing countries which resulted into ten-year defaulting for census enumerations. This challenge compels nations to seek for assistance mostly from various donors nations in every census enumerations exercise. Virtual Census enumerations play a vital role in demographic data enumerations since it does not require physical involvement in Enumeration Area as in traditional enumerations approach. In this paper the main focus is on data integration from different heterogeneous sources, addressing cleansing challenge for data integrated from data sources with no common key for integrations, building virtual data integration framework for enhancing virtual censuses enumeration process. The developed framework and algorithms can be used to guide design of any other data integration system that need to address similar challenges in related aspects. The outcome of this work is suitable and cheaper technique of demographic data enumeration as compared to traditional technique which involves a lot of manual works and processes.
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A Framework for Homomorphic, Private Information Retrieval Protocols in the Cloud
Статья научная
Professional use of cloud health storage around the world implies Information-Retrieval extensions. These developments should help users find what they need among thousands or billions of enterprise documents and reports. However, extensions must offer protection against existing threats, for instance, hackers, server administrators and service providers who use people's personal data for their own purposes. Indeed, cloud servers maintain traces of user activities and queries, which compromise user security against network hackers. Even cloud servers can use those traces to adapt or personalize their platforms without users' agreements. For this purpose, we suggest implementing Private Information Retrieval (PIR) protocols to ease the retrieval task and secure it from both servers and hackers. We study the effectiveness of this solution through an evaluation of information retrieval time, recall and precision. The experimental results show that our framework ensures a reasonable and acceptable level of confidentiality for retrieval of data through cloud services.
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A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques
Статья научная
Testing is one of the crucial activities of software development life cycle which ensures the delivery of high quality product. As software testing consumes significant amount of resources so, if, instead of all software modules, only those are thoroughly tested which are likely to be defective then a high quality software can be delivered at lower cost. Software defect prediction, which has now become an essential part of software testing, can achieve this goal. This research presents a framework for software defect prediction by using feature selection and ensemble learning techniques. The framework consists of four stages: 1) Dataset Selection, 2) Pre Processing, 3) Classification, and 4) Reflection of Results. The framework is implemented on six publically available Cleaned NASA MDP datasets and performance is reflected by using various measures including: F-measure, Accuracy, MCC and ROC. First the performance of all search methods within the framework on each dataset is compared with each other and the method with highest score in each performance measure is identified. Secondly, the results of proposed framework with all search methods are compared with the results of 10 well-known supervised classification techniques. The results reflect that the proposed framework outperformed all of other classification techniques.
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A Framework to Formulate Adaptivity for Adaptive e-Learning System Using User Response Theory
Статья научная
These days different e-learning architecture provide different kinds of e-learning experiences due to “one size fits for all” concept. This is no way better than the traditional learning and does not exploit the technological advances. Thus the e-learning system began to evolve to adaptable e-learning systems which adapts or personalizes the learning experience of the learners. Systems infer the characteristics of the learners and identify the preferences of the learners and automatically generate personalized learning path and customize learning contents to the individuals needs. This process is known as adaptation and systems which adapt are known are adaptive systems. So the main objective of this research was to provide an adaptive e-learning system framework which personalizes the learning experience in an efficient way. In this paper a framework for adaptive e-learning system using user response theory is proposed to meet the research objectives identified in section 1.D.
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Статья научная
The main objective of this paper is to suggest a model for improving and retaining the highly qualified teachers in higher technical education. There are numerous researches going on all over the world regarding the key quality factors which are directly linked with teacher's performance and the methods to improve them. Whatever the methods and measures, the teacher's active participation and dedication is very important to achieve these objectives. A detailed questionnaire was distributed to highly qualified and experienced teachers who are working in engineering colleges for more than five years. Since the variables in this study are quality factors, the collected data is analyzed using the fuzzy logic and inference is drawn for getting more accurate results compared to probability study of the same case. Based on the results obtained from fuzzy inference system, a new model called Adaptive Performance-Incentive-Development (PID) control system for improving the quality as well as retaining the highly qualified teachers in the teaching profession is created.
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A Fuzzy Logic Approach to Assess Web Learner's Joint Skills
Статья научная
Skill assessment is an important but complicated task in the entire web based teaching and learning process. The learner's performance assessment has a strong influence on learners' approaches to learn and their learning outcomes like professional acceptability on desired skills. Most educators focus either on assessing a learner's technical skill set or non-technical skill set, individually, rather than focusing on both the aspects. This paper bridges the gap by applying fuzzy logic approach to analyze a learner's joint skills incorporating both skills-set. An already proven e-commerce website's evaluation technique has been chosen and applied in two situations of learner's skill assessment through case studies namely: technical skills evaluation, and non-technical skills evaluation. Experiments show that the learner's success depends on both sets of skill attributes. This work then proposed a novel method for skill assessment considering two (instead of one) sets of skill attributes invoking parallel or joint application of the technique. This new technique has also been analysed through a case study.
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A GA-Tabu Based User Centric Approach for Discovering Optimal Qos Composition
Статья научная
Cloud computing is an emerging internet-based paradigm of rendering services on pay- as -per -use basis. Increasing growth of cloud service providers and services creates the need to provide a tool for retrieval of the high-quality optimal cloud services composition with relevance to the user priorities. Quality of Service rank-ings provides valuable information for making optimal cloud service selection from a set of functionally equiva-lent service candidates. To obtain weighted user-centric Quality of Service Composition, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes framework for predic-tion of optimal composition of services requested by the user. Taking advantage of the past service usage experi-ences of the consumers more cost effective results are achieved. Our proposed framework enables the end user to determine the optimal service composition based on the input weight for individual service Quality of Service. The Genetic algorithm and basic Tabu search is applied for the user-centric Quality of Service ranking prediction and the optimal service composition. The experimental results proves that our approaches outperform other competing approaches.
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A Gaussian Filter based SVM Approach for Vehicle Class Identification
Статья научная
Vehicle identification or classification is one of the application areas that come under real time image processing. Vehicle recognition is having the significance in various applications including the traffic monitoring, load monitoring, number plate recognition, vehicle theft prevention, traffic violation detection, management of traffic etc. As the images are captured as primary data source, it can have number of associated impurities which include the background inclusion, object overlapping etc. Because of this, object detection and recognition is always a challenge in real time scenario. In present work, a robust feature based model is presented for feature extraction and classification of vehicle images. The presented model is applied on real time captured image to categorize the vehicle in light, medium and heavy vehicle. Firstly, the vehicle area segmentation is performed and later on the Gaussian filter is applied to extract the image features. This featured dataset is processed under Support Vector Machine (SVM) based distance analysis model for vehicle recognition and vehicle class identification. The experimentation results of present investigation shows the recognition rate of devised system over 90%.
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Статья научная
The representation task in time series data mining has been a critical issue because the direct manipulation of continuous, high-dimensional data is extremely difficult to complete efficiently. One time series representation approach is a symbolic representation called the Symbolic Aggregate Approximation (SAX). The main function of SAX is to find the appropriate numbers of alphabet symbols and word size that represent the time series. The aim is to achieve the largest alphabet size and maximum word length with the minimum error rate. The purpose of this study is to propose an integrated approach for a symbolic time series data representation that attempts to improve SAX by improving alphabet and word size. The Relative Frequency (RF) binning method is employed to obtain alphabet size and is integrated with the proposed Multi-pitch Harmony Search (HSMPAR) algorithm to calculate the optimum alphabet and word size. RF is used because of its ability to obtain a sufficient number of intervals with a low error rate compared to other related techniques. HSMPAR algorithm is an optimization algorithm that randomly generates solutions for alphabet and word sizes and selects the best solutions. HS algorithms are compatible with multi-pitch adjustment. The integration of the RF and HSMPAR algorithms is developed to maximize information rather than to improve the error rate. The algorithms are tested on 20 standard time series datasets and are compared with the meta-heuristic algorithms GENEBLA and the original SAX algorithm. The experimental results show that the proposed method generates larger alphabet and word sizes and achieves a lower error rate than the compared methods. With larger alphabet and word sizes, the proposed method is capable of preserving important information.
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A High-Performance Communication Service for Parallel Servo Computing
Статья научная
Complexity of algorithms for the servo control in the multi-dimensional, ultra-precise stage application has made multi-processor parallel computing technology needed. Considering the specific communication requirements in the parallel servo computing, we propose a communication service scheme based on VME bus, which provides high-performance data transmission and precise synchronization trigger support for the processors involved. Communications service is implemented on both standard VME bus and user-defined Internal Bus (IB), and can be redefined online. This paper introduces parallel servo computing architecture and communication service, describes structure and implementation details of each module in the service, and finally provides data transmission model and analysis. Experimental results show that communication services can provide high-speed data transmission with sub-nanosecond-level error of transmission latency, and synchronous trigger with nanosecond-level synchronization error. Moreover, the performance of communication service is not affected by the increasing number of processors.
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Статья научная
In recent years, Aspect Based Sentiment Analysis (ABSA) has gained significant importance, particularly for enterprises operating in the commercial domain. These enterprises tend to analyze the customers’ opinions concerning the different aspects of their products. The primary objective of ABSA is to first identify the aspects (such as battery) associated with a given product (such as a smartphone) and then assign a sentiment polarity to each aspect. In this paper, we focus on the Aspects Extraction (AE) task, specifically for the French language. Previous research studies have mainly focused on the extraction of single-word aspects without giving significant attention to the multi-word aspects. To address this issue, we propose a hybrid method that combines linguistic knowledge-based methods with deep learning-based methods to identify both single-word aspects and multi-word aspects. Firstly, we combined a set of rules with a deep learning-based model to extract the candidate aspects. Subsequently, we introduced a new filtering algorithm to detect the single-word aspect terms. Finally, we created a set of 52 patterns to extract the multi-word aspect terms. To evaluate the performance of the proposed hybrid method, we collected a dataset of 2400 French mobile phone comments from the Amazon website. The final outcome proves the encouraging results of the proposed hybrid method for both mobile phones (F-measure value: 87.27% for single-word aspects and 82.38% for multi-word aspects) and restaurants (F-measure value: 78.79% for single-word aspects and 76.04% for multi-word aspects) domains. By highlighting the practical implications of these results, our hybrid method offers a promising outlook for Aspect Based Sentiment Analysis task, opening new avenues for businesses and future research.
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Статья научная
Using convolution neural network (CNN) for face recognition is being widely research with a promising significant in applications and it is interested by many authors. Moreover, the CNN model has brought successful applications in practice such as detection and identification face of people on Facebook users' photos application, they use DeepFace model. There are many articles which proposed CNN models for face recognition with using some modifications of popular models of large architectures such as VGG, ResNet, OpenFace or FaceNet. However, these models are large complexity for some applications in reality with limitations of computing resources. This paper proposes a design of CNN model with moderate complexity but still ensures the quality and efficiency of face recognition. We run experiments for evaluating the model on some popular datasets, the experiment shows effective results and indicates that the proposed model can be practically used.
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A Low Cost High Speed FPGA-Based Image Processing Framework
Статья научная
In this paper, a high-speed and low-cost image processing framework based on MATLAB-FPGA interface is proposed that can be used in researches aiming at developing wide variety of not only image processing tasks but also many signal processing applications. In addition, this new framework could be exploited for several other tasks such as on-chip verification, using PC as an enormous external RAM for FPGA while preserving high speed data access, developing hardware-software co-designs, etc. The communication between FPGA and MATLAB is via 1Gbs Ethernet based on UDP/IP protocol which is very promising for high speed data transmission in point-to-point communications. UDP stack is efficiently designed in FPGA based on a fully pipelined architecture with minimum level of logic in order to reach high performance.. Dynamic data transmission between the UDP stack, memory and an arbitrary image processing module makes it possible to practically simulate, debug and implement most relevant applications. The hardware system is relatively low-cost and it consumes a negligible area of a Spartan-6 LXT45 Xilinx FPGA. Operating at 1 Gb/s, theoretically, the system is capable of processing 132 frames of 640*480 color images in a second. The effectiveness of the system is evaluated by means of both place and route simulation and practical implementation of a skin detection algorithm and a motion detector.
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