rss_2.0Computer Sciences FeedSciendo RSS Feed for Computer Sciences Sciences Feed three-phase switched-capacitor-based MLI<abstract> <title style='display:none'>Abstract</title> <p>This article proposes a novel three-phase inverter based on the concept of switched capacitors (SCs), which uses a single DC source. A three-phase, seven-level line-to-line output voltage waveform is synthesised by the proposed topology, which includes eight switches, two capacitors, and one diode per phase leg. The proposed topology offers advantages in terms of inherent voltage gain, lower voltage stresses on power switches, and a reduced number of switching components. Additionally, the switched capacitors are self-balanced, thereby eliminating the need for a separate balancing circuit. The proposed structure and its operating principle, the self-balancing mechanism of the capacitors, and the control strategy are all thoroughly explained in the article. The proposed topology has also been compared with some recent SC topologies. Lastly, the proposed topology has been shown to be feasible through simulation and experimentation.</p> </abstract>ARTICLE2022-09-25T00:00:00.000+00:00One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition<abstract> <title style='display:none'>Abstract</title> <p>Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large number of parameters and a huge training data requirements limit CNN’s use in SAR ATR. While previous works have primarily focused on model compression and structural modification of CNN, this paper employs the One-Vs-All (OVA) technique on CNN to address these issues. OVA-CNN comprises several Binary classifying CNNs (BCNNs) that act as an expert in correctly recognizing a single target. The BCNN that predicts the highest probability for a given target determines the class to which the target belongs. The evaluation of the model using various metrics on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset illustrates that the OVA-CNN has fewer weight parameters and training sample requirements while exhibiting a high recognition rate.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Toward Programmability of Radio Resource Control Based on O-RAN<abstract> <title style='display:none'>Abstract</title> <p>Open Radio Access Network (O-RAN) is a concept that aims at embedding intelligence at the network edge and at disaggregating of network functionality from the hardware. The paper studies how the O-RAN concept can be used for optimization of radio resource management. The research focuses on adaptive radio resource allocation based on predictions of device activity. For narrowband devices which send sporadically small volumes of data, a feature is defined which enables a device with no activity for a short time to suspend its session and to resume it moving in active state. Dynamic configuration of the inactivity timer based on prediction of device activity may further optimize radio resource allocation. The paper studies an O-RAN use case for dynamic radio resource control and presents the results of emulation of the RESTful interface defined between the O-RAN non-real-time and near real-time functions.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Investigation of Dense Family of Closure Operations<abstract> <title style='display:none'>Abstract</title> <p>As a basic notion in algebra, closure operations have been successfully applied to many fields of computer science. In this paper we study dense family in the closure operations. In particular, we prove some families to be dense in any closure operation, in which the greatest and smallest dense families, including the collection of the whole closed sets and the minimal generator of the closed sets, are also pointed out. More important, a necessary and sufficient condition for an arbitrary family to be dense is provided in our paper. Then we use these dense families to characterize minimal keys of the closure operation under the viewpoint of transversal hypergraphs and construct an algorithm for determining the minimal keys of a closure operation.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00A New Network Digital Forensics Approach for Internet of Things Environment Based on Binary Owl Optimizer<abstract> <title style='display:none'>Abstract</title> <p>The Internet of Things (IoT) is widespread in our lives these days (e.g., Smart homes, smart cities, etc.). Despite its significant role in providing automatic real-time services to users, these devices are highly vulnerable due to their design simplicity and limitations regarding power, CPU, and memory. Tracing network traffic and investigating its behavior helps in building a digital forensics framework to secure IoT networks. This paper proposes a new Network Digital Forensics approach called (NDF IoT). The proposed approach uses the Owl optimizer for selecting the best subset of features that help in identifying suspicious behavior in such environments. The NDF IoT approach is evaluated using the Bot IoT UNSW dataset in terms of detection rate, false alarms, accuracy, and f-score. The approach being proposed has achieved 100% detection rate and 99.3% f-score and outperforms related works that used the same dataset while reducing the number of features to three features only.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Information Systems Reliability in Traditional Entropy and Novel Hierarchy<abstract> <title style='display:none'>Abstract</title> <p>The continuous progress of computing technologies increases the need for improved methods and tools for assessing the performance of information systems in terms of reliability, conformance, and quality of service. This paper presents an extension of Information Theory by introducing a novel hierarchy concept as a complement to the traditional entropy approach. The methodology adjustments are applied to a simulative numerical example for assessing the reliability of systems with different complexity and performance behavior.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Mathematical Modelling of Malware Intrusion in Computer Networks<abstract> <title style='display:none'>Abstract</title> <p>Malware attacks cause great harms in the contemporary information systems and that requires analysis of computer networks reaction in case of malware impact. The focus of the present study is on the analysis of the computer network’s states and reactions in case of malware attacks defined by the susceptibility, exposition, infection and recoverability of computer nodes. Two scenarios are considered – equilibrium without secure software and not equilibrium with secure software in the computer network. The behavior of the computer network under a malware attack is described by a system of nonhomogeneous differential equations. The system of the nonhomogeneous differential equations is solved, and analytical expressions are derived to analyze network characteristics in case of susceptibility, exposition, infection and recoverability of computer nodes during malware attack. The analytical expressions derived are illustrated with results of numerical experiments. The conception developed in this work can be applied to control, prevent and protect computer networks from malware intrusions.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00A Color-Texture-Based Deep Neural Network Technique to Detect Face Spoofing Attacks<abstract> <title style='display:none'>Abstract</title> <p>Given the face spoofing attack, adequate protection of human identity through face has become a significant challenge globally. Face spoofing is an act of presenting a recaptured frame before the verification device to gain illegal access on behalf of a legitimate person with or without their concern. Several methods have been proposed to detect face spoofing attacks over the last decade. However, these methods only consider the luminance information, reflecting poor discrimination of spoofed face from the genuine face. This article proposes a practical approach combining Local Binary Patterns (LBP) and convolutional neural network-based transfer learning models to extract low-level and high-level features. This paper analyzes three color spaces (i.e., RGB, HSV, and YCrCb) to understand the impact of the color distribution on real and spoofed faces for the NUAA benchmark dataset. In-depth analysis of experimental results and comparison with other existing approaches show the superiority and effectiveness of our proposed models.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Serverless High-Performance Computing over Cloud<abstract> <title style='display:none'>Abstract</title> <p>HPC clouds may provide fast access to fully configurable and dynamically scalable virtualized HPC clusters to address the complex and challenging computation and storage-intensive requirements. The complex environmental, software, and hardware requirements and dependencies on such systems make it challenging to carry out our large-scale simulations, prediction systems, and other data and compute-intensive workloads over the cloud. The article aims to present an architecture that enables HPC workloads to be serverless over the cloud (Shoc), one of the most critical cloud capabilities for HPC workloads. On one hand, Shoc utilizes the abstraction power of container technologies like Singularity and Docker, combined with the scheduling and resource management capabilities of Kubernetes. On the other hand, Shoc allows running any CPU-intensive and data-intensive workloads in the cloud without needing to manage HPC infrastructure, complex software, and hardware environment deployments.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms<abstract> <title style='display:none'>Abstract</title> <p>With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more popularity in many applications of computer vision like object classification, object detection, Human detection, etc., ML and DL approaches are highly compute-intensive and require advanced computational resources for implementation. Multicore CPUs and GPUs with a large number of dedicated processor cores are typically the more prevailing and effective solutions for the high computational need. In this manuscript, we have come up with an analysis of how these multicore hardware technologies respond to DL algorithms. A Convolutional Neural Network (CNN) model have been trained for three different classification problems using three different datasets. All these experimentations have been performed on three different computational resources, i.e., Raspberry Pi, Nvidia Jetson Nano Board, &amp; desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Semantic-Based Dynamic Service Adaptation in Context-Aware Mobile Cloud Learning<abstract> <title style='display:none'>Abstract</title> <p>Self-adaptable system concerns on service adaptation whenever errors persist within the system. Changes in contextual information such as networks or sensors will affect the system’s effectiveness because the service adaptation process is not comprehensively handled in those contexts. Besides, the correctness to get the most equivalence services to be substituted is limitedly being addressed from previous works. A dynamic service adaptation framework is introduced to monitor and run a reasoning control to solve these issues. Hence, this paper presents a case study to proof the dynamic service adaptation framework that leverages on semantic-based approach in a context-aware environment. The evaluation of the case study resulted in a significant difference for the effectiveness at a 95% confidence level, which can be interpreted to confirm that the framework is promising to be used in operating dynamic adaptation process in a pervasive environment.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Noise Generation Methods Preserving Image Color Intensity Distributions<abstract> <title style='display:none'>Abstract</title> <p>In many visual perception studies, external visual noise is used as a methodology to broaden the understanding of information processing of visual stimuli. The underlying assumption is that two sources of noise limit sensory processing: the external noise inherent in the environmental signals and the internal noise or internal variability at different levels of the neural system. Usually, when external noise is added to an image, it is evenly distributed. However, the color intensity and image contrast are modified in this way, and it is unclear whether the visual system responds to their change or the noise presence. We aimed to develop several methods of noise generation with different distributions that keep the global image characteristics. These methods are appropriate in various applications for evaluating the internal noise in the visual system and its ability to filter the added noise. As these methods destroy the correlation in image intensity of neighboring pixels, they could be used to evaluate the role of local spatial structure in image processing.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing<abstract> <title style='display:none'>Abstract</title> <p>Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.</p> </abstract>ARTICLE2022-09-22T00:00:00.000+00:00Influence of the Expected Wind Speed Fluctuation on the Number of Batteries of the Balancing System<abstract> <title style='display:none'>Abstract</title> <p>The article discusses a method of assessing the of dependence of the number of batteries that would be needed to achieve energy balance in distributed generation systems with wind turbines on ambient temperature and on the error involved in predicting the parameters of wind flow (wind speed). To describe the relationship between current rate and capacity in a given current range, Peukert’s law is used. Dependence of the Peukert’s constant on ambient temperature for the lead-acid battery HZB12-180FA is calculated. Taking the lead-acid battery and wind turbine VE-2 as a reference, dependence of area of controlled operation of the battery on the wind speed forecasting error is calculated. The technique of considering ambient temperature, depth of discharge, and wind speed forecasting error when deciding the size of energy storage of the balancing system (the number of batteries and their capacity) is provided. A family of curves representing the dependence of the number of batteries constituting the balancing system on the ambient temperature and the wind speed forecasting error are presented. It is shown that as the wind speed forecasting error increases from 0% to 15% and the ambient temperature decreases from 20 °C to 20 °C, the number of batteries should be increased by approximately 2.81 times in order to maintain the same area of controlled operation of a battery.</p> </abstract>ARTICLE2022-09-15T00:00:00.000+00:00What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature<abstract> <title style='display:none'>Abstract</title> <p>This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.</p> </abstract>ARTICLE2022-09-08T00:00:00.000+00:00Developing a Model and Questionnaire for Predicting Intention to Use Job Boards: A Jobseeker-Oriented Research on the E-Recruitment Adoption in Iran<abstract> <title style='display:none'>Abstract</title> <p>By integrating several prominent theories and models in technology adoption, this study develops a research model to examine the factors affecting jobseekers’ intention to use job boards. The validity and reliability of the researcher-developed instrument, the SEDA-IUQ (SEDA-Intention to Use Questionnaire), were assessed using a combination of statistical methods. Utilizing the data collected from 447 Iranian respondents, findings from the exploratory factor analysis provided evidence for the five-factor solution, that is, technological innovation, functional adequacy, content accessibility, practical utility, and reputation-seeking intention, for the 36-item SEDA-IUQ. Various relationships in the research model are tested using confirmatory factor analysis and structural equation modeling. In addition to developing a practical questionnaire that the e-recruitment researchers and practitioners can deploy to study jobseekers’ behavioral intentions, this is the first study on job search websites’ adoption in the Iranian context. The results support existing literature on e-recruitment adoption and extend it by demonstrating the multidimensionality of jobseekers’ perceptions and behavioral intentions toward e-recruitment.</p> </abstract>ARTICLE2022-09-07T00:00:00.000+00:00Sliding Mode Control-Based MPPT and Output Voltage Regulation of a Stand-alone PV System<abstract> <title style='display:none'>Abstract</title> <p>When it comes to reducing emissions caused by the generation of electricity, among different renewable energy sources, the solar energy gains prominence, due to its geographical availability, simplicity of implementation, and absence of physical moving parts. However, the performance of photovoltaic systems is dependent on environmental conditions. Depending on temperature and solar irradiation, the photovoltaic (PV) system has an operating point where maximum power can be generated. The techniques that are implemented to find this operating point are the so-called maximum power point tracking (MPPT) algorithms. Since weather conditions are variable in nature, the output voltage of the PV system needs to be regulated to remain equal to the reference. Most of the existing studies focus either on MPPT or on voltage regulation of the PV system. In this paper, the two-stage PV system is implemented so that both MPPT and voltage regulation are achieved simultaneously. Additionally, an improved version of the perturb and observe (P&amp;O) algorithm based on artificial potential fields (APF), called APF-P&amp;O, is presented. According to the results of the simulations carried out in MATLAB/Simulink software, the APF-P&amp;O method is more efficient than the conventional method.</p> </abstract>ARTICLE2022-08-31T00:00:00.000+00:00VSC-Based DSTATCOM for PQ Improvement: A Deep-Learning Approach<abstract> <title style='display:none'>Abstract</title> <p>With the rapid advancement of the technology, deep learning supported voltage source converter (VSC)-based distributed static compensator (DSTATCOM) for power quality (PQ) improvement has attracted significant interest due to its high accuracy. In this paper, six subnets are structured for the proposed deep learning approach (DL-Approach) algorithm by using its own mathematical equations. Three subnets for active and the other three for reactive weight components are used to extract the fundamental component of the load current. These updated weights are utilised for the generation of the reference source currents for VSC. Hysteresis current controllers (HCCs) are employed in each phase in which generated switching signal patterns need to be carried out from both predicted reference source current and actual source current. As a result, the proposed technique achieves better dynamic performance, less computation burden and better estimation speed. Consequently, the results were obtained for different loading conditions using MATLAB/Simulink software. Finally, the feasibility was effective as per the benchmark of IEEE guidelines in response to harmonics curtailment, power factor (p.f) improvement, load balancing and voltage regulation.</p> </abstract>ARTICLE2022-08-31T00:00:00.000+00:00A Hyper-Heuristic for the Preemptive Single Machine Scheduling Problem to Minimize the Total Weighted Tardiness<abstract> <title style='display:none'>Abstract</title> <p>A problem of minimizing the total weighted tardiness in the preemptive single machine scheduling for discrete manufacturing is considered. A hyper-heuristic is presented, which is composed of 24 various heuristics, to find an approximately optimal schedule whenever finding the exact solution is practically intractable. The three heuristics are based on the well-known rules, whereas the 21 heuristics are introduced first. Therefore, the hyper-heuristic selects the best heuristic schedule among 24 schedule versions, whose total weighted tardiness is minimal. Each of the 24 heuristics can solely produce a schedule which is the best one for a given scheduling problem. Despite the percentage of zero gap instances decreases as the greater number of jobs is scheduled, the average and maximal gaps decrease as well. In particular, the percentage is not less than 80 % when up to 10 jobs are scheduled. The average gap calculated over nonzero gaps does not exceed 4 % in the case of scheduling 7 jobs. When manufacturing consists of hundreds of jobs, the hyper-heuristic is made an online scheduling algorithm by applying it only to a starting part of the manufacturing process.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00Proposing a Layer to Integrate the Sub-classification of Monitoring Operations Based on AI and Big Data to Improve Efficiency of Information Technology Supervision<abstract> <title style='display:none'>Abstract</title> <p>Intelligent monitoring of a computer network provides a clear understanding of its behaviour at various times and in various situations. It also provides relief to support teams that spend most of their time troubleshooting problems caused by hardware or software failures. This type of monitoring ensures the accuracy and efficiency of the network to meet the expectations of its users. However, to ensure intelligent monitoring, it is necessary to start by automating this process, which often leads to long and costly interventions. The success of such automation implies the establishment of predictive maintenance as a prerequisite for good preventive maintenance governance. However, even when it is practiced effectively, preventive maintenance requires a great deal of time and the mobilization of several full-time resources, especially for large IT structures. This paper gives an overview of the monitoring of a computer network and explains its process and the problems encountered. It also proposes a method based on machine learning to allow for prediction and support decision making to proactively anticipate interventions.</p> </abstract>ARTICLE2022-08-23T00:00:00.000+00:00en-us-1