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16th IEEE International Conference on computer Science and Information Technologies, CSIT 2021

作者： Hovorushchenko, Tetiana Pavlova, Olga Avsiyevych, Volodymyr Computer Engineering and System Programming Department Khmelnytskyi Ukraine

ISBN: (纸本)9781665442572

The main methods of analysis the accuracy of the geolocation system were studied. The aspects of technology for obtaining geographical coordinates using Google geolocation system (GPS) were considered. The best method of obtaining data from satellites was searched. Today the geolocation system has become widely used in many industries, such as military, aviation, maritime, navigation of ordinary users. Also, a significant spread of this technology is noticed in many geographical areas, such as cartography, geodesy and is an area of interests of many scientists. A mobile app based on Android platform for conducting the experiments to determine the accuracy of geolocation services in the form of Web Quests was developed. © 2021 IEEE.

关键词： Global positioning system

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A Practical Approach for Reliability Prediction of Safety Critical Software Using Multi-Model Ensemble Techniques 7

A Practical Approach for Reliability Prediction of Safety Cr...

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7th International Conference on system Reliability and Safety, ICSRS 2023

作者： Ouertani, Aymen Krini, Ossmane Börcsök, Habil. Josef Institute of Functional Safety & Cyber Security Baden-Wuerttemberg Cooperative State University Lörrach Germany Computer Architecture und System Programming University of Kassel Kassel Germany

ISBN: (纸本)9798350306057

This paper introduces a novel and comprehensive approach for estimating the reliability of safety critical software components in autonomous vehicle motion systems. The proposed approach in this paper presents a combination software reliability model (CSRM) that integrates multiple non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMs) to achieve a reasonable compromise between accuracy, the trade-off between the goodness of fit and the simplicity, and stability. By using machine learning techniques, the CSRM effectively combines the strengths of individual SRGMs while mitigating their weaknesses through suitable evaluation and calibration techniques. The developed CSRM has been successfully applied and validated to facilitate a smoother and more efficient evaluation of reliability targets for software components in autonomous robotic wheelchair (ARW). Based on the validation results, the new CSRM has significantly enhanced the efficiency of the process for evaluating whether the defined reliability goals were being achieved. Furthermore, has facilitated a more accurate assessment of the need for further test executions and better planning of the required verification and validation session. This new approach provides valuable insights into the reliability of the developed software, particularly for software developers lacking extensive experience in identifying and applying appropriate SGRM. © 2023 IEEE.

关键词： Economic and social effects

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Object Specialization to Partially Reduce Polymorphism of Attributes 23

Object Specialization to Partially Reduce Polymorphism of At...

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Proceedings of the 2023 7th International Conference on computer Science and Artificial Intelligence

作者： Alena Vasileva Yegor Bugayenko Faculty of Computer Science HSE University Russia System Programming Laboratory Huawei RRI Russia

ISBN: (纸本)9798400708688

In object-oriented programming languages, objects with polymorphic attributes can negatively impact performance and hinder static analysis. These attributes require dynamic dispatch, which is slower than static binding, and complicate the analysis process. We propose a novel algorithm for object specialization that addresses this issue by replacing polymorphic attributes with monomorphic ones, resulting in improved performance and simplified static analysis. Our algorithm is a new approach compared to existing function specialization algorithms. We provide a proof of the algorithm’s soundness and correctness, and present an implementation of the algorithm as a software tool. Empirical evaluation shows that our approach achieves significant improvements in performance and simplifies the static analysis process. Our algorithm can be applied to a variety of object-oriented languages such as Java and Python.

关键词： Object-Oriented programming Polymorphism Specialization

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Model Based Development Methodology in Compliance with ISO 26262 for Safe Automotive ECU

Model Based Development Methodology in Compliance with ISO 2...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Muhammad Ikram Hafiz Department of Computer Architecture and System Programming University of Kassel Kassel Germany

This work presents a practical approach of designing a functionally safe ECU for automotive application by implementing the Model Based Development (MBD) methodology. Functional safety (FuSa) in automotive can be achieved by strictly following the processes of electrical and electronic systems development as given in the ISO 26262. The scope of FuSa encompasses the risks emerging from systematic failures and random hardware failures. One approach to mitigate these failures is the Model Based Development (MBD) on which the ISO 26262 provides guidance. This approach enables to model an ECU and accelerates an IP creation by automatic RTL code generation for rapid prototyping on an FPGA level. In this work the MBD is implemented in accordance to the ISO 26262 to design and test a hardware model of an embedded ECU. The embedded Design under Test (DUT) is simulated and its output verified in Matlab/Simulink. Upon successful results, the RTL codes of the DUT are generated for a target FPGA. The RTL codes are simulated and the functionality of the DUT is verified at the FPGA level.

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Dense subgraphs of power-law random graphs

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Moscow Journal of Combinatorics and Number Theory 2021年第1期10卷 1-14页

作者： Lazarev, Denis O. Kuzyurin, Nikolay N. Theoretical Computer Science Departement Ivannikov Institute for System Programming of the RAS Moscow Russian Federation

The problem of finding a maximal dense subgraph of a power-law random graph G(n, α) is considered for every value of density c ε (0, 1) and for every a ε (0,+∞). It is shown that in case α <2 a maximal c-dense subgraph has size Θ_p(n^1-α/2), in case α = 2 it is limited whp, and in case α >2 it is whp less than 4/c. © 2021 Mathematical Sciences Publishers.

关键词： Graph density Maximal dense subgraph Maximal dense subgraph of random graph Poissonian model Power-law random graph

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RISC-V Based Safety system-on-Chip with Hardware Comparator 11

RISC-V Based Safety System-on-Chip with Hardware Comparator

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11th International Conference on Electronics, Communications and Networks, CECNet 2021

作者： Hahn, Eike Kalinowski, Dominik Mueller, Waldemar Abdelawwad, Mohamed Boercsoek, Josef ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (纸本)9781643682402

In this paper, a Safety system-on-Chip based on the open-source RISC-V processor SweRV EH1 from Western Digital is presented. A hardware comparator concept is followed. The SSoC is implemented on a Xilinx FPGA system and extended with standard peripherals from the Xilinx IP library and from Cobham Gaisler, so that the overall system has an Ethernet interface in addition to GPIO and UART. The goal is to create a complete redundancy approach with a hardware fault tolerance of nearly 1 from input to output based on the freely available RISC-V instruction set and prove its feasibility. © 2022 The authors and IOS Press.

关键词： system-on-chip

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Optimizations and Deoptimizations for Escape Analysis in Open World

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Chinese Journal of Electronics 2023年第2期19卷 211-216页

作者： Xiaohua Shi Gansha Wu Jin Maozhong Guei-Yuan Lueh School of Computer Science Beihang University Beijing China Programming System Laboratory Microprocessor Technology Labs Intel Corporation Beijing China

This paper introduced the optimization and deoptimization technologies for Escape analysis in open world. These technologies are used in a novel Escape analysis framework that has been implemented in Open runtime platform, Intel's open-source Java virtual machine. We introduced the optimization technologies for synchronization removal and object stack allocation, as well as the runtime deoptimization and compensation work. The deoptimization and compensation technologies are crucial for a practical Escape analysis in open world. We evaluated the runtime efficiency of the deoptimization and compensation work on benchmarks like SPECjbb2000 and SPECjvm98.

关键词： Java Runtime Education Medical services Benchmark testing Virtual machining Synchronization

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Al-Biruni Earth Radius Optimization for COVID-19 Forecasting

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computer systems Science & Engineering 2023年第7期46卷 883-896页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Tatiana Makarovskikh Amal H.Alharbi Doaa Sami Khafaga Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of System Programming South Ural State University454080ChelyabinskRussia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Several instances of pneumonia with no clear etiology were recorded in Wuhan,China,on December 31,*** world health organization(WHO)called it COVID-19 that stands for“Coronavirus Disease 2019,”which is the second version of the previously known severe acute respiratory syndrome(SARS)Coronavirus and identified in short as(SARSCoV-2).There have been regular restrictions to avoid the infection spread in all countries,including Saudi *** prediction of new cases of infections is crucial for authorities to get ready for early handling of the virus ***:Analysis and forecasting of epidemic patterns in new SARSCoV-2 positive patients are presented in this research using metaheuristic optimization and long short-term memory(LSTM).The optimization method employed for optimizing the parameters of LSTM is Al-Biruni Earth Radius(BER)***:To evaluate the effectiveness of the proposed methodology,a dataset is collected based on the recorded cases in Saudi Arabia between March 7^(th),2020 and July 13^(th),*** addition,six regression models were included in the conducted experiments to show the effectiveness and superiority of the proposed *** achieved results show that the proposed approach could reduce the mean square error(MSE),mean absolute error(MAE),and R^(2)by 5.92%,3.66%,and 39.44%,respectively,when compared with the six base *** the other hand,a statistical analysis is performed to measure the significance of the proposed ***:The achieved results confirm the effectiveness,superiority,and significance of the proposed approach in predicting the infection cases of COVID-19.

关键词： COVID-19 prediction meta-heuristic optimization LSTM Al-Biruni earth radius algorithm

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Social Engineering Attacks Detection Approach 13

Social Engineering Attacks Detection Approach

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13th International Conference on Dependable systems, Services and Technologies, DESSERT 2023

作者： Lysenko, Sergii Vorobiov, Volodymyr Bokhonko, Oleksandr Gaj, Piotr Savenko, Oleg Woloszyn, Jacek Khmelnitsky National University Department of Computer Engineering and Information Systems Khmelnitsky Ukraine Silesian University of Technology Gliwice Poland Khmelnitsky National University Department of Computer Engineering and System Programming Khmelnitsky Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350396119

The paper focuses on the problem of technical social engineering attacks that encompass the manipulation of individuals to reveal sensitive information, execute actions, or breach security systems. These exploits frequently capitalize on human psychology, trust, and a lack of vigilance to attain unauthorized entry to networks, systems, or data. In contrast to traditional social engineering tactics that center on psychological manipulation, technical social engineering attacks employ technological means and strategies to manipulate and beguile individuals. The paper presents an attempt to detect social engineering attacks. The approach utilized four machine learning algorithms (decision tree, random forest, K-nearest neighbor, and extreme gradient boosting). The analysis is centered on data collected from network hosts, which may serve as indicators of a potential social engineering attack. The empirical results demonstrated high detection accuracy. © 2023 IEEE.

关键词： Cybersecurity

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