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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Aung Kyaw Myo Evgeni M. Portnov Alexey R. Fedorov Institute of Systems and Software Engineering and Information Technology National Research University of Electronic Technology (MIET) Zelenograd Moscow Russia

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Currently, the search for malicious code in information systems is relevant. The analysis showed that existing static analyzers detect vulnerabilities in the code, but not malicious code, as a result of which it was decided to create a new static analyzer with zero false positives, which will not only search for malicious code, but also indicate its location. A formalized representation of the task of detecting malicious code based on static analysis using set theory based on the introduction of a function that matches each element with a maliciousness factor is proposed. A technique for detecting malicious code in the source code based on lexical, semantic and static analysis has been developed. An experimental study has shown that the accuracy of detecting malicious code has increased by three percent, and the F1 measure - by five percent, compared to the best similar tool, which indicates a more effective performance of the task of detecting malicious code by the developed algorithm compared to analogs.

关键词： Measurement Accuracy Source coding Semantics Static analysis Syntactics Set theory Malware Libraries information systems

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Tactile Data Comics: A Step-by-step Multimodal Presentation Method on a Refreshable Tactile Display for Blind and Visually Impaired Individuals

Tactile Data Comics: A Step-by-step Multimodal Presentation ...

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2025 CHI Conference on Human Factors in computing systems, CHI EA 2025

作者： Sun, Ruoting Luo, Rong Yao, Xiwen She, Xinran Hara, Kotaro Jiao, Yang Tsinghua University Beijing China University of Science and Technology Beijing Beijing China South China University of Technology GuangZhou China School of Computing and Information Systems Singapore Management University Singapore Singapore

ISBN: (纸本)9798400713958

Refreshable tactile displays (RTDs) could present dynamic tactile graphics to support blind and low-vision individuals in learning. However, existing research offers limited evidence on the effectiveness of dynamic tactile graphics on RTDs in supporting blind students’ learning outcomes. To study the value of presenting dynamic contents in the learning context, we designed tactile data comics, a method that combines step-by-step presentation of tactile graphics with verbal narration. We conducted a user study with 16 visually impaired students to compare tactile data comics against verbal-only and static tactile graphic presentation approaches. Our findings show that tactile data comics significantly improves participants’ content comprehension. The results highlight the potential of tactile data comics to benefit the visually impaired community, particularly in educational applications. © 2025 Copyright held by the owner/author(s).

关键词： Students

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EEG and fMRI Artifact Detection Techniques: A Survey of Recent Developments

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SN Computer Science 2023年第5期4卷 528页

作者： Mili, Rahma Bouaziz, Bassem Maalel, Ahmed Gargouri, Faiez Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Faculty of Economics and Management of Sfax University of Sfax Sfax 3021 Tunisia Multimedia InfoRmation Systems and Advanced Computing Laboratory (MIRACL) Higher Institute of Computer and Multimedia of Sfax Sfax 3021 Tunisia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba 2010 Tunisia Higher Institute of Applied Sciences and Technology University of Sousse Sousse 4003 Tunisia

The evolution of different techniques for exploring cerebral activity and the development of signal processing and analysis methods have enabled a better understanding of the dynamic cerebral mechanisms in favor of fine diagnosis and characterization of certain diseases such as dementia and epilepsy. Integration of functional magnetic resonance imaging (fMRI) with electroencephalography (EEG) has offered the possibility of understanding new insights into neuroscientific studies because of the higher temporal and spatial measurements of brain activity when compared with the use of each technique separately. However, the experimental limitations of fMRI/EEG are mainly the numerous artifacts, mainly due to switching of magnetic field gradients and cardiac activity. To provide a comprehensive overview of this research area, a systematic mapping study has been conducted. This mapping study aims to identify the topics studied within the artifacts’ detection in EEG and fMRI data, as well as examine the adopted research methodologies, identify the gaps in the current research and point to directions for future research. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artifacts Deep learning EEG fMRI Systematic mapping study

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Development of Load Balancing Method in a Microservice Architecture System

Development of Load Balancing Method in a Microservice Archi...

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International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM)

作者： Aung Kyaw Myo Evgeni M. Portnov Alexander S. Alekseev Institute of Systems and Software Engineering and Information Technology National Research University of Electronic Technology (MIET) Zelenograd Moscow Russia

ISBN: (数字)9798331512194

ISBN: (纸本)9798331512200

This paper presents a novel load balancing method for microservice architectures, focusing on optimizing resource utilization and enhancing system performance. The proposed approach employs a dynamic load balancer that continuously monitors microservice instances, distributing incoming requests based on real-time workload metrics. By integrating queuing theory and simulation modeling, the method maximizes the output stream intensity of processed requests while minimizing rejection rates. Comparative analysis demonstrates a 15% improvement in processing speed compared to traditional uniform distribution methods. The solution addresses key challenges such as uneven request distribution and service failures, ensuring high availability and fault tolerance. The developed algorithm is validated through simulation models, showcasing its effectiveness in real-world scenarios. This research contributes to advancing efficient data distribution in microservice-based systems, offering practical insights for scalable web applications.

关键词： Fault tolerance System performance Heuristic algorithms Fault tolerant systems Microservice architectures Load management Real-time systems Resource management Queueing analysis Load modeling

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Vibrating Particles System Algorithm for Solving Classification Problems

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Computer systems Science & Engineering 2022年第12期43卷 1189-1206页

作者： Mohammad Wedyan Omar Elshaweesh Enas Ramadan Ryan Alturki Department of Autonomous Systems Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Software Engineering Information Technology CollegeAL-Hussein bin Talal UniversityMa’an71111Jordan Department of Computer Science Prince Abdullah Bin Ghazi Faculty of Communication and Information TechnologyAl-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21961Saudi Arabia

Big data is a term that refers to a set of data that,due to its largeness or complexity,cannot be stored or processed with one of the usual tools or applications for data management,and it has become a prominent word in recent years for the massive development of *** immediately thereafter,the term“big data mining”emerged,i.e.,mining from big data even as an emerging and interconnected field of *** is an important stage in data mining since it helps people make better decisions in a variety of situations,including scientific endeavors,biomedical research,and industrial *** probabilistic neural network(PNN)is a commonly used and successful method for handling classification and pattern recognition *** this study,the authors proposed to combine the probabilistic neural network(PPN),which is one of the data mining techniques,with the vibrating particles system(VPS),which is one of the metaheuristic algorithms named“VPS-PNN”,to solve classi-fication problems more *** data set is eleven common benchmark medical datasets from the machine-learning library,the suggested method was *** suggested VPS-PNN mechanism outperforms the PNN,biogeography-based optimization,enhanced-water cycle algorithm(E-WCA)and the firefly algorithm(FA)in terms of convergence speed and classification accuracy.

关键词： Vibrating particles system(VPS) probabilistic neural network(PNN) classification problem data mining

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Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

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Computers, Materials & Continua 2024年第8期80卷 2917-2939页

作者： Islam Zada Mohammed Naif Alatawi Syed Muhammad Saqlain Abdullah Alshahrani Adel Alshamran Kanwal Imran Hessa Alfraihi Department of Software Engineering International Islamic UniversityIslamabad25000Pakistan Information Technology Department Faculty of Computers and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia Department of Computer Science University of PeshawarPeshawar25121Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention *** machine learning classifiers have emerged as promising tools for malware ***,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware *** this gap can provide valuable insights for enhancing cybersecurity *** numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware *** the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security *** study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows *** objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows *** the accuracy,efficiency,and suitability of each classifier for real-world malware detection *** the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and *** recommendations for selecting the most effective classifier for Windows malware detection based on empirical *** study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and *** data analysis involves understanding the dataset’s characteristics and identifying preprocessing *** preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for *** training utilizes various

关键词： Security and privacy challenges in the context of requirements engineering supervisedmachine learning malware detection windows systems comparative analysis Gaussian Naive Bayes K Nearest Neighbors Stochastic Gradient Descent Classifier Decision Tree

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Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach

Designing of a Voice-Based Programming IDE for Source Code G...

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2022 International Research Conference on Smart computing and systems Engineering, SCSE 2022

作者： Nizzad, A.R.M. Thelijjagoda, Samantha British College of Applied Studies School of Computing Sri Lanka Sri Lanka Institute of Information Technology Department of Information Systems Engineering Sri Lanka

ISBN: (数字)9781665473750

ISBN: (纸本)9781665473750

Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model's training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only. © 2022 IEEE.

关键词： Python

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A Scoring Model for Public Administration Process Prioritization in Germany 6

A Scoring Model for Public Administration Process Prioritiza...

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6. Fachtagung Rechts- und Verwaltungsinformatik, RVI 2023 - 6th Conference on Legal and Public Administrative Informatics, RVI 2023

作者： Pidun, Tim Müller, Dirk HTWD University of Applied Sciences Chair of Information Systems/Digital Administration Friedrich-List-Platz 1 Dresden01069 Germany HTWD University of Applied Sciences Chair of Software Technology/Operating Systems Friedrich-List-Platz 1 Dresden01069 Germany

ISBN: (纸本)9783885797357

The German public administration still struggles on becoming digital though considerable effort has been made by accompanying laws and institutions and a good will to digitalize all user-centered processes. But still there is no structured prioritization of processes, and Stakeholders fear the ongoing rather ad-hoc processing of their digitalization tasks. This contribution aims to construct a suitable prioritization score by deriving domain-specific selective aspects along an IS evaluation approach and formulation of a score that relates to the priority of the service process. It can be used to sort a given amount of processes independently of the administrative level. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： information systems

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A New S-Box Design System for Data Encryption Using Artificial Bee Colony Algorithm

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Computers, Materials & Continua 2023年第10期77卷 781-797页

作者： Yazeed Yasin Ghadi Mohammed SAlshehri Sultan Almakdi Oumaima Saidani Nazik Alturki Fawad Masood Muhammad Shahbaz Khan Department of Computer Science Al Ain UniversityAbu Dhabi112612United Arab Emirates Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Institute of Space TechnologyIslamabad4400Pakistan School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghEH105DTUK

Securing digital image data is a key concern in today’s information-driven *** encryption techniques are required to protect sensitive image data,with the Substitution-box(S-box)often playing a pivotal role in many symmetric encryption *** study introduces an innovative approach to creating S-boxes for encryption *** proposed S-boxes are tested for validity and non-linearity by incorporating them into an image encryption *** nonlinearity measure of the proposed S-boxes is *** qualities significantly enhance its resistance to common cryptographic attacks,ensuring high image data ***,to assess the robustness of the S-boxes,an encryption system has also been proposed and the proposed S-boxes have been integrated into the designed encryption *** validate the effectiveness of the proposed encryption system,a comprehensive security analysis including brute force attack and histogram analysis has been *** addition,to determine the level of security during the transmission and storage of digital content,the encryption system’s Number of Pixel Change Rate(NPCR),and Unified Averaged Changed Intensity(UACI)are *** results indicate a 99.71%NPCR and 33.51%*** results demonstrate that the proposed S-boxes offer a significant level of security for digital content throughout its transmission and storage.

关键词： S-box chaos artificial bee colony image encryption

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Bullyproof Sentinel: Fortifying Online Spaces Against Bullying using Machine Learning 7

Bullyproof Sentinel: Fortifying Online Spaces Against Bullyi...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Dilshani, M.M.T. Vinoshan, T. Wijesinghe, W.A.S.D. Jathushan, V. Senarathne, Amila Gunasinghe, Amali Sri Lanka Institute of Information Technology Faculty of Computing Cyber Security Specialization Malabe Sri Lanka Sliit Cisco Academy Sri Lanka Institute of Information Technology Industry Engagement Unit Department of Computer Systems Engineering Manager Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341485

In the current social media landscape, cyberbullying and online harassment remain pressing issues. Therefore, we planned a cyberbullying detection system for social media platforms. It can protect online communities from toxic behaviors and make a global impact by fostering a safer and more constructive online environment. We are mainly focused on developing an improved technique to detect bot profiles on social media, developing a mechanism to detect abusive content and sarcasm, developing a mechanism to detect defamatory comments on social media, and developing an effective method for detecting offensive and abusive content in image-format reviews on social media using machine learning algorithms. © 2023 IEEE.

关键词： Machine learning

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