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International Symposium on Networks, computers and Communications (ISNCC)

作者： Abbas Hussein Ali Uraz YavanoğLu Dept. Of Computer Science Graduate School of Informatics Gazi University Ankara Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

The latest advancements in blockchain technology have significantly influenced several sectors, such as banking, healthcare, and supply chain networks. Because of its distinct attributes, like decentralization, trustworthiness, and security, this technology has recently been employed in the field of education. While the blockchain is bright, several problems, such as legal, immutability, and scalability questions, continue to hinder its adoption in education. In this paper, we look at educational application system in terms of blockchain-based solutions that have been created for educational purposes, as well as the potential challenges of blockchain technology in the field of education.

关键词： computers Law Scalability Education Supply chains Medical services Blockchains Stakeholders Security Guidelines

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Leveraging Artificial Intelligence for Active Cyber Defense Against to Advanced Persistent Threats to Homeland Security

Leveraging Artificial Intelligence for Active Cyber Defense ...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Recep ÖZbay Uraz Yavanoğlu Dept. Of Computer Forensics Graduate School of Informatics Gazi University Ankara Turkey Dept. of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

Increasing cyber threats have rendered traditional security solutions such as firewalls and intrusion-detection/prevention systems insufficient in providing adequate protection against advanced persistent threats to homeland security. To enhance protection, the implementation of active cyber defense is recommended, offering alternative yet effective measures against these threats. This paper examines the phases of the cyber kill chain, one of the cyber defense techniques, which has been enhanced to analyze and understand advanced persistent threats. It explains the use of deception, slowdown, and counterattacks for active cyber defense. Additionally, the utilization of artificial intelligence in active cyber defense techniques is discussed. Finally, the paper elucidates how these techniques can be employed within the scope of active cyber defense to enhance security.

关键词： computers Firewalls (computing) US Department of Homeland Security Security Protection Artificial intelligence

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Chaotic Dynamics in the 2D System of Nonsmooth Ordinary Differential Equations

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer Science and Mathematics 2022年第2期3卷 8-17页

作者： Rahman, Zain-Aldeen S.A. Jasim, Basil H. Al-Yasir, Yasir I.A. Dept. of Electrical Techniques Technical Institute/Qurna Southern Technical University Basra Iraq Dept. of Electrical Engineering College of Engineering University of Basrah Basra Iraq School of Electrical Engineering and Computer Science Faculty of Engineering and Informatics University of Bradford BradfordBD7 1DP United Kingdom

Over the last decade, the chaotic behaviors of dynamical systems have been extensively explored. Recently, discovering or developing a 2D system of ordinary differential equations (ODEs) capable of exhibiting chaotic dynamical behaviors is an attractive research topic. In this study, a chaotic system with a 2D system of nonsmooth ODEs has been developed. This system is can exhibit chaotic dynamical behaviors. Its main dynamical behaviors, including time-series trajectories, phase portraits of attractors, and equilibria and their stability, have been investigated. The developed system has been verified by an excessive variety of fascinating chaotic behaviors, such as chaotic attractor, symmetry, sensitivity to initial conditions (ICs), fractal dimension, autocorrelation, power spectrum, Lyapunov exponent, and bifurcation diagram. Analytical and numerical simulations are used to study the dynamical behaviors of such a system. The developed system has extreme sensitivity to ICs, a fractal dimension of more than 1.8 and less than 2.05, an autocorrelation fluctuating randomly about an average of zero, a broadband power spectrum, and one positive Lyapunov exponent. The obtained numerical simulation results have proven the capability of the developed 2D system for exciting chaotic dynamical behaviors. © 2022 Autoctonía. Revista de Ciencias Sociales e Historia. All rights reserved.

关键词： Fractal dimension

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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An exact based approach for the Post Enrollment Course Timetabling Problem 26

An exact based approach for the Post Enrollment Course Timet...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Dimitsas, Angelos Nastos, Vasileios Gogos, Christos Valouxis, Christos Dept. of Informatics & Telecommunications University of Ioannina Arta Epirus Greece Dept. of Electrical & Computer Engineering University of Patras Rio Greece

ISBN: (纸本)9781450398541

Educational timetabling has long piqued the interest of the scheduling community. Since timetabling governs how universities operate daily, it has been vital in academia to put substantial effort into creating schedules of the highest caliber. To create schedules that satisfy all stakeholders, various approaches have been tried to solve educational timetabling problems, which are typically NP-Hard. Over the past 20 years, several scheduling contests have been held with a focus on issues related to educational operations. In this paper, we focus on the Post Enrollment Course Timetabling Problem and two datasets from the International Timetabling Competitions held in 2002 and 2007. We propose a local search procedure augmented by a base mathematical model and variations of this model that yields competitive results, within reasonable execution times, to some of the best-known solutions. © 2022 ACM.

关键词： Integer programming

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Mining Road Traffic Accident Data for Prediction of Accident Severity

Mining Road Traffic Accident Data for Prediction of Accident...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bahiru, Tadesse Kebede Manjula, V.S. Akele, Tadesse Birara Tesfaw, Engdaw Ayalew Belay, Tadesse Destaw Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information Technology Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Pg Dept of Computer Science Amhara Kombolcha Ethiopia Kombolcha Institute of Technology Wollo University College of Informatics Dept. of Information System Amhara Kombolcha Ethiopia Wachamo University School of Computing and Informatics College of Engineering and Technology Dept. of Information Technology SSNPR Hosaena Ethiopia

ISBN: (纸本)9781665474511

As World Health Organization reports show more than 1.3 million people die each year from traffic accidents and more than twenty to fifty million people are harmed by non-fatal accidents. Understanding and identifying the major factors of road traffic accidents can help the stakeholders to take appropriate measures to minimize the accidents. Support Vector Machine (SVM), J48, and Naïve Bayes (NB) classification techniques are implemented to build a model that predicts accident severities and to identify road traffic accident factors. As experimental findings showed weather conditions, number of lanes, road lighting conditions, and speed limit are the determinant factors that cause more accident severity. Finally, the classification and prediction performance of the models are compared using various performance evaluation techniques. J48 classifier has better classification performance than Support Vector Machine and Naïve Bayes classifiers. Naïve Bayes is the classifier that showed the lowest performance than others, but it has good performance in classifying fatal accident severities rather than serious and slight accident severities. © 2023 IEEE.

关键词： Support vector machines

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Sensitive Content Detection in Social Networks Using Deep Learning Models and Explainability Techniques

Sensitive Content Detection in Social Networks Using Deep Le...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Isidoros Perikos Computer Engineering and Informatics Department University of Patras Patras Greece Electrical and Computer Engineering Dept. Computer Technology Institute and Press “Diophantus” University of Peloponnese Patras Greece

ISBN: (数字)9798350394191

ISBN: (纸本)9798350394207

In the dynamic landscape of online social networks, recognizing sensitive content is essential for safeguarding user privacy, fostering inclusivity, and enhancing diversity awareness. Building on prior research, this study explores new dimensions and methodologies for detecting sensitive content. We examine the temporal evolution of sensitive content, revealing how patterns shift over time, and address cross-linguistic challenges, emphasizing cultural and contextual nuances in detection. We employ advanced machine learning techniques, including deep learning models and BERT that improve the accuracy and robustness of the detection procedure. In the experimental study, BERT transformer reported the best performance in detecting sensitive content in text. Additionally, we incorporate explainability techniques such as LIME and SHAP to provide deeper insights into the model's decision-making processes, ensuring predictions are interpretable and reliable. Our work enhances the theoretical framework of sensitive content detection in social networks and provide methods that are accurate and scalable and can facilitate the creation of user-centric interaction that prioritize privacy and user experience.

关键词： Deep learning Privacy Sensitivity Accuracy Social networking (online) Decision making Predictive models Transformers User experience Cultural differences

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Federated Learning with Clustering-Based Participant Selection for IoT Applications

Federated Learning with Clustering-Based Participant Selecti...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Wang, Kevin I-Kai Ye, Xiaozhou Sakurai, Kouichi The University of Auckland Dept. of Electrical Computer and Software Engineering Auckland New Zealand Kyushu University Dept. of Informatics Fukuoka Japan

ISBN: (纸本)9781665480451

Modern Internet of Things (IoT) systems are highly complex due to its mobile, ad-hoc and geographically distributed nature. Very often, an edge-cloud infrastructure is established to offer intelligent services in modern IoT systems. However, IoT edge devices are typically resource-constrained and can not perform sophisticated machine learning algorithm on board. Data sharing with a central server is a common approach of crowdsourcing, but also brings privacy and security concerns. The emerging federated learning offers a promising pathway to achieve an accurate model through distributed machine learning while ensuring data privacy. The existing federated learning process is not tailored to the mobile and adhoc nature of IoT systems where devices are of varying data and system qualities and may not be able to participate the entire training process. Therefore, in this paper, a new federated learning framework is proposed to support asynchronous model fusion with clustering-based participant selection. The proposed framework aims to accommodate the ad-hoc nature of IoT devices, and at the same time avoiding low quality or even malicious data from its participants to ensure model convergence and performance. © 2022 IEEE.

关键词： Internet of things

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Perceptual Crack Detection for Rendered 3D Textured Meshes

Perceptual Crack Detection for Rendered 3D Textured Meshes

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Armin Shafiee Sarvestani Wei Zhou Zhou Wang Dept. of Electrical & Computer Engineering University of Waterloo Canada School of Computer Science and Informatics Cardiff University United Kingdom

ISBN: (数字)9798350361582

ISBN: (纸本)9798350361599

Recent years have witnessed many advancements in the applications of 3D textured meshes. As the demand continues to rise, evaluating the perceptual quality of this new type of media content becomes crucial for quality assurance and optimization purposes. Different from traditional image quality assessment, crack is an annoying artifact specific to rendered 3D meshes that severely affects their perceptual quality. In this work, we make one of the first attempts to propose a novel Perceptual Crack Detection (PCD) method for detecting and localizing crack artifacts in rendered meshes. Specifically, motivated by the characteristics of the human visual system (HVS), we adopt contrast and Laplacian measurement modules to characterize crack artifacts and differentiate them from other undesired artifacts. Extensive experiments on large-scale public datasets of 3D textured meshes demonstrate effectiveness and efficiency of the proposed PCD method in correct localization and detection of crack artifacts. Moreover, to quantify the performance of the proposed detection method and validate its effectiveness, we propose a simple yet effective weighting mechanism to incorporate the resulting crack map into classical quality assessment (QA) models, which creates significant performance improvement in predicting the perceptual image quality when tested on public datasets of static 3D textured meshes. A software release of the proposed method is publicly available at: https://***/arshafiee/crack-detection-VVM

关键词： Image quality Solid modeling Visualization Three-dimensional displays Quality assurance Visual systems Predictive models

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Perceptual Crack Detection for Rendered 3D Textured Meshes

arXiv

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arXiv 2024年

作者： Sarvestani, Armin Shafiee Zhou, Wei Wang, Zhou Dept. of Electrical & Computer Engineering University of Waterloo Canada School of Computer Science and Informatics Cardiff University United Kingdom

关键词： Quality assurance

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