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18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Kanavos, Andreas Antonopoulos, Nikos Karamitsos, Ioannis Mylonas, Phivos Ionian University Department of Informatics Corfu Greece Ionian University Department of Digital Media and Communication NeMeCULAB Argostoli Greece Rochester Institute of Technology Department of Graduate and Research Dubai United Arab Emirates University of West Attica Department of Informatics and Computer Engineering Athens Greece

ISBN: (纸本)9798350327717

Online Social Networks (OSNs) have become integral platforms for information sharing, attracting both legitimate users and spammers. Detecting and mitigating spam within these networks pose significant challenges due to the dynamic nature of content and user behavior. In this paper, we present a comprehensive comparative analysis of algorithms for tweet analysis, focusing on Natural Language Processing (NLP) and Machine Learning (ML) models. We evaluate these algorithms through sentiment analysis and multiple attribute analysis, utilizing diverse methodologies and datasets. Our study explores feed-forward neural networks, Bayesian classifiers, and transformer-based models for NLP tasks, achieving high prediction accuracy and insightful metrics such as precision, recall, and F1 score. Furthermore, we delve into multiple attribute analysis using Random Forest, Logistic Regression, and Gradient Boosting algorithms. Through a systematic exploration of various approaches, this work contributes to a deeper understanding of spam detection and sentiment analysis within the context of OSNs, paving the way for enhanced social network security and content analysis. © 2023 IEEE.

关键词： Social networking (online)

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Tangles in IOTA to make crypto currency transactions free and secure

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International Journal of Cloud Computing 2022年第1期11卷 1-13页

作者： Anakath, A.S. Kannadasan, R. Prabakaran, N. Basha, M.S. Saleem Department of MCA EGS Pillai Engineering College Nagapattinam 611002 India School Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computing and Informatics Mazoon College Muscat133 Oman

Blockchain introduction has made a revolutionary change in the crypto currency around the world but it has not delivered on its promises of free and faster transaction confirmation. Serguei Popov's proposal of using tangles, a directed acyclic graph, which essentially is considered to be the successor of blockchains and offers the required features like machine to machine micro payment and fee-less transaction. It requires the user to approve the previous two transactions in the web to participate in the network. This essentially eliminates the miners and the mining part form the currency exchange and provides the user or participants to do their transactions fee-less. Since the participant verifies the previous two transactions it also contributes to the security of the tangle. In this paper, features of internet of things application (IOTA) and all the improvements in it using tangles are discussed along with how it contributed to the security and how it enables the participants to have fee-less transactions is also discussed. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Distributed ledger

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Analysis of the Performance of Transformers and Ensemble Schemes for Twitter US Airlines Sentiment Analysis 24

Analysis of the Performance of Transformers and Ensemble Sch...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Konstantinos Kyritsis Isidoros Perikos Charalampos M. Liapis Vaggelis Kapoulas Michael Paraskevas Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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Data-driven real-time image super-resolution with enhanced generative adversarial networks

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International Journal of Information technology (Singapore) 2025年 1-12页

作者： Annaiah, Naveen Ananda Kumar Joseph Mahanty, Mohan Jagan, B. Omkar Lakshmi Data Engineer Tekinvaderz LLC Temple Terrace FL United States Department of Computer Science and Engineering Vignan’s Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Department of Electrical and Electronics Engineering Vignan’s Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Medical Physics and Informatics Laboratory of Electronics Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

Image enhancement aims to enhance the visual information for improved human and machine interpretation. The applications of image enhancement techniques span numerous fields, from photography and medical imaging to surveillance, satellite image analysis, and digital forensics. Recent advancements in deep learning-driven image enhancement remains constrained by challenges such as training dataset scarcity and inherent architectural inadequacies, underscores the imperative for innovative research to yield more resilient and performant DL models. This paper addresses these limitations of the existing models, by introducing a novel Self-Attentive Residual Super-Resolution Generative Adversarial Network (SAR-SRGAN), specifically designed for real time image enhancement. The proposed GAN model departs from the conventional approaches by integrating a series of residual blocks, a self-attention mechanism (SAM), and an effective upscaling module within a unique learning framework. This ensembled model facilitates the generation of perceptually high-resolution (HR) images, particularly in the scenarios characterized by stringent computational constraints and limited training data. Empirical evaluation on a customized image dataset demonstrates the efficacy of SAR-SRGAN, showcasing significant improvements in image quality and visual fidelity compared to the existing deep learning benchmarks. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Image enhancement Noisy images Residual blocks SAM SAR-SRGAN Super resolution

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A Convolutionaly-Informed XLM-RoBERTa for Classifying Short Text Inquires in Greek School Network 24

A Convolutionaly-Informed XLM-RoBERTa for Classifying Short ...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Charalampos M. Liapis Isidoros Perikos Konstantinos Kyritsis Vaggelis Kapoulas Michael Paraskevas Department of Mathematics University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Computer Engineering and Informatics Department University of Patras Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece Computer Technology Institute and Press Diophantus Patras Greece Electrical and Computer Engineering University of the Peloponnese Patras Greece and Computer Technology Institute and Press Diophantus Patras Greece

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Games for Scrum Team Collaboration in the Global Software Development Environment: A Literature Review 7

Games for Scrum Team Collaboration in the Global Software De...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Hidayati, Anita Ismail, Iklima Ermis Yuly, Ade Rahma Edison, Henry Politeknik Negeri Jakarta Department of Computer and Informatics Engineering Depok Indonesia Universitas Dian Nuswantoro Faculty of Computer Science Semarang Indonesia Blekinge Institute of Technology Department of Software Engineering Sweden

ISBN: (纸本)9798350345711

Software development companies commonly use Global Software Development (GSD) in their industry. A competent Scrum team supports the success of the GSD project. This research aims to identify the game components in the form of types, elements, and technologies that contribute to building experience for the Scrum team in a GSD environment. We used a Systematic Literature Review (SLR) with the Kitchenham and snowball method. From the 12 papers, we concluded that none of the games specifically focused on the competence of the Scrum team at GSD. The SLR results also identify ten game elements and technologies as a reference for game development to meet the challenges in GSD. The conclusion is that there are still great opportunities to develop a game that can improve the competence of the GSD Scrum team. Game development should refer to elements and identified technologies. © 2022 IEEE.

关键词： Software design

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Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

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computers, Materials & Continua 2022年第8期72卷 2175-2190页

作者： Mustafa Musa Jaber Salman Yussof Amer S.Elameer Leong Yeng Weng Sura Khalil Abd Anand Nayyar Institute of Informatics and Computing in Energy Universiti Tenaga NasionalMalaysia Department of Computer Science Dijlah University CollageBaghdad10021Iraq Biomedical Informatics College University of Information Technology and CommunicationsBaghdadIraq Faculty of Information Technology Duy Tan UniversityDa Nang550000Viet Nam Graduate School Duy Tan UniversityDa Nang550000Viet Nam Department of Computer Science Al-turath University CollegeBaghdadIraq

Artificial intelligence plays an essential role in the medical and health *** convolution networks offer valuable services and help create automated systems to perform medical image ***,convolution networks examine medical images effectively;such systems require high computational complexity when recognizing the same disease-affected ***,an optimized deep convolution network is utilized for analyzing disease-affected regions in this *** disease-relatedmedical images are selected and examined pixel by pixel;this analysis uses the gray wolf optimized deep learning *** method identifies affected pixels by the gray wolf hunting *** convolution network uses an automatic learning function that predicts the disease affected by previous imaging *** optimized algorithm-based selected regions are further examined using the distribution pattern-matching *** pattern-matching process recognizes the disease effectively,and the system’s efficiency is evaluated using theMATLAB implementation *** process ensures high accuracy of up to 99.02%to 99.37%and reduces computational complexity.

关键词： Artificial intelligence medical field gray wolf-optimized deep convolution networks distribution pattern-matching rule

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Comparing the Effectiveness of Generative Adversarial Networks for Clustering Tasks 19

Comparing the Effectiveness of Generative Adversarial Networ...

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19th IEEE International Conference on computer Science and Information Technologies, CSIT 2024

作者： Lytvynenko, Volodymyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution 'Kundiiev Institute of Occupational Health of the National Academy of Medical Sciences of Ukraine' Kyiv Ukraine

ISBN: (纸本)9798331542627

This study compares the effectiveness of various Generative Adversarial Network architectures, including WGAN and WGAN-GP, in data clustering using the Iris dataset. Performance was evaluated with metrics such as Silhouette Score, Davies-Bouldin Index, Adjusted Rand Index, and Mutual Information. WGANs demonstrated the best clustering performance, while GANs showed promising results but were outperformed by WGAN. Contrary to expectations, WGAN-GP performed the worst. The research underscores the potential of GANs in clustering and highlights the need for further optimization based on specific datasets. © 2024 IEEE.

关键词： adjusted mutual information Davies-Bouldin index generative adversarial networks adjusted rand index mutual information silhouette index

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Comparative Analysis of the Use of Variational Autoencoders and Generative Adversarial Networks for Anomalous Image Detection Tasks 7

Comparative Analysis of the Use of Variational Autoencoders ...

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7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024

作者： Lytvynenko, Volodtmyr Olszewski, Serge Osypenko, Volodymyr Lurie, Iryna Demchenko, Violeta Dontsova, Dariia Kherson National Technical University Department of Informatics and Computer Science Kherson Ukraine Taras Shevchenko National University Department of Radio Engineering and Electronic Systems Kyiv Ukraine Kyiv National University of Technology and Design Department of Computer Science Kyiv Ukraine State Institution Kundiiev Institute of Occupational Health National Academy of Medical Sciences of Ukraine Kyiv Ukraine

ISBN: (纸本)9798331534141

This paper presents a comparative analysis of the application of Variational Autoencoders (VAE) and Wasserstein Generative Adversarial Networks (WGAN) for detecting anomalous images. The performance evaluation of the models is conducted using metrics such as AUC, Precision, Recall, and F1-Score. The results indicate that VAEs provide high Recall in anomaly detection but with low Precision, whereas WGANs demonstrate more balanced results with fewer false positives. Recommendations are proposed for selecting an appropriate model depending on the specific task requirements and the need to minimize false alarms or achieve high detection completeness. © 2024 IEEE.

关键词： Generative adversarial networks

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Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

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arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

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