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Adjunct Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

作者： Avital Shulner-Tal Julia Sheidin Braude College of Engineering Software Engineering & Information Systems Karmiel Israel

ISBN: (纸本)9798400713996

As Artificial Intelligence (AI) systems are increasingly integrated into high-stakes domains, the demand for transparency has become paramount. The opacity of "black-box" models poses significant challenges in trust, fairness, and accountability. Explainable AI (XAI) is a vital approach for addressing these concerns by enabling transparency, fostering trust, and ensuring ethical deployment across various sectors, including healthcare, human resources, finance, autonomous systems, and more. This paper explores how XAI methods can be used throughout the AI lifecycle for creating human-centered, ethical, and responsible AI systems by enhancing transparency, reducing bias, and protecting data privacy. Furthermore, the paper introduces XAI4RE, a theoretical framework that links XAI principles and purposes to concrete stages of the AI lifecycle, demonstrating how to address ethical considerations effectively. This approach involves engaging different stakeholders, such as developers, regulators, and users, at each stage. The framework highlights the critical role of XAI in promoting fairness, accountability, and human-centric design using general guidelines that discuss the relevant insights that can be drawn from XAI at each lifecycle stage. Ultimately, this paper underscores the importance of XAI in bridging the gap between technical advancements and ethical AI practices to foster societal trust and responsible systems.

关键词： Accountability

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Daily activity monitoring system designed for elderly people using hidden Markov models based on real world datasets

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International Journal of Intelligent systems Technologies and Applications 2023年第1期21卷 40-55页

作者： Bouali, Chaima Habert, Olivier Tahiri, Abderrahim Laboratory of Information Systems and Software Engineering National School of Applied Sciences Tetouan Morocco Laboratoire de Conception Optimisation et Modélisation des Systèmes Metz France

This work describes how our abnormal behaviour detection system functions for seniors in their home. Our research is based on the data gathered by a domotic box that is available for purchase. The box was initially intended to continuously detect the owners' daily actions using non-intrusive home automation sensors. The enhancement of the detection of the health changes, deducted by the abnormal behaviour of the user, is closely related to the evolution of the activity recognition of the box. Our system aims to report a relevant context-aware alert to health care service experts. By refining the detection of the activity level of the occupants, we could identify warning manifestations for early intervention. In this paper, we will describe the process of pointing out irregularity in the daily activity pattern of a user or the detection of a malfunction of the box to maintain the accuracy of the service it offers. © 2023 Inderscience Enterprises Ltd.

关键词： Assisted living

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Analyzing XSS Attack information Content on Social Media 6

Analyzing XSS Attack Information Content on Social Media

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Hanapi, Khaerunnisa Harlina, Sitti Arifin, Suci Ramadhani Arifin, Arham Oktavianus, Michael Ahyuna Department of Information Systems Dipa Makassar University Makassar Indonesia Department of Informatics Engineering Dipa Makassar University Makassar Indonesia Department of Software Engineering Dipa Makassar University Makassar Indonesia

ISBN: (纸本)9798331539603

Social media has become more than just a communication tool;it's now a rapidly evolving platform for sharing information, including cybersecurity threats like XSS attacks. This study investigates if analyzing collections of tweets can identify those containing XSS attack information. By accurately extracting this information, we can effectively classify tweets as containing or not containing XSS attacks. Our research focuses on evaluating how well feature extraction methods perform in achieving this classification accuracy. As the result, the 2-gram model with TF-IDF Vectorizer might be a more suitable choice with accuracy exceeding 97% and demonstrates better tolerance for overfitting due to the inherent properties of TF-IDF. © 2024 IEEE.

关键词： Tweets

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Deep Learning-Based Audio Splicing Forgery Detection Using PVTv2 Transformer

Deep Learning-Based Audio Splicing Forgery Detection Using P...

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2024 Innovations in Intelligent systems and Applications Conference, ASYU 2024

作者： Kanca, Elif Gulsoy, Tolgahan Ustubioglu, Arda Ustubioglu, Beste Ayas, Selen Baykal Kablan, Elif Ulutas, Guzin Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Management Information Systems Trabzon University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (纸本)9798350379433

Attacks on multimedia files by malicious users have become quite common, especially with the increase in the number of editing tools and their ease of use. Considering that such files can now be used both as evidence and for social visibility in all kinds of environments, it has become important to prove their authenticity. With the proposed method, the detection of merging forgeries in audio files has been carried out. For this purpose, the audio files received from the input are converted into cochleagram images. The PVTv2 based deep network architecture is trained with the generated cochleagram images. As a result of the training, the suspicious audio file given as input is labelled as original/fake. The proposed method gives 96.11% accuracy for 2s database, 94.63% accuracy for 3s database and 95.19% accuracy for 2s-3s database. © 2024 IEEE.

关键词： Deep learning

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The Future of Mobile Payments: Blockchain-Based Solutions 24

The Future of Mobile Payments: Blockchain-Based Solutions

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24th International Arab Conference on information Technology, ACIT 2023

作者： Obaid, Mahmoud Abumwais, Allam Hodrob, Rami Odeh, Suhail Obaid, Murad Arab American University Computer Systems Engineering Jenin Palestine Bethlehem University Software Engineering Bethlehem Palestine Cyprus International University Management Information Systems Northern Cyprus Turkey

ISBN: (纸本)9798350384307

Blockchain technology has the potential to disrupt the banking and financial sector, even if existing institutions are unable to benefit from it. Most banks are looking to use blockchain technology for smart contracts, payments, and trading platforms in order to reduce fraud, secure and expedite transactions, cut costs, increase data quality, and enable Know Your Customer (KYC). In recent years, blockchain has gained a lot of attention as one of the most popular ways to secure data transfer through decentralized peer-to-peer systems. Blockchain is an immutable ledger that enables secure, decentralized transaction execution. This complex yet secure system has a stellar reputation and is attracting a growing number of clients. This paper proposes a new conceptual framework for using mobile payment blockchain technology to address the needs of customers (both consumers and business owners) for faster, safer, more affordable, real-time, and secure payments that also eliminate the need for intermediary parties to approve and reconcile transactions. The framework reduces operational risk by ensuring the transparency and immutability of all transactions. © 2023 IEEE.

关键词： Blockchain Payment security Private Blockchain real-time mobile payment

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How to Do Things without Words: Modeling Semantic Drift of Emoji

How to Do Things without Words: Modeling Semantic Drift of E...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Arviv, Eyal Tsur, Oren Software and Information Systems Engineering Ben Gurion University of the Negev Israel

Emoji have become a significant part of our informal textual communication. Previous work, addressing the societal and linguistic functions of emoji, overlooked the relation between the semantics and the visual variations of the symbols. In this paper we model and analyze the semantic drift of emoji and discuss the features that may be contributing to the drift, some are unique to emoji and some are more general. Specifically, we explore the relations between graphical changes and semantic changes. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Federated Collaborative Graph Neural Networks for Few-shot Graph Classification

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Machine Intelligence Research 2024年第6期21卷 1077-1091页

作者： Yu Xie Yanfeng Liang Chao Wen A.K.Qin Maoguo Gong School of Computer and Information Technology Shanxi UniversityTaiyuan030000China Key Laboratory of Collaborative Intelligence Systems of Ministry of Education Xidian UniversityXi’an710071China Department of Computer Science and Software Engineering Swinburne University of TechnologyMelbourne3000Australia

Graph neural networks(GNNs)have achieved state-of-the-art performance on graph classification tasks,which aim to pre-dict the class labels of entire graphs and have widespread ***,existing GNN based methods for graph classification are data-hungry and ignore the fact that labeling graph examples is extremely expensive due to the intrinsic *** import-antly,real-world graph data are often scattered in different *** by these observations,this article presents federated collaborative graph neural networks for few-shot graph classification,termed *** its owned graph examples,each client first trains two branches to collaboratively characterize each graph from different views and obtains a high-quality local few-shot graph learn-ing model that can generalize to novel categories not seen while *** each branch,initial graph embeddings are extracted by any GNN and the relation information among graph examples is incorporated to produce refined graph representations via relation aggrega-tion layers for few-shot graph classification,which can reduce over-fitting while learning with scarce labeled graph ***,multiple clients owning graph data unitedly train the few-shot graph classification models with better generalization ability and effect-ively tackle the graph data island *** experimental results on few-shot graph classification benchmarks demonstrate the ef-fectiveness and superiority of our proposed framework.

关键词： Graph neural networks federated learning collaborative learning few-shot learning graph classification

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based software systems

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Computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Performance Evaluation of Clustering Algorithms for Enhancing Test Case Prioritization in Regression Testing 4

Performance Evaluation of Clustering Algorithms for Enhancin...

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4th International Conference on Advanced Research in Computing, ICARC 2024

作者： Puviskar, S. Prabath Wasalthilaka, W.V.S.K. Kumara, B.T.G.S. Sabaragamuwa University of Sri Lanka Department of Computing & Information Systems Sri Lanka Sabaragamuwa University of Sri Lanka Department of Software Engineering Sri Lanka

ISBN: (纸本)9798350384864

Regression testing plays a crucial role in maintain software quality as applications evolving and getting more complex. Selection of a regression testing technique significantly influences overall software quality. In initial stages, this involves picking relevant test cases and remove unnecessary redundancies during minimization phase. Finally, prioritization of test cases phases most important ones is executed first which continue process iteratively or whole testing carried out which effect time and cost intensively. Test Case Prioritization method effective way to address the issue. In this study, we proposed approach to enhance Test Case Prioritization by integrating fault based methods and time of execution analysis. The study also evaluates the efficiency in proposed methodology through, employing APFD metric. The study aims to improve the precision and effectiveness of test case prioritization methods, advancing overall quality and reliability in dynamic software development environments. Notably, AHC outperforms in comparisons, showcasing its efficacy in improving outcomes. © 2024 IEEE.

关键词： software testing

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Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年第12期12卷 19189-19201页

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

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