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2nd International Conference on IT Innovations and Knowledge Discovery, ITIKD 2024

作者： Mogith, P. Mothiswar, T.B.G. Abubak, Mohammad Jagannathan, J. Balaganesh, N. Vellore Institute of Technology Software Engineering Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350355468

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information. © 2025 IEEE.

关键词： data transformation data visualization Exploratory Data Analysis (EDA) Indian Premier League (IPL) key performance indicators (KPIs) performance trends player averages team performance venue effects

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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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Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait

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PeerJ Computer Science 2025年 11卷 1-42页

作者： Naz, Anam Khan, Hikmat Ullah Alsahfi, Tariq Alhajlah, Mousa Alshemaimri, Bader Daud, Ali Department of Information Technology University of Sargodha Punjab Sargodha Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Applied of Computer Science College King Saud University Riyadh Saudi Arabia Software Engineering Department College of Computing and Information Sciences King Saud University Riyadh Saudi Arabia Faculty of Resilience Rabdan Academy Abu Dhabi United Arab Emirates

Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. Subjects Human-Computer Interaction, Artificial Intelligence, Data Mining and Machine Learning, Natural Language and Speech © 2025 xxxx

关键词： Deep learning

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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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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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