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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Geetha Kanaparan Diane E. Strode School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Agile software development (ASD) enables project teams to coordinate their work. Many different ASD practices may contribute to effective coordination. This paper investigates the influence of three structural elements of agile project coordination, proximity, availability, and substitutability, on effective implicit coordination. Survey data from 340 agile practitioners worldwide was analysed using PLS-SEM. The results show that the structural elements of coordination each have a positive effect on the effective implicit coordination of agile software development projects. This paper guides practitioners on what agile practices contribute to improved coordination. This paper also validates the structural elements of an existing theoretical model of ASD coordination and shows that the model is generalisable to ASD contexts.

关键词： Surveys Technological innovation Computational modeling Education Agile software development Sustainable development Context modeling

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Receding-Horizon Trajectory Planning for Under-Actuated Autonomous Vehicles Based on Collaborative Neurodynamic Optimization

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1909-1923页

作者： Jiasen Wang Jun Wang Qing-Long Han IEEE the Future Network Research Center Purple Mountain LaboratoriesNanjing 211111China the Department of Computer Science the School of Data ScienceCity University of Hong KongHong KongChina the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal *** feasibility of the formulated optimization problem is guaranteed under derived *** optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/*** results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.

关键词： Collaborative neurodynamic optimization receding-horizon planning trajectory planning under-actuated vehicles

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Exploring the Impacts of the Stylized Semi-Open-World Virtual Reality (VR) Game on Presence

Exploring the Impacts of the Stylized Semi-Open-World Virtua...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Heng Chi En Heng Yu Ping Ahmad Affandi Supli School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Virtual Reality (VR) is an immersive technology that immerses users in a simulation of the actual world or completely alters it. This research aimed to fill the gaps in existing knowledge by investigating the effects of stylized semi-open-world VR games on the sense of presence in players. Through the development of a stylized 3D virtual environment, the study examined the semi-open-world VR game design elements that significantly influence presence while also exploring the impact of various factors such as motion sickness, VR gaming experience, and gender on gaming satisfaction and the degree of presence experienced by participants in semi-open-world VR games. It was tested by 26 participants using the Presence Questionnaire (PQ 3.0) and the Game User Experience Satisfaction Scale (GUESS-18) survey. The findings revealed that usability/playability, player engrossment, and visual aesthetics significantly influence presence, with male participants without motion sickness and prior VR gaming experience reporting higher gaming satisfaction, while participants without VR gaming experience surprisingly reported a higher sense of presence. These insights served as valuable references for developers aiming to create more immersive and engaging virtual experiences.

关键词： Surveys Visualization Technological innovation Three-dimensional displays Education Virtual environments Games Motion sickness User experience Testing

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Unveiling AI’s Blind Spots: An Oracle for In-Domain, Out-of-Domain, and Adversarial Errors

arXiv

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arXiv 2024年

作者： Han, Shuangpeng Zhang, Mengmi College of Computing and Data Science Nanyang Technological University Singapore Singapore

AI models make mistakes when recognizing images—whether in-domain, out-of-domain, or adversarial. Predicting these errors is critical for improving system reliability, reducing costly mistakes, and enabling proactive corrections in real-world applications such as healthcare, finance, and autonomous systems. However, understanding what mistakes AI models make, why they occur, and how to predict them remains an open challenge. Here, we conduct comprehensive empirical evaluations using a "mentor" model—a deep neural network designed to predict another "mentee" model’s errors. Our findings show that the mentor excels at learning from a mentee’s mistakes on adversarial images with small perturbations and generalizes effectively to predict in-domain and out-of-domain errors of the mentee. Additionally, transformer-based mentor models excel at predicting errors across various mentee architectures. Subsequently, we draw insights from these observations and develop an "oracle" mentor model, dubbed SuperMentor, that can outperform baseline mentors in predicting errors across different error types from the ImageNet-1K dataset. Our framework paves the way for future research on anticipating and correcting AI model behaviors, ultimately increasing trust in AI systems. All code, models, and data will be made publicly available. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Enhancing Phishing Website Detection with Machine Learning Algorithms

Enhancing Phishing Website Detection with Machine Learning A...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Weijie Pang Ayrton Joseph DiPina Ashar Neyaz School of Computing & Data Science Wentworth Institute of Technology Boston MA USA

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

This paper examines the impact of feature selection on phishing website detection using machine learning techniques. We utilize the UCI Phishing Website dataset, which consists of 11,055 instances and 30 features with no missing values. Our results show that the random forest outperforms logistic regression, achieving an accuracy of 97.11%. By analyzing feature importance, we optimize the model by removing detrimental features, further improving accuracy to 97.74%. This study highlights the effectiveness of the random forest in phishing detection and underscores the importance of feature selection in enhancing model performance.

关键词： Logistic regression Analytical models Accuracy Machine learning algorithms Phishing Digital forensics Feature extraction Security Random forests

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Securing Emerging IoT Environments with Super Learner Ensembles 2

Securing Emerging IoT Environments with Super Learner Ensemb...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ishtaiwi, Abdelraouf Al Maqousi, Ali Aldweesh, Amjad University Of Petra Department Of Data Science And Artificial Intelligence Amman Jordan University Of Petra Department Of Information Security Amman Jordan College Of Computing And It Shaqra University Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

This paper investigates the efficacy of the Super Learner ensemble algorithm for robust anomaly detection in Internet of Things (IoT) network traffic. The recently released CIC IoT dataset 2023, which contains both normal background traffic and common cyber attack patterns, is utilized as an evaluation benchmark. The Super Learner ensemble integrates five diverse base classifiers - Support Vector Machines (SVMs), logistic regression, neural networks, random forests, and K-Nearest Neighbors (KNNs). Extensive empirical analysis is conducted by training on a large stratified sample and testing generalization performance on a held-out set. Results demonstrate that the heterogeneous Super Learner ensemble achieves 94.2% test accuracy in detecting anomalies, which significantly outperforms any individual base model by over 7 percentage points. Precision, recall, and F1 metrics are also markedly improved by the ensemble approach compared to single-learner solutions. © 2024 IEEE.

关键词： Anomaly detection

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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Comments on Friedman’s Method for Class Distribution Estimation

arXiv

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arXiv 2024年

作者： Tasche, Dirk Unit for Data Science and Computing North-West University South Africa

The purpose of class distribution estimation (also known as quantification) is to determine the values of the prior class probabilities in a test dataset without class label observations. A variety of methods to achieve this have been proposed in the literature, most of them based on the assumption that the distributions of the training and test data are related through prior probability shift (also known as label shift). Among these methods, Friedman’s method has recently been found to perform relatively well both for binary and multi-class quantification. We discuss the properties of Friedman’s method and another approach mentioned by Friedman (called DeBias method in the literature) in the context of a general framework for designing linear equation systems for class distribution *** Codes 62F10, 62P30 © 2024, CC BY.

关键词： Probability distributions

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Enhancing AI Security: A Review of Blockchain-Integrated Solutions

Enhancing AI Security: A Review of Blockchain-Integrated Sol...

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Computer science and Blockchain (CCSB), International Conference on

作者： Cai Wei School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia

ISBN: (数字)9798350356502

ISBN: (纸本)9798350356519

Artificial Intelligence (AI) technology is advancing rapidly, driving innovation across various industries while also presenting significant security challenges. These challenges include protecting model ownership, ensuring data privacy, and defending against malicious attacks. Blockchain technology, recognized for its decentralized, transparent, and tamper-proof nature, offers a promising solution to address these issues. This review article explores three key research questions: How can blockchain technology protect AI model ownership? In what ways does blockchain safeguard data privacy within AI systems? What other security-related concerns arise with blockchain-driven AI? Through exploring these questions, the article addresses discussions on ownership, data privacy, attacks, scalability, quantum threats, and ethical security challenges, Through a comprehensive analysis of the existing literature, this article identifies the benefits and limitations of combining blockchain with AI, highlights technical and practical challenges, and proposes directions for future research to enhance the security, effectiveness, and ethical governance of blockchain-integrated AI systems.

关键词： Ethics data privacy Technological innovation Reviews Scalability Computer architecture Blockchains Security Artificial intelligence Standards

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Characterizations of Weighted Besov Spaces with Variable Exponents

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Acta Mathematica Sinica,English Series 2024年第11期40卷 2855-2878页

作者： Sheng Rong WANG Peng Fei GUO Jing Shi XU School of Mathematics and Statistics Hainan Normal UniversityHaikou571158P.R.China Center for Applied Mathematics of Guangxi Guangxi Colleges and Universities Key Laboratory of Data Analysis and ComputationSchool of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilin541004P.R.China

In this paper,we first give characterizations of weighted Besov spaces with variable exponents via Peetre’s maximal *** we obtain decomposition characterizations of these spaces by atom,molecule and *** an applicatio... 详细信息

关键词： Besov space Muckenhoupt weight variable exponent Peetre’s maximal function atom molecule wavelet pseudo-differential operator

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