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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Yiwei Zhang Rouzbeh Behnia Attila A. Yavuz Reza Ebrahimi Elisa Bertino Department of Computer Science Purdue University West Lafayette USA School of Information Systems and Management University of South Florida Sarasota USA Department of Computer Science and Engineering University of South Florida Tampa USA School of Information Systems and Management University of South Florida Tampa USA

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Federated learning enables the collaborative learning of a global model on diverse data, preserving data locality and eliminating the need to transfer user data to a central server. However, data privacy remains vulnerable, as attacks can target user training data by exploiting the updates sent by users during each learning iteration. Secure aggregation protocols are designed to mask/encrypt user updates and enable a central server to aggregate the masked information. MicroSecAgg (PoPETS 2024) proposes a single server secure aggregation protocol that aims to mitigate the high communication complexity of the existing approaches by enabling a one-time setup of the secret to be re-used in multiple training iterations. In this paper, we identify a security flaw in the MicroSecAgg that undermines its privacy guarantees. We detail the security flaw and our attack, demonstrating how an adversary can exploit predictable masking values to compromise user privacy. Our findings highlight the critical need for enhanced security measures in secure aggregation protocols, particularly the implementation of dynamic and unpredictable masking strategies. We propose potential countermeasures to mitigate these vulnerabilities and ensure robust privacy protection in the secure aggregation frameworks.

关键词： Deep learning Training Privacy Data privacy Protocols Federated learning Training data Vectors Security Servers

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Handling Imbalanced Data for Credit Card Fraudulent Detection: A Machine Learning Approach 1st

Handling Imbalanced Data for Credit Card Fraudulent Detectio...

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1st International Conference on Advances in Artificial Intelligence and Machine Learning in Big Data Processing, AAIMB 2023

作者： Sujatha, E. Umarani, V. Rekha, K.S. Gopirajan, P.V. Manickavasagan, V. Sriperumbudur India Department of Computational Intelligence SRM Institute of Science and Technology Chennai India Department of Computer Science and Business Systems Sri Sairam Engineering College Chennai India

ISBN: (纸本)9783031730672

Imbalanced data is crucial for contemporary machine learning models. Evaluating the effectiveness of machine learning classifiers under an unfair dataset is problematic. Several resampling techniques were applied to the unwarranted dataset and pre-processing stages to increase the accuracy of prognosis classifiers. This proposed work discusses the basics of resampling techniques such as Random under-sampling, Random oversampling, SMOTE (Synthetic Minority Over-sampling Technique), SMOTE-ENN (Synthetic minority over-sampling technique and Edited nearest neighbor), ADASYN (Adaptive Synthetic), and SMOTE-Tomek to handle this Weakness with unbalanced data. The proposed system uses an unstable credit card fraudulent dataset collected from Kaggle, which contains 21 columns and 28382 rows, of which 18497 negative samples and 4208 positive samples. The balanced dataset got by applying resampling methods SMOTE, SMOTE-ENN, ADASYN, and SMOTE-Tomek. Afterward, it uses machine learning techniques such as Logistic Regression (LR), Decision tree (DT), and Random Forest (RF) for classification. From this analysis, it selects the optimal scheme and classifier. The grey wolf optimization technique improves the classifier’s performance, which optimizes the samples after sampling. This hybrid method’s effectiveness was evaluated with standard evaluation parameters. The hybrid Random Forest technique achieved 92% accuracy from this analysis compared to hybrid models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Improving ASTRO's Perception Capability for a more Natural Follow-Me Task 6

Improving ASTRO's Perception Capability for a more Natural F...

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6th Experiment at International Conference, exp.at 2023

作者： Ferreira, Bruno Sorrentino, Alessandra Menezes, Paulo Batista, Jorge Dias, Jorge Fiorini, Laura Cavallo, Filipo University of Coimbra Institute of Systems and Robotics Dept. of Electrical and Computer Engineering Coimbra Portugal Khalifa University of Science and Technology Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates University of Florence Dept. of Industrial Engineering Florence Italy BioRobotics Institute Scuola Superiore Sant'Anna Pisa Italy

ISBN: (纸本)9798350314403

Social assistive robots usually encompass a great compromise between the advanced perception models that one can use and their computing capabilities. The ideal approaches are always oriented towards low power consumption while maintaining a higher order of responsiveness to the surroundings. Therefore, we present in this paper, an improvement of the follow-me system on ASTRO. In detail, we propose the use of Mediapipe SDK for human detection and tracking, when ASTRO is meant to accompany someone that is walking. A study on the new velocity and distance profiles that the robot keeps from the individuals is presented and we have also evaluated how it affects their perception of being safe. The presented results show that our new approach allows the system to achieve real-time performance by becoming ≈ 9.1× faster, smoothing, and keeping a more natural distance from the user. © 2023 IEEE.

关键词： Human robot interaction

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ChatGPT vs. Stack Overflow: An Exploratory Comparison of Programming Assistance Tools

ChatGPT vs. Stack Overflow: An Exploratory Comparison of Pro...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Jinrun Liu Xinyu Tang Linlin Li Panpan Chen Yepang Liu Southern University of Science and Technology Shenzhen Guangdong China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Programmers often seek help from Q&A websites to resolve issues they encounter during programming. Stack Overflow has been a widely used platform for this purpose for over a decade. Recently, revolutionary AI-powered platforms like ChatGPT have quickly gained popularity among programmers for their efficient and personalized programming assistance via natural language interactions. Both platforms can offer valuable assistance to programmers, but it's unclear which is more effective at enhancing programmer productivity. In our paper, we conducted an exploratory user study to compare the performance of Stack Overflow and ChatGPT in enhancing programmer productivity. Two groups of students with similar programming abilities were instructed to use the two platforms to solve three different types of programming tasks: algorithmic challenges, library usage, and debugging. During the experiments, we measured and compared the quality of code produced and the time taken to complete tasks for the two groups. The results show that, concerning code quality, ChatGPT outperforms Stack Overflow significantly in helping complete algorithmic and library-related tasks, while Stack Overflow is better for debugging tasks. Regarding task completion speed, the ChatGPT group is obviously faster than the Stack Overflow group in the algorithmic challenge, but the two groups have a similar performance in the other two tasks. Additionally, we conducted a post-experiment survey with the participants to understand how the platforms have helped them complete the programming tasks. We analyzed the questionnaires to summarize ChatGPT and Stack Overflow's strengths and weaknesses pointed out by the participants. By comparing these, we identified the reasons behind the two platforms' divergent performances in programming assistance.

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Evaluating Propagation Models for IIoT in Underground Mining: an Experimental Comparative Study in Underground Coal Mines

Evaluating Propagation Models for IIoT in Underground Mining...

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IEEE ANDESCON

作者： Fabian Medina Oscar Montañez Cristian Suancha Eduardo Avendaño Sandra Céspedes Systems and Computing Engineering school UPTC Sogamoso Colombia Electronic Engineering school UPTC Sogamoso Colombia Department of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (数字)9798350355284

ISBN: (纸本)9798350355291

Planning the Industrial Internet of Things (IIoT) systems for underground mining is critical in guaranteeing communication between nodes (sensor/actuator) and ensuring the effectiveness of their functionalities. Therefore, it is necessary to understand how electromagnetic waves disperse in under- ground environments through models that attempt to reproduce their behavior. This understanding is critical to designing and simulating efficient communications for IIoT systems requiring reliable and continuous data transmission. This paper evaluates two propagation models for underground mining tunnels and emphasizes the analysis of the materials that make up the side walls, the floor, and the roof. The analysis of the models is compared with experimental measurements of received power in the 433 MHz band made in coal mines in Colombia. The results, which have direct practical implications, show that the True Rays model generates a behavior similar to that observed in the experimental measurements, with determination coefficients above 74%. At the same time, we assessed the impact of the standard deviation of surface roughness on the accuracy of the received power predictions in the True Rays model.

关键词： Analytical models Accuracy Predictive models Surface roughness Coal mining Rough surfaces Standards Floors Tuning Industrial Internet of Things

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Exploiting Partial Observability and Optimized Simple State Representations in Deep Q-Learning 20

Exploiting Partial Observability and Optimized Simple State ...

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20th International Conference on Frontiers of Information Technology, FIT 2023

作者： Mahmood, Danyaal Arshad, Usama Ali, Raja Hashim Ul Abideen, Zain Shah, Muhammad Huzaifa Khan, Talha Ali Ijaz, Ali Zeeshan Ali, Nisar Siddique, Abu Bakar Gik Institute of Engg. Sciences & Technology Faculty of Computer Science & Engineering Topi Pakistan University of Europe for Applied Sciences Dept. of Technology & Software Engineering Berlin Germany Applications & Nanostructure BCMaterials Basque Center for Materials Spain University of Regina Faculty of Electronic Systems Engineering Regina Canada

ISBN: (纸本)9798350395785

The advent of deep Q-learning has opened up new possibilities in training autonomous agents to perform intelligently in intricate settings. This research work examines the potential of deep Q-learning in the paradigmatic Snake game, which requires an agent to navigate a grid, ingest food items, and avoid collisions. We incorporated a partially observable game state, introducing a novel level of difficulty to the task. Our proposed approach utilizes a straightforward feed-forward neural network to estimate Q-values of potential actions, given the current state. The agent progressively learns to balance between exploration and exploitation, allowing it to navigate optimally in the game scenario. Most existing studies in deep Q-learning for the Snake game overlook the complexities posed by partial observability, focusing mainly on fully observable state representations. Furthermore, the majority of the investigations delve into the efficacy of complex neural network architectures, leaving a gap in the understanding of the performance of more simplistic feed-forward networks. Our study fills this gap by offering an in-depth exploration and analysis of deep Q-learning strategies, especially when applied to partially observable game states and simple neural networks. Simulations and results reveal that adopting simpler state representations can significantly boost the agent's performance in the Snake game. A more detailed and intricate reward function is also observed to enhance the agent's decision-making skills and overall proficiency in the game. These observations underscore the significance of optimizing state representations and reward functions for maximizing the performance of deep Q-learning agents in the Snake game. © 2023 IEEE.

关键词： Autonomous agents

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Physical and Semantic-Driven Transformation of Everyday Objects into Smart Interfaces in MR

Physical and Semantic-Driven Transformation of Everyday Obje...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Liang, Xiaozhan Yong, Hu Shen, Xukun Yang, Tianyu Wang, Yu Zheng, Chenyue Piao, Dongxu School of New Media Art and Design Beihang University Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing Beijing China

ISBN: (纸本)9798400713958

MR merges virtual and physical worlds, with MLLM enhancing contextual understanding and multisensory perception for smarter interactions. This study explores user behaviors and task needs through contextual inquiry and retrospective interviews (N=12), identifying key daily tasks and information requirements. A participatory design workshop (N=18) further refines interface displays and interactions based on the physical and semantic attributes of objects, resulting in a dynamic design space. By leveraging MLLM, this design space adapts to user needs, paving the way for improving intelligent perception and redefining the role of everyday objects in MR. © 2025 Copyright held by the owner/author(s).

关键词： Intelligent Interfaces Mixed Reality (MR) Multimodal Large Language Models (MLLM) Physical and Semantic Substitution Transformation User Interaction

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Correlation-aware Graph Data Augmentation with Implicit and Explicit Neighbors

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ACM Transactions on Knowledge Discovery from Data 2024年第5期18卷 1-23页

作者： Kuo, Chuan-Wei Chen, Bo-Yu Peng, Wen-Chih Hung, Chih-Chieh Su, Hsin-Ning Institute of Computer Science and Engineering National Yang Ming Chiao Tung University No. 1001 University Road Hsinchu300 Taiwan Department of Management Information Systems National Chung Hsing University 145 Xingda Rd. South Dist. Taichung Taiwan Institute of Management of Technology National Yang Ming Chiao Tung University No. 1001 University Road Hsinchu300 Taiwan

In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub.1 © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graph neural networks

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CoDriveVLM: VLM-Enhanced Urban Cooperative Dispatching and Motion Planning for Future Autonomous Mobility on Demand systems

arXiv

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arXiv 2025年

作者： Liu, Haichao Yao, Ruoyu Liu, Wenru Huang, Zhenmin Shen, Shaojie Ma, Jun The Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology Guangzhou China The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

The increasing demand for flexible and efficient urban transportation solutions has spotlighted the limitations of traditional Demand Responsive Transport (DRT) systems, particularly in accommodating diverse passenger needs and dynamic urban environments. Autonomous Mobility-on-Demand (AMoD) systems have emerged as a promising alternative, leveraging connected and autonomous vehicles (CAVs) to provide responsive and adaptable services. However, existing methods primarily focus on either vehicle scheduling or path planning, which often simplify complex urban layouts and neglect the necessity for simultaneous coordination and mutual avoidance among CAVs. This oversimplification poses significant challenges to the deployment of AMoD systems in real-world scenarios. To address these gaps, we propose CoDriveVLM, a novel framework that integrates high-fidelity simultaneous dispatching and cooperative motion planning for future AMoD systems. Our method harnesses Vision-Language Models (VLMs) to enhance multi-modality information processing, and this enables comprehensive dispatching and collision risk evaluation. The VLM-enhanced CAV dispatching coordinator is introduced to effectively manage complex and unforeseen AMoD conditions, thus supporting efficient scheduling decision-making. Furthermore, we propose a scalable decentralized cooperative motion planning method via consensus alternating direction method of multipliers (ADMM) focusing on collision risk evaluation and decentralized trajectory optimization. Simulation results demonstrate the feasibility and robustness of CoDriveVLM in various traffic conditions, showcasing its potential to significantly improve the fidelity and effectiveness of AMoD systems in future urban transportation networks. The code is available at https://***/henryhcliu/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Risk assessment

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