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IEEE Symposium on Security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization data augmentation Robustness

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A Multi-module Robust Method for Transient Stability Assessment against False Label Injection Cyberattacks

arXiv

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arXiv 2024年

作者： Wang, Hanxuan Lu, Na Liu, Yinhong Wang, Zhuqing Wang, Zixuan Systems Engineering Institute School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China School of Computer Science Northwestern Polytechnical University Xi’an710129 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

The success of deep learning in transient stability assessment (TSA) heavily relies on high-quality training data. However, the label information in TSA datasets is vulnerable to contamination through false label injection (FLI) cyberattacks, resulting in degraded performance of deep TSA models. To address this challenge, a Multi-Module Robust TSA method (MMR) is proposed to rectify the supervised training process misguided by FLI in an unsupervised manner. In MMR, a supervised classification module and an unsupervised clustering module are alternatively trained to improve the clustering friendliness of representation leaning, thereby achieving accurate clustering assignments. Leveraging the clustering assignments, we construct a training label corrector to rectify the injected false labels and progressively enhance robustness and resilience against FLI. However, there is still a gap on accuracy and convergence speed between MMR and FLI-free deep TSA models. To narrow this gap, we further propose a human-in-the-loop training strategy, named MMR-HIL. In MMR-HIL, potential false samples can be detected by modeling the training loss with a Gaussian distribution. From these samples, the most likely false samples and most ambiguous samples are re-labeled by a TSA experts guided bi-directional annotator and then subjected to penalized optimization, aimed at improving accuracy and convergence speed. Extensive experiments indicate that MMR and MMR-HIL both exhibit powerful robustness against FLI in TSA performance. Moreover, the contaminated labels can also be effectively corrected, demonstrating superior resilience of the proposed methods. © 2024, CC BY.

关键词： Transients

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Joint Deployment of UAV and Edge Server In Edge Computing

Joint Deployment of UAV and Edge Server In Edge Computing

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Huang, Wei Zhang, Deyu Luo, Wei Tang, Yin School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China

ISBN: (纸本)9798350368369

Edge server placement is a hot issue in mobile edge computing. It is a key prerequisite for deploying edge servers that can meet computing needs and improve resource utilization. This paper studies the joint location deployment problem of edge server and UAV, and proposes a dynamic placement solution for heterogeneous servers based on dynamically changing network environments and computing resource requirements, with the goal of minimizing migration costs. Extensive comparative experiments based on real datasets demonstrate that our algorithm outperforms other algorithms in minimizing costs. © 2025 IEEE.

关键词： Edge computing

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Text2NKG: fine-grained N-ary relation extraction for N-ary relational knowledge graph construction 24

Text2NKG: fine-grained N-ary relation extraction for N-ary r...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haoran Luo Haihong E. Yuhao Yang Tianyu Yao Yikai Guo Zichen Tang Wentai Zhang Shiyao Peng Kaiyang Wan Meina Song Wei Lin Yifan Zhu Luu Anh Tuan School of Computer Science Beijing University of Posts and Telecommunications China School of Automation Science and Electrical Engineering Beihang University China Beijing Institute of Computer Technology and Application Inspur Group Co. Ltd. China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798331314385

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable arity of entities. To address these restrictions, we propose Text2NKG, a novel fine-grained n-ary relation extraction framework for n-ary relational knowledge graph construction. We introduce a span-tuple classification approach with hetero-ordered merging and output merging to accomplish fine-grained n-ary relation extraction in different arity. Furthermore, Text2NKG supports four typical NKG schemas: hyper-relational schema, event-based schema, role-based schema, and hypergraph-based schema, with high flexibility and practicality. The experimental results demonstrate that Text2NKG achieves state-of-the-art performance in F1 scores on the fine-grained n-ary relation extraction benchmark. Our code and datasets are publicly available https://***/LHRLAB/Text2NKG.

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Enhancing Metaverse using Quantum Technologies

Enhancing Metaverse using Quantum Technologies

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Advances in Computing Research on science engineering and Technology (ACROSET), International Conference on

作者： Jash Doshi Sanober Sultana Shaikh Dept.of Computer Engineering Thadomal Shahani Engineering College Mumbai India Dept.of Artifical Intelligence and Data Science Thadomal Shahani Engineering College Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Recently, metaverse technology has seen a boom in users. However, data privacy, security, connectivity, and computational complexity are still the main issues that ruin the user experience and make the metaverse less likely to be used. In this context, we propose a 3-layer quantum-enabled architecture system for the metaverse that holds the promise of overcoming these issues. We also cover the applications of the metaverse, the current challenges faced by the metaverse, and the exciting potential of Quantum Computing to revolutionize the metaverse.

关键词： data privacy Quantum computing Metaverse computer architecture Games User experience Cryptography Computational complexity

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LightDAG: A Low-latency DAG-based BFT Consensus through Lightweight Broadcast

LightDAG: A Low-latency DAG-based BFT Consensus through Ligh...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Xiaohai Dai Guanxiong Wang Jiang Xiao Zhengxuan Guo Rui Hao Xia Xie Hai Jin 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 Huazhong University of Science and Technology China School of Computer Science and Artificial Intelligence Wuhan University of Technology China School of Computer Science and Technology Hainan University China

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

To improve the throughput of Byzantine Fault Tolerance (BFT) consensus protocols, the Directed Acyclic Graph (DAG) topology has been introduced to parallel data processing, leading to the development of DAG-based BFT consensus. However, existing DAG-based works heavily rely on Reliable Broadcast (RBC) protocols for block broadcasting, which introduces significant latency due to the three communication steps involved in each RBC. For instance, DAGRider, a representative DAG-based protocol, exhibits the best latency of 12 steps, considerably higher than non-DAG protocols like PBFT, which only requires 3 steps. To tackle this issue, we propose LightDAG, which replaces RBC with lightweight broadcasting protocols such as Consistent Broadcast (CBC) and Plain Broadcast (PBC). Since CBC and PBC can be implemented in two and one communication steps, respectively, LightDAG achieves low *** our proposal, we present two variants of LightDAG, namely LightDAG1 and LightDAG2, each providing a trade-off between the best latency and the expected worst latency. In LightDAG1, every block is broadcast using CBC, which exhibits a best latency of 5 steps and an expected worst latency of 14 steps. Since CBC cannot guarantee the totality property, we design a block retrieval mechanism in LightDAG1 to assist replicas in retrieving missing blocks. LightDAG2 utilizes a combination of PBC and CBC for block broadcasting, resulting in the best latency of 4 steps and an expected worst latency of 12(t+1) steps, where t represents the number of actual Byzantine replicas. Since a Byzantine replica may equivocate through PBC, LightDAG2 prohibits blocks from directly referencing contradictory blocks. To ensure liveness, we propose a mechanism to identify and exclude Byzantine replicas if they engage in equivocation attacks. Extensive experiments have been conducted to evaluate LightDAG, and the results demonstrate its feasibility and efficiency.

关键词： Fault tolerance Distributed processing Directed acyclic graph Fault tolerant systems Broadcasting Throughput data processing

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Implementing Front Axle Reference Points for Enhanced Autonomous Vehicle Navigation

Implementing Front Axle Reference Points for Enhanced Autono...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Premkumar Duraisamy Sibishree M Nandhini S Parvathy K Anukarthika S Devatharani G S Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering Dr NGP Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Sri Eswar College of Engineering Coimbatore India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

In response to the rapid progress of autonomous vehicle technology, the imperative for sophisticated navigation systems has emerged. This paper extensively investigates the fundamental mechanisms employed for reference and positioning in autonomous vehicles, with a specific focus on the Global Positioning System (GPS), Lidar (Light Detection and Ranging), Radar (Radio Detection and Ranging), and Camera-Based Vision Systems. Amidst the evolving landscape of autonomous navigation, numerous challenges persist, ranging from environmental variability to the need for real-time accuracy. A comprehensive comparative analysis is conducted for each technology, elucidating their fundamental operations, advantages, and limitations. This study highlights the performance of the front axle reference point mechanism and compared it the rear and center axle reference points. This study analyzes the distinct roles and applications of these mechanisms, underscoring the significance of the front axle reference point in achieving precision and reliability in navigation, especially in intricate and dynamic environments. The integration of these diverse technologies, with a specific emphasis on the front axle reference point, is pivotal for enabling autonomous vehicles to navigate with enhanced accuracy and safety. Additionally, the paper explores future trends, potential challenges, and emerging technologies, providing valuable insights into the evolving landscape of autonomous vehicle navigation. In essence, this paper endeavors to furnish a detailed overview of current technologies while offering a new perspective on how these systems, particularly those focused on the front axle reference point, will continue to mold the trajectory of autonomous transportation in the future.

关键词： Accuracy Laser radar Navigation Machine vision Wheels Distance measurement Vehicle dynamics

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Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

Rethinking the Evaluation for Conversational Recommendation ...

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2023 Conference on Empirical Methods in Natural Language Processing, EMNLP 2023

作者： Wang, Xiaolei Tang, Xinyu Zhao, Wayne Xin Wang, Jingyuan Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China China School of Information Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)9798891760608

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for CRSs, revealing the inadequacy of the existing evaluation protocol. It might overemphasize the matching with ground-truth items annotated by humans while neglecting the interactive nature of CRSs. To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs, named iEvaLM, which harnesses LLM-based user simulators. Our evaluation approach can simulate various system-user interaction scenarios. Through the experiments on two public CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and realistic evaluation approach for future research about LLM-based CRSs. The code is available at https://***/RUCAIBox/iEvaLM-CRS. © 2023 Association for Computational Linguistics.

关键词： Computational linguistics

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Enhancing Educational Strategy Through K-Means Clustering: A Study on Academic Departments

Enhancing Educational Strategy Through K-Means Clustering: A...

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International Joint Conference on computer science and Software engineering (JCSSE)

作者： Aomsap Inkongngarm Jakramate Bootkrajang Samerkae Somhom Areerat Trongratsameethong Prasert Luekhong Data Science Consortium Faculty of Engineering Chiang Mai University Chiang Mai Thailand Department of Computer Science Faculty of Science Chiang Mai University Chiang Mai Thailand College of Integrated Science and Technology Rajamangala University of Technology Chiang Mai Thailand

ISBN: (数字)9798350381764

ISBN: (纸本)9798350381771

This study explores the use of k-means clustering for strategic analysis of academic departments within universities, focusing on enrollment and performance metrics. This research aimed at enhancing university administration through data-driven decision making. The research highlights the potential of k-means clustering for resource allocation, curriculum planning, and improving student success. The study categorizes departments into three distinct groups through data collection and cleansing, followed by the application of the Elbow method for optimal cluster determination. These clusters offer insights into departmental dynamics, guiding strategic interventions in areas ranging from high-performance departments to those facing enrollment challenges. The findings underscore the significance of aligning academic offerings with performance excellence, revealing the strategic benefits of a data-informed approach in higher education. This research not only fills a gap in applying quantitative analysis at the departmental level but also opens pathways for further exploration of machine learning in educational strategy, underscoring the transformative potential of analytics in academic management.

关键词： Machine learning algorithms Statistical analysis Education Decision making Refining Focusing Machine learning

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Assessing the Improvements Brought by Artificial Intelligence on the Prediction of Aerodynamic Coefficients

Assessing the Improvements Brought by Artificial Intelligenc...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Zerouaoui, Jad Alaoui, Altaf Ettaki, Badia Chakir, ElMahjoub Laboratory of Material and Subatomic Physics Faculty of Sciences Ibn Tofail University Campus Universitaire BP 133 Kenitra Morocco LyRICA: Laboratory of Research in Computer Science Data Sciences and Knowledge Engineering School of Information Sciences Rabat Rabat Morocco

ISBN: (纸本)9783031262531

The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improvements of Artificial Intelligence as a pilar of the next generation of scientific research. Applications and developments brought by Artificial Intelligence and Deep Learning to the aerospace industry generally, mainly the prediction of aerodynamic coefficients and design optimization using intricate neural networks replicating the process of the human brain. Scientific developments participated in improving major areas in the aerospace field. Due to the expensive and time-consuming process related to the complexity of the geometry and the precision of the results required, researchers integrated surrogate models based on Deep Learning in order to improve the calculation process of the aerodynamic coefficients. On one hand, this paper introduces and discusses the general architecture of several networks used to enhance the calculation ability of the machine, while presenting the process of each work along with recounting the different methods employed. On the other hand, a discussion regarding the challenges presented with utilizing Computational Fluid Dynamics while assessing and comparing the results brought by surrogate models based on efficiency and time consumption is also tackled. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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