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arXiv 2024年

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of School of Software Engineering Huazhong University of Science and Technology China

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model’s incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method’s superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method’s effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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DST: Personalized Charging Station Recommendation for Electric Vehicles Based on Deep Reinforcement Learning and Spatio-Temporal Preference Analysis

DST: Personalized Charging Station Recommendation for Electr...

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IEEE International Conference on Web Services (ICWS)

作者： Xiaoying Fan Yongqiang Gao Fujun Zhang College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

With the rapid development and popularization of electric vehicles (EVs), the increased demand for charging stations (CSs) has made configuring charging facilities and optimizing charging station recommendations crucial. Consequently, personalized recommendation services for charging stations have emerged as a crucial strategy to mitigate the "mileage anxiety" experienced by drivers. Previous work has typically focused on optimizing CSs’ resource allocation or drivers’ costs, with little consideration of the complexity of the charging behavior of individuals, such as the distribution of Point Of Interests (POIs) surrounding the CS, the similarity of driver selection in different temporal scales, and the tradeoff between different users’ inherent preferences and external cost (e.g., travel times and electric prices), which may result in lower satisfaction, decreased accuracy of recommendations, and sub-optimal decision-making. To address this problem, we propose a charging station recommendation framework based on Actor-Critic Deep Reinforcement Learning, called DST, to assist electric vehicle drivers in finding the proper spots for charging. In the DST, both the actor and critic networks are implemented by Deep Neural Networks (DNNs). The Actor networks, utilizing a Semantic-based Bidirectional Long Short-Term Memory Network and a Stacked Convolutional Neural Network with Multiple Channels, extract the geographical semantic features of CSs and specific preference patterns from different temporal scales as hidden state representations. The critic networks employ a Twin-Critic architecture for joint optimization of inherent preference rewards and external environmental rewards. Extensive experiments on two real-world datasets demonstrate that the DST achieves the best comprehensive performance compared with seven baseline approaches.

关键词： Costs Web services Semantics Computer architecture Charging stations Feature extraction Electric vehicles Deep reinforcement learning Spatiotemporal phenomena Vehicles

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A Spectral Bipartite Analogue of the Erdős-Sós Conjecture

SSRN

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SSRN 2024年

作者： Geng, Xianya Wei, Wei Feng, Zhiming Center of Intelligent Computing and Applied Statistics School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai China School of Mathematics and Big Data Anhui University of Science & Technology Huainan China School of Mathematics and Statistics Xinyang Normal University Xinyang China

A well-known conjecture of Erdős and Sós states that each graph on ν vertices with size more than ν(t2−1) contains every tree with order t + 1 as a subgraph. In 2010, Nikiforov presented the spectral Erdős-Sós conjecture: Let G be a graph with sufficiently large order ν and spectral radius λ(G), and let t ≥ 2 be an integer, (i) if λ(G) ≥ λ(Kt ∨ Kν−t), then G contains all trees of order 2t + 2 unless G ∼= Kt ∨ Kν−t;(ii) if λ(G) ≥ λ(Kt ∨ (Kν−t−2 ∪ K2)), then G contains all trees of order 2t + 3 unless G ∼= Kt ∨ (Kν−t−2 ∪ K2). Recently, this conjecture was confirmed by Cioabă, Desai and Tait (2022). In this contribution, we resolve the spectral bipartite analogue of the Erdős-Sós conjecture. For sufficiently large ν and positive integer t ≥ 2, we show that the complete bipartite graph Kt,ν−t uniquely maximizes the spectral radius over the set of ν-vertex bipartite graphs each of which does not contain some trees with order 2t + 2 (resp. 2t + 3) as subgraphs. Our proof technique is mainly based on the Zarankiewicz number of a tree, and with the aid of the eigenvector method from Cioabă, Desai and Tait (2022). Some problems are proposed in the end for future research. © 2024, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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BADROBOT: JAILBREAKING EMBODIED LLMS IN THE PHYSICAL WORLD

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Zhu, Chenyu Wang, Xianlong Zhou, Ziqi Yin, Changgan Li, Minghui Xue, Lulu Wang, Yichen Hu, Shengshan Liu, Aishan Guo, Peijin Zhang, Leo Yu National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Beihang University China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these embodied LLMs perpetrate harmful behaviors? In response, we introduce BADROBOT, a novel attack paradigm aiming to make embodied LLMs violate safety and ethical constraints through typical voice-based user-system interactions. Specifically, three vulnerabilities are exploited to achieve this type of attack: (i) manipulation of LLMs within robotic systems, (ii) misalignment between linguistic outputs and physical actions, and (iii) unintentional hazardous behaviors caused by world knowledge’s flaws. Furthermore, we construct a benchmark of various malicious physical action queries to evaluate BADROBOT’s attack performance. Based on this benchmark, extensive experiments against existing prominent embodied LLM frameworks (e.g., Voxposer, Code as Policies, and ProgPrompt) demonstrate the effectiveness of our BADROBOT. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Joint Task Offloading and Resource Allocation for NOMA-Based Vehicular Networks

Joint Task Offloading and Resource Allocation for NOMA-Based...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yunfei Song Yongqiang Gao Yipei He Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China College of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Vehicle Edge computing (VEC) is a critical technology that can achieve low latency and energy consumption for Telematics. However, with the high-speed mobility of new energy-electric vehicles and their cross-regional nature, performing high-quality service of vehicle tasks on the VEC model is still challenging. Considering the electric vehicle range problem, this paper plans to jointly optimize task offloading, task result forwarding and computational resource allocation (OOFR) within the maximum tolerable delay of vehicle tasks to minimize vehicle tasks' delay and energy consumption. The non-orthogonal multiple access (NOMA) technology is used with roadside units (RSUs) to achieve multiplexing of limited spectrum resources. This allows multiple vehicle users to perform task transmission simultaneously, thus reducing vehicle task transmission delay. In addition, we propose a cooperative game approach based on NOMA for task grouping to reduce the signal interference of vehicle task transmission. Finally, a deep reinforcement learning method is proposed for task offloading decision selection. A simulation platform is built to compare with MEC, COMO and MADDPG methods, combined with simulation results, showing that the superiority of our proposed scheme is verified.

关键词： NOMA Energy consumption Simulation Games Electric vehicles Deep reinforcement learning Telematics

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Hyper-Parameter Optimization and Proxy Re-encryption for Federated Learning 6th

Hyper-Parameter Optimization and Proxy Re-encryption for F...

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6th International Conference on Blockchain and Trustworthy Systems, BlockSys 2024

作者： Du, Wanrong Wen, Bin Ma, Mengshuai Liu, Wenlong Wei, Xiaoxun Key Laboratory of Data Science and Smart Education Ministry of Education Hainan Normal University Haikou571158 China School of Information Science and Technology Hainan Normal University Haikou571158 China Cloud Computing and Big Data Research Center Hainan Normal University Haikou571158 China

ISBN: (纸本)9789819614103

Federated learning is a distributed machine learning paradigm that allows multiple participants to collaborate on training a shared model without revealing their own data. However, federated learning faces challenges such as data heterogeneity, communication overhead, security attacks, etc., which result in low efficiency of data training. To address these issues, this paper proposes a strategy to enhance the training efficiency of federated learning data through hyperparameter optimization and proxy re-encryption. A semi-supervised model of federated learning based on generative adversarial network is designed so that each participant can automatically adjust and optimize their own training parameters according to their own data distribution and environment in order to adapt to the data heterogeneity and dynamics of federated learning. Finally, experiments demonstrate that the proposed method can significantly improve the efficiency of data training and model performance compared with traditional federated learning methods across different datasets and scenarios. Experimental results show that the proposed strategy can achieve the same or even better accuracy than centralized machine learning while safeguarding data privacy and enabling ciphertext conversion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Autonomous Navigation of Agricultural Robots Utilizing Path Points and Deep Reinforcement Learning

Autonomous Navigation of Agricultural Robots Utilizing Path ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jipeng Kang Zhibin Zhang Yajie Liang Xu Chen Key Laboratory of Wireless Networks and Mobile Computing School of Computer Science Inner Mongolia University Hohhot China Engineering Research Center of Ecological Big Data School of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

When using deep reinforcement learning algorithms for reactive navigation, it is common to encounter situations where the agent gets stuck in a local optimum. In autonomous exploration without any prior knowledge, the exploration of environments is often suboptimal. Moreover, the agricultural robots performing tasks in farm fields face even more great challenges. To address this, we propose an Agricultural Robot Autonomous Exploration System (ARAES) that uses deep reinforcement learning algorithms to drive the robot to explore unknown farm environments. The whole system consists of a Global Path Planning (GPP) module and a Local Path Planning (LPP) module. The GPP module utilizes path points acquired along the heading direction in the environment and selects waypoints based on the collected path points to guide the robot towards the global goal, effectively alleviating local optima issues in reactive navigation. The LPP module learns local navigation motion policies in a simulated environment. It incorporates the learned policies into the motion planning stack, enabling the robot to move towards global goals based on the path points and way points. The advantages of the ARAES system are its autonomy and real-time performance, allowing the robot to operate without needing to know detailed prior information about the environment and enabling real-time adjustments of navigation policies according to current environments. Meanwhile, ARAES can construct maps in real-time as the robot moves in the environment. Experiments demonstrate that ARAES is advantageous because it does not rely on complex static and dynamic environment maps or prior information and delivers superior performance compared to other methods.

关键词： Agricultural robots Simultaneous localization and mapping Navigation Production Deep reinforcement learning Real-time systems Path planning

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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Multi-View Clustering Via Multi-Manifold Regularized Concept Factorization

SSRN

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SSRN 2024年

作者： Ban, Yong Su, Shaosheng Cai, Yongming Huang, Zhanpeng College of Medical Information Engineering Guangdong Pharmaceutical University Guangzhou510006 China Information department of Zhongshan City People’s Hospital Zhongshan528403 China Guangdong Provincial Traditional Chinese Medicine Precision Medicine Big Data Engineering Technology Research Center Guangzhou510006 China Cloud-Based Computing Precision Medical Big Data Engineering Technology Research Center of Guangdong Universities Guangzhou510006 China

Matrix factorization stands as a crucial technology for deriving latent representations of data by uncovering underlying structures. Non-negative matrix factorization (NMF)-based multi-view clustering algorithms have demonstrated their competitive edge in this *** order to eliminate the non-negativity constraint, concept factorization (CF) based multi-view clustering methods have been widely used. However, concept factorization fails to preserve the local geometric structure local geometric structure and form a consensus matrix in a unified framework. In this paper, a multimanifold regularized concept factorization for multi-view clustering (MVMMCF) is proposed, which can preserve the local geometric structure of the feature and data manifold. MVMMCF uses kernelbased approaches to refine the latent data representation. Dual-graph regularization is incorporated to form the geometrical framework of the feature and data manifolds, and representation matrix is regularized toward a common consensus matrix. And an iterative alternating method is used to achieve the consensus representation matrix for multi-view data. Finally, extensive experiments are carried out on five *** result shows that our method outperform several popular non-negative matrix factorization and concept factorization based multi-view clustering algorithms. © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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