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arXiv 2023年

作者： Zhou, Ziqi Hu, Shengshan Zhao, Ruizhi Wang, Qian Zhang, Leo Yu Hou, Junhui Jin, Hai 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 Cyber Science and Engineering Wuhan University China School of Information and Communication Technology Griffith University Australia Department of Computer Science City University of Hong Kong Hong Kong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial use. In this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://github. com/CGCL-codes/AdvEncoder. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Joint Power and Spectrum Orchestration for D2D Semantic Communication Underlying Energy-Efficient Cellular Networks

arXiv

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arXiv 2025年

作者： Xia, Le Sun, Yao Sun, Haijian Hu, Rose Qingyang Niyato, Dusit Imran, Muhammad Ali James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom School of Electrical and Computer Engineering University of Georgia AthensGA30602 United States Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Semantic communication (SemCom) has been recently deemed a promising next-generation wireless technique to enable efficient spectrum savings and information exchanges, thus naturally introducing a novel and practical network paradigm where cellular and device-to-device (D2D) SemCom approaches coexist. Nevertheless, the involved wireless resource management becomes complicated and challenging due to the unique semantic performance measurements and energy-consuming semantic coding mechanism. To this end, this paper jointly investigates power control and spectrum reuse problems for energy-efficient D2D SemCom cellular networks. Concretely, we first model the user preference-aware semantic triplet transmission and leverage a novel metric of semantic value to identify the semantic information importance conveyed in SemCom. Then, we define the additional power consumption from semantic encoding in conjunction with basic power amplifier dissipation to derive the overall system energy efficiency (semantics/Joule). Next, we formulate an energy efficiency maximization problem for joint power and spectrum allocation subject to several SemCom-related and practical constraints. Afterward, we propose an optimal resource management solution by employing the fractional-to-subtractive problem transformation and decomposition while developing a three-stage method with theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the adequate performance superiority of our proposed solution compared with different benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Energy utilization

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RELI11D: A Comprehensive Multimodal Human Motion dataset and Method

RELI11D: A Comprehensive Multimodal Human Motion Dataset and...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ming Yan Yan Zhang Shuqiang Cai Shuqi Fan Xincheng Lin Yudi Dai Siqi Shen Chenglu Wen Lan Xu Yuexin Ma Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However, solely using these modalities or a combination of them may not be adequate for HPE, particularly for complex and fast movements. For holistic human motion understanding, we present RELI11D, a high-quality multimodal human motion dataset involves LiDAR, IMU system, RGB camera, and Event camera. It records the motions of 10 actors performing 5 sports in 7 scenes, including 3.32 hours of synchronized LiDAR point clouds, IMU measurement data, RGB videos and Event steams. Through extensive experiments, we demonstrate that the RELI 11 D presents considerable challenges and opportunities as it contains many rapid and complex motions that require precise location. To address the challenge of integrating different modalities, we propose LEIR, a multimodal baseline that effectively utilizes LiDAR Point Cloud, Event stream, and RGB through our cross-attention fusion strategy. We show that LEIR exhibits promising results for rapid motions and daily motions and that utilizing the characteristics of multiple modalities can indeed improve HPE performance. Both the dataset and source code release publicly in http://***/reli11d/, fostering collaboration and enabling further exploration in this field.

关键词： Point cloud compression Location awareness Laser radar Source coding Cameras Motion detection Pattern recognition

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Identifying spatiotemporal traffic patterns in large-scale urban road networks using a modified nonnegative matrix factorization algorithm

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Journal of Traffic and Transportation engineering(English Edition) 2020年第4期7卷 529-539页

作者： Xiaolei Ma Yi Li Peng Chen School of Transportation Science and Engineering Beihang UniversityBeijing 100191China Beiing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeiing 100191China

The identification and analysis of spatiotemporal traffic patterns in road networks constitute a crucial process for sophisticated traffic management and *** methods based on mathematical equations and statistical models can hardly be applicable to large-scale urban road networks,where traffic states exhibit high degrees of dynamics and ***,advances in data collection and processing have provided new opportunities to effectively understand spatiotemporal traffic patterns in large-scale road networks using data-driven ***,limited efforts have been exerted to explore the essential structure of the networks when conducting a spatiotemporal analysis of traffic *** this end,this study proposes a modified nonnegative matrix factorization algorithm that processes high-dimensional traffic data and provides an improved representation of the global traffic *** matrix factorization,cluster analysis is conducted based on the obtained low-dimensional representative matrices,which contain different traffic patterns and serve as the basis for exploring the temporal dynamics and spatial structure of network *** applicability and effectiveness of the proposed approach are examined in a road network of Beijing,*** show that the methods exhibit considerable potential for identifying and interpreting the spatiotemporal traffic patterns over the entire network and provide a systematic and efficient approach for analyzing the network-level traffic state.

关键词： Large-scale network Traffic state Spatiotemporal patterns Nonnegative matrix factorization

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Frequency Diverse Array-enabled RIS-aided Integrated Sensing and Communication

arXiv

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arXiv 2024年

作者： Yang, Hanyu Gong, Shiqi Liu, Heng Xing, Chengwen Zhao, Nan Niyato, Dusit The School of Information and Electronics Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The School of Information and Communication Engineering Dalian University of Technology Dalian116024 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore

Integrated sensing and communication (ISAC) has been envisioned as a prospective technology to enable ubiquitous sensing and communications in next-generation wireless networks. In contrast to existing works on reconfigurable intelligent surface (RIS) aided ISAC systems using conventional phased arrays (PAs), this paper investigates a frequency diverse array (FDA)-enabled RIS-aided ISAC system, where the FDA aims to provide a distance-angle-dependent beampattern to effectively suppress the clutter, and RIS is employed to establish high-quality links between the BS and users/target. We aim to maximize sum rate by jointly optimizing the BS transmit beamforming vectors, the covariance matrix of the dedicated radar signal, the RIS phase shift matrix, the FDA frequency offsets and the radar receive equalizer, while guaranteeing the required signal-to-clutter-plus-noise ratio (SCNR) of the radar echo signal. To tackle this challenging problem, we first theoretically prove that the dedicated radar signal is unnecessary for enhancing target sensing performance, based on which the original problem is much simplified. Then, we turn our attention to the single-user single-target (SUST) scenario to demonstrate that the FDA-RIS-aided ISAC system always achieves a higher SCNR than its PA-RIS-aided counterpart. Moreover, it is revealed that the SCNR increment exhibits linear growth with the BS transmit power and the number of BS receive antennas. In order to effectively solve this simplified problem, we leverage the fractional programming (FP) theory and subsequently develop an efficient alternating optimization (AO) algorithm based on symmetric alternating direction method of multipliers (SADMM) and successive convex approximation (SCA) techniques. Numerical results demonstrate the superior performance of our proposed algorithm in terms of sum rate and radar SCNR. Copyright © 2024, The Authors. All rights reserved.

关键词： Radar clutter

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Safe Reinforcement Learning for Social Motor Coordination in Two Dimensional Space

Safe Reinforcement Learning for Social Motor Coordination in...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Fan Yang Yuening Huang Chao Zhai Hehong Zhang China University of Geosciences Wuhan Campus School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Rice University George R. Brown School of Engineering and Computing Houston Texas United States Fuzhou University School of Computer and Data Science Fuzhou China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper presents an interactive motion control method based on reinforcement learning, designed to assist children with autism who have social motor impairments through a mirror game intervention. The virtual teacher uses the Deep Deterministic Policy Gradient (DDPG) algorithm to optimize its actions, guiding the participant to follow a Lissajous trajectory. To ensure safety, a motion-correction mechanism was developed, which automatically adjusts actions when the predicted trajectory surpasses predefined safety boundaries. The reward function considers both the distance between the virtual teacher and the target trajectory, as well as the distance between the virtual teacher and the participant, with dynamic adjustments applied by the motion-correction mechanism. Experimental results demonstrate that the virtual teacher effectively guides the participant towards the target trajectory while adhering to safety constraints.

关键词： Autism Filters Reinforcement learning Games Variable speed drives Motors Trajectory Safety Mirrors Motion control

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed computing Systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li 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 Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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Hybrid System with Modified Cuckoo Search to Design Airfoil

Hybrid System with Modified Cuckoo Search to Design Airfoil

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data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Thinakaran M. Anand T.Rajesh Kumar Saira Banu Atham K.M. Subramanian Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering School of CSE & IS Presidency University Bangaluru Department of Computer Science Engineering Shadan College of Engineering and Technology Hyderabad

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error.

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Enhancing Cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and data Protection in Cloud Platforms 5

Enhancing Cloud Security with AI: Developing Robust, Scalabl...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Abusalih Rahumath Ali, AbuFaizur Rahman Saravanan, K. Abirami, B.Bala Pandi, V.Samuthira Aroulanandam, Vijay Vasanth Parthiban, S. Enterprise Solution Architect Tekskills Inc. PrincetonNJ08540 United States R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India Panimalar Engineering College Department of Artificial Intelligence and Data Science India Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Tamil Nadu Chennai India School of Computing SRM Institute of Science & Technology Networking and Communications Department Tamil Nadu Kattankulathur India SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of CSE Chennai Thandalam India

ISBN: (纸本)9798331530013

As cloud computing continues to gain popularity, guaranteeing the security of cloud environments has become an issue of the utmost importance for businesses all over the world. With a particular emphasis on the development of solutions that are both robust and scalable for the purpose of threat mitigation and data protection, this study investigates the application of artificial intelligence (AI) in the process of improving cloud security. Security frameworks that are driven by artificial intelligence are able to proactively discover, evaluate, and respond to new threats in real time. This is accomplished by employing machine learning algorithms and advanced analytics. With the purpose of demonstrating the efficacy of various artificial intelligence approaches in tackling common vulnerabilities such as data breaches, unauthorized access, and distributed denial of service attacks, we investigate a variety of AI methodologies, such as anomaly detection, behavior analysis, and automated incident response. In addition, we analyze the significance of a multilayered security approach, which involves integrating artificial intelligence tools with conventional security measures in order to develop an all-encompassing defense plan. The purpose of this article is to present case studies that illustrate successful implementations of artificial intelligence-enhanced security solutions on cloud platforms. These case studies demonstrate the influence that these solutions have on increasing security posture and operational efficiency. Ultimately, the findings of this study highlight the significant role that artificial intelligence plays in the transformation of cloud security practices. This paves the way for cloud infrastructures that are more robust and secure in the face of changing cyber threats. © 2024 IEEE.

关键词： Cloud platforms

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TransformerFusionNet: A Real-Time Multimodal Framework for ICU Heart Failure Mortality Prediction Using Big data Streaming

TransformerFusionNet: A Real-Time Multimodal Framework for I...

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2024 International Conference on Computer and Applications, ICCA 2024

作者： Saleh, Hager McCann, Michael El-Sappagh, Shaker Breslin, John G. Hurghada University Faculty of Computers and Artificial Intelligence Hurghada Egypt University of Galway Insight Research Ireland Centre for Data Analytics GalwayH91 TK33 Ireland Atlantic Technological University Letterkenny Ireland Atlantic Technological University Department of Computing Letterkenny Ireland Galala University Faculty of Computer Science and Engineering Suez Egypt Benha University Faculty of Computers and Artificial Intelligence Benha Egypt School of Engineering University of Galway GalwayH91 TK33 Ireland

ISBN: (纸本)9798350367560

This paper presents a real-time multimodal frame-work to enhance ICU mortality prediction for heart disease patients by integrating structured data, clinical notes, and big data streaming platforms. The proposed framework comprises two components: an offline multimodal model and a real-time pipeline. In the offline component, we proposed the Transformer-FusionNet model leverages BioBERT-Transformer for clinical notes and a Recurrent Neural Network (RNN) for structured data. The model integrates outputs through a concatenation layer to make predictions and is rigorously evaluated using the MIMIC-III dataset. It demonstrates superior performance across evaluation metrics and achieves an accuracy of 91.720%, precision of 91.85%, recall of 91.720%, and F1-score of 91.693%, outperforming other single and multimodal models. The real-time pipeline component incorporates Apache Spark and Apache Kafka for ingesting, preprocessing, and streaming structured data and clinical notes. This integration enables real-time mor-tality prediction with TransformerFusionNet, addressing critical gaps in the utilization of clinical notes in healthcare analytics. The framework significantly advances multimodal deep learning applications in healthcare by combining state-of-the-art models with scalable streaming technologies, offering a practical solution for improving clinical decision support systems and patient outcomes in real-time, especially in intensive care unit settings. © 2024 IEEE.

关键词： Recurrent neural networks

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