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38th AAAI Conference on Artificial Intelligence (AAAI) / 36th Conference on Innovative Applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Guangdong Key Lab Big Data Anal & Proc Guangzhou Peoples R China Mohamed bin Zayed Univ Artificial Intelligence Dept Machine Learning Abu Dhabi U Arab Emirates Hong Kong Univ Sci & Technol Guangzhou AI Thrust Informat Hub Guangzhou Peoples R China Hong Kong Univ Sci & Technol Dept CSE Hong Kong Peoples R China

ISBN: (纸本)1577358872

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS.

关键词： Distillation

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An improved finegrained ciphertext policy based temporary keyword search on encrypted data for secure cloud storage

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SCIENTIFIC REPORTS 2024年第1期14卷 1-13页

作者： Dabra, Mamta Sharma, Shivani Kumar, Sachin Min, Hong Punjab Engn Coll Dept Comp Sci & Engn Chandigarh India Thapar Univ Dept Comp Sci & Engn Patiala India South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk Russia Gachon Univ Sch Comp Seongnam South Korea

We present a temporary keyword search over sensitive and confidential health data in a cloud environment. The cloud constitutes a semi-trusted domain, making it necessary for data owners to secure their data before outsourcing it through techniques like encryption. Attribute-based keyword search techniques tend to perform a search operation using a search token generated by an authorized user. These search tokens can lead to serious privacy threats, as they can extract all ciphertexts that may have been generated along with their keyword. Therefore, restricting search tokens to extract ciphertexts generated within a time interval is a more promising solution. In this paper, we present a novel ciphertext policy fine-grained temporary keyword that prevents the misuse of these search tokens. Further, it mitigates the risk of insider threats within healthcare organizations by limiting the window of opportunity for unauthorized access to minimum. To assess the security, our proposed scheme is formally proven to be secure against Selectively Chosen Keyword Attacks in the generic bilinear group model. Additionally, we demonstrate that the encryption algorithm's complexity is linear in relation to the number of attributes. Our scheme's significance and practicality are revealed by the performance evaluation.

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Federated learning-Based Misbehavior Detection for the 5G-Enabled Internet of Vehicles

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第2期70卷 4656-4664页

作者： Rani, Preeti Sharma, Chandani Ramesh, Janjhyam Venkata Naga Verma, Sonia Sharma, Rohit Alkhayyat, Ahmed Kumar, Sachin SRM Inst Sci & Technol Fac Engn & Technol Dept Elect & Commun Engn Delhi NCR Campus Ghaziabad 201204 India Maharishi Markandeshwar Deemed Univ Dept Comp Sci & Engn MMICTBM MCA Mullana Ambala 133207 India Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram 522502 India ABES Engn Coll Dept Comp Sci Ghaziabad 201204 India SRM Inst Sci & Technol Dept ECE Ghaziabad 201204 India ABES Engn Coll Dept Elect & Commun Engn Ghaziabad India Islamic Univ Coll Tech Engn Najaf 54005 Iraq South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk 454080 Russia

The concept of federated learning (FL) is becoming increasingly popular as a method for training collaborative models without loss the sensitive information. The term has become ubiquitous due to the extensive development of autonomous vehicles. Vehicular Networks and the Internet of Vehicles (IoV) enable cooperative learning through federated learning. It is still necessary to address several technical challenges. In recent years, Federated learning (FL) has attracted significant interest in various sectors, including smart cities and transportation systems. FL-enabled attack detection for IoVs are still in its infancy. However, to determine the main challenges of deployment in real-world scenarios, there needs to be research efforts from various areas. Performance metrics are used to evaluate the effectiveness of the proposed FL framework. According to experiments, the proposed FL approach detected attacks in IOV networks with a maximum accuracy of 99.72%. In addition to precision, recall, and F1 scores, 99.70%, 99.20%, and 99.26% were achieved. A comparison of the proposed model with the existing model shows that the proposed model is more accurate.

关键词： data privacy Federated learning Internet of Vehicles Security Artificial intelligence 5G mobile communication Training Internet of Vehicles (IoV) intelligent transportation system (ITS) 5G vehicular ad-hoc networks (VANETs)

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Multi-User QoE Enhancement: Federated Multi-Agent Reinforcement learning for Cooperative Edge Intelligence

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IEEE NETWORK 2022年第5期36卷 144-151页

作者： Li, Xiuhua Sun, Chuan Wen, Junhao Wang, Xiaofei Guizani, Mohsen Leung, Victor C. M. Chongqing Univ Sch Big Data & Software Engn Educ Minist Chongqing Peoples R China Chongqing Univ Inst Intelligent Software & Serv Comp Key Lab Dependable Serv Comp Cyber Phys Soc Educ Minist Chongqing Peoples R China Chongqing Univ Sch Big Data & Software Engn Chongqing Peoples R China Chongqing Univ Chongqing Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Mohamed bin Zayed Univ Artificial Intelligence MB Machine Learning Dept Abu Dhabi U Arab Emirates Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Univ British Columbia Vancouver BC Canada

Federated learning (FL) as a new decentralized learning/computing technique has potential advantages (e.g., accelerating computation task processing and protecting user privacy) for edge intelligence. However, due to limited computing/ caching capacities of network edges and dynamic arrivals of computation tasks, edge intelligence with FL cannot appropriately offload and effectively process computation tasks, which will degrade multi-user quality of experience (QoE). To address these challenges, it is critical to enhance the cooperation of network edges and quantify the multi-user QoE. In this article, we investigate the issue of cooperative edge intelligence by considering federated multi-agent reinforcement learning to enhance the multi-user QoE. Particularly, we present a cooperative edge intelligence architecture with vertical-horizontal cooperation supporting computation offloading. We model a comprehensive system cost to quantify the multi-user QoE and formulate the optimization problem as minimizing the expected long-term system cost. We further propose a decentralized intelligent offloading framework based on soft actor-critic and FL with an attention mechanism. Evaluation results demonstrate that the proposed scheme outperforms existing offloading schemes in terms of convergence and multi-user QoE. Finally, we discuss several open issues and opportunities of edge intelligence with FL.

关键词： Privacy Costs Federated learning Reinforcement learning Computer architecture Multitasking Quality of experience

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A Transformer-based multi-modal fusion network for 6D pose estimation

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INFORMATION FUSION 2024年 105卷

作者： Hong, Jia-Xin Zhang, Hong-Bo Liu, Jing-Hua Lei, Qing Yang, Li-Jie Du, Ji-Xiang Huaqiao Univ Dept Comp Sci & Technol Xiamen 361021 Peoples R China Huaqiao Univ Fujian Key Lab Big Data Intelligence & Secur Xiamen 361021 Peoples R China Huaqiao Univ Xiamen Key Lab Comp Vis & Pattern Recognit Xiamen 361021 Peoples R China Huaqiao Univ Key Lab Comp Vis & Machine Learning Fujian Prov Univ Xiamen 361021 Peoples R China

6D pose estimation has garnered significant attention and research. RGB images and point clouds converted from RGB-D images provide complementary color and geometry information, making them the mainstream data sources for object 6D pose estimation. However, due to the fact that RGB image and point cloud belong to different dimensional spaces and have different distribution characteristics, the fusion of these two complementary data sources remains a key technical challenge for 6D pose estimation. In contrast to prior approaches that simply concatenate separately processed RGB images and point clouds, this work introduces a Transformer -based multi -modal fusion network to address this challenge. More precisely, We build a Transformer architecture based pixel -wise feature extraction to optimize feature extraction from RGB images and point clouds. Subsequently, we investigate various multi -modal feature fusion methods to process these features, enabling deeper fusion of complementary data. Additionally, during the experimental phase, we design a 6D pose estimation network based on depth prediction to assess the impact of point cloud accuracy on the multi -modal fusion module. Finally, the proposed method is verified on four datasets: LineMOD, Occlusion Linemod, MP6D and YCB-Video. Experimental results show that the proposed method outperforms similar methods on these datasets.

关键词： 6D pose estimation Multi-modal feature fusion Pixel-wise feature extraction Transformer encoder

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An improved cost-sensitive approach toward the selection of wart treatment methods

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2023年第1期12卷 39-39页

作者： Mishra, Abinash Reddy, U. Srinivasulu Reddy, A. Venkataswamy Natl Forens Sci Univ Sch Cyber Secur & Digital Forens Delhi Campus Delhi 382007 India Natl Inst Technol Dept Comp Applicat Machine Learning & Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India Natl Inst Technol Dept Comp Applicat Big Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India

Warts are benign tumors, caused due to the infection of human papillomavirus (HPV). The identification of wart-specific treatment methods is pertaining to major challenges such as class imbalance, prediction accuracy, and biased nature of learning algorithm. In this article, a bagged ensemble of cost-sensitive extra tree classifier (BECSETC) is developed toward the selection of wart-specific treatment methods. BECSETC outperforms the state-of-the-art techniques (SOTA) by a margin of (0-45, 0-31.60), (0-12, 0-2.6) in terms of sensitivity and specificity which overcome the imbalanced distribution on both immunotherapy and cryotherapy datasets. However, on merged dataset, BECSETC algorithm gave an improvement of 6.04-10.57%, and 4.63% in terms of sensitivity and specificity, as compared to SOTA techniques.

关键词： Immunotherapy Cryotherapy Imbalance distribution Bagging ensemble Extra tree classifier

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DSGD-CECA: Decentralized SGD with Communication-Optimal Exact Consensus Algorithm 40

DSGD-CECA: Decentralized SGD with Communication-Optimal Exac...

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40th International Conference on machine learning

作者： Ding, Lisang Jin, Kexin Ying, Bicheng Yuan, Kun Yin, Wotao Univ Calif Los Angeles Dept Math Los Angeles CA 90024 USA Princeton Univ Dept Math Princeton NJ 08544 USA Google Inc Los Angeles CA USA Peking Univ Ctr Machine Learning Res Beijing Peoples R China AI Sci Inst Beijing Peoples R China Natl Engn Labratory Big Data Analyt & Applicat Beijing Peoples R China Alibaba US Decis Intelligence Lab Bellevue WA 98004 USA

Decentralized Stochastic Gradient Descent (SGD) is an emerging neural network training approach that enables multiple agents to train a model collaboratively and simultaneously. Rather than using a central parameter server to collect gradients from all the agents, each agent keeps a copy of the model parameters and communicates with a small number of other agents to exchange model updates. Their communication, governed by the communication topology and gossip weight matrices, facilitates the exchange of model updates. The state-of-the-art approach uses the dynamic one-peer exponential-2 topology, achieving faster training times and improved scalability than the ring, grid, torus, and hypercube topologies. However, this approach requires a power-of-2 number of agents, which is impractical at scale. In this paper, we remove this restriction and propose Decentralized SGD with Communication-optimal Exact Consensus Algorithm (DSGD-CECA), which works for any number of agents while still achieving state-of-the-art properties. In particular, DSGD-CECA incurs a unit per-iteration communication overhead and an (O) over tilde (n(3)) transient iteration complexity. Our proof is based on newly discovered properties of gossip weight matrices and a novel approach to combine them with DSGD's convergence analysis. Numerical experiments show the efficiency of DSGD-CECA.

关键词： Stochastic systems

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Weakly Supervised Video Individual Counting

Weakly Supervised Video Individual Counting

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xinyan Liu Guorong Li Yuankai Qi Ziheng Yan Zhenjun Han Anton van den Hengel Ming-Hsuan Yang Qingming Huang University of Chinese Academy of Science Key Lab of Big Data Mining and Knowledge Management UCAS China Macquarie University Australian Institute for Machine Learning The University of Adelaide University of California at Merced Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Video Individual Counting (VIC) aims to predict the number of unique individuals in a single video. Existing methods learn representations based on trajectory labels for individuals, which are annotation-expensive. To provide a more realistic reflection of the underlying practical challenge, we introduce a weakly supervised VIC task, wherein trajectory labels are not provided. Instead, two types of labels are provided to indicate traffic entering the field of view (inflow) and leaving the field view (outflow). We also propose the first solution as a baseline that formulates the task as a weakly supervised contrastive learning problem under group-level matching. In doing so, we devise an end-to-end trainable soft contrastive loss to drive the network to distin-guish inflow, outflow, and the remaining. To facilitate future study in this direction, we generate annotations from the existing VIC datasets Sense Crowd and CroHD and also build a new dataset, UAVVIC. Extensive results show that our baseline weakly supervised method outperforms supervised methods, and thus, little information is lost in the transition to the more practically relevant weakly supervised task. The code and trained model can be found at CGNet.

关键词： Computer vision Costs Codes Annotations Contrastive learning Drives Reflection

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Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review

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KNOWLEDGE-BASED SYSTEMS 2023年第1期274卷

作者： Rani, Sita Kataria, Aman Kumar, Sachin Tiwari, Prayag Guru Nanak Dev Engn Coll Dept Comp Sci & Engn Ludhiana 141006 Punjab India CSIR CSIO Chandigarh 160030 India Natl Res Univ South Ural State Univ Big Data & Machine Learning Lab Chelyabinsk 454080 Russia Halmstad Univ Sch Informat Technol Halmstad Sweden

Recent developments in the Internet of Things (IoT) and various communication technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into various medical devices and equipment, leading to the progression of the Internet of Medical Things (IoMT). Therefore, various IoMT-based healthcare applications are deployed and used in the day-to-day scenario. Traditionally, machine learning (ML) models use centralized data compilation and learning that is impractical in pragmatic healthcare frameworks due to rising privacy and data security issues. Federated learning (FL) has been observed as a developing distributed collective paradigm, the most appropriate for modern healthcare framework, that manages various stakeholders (e.g., patients, hospitals, laboratories, etc.) to carry out training of the models without the actual exchange of sensitive medical data. Consequently, in this work, the authors present an exhaustive survey on the security of FL-based IoMT applications in smart healthcare frameworks. First, the authors introduced IoMT devices, their types, applications, datasets, and the IoMT security framework in detail. Subsequently, the concept of FL, its application domains, and various tools used to develop FL applications are discussed. The significant contribution of FL in deploying secure IoMT systems is presented by focusing on FL-based IoMT applications, patents, real-world FL-based healthcare projects, and datasets. A comparison of FL-based security techniques with other schemes in the smart healthcare ecosystem is also presented. Finally, the authors discussed the challenges faced and potential future research recommendations to deploy secure FL-based IoMT applications in smart healthcare frameworks.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： data security Encryption Federated learning IoT IoMT machine learning Privacy Smart healthcare Security mechanisms

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Edge-Enabled Blockchain-Based V2X Scheme for Secure Communication Within the Smart City Development

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IEEE INTERNET OF THINGS JOURNAL 2023年第24期10卷 21282-21293页

作者： Chavhan, Suresh Kumar, Sachin Tiwari, Prayag Liang, Xueqin Lee, Ik Hyun Muhammad, Khan Indian Inst Informat Technol Raichur Dept Comp Sci & Engn Raichur 584135 India South Ural State Univ Natl Res Univ Big Data & Machine Learning Lab Chelyabinsk 454080 Russia Halmstad Univ Sch Informat Technol S-30118 Halmstad Sweden Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Tech Univ Korea Dept Mechatron Engn Shihung 15073 South Korea IKLAB Inc Shihung 15073 South Korea Sungkyunkwan Univ Coll Comp & Informat Sch Convergence Dept Appl Artificial IntelligenceVisual Analyt Kn Seoul 03063 South Korea

As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.

关键词： Security Vehicle-to-everything Smart cities Blockchains Servers Transportation Electronic mail Blockchain intelligent transport systems smart city V2X

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