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arXiv 2024年

作者： Yang, Chen He, Yangfan Tian, Aaron Xuxiang Chen, Dong Wang, Jianhui Shi, Tianyu Heydarian, Arsalan Liu, Pei Department of Computer Science and Informatics Cardiff University United Kingdom College of Libera Arts University of Minnesota - Twin Cities United States United States Agricultural & Biological Engineering Mississippi State University United States Information and Software Engineering University of Electronic Science and Technology of China China Transportation Research Institute University of Toronto Canada Link Lab & Civil and Environmental Engineering University of Virginia United States Intelligent Transportation Thrust The Hong Kong University of Science and Technology Guangzhou China

In this paper, we introduce a novel approach for autonomous driving trajectory generation by harnessing the complementary strengths of diffusion probabilistic models (a.k.a., diffusion models) and transformers. Our proposed framework, termed the "World-centric Diffusion Transformer"(WcDT), optimizes the entire trajectory generation process, from feature extraction to model inference. To enhance the scene diversity and stochasticity, the historical trajectory data is first preprocessed into "Agent Move Statement" and encoded into latent space using Denoising Diffusion Probabilistic Models (DDPM) enhanced with Diffusion with Transformer (DiT) blocks. Then, the latent features, historical trajectories, HD map features, and historical traffic signal information are fused with various transformer-based encoders that is used to enhance the interaction of agents with other elements in the traffic scene. The encoded traffic scenes are then decoded by a trajectory decoder to generate multimodal future trajectories. Comprehensive experimental results show that the proposed approach exhibits superior performance in generating both realistic and diverse trajectories, showing its potential for integration into automatic driving simulation systems. Our code is available at https://***/yangchen1997/WcDT. © 2024, CC BY.

关键词： Stochastic systems

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Distributed Compressed Sensing Based on Local Transformer Network

SSRN

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SSRN 2023年

作者： Zhou, Yu Xie, Wei Yang, Yingkai Sun, Shilong Zhang, Xiao Qin, Jun Key Laboratory of Cyber-Physical Fusion Intelligent Computing State Ethnic Affairs Commission Wuhan China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Department of Computer Science South-Central Minzu University Wuhan430074 China

Among compressed sensing algorithms, Distributed Compressed Sensing (DCS) stands out for its ability to jointly reconstruct multiple signals, making it a focal point of interest for researchers and engineers , thanks to its outstanding efficiency in signal reconstruction. In contrast to conventional approaches that depend on a priori estimates, like joint sparsity of signals, deep neural networks leverage their robust feature extraction and sequence modeling abilities to automatically capture inter-signal dependencies. This confers them with the benefits of faster solving speed and superior reconstruction accuracy. Most of the current methods, however, do not fully utilize the global dependency information between signals, while also ignoring the detailed features within the signals. Therefore, this paper proposes a Local Transformer Network for DCS (LTN-DCS). It utilizes the self-attention mechanism to extract global contextual information between the reconstructed signal sequences, while also addressing local redundancy partitioning within the signal sequences for finer-grained information capture. This approach generates probabilistic estimates of the non-zero element positions in each signal, forming the foundation for selecting basis atoms from the sampling matrix. Finally, the least-squares method is used to achieve the signal reconstruction. Through extensive experiments, the results show that our proposed method outperforms state-ofthe art DCS methods. © 2023, The Authors. All rights reserved.

关键词： Signal reconstruction

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A Robust Key Exchange and Tamper-Resistant Protocol for HAN and NAN Networks in Smart Grids

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IEEE Internet of Things Journal 2025年第12期12卷 21394-21403页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Shamshad, Salman Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250353 China

Smart Grids (SG) rely on Home Area Networks (HAN) and Neighborhood Area Networks (NAN) to ensure efficient power distribution, real-time monitoring, and seamless communication between smart devices. Despite these advantages, the use of public communication channels in HAN and NAN networks introduces critical challenges, such as vulnerability to impersonation, physical tampering, and scalability issues in resource-constrained environments. These issues compromise the stability, reliability, and security of SG environments. Existing protocols often fail to adequately address these challenges, particularly in ensuring resistance to physical tampering of supply lines and impersonation attacks. Additionally, they struggle to minimize the computation, communication, and energy costs associated with securing a resource-constrained SG environment. Therefore, we propose a robust key exchange and tamper-resistant protocol for HAN and NAN networks to address these limitations. The proposed protocol leverages the Physical Unclonable Function (PUF) mechanism to offer physical tampering resistance to smart meters. We validate our protocol formally using the Random or Real (RoR) model, which confirms its security robustness. Additionally, our informal security analysis highlights the protocol's resilience against impersonation, physical tampering attacks, etc. We analyze and compare the performance of the proposed protocol, which further demonstrates our protocol's effectiveness and security compared to competing protocols. Moreover, the proposed protocol achieves resource efficiency with 45.99% and 58.85% substantial reductions in computation overhead and energy overhead, respectively, compared to competing protocols. These results showcase the protocol's enhanced security and practicality for resource-constrained SG environments. © 2014 IEEE.

关键词： Authentication Protocol

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Forecasting COVID-19 Infections in Gulf Cooperation Council (GCC) Countries using Machine Learning

arXiv

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arXiv 2023年

作者： Ismail, Leila Materwala, Huned Hennebelle, Alain Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates Independent Data Scientist and Engineer Al Ain Abu Dhabi United Arab Emirates

COVID-19 has infected more than 68 million people worldwide since it was first detected about a year ago. Machine learning time series models have been implemented to forecast COVID-19 infections. In this paper, we develop time series models for the Gulf Cooperation Council (GCC) countries using the public COVID-19 dataset from Johns Hopkins. The dataset set includes the one-year cumulative COVID-19 cases between 22/01/2020 to 22/01/2021. We developed different models for the countries under study based on the spatial distribution of the infection data. Our experimental results show that the developed models can forecast COVID-19 infections with high *** Codes 68-04, 68T05, 68T07, 68T20 © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Secure and Efficient Data Storage and Sharing Scheme Based on Double Blockchain

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Computers, Materials & Continua 2021年第1期66卷 499-515页

作者： Lejun Zhang Minghui Peng Weizheng Wang Yansen Su Shuna Cui Seokhoon Kim College of Information Engineering Yangzhou UniversityYangzhou225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou362000China Division of Computer Science University of AizuAizu–Wakamatsu9658580Japan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei230601China Medical College of Yangzhou University Yangzhou225001China Department of Gynecology and Obstetrics Affiliated Hospital of Yangzhou UniversityYangzhouChina Department of Computer Software Engineering Soonchunhyang UniversityAsanKorea

In the digital era,electronic medical record(EMR)has been a major way for hospitals to store patients’medical *** traditional centralized medical system and semi-trusted cloud storage are difficult to achieve dynamic balance between privacy protection and data *** storage capacity of blockchain is limited and single blockchain schemes have poor scalability and low *** address these issues,we propose a secure and efficient medical data storage and sharing scheme based on double *** our scheme,we encrypt the original EMR and store it in the *** storage blockchain stores the index of the complete EMR,and the shared blockchain stores the index of the shared part of the *** with different attributes can make requests to different blockchains to share different parts according to their own *** experiments,it was found that cloud storage combined with blockchain not only solved the problem of limited storage capacity of blockchain,but also greatly reduced the risk of leakage of the original *** Extraction Signature(CES)combined with the double blockchain technology realized the separation of the privacy part and the shared part of the original *** symmetric encryption technology combined with Ciphertext-Policy Attribute-Based Encryption(CP–ABE)not only ensures the safe storage of data in the cloud,but also achieves the consistency and convenience of data update,avoiding redundant backup of *** analysis and performance analysis verified the feasibility and effectiveness of our scheme.

关键词： Cloud storage blockchain electronic medical records access control data sharing privacy

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Positional Encoding-Guided Transformer-Based Multiple Instance Learning for Histopathology Whole Slide Images Classification

SSRN

引用

SSRN 2024年

作者： Shi, Jun Sun, Dongdong Wu, Kun Jiang, Zhiguo Kong, Xue Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Anhui Province Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Province Hefei230601 China Beijing102206 China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering China

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slidelevel classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches which is likely to strengthen the discriminative ability of WSI-level features. In this paper, we propose a novel position encoding-guided transformerbased multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatialaware features. After incorporating the semantic features and spatial embeddings, multi-head selfattention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial-semantic consistency and generalization ability of features. The proposed method is evaluated on one public TCGA dataset and two in-house datasets. Experimental results validate it is effective in the tasks of lung cancer subtyping and gene mutation status prediction for lung cancer and gastrointestinal stromal tumor (GIST), and superior to the state-of-the-art MIL-based methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

引用

IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Knowledge Augmentation in Federation: Rethinking What Collaborative Learning Can Bring Back to Decentralized Data

arXiv

引用

arXiv 2025年

作者： Wu, Wentai He, Ligang Long, Saiqin Abdelmoniem, Ahmed M. Wu, Yingliang Mao, Rui Department of Computer Science College of Information Science Technology Jinan University Guangzhou510632 China Department of Computer Science University of Warwick CoventryCV4 7AL United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Electronic Business The Institute of Digital Business & Intelligent Logistics South China University of Technology Guangzhou510006 China College of Computer Science and Software Engineering The Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen518060 China

Data, as an observable form of knowledge, has become one of the most important factors of production for the development of Artificial Intelligence (AI). Meanwhile, increasing legislation and regulations on private and proprietary information results in scattered data sources also known as the "data islands". Although some collaborative learning paradigms such as Federated Learning (FL) can enable privacy-preserving training over decentralized data, they have inherent deficiencies in fairness, costs and reproducibility because of being learning-centric, which greatly limits the way how participants cooperate with each other. In light of this, we present a knowledge-centric paradigm termed Knowledge Augmentation in Federation (KAF), with focus on how to enhance local knowledge through collaborative effort. We provide the suggested system architecture, formulate the prototypical optimization objective, and review emerging studies that employ methodologies suitable for KAF. On our roadmap, with a three-way categorization we describe the methods for knowledge expansion, knowledge filtering, and label and feature space correction in the federation. Further, we highlight several challenges and open questions that deserve more attention from the community. With our investigation, we intend to offer new insights for what collaborative learning can bring back to decentralized data. © 2025, CC BY-NC-SA.

关键词： Federated learning

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A Novel 6dof Pose Estimation Method Using Transformer Fusion

SSRN

引用

SSRN 2023年

作者： Wang, Huafeng Zhang, Haodu Liu, Wanquan Hu, Zhimin Gao, Haoqi Lv, Weifeng Gu, Xianfeng School of Information Technology North China University of Technology Beijing100122 China School of Software Beihang University Beijing100191 China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510335 China University of Wisconsin Madison United States Department of Computer Science Stony Brook University NY11794-2424 United States

Integrating heterogeneous information in multimodal tasks presents challenges in effectively leveraging complementary information. This is particularly evident in the context of 6D pose estimation, where the effective utilization of RGB-D data is crucial for extracting supplementary information that complements 2D and 3D data. Another challenge is aligning modalities implicitly to obtain robust estimation results. To tackle these challenges, we propose a Transformer-based fusion module that facilitates hierarchical fusion across modalities at different stages. This approach effectively combines disparate features and complements global information in a non-convolutional manner. Additionally, we employ an attentional residual structure to address two primary challenges. Firstly, it mitigates information loss that may occur due to the stacking depth of layers. Secondly, it leverages attention weights to adapt the output of the preceding layer, thereby enhancing modal alignment. We validate our approach on two benchmark datasets: LineMOD [1] and YCB-Video [2]. Our method outperforms most existing methods on the LineMOD dataset and achieves state-of-the-art results on the YCB-Video dataset, showcasing its superior generalization ability. © 2023, The Authors. All rights reserved.

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Hybrid Reinforced Medical Report Generation with M-Linear Attention and Repetition Penalty

arXiv

引用

arXiv 2022年

作者： Xu, Wenting Xu, Zhenghua Chen, Junyang Qi, Chang Lukasiewicz, Thomas State Key Laboratory of Reliability and Intelligence of Electrical Equipment Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health Hebei University of Technology China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science University of Oxford United Kingdom

To reduce doctors’ workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where deep convolutional neural networks (CNNs) are employed to encode the input images, and recurrent neural networks (RNNs) are used to decode the visual features into medical reports automatically. However, these state-of-the-art methods mainly suffer from three shortcomings: (i) incomprehensive optimization, (ii) low-order and unidimensional attention mechanisms, and (iii) repeated generation. In this article, we propose a hybrid reinforced medical report generation method with m-linear attention and repetition penalty mechanism (HReMRG-MR) to overcome these problems. Specifically, a hybrid reward with different weights is employed to remedy the limitations of single-metric-based rewards. We also propose a search algorithm with linear complexity to approximate the best weight combination. Furthermore, we use m-linear attention modules to explore high-order feature interactions and to achieve multi-modal reasoning, while a repetition penalty applies penalties to repeated terms during the model’s training process. Extensive experimental studies on two public datasets show that HReMRG-MR greatly outperforms the state-of-the-art baselines in terms of all metrics. We also conducted a series of ablation experiments to prove the effectiveness of all our proposed components. We also performed a reward search toy experiment to give evidence that our proposed search approach can significantly reduce the search time while approximating the best performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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