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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

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

ISBN: (纸本)9798350331684

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models. © 2023 IEEE.

关键词： Knowledge graph

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PUF-Based Privacy-Preserving Simultaneous Authentication Among Multiple Vehicles in VANET

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IEEE Transactions on Vehicular Technology 2024年第5期73卷 6727-6739页

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Akram, Muhammad Wahid Hussain, Shafiq Das, Ashok Kumar Park, Youngho Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Lahore Department of Software Engineering Punjab Lahore54590 Pakistan University of Sahiwal Department of Computer Science Punjab Sahiwal57000 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Telangana Hyderabad500032 India Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of

The intellectual and immense attainment of a smart city and auto-mobile vehicles are gradually growing. This growth drives Vehicular Ad-Hoc Network (VANET), a broadly operated complex network for inter-vehicular transmission to acquire information relevant to the vehicle location, road condition, traffic congestion, and speed. Generally, this information is procured through an open-source communication, which is susceptible to various security threats. In broad terms, the privacy of data in VANET is considered an imperative task. In this regard, many authentication protocols are formerly initiated for VANET. Most of these protocols are precarious owing to eavesdropping, replay, and impersonation attacks, etc. Furthermore, most existing protocols operates on enormous communication and computation rate, which turns them inadequate for VANET. This paper exhibits a lightweight privacy-preserving authentication protocol for n number of vehicles in VANET using the physical unclonable function (PUF) technique, and offers efficient and secure communication via public channels. Moreover, we evaluate our protocol formally and informally, showing that it is scalable, lightweight, and safe compared to formerly protocols. The rate of computation and communication is less than most related protocols. We also simulate the proposed scheme using the NS-3 simulator to measure its various network performance parameters. As a result, it is apparent through the beheld statistics that the presented protocol is beyond practical, efficient, and reliable for the secure dissemination of information in VANET. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing Systems 39

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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39th IEEE International Conference on data engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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Intelligent New Energy Vehicle Battery Swap Scheme based on Blockchain Technology 23

Intelligent New Energy Vehicle Battery Swap Scheme based on ...

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Proceedings of the 2023 5th International Conference on Blockchain Technology

作者： Ya Han Jianbo Gao Zhi Guan Zhong Chen Lifeng Ren School of Computer Science Peking University China and Peking University Chongqing Research Institute of Big Data China National Engineering Research Center for Software Engineering Peking University China Peking University Chongqing Research Institute of Big Data China

ISBN: (纸本)9798400708930

With the development of electric vehicle technology, electric vehicles surpass fuel vehicles in terms of power performance and intelligence, and the cruising range has also increased to more than 400km with the increase of battery energy density and power consumption. However, the overall is still facing the problem of energy refueling anxiety, and the fuel vehicle refueling time is 5 minutes, and the current fast charge takes at least 60 minutes. The waiting time for charging during peak hours has also been further lengthened. Increasing the speed of energy replenishment has become an urgent need for new energy vehicle owners. The way electric vehicles are supplied with energy is fundamentally different from that of traditional cars. The battery swap mode naturally separates the vehicle and electricity, providing the possibility for novel business models (vehicle electricity separation, battery sharing). Let the battery return to its "energy carrier" use attribute, realize the sharing of batteries, create conditions for battery financialization, carry out full life cycle value management of batteries, implement battery gradient utilization, and provide a feasible path for future energy storage business. However, in battery swap mode, there are many divisions of responsibilities. For example, if the battery burns the car, should I find the main engine factory or the power exchange company/high-voltage electrical failure after the power exchange, especially when the responsibility is not clear, who will check and repair. This paper proposes a new energy vehicle monitoring platform based on blockchain technology, which can manage the whole process life cycle of new energy batteries through blockchain traceability technology. At the same time, according to the current responsibility division system, the transparent division of responsibilities is realized in combination with blockchain smart contracts.

关键词： Battery swap mode Blockchain New energy Responsibility division

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Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal data 24

Backdoor Attacks on Bimodal Salient Object Detection with RG...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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Patch Target Guided Dual-Branch Deep Multiple Instance Learning for 3D MRI Analysis

Patch Target Guided Dual-Branch Deep Multiple Instance Learn...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Dai, Mingzhe Shi, Xiaoshuang Zhu, Xiaofeng Pan, Tingrui Li, Kang University of Science and Technology of China School of Software Engineering Hefei China West China Hospital of Sichuan University West China Biomedical Big Data Center Chengdu China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350386226

Deep multiple instance learning (MIL) has attracted considerable attention in medical image analysis, since it only requires image-level labels for model training without using fine-grained (or patch) annotations. Unfortunately, MIL-based methods might lose some significant patch features. Although pseudo-label-based methods, which assign a pre-defined label to each patch, can explore more patch-level features, they might bring label noise and make the patch-level features lose diversity, thereby possibly restricting the model performance. To overcome this issue, we propose a novel gradient-based patch target generation (PTG) module to dynamically produce a feature vector for each patch as its target. Additionally, based on the PTG module, we propose a patch-target guided dual-branch deep MIL framework for 3D MRI data analysis, where both the two branches consist of a CNN model to extract patch-level features, an attention module to interpret the significance of patches, and a bag-level classifier, while the second branch also contains the PTG module to generate patch targets of patches. Moreover, the two branches are alternatively updated in our framework, resulting in a bi-level optimization problem, and thus we design a bi-level optimization algorithm to solve our proposed objective function. Extensive experiments demonstrate the superior classification and interpretation performance of the proposed framework over recent state-of-the-art methods. Codes are available at https://***/daimz1213/mil2024. © 2024 IEEE.

关键词： Image annotation

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Natural language processing applications in social network analysis: A data mining approach 7

Natural language processing applications in social network a...

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2024 7th International Symposium on big data and Applied Statistics, ISBDAS 2024

作者： Yu, Yunxia Guan, Yibo Hu, Yurong School of Computer Engineering Jingchu University of Technology Hubei Jingmen448000 China Big Data Research Center Jingchu University of Technology Hubei Jingmen448000 China Beijing Aerocim Technology Co. Ltd. Beijing102308 China

Social media texts, such as tweets and user profiles, reflect the opinions, emotions, preferences, and behaviors of social media users, as well as the structure and dynamics of their social networks. Analyzing social media texts can provide valuable insights and applications for various domains and stakeholders, such as marketing, politics, healthcare, and education. However, analyzing social media texts poses significant challenges, as they are often noisy, informal, unstructured, and heterogeneous. Moreover, social media texts are not only influenced by the individual characteristics and contexts of the users but also by the complex and evolving interactions and relationships among them. Therefore, traditional methods and techniques for natural language processing (NLP), social network analysis (SNA), and data mining (DM) may not be sufficient or effective for social media text analysis. In this paper, we propose a data mining approach that integrates and leverages NLP, SNA, and DM methods and techniques for social media text analysis. We demonstrate the feasibility and effectiveness of our approach on a large and comprehensive data set of tweets and user profiles related to the domain of consumer spending. We show that our approach can provide comprehensive and effective solutions and applications for various domains and stakeholders, such as marketing, and education. We also compare and contrast our results and outcomes with the existing works, and show the advantages and improvements of our approach over the existing works. © Published under licence by IOP Publishing Ltd.

关键词： Tweets

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Futures Quantitative Investment With Heterogeneous Continual Graph Neural Network 24

Futures Quantitative Investment With Heterogeneous Continual...

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24th IEEE International Conference on data Mining, ICDM 2024

作者： Tan, Zhizhong Hu, Min Liu, Bin Yin, Guosheng School of Computer Science and Engineering Macau University of Science and Technolog China School of Finance Southwestern University of Finance and Economics China Center of Statistical Research School of Statistics Southwestern University of Finance and Economics China School of Computing and Data Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798331506681

It is challenging to predict futures prices with traditional econometric models as it necessitates a comprehensive consideration of both historical observations and correlations among various futures. Spatial-temporal graph neural networks (STGNNs) offer a promising approach for modeling such complex spatio-temporal data. Nevertheless, the direct application of STGNNs to high-frequency futures data remains a challenge, as traders must account for both short- and long-term characteristics when making decisions. To capture these distinct timeframes, we leverage additional label information by devising four heterogeneous tasks: price regression, price gap regression, price moving average regression, and change-point detection. To make full use of these labels, we train the model in a continual manner. Traditional continual GNNs define the gradient of losses as parameter importance to overcome the catastrophic forgetting (CF) issue, while this is unsuitable for our heterogeneous tasks with losses in distinct spaces. Therefore, we propose to calculate the parameter importance with mutual information between the original observations and the extracted features. The empirical results based on 49 varieties of commodity futures demonstrate that our model achieves superior performance compared to the leading approaches, highlighting the effectiveness of our approach in modeling the intricate dynamics of the futures market. © 2024 IEEE.

关键词： Graph neural networks

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WheatNet: Attentional Path Aggregation Feature Pyramid Network for Precise Detection and Counting of Dense and Arbitrary-Oriented Wheat Spikes

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 606-616页

作者： Jiao, Lin Liu, Qihuang Liu, Haiyun Chen, Peng Wang, Rujing Liu, Kang Dong, Shifeng Anhui University School of Internet Hefei230031 China Chinese Academy of Sciences National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Hefei230031 China Chinese Academy of Sciences Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Science and Technology of China Hefei230031 China Anhui University National Engineering Research Center for Agro-Ecological Big Data Analysis and Application Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Internet Hefei230601 China Anhui University Institutes of Physical Science and Information Technology Hefei230601 China Chinese Academy of Science Institute of Intelligent Machines Hefei Institutes of Physical Science Hefei230031 China University of Sheffield Department of Computer Science SheffieldS10 2TN United Kingdom Institute of Dataspace Hefei230093 China

Achieving the precise and real-time detection of wheat spikes play a crucial role in wheat growth monitoring for precision agriculture community. Machine-learning methods are commonly introduced to automatically detect and count the wheat spikes, which need carefully selected hand-crafted feature descriptors, leading to time-consuming and poor performance. The deep learning has become a promising technology for the accurate detection wheat spikes, owing to its powerful ability of feature extraction. However, the obtained wheat spike images from UAV still have serious overlap, dense distribution, various orientations, and large aspect ratios, leading to poor performance of recent wheat spike detection method. To address the demand of precise and fast detection and counting of wheat spike with dense distribution and arbitrary-orientation, a novel deep learning-based method, WheatNet, has been proposed. The attention mechanism has been introduced the process of feature fusing to highlight the important features of wheat spike as well as inhibit the useless information. Additionally, to optimize the parameters of the network, a loss function with soft dynamic label assignment is adopted to reduce the number of low-quality matches, which provides significant performance gains over other wheat spike detectors. Furthermore, to achieve the precise detection of wheat spike with multi-orientations, a large-scale oriented wheat spike dataset has been constructed, named RoWheat, including 900 images and 50419 annotations with dense distribution and various orientation. Experimental studies demonstrate that the proposed WheatNet achieves a recall of 99.7% and mAP of 91.8%, showing its promising performance gain compared to other state-of-the-art methods. © 2023 IEEE.

关键词： Image annotation

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