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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Tang, Hao Wang, Cheng Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200030 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9789819794119

Social behavior metadata has altered business models and human lifestyles. However, the inclusion of personal information in social behavior metadata poses risks of identity exposure for users. Under the requirements of modern data management laws, service providers now employ anonymization techniques when collecting user behavior metadata, which aims to break the link between personal identifiers and behavioral attributes for privacy protection. This paper conducts experiments on an open-source dataset of anonymized social behavior metadata and finds that even with privacy protection techniques such as data generalization, there remains a high risk of reidentification. Specifically, four social behavior metadata records are sufficient to reidentify users from the anonymized social behavior metadata, achieving a unicity of 90.10%. This indicates that the current widespread collection of social behavior metadata brings a high risk of privacy breaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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DA-Ada: Learning Domain-Aware Adapter for Domain Adaptive Object Detection

arXiv

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arXiv 2024年

作者： Li, Haochen Zhang, Rui Yao, Hantao Zhang, Xin Hao, Yifan Song, Xinkai Li, Xiaqing Zhao, Yongwei Li, Ling Chen, Yunji Intelligent Software Research Center Institute of Software CAS Beijing China State Key Lab of Processors Institute of Computing Technology CAS Beijing China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing China

Domain adaptive object detection (DAOD) aims to generalize detectors trained on an annotated source domain to an unlabelled target domain. As the visual-language models (VLMs) can provide essential general knowledge on unseen images, freezing the visual encoder and inserting a domain-agnostic adapter can learn domain-invariant knowledge for DAOD. However, the domain-agnostic adapter is inevitably biased to the source domain. It discards some beneficial knowledge discriminative on the unlabelled domain, ***-specific knowledge of the target domain. To solve the issue, we propose a novel Domain-Aware Adapter (DA-Ada) tailored for the DAOD task. The key point is exploiting domain-specific knowledge between the essential general knowledge and domain-invariant knowledge. DA-Ada consists of the Domain-Invariant Adapter (DIA) for learning domain-invariant knowledge and the Domain-Specific Adapter (DSA) for injecting the domain-specific knowledge from the information discarded by the visual encoder. Comprehensive experiments over multiple DAOD tasks show that DA-Ada can efficiently infer a domain-aware visual encoder for boosting domain adaptive object detection. Our code is available at https://***/Therock90421/DA-Ada © 2024, CC BY-NC-SA.

关键词： Ada (programming language)

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MLiquID: Towards Mobile Liquid Sensing with COTS RFIDs

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Zijuan Liu, Xiulong Tong, Xinyu Xie, Xin Chen, Jiancheng Yang, Ming Li, Keqiu Tsinghua University Department of Automation Beijing China Tianjin University College of Intelligence and Computing Tianjin China Xiamen Intretech Inc. Xiamen Key Laboratory of Intelligent Scene Technology in Industrial Metaverse Fujian China Xiamen Intretech Inc. Fujian Provincial Key Laboratory of Industrial Internet & IoT Fujian China

Liquid sensing in ubiquitous contexts plays an essential role in various scenarios. Recently, some wireless sensing systems have been proposed for liquid identification. However, existing works usually require specific equipment or capture the signals penetrating a target, limiting the deployability of liquid sensing. In large-scale scenarios, multiple devices are usually required to expand the coverage area due to the RFID reader antenna's reading range limitation. To enlarge the sensing range and make the liquid sensing method can be adopted in real moving scenarios, in this paper, we present Mobile Liquid IDentification (MLiquID), a liquid sensing system that can recognize the type of liquid in a mobile manner with commercial off-the-shelf (COTS) RFID devices. This mobile process leads to continuous variation in location, so the major challenge in this paper is how to extract signal features from the superimposed information of movement and material. The key insight is to regard movement as an opportunity to acquire data from different perspectives instead of a challenge to hinder feature extraction. We construct a Phase-RSS model by analyzing the influence of moving and liquid on the phase and RSS signals. First, we propose a method to calculate the distance from the tag to the reader antenna. Second, we explore an identification method to identify liquid type by extracting signal features Phase-RSS coefficient C{P-R} and Maximum Response Distance (MRD). Experimental results demonstrate an average accuracy of 96.80% in identifying 10 common liquids, which shows the great potential of MLiquID for mobile liquid sensing. © 2002-2012 IEEE.

关键词： Antennas

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Temporal Enhanced Multi-Stream Graph Convolutional Nerual Networks for Skeleton-Based Action Recognition

Temporal Enhanced Multi-Stream Graph Convolutional Nerual Ne...

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2021 China Automation Congress, CAC 2021

作者： Wang, Tianyi Sun, Zhonghua Jia, Kebin Feng, Jinchao Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Laboratory of Advanced Information Networks Beijing100124 China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781665426473

Compared with image sequence, skeleton sequence is an ideal choice for human action recognition because of no redundant information and lightweight data. Recently,a tremendous breakthrough in skeleton-based human activity recognition methods. By way of illustration, Spatial- Temporal Graph Convolutional Networks (ST-GCN) creatively distill the information of human joints into the structure of a graph and Two-Stream Adaptive Graph Convolutional Networks (2S-AGCN) propose to use the length and orientation information of bones and combine it with joint information to predict in an explicit way. However there are still some issues exist in these GCN-based models. The model lacks long-term dependency modeling capabilities and does not explore the deep correlation between joints and bones. In this work, we propose a temporal enhanced multi-stream graph convolutional nerual networks (TAMS-GCN) for skeleton-based action *** combine the temporal attention module with a graph convolutional neural network to extract skeletal information of each frame and actively incorporates it into the global features by global pooling of all joints for each frame to acquire attention at the frame level of the action. In addition we propose a fusion network of joint and bone information that implicitly learns the connection between joints and bones, increasing the compactness between joints and bones. We tested our TAMS-GCN model on NTU-RGBD datasets, the model achieves excellent performance compared to the state-of-the-art. © 2021 IEEE

关键词： Convolution

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ESNet: evolution and succession network for high-resolution salient object detection 24

ESNet: evolution and succession network for high-resolution ...

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Proceedings of the 41st International Conference on Machine Learning

作者： Hongyu Liu Runmin Cong Hua Li Qianqian Xu Qingming Huang Wei Zhang Institute of Information Science Beijing Jiaotong University & Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University & Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature; the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24.

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A second-order knowledge filter transfer learning algorithm for modeling nonlinear systems

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Science China Technological Sciences 2025年第6期 105-120页

作者： Honggui HAN Mengmeng LI Xiaolong WU Hongyan YANG Junfei QIAO School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education

Transfer learning algorithms can transform prior knowledge into linearization knowledge to model nonlinear systems. However, the linearization knowledge-based models tend to diverge in the process of knowledge linearization due to the neglected information of higher-order terms. To overcome this problem, a second-order knowledge filter transfer learning algorithm(SOFTLA) is developed for modeling nonlinear systems. First, a knowledge transformation strategy is introduced to transform the linearization source knowledge into comprehensive knowledge containing first-order and second-order *** with the original knowledge, the transformed source knowledge with second-order term can prevent information loss during the knowledge linearization. Second, a knowledge filter algorithm is proposed to eliminate the useless information in the source knowledge. Subsequently, a suitable filter gain is designed to reduce the cumulative error in knowledge updating process. Third, a model adaptation mechanism is designed to enable effective knowledge transfer by updating the structure and parameters of the target model simultaneously. Subsequently, the adaptability of the source knowledge is enhanced to facilitate learning tasks in the target domain. Finally, a benchmark problem and several practical industrial applications are presented to validate the superiority of SOFTLA. The experimental discussions illustrate that SOFTLA can obtain obvious advantages over contrastive methods.

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Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learning 24

Towards High-resolution 3D Anomaly Detection via Group-Level...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhu, Hongze Xie, Guoyang Hou, Chengbin Dai, Tao Gao, Can Wang, Jinbao Shen, Linlin National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Department of Intelligent Manufacturing CATL Ningde China Fuzhou Fuyao Institute for Advanced Study Fuyao University of Science and Technology Fuzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9798400706868

High-resolution point clouds (HRPCD) anomaly detection (AD) plays a critical role in precision machining and high-end equipment manufacturing. Despite considerable 3D-AD methods that have been proposed recently, they still cannot meet the requirements of the HRPCD-AD task. There are several challenges: i) It is difficult to directly capture HRPCD information due to large amounts of points at the sample level;ii) The advanced transformer-based methods usually obtain anisotropic features, leading to degradation of the representation;iii) The proportion of abnormal areas is very small, which makes it difficult to characterize. To address these challenges, we propose a novel group-level feature-based network, called Group3AD, which has a significantly efficient representation ability. First, we design an Intercluster Uniformity Network (IUN) to present the mapping of different groups in the feature space as several clusters, and obtain a more uniform distribution between clusters representing different parts of the point clouds in the feature space. Then, an Intracluster Alignment Network (IAN) is designed to encourage groups within the cluster to be distributed tightly in the feature space. In addition, we propose an Adaptive Group-Center Selection (AGCS) based on geometric information to improve the pixel density of potential anomalous regions during inference. The experimental results verify the effectiveness of our proposed Group3AD, which surpasses Reg3D-AD by the margin of 5% in terms of object-level AUROC on Real3D-AD. We provide the code and supplementary information on our website: https://***/M-3LAB/Group3AD. © 2024 ACM.

关键词： Contrastive Learning

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Accurate interpolation for scattered data through hierarchical residual refinement 23

Accurate interpolation for scattered data through hierarchic...

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Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Shizhe Ding Boyang Xia Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China and Central China Institute for Artificial Intelligence Technologies Zhengzhou China

Accurate interpolation algorithms are highly desired in various theoretical and engineering scenarios. Unlike the traditional numerical algorithms that have exact zero-residual constraints on observed points, the neural network-based interpolation methods exhibit non-zero residuals at these points. These residuals, which provide observations of an underlying residual function, can guide predicting interpolation functions, but have not been exploited by the existing approaches. To fill this gap, we propose Hierarchical INTerpolation Network (HINT), which utilizes the residuals on observed points to guide target function estimation in a hierarchical fashion. HINT consists of several sequentially arranged lightweight interpolation blocks. The first interpolation block estimates the main component of the target function, while subsequent blocks predict the residual components using observed points residuals of the preceding blocks. The main component and residual components are accumulated to form the final interpolation results. Furthermore, under the assumption that finer residual prediction requires a more focused attention range on observed points, we utilize hierarchical local constraints in correlation modeling between observed and target points. Extensive experiments demonstrate that HINT outperforms existing interpolation algorithms significantly in terms of interpolation accuracy across a wide variety of datasets, which underscores its potential for practical scenarios.

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Deep Residual Feature Generating Network:A Novel Method for Few-Shot Fault Diagnosis of Chillers

Deep Residual Feature Generating Network:A Novel Method for ...

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第43届中国控制会议

作者： Kexin Jiang Xuejin Gao Huihui Gao Huayun Han Yongsheng,Qi Faculty of Information Technology Beijing University of Technology Engineering Research Centre of Digital Community Ministry of EducationBeijing University of Technology Beijing Laboratory for Urban Mass Transit Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology School of Electric Power Inner Mongolia University of Technology

The scarcity of historical data on fault states is a common phenomenon in the health management of *** phenomenon leads to poor performance of fault diagnosis methods based on deep learning for *** Adversarial Network(GAN),a common data augmentation method,is an effective approach to conquer this ***,dynamic coupling characteristics exist in the operational data of *** characteristics lead to distribution differences in operational data at different times within the same *** coupling characteristics result in higher inter-class similarity in the operational *** characteristics pose challenges for GAN-based data augmentation ***,an encoder-decoder-based residual feature extraction method is introduced to enhance inter-class separability while improving intra-class ***,a residual feature generating network(DRFGN) is designed to augment the limited historical *** the DRFGN,to address the issue of insufficient distribution paradigm,an extended class characteristic information called latent attributes is *** the training process of DRFGN,latent attributes are introduced as extended discriminative information,allowing the generator to comprehensively learn the class characteristics of real ***,smooth L1 loss is employed to further enhance the similarity between the synthesized and real ***,a dataset collected from the actual equipment,referred to as HY-31 C,is used for the validation of the proposed *** results indicate satisfactory distribution similarity between the synthesized and real data,benefiting the health management and maintenance decision of the equipment.

关键词： Generative Adversarial Network(GAN) chillers fault diagnosis latent attribute smooth L1

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Wafer map defect recognition with few shot learning based on hybrid self-attention mechanism and prototype network

Wafer map defect recognition with few shot learning based on...

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2021 China Automation Congress, CAC 2021

作者： Yu, Naigong Chen, Huaisheng Xu, Qiao Hasan, Mohammad Mehedi Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China

ISBN: (纸本)9781665426473

Semiconductor manufacturing is a very complex dynamic manufacturing process. Wafer manufacturing is the most critical part of it. Abnormalities in any process in wafer production may cause wafer defects. Accurately identifying various defect patterns on the wafer surface can help find and adjust abnormal factors in the online manufacturing process and improve the yield of integrated circuit production. Aiming at the very small number of wafer defect patterns that appear in the wafer manufacturing process, this paper proposes a small sample wafer defect pattern classification method based on a hybrid self-attention mechanism and a prototype network. This method introduces a hybrid self-attention module into the feature embedding network that helps to automatically learn the global dependence of channels and positions in the wafer map to obtain more discriminative features. The metric classifier is used for classification to realize the pattern recognition of wafer defects in small samples. A large number of experiments were conducted on the WM-811K data set and the MixedWM38 data set. The recognition accuracy of this method in the 3-way 1-shot, 3-way 5-shot, 5-way 1-shot and 5-way 5-shot tasks achieved average classification accuracy rates of 89.99%, 94.96%, 85.77%, and 92.32%, respectively. The experimental results show that compared with the traditional deep learning method, the method in this paper can learn new defect pattern features and realize automatic classification only through a small number of wafer samples. © 2021 IEEE

关键词： Silicon wafers

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