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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Zhu, Yi Chen, Xiaoying Zhao, Yu School of Computer Science and Technology Jiangsu Normal University XuZhou China Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics Key Laboratory of Safety-Critical Software Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Automated vehicle combines physics and computation on the basis of environment perception. It can realize intelligent interaction with the environment. Automated vehicle is a typical CPS. However, the continuous changes of driving physical space bring certain challenges to the safety of CPS resources. Therefore, how to solve this kind of CPS resource safety problems caused by space and time changes becomes the key. We propose a space-time constraint resource modeling and safety verification method for automated vehicle to solve this problem. Firstly, the physical topology model is proposed to model the physical topology space of CPS, which is able to describe the topology space. Secondly, the Resource-Space Time Communicating Sequential Process (RS- TCSP) is proposed by extending the resource vector on the basis of Time Communicating Sequential Process(TCSP) to describe the resources in CPS topology. Thirdly, the physical topology model and RS- TCSP are mapped to bigraphs and bigraphs reactive system, respectively. The safety of CPS resources is verified by BigMC, the verification tool of bigraphs, and the counterexample path is modified. Finally, a driving scene is given to verify the effectiveness of the proposes method. © 2022 IEEE.

关键词： Formal verification

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Out-of-Distribution Fault Diagnosis of Industrial Cyber-Physical Systems Based on Orthogonal Anchor Clustering With Adaptive Balance

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 48-60页

作者： Liu, Ruonan Hu, Puyuan Zhao, Siheng Sun, Zhijian Han, Te Pang, Zhibo Zhang, Weidong Shanghai Jiao Tong University Department of Automation Shanghai200240 China National Key Laboratory of Marine Engine Science and Technology Shanghai201108 China Crossocean of Suzhou Technology Suzhou215121 China Beijing Institute of Technology Center for Energy and Environmental Policy Research School of Management Beijing100081 China ABB Corporate Research Sweden Department of Automation Technology Vasteras722 26 Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm114 28 Sweden Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng475001 China Shanghai Jiaotong University Department of Automation Shanghai200240 China

Given the critical role of rotating machinery in industrial cyber-physical systems (ICPS), ensuring their reliable operation is essential for the stability and safety of ICPS. Deep neural networks have demonstrated competitive results for intelligent fault diagnosis, which are usually trained via the historical data of all fault modes. However, in real engineering, it is usually difficult to collect samples and exhaust all failures during the training stage. As a result, out-of-distribution fault diagnosis (OOD-FD) becomes a more realistic problem that requires the methods to not only accurately diagnose the known faults, but also effectively recognize unknown ones. Therefore, a novel orthogonal anchor clustering with focal attention (FA-OAC) is proposed in this paper for OOD-FD. Firstly, an orthogonal anchor clustering (OAC) algorithm is proposed to fix the class center of each fault mode orthogonally and distinguish the known and unknown faults at the class level. Then, because the identifiability of different fault modes changes a lot in OOD-FD, the focal attention mechanism is applied to dynamically adjust the attention to different fault modes according to the distance loss of OAC, thus addressing the identifiability imbalance problem. To verify the effectiveness of the proposed method, different OOD-FD tasks are designed based on two rotating machinery datasets. The experimental results and comparison with state-of-the-art fault diagnosis methods have demonstrated that the proposed method has improved the OOD-FD performance greatly and therefore provides an effective tool in real engineering. © 2023 IEEE.

关键词： Rotating machinery

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Lightweight Network Based Real-time Anomaly Detection Method for Caregiving at Home 25

Lightweight Network Based Real-time Anomaly Detection Method...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Bin Wu, Xingjiao Gong, Miaomiao Zhao, Jin Sun, Yuling School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781665405270

Using data-driven technologies to support the healthcare of the elderly has been largely celebrated as an effective means. This paper focuses on the issue of using video-based sensing technologies to remotely monitor the activities and conditions of the elderly. Although it is a widely explored field, the high cost and high infrastructural requirements of most existing technologies usually challenge their effectiveness and efficiency in practical caregiving context. To address these challenges, we propose a lightweight network based real-time anomaly detection system, which consists of video-based ADL sensing and pre-processing, AI streaming aggregating and cluster computing. We examine our method by implementing and deploying it into a real-world care facility for the elderly in Shanghai China. The results show that our method has good performance in expansibility, reliability, bandwidth availability, accuracy and privacy protection. © 2022 IEEE.

关键词： Fall detection

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Classification Method of Ethereum Smart Contracts Based on Statistical Model Checking

Classification Method of Ethereum Smart Contracts Based on S...

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IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Miaoer Li Yi Zhu Yali Liu Chan Yin School of Computer Science and Technology Jiangsu Normal University Xuzhou Jiangsu China Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information Technology Nanjing Jiangsu China

ISBN: (数字)9798350365634

ISBN: (纸本)9798350365641

The integration of blockchain and smart contracts facilitates efficient and secure data exchange and value transfer. Nevertheless, the reliability of smart contracts has emerged as a critical concern. The vulnerabilities in contracts are intricately linked to their categories, emphasizing the significance of smart contract classification for enhancing code, user, and system security. Formal verification methods offer a robust means to validate the accuracy of contract classification and mitigate vulnerabilities. However, conventional machine learning approaches often lack precision and overlook the impact of account transaction behavior on classification during contract execution. This study introduces an Ethereum smart contract classification methodology based on statistical model detection. Through an examination of five smart contract types and ensuring the logical exclusivity of each, we delineate and formalize the internal logic of each contract type. We establish the contract automata network, devise conversion rules from contract source code to the automata network, and verify class properties using the statistical model detection tool UPPAAL-SMC. Lastly, we showcase the efficacy of our proposed methodology through a practical contract case.

关键词： Solid modeling Analytical models Atmospheric modeling Smart contracts Automata Model checking Blockchains

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A Model-Agnostic Interpretability Approach Based on Enhanced Hierarchical Clustering

A Model-Agnostic Interpretability Approach Based on Enhanced...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Xi Ding Yi Zhu Qiao Yu Chongyang Huang School of Computer Science and Technology Jiangsu Normal University Xuzhou Jiangsu China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing Jiangsu China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

The Local Interpretable Model-Agnostic Explanations (LIME) is a relatively novel technique for explaining black-box models. LIME generates perturbed instances of the model based on a random instance, and then approximates the original model with a linear model derived from the weighted perturbed instances, thereby providing an explanation for individual predictions. While LIME effectively addresses the opacity of black-box models, the randomness in instance generation often leads to unstable and inaccurate predictions. For a given prediction, different explanations may be generated, posing critical issues, particularly in security-sensitive domains. In response to these challenges, we propose a stable version of LIME, named DDLIME(Density and Distance-based LIME). DDLIME leverages improved hierarchical clustering and KNN algorithms to select instances that are most similar to the data, thus reducing the impact of perturbations. Additionally, the enhanced hierarchical clustering, incorporating density and distance metrics, enables DDLIME to address the limitations of traditional hierarchical clustering in clustering non-spherical clusters. Consequently, DDLIME can effectively capture complex structures and patterns within the data. The experimental results on three binary public datasets from the UCI repository demonstrate that DDLIME provides more stable explanations compared to LIME. Furthermore, compared to both baseline models, DDLIME exhibits superior performance across various evaluation metrics.

关键词： Measurement Accuracy Perturbation methods Clustering algorithms Closed box Predictive models Nearest neighbor methods Prediction algorithms Stability analysis Software reliability

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Safety decision of autonomous driving behavior based on Bayesian network and its verification method

Safety decision of autonomous driving behavior based on Baye...

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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zexin Li Yi Zhu Li Zhao Ying Zhao School of Computer Science and Technology Jiangsu Normal University Xuzhou Jiangsu China Key Laboratory of Safety-Critical Software Nanjing University of Aeronautics and Astronautics Ministry of Industry and Information Technology Nanjing Jiangsu China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

With the increasing visibility of autonomous vehicles, the assurance of safe and precise behavioral decisions presents a substantial challenge within the realm of autonomous driving. The complexity of physical space, uncertainty in duration-space data, and human subjectivity complicate the assurance of existing behavior decision-making methods. To address these challenges, a safety decision method for autonomous driving based on Bayesian network is proposed. Firstly, semantic relations among risk elements in driving scenario are represented using knowledge graph, and the hierarchical structure and node information of the Bayesian network are determined based on expert knowledge and historical experience. Behavior decision actions are derived through Bayesian network inference. Secondly, leveraging the Bayesian network structure and inference results, a Duration-space Resource Timed Communicating Sequential Process is construct to verify the security of time and space resources. Thirdly, the constructed process is converted into Bigraphs and use the CPS MODEL CHECKER for security verification. Finally, the effectiveness of the proposed method is verified using a highway lane change overtaking scenario.

关键词： Uncertainty Roads Decision making Bayes methods Safety Software reliability Security Time factors Automobiles Autonomous vehicles

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Frame-Level Multi-Label Playing Technique Detection Using Multi-Scale Network and Self-Attention Mechanism 48

Frame-Level Multi-Label Playing Technique Detection Using Mu...

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48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Li, Dichucheng Che, Mingjin Meng, Wenwu Wu, Yulun Yu, Yi Xia, Fan Li, Wei Sichuan Conservatory of Music College of Experimental Art Sichuan China Digital Content and Media Sciences Research Division Japan Fudan University School of Computer Science and Technology Shanghai China Fudan University Shanghai Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781728163277

Instrument playing technique (IPT) is a key element of musical presentation. However, most of the existing works for IPT detection only concern monophonic music signals, yet little has been done to detect IPTs in polyphonic instrumental solo pieces with overlapping IPTs or mixed IPTs. In this paper, we formulate it as a frame-level multi-label classification problem and apply it to Guzheng, a Chinese plucked string instrument. We create a new dataset, Guzheng Tech99, containing Guzheng recordings and onset, offset, pitch, IPT annotations of each note. Because different IPTs vary a lot in their lengths, we propose a new method to solve this problem using multi-scale network and self-attention. The multi-scale network extracts features from different scales, and the self-attention mechanism applied to the feature maps at the coarsest scale further enhances the long-range feature extraction. Our approach outperforms existing works by a large margin, indicating its effectiveness in IPT detection. © 2023 IEEE.

关键词： multi-scale net-work music information retrieval Playing technique detection self-attention

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Boosting COVID-19 Severity Detection with Infection-Aware Contrastive Mixup Classification 17th

Boosting COVID-19 Severity Detection with Infection-Aware C...

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17th European Conference on computer Vision, ECCV 2022

作者： Hou, Junlin Xu, Jilan Zhang, Nan Zhang, Yuejie Zhang, Xiaobo Feng, Rui School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China National Children’s Medical Center Children’s Hospital of Fudan University Shanghai China

ISBN: (纸本)9783031250811

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifically, we train two segmentation networks to first extract the lung region and then the inner lesion region. The lesion segmentation mask serves as complementary information for the original CT slices. To relieve the issue of imbalanced data distribution, we further improve the advanced Contrastive Mixup Classification network by weighted cross-entropy loss. On the COVID-19 severity detection leaderboard, our approach won the first place with a Macro F1 Score of 51.76%. It significantly outperforms the baseline method by over 11.46%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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CareMap: Human-Space-Service Based Healthcare Modeling and Quantifying for the Elderly Aging in Place 25

CareMap: Human-Space-Service Based Healthcare Modeling and Q...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Guo, Jiancong Wang, Bin Zhao, Jin Sun, Yuling School of Computer Science and Technology East China Normal University Shanghai China School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (纸本)9781665405270

With the aging of the population, caregiving for the elderly has become an urgent social topic. While the rapid development of data-driven technologies provides tremendous promises to deal with this issue, the gap between data-driven and practical healthcare challenges their effectiveness. With the aim of providing strong data basis for the large-scale data-driven healthcare, this paper proposes a human-space-service based method, named CareMap, to model and quantity the caregiving process for the elderly aging at home. We build the intelligent individual profile to model and compute seniors' health conditions on the one hand and care network profile to model and quantify the care resources around the elderly on the other hand. We design a CareMap based prototype to illustrate the possible application and discuss its potentials and limitations. © 2022 IEEE.

关键词： Computational modeling Conferences Sociology Prototypes Medical services Aging Collaborative work

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OneVOS: Unifying Video Object Segmentation with All-in-One Transformer Framework

arXiv

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arXiv 2024年

作者： Li, Wanyun Guo, Pinxue Zhou, Xinyu Hong, Lingyi He, Yangji Zheng, Xiangyu Zhang, Wei Zhang, Wenqiang School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China Academy for Engineering and Technology Fudan University China

Contemporary Video Object Segmentation (VOS) approaches typically consist stages of feature extraction, matching, memory management, and multiple objects aggregation. Recent advanced models either employ a discrete modeling for these components in a sequential manner, or optimize a combined pipeline through substructure aggregation. However, these existing explicit staged approaches prevent the VOS framework from being optimized as a unified whole, leading to the limited capacity and suboptimal performance in tackling complex videos. In this paper, we propose OneVOS, a novel framework that unifies the core components of VOS with All-in-One Transformer. Specifically, to unify all aforementioned modules into a vision transformer, we model all the features of frames, masks and memory for multiple objects as transformer tokens, and integrally accomplish feature extraction, matching and memory management of multiple objects through the flexible attention mechanism. Furthermore, a Unidirectional Hybrid Attention is proposed through a double decoupling of the original attention operation, to rectify semantic errors and ambiguities of stored tokens in OneVOS framework. Finally, to alleviate the storage burden and expedite inference, we propose the Dynamic Token Selector, which unveils the working mechanism of OneVOS and naturally leads to a more efficient version of OneVOS. Extensive experiments demonstrate the superiority of OneVOS, achieving state-of-the-art performance across 7 datasets, particularly excelling in complex LVOS and MOSE datasets with 70.1% and 66.4% J&F scores, surpassing previous state-of-the-art methods by 4.2% and 7.0%, respectively. And our code will be available for reproducibility and further research. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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