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International Conference on Systems and Informatics (ICSAI)

作者： Jin Liu Yanqin Kang Tao Liu TingYu Zhang Yikun Zhang School of Computer and Information Anhui Polytechnic University Wuhu China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9781665474498

clinical low X-ray dose computed tomography (LDCT) scanner often induce high intensity strip artifact and spot nosie, compromising diagnoses and intervention plans. Recently, sparsely constrained and network learning-based frameworks have been shown to be efficient in mitigating such issue. In this work, we propose a deep iterative reconstruction network (DIRNet) model with a residual constraint to synergize the advantages of feature learning and image reconstruction to address the LDCT imaging problem. DIR-Net compose by few iteration units, and all iteration units include three different network modules: projection restoration, residual constraint and image update block. DIR-Net is a promising approach for building an end-to-reconstruction mapping strategy and directly obtaining high-quality CT images. Furthermore, LISTA is used to conFigure the network, and the whole network architecture yields improved interpretability. Qualitative and quantitative analysis in test data shown the promising imaging effects of DIR-Net in quantum noise reduction, block artifact removal and tissue detail texture mantian.

关键词： Strips Statistical analysis Sonogram Computed tomography Imaging network architecture Image restoration

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An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

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Smart World Congress (SWC), IEEE

作者： HuiJuan Hao Yue Feng HongGe Zhao SiJian Zhu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of $\mathbf{9 8. 7 5 \%}$ on the CWRU dataset and $\mathbf{9 7. 9 2 \%}$ on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning.

关键词： Fault diagnosis Accuracy Adaptive systems Extreme learning machines Transfer learning Clustering algorithms Feature extraction Classification algorithms Data mining Elbow

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Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification 33

Ranking Aggregation with Interactive Feedback for Collaborat...

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Huang, Ji Liang, Chao Zhang, Yue Wang, Zhongyuan Zhang, Chunjie National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China

Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： computer vision

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A Category-Driven Contrastive Recovery network for Double Incomplete Multi-view Multi-label Classification

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IEEE Transactions on Multimedia 2025年

作者： Wang, Yiming Li, Qun Chang, Dongxia Wen, Jie Xiao, Fu Zhao, Yao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing China Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Harbin Institute of Technology Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China

In the field of multi-view multi-label learning, the challenges of incomplete views and missing labels are prevalent due to the complexity of manual labeling and data acquisition errors. These challenges significantly reduce the quality of latent representations and hinder prediction by multi-label classification. To address this issue, we propose a novel Category-driven Semi-supervised Contrastive Recovery (CSCR) framework in this study. Our framework aims to fully integrate existing label information into incomplete representation learning and classification. Specifically, to address the limitations posed by incomplete views and labels, we construct a label coincidence matrix based on existing labels, which serves as a similarity matrix in subsequent semi-supervised contrastive learning and multi-view classification. By leveraging this matrix, we design a semi-supervised multi-view contrastive learning module, which constructs sample pairs on the basis of inter-view correspondences and label similarity. It learns discriminative latent representations without the need for data augmentation. A weighted multi-label classification module is subsequently employed to integrate the predictions from each view to obtain the final classification result. Experimental evaluations on five challenging datasets demonstrate the superiority of our model over existing state-of-the-art methods. © 1999-2012 IEEE.

关键词： Contrastive Learning

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A Dirty Data Identification Method in Mechanical Equipment Condition Monitoring

A Dirty Data Identification Method in Mechanical Equipment C...

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Smart World Congress (SWC), IEEE

作者： Huijuan Hao Hongge Zhao Sijian Zhu Yue Feng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is $\mathbf{0. 9 7 0 0}$ and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability.

关键词： Condition monitoring Correlation Buildings Production Transformers Generative adversarial networks Market research Data models Monitoring Industrial Internet of Things

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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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Enhancing Unknown Encrypted Traffic Clustering with Self-Supervised Learning

Enhancing Unknown Encrypted Traffic Clustering with Self-Sup...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ke Ding Xia Zhou Xiaoyan Hu Guang Cheng Ruidong Li Hua Wu School of Cyber Science & Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Institute of Science and Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Many malicious attacks are launched through encrypted traffic from unknown proprietary network protocols. Timely identification of such malicious unknown encrypted traffic is essential for the defense. However, it is challenging to acquire labels for unknown protocols in the context of encrypted traffic. Due to the lack of prior knowledge, unsupervised learning is adopted to cluster unknown encrypted traffic. The existing unsupervised encrypted traffic clustering methods do not customize feature extraction and representation of unknown encrypted traffic, resulting in imperfect clustering results. This work innovatively proposes BiFR-SSL to accurately cluster unknown encrypted traffic without prior knowledge. BiFRSSL extracts features of each unknown encrypted bidirectional network flow based on the lengths, arrival time, and directions of packets within the flow to construct a Bidirectional Flowpic Representation (BiFR). Subsequently, it exploits Self-Supervised Learning (SSL) to pre-train a feature extractor that produces feature representations from BiFRs, guaranteeing the closeness of encrypted traffic flows from the same protocol in the representation space. Finally, it clusters unknown encrypted traffic based on their feature representations generated by the pre-trained feature extractor. Our experimental studies demonstrate that BiFR-SSL can effectively cluster encrypted traffic of unknown protocols and outperforms state-of-the-art methods.

关键词： Protocols Clustering methods Clustering algorithms Self-supervised learning Telecommunication traffic Feature extraction Data augmentation Cryptography Global communication Unsupervised learning

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A Multidimensional Adaptive Perception Model for Security Contraband Detection

SSRN

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SSRN 2023年

作者： Bian, Weixin Feng, Luo Xu, Deqin Jie, Biao Huang, Yi The School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Province Key Laboratory of Network and Information Security Wuhu241002 China

Identifying prohibited items in security X-ray images is a challenging task. Since prohibited items may be intentionally concealed, and the background of X-ray images is complex and the image representation ability is limited, it is easy to cause missed detection or false alarms. These questions are critical to ensuring safety. To address these challenges, the Multidimensional Adaptive Perception Module (MAPM) is proposed, and coordinated attention (CA) adaptive learning weights are introduced to make the learning focus of the model more accurate. The MAPM module fuses high semantic information into deep networks through residual connections, while integrating detailed features into shallow networks, allowing the model to obtain deeper and more comprehensive information and improve its ability to understand complex backgrounds. At the same time, we adopt a decoupling head mechanism to accelerate the convergence speed of the network. Experiments have proven that our model significantly improves the detection accuracy of X-ray security inspection images. Compared with the YOLOv5s model, the average accuracy is increased by 7.9mAP. Compared with many current advanced algorithms, our model has higher accuracy and stronger robustness. This research aims to provide an effective and reliable solution to the security inspection field to improve safety and operability. © 2023, The Authors. All rights reserved.

关键词： Object detection

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WMLink: Wearable Smart Devices and Mobile Phones Linking through Passive Sniffing BLE and WiFi Signals

WMLink: Wearable Smart Devices and Mobile Phones Linking thr...

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International Conference on Advanced Cloud and Big Data (CBD)

作者： Naixuan Guo Zhaofeng Chen Heyang Xu Zhechun Zhao Yu Liu Sen Xu School of Information Engineering Yancheng Institute of Technology Yancheng China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University)

ISBN: (纸本)9798350309720

In recent years, wearable smart devices have entered people’s lives. These devices can help users to record their daily activities, health data and so on. These devices periodically send out packets to keep connected with mobile phones, which can be observed by any external observer. By analyzing these packets, some information about the device owner will be revealed. Although the observer can sniff the wireless packets, the actual person using the device is unknown. In this paper, we link wearable smart devices with users’ mobile phones which are recognized as users’ electronic identity. Linking these two types of devices opens up new opportunities for applications such as authenticating users by multi-devices or constructing user device graph for advertisements. We deploy sniffing nodes to sniff BLE and WiFi signals, and put forward a multi-stage filtering method to reduce signals’ noises, then link two types of devices by an algorithm based on dynamic time warping. At last, our experiments show that 7 wearable devices can be linked with a correct rate more than 80%. We also find that when one device is observed more than 11 times, its linking accuracy can be close to 100%.

关键词： Meters Filtering Heuristic algorithms Wearable computers Observers Big Data Market research

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Adaptive NAD: Online and Self-adaptive Unsupervised network Anomaly Detector

arXiv

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arXiv 2024年

作者： Yuan, Yachao Huang, Yu Yuan, Yali Wang, Jin School of Future Science and Engineering Soochow University Jiangsu Suzhou China Independent Researcher Guangxi Nanning China School of Cyber Science and Engineering Southeast University Jiangsu Nanjing China the Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Bangladesh

The widespread usage of the Internet of Things (IoT) has raised the risks of cyber threats, thus developing Anomaly Detection Systems (ADSs) that can adapt to evolving or new attacks is critical. Previous studies primarily focused on offline unsupervised learning methods to safeguard ADSs, which is not applicable in practical real-world applications. Besides, most of them strongly rely on assumptions of known legitimates and fail to satisfy the interpretable requirements in security applications, creating barriers to the adoption in practice. In this paper, we design Adaptive NAD, a general framework to improve and interpret online and Adaptive unsupervised network Anomaly Detection in security domains. An interpretable two-layer anomaly detection strategy is proposed to generate reliable high-confidence pseudo-labels. Then, an online learning scheme is introduced to update Adaptive NAD by a novel threshold calculation technique to adapt to new threats. Experimental results demonstrate that Adaptive NAD achieves more than 5.4%, 23.0%, and 3.2% improvements in SPAUC compared with state-of-the-art solutions on the CIC-Darknet2020, CIC-DoHBrw-2020, and Edge-IIoTset datasets, respectively. The code is released at https://***/MyLearnCodeSpace/Adaptive-NAD. © 2024, CC BY-SA.

关键词： Contrastive Learning

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