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IEEE International Symposium on information (IT) in Medicine and Education, ITME

作者： Meiping Zhong Zuoyong Li ChuanSheng Wang Jiayan Huang WanQi Zhao Haoyi Fan College of Computer and Big Data Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona Spain New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Quality assessment of electrocardiogram (ECG) signals holds significant importance in reducing the high-frequency false alarms in automated arrhythmia diagnosis. Existing quality assessment methods primarily rely on empirical threshold setting or the calculation of relevant statistical values to formulate decision rules. However, the thresholds set in this manner are typically based on experience and knowledge, resulting in decision rules that lack flexibility and struggle to adapt to changes in the dataset. To address this issue, many researchers have turned to deep learning algorithms for ECG signal quality assessment. Nevertheless, most existing deep learning methods usually require large-scale and labeled ECG data for training, whose acquiring is typically challenging. Therefore, this paper proposes an efficient method for ECG signal quality assessment. First, we use a contrast learning framework to construct sample pairs through multilevel data augmentation. Second, a densely connected convolutional network (DenseNet) is used to improve the model's ability of extracting local features in ECG signals, and a Squeeze-and-Excitation attention module is introduced to make the model pay more attention to critical features. Finally, the feature representation is learned by minimizing the contextual contrast loss to bring similar samples closer and push dissimilar samples farther away. In this way, we can utilize the contrastive learning framework to extract temporal representations from unlabeled data and transfer them to downstream ECG signal quality assessment tasks. Extensive experiments on the Physionet/CinC Challenge 2011 dataset demonstrate that our approach outperforms most existing quality assessment methods.

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A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Research Square

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Research Square 2022年

作者： Zhang, Zhenghui Zou, Juan Zheng, Jinhua Yang, Shengxiang Gong, Dunwei Pei, Tingrui Key Laboratory of Hunan Province for Internet of Things and Information Security Xiangtan University Hunan Xiangtan411105 China Key Laboratory of Intelligent Computing and Information Processing Ministry of Education Information Engineering College of Xiangtan University Hunan Xiangtan411105 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom School of Information and Electronic Engineering China University of Mining and Technology Xuzhou221116 China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm’s design is to exploit all available information and computationally suggest matches to increase the algorithm’s ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios. © 2022, CC BY.

关键词： Knowledge acquisition

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Unsupervised Graph-Sequence Anomaly Detection for 5G Core Network Control Plane Traffic

Unsupervised Graph-Sequence Anomaly Detection for 5G Core Ne...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Wang Peng Li Zhang Cheng Wenmao Liu Lei Nie Haizhou Bao Qin Liu Kai Zhang School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System China NSFOCUS Technologies Group Co. Ltd School of Cyber Science and Engineering Wuhan University China

5G Core network (5GC) employs a Service Based Architecture (SBA). This architecture decomposes the control plane into multiple independent Network Functions (NFs). NFs open interfaces to provide services to other NFs, which makes the control plane more susceptible to external malicious attacks. However, existing anomaly detection methods focus more on traffic statistics features and are difficult to apply to the 5GC control plane. In this paper, we proposed GSAD, a Graph-Sequence analysis-based Anomaly Detection method for 5GC control plane traffic. We model control plane traffic as a directed graph to depict topological and NF interaction information. Further, we use the normalizing flows with temporal dependencies to mine the sequential information in the traffic. GSAD combines the topological and sequential information to provide fine-grained detection. We evaluate our proposed framework on the 5GC testbed using Free5GC and UERANSIM in various scenarios. Experimental results demonstrate that our framework outperforms the baselines significantly in terms of detection performance.

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EEG based Parkinson Detection through Supervised information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jian Song Xiang Li Wenjing Jiang Chunxiao Wang Zhigang Zhao Jialiang Lv Bin Hu 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Qilu Hospital of Shandong University Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study presents a novel Supervised information Enhanced Contrastive Learning Algorithm for Parkinson’s Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks.

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Semi-Supervised Time Series Classification by Temporal Relation Prediction

Semi-Supervised Time Series Classification by Temporal Relat...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Haoyi Fan Fengbin Zhang Ruidong Wang Xunhua Huang Zuoyong Li School of Computer Science and Technology Harbin University of Science and Technology Harbin China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China

Semi-supervised learning (SSL) has proven to be a powerful algorithm in different domains by leveraging unlabeled data to mitigate the reliance on the tremendous annotated data. However, few efforts consider the underlying temporal relation structure of unlabeled time series data in the semi-supervised learning paradigm. In this work, we propose a simple and effective method of Semi-supervised Time series classification architecture (termed as SemiTime) by gaining from the structure of unlabeled data in a self-supervised manner. Specifically, for the labeled time series, SemiTime conducts the supervised classification directly under the supervision of the annotated class label. For the unlabeled time series, the segments of past-future pair are sampled from time series, where two segments of pair from the same time series candidate are in positive temporal relation, while two segments from the different candidates are in negative temporal relation. Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner. Finally, by jointly classifying labeled data and predicting the temporal relation of unlabeled data, the useful representation of unlabeled time series can be captured by SemiTime. Extensive experiments on multiple real-world datasets show that SemiTime consistently out-performs the state-of-the-arts, which demonstrates the effectiveness of the proposed method.

关键词： Conferences Time series analysis Semantics Semisupervised learning Signal processing Feature extraction Classification algorithms

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Large AI Model-Based Semantic Communications

arXiv

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arXiv 2023年

作者： Jiang, Feibo Peng, Yubo Dong, Li Wang, Kezhi Yang, Kun Pan, Cunhua You, Xiaohu Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China School of Information Science and Engineering Hunan Normal University Changsha China Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Department of Computer Science Brunel University London United Kingdom School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Changchun Institute of Technology China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China

Semantic communication (SC) is an emerging intelligent paradigm, offering solutions for various future applications like metaverse, mixed reality, and the Internet of Everything. However, in current SC systems, the construction of the knowledge base (KB) faces several issues, including limited knowledge representation, frequent knowledge updates, and insecure knowledge sharing. Fortunately, the development of the large AI model (LAM) provides new solutions to overcome the above issues. Here, we propose a LAM-based SC framework (LAM-SC) specifically designed for image data, where we first apply the segment anything model (SAM)-based KB (SKB) that can split the original image into different semantic segments by universal semantic knowledge. Then, we present an attention-based semantic integration (ASI) to weigh the semantic segments generated by SKB without human participation and integrate them as the semanticaware image. Additionally, we propose an adaptive semantic compression (ASC) encoding to remove redundant information in semantic features, thereby reducing communication overhead. Finally, through simulations, we demonstrate the effectiveness of the LAM-SC framework and the possibility of applying the LAM-based KB in future SC paradigms. Copyright © 2023, The Authors. All rights reserved.

关键词： Latent semantic analysis

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Design and Implementation of Firmware Data Acquisition System Based on Scrapy Framework

Design and Implementation of Firmware Data Acquisition Syste...

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2020 IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2020

作者： Han, Xiaowei Zheng, Likun School of Computer and Information Engineering Harbin University of Commerce Harbin China Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin China

ISBN: (纸本)9781728198736

In recent years, the scale of Internet data grows exponentially with the development of Internet technology. Such huge amount of Internet data is valuable. Web crawler is one of the most popular technology, which is often used to obtain these data. Scrapy is a popular framework of web crawler which is widely used in various Internet information collection systems. This paper optimizes the framework of Scrapy, designs a firmware data acquisition system based on the framework of Scrapy based on the technologies of distributed, anti-crawler, ELK and automatic construction, and crawls the firmware information of various manufacturers on the Internet. The experimental results show that after employing the distributed and prevent anti-crawler technology, the number of target acquisition increases by 10%, and the time is shortened by 70%. The large-scale logs analysis using ELK solves the problem that the log number is too large for analysis, and the crawler can be automatically constructed and crawls through the code automatic construction technology. This method is efficient for the optimization of firmware crawler. © 2020 IEEE.

关键词： Firmware

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A Profile Matching Scheme based on Private Set Intersection for Cyber-Physical-Social Systems

A Profile Matching Scheme based on Private Set Intersection ...

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IEEE Conference on Dependable and Secure Computing

作者： Yalian Qian Xueya Xia Jian Shen School of Computer and Software Nanjing university of information science and technology Nanjing China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fujian China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

The cyber-physical-social system (CPSS) is a three-layer system framework that combines the human society on the basis of the cyber-physical system (CPS), so that the human society, the cyber world and the physical world are interconnected. In the CPSS, similar profile attributes are matched to socialize and ultimately achieve the purpose of information sharing. However, some personal information may be included in the profile attributes, thus the users' privacy cannot be protected during the process. To meet this challenge, a privacy-preserving profile matching scheme based on private set intersection is proposed in this paper. Multi-tag is utilized to partition the dataset of users to achieve fine-grained profile matching. In addition, the privacy of users is protected by re-encryption technique. Security analysis shows that our scheme is secure against the semi-honest adversary and theoretical analysis of the experiment shows that that the scheme is efficient for profile matching in the CPSS.

关键词： Privacy Conferences Resists Cyber-physical systems Performance analysis information management Security

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Geometry-based non-line-of-sight error mitigation and localization in wireless communications

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Science China(information Sciences) 2019年第10期62卷 149-163页

作者： Jingyu HUA Yejia YIN Anding WANG Yu ZHANG Weidang LU School of Information and Electronic Engineering Zhejiang Gongshang University Zhejiang Provincial Key Laboratory of Information Processing Communication and Networking College of Information Engineering Zhejiang University of Technology

Recently, positioning services have received considerable attention. The primary source of the positioning error is non-line-of-sight(NLOS) propagation. To address this problem, we propose a novel NLOS mitigation scheme, in which the geometric relationship between a base station and a mobile station is *** makes it possible to identify range measurements corrupted by NLOS errors, and the mobile station can then estimate its position through line-of-sight(LOS) measurements. Moreover, the threshold of the NLOS detector is derived via a hybrid method using both the analytical derivation and computer simulation, which significantly reduces the difficulty of identifying thresholds. After identifying the NLOS measurements, a two-step weighted-least-squares algorithm is used to obtain the localization, in which both range and angle measurements are considered. The simulation results reveal that the proposed algorithm yields a high identification probability of NLOS measurements, which results in improved localization performance.

关键词： wireless localization non-line-of-sight error geometry cellular network residual

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A Novel Dynamic Group Signature with Membership Privacy

A Novel Dynamic Group Signature with Membership Privacy

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IEEE Conference on Dependable and Secure Computing

作者： Junqing Lu Rongxin Qi Jian Shen School of Computer and Software Nanjing University of Information Science & Technology Nanjing China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

Group signature is a cryptography primitive that has been widely researched. It strikes a balance between digital signature and the user's demand for anonymity. A valid member in the group can generate a signature on behalf of the whole group. The public can only know that it was provided by a valid group member and learn nothing about the actual identity of the signer when verifying a group signature. Backes et al pointed out that the existing dynamic group signature schemes implicitly assume that the membership of everyone in the group is open to the public. Thus, they put forward a property called membership privacy for dynamic group signature. In this paper, we design a dynamic group signature scheme with membership privacy on top of Signature Proofs of Knowledge (SPK) and BBS+ signature. Further more, dynamic accumulator mechanism is adopted to revoke a group member's authority to sign. Then, a security analysis demonstrates that the proposed group signature scheme satisfies join-leave privacy. Finally, quantitative analysis and experimental results show that the proposed group signature scheme achieves the fewer signature size and less computation overhead compared with Backes's scheme.

关键词： Privacy Statistical analysis Conferences Cryptography Digital signatures

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