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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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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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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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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

arXiv

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arXiv 2024年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Grau, Antoni Zhang, Fuquan The College of Computer and Big Data Fuzhou University Fuzhou China The Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain The New Engineering Industry College Putian University Putian China School of Computer and Artificial Intelligence Zhengzhou University China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of Computer and Control Engineering Minjiang University Fuzhou China Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Minjiang University Fuzhou China Fujian Polytechnic Normal University Fujian Province University Fuzhou China

Driver drowsiness electroencephalography (EEG) signal monitoring can timely alert drivers of their drowsiness status, thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end lightweight dual graph convolutional network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. Furthermore, to deploy our method on the fourth-generation Raspberry Pi, we utilize Adaptive Pruning Optimization (APO) on both channels and neurons, reducing inference latency by almost half. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization compared to existing state-of-the-art algorithms. All our source code can be found at https://***/BryantDom/Driver-Drowsiness-Monitoring. Copyright © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling Image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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Image Definition Evaluation Function Based on Improved Maximum Local Variation and Focusing Window Selection 1

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6th International Conference on Data Mining and Big Data, DMBD 2021

作者： Li, Shiyun Chen, Jian Wan, Jiaze Li, Zuoyong Lin, Li School of Electronic Electrical Engineering and Physics Fujian University of Technology Xueyuan Road Fujian Fuzhou350118 China College of Computer and Control Engineering Minjiang University No. 200 Xiyuangong Road Fujian Fuzhou350121 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou350121 China

ISBN: (数字)9789811675027

ISBN: (纸本)9789811675010

Aiming at the problem that commonly used image definition evaluation functions in the focusing process are sensitive to noise, we propose a new image definition evaluation function based on improved maximum local variation and focusing window selection. Firstly, the focusing window is selected by gradient accumulation of a 4-directional Scharr operator in order to reduce the calculation complexity and improve the accuracy of evaluation results. Secondly, an improved 3-neighbors method based on the maximum local variation is proposed to decrease the change in scores for noisy images. Finally, the standard deviation of the improved maximum local variation distribution is used as the measure of clarity. The experimental results show that compared with the method using maximum local variation, the proposed method has better unbiasedness and sensitivity. Compared with the commonly used evaluation functions, the proposed method has better noise immunity and high sensitivity. Compare with other no-reference image quality assessment algorithms, it has better monotonicity, unimodality, unbiasedness, sensitivity and real-time performance as well. The proposed method is suitable for the fine focusing stage with high real-time performance. © 2021, Springer Nature Singapore Pte Ltd.

关键词： computer vision

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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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Predicting the pathological status of mammographic microcalcifications through a radiomics approach

引用

Intelligent Medicine 2021年第3期1卷 95-103页

作者： Min Li Liyu Zhu Guangquan Zhou Jianan He Yanni Jiang Yang Chen School of Biological Science and Medical Engineering Southeast UniversityNanjingJiangsu 210096China Department of Radiology the First Affiliated Hospital of Nanjing Medical UniversityNanjingJiangsu 210029China Department of Radiology the Second Affiliated Hospital of Nantong UniversityNantongJiangsu 226001China The State Key Laboratory of Bioelectronics Southeast UniversityNanjingJiangsu 210096China Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China The Laboratory of Image Science and Technology Key Laboratory of Ministry of EducationSchool of Computer Science and EngineeringSoutheast UniversityNanjingJiangsu 210096China

Objective The study aimed to develop a machine learning(ML)-coupled interpretable radiomics signature to predict the pathological status of non-palpable suspicious breast microcalcifications(MCs).Methods We enrolled 463 digital mammographical view images from 260 consecutive patients detected with non-palpable MCs and BI-RADS scored at 4(training cohort,n=428;independent testing cohort,n=35)in the First Affiliated Hospital of Nanjing Medical University between September 2010 and January ***,837 textures and 9 shape features were subsequently extracted from each view and finally selected by an XGBoostembedded recursive feature elimination technique(RFE),followed by four machine learning-based classifiers to build the radiomics *** Ten radiomic features constituted a malignancy-related signature for breast MCs as logistic regression(LR)and support vector machine(SVM)yielded better positive predictive value(PPV)/sensitivity(SE),0.904(95%CI,0.865–0.949)/0.946(95%CI,0.929–0.977)and 0.891(95%CI,0.822–0.939)/0.939(95%CI,0.907–0.973)respectively,outperforming their negative predictive value(NPV)/specificity(SP)from 10-fold crossvalidation(10FCV)of the training *** optimal prognostic model was obtained by SVM with an area under the curve(AUC)of 0.906(95%CI,0.834–0.969)and accuracy(ACC)0.787(95%CI,0.680–0.855)from 10FCV against AUC 0.810(95%CI,0.760–0.960)and ACC 0.800 from the testing *** The proposed radiomics signature dependens on a set of ML-based advanced computational algorithms and is expected to identify pathologically cancerous cases from mammographically undecipherable MCs and thus offer prospective clinical diagnostic guidance.

关键词： Nonpalpable microcalcifications Radiomics Machine learning

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A Photonics-aided MMW OFDM Joint Radar and Communication System with Velocity Accuracy Improvement

A Photonics-aided MMW OFDM Joint Radar and Communication Sys...

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Applied Sensing Conference (APSCON), IEEE

作者： Lanfeng Peng Dongxiang Luo Yaoqiang Xiao Fan Li School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Opto-Electronic Information Processing Chips and Systems Sun Yat-Sen University Guangzhou China Institute of Semiconductors South China Normal University Guangzhou China School of Chemistry and Chemical Engineering/Huangpu Hydrogen Innovation Center Guangzhou University Guangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

Photonics-aided millimeter wave (MMW) Orthogonal frequency division multiplexing (OFDM) system is a novel approach to realize the joint radar and communication system. The phase noise introduced by the laser will degrade the system performance. In this paper, we analyze the mechanism of phase noise acting on OFDM radar system. A method that combines chirp-Z transform (CZT) and compensation enabled by a reference signal is adopted to improve the velocity accuracy. RF pilot (RFP) and blind phase search (BPS) algorithm are adopted to improve communication performance. A simulation is demonstrated at 30 GHz with 2 GHz communication bandwidth. A twodimensional radar imaging with a range resolution of 0.075 m and velocity resolution of 0.980 m/s and a communication capacity of 6.4 Gbps are obtained in the proposed joint radar and communication system.

关键词： Phase noise Laser radar Image resolution Communication systems OFDM Bandwidth Radar imaging

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FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment

arXiv

引用

arXiv 2023年

作者： Lin, Qinghua Li, Zuoyong Zeng, Kun Fan, Haoyi Li, Wei Zhou, Xiaoguang School of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China School of Automation Beijing University of Post and Telecommunications Beijing100876 China School of Economics and Management Minjiang University Fuzhou350121 China

Deep learning techniques have greatly enhanced the performance of fire detection in videos. However, video-based fire detection models heavily rely on labeled data, and the process of data labeling is particularly costly and time-consuming, especially when dealing with videos. Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment. Specifically, we first combine consistency regularization with pseudo-label. For unlabeled data, we design video data augmentation to obtain corresponding weakly augmented and strongly augmented samples. The proposed model predicts weakly augmented samples and retains pseudo-label above a threshold, while training on strongly augmented samples to predict these pseudo-labels for learning more robust feature representations. Secondly, we generate video cross-set augmented samples by adversarial distribution alignment to expand the training data and alleviate the decline in classification performance caused by insufficient labeled data. Finally, we introduce a fairness loss to help the model produce diverse predictions for input samples, thereby addressing the issue of high confidence with the non-fire class in fire classification scenarios. The FireMatch achieved an accuracy of 76.92% and 91.81% on two real-world fire datasets, respectively. The experimental results demonstrate that the proposed method outperforms the current state-of-the-art semi-supervised classification methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Fires

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