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International Conference on Computer Supported Cooperative Work in Design

作者： Qianqian Li Yubing Han Qi Liu Guijuan Wang Hao Yu Jiguo Yu 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 School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In Internet of Things(IoT) systems, ensuring the secure exchange of information between devices from different domains is crucial. Current cross-domain authentication schemes based on a single blockchain struggle to meet the confidentiality requirements for data and information exchange in large-scale IoT systems. This paper proposes a blockchain-based identity authentication scheme (BCPPAS) for IoT, featuring dual-chain collaboration, and designs a novel certificateless aggregate signature algorithm to address complex certificate management and key escrow issues. The edge server is capable of aggregating different signatures to achieve batch authentication, markedly improving authentication efficiency and reducing computational and storage overhead. Also, BCPPAS is designed to avoid costly bilinear pairing operations, providing less computational overhead for IoT devices with limited resources. To protect the privacy of IoT devices, BCPPAS uses the pseudonym instead of the real identity. Finally, efficiency of BCPPAS are demonstrated through theoretical analysis and experiments.

关键词： Privacy Federated learning Aggregates Authentication Blockchains Computational efficiency Internet of Things Servers Protection Information exchange

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Event-Triggered Active Disturbance Rejection Control for Uncertain Random Nonlinear Systems

arXiv

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arXiv 2023年

作者： Wu, Ze-Hao Deng, Feiqi Zeng, Pengyu Zhou, Hua-Cheng Li, Hongyi School of Mathematics and Big Data Foshan University Foshan528000 China Systems Engineering Institute South China University of Technology Guangzhou510640 China School of Mathematics and Statistics HNP-LAMA Central South University Changsha410075 China School of Electronic and Information Engineering Chongqing Key Laboratory of Generic Technology System of Service Robots Southwest University Chongqing400715 China

In this paper, event-triggered active disturbance rejection control (ADRC) is first addressed for a class of uncertain random nonlinear systems driven by bounded noise and colored noise. The event-triggered extended state observer (ESO) and ADRC controller are designed, where two respective event-triggering mechanisms with a fixed positive lower bound for the inter-execution times are proposed. The random total disturbance representing the coupling of nonlinear unmodeled dynamics, external deterministic disturbance, bounded noise, and colored noise is estimated in real time by the event-triggered ESO and compensated in the event-triggered feedback loop. Both the mean square and almost surely practical convergence of the closed-loop systems is shown with rigorous theoretical analysis. Finally, some numerical simulations are implemented to validate the proposed control scheme and theoretical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Closed loop systems

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Multilayer Feature Fusion and Joint Loss Optimization for Emotion Recognition in Conversations

Multilayer Feature Fusion and Joint Loss Optimization for Em...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using RNN-based and GCN-based models to capture emotional dynamics and model speaker relationships, there remain two primary limitations: first, an insufficient integration of multiple feature representations and commonsense knowledge, which hampers the model's ability for deep emotional understanding; and second, the reliance on a single cross-entropy loss for classification optimization, which restricts the accuracy and robustness of emotion recognition. To address these issues, we propose a method for ERC using Multilayer Feature Fusion and Joint Loss Optimization (MFFJL). This approach combines contextual information, speaker dependency, and commonsense knowledge features by extracting feature vectors through RoBERTa and COMET, utilizing bidirectional LSTM and attention mechanisms to capture conversational context, and applying a cross-fusion module to deeply integrate various features, thus enhancing comprehension of complex emotional expressions. Additionally, the feature classification module incorporates joint cross-entropy and KL divergence optimization, further improving classification accuracy and consistency. Experimental results demonstrate the effectiveness of our method, as evidenced by superior performance on the IEMOCAP and MELD datasets. Our code is available at https://***/r/MFFJL-D9D8.

关键词： Emotion recognition Accuracy Federated learning Computational modeling Feature extraction Nonhomogeneous media Vectors Robustness Optimization Commonsense reasoning

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Pulse shape discrimination based on the Tempotron: a powerful classifier on GPU

arXiv

引用

arXiv 2023年

作者： Liu, Haoran Li, Peng Liu, Mingzhe Wang, Kaimin Zuo, Zhuo Liu, Bingqi The State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu610059 China The School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou325000 China The Engineering & Technical College of Chengdu University of Technology Leshan614000 China The Southwestern Institute of Physics Chengdu610225 China The School of Mechanical Engineering Chengdu University Chengdu610106 China

This study utilized the Tempotron, a robust classifier based on a third-generation neural network model, for pulse shape discrimination. By eliminating the need for manual feature extraction, the Tempotron model can process pulse signals directly, generating discrimination results based on prior knowledge. The study performed experiments using GPU acceleration, resulting in over 500 times faster compared to the CPU-based model, and investigated the impact of noise augmentation on the Tempotron performance. Experimental results substantiated that Tempotron serves as a formidable classifier, adept at accomplishing high discrimination accuracy on both AmBe and time-of-flight PuBe datasets. Furthermore, analyzing the neural activity of Tempotron during training shed light on its learning characteristics and aided in selecting its hyperparameters. Moreover, the study addressed the constraints and potential avenues for future development in utilizing the Tempotron for pulse shape discrimination. The dataset used in this study and the GPU-based Tempotron are publicly available on GitHub at https://***/HaoranLiu507/TempotronGPU. © 2023, CC BY-NC-ND.

关键词： Gamma rays

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Differentially-Fed Rectangular Patch Antenna under TM21and TM03Modes for Enhanced Bandwidth and Stable High Gain 13

Differentially-Fed Rectangular Patch Antenna under TM21and T...

引用

13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Xu, Zhi-Yong Zhang, Xiao Hong, Kai-Dong Zhu, Lei Yuan, Tao Shenzhen University Guangdong Prov. Mobile Terminal Microwave And Millimeter-Wave Antenna Engineering Research Center College Of Electronics And Information Engineering Shenzhen518060 China Guangdong-Hong Kong Joint Laboratory For Big Data Imaging And Communication Shenzhen Guangdong518048 China ATR National Key Laboratory Of Defense Technology College Of Electronics And Information Engineering Shenzhen University Shenzhen Guangdong518060 China Department Of Electrical And Computer Engineering Faculty Of Science And Technology University Of Macau China Postgraduate Education Branch Of The China Education Society Of Electronics China

ISBN: (纸本)9781665434379

A differentially-fed rectangular patch antenna under TM21 and TM03 modes is proposed to realize bandwidth enhancement and stable high gain. Firstly, two open slots along the median of patch are introduced to reconstruct the current distribution of TM21 mode so that its broadside radiation is strengthened greatly. Secondly, another central slot along with the two sided ones interrupt out-of-phase current of TM03 mode and make it circulate with little radiation, hence substantially reducing the sidelobe level (SLL). With the help of loading three cascaded slots and pins, the frequencies of the two modes are merged together, and so the antenna obtains an enhanced impedance bandwidth of 11.8% (2.24-2.52 GHz). Finally, the simulation results show that the proposed rectangular patch antenna achieves gain level as high as 13.8 dBi, which has little variation in the working bandwidth. © 2021 IEEE.

关键词： Bandwidth

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Fault Identification and Remaining Useful Life Prediction for Complex Equipment under Multiple Fault Modes

Fault Identification and Remaining Useful Life Prediction fo...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Jiapeng Cui Kaixiang Peng Pengxue Lang Shuai Lu Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665401166

To ensure the safe operation of complex equipment and specify maintenance strategies, the prediction of the remaining useful life (RUL) and the identification of failure modes are of great significance. Generally, it is difficult to directly differentiate the fault modes from the raw data. Thus, a novel fault clustering and RUL prediction framework under multiple failure modes is proposed in this paper. First, dynamic time warping (DTW) distance is combined with k-medoids to identify multiple failure modes. Following that, a convolutional neural network connecting with a long short-term memory (CNN-LSTM) is constructed to extract hidden features and predict RUL. In the end, the experiment is conducted on an engine dataset provided by NASA. The results show that the proposed framework has a better performance compared with the competitive prediction methods.

关键词： Degradation NASA Predictive models Maintenance engineering Feature extraction Prediction algorithms Spatiotemporal phenomena

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data augmentation for fault prediction of aircraft engine with generative adversarial networks

Data augmentation for fault prediction of aircraft engine wi...

引用

Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Pengxue Lang Kaixiang Peng Jiapeng Cui Jie Yang Yingxin Guo Key Laboratory of Knowledge Automation for Industrial Process of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (纸本)9781665401166

Fault prediction is to predict the remaining useful life(RUL) of the equipment by constructing models using historical data. However, the run-to-failure data is difficult to obtain, and it is impossible to build an accurate prediction model. To address this problem, an innovative forecasting method based on data augmentation technology is proposed in this paper. First, a generative adversarial network (GAN) is used to study the distribution of the original dataset, and generate a new train set. Then, the original dataset is fused with the new one to train a convolutional neural network and long short-term memory network (CNN-LSTM) prediction model. Finally, the experiment is conducted on the original C-MAPSS and fusion dataset, which results show that the proposed feature extraction method can effectively predict the RUL compared with the existed methods.

关键词： Atmospheric modeling Neural networks Merging Predictive models Generative adversarial networks data models Hybrid power systems

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MF2-MVQA: A MULTI-STAGE FEATURE FUSION METHOD FOR MEDICAL VISUAL QUESTION ANSWERING

arXiv

引用

arXiv 2022年

作者： Song, Shanshan Li, Jiangyun Wang, Jing Cai, Yuanxiu Dong, Wenkai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China

There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets. In order to better utilize multi-scale information of medical images, previous methods directly embed the multi-stage visual feature maps as tokens of same size respectively and fuse them with text representation. However, this will cause the confusion of visual features at different stages. To this end, we propose a simple but powerful multi-stage feature fusion method, MF2-MVQA, which stage-wise fuses multi-level visual features with textual semantics. MF2-MVQA achieves the State-Of-The-Art performance on VQA-Med 2019 and VQA-RAD dataset. The results of visualization also verify that our model outperforms previous work. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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HOUGH-DCT Based Robust Watermarking for Medical Image 1

引用

12th International Symposium on Cyberspace Safety and Security, CSS 2020

作者： Mo, Ruiqi Liu, Jing Li, Jingbing Wang, Yulin Zhou, Jingjun School of Information and Communication Engineering Hainan University Haikou570228 China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan University Haikou570228 China Research Center for Healthcare Data Science Zhejiang Lab Zhejiang Hangzhou China

ISBN: (数字)9783030736712

ISBN: (纸本)9783030736705

The patients’ case files may be maliciously tampered with during network transmission. Once the patients’ medical record information is altered by criminals, serious medical accidents will occur. To solve the problem of information leakage, a robust medical image watermarking algorithm based on Hough transform and discrete cosine transform (HOUGH-DCT) is proposed. This method combines the feature vector of medical image, cryptography, hash function and zero watermark technology, and it makes up for the shortcoming that the traditional digital watermark method cannot protect the medical image. It has strong robustness and invisibility which can protect the patient's private information and medical image data security at the same time. Experimental results show that the algorithm can embed the watermark information at a faster speed without changing the medical image information and extract the watermark safely with better invisibility and robustness. © 2021, Springer Nature Switzerland AG.

关键词： Discrete cosine transforms

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