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4th International Symposium on Computer Engineering and intelligent Communications, ISCEIC 2023

作者： Huang, Guimin Wei, Xiong Guo, Qingkai Deng, Nanxiao Lin, Jianxing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350306323

The integration of large education and artificial intelligence technologies is gradually deepening, and how to provide personalized user profiling services for learners is an important research problem. In response to the long-term dependency problem and lack of learning features of today's deep knowledge tracking models, a deep knowledge tracking model (FSA-MuLSTM) based on forgetting features and the self-attentiveness mechanism is proposed. Firstly, the model combines the learner's historical learning interaction tuple sequences with learning times and memory residuals to obtain the input vector based on the LSTM;secondly, the correlation weights between tuples are captured using the self-attention mechanism;finally, the deep information between tuple sequences is captured using the stacking of multilayer LSTMs. The probability of correctly answering the current question is derived from the hidden vectors and the self-attentive weights of the output of the multilayer LSTM. Two public datasets are used for the experiments, and the combined analysis outperforms existing knowledge-tracking models in terms of performance and improves the accuracy of prediction. © 2023 IEEE.

关键词： Multilayers

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Self-powered sensor based on compressible ionic gel electrolyte for simultaneous determination of temperature and pressure

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InfoMat 2024年第7期6卷 62-75页

作者： Junjie Zou Yanan Ma Chenxu Liu Yimei Xie Xingyao Dai Xinhui Li Shuxuan Li Shaohui Peng Yang Yue Shuo Wang Ce-Wen Nan Xin Zhang State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center of Smart Materials and Devices&International School of Materials Science and EngineeringWuhan University of TechnologyWuhanthe People's Republic of China Hubei Key Laboratory of Energy Storage and Power Battery School of MathematicsPhysics and Optoelectronic EngineeringHubei University of Automotive TechnologyShiyanthe People's Republic of China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Structure and Functional Regulation of Hybrid Materials of Ministry of EducationInstitutes of Physical Science and Information TechnologyAnhui UniversityHefeithe People's Republic of China State Key Lab of New Ceramics and Fine Processing School of Materials Science and EngineeringTsinghua UniversityBeijingthe People's Republic of China

The simultaneous detection of multiple stimuli,such as pressure and temperature,has long been a persistent challenge for developing electronic skin(eskin)to emulate the functionality of human ***,the demand for integrated power supply units is an additional pressing concern to achieve its lightweightness and ***,we propose a self-powered dual temperature–pressure(SPDM)sensor,which utilizes a compressible ionic gel electrolyte driven by the potential difference between MXene and Al *** SPDM sensor exhibits a rapid and timely response to changes in pressure-induced deformation,while exhibiting a slow and hysteretic response to temperature *** distinct response characteristics enable the differentiation of current signals generated by different stimuli through machine learning,resulting in an impressive accuracy rate of 99.1%.Furthermore,the developed SPDM sensor exhibits a wide pressure detection range of 0–800 kPa and a broad temperature detection range of 5–75C,encompassing the environmental conditions encountered in daily human *** dual-mode coupled strategy by machine learning provides an effective approach for temperature and pressure detection and discrimination,showcasing its potential applications in wearable electronics,intelligent robots,human–machine interactions,and so on.

关键词： e-skin gel electrolyte machine learning self-powered dual-mode sensor

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MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

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Computers, Materials & Continua 2024年第6期79卷 4863-4882页

作者： Tao Zhou Yujie Guo Caiyue Peng Yuxia Niu Yunfeng Pan Huiling Lu School of Computer Science and Engineering North Minzu UniversityYinchuan750021China School of Medical Information&Engineering Ningxia Medical UniversityYinchuan750004China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu UniversityYinchuan750021China

Computer-aided diagnosis of pneumonia based on deep learning is a research ***,there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images.A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this *** main innovations are as follows:Firstly,the Multi-scale Residual Feature Extraction Module(MRFEM)is designed to effectively extract multi-scale *** MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features ***,the Multi-scale Directional Feature Perception Module(MDFPM)is designed,which uses a three-branch structure of different sizes convolution to transmit direction feature layer by layer,and focuses on the target region to enhance the feature ***,the Axial Compression Former Module(ACFM)is designed to perform global calculations to enhance the perception ability of global features in different *** verify the effectiveness of the MSD-Net,comparative experiments and ablation experiments are carried *** the COVID-19 RADIOGRAPHY DATABASE,the Accuracy,Recall,Precision,F1 Score,and Specificity of MSD-Net are 97.76%,95.57%,95.52%,95.52%,and 98.51%,*** the chest X-ray dataset,the Accuracy,Recall,Precision,F1 Score and Specificity of MSD-Net are 97.78%,95.22%,96.49%,95.58%,and 98.11%,*** model improves the accuracy of lung image recognition effectively and provides an important clinical reference to pneumonia Computer-Aided Diagnosis.

关键词： Pneumonia X-ray image ResNet multi-scale feature direction feature transformer

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Blockchain-Enabled Secure, Fair and Scalable Data Sharing in Zero-Trust Edge-End Environment

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 2056-2069页

作者： Xu, Xiaolong Meng, Ke Xiang, Haolong Cui, Guangming Xia, Xiaoyu Dou, Wanchun Nanjing210044 China Nanjing University of Information Science and Technology School of Software and the Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing210044 China RMIT University School of Computing Technologies MelbourneVIC3000 Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China

In edge computing, the Zero-Trust Security Model (ZTSM), as a key enabling technology for next-generation networks, plays a crucial role in providing authentication for addressing data sharing concerns, such as frequent data breaches, data misuse, and cyberattacks. However, due to the complexity and diversity of edge environments, ZTSM struggles to meet the security requirements of data sharing frameworks solely through enhanced authentication. Consequently, such frameworks with ZTSM still face challenges in ensuring data integrity, evaluating various node behaviors, and coping with the increasing complexity of node attributes. To address these issues, we propose a blockchain-enabled secure, fair and scalable data sharing framework in a zero-trust edge-end environment in this paper. Specifically, we first propose a Merkle forest-based data storage model for the classified storage of loosely coupled data, consequently enhancing the scalability of the model. Then, we design a node behavior-based reputation assessment mechanism to ensure fairness during data sharing. Moreover, a data sharing protocol supervised by smart contract is proposed, working with the aforementioned storage and assessment schemes, to ensure the security of data sharing. Finally, comprehensive security analysis validates the security, fairness and scalability of the proposed framework. Extensive experimental results show that, as transaction volume grows, the time cost of data traversal in the storage model becomes progressively more efficient. Additionally, when the size of the smart contract is increased tenfold, the maximum time cost of the data sharing protocol rises by only 4.98 times. © 1983-2012 IEEE.

关键词： Data integrity

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An Analysis Model of English Text Coherence Based on RST Dependency Relationship 4

An Analysis Model of English Text Coherence Based on RST Dep...

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4th International Conference on information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Huang, Guimin Shuai, Wanying Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (纸本)9798350337181

Text coherence analysis is an important and challenging task that is essential for subtasks such as automatic summarisation, viewpoint extraction and machine translation in natural language processing (NLP). A large body of previous work has used linguistic features such as lexical, syntactic and entity features to capture relatively shallow coherence features, while ignoring deeper logically relevant features hidden in the text. We propose an end-to-end English text coherence analysis model (RGCM for short) that incorporates RST and graph convolutional neural networks (GCN). The intra- and inter-sentence logical relations of the input text are first mapped onto a discourse relationship tree, and then transformed into an RST-dependent context graph by certain pruning strategies. Subsequently, we propose a GCN-based text coherence assessment framework to capture intra- and inter-sentence interactions to assess text coherence. We conducted experiments on two different coherence assessment datasets and achieved accuracy rates of 95.5% and 97.8% respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Representation Purification for End-to-End Speech Translation 31

Representation Purification for End-to-End Speech Translatio...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Chengwei Zhou, Yue Zhao, Rui Chen, Yidong Shi, Xiaodong School of Informatics Xiamen University China Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University Xiamen China

ISBN: (纸本)9798891761964

Speech-to-text translation (ST) is a cross-modal task that involves converting spoken language into text in a different language. Previous research primarily focused on enhancing speech translation by facilitating knowledge transfer from machine translation, exploring various methods to bridge the gap between speech and text modalities. Despite substantial progress made, factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. In this paper, we conceptualize speech representation as a combination of content-agnostic and content-relevant factors. We examine the impact of content-agnostic factors on translation performance through preliminary experiments and observe a significant performance deterioration when content-agnostic perturbations are introduced to speech signals. To address this issue, we propose a Speech Representation Purification with Supervision Enhancement (SRPSE) framework, which excludes the content-agnostic components within speech representations to mitigate their negative impact on ST. Experiments on MuST-C and CoVoST-2 datasets demonstrate that SRPSE significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a transcript-free setting. © 2025 Association for Computational Linguistics.

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DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis

arXiv

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arXiv 2024年

作者： Yang, Shangshang Chen, Mingyang Wang, Ziwen Yu, Xiaoshan Zhang, Panpan Ma, Haiping Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Anhui Hefei230601 China Anhui Province Key Laboratory of Intelligent Computing and Applications China Department of Information Materials Intelligent Sensing Laboratory of Anhui Province China

Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students’ interactions. Besides, lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction, exercise-concept relation, and concept dependency graphs, respectively. Specifically, the latter two graphs are first disentangled from the interaction graph. Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module;multiple learnable propagation paths in this module enable current student latent representation to access lower-order exercise latent representations, which can lead to more effective nad robust student representations learned;the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction. Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module. The source code is available at https://***/BIMK/intelligent-Education/tree/main/DisenGCD. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Femtosecond laser-induced flexible and highly sensitive capacitive pressure sensor with treefrog suckers bionic microstructure 2

Femtosecond laser-induced flexible and highly sensitive capa...

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2nd International Symposium on Sensor Technology and Control, ISSTC 2023

作者： Wang, Zhiyun Chu, Mengyang Su, Yahui Guo, Xiaohui Liu, Rui Anhui University Information Materials and Intelligent Sensing Laboratory of Anhui Province School of Electronic and Information Engineering Hefei China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University School of Integrated Circuits Hefei China Anhui Xinhuai Electronics Co. Ltd Huaibei China

ISBN: (纸本)9798350314465

Flexible capacitive pressure sensors are optimal for wearable electronic equipment owing to their advantages of simplicity in architecture and low power consumption. However, fabricating a bionic flexible capacitive pressure sensor with high sensitivity and a wide dynamic range of detection is still a great challenge. In this article, we propose a flexible pressure sensor based on femtosecond laser ablation with a treefrog suckers bionic microstructure. The treefrog suckers capacitive pressure sensor (TSCPS) has high sensitivity (0-10 kPa, 0.0762 kPa-1), wide detection range (500 kPa), high repeatability (>12000 s at 10 kPa), and fast response time (62.5 ms). For practical applications, the TSCPS was positioned on the wrist, palm, and sole of the hand to detect the movements of each part accordingly. The experimental findings indicate that the TSCPS has broad promising applications in bionic wearable products such as human locomotion monitoring. © 2023 IEEE.

关键词： Pressure sensors

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Fusion Multi-scale Features and Attention Mechanism for Multi-view 3D Reconstruction 3

Fusion Multi-scale Features and Attention Mechanism for Mult...

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3rd IEEE International Conference on Electronic Technology, Communication and information, ICETCI 2023

作者： Xie, Qiqi Xin, Yuelan Sun, Kexin Zeng, Xi Qinghai Normal University Xining China Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining China

ISBN: (纸本)9798350398410

In order to solve the problem that it is difficult to carry out the reconstruction accurately and completely in the process of multi-view 3D reconstruction, we puts forward to a 3D reconstruction method that combined with multi-scale features and attention mechanisms. To begin with, multi-scale features are extracted from the input source and reference images using a Feature Pyramid Network that adds a bottom-up structure to obtain the connections between the different scales of the feature map. What's more, the attention mechanism is used to optimize the cost volume regularization process and improve the accuracy of depth estimation. In the end, the improved network is trained. About the experimental results of DTU dataset, it can be seen that the 3D reconstruction results of this network reached 0.358mm and 0.366mm in Completeness (Comp) and Overall respectively, which are higher than traditional methods as well as some 3D reconstruction methods that are based on deep learning methods. At the same time, the results of visualization in the point cloud model have been significantly improved. In addition, experimental results on Tanks and Temples dataset show that this network also have a better generalization performance. © 2023 IEEE.

关键词： Deep learning

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JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASYMMETRIC RESIDUAL CROSS TRANSFORMER

JOINT CLASSIFICATION OF HYPERSPECTRAL AND SAR DATA USING ASY...

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IET International Radar Conference 2023, IRC 2023

作者： Sun, Yaxiu Wang, Minhui Sun, Rui Hao, Lihong Xiang, Jianhong National Key Laboratory of Communication Anti-Jamming Technology College of Information and Communication Engineering Harbin Engineering University Harbin China Agile and Intelligent Computing Key Laboratory Chengdu China

ISBN: (纸本)9781839539954

With the increasing amount of remote sensing (RS) data obtained from satellites or airborne platforms, The combined use of multimodal remote sensing data to improve classification performance has been widely concerned. Therefore, a new transformer-based hyperspectral and synthetic aperture radar (HS-SAR) data joint classification network ACCT was proposed in the paper. Combining the strengths of convolutional neural networks (CNN) and transformer, focus on extracting specific and common features from HS and SAR data. In addition, adding layer scale to the output of each residual block can learn diagonal matrix and improve the dynamics of training. Then, combining the advantages of residual join and asymmetric convolution kernel, the parameters are reduced while the feature information is fully learned. Experiments in this paper are carried out on two famous multimodal fusion datasets: Augsburg (AU), and Berlin (BL) datasets. Results show that ACCT achieves better classification accuracy than several existing models. OA reaches 97.20%, and 85.02% on the two datasets. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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