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International Conference on Communication Technology (ICCT)

作者： Zeqing Chen Rui Chen Huan Zhou Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China College of Computer and Information Technology China Three Gorges University Yichang Hubei China School of Computer Science Northwestern Polytechnical University Xi'an China

Frequency hopping (FH) technique has been extensively used for high security communication because of its strong anti-jamming ability and good confidentiality. However, with the continuous advancement of electronic attacks, plenty of energy limited devices in the Internet of Things (IoT) are experiencing a sharp increase in interference, which poses serious challenges to the current FH sequence generation techniques. In this paper, we propose a novel FH sequence generation scheme by exploiting an ultra-lightweight block cipher (PRESENT), which strikes an effective balance between security and efficiency. Specifically, the values related to channel gain are mapped to corresponding FH codes through the output of lightweight block cipher algorithm. Moreover, the security of the proposed scheme has been analyzed. It turns out that the FH sequences generated by PRESENT algorithm outperform the FH sequences generated by a bit-slice lightweight block cipher suitable for multiple platforms (RECTANGLE) and a lightweight block cipher (LBlock) in terms of uniformity, randomness and complexity.

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Surface Electric Field Measurement of Composite Insulator Using an Optical Waveguide Sensor

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SSRN 2023年

作者： Zhang, Jiahong Li, Weichang Shi, Jiali Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650000 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming650000 China

In this paper, an integrated optical waveguide sensor is designed and developed to comprehensively analyze the surface electric field distribution characteristics of composite insulators. Based on the optical waveguide electric field sensor, a measuring system for the surface axial and radial electric fields of composite insulators is built, and the axial and radial electric fields along the surface of composite insulator is measured under normal, damaged and internally defects. Based on the optical waveguide electric field sensor, an experimental system is setup for measuring the axial and radial electric fields along surface of composite insulators under normal, damaged and internally defective conditions. The experimental results showed that when a 10 kV power frequency voltage is connected, the radial and axial electric fields at the low and high voltage ends of the normal insulator are 21.53 kV/m, 53.07 kV/m and 47.55 kV/m, 105.12 kV/m respectively. When the insulator surface is damaged, the radial and axial electric field intensities at the damaged locations increased by 37.59%-204.85% and 12.89%-32.69%, respectively. When an internal defect occurred, the radial and axial electric field along the defect area increased by 18% and decreased by 127.72%, respectively. The radial electric field is more affected by the defect. The comparison with the calculated results shows that the integrated optical waveguide electric field sensor has great potential capability to be used for measuring the surface electric field of insulators. © 2023, The Authors. All rights reserved.

关键词： Electric fields

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on computer Supported Cooperative Work in Design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media information retrieval Encoding

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Semantic information Reasoning and Multi-Step Cross-Modal Interaction Network for Image-Text Retrieval

Semantic Information Reasoning and Multi-Step Cross-Modal In...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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Improved harris hawks optimization algorithm based on collaborative quantization 6

Improved harris hawks optimization algorithm based on collab...

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6th International Conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Li, Yan Zhang, Xiang Qian, Qian Pan, Jiawen Feng, Yong Fu, Yunfa Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming Yunnan650500 China Yunnan Key Laboratory of Computer Technology Applications Kunming University of Science and Technology Kunming Yunnan650500 China Logistic Service Group Yunnan University Kunming Yunnan650500 China

ISBN: (纸本)9781510656635

Harris hawk optimization algorithm (HHO) is one of the population-based algorithms proposed by in *** has received great attention from researchers. However, HHO algorithm still has some problems, such as the exploitation ability is too large compared with the exploration ability, which leads to low optimization accuracy, slow convergence speed and so on. Therefore, the collaborative strategy and quantization strategy are introduced. With the support of the two strategies, the algorithm can avoid falling into local extremum in the early phase of iteration and improve its optimization accuracy at the late phase of iteration. Through the test of four representative functions, compared with the other three basic algorithms, the proposed algorithm greatly improves the optimization accuracy and convergence speed of the optimal solution, and the ability to find global extremum is greatly improved. © 2022 SPIE.

关键词： Optimization

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Indoor Staircase Detection for Supporting Security Systems in Autonomous Smart Wheelchairs Based on Deep Analysis of the Co-Occurrence Matrix

SSRN

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SSRN 2023年

作者： Utaminingrum, Fitri Wahyudi, Slamet Karim, Corina Johan, Ahmad Wali Satria Bahari Shih, Timothy K. Lin, Chih-Yang Computer Vision Research Groups Faculty of Computer Science Brawijaya University Malang65145 Indonesia Mechanical Department Brawijaya University Malang65145 Indonesia Mathematics Department Brawijaya University Malang65145 Indonesia Department of Informatics Institut Teknologi Telkom Purwokerto Surabaya65145 Indonesia Department of Computer Science and Information Engineering National Central University Taoyuan City32001 Taiwan Computer Vision and Interactive Technology Department of Electrical Engineering Yuan-Ze University Taoyuan City32003 Taiwan

Detection of descending stairs and floors is important in implementing autonomous systems in smart wheelchairs. Suppose the obstacle detection system applied to wheelchairs cannot properly detect the descending stairs. In that case, it will have fatal consequences for its users, one of which is an injury or even the worst impact can result in death. Hence, we need an algorithm that has good accuracy in detecting obstacles in descending stairs and a fast-computing time so that the system's response for braking on a wheelchair can be made immediately. In this study, we use the Gray Level Co-Occurrence Matrix (GLCM) method for the basis of feature ***, we analyzed the performance of several types of binary classification methods, including Naive Bayes, Decision Tree, Logistic Regression, k-Nearest Neighbors (k-NN), and Support Vector Machines (SVM). Based on the results obtained, the Decision Tree has the highest accuracy value compared to the others. The Accuracy value is 94%, and the computing time is 0.01299 seconds. These results were obtained in a combination of the GLCM method, the distance is equal to 2, and the angle is 45o. © 2023, The Authors. All rights reserved.

关键词： Stairs

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Cross-modal retrieval based on Attention Embedded Variational Auto-Encoder

Cross-modal retrieval based on Attention Embedded Variationa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Kaili Zhou Yiyun Xing Yushui Geng Jing Zhao Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

This study proposes a cross-modal retrieval technique, which employs an Attention Embedded Variational AutoEncoder (AE-VAE) to tackle the problem of reduced retrieval accuracy resulting from data noise and data absence in cross-modal retrieval tasks. First, the variational auto-encoder(VAE) is used as an infrastructure to transform modal data into latent variables by encoding and decoding different modal features, thus preserving the semantic structure of the raw modal data and suppressing noise and missing information in the data. Secondly, to better capture the important semantic features of image and text modalities, an improved multi-attention module is introduced in the encoder. In addition, a cross-modal alignment module is designed to further align the latent variables of different modalities in the latent space to solve the confusion problem and improve the accuracy and robustness of cross-modal retrieval. Finally, experiments are conducted on four public datasets, CUB, SUN, AwA1 and AwA2, and the experimental results show that our proposed model is effective in improving the accuracy and robustness of retrieval.

关键词： Accuracy Uncertainty Noise Semantics Transforms Probabilistic logic Robustness

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Semantic Relation-Based Cross Attention Network for Image-Text Retrieval

Semantic Relation-Based Cross Attention Network for Image-Te...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huanxiao Zhou Yushui Geng Jing Zhao Xishan Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Image-text retrieval refers to querying the text or image of another modality given an image or text, and its key lies in the ability to accurately measure the similarity between text and image. Most of the existing retrieval methods only utilize the intramodal relations of each modality or the intermodal relations of the two modalities to perform the retrieval task. To address the above problems, we integrate the intra-modal and inter-modal relations and propose a Semantic Relation-based Cross Attention Network(SRCAN). In our proposed method, we first mine the possible intra-modal associations between regions in an image and between words in a text to obtain features with semantic relationships, and then capture the fine-grained associations between segments through the cross-attention mechanism. Finally, we balance the intra-modal and inter-modal relationships to improve the performance of the model. Our proposed method is experimentally validated on two datasets, Flickr30K and MS-COCO, and the experimental results show that our method achieves superior results.

关键词： Image segmentation Semantics Bidirectional control Feature extraction Cognition Feeds Cybernetics

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MFFLEN: Multi-Label Text Classification Based on Multi-Feature Fusion and Label Embedding

MFFLEN: Multi-Label Text Classification Based on Multi-Featu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiliang Gu Shuo Zhao Jianqiang Zhang Gongpeng Song Qin Lu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Shandong Engineering Research Center of Big Data Applied Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

To address the challenges associated with insufficiently extracting and utilizing features at different levels, overlooking the connection between label meanings and text, and facing problems of over-compression or information loss when extracting global information using recurrent neural networks in the field of multi-label text categorization, this paper introduces an innovative model known as MFFLEN (MultiFeature Fusion and Label Embedding Neural Network). First, a back-translated enhanced label set is constructed by back-translated splicing enhancement of the original label set. This set, together with the text, is then input into the embedding layer, which consists of the pre-trained model of bert-baseChinese, thus establishing the initial connection between the text and the labels within the same vector space. Then, to comprehensively extract multi-level semantic features, the model uses a convolutional layer to extract local features and an embedding layer to extract sentence-level features. A bidirectional attention embedded GRU (BAE-GRU) layer is used to extract hybrid finegrained features, which are then fed into the attention layer to further extract hybrid labeled features based on labeling information. Finally, these three different types of features are fused and multi-label text classification results are obtained using a classifier. The experiments proved that the MFFLEN model achieved 73.82% and 88.44% macro-F1 and 88.00% and 88.86% micro-F1 on the two datasets CAIL 2018 Small and CAIL 2018 Split, respectively, which is better than other baseline models.

关键词： Measurement Recurrent neural networks Splicing Text categorization Semantics Multi label classification Logic gates Feature extraction Vectors Data mining

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AuthUp: A Privacy-Preserving V2I Authentication Scheme for Vehicular Ad Hoc Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Li, Xincheng Yin, Xinchun Ning, Jianting Zhang, Tianwei Zhejiang Sci-Tech University School of Information Science and Engineering School of Cyber Science and Technology the Zhejiang Key Laboratory of Digital Fashion and Data Governance Hangzhou310018 China Jiangxi Vocational College of Science and Technology Nanchang330200 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science China College of Computer and Cyber Security Fuzhou350117 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore

Vehicular ad hoc networks (VANETs) play a crucial role in enhancing modern transportation systems, leading to the development of various authentication schemes to meet the stringent security and privacy requirements of vehicular communications. Many existing schemes rely on tamper- proof devices (TPDs) that store extensive secret parameters for identity authentication, making them susceptible to Sybil and side-channel attacks. This paper introduces a sophisticated privacy-preserving authentication protocol, referred to as Authentication and Update (AuthUp), specifically designed to solve the problem. A trusted authority (TA) issues one-time access tokens to vehicles during the registration process. After each vehicle-to-infrastructure (V2I) authentication process, cryptographic key material is updated without compromising privacy. Consequently, Sybil attacks are promptly identified and thwarted, as manipulated tokens fail authentication. Additionally, adversaries are prevented from accessing sensitive data stored in TPDs via side-channel attacks because of the dynamic updating of secret keys. A formal security proof validates the robustness of AuthUp, and extensive evaluations demonstrate its superior efficiency compared to alternative schemes, highlighting its suitability for VANET deployment. © 1967-2012 IEEE.

关键词： Side channel attack

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