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An Efficient and Provably Secure SM2 key-Insulated Signature Scheme for Industrial Internet of Things

computer Modeling in Engineering & Sciences 2024年第1期138卷 903-915页

作者： Senshan Ouyang Xiang Liu Lei Liu Shangchao Wang Baichuan Shao Yang Zhao School of Mechanical Engineering Northwestern Polytechnical UniversityXi’anChina Department of Process and Information Technology Chengdu Aircraft Industrial(Group)Co.Ltd.ChengduChina School of Information and Software Engineering The Network and Data Security Key Laboratory of Sichuan ProvinceUniversity of Electronic Science and Technology of ChinaChengduChina

With the continuous expansion of the Industrial Internet of Things(IIoT),more andmore organisations are placing large amounts of data in the cloud to reduce ***,the channel between cloud servers and smart equipment is not trustworthy,so the issue of data authenticity needs to be *** SM2 digital signature algorithm can provide an authentication mechanism for data to solve such ***,it still suffers from the problem of key *** order to address this concern,this study first introduces a key-insulated scheme,SM2-KI-SIGN,based on the SM2 *** scheme boasts strong key insulation and secure *** scheme uses the elliptic curve algorithm,which is not only more efficient but also more suitable for IIoT-cloud ***,the security proof of SM2-KI-SIGN is given under the Elliptic Curve Discrete Logarithm(ECDL)assumption in the random oracle.

关键词： key-insulated SM2 algorithm digital signature Industrial Internet of Things(IIoT) provable security

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Context-Aware Grammatical Error Correction Model 5

Context-Aware Grammatical Error Correction Model

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Lin, Jianxing Huang, Guimin Wang, JiaHao 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)9798331506612

The pursuit of accurate and fluent English communication is the cornerstone of global academic exchange and expression. The accuracy of written English is crucial for effective discourse;however, despite advancements in grammatical error correction (GEC) technology, these systems often fall short when analyzing coherent text. This paper delves into the complexities of English writing and the challenges posed by current GEC systems, advocating for a shift toward understanding document-level context in error correction. We propose a dual-encoder architecture within an encoder-decoder model, referred to as the Context-Aware Grammatical Error Correction (CAGEC) model. The CAGEC model employs an innovative dual-encoder structure, combining the encoder of the Transformer model with a Bi-GRU (Bidirectional Gated Recurrent Unit) neural network, and integrates encoding into the decoder through attention and gating mechanisms to achieve a deep understanding of the source sentence and its context. © 2024 IEEE.

关键词： Decoding

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Dynamic Student Classification with Forgetting Mechanisms on Memory networks for Knowledge Tracing 5

Dynamic Student Classification with Forgetting Mechanisms on...

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5th International Conference on Intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Huang, Guimin Qin, Xiangqian 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)9798350368284

Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods. © 2024 IEEE.

关键词： Contrastive Learning

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Shorter ZK-SNARKs from square span programs over ideal lattices

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Cybersecurity 2025年第1期8卷 208-226页

作者： Xi Lin Heyang Cao Feng‑Hao Liu Zhedong Wang Mingsheng Wang Key Laboratory of Cyberspace Security Defense Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina School of Electrical Engineering&Computer Science Washington State UniversityPullmanWAUSA School of Cyber Science and Engineering Shanghai Jiaotong UniversityShanghaiChina

Zero-knowledge succinct non-interactive arguments of knowledge(zk-SNARKs)are cryptographic protocols that ofer efcient and privacy-preserving means of verifying NP language relations and have drawn considerable atten‑tion for their appealing applications,e.g.,verifable computation and anonymous payment *** with the pre-quantum case,the practicability of this primitive in the post-quantum setting is still unsatisfactory,espe‑cially for the space *** tackle this issue,this work seeks to enhance the efciency and compactness of lat‑tice-based zk-SNARKs,including proof length and common reference string(CRS)*** this paper,we develop the framework of square span program-based SNARKs and design new zk-SNARKs over cyclotomic *** with previous works,our construction is without parallel repetition and achieves shorter proof and CRS lengths than previous lattice-based zk-SNARK ***,the proof length of our scheme is around 23.3%smaller than the recent shortest lattice-based zk-SNARKs by Ishai et al.(in:Proceedings of the 2021 ACM SIGSAC conference on computer and communications security,pp 212-234,2021),and the CRS length is 3.6×*** constructions follow the framework of Gennaro et al.(in:Proceedings of the 2018 ACM SIGSAC conference on computer and com‑munications security,pp 556-573,2018),and adapt it to the ring setting by slightly modifying the knowledge *** develop concretely small constructions by using module-switching and key-switching procedures in a novel way.

关键词： Zk-SNARKs Post-quantum Succinct argument

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5th International Conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2024

作者： Zhou, Ya Chen, Chen Huang, Guimin 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)9798331506612

In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%. © 2024 IEEE.

关键词： Semantics

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Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare 18th

Genetic-A* Algorithm-Based Routing for Continuous-Flow Micr...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Huang, Huichang Yang, Zhongliao Zhong, Jiayuan Xu, Li Dong, Chen Bao, Ruishen College of Computer and Data Science Fuzhou University Fuzhou350116 China College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9789819998951

In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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A Personalized Recommendation Model based on Collaborative Filtering and Federated Learning for Cultural Tourism Attractions in Fujian-Taiwan

A Personalized Recommendation Model based on Collaborative F...

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2023 International Conference on Software and System Engineering, ICoSSE 2023

作者： Cai, Yunsen Gao, Haolin Liao, Junkai Li, Xinwei Xu, Yushan Xiong, Jinbo Fujian Normal University College of Computer and Cyber Security Fuzhou350117 China Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou350007 China

ISBN: (纸本)9798350324242

In the era of big data, the explosive growth of cultural tourism information should provide personalized and privacy requirements for people's travel. However, the existing travel recommendation algorithms have some obvious challenges, such as weak personalization, significant cold-start problem, and poor user privacy protection, etc. We propose a personalized intelligent recommendation model and method, which constructs a new collaborative filtering recommendation algorithm based on user characteristics portrait and historical behaviors, and designs an user-attraction data correction model based on federal learning to implement user privacy protection. Furthermore, We fully combine user age, gender, hobbies, and explicit and implicit data feedback to improve the accuracy of the proposed recommendation model. A sufficient experiments have been done on the real Fujian-Taiwan cultural and tourist attractions dataset, and the results show that, the proposed model protects the user's private information and ensures a higher recommendation accuracy. Compared with the traditional collaborative filtering recommendation, it is concluded that the recommended attractions can accurately hit 50%-90% of users' clear travel destinations, and the satisfaction rate of users can reach 77% among the attractions recommended to users outside the expectation, which shows good performance and can be effectively extended to other recommendation systems for cultural tourism attractions from the experimental results. © 2023 IEEE.

关键词： Collaborative filtering

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Multi-Label Open Set Recognition 38

Multi-Label Open Set Recognition

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Wang, Yi-Bo Hang, Jun-Yi Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

In multi-label learning, each training instance is associated with multiple labels simultaneously. Traditional multi-label learning studies primarily focus on closed set scenario, i.e. the class label set of test data is identical to those used in training phase. Nevertheless, in numerous real-world scenarios, the environment is open and dynamic where unknown labels may emerge gradually during testing. In this paper, the problem of multi-label open set recognition (MLOSR) is investigated, which poses significant challenges in classifying and recognizing instances with unknown labels in multi-label setting. To enable open set multi-label prediction, a novel approach named SLAN is proposed by leveraging sub-labeling information enriched by structural information in the feature space. Accordingly, unknown labels are recognized by differentiating the sub-labeling information from holistic supervision. Experimental results on various datasets validate the effectiveness of the proposed approach in dealing with the MLOSR problem. © 2024 Neural information processing systems foundation. All rights reserved.

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A divide-and-conquer reconstruction method for defending against adversarial example attacks

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Visual Intelligence 2024年第1期2卷 1-17页

作者： Liu, Xiyao Hu, Jiaxin Yang, Qingying Jiang, Ming He, Jianbiao Fang, Hui School of Computer Science and Engineering Central South University Hunan Changsha 410083 China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Guilin China Department of Computer Science Loughborough University Loughborough LE113TU United Kingdom

In recent years, defending against adversarial examples has gained significant importance, leading to a growing body of research in this area. Among these studies, pre-processing defense approaches have emerged as a prominent research direction. However, existing adversarial example pre-processing techniques often employ a single pre-processing model to counter different types of adversarial attacks. Such a strategy may miss the nuances between different types of attacks, limiting the comprehensiveness and effectiveness of the defense strategy. To address this issue, we propose a divide-and-conquer reconstruction pre-processing algorithm via multi-classification and multi-network training to more effectively defend against different types of mainstream adversarial attacks. The premise and challenge of the divide-and-conquer reconstruction defense is to distinguish between multiple types of adversarial attacks. Our method designs an adversarial attack classification module that exploits the high-frequency information differences between different types of adversarial examples for their multi-classification, which can hardly be achieved by existing adversarial example detection methods. In addition, we construct a divide-and-conquer reconstruction module that utilizes different trained image reconstruction models for each type of adversarial attack, ensuring optimal defense effectiveness. Extensive experiments show that our proposed divide-and-conquer defense algorithm exhibits superior performance compared to state-of-the-art pre-processing methods. © The Author(s) 2024.

关键词： Adversarial attack multi-classification Adversarial example defense Divide-and-conquer strategy Reconstruction network

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