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10th International Conference on Software Defined Systems, SDS 2023

作者： Song, Wenlue Wu, Hanyuan Meng, Hongwei Bian, Yifu Tang, Cong Xi, Jiaqi Zhu, Haogang School of Computer Science and Engineering Beihang University Beijing China Xiong'An Intelligent City Innovation Federation Xiong'An Blockchain Lab New Area Xiong'an China Temi Tech Ltd Beijing Xiong'An Huoshu Tech Ltd Hebei Beijing China Beijing University of Technology Beijing China

ISBN: (纸本)9798350382235

As large-scale construction projects become increasingly complex, the use and integration of advanced technologies are being emphasized more and more. However, the construction industry often lags behind most industries in the application of digital technologies. In recent years, a decentralized, peer-to-peer blockchain technology has attracted widespread attention from academia and industry. This paper provides a solution that combines blockchain technology with construction project fund management. The system involves participants such as the owner's unit, construction companies, government departments, banks, etc., adopting the technical architecture of the 'Xiong'an blockchain Underlying System'. The core business and key logic processing are all implemented through smart contracts, ensuring the transparency and traceability of the fund payment process. The goal of ensuring investment quality, standardizing investment behavior, and strengthening cost control is achieved through blockchain technology. The application of this system in the management of Xiong'an construction projects has verified that blockchain technology plays a significant positive role in strengthening fund management, enhancing fund supervision, and ensuring fund safety in the construction process of engineering projects. It helps to eliminate the common problems of multi-party trust and transparent supervision in the industry and can further improve the investment benefits of government investment projects and improve the management system and operation mechanism of investment projects. © 2023 IEEE.

关键词： Smart contract

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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A Legal Judgment Prediction Model Based on BERT, Attention, and Graph Convolutional Network 36

A Legal Judgment Prediction Model Based on BERT, Attention, ...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Yang, Binxia Chen, Guibin Luo, Xudong School of Computer Science and Engineering Guangxi Normal University Guangxi Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guangxi Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Guilin541004 China

ISBN: (纸本)9798331527235

Legal judgment prediction is one of essential tasks in legal practice. However, most existing models of legal judgment prediction struggle with long documents due to limitations in position encoding and pre-trained language models' capabilities. To address these issues, we propose a hybrid model for legal judgment prediction. The model uses BERT to captures deep semantic information of a legal text, position encoding to preserve its text structure, a graph convolutional network to analyse complex relationships between the entities in the text, and a decoupled attention mechanism to fuse the textual content and positional information. Our experiments show that the proposed model excels in legal judgment prediction tasks, outperforming existing technologies in accuracy and recall, with significant advantages in handling lengthy legal texts. © 2024 IEEE.

关键词： Prediction models

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EduChain: A blockchain-Based Privacy-Preserving Lifelong Education Platform 28th

EduChain: A Blockchain-Based Privacy-Preserving Lifelong Edu...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Huang, Xinzhe Wang, Yujue Liang, Hai Ding, Yong Wu, Qianhong Zhang, Ziyi Qu, Qiang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute Beihang University Hangzhou China Cyberspace Security Research Center Pengcheng Laboratory Shenzhen China School of Cyber Science and Technology Beihang University Beijing China PAAS Blockchain Service Team Huawei Cloud Tech Co. Ltd Beijing China Blockchain Lab Huawei Cloud Tech Co. Ltd Shenzhen China

ISBN: (纸本)9783031306778

With the popularization of lifelong education, academic and vocational education run through the life of students. However, the traditional lifelong education platform has three problems, namely incoherent achievement data, poor privacy protection, and vulnerability to single-point attacks, which seriously hinder the development of the lifelong education industry. This paper proposes a lifelong education platform based on blockchain supporting privacy protection (EduChain). The access control on achievement data is realized based on proxy re-encryption, and anyone can trace achievement data to the issuing organization through digital signature technology. EduChain can realize the credible transfer and privacy protection of lifelong education archives between students and checkers. The experiments demonstrate the practicality of EduChain in education scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： blockchain

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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Modeling High-Order Relationships Between Human and Video for Emotion Recognition in Video Learning 31st

Modeling High-Order Relationships Between Human and Video f...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Ai, Hanxu Tao, Xiaomei Li, Xingbing Gan, Yanling Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (纸本)9789819620630

In video learning, accurately identifying learners’ emotional state is fundamental for enhancing the learner experience and improving teaching effectiveness. Most existing methods focus on analyzing learners’ physiological signals, neglecting the impact of instructional videos on learners, especially the hidden high-order relationships between video stimuli and physiological signals. This paper introduces a Multimodal Multi-Hpergraph emotion recognition framework, MMH, leveraging the strengths of hypergraphs in modeling high-order relationships. MMH captures high-order relationships between different modalities within instructional videos and between videos and learners, aiming to identify learners’ emotional states during video learning. During the hypergraph construction process, we employ two strategies to construct the hypergraph: (1) using multiple k-value feature KNN construction methods, and (2) construction methods based on vertex attributes. In the hypergraph learning process, we introduce a vertex attention mechanism to update vertex features during the hypergraph learning process, further improving the emotion recognition performance of MMH. Our method achieves state-of-the-art cross-subject emotion recognition performance on the VLMED dataset. Experimental results on two public benchmark datasets, MAHNOB-HCI and DEAP, further verify the effectiveness of our method in emotion recognition tasks. Our source code will be made publicly available upon acceptance of the paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Emotion Recognition

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Privacy-preserving data aggregation achieving completeness of data queries in smart grid

Privacy-preserving data aggregation achieving completeness o...

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作者： Li, Xinyang Zhao, Meng Ding, Yong Yang, Changsong Wang, Huiyong Liang, Hai Wang, Yujue Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin China Hangzhou Innovation Institute of Beihang University Hangzhou China Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

In smart grid systems, the control center formulates strategies and provides services by analyzing electricity consumption data. However, ensuring the privacy and security of user data is a critical concern. While traditional data aggregation schemes can provide a certain level of privacy protection for users, they also impose limitations on the control center's access to fine-grained data. To address these challenges, we propose a privacy-preserving data aggregation scheme supporting data query (PAQ). We designed a multi-level data aggregation mechanism based on Paillier semi-homomorphic encryption to achieve efficient aggregation of user data in the control center. Additionally, a data query mechanism based on electricity consumption intervals is introduced, allowing the control center to query aggregated ciphertexts for different user categories from outsourced data on the cloud server. Security analysis demonstrates that PAQ design effectively solves security issues in data aggregation and query processes. Performance analysis indicates that the proposed scheme outperforms existing solutions in terms of efficiency. © 2024 John Wiley & Sons, Ltd.

关键词： Smart power grids

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RF Fingerprinting-Based IoT Node Authentication Using Mahalanobis Distance Correlation Theory

IEEE Networking Letters

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IEEE Networking Letters 2022年第2期4卷 78-81页

作者： Nguyen, Dinh Duc Nha Sood, Keshav Nosouhi, Mohammad Reza Xiang, Yong Gao, Longxiang Chi, Lianhua Deakin University Centre of Cyber Security Research Innovation GeelongVIC3220 Australia Deakin University Deakin Blockchain Innovation Laboratory School of Information Technology GeelongVIC3220 Australia Qilu University of Technology Shandong Academy of Sciences Jinan250316 China National Supercomputer Center in Jinan Shandong Computer Science Center Jinan250101 China La Trobe University Department of Computer Science and Information Technology MelbourneVIC3086 Australia

The wireless Internet of Things (IoT) node authentication approaches also used Radio Frequency (RF) fingerprinting or physical unclonable features (PUF) of IoT devices for node authentication. Machine learning based models play vital role in these approaches. In this letter, we introduce an effective and novel IoT node authentication approach using Mahalanobis Distance correlation and Chi-square distribution theories. Further, it has lower computational time to determine node authenticity. The comparative results of our proposal with three recent machine learning based approaches and PUF based approaches are promising which validates the effectiveness and the novelty of our proposal. © 2019 IEEE.

关键词： Next generation networks

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A BERT-Based Model for Legal Document Proofreading 13th

A BERT-Based Model for Legal Document Proofreading

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Liu, Jinlong Luo, Xudong School of Computer Science and Engineering Guangxi Normal University Guilin541004 China Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China

ISBN: (纸本)9783031578076

Legal documents require high precision and accuracy in language use, leaving no room for grammatical and spelling errors. To address the issue, this paper proposes a novel application of the BERT pre-trained language model for legal document proofreading. The BERT-based model is trained to detect and correct legal texts’ grammatical and spelling errors. On a dataset of annotated legal documents, we experimentally show that our BERT-based model significantly outperforms state-of-the-art proofreading models in precision, recall, and F1 score, showing its potential as a valuable tool in legal document preparation and revision processes. The application of such advanced deep learning techniques could revolutionise the field of legal document proofreading, enhancing accuracy and efficiency. © IFIP International Federation for Information Processing 2024.

关键词： Authentication

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Research on the seismic signal classification algorithm based on SE-DenseNet 4

Research on the seismic signal classification algorithm base...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Wang, Yuan Huang, Hanming Ding, Weiwei Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

ISBN: (数字)9781510680265

ISBN: (纸本)9781510680258

Addressing the issue of seismic signal classification and recognition for natural earthquakes and artificial blasting, a deep learning model suitable for seismic signal classification and recognition is proposed. In this study, a dataset is created by selecting 14,001 natural earthquake waveforms and 12,870 artificial blasting waveforms from the seismic waveform data provided by the China National Earthquake science Data Sharing Centre for the years 2010-2016. Based on the DenseNet architecture, an attention mechanism is introduced into the original Dense Block, adaptively enhancing valuable feature channels, thereby improving the model's perception and expression capabilities for important information. The model achieves an accuracy of 96.25% on the test set, surpassing the compared methods. Experimental results demonstrate that the attention-enhanced DenseNet exhibits excellent classification effectiveness and generalization ability when applied to seismic signals. © 2024 SPIE.

关键词： Earthquakes

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