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Access control of blockchain based on dual-policy attribute-based encryption 22

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Access control of blockchain based on dual-policy attribute-...

22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Han, Daojun Chen, Jinyu Zhang, Lei Shen, Yatian Wang, Xueheng Gao, Yihua Institute of Data and Knowledge Engineering School of Computer and Information Engineering Henan University Kaifeng China Henan University Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Kaifeng China

ISBN: (纸本)9781728176499

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through 'pseudo-Anonymity'. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center;instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally,

关键词： Blockchain

Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation

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arXiv 2024年

作者： Li, Jiyong Azizov, Dilshod Li, Yang Liang, Shangsong School of Computer Science and Engineering Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates AI Thrust Information Hub The Hong Kong University of Science and Technology Guangzhou China Department of CSE The Hong Kong University of Science and Technology China

Recently, because of the high-quality representations of contrastive learning methods, rehearsal-based contrastive continual learning has been proposed to explore how to continually learn transferable representation embeddings to avoid the catastrophic forgetting issue in traditional continual settings. Based on this framework, we propose Contrastive Continual Learning via Importance Sampling (CCLIS) to preserve knowledge by recovering previous data distributions with a new strategy for Replay Buffer Selection (RBS), which minimize estimated variance to save hard negative samples for representation learning with high quality. Furthermore, we present the Prototype-instance Relation Distillation (PRD) loss, a technique designed to maintain the relationship between prototypes and sample representations using a self-distillation process. Experiments on standard continual learning benchmarks reveal that our method notably outperforms existing baselines in terms of knowledge preservation and thereby effectively counteracts catastrophic forgetting in online contexts. The code is available at https://***/lijy373/CCLIS. Copyright © 2024, The Authors. All rights reserved.

关键词： Distillation

FF-KGAT: Feature Fusion Based Knowledge Graph Attention Network for Recommendation 5

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FF-KGAT: Feature Fusion Based Knowledge Graph Attention Netw...

5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Tao, Li Xiaoge, Li Chaodong, Wang Xianliang, Li Shuai, Gao Dan, Han Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Computer Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China Inspur Software Technology Co Ltd Software Development Project Management China Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China School of Communication and Information Engineering Xian University of Posts and Telecommunications Shaanxi Xi'an710121 China

ISBN: (纸本)9781450396899

It is commonly agreed that a recommender system based on knowledge graph (KG) should not only use user-item interactions, but also take side information into account to deal with the problem of data sparsity. However, existing KG-based models present unique challenges that have the shortcoming of cold start and redundant iterations. To address the issue, we propose a Feature Fusion-based Knowledge Graph Attention Network (FF-KGAT) for the recommendation. FF-KGAT handles the cold-start problem through introducing user characteristics to fully explore the information of users. Additionally, FF-KGAT introduces the feature fusion graph, which removes slight nodes to obtain fewer iterations. Experimental results show that the proposed model significantly outperforms baseline methods. Particularly, the ndcg is increased by 9.83% and 10.25% on two public datasets, compared with the best performance of existing methods. © 2022 ACM.

关键词： Recommender systems

Appformer: A Novel Framework for Mobile App Usage Prediction Leveraging Progressive Multi-Modal data Fusion and Feature Extraction

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arXiv 2024年

作者： Sun, Chuike Chen, Junzhou Zhao, Yue Han, Hao Jing, Ruihai Tan, Guang Wu, Di School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University No. 66 Gongchang Road Guangming District Guangdong Shenzhen518107 China Transsion Holdings Co. Ltd Shanghai201203 China The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China The Guangdong Key Laboratory of Big Data Analysis and Processing Guangdong510006 China

This article presents Appformer, a novel mobile application prediction framework inspired by the efficiency of Transformer-like architectures in processing sequential data through self-attention mechanisms. Combining a Multi-Modal data Progressive Fusion Module with a sophisticated Feature Extraction Module, Appformer leverages the synergies of multimodal data fusion and data mining techniques while maintaining user privacy. The framework employs Points of Interest (POIs) associated with base stations, optimizing them through comprehensive comparative experiments to identify the most effective clustering method. These refined inputs are seamlessly integrated into the initial phases of cross-modal data fusion, where temporal units are encoded via word embeddings and subsequently merged in later stages. The Feature Extraction Module, employing Transformer-like architectures specialized for time series analysis, adeptly distils comprehensive features. It meticulously fine-tunes the outputs from the fusion module, facilitating the extraction of high-calibre, multi-modal features, thus guaranteeing a robust and efficient extraction process. Extensive experimental validation confirms Appformer’s effectiveness, attaining state-of-the-art (SOTA) metrics in mobile app usage prediction, thereby signifying a notable progression in this field. © 2024, CC BY-NC-SA.

关键词： Spatio-temporal data

Internal drivers of the global pandemic of the Omicron variants of SARS-CoV-2

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Medicine Advances

Medicine Advances 2024年第3期2卷 262-273页

作者： Yilin Niu Wuxiu Quan Yunyi Li Ziying Chen Yunfei Gao School of Public Health Southern Medical University Guangzhou Guangdong China Medical Big Data Center Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangzhou Guangdong China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China

Background The high transmissibility of the Omicron variants of severe acute respiratory syndrome coronavirus 2 continues to impose a significant burden on public health systems worldwide. Continuous mutations in the virus challenge the efficacy of vaccines and prior immunity from other variants, posing a severe threat to human health. Therefore, there is an urgent need to predict mutations of the Omicron variants and identify key factors influencing their spread. This study investigated critical amino acid mutations of the Omicron variants using epidemiological data and the mechanisms underlying their transmission. The aim was to understand the key factors driving the spread of the Omicron variants and provide insights for effective control and prevention strategies. Methods A total of 488,646 Omicron cases recorded between December 2021 and February 2023 were analyzed using a sliding time window and the Epi Score model, which has high accuracy for the identification of mutation sites. MutPred2, PolyPhen2 and VarSite tools were used to predict future mutations and identify factors driving the prevalence of the Omicron variants. Results Epi Scores showed fluctuating patterns at mutation sites, highlighting N969K, Y505H, N764K, T478K, and S371F mutations on the spike protein as significant for future prevention efforts. The spread of the Omicron variants was linked to changes in the viral entry pathway and improved angiotensin-converting enzyme 2 (ACE2) binding and immune evasion tactics. Conclusions The findings of this study reveal trends in the evolution of the Omicron variants, including altered cell entry, increased affinity for ACE2, and evasion of the immune system. These factors are critical for understanding the global spread of the Omicron variants and developing effective control strategies.

关键词： amino acids coronavirus immune evasion mutation public health SARS-CoV-2 spike glycoprotein

Deep Learning in Palmprint Recognition-A Comprehensive Survey

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

Sparse Deep Neural Network for Nonlinear Partial Differential Equations

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arXiv 2022年

作者： Xu, Yuesheng Zeng, Taishan Department of Mathematics and Statistics Old Dominion University NorfolkVA23529 United States School of Mathematical Science South China Normal University Guangzhou510631 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

More competent learning models are demanded for data processing due to increasingly greater amounts of data available in applications. data that we encounter often have certain embedded sparsity structures. That is, if they are represented in an appropriate basis, their energies can concentrate on a small number of basis functions. This paper is devoted to a numerical study of adaptive approximation of solutions of nonlinear partial differential equations whose solutions may have singularities, by deep neural networks (DNNs) with a sparse regularization with multiple parameters. Noting that DNNs have an intrinsic multi-scale structure which is favorable for adaptive representation of functions, by employing a penalty with multiple parameters, we develop DNNs with a multi-scale sparse regularization (SDNN) for effectively representing functions having certain singularities. We then apply the proposed SDNN to numerical solutions of the Burgers equation and the Schrödinger equation. Numerical examples confirm that solutions generated by the proposed SDNN are sparse and accurate. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral image Classification

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arXiv 2023年

作者： Liu, Chun Yang, Longwei Li, Zheng Yang, Wei Han, Zhigang Guo, Jianzhong Yu, Junyong School of Computer and Information Engineering Henan University Henan Zhengzhou450046 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Henan Zhengzhou450046 China Henan Engineering Laboratory of Spatial Information Processing Henan University Henan Zhengzhou450046 China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan University Henan Zhengzhou450046 China College of Geography and Environmental Science Henan University Henan Zhengzhou450046 China Henan DeFan High-tech Software Co. Ltd Henan Zhengzhou450046 China

Cross-domain few-shot hyperspectral image classification focuses on learning prior knowledge from a large number of labeled samples from source domains and then transferring the knowledge to the tasks which contain few labeled samples in target domains. Following the metric-based manner, many current methods first extract the features of the query and support samples, and then directly predict the classes of query samples according to their distance to the support samples or prototypes. The relations between samples have not been fully explored and utilized. Different from current works, this paper proposes to learn sample relations on different levels and take them into the model learning process, to improve the cross-domain few-shot hyperspectral image classification. Building on current method of"Deep Cross-Domain Few-Shot Learning for Hyperspectral image Classification" which adopts a domain discriminator to deal with domain-level distribution difference, the proposed method applies contrastive learning to learn the class-level sample relations to obtain more discriminable sample features. In addition, it adopts a transformer based cross-attention learning module to learn the set-level sample relations and acquire the attention from query samples to support samples. Our experimental results have demonstrated the contribution of the multi-level relation learning mechanism for few-shot hyperspectral image classification when compared with the state of the art methods. All the codes are available at github https://***/HENULWY/STBDIP. © 2023, CC BY.

关键词： image enhancement

An Adaptive Framework of Multimodal Emotion Recognition Based on Collaborative Discriminative Learning

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An Adaptive Framework of Multimodal Emotion Recognition Base...

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Yadi Wang Xiaoding Guo Yibo Zhang Yiyuan Ren Wendi Huang Zunyan Liu Yuming Feng Xiangguang Dai Wei Zhang Hangjun Che School of Computer and Information Engineering Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China Key Laboratory of Intelligent Information Processing and Control Chongqing Three Gorges University Chongqing China School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China School of Three Gorges Artificial Intelligence Chongqing Three Gorges University Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China

Multimodal emotion recognition has great application scenarios in the field of human-computer interaction, and the main task lies in how to capture and handle the consistency and complementarity of multimodal signals. However, in numerous scenarios, multimodal fusion is significantly challenged by variances in the strengths and weaknesses of different modality signals in carrying emotional information. This paper aims to learn the essential information of strong modalities and inter-modality correlations thus achieving better emotion recognition. We propose an adaptive multimodal emotion recognition framework based on collaborative discriminative learning (AMCDL), a framework for emotion recognition that engages in collaborative learning and adjusts to the information-processing capabilities of various emotion recognition models. AMCDL calculates the inter-modality correlation using Canonical Correlation analysis (CCA) and uses it as an influencing factor to boost the multiplicative combination method to create a class loss function. When trained on several modalities, the class loss function can integrate complementary information to enable strong modal expression, enhancing emotion perception. On the benchmark dataset CMU-MOSI, AMCDL significantly improves classification accuracy when compared to the state-of-the-art models. The experiments showed the flexibility and generality of AMCDL by applying several emotion recognition models to it and producing better classification results than the original model.

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