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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Graph Regularized Sparse Non-Negative Matrix Factorization for Clustering

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IEEE Transactions on Computational Social Systems 2023年第3期10卷 910-921页

作者： Deng, Ping Li, Tianrui Wang, Hongjun Wang, Dexian Horng, Shi-Jinn Liu, Rui Southwest Jiaotong University National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Computing and Artificial Intelligence Chengdu611756 China National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei106 Taiwan North Sichuan Medical College School of Preclinical Medicine Department of Computer and Mathematics Nanchong637100 China

The graph regularized nonnegative matrix factorization (GNMF) algorithms have received a lot of attention in the field of machine learning and data mining, as well as the square loss method is commonly used to measure the quality of reconstructed data. However, noise is introduced when data reconstruction is performed;and the square loss method is sensitive to noise, which leads to degradation in the performance of data analysis tasks. To solve this problem, a novel graph regularized sparse NMF (GSNMF) is proposed in this article. To obtain a cleaner data matrix to approximate the high-dimensional matrix, the l1-norm to the low-dimensional matrix is added to achieve the adjustment of data eigenvalues in the matrix and sparsity constraint. In addition, the corresponding inference and alternating iterative update algorithm to solve the optimization problem are given. Then, an extension of GSNMF, namely, graph regularized sparse nonnegative matrix trifactorization (GSNMTF), is proposed, and the detailed inference procedure is also shown. Finally, the experimental results on eight different datasets demonstrate that the proposed model has a good performance. © 2014 IEEE.

关键词： Matrix algebra

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Root-Of-Trust for Continuous Integration and Continuous Deployment Pipeline in Cloud computing

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computers, Materials & Continua 2022年第11期73卷 2223-2239页

作者： Abdul Saboor Mohd Fadzil Hassan Rehan Akbar Erwin Susanto Syed Nasir Mehmood Shah Muhammad Aadil Siddiqui Saeed Ahmed Magsi High-Performance Cloud Computing Centre(HPC3) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer&Information SciencesUniversiti Teknologi PETRONASSeri IskandarPerakMalaysia School of Electrical Engineering Telkom UniversityBandungIndonesia KICSIT Institute of Space Technology(IST)IslamabadPakistan Department of Electrical and Electronics Engineering Universiti Teknologi PETRONASSeri IskandarPerakMalaysia

Cloud computing has gained significant use over the last decade due to its several benefits,including cost savings associated with setup,deployments,delivery,physical resource sharing across virtual machines,and availability of on-demand cloud ***,in addition to usual threats in almost every computing environment,cloud computing has also introduced a set of new threats as consumers share physical resources due to the physical co-location ***,since there are a growing number of attacks directed at cloud environments(including dictionary attacks,replay code attacks,denial of service attacks,rootkit attacks,code injection attacks,etc.),customers require additional assurances before adopting cloud ***,the continuous integration and continuous deployment of the code fragments have made cloud services more prone to security *** this study,the model based on the root of trust for continuous integration and continuous deployment is proposed,instead of only relying on a single signon authentication method that typically uses only id and *** underlying study opted hardware security module by utilizing the Trusted Platform Module(TPM),which is commonly available as a cryptoprocessor on the motherboards of the personal computers and data center *** preliminary proof of concept demonstrated that the TPM features can be utilized through RESTful services to establish the root of trust for continuous integration and continuous deployment pipeline and can additionally be integrated as a secure microservice feature in the cloud computing environment.

关键词： Root of Trust(RoT) Trusted Platform Module(TPM) cryptoprocessor microservices Hardware Security Modules(HSM) DevOps

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The 20-nm Skyrmion Generated at Room Temperature by Spin-Orbit Torques

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Chinese Physics Letters 2022年第1期39卷 40-45页

作者： Jiahao Liu Zidong Wang Teng Xu Hengan Zhou Le Zhao Soong-Guen Je Mi-Young Im Liang Fang Wanjun Jiang State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua UniversityBeijing 100084China Frontier Science Center for Quantum Information Tsinghua UniversityBeijing 100084China Institute for Quantum Information&State Key Laboratory of High Performance Computing College of ComputerNational University of Defense TechnologyChangsha 410073China Center for X-ray Optics Lawrence Berkeley National LaboratoryCyclotron RoadBerkeleyCA 94720USA Department of Physics Chonnam National UniversityGwangju61186Republic of Korea

The discovery of magnetic skyrmions provides a promising pathway for developing functional spintronic memory and logic devices. Towards the future high-density memory application, nanoscale skyrmions with miniaturized diameters, ideally down to 20 nm are required. Using x-ray magnetic circular dichroism transmission microscopy,nanoscale skyrmions are observed in the [Pt/Co/Ir]15 multilayer at room temperature. In particular, small skyrmions with minimum diameters approaching 20 nm could be generated by the current-induced spin-orbit torques. Through implementing material specific parameters, the dynamic process of skyrmion generation is further investigated by performing micromagnetic simulations. According to the simulation results, we find that both the tube-like Neel-type skyrmions and the bobber-like Neel-type skyrmions can be electrically generated. In particular, the size of the bobber-like Neel-type skyrmions can be effectively reduced by the spin-orbit torques,which leads to the formation of 20 nm Neel-type skyrmions. Our findings could be important for understanding the formation dynamics of nanoscale Neel-type spin textures, skyrmions and bobber in particular, which could also be useful for promoting nanoscale skyrmionic memories and logic devices.

关键词： temperature diameters approaching

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Recommender System for Social Media: Research Challenges and Future Applications 5

Recommender System for Social Media: Research Challenges and...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ge, Mouzhi Pilato, Giovanni Persia, Fabio D'Auria, Daniela Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf Germany Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy University of l'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila Italy University of Bozen-Bolzano Faculty of Computer Science Free Bozen-Bolzano Italy

ISBN: (纸本)9798350358018

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning. © 2023 IEEE.

关键词： Recommender systems

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Qiu, Yanlong Wang, Siqi Yang, Xi Qiu, Xinyuan Wu, Chengkun Cui, Yingbo Yang, Canqun National University of Defense Technology Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science Hunan Changsha410073 China National Supercomputer Center in Tianjin Tianjin300457 China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Hunan Changsha410073 China National University of Defense Technology Department of Biology and Chemistry College of Science Hunan Changsha410073 China

ISBN: (纸本)9798350337488

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

关键词： Drug-Protein Interaction Knowledge Graph Embedding Triple Embedding Weakly Supervised Learning

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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CLIMS++: Cross Language Image Matching with Automatic Context Discovery for Weakly Supervised Semantic Segmentation

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International Journal of computer Vision 2025年 1-20页

作者： Xie, Jinheng Deng, Songhe Hou, Xianxu Luo, Zhaochuan Shen, Linlin Huang, Yawen Zheng, Yefeng Shou, Mike Zheng Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Computer Science Wenzhou-Kean University Wenzhou325060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore639798 Singapore School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Suzhou215400 China Jarvis Research Center Tencent YouTu Lab Shenzhen518703 China

While promising results have been achieved in weakly-supervised semantic segmentation (WSSS), limited supervision from image-level tags inevitably induces discriminative reliance and spurious relations between target classes and background regions. Thus, Class Activation Map (CAM) usually tends to activate discriminative object regions and falsely includes lots of class-related backgrounds. Without pixel-level supervisions, it could be very difficult to enlarge the foreground activation and suppress those false activation of background regions. In this paper, we propose a novel framework of Cross Language Image Matching with Automatic Context Discovery (CLIMS++), based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress class-related background regions in CAM. In particular, we design object, background region, and text label matching losses to guide the model to excite more reasonable object regions of each category. In addition, we propose to automatically find spurious relations between foreground categories and backgrounds, through which a background suppression loss is designed to suppress the activation of class-related backgrounds. The above designs enable the proposed CLIMS++ to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC 2012 and MS COCO 2014 datasets show that our CLIMS++ significantly outperforms the previous state-of-the-art methods. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantic Segmentation

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IAACS: Image aesthetic assessment through color composition and space formation

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 42-56页

作者： Bailin YANG Changrui ZHU Frederick WBLI Tianxiang WEI Xiaohui LIANG Qingxu WANG School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Computer Science University of DurhamDH13LEUnited Kingdom College of Intelligence and Computing Tianjin UniversityTianjin 300072China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

关键词： Image aesthetic assessment Color composition Space formation Deep learning

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