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BCDAS: Blockchain-assisted Classifiable Data Auditing Scheme with Dynamic Operations

Blockchain: Research and Applications 2025年

作者： Yuxin Cui Chen Wang Bing Yang Wei Tong Jian Shen School of Information Science and Engineering (School of Cyber Science and Technology) Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Key Laboratory of Digital Fashion and Data Governance Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou 310018 Zhejiang China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing 312000 Zhejiang China School of Computer Science Nanjing University of Information Science Technology Nanjing 210044 Jiangsu China

As cloud computing gains widespread adoption, cloud storage services have become the primary means of data management for users. Authenticated data structures (ADS) are a novel computational model designed to address data authentication problems in distributed environments. With the growing demand for robust data security, vulnerability detection in storage systems has become a critical area of focus to ensure resilience against potential threats. However, traditional ADS, while ensuring consistency between cloud data and source data, have limitations in handling dynamic data operations on multiple types of files, storage space expansion, and single-point failure issues. To tackle these issues, this paper proposes a blockchain-assisted classifiable data auditing scheme with dynamic operations. First, trapdoor hash functions are used to construct a binary tree. During dynamic data operations, the impact of hash updates is confined to a subset of nodes, ensuring global stability and reducing computational resource consumption. Second, innovative data structures and verification mechanisms are introduced, reducing the risk of single-point failures by decentralizing the dependency on verification paths. Finally, data types are confirmed based on data identifiers, and corresponding path information is recorded, enabling efficient and rapid dynamic operations on specific types of files within multi-source data. Both security analysis and performance assessment demonstrate that BCDAS conducts data auditing for with reliability and efficiency.

关键词： Data auditing Dynamic operations Blockchain Trapdoor hash function

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Research of etching process of Al0.8Sc0.2N based on ICP etching equipment

Research of etching process of Al0.8Sc0.2N based on ICP etch...

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IEEE Electron Devices tech.ology and Manufacturing Conference (EDTM)

作者： Xiaoyi Wang Wenkui Lin Xiaofan Yun Qiang Zha Haiou Li Baoshun Zhang Key Laboratory of Multifunctional Nanomaterials and Smart Systems Suzhou Institute of Nano-Tech and Nano-Bionics CAS China School of Nano-Tech and Nano-Bionics Univ. of Sci. and Tech. of China China School of Materials Science and Engineering Nanjing University of Science and Technology China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology China

Etching process is the basis of fabrication of AlScN piezoelectric devices. The mechanism of etching Al0.8Sc0.2N film by inductively coupled plasma etching machine was studied. When the gas flow rates of BCl3/Ar at 80... 详细信息

ISBN: (纸本)9781728181769;9781728181776

Etching process is the basis of fabrication of AlScN piezoelectric devices. The mechanism of etching Al_0.8Sc_0.2N film by inductively coupled plasma etching machine was studied. When the gas flow rates of BCl₃/Ar at 80/5 seem, and the tray temperature was 293 K, the etching selectivity ratio of Al₀.₈Sc_0.2N to photoresist was 0.7: 1, and the etching rate was 79 nm/min, the etching inclination angle is 20.2°, and the etching surface roughness is 1.28 nm, which can meet the application of piezoelectric devices based on AlScN thin films.

关键词： Radio frequency Fabrication Fluid flow Resists Etching Surface roughness Rough surfaces

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NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results

arXiv

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arXiv 2022年

作者： Li, Yawei Zhang, Kai Timofte, Radu Van Gool, Luc Kong, Fangyuan Li, Mingxi Liu, Songwei Du, Zongcai Liu, Ding Zhou, Chenhui Chen, Jingyi Han, Qingrui Li, Zheyuan Liu, Yingqi Chen, Xiangyu Cai, Haoming Qiao, Yu Dong, Chao Sun, Long Pan, Jinshan Zhu, Yi Zong, Zhikai Liu, Xiaoxiao Hui, Zheng Yang, Tao Ren, Peiran Xie, Xuansong Hua, Xian-Sheng Wang, Yanbo Ji, Xiaozhong Lin, Chuming Luo, Donghao Tai, Ying Wang, Chengjie Zhang, Zhizhong Xie, Yuan Cheng, Shen Luo, Ziwei Yu, Lei Wen, Zhihong Wu, Qi Li, Youwei Fan, Haoqiang Sun, Jian Liu, Shuaicheng Huang, Yuanfei Jin, Meiguang Huang, Hua Liu, Jing Zhang, Xinjian Wang, Yan Long, Lingshun Li, Gen Zhang, Yuanfan Cao, Zuowei Sun, Lei Alexander, Panaetov Wang, Yucong Cai, Minjie Wang, Li Tian, Lu Wang, Zheyuan Ma, Hongbing Liu, Jie Chen, Chao Cai, Yidong Tang, Jie Wu, Gangshan Wang, Weiran Huang, Shirui Lu, Honglei Liu, Huan Wang, Keyan Chen, Jun Chen, Shi Miao, Yuchun Huang, Zimo Zhang, Lefei Ayazoglu, Mustafa Xiong, Wei Xiong, Chengyi Wang, Fei Li, Hao Wen, Ruimian Yang, Zhijing Zou, Wenbin Zheng, Weixin Ye, Tian Zhang, Yuncheng Kong, Xiangzhen Arora, Aditya Zamir, Syed Waqas Khan, Salman Hayat, Munawar Khan, Fahad Shahbaz Gao, Dandan Zhou, Dengwen Ning, Qian Tang, Jingzhu Huang, Han Wang, Yufei Peng, Zhangheng Li, Haobo Guan, Wenxue Gong, Shenghua Li, Xin Liu, Jun Wang, Wanjun Zeng, Kun Lin, Hanjiang Chen, Xinyu Fang, Jinsheng Zhang, Shuhao Zhang, Yuhao Sinha, Abhishek Kumar Moorthi, S. Manthira Dhar, Debajyoti Yang, Hao-Hsiang Huang, Zhi-Kai Chen, Wei-Ting Chang, Hua-En Kuo, Sy-Yen Tan, Wei Chen, Hao Xu, Qian Narang, Pratik Singh, Usneek Sameen, Syed Khaitan, Harsh Yinghua, Liu Tianlin, Zhang Xiaoming, Zhang Meng, Dingxuan Tian, Chunwei Morshed, Mashrur M. Ahsan, Ahmad Omar Computer Vision Lab ETH Zurich Switzerland University of Würzburg Germany ByteDance Shenzhen China State Key Laboratory for Novel Software Technology Nanjing University China ByteDance Inc China NetEase Inc. China Shenzhen Institutes of Advanced Technology CAS China University of Macau China Shanghai AI Lab Shanghai China Nanjing University of Science and Technology China Amazon Web Services United States China Alibaba DAMO Academy EFC Yuhang District Zhejiang Hangzhou China East China Normal University China Youtu Lab Tencent China Megvii Technology China University of Electronic Science and Technology of China China School of Artificial Intelligence Beijing Normal University China Alibaba Group China Bilibili AI China Nankai-Baidu Joint Lab Nankai University Tianjin China Platform Technologies Tencent Online Video China Higher School of Economics Russia Huawei Moscow Research Center Russia Hunan University China Xidian University China Xilinx Technology Beijing Limited China College of Information Science and Engineering Xinjiang University Urumqi China Department of Electronic Engineering Tsinghua University Beijing China Nanjing University China McMaster University Canada School of Telecommunication Engineering Xidian University Xi’an China School of Computer Science Wuhan University Wuhan China School of Mathematical Science University of Electronic Science and Technology of China Chengdu China School of Computer Science The University of Sydney Sydney Australia Aselsan Research Ankara Turkey School of Electronic and Information Engineering South-Central University for Nationalities Wuhan China Guangdong University of Technology China Fujian Normal University Fuzhou University Jimei University China Design Group China Abu Dhabi United Arab Emirates Monash University Melbourne Australia Mohamed bin Zayed University of AI United Arab Emirates North China Electric Power University Changping District Beijing China The School of Art

This paper reviews the NTIRE 2022 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The task of the challenge was to super-resolve an input image with a magnification factor of ×4 based on pairs of low and corresponding high resolution images. The aim was to design a network for single image super-resolution that achieved improvement of efficiency measured according to several metrics including runtime, parameters, FLOPs, activations, and memory consumption while at least maintaining the PSNR of 29.00dB on DIV2K validation set. IMDN is set as the baseline for efficiency measurement. The challenge had 3 tracks including the main track (runtime), sub-track one (model complexity), and sub-track two (overall performance). In the main track, the practical runtime performance of the submissions was evaluated. The rank of the teams were determined directly by the absolute value of the average runtime on the validation set and test set. In sub-track one, the number of parameters and FLOPs were considered. And the individual rankings of the two metrics were summed up to determine a final ranking in this track. In sub-track two, all of the five metrics mentioned in the description of the challenge including runtime, parameter count, FLOPs, activations, and memory consumption were considered. Similar to sub-track one, the rankings of five metrics were summed up to determine a final ranking. The challenge had 303 registered participants, and 43 teams made valid submissions. They gauge the state-of-the-art in efficient single image super-resolution. © 2022, CC BY.

关键词： Chemical activation

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Predicting critical nodes in temporal networks by dynamic graph convolutional networks

arXiv

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arXiv 2021年

作者： Yu, En-Yu Fu, Yan Zhou, Jun-Lin Sun, Hong-Liang Chen, Duan-Bing Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China School of Information Engineering Nanjing University of Finance and Economics 210023 China School of Computer Science and Technology University of Nottingham Ningbo315100 China The Research Base of Digital Culture and Media Sichuan Provincial Key Research Base of Social Science Chengdu611731 China Union Big Data Tech. Inc. Chengdu610041 China

Many real-world systems can be expressed in temporal networks with nodes playing far different roles in structure and function and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinions or epidemics, predict leading figures in academia, conduct advertisements for various commodities, and so on. However, it is rather difficult to identify critical nodes because the network structure changes over time in temporal networks. In this paper, considering the sequence topological information of temporal networks, a novel and effective learning framework based on the combination of special GCNs and RNNs is proposed to identify nodes with the best spreading ability. The effectiveness of the approach is evaluated by weighted Susceptible-Infected-Recovered model. Experimental results on four real-world temporal networks demonstrate that the proposed method outperforms both traditional and deep learning benchmark methods in terms of the Kendall τ coefficient and top k hit rate. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Shattered chain of trust: understanding security risks in cross-cloud IoT access delegation 20

Shattered chain of trust: understanding security risks in cr...

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Proceedings of the 29th USENIX Conference on Security Symposium

作者： Bin Yuan Yan Jia Luyi Xing Dongfang Zhao XiaoFeng Wang Deqing Zou Hai Jin Yuqing Zhang School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center and Indiana University Bloomington and Shenzhen Huazhong University of Science and Technology Research Institute China School of Cyber Engineering Xidian University China and National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and Indiana University Bloomington Indiana University Bloomington School of Cyber Science and Engineering Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong Univ. of Sci. & Tech. China and National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab and Big Data Security Engineering Research Center Huazhong Univ. of Sci. & Tech. China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences China and School of Cyber Engineering Xidian University China

ISBN: (纸本)9781939133175

IoT clouds facilitate the communication between IoT devices and users, and authorize users' access to their devices. In this paradigm, an IoT device is usually managed under a particular IoT cloud designated by the device vendor, e.g., Philips bulbs are managed under Philips Hue cloud. Today's mainstream IoT clouds also support device access delegation across different vendors (e.g., Philips Hue, LIFX, etc.) and cloud providers (e.g., Google, IFTTT, etc.): for example, Philips Hue and SmartThings clouds support to delegate device access to another cloud such as Google Home, so a user can manage multiple devices from different vendors all through Google Home. Serving this purpose are the IoT delegation mechanisms developed and utilized by IoT clouds, which we found are heterogeneous and ad-hoc in the wild, in the absence of a standardized delegation protocol suited for IoT environments. In this paper, we report the first systematic study on real-world IoT access delegation, based upon a semi-automatic verification tool we developed. Our study brought to light the pervasiveness of security risks in these delegation mechanisms, allowing the adversary (e.g., Airbnb tenants, former employees) to gain unauthorized access to the victim's devices (e.g., smart locks) or impersonate the devices to trigger other devices. We confirmed the presence of critical security flaws in these mechanisms through end-to-end exploits on them, and further conducted a measurement study. Our research demonstrates the serious consequences of these exploits and the security implications of the practice today for building these mechanisms. We reported our findings to related parties, which acknowledged the problems. We further propose principles for developing more secure cross-cloud IoT delegation services, before a standardized solution can be widely deployed.

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Text emotion distribution learning via multi-task convolutional neural network 27

Text emotion distribution learning via multi-task convolutio...

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27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Zhang, Yuxiang Fu, Jiamei Zhang, Ying Yang, Jufeng College of Computer Science and Technology Civil Aviation University of China Tianjin China College of Computer and Control Engineering Nankai University Tianjin China College of Comp. Sci. and Tech. Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9780999241127

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users' emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexicon-based strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches. © 2018 International Joint Conferences on Artificial *** right reserved.

关键词： Classification (of information)

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Teaching Cybersecurity Using Guided Inquiry Collaborative Learning

Teaching Cybersecurity Using Guided Inquiry Collaborative Le...

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Frontiers in Education (FIE) Conference

作者： Xiaohong Yuan Tian Zhang Ali Albu Shama Jinsheng Xu Li Yang Jennifer Ellis Wu He Cynthia Waters Dept. of Computer Science North Carolina A&T State University Greensboro USA Dept. of Computer Sci. and Eng. University of Tennessee at Chattanooga Chattanooga TN School of Education University of Tennessee at Chattanooga Chattanooga TN Dept. of Info. Tech. & Decision Sci. Old Dominion University Norfolk VA Dept. of Mechanical Engineering North Carolina A&T State University Greensboro USA

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This Innovate Practice Full Paper describes our experience with teaching cybersecurity topics using guided inquiry collaborative learning. The goal is to not only develop the students' in-depth tech.ical knowledge, but also “soft skills” such as communication, attitude, team work, networking, problem-solving and critical thinking. This paper reports our experience with developing and using the Guided Inquiry Collaborative Learning materials on the topics of firewall and IPsec. Pre- and post-surveys were conducted to access the effectiveness of the developed materials and teaching methods in terms of learning outcome, attitudes, learning experience and motivation. Analysis of the survey data shows that students had increased learning outcome, participation in class, and interest with Guided Inquiry Collaborative Learning.

关键词： Education Collaborative work Firewalls (computing) Communication networks Logic gates

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Experimental investigation on ignition characteristic of NEPE propellant 30

Experimental investigation on ignition characteristic of NEP...

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30th International Symposium on Ballistics, BALLISTICS 2017

作者： Li, Lian-Bo Chen, Xiong Zhou, Chang-Sheng Zhu, Min Musa, Omer School of Mechanical Engineering Nanjing University of Science and Technology Nanjing210094 China Taizhou Institute of Sci. and Tech. Nanjing University of Science and Technology Taizhou225300 China

ISBN: (纸本)9781605954196

The ignition characteristic of solid propellant is the main content in internal ballistic research. In this study, the factors which may have an impact on ignition delay time of Nitrate Ester Plasticized Polyether Propellant (NEPE) are analyzed by a CO2 laser radiation experiment. Using high-speed camera, the ignition process of NEPE propellant is recorded under different environmental conditions. Results show that the ignition delay time decreases with the increase of laser heat flux, ambient pressure and initial temperature of NEPE propellant. At the same time, with the increase of laser heat flux, the influences of ambient pressure and initial temperature on the ignition delay time decrease. Besides, the effect of oxygen concentration on the ignition delay time is confirmed that the oxygen content has little effect on the ignition delay time when the oxygen content of the ambient gas is less than that of the propellant pyrolysis product.

关键词： High speed cameras

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Privacy Leakage in Smart Homes and Its Mitigation: IFTTT as a Case Study

Privacy Leakage in Smart Homes and Its Mitigation: IFTTT as ...

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IEEE International Performance Computing and Communications Conference

作者： Rixin Xu Qiang Zeng Liehuang Zhu Haotian Chi Xiaojiang Du School of Computer Sci. & Tech. Beijing Institute of Technology Beijing China Computer Science & Engineering Dept. University of South Carolina Columbia SC USA Dept. of Computer and Information Sciences Temple University Philadelphia PA USA

ISBN: (纸本)9781538668092;9781538668085

The combination of an appified smart home platform and third-party apps have enabled developers to contribute their novel ideas to bring more convenience to their users. However, this also brings the potential of privacy leakage. If a third-party app is permitted to monitor a user day and night, then it will learn the behavior pattern of this user before long. In this paper, we exploited how IFTTT monitors the daily life of a user in several ways that are hardly noticeable. We propose the "Specific-fuzzification" to protect the privacy of a user in two steps: filter the unnecessary events to the IFTTT, then fuzz the value of the events that must be uploaded. We evaluated the "Specific-fuzzification" on event records of seven users, the result showed comparing the original IFTTT, the modified IFTTT patched with "Specific-fuzzification" only gained rare events and thus could no longer recognize any behavior patterns of a user.

关键词： Smart home Privacy leakage SmartThings IFTTT

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Modeling recommender systems via weighted bipartite network

Modeling recommender systems via weighted bipartite network

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作者： Xia, Jianxun Wu, Fei Xiong, Zenggang Qiu, Meikang Xie, Changsheng School of Computer Sci. and Tech. Huazhong University of Science and Technology WuhanHubei430074 China School of Computer and Information Science Hubei Engineering University XiaoganHubei432000 China Computer Science Department Pace University New YorkNY10038 United States

Recommender systems have shown great potential to address information overload problems, namely, to help users find interesting and relevant objects within a huge information space. To achieve more accurate recommendation, in this paper, we proposed a recommendation algorithm Improved weighted Network-Based Inference (INBIw) that improves on the original weighted network-based inference by introducing a tunable parameter β to depress the influence of high-degree nodes. In order to evaluate the recommendation performance of INBIw, ranking position rate and hitting rate are calculated. The results of experiment based on MovieLens data set show that the INBIw outperforms previous methods, including the global ranking method, collaborative filtering, network-based inference, and weighted network-based inference with respect to ranking position rate and hitting rate. Specifically, it performs well and gives a more accurate prediction. After further analysis, we discovered that the recommendation results of INBIw are insensitive to the amount of data and length of the recommendation list. Thus, INBIw can deal with data sparsity and is able to satisfy the varied requirements of real situations. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Recommender systems

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