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Copyright Protection Application Based on Blockchain technology

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yi Ouyang Xianghan Zheng Xiaoliang Lu Lin Xiaowei Shengyin Zhang Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China MingByte Technology (Qingdao) Co. Ltd MingByte Technology Co. Ltd Qingdao China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

In the traditional copyright protection platform, there are problems such as copyright information security and high registration cost. With the rapid development of blockchain technology, blockchain technology, as a new technology architecture, has the characteristics of decentralization, nontamperability, traceability, openness and transparency, and has a natural combination with copyright protection, which is expected to solve the existence of traditional copyright protection. The pain point of this paper is to apply the blockchain technology to the copyright management platform. It is hoped that in this way, the problems of poor security, difficult rights, complicated transaction procedures, etc. in the traditional copyright management platform are solved. An attempt to combine chain technology with copyright protection.

关键词： Blockchain Peer-to-peer computing Distributed databases Smart contracts security Fabrics Distributed ledger

Deep Variational network Toward Blind Image Restoration

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arXiv 2020年

作者： Yue, Zongsheng Yong, Hongwei Zhao, Qian Zhang, Lei Meng, Deyu Wong, Kwan-Yee K. The School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China The Department of Computer Science The University of Hong Kong Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China The Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

Blind image restoration (IR) is a common yet challenging problem in computer vision. Classical model-based methods and recent deep learning (DL)-based methods represent two different methodologies for this problem, each with its own merits and drawbacks. In this paper, we propose a novel blind image restoration method, aiming to integrate both the advantages of them. Specifically, we construct a general Bayesian generative model for the blind IR, which explicitly depicts the degradation process. In this proposed model, a pixel-wise non-i.i.d. Gaussian distribution is employed to fit the image noise. It is with more flexibility than the simple i.i.d. Gaussian or Laplacian distributions as adopted in most of conventional methods, so as to handle more complicated noise types contained in the image degradation. To solve the model, we design a variational inference algorithm where all the expected posteriori distributions are parameterized as deep neural networks to increase their model capability. Notably, such an inference algorithm induces a unified framework to jointly deal with the tasks of degradation estimation and image restoration. Further, the degradation information estimated in the former task is utilized to guide the latter IR process. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts. The source code is available at https://***/zsyOAOA/VIRNet. © 2020, CC BY-NC-SA.

关键词： Image reconstruction

内蒙古深季节冻土区公路水热过程的实体工程试验

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Journal of Central South University 2020年第7期 2082-2093页

作者：张洪伟王学营赵鑫刘鹏飞 Key Laboratory of Transport Industry of Management Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area Inner Mongolia Transport Construction Engineering Quality Supervision Bureau Institute of Highway Engineering RWTH Aachen University

季节冻土区道路工程冻融病害频繁,路基的水热耦合作用是病害发育的控制因素。为揭示该地区公路路基的水热作用过程,分析其影响因素和变化规律,基于内蒙古深季节冻土区室韦—拉布达林公路实体工程3年的观测数据,分析了沥青路面结构层和... 详细信息

季节冻土区道路工程冻融病害频繁,路基的水热耦合作用是病害发育的控制因素。为揭示该地区公路路基的水热作用过程,分析其影响因素和变化规律,基于内蒙古深季节冻土区室韦—拉布达林公路实体工程3年的观测数据,分析了沥青路面结构层和路基的冻融规律、含水率变化特征以及水热相互作用过程。观测数据表明,该地区公路路基冻融频繁,面层处的最高、最低温度和融化指数、冻结指数均为最大,其他部位的地温随着路基深度的增加有所减小,路基底部附近的融冻指数比约为天然地表的1.6倍以上。路基的完全冻结时间在150 d左右,且随着运营时间的延长,融化期有所延长。路基内含水率表现出显著的季节交替特性,底基层处的含水率最大,暖季时达到40%左右,基层处的含水率最小,在10%以下,路基体内的含水率在20%～25%。地温与含水率存在显著的线性相关性,水分的波动受温度影响显著。沥青路面的温度梯度和水分通量的统计关系表明,水分通量的变化主要集中在融化期内,且与温度梯度的变化吻合较好,底基层内的水分通量最为活跃,说明温度梯度是水分迁移和聚集的重要驱动力,沥青路面吸热和封水的结构特性是诱发路基下部水分在底基层处聚集的主要原因,提出此类地区道路工程的冻融病害防治需从保温和隔水两方面入手。

关键词：路基工程水热过程现场观测季节冻土区冻融病害

Artificial unintelligence: Anti-intelligence of intelligent algorithms 3rd

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Artificial unintelligence: Anti-intelligence of intelligent ...

3rd International Conference on Intelligence Science, ICIS 2018

作者： Zhang, Yuhong Nauman, Umer Information Science and Engineering College Henan University of Technology Zhengzhou450001 China Network and Data Security Key Laboratory of Sichuan University of Electronic Science and Technology Chengdu610054 China

ISBN: (纸本)9783030013127

In the age of big data, artificial intelligence (AI) algorithms play an important role in demonstrating the value of data, but its negative effects are increasingly prominent. Researchers focus more on the obvious issues like privacy protection these days, and we assume that technologies like AI will make people wiser. But due to the not-so-obvious limits of AI, it may push us to the opposite side–artificial unintelligence. In this paper, a detailed analysis of the process of value creation from data is presented first. Then, based on the above process, the role played by AI algorithms is analyzed. Next, the anti-intelligence features of AI algorithms, such as the tendency of ‘fatuous King’, the generalization of quick thinking, a faster transformation of master/slave at the individual level, and retribalization at the group level, are deeply discussed. Finally, some possilbe ways of circumventing the aforementioned problems are offered. © IFIP International Federation for Information Processing 2018.

关键词： Big data

Sparse code multiple access for 6G wireless communication networks: Recent advances and future directions

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arXiv 2021年

作者： Yu, Lisu Liu, Zilong Wen, Miaowen Cai, Donghong Dang, Shuping Wang, Yuhao Xiao, Pei School of Information Engineering Nanchang University Nanchang330031 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Electronics Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Electronic and Information Engineering South China University of Technology Guangzhou510640 China College of Cyber Security Jinan University Guangzhou510632 China Thuwal23955-6900 Saudi Arabia Institute for Communication Systems 5G Innovation Centre University of Surrey GuildfordGU2 7XH United Kingdom

As 5G networks rolling out in many different countries nowadays, the time has come to investigate how to upgrade and expand them towards 6G, where the latter is expected to realize the interconnection of everything as well as the development of a ubiquitous intelligent mobile world for intelligent life. To enable this epic leap in communications, this article provides an overview and outlook on the application of sparse code multiple access (SCMA) for 6G wireless communication systems, which is an emerging disruptive non-orthogonal multiple access (NOMA) scheme for the enabling of massive connectivity. We propose to apply SCMA to a massively distributed access system (MDAS), whose architecture is based on fiber-based visible light communication (FVLC), ultra-dense network (UDN), and NOMA. Under this framework, we consider the interactions between optical front-hauls and wireless access links. In order to stimulate more upcoming research in this area, we outline a number of promising directions associated with SCMA for faster, more reliable, and more efficient multiple access in future 6G communication networks. © 2021, CC BY-NC-ND.

关键词： Visible light communication

Improved heuristic algorithms for UAVs path planning in hazardous environment 14

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Improved heuristic algorithms for UAVs path planning in haza...

14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Li, Zhenghao Yang, Peng Tong, Cen Shen, Jiaqi Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China Explosive Case Laboratory Institute of Forensic Science Tianjin Public Security Bureau Tianjin300020 China

ISBN: (纸本)9781538680971

As the route planning of UAV searching in a risky environment is a complicated combinatorial optimization problem, which is characterized by a variety of unpredictable factors. Heuristic methods can be used to speed up the process of finding a satisfactory solution. In this paper, Greedy algorithm and Q-learning algorithm are designed to efficiently produce high quality results for this problem. Regarding the total risk of the UAV crashing as the objective, a discrete routing model is established. Based on a nonlinear relationship between grid areas and risk, an evaluation optimization model for the UAV is also established, and the value of potential areas is introduced to improve it. The simulation experiments verify that the two algorithms can both reduce the operation time and find the target in less risky situations. Results indicate that the Greedy algorithm is robust, and it exponentially drives toward high-quality solutions in relatively short time. While the Q-learning algorithm prefer to get less risky solution. © 2018 IEEE.

关键词： Unmanned aerial vehicles (UAV)

Multi-view graph learning by joint modeling of consistency and inconsistency

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arXiv 2020年

作者： Liang, Youwei Huang, Dong Wang, Chang-Dong Yu, Philip S. College of Mathematics and Informatics South China Agricultural University Guangzhou China Tencent Shenzhen China College of Mathematics and Informatics South China Agricultural University Guangzhou China Pazhou Lab Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States

—Graph learning has emerged as a promising technique for multi-view clustering due to its ability to learn a unified and robust graph from multiple views. However, existing graph learning methods mostly focus on the multi-view consistency issue, yet often neglect the inconsistency between views, which makes them vulnerable to possibly low-quality or noisy datasets. To overcome this limitation, we propose a new multi-view graph learning framework, which for the first time simultaneously and explicitly models multi-view consistency and inconsistency in a unified objective function, through which the consistent and inconsistent parts of each single-view graph as well as the unified graph that fuses the consistent parts can be iteratively learned. Though optimizing the objective function is NP-hard, we design a highly efficient optimization algorithm that can obtain an approximate solution with linear time complexity in the number of edges in the unified graph. Furthermore, our multi-view graph learning approach can be applied to both similarity graphs and dissimilarity graphs, which lead to two graph fusion-based variants in our framework. Experiments on twelve multi-view datasets have demonstrated the robustness and efficiency of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Optimization

On the Secrecy of UAV Systems With Linear Trajectory

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arXiv 2020年

作者： Pan, Gaofeng Lei, Hongjiang An, Jianping Zhang, Shuo Alouini, Mohamed-Slim School of Information and Electronics Engineering Beijing Institute of Technology Beijing100081 China Thuwal23955-6900 Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Shannxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts and Telecommunications Xi’anShaanxi710121 China School of Information and Electronics Engineering Beijing Institute of Technology Beijing100081 China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing100854 China Beijing Aerospace Automatic Control Institute Beijing100854 China Thuwal23955-6900 Saudi Arabia

By observing the fact that moving in a straight line is a common flying behavior of unmanned aerial vehicles (UAVs) in normal applications, e.g., power line inspections, and air patrols along with highway/streets/borders, in this paper we investigate the secrecy outage performance of a UAV system with linear trajectory, where a UAV (S) flies in a straight line and transmits its information over the downlink to a legitimate receiver (D) on the ground while an eavesdropping UAV (E) trying to overhear the information delivery between S and D. Meanwhile, some information is delivered to S over the uplink from D, such as commanding messages to control S’s detecting operations, which can also be eavesdropped by E. The locations of S, D, and E are randomly distributed. We first characterize the statistical characteristics (including cumulative distribution functions and probability density function) of the received signal-to-noise ratio over both downlink and uplink, and then the closed-form analytical Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

Progressive Teacher-Student Learning for Early Action Prediction

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Progressive Teacher-Student Learning for Early Action Predic...

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xionghui Wang Jian-Fang Hu Jian-Huang Lai Jianguo Zhang Wei-Shi Zheng Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Sun Yat-sen University Guangzhou China University of Dundee United Kingdom Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781728132945

The goal of early action prediction is to recognize actions from partially observed videos with incomplete action executions, which is quite different from action recognition. Predicting early actions is very challenging since the partially observed videos do not contain enough action information for recognition. In this paper, we aim at improving early action prediction by proposing a novel teacher-student learning framework. Our framework involves a teacher model for recognizing actions from full videos, a student model for predicting early actions from partial videos, and a teacher-student learning block for distilling progressive knowledge from teacher to student, crossing different tasks. Extensive experiments on three public action datasets show that the proposed progressive teacher-student learning framework can consistently improve performance of early action prediction model. We have also reported the state-of-the-art performances for early action prediction on all of these sets.

关键词： Action Videotapes Teachers Learning Students Frameworks user modelling Recognition (Psychology)