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arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Tensor Graph Convolutional network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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A Node-collaboration-informed Graph Convolutional network for Precise Representation to Undirected Weighted Graphs

arXiv

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arXiv 2022年

作者： Wang, Ying Yuan, Ye Luo, Xin The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module;b) Building the residual connection and weighted representation propagation to obtain high representation capacity;and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution

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A Symmetric Uniform Formula and Sole Index Method for Sieving (Twin) Primes

A Symmetric Uniform Formula and Sole Index Method for Sievin...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Wei Ren School of Computer Science China University of Geosciences Wuhan P.R. China Guangxi Key Laboratory of Cryptography and Information Security Guilin P.R. China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing P.R. China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

All primes can be indexed by k, as primes must be in the form of 6k+1 or 6k-1. In this paper, we explore for the set of k such that either 6k+1 or 6k-1 is not a prime. Our method provides a uniform formula for k that can sieve primes and twin primes as well. The uniform presents symmetry in terms of k that works as single index in sieving. We also propose a new conjecture that is equivalent to Twin Prime Conjecture but possibly be easier to approach by merely exploring of sole index in term of k. The uniform formula for prime sieving are as follows: k ∈ S l ⇒ 6k-1 ∉ P, where s l = [-I]6I+1 = [I] 6I-1 \min([I] 6I-1 ), I ∈ N. ∈ s r ⇒ 6k+1 ∉ P, where s r = [-I] 6I-1 ∪ ([I] 6I+1 \min([I] 6I+1 )), I ∈ N.

关键词： data privacy Conferences Indexes Computer security

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Dual Graph-Laplacian PCA: A closed-form solution for bi-clustering to find "checkerboard" structures on gene expression data

arXiv

引用

arXiv 2019年

作者： Liu, Jin-Xing Feng, Chun-Mei Kong, Xiang-Zhen Xu, Yong School of Information Science and Engineering Qufu Normal University Rizhao Shandong276826 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Key Laboratory of Network Oriented Intelligent Computation Shenzhen Guangdong518055 China

In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or down-regulated in patients with specific types of tumors. In this paper, we propose a novel method, called dual graph-regularization principal component analysis (DGPCA). The main innovation of this method is that it simultaneously considers the internal geometric structures of the condition manifold and the gene manifold. Specifically, we obtain principal components (PCs) to represent the data and approximate the cluster membership indicators through Laplacian embedding. This new method is endowed with internal geometric structures, such as the condition manifold and gene manifold, which are both suitable for bi-clustering. A closed-form solution is provided for DGPCA. We apply this new method to simultaneously cluster genes and conditions (e.g., different samples) with the aim of finding internal "checkerboard" structures on gene expression data, if they exist. Then, we use this new method to identify regulatory genes under the particular conditions and to compare the results with those of other state-of-the-art PCA-based methods. Promising results on gene expression data have been verified by extensive experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

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Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer

Parallel Optimization of Computational Fluid Dynamics Applic...

引用

International Symposium on Parallel Architectures, Algorithms and Programming (PAAP)

作者： Anjun Liu Yin Liao Xihao Xu YanYan Chen Hongjun Zhong Jianming Wu Bo Kong Alexander Raikov Meng Guo Jinan Key Laboratory of High Performance Industrial Software Jinan Institute of Supercomputing Technology Jinan China Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Zhejiang Yuansuan Technology Co. Ltd Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan ) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1–2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry.

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Generative AI for Advanced UAV networking

引用

IEEE network 2024年

作者： Sun, Geng Xie, Wenwen Niyato, Dusit Du, Hongyang Kang, Jiawen Wu, Jing Sun, Sumei Zhang, Ping Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong999077 Hong Kong Guangdong University of Technology School of Automation Guangzhou510006 China Agency for Science Technology and Research Institute for Infocomm Research Singapore639798 Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China

With the impressive achievements of chatGPT and Sora, generative artificial intelligence (GAI) has received increasing attention. Not limited to the field of content generation, GAI is also widely used to solve the problems in wireless communication scenarios due to its powerful learning and generalization capabilities. Therefore, we discuss key applications of GAI in improving unmanned aerial vehicle (UAV) communication and networking performance in this article. Specifically, we first review the key technologies of GAI and the important roles of UAV networking. Then, we show how GAI can improve the communication, networking, and security performances of UAV systems. Subsequently, we propose a novel framework of GAI for advanced UAV networking, and then present a case study of UAV-enabled spectrum map estimation and transmission rate optimization based on the proposed framework to verify the effectiveness of GAI-enabled UAV systems. Finally, we discuss some important open directions. © 2024 IEEE.

关键词： Aircraft communication

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SCScan: A SVM-Based Scanning System for Vulnerabilities in Blockchain Smart Contracts

SCScan: A SVM-Based Scanning System for Vulnerabilities in B...

引用

IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Xiaohan Hao Wei Ren Wenwen Zheng Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Guangxi Key Laboratory of Cryptography and Information Security Guilin P.R. China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing P.R. China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The application of blockchain has moved beyond cryptocurrencies, to applications such as credentialing and smart contracts. The smart contract allows ones to achieve fair exchange for values without relying on a centralized entity. However, as the smart contract can be automatically executed with token transfers, an attacker can seek to exploit vulnerabilities in smart contracts for illicit profits. Thus, this paper proposes a support vector machine (SVM)-based scanning system for vulnerabilities on smart contracts. Our evaluation on Ethereum demonstrate that we achieve a identification rate of over 90% based on several popular attacks.

关键词： Support vector machines Deep learning Privacy Smart contracts Supervised learning Blockchain Pattern matching

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Optical quantum sensing for agnostic environments via deep learning

引用

Physical Review Research 2024年第4期6卷 043267-043267页

作者： Zeqiao Zhou Yuxuan Du Xu-Fei Yin Shanshan Zhao Xinmei Tian Dacheng Tao MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei 230026 China JD Explore Academy Beijing 101111 China College of Computing and Data Science Nanyang Technological University Singapore 639798 Singapore Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei 230026 China CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai 201315 China School of Computer Science The University of Sydney NSW 2008 Australia

Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative deep-learning-based quantum sensing scheme (DQS), enabling optical quantum sensors to attain HL in agnostic environments. DQS incorporates two essential components: a graph neural network (GNN) predictor and a trigonometric interpolation algorithm. Operating within a data-driven paradigm, DQS utilizes the GNN predictor, trained on offline data, to unveil the intrinsic relationships between the optical setups employed in preparing the probe state and the resulting quantum Fisher information (QFI) after interaction with the agnostic environment. This distilled knowledge facilitates the identification of optimal optical setups associated with maximal QFI. Subsequently, DQS employs a trigonometric interpolation algorithm to recover the unknown parameter estimates for the identified optical setups. Extensive experiments are conducted to investigate the performance of DQS under different settings up to eight photons. Our findings not only offer a different lens through which to accelerate optical quantum sensing tasks but also catalyze future research integrating deep learning and quantum mechanics.

关键词： Graph neural networks

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A Blockchain-based Fast Authentication and Collaborative Video data Forwarding Scheme for Vehicular networks

A Blockchain-based Fast Authentication and Collaborative Vid...

引用

IEEE/IFIP International Conference on Embedded and Ubiquitous computing, EUC

作者： Weihui Qiu Xin Yang Ming Wei Wei Ren Tianqing Zhu School of Computer Science China University of Geosciences Wuhan P.R. China Wuhan Institute of Marine Electric Propulsion CSSC P.R. China Key Laboratory of Network Assessment Technology CAS Chinese Academy of Sciences Institute of Information Engineering Beijing P.R. China Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guiyang P.R. China

ISBN: (纸本)9781665400374

Internet of Things (IoT) current present two trends with respect to ubiquity and mobility. The number of devices increases remarkably and most forthcoming devices are mobile, e.g., Internet of Vehicles (IoV). In large scale mobile IoT, the management of device identification, fast authentication, and certification of public keys experiences upcoming difficulties. Centralized security architecture may not be suitable and in contrast cross-domain security architecture will tackle the ubiquity and mobility better. We note that blockchain is a new decentralized architecture equipped with basic cryptographic settings, which provides new tools such as token and guarantees many security properties such as block integrity. In this paper we design a blockchain-based fast authentication video data forwarding scheme for IoV. Compared with existing protocols, it has the advantages of decentralization, high authentication efficiency, strong trust, and resistance to common attacks. We evaluate our scheme with real experiments over Hyperledger Fabric and the authentication delay is manageable (e.g, 4 seconds in IoV).

关键词： Resistance Protocols Authentication Public key Computer architecture Near field communication Ubiquitous computing

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