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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

—By taking full advantage of computing, Communication and Caching (3C) resources at the network edge, Mobile Edge computing (MEC) is envisioned as one of the key enablers for the next generation network and services. However, current fixed-location MEC architecture may not be able to make real-time decision in dynamic environment, especially in large-scale scenarios. To address this issue, in this paper, a Heterogeneous MEC (H-MEC) architecture is proposed, which is composed of fixed unit, i.e., Ground Stations (GSs) as well as moving nodes, i.e., Ground Vehicles (GVs) and Unmanned Aerial Vehicles (UAVs), all with 3C resource enabled. The key challenges in H-MEC, e.g., mobile edge node management, real-time decision making, user association and resource allocation along with the possible Artificial Intelligence (AI)-based solutions are discussed in this paper. In addition, an AI-based joint Resource schEduling (ARE) framework is proposed, with simulation results shown to verify the efficiency of our proposed framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Design and Realization of MATLAB-based Face Recognition System

Design and Realization of MATLAB-based Face Recognition Syst...

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1st International Conference on Advanced Algorithms and Control Engineering, ICAACE 2018

作者： Liu, Jianming College of Mathematics and Computer Science Quanzhou Normal University Fujian Quanzhou362000 China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University Quanzhou Normal University Fujian Quanzhou362000 China

Through MATLAB, the paper makes a comparison between Principal Component Analysis (PCA)face recognition algorithm and Adaboost recognition algorithm and selects the algorithm with higher recognition rates to develop an auto face recognition system. The paper explicates primary techniques the system adopts and its specific realization process. By downloading face database online, the paper conducts an all-round test to the system, the result of which proves that this face recognition system is completely practical and feasible. © Published under licence by IOP Publishing Ltd.

关键词： Principal component analysis

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Systematic Resource Allocation in Cloud RAN with Caching as a Service under Two Timescales

Systematic Resource Allocation in Cloud RAN with Caching as ...

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作者： Tang, Jianhua Quek, Tony Q. S. Chang, Tsung-Hui Shim, Byonghyo Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Normal University Changsha410081 China Institute of New Media and Communications Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of School of Science and Engineering Chinese University of Hong Kong Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China Department of Electrical and Computer Engineering Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of

Recently, cloud radio access network (C-RAN) with caching as a service (CaaS) was proposed to merge the functionalities of communication, computing, and caching (CCC) together. In this paper, we dissect the interactions of CCC in C-RAN with CaaS from two dimensions: physical resource dimension and time dimension. In the physical resource dimension, we identify how to segment the baseband unit (BBU) pool resources (i.e., computation and storage) into different types of virtual machines (VMs). In the time dimension, we address how the long-term resource segmentation in the BBU pool impacts on the short-term transmit beamforming at the remote radio heads. We formulate the problem as a stochastic mixed-integer nonlinear programming (SMINLP) to minimize the system cost, including the server cost, VM cost and wireless transmission cost. After a series of approximation, including sample average approximation, successive convex approximation, and semidefinite relaxation, the SMINLP is approximated as a global consensus problem. The alternating direction method of multipliers (ADMM) is utilized to obtain the solution in a parallel fashion. Simulation results verify the convergence of our proposed algorithm, and also confirm that the proposed scheme is more cost-saving than that without considering the integration of CCC. © 1972-2012 IEEE.

关键词： Integer programming

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ANDROIDOFF:Offloading android application based on cost estimation

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Journal of Systems and Software 2019年 158卷

作者： Chen, Xing Chen, Jiaqing Liu, Bichun Ma, Yun Zhang, Ying Zhong, Hao College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China School of Software Tsinghua University Beijing100084 China National Engineering Research Center of Software Engineering Peking University China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Computation offloading is a promising way of improving the performance and reducing the battery power consumption, since it moves some time-consuming computation activities to nearby servers. Although various approaches have been proposed to support computation offloading, we argue that there is still sufficient space for improvements, since existing approaches cannot accurately estimate the execution costs. As a result, we find that their offloading plans are less optimized. To handle the problem, in this paper, given an Android application, we propose a novel approach, called ANDROIDOFF, that supports offloading at the granularity of objects. Supporting such capability is challenging due to the two reasons: (1) through dynamic execution, it is feasible to collect the execution costs of only partial methods, and (2) it is difficult to accurately estimate the execution costs of the remaining methods. To overcome the challenges, given an Android application, ANDROIDOFF first combines static and dynamic analysis to predict the execution costs of all its methods. After all the costs are estimated, ANDROIDOFF synthesizes an offloading plan, in which determines the offloading details. We evaluate ANDROIDOFF on a real-world application, with two mobile devices. Our results show that, compared with other approaches, ANDROIDOFF saves the response time by 8%–49% and reduces the energy consumption by 12%–49% on average for computation-intensive applications. © 2019 Elsevier Inc.

关键词： computation offloading

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Identification and Prediction of Key Nucleotide Sites Using Machine Learning in Bioinformatics: A Brief Overview

Identification and Prediction of Key Nucleotide Sites Using ...

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

作者： Jianhua Cai Leyi Wei Kun Zeng Guobao Xiao Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou P.R. China College of Mathematics and Computer Science Fuzhou University Fuzhou P.R. China College of Intelligence and Computing Tianjin University Tianjin P.R. China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Nowadays, the burgeon of machine learning has promoted its wide application in various fields. In Bioinformatics, machine learning computational method has become an indispensable part. Its efficiency and simplicity break the expensive and inefficient barriers of previous experimental methods. This article briefly reviews the application and development of machine learning in identifying and predicting key nucleotide sites (such as 6mA, 5mC, 4mC, etc.) in DNA or RNA. Among them, supervised learning is one of the main methods to this work. Its wide application has provided great convenience for the development of identification and prediction of key nucleotides in Bioinformatics.

关键词： Bioinformatics Feature extraction Machine learning Predictive models Computational modeling DNA Prediction algorithms

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Towards Privacy-preserving Incentive for Mobile Crowdsensing Under An Untrusted Platform

Towards Privacy-preserving Incentive for Mobile Crowdsensing...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Jingxin Li Jiahui Hu Ju Ren Zhetao Li Yanjun Li State Key Laboratory for Novel Software Technology Nanjing University P. R. China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China School of Information Science and Engineering Central South University P. R. China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education College of Information Engineering Xiangtan University P. R. China School of Computer Science and Technology Zhejiang University of Technology P. R. China

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.

关键词： Task analysis Privacy Differential privacy Sensors Mobile handsets Cryptography Integer linear programming

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A chaotic artificial bee colony algorithm based on lévy search

A chaotic artificial bee colony algorithm based on lévy sea...

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作者： Lin, Shijie Dong, Chen Wang, Zhiqiang Guo, Wenzhong Chen, Zhenyi Ye, Yin College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China Key Laboratory of Information Security of Network Systems Fuzhou350116 China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China Department of Electrical Engineering University of South Florida TampaFL33620 United States

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC. Copyright © 2018 The Institute of Electronics, information and Communication Engineers.

关键词： Evolutionary algorithms

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GLA: Global-Local Attention for Image Description

GLA: Global-Local Attention for Image Description

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作者： Li, Linghui Tang, Sheng Zhang, Yongdong Deng, Lixi Tian, Qi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of the ChineseAcademy of Sciences BeijingTX100049 China Department of Computer Science University of Texas at San Antonio San Antonio78249-1604 United States

In recent years, the task of automatically generating image description has attracted a lot of attention in the field of artificial intelligence. Benefitting from the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), many approaches based on the CNN-RNN framework have been proposed to solve this task and achieved remarkable process. However, two problems remain to be tackled in which the most existing methods use only the image-level representation. One problem is object missing, in which some important objects may be missing when generating the image description and the other is misprediction, when one object may be recognized in a wrong category. In this paper, to address these two problems, we propose a new method called global-local attention (GLA) for generating image description. The proposed GLA model utilizes an attention mechanism to integrate object-level features with image-level feature. Through this manner, our model can selectively pay attention to objects and context information concurrently. Therefore, our proposed GLA method can generate more relevant image description sentences and achieve the state-of-The-Art performance on the well-known Microsoft COCO caption dataset with several popular evaluation metrics-CIDEr, METEOR, ROUGE-L and BLEU-1, 2, 3, 4. © 1999-2012 IEEE.

关键词： Recurrent neural networks

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Reversible data hiding in encrypted images with Two-MSB prediction 10

Reversible data hiding in encrypted images with Two-MSB pred...

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10th IEEE International Workshop on information Forensics and Security, WIFS 2018

作者： Puyang, Yi Yin, Zhaoxia Qian, Zhenxing Key Laboratory of Intelligent Computing Signal Processing Ministry of Education Anhui University Hefei230601 China Department of Computer Science Purdue University West lafayette47906 United States Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200072 China

ISBN: (纸本)9781538665367

In recent years, reversible data hiding in encrypted images (RDHEI) that embeds additional data into the encrypted image content has received more and more attention. In previous RDHEI methods, there is no one considering the correlation between neighboring pixels. In this paper, to better explore the correlation between neighbor pixels, a novel reversible data hiding method in encrypted images with two-MSB (MSB and second MSB) prediction is proposed. After analyzing the original image content and detecting all the possible prediction errors, we encrypt the original image content using the bitwise exclusive-or (XOR) operation and highlight prediction error based on the error location binary map. Finally, all available pixels are embedded with additional data by two-MSB substitution. In our method, reversibility, separability and error-free data extraction are realized. The experimental results show that the proposed method providing a higher embedding rate and better quality of directly decrypted image compared with other start-of-the-art methods. © 2018 IEEE.

关键词： Pixels

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A visual self-positioning method for inspection robot based on symmetrical tangential artificial landmarks

A visual self-positioning method for inspection robot based ...

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2018 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Yang, Shu-Yu Deng, Shun-Ming Zhao, Peng-Peng Li, Jin-Ping School of Information Science and Engineering University of Jinan Jinan Shandong250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan Shandong250022 China Shandong College University Key Laboratory of Information Processing and Cognitive Computing in 13th Five-year Jinan Shandong250022 China Chongqing Vocational Institute of Engineering Chongqing400000 China

ISBN: (纸本)9781728105512

The artificial landmark positioning technology is one of the visual positioning methods of the inspection robot. Although this technology has simple algorithm and low maintenance cost, it is difficult to be widely used due to the limitations in some special environment. For example, people are not allowed to set a large number of landmarks on the ground in some workshops. In this paper, we propose a visual self-positioning method based on the symmetrical tangential artificial landmarks. The basic idea of this method is: firstly, a small number of tangential artificial landmarks are set on the walls or machine tools on both sides, secondly, the perspective model and triangulation method are used to realize the self-positioning of the inspection robot. The experimental results show that the method is simple to operate and has high positioning accuracy, which can meet the needs of the inspection robot. © 2018 IEEE.

关键词： Machine tools

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