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3rd IEEE International Conference on Data Science in Cyberspace, DSC 2018

作者： Yang, Honghao Liang, Dong Kuang, Xiaohui Xu, Changqiao Institute of Network Technology Beijing University of Posts and Telecommunications Beijing China National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9781538642108

With the development of information technology and the popularity of smart devices, voice recognition system, which is installed in many devices such as smartphones and automatic control equipment, is playing an increasingly important role in people's lives. Its appearance has greatly facilitated people's lives. However, their potential safety issues should also receive sufficient attention. Human perception of speech signals is affected by prior knowledge and context. It is natural for a people to unconsciously make up the obscure part of a voice signals he heard. But it doesn't work this way for a voice recognition software. In this condition, wrong results which are far from normal people's recognition will be returned from the software. That case rose above shows that voice recognition systems have potential vulnerability, which makes it possible that designed dangerous commands may be executed without operator's conscious. This paper firstly proposes a method to process voice signal and produce a lot of processed signals which are similar in human auditory but different in actual. Then, we propose a strategy to select processed voice signals aiming at a target speech recognition system. Finally, a way to test the vulnerability of voice recognition system is proposed in passing. Experimental results revealed that the above strategies effectively changed the output texts from speech recognition systems while the input voices were auditory changed little. This result confirmed the vulnerability of voice recognition system. © 2018 IEEE.

关键词： Speech recognition

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A Slice-Guided Method of Indoor Scene Structure Retrieving 12th

A Slice-Guided Method of Indoor Scene Structure Retrieving

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12th International Conference on e-Learning and Games, Edutainment 2018

作者： Wang, Lijuan Wang, Yinghui Wang, Ningna Ning, Xiaojuan Lv, Ke Huang, Liangyi Xi’an University of Technology South Jinhua Road no. 5 Xi’an710032 China Xi’an Technology University Mid Xuefu Road no. 2 Xi’an710021 China Shaanxi Key Laboratory of Network Computing and Security Technology South Jinhua Road no. 5 Xi’an710032 China *** B.V. Vijzelstraat 66-80 Amsterdam1017HL Netherlands Institute of Automation Chinese Academy of Sciences Beijing China School of Engineering and Applied Science George Washington University Washington DC20052 United States

ISBN: (纸本)9783030237110

The structure information of indoor scene is necessary for a robot who works in a room. In order to achieve structure of an indoor scene, a slice-guided method of indoor scene structure retrieving is proposed in this paper. We present a slicing based approach that transforms three-dimensional (3D) segmentations into two-dimensional (2D) segmentation and segments different kinds of primitive shapes while keeping the global topology structure of the indoor scene. The global topology structure is represented by a graph. The graph is compared with the given indoor scene template. The matched objects and the topology relation between them are finally presented. Our experiment results show that the proposed method performs well on several typical indoor scenes, even if some data are missing. © 2019, Springer Nature Switzerland AG.

关键词： Topology

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A Privacy-Preserving TPA-aided Remote Data Integrity Auditing Scheme in Clouds

A Privacy-Preserving TPA-aided Remote Data Integrity Auditin...

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2019国际计算机前沿大会

作者： Meng Zhao Yong Ding Yujue Wang Huiyong Wang Bingyao Wang Lingang Liu School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic Technology School of Mathematics and Computing Science Guilin University of Electronic Technology

The remote data integrity auditing technology can guarantee the integrity of outsourced data in *** can periodically run an integrity auditing protocol by interacting with cloud server,to verify the latest status of outsourced *** auditing requires user to take massive time-consuming computations,which would not be affordable by weak *** this paper,we propose a privacy-preserving TPA-aided remote data integrity auditing scheme based on Li et al.'s data integrity auditing scheme without bilinear pairings,where a third party auditor(TPA) is employed to perform integrity auditing on outsourced data for *** privacy of outsourced data can be guaranteed against TPA in the sense that TPA could not infer its contents from the returned proofs in the integrity auditing *** construction is as efficient as Li et al.'s scheme,that is,each procedure takes the same time-consuming operations in both schemes,and our solution does not increase the sizes of processed data,challenge and proof.

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Learning from context： a mutual reinforcement model for Chinese microblog opinion retrieval

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Frontiers of Computer Science 2018年第4期12卷 714-724页

作者： Jingjing WEI Xiangwen LIAO Houdong ZHENG Guolong CHEN Xueqi CHENG College of Mathematics and Computer Science Fuzhou University Fuzhou 350108 China College of Electronics and Information Science Fujian Jiangxia University Fuzhou 350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligence Processing Fuzhou University Fuzhou 350108 China Institute of Computing Technology Chinese Academic of Sciences Beijing 100190 China

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentiment words relying on the document features. However, this approach could not be effectively applied to mi- croblogs that have typical user-generated content with valu- able contextual information：＂user-user＂ interpersonal interactions and ＂user-post/comment＂ intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.

关键词： opinion retrieval sentiment words lexiconweighting mutual reinforcement model

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CPicker： Leveraging Performance-Equivalent Configurations to Improve Data Center Energy Efficiency

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Journal of Computer Science & technology 2018年第1期33卷 131-144页

作者： Fa-Qiang Sun Gui-Hai Yan Xin He Hua-Wei Li Yin-He Han State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Computer Network Emergency Response Technical Team of China Beijing 100029 China University of Chinese Academy of Sciences Beijing 100049 China

The poor energy proportionality of server is seen as the principal source for low energy efficiency of modern data centers. We find that different resource configurations of an application lead to similar performance, but have distinct energy consumption. We call this phenomenon as ＂performance-equivalent resource configurations （PERC）＂, and its performance range is called equivalent region （ER）. Based on PERC, one basic idea for improving energy efficiency is to select the most efficient configuration from PERC for each application. However, it cannot support every application to obtain optimal solution when thousands of applications are run simultaneously on resource-bounded servers. Here we propose a heuristic scheme, CPicker, based on genetic programming to improve energy efficiency of servers. To speed up convergence, CPicker initializes a high quality population by first choosing configurations from regions that have high energy variation. Experiments show that CPicker obtains above 17% energy efficiency improvement compared with the greedy approach, and less than 4% efficiency loss compared with the oracle case.

关键词： performance equivalence energy efficiency data center power management dynamic voltage and frequencyscaling （DVFS）

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Dynamic MCMC sampling

arXiv

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arXiv 2019年

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from Gibbs distributions arising by various graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from a MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with n variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate samples within 1/Poly(n) total variation errors, or perfect samples, while the MRF is dynamically changing. Furthermore, the dynamic sampling algorithms are efficient with O(n) space cost, and O(log2 n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic (approximate or perfect) sampling algorithms with O(n) space cost and O (log2 n) incremental time cost, for the following models when the maximum degree is bounded: generalMRF satisfying the Dobrushin-Shlosman condition (for approximate sampling);Ising model with temperature β where e-2|β| > 1 - 2 β+1 (for both approximate and perfect samplings);hardcore model with fugacity λ 2λ (for approximate sampling);or q > 2Δ2 + 3Δ (for perfect sampling). These results show that the coupling of single-site Markov chains that

关键词： Learning algorithms

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MAC-AC: A Novel Distributed MAC Protocol for Accessing Channel in Vehicular Ad Hoc networks

MAC-AC: A Novel Distributed MAC Protocol for Accessing Chann...

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IEEE Conference on Vehicular technology (VTC)

作者： Baozhu Li Fen Hou Changyue Zhang Shaohua Li Shujuan Ji Shanzhi Chen School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Faculty of Science and Technology University of Macau Macau China Automotive Research Institute China National Heavy Duty Truck Group Co. Ltd Jinan China Shandong Key Laboratory of Wisdom Mine Information Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Lab of Wireless Mobile Communication Beijing China China Academy of Telecommunication Technology Beijing China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

关键词： Vehicular and wireless technologies Time division multiple access network topology Vehicular ad hoc networks Media Access Protocol Topology Vehicle dynamics

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Reversible data hiding based on reducing invalid shifting of pixels in histogram shifting

arXiv

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arXiv 2019年

作者： Jia, Yujie Yin, Zhaoxia Zhang, Xinpeng Luo, Yonglong Key Laboratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei230601 China School of Computer Science Fudan University Shanghai201203 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu Anhui240002 China

In recent years, reversible data hiding (RDH), a new research hotspot in the field of information security, has been paid more and more attention by researchers. Most of the existing RDH schemes do not fully take it into account that natural image's texture has influence on embedding distortion. The image distortion caused by embedding data in the image's smooth region is much smaller than that in the unsmooth region, essentially, it is because embedding additional data in the smooth region corresponds to fewer invalid shifting pixels (ISPs) in histogram shifting. Thus, we propose a RDH scheme based on the images texture to reduce invalid shifting of pixels in histogram shifting. Specifically, first, a cover image is divided into two sub-images by the checkerboard pattern, and then each sub-image's fluctuation values are calculated. Finally, additional data can be embedded into the region of sub-images with smaller fluctuation value preferentially. The experimental results demonstrate that the proposed method has higher capacity and better stego-image quality than some existing RDH schemes. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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DPANet: Depth potentiality-aware gated attention network for RGB-D salient object detection

arXiv

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arXiv 2020年

作者： Chen, Zuyao Cong, Runmin Xu, Qianqian Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong Hong Kong The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518055 China

There are two main issues in RGB-D salient object detection: (1) how to effectively integrate the complementarity from the cross-modal RGB-D data;(2) how to prevent the contamination effect from the unreliable depth map. In fact, these two problems are linked and intertwined, but the previous methods tend to focus only on the first problem and ignore the consideration of depth map quality, which may yield the model fall into the sub-optimal state. In this paper, we address these two issues in a holistic model synergistically, and propose a novel network named DPANet to explicitly model the potentiality of the depth map and effectively integrate the cross-modal complementarity. By introducing the depth potentiality perception, the network can perceive the potentiality of depth information in a learning-based manner, and guide the fusion process of two modal data to prevent the contamination occurred. The gated multi-modality attention module in the fusion process exploits the attention mechanism with a gate controller to capture long-range dependencies from a cross-modal perspective. Experimental results compared with 16 state-of-the-art methods on 8 datasets demonstrate the validity of the proposed approach both quantitatively and qualitatively. Copyright © 2020, The Authors. All rights reserved.

关键词： Modal analysis

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Localization of Deep Inpainting Using High-Pass Fully Convolutional network

Localization of Deep Inpainting Using High-Pass Fully Convol...

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International Conference on Computer Vision (ICCV)

作者： Haodong Li Jiwu Huang Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Image inpainting has been substantially improved with deep learning in the past years. Deep inpainting can fill image regions with plausible contents, which are not visually apparent. Although inpainting is originally designed to repair images, it can even be used for malicious manipulations, e.g., removal of specific objects. Therefore, it is necessary to identify the presence of inpainting in an image. This paper presents a method to locate the regions manipulated by deep inpainting. The proposed method employs a fully convolutional network that is based on high-pass filtered image residuals. Firstly, we analyze and observe that the inpainted regions are more distinguishable from the untouched ones in the residual domain. Hence, a high-pass pre-filtering module is designed to get image residuals for enhancing inpainting traces. Then, a feature extraction module, which learns discriminative features from image residuals, is built with four concatenated ResNet blocks. The learned feature maps are finally enlarged by an up-sampling module, so that a pixel-wise inpainting localization map is obtained. The whole network is trained end-to-end with a loss addressing the class imbalance. Extensive experimental results evaluated on both synthetic and realistic images subjected to deep inpainting have shown the effectiveness of the proposed method.

关键词： Forensics Feature extraction Convolution Kernel Machine learning Computer vision Probability

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