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arXiv 2019年

作者： Liao, Hao Liu, Qi-Xin Huang, Ze-Cheng Yeung, Chi Ho Zhang, Yi-Cheng National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen518060 China Department of Science and Environmental Studies The Education University of Hong Kong Hong Kong Hong Kong Department of Physics University of Fribourg Fribourg Switzerland

With the rapid development of modern technology, the Web has become an important platform for users to make friends and acquire information. However, since information on the Web is over-abundant, information filtering becomes a key task for online users to obtain relevant suggestions. As most Websites can be ranked according to users’ rating and preferences, relevance to queries, and recency, how to extract the most relevant item from the over-abundant information is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed accumulative time-based ranking (ATR) algorithm, we incorporate two behavioral weighting factors which are updated when users select or rate items, to reflect the evolution of user reputation and item quality over time. We showed that our algorithm outperforms state-of-the-art ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Copyright © 2019, The Authors. All rights reserved.

关键词： Information filtering

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PNet: An Efficient Network for Pneumonia Detection 12

PNet: An Efficient Network for Pneumonia Detection

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12th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2019

作者： Li, Zhongliang Yu, Juan Li, Xuechen Li, Yingqi Dai, Weicai Shen, Linlin Mou, Lisha Pu, Zuhui Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Institute of Translational Medicine Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Imaging Department Shenzhen University Health Science Center Shenzhen University School of Medicine Shenzhen China Shenzhen Second People's Hospital First Affiliated Hospital Shenzhen University Shenzhen China Imaging Department Shenzhen Bao'an District Songgang People's Hospital Shenzhen China Imaging Department Fifth People's Hospital of Longgang District Shenzhen China

ISBN: (纸本)9781728148526

Pneumonia is a common lung disease and affects millions of people worldwide each year. The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosing lung diseases. Usually, chest X-ray images (CXRs) are taken at physical examination and evaluated by radiologists. The unbalance between tremendous numbers of CXRs and limited number of radiologists need to be solved by Computer Assisted Diagnosis (CAD). As deep neural networks have shown promising results in CAD, we implemented a deep learning-based framework, PNet, for pneumonia detection. 10784 chest X-ray images collected at the Shenzhen No.2 People's Hospital were employed for training and evaluation. The experimental results showed that the proposed PNet (1,695,777 parameters) achieved higher accuracy and F1 score than classical networks like AlexNet (42,725,889 parameters) and VGG16 (27,560,769 parameters). © 2019 IEEE.

关键词： Deep neural networks

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Learning Temporal Relationships Between Financial Signals

Learning Temporal Relationships Between Financial Signals

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Dawei Cheng Yi Tu Zhibin Niu Liqing Zhang Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Dep. of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Software Tianjin University China

ISBN: (纸本)9781538646595

Portfolio risk control is vital to financial institutions: investors seek to build equities with the highest return but with minimum risk. However, a general phenomenon is significant comovement among many financial signals, such as stocks and futures. One investment strategy is to choose less correlated assets. Classic approaches quantifying such relationships in real financial markets make it difficult to exclude factors such as market trends and autocorrelation. In this paper, we propose a signal process perspective for quantitative measurement. A machine learning based algorithm is designed to model returns, taking account of market sensitivity, autocorrelation, and relationships with other stocks. We then extend the model training algorithm using regularized least square and gradient descent to estimate parameters. A penalty factor is designed in the optimization function to address extreme large negative returns. After denoising common factors, the learned pure relationship parameters are applied to construct a relationship matrix. Finally, we use this matrix to build portfolios by constrained optimization. Empirical experiments on two stock datasets show that the proposed method outperforms several state-of-the-art methods in terms of mean average precision and cumulative returns.

关键词： financial signal temporal relationship factor model portfolio risk signals Autocorrelation relational matrix stocks Least squares method signal processing

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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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AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography

arXiv

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arXiv 2020年

作者： Fu, Huazhu Li, Fei Sun, Xu Cao, Xingxing Liao, Jingan Orlando, José Ignacio Tao, Xing Li, Yuexiang Zhang, Shihao Tan, Mingkui Yuan, Chenglang Bian, Cheng Xie, Ruitao Li, Jiongcheng Li, Xiaomeng Wang, Jing Geng, Le Li, Panming Hao, Huaying Liu, Jiang Kong, Yan Ren, Yongyong Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu State Key Laboratory Zhongshan Ophthalmic Center Sun Yat-sen University Guangzhou510060 China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates Intelligent Healthcare Unit Baidu Beijing China School of Computer Science and Engineering South China University of Technology GuangzhouGuangdong China National Scientific and Technical Research Council CONICET Argentina Tandil Argentina School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Tencent Jarvis Lab Shenzhen China School of Software Engineering South China University of Technology Guangzhou China School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China School of Electronic and Information Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong School of Electronic and Information Engineering Soochow University Suzhou China Ningbo University Zhejiang China Ningbo Institute of Industrial Technology Chinese Academy of Sciences Zhejiang China Southern University of Science and Technology Shenzhen China Shanghai Jiaotong University Shanghai China Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University of Vienna Vienna Austria

Angle closure glaucoma (ACG) is a more aggressive disease than open-angle glaucoma, where the abnormal anatomical structures of the anterior chamber angle (ACA) may cause an elevated intraocular pressure and gradually leads to glaucomatous optic neuropathy and eventually to visual impairment and blindness. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging provides a fast and contactless way to discriminate angle closure from open angle. Although many medical image analysis algorithms have been developed for glaucoma diagnosis, only a few studies have focused on AS-OCT imaging. In particular, there is no public AS-OCT dataset available for evaluating the existing methods in a uniform way, which limits the progress in the development of automated techniques for angle closure detection and assessment. To address this, we organized the Angle closure Glaucoma Evaluation challenge (AGE), held in conjunction with MICCAI 2019. The AGE challenge consisted of two tasks: scleral spur localization and angle closure classification. For this challenge, we released a large data of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models. During the AGE challenge, over 200 teams registered online, and more than 1100 results were submitted for online evaluation. Finally, eight teams participated in the onsite challenge. In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results in the two tasks. We further discuss limitations and future directions. In the AGE challenge, the top-performing approach had an average Euclidean Distance of 10 pixel (10µm) in scleral spur localization, while in the task of angle closure classification, all the algorithms achieved the satisfactory performances, especially, 100% accuracy rate for top-two performances. These artificial intelligence techniques were shown to have the potential to enable new developments in AS-OCT

关键词： Ophthalmology

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Detection of deep network generated images using disparities in color components

arXiv

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arXiv 2018年

作者： Li, Haodong Li, Bin Tan, Shunquan Huang, Jiwu Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

With the powerful deep network architectures, such as generative adversarial networks and variational autoencoders, large amounts of photorealistic images can be generated. The generated images, already fooling human eyes successfully, are not initially targeted for deceiving image authentication systems. However, research communities as well as public media show great concerns on whether these images would lead to serious security issues. In this paper, we address the problem of detecting deep network generated (DNG) images by analyzing the disparities in color components between real scene images and DNG images. Existing deep networks generate images in RGB color space and have no explicit constrains on color correlations;therefore, DNG images have more obvious differences from real images in other color spaces, such as HSV and YCbCr, especially in the chrominance components. Besides, the DNG images are different from the real ones when considering red, green, and blue components together. Based on these observations, we propose a feature set to capture color image statistics for detecting the DNG images. Moreover, three different detection scenarios in practice are considered and the corresponding detection strategies are designed. Extensive experiments have been conducted on face image datasets to evaluate the effectiveness of the proposed method. The experimental results show that the proposed method is able to distinguish the DNG images from real ones with high accuracies. Copyright © 2018, The Authors. All rights reserved.

关键词： Color

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Allosteric DNAzyme-based DNA logic circuit: operations and dynamic analysis

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Nucleic acids research 2019年第3期47卷 1097-1109页

作者： Xuedong Zheng Jing Yang Changjun Zhou Cheng Zhang Qiang Zhang Xiaopeng Wei College of Computer Science Shenyang Aerospace University Shenyang 110136 China. School of Control and Computer Engineering North China Electric Power University Beijing 102206 China. College of Mathematics and Computer sciences Zhejiang Normal University Jinhua 321004 China. School of Electronics Engineering and Computer Science Peking University Key laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 China. Key Laboratory of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian 116622 China. School of Computer Scicence and Technology Dalian University of Technology Dalian 116024 China.

Recently, due to the dual roles of DNA and enzyme, DNAzyme has been widely used in the field of DNA circuit, which has a wide range of applications in bio-engineered system, information processing and biocomputing. In fact, the activity of DNAzymes was regulated by subunits assembly, pH control and metal ions triggers. However, those regulations required to change the sequences of whole DNAzyme, as separating parts and inserting extra DNA sequence. Inspired by the allosteric regulation of proteins in nature, a new allosteric strategy is proposed to regulate the activity of DNAzyme without DNA sequences changes. In this strategy, DNA strand displacement was used to regulate the DNAzyme structure, through which the activity of DNAzyme was well controlled. The strategy was applied to E6-type DNAzymes, and the operations of DNA logic circuit (YES, OR, AND, cascading and feedback) were established and simulated with the dynamic analyses. The allosteric regulation has potential to construct more complicated molecular systems, which can be applied to bio-sensing and detection.

关键词：

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Secure Beamforming Design for MISO SWIPT Systems: An Indirectly Optimized Approach

Secure Beamforming Design for MISO SWIPT Systems: An Indirec...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Yao Yu Siyu Wang Shumei Liu Lei Guo Zhaolong Ning Shimin Gong Mohammad S. Obaidat School of Computer Science and Engineering Northeastern University Shenyang China Chongqing University of Posts and Telecommunications China State Key Laboratory for Novel Software Technology Nanjing University China School of Intelligent Systems Engineering Sun Yat-sen University China ECE Department Nazarbayev University Astana Kazakhstan College of Computing and Informatics University of Sharjah UAE The King Abdullah II School of Information Technology The University of Jordan Amman Jordan University of Science and Technology Beijing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

By considering the Simultaneous Wireless Information and Power Transfer (SWIPT) schemes, this paper focuses on secure transmission model design in multiple-input-single-output (MISO) channels. In these channels, the channel state information is assumed to be perfect. Our objective is to maximize the worst-case secrecy rate with respect to both potential eavesdroppers and obvious eavesdroppers under the constraints of energy-harvesting and total transmission power. We present an optimization model to indirectly obtain maximum security rate in a single receiver system. Due to the high computational complexity of the solution process caused by the formulated non-convex optimization problem, we propose a novel indirect method to handle this issue. Then, a Semi-Definite Programming (SDP) relaxation method is used to approach the optimal solution. Moreover, we reveal the conditions for ensuring that the above semi-definite relaxation is compact. Simulation results demonstrate that the gained performance in our system is much better than those of the existing competing schemes.

关键词： Receivers Interference Signal to noise ratio Transmitters Optimization Security Communication system security

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QoS-oriented packet scheduling scheme for opportunistic networks

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The Journal of China Universities of Posts and Telecommunications 2017年第3期24卷 51-57页

作者： Yao Wenbin Chen Yin Zong Ming Wang Dongbin Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Computer Science Beijing University of Posts and Telecommunications Yingcai Honors College University of Electronic Science and Technology of China National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications

The unique characteristics of opportunistic networks （ONs）, such as intermittent connectivity and limited network resources, makes it difficult to support quality of service （QoS） provisioning, particularly to guarantee delivery ratio and delivery delay. In this paper, we propose a QoS-oriented packet scheduling scheme （QPSS） to make decisions for bundle transmissions to satisfy the needs for the delivery ratio and delivery delay constraints of bundles. Different from prior works, a novel bundle classification method based on the reliability and latency requirements is utilized to decide the traffic class of bundles. A scheduling algorithm of traffic class and bundle redundancy is used to maintain a forwarding and dropping priority queue and allocate network resources in QPSS. Simulation results indicate that our scheme not only achieves a higher overall delivery ratio but also obtains an approximate 14% increase in terms of the amount of eligible bundles.

关键词： opportunistic networks packet scheduling QoS traffic class of bundles

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Protecting privacy of users in brain-computer interface applications

arXiv

引用

arXiv 2019年

作者： Agarwal, Anisha Dowsley, Rafael McKinney, Nicholas D. Wu, Dongrui Lin, Chin-Teng De Cock, Martine Nascimento, Anderson C.A. School of Engineering and Technology University of Washington TacomaWA98402 United States Department of Computer Science Bar-Ilan University Israel School of Engineering and Technology University of Washington TacomaWA98402 United States Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Automation Huazhong University of Science and Technology Wuhan China School of Software University of Technology Sydney Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent9000 Belgium

Machine learning (ML) is revolutionizing research and industry. Many ML applications rely on the use of large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data, a kind of data that is so rich with information that application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other intimate data. The challenge we address is how to engage in meaningful ML with EEG data while protecting the privacy of users. Hence, we propose cryptographic protocols based on Secure Multiparty Computation (SMC) to perform linear regression over EEG signals from many users in a fully privacy-preserving (PP) fashion, i.e. such that each individual's EEG signals are not revealed to anyone else. To illustrate the potential of our secure framework, we show how it allows estimating the drowsiness of drivers from their EEG signals as would be possible in the unencrypted case, and at a very reasonable computational cost. Our solution is the first application of commodity-based SMC to EEG data, as well as the largest documented experiment of secret sharing based SMC in general, namely with 15 players involved in all the computations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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