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arXiv 2018年

作者： Liu, Mingrui Zhang, Xiaoxuan Zhang, Lijun Jin, Rong Yang, Tianbao Department of Computer Science University of Iowa Iowa CityIA52242 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Alibaba Group Seattle United States

Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received increasing attention in the field of optimization for developing optimization algorithms with fast convergence. However, the studies of EBC in statistical learning are hitherto still limited. The main contributions of this paper are two-fold. First, we develop fast and intermediate rates of empirical risk minimization (ERM) under EBC for risk minimization with Lipschitz continuous, and smooth convex random functions. Second, we establish fast and intermediate rates of an efficient stochastic approximation (SA) algorithm for risk minimization with Lipschitz continuous random functions, which requires only one pass of n samples and adapts to EBC. For both approaches, the convergence rates span a full spectrum between Oe(1/√n) and Oe(1/n) depending on the power constant in EBC, and could be even faster than O(1/n) in special cases for ERM. Moreover, these convergence rates are automatically adaptive without using any knowledge of EBC. Overall, this work not only strengthens the understanding of ERM for statistical learning but also brings new fast stochastic algorithms for solving a broad range of statistical learning problems. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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Triangle extension: Efficient localizability detection in wireless sensor networks

arXiv

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arXiv 2018年

作者： Wu, Hejun Ding, Ao Liu, Weiwei Li, Lvzhou Yang, Zheng Guangdong Key Laboratory of Big Data Analysis and Processing Department of Computer Science Sun Yat-sen University Guangzhou China Horizon Robotics Beijing China School of Software Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing China

Determining whether nodes can be localized, called localizability detection, is essential for wireless sensor networks (WSNs). This step is required for localizing nodes, achieving low-cost deployments, and identifying prerequisites in location-based applications. Centralized graph algorithms are inapplicable to a resource-limited WSN because of their high computation and communication costs, whereas distributed approaches may miss a large number of theoretically localizable nodes in a resource-limited WSN. In this paper, we propose an efficient and effective distributed approach in order to address this problem. Furthermore, we prove the correctness of our algorithm and analyze the reasons our algorithm can find more localizable nodes while requiring fewer known location nodes than existing algorithms, under the same network configurations. The time complexity of our algorithm is linear with respect to the number of nodes in a network. We conduct both simulations and real-world WSN experiments to evaluate our algorithm under various network settings. The results show that our algorithm significantly outperforms the existing algorithms in terms of both the latency and the accuracy of localizability *** Codes 68T40 Copyright © 2018, The Authors. All rights reserved.

关键词： Sensor nodes

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Preventing "bad" content dispersal in Named Data Networking 17

Preventing "bad" content dispersal in Named Data Networking

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50th ACM Turing Conference - China, ACM TUR-C 2017

作者： Wang, Yi Qi, Zhuyun Lei, Kai Liu, Bin Tian, Chen Huawei Future Network Theory Lab Hong Kong School of Electronics and Computer Engineering Peking University Shenzhen518055 China Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing100084 China State Key Laboratory for Novel Software Technology Nanjing University Najing210023 China

ISBN: (纸本)9781450348737

Named Data Networking (NDN) improves the data delivery efficiency by caching contents in routers. To prevent corrupted and faked contents be spread in the network, NDN routers should verify the digital signature of each published content. Since the verification scheme in NDN applies the asymmetric encryption algorithm to sign contents, the speed of content verification is too slow to satisfy the high speed requirement. In this paper, we propose two schemes to improve the verification performance of NDN routers to prevent content poisoning. The first content verification scheme, called "user-assisted", leads to the best performance, but can be bypassed if the clients and the content producer collude. To prevent the aforementioned collusion attack, we improve the user-assisted content verification scheme and propose the second content verification scheme, named "Router-Cooperation", in which the edge routers verify the contents independently without the assistance of users and the core routers no longer verify the contents. The Router-Cooperation verification scheme reduces the computing complexity of cryptographic operation by replacing the asymmetric encryption algorithm with symmetric encryption algorithm. The simulation results demonstrate that this Router-Cooperation scheme can speed up 145.5× (in hardware) and 18.85× (in software) of the original content verification scheme with merely extra transmission overhead. © 2017 ACM.

关键词： Verification

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Recognizing keystrokes Using WiFi Devices

Recognizing Keystrokes Using WiFi Devices

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作者： Ali, Kamran Liu, Alex X. Wang, Wei Shahzad, Muhammad Department of Computer Science and Engineering Michigan State University East LansingMI48823 United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science North Carolina State University RaleighNC27695 United States

keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called Wikey. Wikey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, Wikey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the Wikey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. Wikey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, Wikey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. Wikey can also recognize complete words inside a sentence with over 85% accuracy. © 2017 IEEE.

关键词： Channel state information

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Device-Free Human Activity Recognition Using Commercial WiFi Devices

Device-Free Human Activity Recognition Using Commercial WiFi...

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作者： Wang, Wei Liu, Alex X. Shahzad, Muhammad Ling, Kang Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Computer Science North Carolina State University RaleighNC27695 United States

Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel state Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes. © 2017 IEEE.

关键词： Channel state information

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Short-term wind power forecast for wind farm base on artificial neural network

Short-term wind power forecast for wind farm base on artific...

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2017 International Conference on computer technology, Electronics and Communication, ICCTEC 2017

作者： Wu, Feiyan Liao, Xianwen Guangxi Colleges and Universities Key Laboratory of Professional Software Technology Wuzhou University Wuzhou543002 China Beihai Campus of the Guilin University of Electronic Technology Department of Computer Science Beihai536000 China

ISBN: (纸本)9781538657843

Wind power forecasting is a critical method in minimizing the impact caused by integrated power grids. Firstly, the general steps of the establishment of neural network based load forecasting model for wind farm are presented and the principles are introduced. Next, in an example of a wind farm, the paper focuses on the relationship of weather data and output power by analyzing this relationship. A number of factors having significant impact on the output power are selected and used as network input. This network is an excellent predictive model which has been proved high prediction accuracy in experiments. © 2017 IEEE.

关键词： Neural networks

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Amplified locality-sensitive hashing for privacy-preserving distributed service recommendation 10th

Amplified locality-sensitive hashing for privacy-preserving ...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Qi, Lianyong Dou, Wanchun Zhang, Xuyun Yu, Shui School of Information Science and Engineering Qufu Normal University Rizhao276826 China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China Department of Electrical and Computer Engineering University of Auckland Auckland1023 New Zealand School of Information Technology Deakin University Melbourne3125 Australia

ISBN: (纸本)9783319723884

With the ever-increasing volume of services registered in various web communities, service recommendation techniques, e.g., Collaborative Filtering (i.e., CF) have provided a promising way to alleviate the heavy burden on the service selection decisions of target users. However, traditional CF-based service recommendation approaches often assume that the recommendation bases, i.e., historical service quality data are centralized, without considering the distributed service recommendation scenarios as well as the resulted privacy leakage risks. In view of this shortcoming, Locality-Sensitive Hashing (LSH) technique is recruited in this paper to protect the private information of users when distributed service recommendations are made. Furthermore, LSH is essentially a probability-based search technique and hence may generate "False-positive" or "False-negative" recommended results;therefore, we amplify LSH by AND/OR operations to improve the recommendation accuracy. Finally, through a set of experiments deployed on a real distributed service quality dataset, i.e., WS-DREAM, we validate the feasibility of our proposed recommendation approach named DistSRAmplify-LSH in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation in the distributed environment. © 2017, Springer International Publishing AG.

关键词： Collaborative filtering

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Data Collection Strategy in Low Duty Cycle Wireless Sensor Networks with Mobile Sink

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International Journal of Communications, Network and System sciences 2017年第5期10卷 227-239页

作者： Xiaofeng Tang Li Xie State Key Laboratory of Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

The lifetime of wireless sensor networks can be improved by imposing low duty cycle, but doing so could not solve unbalanced energy consumption and will increase transmission latency. To avoid this, this paper gives a new method to collect data by mobile sink. The proper data collection route is selected according to the sink speed and buffer size of the sensors. The sensors only wake up when the sink approaches them. When certain sensors detect an emergency, the sink catches the message quickly and moves to the hotspot to decrease message relay in the network. The result of simulation by OPNET shows that this protocol can reduce transmission data in the network and prolong the network lifetime.

关键词： Wireless Sensor Networks Low Duty Cycle Mobile Sink

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Predicting commercial activeness over urban big data

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017年第3期1卷 1-20页

作者： Yang, Su Wang, Minjie Wang, Wenshan Sun, Yi Gao, Jun Zhang, Weishan Zhang, Jiulong Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University 825 Zhangheng Rd Shanghai201203 China Department of Software Engineering China University of Petroleum Qingdao266580 China School of Computer Science Xian University of Technology Xian710048 China

This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of visitors, the region functions, the population and buying power of local residents, the average price of services, and the rating scores of customers, which are computed from heterogeneous data including taxi GPS trajectories, point of interests, geographical data, and user-generated comments. Then, we apply sparse representation to discover the impactor factor of each variable of the social contexts in terms of predicting commercial activeness of UCDs under a linear predictive model. The experiments show that a linear correlation between social contexts and commercial activeness exists for Beijing and Shanghai based on an average prediction accuracy of 77.69% but the impact factors of social contexts vary from city to city, where the key factors are rich life services, diversity of restaurants, good shopping experience, large number of local residents with relatively high purchasing power, and convenient transportation. This study reveals the underlying mechanism of urban business ecosystems, and promise social context-aware business planning over heterogeneous urban big data. © 2017 Association for Computing Machinery.

关键词： Ecosystems

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Robust dense reconstruction by range merging based on confidence estimation

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science China(Information sciences) 2016年第9期59卷 22-32页

作者： Yadang CHEN Chuanyan HAO Wen WU Enhua WU School of Computer and Software Nanjing University of Information Science and Technology Department of Computer and Information Science Faculty of Science and Technology University of Macau School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences

Although the stereo matching problem has been extensively studied during the past decades, automatically computing a dense 3D reconstruction from several multiple views is still a difficult task owing to the problems of textureless regions, outliers, detail loss, and various other factors. In this paper, these difficult problems are handled effectively by a robust model that outputs an accurate and dense reconstruction as the final result from an input of multiple images captured by a normal camera. First, the positions of the camera and sparse 3D points are estimated by a structure-from-motion algorithm and we compute the range map with a confidence estimation for each image in our approach. Then all the range maps are integrated into a fine point cloud data set. In the final step we use a Poisson reconstruction algorithm to finish the reconstruction. The major contributions of the work lie in the following points: effective range-computation and confidence-estimation methods are proposed to handle the problems of textureless regions, outliers and detail loss. Then, the range maps are merged into the point cloud data in terms of a confidence-estimation. Finally, Poisson reconstruction algorithm completes the dense mesh. In addition, texture mapping is also implemented as a post-processing work for obtaining good visual effects. Experimental results are presented to demonstrate the effectiveness of the proposed approach.

关键词： stereo matching 3D reconstruction textureless regions outliers details loss range map

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