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arXiv 2018年

作者： Wang, Cheng Yang, Bo Department of Computer Science and Technology Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China

In this work, we aim at building a bridge from poor behavioral data to an effective, quick-response, and robust behavior model for online identity theft detection. We concentrate on this issue in online social networks (OSNs) where users usually have composite behavioral records, consisting of multi-dimensional low-quality data, e.g., offline check-ins and online user generated content (UGC). As an insightful result, we find that there is a complementary effect among different dimensions of records for modeling users' behavioral patterns. To deeply exploit such a complementary effect, we propose a joint model to capture both online and offline features of a user's composite behavior. We evaluate the proposed joint model by comparing with some typical models on two real-world datasets: Foursquare and Yelp. In the widely-used setting of theft simulation (simulating thefts via behavioral replacement), the experimental results show that our model outperforms the existing ones, with the AUC values 0:956 in Foursquare and 0:947 in Yelp, respectively. Particularly, the recall (True Positive Rate) can reach up to 65:3% in Foursquare and 72:2% in Yelp with the corresponding disturbance rate (False Positive Rate) below 1%. It is worth mentioning that these performances can be achieved by examining only one composite behavior (visiting a place and posting a tip online simultaneously) per authentication, which guarantees the low response latency of our method. This study would give the cybersecurity community new insights into whether and how a real-time online identity authentication can be improved via modeling users' composite behavioral patterns. Copyright © 2018, The Authors. All rights reserved.

关键词： Behavioral research

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A robust facial descriptor for face recognition

A robust facial descriptor for face recognition

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International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Liu, Na Duan, Huixian Song, Lei Yan, Zhiguo Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

Illumination, occlusion, pose and expression variations are the most common challenging problems for face recognition in many real-world applications. However, existing face recognition methods are proposed to handle part of these variations. In this paper, we propose a robust facial descriptor to address this issue. First, we apply a chain of three processing to tackle the illumination variation. Second, we compute the facial sparse local descriptor to handle the occlusion, pose, and expression variations. Experimental evaluation on the FRGC database shows that our approach is able to achieve very promising recognition rates under uncontrolled environments. © 2018, Springer International Publishing AG.

关键词： Face recognition

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Artificial impostors for location privacy preservation

arXiv

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arXiv 2018年

作者： Wang, Cheng Xie, Zhiyang Department of Computer Science and Engineering Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education China

The progress of location-based services has led to severe concerns about location privacy leakage. However, existing methods are still incompetent for effective and efficient location privacy preservation (LPP). They are often vulnerable under the identification attack with side information, or hard to be implemented due to the high computational complexity. In this paper, we pursue the high protection efficacy and low computational complexity simultaneously. We propose a scalable LPP method based on the paradigm of counterfeiting locations. To make fake locations extremely plausible, we forge them by synthesizing artificial impostors (AIs). The so-called AIs refer to the synthesized traces that have similar semantic features to the actual traces and do not contain any target location. We devise two dedicated techniques: the sampling-based synthesis method and population-level semantic model. They respectively play the significant roles in two critical steps of synthesizing AIs. We conduct the experiments on real datasets in two cities (Shanghai of China and Asturias of Spain) to validate the high efficacy and scalability of the proposed method. In these two datasets, the experimental results show that our method achieves the preservation efficacy of 97:68% and 96:24%, and the time spent for building generators is only 230:47 and 215:92 seconds, respectively. This study would give the research community new insights into improving the practicality of the state-of-the-art LPP paradigm via counterfeiting locations. Copyright © 2018, The Authors. All rights reserved.

关键词： Location

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

Vision-based Semantic Mapping and Localization for Autonomou...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China School of Surveying and Geo-Informatics Tongji University Shanghai China

ISBN: (纸本)9781538644522

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. © 2018 IEEE.

关键词： Autonomous vehicles

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A speed estimation method of vehicles based on road monitoring video-images

A speed estimation method of vehicles based on road monitori...

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International Conference on Applications and Techniques in Cyber Security and Intelligence, ATCSI 2017

作者： Huixian, Duan Jun, Wang Lei, Song Yixin, Zhao Liu, Na The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai China Shanghai International Technology and Trade United Co. Ltd Shanghai China

ISBN: (纸本)9783319670706

In order to reduce traffic accidents and road congestion in many cities, vehicle speed estimation is very critical and important to observe speed limitation law and traffic conditions. In this paper, we present a speed estimation method of vehicles based on road monitoring video-images. Firstly, we set up a word coordinate system on the license plate in one vehicle image. Next, for small vehicles in China, according to the known length and width of the license plate, the spatial transformation matrix between the word coordinate system and the image coordinate system is derived. Then, based on the spatial transform matrix, compute the corner spatial coordinates of the license plate in each frame, and then estimate the vehicle speed. Finally, experimental results on read data have shown that the vehicle speed can be estimated within the acceptable error range (±3 km/h), and then have demonstrated the effectiveness of the proposed vehicle speed estimation method. © 2018, Springer International Publishing AG.

关键词： Speed

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Object measurement system based on Surveillance video-images 1

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6th International Conference on Frontier computing, FC 2017

作者： Duan, Hui-Xian Wang, Jun Song, Lei Liu, Na Cyber Physical System R&D Center The Third Research Institute of Ministry of Public Security Shanghai201204 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai200092 China Shanghai International Technology and Trade United Co. Ltd Shanghai200031 China

ISBN: (数字)9789811073984

ISBN: (纸本)9789811073977

Object measurement based on surveillance video-images is a critical task in the field of intelligent video surveillance. In this paper, we proposed an object measurement system based on surveillance video-images. Firstly, considering the scene of the urban video surveillance, a novel calibration tool is designed for such scene, which is portable, collapsible and operational. Next, based on the portable calibration tool, the intrinsic and extrinsic parameters of the surveillance camera can be estimated. Then, an object measurement system based on surveillance video-images is developed, which include the acquisition module of calibration data, the computation module of camera parameters, the selection module of measurement endpoint and the computation module of object. Finally, in the traffic checkpoint, experiments are carried out to verify the effectiveness of the object measurement system. The results show that as long as the surveillance camera is one-time calibrated based on the portable calibration tool, this system can measure the object on the surveillance video-images captured by the calibrated camera. What’s more, experimental results have shown that the object measurement system can be used to estimate the vehicle speed and measure the body height within the acceptable error range, and then have demonstrated the effectiveness of the object measurement system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Calibration

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Observable liveness of Petri nets with controllable and observable transitions

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Science China(Information Sciences) 2017年第11期60卷 256-264页

作者： Guanjun LIU Changjun JIANG Department of Computer Science Tongji University Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University E-commerce Transactions and Information Services Collaborative Innovation Center of Shanghai University Knowledge Service Platform Tongji University

Observability and/or controllability are paid much attention to by researchers not only from the control science field[1–3]but also from the computer science field[4–9].Recently,Desel and Kilinc[4]proposed the notion of observable liveness for Petri nets with(un-)observable and(un-)controllable *** purpose of defining observable liveness is to represent that"a user can always enforce the occurrence of any observable transition only by stimulating the net by choosing appropriate enabled controllable transition"[4].An important assumption of

关键词： net cot PN Observable liveness of Petri nets with controllable and observable transitions

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Greedy signal space recovery algorithm with overcomplete dictionaries in compressive sensing

arXiv

引用

arXiv 2019年

作者： Zhu, Jianchen Zhao, Shengjie Shi, Qingjiang Arce, Gonzalo R. College of Electronics and Information Engineering Tongji University Shanghai201804 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China School of Software Engineering Tongji University Shanghai201804 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Compressed sensing

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Clustering and Analysis of Household Power Load Based on HMM and Multi-factors

Clustering and Analysis of Household Power Load Based on HMM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hao Fang Yue Zhang Min Liu Weiming Shen School of Electronic and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the advance of the information and communication technology, smart grid, and smart metering, residential electricity usage data are available for analyzing household usage pattern. However, most such usage pattern analyses have been based on smart meter data. Geographical location and environment factors have not been well considered. In order to have a better understanding of residential electricity usage pattern, this paper studies the usage pattern based on both environment data and smart meter data. A Hidden Markov Model (HMM) is applied to learn the consumption dynamic behavior under the corresponding environments and a clustering method is applied to discover the typical usage patterns. The environmental adaptation which indicates the household reaction to the environment during the electricity consumption is revealed.

关键词： Hidden Markov models Clustering algorithms Smart meters Meteorology Pattern analysis Analytical models Biological system modeling

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A Location Privacy Preserving Scheme Based on Repartitioning Anonymous Region in Mobile Social Network

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Procedia Computer Science 2018年 129卷 368-371页

作者： Lina Ni Yanfeng Yuan Xiao Wang Mengmeng Zhang Jinquan Zhang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Applying the proliferated location-based services (LBS) to social networks has spawned mobile social network (MSN) services that it allows users to discover potential friends around them. In this paper, we focus on the problem of location privacy preserving in MSN. Particularly, we propose a location privacy preserving (RPAR) scheme via to repartition anonymous region where the central anonymous location minimizes the traffic between the anonymous server and the LBS server while protecting the privacy of the user location.

关键词： Privacy preserving mobile social network location-based service location privacy anonymous region repartition

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