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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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Composite behavioral modeling for identity theft detection in online social networks

arXiv

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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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Non-Intrusive Load Disaggregation Using Semi-Supervised Learning Method

Non-Intrusive Load Disaggregation Using Semi-Supervised Lear...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Nan Miao Shengjie Zhao Qingjiang Shi Rongqing Zhang The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches.

关键词： Training Hidden Markov models Home appliances Neural networks Convolution Security Pattern analysis

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Low-Rate Non-Intrusive Appliance Load Monitoring Based on Graph Signal Processing

Low-Rate Non-Intrusive Appliance Load Monitoring Based on Gr...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Bing Zhang Shengjie Zhao Qingjiang Shi Rongqing Zhang The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering The Key Laboratory of Embedded System and Service Computing of Ministry of Education Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China

ISBN: (数字)9781728159287

ISBN: (纸本)9781728159294

Thanks to the large-scale smart meters deployments around the world, non-intrusive appliance load monitoring (NILM) is receiving popularity. It aims to disaggregate the total electricity load of a home into individual appliances without resorting to any specific appliance power monitors. NILM is worthy of broad attention owing to its facilitation in energy savings. This paper regards NILM as a classification task and proposes a two-step method based on graph signal processing (GSP). In the first step, a smoothest solution is obtained by minimizing the regularization term. In the second step, gradient projection method, which uses the obtained minimizer as a start point, is adopted to optimize the while objective function, where NILM is regarded as a constrained nonlinear programming problem. The experiment results based on the open-access data set REDD clearly demonstrate that the proposed GSP-based method achieves improved performance compared with other state-of-the-art low-rate NILM approaches.

关键词： domestic appliances gradient methods graph theory nonlinear programming power system measurement signal processing smart meters Smart meters Power system measurements signal processing Nonlinear Programming gradient methods Graph theory Appliances load monitoring

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Adaptive rate-distortion prediction for multiple reference selection and inter-mode decision

Adaptive rate-distortion prediction for multiple reference s...

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13th Pacific-Rim Conference on Multimedia, PCM 2012

作者： Zhao, Tiesong Zhang, Yun Kwong, Sam Wang, Hanli Chen, Qian Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Computer Science City University of Hong Kong Hong Kong Hong Kong Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9783642347771

To minimize Rate-Distortion (RD) cost in video coding, RD Optimization (RDO) technique is adopted, which also brings increasingly computational complexity in Motion Estimation (ME), multiple reference selection and mode decision. In this paper, a Gaussian model based adaptive RD cost prediction method is developed to jointly speed up multiple reference selection and inter-mode decision. Besides the ability of achieving good coding efficiency with much reduced computational power, our method is also with two major advantages. First, it can achieve robust performance, including computational complexity reduction and almost intact video quality, with a wide range of video sequences resolutions and even for various scene changes;second, it could be either individually used or combined with the other efficient mode decision algorithms, and in both cases, our method could achieve remarkable performance, which is revealed by exhaustive experimental results. © 2012 Springer-Verlag.

关键词： Image coding

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A Kind of Identity Authentication Method Based on Browsing Behaviors

A Kind of Identity Authentication Method Based on Browsing B...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Junzhu Zhong Chungang Yan Wangyang Yu Peihai Zhao Mimi Wang The Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Shaanxi Normal University Xi'an China

ISBN: (纸本)9781479970063

Due to the continued growth threat in Phishing, a kind of stable identity authentication method is highly needed based on individual characteristics just like browsing behaviors. Most of the existing researches focused on browsing behavior patterns of group users are used in personal recommendation, website structure optimization or web prediction. In order to ensure the validity of user identity and the security of e-commerce, we construct personalized user browsing behavior model based on ARM (Association Rule Mining) from Web usage log. We compare real-time browsing behaviors with history model to identify a user's real identity in Web pages accessed. According to the results of the experiments, for the illegal users, this method can attain 91.3% detection rate with below 10% false alarm rate. Thus, it can achieve high real-time and recognition efficiency.

关键词： Association rules Authentication Web pages Decision trees Real-time systems Classification algorithms

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Feature selection on chinese text classification using character N-grams

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3rd International Conference on Rough Sets and Knowledge Technology, RSKT 2008

作者： Wei, Zhihua Miao, Duoqian Chauchat, Jean-Hugues Zhong, Caiming Tongji University Key Laboratory Embedded System and Service Computing Ministry of Education Cao'an Road Shanghai 201804 China Laboratoire ERIC Université Lumière Lyon 2 5 avenue Pierre Mendès-France Bron Cedex 69676 France

ISBN: (纸本)3540797203

In this paper, we perform Chinese text classification using n-gram text representation on TanCorp which is a new large corpus special for Chinese text classification more than 14,000 texts divided into 12 classes. We use different n-gram feature (1-, 2-grams or 1-, 2-, 3-grams) to represent documents. Different feature weights (absolute text frequency, relative text frequency, absolute n-gram frequency and relative n-gram frequency) are compared. The sparseness of "document by feature" matrices is analyzed in various cases. We use the C-SVC classifier which is the SVM algorithm designed for the multi-classification task. We perform our experiments in the TANAGRA platform. We found out that the feature selection methods based on n-gram frequency (absolute or relative) always give better results and produce denser matrices. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Feature Selection

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Artificial impostors for location privacy preservation

arXiv

引用

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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A New Modified Reachability Graph of ω-independent Petri Nets and its Application

A New Modified Reachability Graph of ω-independent Petri Ne...

引用

2nd International Conference on Communication Technology(ICCT 2015)

作者： Ru Yang Zhijun Ding Meiqin Pan The Key Laboratory of Embedded System and Service Computing Ministry of Education College of international business Shanghai International Studies University

Analysis approaches of liveness are important to Petri nets theory,but the relevant research work in the past is still *** paper proposed a new modified reachability graph(NMRG) approach for liveness analysis of ω-independent Petri *** NMRG of a Petri net is transformed from its NMRT and can be used to analyze liveness and deadlock of ω-independent unbound Petri nets.A sufficient condition of liveness and a sufficient and necessary condition of deadlock of ω-independent Petri nets are proposed on the basis of *** are given to illustrate the method.

关键词： ω-Independent Petri net Liveness Deadlock NMRG

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

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

引用

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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