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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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Information Security Evaluation by Information Flow Analysis Based on Stochastic Petri Nets

Information Security Evaluation by Information Flow Analysis...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Tu, Hanqian Xiang, Dongming Lin, Wang Liu, Guanjun Zhejiang Sci-Tech University Department of Computer Science and Technology Hangzhou310018 China Shanghai Electronic Transactions and Information Service Collaborative Innovation Center Tongji University Key Laboratory of Embedded System and Service Computing Ministry of Education Department of Computer Science Shanghai201804 China

ISBN: (纸本)9781665410205

The Petri-net-based information flow analysis offers an effective approach for detecting information leakage by the concept of non-interference. Although the related studies propose efficient solutions, they lack quantitative evaluation on information leakage. In this paper, we propose a novel method for quantitative evaluation of information security based on stochastic labeled Petri nets (SLPNs) and information flow analysis. Specifically, we introduce four different levels of security metrics, and provide a methodology for evaluating the information security. Furthermore, a case study is presented to show the feasibility of our method. © 2024 IEEE.

关键词： Information leakage

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Spatial-Temporal Dynamic Graph Diffusion Convolutional Network for Traffic Flow Forecasting

Spatial-Temporal Dynamic Graph Diffusion Convolutional Netwo...

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IEEE International Conference on Big Data

作者： Yang Li Yaying Zhang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Artificial Intelligence Laboratory Shanghai China

Traffic forecasting plays a crucial role in intelligent transportation systems and finds application in various domains. Accurate traffic forecasting remains challenging due to the time-varying correlations within the data and the heterogeneous correlations between regions. Although various dynamic spatial-temporal graph models have been proposed to address these challenges in recent years, most of them are burdened by high computation costs and not intuitive to understand. In this paper, we propose a spatial-temporal graph model, Spatial-Temporal Dynamic Graph Diffusion Convolutional Network (SDGDN) that provides an effective and efficient approach to traffic forecasting. From the perspective of traffic flow transition probabilities, SDGDN learns dynamic graph structures to capture the time-varying traffic transition relationships. Besides dynamic graph structures, static node features are employed in diffusion convolution to better capture heterogeneous regional features. Furthermore, we utilize temporal encoding and also generate varying graphs in each stacked layer to enhance the forecasting performance. Experiments results on five real-world datasets demonstrate that SDGDN outperforms most baseline models in terms of both performance and computation efficiency.

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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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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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Mobile message-aware enhancement using fuzzy lattice reasoning

Mobile message-aware enhancement using fuzzy lattice reasoni...

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2012 9th International Conference on Fuzzy systems and Knowledge Discovery, FSKD 2012

作者： Al-Sultany, Ghaidaa Li, Maozhen Ponraj, Mahesh Al-Raweshidy, Hamid School of Engineering and Design Brunel University Uxbridge UB8 3PH United Kingdom Key Laboratory of Embedded Systems and Service Computing Ministry of Education Tongji University China

ISBN: (纸本)9781467300223

Developing service discovery, information retrieval and context-aware to adapt to user everywhere/anytime and attain the ambient intelligence arise the issue of handling misclassification, imperfect reasoning and estimation with respect to the current context. To deal with this issue, we propose the use of Fuzzy set theory with the purpose of inferring and reasoning that adopt fuzzy lattice classifier for decision making. It is applied on top of SOIM framework to increase the accuracy of the classification process with clearer decisions. © 2012 IEEE.

关键词： Fuzzy set theory

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

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

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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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