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On complementary effect of blended behavioral analysis for identity theft detection in mobile social networks 1

13th International Conference on Mobile Ad-hoc and Sensor Networks, MSN 2017

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

ISBN: (数字)9789811088902

ISBN: (纸本)9789811088896

User behavioral analysis is expected to act as a promising technique for identity theft detection in the Internet. The performance of this paradigm extremely depends on a good individual-level user behavioral model. Such a good model for a specific behavior is often hard to obtain due to the insufficiency of data for this behavior. The insufficiency of specific data is mainly led by the prevalent sparsity of users’ collectable behavioral footprints. This work aims to address whether it is feasible to effectively detect identify thefts by jointly using multiple unreliable behavioral models from sparse individual-level records. We focus on this issue in mobile social networks (MSNs) with multiple dimensions of collectable but sparse data of user behavior, i.e., making check-ins, posing tips and forming friendships. Based on these sparse data, we build user spatial distribution model, user post interest model and user social preference model, respectively. Here, as the arguments, we validate that there is indeed a complementary effect in multi-dimensional blended behavioral analysis for identity theft detection in MSNs. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Social networking (online)

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AutoDE: Automated vulnerability discovery and exploitation 4

AutoDE: Automated vulnerability discovery and exploitation

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Zhang, Yuntao Liu, Tong Wang, Zhongru Ruan, Qiang Fang, Binxing Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China Zhejiang Lab Hangzhou China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (纸本)9781728145280

Recently, automated testing for software vulnerability finding and exploitation has attracted more and more attention, due to the high scalability and efficiency. However, it is non-trivial and challenging since the existing methods have limited effectiveness to discover vulnerabilities, that are exploitable to hack the software. To address these challenges, we propose an Automated Vulnerability Discovery and Exploitation framework, AutoDE, which aims to improve the effectiveness of the vulnerability discovery and exploitation. In the vulnerability discovery stage, we propose Anti-Driller to alleviate the 'path explosion' problem. It first generates a specific input through the symbolic execution by using the control ?ow graph (CFG), then leverages a mutation-based fuzzer to mutate this case to find vulnerabilities which can avoid unqualified mutation. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits that produce a shell based on the detected vulnerabilities with several attack techniques. We have conducted extensive experiments on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset. The experimental results clearly demonstrate the effectiveness and scalability of the proposed framework. © 2019 IEEE.

关键词： Scalability

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Temperature prediction of generator carbon brush based on LSTM neural network

Temperature prediction of generator carbon brush based on LS...

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Chinese Automation Congress (CAC)

作者： Qing Zhang Tingyang Jiao Yinxing Ma Youmin Zhang Xiaohui Wu Pengyue Wu Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an China The Faculty of Computing Science The University of Alberta Edmonton Alberta Canada Concordia University Montreal Canada Huaneng Weihai Power Generation Co. Ltd Wei’hai China Xi’an Thermal Power Research Institute Co. Ltd Xi’an China

ISBN: (纸本)9781665426480

In order to improve the prediction accuracy of the carbon brush temperature trend of the generator, analyzing the operating state of the generator more accurately, and reduce other accidents like the unplanned outage caused by carbon brush failure of the generator in the power plant. A multi-step temperature prediction model based on the LSTM (Long Short-Term Memory) neural network is proposed. The data comes from real operating carbon brush temperature of Weihai Power Plant in ten days. Then the temperature prediction model is established to achieve an accurate prediction of the carbon brush temperature in the future day. The prediction error is stable within 0.4 °C. By comparing the predicted results of the BP model and Elman model, the error between the predicted results of each model and the actual temperature data is analyzed. By comparing the indicators and analyzing the actual curves, the results show that LSTM neural network has higher accuracy in predicting the temperature of generator carbon brush. This method is of reference significance for the accurate analysis of the operation state of the generator and the load analysis of the excitation system.

关键词： Analytical models Temperature distribution Brushes Neural networks Predictive models Market research Generators

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Model Parameter Analysis of Cracked Photovoltaic Module under Outdoor Conditions

Model Parameter Analysis of Cracked Photovoltaic Module unde...

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2020 47th IEEE Photovoltaic Specialists Conference (PVSC)

作者： Jingwei Zhang Yongjie Liu Kun Ding Li Feng Frank U. Hamelmann Xiang Chen College of Mechanical and Electrical Engineering Hohai University Changzhou China Solar Computing Lab University of Applied Science Bielefeld Minden Germany Changzhou Key Laboratory of Photovoltaic System Integration and Production Equipment Technology Changzhou China

ISBN: (数字)9781728161150

ISBN: (纸本)9781728161167

In the recent decade, the intelligent maintenance of the PV systems is a new trend in this area. In order to facilitate the further fault prognosis of the PV module, in this paper, the model parameters of the single diode model for a glass-cracked PV module with cracked cells, which cannot be visually inspected, are extracted based on the outdoor measured current-voltage (I-V) curves. The cracks of the solar cells are also observed from the electroluminescence images. The comparison results of the I-V curves from a referenced normal PV module and the glass-cracked PV module indicate that the photocurrent and the equivalent shunt resistance of the cracked solar cells in the fault PV module decreases significantly. The extracted parameters can quantitatively describe the failure degree of the cracked solar cells. The extracted parameter can also be used to further predict the degradation trend of the fault PV module and realize fault prognosis of PV modules.

关键词： Photovoltaic cells Resistance Temperature measurement Photovoltaic systems Photoconductivity Analytical models Market 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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The effects of different travel modes and travel destinations on COVID-19 transmission in global cities

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Science Bulletin 2022年第6期67卷 588-592,M0003页

作者： Rui Zhu Luc Anselin Michael Batty Mei-Po Kwan Min Chen Wei Luo Tao Cheng Che Kang Lim Paolo Santi Cheng Cheng Qiushi Gu Man Sing Wong Kai Zhang Guonian Lü Carlo Ratti Department of Land Surveying and Geo-Informatics the Hong Kong Polytechnic UniversityHong KongChina Key Laboratory of Virtual Geographic Environment(Ministry of Education) Nanjing Normal UniversityNanjing 210023China Senseable City Laboratory Future Urban Mobility IRGSingapore-MIT Alliance for Research and TechnologySingapore 138602Singapore Center for Spatial Data Science Division of the Social SciencesUniversity of ChicagoChicago IL 60637USA School of Geography Science Nanjing Normal UniversityNanjing 210023China Centre for Advanced Spatial Analysis Faculty of the Built EnvironmentUniversity College LondonLondon WC1E 6BTUK Department of Geography and Resource Management the Chinese University of Hong KongHong KongChina Institute of Space and Earth Information Science the Chinese University of Hong KongHong KongChina Department of Human Geography and Spatial Planning Utrecht UniversityUtrecht 3584 CSNetherlands State Key Laboratory Cultivation Base of Geographical Environment Evolution Nanjing 210023China Department of Geography National University of SingaporeSingapore 117570Singapore SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College LondonLondon WC1E 6BTUK Department of Clinical Translational Research Singapore General HospitalSingapore 169856Singapore Division of Clinical Immunology and Transfusion Medicine Department of Laboratory MedicineKarolinska InstitutetStockholm 17177Sweden Senseable City Laboratory Department of Urban Studies and PlanningMassachusetts Institute of TechnologyCambridgeMA 02139USA Istituto di Informatica e Telematica del CNR Pisa 56124Italy Department of System Science Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Tourism Studies School of HumanitiesSoutheast UniversityNanjing 210096China

The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their ***,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].

关键词：多变量时间序列疫情传播出行方式谷歌公司交通出行病毒传播出行行为大城市

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Performance Characterization and Receiver Design for Random Temporal Multiple Access in Non-Coordinated Networks

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China Communications 2019年第6期16卷 173-184页

作者： Yin Lu Jun Fang Zhong Guo J.Andrew Zhang Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Wuxi Longi Intelligent Technology Co.Ltd. Wuxi 214400China University of Technology Sydney Ultimo NSW 2007Australia

Random access is a well-known multiple access method for uncoordinated communication *** work mainly focuses on optimizing iterative access protocols,assuming that packets are corrupted once they are collided,or that feedback is available and can be *** practice,a packet may still be able to be recovered successfully even when collided with other *** design and performance analysis under such a situation,particularly when the details of collision are taken into consideration,are less *** this paper,we provide a framework for analytically evaluating the actual detection performance in a random temporal multiple access system where nodes can only *** expressions are provided for collision probability and signal to interference and noise ratio(SINR)when different numbers of packets are *** then discuss and compare two receiver options for the AP,and provide detailed receiver design for the premium *** particular,we propose a synchronization scheme which can largely reduce the preamble *** also demonstrate that system performance could be a convex function of preamble length both analytically and via simulation,as well as the forward error correction(FEC)coding rate.

关键词： random temporal multiple access non-coordination networks packet collision

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A knowledge acquisition automatizing framework from literature exemplified by Na + activation energy prediction of NASICON solid-state electrolyte

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Energy Storage Materials 2025年 80卷

作者： Yue Liu Dahui Liu Zhengwei Yang Xianyuan Ge Wenxuan Yao Jie Wu Maxim Avdeev Siqi Shi State Key Laboratory of Materials for Advanced Nuclear Energy & School of Computer Engineering and Science Shanghai University Shanghai 200444 China Shanghai Engineering Research Center of Intelligent Computing System Shanghai 200444 China Australian Nuclear Science and Technology Organisation Sydney 2232 Australia School of Chemistry The University of Sydney Sydney 2006 Australia State Key Laboratory of Materials for Advanced Nuclear Energy & School of Materials Science and Engineering Shanghai University Shanghai 200444 China Materials Genome Institute Shanghai University Shanghai 200444 China

Materials science literature contains vast amount of structure-activity relationship knowledge crucial for materials discovery and design. However, automatic extraction of domain knowledge from literature remains challenging due to its unstructured and heterogeneous format. Herein, we propose a framework for automating knowledge acquisition, which involves a materials entity-aware relational extraction model (MatRE) to mine triples, an approach to construct a knowledge graph (KG) for the detection of associations among triples, as well as inference and representation of structure-activity relationships in a machine learning (ML)-compatible format. We demonstrate its application in predicting sodium ion activation energy for the NASICON solid-state electrolyte (SSE) system. MatRE trained on a NASICON SSE dataset, achieves an F1-score of 0.80, and is used to extract 260,475 entity–relation triples from 1,808 scientific publications. Furthermore, embedding 24 knowledge bullets from the KG into data pre-processing and feature engineering stages improves the performance and interpretability of six common ML models by up to 25.7%. This work offers key insights into automatic knowledge acquisition from literature and heralds a new paradigm for AI-assisted materials genome engineering driven by both data and knowledge.

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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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HDTCNet: A hybrid-dimensional convolutional network for multivariate time series classification

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Pattern Recognition 2025年 168卷

作者： Yongli Gu Xiang Yan Hanlin Qin Naveed Akhtar Shuai Yuan Honghao Fu Shuowen Yang Ajmal Mian School of Optoelectronic Engineering Xidian University Xi’an 710071 China Xi’an Key Laboratory of Infrared Technology and System Xi’an 710071 China School of Computing and Information Systems The University of Melbourne Parkville VIC 3052 Australia School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore School of Computer Science and Software Engineering The University of Western Australia Crawley WA 6009 Australia

Currently, transformer-based models dominate time series classification due to their ability to capture global data dependencies. However, their large memory footprint and high computational demands make them less desirable for long multivariate time series modeling. At the same time, convolution-based models promise a desirable performance versus efficiency trade-off for the task. Nevertheless, the popularity of transformer models has impeded exploration of sophisticated convolution-based modeling for time series classification. In this work, we introduce a novel convolution-based model, called HDTCNet, that alternates between 1D and 2D convolutional modes to capture temporal pattern correlations between intra- and inter-sensor variations. We develop a Wavelet Time Convolution block to expand the effective receptive field of the underlying neural model with a small convolutional kernel that embeds a set of 1D convolution operators between a cascade of wavelet decomposition and reconstruction of the input time series. To efficiently represent the local features of multivariate time series, we devise a hierarchical difference convolution network to capture intra- and inter-sensor gradient variations. To refine the feature representation of sensor-level time series and achieve multi-view self-enhancement, we design a self-enhancing module. These innovations enable HDTCNet to achieve superior performance while maintaining efficiency as a purely convolutional model. Experimental results demonstrate that HDTCNet achieves 2.1% accuracy and 1% F1-score performance improvements over state-of-the-art methods on 25 UEA classification datasets and 3 anomaly detection datasets, respectively, while reducing parameter counts by two orders of magnitude. Our code will be available at https://***/Guuuli/HDTCNet .

关键词： Classification Convolutional network Difference calculation Multivariate time series Wavelet transform

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