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Infrastructure-Free Floor Localization Through Crowdsourcing

Journal of computer science & technology 2015年第6期30卷 1249-1273页

作者：叶海波顾涛陶先平吕建 State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China Department of Computer Science Nanjing University Nanjing 210023 China School of Computer Science and Information Technology RMIT University Melbourne Victoria 3001 Australia

Mobile phone localization plays a key role in the fast-growing location-based applications domain. Most of the existing localization schemes rely on infrastructure support such as GSM, Wi-Fi or GPS. In this paper, we present FTrack, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. FTrack uses the mobile phone＇s sensors only without any infrastructure support. It does not require any prior knowledge of the building such as floor height or floor levels. Through crowdsourcing, FTrack builds a mapping table which contains the magnetic field signature of users taking the elevator/escalator or walking on the stairs between any two floors. The table can then be used for mobile users to pinpoint their current floor levels. We conduct both simulation and field studies to demonstrate the eiTiciency, scalability and robustness of FTrack. Our field trial shows that FTrack achieves an accuracy of over 96% in three different buildings.

关键词： mobile phone localization floor localization crowdsourcing mobile phone sensing

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Automatic Detection and Repair Recommendation for Missing Checks

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Journal of computer science & technology 2019年第5期34卷 972-992页

作者： Ling-Yun Situ Lin-Zhang Wang Yang Liu Bing Mao Xuan-Dong Li State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China School of Computer Science and Engineering Nanyang Technological UniversitySingapore 639798Singapore

Missing checks for untrusted inputs used in security-sensitive operations is one of the major causes of various vulnerabilities. Efficiently detecting and repairing missing checks are essential for prognosticating potential vulnerabilities and improving code reliability. We propose a systematic static analysis approach to detect missing checks for manipulable data used in security-sensitive operations of C/C++ programs and recommend repair references. First, customized securitysensitive operations are located by lightweight static analysis. Then, the assailability of sensitive data used in securitysensitive operations is determined via taint analysis. And, the existence and the risk degree of missing checks are assessed. Finally, the repair references for high-risk missing checks are recommended. We implemented the approach into an automated and cross-platform tool named Vanguard based on Clang/LLVM 3.6.0. Large-scale experimental evaluation on open-source projects has shown its effectiveness and efficiency. Furthermore, Vanguard has helped us uncover five known vulnerabilities and 12 new bugs.

关键词： static analysis missing check vulnerability detection repair recommendation

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Automatic Buffer Overflow Warning Validation

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Journal of computer science & technology 2020年第6期35卷 1406-1427页

作者： Feng-Juan Gao Yu Wang Lin-Zhang Wang Zijiang Yang Xuan-Dong Li State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science and Technology Nanjing UniversityNanjing 210023China Department of Computer Science Western Michigan UniversityKalamazoo 49008-5466U.S.A.

Static buffer overflow detection techniques tend to report too many false positives fundamentally due to the lack of software execution information. It is very time consuming to manually inspect all the static warnings. In this paper, we propose BovInspector, a framework for automatically validating static buffer overflow warnings and providing suggestions for automatic repair of true buffer overflow warnings for C programs. Given the program source code and the static buffer overflow warnings, BovInspector first performs warning reachability analysis. Then, BovInspector executes the source code symbolically under the guidance of reachable warnings. Each reachable warning is validated and classified by checking whether all the path conditions and the buffer overflow constraints can be satisfied simultaneously. For each validated true warning, BovInspector provides suggestions to automatically repair it with 11 repair strategies. BovInspector is complementary to prior static buffer overflow discovery schemes. Experimental results on real open source programs show that BovInspector can automatically validate on average 60% of total warnings reported by static tools.

关键词： buffer overflow static analysis warning symbolic execution automatic repair

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Model Checking over Paraconsistent Temporal Logic

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Journal of Donghua University(English Edition) 2008年第5期25卷 571-580页

作者：陈冬火王林章崔家林 State Key Laboratory of Novel Software Technology Nanjing University School of Computer Science and Technology Soochow University Department of Information Science and Engineering Ningbo Institute of TechnologyZhejiang University

Classical logic cannot be used to effectively reason about concurrent systems with inconsistencies (inconsistencies often occur, especially in the early stage of the development, when large and complex concurrent systems are developed). In this paper, we propose the use of a guasi-classical temporal logic (QCTL) for supporting the verification of temporal properties of such systems even where the consistent model is not available. Our models are paraKripke structures (extended standard Kripke structures), in which both a formula and its negation are satisfied in a same state, and properties to be verified are expressed by QCTL with paraKripke structures semantics. We introduce a novel notion of paraKripke models, which grasps the paraconsistent character of the entailment relation of QCTL. Furthermore, we explore the methodology of model checking over QCTL, and describe the detailed algorithm of implementing QCTL model checker. In the sequel, a simple example is presented, showing how to exploit the proposed model checking technique to verify the temporal properties of inconsistent concurrent systems.

关键词： inconsistency concurrent systems paraconsistent temporal logic model checking

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Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks

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Journal of computer science & technology 2011年第2期26卷 283-291页

作者：王珺曹涌涛谢俊元陈世福 State Key Lab for Novel Software Technology at Nanjing University Department of Computer Science and Technology Nanjing University Institute of Communication and Information Engineering Nanjing University of Posts and Telecommunications China Development Center Trend Micro Corp.

Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the multi-hop technique with a backoff-based clustering algorithm to organize sensors. By using an adaptive backoff strategy, the algorithm not only realizes load balance among sensor node, but also achieves fairly uniform cluster head distribution across the network. Simulation results also demonstrate our algorithm is more energy-efficient than classical ones. Our algorithm is also easily extended to generate a hierarchy of cluster heads to obtain better network management and energy-efficiency.

关键词： multi-hop wireless sensor network clustering algorithm backoff strategy

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Grey-box Adversarial Attack on Communication in Multi-agent Reinforcement Learning 22

Grey-box Adversarial Attack on Communication in Multi-agent ...

引用

22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Ma, Xiao Li, Wu-Jun National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

Although research on communication in multi-agent reinforcement learning (MARL) has achieved some progress, the vulnerability of the communication mechanism in MARL caused by adversarial communication messages generated by malicious agents has not been well investigated. Existing works about adversarial communication messages in MARL focus on the black-box scenario where the attacker cannot access any model information about the multi-agent system (MAS). But a more practical setting is the grey-box scenario where the attacker can access the model information about its controlled agent. To the best of our knowledge, there has not been any work investigating grey-box attacks on communication in MARL. In this paper, we propose the first grey-box attack method on communication in MARL, which is called victim-simulation based adversarial attack (VSA). At each timestep, the attacker simulates a victim attacked by other regular agents' communication messages and generates adversarial perturbations on its received communication messages. The aggregation of these perturbations is sent by the attacker to the regular agents through communication messages, which will induce non-optimal actions of the regular agents. Experimental results show that VSA can effectively degrade the performance of the MAS on Predator-Prey. The findings in this paper will make researchers aware of the grey-box attack in MARL. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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Multi-view Multi-label Learning with Dual-Attention Networks for Stroke Screen

Multi-view Multi-label Learning with Dual-Attention Networks...

引用

2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020

作者： Shen, Jundong Zhang, Yi Yu, Cheng Wang, Chongjun National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing China

ISBN: (纸本)9781728162157

With the acceleration of urbanization, there is an explosive growth trend of the stroke burden in China. Consequently, it is necessary to adopt a regular screen to reduce brain damage and other complications. For one thing, the risk factors screened can be divided into 6 subsets: medical history, family history, fasting blood, lifestyle, physical characteristics, and demographic information. For another, the screen results can be categorized into 5 groups: normal carotid plaque, thickening, unstable plaque, and arterial stenosis. To address the above problems, Multi-View Multi-label (MVML) learning provides a fundamental framework for handling such tasks, which treats the risk factors as multi-view data and the screen results as multi-label data. Besides, it is a challenging issue of how to effectively utilize view correlations and label correlations in MVML learning. In this paper, we propose a novel Multi-view Multi-label Learning with Dual-Attention Networks (MML-DAN) for stroke screen, which can strike a balance between label-specific views and label correlations. Meanwhile, the attention mechanism aims to focus on the most pertinent information of the corresponding labels rather than using all available risk factors equally. Experiments on one real-world dataset of stroke screen demonstrate the superiority of the proposed MML-DAN for stroke screen prediction. To further validate the effectiveness and generalization capability of MML-DAN, extensive experiments on 5 benchmark MVML datasets show that MML-DAN significantly outperforms other state-of-the-art MVML algorithms. © 2020 IEEE.

关键词： computer vision

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Research on complex structure-oriented accurate web information extraction rules

Research on complex structure-oriented accurate web informat...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Xie, Tao Shi, Shengsheng Quan, Fuliang Yuan, Chunfeng Huang, Yihua Department of Computer Science and Technology National Key Laboratory of Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781424467860

With the rapid growth of web information, there is an increasing need to easily and efficiently acquire accurate information from the massive and heterogamous web. Web information extraction is such a research area to meet these needs. In this paper, we analyze the shortcomings of related researches and systems and find that when extracting accurate web information with complex structures, few systems can do so without being too much of a burden to users. Aiming at overcoming this type of pitfalls, this paper will study and propose a comprehensive model and framework that can combine the automatic web data analysis and extraction with the user interaction-based semi-supervised web data extraction. The new model and framework has a good trade-off between the automatic generation of extraction rules and their expression capability towards the accurate information extraction. Based on this, we further present a multi-functional data extraction rule system that will use a variety of structural and textual extraction rules of different functions to achieve powerful expression capability. Furthermore, to offer powerful expression mechanism for data extraction, this paper will describe a well-designed, XML-based data extraction language which works well for rule generation based on both automatic web structure analysis and user interaction. ©2010 IEEE.

关键词： Economic and social effects

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Link communities detection via local approach

Link communities detection via local approach

引用

7th International Conference on Rough Sets and Knowledge technology, RSKT 2012

作者： Pan, Lei Wang, Chongjun Xie, Junyuan National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783642318993

The traditional community detection algorithms were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. Here, we proposed a novel algorithm LBLC (local based link community) to detect link communities in networks based on some local information. A local link community can be detected by maximizing a local link fitness function from a seed link, which was ranked by another algorithm previously. The proposed LBLC algorithm has been tested on both synthetic and real world networks. The experimental results showed LBLC achieves meaningful link community structure. © 2012 Springer-Verlag.

关键词： Population dynamics

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

引用

arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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