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Articles 4831 - 4860 of 9025

Full-Text Articles in Computer Sciences

Law Enforcement Resource Optimization With Response Time Guarantees, Jonathan Chase, Jiali Du, Na Fu, Truc Viet Le, Hoong Chuin Lau Dec 2017

Law Enforcement Resource Optimization With Response Time Guarantees, Jonathan Chase, Jiali Du, Na Fu, Truc Viet Le, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In a security-conscious world, and with the rapid increase in the global urbanized population, there is a growing challenge for law enforcement agencies to efficiently respond to emergency calls. We consider the problem of spatially and temporally optimizing the allocation of law enforcement resources such that the quality of service (QoS) in terms of emergency response time can be guaranteed. To solve this problem, we provide a spatio-temporal MILP optimization model, which we learn from a real-world dataset of incidents and dispatching records, and solve by existing solvers. One key feature of our proposed model is the introduction of risk …

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Compact Hierarchical Ibe From Lattices In The Standard Model, Daode Zhang, Fuyang Fang, Bao Li, Haiyang Xue, Bei Liang Dec 2017

Compact Hierarchical Ibe From Lattices In The Standard Model, Daode Zhang, Fuyang Fang, Bao Li, Haiyang Xue, Bei Liang

Research Collection School Of Computing and Information Systems

At Crypto’10, Agrawal et al. proposed a lattice-based selectively secure Hierarchical Identity-based Encryption (HIBE) scheme (ABB10b) with small ciphertext on the condition that (the length of identity at each level) is small in the standard model. In this paper, we present another lattice-based selectively secure HIBE scheme with depth d, using a gadget matrix with enough large to replace the matrix in the HIBE scheme proposed by Agrawal et al. at Eurocrypt’10. In our HIBE scheme, not only the size of ciphertext at level is larger than the size in ABB10b and at least smaller than the sizes in the …

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Towards Tightly Secure Deterministic Public Key Encryption, Daode Zhang, Bao Li, Yamin Liu, Haiyang Xue, Xianhui Lu, Dingding Jia Dec 2017

Towards Tightly Secure Deterministic Public Key Encryption, Daode Zhang, Bao Li, Yamin Liu, Haiyang Xue, Xianhui Lu, Dingding Jia

Research Collection School Of Computing and Information Systems

In this paper, we formally consider the construction of tightly secure deterministic public key encryption (D-PKE). Initially, we compare the security loss amongst the D-PKE schemes under the concrete assumptions and also analyze the tightness of generic D-PKE constructions. Furthermore, we prove that the CPA secure D-PKE scheme of Boldyreva et al. (Crypto’08) is tightly PRIV-IND-CPA secure for block-sources. Our security reduction improves the security loss of their scheme from O(nc∗) to O(1). Additionally, by upgrading the all-but-one trapdoor function (TDF) in the construction of Boldyreva et al. to all-but-n TDF defined by Hemenway et al. (Asiacrypt’11), we give general …

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Enterprise Social Media Use And Impact On Performance: The Role Of Workplace Integration And Positive Emotions, Murad Moqbel, Fiona Fui-Hoon Nah Dec 2017

Enterprise Social Media Use And Impact On Performance: The Role Of Workplace Integration And Positive Emotions, Murad Moqbel, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Organizations struggle to find ways to improve employees’ performance. To date, little research has empirically examined the relationship between enterprise social media use and knowledge workers’ performance. Using social capital theory and the broaden-and-build theory of positive emotions as our theoretical framework, we investigate the relationship between enterprise social media use and knowledge workers’ performance. We tested our research model by collecting data from employees working for a large information technology firm in the Midwestern United States and analyzing the data using a structural equation modeling approach. The results suggest that enterprise social media use can increase workplace integration, which …

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Who Are Your Users? Comparing Media Professionals' Preconception Of Users To Data-Driven Personas, Lene Nielsen, Soon-Gyu Jung, Jisun An, Joni Salminen, Haewoon Kwak, Bernard J. Jansen Dec 2017

Who Are Your Users? Comparing Media Professionals' Preconception Of Users To Data-Driven Personas, Lene Nielsen, Soon-Gyu Jung, Jisun An, Joni Salminen, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

One of the reasons for using personas is to align user understandings across project teams and sites. As part of a larger persona study, at Al Jazeera English (AJE), we conducted 16 qualitative interviews with media producers, the end users of persona descriptions. We asked the participants about their understanding of a typical AJE media consumer, and the variety of answers shows that the understandings are not aligned and are built on a mix of own experiences, own self, assumptions, and data given by the company. The answers are sometimes aligned with the data-driven personas and sometimes not. The end …

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Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen Dec 2017

Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ …

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Bikemate: Bike Riding Behavior Monitoring With Smartphones, Weixi Gu, Zimu Zhou, Yuxun Zhou, Han Zou, Yunxin Liu, Costas J. Spanos, Lin Zhang Nov 2017

Bikemate: Bike Riding Behavior Monitoring With Smartphones, Weixi Gu, Zimu Zhou, Yuxun Zhou, Han Zou, Yunxin Liu, Costas J. Spanos, Lin Zhang

Research Collection School Of Computing and Information Systems

Detecting dangerous riding behaviors is of great importance to improve bicycling safety. Existing bike safety precautionary measures rely on dedicated infrastructures that incur high installation costs. In this work, we propose BikeMate, a ubiquitous bicycling behavior monitoring system with smartphones. BikeMate invokes smartphone sensors to infer dangerous riding behaviors including lane weaving, standing pedalling and wrong-way riding. For easy adoption, BikeMate leverages transfer learning to reduce the overhead of training models for different users, and applies crowdsourcing to infer legal riding directions without prior knowledge. Experiments with 12 participants show that BikeMate achieves an overall accuracy of 86.8% for lane …

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Learning Likely Invariants To Explain Why A Program Fails, Long H. Pham, Jun Sun, Lyly Tran Thi, Jingyi Wang, Xin Peng Nov 2017

Learning Likely Invariants To Explain Why A Program Fails, Long H. Pham, Jun Sun, Lyly Tran Thi, Jingyi Wang, Xin Peng

Research Collection School Of Computing and Information Systems

Debugging is difficult. Recent studies show that automatic bug localization techniques have limited usefulness. One of the reasons is that programmers typically have to understand why the program fails before fixing it. In this work, we aim to help programmers understand a bug by automatically generating likely invariants which are violated in the failed tests. Given a program with an initial assertion and at least one test case failing the assertion, we first generate random test cases, identify potential bug locations through bug localization, and then generate program state mutation based on active learning techniques to identify a predicate 'explaining' …

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Mining Implicit Design Templates For Actionable Code Reuse, Yun Lin, Guozhu Meng, Yinxing Yue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, Jin Song Dong Nov 2017

Mining Implicit Design Templates For Actionable Code Reuse, Yun Lin, Guozhu Meng, Yinxing Yue, Zhenchang Xing, Jun Sun, Xin Peng, Yang Liu, Wenyun Zhao, Jin Song Dong

Research Collection School Of Computing and Information Systems

In this paper, we propose an approach to detecting project-specific recurring designs in code base and abstracting them into design templates as reuse opportunities. The mined templates allow programmers to make further customization for generating new code. The generated code involves the code skeleton of recurring design as well as the semi-implemented code bodies annotated with comments to remind programmers of necessary modification. We implemented our approach as an Eclipse plugin called MICoDe. We evaluated our approach with a reuse simulation experiment and a user study involving 16 participants. The results of our simulation experiment on 10 open source Java …

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Automatic Loop-Invariant Generation And Refinement Through Selective Sampling, Jiaying Li, Jun Sun, Li Li, Quang Loc Le, Shang-Wei Lin Nov 2017

Automatic Loop-Invariant Generation And Refinement Through Selective Sampling, Jiaying Li, Jun Sun, Li Li, Quang Loc Le, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Automatic loop-invariant generation is important in program analysis and verification. In this paper, we propose to generate loop-invariants automatically through learning and verification. Given a Hoare triple of a program containing a loop, we start with randomly testing the program, collect program states at run-time and categorize them based on whether they satisfy the invariant to be discovered. Next, classification techniques are employed to generate a candidate loop-invariant automatically. Afterwards, we refine the candidate through selective sampling so as to overcome the lack of sufficient test cases. Only after a candidate invariant cannot be improved further through selective sampling, we …

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Fib: Squeezing Loop Invariants By Interpolation Between Forward/Backward Predicate Transformers, Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sana, Henri Hansen Nov 2017

Fib: Squeezing Loop Invariants By Interpolation Between Forward/Backward Predicate Transformers, Shang-Wei Lin, Jun Sun, Hao Xiao, Yang Liu, David Sana, Henri Hansen

Research Collection School Of Computing and Information Systems

Loop invariant generation is a fundamental problem in program analysis and verification. In this work, we propose a new approach to automatically constructing inductive loop invariants. The key idea is to aggressively squeeze an inductive invariant based on Craig interpolants between forward and backward reachability analysis. We have evaluated our approach by a set of loop benchmarks, and experimental results show that our approach is promising.

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Uncovering User-Triggered Privacy Leaks In Mobile Applications And Their Utility In Privacy Protection, Joo Keng Joseph Chan Nov 2017

Uncovering User-Triggered Privacy Leaks In Mobile Applications And Their Utility In Privacy Protection, Joo Keng Joseph Chan

Dissertations and Theses Collection

Mobile applications are increasingly popular, and help mobile users in many aspects of their lifestyle. Applications have access to a wealth of information about the user through powerful developer APIs. It is known that most applications, even popular and highly regarded ones, utilize and leak privacy data to the network. It is also common for applications to over-access privacy data that does not fit the functionality profile of the application. Although there are available privacy detection tools, they might not provide sufficient context to help users better understand the privacy behaviours of their applications. In this dissertation, I present the …

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An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le Nov 2017

An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le

Dissertations and Theses Collection (Open Access)

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …

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Scalable Online Kernel Learning, Jing Lu Nov 2017

Scalable Online Kernel Learning, Jing Lu

Dissertations and Theses Collection (Open Access)

One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …

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Eeg-Based Emotion Recognition Via Fast And Robust Feature Smoothing, Cheng Tang, Di Wang, Ah-Hwee Tan, Chunyan Miao Nov 2017

Eeg-Based Emotion Recognition Via Fast And Robust Feature Smoothing, Cheng Tang, Di Wang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Electroencephalograph (EEG) signals reveal much of our brain states and have been widely used in emotion recognition. However, the recognition accuracy is hardly ideal mainly due to the following reasons: (i) the features extracted from EEG signals may not solely reflect one’s emotional patterns and their quality is easily affected by noise; and (ii) increasing feature dimension may enhance the recognition accuracy, but it often requires extra computation time. In this paper, we propose a feature smoothing method to alleviate the aforementioned problems. Specifically, we extract six statistical features from raw EEG signals and apply a simple yet cost-effective feature …

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Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo Nov 2017

Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In this paper, we describe the systems developed for Video-to-Text (VTT), Ad-hoc Video Search (AVS) and Video Hyper-linking (LNK) tasks at TRECVID 2017 [1] and the achieved results.

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Leveraging Social Analytics Data For Identifying Customer Segments For Online News Media, Jansen, Bernard J, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Haewoon Kwak Nov 2017

Leveraging Social Analytics Data For Identifying Customer Segments For Online News Media, Jansen, Bernard J, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Haewoon Kwak

Research Collection School Of Computing and Information Systems

In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, presenting a holistic picture of the customer base of an organization. We validate our methodology via the implementation of a working system that rapidly and in near real-time processes tens of millions of online customer interactions with content posted on …

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Color-Sketch Simulator: A Guide For Color-Based Visual Known-Item Search, Jakub Lokoč, Anh Nguyen Phuong, Marta Vomlelová, Chong-Wah Ngo Nov 2017

Color-Sketch Simulator: A Guide For Color-Based Visual Known-Item Search, Jakub Lokoč, Anh Nguyen Phuong, Marta Vomlelová, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In order to evaluate the effectiveness of a color-sketch retrieval system for a given multimedia database, tedious evaluations involving real users are required as users are in the center of query sketch formulation. However, without any prior knowledge about the bottlenecks of the underlying sketch-based retrieval model, the evaluations may focus on wrong settings and thus miss the desired effect. Furthermore, users have usually no clues or recommendations to draw color-sketches effectively. In this paper, we aim at a preliminary analysis to identify potential bottlenecks of a flexible color-sketch retrieval model. We present a formal framework based on position-color feature …

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Detecting Semantic Uncertainty By Learning Hedge Cues In Sentences Using An Hmm, Xiujun Li, Wei Gao, Jude Shavlik Nov 2017

Detecting Semantic Uncertainty By Learning Hedge Cues In Sentences Using An Hmm, Xiujun Li, Wei Gao, Jude Shavlik

Research Collection School Of Computing and Information Systems

Detecting speculative assertions is essential to distinguish semantically uncertain information from the factual ones in text. This is critical to the trustworthiness of many intelligent systems that are based on information retrieval and natural language processing techniques, such as question answering or information extraction. We empirically explore three fundamental issues of uncertainty detection: (1) the predictive ability of different learning methods on this task; (2) whether using unlabeled data can lead to a more accurate model; and (3) whether closed-domain training or crossdomain training is better. For these purposes, we adopt two statistical learning approaches to this problem: the commonly …

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Language Inclusion Checking Of Timed Automata With Non-Zenoness, Xinyu Wang, Jun Sun, Ting Wang, Shengchao Qin Nov 2017

Language Inclusion Checking Of Timed Automata With Non-Zenoness, Xinyu Wang, Jun Sun, Ting Wang, Shengchao Qin

Research Collection School Of Computing and Information Systems

Given a timed automaton P modeling an implementation and a timed automaton S as a specification, the problem of language inclusion checking is to decide whether the language of P is a subset of that of S. It is known to be undecidable. The problem gets more complicated if non-Zenoness is taken into consideration. A run is Zeno if it permits infinitely many actions within finite time. Otherwise it is non-Zeno. Zeno runs might present in both P and S. It is necessary to check whether a run is Zeno or not so as to avoid presenting Zeno runs as …

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Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun Nov 2017

Anomaly Detection For A Water Treatment System Using Unsupervised Machine Learning, Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun

Research Collection School Of Computing and Information Systems

In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS). We compare two methods: Deep Neural Networks (DNN) adapted to time series data generated by a CPS, and one-class Support Vector Machines (SVM). These methods are evaluated against data from the Secure Water Treatment (SWaT) testbed, a scaled-down but fully operational raw water purification plant. For both methods, we first train detectors using a log generated by SWaT operating under normal conditions. Then, we evaluate the performance of both methods using a log generated by SWaT operating under 36 …

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Classification-Based Parameter Synthesis For Parametric Timed Automata, Jiaying Li, Jun Sun, Bo Gao, Étienne Andre Nov 2017

Classification-Based Parameter Synthesis For Parametric Timed Automata, Jiaying Li, Jun Sun, Bo Gao, Étienne Andre

Research Collection School Of Computing and Information Systems

Parametric timed automata are designed to model timed systems with unknown parameters, often representing design uncertainties of external environments. In order to design a robust system, it is crucial to synthesize constraints on the parameters, which guarantee the system behaves according to certain properties. Existing approaches suffer from scalability issues. In this work, we propose to enhance existing approaches through classification-based learning. We sample multiple concrete values for parameters and model check the corresponding non-parametric models. Based on the checking results, we form conjectures on the constraint through classification techniques, which can be subsequently confirmed by existing model checkers for …

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Improving Probability Estimation Through Active Probabilistic Model Learning, Jingyi Wang, Xiaohong Chen, Jun Sun, Shengchao Qin Nov 2017

Improving Probability Estimation Through Active Probabilistic Model Learning, Jingyi Wang, Xiaohong Chen, Jun Sun, Shengchao Qin

Research Collection School Of Computing and Information Systems

It is often necessary to estimate the probability of certain events occurring in a system. For instance, knowing the probability of events triggering a shutdown sequence allows us to estimate the availability of the system. One approach is to run the system multiple times and then construct a probabilistic model to estimate the probability. When the probability of the event to be estimated is low, many system runs are necessary in order to generate an accurate estimation. For complex cyber-physical systems, each system run is costly and time-consuming, and thus it is important to reduce the number of system runs …

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A Verification Framework For Stateful Security Protocols, Li Li, Naipeng Dong, Jun Pang, Jun Sun, Guangdong Bai, Yang Liu, Jin Song Dong Nov 2017

A Verification Framework For Stateful Security Protocols, Li Li, Naipeng Dong, Jun Pang, Jun Sun, Guangdong Bai, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

A long-standing research problem is how to efficiently verify security protocols with tamper-resistant global states, especially when the global states evolve unboundedly. We propose a protocol specification framework, which facilitates explicit modeling of states and state transformations. On the basis of that, we develop an algorithm for verifying security properties of protocols with unbounded state-evolving, by tracking state transformation and checking the validity of the state-evolving traces. We prove the correctness of the verification algorithm, implement both of the specification framework and the algorithm, and evaluate our implementation using a number of stateful security protocols. The experimental results show that …

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A Semantics Comparison Workbench For A Concurrent, Asynchronous, Distributed Programming Language, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt Nov 2017

A Semantics Comparison Workbench For A Concurrent, Asynchronous, Distributed Programming Language, Claudio Corrodi, Alexander Heußner, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

A number of high-level languages and libraries have been proposed that offer novel and simple to use abstractions for concurrent, asynchronous, and distributed programming. The execution models that realise them, however, often change over time---whether to improve performance, or to extend them to new language features---potentially affecting behavioural and safety properties of existing programs. This is exemplified by SCOOP, a message-passing approach to concurrent object-oriented programming that has seen multiple changes proposed and implemented, with demonstrable consequences for an idiomatic usage of its core abstraction. We propose a semantics comparison workbench for SCOOP with fully and semi-automatic tools for analysing …

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Enabling Phased Array Signal Processing For Mobile Wifi Devices, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu Nov 2017

Enabling Phased Array Signal Processing For Mobile Wifi Devices, Kun Qian, Chenshu Wu, Zheng Yang, Zimu Zhou, Xu Wang, Yunhao Liu

Research Collection School Of Computing and Information Systems

Modern mobile devices are equipped with multiple antennas, which brings various wireless sensing applications such as accurate localization, contactless human detection, and wireless human-device interaction. A key enabler for these applications is phased array signal processing, especially Angle of Arrival (AoA) estimation. However, accurate AoA estimation on commodity devices is non-trivial due to limited number of antennas and uncertain phase offsets. Previous works either rely on elaborate calibration or involve contrived human interactions. In this paper, we aim to enable practical AoA measurements on commodity off-the-shelf (COTS) mobile devices. The key insight is to involve users’ natural rotation to formulate …

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Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng Nov 2017

Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng

Research Collection School Of Computing and Information Systems

Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by …

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Understanding Inactive Yet Available Assignees In Github, Jing Jiang, David Lo, Xinyu Ma, Fuli Feng, Li Zhang Nov 2017

Understanding Inactive Yet Available Assignees In Github, Jing Jiang, David Lo, Xinyu Ma, Fuli Feng, Li Zhang

Research Collection School Of Computing and Information Systems

Context In GitHub, an issue or a pull request can be assigned to a specific assignee who is responsible for working on this issue or pull request. Due to the principle of voluntary participation, available assignees may remain inactive in projects. If assignees ever participate in projects, they are active assignees; otherwise, they are inactive yet available assignees (inactive assignees for short). Objective Our objective in this paper is to provide a comprehensive analysis of inactive yet available assignees in GitHub. Method We collect 2,374,474 records of activities in 37 popular projects, and 797,756 records of activities in 687 projects …

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Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu Nov 2017

Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu

Research Collection School Of Computing and Information Systems

Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these graphs and …

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A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng Nov 2017

A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific …

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