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Articles 5761 - 5790 of 9024

Full-Text Articles in Computer Sciences

Lesinn: Detecting Anomalies By Identifying Least Similar Nearest Neighbours, Guansong Pang, Kai Ming Ting, David Albrecht Nov 2015

Lesinn: Detecting Anomalies By Identifying Least Similar Nearest Neighbours, Guansong Pang, Kai Ming Ting, David Albrecht

Research Collection School Of Computing and Information Systems

We introduce the concept of Least Similar Nearest Neighbours (LeSiNN) and use LeSiNN to detect anomalies directly. Although there is an existing method which is a special case of LeSiNN, this paper is the first to clearly articulate the underlying concept, as far as we know. LeSiNN is the first ensemble method which works well with models trained using samples of one instance. LeSiNN has linear time complexity with respect to data size and the number of dimensions, and it is one of the few anomaly detectors which can apply directly to both numeric and categorical data sets. Our extensive …

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Deep Multimodal Learning For Affective Analysis And Retrieval, Lei Pang, Shiai Zhu, Chong-Wah Ngo Nov 2015

Deep Multimodal Learning For Affective Analysis And Retrieval, Lei Pang, Shiai Zhu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Social media has been a convenient platform for voicing opinions through posting messages, ranging from tweeting a short text to uploading a media file, or any combination of messages. Understanding the perceived emotions inherently underlying these user-generated contents (UGC) could bring light to emerging applications such as advertising and media analytics. Existing research efforts on affective computation are mostly dedicated to single media, either text captions or visual content. Few attempts for combined analysis of multiple media are made, despite that emotion can be viewed as an expression of multimodal experience. In this paper, we explore the learning of highly …

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Human Action Recognition In Unconstrained Videos By Explicit Motion Modeling, Yu-Gang Jiang, Qi Dai, Wei Liu, Xiangyang Xue, Chong-Wah Ngo Nov 2015

Human Action Recognition In Unconstrained Videos By Explicit Motion Modeling, Yu-Gang Jiang, Qi Dai, Wei Liu, Xiangyang Xue, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Human action recognition in unconstrained videos is a challenging problem with many applications. Most state-of-the-art approaches adopted the well-known bag-of-features representations, generated based on isolated local patches or patch trajectories, where motion patterns, such as object-object and object-background relationships are mostly discarded. In this paper, we propose a simple representation aiming at modeling these motion relationships. We adopt global and local reference points to explicitly characterize motion information, so that the final representation is more robust to camera movements, which widely exist in unconstrained videos. Our approach operates on the top of visual codewords generated on dense local patch trajectories, …

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Automating The Performance Deviation Analysis For Multiple System Releases: An Evolutionary Study, Felipe Pinto, Uirá Kulesza, Christoph Treude Nov 2015

Automating The Performance Deviation Analysis For Multiple System Releases: An Evolutionary Study, Felipe Pinto, Uirá Kulesza, Christoph Treude

Research Collection School Of Computing and Information Systems

This paper presents a scenario-based approach for the evaluation of the quality attribute of performance, measured in terms of execution time (response time). The approach is implemented by a framework that uses dynamic analysis and repository mining techniques to provide an automated way for revealing potential sources of performance degradation of scenarios between releases of a software system. The approach defines four phases: (i) preparation – choosing the scenarios and preparing the target releases; (ii) dynamic analysis – determining the performance of scenarios and methods by calculating their execution time; (iii) degradation analysis – processing and comparing the results of …

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Challenges In Analyzing Software Documentation In Portuguese, Christoph Treude, Carlos A. Prolo, Fernando Figueira Filho Nov 2015

Challenges In Analyzing Software Documentation In Portuguese, Christoph Treude, Carlos A. Prolo, Fernando Figueira Filho

Research Collection School Of Computing and Information Systems

Many tools that automatically analyze, summarize, or transform software artifacts rely on natural language processing tooling for the interpretation of natural language text produced by software developers, such as documentation, code comments, commit messages, or bug reports. Processing natural language text produced by software developers is challenging because of unique characteristics not found in other texts, such as the presence of code terms and the systematic use of incomplete sentences. In addition, texts produced by Portuguese-speaking developers mix languages since many keywords and programming concepts are referred to by their English name. In this paper, we provide empirical insights into …

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Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao Nov 2015

Codehow: Effective Code Search Based On Api Understanding And Extended Boolean Model (E), Fei Lv, Jian-Guang Lou, Shaowei Wang, Dongmei Zhang, Jainjun Zhao

Research Collection School Of Computing and Information Systems

Over the years of software development, a vast amount of source code has been accumulated. Many code search tools were proposed to help programmers reuse previously-written code by performing free-text queries over a large-scale codebase. Our experience shows that the accuracy of these code search tools are often unsatisfactory. One major reason is that existing tools lack of query understanding ability. In this paper, we propose CodeHow, a code search technique that can recognize potential APIs a user query refers to. Having understood the potentially relevant APIs, CodeHow expands the query with the APIs and performs code retrieval by applying …

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Interpolation Guided Compositional Verification, Shang-Wei Lin, Jun Sun, Truong Khanh Nguyen, Yang Liu, Jin Song Dong Nov 2015

Interpolation Guided Compositional Verification, Shang-Wei Lin, Jun Sun, Truong Khanh Nguyen, Yang Liu, Jin Song Dong

Research Collection School Of Computing and Information Systems

Model checking suffers from the state space explosion problem. Compositional verification techniques such as assume-guarantee reasoning (AGR) have been proposed to alleviate the problem. However, there are at least three challenges in applying AGR. Firstly, given a system M1 M2, how do we automatically construct and refine (in the presence of spurious counterexamples) an assumption A2, which must be an abstraction of M2? Previous approaches suggest to incrementally learn and modify the assumption through multiple invocations of a model checker, which could be often time consuming. Secondly, how do we keep the state space small when checking M1 A2 |= …

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Intelligshop: Enabling Intelligent Shopping In Malls Through Location-Based Augmented Reality, Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang, Kevin Chen-Chuan Chang Nov 2015

Intelligshop: Enabling Intelligent Shopping In Malls Through Location-Based Augmented Reality, Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

Shopping experience is important for both citizens and tourists. We present IntelligShop, a novel location-based augmented reality application that supports intelligent shopping experience in malls. As the key functionality, IntelligShop provides an augmented reality interface-people can simply use ubiquitous smartphones to face mall retailers, then IntelligShop will automatically recognize the retailers and fetch their online reviews from various sources (including blogs, forums and publicly accessible social media) to display on the phones. Technically, IntelligShop addresses two challenging data mining problems, including robust feature learning to support heterogeneous smartphones in localization and learning to query for automatically gathering the retailer content …

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Event Detection In Wireless Sensor Networks In Random Spatial Sensors Deployments, Pengfei Zhang, Ido Nevat, Gareth W. Peters, Gaoxi Xiao, Hwee-Pink Tan Nov 2015

Event Detection In Wireless Sensor Networks In Random Spatial Sensors Deployments, Pengfei Zhang, Ido Nevat, Gareth W. Peters, Gaoxi Xiao, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

We develop a new class of event detection algorithms in Wireless Sensor Networks where the sensors are randomly deployed spatially. We formulate the detection problem as a binary hypothesis testing problem and design the optimal decision rules for two scenarios, namely the Poisson Point Process and Binomial Point Process random deployments. To calculate the intractable marginal likelihood density, we develop three types of series expansion methods which are based on an Askey-orthogonal polynomials. In addition, we develop a novel framework to provide guidance on which series expansion is most suitable (i.e., most accurate) to use for different system parameters. Extensive …

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A Passive Testing Approach For Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee Xian Tan, Hwee-Pink Tan, Zhangbing Zhou Nov 2015

A Passive Testing Approach For Protocols In Wireless Sensor Networks, Xiaoping Che, Stephane Maag, Hwee Xian Tan, Hwee-Pink Tan, Zhangbing Zhou

Research Collection School Of Computing and Information Systems

Smart systems are today increasingly developed with the number of wireless sensor devices drastically increasing. They are implemented within several contexts throughout our environment. Thus, sensed data transported in ubiquitous systems are important, and the way to carry them must be efficient and reliable. For that purpose, several routing protocols have been proposed for wireless sensor networks (WSN). However, one stage that is often neglected before their deployment is the conformance testing process, a cruicial and challenging step. Compared to active testing techniques commonly used in wired networks, passive approaches are more suitable to the WSN environment. While some works …

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Stack Layout Randomization With Minimal Rewriting Of Android Binaries, Yu Liang, Xinjie Ma, Daoyuan Wu, Xiaoxiao Tang, Debin Gao, Guojun Peng, Chunfu Jia, Huanguo Zhang Nov 2015

Stack Layout Randomization With Minimal Rewriting Of Android Binaries, Yu Liang, Xinjie Ma, Daoyuan Wu, Xiaoxiao Tang, Debin Gao, Guojun Peng, Chunfu Jia, Huanguo Zhang

Research Collection School Of Computing and Information Systems

Stack-based attacks typically require that attackers have a good understanding of the stack layout of the victim program. In this paper, we leverage specific features on ARM architecture and propose a practical technique that introduces randomness to the stack layout when an Android application executes. We employ minimal binary rewriting on the Android app that produces randomized executable of the same size which can be executed on an unmodified Android operating system. Our experiments on applying this randomization on the most popular 20 free Android apps on Google Play show that the randomization coverage of functions increases from 65% (by …

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Cost-Sensitive Online Classification With Adaptive Regularization And Its Applications, Peilin Zhao, Furen Zhuang, Min Wu, Xiao-Li Li, Hoi, Steven C. H. Nov 2015

Cost-Sensitive Online Classification With Adaptive Regularization And Its Applications, Peilin Zhao, Furen Zhuang, Min Wu, Xiao-Li Li, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

Cost-Sensitive Online Classification is recently proposed to directly online optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. However, the previous existing learning algorithms only utilized the first order information of the data stream. This is insufficient, as recent studies have proved that incorporating second order information could yield significant improvements on the prediction model. Hence, we propose a novel cost-sensitive online classification algorithm with adaptive regularization. We theoretically analyzed the proposed algorithm and empirically validated its effectiveness with extensive experiments. We also demonstrate the application of the …

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Cnl: Collective Network Linkage Across Heterogeneous Social Platforms, Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, Aoying Zhou Nov 2015

Cnl: Collective Network Linkage Across Heterogeneous Social Platforms, Ming Gao, Ee-Peng Lim, David Lo, Feida Zhu, Philips Kokoh Prasetyo, Aoying Zhou

Research Collection School Of Computing and Information Systems

The popularity of social media has led many users to create accounts with different online social networks. Identifying these multiple accounts belonging to same user is of critical importance to user profiling, community detection, user behavior understanding and product recommendation. Nevertheless, linking users across heterogeneous social networks is challenging due to large network sizes, heterogeneous user attributes and behaviors in different networks, and noises in user generated data. In this paper, we propose an unsupervised method, Collective Network Linkage (CNL), to link users across heterogeneous social networks. CNL incorporates heterogeneous attributes and social features unique to social network users, handles …

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Experience Report: An Industrial Experience Report On Test Outsourcing Practices, Xin Xia, David Lo, Pavneet Singh Kochhar, Zhenchang Xing, Xinyu Wang, Shanping Li Nov 2015

Experience Report: An Industrial Experience Report On Test Outsourcing Practices, Xin Xia, David Lo, Pavneet Singh Kochhar, Zhenchang Xing, Xinyu Wang, Shanping Li

Research Collection School Of Computing and Information Systems

Nowadays, many companies contract their testing functionalities out to third-party IT outsourcing companies. This process referred to as test outsourcing is common in the industry, yet it is rarely studied in the research community. In this paper, to bridge the gap, we performed an empirical study on test outsourcing with 10 interviewees and 140 survey respondents. We investigated various research questions such as the types, the process, and the challenges of test outsourcing, and the differences between test outsourcing and in-house testing. We found customer satisfaction, tight project schedule, and domain unfamiliarity are the top-3 challenges faced by the testers. …

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Should Fixing These Failures Be Delegated To Automated Program Repair?, Le Dinh Xuan Bach, Le Bui Tien Duy, David Lo Nov 2015

Should Fixing These Failures Be Delegated To Automated Program Repair?, Le Dinh Xuan Bach, Le Bui Tien Duy, David Lo

Research Collection School Of Computing and Information Systems

Program repair constitutes one of the major components of software maintenance that usually incurs a significant cost in software production. Automated program repair is supposed to help in reducing the software maintenance cost by automatically fixing software defects. Despite the recent advances in automated software repair, it is still very costly to wait for repair tools to produce valid repairs of defects. This paper addresses the following question: "Will an automated program repair technique find a repair for a defect within a reasonable time?". To answer this question, we build an oracle that can predict whether fixing a failure should …

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Not All Trips Are Equal: Analyzing Foursquare Check-Ins Of Trips And City Visitors, Wen Haw Chong, Bingtian Dai, Ee Peng Lim Nov 2015

Not All Trips Are Equal: Analyzing Foursquare Check-Ins Of Trips And City Visitors, Wen Haw Chong, Bingtian Dai, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Location-Based Social Networks (LBSN) such as Foursquare allow users to indicate venue visits via check-ins. This results in much fine grained context-rich data, useful for studying user mobility. In this work, we use check-ins to characterize trips and visitors to two cities, where visitors are defined as having their home cities elsewhere. First, we divide trips into two duration types: long and short. We then show that trip types differ in check-in distributions over venue categories, time slots, as well as check-in intensity. Based on the trip types, we then divide visitors into long-term and short-term visitors. We compare visitor …

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Real-Time Detection Of Seat Occupancy And Hogging, Huy Hoang Nguyen, Nakul Gulati, Youngki Lee, Rajesh Krishna Balan Nov 2015

Real-Time Detection Of Seat Occupancy And Hogging, Huy Hoang Nguyen, Nakul Gulati, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

In this paper, we propose a cheap and effective solution to detect if specific seats at a shared public table are occupied -- either by humans or by objects (i.e., the seats are being "hogged"). The hogging of seats, in particular, is a big problem for our campus library and required a large amount of manpower to correct (to find and clear hogged seats). We propose using two different cheap sensors, a capacitance sensor and an infrared (IR) sensor, to solve this problem. In the rest of this paper, we show how using these sensors can accurately determine if a …

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Powerforecaster: Predicting Smartphone Power Impact Of Continuous Sensing Applications At Pre-Installation Time, Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, Junehwa Song Nov 2015

Powerforecaster: Predicting Smartphone Power Impact Of Continuous Sensing Applications At Pre-Installation Time, Chulhong Min, Youngki Lee, Chungkuk Yoo, Seungwoo Kang, Sangwon Choi, Pillsoon Park, Inseok Hwang, Younghyun Ju, Seungpyo Choi, Junehwa Song

Research Collection School Of Computing and Information Systems

Today's smartphone application (hereinafter 'app') markets miss a key piece of information, power consumption of apps. This causes a severe problem for continuous sensing apps as they consume significant power without users' awareness. Users have no choice but to repeatedly install one app after another and experience their power use. To break such an exhaustive cycle, we propose PowerForecaster, a system that provides users with power use of sensing apps at pre-installation time. Such advanced power estimation is extremely challenging since the power cost of a sensing app largely varies with users' physical activities and phone use patterns. We observe …

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Modelling Cascades Over Time In Microblogs, Xie Wei, Feida Zhu, Siyuan Liu, Ke Wang Nov 2015

Modelling Cascades Over Time In Microblogs, Xie Wei, Feida Zhu, Siyuan Liu, Ke Wang

Research Collection School Of Computing and Information Systems

One of the most important features of microblogging services such as Twitter is how easy it is to re-share a piece of information across the network through various user connections, forming what we call a "cascade". Business applications such as viral marketing have driven a tremendous amount of research effort predicting whether a certain cascade will go viral. Yet the rarity of viral cascades in real data poses a challenge to all existing prediction methods. One solution is to simulate cascades that well fit the real viral ones, which requires our ability to tell how a certain cascade grows over …

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Analysis Of Aspects And Star Ratings In Consumer Reviews, Maruthi Prithivirajan, Vivian Lai, Kyong Jin Shim Nov 2015

Analysis Of Aspects And Star Ratings In Consumer Reviews, Maruthi Prithivirajan, Vivian Lai, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

This paper presents an analysis of star ratings in consumer reviews in Yelp, an online social platform for sharing consumer reviews about local businesses. In particular, we analyze consumer reviews about food businesses. We analyze how well or poorly the star ratings (on a scale of one star to five stars) associated with these reviews tally with the sentiment derived from the textual portion of the consumer review.

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Security And Privacy Of Electronic Health Information Systems: Editorial, Elisa Bertino, Robert H. Deng, Xinyi Huang, Jianying Zhou Nov 2015

Security And Privacy Of Electronic Health Information Systems: Editorial, Elisa Bertino, Robert H. Deng, Xinyi Huang, Jianying Zhou

Research Collection School Of Computing and Information Systems

Digital technologies have dramatically transformed our daily lives by bringing countless conveniences and benefits. As an evolving concept, electronic health information has become the focus of attention in both academia and industry. By leveraging modern digital technologies like the internet and the cloud, electronic health information systems will be a key enabling technology in improving the quality and convenience of patient care, encouraging patient participation in their care, reducing medical errors, improving practice efficiencies, and saving time and cost. The complexity of electronic health information systems, however, raises several new security and privacy issues. It is thus critical to investigate …

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Dictionary Pair Learning On Grassmann Manifolds For Image Denoising, Xianhua Zeng, Wei Bian, Wei Liu, Jialie Shen, Dacheng Tao Nov 2015

Dictionary Pair Learning On Grassmann Manifolds For Image Denoising, Xianhua Zeng, Wei Bian, Wei Liu, Jialie Shen, Dacheng Tao

Research Collection School Of Computing and Information Systems

Image denoising is a fundamental problem in computer vision and image processing that holds considerable practical importance for real-world applications. The traditional patch-based and sparse coding-driven image denoising methods convert 2D image patches into 1D vectors for further processing. Thus, these methods inevitably break down the inherent 2D geometric structure of natural images. To overcome this limitation pertaining to the previous image denoising methods, we propose a 2D image denoising model, namely, the dictionary pair learning (DPL) model, and we design a corresponding algorithm called the DPL on the Grassmann-manifold (DPLG) algorithm. The DPLG algorithm first learns an initial dictionary …

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Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim Nov 2015

Where Are The Passengers? A Grid-Based Gaussian Mixture Model For Taxi Bookings, Meng-Fen Chiang, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Taxi bookings are events where requests for taxis are made by passengers either over voice calls or mobile apps. As the demand for taxis changes with space and time, it is important to model both the space and temporal dimensions in dynamic booking data. Several applications can benefit from a good taxi booking model. These include the prediction of number of bookings at certain location and time of the day, and the detection of anomalous booking events. In this paper, we propose a Grid-based Gaussian Mixture Model (GGMM) with spatio-temporal dimensions that groups booking data into a number of spatio-temporal …

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Using Digital Genomics To Create An Intelligent Enterprise, Mario Domingo Nov 2015

Using Digital Genomics To Create An Intelligent Enterprise, Mario Domingo

Asian Management Insights

Every business knows that it needs to leverage customer data, but few know the potential it has to transform business processes, decisions and performance.

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A Method And System For Sentiment Classification And Emotion Classification [Us Patent 20170308523a1], Zhaoxia Wang, Rick Siow Mong Goh, Yinping Yang Nov 2015

A Method And System For Sentiment Classification And Emotion Classification [Us Patent 20170308523a1], Zhaoxia Wang, Rick Siow Mong Goh, Yinping Yang

Research Collection School Of Computing and Information Systems

A system and a method for classifying text messages, such as social media messages into sentiment valence categories are provided. The system comprising a module for decomposing text messages, a module for cleaning text messages, a module for producing feature data of text messages, and a module for classifying text messages into sentiment valence categories. The module for decomposing text messages is configured to: receive a text message, parse the text message into separate portions in response to parsing criteria based on sentence delimiters, wherein the separate portions are sentences, phrases and words, and rejoin at least some of the …

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Using Gamification As A Collaboration Motivator For Software Development Teams: A Preliminary Framework, Flavio Steffens, Sabrina Marczak, Fernando Figueira Filho, Christoph Treude, Leif Singer, David Redmiles, Ban Al-Ani Nov 2015

Using Gamification As A Collaboration Motivator For Software Development Teams: A Preliminary Framework, Flavio Steffens, Sabrina Marczak, Fernando Figueira Filho, Christoph Treude, Leif Singer, David Redmiles, Ban Al-Ani

Research Collection School Of Computing and Information Systems

Gamification is the use of game elements in non-game context to engage and to motivate people to achieve goals. Its use is becoming very popular in software development organizations due to work being based upon human-centric and brain-intensive activity. This paper presents the topics of collaboration and gamification in the context of software engineering, and proposes a framework that identifies the most common collaboration issues that affect software development teams, and how to apply game elements to motivate a change on their behaviors.

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Face Recognition On Large-Scale Video In The Wild With Hybrid Euclidean-And-Riemannian Metric Learning, Zhiwu Huang, R. Wang, S. Shan, X Chen Oct 2015

Face Recognition On Large-Scale Video In The Wild With Hybrid Euclidean-And-Riemannian Metric Learning, Zhiwu Huang, R. Wang, S. Shan, X Chen

Research Collection School Of Computing and Information Systems

Face recognition on large-scale video in the wild is becoming increasingly important due to the ubiquity of video data captured by surveillance cameras, handheld devices, Internet uploads, and other sources. By treating each video as one image set, set-based methods recently have made great success in the field of video-based face recognition. In the wild world, videos often contain extremely complex data variations and thus pose a big challenge of set modeling for set-based methods. In this paper, we propose a novel Hybrid Euclidean-and-Riemannian Metric Learning (HERML) method to fuse multiple statistics of image set. Specifically, we represent each image …

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Assessing Developer Contribution With Repository Mining-Based Metrics, Jalerson Lima, Christoph Treude, Fernando Figueira Filho, Uirá Kulesza Oct 2015

Assessing Developer Contribution With Repository Mining-Based Metrics, Jalerson Lima, Christoph Treude, Fernando Figueira Filho, Uirá Kulesza

Research Collection School Of Computing and Information Systems

Productivity as a result of individual developers' contributions is an important aspect for software companies to maintain their competitiveness in the market. However, there is no consensus in the literature on how to measure productivity or developer contribution. While some repository mining-based metrics have been proposed, they lack validation in terms of their applicability and usefulness from the individuals who will use them to assess developer contribution: team and project leaders. In this paper, we propose the design of a suite of metrics for the assessment of developer contribution, based on empirical evidence obtained from project and team leaders. In …

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Scheduled Approximation For Personalized Pagerank With Utility-Based Hub Selection, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying Oct 2015

Scheduled Approximation For Personalized Pagerank With Utility-Based Hub Selection, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying

Research Collection School Of Computing and Information Systems

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an “organized” way, such that we can gradually improve our PPV estimation in an incremental manner and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub-based realization, where we adopt the metric …

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Detect Rumors Using Time Series Of Social Context Information On Microblogging Websites, Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong Oct 2015

Detect Rumors Using Time Series Of Social Context Information On Microblogging Websites, Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm …

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