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Articles 6121 - 6150 of 9024
Full-Text Articles in Computer Sciences
Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang
Band Selection For Hyperspectral Images Using Probabilistic Memetic Algorithm, Liang Feng, Ah-Hwee Tan, Meng-Hiot Lim, Si Wei Jiang
Research Collection School Of Computing and Information Systems
Band selection plays an important role in identifying the most useful and valuable information contained in the hyperspectral images for further data analysis such as classification, clustering, etc. Memetic algorithm (MA), among other metaheuristic search methods, has been shown to achieve competitive performances in solving the NP-hard band selection problem. In this paper, we propose a formal probabilistic memetic algorithm for band selection, which is able to adaptively control the degree of global exploration against local exploitation as the search progresses. To verify the effectiveness of the proposed probabilistic mechanism, empirical studies conducted on five well-known hyperspectral images against two …
Web Application Vulnerability Prediction Using Hybrid Program Analysis And Machine Learning, Lwin Khin Shar, Lionel Briand, Hee Beng Kuan Tan
Web Application Vulnerability Prediction Using Hybrid Program Analysis And Machine Learning, Lwin Khin Shar, Lionel Briand, Hee Beng Kuan Tan
Research Collection School Of Computing and Information Systems
Due to limited time and resources, web software engineers need support in identifying vulnerable code. A practical approach to predicting vulnerable code would enable them to prioritize security auditing efforts. In this paper, we propose using a set of hybrid (staticþdynamic) code attributes that characterize input validation and input sanitization code patterns and are expected to be significant indicators of web application vulnerabilities. Because static and dynamic program analyses complement each other, both techniques are used to extract the proposed attributes in an accurate and scalable way. Current vulnerability prediction techniques rely on the availability of data labeled with vulnerability …
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Linguistic Analysis Of Toxic Behavior In An Online Video Game, Haewoon Kwak, Telefonica
Research Collection School Of Computing and Information Systems
In this paper we explore the linguistic components of toxic behavior by using crowdsourced data from over 590 thousand cases of accused toxic players in a popular match-based competition game, League of Legends. We perform a series of linguistic analyses to gain a deeper understanding of the role communication plays in the expression of toxic behavior. We characterize linguistic behavior of toxic players and compare it with that of typical players in an online competition game. We also find empirical support describing how a player transitions from typical to toxic behavior. Our findings can be helpful to automatically detect and …
Vector Abstraction And Concretization For Scalable Detection Of Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Vector Abstraction And Concretization For Scalable Detection Of Refactorings, Narcisa Andreea Milea, Lingxiao Jiang, Siau-Cheng Khoo
Research Collection School Of Computing and Information Systems
Automated techniques have been proposed to either identify refactoring opportunities (i.e., code fragments that can be but have not yet been restructured in a program), or reconstruct historical refactorings (i.e., code restructuring operations that have happened between different versions of a program). In this paper, we propose a new technique that can detect both refactoring opportunities and historical refactorings in large code bases. The key of our technique is the design of vector abstraction and concretization operations that can encode code changes induced by certain refactorings as characteristic vectors. Thus, the problem of identifying refactorings can be reduced to the …
Grumon: Fast And Accurate Group Monitoring For Heterogeneous Urban Spaces, Rijurekha Sen, Youngki Lee, Kasthuri Jayarajah, Rajesh Krishna Balan, Archan Misra
Grumon: Fast And Accurate Group Monitoring For Heterogeneous Urban Spaces, Rijurekha Sen, Youngki Lee, Kasthuri Jayarajah, Rajesh Krishna Balan, Archan Misra
Research Collection School Of Computing and Information Systems
Real-time monitoring of groups and their rich contexts will be a key building block for futuristic, group-aware mobile services. In this paper, we propose GruMon, a fast and accurate group monitoring system for dense and complex urban spaces. GruMon meets the performance criteria of precise group detection at low latencies by overcoming two critical challenges of practical urban spaces, namely (a) the high density of crowds, and (b) the imprecise location information available indoors. Using a host of novel features extracted from commodity smartphone sensors, GruMon can detect over 80% of the groups, with 97% precision, using 10 minutes latency …
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Cama: Efficient Modeling Of The Capture Effect For Low Power Wireless Networks, Behnam Dezfouli, Marjan Radi, Kamin Whitehouse, Shukor Abd Razak, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Network simulation is an essential tool for the design and evaluation of wireless network protocols, and realistic channel modeling is essential for meaningful analysis. Recently, several network protocols have demonstrated substantial network performance improvements by exploiting the capture effect, but existing models of the capture effect are still not adequate for protocol simulation and analysis. Physical-level models that calculate the signal-to-interference-plus-noise ratio (SINR) for every incoming bit are too slow to be used for large-scale or long-term networking experiments, and link-level models such as those currently used by the NS2 simulator do not accurately predict protocol performance. In this article, …
Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Hybrid Euclidean-And-Riemannian Metric Learning For Image Set Classification, Zhiwu Huang, R. Wang, S. Shan, X. Chen
Research Collection School Of Computing and Information Systems
We propose a novel hybrid metric learning approach to combine multiple heterogenous statistics for robust image set classification. Specifically, we represent each set with multiple statistics – mean, covariance matrix and Gaussian distribution, which generally complement each other for set modeling. However, it is not trivial to fuse them since the mean vector with dd-dimension often lies in Euclidean space RdRd, whereas the covariance matrix typically resides on Riemannian manifold Sym+dSymd+. Besides, according to information geometry, the space of Gaussian distribution can be embedded into another Riemannian manifold Sym+d+1Symd+1+. To fuse these statistics from heterogeneous spaces, we propose a Hybrid …
Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu
Celelabel: An Interactive System For Annotating Celebrities In Web Videos, Zhineng Chen, Jinfeng Bai, Chong-Wah Ngo, Bailan Feng, Bo Xu
Research Collection School Of Computing and Information Systems
Manual annotation of celebrities in Web videos is an essential task in many people-related Web services. The task, however, poses a significant challenge even to skillful annotators, mainly due to the large quantity of unfamiliar and greatly varied celebrities, and the lack of a customized system for it. This work develops CeleLabel, an interactive system for manually annotating celebrities in the Web video domain. The peculiarity of CeleLabel is to exploit and display multiple types of information that could assist the annotation, including video content, context surrounding and within a video, celebrity images on the Web, and human factors. Using …
Vireo @ Trecvid 2014: Instance Search And Semantic Indexing, Wei Zhang, Hao Zhang, Ting Yao, Yijie Lu, Jingjing Chen, Chong-Wah Ngo
Vireo @ Trecvid 2014: Instance Search And Semantic Indexing, Wei Zhang, Hao Zhang, Ting Yao, Yijie Lu, Jingjing Chen, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
This paper summarizes the following two tasks participated by VIREO group: instance search and semantic indexing. We will present our approaches and analyze the results obtained in TRECVID 2014 benchmark evaluation
K-Sketch: Digital Storytelling With Animation Sketches, Richard Christopher Davis, Nur Camellia Binte Zakaria
K-Sketch: Digital Storytelling With Animation Sketches, Richard Christopher Davis, Nur Camellia Binte Zakaria
Research Collection School Of Computing and Information Systems
K-Sketch gives novice animators an easy way to tell stories with animation sketches. It relies on users’ intuitive sense of space and time, and makes animation easy through the use of sketching and demonstration. Our studies have shown that people take naturally to telling stories with K-Sketch, and it is particularly helpful for exploring the timing of events. We also found that K-Sketch is a good collaborative medium for telling stories. In this demonstration we will show how K-Sketch works and explain how these advantages are realized in practice.
Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw
Dynamic Clustering Of Contextual Multi-Armed Bandits, Trong T. Nguyen, Hady W. Lauw
Research Collection School Of Computing and Information Systems
With the prevalence of the Web and social media, users increasingly express their preferences online. In learning these preferences, recommender systems need to balance the trade-off between exploitation, by providing users with more of the "same", and exploration, by providing users with something "new" so as to expand the systems' knowledge. Multi-armed bandit (MAB) is a framework to balance this trade-off. Most of the previous work in MAB either models a single bandit for the whole population, or one bandit for each user. We propose an algorithm to divide the population of users into multiple clusters, and to customize the …
Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis
Historical Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
Historical traffic information is valuable for transportation analysis and planning, as well as for route search services. In view of these applications, we propose the k traffic-tolerant paths problem (TTP) on road networks, which takes a source-destination pair and historical traffic information as input, and returns k paths that minimize the aggregate (historical) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to compute. We propose an exact algorithm and a heuristic algorithm for this problem. Experiments on real traffic data demonstrate the effectiveness of TTP paths and …
Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Generative Modeling Of Entity Comparisons In Text, Maksim Tkachenko, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Users frequently rely on online reviews for decision making. In addition to allowing users to evaluate the quality of individual products, reviews also support comparison shopping. One key user activity is to compare two (or more) products based on a specific aspect. However, making a comparison across two different reviews, written by different authors, is not always equitable due to the different standards and preferences of individual authors. Therefore, we focus instead on comparative sentences, whereby two products are compared directly by a review author within a single sentence. We study the problem of comparative relation mining. Given a set …
Predicting Effectiveness Of Ir-Based Bug Localization Techniques, Tien-Duy B. Le, Ferdian Thung, David Lo
Predicting Effectiveness Of Ir-Based Bug Localization Techniques, Tien-Duy B. Le, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Recently, many information retrieval (IR) based bug localization approaches have been proposed in the literature. These approaches use information retrieval techniques to process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recent approaches can achieve reasonable accuracy, however, even a state-of-the-art bug localization tool outputs many ranked lists where buggy files appear very low in the lists. This potentially causes developers to distrust bug localization tools. Parnin and Orso recently conduct a user study and highlight that developers …
Buglocalizer: Integrated Tool Support For Bug Localization, Ferdian Thung, Tien-Duy B. Le, Pavneet Singh Kochhar, David Lo
Buglocalizer: Integrated Tool Support For Bug Localization, Ferdian Thung, Tien-Duy B. Le, Pavneet Singh Kochhar, David Lo
Research Collection School Of Computing and Information Systems
To manage bugs that appear in a software, developers often make use of a bug tracking system such as Bugzilla. Users can report bugs that they encounter in such a system. Whenever a user reports a new bug report, developers need to read the summary and description of the bug report and manually locate the buggy files based on this information. This manual process is often time consuming and tedious. Thus, a number of past studies have proposed bug localization techniques to automatically recover potentially buggy files from bug reports. Unfortunately, none of these techniques are integrated to bug tracking …
Rapid: A Toolkit For Reliability Analysis Of Non-Deterministic Systems, Lin Gui, Jun Sun, Yang Liu, Truong Khanh Nguyen, Jin Song Dong Dong
Rapid: A Toolkit For Reliability Analysis Of Non-Deterministic Systems, Lin Gui, Jun Sun, Yang Liu, Truong Khanh Nguyen, Jin Song Dong Dong
Research Collection School Of Computing and Information Systems
Non-determinism in concurrent or distributed software systems (i.e., various possible execution orders among different distributed components) presents new challenges to the existing reliability analysis methods based on Markov chains. In this work, we present a toolkit RaPiD for the reliability analysis of non-deterministic systems. Taking Markov decision process as reliability model, RaPiD can help in the analysis of three fundamental and rewarding aspects regarding software reliability. First, to have reliability assurance on a system, RaPiD can synthesize the overall system reliability given the reliability values of system components. Second, given a requirement on the overall system reliability, RaPiD can distribute …
The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao
The Evolution Of Research On Multimedia Travel Guide Search And Recommender Systems, Junge Shen, Zhiyong Cheng, Jialie Shen, Tao Mei, Xinbo Gao
Research Collection School Of Computing and Information Systems
The importance of multimedia travel guide search and recommender systems has led to a substantial amount of research spanning different computer science and information system disciplines in recent years. The five core research streams we identify here incorporate a few multimedia computing and information retrieval problems that relate to the alternative perspectives of algorithm design for optimizing search/recommendation quality and different methodological paradigms to assess system performance at large scale. They include (1) query analysis, (2) diversification based on different criteria, (3) ranking and reranking, (4) personalization and (5) evaluation. Based on a comprehensive discussion and analysis of these streams, …
Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder
Developer Involvement Considered Harmful? An Empirical Examination Of Android Bug Resolution Times, Subhajit Datta, Proshanta Sarkar, Subhashis Majumder
Research Collection School Of Computing and Information Systems
In large scale software development ecosystems, there is a common perception that higher developer involvement leads to faster resolution of bugs. This is based on conjectures around more ``eyeballs" making bugs "shallow" -- whose validity and applicability are not without dispute. In this paper, we posit that the level of developer attention as well as its extent of diversity influence how quickly bugs get resolved. We report results from a study of 1,000+ Android bugs. We find statistically significant evidence that attention and diversity have contrasting relationships with the resolution time of bugs, even after controlling for factors such as …
On Joint Modeling Of Topical Communities And Personal Interest In Microblogs, Tuan-Anh Hoang, Ee Peng Lim
On Joint Modeling Of Topical Communities And Personal Interest In Microblogs, Tuan-Anh Hoang, Ee Peng Lim
Research Collection School Of Computing and Information Systems
In this paper, we propose the Topical Communities and Personal Interest (TCPI) model for simultaneously modeling topics, topical communities, and users’ topical interests in microblogging data. TCPI considers different topical communities while differentiating users’ personal topical interests from those of topical communities, and learning the dependence of each user on the affiliated communities to generate content. This makes TCPI different from existing models that either do not consider the existence of multiple topical communities, or do not differentiate between personal and community’s topical interests. Our experiments on two Twitter datasets show that TCPI can effectively mine the representative topics for …
Managing Seller Heterogeneity In A Competitive Marketplace, R. Wu, Mei Lin
Managing Seller Heterogeneity In A Competitive Marketplace, R. Wu, Mei Lin
Research Collection School Of Computing and Information Systems
The growth of online marketplaces is accompanied by significant heterogeneity of the third-party sellers. The marketplace owner often applies policies that favor the sellers who offer higher values to buyers, which puts the lower-value sellers at an even greater disadvantage. This leads to the phenomenon of Matthew Effect. Our study focuses on a marketplace owner’s policy in managing seller heterogeneity and analyzes Matthew Effect in a competitive market environment. By extending the circular city model, we analytically examine the price competition among a large number of sellers that differ both in variety and in their value offerings. We present the …
Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen
Combining Multiple Kernel Methods On Riemannian Manifold For Emotion Recognition In The Wild, M. Liu, R. Wang, S. Li, S. Shan, Zhiwu Huang, X. Chen
Research Collection School Of Computing and Information Systems
In this paper, we present the method for our submission to the Emotion Recognition in the Wild Challenge (EmotiW 2014). The challenge is to automatically classify the emotions acted by human subjects in video clips under realworld environment. In our method, each video clip can be represented by three types of image set models (i.e. linear subspace, covariance matrix, and Gaussian distribution) respectively, which can all be viewed as points residing on some Riemannian manifolds. Then different Riemannian kernels are employed on these set models correspondingly for similarity/distance measurement. For classification, three types of classifiers, i.e. kernel SVM, logistic regression, …
Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau
Multi-Agent Orienteering Problem With Time-Dependent Capacity Constraints, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we formulate and study the Multi-agent Orienteering Problem with Time-dependent Capacity Constraints (MOPTCC). MOPTCC is similar to the classical orienteering problem at single-agent level: given a limited time budget, an agent travels around the network and collects rewards by visiting different nodes, with the objective of maximizing the sum of his collected rewards. The most important feature we introduce in MOPTCC is the inclusion of multiple competing agents. All agents in MOPTCC are assumed to be self-interested, and they interact with each other when arrive at certain nodes simultaneously. As all nodes are capacitated, if a particular …
Survey On Wakeup Scheduling For Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Wee-Seng Soh, Hwee-Pink Tan
Survey On Wakeup Scheduling For Environmentally-Powered Wireless Sensor Networks, Alvin Cerdena Valera, Wee-Seng Soh, Hwee-Pink Tan
Research Collection School Of Computing and Information Systems
Advances in energy harvesting technologies and ultra low-power computing and communication devices are enabling the realization of environmentally-powered wireless sensor networks (EPWSNs). Because of limited and dynamic energy supply, EPWSNs are duty-cycled to achieve energy-neutrality, a condition where the energy demand does not exceed the energy supply. Duty cycling entails nodes to sleep and wakeup according to a wakeup scheduling scheme. In this paper, we survey the various wakeup scheduling schemes, with focus on their suitability for EPWSNs. A classification scheme is proposed to characterize existing wakeup scheduling schemes, with three main categories, namely, asynchronous, synchronous, and …
Enabling National Software Development Competitions To Identify And Enhance Student Mentor Capability In Singapore, Chris Boesch, Sandra Boesch
Enabling National Software Development Competitions To Identify And Enhance Student Mentor Capability In Singapore, Chris Boesch, Sandra Boesch
Research Collection School Of Computing and Information Systems
The authors previously developed a system to facilitate the self-directed learning and practicing of software languages in Singapore. One of the goals of this self-directed learning was to enable the creation of student mentors who would be able to assist other students during classroom sessions. Building on this work, the authors extended the platform to support the promotion and coordination of multiple programming competitions including multiple schools systems within Singapore with the goals of identifying, enabling, and mentoring students who might be better prepared to mentor their peers at their school after participating in the country wide competition. This paper …
Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang
Entity Linking On Microblogs With Spatial And Temporal Signals, Yuan Fang, Ming-Wei Chang
Research Collection School Of Computing and Information Systems
Microblogs present an excellent opportunity for monitoring and analyzing world happenings. Given that words are often ambiguous, entity linking becomes a crucial step towards understanding microblogs. In this paper, we re-examine the problem of entity linking on microblogs. We first observe that spatiotemporal (i.e., spatial and temporal) signals play a key role, but they are not utilized in existing approaches. Thus, we propose a novel entity linking framework that incorporates spatiotemporal signals through a weakly supervised process. Using entity annotations1 on real-world data, our experiments show that the spatiotemporal model improves F1 by more than 10 points over existing systems. …
A Multi-Dimensional Image Quality Prediction Model For User-Generated Images In Social Networks, You Yang, Xu Wang, Tao Guan, Jialie Shen, Li Yu
A Multi-Dimensional Image Quality Prediction Model For User-Generated Images In Social Networks, You Yang, Xu Wang, Tao Guan, Jialie Shen, Li Yu
Research Collection School Of Computing and Information Systems
User-generated images (UGIs) are currently proliferating within social networks. These images contain multi-dimensional data, including the image itself, text and the social links of the owner. UGIs can be utilized for self-presentation, news dissemination and other purposes, and the quality of the image should be able to reveal these social functionalities. However, it is challenging to predict UGI quality utilizing existing models, such as image quality assessment, recommender systems or others, because these models have difficulties processing multi-dimensional data simultaneously. To address this problem, we propose a multi-dimensional image quality prediction model for UGIs in social networks. In this model, …
Auction With Rolling Horizon For Urban Consolidation Centre, Chen Wang, Stephanus Daniel Handoko, Hoong Chuin Lau
Auction With Rolling Horizon For Urban Consolidation Centre, Chen Wang, Stephanus Daniel Handoko, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
A number of cities around the world have adopted urban consolidation centres (UCCs) to address some challenges of their last-mile deliveries. At the UCC, goods are consolidated based on their destinations prior to their deliveries into the city centre. In many examples, the UCC owns a fleet of eco-friendly vehicles to carry out the deliveries. A carrier/shipper who buys the UCC’s service hence no longer needs to enter the city centre in which time-window and vehicle-type restrictions may apply. As a result, it becomes possible to retain the use of large trucks for the economies of scale outside the city …
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Cost-Sensitive Online Classification, Jialei Wang, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, “Cost-Sensitive Online Classification". In this paper, we formally study this problem, and propose a new framework for Cost-Sensitive Online Classification by directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of …
Partisan Sharing: Facebook Evidence And Societal Consequences, Jisun An, Daniele Quercia, Jon Crowcroft
Partisan Sharing: Facebook Evidence And Societal Consequences, Jisun An, Daniele Quercia, Jon Crowcroft
Research Collection School Of Computing and Information Systems
The hypothesis of selective exposure assumes that people seek out information that supports their views and eschew information that conflicts with their beliefs, and that has negative consequences on our society. Few researchers have recently found counter evidence of selective exposure in social media: users are exposed to politically diverse articles. No work has looked at what happens after exposure, particularly how individuals react to such exposure, though. Users might well be exposed to diverse articles but share only the partisan ones. To test this, we study partisan sharing on Facebook: the tendency for users to predominantly share like-minded news …
Singapore Management University Establishes A New Research Centre On Secure Mobile Computing Technologies And Solutions, Singapore Management University
Singapore Management University Establishes A New Research Centre On Secure Mobile Computing Technologies And Solutions, Singapore Management University
SMU Press Releases and News
The Singapore Management University (SMU) has announced today the establishment of a new centre of research excellence that focuses on mobile computing security. Funded by Singapore’s National Research Foundation (NRF), the Secure Mobile Centre is developing efficient and scalable technologies and solutions that strengthen the security of mobile computing systems, applications and services. The Secure Mobile Centre is led by a team of five faculty members from SMU’s School of Information Systems who specialise in information security and trust: Professor Robert DENG (Centre Director), Professor PANG Hwee Hwa, Associate Professor LI Yingjiu, Associate Professor DING Xuhua and Assistant Professor Debin …