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Articles 5971 - 6000 of 9024

Full-Text Articles in Computer Sciences

Direct: A Scalable Approach For Route Guidance In Selfish Orienteering Problems, Pradeep Varakantham, Hala Mostafa, Na Fu, Hoong Chuin Lau May 2015

Direct: A Scalable Approach For Route Guidance In Selfish Orienteering Problems, Pradeep Varakantham, Hala Mostafa, Na Fu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of crowd congestion at venues like theme parks, museums and world expos by providing route guidance to multiple selfish users (with budget constraints) moving through the venue simultaneously. To represent these settings, we introduce the Selfish Orienteering Problem (SeOP) that combines two well studied problems from literature, namely Orienteering Problem (OP) and Selfish Routing (SR). OP is a single agent routing problem where the goal is to minimize latency (or maximize reward) in traversing a subset of nodes while respecting budget constraints. SR is a game between selfish agents looking for minimum latency routes from source …

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Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra May 2015

Multi-Agent Task Assignment For Mobile Crowdsourcing Under Trajectory Uncertainties, Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, Archan Misra

Research Collection School Of Computing and Information Systems

In this work, we investigate the problem of mobile crowdsourcing, where workers are financially motivated to perform location-based tasks physically. Unlike current industry practice that relies on workers to manually browse and filter tasks to perform, we intend to automatically make task recommendations based on workers' historical trajectories and desired time budgets. However, predicting workers' trajectories is inevitably faced with uncertainties, as no one will take exactly the same route every day; yet such uncertainties are oftentimes abstracted away in the known literature. In this work, we depart from the deterministic modeling and study the stochastic task recommendation problem where …

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Oscar: Online Selection Of Algorithm Portfolios With Case Study On Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau May 2015

Oscar: Online Selection Of Algorithm Portfolios With Case Study On Memetic Algorithms, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

This paper introduces an automated approach called OSCAR that combines algorithm portfolios and online algorithm selection. The goal of algorithm portfolios is to construct a subset of algorithms with diverse problem solving capabilities. The portfolio is then used to select algorithms from for solving a particular (set of) instance(s). Traditionally, algorithm selection is usually performed in an offline manner and requires the need of domain knowledge about the target problem; while online algorithm selection techniques tend not to pay much attention to a careful construction of algorithm portfolios. By combining algorithm portfolios and online selection, our hope is to design …

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Adviser: A Web-Based Algorithm Portfolio Deviser, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau May 2015

Adviser: A Web-Based Algorithm Portfolio Deviser, Mustafa Misir, Stephanus Daniel Handoko, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

The basic idea of algorithm portfolio [1] is to create a mixture of diverse algorithms that complement each other’s strength so as to solve a diverse set of problem instances. Algorithm portfolios have taken on a new and practical meaning today with the wide availability of multi-core processors: from an enterprise perspective, the interest is to make best use of parallel machines within the organization by running different algorithms simultaneously on different cores to solve a given problem instance. Parallel execution of a portfolio of algorithms as suggested by [2, 3] a number of years …

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Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu May 2015

Moving Average Reversion Strategy For On-Line Portfolio Selection, Bin Li, Steven C. H. Hoi, Doyen Sahoo, Zhi-Yong Liu

Research Collection School Of Computing and Information Systems

On-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interest from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock prices are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and …

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Rclinker: Automated Linking Of Issue Reports And Commits Leveraging Rich Contextual Information, Tien-Duy B. Le, Mario Linares Vasquez, David Lo, Denys Poshyvanyk May 2015

Rclinker: Automated Linking Of Issue Reports And Commits Leveraging Rich Contextual Information, Tien-Duy B. Le, Mario Linares Vasquez, David Lo, Denys Poshyvanyk

Research Collection School Of Computing and Information Systems

Links between issue reports and their corresponding commits in version control systems are often missing. However, these links are important for measuring the quality of a software system, predicting defects, and many other tasks. Several approaches have been designed to solve this problem by automatically linking bug reports to source code commits via comparison of textual information in commit messages and bug reports. Yet, the effectiveness of these techniques is oftentimes suboptimal when commit messages are empty or contain minimum information; this particular problem makes the process of recovering traceability links between commits and bug reports particularly challenging. In this …

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Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo May 2015

Active Semi-Supervised Defect Categorization, Ferdian Thung, Xuan-Bach D. Le, David Lo

Research Collection School Of Computing and Information Systems

Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of …

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Elblocker: Predicting Blocking Bugs With Ensemble Imbalance Learning, Xin Xia, David Lo, Emad Shihab, Xinyu Wang, Xiaohu Yang May 2015

Elblocker: Predicting Blocking Bugs With Ensemble Imbalance Learning, Xin Xia, David Lo, Emad Shihab, Xinyu Wang, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Context: Blocking bugs are bugs that prevent other bugs from being fixed. Previous studies show that blocking bugs take approximately two to three times longer to be fixed compared to non-blocking bugs. Objective: Thus, automatically predicting blocking bugs early on so that developers are aware of them, can help reduce the impact of or avoid blocking bugs. However, a major challenge when predicting blocking bugs is that only a small proportion of bugs are blocking bugs, i.e., there is an unequal distribution between blocking and non-blocking bugs. For example, in Eclipse and OpenOffice, only 2.8% and 3.0% bugs are blocking …

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Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu May 2015

Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of …

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Matchmaking Game Players On Public Transport, Nairan Zhang, Youngki Lee, Rajesh Krishna Balan May 2015

Matchmaking Game Players On Public Transport, Nairan Zhang, Youngki Lee, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

This paper extends our recent work, called GameOn, which presented a system for allowing public transport commuters to engage in multiplayer games with fellow commuters traveling on the same bus or train. An important challenge for GameOn is to group players with reliable connections into the same game. In this case, the meaning of reliability has two dimensions. First, the network connectivity (TCP, UDP etc.) should be robust. Second, the players should be collocated with each other for a sufficiently long duration so that a game session will not be terminated by players leaving the public transport modality such as …

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Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda May 2015

Advances In Knowledge Discovery And Data Mining Part Ii, Tru Cao, Ee Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda

Research Collection School Of Computing and Information Systems

No abstract provided.

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Advances In Knowledge Discovery And Data Mining: 19th Pacific-Asia Conference, Pakdd 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda May 2015

Advances In Knowledge Discovery And Data Mining: 19th Pacific-Asia Conference, Pakdd 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part I, Tru Cao, Ee-Peng Lim, Zhi-Hua Zhou, Tu-Bao Ho, David Wai-Lok Cheung, Hiroshi Motoda

Research Collection School Of Computing and Information Systems

No abstract provided.

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Near-Optimal Decentralized Power Supply Restoration In Smart Grids, Pritee Agrawal, Akshat Kumar, Pradeep Varakantham May 2015

Near-Optimal Decentralized Power Supply Restoration In Smart Grids, Pritee Agrawal, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Next generation of smart grids face a number of challenges including co-generation from intermittent renewable power sources, a shift away from monolithic control due to increased market deregulation, and robust operation in the face of disasters. Such heterogeneous nature and high operational readiness requirement of smart grids necessitates decentralized control for critical tasks such as power supply restoration (PSR) after line failures. We present a novel multiagent system based approach for PSR using Lagrangian dual decomposition. Our approach works on general graphs, provides provable quality-bounds and requires only local message-passing among different connected sub-regions of a smart grid, enabling decentralized …

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Predicting Bundles Of Spatial Locations From Learning Revealed Preference Data, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya Krishnan May 2015

Predicting Bundles Of Spatial Locations From Learning Revealed Preference Data, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau, Ramayya Krishnan

Research Collection School Of Computing and Information Systems

We propose the problem of predicting a bundle of goods, where the goods considered is a set of spatial locations that an agent wishes to visit. This typically arises in the tourism setting where attractions can often be bundled and sold as a package to visitors. While the problem of predicting future locations given the current and past trajectories is well-established, we take a radical approach by looking at it from an economic point of view. We view an agent's past trajectories as revealed preference (RP) data, where the choice of locations is a solution to an optimisation problem according …

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Gameon: P2p Gaming On Public Transport, Nairan Zhang, Youngki Lee, Meera Radhakrishnan, Rajesh Krishna Balan May 2015

Gameon: P2p Gaming On Public Transport, Nairan Zhang, Youngki Lee, Meera Radhakrishnan, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Mobile games, and especially multiplayer games are a very popular daily distraction for many users. We hypothesise that commuters travelling on public buses or trains would enjoy being able to play multiplayer games with their fellow commuters to alleviate the commute burden and boredom. We present quantitative data to show that the typical one-way commute time is fairly long (at least 25 minutes on average) as well as survey results indicating that commuters are willing to play multiplayer games with other random commuters. In this paper, we present GameOn, a system that allows commuters to participate in multiplayer games with …

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Towards Practical Graph-Based Verification For An Object-Oriented Concurrency Model, Alexander Heußner, Christopher M. Poskitt, Claudio Corrodi, Benjamin Morandi Apr 2015

Towards Practical Graph-Based Verification For An Object-Oriented Concurrency Model, Alexander Heußner, Christopher M. Poskitt, Claudio Corrodi, Benjamin Morandi

Research Collection School Of Computing and Information Systems

To harness the power of multi-core and distributed platforms, and to make the development of concurrent software more accessible to software engineers, different object-oriented concurrency models such as SCOOP have been proposed. Despite the practical importance of analysing SCOOP programs, there are currently no general verification approaches that operate directly on program code without additional annotations. One reason for this is the multitude of partially conflicting semantic formalisations for SCOOP (either in theory or by-implementation). Here, we propose a simple graph transformation system (GTS) based run-time semantics for SCOOP that grasps the most common features of all known semantics of …

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Tlv: Abstraction Through Testing, Learning, And Validation, Jun Sun, Hao Xiao, Yang Liu, Shang-Wei Lin, Shengchao Qin Apr 2015

Tlv: Abstraction Through Testing, Learning, And Validation, Jun Sun, Hao Xiao, Yang Liu, Shang-Wei Lin, Shengchao Qin

Research Collection School Of Computing and Information Systems

A (Java) class provides a service to its clients (i.e., programs which use the class). The service must satisfy certain specifications. Different specifications might be expected at different levels of abstraction depending on the client's objective. In order to effectively contrast the class against its specifications, whether manually or automatically, one essential step is to automatically construct an abstraction of the given class at a proper level of abstraction. The abstraction should be correct (i.e., over-approximating) and accurate (i.e., with few spurious traces). We present an automatic approach, which combines testing, learning, and validation, to constructing an abstraction. Our approach …

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Measuring User Influence, Susceptibility And Cynicalness In Sentiment Diffusion, Roy Ka-Wei Lee, Ee Peng Lim Apr 2015

Measuring User Influence, Susceptibility And Cynicalness In Sentiment Diffusion, Roy Ka-Wei Lee, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Diffusion in social networks is an important research topic lately due to massive amount of information shared on social media and Web. As information diffuses, users express sentiments which can affect the sentiments of others. In this paper, we analyze how users reinforce or modify sentiment of one another based on a set of inter-dependent latent user factors as they are engaged in diffusion of event information. We introduce these sentiment-based latent user factors, namely influence, susceptibility and cynicalness. We also propose the ISC model to relate the three factors together and develop an iterative computation approach to …

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Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth Apr 2015

Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth

Research Collection School Of Computing and Information Systems

The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results …

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Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han Apr 2015

Exploring Cyberbullying And Other Toxic Behavior In Team Competition Online Games, Haewoon Kwak, Jeremy Blackburn, Seungyeop. Han

Research Collection School Of Computing and Information Systems

In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.

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Sensorem – An Efficient Mobile Platform For Wireless Sensor Network Visualization, Jin Ming Koh, Marcus Sak, Hwee Xian Tan, Huiguang Liang, Fachmin Folianto, Tony Quek Apr 2015

Sensorem – An Efficient Mobile Platform For Wireless Sensor Network Visualization, Jin Ming Koh, Marcus Sak, Hwee Xian Tan, Huiguang Liang, Fachmin Folianto, Tony Quek

Research Collection School Of Computing and Information Systems

No abstract provided.

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Multi-Roles Affiliation Model For General User Profiling, Lizi Liao, Heyan Huang, Yashen Wang Apr 2015

Multi-Roles Affiliation Model For General User Profiling, Lizi Liao, Heyan Huang, Yashen Wang

Research Collection School Of Computing and Information Systems

Online social networks release user attributes, which is important for many applications. Due to the sparsity of such user attributes online, many works focus on profiling user attributes automatically. However, in order to profile a specific user attribute, an unique model is built and such model usually does not fit other profiling tasks. In our work, we design a novel, flexible general user profiling model which naturally models users’ friendships with user attributes. Experiments show that our method simultaneously profile multiple attributes with better performance.

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Mobility Increases Localizability: A Survey On Wireless Indoor Localization Using Inertial Sensors, Zheng Yang, Chenshu Wu, Zimu Zhou, Xinglin Zhang, Xu Wang, Yunhao Liy Apr 2015

Mobility Increases Localizability: A Survey On Wireless Indoor Localization Using Inertial Sensors, Zheng Yang, Chenshu Wu, Zimu Zhou, Xinglin Zhang, Xu Wang, Yunhao Liy

Research Collection School Of Computing and Information Systems

Wireless indoor positioning has been extensively studied for the past two decades and continuously attracted growing research efforts in mobile computing context. As the integration of multiple inertial sensors (e.g., accelerometer, gyroscope, and magnetometer) to nowadays smartphones in recent years, human-centric mobility sensing is emerging and coming into vogue. Mobility information, as a new dimension in addition to wireless signals, can benefit localization in a number of ways, since location and mobility are by nature related in physical world. In this article, we survey this new trend of mobility enhancing smartphone-based indoor localization. Specifically, we first study how to measure …

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Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen Apr 2015

Memory Dynamics In Attractor Networks, Guoqi Li, Kiruthika Ramanathan, Ning Ning, Luping Shi, Changyun Wen

Research Collection School Of Computing and Information Systems

As can be represented by neurons and their synaptic connections, attractor networks are widely believed to underlie biological memory systems and have been used extensively in recent years to model the storage and retrieval process of memory. In this paper, we propose a new energy function, which is nonnegative and attains zero values only at the desired memory patterns. An attractor network is designed based on the proposed energy function. It is shown that the desired memory patterns are stored as the stable equilibrium points of the attractor network. To retrieve a memory pattern, an initial stimulus input is presented …

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Physio@Home: Exploring Visual Guidance And Feedback Techniques For Physiotherapy Exercises, Richard Tang, Xing-Dong Yang, Scott Bateman, Joaquim Jorge, Anthony Tang Apr 2015

Physio@Home: Exploring Visual Guidance And Feedback Techniques For Physiotherapy Exercises, Richard Tang, Xing-Dong Yang, Scott Bateman, Joaquim Jorge, Anthony Tang

Research Collection School Of Computing and Information Systems

Physiotherapy patients exercising at home alone are at risk of re-injury since they do not have corrective guidance from a therapist. To explore solutions to this problem, we designed Physio@Home, a prototype that guides people through pre-recorded physiotherapy exercises using realtime visual guides and multi-camera views. Our design addresses several aspects of corrective guidance, including: plane and range of movement, joint positions and angles, and extent of movement. We evaluated our design, comparing how closely people could follow exercise movements under various feedback conditions. Participants were most accurate when using our visual guide and multi-views. We provide suggestions for exercise …

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Best Upgrade Plans For Single And Multiple Source-Destination Pairs, Yimin Lin, Kyriakos Mouratidis Apr 2015

Best Upgrade Plans For Single And Multiple Source-Destination Pairs, Yimin Lin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

In this paper, we study Resource Constrained Best Upgrade Plan (BUP) computation in road network databases. Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. In the single-pair version of BUP, the input includes a source and a destination in G, and a budget B (resource constraint). The goal is to identify which upgradable edges should be upgraded so that the shortest path distance between source and …

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Review Selection Using Micro-Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas Apr 2015

Review Selection Using Micro-Reviews, Thanh-Son Nguyen, Hady W. Lauw, Panayiotis Tsaparas

Research Collection School Of Computing and Information Systems

Given the proliferation of review content, and the fact that reviews are highly diverse and often unnecessarily verbose, users frequently face the problem of selecting the appropriate reviews to consume. Micro-reviews are emerging as a new type of online review content in the social media. Micro-reviews are posted by users of check-in services such as Foursquare. They are concise (up to 200 characters long) and highly focused, in contrast to the comprehensive and verbose reviews. In this paper, we propose a novel mining problem, which brings together these two disparate sources of review content. Specifically, we use coverage of micro-reviews …

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Maximizing Lifetime In Clustered Wsns With Energy Harvesting Relay: Profiling And Modeling, Pengfei Zhang, Hwee-Pink Tan, Gaoxi Xiao, Yi Yu Apr 2015

Maximizing Lifetime In Clustered Wsns With Energy Harvesting Relay: Profiling And Modeling, Pengfei Zhang, Hwee-Pink Tan, Gaoxi Xiao, Yi Yu

Research Collection School Of Computing and Information Systems

Inspired by clustering and energy harvesting techniques, we study multiple-cluster wireless sensor networks with energy harvesting (EH) sensors serving as relay for cluster heads. In this paper, we derive the model for realistic energy harvesting rate. Then we propose distributed matching algorithm for EHs to serve as relay for CHs. The proposed algorithm could find optimal/near-optimal CH-EH matching in short time and still achieve good performance. We evaluate the performance of our method through theoretical analysis as well as simulation.

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Effect Of Machine Translation In Interlingual Conversation: Lessons From A Formative Study, Kotaro Hara, Shamsi T. Iqbal Apr 2015

Effect Of Machine Translation In Interlingual Conversation: Lessons From A Formative Study, Kotaro Hara, Shamsi T. Iqbal

Research Collection School Of Computing and Information Systems

Language barrier is the primary challenge for effectivecross-lingual conversations. Spoken language translation(SLT) is perceived as a cost-effective alternative to lessaffordable human interpreters, but little research has beendone on how people interact with such technology. Using aprototype translator application, we performed a formativeevaluation to elicit how people interact with the technologyand adapt their conversation style. We conducted two setsof studies with a total of 23 pairs (46 participants).Participants worked on storytelling tasks to simulate naturalconversations with 3 different interface settings. Ourfindings show that collocutors naturally adapt their style ofspeech production and comprehension to compensate forinadequacies in SLT. We conclude the paper …

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Chalk And Cheese In Twitter: Discriminating Personal And Organization Accounts, Richard Jayadi Oentaryo, Jia-Wei Low, Ee Peng Lim Apr 2015

Chalk And Cheese In Twitter: Discriminating Personal And Organization Accounts, Richard Jayadi Oentaryo, Jia-Wei Low, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Social media have been popular not only for individuals to share contents, but also for organizations to engage users and spread information. Given the trait differences between personal and organization accounts, the ability to distinguish between the two account types is important for developing better search/recommendation engines, marketing strategies, and information dissemination platforms. However, such task is non-trivial and has not been well studied thus far. In this paper, we present a new generic framework for classifying personal and organization accounts, based upon which comprehensive and systematic investigation on a rich variety of content, social, and temporal features can be …

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