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Research Collection School Of Computing and Information Systems

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Full-Text Articles in Computer Sciences

Ui X-Ray: Interactive Mobile Ui Testing Based On Computer Vision, Chun-Fu Richard Chen, Marco Pistoia, Conglei Shi, Paolo Girolami, Joseph W. Ligman, Yong Wang Mar 2017

Ui X-Ray: Interactive Mobile Ui Testing Based On Computer Vision, Chun-Fu Richard Chen, Marco Pistoia, Conglei Shi, Paolo Girolami, Joseph W. Ligman, Yong Wang

Research Collection School Of Computing and Information Systems

User Interface/eXperience (UI/UX) significantly affects the lifetime of any software program, particularly mobile apps. A bad UX can undermine the success of a mobile app even if that app enables sophisticated capabilities. A good UX, however, needs to be supported of a highly functional and user friendly UI design. In spite of the importance of building mobile apps based on solid UI designs, UI discrepancies- inconsistencies between UI design and implementation-Are among the most numerous and expensive defects encountered during testing. This paper presents UI X-RAY, an interactive UI testing system that integrates computer-vision methods to facilitate the correction of …

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Are The Trade-Offs For Reducing Cross-Border Cybercrime Manageable?, Steven Mark Miller, Qiu-Hong Wang, Robert John Kauffman Mar 2017

Are The Trade-Offs For Reducing Cross-Border Cybercrime Manageable?, Steven Mark Miller, Qiu-Hong Wang, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Without increased government intervention and government-industry collaboration, the advantages inherent in the next wave of Internet-enabled digital transformation will increasingly tilt toward cybercriminals, and their influence will disproportionately increase. The dilemma that immediately presents itself in such a scenario, however, is that an increased level of government involvement can also lead to undesirable consequences. Increasing security always comes with trade-offs that must be managed. The obvious concerns relate to the erosion of privacy, illegal or extralegal persecution, the abuse of Internet censorship and the impediment to or stifling of innovation.

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Collaboration Trumps Homophily In Urban Mobile Crowdsourcing, Thivya Kandappu, Archan Misra, Randy Tandriansyah Mar 2017

Collaboration Trumps Homophily In Urban Mobile Crowdsourcing, Thivya Kandappu, Archan Misra, Randy Tandriansyah

Research Collection School Of Computing and Information Systems

This paper establishes the power of dynamic collaborative task completion among workers for urban mobile crowdsourcing. Collaboration is defined via the notion of peer referrals, whereby a worker who has accepted a location-specific task, but is unlikely to visit that location, offloads the task to a willing friend. Such a collaborative framework might be particularly useful for task bundles, especially for bundles that have higher geographic dispersion. The challenge, however, comes from the high similarity observed in the spatiotemporal pattern of task completion among friends. Using extensive real-world crowd-sourcing studies conducted over 7 weeks and 1000+ workers on a campus-based …

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Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau Mar 2017

Privacy In Context-Aware Mobile Crowdsourcing Systems, Thivya Kandappu, Archan Misra, Shih-Fen Cheng, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Mobile crowd-sourcing can become as a strategy to perform time-sensitive urban tasks (such as municipal monitoring and last mile logistics) by effectively coordinating smartphone users. The success of the mobile crowd-sourcing platform depends mainly on its effectiveness in engaging crowd-workers, and recent studies have shown that compared to the pull-based approach, which relies on crowd-workers to browse and commit to tasks they would want to perform, the push-based approach can take into consideration of worker’s daily routine, and generate highly effective recommendations. As a result, workers waste less time on detours, plan more in advance, and require much less planning …

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Whole-System Analysis For Understanding Publicly Accessible Functions In Android, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan Mar 2017

Whole-System Analysis For Understanding Publicly Accessible Functions In Android, Huu Hoang Nguyen, Lingxiao Jiang, Thanh Tho Quan

Research Collection School Of Computing and Information Systems

Android has become the most popular mobile operating system. Millions of applications, including many malwares, haven been developed for it. Android itself evolves constantly with changing features and higher complexities. It is challenging for application developers to keep up with the changes and maintain the compatibility of their apps across Android versions. Therefore, there are many challenges for application analysis tools to accurately model and analyze app behaviors across Android versions. Even though the overall system architecture of Android and many APIs are documented, many other APIs and implementation details are not, not to mention potential bugs and vulnerabilities. Techniques …

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Location Matters: Geospatial Policy Analytics Over Time For Household Hazardous Waste Collection In California, Kustini Lim-Wavde, Robert John Kauffman, Tin Seong Kam, Gregory S. Dawson Mar 2017

Location Matters: Geospatial Policy Analytics Over Time For Household Hazardous Waste Collection In California, Kustini Lim-Wavde, Robert John Kauffman, Tin Seong Kam, Gregory S. Dawson

Research Collection School Of Computing and Information Systems

By integrating mapping and geospatial data into a county-level datasetfor exploratory analysis, we will demonstrate how to provide useful insightsfor waste managers and local governments regarding spatial patterns ofhousehold hazardous waste (HHW) collection and how it changes over time. We usemap-based visualization to display patterns of spatial intensity and countylocations for HHW collection in California from 2004 to 2015. We use exploratory spatial data analyticsmethods to characterize the spatial distribution of HHW collected per person.When we considered the spatial relationships, we were able to develop andestimate a geographically-weighted regression to explain how different regionalfactors influence the amount of HHW collected. …

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Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen Mar 2017

Scalable Image Retrieval By Sparse Product Quantization, Qingqun Ning, Jianke Zhu, Zhiyuan Zhong, Steven C. H. Hoi, Chun Chen

Research Collection School Of Computing and Information Systems

Fast approximate nearest neighbor (ANN) search technique for high-dimensional feature indexing and retrieval is the crux of large-scale image retrieval. A recent promising technique is product quantization, which attempts to index high-dimensional image features by decomposing the feature space into a Cartesian product of low-dimensional subspaces and quantizing each of them separately. Despite the promising results reported, their quantization approach follows the typical hard assignment of traditional quantization methods, which may result in large quantization errors, and thus, inferior search performance. Unlike the existing approaches, in this paper, we propose a novel approach called sparse product quantization (SPQ) to encoding …

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Are The Trade-Offs For Reducing Cross-Border Cybercrime Manageable?, Steven Mark Miller, Qiu-Hong Wang, Robert John Kauffman Mar 2017

Are The Trade-Offs For Reducing Cross-Border Cybercrime Manageable?, Steven Mark Miller, Qiu-Hong Wang, Robert John Kauffman

Research Collection School Of Computing and Information Systems

Without increased government intervention andgovernment-industry collaboration, the advantages inherent in the next wave ofInternet-enabled digital transformation will increasingly tilt towardcyber criminals, and their influence will disproportionately increase. The dilemma that immediately presents itself in such ascenario, however, is that an increased level of government involvement canalso lead to undesirable consequences. Increasing security always comes withtrade-offs that must be managed. The obvious concerns relate to the erosion ofprivacy, illegal or extralegal persecution, the abuse of Internet censorshipand the impediment to or stifling of innovation.

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Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang Feb 2017

Soal: Second-Order Online Active Learning, Shuji Hao, Peilin Zhao, Jing Lu, Steven C. H. Hoi, Chunyan Miao, Chi Zhang

Research Collection School Of Computing and Information Systems

This paper investigates the problem of online active learning for training classification models from sequentially arriving data. This is more challenging than conventional online learning tasks since the learner not only needs to figure out how to effectively update the classifier but also needs to decide when is the best time to query the label of an incoming instance given limited label budget. The existing online active learning approaches are often based on first-order online learning methods which generally fall short in slow convergence rate and suboptimal exploitation of available information when querying the labeled data. To overcome the limitations, …

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Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau Feb 2017

Collective Multiagent Sequential Decision Making Under Uncertainty, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Multiagent sequential decision making has seen rapid progress with formal models such as decentralized MDPs and POMDPs. However, scalability to large multiagent systems and applicability to real world problems remain limited. To address these challenges, we study multiagent planning problems where the collective behavior of a population of agents affects the joint-reward and environment dynamics. Our work exploits recent advances in graphical models for modeling and inference with a population of individuals such as collective graphical models and the notion of finite partial exchangeability in lifted inference. We develop a collective decentralized MDP model where policies can be computed based …

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A Riemannian Network For Spd Matrix Learning, Zhiwu Huang, Gool L. Van Feb 2017

A Riemannian Network For Spd Matrix Learning, Zhiwu Huang, Gool L. Van

Research Collection School Of Computing and Information Systems

Symmetric Positive Definite (SPD) matrix learning methods have become popular in many image and video processing tasks, thanks to their ability to learn appropriate statistical representations while respecting Riemannian geometry of underlying SPD manifolds. In this paper we build a Riemannian network architecture to open up a new direction of SPD matrix non-linear learning in a deep model. In particular, we devise bilinear mapping layers to transform input SPD matrices to more desirable SPD matrices, exploit eigenvalue rectification layers to apply a non-linear activation function to the new SPD matrices, and design an eigenvalue logarithm layer to perform Riemannian computing …

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Maximizing The Probability Of Arriving On Time: A Practical Q-Learning Method, Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek, Ulrich Fastenrath Feb 2017

Maximizing The Probability Of Arriving On Time: A Practical Q-Learning Method, Zhiguang Cao, Hongliang Guo, Jie Zhang, Frans Oliehoek, Ulrich Fastenrath

Research Collection School Of Computing and Information Systems

The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. …

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Security Slicing For Auditing Common Injection Vulnerabilities, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand Feb 2017

Security Slicing For Auditing Common Injection Vulnerabilities, Julian Thome, Lwin Khin Shar, Domenico Bianculli, Lionel Briand

Research Collection School Of Computing and Information Systems

Cross-site scripting and injection vulnerabilities are among the most common and serious security issues for Web applications. Although existing static analysis approaches can detect potential vulnerabilities in source code, they generate many false warnings and source-sink traces with irrelevant information, making their adoption impractical for security auditing. One suitable approach to support security auditing is to compute a program slice for each sink, which contains all the information required for security auditing. However, such slices are likely to contain a large amount of information that is irrelevant to security, thus raising scalability issues for security audits. In this paper, we …

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Robust Optimization For Tree-Structured Stochastic Network Design, Xiaojian Wu, Akshat Kumar, Daniel Sheldon Feb 2017

Robust Optimization For Tree-Structured Stochastic Network Design, Xiaojian Wu, Akshat Kumar, Daniel Sheldon

Research Collection School Of Computing and Information Systems

Stochastic network design is a general framework for optimizing network connectivity. It has several applications in computational sustainability including spatial conservation planning, pre-disaster network preparation, and river network optimization. A common assumption in previous work has been made that network parameters (e.g., probability of species colonization) are precisely known, which is unrealistic in real- world settings. We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers. We assume that fish passability probabilities are known only imprecisely, but are within some interval bounds. We then develop a planning …

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Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang Feb 2017

Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …

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Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar Feb 2017

Decentralized Planning In Stochastic Environments With Submodular Rewards, Rajiv Ranjan Kumar, Pradeep Varakantham, Akshat Kumar

Research Collection School Of Computing and Information Systems

Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show …

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Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou Feb 2017

Streaming Classification With Emerging New Class By Class Matrix Sketching, Xin Mu, Feida Zhu, Juan Du, Ee-Peng Lim, Zhi-Hua Zhou

Research Collection School Of Computing and Information Systems

Streaming classification with emerging new class is an important problem of great research challenge and practical value. In many real applications, the task often needs to handle large matrices issues such as textual data in the bag-of-words model and large-scale image analysis. However, the methodologies and approaches adopted by the existing solutions, most of which involve massive distance calculation, have so far fallen short of successfully addressing a real-time requested task. In this paper, the proposed method dynamically maintains two low-dimensional matrix sketches to 1) detect emerging new classes; 2) classify known classes; and 3) update the model in the …

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Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang Feb 2017

Discovering Burst Patterns Of Burst Topic In Twitter, Guozhong Dong, Wu Yang, Feida Zhu, Wei Wang

Research Collection School Of Computing and Information Systems

Twitter has become one of largest social networks for users to broadcast burst topics. There have been many studies on how to detect burst topics. However, mining burst patterns in burst topics has not been solved by the existing works. In this paper, we investigate the problem of mining burst patterns of burst topic in Twitter. A burst topic user graph model is proposed, which can represent the topology structure of burst topic propagation across a large number of Twitter users. Based on the model, hierarchical clustering is applied to cluster burst topics and reveal burst patterns from the macro …

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Collaboration Trumps Homophily In Urban Mobile Crowd-Sourcing, Thivya Kandappu, Archan Misra, Randy Tandriansyah Daratan Feb 2017

Collaboration Trumps Homophily In Urban Mobile Crowd-Sourcing, Thivya Kandappu, Archan Misra, Randy Tandriansyah Daratan

Research Collection School Of Computing and Information Systems

This paper establishes the power of dynamic collaborative task completion among workers for urban mobile crowdsourcing. Collaboration is defined via the notion of peer referrals, whereby a worker who has accepted a location-specific task, but is unlikely to visit that location, offloads the task to a willing friend. Such a collaborative framework might be particularly useful for task bundles, especially for bundles that have higher geographic dispersion. The challenge, however, comes from the high similarity observed in the spatiotemporal pattern of task completion among friends. Using extensive real-world crowd-sourcing studies conducted over 7 weeks and 1000+ workers on a campus-based …

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Clcminer: Detecting Cross-Language Clones Without Intermediates, Xiao Cheng, Zhiming Peng, Lingxiao Jiang, Hao Zhong, Haibo Yu, Jianjun Zhao Feb 2017

Clcminer: Detecting Cross-Language Clones Without Intermediates, Xiao Cheng, Zhiming Peng, Lingxiao Jiang, Hao Zhong, Haibo Yu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

The proliferation of diverse kinds of programming languages and platforms makes it a common need to have the same functionality implemented in different languages for different platforms, such as Java for Android applications and C# forWindows phone applications. Although versions of code written in different languages appear syntactically quite different from each other, they are intended to implement the same software and typically contain many code snippets that implement similar functionalities, which we call cross-language clones. When the version of code in one language evolves according to changing functionality requirements and/or bug fixes, its cross-language clones may also need be …

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Crowdsensing And Analyzing Micro-Event Tweets For Public Transportation Insights, Thoong Hoang, Pei Hua (Xu Peihua) Cher, Philips Kokoh Prasetyo, Ee-Peng Lim Feb 2017

Crowdsensing And Analyzing Micro-Event Tweets For Public Transportation Insights, Thoong Hoang, Pei Hua (Xu Peihua) Cher, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Efficient and commuter friendly public transportation system is a critical part of a thriving and sustainable city. As cities experience fast growing resident population, their public transportation systems will have to cope with more demands for improvements. In this paper, we propose a crowdsensing and analysis framework to gather and analyze realtime commuter feedback from Twitter. We perform a series of text mining tasks identifying those feedback comments capturing bus related micro-events; extracting relevant entities; and, predicting event and sentiment labels. We conduct a series of experiments involving more than 14K labeled tweets. The experiments show that incorporating domain knowledge …

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Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet Feb 2017

Dynamic Repositioning To Reduce Lost Demand In Bike Sharing Systems, Supriyo Ghosh, Pradeep Varakantham, Yossiri Adulyasak, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Bike Sharing Systems (BSSs) are widely adopted in major cities of the world due to concerns associated with extensive private vehicle usage, namely, increased carbon emissions, traffic congestion and usage of nonrenewable resources. In a BSS, base stations are strategically placed throughout a city and each station is stocked with a pre-determined number of bikes at the beginning of the day. Customers hire the bikes from one station and return them at another station. Due to unpredictable movements of customers hiring bikes, there is either congestion (more than required) or starvation (fewer than required) of bikes at base stations. Existing …

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Why And How Developers Fork What From Whom In Github, Jing Jiang, David Lo, Jiahuan He, Xin Xia, Pavneet Singh Kochhar, Li Zhang Feb 2017

Why And How Developers Fork What From Whom In Github, Jing Jiang, David Lo, Jiahuan He, Xin Xia, Pavneet Singh Kochhar, Li Zhang

Research Collection School Of Computing and Information Systems

Forking is the creation of a new software repository by copying another repository. Though forking is controversial in traditional open source software (OSS) community, it is encouraged and is a built-in feature in GitHub. Developers freely fork repositories, use codes as their own and make changes. A deep understanding of repository forking can provide important insights for OSS community and GitHub. In this paper, we explore why and how developers fork what from whom in GitHub. We collect a dataset containing 236,344 developers and 1,841,324 forks. We make surveys, and analyze programming languages and owners of forked repositories. Our main …

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Seapot-Rl: Selective Exploration Algorithm For Policy Transfer In Rl, Akshay Narayan, Zhuoru Li, Tze-Yun Leong Feb 2017

Seapot-Rl: Selective Exploration Algorithm For Policy Transfer In Rl, Akshay Narayan, Zhuoru Li, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We propose a new method for transferring a policy from a source task to a target task in model-based reinforcement learning. Our work is motivated by scenarios where a robotic agent operates in similar but challenging environments, such as hospital wards, differentiated by structural arrangements or obstacles, such as furniture. We address problems that require fast responses adapted from incomplete, prior knowledge of the agent in new scenarios. We present an efficient selective exploration strategy that maximally reuses the source task policy. Reuse efficiency is effected through identifying sub-spaces that are different in the target environment, thus limiting the exploration …

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Optimizing Expectation With Guarantees In Pomdps, Krishnendu Chatterjee, Guillermo A. Perez, Jean-François Raskin, Dorde Zikelic Feb 2017

Optimizing Expectation With Guarantees In Pomdps, Krishnendu Chatterjee, Guillermo A. Perez, Jean-François Raskin, Dorde Zikelic

Research Collection School Of Computing and Information Systems

A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may still permit unlikely but highly undesirable outcomes, which is problematic especially in safety-critical applications. Recently, there has been a surge of interest in POMDPs where the goal is to maximize the probability to ensure that the payoff is at least a given threshold, but these approaches do not consider any optimization beyond satisfying this threshold constraint. In this work we go beyond both the "expectation" and "threshold" approaches and consider a "guaranteed payoff optimization (GPO)" problem …

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Bike Route Choice Modeling Using Gps Data Without Choice Sets Of Paths, Maëlle Zimmermann, Tien Mai, Emma Frejinger Feb 2017

Bike Route Choice Modeling Using Gps Data Without Choice Sets Of Paths, Maëlle Zimmermann, Tien Mai, Emma Frejinger

Research Collection School Of Computing and Information Systems

Concerned by the nuisances of motorized travel on urban life, policy makers are faced with the challenge of making cycling a more attractive alternative for everyday transportation. Route choice models can help achieve this objective by gaining insights into the trade-offs cyclists make when choosing their routes and by allowing the effect of infrastructure improvements to be analyzed. We estimate a link-based bike route choice model from a sample of GPS observations in the city of Eugene on a network comprising over 40,000 links. The so-called recursive logit (RL) model (Fosgerau et al., 2013) does not require to sample any …

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An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong Feb 2017

An Efficient Approach To Model-Based Hierarchical Reinforcement Learning, Zhuoru Li, Akshay Narayan, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We propose a model-based approach to hierarchical reinforcement learning that exploits shared knowledge and selective execution at different levels of abstraction, to efficiently solve large, complex problems. Our framework adopts a new transition dynamics learning algorithm that identifies the common action-feature combinations of the subtasks, and evaluates the subtask execution choices through simulation. The framework is sample efficient, and tolerates uncertain and incomplete problem characterization of the subtasks. We test the framework on common benchmark problems and complex simulated robotic environments. It compares favorably against the stateof-the-art algorithms, and scales well in very large problems.

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Active Video Summarization: Customized Summaries Via On-Line Interaction With The User, Ana Garcia Del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan Feb 2017

Active Video Summarization: Customized Summaries Via On-Line Interaction With The User, Ana Garcia Del Molino, Xavier Boix, Joo-Hwee Lim, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

To facilitate the browsing of long videos, automatic video summarization provides an excerpt that represents its content. In the case of egocentric and consumer videos, due to their personal nature, adapting the summary to specific user’s preferences is desirable. Current approaches to customizable video summarization obtain the user’s preferences prior to the summarization process. As a result, the user needs to manually modify the summary to further meet the preferences. In this paper, we introduce Active Video Summarization (AVS), an interactive approach to gather the user’s preferences while creating the summary. AVS asks questions about the summary to update it …

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Detecting Similar Repositories On Github, Yun Zhang, David Lo, Pavneet Singh Kochhar, Xin Xia, Quanlai Li, Jianling Sun Feb 2017

Detecting Similar Repositories On Github, Yun Zhang, David Lo, Pavneet Singh Kochhar, Xin Xia, Quanlai Li, Jianling Sun

Research Collection School Of Computing and Information Systems

GitHub contains millions of repositories among which many are similar with one another (i.e., having similar source codes or implementing similar functionalities). Finding similar repositories on GitHub can be helpful for software engineers as it can help them reuse source code, build prototypes, identify alternative implementations, explore related projects, find projects to contribute to, and discover code theft and plagiarism. Previous studies have proposed techniques to detect similar applications by analyzing API usage patterns and software tags. However, these prior studies either only make use of a limited source of information or use information not available for projects on GitHub. …

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Empath-D: Empathetic Design For Accessibility, Kenny Tsu Wei Choo, Rajesh Krishna Balan, Kiat Wee Tan, Archan Misra, Youngki Lee Feb 2017

Empath-D: Empathetic Design For Accessibility, Kenny Tsu Wei Choo, Rajesh Krishna Balan, Kiat Wee Tan, Archan Misra, Youngki Lee

Research Collection School Of Computing and Information Systems

We describe our vision for Empath-D, our system to enable Empathetic User Interface Design. Our key idea is to leverage Virtual and Augmented Reality (VR / AR) displays to provide an Immersive Reality environment, where developers/designers can emulate impaired interactions by elderly or disabled users while testing the usability of their applications. Our early experiences with the Empath-D prototype show that Empath-D can emulate a cataract vision impairment of the elderly and guide designers to create accessible web pages with less mental workload.

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