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Articles 6001 - 6030 of 8479
Full-Text Articles in Computer Sciences
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Predicting Best Answerers For New Questions: An Approach Leveraging Topic Modeling And Collaborative Voting, Yuan Tian, Pavneet Singh Kochhar, Ee Peng Lim, Feida Zhu, David Lo
Research Collection School Of Computing and Information Systems
Community Question Answering (CQA) sites are becoming increasingly important source of information where users can share knowledge on various topics. Although these platforms bring new opportunities for users to seek help or provide solutions, they also pose many challenges with the ever growing size of the community. The sheer number of questions posted everyday motivates the problem of routing questions to the appropriate users who can answer them. In this paper, we propose an approach to predict the best answerer for a new question on CQA site. Our approach considers both user interest and user expertise relevant to the topics …
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Attempts have been made to understand driver development in terms of code clones. In this paper, we propose an alternate view, based on the metaphor of a gene. Guided by this metaphor, we study the structure of Linux 3.10 ethernet platform driver probe functions.
Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz
Mining Branching-Time Scenarios, Dirk Fahland, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows …
Automatic Recommendation Of Api Methods From Feature Requests, Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall
Automatic Recommendation Of Api Methods From Feature Requests, Ferdian Thung, Shaowei Wang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Developers often receive many feature requests. To implement these features, developers can leverage various methods from third party libraries. In this work, we propose an automated approach that takes as input a textual description of a feature request. It then recommends methods in library APIs that developers can use to implement the feature. Our recommendation approach learns from records of other changes made to software systems, and compares the textual description of the requested feature with the textual descriptions of various API methods. We have evaluated our approach on more than 500 feature requests of Axis2/Java, CXF, Hadoop Common, HBase, …
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Upsizer: Synthetically Scaling An Empirical Relational Database, Y. C. Tay, Bing Tian Dai, Daniel T. Wang, Eldora Y. Sun, Yong Lin, Yuting Lin
Research Collection School Of Computing and Information Systems
The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state e D that is similar to D but s …
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Covariance Selection By Thresholding The Sample Correlation Matrix, Binyan Jiang
Research Collection School Of Computing and Information Systems
This article shows that when the nonzero coefficients of the population correlation matrix are all greater in absolute value than (C1logp/n)1/2 for some constant C1, we can obtain covariance selection consistency by thresholding the sample correlation matrix. Furthermore, the rate (logp/n)1/2 is shown to be optimal.
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Understanding The Genetic Makeup Of Linux Device Drivers, Peter Senna Tschudin, Laurent Reveillere, Lingxiao Jiang, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
No abstract provided.
Got Issues? Who Cares About It? A Large Scale Investigation Of Issue Trackers From Github, Tegawende F. Bissyande, David Lo, Lingxiao Jiang, Laurent Reveillere, Jacques Klein, Yves Le Traon
Got Issues? Who Cares About It? A Large Scale Investigation Of Issue Trackers From Github, Tegawende F. Bissyande, David Lo, Lingxiao Jiang, Laurent Reveillere, Jacques Klein, Yves Le Traon
Research Collection School Of Computing and Information Systems
Feedback from software users constitutes a vital part in the evolution of software projects. By filing issue reports, users help identify and fix bugs, document software code, and enhance the software via feature requests. Many studies have explored issue reports, proposed approaches to enable the submission of higher-quality reports, and presented techniques to sort, categorize and leverage issues for software engineering needs. Who, however, cares about filing issues? What kind of issues are reported in issue trackers? What kind of correlation exist between issue reporting and the success of software projects? In this study, we address the need for answering …
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Predicting User's Political Party Using Ideological Stances, Swapna Gottopati, Minghui Qiu, Liu Yang, Feida Zhu, Jing Jiang
Research Collection School Of Computing and Information Systems
Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task …
Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan
Challenges And Opportunities In Taxi Fleet Anomaly Detection, Rijurekha Sen, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
To enhance fleet operation and management, logistics companies instrument their vehicles with GPS receivers and network connectivity to servers. Mobility traces from such large fleets provide significant information on commuter travel patterns, traffic congestion and road anomalies, and hence several researchers have mined such datasets to gain useful urban insights. These logistics companies, however, incur significant cost in deploying and maintaining their vast network of instrumented vehicles. Thus research problems, that are not only of interest to urban planners, but to the logistics companies themselves are important to attract and engage these companies for collaborative data analysis. In this paper, …
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Classification In P2p Networks With Cascade Support Vendor Machines, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee-Keong Ng
Research Collection School Of Computing and Information Systems
Classification in Peer-to-Peer (P2P) networks is important to many real applications, such as distributed intrusion detection, distributed recommendation systems, and distributed antispam detection. However, it is very challenging to perform classification in P2P networks due to many practical issues, such as scalability, peer dynamism, and asynchronism. This article investigates the practical techniques of constructing Support Vector Machine (SVM) classifiers in the P2P networks. In particular, we demonstrate how to efficiently cascade SVM in a P2P network with the use of reduced SVM. In addition, we propose to fuse the concept of cascade SVM with bootstrap aggregation to effectively balance the …
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Second Order Online Collaborative Filtering, Jing Lu, Steven C. H. Hoi, Jialei Wang, Peilin Zhao
Research Collection School Of Computing and Information Systems
Collaborative Filtering (CF) is one of the most successful learning techniques in building real-world recommender systems. Traditional CF algorithms are often based on batch machine learning methods which suffer from several critical drawbacks, e.g., extremely expensive model retraining cost whenever new samples arrive, unable to capture the latest change of user preferences over time, and high cost and slow reaction to new users or products extension. Such limitations make batch learning based CF methods unsuitable for real-world online applications where data often arrives sequentially and user preferences may change dynamically and rapidly. To address these limitations, we investigate online collaborative …
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Mining Fraudulent Patterns In Online Advertising, Richard J. Oentaryo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Advances in web technologies have rendered onlineadvertising as an effective means for small and large businesses to target different market segments on the fly. Online advertising is a huge industry. According to Gartner Inc., worldwide online advertising revenue is projected tohit $11.4 billion in 2013, up from $9.6 billion in 2012. Global revenue will also reach $24.5 billion in 2016, with online advertising creating opportunities for app developers, advertising networks, and service providersin various regions. An online advertising ecosystem is typically coordinated by an advertising commissioner, acting as a broker between advertisers and content publishers. An advertiser plans a budget, …
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Social Informatics, Adam Jatowt, Ee-Peng Lim, Ying Ding, Asako Miura, Taro Tezuka, Gael Dias, Katsumi Tanaka, Andrew J. Flanagin, Bing Tian Dai
Research Collection School Of Computing and Information Systems
This book constitutes the proceedings of the 5th International Conference on Social Informatics, SocInfo 2013, held in Kyoto, Japan, in November 2013. The 23 full papers, 15 short papers, and three poster papers included in this volume were carefully reviewed and selected from 103 submissions. The papers present original research work on studying the interplay between socially-centric platforms and social phenomena.
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Social Listening For Customer Acquisition, Juan Du, Biying Tan, Feida Zhu, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Social network analysis has received much attention from corporations recently. Corporations are trying to utilize social media platforms such as Twitter, Facebook and Sina Weibo to expand their own markets. Our system is an online tool to assist these corporations to 1) find potential customers, and 2) track a list of users by specific events from social networks. We employ both textual and network information, and thus produce a keyword-based relevance score for each user in pre-defined dimensions, which indicates the probability of the adoption of a product. Based on the score and its trend, out tool is able to …
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Image Search By Graph-Based Label Propagation With Image Representation From Dnn, Yingwei Pan, Yao Ting, Kuiyuan Yang, Houqiang Li, Chong-Wah Ngo, Jingdong Wang, Tao Mei
Research Collection School Of Computing and Information Systems
Our objective is to estimate the relevance of an image to a query for image search purposes. We address two limitations of the existing image search engines in this paper. First, there is no straightforward way of bridging the gap between semantic textual queries as well as users’ search intents and image visual content. Image search engines therefore primarily rely on static and textual features. Visual features are mainly used to identify potentially useful recurrent patterns or relevant training examples for complementing search by image reranking. Second, image rankers are trained on query-image pairs labeled by human experts, making the …
Annotation For Free: Video Tagging By Mining User Search Behavior, Yao Ting, Tao Mei, Chong-Wah Ngo, Shipeng Li
Annotation For Free: Video Tagging By Mining User Search Behavior, Yao Ting, Tao Mei, Chong-Wah Ngo, Shipeng Li
Research Collection School Of Computing and Information Systems
The problem of tagging is mostly considered from the perspectives of machine learning and data-driven philosophy. A fundamental issue that underlies the success of these approaches is the visual similarity, ranging from the nearest neighbor search to manifold learning, to identify similar instances of an example for tag completion. The need to searching for millions of visual examples in high-dimensional feature space, however, makes the task computationally expensive. Moreover, the results can suffer from robustness problem, when the underlying data, such as online videos, are rich of semantics and the similarity is difficult to be learnt from low-level features. This …
Error Recovered Hierarchical Classification, Shiai Zhu, Xiao-Yong Wei, Chong-Wah Ngo
Error Recovered Hierarchical Classification, Shiai Zhu, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Hierarchical classification (HC) is a popular and efficient way for detecting the semantic concepts from the images. However, the conventional HC, which always selects the branch with the highest classification response to go on, has the risk of propagating serious errors from higher levels of the hierarchy to the lower levels. We argue that the highestresponse-first strategy is too arbitrary, because the candidate nodes are considered individually which ignores the semantic relationship among them. In this paper, we propose a novel method for HC, which is able to utilize the semantic relationship among candidate nodes and their children to recover …
The Vireo Team At Mediaeval 2013: Violent Scenes Detection By Mid-Level Concepts Learnt From Youtube, Chun Chet Tan, Chong-Wah Ngo
The Vireo Team At Mediaeval 2013: Violent Scenes Detection By Mid-Level Concepts Learnt From Youtube, Chun Chet Tan, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
The Violent Scenes Detection task continues to pose challenge in detecting violent scenes in Hollywood movies. In this working notes paper, we present the framework of our system and briefly discuss the performance results obtained in both objective and subjective subtasks. Besides using the low-level features for training the SVM classifiers for violent scenes detection, we show the feasibility in using the concept detectors to infer the occurrence of violent scenes. External Youtube data is exploited in our implementation to provide more diverse definition to violent scene concepts. Furthermore, we explore the feasibility of using Conditional Random Fields (CRF) to …
Cell: A Compositional Verification Framework, Kun Ji, Yang Liu, Jun Sun, Jun Sun, Jin Song Dong, Truong Khanh Nguyen
Cell: A Compositional Verification Framework, Kun Ji, Yang Liu, Jun Sun, Jun Sun, Jin Song Dong, Truong Khanh Nguyen
Research Collection School Of Computing and Information Systems
This paper presents CELL, a comprehensive and extensible framework for compositional verification of concurrent and real-time systems based on commonly used semantic models. For each semantic model, CELL offers three libraries, i.e., compositional verification paradigms, learning algorithms and model checking methods to support various state-of-the-art compositional verification approaches. With well-defined APIs, the framework could be applied to build customized model checkers. In addition, each library could be used independently for verification and program analysis purposes. We have built three model checkers with CELL. The experimental results show that the performance of these model checkers can offer similar or often better …
K-Time Proxy Signature: Formal Definition And Efficient Construction, Weiwei Liu, Guomin Yang, Yi Mu, Jiannan Wei
K-Time Proxy Signature: Formal Definition And Efficient Construction, Weiwei Liu, Guomin Yang, Yi Mu, Jiannan Wei
Research Collection School Of Computing and Information Systems
Proxy signature, which allows an original signer to delegate his/her signing right to another party (or proxy signer), is very useful in many applications. Conventional proxy signature only allows the original signer to specify in the warrant the validity time period of the delegation but not the number of proxy signatures the proxy signer can generate. To address this problem, in this paper, we provide a formal treatment for k-time proxy signature. Such a scheme allows a designated proxy signer to produce only a fixed number of proxy signatures on behalf of the original signer. We provide the formal definitions …
Skyhunter: A Multi-Surface Environment For Supporting Oil And Gas Exploration, Teddy Seyed, Mario Costa Sousa, Frank Maurer, Anthony Tang
Skyhunter: A Multi-Surface Environment For Supporting Oil And Gas Exploration, Teddy Seyed, Mario Costa Sousa, Frank Maurer, Anthony Tang
Research Collection School Of Computing and Information Systems
The process of oil and gas exploration and its result, the decision to drill for oil in a specific location, relies on a number of distinct but related domains. These domains require effective collaboration to come to a decision that is both cost effective and maintains the integrity of the environment. As we show in this paper, many of the existing technologies and practices that support the oil and gas exploration process overlook fundamental user issues such as collaboration, interaction and visualization. The work presented in this paper is based upon a design process that involved expert users from an …
Consistent Stereo Image Editing, Tao Yan, Shengfeng He, Rynson W.H. Lau, Yun Xu
Consistent Stereo Image Editing, Tao Yan, Shengfeng He, Rynson W.H. Lau, Yun Xu
Research Collection School Of Computing and Information Systems
Stereo images and videos are very popular in recent years, and techniques for processing this media are attracting a lot of attention. In this paper, we extend the shift-map method for stereo image editing. Our method simultaneously processes the left and right images on pixel level using a global optimization algorithm. It enforces photo consistence between the two images and preserves 3D scene structures. It also addresses the occlusion and disocclusion problem, which may enable many stereo image editing functions, such as depth mapping, object depth adjustment and non-homogeneous image resizing. Our experiments show that the proposed method produces high …
Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu
Merged Aggregate Nearest Neighbor Query Processing In Road Networks, Weiwei Sun, Chong Chen, Baihua Zheng, Chunan Chen, Liang Zhu
Research Collection School Of Computing and Information Systems
Aggregate nearest neighbor query, which returns a common interesting point that minimizes the aggregate distance for a given query point set, is one of the most important operations in spatial databases and their application domains. This paper addresses the problem of finding the aggregate nearest neighbor for a merged set that consists of the given query point set and multiple points needed to be selected from a candidate set, which we name as merged aggregate nearest neighbor(MANN) query. This paper proposes an effective algorithm to process MANN query in road networks based on our pruning strategies. Extensive experiments are conducted …
Todmis: Mining Communities From Trajectories, Siyuan Liu, Shuhui Wang, Kasthuri Jayarajah, Archan Misra, Rammaya Krishnan
Todmis: Mining Communities From Trajectories, Siyuan Liu, Shuhui Wang, Kasthuri Jayarajah, Archan Misra, Rammaya Krishnan
Research Collection School Of Computing and Information Systems
Existing algorithms for trajectory-based clustering usually rely on simplex representation and a single proximity-related distance (or similarity) measure. Consequently, additional information markers (e.g., social interactions or the semantics of the spatial layout) are usually ignored, leading to the inability to fully discover the communities in the trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) can help capture latent relationships between cluster members. To address this limitation, we propose TODMIS: a general framework for Trajectory cOmmunity Discovery using Multiple Information Sources. TODMIS combines …
Understanding Online Consumer’S Inter-Purchase Time, Youngsoo Kim
Understanding Online Consumer’S Inter-Purchase Time, Youngsoo Kim
Research Collection School Of Computing and Information Systems
This study is motivated by the premise that online consumers can make a purchase at any time of day if they have even a tiny time slot along with Internet access. Given the increased shopping time flexibility, we first examine the patterns of online purchase timing at an online shopping mall handling diverse products (e.g., Amazon.com). The results show (1) the breakdown of purchase timing regularity and (2) the change of weekly spike purchase occurrence. Second, we build online inter-purchase time and price promotion utilization models and estimate them with the data collected from one of the premier online shopping …
Accurate Developer Recommendation For Bug Resolution, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Accurate Developer Recommendation For Bug Resolution, Xin Xia, David Lo, Xinyu Wang, Bo Zhou
Research Collection School Of Computing and Information Systems
Bug resolution refers to the activity that developers perform to diagnose, fix, test, and document bugs during software development and maintenance. It is a collaborative activity among developers who contribute their knowledge, ideas, and expertise to resolve bugs. Given a bug report, we would like to recommend the set of bug resolvers that could potentially contribute their knowledge to fix it. We refer to this problem as developer recommendation for bug resolution. In this paper, we propose a new and accurate method named DevRec for the developer recommendation problem. DevRec is a composite method which performs two kinds of analysis: …
Automatic Recovery Of Root Causes From Bug-Fixing Changes, Ferdian Thung, David Lo, Lingxiao Jiang
Automatic Recovery Of Root Causes From Bug-Fixing Changes, Ferdian Thung, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
What is the root cause of this failure? This question is often among the first few asked by software debuggers when they try to address issues raised by a bug report. Root cause is the erroneous lines of code that cause a chain of erroneous program states eventually leading to the failure. Bug tracking and source control systems only record the symptoms (e.g., bug reports) and treatments of a bug (e.g., committed changes that fix the bug), but not its root cause. Many treatments contain non-essential changes, which are intermingled with root causes. Reverse engineering the root cause of a …
Automated Library Recommendation, Ferdian Thung, David Lo, Julia Lawall
Automated Library Recommendation, Ferdian Thung, David Lo, Julia Lawall
Research Collection School Of Computing and Information Systems
Many third party libraries are available to be downloaded and used. Using such libraries can reduce development time and make the developed software more reliable. However, developers are often unaware of suitable libraries to be used for their projects and thus they miss out on these benefits. To help developers better take advantage of the available libraries, we propose a new technique that automatically recommends libraries to developers. Our technique takes as input the set of libraries that an application currently uses, and recommends other libraries that are likely to be relevant. We follow a hybrid approach that combines association …
Adaptive Gameplay For Programming Practice, Chris Boesch, Sandra Boesch
Adaptive Gameplay For Programming Practice, Chris Boesch, Sandra Boesch
Research Collection School Of Computing and Information Systems
Over the past four years, we have collaborated to develop a set of online games to enable users to practice software languages in a self-directed manner and as part of a class. Recently we introduced a new adaptive difficulty feature that enables players to self-regulate the difficulty of the games they are playing to practice. These new features also provide additional information to further adapt the problem content to better meet the needs of the users.