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Research Collection School Of Computing and Information Systems

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Articles 2791 - 2820 of 3436

Full-Text Articles in Databases and Information Systems

An Effective Approach To 3d Deformable Surface Tracking, Jianke Zhu, Steven C. H. Hoi, Zenglin Xu, Michael R. Lyu Oct 2008

An Effective Approach To 3d Deformable Surface Tracking, Jianke Zhu, Steven C. H. Hoi, Zenglin Xu, Michael R. Lyu

Research Collection School Of Computing and Information Systems

The key challenge with 3D deformable surface tracking arises from the difficulty in estimating a large number of 3D shape parameters from noisy observations. A recent state-of-the-art approach attacks this problem by formulating it as a Second Order Cone Programming (SOCP) feasibility problem. The main drawback of this solution is the high computational cost. In this paper, we first reformulate the problem into an unconstrained quadratic optimization problem. Instead of handling a large set of complicated SOCP constraints, our new formulation can be solved very efficiently by resolving a set of sparse linear equations. Based on the new framework, a …

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Representative Entry Selection For Profiling Blogs, Jinfeng Zhuang, Steven C. H. Hoi, Aixin Sun, Rong Jin Oct 2008

Representative Entry Selection For Profiling Blogs, Jinfeng Zhuang, Steven C. H. Hoi, Aixin Sun, Rong Jin

Research Collection School Of Computing and Information Systems

Many applications on blog search and mining often meet the challenge of handling huge volume of blog data, in which one single blog could contain hundreds or even thousands of entries. We investigate novel techniques for profiling blogs by selecting a subset of representative entries for each blog. We propose two principles for guiding the entry selection task: representativeness and diversity. Further, we formulate the entry selection task into a combinatorial optimization problem and propose a greedy yet effective algorithm for finding a good approximate solution by exploiting the theory of submodular functions. We suggest blog classification for judging the …

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Leveraging Social Context For Searching Social Media, Marc Smith, Vladimir Barash, Lise Getoor, Hady W. Lauw Oct 2008

Leveraging Social Context For Searching Social Media, Marc Smith, Vladimir Barash, Lise Getoor, Hady W. Lauw

Research Collection School Of Computing and Information Systems

The ability to utilize and benefit from today's explosion of social media sites depends on providing tools that allow users to productively participate. In order to participate, users must be able to find resources (both people and information) that they find valuable. Here, we argue that in order to do this effectively, we should make use of a user's "social context". A user's social context includes both their personal social context (their friends and the communities to which they belong) and their community social context (their role and identity in different communities).

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Ontology Enhanced Web Image Retrieval: Aided By Wikipedia And Spreading Activation Theory, Huan Wang, Xing Jiang, Liang-Tien Chia, Ah-Hwee Tan Oct 2008

Ontology Enhanced Web Image Retrieval: Aided By Wikipedia And Spreading Activation Theory, Huan Wang, Xing Jiang, Liang-Tien Chia, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Ontology, as an efective approach to bridge the semantic gap in various domains, has attracted a lot of interests from multimedia researchers. Among the numerous possibilities enabled by ontology, we are particularly interested in exploiting ontology for a better understanding of media task (particularly, images) on the World Wide Web. To achieve our goal, two open issues are inevitably involved: 1) How to avoid the tedious manual work for ontology construction? 2) What are the effective inference models when using an ontology? Recent works about ontology learned from Wikipedia has been reported in conferences targeting the areas of knowledge management …

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Spatio-Temporal Efficiency In A Taxi Dispatch System, Darshan Santani, Rajesh Krishna Balan, C. Jason Woodard Oct 2008

Spatio-Temporal Efficiency In A Taxi Dispatch System, Darshan Santani, Rajesh Krishna Balan, C. Jason Woodard

Research Collection School Of Computing and Information Systems

In this paper, we present an empirical analysis of the GPS-enabled taxi dispatch system used by the world’s second largest land transportation company. We first summarize the collective dynamics of the more than 6,000 taxicabs in this fleet. Next, we propose a simple method for evaluating the efficiency of the system over a given period of time and geographic zone. Our method yields valuable insights into system performance—in particular, revealing significant inefficiencies that should command the attention of the fleet operator. For example, despite the state of the art dispatching system employed by the company, we find imbalances in supply …

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Special Issue Introduction: Hci Studies In Mis, Fiona Fui-Hoon Nah, Xiaowen Fang, Traci Hess, Weiyin Hong Sep 2008

Special Issue Introduction: Hci Studies In Mis, Fiona Fui-Hoon Nah, Xiaowen Fang, Traci Hess, Weiyin Hong

Research Collection School Of Computing and Information Systems

We are grateful to the editors-in-chief for this opportunity and their strong support of the second AIS SIGHCI-sponsored special issue on HCI studies in MIS. We also thank the following reviewers who have played an important role in the development of the manuscripts included in this special issue: Steven Bellman, Damon Campbell, Jinwei Cao, Jane Carey, Andrea Everard, Mark Fuller, Matt Germonprez, Maggie Guo, Susanna Ho, De Liu, Hong Sheng, Chuan Hoo Tan, Horst Treiblmaier, June Wei, and Yunjie Calvin Xu.

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Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng Sep 2008

Cascade Rsvm In Peer-To-Peer Network, Hock Hee Ang, Vivekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng

Research Collection School Of Computing and Information Systems

The goal of distributed learning in P2P networks is to achieve results as close as possible to those from centralized approaches. Learning models of classification in a P2P network faces several challenges like scalability, peer dynamism, asynchronism and data privacy preservation. In this paper, we study the feasibility of building SVM classifiers in a P2P network. We show how cascading SVM can be mapped to a P2P network of data propagation. Our proposed P2P SVM provides a method for constructing classifiers in P2P networks with classification accuracy comparable to centralized classifiers and better than other distributed classifiers. The proposed algorithm …

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Relative Importance, Specific Investment And Ownership In Interorganizational Systems., Kunsoo Han, Robert J. Kauffman, Barrie R. Nault Sep 2008

Relative Importance, Specific Investment And Ownership In Interorganizational Systems., Kunsoo Han, Robert J. Kauffman, Barrie R. Nault

Research Collection School Of Computing and Information Systems

Implementation and maintenance of interorganizational systems (IOS) require investments by all the participating firms. Compared with intraorganizational systems, however, there are additional uncertainties and risks. This is because the benefits of IOS investment depend not only on a firm's own decisions, but also on those of its business partners. Without appropriate levels of investment by all the firms participating in an IOS, they cannot reap the full benefits. Drawing upon the literature in institutional economics, we examine IOS ownership as a means to induce value-maximizing noncontractible investments. We model the impact of two factors derived from the theory of incomplete …

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Classification In P2p Networks By Bagging Cascade Rsvms, Hock Hee Ang, Vikvekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng, Anwitaman Datta Aug 2008

Classification In P2p Networks By Bagging Cascade Rsvms, Hock Hee Ang, Vikvekanand Gopalkrishnan, Steven C. H. Hoi, Wee Keong Ng, Anwitaman Datta

Research Collection School Of Computing and Information Systems

Data mining tasks in P2P are bound by issues like scalability, peer dynamism, asynchronism, and data privacy preservation. These challenges pose difficulties for deploying conventional machine learning techniques in P2P networks, which may be hard to achieve classification accuracies comparable to regular centralized solutions. We recently investigated the classification problem in P2P networks and proposed a novel P2P classification approach by cascading Reduced Support Vector Machines (RSVM). Although promising results were obtained, the existing solution has some drawback of redundancy in both communication and computation. In this paper, we present a new approach to over the limitation of the previous …

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Impacts Of Social Network Structure On Knowledge Sharing In Open Source Software Development Teams, Y. Long, Keng Siau Aug 2008

Impacts Of Social Network Structure On Knowledge Sharing In Open Source Software Development Teams, Y. Long, Keng Siau

Research Collection School Of Computing and Information Systems

The study examines the relationship between social network structure and knowledge sharing in Open Source Software (OSS) development teams. One hundred and fifty projects were selected from SourceForge.net using stratified sampling. Social network structure was measured by two indices: degree of centralization and core/periphery fitness. Knowledge sharing was measured from two aspects: the quality of knowledge sharing that is indicated by the helpfulness of messages and the quantity of knowledge sharing that is indicated by the number of messages. The results show that social network structure significantly affects the quantity of knowledge sharing. However, social network structure does not influence …

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Knowledge Transfer Via Multiple Model Local Structure Mapping, Jing Gao, Wei Fan, Jing Jiang, Jiawei Han Aug 2008

Knowledge Transfer Via Multiple Model Local Structure Mapping, Jing Gao, Wei Fan, Jing Jiang, Jiawei Han

Research Collection School Of Computing and Information Systems

The effectiveness of knowledge transfer using classification algorithms depends on the difference between the distribution that generates the training examples and the one from which test examples are to be drawn. The task can be especially difficult when the training examples are from one or several domains different from the test domain. In this paper, we propose a locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example. It can integrate the advantages of various learning algorithms and the labeled information from multiple …

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Authenticating The Query Results Of Text Search Engines, Hwee Hwa Pang, Kyriakos Mouratidis Aug 2008

Authenticating The Query Results Of Text Search Engines, Hwee Hwa Pang, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

The number of successful attacks on the Internet shows that it is very difficult to guarantee the security of online search engines. A breached server that is not detected in time may return incorrect results to the users. To prevent that, we introduce a methodology for generating an integrity proof for each search result. Our solution is targeted at search engines that perform similarity-based document retrieval, and utilize an inverted list implementation (as most search engines do). We formulate the properties that define a correct result, map the task of processing a text search query to adaptations of existing threshold-based …

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Critical Success Factors In Soa Implementation, J. Erickson, Keng Siau Aug 2008

Critical Success Factors In Soa Implementation, J. Erickson, Keng Siau

Research Collection School Of Computing and Information Systems

Service Oriented Architecture (SOA) has become flavor du jour for many businesses. Seemingly, almost every company has implemented, is in the midst of implementing or is seriously considering a SOA project. A critical question many organizations are facing now is – what are the critical success factors for SOA implementations? This research aims to identify a list of factors relating to SOA implementation success. A Delphi study forms the research method, and inputs regarding SOA critical success factors are requested from a panel of experts.

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Integrating Lightweight Systems Analysis Into The Unified Process By Using Service Responsibility Tables, X. Tan, S. Alter, Keng Siau Aug 2008

Integrating Lightweight Systems Analysis Into The Unified Process By Using Service Responsibility Tables, X. Tan, S. Alter, Keng Siau

Research Collection School Of Computing and Information Systems

This paper is a step toward establishing direct, but non-automatic links between lightweight (semi-formal) analysis methods for business professionals and heavyweight analysis methods for IT professionals. After noting the importance of user involvement in obtaining accurate and meaningful user requirements, the paper summarizes the Unified Process, a software development methodology that employs Unified Modeling Language (UML). Another section in the paper summarizes previous extensions of the work system method that produced a lightweight analysis tool called Service Responsibility Tables (SRTs). This paper uses a straightforward example to demonstrate a set of heuristics for translating between service responsibility tables produced by …

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Understanding Factors Influencing Proficient Information Systems Usage, Brenda Eschenbrenner, Fiona Fui-Hoon Nah Aug 2008

Understanding Factors Influencing Proficient Information Systems Usage, Brenda Eschenbrenner, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Variations exist among information system (IS) users’ abilities to effectively utilize an IS. Some users are able to maximize IS potential, while others are not. This research proposes to understand the attributes of individuals who are most capable of exploiting IS to its fullest potential as well as the management and organizational factors that facilitate the development of highly competent users. The Repertory Grid Technique was utilized to identify user attributes contributing to IS proficiency in Phase One of this research and will be utilized to identify management and organizational factors in Phase Two. The results will provide a comprehensive …

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A Lightweight Buyer-Seller Watermarking Protocol, Yongdong Wu, Hwee Hwa Pang Aug 2008

A Lightweight Buyer-Seller Watermarking Protocol, Yongdong Wu, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

The buyer-seller watermarking protocol enables a seller to successfully identify a traitor from a pirated copy, while preventing the seller from framing an innocent buyer. Based on finite field theory and the homomorphic property of public key cryptosystems such as RSA, several buyer-seller watermarking protocols (N. Memon and P. W. Wong (2001) and C.-L. Lei et al. (2004)) have been proposed previously. However, those protocols require not only large computational power but also substantial network bandwidth. In this paper, we introduce a new buyer-seller protocol that overcomes those weaknesses by managing the watermarks. Compared with the earlier protocols, ours is …

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Estimating Local Optimums In Em Algorithm Over Gaussian Mixture Model, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung Jul 2008

Estimating Local Optimums In Em Algorithm Over Gaussian Mixture Model, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung

Research Collection School Of Computing and Information Systems

EM algorithm is a very popular iteration-based method to estimate the parameters of Gaussian Mixture Model from a large observation set. However, in most cases, EM algorithm is not guaranteed to converge to the global optimum. Instead, it stops at some local optimums, which can be much worse than the global optimum.

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Tree-Based Partition Querying: A Methodology For Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou Jul 2008

Tree-Based Partition Querying: A Methodology For Computing Medoids In Large Spatial Datasets, Kyriakos Mouratidis, Dimitris Papadias, Spiros Papadimitriou

Research Collection School Of Computing and Information Systems

Besides traditional domains (e.g., resource allocation, data mining applications), algorithms for medoid computation and related problems will play an important role in numerous emerging fields, such as location based services and sensor networks. Since the k-medoid problem is NP hard, all existing work deals with approximate solutions on relatively small datasets. This paper aims at efficient methods for very large spatial databases, motivated by: (i) the high and ever increasing availability of spatial data, and (ii) the need for novel query types and improved services. The proposed solutions exploit the intrinsic grouping properties of a data partition index in order …

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Mobile Interaction Design: Integrating Individual And Organizational Perspectives, Peter Tarasewich, Jun Gong, Fiona Fui-Hoon Nah, David Dewester Jul 2008

Mobile Interaction Design: Integrating Individual And Organizational Perspectives, Peter Tarasewich, Jun Gong, Fiona Fui-Hoon Nah, David Dewester

Research Collection School Of Computing and Information Systems

While mobile computing provides organizations with many information systems implementation alternatives, it is often difficult to predict the potential benefits, limitations, and problems with mobile applications. Given the inherent portability of mobile devices, many design and use issues can arise which do not exist with desktop systems. While many existing rules of thumb for design of stationary systems apply to mobile systems, many new ones emerge. Issues such as the security and privacy of information take on new dimensions, and potential conflicts can develop when a single mobile device serves both personal and business needs. This paper identifies potential issues …

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Semi-Supervised Ensemble Ranking, Steven C. H. Hoi, Rong Jin Jul 2008

Semi-Supervised Ensemble Ranking, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance by combining the outputs from multiple ranking algorithms. Many ensemble ranking approaches employ supervised learning techniques to learn appropriate weights for combining multiple rankers. The main shortcoming with these approaches is that the learned weights for ranking algorithms are query independent. This is suboptimal since a ranking algorithm could perform well for certain queries but poorly for others. In this paper, we propose a novel semi-supervised ensemble ranking (SSER) algorithm that learns query-dependent weights when combining …

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Comments-Oriented Document Summarization: Understanding Documents With Readers' Feedback, Meishan Hu, Aixin Sun, Ee Peng Lim Jul 2008

Comments-Oriented Document Summarization: Understanding Documents With Readers' Feedback, Meishan Hu, Aixin Sun, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Comments left by readers on Web documents contain valuable information that can be utilized in different information retrieval tasks including document search, visualization, and summarization. In this paper, we study the problem of comments-oriented document summarization and aim to summarize a Web document (e.g., a blog post) by considering not only its content, but also the comments left by its readers. We identify three relations (namely, topic, quotation, and mention) by which comments can be linked to one another, and model the relations in three graphs. The importance of each comment is then scored by: (i) graph-based method, where the …

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User Guidance Of Resource-Adaptive Systems, João Pedro Sousa, Rajesh Krishna Balan, Vahe Poladian, David Garlan, Mahadev Satyanarayanan Jul 2008

User Guidance Of Resource-Adaptive Systems, João Pedro Sousa, Rajesh Krishna Balan, Vahe Poladian, David Garlan, Mahadev Satyanarayanan

Research Collection School Of Computing and Information Systems

This paper presents a framework for engineering resource-adaptive software systems targeted at small mobile devices. The proposed framework empowers users to control tradeoffs among a rich set of ervicespecific aspects of quality of service. After motivating the problem, the paper proposes a model for capturing user preferences with respect to quality of service, and illustrates prototype user interfaces to elicit such models. The paper then describes the extensions and integration work made to accommodate the proposed framework on top of an existing software infrastructure for ubiquitous computing. The research question addressed here is the feasibility of coordinating resource allocation and …

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Active Kernel Learning, Steven C. H. Hoi, Rong Jin Jul 2008

Active Kernel Learning, Steven C. H. Hoi, Rong Jin

Research Collection School Of Computing and Information Systems

Identifying the appropriate kernel function/matrix for a given dataset is essential to all kernel-based learning techniques. A number of kernel learning algorithms have been proposed to learn kernel functions or matrices from side information (e.g., either labeled examples or pairwise constraints). However, most previous studies are limited to “passive” kernel learning in which side information is provided beforehand. In this paper we present a framework of Active Kernel Learning (AKL) that actively identifies the most informative pairwise constraints for kernel learning. The key challenge of active kernel learning is how to measure the informativeness of an example pair given its …

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A Self-Organizing Neural Model For Multimedia Information Fusion, Luong-Dong Nguyen, Kia-Yan Woon, Ah-Hwee Tan Jul 2008

A Self-Organizing Neural Model For Multimedia Information Fusion, Luong-Dong Nguyen, Kia-Yan Woon, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents a self-organizing network model for the fusion of multimedia information. By synchronizing the encoding of information across multiple media channels, the neural model known as fusion Adaptive Resonance Theory (fusion ART) generates clusters that encode the associative mappings across multimedia information in a real-time and continuous manner. In addition, by incorporating a semantic category channel, fusion ART further enables multimedia information to be fused into predefined themes or semantic categories. We illustrate the fusion ART’s functionalities through experiments on two multimedia data sets in the terrorist domain and show the viability of the proposed approach.

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Ranked Reverse Nearest Neighbor Search, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee Jul 2008

Ranked Reverse Nearest Neighbor Search, Ken C. K. Lee, Baihua Zheng, Wang-Chien Lee

Research Collection School Of Computing and Information Systems

Given a set of data points P and a query point q in a multidimensional space, Reverse Nearest Neighbor (RNN) query finds data points in P whose nearest neighbors are q. Reverse k-Nearest Neighbor (RkNN) query (where k ≥ 1) generalizes RNN query to find data points whose kNNs include q. For RkNN query semantics, q is said to have influence to all those answer data points. The degree of q's influence on a data point p (∈ P) is denoted by κp where q is the κp-th NN of p. We introduce a new variant of RNN query, namely, …

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Self-Organizing Neural Models Integrating Rules And Reinforcement Learning, Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan Jun 2008

Self-Organizing Neural Models Integrating Rules And Reinforcement Learning, Teck-Hou Teng, Zhong-Ming Tan, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Traditional approaches to integrating knowledge into neural network are concerned mainly about supervised learning. This paper presents how a family of self-organizing neural models known as fusion architecture for learning, cognition and navigation (FALCON) can incorporate a priori knowledge and perform knowledge refinement and expansion through reinforcement learning. Symbolic rules are formulated based on pre-existing know-how and inserted into FALCON as a priori knowledge. The availability of knowledge enables FALCON to start performing earlier in the initial learning trials. Through a temporal-difference (TD) learning method, the inserted rules can be refined and expanded according to the evaluative feedback signals received …

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Context Modeling With Evolutionary Fuzzy Cognitive Map In Interactive Storytelling, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen Jun 2008

Context Modeling With Evolutionary Fuzzy Cognitive Map In Interactive Storytelling, Yundong Cai, Chunyan Miao, Ah-Hwee Tan, Zhiqi Shen

Research Collection School Of Computing and Information Systems

To generate a believable and dynamic virtual world is a great challenge in interactive storytelling. In this paper, we propose a model, namely evolutionary fuzzy cognitive map (E-FCM), to model the dynamic causal relationships among different context variables. As an extension to conventional FCM, E-FCM models not only the fuzzy causal relationships among the variables, but also the probabilistic property of causal relationships, and asynchronous activity update of the concepts. With this model, the context variables evolve in a dynamic and uncertain manner with the according evolving time. As a result, the virtual world is presented more realistically and dynamically.

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Wikinetviz: Visualizing Friends And Adversaries In Implicit Social Networks, Minh-Tam Le, Hoang-Vu Dang, Ee Peng Lim, Anwitaman Datta Jun 2008

Wikinetviz: Visualizing Friends And Adversaries In Implicit Social Networks, Minh-Tam Le, Hoang-Vu Dang, Ee Peng Lim, Anwitaman Datta

Research Collection School Of Computing and Information Systems

When multiple users with diverse backgrounds and beliefs edit Wikipedia together, disputes often arise due to disagreements among the users. In this paper, we introduce a novel visualization tool known as WikiNetViz to visualize and analyze disputes among users in a dispute-induced social network. WikiNetViz is designed to quantify the degree of dispute between a pair of users using the article history. Each user (and article) is also assigned a controversy score by our proposed controversy rank model so as to measure the degree of controversy of a user (and an article) by the amount of disputes between the user …

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Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang Jun 2008

Semi-Supervised Distance Metric Learning For Collaborative Image Retrieval, Steven Hoi, Wei Liu, Shih-Fu Chang

Research Collection School Of Computing and Information Systems

Typical content-based image retrieval (CBIR) solutions with regular Euclidean metric usually cannot achieve satisfactory performance due to the semantic gap challenge. Hence, relevance feedback has been adopted as a promising approach to improve the search performance. In this paper, we propose a novel idea of learning with historical relevance feedback log data, and adopt a new paradigm called “Collaborative Image Retrieval” (CIR). To effectively explore the log data, we propose a novel semi-supervised distance metric learning technique, called “Laplacian Regularized Metric Learning” (LRML), for learning robust distance metrics for CIR. Different from previous methods, the proposed LRML method integrates both …

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A Multimodal And Multilevel Ranking Scheme For Large-Scale Video Retrieval, Steven C. H. Hoi, Michael R. Lyu Jun 2008

A Multimodal And Multilevel Ranking Scheme For Large-Scale Video Retrieval, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

A critical issue of large-scale multimedia retrieval is how to develop an effective framework for ranking the search results. This problem is particularly challenging for content-based video retrieval due to some issues such as short text queries, insufficient sample learning, fusion of multimodal contents, and large-scale learning with huge media data. In this paper, we propose a novel multimodal and multilevel (MMML) ranking framework to attack the challenging ranking problem of content-based video retrieval. We represent the video retrieval task by graphs and suggest a graph based semi-supervised ranking (SSR) scheme, which can learn with small samples effectively and integrate …

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