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Singapore Management University

2008

Articles 1 - 30 of 62

Full-Text Articles in Databases and Information Systems

A Fast Pruned‐Extreme Learning Machine For Classification Problem, Hai-Jun Rong, Yew-Soon Ong, Ah-Hwee Tan, Zexuan Zhu Dec 2008

A Fast Pruned‐Extreme Learning Machine For Classification Problem, Hai-Jun Rong, Yew-Soon Ong, Ah-Hwee Tan, Zexuan Zhu

Research Collection School Of Computing and Information Systems

Extreme learning machine (ELM) represents one of the recent successful approaches in machine learning, particularly for performing pattern classification. One key strength of ELM is the significantly low computational time required for training new classifiers since the weights of the hidden and output nodes are randomly chosen and analytically determined, respectively. In this paper, we address the architectural design of the ELM classifier network, since too few/many hidden nodes employed would lead to underfitting/overfitting issues in pattern classification. In particular, we describe the proposed pruned-ELM (P-ELM) algorithm as a systematic and automated approach for designing ELM classifier network. P-ELM uses …

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Robust Regularized Kernel Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu Dec 2008

Robust Regularized Kernel Regression, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Robust regression techniques are critical to fitting data with noise in real-world applications. Most previous work of robust kernel regression is usually formulated into a dual form, which is then solved by some quadratic program solver consequently. In this correspondence, we propose a new formulation for robust regularized kernel regression under the theoretical framework of regularization networks and then tackle the optimization problem directly in the primal. We show that the primal and dual approaches are equivalent to achieving similar regression performance, but the primal formulation is more efficient and easier to be implemented than the dual one. Different from …

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Innovation In The Programmable Web: Characterizing The Mashup Ecosystem, C. Jason Woodard, Shuli Yu Dec 2008

Innovation In The Programmable Web: Characterizing The Mashup Ecosystem, C. Jason Woodard, Shuli Yu

Research Collection School Of Computing and Information Systems

This paper investigates the structure and dynamics of the Web 2.0 software ecosystem by analyzing empirical data on web service APIs and mashups. Using network analysis tools to visualize the growth of the ecosystem from December 2005 to 2007, we find that the APIs are organized into three tiers, and that mashups are often formed by combining APIs across tiers. Plotting the cumulative distribution of mashups to APIs reveals a power-law relationship, although the tail is short compared to previously reported distributions of book and movie sales. While this finding highlights the dominant role played by the most popular APIs …

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Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang Dec 2008

Explaining Inferences In Bayesian Networks, Ghim-Eng Yap, Ah-Hwee Tan, Hwee Hwa Pang

Research Collection School Of Computing and Information Systems

While Bayesian network (BN) can achieve accurate predictions even with erroneous or incomplete evidence, explaining the inferences remains a challenge. Existing approaches fall short because they do not exploit variable interactions and cannot account for compensations during inferences. This paper proposes the Explaining BN Inferences (EBI) procedure for explaining how variables interact to reach conclusions. EBI explains the value of a target node in terms of the influential nodes in the target's Markov blanket under specific contexts, where the Markov nodes include the target's parents, children, and the children's other parents. Working back from the target node, EBI shows the …

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Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan Dec 2008

Planning With Ifalcon: Towards A Neural-Network-Based Bdi Agent Architecture, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper presents iFALCON, a model of BDI (beliefdesire-intention) agents that is fully realized as a selforganizing neural network architecture. Based on multichannel network model called fusion ART, iFALCON is developed to bridge the gap between a self-organizing neural network that autonomously adapts its knowledge and the BDI agent model that follows explicit descriptions. Novel techniques called gradient encoding are introduced for representing sequences and hierarchical structures to realize plans and the intention structure. This paper shows that a simplified plan representation can be encoded as weighted connections in the neural network through a process of supervised learning. A case …

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Text Cube: Computing Ir Measures For Multidimensional Text Database Analysis, Cindy Xinde Lin, Bolin Ding, Jiawei Han, Feida Zhu, Bo Zhao Dec 2008

Text Cube: Computing Ir Measures For Multidimensional Text Database Analysis, Cindy Xinde Lin, Bolin Ding, Jiawei Han, Feida Zhu, Bo Zhao

Research Collection School Of Computing and Information Systems

Since Jim Gray introduced the concept of ”data cube” in 1997, data cube, associated with online analytical processing (OLAP), has become a driving engine in data warehouse industry. Because the boom of Internet has given rise to an ever increasing amount of text data associated with other multidimensional information, it is natural to propose a data cube model that integrates the power of traditional OLAP and IR techniques for text. In this paper, we propose a Text-Cube model on multidimensional text database and study effective OLAP over such data. Two kinds of hierarchies are distinguishable inside: dimensional hierarchy and term …

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On Visualizing Heterogeneous Semantic Networks From Multiple Data Sources, Maureen Maureen, Aixin Sun, Ee Peng Lim, Anwitaman Datta, Kuiyu Chang Dec 2008

On Visualizing Heterogeneous Semantic Networks From Multiple Data Sources, Maureen Maureen, Aixin Sun, Ee Peng Lim, Anwitaman Datta, Kuiyu Chang

Research Collection School Of Computing and Information Systems

In this paper, we focus on the visualization of heterogeneous semantic networks obtained from multiple data sources. A semantic network comprising a set of entities and relationships is often used for representing knowledge derived from textual data or database records. Although the semantic networks created for the same domain at different data sources may cover a similar set of entities, these networks could also be very different because of naming conventions, coverage, view points, and other reasons. Since digital libraries often contain data from multiple sources, we propose a visualization tool to integrate and analyze the differences among multiple social …

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Cognitive Agents Integrating Rules And Reinforcement Learning For Context-Aware Decision Support, Teck-Hou Teng, Ah-Hwee Tan Dec 2008

Cognitive Agents Integrating Rules And Reinforcement Learning For Context-Aware Decision Support, Teck-Hou Teng, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

While context-awareness has been found to be effective for decision support in complex domains, most of such decision support systems are hard-coded, incurring significant development efforts. To ease the knowledge acquisition bottleneck, this paper presents a class of cognitive agents based on self-organizing neural model known as TD-FALCON that integrates rules and learning for supporting context-aware decision making. Besides the ability to incorporate a priori knowledge in the form of symbolic propositional rules, TD-FALCON performs reinforcement learning (RL), enabling knowledge refinement and expansion through the interaction with its environment. The efficacy of the developed Context-Aware Decision Support (CaDS) system is …

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Scaling Up Multi-Agent Reinforcement Learning In Complex Domains, Dan Xiao, Ah-Hwee Tan Dec 2008

Scaling Up Multi-Agent Reinforcement Learning In Complex Domains, Dan Xiao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

TD-FALCON (Temporal Difference - Fusion Architecture for Learning, COgnition, and Navigation) is a class of self-organizing neural networks that incorporates Temporal Difference (TD) methods for real-time reinforcement learning. In this paper, we present two strategies, i.e. policy sharing and neighboring-agent mechanism, to further improve the learning efficiency of TD-FALCON in complex multi-agent domains. Through experiments on a traffic control problem domain and the herding task, we demonstrate that those strategies enable TD-FALCON to remain functional and adaptable in complex multi-agent domains

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Modality Mixture Projections For Semantic Video Event Detection, Jialie Shen, Dacheng Tao, Xuelong Li Nov 2008

Modality Mixture Projections For Semantic Video Event Detection, Jialie Shen, Dacheng Tao, Xuelong Li

Research Collection School Of Computing and Information Systems

Event detection is one of the most fundamental components for various kinds of domain applications of video information system. In recent years, it has gained a considerable interest of practitioners and academics from different areas. While detecting video event has been the subject of extensive research efforts recently, much less existing approach has considered multimodal information and related efficiency issues. In this paper, we use a subspace selection technique to achieve fast and accurate video event detection using a subspace selection technique. The approach is capable of discriminating different classes and preserving the intramodal geometry of samples within an identical …

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Bias And Controversy In Evaluation Systems, Hady Wirawan Lauw, Ee Peng Lim, Ke Wang Nov 2008

Bias And Controversy In Evaluation Systems, Hady Wirawan Lauw, Ee Peng Lim, Ke Wang

Research Collection School Of Computing and Information Systems

Evaluation is prevalent in real life. With the advent of Web 2.0, online evaluation has become an important feature in many applications that involve information (e.g., video, photo, and audio) sharing and social networking (e.g., blogging). In these evaluation settings, a set of reviewers assign scores to a set of objects. As part of the evaluation analysis, we want to obtain fair reviews for all the given objects. However, the reality is that reviewers may deviate in their scores assigned to the same object, due to the potential bias of reviewers or controversy of objects. The statistical approach of averaging …

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Beyond Semantic Search: What You Observe May Not Be What You Think, Chong-Wah Ngo, Yu-Gang Jiang, Xiaoyong Wei, Wanlei Zhao, Feng Wang, Xiao Wu, Hung-Khoon Tan Nov 2008

Beyond Semantic Search: What You Observe May Not Be What You Think, Chong-Wah Ngo, Yu-Gang Jiang, Xiaoyong Wei, Wanlei Zhao, Feng Wang, Xiao Wu, Hung-Khoon Tan

Research Collection School Of Computing and Information Systems

This paper presents our approaches and results of the four TRECVID 2008 tasks we participated in: high-level feature extraction, automatic video search, video copy detection, and rushes summarization

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A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow Nov 2008

A Neural Network Model For A Hierarchical Spatio-Temporal Memory, Kiruthika Ramanathan, Luping Shi, Jianming Li, Kian Guan Lim, Zhi Ping Ang, Chong Chong Tow

Research Collection School Of Computing and Information Systems

The architecture of the human cortex is uniform and hierarchical in nature. In this paper, we build upon works on hierarchical classification systems that model the cortex to develop a neural network representation for a hierarchical spatio-temporal memory (HST-M) system. The system implements spatial and temporal processing using neural network architectures. We have tested the algorithms developed against both the MLP and the Hierarchical Temporal Memory algorithms. Our results show definite improvement over MLP and are comparable to the performance of HTM.

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Comparison Of Online Social Relations In Volume Vs Interaction: A Case Study Of Cyworld, Hyunwoo Chun, Haewoon Kwak, Young-Ho Eom, Yong-Yeol Ahn, Sue Moon, Hawoong. Jeong Oct 2008

Comparison Of Online Social Relations In Volume Vs Interaction: A Case Study Of Cyworld, Hyunwoo Chun, Haewoon Kwak, Young-Ho Eom, Yong-Yeol Ahn, Sue Moon, Hawoong. Jeong

Research Collection School Of Computing and Information Systems

Online social networking services are among the most popular Internet services according to Alexa.com and have become a key feature in many Internet services. Users interact through various features of online social networking services: making friend relationships, sharing their photos, and writing comments. These friend relationships are expected to become a key to many other features in web services, such as recommendation engines, security measures, online search, and personalization issues. However, we have very limited knowledge on how much interaction actually takes place over friend relationships declared online. A friend relationship only marks the beginning of online interaction.Does the interaction …

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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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Near-Duplicate Keyframe Retrieval By Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan Oct 2008

Near-Duplicate Keyframe Retrieval By Nonrigid Image Matching, Jianke Zhu, Steven C. H. Hoi, Michael R. Lyu, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Near-duplicate image retrieval plays an important role in many real-world multimedia applications. Most previous approaches have some limitations. For example, conventional appearance-based methods may suffer from the illumination variations and occlusion issue, and local feature correspondence-based methods often do not consider local deformations and the spatial coherence between two point sets. In this paper, we propose a novel and effective Nonrigid Image Matching (NIM) approach to tackle the task of near-duplicate keyframe retrieval from real-world video corpora. In contrast to previous approaches, the NIM technique can recover an explicit mapping between two near-duplicate images with a few deformation parameters and …

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Event Detection With Common User Interests, Meishan Hu, Aixin Sun, Ee Peng Lim Oct 2008

Event Detection With Common User Interests, Meishan Hu, Aixin Sun, Ee Peng Lim

Research Collection School Of Computing and Information Systems

In this paper, we aim at detecting events of common user interests from huge volume of user-generated content. The degree of interest from common users in an event is evidenced by a significant surge of event-related queries issued to search for documents (e.g., news articles, blog posts) relevant to the event. Taking the stream of queries from users and the stream of documents as input, our proposed framework seamlessly integrates the two streams into a single stream of query profiles. A query profile is a set of documents matching a query at a given time. With the single stream of …

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Using English Information In Non-English Web Search, Wei Gao, Wei Gao, Ming Zhou Oct 2008

Using English Information In Non-English Web Search, Wei Gao, Wei Gao, Ming Zhou

Research Collection School Of Computing and Information Systems

The leading web search engines have spent a decade building highly specialized ranking functions for English web pages. One of the reasons these ranking functions are effective is that they are designed around features such as PageRank, automatic query and domain taxonomies, and click-through information, etc. Unfortunately, many of these features are absent or altered in other languages. In this work, we show how to exploit these English features for a subset of Chinese queries which we call linguistically non-local (LNL). LNL Chinese queries have a minimally ambiguous English translation which also functions as a good English query. We first …

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Output Regularized Metric Learning With Side Information, Wei Liu, Steven C. H. Hoi, Jianzhuang Liu Oct 2008

Output Regularized Metric Learning With Side Information, Wei Liu, Steven C. H. Hoi, Jianzhuang Liu

Research Collection School Of Computing and Information Systems

Distance metric learning has been widely investigated in machine learning and information retrieval. In this paper, we study a particular content-based image retrieval application of learning distance metrics from historical relevance feedback log data, which leads to a novel scenario called collaborative image retrieval. The log data provide the side information expressed as relevance judgements between image pairs. Exploiting the side information as well as inherent neighborhood structures among examples, we design a convex regularizer upon which a novel distance metric learning approach, named output regularized metric learning, is presented to tackle collaborative image retrieval. Different from previous distance metric …

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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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Bayesian Tensor Approach For 3-D Face Modeling, Dacheng Tao, Mingli Song, Xuelong Li, Jialie Shen, Jimeng Sun, Xindong Wu, Christos Faloutsos, Stephen J. Maybank Oct 2008

Bayesian Tensor Approach For 3-D Face Modeling, Dacheng Tao, Mingli Song, Xuelong Li, Jialie Shen, Jimeng Sun, Xindong Wu, Christos Faloutsos, Stephen J. Maybank

Research Collection School Of Computing and Information Systems

Effectively modeling a collection of three-dimensional (3-D) faces is an important task in various applications, especially facial expression-driven ones, e.g., expression generation, retargeting, and synthesis. These 3-D faces naturally form a set of second-order tensors-one modality for identity and the other for expression. The number of these second-order tensors is three times of that of the vertices for 3-D face modeling. As for algorithms, Bayesian data modeling, which is a natural data analysis tool, has been widely applied with great success; however, it works only for vector data. Therefore, there is a gap between tensor-based representation and vector-based data analysis …

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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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Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan Oct 2008

Recursive Pattern Based Hybrid Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan

Research Collection School Of Computing and Information Systems

We propose, theorize and implement the Recursive Pattern-based Hybrid Supervised (RPHS) learning algorithm. The algorithm makes use of the concept of pseudo global optimal solutions to evolve a set of neural networks, each of which can solve correctly a subset of patterns. The pattern-based algorithm uses the topology of training and validation data patterns to find a set of pseudo-optima, each learning a subset of patterns. It is therefore well adapted to the pattern set provided. We begin by showing that finding a set of local optimal solutions is theoretically equivalent, and more efficient, to finding a single global optimum …

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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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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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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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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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