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Research Collection School Of Computing and Information Systems

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Understanding User Perceptions On Usefulness And Usability Of An Integrated Wiki-G-Portal, Yin-Leng Theng, Yuanyuan Li, Ee Peng Lim, Zhe Wang, Dion Hoe-Lian Goh, Chew-Hung Chang, Kalyani Chatterjea, Jun Zhang Nov 2006

Understanding User Perceptions On Usefulness And Usability Of An Integrated Wiki-G-Portal, Yin-Leng Theng, Yuanyuan Li, Ee Peng Lim, Zhe Wang, Dion Hoe-Lian Goh, Chew-Hung Chang, Kalyani Chatterjea, Jun Zhang

Research Collection School Of Computing and Information Systems

This paper describes a pilot study on Wiki-G-Portal, a project integrating Wikipedia, an online encyclopedia, into G-Portal, a Web-based digital library, of geography resources. Initial findings from the pilot study seemed to suggest positive perceptions on usefulness and usability of Wiki-G-Portal, as well as subjects' attitude and intention to use.

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Low-Latency Broadcast In Multirate Wireless Mesh Networks, Chun Tung Chou, Archan Misra, Junaid Qadir Nov 2006

Low-Latency Broadcast In Multirate Wireless Mesh Networks, Chun Tung Chou, Archan Misra, Junaid Qadir

Research Collection School Of Computing and Information Systems

In a multirate wireless network, a node can dynamically adjust its link transmission rate by switching between different modulation schemes. In the current IEEE802.11a/b/g standards, this rate adjustment is defined for unicast traffic only. In this paper, we consider a wireless mesh network (WMN), where a node can dynamically adjust its link-layer multicast rates to its neighbors, and address the problem of realizing low-latency network-wide broadcast in such a mesh. We first show that the multirate broadcast problem is significantly different from the single-rate case. We will then present an algorithm for achieving low-latency broadcast in a multirate mesh which …

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Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang Oct 2006

Fast Tracking Of Near-Duplicate Keyframes In Broadcast Domain With Transitivity Propagation, Chong-Wah Ngo, Wan-Lei Zhao, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

The identification of near-duplicate keyframe (NDK) pairs is a useful task for a variety of applications such as news story threading and content-based video search. In this paper, we propose a novel approach for the discovery and tracking of NDK pairs and threads in the broadcast domain. The detection of NDKs in a large data set is a challenging task due to the fact that when the data set increases linearly, the computational cost increases in a quadratic speed, and so does the number of false alarms. This paper explores the symmetric and transitive nature of near-duplicate for the effective …

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Service Pattern Discovery Of Web Service Mining In Web Service Registry-Repository, Qianhui Althea Liang, Jen-Yao Chung, Steven M. Miller, Yang Ouyang Oct 2006

Service Pattern Discovery Of Web Service Mining In Web Service Registry-Repository, Qianhui Althea Liang, Jen-Yao Chung, Steven M. Miller, Yang Ouyang

Research Collection School Of Computing and Information Systems

This paper presents and elaborates the concept of Web service usage patterns and pattern discovery through service mining. We define three different levels of service usage data: i) user request level, ii) template level and iii) instance level. At each level, we investigate patterns of service usage data and the discovery of these patterns. An algorithm for service pattern discovery at the template level is presented. We show the system architecture of a service-mining enabled service registry repository. Web service patterns, pattern discovery and pattern mining supports the discovery and composition of complex services, which in turn supports the application …

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Two-Instant Reallocation In Two-Echelon Spare Parts Inventory Systems, Huawei Song, Hoong Chuin Lau Oct 2006

Two-Instant Reallocation In Two-Echelon Spare Parts Inventory Systems, Huawei Song, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of deciding when and how to perform reallocation of existing spare parts in a multi-echelon reparable item inventory system. We present a mathematical model that solves the problem when there are two reallocation instants, in response to the open challenge post by Cao and Silver(2005) to consider two or more possible reallocations within a replenishment cycle.

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Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang Oct 2006

Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang

Research Collection School Of Computing and Information Systems

Web pages from a Web site can often be associated with concepts in an ontology, and pairs of Web pages also can be associated with relationships between concepts. With such associations, the Web site can be searched, browsed, or even reorganized based on the concept and relationship labels of its Web pages. In this article, we study the link chain extraction problem that is critical to the extraction of Web pages that are related. A link chain is an ordered list of anchor elements linking two Web pages related by some semantic relationship. We propose a link chain extraction method …

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Practical Private Data Matching Deterrent To Spoofing Attacks, Yanjiang Yang, Robert H. Deng, Feng Bao Sep 2006

Practical Private Data Matching Deterrent To Spoofing Attacks, Yanjiang Yang, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Private data matching between the data sets of two potentially distrusted parties has a wide range of applications. However, existing solutions have substantial weaknesses and do not meet the needs of many practical application scenarios. In particular, practical private data matching applications often require discouraging the matching parties from spoofing their private inputs. In this paper, we address this challenge by forcing the matching parties to "escrow" the data they use for matching to an auditorial agent, and in the "after-the-fact" period, they undertake the liability to attest the genuineness of the escrowed data.

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Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan Sep 2006

Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan

Research Collection School Of Computing and Information Systems

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …

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Discovering Image-Text Associations For Cross-Media Web Information Fusion, Tao Jiang, Ah-Hwee Tan Sep 2006

Discovering Image-Text Associations For Cross-Media Web Information Fusion, Tao Jiang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The diverse and distributed nature of the information published on the World Wide Web has made it difficult to collate and track information related to specific topics. Whereas most existing work on web information fusion has focused on multiple document summarization, this paper presents a novel approach for discovering associations between images and text segments, which subsequently can be used to support cross-media web content summarization. Specifically, we employ a similarity-based multilingual retrieval model and adopt a vague transformation technique for measuring the information similarity between visual features and textual features. The experimental results on a terrorist domain document set …

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Masking Page Reference Patterns In Encryption Databases On Untrusted Storage, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan Sep 2006

Masking Page Reference Patterns In Encryption Databases On Untrusted Storage, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

To support ubiquitous computing, the underlying data have to be persistent and available anywhere-anytime. The data thus have to migrate from devices that are local to individual computers, to shared storage volumes that are accessible over open network. This potentially exposes the data to heightened security risks. In particular, the activity on a database exhibits regular page reference patterns that could help attackers learn logical links among physical pages and then launch additional attacks. We propose two countermeasures to mitigate the risk of attacks initiated through analyzing the shared storage server’s activity for those page patterns. The first countermeasure relocates …

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Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan Sep 2006

Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan

Research Collection School Of Computing and Information Systems

In this paper, we propose the multi-learner based recursive supervised training (MLRT) algorithm, which uses the existing framework of recursive task decomposition, by training the entire dataset, picking out the best learnt patterns, and then repeating the process with the remaining patterns. Instead of having a single learner to classify all datasets during each recursion, an appropriate learner is chosen from a set of three learners, based on the subset of data being trained, thereby avoiding the time overhead associated with the genetic algorithm learner utilized in previous approaches. In this way MLRT seeks to identify the inherent characteristics of …

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Rights Protection For Data Cubes, Jie Guo, Yingjiu Li, Robert H. Deng, Kefei Chen Sep 2006

Rights Protection For Data Cubes, Jie Guo, Yingjiu Li, Robert H. Deng, Kefei Chen

Research Collection School Of Computing and Information Systems

We propose a rights protection scheme for data cubes. The scheme embeds ownership information by modifying a set of selected cell values. The embedded message will not affect the usefulness of data cubes in the sense that the sum queries at any aggregation level are not affected. At the same time, the errors introduced to individual cell values are under control. The embedded message can be detected with a high probability even in the presence of typical data cube attacks. The proposed scheme can thus be used for protecting data cubes from piracy in an open, distributed environment.

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Cuhk At Imageclef 2005: Cross-Language And Cross Media Image Retrieval, Steven Hoi, Jianke Zhu, Michael R. Lyu Sep 2006

Cuhk At Imageclef 2005: Cross-Language And Cross Media Image Retrieval, Steven Hoi, Jianke Zhu, Michael R. Lyu

Research Collection School Of Computing and Information Systems

In this paper, we describe our studies of cross-language and cross-media image retrieval at the ImageCLEF 2005. This is the first participation of our CUHK (The Chinese University of Hong Kong) group at ImageCLEF. The task in which we participated is the “bilingual ad hoc retrieval” task. There are three major focuses and contributions in our participation. The first is the empirical evaluation of language models and smoothing strategies for cross-language image retrieval. The second is the evaluation of cross-media image retrieval, i.e., combining text and visual contents for image retrieval. The last is the evaluation of bilingual image retrieval …

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Wireless Indoor Positioning System With Enhanced Nearest Neighbors In Signal Space Algorithm, Quang Tran, Juki Wirawan Tantra, Ah-Hwee Tan, Ah-Hwee Tan, Kin-Choong Yow, Dongyu Qiu Sep 2006

Wireless Indoor Positioning System With Enhanced Nearest Neighbors In Signal Space Algorithm, Quang Tran, Juki Wirawan Tantra, Ah-Hwee Tan, Ah-Hwee Tan, Kin-Choong Yow, Dongyu Qiu

Research Collection School Of Computing and Information Systems

With the rapid development and wide deployment of wireless Local Area Networks (WLANs), WLAN-based positioning system employing signal-strength-based technique has become an attractive solution for location estimation in indoor environment. In recent years, a number of such systems has been presented, and most of the systems use the common Nearest Neighbor in Signal Space (NNSS) algorithm. In this paper, we propose an enhancement to the NNSS algorithm. We analyze the enhancement to show its effectiveness. The performance of the enhanced NNSS algorithm is evaluated with different values of the parameters. Based on the performance evaluation and analysis, we recommend some …

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Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis Sep 2006

Continuous Nearest Neighbor Monitoring In Road Networks, Kyriakos Mouratidis, Man Lung Yiu, Dimitris Papadias, Nikos Mamoulis

Research Collection School Of Computing and Information Systems

Recent research has focused on continuous monitoring of nearest neighbors (NN) in highly dynamic scenarios, where the queries and the data objects move frequently and arbitrarily. All existing methods, however, assume the Euclidean distance metric. In this paper we study k-NN monitoring in road networks, where the distance between a query and a data object is determined by the length of the shortest path connecting them. We propose two methods that can handle arbitrary object and query moving patterns, as well as °uctuations of edge weights. The ¯rst one maintains the query results by processing only updates that may invalidate …

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Three Architectures For Trusted Data Dissemination In Edge Computing, Shen-Tat Goh, Hwee Hwa Pang, Robert H. Deng, Feng Bao Sep 2006

Three Architectures For Trusted Data Dissemination In Edge Computing, Shen-Tat Goh, Hwee Hwa Pang, Robert H. Deng, Feng Bao

Research Collection School Of Computing and Information Systems

Edge computing pushes application logic and the underlying data to the edge of the network, with the aim of improving availability and scalability. As the edge servers are not necessarily secure, there must be provisions for users to validate the results—that values in the result tuples are not tampered with, that no qualifying data are left out, that no spurious tuples are introduced, and that a query result is not actually the output from a different query. This paper aims to address the challenges of ensuring data integrity in edge computing. We study three schemes that enable users to check …

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Collaborative Image Retrieval Via Regularized Metric Learning, Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu Aug 2006

Collaborative Image Retrieval Via Regularized Metric Learning, Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu

Research Collection School Of Computing and Information Systems

In content-based image retrieval (CBIR), relevant images are identified based on their similarities to query images. Most CBIR algorithms are hindered by the semantic gap between the low-level image features used for computing image similarity and the high-level semantic concepts conveyed in images. One way to reduce the semantic gap is to utilize the log data of users' feedback that has been collected by CBIR systems in history, which is also called “collaborative image retrieval.” In this paper, we present a novel metric learning approach, named “regularized metric learning,” for collaborative image retrieval, which learns a distance metric by exploring …

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Understanding Intrinsic Factors Influencing Benefit Maximization Of Is Usage, Brenda Eschenbrenner, Fiona Fui-Hoon Nah Aug 2006

Understanding Intrinsic Factors Influencing Benefit Maximization Of Is Usage, Brenda Eschenbrenner, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

This research uses the Repertory Grid technique to understand intrinsic factors influencing benefit maximization of IS usage. The results show that domain-relevant skills, task motivation, cognitive/work style attributes, individual characteristics (identified as creativity traits) and personal characteristics (identified as innovativeness traits) influence benefit maximization of IS usage. The findings not only provide insights on ways to increase quality of IS usage in organizations but are also helpful for identifying approaches that foster attributes leading to increased benefit realization from IS usage.

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Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang Aug 2006

Bias And Controversy: Beyond The Statistical Deviation, Hady W. Lauw, Ee Peng Lim, Ke Wang

Research Collection School Of Computing and Information Systems

In this paper, we investigate how deviation in evaluation activities may reveal bias on the part of reviewers and controversy on the part of evaluated objects. We focus on a 'data-centric approach' where the evaluation data is assumed to represent the ground truth'. The standard statistical approaches take evaluation and deviation at face value. We argue that attention should be paid to the subjectivity of evaluation, judging the evaluation score not just on 'what is being said' (deviation), but also on 'who says it' (reviewer) as well as on 'whom it is said about' (object). Furthermore, we observe that bias …

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Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang Aug 2006

Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang

Research Collection School Of Computing and Information Systems

Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel Machines (UKM) from both labeled and unlabeled data. Our proposed framework integrates supervised learning, semi-supervised kernel learning, and active learning in a unified solution. In the suggested framework, we particularly focus our attention on designing a new semi-supervised kernel learning method, i.e., Spectral Kernel Learning (SKL), which is built on the principles of kernel target alignment and unsupervised kernel design. Our algorithm is related to an equivalent quadratic programming problem that can be efficiently …

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Keyframe Retrieval By Keypoints: Can Point-To-Point Matching Help?, Wanlei Zhao, Yu-Gang Jiang, Chong-Wah Ngo Jul 2006

Keyframe Retrieval By Keypoints: Can Point-To-Point Matching Help?, Wanlei Zhao, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Bag-of-words representation with visual keypoints has recently emerged as an attractive approach for video search. In this paper, we study the degree of improvement when point-to-point (P2P) constraint is imposed on the bag-of-words. We conduct investigation on two tasks: near-duplicate keyframe (NDK) retrieval, and high-level concept classification, covering parts of TRECVID 2003 and 2005 datasets. In P2P matching, we propose a one-to-one symmetric keypoint matching strategy to diminish the noise effect during keyframe comparison. In addition, a new multi-dimensional index structure is proposed to speed up the matching process with keypoint filtering. Through experiments, we demonstrate that P2P constraint can …

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Hierarchical Hidden Markov Model For Rushes Structuring And Indexing, Chong-Wah Ngo, Zailiang Pan, Xiaoyong Wei Jul 2006

Hierarchical Hidden Markov Model For Rushes Structuring And Indexing, Chong-Wah Ngo, Zailiang Pan, Xiaoyong Wei

Research Collection School Of Computing and Information Systems

Rushes footage are considered as cheap gold mine with the potential for reuse in broadcasting and filmmaking industries. However, it is difficult to mine the "gold" from the rushes since usually only minimum metadata is available. This paper focuses on the structuring and indexing of the rushes to facilitate mining and retrieval of "gold". We present a new approach for rushes structuring and indexing based on motion feature. We model the problem by a two-level Hierarchical Hidden Markov Model (HHMM). The HHMM, on one hand, represents the semantic concepts in its higher level to provide simultaneous structuring and indexing, on …

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Prediction-Based Gesture Detection In Lecture Videos By Combining Visual, Speech And Electronic Slides, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong Jul 2006

Prediction-Based Gesture Detection In Lecture Videos By Combining Visual, Speech And Electronic Slides, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong

Research Collection School Of Computing and Information Systems

This paper presents an efficient algorithm for gesture detection in lecture videos by combining visual, speech and electronic slides. Besides accuracy, response time is also considered to cope with the efficiency requirements of real-time applications. Candidate gestures are first detected by visual cue. Then we modifity HMM models for complete gestures to predict and recognize incomplete gestures before the whole gestures paths are observed. Gesture recognition is used to verify the results of gesture detection. The relations between visual, speech and slides are analyzed. The correspondence between speech and gesture is employed to improve the accuracy and the responsiveness of …

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Mobile Healthcare Informatics, Keng Siau, Zixing Shen Jul 2006

Mobile Healthcare Informatics, Keng Siau, Zixing Shen

Research Collection School Of Computing and Information Systems

Advances in wireless technology give pace to the rapid development of mobile applications. The coming mobile revolution will bring dramatic and fundamental changes to our daily life. It will influence the way we live, the way we do things, and the way we take care of our health. For the healthcare industry, mobile applications provide a new frontier in offering better care and services to patients, and a more flexible and mobile way of communicating with suppliers and patients. Mobile applications will provide important real time data for patients, physicians, insurers, and suppliers. In addition, it will revolutionalize the way …

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A Novel Privacy Preserving Authentication And Access Control Scheme For Pervasive Computing Environments, K. Ren, Wenjing Lou, K. Kim, Robert H. Deng Jul 2006

A Novel Privacy Preserving Authentication And Access Control Scheme For Pervasive Computing Environments, K. Ren, Wenjing Lou, K. Kim, Robert H. Deng

Research Collection School Of Computing and Information Systems

Privacy and security are two important but seemingly contradictory objectives in a pervasive computing environment (PCE). On one hand, service providers want to authenticate legitimate users and make sure they are accessing their authorized services in a legal way. On the other hand, users want to maintain the necessary privacy without being tracked down for wherever they are and whatever they are doing. In this paper, a novel privacy preserving authentication and access control scheme to secure the interactions between mobile users and services in PCEs is proposed. The proposed scheme seamlessly integrates two underlying cryptographic primitives, namely blind signature …

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Authenticating Multi-Dimensional Query Results In Data Publishing, Weiwei Cheng, Hwee Hwa Pang, Kian-Lee Tan Jul 2006

Authenticating Multi-Dimensional Query Results In Data Publishing, Weiwei Cheng, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

In data publishing, the owner delegates the role of satisfying user queries to a third-party publisher. As the publisher may be untrusted or susceptible to attacks, it could produce incorrect query results. This paper introduces a mechanism for users to verify that their query answers on a multi-dimensional dataset are correct, in the sense of being complete (i.e., no qualifying data points are omitted) and authentic (i.e., all the result values originated from the owner). Our approach is to add authentication information into a spatial data structure, by constructing certified chains on the points within each partition, as well as …

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Cognitive Mapping Techniques For User-Database Interaction, Keng Siau, X. Tan Jun 2006

Cognitive Mapping Techniques For User-Database Interaction, Keng Siau, X. Tan

Research Collection School Of Computing and Information Systems

In this paper, we first develop a framework of user-database interaction. Based on this framework, we then provide a discussion on how notable human factors influence various dimensions of user-database interaction. Following that, we propose using cognitive mapping techniques to overcome some cognitive and behavioral biases during user-database interaction. Three popular cognitive mapping techniques-causal mapping, semantic mapping, and concept mapping-are introduced as techniques to elicit an individual's belief systems regarding a problem domain. Through an example database application, we demonstrate how to use these cognitive mapping techniques to improve user-database interaction. Finally, we discuss the implications of this research for …

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Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma Jun 2006

Learning Distance Metrics With Contextual Constraints For Image Retrieval, Steven C. H. Hoi, Wei Liu, Michael R. Lyu, Wei-Ying Ma

Research Collection School Of Computing and Information Systems

Relevant Component Analysis (RCA) has been proposed for learning distance metrics with contextual constraints for image retrieval. However, RCA has two important disadvantages. One is the lack of exploiting negative constraints which can also be informative, and the other is its incapability of capturing complex nonlinear relationships between data instances with the contextual information. In this paper, we propose two algorithms to overcome these two disadvantages, i.e., Discriminative Component Analysis (DCA) and Kernel DCA. Compared with other complicated methods for distance metric learning, our algorithms are rather simple to understand and very easy to solve. We evaluate the performance of …

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Multilearner Based Recursive Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan, Laxmi R. Iyer Jun 2006

Multilearner Based Recursive Supervised Training, Kiruthika Ramanathan, Sheng Uei Guan, Laxmi R. Iyer

Research Collection School Of Computing and Information Systems

In supervised learning, most single solution neural networks such as constructive backpropagation give good results when used with some datasets but not with others. Others such as probabilistic neural networks (PNN) fit a curve to perfection but need to be manually tuned in the case of noisy data. Recursive percentage based hybrid pattern training (RPHP) overcomes this problem by recursively training subsets of the data, thereby using several neural networks. MultiLearner based recursive training (MLRT) is an extension of this approach, where a combination of existing and new learners are used and subsets are trained using the weak learner which …

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On The Release Of Crls In Public Key Infrastructure, Chengyu Ma, Nan Hu, Yingjiu Li Jun 2006

On The Release Of Crls In Public Key Infrastructure, Chengyu Ma, Nan Hu, Yingjiu Li

Research Collection School Of Computing and Information Systems

Public key infrastructure provides a promising foundation for verifying the authenticity of communicating parties and transferring trust over the internet. The key issue in public key infrastructure is how to process certificate revocations. Previous research in this aspect has concentrated on the tradeoffs that can be made among different revocation options. No rigorous efforts have been made to understand the probability distribution of certificate revocation requests based on real empirical data. In this study, we first collect real empirical data from VeriSign and derive the probability function for certificate revocation requests. We then prove that a revocation system will become …

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