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Extracting Link Chains Of Relationship Instances From A Website, Myo-Myo Naing, Ee Peng Lim, Roger Hsiang-Li Chiang
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 …
Privacy Enhanced Superdistribution Of Layered Content With Trusted Access Control, Daniel J. T. Chong, Robert H. Deng
Privacy Enhanced Superdistribution Of Layered Content With Trusted Access Control, Daniel J. T. Chong, Robert H. Deng
Research Collection School Of Computing and Information Systems
Traditional superdistribution approaches do not address consumer privacy issues and also do not reliably prevent the malicious consumer from indiscriminately copying and redistributing the decryption keys or the decrypted content. The layered nature of common digital content can also be exploited to efficiently provide the consumer with choices over the quality of the content, allowing him/her to pay less for lower quality consumption and vice versa. This paper presents a system that superdistributes encrypted layered content and (1) allows the consumer to select a quality level at which to decrypt and consume the content; (2) prevents the merchant from knowing …
Viz: A Visual Analysis Suite For Explaining Local Search Behavior, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Viz: A Visual Analysis Suite For Explaining Local Search Behavior, Steven Halim, Roland H. C. Yap, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
NP-hard combinatorial optimization problems are common in real life. Due to their intractability, local search algorithms are often used to solve such problems. Since these algorithms are heuristic-based, it is hard to understand how to improve or tune them. We propose an interactive visualization tool, VIZ, meant for understanding the behavior of local search. VIZ uses animation of abstract search trajectories with other visualizations which are also animated in a VCR-like fashion to graphically playback the algorithm behavior. It combines generic visualizations applicable on arbitrary algorithms with algorithm and problem specific visualizations. We use a variety of techniques such as …
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
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 …
Multi-Learner Based Recursive Supervised Training, Laxmi R. Iyer, Kiruthika Ramanathan, Sheng-Uei Guan
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 …
Masking Page Reference Patterns In Encryption Databases On Untrusted Storage, Xi Ma, Hwee Hwa Pang, Kian-Lee Tan
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 …
Three Architectures For Trusted Data Dissemination In Edge Computing, Shen-Tat Goh, Hwee Hwa Pang, Robert H. Deng, Feng Bao
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 …
Cuhk At Imageclef 2005: Cross-Language And Cross Media Image Retrieval, Steven Hoi, Jianke Zhu, Michael R. Lyu
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 …
Mining Rdf Metadata For Generalized Association Rules, Tao Jiang, Ah-Hwee Tan
Mining Rdf Metadata For Generalized Association Rules, Tao Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In this paper, we present a novel frequent generalized pattern mining algorithm, called GP-Close, for mining generalized associations from RDF metadata. To solve the over-generalization problem encountered by existing methods, GP-Close employs the notion of generalization closure for systematic over-generalization reduction. Empirical experiments conducted on real world RDF data sets show that our method can substantially reduce pattern redundancy and perform much better than the original generalized association rule mining algorithm Cumulate in term of time efficiency.
Discovering Image-Text Associations For Cross-Media Web Information Fusion, Tao Jiang, Ah-Hwee Tan
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 …
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
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 …
Human-Computer Interaction Research In The Management Information Systems Discipline, Fiona Fui-Hoon Nah, Ping Zhang, Scott Mccoy, Munyong Yi
Human-Computer Interaction Research In The Management Information Systems Discipline, Fiona Fui-Hoon Nah, Ping Zhang, Scott Mccoy, Munyong Yi
Research Collection School Of Computing and Information Systems
No abstract provided.
Rights Protection For Data Cubes, Jie Guo, Yingjiu Li, Robert H. Deng, Kefei Chen
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.
Minimum Latency Broadcasting In Multi-Radio Multi-Channel Multi-Rate Wireless Meshes, Junaid Qadir, Archan Misra, Chun Tung Chou
Minimum Latency Broadcasting In Multi-Radio Multi-Channel Multi-Rate Wireless Meshes, Junaid Qadir, Archan Misra, Chun Tung Chou
Research Collection School Of Computing and Information Systems
We address the problem of minimizing the worst-case broadcast delay in multi-radio multi-channel multi-rate (MR2-MC) wireless mesh networks (WMN). The problem of 'efficient' broadcast in such networks is especially challenging due to the numerous interrelated decisions that have to be made. The multi-rate transmission capability of WMN nodes, interference between wireless transmissions, and the hardness of optimal channel assignment adds complexity to our considered problem. We present four heuristic algorithms to solve the minimum latency broadcast problem for such settings and show that the 'best' performing algorithms usually adapt themselves to the available radio interfaces and channels. We also study …
Collaborative Image Retrieval Via Regularized Metric Learning, Luo Si, Rong Jin, Steven C. H. Hoi, Michael R. Lyu
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 …
Use Of A Classroom Response System To Enhance Classroom Interactivity, Keng Siau, H. Sheng, Fiona Fui-Hoon Nah
Use Of A Classroom Response System To Enhance Classroom Interactivity, Keng Siau, H. Sheng, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Classroom interactivity is a critical component of teaching and learning. This paper reports on the use of a classroom response system to enhance classroom interactivity in a systems analysis and design course. The success of the project was assessed using both quantitative and qualitative data. A pretest/posttest design was used to examine the effects of a classroom respons system on interactivity. The results show that a classroom response system can significantly improve classroom interactivity. Qualitative data was also collected to identify the strengths and weaknesses of using a classroom response system to enhance classroom interaction. Based on the quantitative and …
Architectural Control And Value Migration In Platform Industries, C. Jason Woodard
Architectural Control And Value Migration In Platform Industries, C. Jason Woodard
Research Collection School Of Computing and Information Systems
carry some pre-computation information of each region. We also propose multiple client-side algorithms to facilitate the processing of
An Energy-Efficient And Access Latency Optimized Indexing Scheme For Wireless Data Broadcast, Yuxia Yao, Xueyan Tang, Ee Peng Lim, Aixin Sun
An Energy-Efficient And Access Latency Optimized Indexing Scheme For Wireless Data Broadcast, Yuxia Yao, Xueyan Tang, Ee Peng Lim, Aixin Sun
Research Collection School Of Computing and Information Systems
Data broadcast is an attractive data dissemination method in mobile environments. To improve energy efficiency, existing air indexing schemes for data broadcast have focused on reducing tuning time only, i.e., the duration that a mobile client stays active in data accesses. On the other hand, existing broadcast scheduling schemes have aimed at reducing access latency through nonflat data broadcast to improve responsiveness only. Not much work has addressed the energy efficiency and responsiveness issues concurrently. This paper proposes an energy-efficient indexing scheme called MHash that optimizes tuning time and access latency in an integrated fashion. MHash reduces tuning time by …
Social Network Dynamics For Open Source Software Projects, Y. Long, Keng Siau
Social Network Dynamics For Open Source Software Projects, Y. Long, Keng Siau
Research Collection School Of Computing and Information Systems
Drawing on social network theories and previous studies, this research is an initial effort to explore the dynamics of the social network structure in Open Source Software (OSS) teams. Three projects were selected from SourceForge.net in term of their similarities as well as their differences. Monthly data were extracted from the bug tracking system in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of network structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a …
Understanding Intrinsic Factors Influencing Benefit Maximization Of Is Usage, Brenda Eschenbrenner, Fiona Fui-Hoon Nah
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.
A Hybrid Architecture Combining Reactive Plan Execution And Reactive Learning, Samin Karim, Liz Sonenberg, Ah-Hwee Tan
A Hybrid Architecture Combining Reactive Plan Execution And Reactive Learning, Samin Karim, Liz Sonenberg, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Developing software agents has been complicated by the problem of how knowledge should be represented and used. Many researchers have identified that agents need not require the use of complex representations, but in many cases suffice to use “the world” as their representation. However, the problem of introspection, both by the agents themselves and by (human) domain experts, requires a knowledge representation with a higher level of abstraction that is more ‘understandable’. Learning and adaptation in agents has traditionally required knowledge to be represented at an arbitrary, low-level of abstraction. We seek to create an agent that has the capability …
Learning The Unified Kernel Machines For Classification, Steven C. H. Hoi, Michael R. Lyu, Edward Y. Chang
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 …
Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan
Ontosearch: A Full-Text Search Engine For The Semantic Web, Xing Jiang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
OntoSearch, a full-text search engine that exploits ontological knowledge for document retrieval, is presented in this paper. Different from other ontology based search engines, OntoSearch does not require a user to specify the associated concepts of his/her queries. Domain ontology in OntoSearch is in the form of a semantic network. Given a keyword based query, OntoSearch infers the related concepts through a spreading activation process in the domain ontology. To provide personalized information access, we further develop algorithms to learn and exploit user ontology model based on a customized view of the domain ontology. The proposed system has been applied …
A Novel Privacy Preserving Authentication And Access Control Scheme For Pervasive Computing Environments, K. Ren, Wenjing Lou, K. Kim, Robert H. Deng
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 …
Extraction Of Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai
Extraction Of Coherent Relevant Passages Using Hidden Markov Models, Jing Jiang, Chengxiang Zhai
Research Collection School Of Computing and Information Systems
In information retrieval, retrieving relevant passages, as opposed to whole documents, not only directly benefits the end user by filtering out the irrelevant information within a long relevant document, but also improves retrieval accuracy in general. A critical problem in passage retrieval is to extract coherent relevant passages accurately from a document, which we refer to as passage extraction. While much work has been done on passage retrieval, the passage extraction problem has not been seriously studied. Most existing work tends to rely on presegmenting documents into fixed-length passages which are unlikely optimal because the length of a relevant passage …
Autonomic Management Of Stream Processing Applications Via Adaptive Bandwidth Control, Dimitrios Pendarakis, Jeremy Silber, Laura Wynter
Autonomic Management Of Stream Processing Applications Via Adaptive Bandwidth Control, Dimitrios Pendarakis, Jeremy Silber, Laura Wynter
Research Collection School Of Computing and Information Systems
We present a novel autonomic control system for high performance stream processing systems. The system uses bandwidth controls on incoming or outgoing streams to achieve a desired resource utilization balance among a set of concurrently executing stream processing tasks. We show that CPU prioritization and allocation mechanisms in schedulers and virtual machine managers are not sufficient to control such I/O-centric applications, and present an autonomic bandwidth control system that adaptively adjusts incoming and outgoing traffic rates to achieve system management goals. The system dynamically learns the bandwidth rate necessary to meet the system management goals using stochastic nonlinear optimization, and …
Mobile Healthcare Informatics, Keng Siau, Zixing Shen
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 …
Generalized On-Demand Service Architecture For Interactive Applications [Us 2006/0168107 A1], Rajesh K. Balan, Paul C. Castro, Maria R. Ebling, Archan Misra
Generalized On-Demand Service Architecture For Interactive Applications [Us 2006/0168107 A1], Rajesh K. Balan, Paul C. Castro, Maria R. Ebling, Archan Misra
Research Collection School Of Computing and Information Systems
A method, system, and computer program for executing a network-based distributed application. An infrastructure is described that includes application containers configured to execute an application instance of the distributed application and measure quality of service metrics for the application instance. Workload management elements form a decentralized workload management layer. The workload management layer is configured to assign a workload to application containers based on the quality of service metrics received by the application containers.
Hierarchical Hidden Markov Model For Rushes Structuring And Indexing, Chong-Wah Ngo, Zailiang Pan, Xiaoyong Wei
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 …
Prediction-Based Gesture Detection In Lecture Videos By Combining Visual, Speech And Electronic Slides, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
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 …