Computer Sciences Commons^™

Introduction: Special Issue For The Selected Papers In The Fourth International Conference On Intelligent Multimedia Computing And Networking (Immcn) 2005, Chong-Wah Ngo, Hong-Va Leong

Research Collection School Of Computing and Information Systems

This special issue introduces seven papers selected from the IMMCN’ 2005, covering a wide range of emerging topics in multimedia field. These papers receive high scores and good comments from the reviewers in their respective areas of intelligent and nextgeneration networking, technology and application, multimedia coding, content analysis and retrieval. The seven papers are extended to 20 pages and then gone through another review process before the final publication. In this issue, we have two papers for video streaming, two papers for multimedia applications, one paper for video coding, and two papers for image and video retrieval.

National Culture And Its Effects On Knowledge Communication In Online Virtual Communities, Keng Siau, Fiona Fui-Hoon Nah, Min Ling

Research Collection School Of Computing and Information Systems

Online virtual communities provide a powerful means of knowledge sharing. Despite the prevalence of online virtual communities, there is a paucity of research to investigate the effect of national culture differences on knowledge sharing in online virtual communities. Are there differences between online virtual communities from different national cultures? This research studies the differences in knowledge-sharing activities between US-based and China-based online virtual communities. Hofstede's dimensions of national culture serve as the theoretical foundation for this research.

A Growth Theory Perspective On The International Diffusion Of Electronic Commerce., S.C Ho, Robert John Kauffman, T.P. Liang

Research Collection School Of Computing and Information Systems

Information and communication technologies (ICTs) continue to have a profound effect on the economies and societies where they are used. In this article, we propose three related theories to describe the underlying mechanism for growth in e-commerce revenues at the national level. Endogenous growth theory posits that the primary drivers of e-commerce growth are internal to a country. Exogenous growth theory suggests that the primary drivers of e-commerce growth are external to an economic system, and reflect the forces of the regional economy. A blend of these, a mixed endogenous–exogenous growth theory, incorporates drivers from both the economy and …

An Improvement Heuristic For The Timetabling Problem, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh

Research Collection School Of Computing and Information Systems

This paper formulates a timetabling problem, which is often encountered in a university, as a mathematical programming model. The proposed model combines both teacher assignment and course scheduling problems simultaneously, which causes the entire model to become more complex. We propose an improvement heuristic algorithm to solve such a model. The proposed algorithm has been tested with several randomly generated datasets of sizes that are comparable to those occurring in a university in Indonesia. The computational results show that the improvement heuristic is not only able to obtain good solutions, but is also able to do so within reasonable computational …

Editors' Introduction: Ecra Vol. 6, No. 4, Patrick Y. K. Chau, Robert J. Kauffman, Norman M. Sadeh, J. Christopher Westland

Research Collection School Of Computing and Information Systems

This issue features a total of 13 articles. The first four articles are part of a special section on “Intelligent agents in e-services”, edited by Professors William K. Cheung and Jane Y. Hsu under the supervision of Professor Robert J. Kauffman. Summaries of articles appearing in the special section are provided in the guest editors’ introduction. The following is a summary of the nine regular submission articles also published in this issue. The review of these articles was kindly coordinated by Professor Jae Kyu Lee, former Editor in Chief of ECRA.

The general submission articles in this issue focus on …

Solving The Teacher Assignment-Course Scheduling Problem By A Hybrid Algorithm, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh

Research Collection School Of Computing and Information Systems

This paper presents a hybrid algorithm for solving atimetabling problem, which is commonly encountered in manyuniversities. The problem combines both teacher assignment andcourse scheduling problems simultaneously, and is presented as amathematical programming model. However, this problem becomesintractable and it is unlikely that a proven optimal solution can beobtained by an integer programming approach, especially for largeproblem instances. A hybrid algorithm that combines an integerprogramming approach, a greedy heuristic and a modified simulatedannealing algorithm collaboratively is proposed to solve the problem.Several randomly generated data sets of sizes comparable to that ofan institution in Indonesia are solved using the proposed algorithm.Computational results …

Editors' Introduction, Robert John Kauffman, Jae Kyu Lee, Norman M. Sadeh

Research Collection School Of Computing and Information Systems

This issue features a blend of empirical studies andtechnology-related articles, covering a number of aspectsof e-commerce research and industry applications. Thecontent includes works on online advertising and customerservice on the Internet, the social and human aspects ofradio frequency identification (RFID), the usability of theWeb, content delivery and wireless local area networks,electronic auctions, micropayments, and e-government.This reflects the editorial mission of this journal, which isto attract a breadth of scholarship from the Computer Science and technical research community, the InformationSystems and Technology research community, and fromthe Management, Strategy, Marketing, Operations, Finance and Policy communities associated with businessschools and schools of public …

Value-At-Risk In It Services Contracts, Robert J. Kauffman, Ryan Sougstad

Research Collection School Of Computing and Information Systems

As information systems (IS) and technology solutions become increasingly service-driven, managers are faced with the task of choosing parameters such as service-levels, pricing, and contract duration. Information technology (IT) services vendors manage portfolios of contracts in which parameters, decided at inception, are often subject to future risks. The contract profit maximization decision may adversely affect the risk position of the firm's portfolio of services contracts. We propose a model to inform vendors on setting optimal parameters for IS contracts subject to acceptable levels of risk. The analytic model presented draws from IS economics research and the principles of value-at-risk (VaR) …

Mining Multiple Visual Appearances Of Semantics For Image Annotation, Hung-Khoon Tan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This paper investigates the problem of learning the visual semantics of keyword categories for automatic image annotation. Supervised learning algorithms which learn only a single concept point of a category are limited in their effectiveness for image annotation. We propose to use data mining techniques to mine multiple concepts, where each concept may consist of one or more visual parts, to capture the diverse visual appearances of a single keyword category. For training, we use the Apriori principle to efficiently mine a set of frequent blobsets to capture the semantics of a rich and diverse visual category. Each concept is …

Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

TD-FALCON is a self-organizing neural network that incorporates Temporal Difference (TD) methods for reinforcement learning. Despite the advantages of fast and stable learning, TD-FALCON still relies on an iterative process to evaluate each available action in a decision cycle. To remove this deficiency, this paper presents a direct code access procedure whereby TD-FALCON conducts instantaneous searches for cognitive nodes that match with the current states and at the same time provide maximal reward values. Our comparative experiments show that TD-FALCON with direct code access produces comparable performance with the original TD-FALCON while improving significantly in computation efficiency and network complexity.

Integrating Semantic Templates With Decision Tree For Image Semantic Learning, Ying Liu, Dengsheng Zhang, Guojun Lu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Decision tree (DT) has great potential in image semantic learning due to its simplicity in implementation and its robustness to incomplete and noisy data. Decision tree learning naturally requires the input attributes to be nominal (discrete). However, proper discretization of continuous-valued image features is a difficult task. In this paper, we present a decision tree based image semantic learning method, which avoids the difficult image feature discretization problem by making use of semantic template (ST) defined for each concept in our database. A ST is the representative feature of a concept, generated from the low-level features of a collection of …

Towards Efficient Computation Of Quality Bounded Solutions In Pomdps: Expected Value Approximation And Dynamic Disjunctive Beliefs, Pradeep Reddy Varakantham, Rajiv Maheswaran, Tapana Gupta, Milind Tambe

Research Collection School Of Computing and Information Systems

While POMDPs (partially observable markov decision problems) are a popular computational model with wide-ranging applications, the computational cost for optimal policy generation is prohibitive. Researchers are investigating ever-more efficient algorithms, yet many applications demand such algorithms bound any loss in policy quality when chasing efficiency. To address this challenge, we present two new techniques. The first approximates in the value space to obtain solutions efficiently for a pre-specified error bound. Unlike existing techniques, our technique guarantees the resulting policy will meet this bound. Furthermore, it does not require costly computations to determine the quality loss of the policy. Our second …

On The Lower Bound Of Local Optimums In K-Means Algorithms, Zhenjie Zhang, Bing Tian Dai, Anthony K.H. Tung

Research Collection School Of Computing and Information Systems

No abstract provided.

Rapid Identification Of Column Heterogeneity, Bing Tian Dai, Nick Koudas, Beng Chin Ooi, Divesh Srivastava, Suresh Venkatasubramanian

Research Collection School Of Computing and Information Systems

No abstract provided.

Clique Percolation For Finding Naturally Cohesive And Overlapping Document Clusters, Wei Gao, Kam-Fai Wong, Yunqing Xia, Ruifeng Xu

Research Collection School Of Computing and Information Systems

Techniques for find document clusters mostly depend on models that impose strong explicit and/or implicit priori assumptions. As a consequence, the clustering effects tend to be unnatural and stray away from the intrinsic grouping natures of a document collection. We apply a novel graph-theoretic technique called Clique Percolation Method (CPM) for document clustering. In this method, a process of enumerating highly cohesive maximal document cliques is performed in a random graph, where those strongly adjacent cliques are mingled to form naturally overlapping clusters. Our clustering results can unveil the inherent structural connections of the underlying data. Experiments show that CPM …

Finding A Length-Constrained Maximum-Sum Or Maximum-Density Subtree And Its Application To Logistics, Hoong Chuin Lau, Trung Hieu Ngo, Bao Nguyen Nguyen

Research Collection School Of Computing and Information Systems

We study the problem of finding a length-constrained maximum-density path in a tree with weight and length on each edge. This problem was proposed in [R.R. Lin, W.H. Kuo, K.M. Chao, Finding a length-constrained maximum-density path in a tree, Journal of Combinatorial Optimization 9 (2005) 147–156] and solved in O(nU) time when the edge lengths are positive integers, where n is the number of nodes in the tree and U is the length upper bound of the path. We present an algorithm that runs in O(nlog2n) time for the generalized case when the edge lengths are positive real numbers, which …

Measuring Qualities Of Articles Contributed By Online Communities, Ee Peng Lim, Ba-Quy Vuong, Hady W. Lauw, Aixin Sun

Research Collection School Of Computing and Information Systems

Using open source Web editing software (e.g., wiki), online community users can now easily edit, review and publish articles collaboratively. While much useful knowledge can be derived from these articles, content users and critics are often concerned about their qualities. In this paper, we develop two models, namely basic model and peer review model, for measuring the qualities of these articles and the authorities of their contributors. We represent collaboratively edited articles and their contributors in a bipartite graph. While the basic model measures an article's quality using both the authorities of contributors and the amount of contribution from each …

Dynamic Multi-Linked Negotiations In Multi-Echelon Production Scheduling Networks, Hoong Chuin Lau, Guan Li Soh, Wee Chong Wan

Research Collection School Of Computing and Information Systems

In this paper, we are concerned with scheduling resources in a multi-tier production/logistics system for multi-indenture goods. Unlike classical production scheduling problems, the problem we study is concerned with local utilities which are private. We present an agent model and investigate an efficient scheme for handling multi-linked agent negotiations. With this scheme we attempt to overcome the drawbacks of sequential negotiations and negotiation parameter settings. Our approach is based on embedding a credit-based negotiation protocol within a local search scheduling algorithm. We demonstrate the computational efficiency and effectiveness of the approach in solving a real-life dynamic production scheduling problem which …

Query-Based Watermarking For Xml Data, Xuan Zhou, Hwee Hwa Pang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

As increasing amount of XML data is exchanged over the internet, copyright protection of this type of data is becoming an important requirement for many applications. In this paper, we introduce a rights protection scheme for XML data based on digital watermarking. One of the main challenges for watermarking XML data is that the data could be easily reorganized by an adversary in an attempt to destroy any embedded watermark. To overcome it, we propose a query-based watermarking scheme, which creates queries to identify available watermarking capacity, such that watermarks could be recovered from reorganized data through query rewriting. The …

Online Advertising By The Comparison Challenge Approach, Jae Wong Lee, Jae Kyu Lee

Research Collection School Of Computing and Information Systems

To enhance the effectiveness of online comparisons from a manufacturer's point of view, we develop a framework for the comparison challenge approach. To develop the comparison challenge framework, we analyze 12 factors that determine the characteristics of comparison and propose models of valuable comparison challenges using the CompareMe and CompareThem strategies. We demonstrate the approaches with the example of PC selection. To help plan the comparison challenges, we formulate a mathematical programming model that maximizes the total value of comparison under the constraints of comparison opportunity and budgetary limitation. The model is applied to eight comparison scenarios, and its performance …

Designing Web Sites For Customer Loyalty Across Business Domains: A Multilevel Analysis, S. Mithas, Narayanasamy Ramasubbu, M. S. Krishnan, C. Fornell

Research Collection School Of Computing and Information Systems

Web Sites are important components of Internet strategy for organizations. This paper develops a theoretical model for understanding the effect of Web site design elements on customer loyalty to a Web site. We show the relevance of the business domain of a Web site to gain a contextual understanding of relative importance of Web site design elements. We use a hierarchical linear modeling approach to model multilevel and cross-level interactions that have not been explicitly considered in previous research. By analyzing, data on more than 12,000 online customer surveys for 43 Web sites in several business domains, we find that …

Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Combination, Jialie Shen, John Shepherd, Ann H. H. Ngu

Research Collection School Of Computing and Information Systems

In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare …

Reputation Stretching In Online Auctions, Jianqing Chen, Mei Lin, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

Reputation systems are ubiquitously employed in the online marketplaces to provide information on users’ abilities and trustworthiness. We focus particularly on the problem of reputation stretching, the extension of an established reputation into a new market. By examining various factors—market size, the expected performance in the expanding market, and the risk of performance in the expanding market—we characterize conditions that affect bidder’s stretching decision in the expanding market. Moreover, we analyze both the short-term and the long-term expanding cases, and obtain contrasting results in the two settings.

Supply Chain Information Sharing In A Macro Prediction Market, Zhiling Guo, Fang Fang, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

This paper aims to address supply chain partners' incentives for information sharing from an information systems design perspective. Specifically, we consider a supply chain characterized by N geographically distributed retailers who order a homogeneous product from one manufacturer. Each retailer's demand risk consists of two parts: a systematic risk part that affects all retailers and an idiosyncratic risk part that only has a local effect. We propose a macro prediction market to effectively elicit and aggregate useful information about systematic demand risk. We show that such information can be used to achieve accurate demand forecast sharing and better channel coordination …

Set-Based Cascading Approaches For Magnetic Resonance (Mr) Image Segmentation (Scamis), Jiang Liu, Tze-Yun Leong, Kin Ban Chee, Boon Pin Tan, Borys Shuter, Shih Chang Wang

Research Collection School Of Computing and Information Systems

This paper introduces Set-based Cascading Approach for Medical Image Segmentation (SCAMIS), a new methodology for segmentation of medical imaging by integrating a number of algorithms. Existing approaches typically adopt the pipeline methodology. Although these methods provide promising results, the results generated are still susceptible to over-segmentation and leaking. In our methodology, we describe how set operations can be utilized to better overcome these problems. To evaluate the effectiveness of this approach, Magnetic Resonance Images taken from a teaching hospital research programme have been utilised, to reflect the real world quality needed for testing in patient datasets. A comparison between the …

Towards Effective Content-Based Music Retrieval With Multiple Acoustic Feature Composition, Jialie Shen, John Shepherd, Ngu Ahh

Research Collection School Of Computing and Information Systems

In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare …

Cosign: A Parallel Algorithm For Coordinated Traffic Signal Control, Shih-Fen Cheng, Marina A. Epelman, Robert L. Smith

Research Collection School Of Computing and Information Systems

The problem of finding optimal coordinated signal timing plans for a large number of traffic signals is a challenging problem because of the exponential growth in the number of joint timing plans that need to be explored as the network size grows. In this paper, the game-theoretic paradigm of fictitious play to iteratively search for a coordinated signal timing plan is employed, which improves a system-wide performance criterion for a traffic network. The algorithm is robustly scalable to realistic-size networks modeled with high-fidelity simulations. Results of a case study for the city of Troy, MI, where there are 75 signalized …

Plans As Products Of Learning, Samin Karim, Budhitama Subagdja, Liz Sonenberg

Research Collection School Of Computing and Information Systems

This paper presents motivations and current related work in the field of plan learning. Additionally, two approaches that achieve plan learning are presented. The two presented approaches are centred on the BDI framework of agency and have particular focus on plans, which, alongside goals, are the means to fulfil intentions in most pragmatic and theoretical realisations of the BDI framework. The first approach is a hybrid architecture that combines a BDI plan extractor and executor with a generic low-level learner. The second approach uses hypotheses to suggest incremental refinements of a priori plans. Both approaches achieve plan generation that is …

Formal Analysis And Systematic Construction Of Two-Factor Authentication Scheme, Guomin Yang, Duncan S. Wong, Huaxiong Wang, Xiaotie Deng

Research Collection School Of Computing and Information Systems

One of the most commonly used two-factor authentication mechanisms is based on smart card and user’s password. Throughout the years, there have been many schemes proposed, but most of them have already been found flawed due to the lack of formal security analysis. On the cryptanalysis of this type of schemes, in this paper, we further review two recently proposed schemes and show that their security claims are invalid. To address the current issue, we propose a new and simplified property set and a formal adversarial model for analyzing the security of this type of schemes. We believe that the …

An Experimental Study On U-Commerce Adoption: Impact Of Personalization And Privacy Concerns, H. Sheng, Fiona Fui-Hoon Nah, Keng Siau

Research Collection School Of Computing and Information Systems

Ubiquitous commerce (u-commerce) represents "anytime, anywhere" commerce. U-commerce can provide a high level of personalization, which can bring significant benefits to customers. However, privacy is a major concern to customers and an obstacle to the adoption of u-commerce. This research examines how personalization and context can impact customers' privacy concerns as well as intention to adopt u-commerce applications. As u-commerce is new and emerging, we used the scenario-based approach to operationalize personalization and context in an experimental study. The experimental results show that the effects of personalization on customers' privacy concerns and adoption intention are situation dependent.