Digital Commons Network^™

Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang

Research Collection School Of Computing and Information Systems

Traditional text mining techniques transform free text into flat bags of words representation, which does not preserve sufficient semantics for the purpose of knowledge discovery. In this paper, we present a two-step procedure to mine generalized associations of semantic relations conveyed by the textual content of Web documents. First, RDF (resource description framework) metadata representing semantic relations are extracted from raw text using a myriad of natural language processing techniques. The relation extraction process also creates a term taxonomy in the form of a sense hierarchy inferred from WordNet. Then, a novel generalized association pattern mining algorithm (GP-Close) is applied …

Quality Of Service Routing Strategy Using Supervised Genetic Algorithm, Zhaoxia Wang, Yugeng Sun, Zhiyong Wang, Huayu Shen

Research Collection School Of Computing and Information Systems

A supervised genetic algorithm (SGA) is proposed to solve the quality of service (QoS) routing problems in computer networks. The supervised rules of intelligent concept are introduced into genetic algorithms (GAs) to solve the constraint optimization problem. One of the main characteristics of SGA is its searching space can be limited in feasible regions rather than infeasible regions. The superiority of SGA to other GAs lies in that some supervised search rules in which the information comes from the problems are incorporated into SGA. The simulation results show that SGA improves the ability of searching an optimum solution and accelerates …

Lecture Video Enhancement And Editing By Integrating Posture, Gesture, And Text, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong

Research Collection School Of Computing and Information Systems

This paper describes a novel framework for automatic lecture video editing by gesture, posture, and video text recognition. In content analysis, the trajectory of hand movement is tracked and the intentional gestures are automatically extracted for recognition. In addition, head pose is estimated through overcoming the difficulties due to the complex lighting conditions in classrooms. The aim of recognition is to characterize the flow of lecturing with a series of regional focuses depicted by human postures and gestures. The regions of interest (ROIs) in videos are semantically structured with text recognition and the aid of external documents. By tracing the …

Moving-Object Detection, Association, And Selection In Home Videos, Zailiang Pan, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Due to the prevalence of digital video camcorders, home videos have become an important part of life-logs of personal experiences. To enable efficient video parsing, a critical step is to automatically extract objects, events and scene characteristics present in videos. This paper addresses the problem of extracting objects from home videos. Automatic detection of objects is a classical yet difficult vision problem, particularly for videos with complex scenes and unrestricted domains. Compared with edited and surveillant videos, home videos captured in uncontrolled environment are usually coupled with several notable features such as shaking artifacts, irregular motions, and arbitrary settings. These …

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 …

The Philosophy Of Special Relativity: A Comparison Between Indian And Western Interpretations, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

The Western philosophical phenomenalism could be treated as a kind of philosophical basis of the special theory of relativity. The perceptual limitations of our senses hold the key to the understanding of relativistic postulates. The specialness of the speed of light in our phenomenal space and time is more a matter of our perceptual apparatus, than an input postulate to the special theory of relativity. The author believes that the parallels among the phenomenological, Western spiritual and the Eastern Advaita interpretations of special relativity point to an exciting possibility of unifying the Eastern and Western schools of thought to some …

Searching And Tagging: Two Sides Of The Same Coin?, Qiaozhu Mei, Jing Jiang, Hang Su, Chengxiang Zhai

Research Collection School Of Computing and Information Systems

This paper presents the duality hypothesis of search and tagging, two important behaviors of web users. The hypothesis states that if a user views a document D in the search results for query Q, the user would tend to assign document $D$ a tag identical to or similar to Q; similarly, if a user tags a document D with a tag T, the user would tend to view document D if it is in the search results obtained using T as a query. We formalize this hypothesis with a unified probabilistic model for search and tagging, and show that empirical …

Real-Time Supply Chain Control Via Multi-Agent Adjustable Autonomy, Hoong Chuin Lau, Lucas Agussurja, Ramesh Thangarajoo

Research Collection School Of Computing and Information Systems

Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design …

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 …

Iterated Weaker-Than-Weak Dominance, Shih-Fen Cheng, Michael P. Wellman

Research Collection School Of Computing and Information Systems

We introduce a weakening of standard gametheoretic δ-dominance conditions, called dominance, which enables more aggressive pruning of candidate strategies at the cost of solution accuracy. Equilibria of a game obtained by eliminating a δ-dominated strategy are guaranteed to be approximate equilibria of the original game, with degree of approximation bounded by the dominance parameter. We can apply elimination of δ-dominated strategies iteratively, but the for which a strategy may be eliminated depends on prior eliminations. We discuss implications of this order independence, and propose greedy heuristics for determining a sequence of eliminations to reduce the game as far as possible …

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) …

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, 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 …

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.

Businessfinder: Harnessing Presence To Enable Live Yellow Pages For Small, Medium And Micro Mobile Businesses, D. Chakraborty, K. Dasgupta, S. Mittal, Archan Misra, C. Oberle, A. Gupta, E. Newmark

Research Collection School Of Computing and Information Systems

Applications leveraging network presence in next-generation cellular networks have so far focused on subscription queries, where "presence" information is extracted from specific devices and sent to entities who have subscribed to such presence information. In this article we present BusinessFinder, a service that leverages the underlying cellular presence substrate to provide efficient, on-demand, context-aware matching of customer requests to nomadic micro businesses as well as small and medium businesses having a mobile workforce. Presence, in the context of BusinessFinder, is not simply limited to phone location and device status, but also encompasses dynamic attributes of vendors (both "mobile" and "static"), …

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 …

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 …

Image Segmentation Using Multi-Coloured Active Illumination, Tze Ki (Xu Shuqi) Koh, Nicholas Miles, Barrie Hayes-Gill

Research Collection School Of Computing and Information Systems

In this paper, the use of active illumination is extended to image segmentation, specifically in the case of overlapping particles. This work is based on Multi-Flash Imaging (MFI), originally developed by Mitsubishi Electric Labs, to detect depth discontinuities. Illuminations of different wavelengths are projected from multiple positions, providing additional information about a scene compared to conventional segmentation techniques. Shadows are used to identify true object edges. The identification of non- occluded particles is made possible by exploiting the fact that shadows are cast on underlying particles. Implementation issues such as selecting the appropriate colour model and number of illuminations are …

Anticipatory Event Detection Via Classification, He Qi, Kuiyu Chang, Ee Peng Lim

Research Collection School Of Computing and Information Systems

The idea of event detection is to identify interesting patterns from a constant stream of incoming news documents. Previous research in event detection has largely focused on identifying the first event or tracking subsequent events belonging to a set of pre-assigned topics such as earthquakes, airline disasters, etc. In this paper, we describe a new problem, called anticipatory event detection (AED), which aims to detect if a user-specified event has transpired. AED can be viewed as a personalized combination of event tracking and new event detection. We propose using sentence-level and document-level classification approaches to solve the AED problem for …

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.

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.

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 …

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 …

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 …

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 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 …

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 …

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 …

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.