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Cognitive Evaluation Of Information Modeling Methods, Keng Siau, Yuan Wang
Cognitive Evaluation Of Information Modeling Methods, Keng Siau, Yuan Wang
Research Collection School Of Computing and Information Systems
In the field of information system engineering, information modeling method is a technique to capture user requirements and to understand system complexity. The importance of information modeling has been recognized by practitioners and researchers, but little has been explored to analyze the available information modeling methods or to evaluate them in terms of their strengths, weaknesses, and effectiveness. This research analyzes six information-modeling methods: use case diagram, rich picture diagram, entity-relationship diagram, Trochim’s concept mapping, repertory grid, and causal mapping. These information-modeling methods are analyzed from a cognitive perspective in order to better understand their nature, the assumptions, and the …
Deckard: Scalable And Accurate Tree-Based Detection Of Code Clones, Lingxiao Jiang, Ghassan Misherghi, Zhendong Su, Stephane Glondu
Deckard: Scalable And Accurate Tree-Based Detection Of Code Clones, Lingxiao Jiang, Ghassan Misherghi, Zhendong Su, Stephane Glondu
Research Collection School Of Computing and Information Systems
Detecting code clones has many software engineering applications. Existing approaches either do not scale to large code bases or are not robust against minor code modifications. In this paper, we present an efficient algorithm for identifying similar subtrees and apply it to tree representations of source code. Our algorithm is based on a novel characterization of subtrees with numerical vectors in the Euclidean Rn and an efficient algorithm to cluster these vectors w.r.t. the Euclidean distance metric. Subtrees with vectors in one cluster are considered similar. We have implemented our tree similarity algorithm as a clone detection tool called DECKARD …
Forgery Attack To An Asymptotically Optimal Traitor Tracing Scheme, Yongdong Wu, Feng Bao, Robert H. Deng
Forgery Attack To An Asymptotically Optimal Traitor Tracing Scheme, Yongdong Wu, Feng Bao, Robert H. Deng
Research Collection School Of Computing and Information Systems
In this paper, we present a forgery attack to a black-box traitor tracing scheme [2] called as CPP scheme. CPP scheme has efficient transmission rate and allows the tracer to identify a traitor with just one invalid ciphertext. Since the original CPP scheme is vulnerable to the multi-key attack, we improved CPP to thwart the attack. However, CPP is vulnerable to a fatal forgery attack. In the forgery attack, two traitors can collude to forge all valid decryption keys. The forged keys appear as perfect genuine keys, can decrypt all protected content, but are untraceable by the tracer. Fortunately, we …
Mining Colossal Frequent Patterns By Core Pattern Fusion, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, Hong Cheng
Mining Colossal Frequent Patterns By Core Pattern Fusion, Feida Zhu, Xifeng Yan, Jiawei Han, Philip S. Yu, Hong Cheng
Research Collection School Of Computing and Information Systems
Extensive research for frequent-pattern mining in the past decade has brought forth a number of pattern mining algorithms that are both effective and efficient. However, the existing frequent-pattern mining algorithms encounter challenges at mining rather large patterns, called colossal frequent patterns, in the presence of an explosive number of frequent patterns. Colossal patterns are critical to many applications, especially in domains like bioinformatics. In this study, we investigate a novel mining approach called Pattern-Fusion to efficiently find a good approximation to the colossal patterns. With Pattern-Fusion, a colossal pattern is discovered by fusing its small core patterns in one step, …
Social Network Structures In Open Source Software Development Teams, Y. Long, Keng Siau
Social Network Structures In Open Source Software Development Teams, Y. Long, Keng Siau
Research Collection School Of Computing and Information Systems
Drawing on social network theories and previous studies, this research examines the dynamics of social network structures in open source software (OSS) teams. Three projects were selected from SourceForge.net in terms of their similarities as well as their differences. Monthly data were extracted from the bug tracking systems in order to achieve a longitudinal view of the interaction pattern of each project. Social network analysis was used to generate the indices of social structure. The finding suggests that the interaction pattern of OSS projects evolves from a single hub at the beginning to a corel periphery model as the projects …
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One critical task in content-based video retrieval is to rank search results with combinations of multimodal resources effectively. This paper proposes a novel multimodal and multilevel ranking framework for content-based video retrieval. The main idea of our approach is to represent videos by graphs and learn harmonic ranking functions through fusing multimodal resources over these graphs smoothly. We further tackle the efficiency issue by a multilevel learning scheme, which makes the semi-supervised ranking method practical for large-scale applications. Our empirical evaluations on TRECVID 2005 dataset show that the proposed multimodal and multilevel ranking framework is effective and promising for content-based …
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
A Multimodal And Multilevel Ranking Framework For Content-Based Video Retrieval, Steven C. H. Hoi, Michael R. Lyu
Research Collection School Of Computing and Information Systems
One critical task in content-based video retrieval is to rank search results with combinations of multimodal resources effectively. This paper proposes a novel multimodal and multilevel ranking framework for content-based video retrieval. The main idea of our approach is to represent videos by graphs and learn harmonic ranking functions through fusing multimodal resources over these graphs smoothly. We further tackle the efficiency issue by a multilevel learning scheme, which makes the semi-supervised ranking method practical for large-scale applications. Our empirical evaluations on TRECVID 2005 dataset show that the proposed multimodal and multilevel ranking framework is effective and promising for content-based …
Experiences With Tracking The Effects Of Changing Requirements On Morphbank: A Web-Based Bioinformatics Application, Subhajit Datta, Robert Van Engelen, David Gaitros, Neelima Jammigumpula
Experiences With Tracking The Effects Of Changing Requirements On Morphbank: A Web-Based Bioinformatics Application, Subhajit Datta, Robert Van Engelen, David Gaitros, Neelima Jammigumpula
Research Collection School Of Computing and Information Systems
In this paper, we present a case study of applying the metrics Mutation Index, Component Set, Dependency Index on Morphbank- a web based bioinformatics application - to track the effects of changing requirements on a software system and suggest design modifications to mitigate such impact. Morphbank is "an open web repository of biological images documenting specimen-based research in comparative anatomy, morphological phylogenetics, taxonomy and related fields focused on increasing our knowledge about biodiversity". This paper discusses the context of the case study, analyzes the results, highlights observations and learning, and mentions directions of future work.
Efficient Algorithms For Machine Scheduling Problems With Earliness And Tardiness Penalties, Guang Feng, Hoong Chuin Lau
Efficient Algorithms For Machine Scheduling Problems With Earliness And Tardiness Penalties, Guang Feng, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In this paper, we study the multi-machine scheduling problem with earliness and tardiness penalties and sequence dependent setup times. This problem can be decomposed into two subproblems—sequencing and timetabling. Sequencing focuses on assigning each job to a fixed machine and determine the job sequence on each machine. We call such assignment a semi-schedule. Timetabling focuses on finding an executable schedule from the semi-schedule via idle-time insertion. Sequencing is strongly NP-hard in general. Although timetabling is polynomial-time solvable, it can become a computational bottleneck if the procedure is executed many times within a larger framework. This paper makes two contributions. We …
Percentage-Based Hybrid Pattern Training With Neural Network Specific Cross Over, Sheng-Uei Guan, Kiruthika Ramanathan
Percentage-Based Hybrid Pattern Training With Neural Network Specific Cross Over, Sheng-Uei Guan, Kiruthika Ramanathan
Research Collection School Of Computing and Information Systems
In this paper, a new weight-setting method is proposed to improve the training time and generalization accuracy of feed-forward neural networks. This method introduces a percentage-based hybrid pattern training (PHP) scheme and aims to provide a solution to the problem dependency of other Genetic Algorithm (GA)-based Neural Network weight-setting methods. A neural network is trained using a neural network specific GA until a certain percentage of the training patterns is learned. The weights thus obtained are used as the initial weights for backpropagation (BP) training, which is then applied to complete the network training. Further improvement to the method was …
Privacy-Preserving Credentials Upon Trusted Computing Augmented Servers, Yanjiang Yang, Robert H. Deng, Feng Bao
Privacy-Preserving Credentials Upon Trusted Computing Augmented Servers, Yanjiang Yang, Robert H. Deng, Feng Bao
Research Collection School Of Computing and Information Systems
Credentials are an indispensable means for service access control in electronic commerce. However, regular credentials such as X.509 certificates and SPKI/SDSI certificates do not address user privacy at all, while anonymous credentials that protect user privacy are complex and have compatibility problems with existing PKIs. In this paper we propose privacy-preserving credentials, a concept between regular credentials and anonymous credentials. The privacy-preserving credentials enjoy the advantageous features of both regular credentials and anonymous credentials, and strike a balance between user anonymity and system complexity. We achieve this by employing computer servers equipped with TPMs (Trusted Platform Modules). We present a …
Tube (Text-Cube) For Discovering Documentary Evidence Of Associations Among Entities, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang
Tube (Text-Cube) For Discovering Documentary Evidence Of Associations Among Entities, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang
Research Collection School Of Computing and Information Systems
User-driven discovery of associations among entities, and documents that provide evidence for these associations, is an important search task conducted by researchers and do-main information specialists. Entities here refer to real or abstract objects such as people, organizations, ideologies, etc. Associations are the inter-relationships among entities. Most current works in query-driven document retrieval and finding representative subgraphs are ill-suited for the task as they lack an awareness of entity types as well as an intuitive representation of associations. We propose the TUBE model, a text cube approach for discovering associations and documentary evidence of these associations. The model consists of …
Mining Generalized Associations Of Semantic Relations From Textual Web Content, Tao Jiang, Ah-Hwee Tan, We Wang
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 …
Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan
Clustering And Combinatorial Optimization In Recursive Supervised Learning, Kiruthika Ramanathan, Sheng Uei Guan
Research Collection School Of Computing and Information Systems
The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In …
Quality Of Service Routing Strategy Using Supervised Genetic Algorithm, Zhaoxia Wang, Yugeng Sun, Zhiyong Wang, Huayu Shen
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
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
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 …
Towards Efficient Planning For Real World Partially Observable Domains, Pradeep R. Varakantham
Towards Efficient Planning For Real World Partially Observable Domains, Pradeep R. Varakantham
Research Collection School Of Computing and Information Systems
My research goal is to build large-scale intelligent systems (both single- and multi-agent) that reason with uncertainty in complex, real-world environments. I foresee an integration of such systems in many critical facets of human life ranging from intelligent assistants in hospitals to offices, from rescue agents in large scale disaster response to sensor agents tracking weather phenomena in earth observing sensor webs, and others. In my thesis, I have taken steps towards achieving this goal in the context of systems that operate in partially observable domains that also have transitional (non-deterministic outcomes to actions) uncertainty. Given this uncertainty, Partially Observable …
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
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.
Solving The Teacher Assignment-Course Scheduling Problem By A Hybrid Algorithm, Aldy Gunawan, Kien Ming Ng, Kim Leng Poh
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 …
Value-At-Risk In It Services Contracts, Robert J. Kauffman, Ryan Sougstad
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) …
Direct Code Access In Self-Organizing Neural Networks For Reinforcement Learning, Ah-Hwee Tan
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
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 …
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
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"), …
Searching And Tagging: Two Sides Of The Same Coin?, Qiaozhu Mei, Jing Jiang, Hang Su, Chengxiang Zhai
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 …
National Culture And Its Effects On Knowledge Communication In Online Virtual Communities, Keng Siau, Fiona Fui-Hoon Nah, Min Ling
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.
Image Segmentation Using Multi-Coloured Active Illumination, Tze Ki (Xu Shuqi) Koh, Nicholas Miles, Barrie Hayes-Gill
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 …
Iterated Weaker-Than-Weak Dominance, Shih-Fen Cheng, Michael P. Wellman
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 …
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
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 …
Real-Time Supply Chain Control Via Multi-Agent Adjustable Autonomy, Hoong Chuin Lau, Lucas Agussurja, Ramesh Thangarajoo
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 …