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Articles 481 - 493 of 493
Full-Text Articles in Theory and Algorithms
Synchronization Of Lecture Videos And Electronic Slides By Video Text Analysis, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Synchronization Of Lecture Videos And Electronic Slides By Video Text Analysis, Feng Wang, Chong-Wah Ngo, Ting-Chuen Pong
Research Collection School Of Computing and Information Systems
An essential goal of structuring lecture videos captured in live presentation is to provide a synchronized view of video clips and electronic slides. This paper presents an automatic approach to match video clips and slides based on the analysis of text embedded in lecture videos. We describe a method to reconstruct high-resolution video texts from multiple keyframes for robust OCRrecognition. A two-stage matching algorithm based on the title and content similarity measures between video clips and slides is also proposed.
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Mining Of Correlated Rules In Genome Sequences, L. Lin, L. Wong, Tze-Yun Leong, P. S. Lai
Research Collection School Of Computing and Information Systems
With the huge amount of data collected by scientists in the molecular genetics community in recent years, there exists a need to develop some novel algorithms based on existing data mining techniques to discover useful information from genome databases. We propose an algorithm that integrates the statistical method, association rule mining, and classification rule mining in the discovery of allelic combinations of genes that are peculiar to certain phenotypes of diseased patients.
Modeling And Simulation Of Steady State And Transient Behaviors For Emergent Socs, Joann M. Paul, Arne Suppe, Donald E. Thomas
Modeling And Simulation Of Steady State And Transient Behaviors For Emergent Socs, Joann M. Paul, Arne Suppe, Donald E. Thomas
Research Collection School Of Computing and Information Systems
We introduce a formal basis for viewing computer systems as mixed steady state and non-steady state (transient) behaviors to motivate novel design strategies resulting from simultaneous consideration of function, scheduling and architecture. We relate three design styles: Hierarchical decomposition, static mapping and directed platform that have traditionally been separate. By considering them together, we reason that once a steady state system is mapped to an architecture, the unused processing and communication power may be viewed as a platform for a transient system, ultimately resulting in more effective design approaches that ease the static mapping problem while still allowing for effective …
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Nonparametric Techniques To Extract Fuzzy Rules For Breast Cancer Diagnosis Problem, Manish Sarkar, Tze-Yun Leong
Research Collection School Of Computing and Information Systems
This paper addresses breast cancer diagnosis problem as a pattern classification problem. Specifically, the problem is studied using Wisconsin-Madison breast cancer data set. Fuzzy rules are generated from the input-output relationship so that the diagnosis becomes easier and transparent for both patients and physicians. For each class, at least one training pattern is chosen as the prototype, provided (a) the maximum membership of the training pattern is in the given class, and (b) among all the training patterns, the neighborhood of this training pattern has the least fuzzy-rough uncertainty in the given class. Using the fuzzy-rough uncertainty, a cluster is …
Genetic Algorithms For Communications Network Design - An Empirical Study Of The Factors That Influence Performance, Hsinghua Chou, G. Premkumar, Chao-Hsien Chu
Genetic Algorithms For Communications Network Design - An Empirical Study Of The Factors That Influence Performance, Hsinghua Chou, G. Premkumar, Chao-Hsien Chu
Research Collection School Of Computing and Information Systems
We explore the use of GAs for solving a network optimization problem, the degree-constrained minimum spanning tree problem. We also examine the impact of encoding, crossover, and mutation on the performance of the GA. A specialized repair heuristic is used to improve performance. An experimental design with 48 cells and ten data points in each cell is used to examine the impact of two encoding methods, three crossover methods, two mutation methods, and four networks of varying node sizes. Two performance measures, solution quality and computation time, are used to evaluate the performance. The results obtained indicate that encoding has …
Motion-Based Video Representation For Scene Change Detection, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang, Roland T. Chin
Motion-Based Video Representation For Scene Change Detection, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang, Roland T. Chin
Research Collection School Of Computing and Information Systems
We present a new ly developed scheme for automatical ly partitioning videos into scenes. A scene is general ly referred to as a group of shots taken place in the same site. In this paper, we first propose a motion annotation algorithm based on the analysis of spatiotemporal image volumes. The algorithm characterizes the motions within shots by extracting and analyzing the motion trajectories encoded in the temporal slices of image volumes. A motion-based keyframe computing and selection strategy is thus proposed to compactly represent the content of shots. With these techniques, we further present a scene change detection algorithm …
Motion Characterization By Temporal Slices Analysis, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang, Roland T. Chin
Motion Characterization By Temporal Slices Analysis, Chong-Wah Ngo, Ting-Chuen Pong, Hong-Jiang Zhang, Roland T. Chin
Research Collection School Of Computing and Information Systems
This paper describes an approach to characterize camera and object motions based on the analysis of spatio temporal image volumes. In the spatio-temporal slices of image volumes, motion is depicted as oriented patterns. We propose a tensor histogram computation algorithm to represent these oriented patterns. The motion trajectories in a histogram are tracked to describe both the camera and object motions. In addition, we exploit the similarity of the temporal slices in a volume to reliably partition a volume into motion tractable units.
Predictive Adaptive Resonance Theory And Knowledge Discovery In Databases, Ah-Hwee Tan, Hui-Shin Vivien Soon
Predictive Adaptive Resonance Theory And Knowledge Discovery In Databases, Ah-Hwee Tan, Hui-Shin Vivien Soon
Research Collection School Of Computing and Information Systems
This paper investigates the scalability of predictive Adaptive Resonance Theory (ART) networks for knowledge discovery in very large databases. Although predictive ART performs fast and incremental learning, the number of recognition categories or rules that it creates during learning may become substantially large and cause the learning speed to slow down. To tackle this problem, we introduce an on-line algorithm for evaluating and pruning categories during learning. Benchmark experiments on a large scale data set show that on-line pruning has been effective in reducing the number of the recognition categories and the time for convergence. Interestingly, the pruned networks also …
Cascade Artmap: Integrating Neural Computation And Symbolic Knowledge Processing, Ah-Hwee Tan
Cascade Artmap: Integrating Neural Computation And Symbolic Knowledge Processing, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper introduces a hybrid system termed cascade adaptive resonance theory mapping (ARTMAP) that incorporates symbolic knowledge into neural-network learning and recognition. Cascade ARTMAP, a generalization of fuzzy ARTMAP, represents intermediate attributes and rule cascades of rule-based knowledge explicitly and performs multistep inferencing. A rule insertion algorithm translates if-then symbolic rules into cascade ARTMAP architecture. Besides that initializing networks with prior knowledge can improve predictive accuracy and learning efficiency, the inserted symbolic knowledge can be refined and enhanced by the cascade ARTMAP learning algorithm. By preserving symbolic rule form during learning, the rules extracted from cascade ARTMAP can be compared …
Inductive Neural Logic Network And The Scm Algorithm, Ah-Hwee Tan, Loo-Nin Teow
Inductive Neural Logic Network And The Scm Algorithm, Ah-Hwee Tan, Loo-Nin Teow
Research Collection School Of Computing and Information Systems
Neural Logic Network (NLN) is a class of neural network models that performs both pattern processing and logical inferencing. This article presents a procedure for NLN to learn multi-dimensional mapping of both binary and analog data. The procedure, known as the Supervised Clustering and Matching (SCM) algorithm, provides a means of inferring inductive knowledge from databases. In contrast to gradient descent error correction methods, pattern mapping is learned by an inductive NLN using fast and incremental clustering of input and output patterns. In addition, learning/encoding only takes place when both the input and output match criteria are satisfied in a …
Rule Extraction: From Neural Architecture To Symbolic Representation, Gail A. Carpenter, Ah-Hwee Tan
Rule Extraction: From Neural Architecture To Symbolic Representation, Gail A. Carpenter, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
This paper shows how knowledge, in the form of fuzzy rules, can be derived from a supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning, which simplifies the network structure by removing excessive recognition categories and weights; and quantization of continuous learned weights, which allows the final system state to be translated into a usable set of descriptive rules. Three benchmark studies illustrate the rule extraction methods: (1) Pima Indian diabetes diagnosis, (2) mushroom classification and (3) DNA promoter recognition. Fuzzy ARTMAP and ART-EMAP are compared with the ADAP algorithm, the k nearest neighbor system, …
Visual Knowledge Query Language As A Front-End To Relational Systems, Keng Siau, Kok Phuang Tan, Hock Chuan Chan
Visual Knowledge Query Language As A Front-End To Relational Systems, Keng Siau, Kok Phuang Tan, Hock Chuan Chan
Research Collection School Of Computing and Information Systems
Relational query languages like SQL and QUEL require the users to understand the complex database structure. This is a burden on end users, especially novice end users who access the database on a casual and infrequent basis. To alleviate the need to know the logical database organization, this paper proposes the use of a semantic data model, known as the Enhanced Entity-Relationship (EER) model, as a front-end to the relational systems. A formal, high-level Visual Knowledge Query Language (VKQL) has also been designed for this interface. This language provides for knowledge abstraction as the user communicates only domain knowledge with …
Visual Knowledge Query Language As A Front-End To Relational Systems, Keng Siau, Kok Phuang Tan, Hock Chuan Chan
Visual Knowledge Query Language As A Front-End To Relational Systems, Keng Siau, Kok Phuang Tan, Hock Chuan Chan
Research Collection School Of Computing and Information Systems
Relational query languages like SQL and QUEL require the users to understand the complex database structure. This is a burden on end users, especially novice end users who access the database on a casual and infrequent basis. To alleviate the need to know the logical database organization, this paper proposes the use of a semantic data model, known as the Enhanced Entity-Relationship (EER) model, as a front-end to the relational systems. A formal, high-level Visual Knowledge Query Language (VKQL) has also been designed for this interface. This language provides for knowledge abstraction as the user communicates only domain knowledge with …