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Articles 331 - 353 of 353
Full-Text Articles in Theory and Algorithms
Directed Extended Dependency Analysis For Data Mining, Thaddeus T. Shannon, Martin Zwick
Directed Extended Dependency Analysis For Data Mining, Thaddeus T. Shannon, Martin Zwick
Complex Systems Faculty Publications and Presentations
Extended dependency analysis (EDA) is a heuristic search technique for finding significant relationships between nominal variables in large data sets. The directed version of EDA searches for maximally predictive sets of independent variables with respect to a target dependent variable. The original implementation of EDA was an extension of reconstructability analysis. Our new implementation adds a variety of statistical significance tests at each decision point that allow the user to tailor the algorithm to a particular objective. It also utilizes data structures appropriate for the sparse data sets customary in contemporary data mining problems. Two examples that illustrate different approaches …
An Overview Of Reconstructability Analysis, Martin Zwick
An Overview Of Reconstructability Analysis, Martin Zwick
Complex Systems Faculty Publications and Presentations
This paper is an overview of reconstructability analysis (RA), a discrete multivariate modeling methodology developed in the systems literature; an earlier version of this tutorial is Zwick (2001). RA was derived from Ashby (1964), and was developed by Broekstra, Cavallo, Cellier Conant, Jones, Klir, Krippendorff, and others (Klir, 1986, 1996). RA resembles and partially overlaps log‐line (LL) statistical methods used in the social sciences (Bishop et al., 1978; Knoke and Burke, 1980). RA also resembles and overlaps methods used in logic design and machine learning (LDL) in electrical and computer engineering (e.g. Perkowski et al., 1997). Applications of RA, like …
The Staging Transformation Approach To Mixing Initiative, Robert Capra, Michael Narayan, Saverio Perugini, Naren Ramakrishnan, Manuel A. Pérez-Quiñones
The Staging Transformation Approach To Mixing Initiative, Robert Capra, Michael Narayan, Saverio Perugini, Naren Ramakrishnan, Manuel A. Pérez-Quiñones
Computer Science Faculty Publications
Mixed-initiative interaction is an important facet of many conversational interfaces, flexible planning architectures, intelligent tutoring systems, and interactive information retrieval systems. Software systems for mixed-initiative interaction must enable us to both operationalize the mixing of initiative (i.e., support the creation of practical dialogs) and to reason in real-time about how a flexible mode of interaction can be supported (e.g., from a meta-dialog standpoint). In this paper, we present the staging transformation approach to mixing initiative, where a dialog script captures the structure of the dialog and dialog control processes are realized through generous use of program transformation techniques (e.g., partial …
Personalizing Interactions With Information Systems, Saverio Perugini, Naren Ramakrishnan
Personalizing Interactions With Information Systems, Saverio Perugini, Naren Ramakrishnan
Computer Science Faculty Publications
Personalization constitutes the mechanisms and technologies necessary to customize information access to the end-user. It can be defined as the automatic adjustment of information content, structure, and presentation tailored to the individual. In this chapter, we study personalization from the viewpoint of personalizing interaction. The survey covers mechanisms for information-finding on the web, advanced information retrieval systems, dialog-based applications, and mobile access paradigms. Specific emphasis is placed on studying how users interact with an information system and how the system can encourage and foster interaction. This helps bring out the role of the personalization system as a facilitator which reconciles …
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.
The Partial Evaluation Approach To Information Personalization, Naren Ramakrishnan, Saverio Perugini
The Partial Evaluation Approach To Information Personalization, Naren Ramakrishnan, Saverio Perugini
Computer Science Faculty Publications
Information personalization refers to the automatic adjustment of information content, structure, and presentation tailored to an individual user. By reducing information overload and customizing information access, personalization systems have emerged as an important segment of the Internet economy. This paper presents a systematic modeling methodology— PIPE (‘Personalization is Partial Evaluation’) — for personalization. Personalization systems are designed and implemented in PIPE by modeling an information-seeking interaction in a programmatic representation. The representation supports the description of information-seeking activities as partial information and their subsequent realization by partial evaluation, a technique for specializing programs. We describe the modeling methodology at a …
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 …
Personalizing The Gams Cross-Index, Saverio Perugini, Priya Lakshminarayanan, Naren Ramakrishnan
Personalizing The Gams Cross-Index, Saverio Perugini, Priya Lakshminarayanan, Naren Ramakrishnan
Computer Science Faculty Publications
The NIST Guide to Available Mathematical Software (GAMS) system at http://gams.nist .gov serves as the gateway to thousands of scientific codes and modules for numerical computation. We describe the PIPE personalization facility for GAMS, whereby content from the cross-index is specialized for a user desiring software recommendations for a specific problem instance. The key idea is to (i) mine structure, and (ii) exploit it in a programmatic manner to generate personalized web pages. Our approach supports both content-based and collaborative personalization and enables information integration from multiple (and complementary) web resources. We present case studies for the domain of linear, …
Factor Criteria Metric (Fcm) For Requirements Analysis Phase In The Development Of Management Information Systems, Wei Yin Chew
Factor Criteria Metric (Fcm) For Requirements Analysis Phase In The Development Of Management Information Systems, Wei Yin Chew
Student Works (2000-2009)
This project establishes a way to measure a characteristic of a product and a characteristic of a process involved in the requirements analysis phase for the development of management information systems (M!Ss) by adapting and enhancing McCall's Factor Criteria Metric (FCM) model. The two selected characteristics are the understandability of a software requirements specification (SRS) and effectiveness of a requirements gathering interview (RGI). To define the measurement for these two characteristics, a structure of factors, criteria, checklists and metrics for the characteristics of the products and processes is established based on McCall's FCM model. In addition, a software tool, FCMware, …
Feedback Control Solutions To Network Level User-Equilibrium Real-Time Dynamic Traffic Assignment Problems, Pushkin Kachroo, Kaan Ozbay
Feedback Control Solutions To Network Level User-Equilibrium Real-Time Dynamic Traffic Assignment Problems, Pushkin Kachroo, Kaan Ozbay
Electrical & Computer Engineering Faculty Research
A new method for performing dynamic traffic assignment (DTA) is presented which is applicable in real time, since the solution is based on feedback control. This method employs the design of nonlinear H∞ feedback control systems which is robust to certain class of uncertainties in the system. The solution aims at achieving user equilibrium on alternate routes in a network setting.
Investigating The Use Of Kalman Filtering Approaches For Dynamic Origin-Destination Trip Table Estimation, Pushkin Kachroo, Kaan Ozbay, Arvind Narayanan
Investigating The Use Of Kalman Filtering Approaches For Dynamic Origin-Destination Trip Table Estimation, Pushkin Kachroo, Kaan Ozbay, Arvind Narayanan
Electrical & Computer Engineering Faculty Research
This paper studies the applicability of Kalman filtering approaches for network wide traveler origin-destination estimation from link traffic volumes. The paper evaluates the modeling assumptions of the Kalman filters and examines the implications of such assumptions.
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 …
Visual Speech Recognition Using Multiple Deformable Lip Models, Devi Chandramohan
Visual Speech Recognition Using Multiple Deformable Lip Models, Devi Chandramohan
Electrical & Computer Engineering Theses & Dissertations
Motivated by the fact that human speech perception is a bimodal process (auditory and visual), several researchers have designed and implemented automatic speech recognition (ASR) systems consisting of both audio and visual subsystems, and shown improved performance relative to traditional purely auditory systems. Several visual speech reading approaches have used deformable templates to model the shape of a speaker's lips. Deformable templates are models of image objects, which can be deformed by adjusting a set of parameters to match the object in some optimal way, as defined by a cost function. Using a single deformable lip model has disadvantages such …
The Quest For The Gnarl, Rudy Rucker
The Quest For The Gnarl, Rudy Rucker
SWITCH
The article describes some of the author’s own image-generating computer programs that he describes as “gnarly”. He began writing a simple spirograph program based off simple sine wave function called Spiro. Later transitioned into writing with C and better programs using more nonlinear feedback. Where Spiro is based on a simple sine wave function, Vine uses a nested sine function: the sine of the sine. The need for a more complicated computational approach lead to iteration and parallelism. Julgnarl uses Iteration and Calife uses parallelism. Calife shows one-dimensional cellular automata: spaces in which virtual computers are lined up like beads …
Gnarly Rantings About The Hacker And The Ants, Rudy Rucker
Gnarly Rantings About The Hacker And The Ants, Rudy Rucker
SWITCH
The article is an excerpt from Rucker’s book “The Happy Mutant”. It begins with his reflection of his career with GoMotion. He discusses the relation that he saw between design and cyberspace. Later he discusses his experience with a game a colleague found on the net: a virtual world where player is an ant. He talks about the struggles he goes through in this virtual world because of game difficulty and poor visuals. He ties it all in with how the Silicon Valley works in a similar way, and is filled with hackers and programers all needing each other to …
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, …
A Greedy Hypercube-Labeling Algorithm, D. Bhagavathi, C. E. Grosch, S. Olariu
A Greedy Hypercube-Labeling Algorithm, D. Bhagavathi, C. E. Grosch, S. Olariu
Computer Science Faculty Publications
Due to its attractive topological properties, the hypercube multiprocessor has emerged as one of the architectures of choice when it comes to implementing a large number of computational problems. In many such applications, Gray-code labelings of the hypercube are a crucial prerequisite for obtaining efficient algorithms. We propose a greedy algorithm that, given an n-dimensional hypercube H with N=22 nodes, returns a Gray-code labeling of H, that is, a labeling of the nodes with binary strings of length n such that two nodes are neighbors in the hypercube if, and only if, their labels differ in exactly …
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 …
A Mergeable Double-Ended Priority Queue, S. Olariu, Z. Wen
A Mergeable Double-Ended Priority Queue, S. Olariu, Z. Wen
Computer Science Faculty Publications
An implementation of a double-ended priority queue is discussed. This data structure referred to as min–max–pair heap can be built in linear time; the operations Delete-min, Delete-max and Insert take O(log n) time, while Find-min and Find-max run in O(1) time. In contrast to the min-max heaps, it is shown that two min–max–pair heaps can be merged in sublinear time. More precisely, two min–max–pair heaps of sizes n and k can be merged in time O(log (n/k) * log k).
Pipelining Data Compression Algorithms, R. L. Bailey, R. Mukkamala
Pipelining Data Compression Algorithms, R. L. Bailey, R. Mukkamala
Computer Science Faculty Publications
Many different data compression techniques currently exist. Each has its own advantages and disadvantages. Combining (pipelining) multiple data compression techniques could achieve better compression rates than is possible with either technique individually. This paper proposes a pipelining technique and investigates the characteristics of two example pipelining algorithms. Their performance is compared with other well-known compression techniques.
Efficient Schemes To Evaluate Transaction Performance In Distributed Database Systems, R. Mukkamala, S. C. Bruell
Efficient Schemes To Evaluate Transaction Performance In Distributed Database Systems, R. Mukkamala, S. C. Bruell
Computer Science Faculty Publications
Database designers and researchers often need efficient schemes to evaluate transaction performance. In this paper, we chose two important performance measures: the average number of nodes accessed and the average number of data items accessed per node by a transaction in a distributed database system. We derive analytical expressions to evaluate these metrics. For general applicability, we consider partially replicated distributed database systems. Our first set of analytic results are closed-form expressions for these two measures. These are based on some fairly restrictive simplifying assumptions. When these assumptions are relaxed, no closed-form expressions exist for these averages. Hence, we develop …