Open Source Software: A History, David Bretthauer

Published Works

In the 30 years from 1970-2000, open source software began as an assumption without a name or a clear alternative. It has evolved into a sophisticated movement which has produced some of the most stable and widely used software packages ever produced. This paper traces the evolution of three operating systems: GNU, BSD, and Linux, as well as the communities which have evolved with these systems and some of the commonly-used software packages developed using the open source model. It also discusses some of the major figures in open source software, and defines both “free software” and “open source software.”

Knowledge Discovery In Biological Datasets Using A Hybrid Bayes Classifier/Evolutionary Algorithm, Michael L. Raymer, Leslie A. Kuhn, William F. Punch

Kno.e.sis Publications

A key element of bioinformatics research is the extraction of meaningful information from large experimental data sets. Various approaches, including statistical and graph theoretical methods, data mining, and computational pattern recognition, have been applied to this task with varying degrees of success. We have previously shown that a genetic algorithm coupled with a k-nearest-neighbors classifier performs well in extracting information about protein-water binding from X-ray crystallographic protein structure data. Using a novel classifier based on the Bayes discriminant function, we present a hybrid algorithm that employs feature selection and extraction to isolate salient features from large biological data sets. The …

Profile Combinatorics For Fragment Selection In Comparative Protein Structure Modeling, Deacon Sweeney, Travis E. Doom, Michael L. Raymer

Kno.e.sis Publications

Sequencing of the human genome was a great stride towards modeling cellular complexes, massive systems whose key players are proteins and DNA. A major bottleneck limiting the modeling process is structure and function annotation for the new genes. Contemporary protein structure prediction algorithms represent the sequence of every protein of known structure with a profile to which the profile of a protein sequence of unknown structure is compared for recognition. We propose a novel approach to increase the scope and resolution of protein structure profiles. Our technique locates equivalent regions among the members of a structurally similar fold family, and …

Reifying Communication At The Application Level, Andrew P. Black, Jie Huang, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Middleware, from the earliest RPC systems to recent Object-Oriented Remote Message Sending (RMS) systems such as Java RMI and CORBA, claims transparency as one of its main attributes. Coulouris et al. define transparency as “the concealment from the … application programmer of the separation of components in a distributed system.” They go on to identify eight different kinds of transparency.

We considered titling this paper “Transparency Considered Harmful”, but that title is misleading because it implies that all kinds of transparency are bad. This is not our view. Rather, we believe that the choice of which transparencies should be offered …

Online Bayesian Tree-Structured Transformation Of Hmms With Optimal Model Selection For Speaker Adaptation, Shaojun Wang, Yunxin Zhao

Kno.e.sis Publications

This paper presents a new recursive Bayesian learning approach for transformation parameter estimation in speaker adaptation. Our goal is to incrementally transform or adapt a set of hidden Markov model (HMM) parameters for a new speaker and gain large performance improvement from a small amount of adaptation data. By constructing a clustering tree of HMM Gaussian mixture components, the linear regression (LR) or affine transformation parameters for HMM Gaussian mixture components are dynamically searched. An online Bayesian learning technique is proposed for recursive maximum a posteriori (MAP) estimation of LR and affine transformation parameters. This technique has the advantages of …

Semantic Operators And Fixed-Point Theory In Logic Programming, Anthony K. Seda, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We consider rather general operators mapping valuations to (sets of) valuations in the context of the semantics of logic programming languages. This notion generalizes several of the standard operators encountered in this subject and is inspired by earlier work of M.C. Fitting. The fixed points of such operators play a fundamental role in logic programming semantics by providing standard models of logic programs and also in determining the computability properties of these standard models. We discuss some of our recent work employing topological ideas, in conjunction with order theory, to establish methods by which one can find the fixed points …

Summarizing Data Sets For Classification, Christopher W. Kinzig, Krishnaprasad Thirunarayan, Gary B. Lamont, Robert E. Marmelstein

Kno.e.sis Publications

This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein’s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.

Query Processing With An Fpga Coprocessor Board, Jack S. Jean, Guozhu Dong, Hwa Zhang, Xinzhong Guo, Baifeng Zhang

Kno.e.sis Publications

In this paper, a commercial FPGA coprocessor board is used to accelerate the processing of queries on a relational database that contains texts and images. FPGA designs for text searching and image matching are described and their performances summarized. A potential design for a database JOIN operator is then studied. A query optimization preprocessor is then proposed.

Making Use Of The Most Expressive Jumping Emerging Patterns For Classification, Jinyan Li, Guozhu Dong, Kotagiri Ramamohanarao

Kno.e.sis Publications

Classification aims to discover a model from training data that can be used to predict the class of test instances. In this paper, we propose the use of jumping emerging patterns (JEPs) as the basis for a new classifier called the JEP-Classifier. Each JEP can capture some crucial difference between a pair of datasets. Then, aggregating all JEPs of large supports can produce a more potent classification power. Procedurally, the JEP-Classifier learns the pair-wise features (sets of JEPs) contained in the training data, and uses the collective impacts contributed by the most expressive pair-wise features to determine the class labels …

Predictive Self-Organizing Networks For Text Categorization, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

This paper introduces a class of predictive self-organizing neural networks known as Adaptive Resonance Associative Map (ARAM) for classification of free-text documents. Whereas most sta- tistical approaches to text categorization derive classification knowledge based on training examples alone, ARAM performs supervised learn- ing and integrates user-defined classification knowledge in the form of IF-THEN rules. Through our experiments on the Reuters-21578 news database, we showed that ARAM performed reasonably well in mining categorization knowledge from sparse and high dimensional document feature space. In addition, ARAM predictive accuracy and learning efficiency can be improved by incorporating a set of rules derived from …

Survivability Architecture For Workflow Management Systems, Jorge Cardoso, Zongwei Luo, John A. Miller, Amit P. Sheth, Krzysztof J. Kochut

Kno.e.sis Publications

The survivability of critical infrastructure systems has been gaining increasing concern from the industry. The survivability research area addresses the issue of infrastructure systems that continues to provide pre-established service levels to users in the face of disorders and react to changes in the surrounding environment. Workflow management systems need to be survivable since they are used to support critical and sensitive business processes. They require a high level of dependability and should not allow process instances to be interrupted or aborted due to failures. Moreover, due to their sensitivity, business process should reflect any change in the environment. In …

Moving Towards Massively Scalable Video-Based Sensor Networks, Wu-Chi Feng, Jonathan Walpole, Calton Pu, Wu-Chang Feng

Computer Science Faculty Publications and Presentations

Networking and computing technologies are becoming advanced enough to enable a wealth of diverse applications that will drastically change our everyday lives. Some past examples of these developments include the World Wide Web and wireless data networking infrastructures. As is quite obvious, the World Wide Web has enabled a fundamental change in the way many people deal with day-to-day tasks. Through the web, one can now make on-line reservations for travel, pay bills through on-line banking services, and view personalized on-line newscasts. More recently, developments in wireless technologies have enabled anywhere, anytime access to information over wireless medium. As wireless …

Modeling The Transient Rate Behavior Of Bandwidth Sharing As A Hybrid Control System, Kang Li, Molly H. Shor, Jonathan Walpole, Calton Pu

Computer Science Faculty Publications and Presentations

This paper uses hybrid control to model a problem of computer network systems, the dynamic behavior of bandwidth sharing among competing TCP traffic. It has been well known in the computer network community that well-behaved (TCP-friendly) congestion control mechanisms are crucial to the robustness of the Internet. Congestion control determines the transmission rate for each flow. Right now, most TCP-friendly research focuses only on the average throughput behavior without considering how the data is sent out in the short-term (e.g. bursty or smooth). However, recent experimental results show that short-term rate adjustments can change the bandwidth sharing result. Therefore, it …

A "Converse" Of The Banach Contraction Mapping Theorem, Pascal Hitzler, Anthony K. Seda

Computer Science and Engineering Faculty Publications

We prove a type of converse of the Banach contraction mapping theorem for metric spaces: if X is a T₁ topological space and f: X -> X is a function with the unique fixed point a such that fⁿ(x) converges to a for each x is a member of X, then there exists a distance function d on X such that f is a contraction on the complete ultrametric space (X,d) with contractivity factor 1/2. We explore properties of the resulting space (X,d).

Unique Supported-Model Classes Of Logic Programs, Pascal Hitzler, Anthony K. Seda

Computer Science and Engineering Faculty Publications

We study classes of programs, herein called unique supported-model classes, with the property that each program in the class has a unique supported model. Elsewhere, the authors examined these classes from the point of view of operators defined relative to certain three-valued logics. In this paper, we complement our earlier results by considering how unique supported-model classes fit into the framework given by various classes of programs in several well-known approaches to semantics.

Kontraktionssatze Auf Verallgemeinerten Metrischen Raumen, Pascal Hitzler

Computer Science and Engineering Faculty Publications

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 …

Latent Maximum Entropy Principle For Statistical Language Modeling, Shaojun Wang, Ronald Rosenfeld, Yunxin Zhao

Kno.e.sis Publications

We describe a unified probabilistic framework for statistical language modeling, the latent maximum entropy principle. The salient feature of this approach is that the hidden causal hierarchical dependency structure can be encoded into the statistical model in a principled way by mixtures of exponential families with a rich expressive power. We first show the problem formulation, solution, and certain convergence properties. We then describe how to use this machine learning technique to model various aspects of natural language, such as syntactic structure of sentences, semantic information in a document. Finally, we draw a conclusion and point out future research directions.

Rate-Matching Packet Scheduler For Real-Rate Applications, Kang Li, Jonathan Walpole, Dylan Mcnamee, Calton Pu, David Steere

Computer Science Faculty Publications and Presentations

A packet scheduler is an operating system component that controls the allocation of network interface bandwidth to outgoing network flows. By deciding which packet to send next, packet schedulers not only determine how bandwidth is shared among flows, but also play a key role in determining the rate and timing behavior of individual flows. The recent explosion of rate and timing-sensitive flows, particularly in the context of multimedia applications, has focused new interest on packet schedulers. Next generation packet schedulers must not only ensure separation among flows and meet real-time performance constraints, they must also support dynamic fine-grain reallocation of …