Thomas E. Potok

Oak Ridge National Laboratory

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Robert M. Patton

Oak Ridge National Laboratory

Catherine D. Schuman

University of Tennessee at Knoxville

Xiaohui Cui

Wuhan University

Prasanna Date

Oak Ridge National Laboratory

Steven R. Young

Oak Ridge National Laboratory

Yu Jiao

Shandong University

Ali R. Hurson

Missouri University of Science and Technology

Mark Elmore

University of California, Davis

Jim N. Treadwell

Oak Ridge National Laboratory

Ramakrishnan Kannan

Oak Ridge National Laboratory

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Oak Ridge National Laboratory

525

University of Tennessee at Knoxville

201

Office of Scientific and Technical Information

National Technical Information Service

Oak Ridge Associated Universities

Knoxville College

Argonne National Laboratory

Government of the United States of America

Center for Nanophase Materials Sciences

National Transportation Research Center

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A VECTOR PERTURBATION APPROACH TO THE GENERALIZED AIRCRAFT SPARE PARTS GROUPING PROBLEM

Nagiza F. Samatova Thomas E. Potok Michael R. Leuze

The Vector Perturbation Approach is introduced for addressing the generalized parts grouping problem, identifying part families for a general set of suppliers, not just a single supplier. This method is driven by the need for flexible and lean supply chain systems. A vector space model is used to represent a set of operation sequences as opposed to the traditional matrix and integer programming models in Group Technology. Using this approach we found that we are able to generate part groups from 90% of the available parts, where all the operation sequences are preserved. This contrasts with the 66% of available parts grouped using the traditional methods. Furthermore, a vector representation of operation sequences provides an intuitive means for discovering the natural structure of the part data. From these results we conclude that this technique can dramatically improve the effectiveness of the entire supply chain.

Spare part

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Visual Mining Intrusion Behaviors by Using Swarm Technology

Xiaohui Cui Justin M. Beaver Thomas E. Potok Li Yang

The alerts produced by the real time intrusion detection systems, e.g. Snort, can be difficult for security administrators to efficiently review and respond to, due to the enormous amount of messages generated in a short time frame. In this research, we developed a technique, the swarm based visual data mining approach (SVDM), to help user gain insight into the alert event data of the intrusion detection system, come up with new hypothesis, and verify the hypothesis via the interaction between the human and the system. The SVDM system can efficiently help security administrators detect anomaly behaviors of malicious user in a large volume of high dimensional time-dependent state spaces. The output visual representation from this system exploits the human being's innate ability to recognize patterns and utilizes this ability to help security administrators understand the relationship between the seemingly discrete security breaches.

Attack patterns

Representation

10.1109/hicss.2011.486

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Citations (4)

A model of correlated team behaviour in a software development environment

Thomas E. Potok Mladen A. Vouk

In today's highly competitive software development environments, accurately estimating software duration and cost can often mean the difference between project success or failure. Traditional software development estimation techniques often assume that software development teams operate independently from task to task. This assumption allows task covariances to be ignored. However, there is ample evidence that, in practice, the behaviour of a software development team over a project life-cycle has strong "memory" of previous tasks and external influences. This means that the accuracy of software development estimates may suffer due to this simplifying assumption of no-correlation. We present a model that we use to describe correlated team behaviour in a software development environment.

Team software process

Personal software process

Software sizing

Software metric

Software project management

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Software analytics

10.1109/asset.1999.756781

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Citations (7)

A Classical-Quantum Hybrid Approach for Unsupervised Probabilistic Machine Learning

Lecture notes in networks and systems (2019)

Prasanna Date Catherine D. Schuman Robert M. Patton Thomas E. Potok

10.1007/978-3-030-12385-7_9

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Citations (22)

XML and the Semantic Web: Implications and Applications Minitrack Introduction

Hawaii International Conference on System Sciences (2003)

Joanne Curry Carolyn McGregor Thomas E. Potok Mark Elmore

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Boosting-Based Distributed and Adaptive Security-Monitoring through Agent Collaboration

2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops (2007)

Evens Jean Yu Jiao Ali R. Hurson Thomas E. Potok

The use of mobile agents to support the development of practical applications is limited primarily by the risks to which hosts in the system are subject to. This article introduces a distributed and adaptive security-monitoring framework to decrease such potential threats. The proposed framework is based on a modified version of the popular Boosting algorithm to classify malicious agents based on their execution patterns on current and prior hosts. Having implemented the framework for the Aglet platform, we herein present the results of our experiments showcasing the detection of agent entities in the system with intention deviating from that of their well-behaved counterparts.

Boosting

10.1109/wi-iatw.2007.52

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Citations (3)

Island model for parallel evolutionary optimization of spiking neuromorphic computing

Proceedings of the Genetic and Evolutionary Computation Conference Companion (2019)

Catherine D. Schuman James S. Plank Robert M. Patton Thomas E. Potok

Parallel genetic algorithms (PGAs) can be used to accelerate optimization by exploiting large-scale computational resources. In this work, we describe a PGA framework for evolving spiking neural networks (SNNs) for neuromorphic hardware implementation. The PGA framework is based on an islands model with migration. We show that using this framework, better SNNs for neuromorphic systems can be evolved faster.

Neuromorphic engineering

10.1145/3319619.3322016

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Citations (2)

Development productivity for commercial software using object-oriented methods

Conference of the Centre for Advanced Studies on Collaborative Research (1995)

Thomas E. Potok Mladen A. Vouk

Object-oriented software development is widely believed to improve programmer productivity, however, surprisingly little evidence of this has been published. This paper presents an initial analysis of the productivity measured for 19 software products developed at IBM Programming Laboratories. Half of the products were developed using object-oriented methods, while the other half were developed using traditional methods. Our study indicates that first generation object-oriented projects achieve the same productivity rates as follow-on releases of procedural projects. There does not appear to be a productivity penalty for moving to object-oriented technology, but there is apparently no immediate productivity benefit either. Furthermore, for this data, productivity rates increase as project size increases. This runs against the conventional wisdom about large projects, and is more surprising given that smaller projects had proportionately larger development teams than larger projects. An initial study of this phenomenon indicates that scheduling effects, such as the Parkinson effect and the Deadline effect, may be responsible for this economy of scale.

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IBM

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COVID-19 Knowledge Graph -- Dataset for SMCDC 2021 Challenge 2

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (2021)

Drahomíra Herrmannová Ramakrishnan Kannan Seung-Hwan Lim Thomas E. Potok

10.13139/olcf/1782714

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Citations (0)