Gary Holness

@article{pokrajac2019modeling,
 author = {David D. Pokrajac and Poopalasingam Sivakumar and Yuriy Markushin and Daniela Milovic and Gary Holness and Jinjie Liu and Noureddine Melikechi and Mukti Rana},
 title = {Modeling of Laser-Induced reakdown Spectroscopic Data Analysis by an Automatic Classifier},
 journal = {International Journal of Data Science and and Analytics},
 volume= {8},
 number= {2},
 month = {September},
 pages = {213-220},
 year = {2019}
 }

@article{sharma2017l2norm,
 author = {Piyush Sharma and Gary Holness},
 title = {L2-norm Transformation for Improving k-means Clustering:  Finding A Suitable Model By Range Transformation for Novel Data Analysis},
 journal = {International Journal of Data Science and and Analytics},
 volume= {TBD},
 number= {TBD},
 month = {TBD},
 pages = {TBD},
 year = {2017}
 }

Abstract:

@misc{boyd2017understanding,
 author = {Janelle Boyd and Gary Holness},
 title = {Understanding Social Queues to Participate in a Turn-Taking Interaction with Autistic Children},
 booktitle = {Academic and Research Leadership Symposium, National Society of Black Engneers 43rd Annual Convention},
 year = {2017}
 }

Abstract:

@inproceedings{sharma2016dialation,
 author = {Piyush Sharma and Gary Holness},
 title = {Dialation of Chisini Jensen-Shannon Divergence},
 booktitle = {3rd IEEE International Conference on Data Science and Advanced Analytics}
 month = {October},
 year = {2016}
 }

Abstract:

@inproceedings{sharma2015investigating,
 author = {Piyush Sharma and Gary Holness and Yuri Markushin and Noureddine Melikechi},
 title = {A Family of Chisini Mean Based Jensen-Shannon Divergence Kernels},
 booktitle = {14th IEEE International Conference on Machine Learning and Applications}
 month = {December},
 year = {2015}
 }

Abstract:

@inproceedings{sharma2015investigating,
 author = {Piyush Sharma and Gary Holness and Sivakumar Poopalasingam and Yuri Markushin and Noureddine Melikechi},
 title = {Investigating Manifold Neighborhood size for Nonlinear Analysis of LIBS Amino Acid Spectra},
 booktitle = {24th International Conference on Software Engineering and Data Engineering (SEDE 2015)}
 month = {October},
 year = {2015}
 }

Abstract:

@inproceedings{sharma2015analysis,
 author = {Piyush Sharma and Gary Holness and Poopalasingam Sivakumar and Yuri Markushin and Noureddine Melikechi},
 title = {Analysis of LIBS Amino Acid Spectra and the Impact of Neighborhood Size on the efficacy of Nonlinear Analysis},
 booktitle = {1st Delaware Optics Symposium}
 month = {October},
 year = {2015}
 }

Abstract:

@inproceedings{Holness2015Prototype,
 author = {Gary Holness and Leon Hunter},
 title = {A Prototype Distributed Framework for Identification and Alerting for Medical Events in Home Care},
 booktitle = {Technology Interface International Journal},
 month = {Spring/Summer},
 volume = {15},
 number = {2},
 year = {2015}
 }

Abstract:

@inproceedings{boyd2015framework,
 author = {Janelle Boyd and Gary Holness},
 title = {A Framework for Perceptual Processing in Autonomous Wheelchairs},
 booktitle = {Emerging Researchers National Conference in STEM},
 month = {February},
 year = {2015}
 }

Abstract:

@inproceedings{Pokie2014towards,
 author = {David D. Pokrajac and Poopalasingam Sivakumar and Yuri Markushin and Daniela Milovic and Mukti Ranai and Gary Holness and Jinjj
ie Liu and Noureddine Melikechi},
 title = {Towards Optimal CLassifier of Spectroscopy Data},
 booktitle = {Proceedings of the 1st International Conference onElectrical, Electronic, and Computer Engineering (icETRAN)},
 month = {June},
 year = {2014}
 }

@inproceedings{Holness2014Prototpe,
 author = {Gary Holness and Leon Hunter},
 title = {A Prototype Distributed Framework for Identification and Alerting for Medical Events in Home Care},
 booktitle = {Proceedings of the 4th IJAC/ISAM Joint International Conference},
 month = {Sept},
 year = {2014}
 }

Abstract:

@inproceedings{Brown2014Improving,
 author = {Tevin Brown and Gary Holness},
 title = {IMproving LIBS Analysis using Nonlinear Dimensionality Reduction},
 booktitle = {Summer Research Symposium, Delaware State University},
 month = {July},
 year = {2014}
 }

Abstract:

@inproceedings{Hunter2014Identifying,
 author = {Leon Hunter and Gary Holness},
 title = {Identifying Precursor Warnings of Potentially Fatal Afflictions via Web Service},
 booktitle = {Emerging Researchers National Conference in STEM},
 month = {February},
 year = {2014}
 }

Abstract:

@ARTICLE{Ortiz2013Robots,
 author = {Gabriel Calderon Ortiz and Gary Holness},
 title = {Using Robots to Improve Socialization Skills of Autistic Children},
 booktitle = {Summer Research Symposium, Delaware State University},
 month = {July},
 year = {2013}
 }

Abstract:

@ARTICLE{Hunter2013Identifying,
 author = {Leon Hunter and Gary Holness},
 title = {Identifying Precursor Warnings of Potentially Fatal Afflictions via Web Service},
 booktitle = {Summer Research Symposium, Delaware State University},
 month = {July},
 year = {2013}
 }

Abstract:

@ARTICLE{Brown2013Linkage,
 author = {Tevin Brown and Gary Holness},
 title = {Linkage Analysis of LIBS Protein Spectra.},
 booktitle = {Summer Research Symposium, Delaware State University}, month = {July},
 year = {2013}
 }

Abstract:

@ARTICLE{Biancaniello2012AIR,
 author = {Paul Biancaniello and Gary Holness and Jonathan Darvill and Matt Craven and Patrick Lardieri},
 title = {AIR: A Framework for Adaptive Immune Response for Cyber Defense},
 booktitle = {Journal of Intelligence Community Research and Development},
 month = {May},
 year = {2012}
 }

Abstract:

@INPROCEEDINGS{Eaton2010Interactive,
 author = {Eric Eaton and Gary Holness and Daniel McFarlane},
 title = {Interactive Learning using Manifold Geometry},
 booktitle = {Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10)},
 month = {July 11--15},
 location = {Atlanta, GA},
 publisher = {AAAI Press},
 pages = {437--443},
 year = {2010},
 abstract = {
	We present an interactive learning method that enables 
	a user to iteratively refine a regression model.  The 
	user examines the output of the model, visualized as 
	the vertical axis of a 2D scatterplot, and provides 
	corrections by repositioning individual data instances 
	to the correct output level. Each repositioned data 
	instance acts as a control point for altering the 
	learned model, using the geometry underlying the data.
	We capture the underlying structure of the data as a
	manifold, on which we compute a set of basis functions
	as the foundation for learning.  Our results show that
	manifold-based interactive learning improves performance 
	monotonically with each correction, outperforming 
	alternative approaches.}
 }

Abstract:

@INPROCEEDINGS{Holness09Training,
 author = {Gary Holness and Paul Utgoff},
 title = {Training Ensembles Using Max-Entropy Error Diversity},
 booktitle = {Proceedings of the 29th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering},
 month = {December 9},
 publisher = {AIP Conference Proceedings},
 pages = {202-209},
 year = {2009},
 }

@INPROCEEDINGS{Eaton2009Interactive,
 author = {Eric Eaton and Gary Holness and Daniel McFarlane},
 title = {Interactive Learning using Manifold Geometry},
 booktitle = {Proceedings of the AAAI Fall Symposium on Manifold Learning and Its Applications (AAAI Technical Report FS-09-04)},
 month = {November 5--7},
 location = {Arlington, VA},
 publisher = {AAAI Press},
 pages = {10--17},
 year = {2009},
 }

@THESIS{Holness2009StatisticalApproach,
 author = {Gary Holness},
 year = {2008},
 title = {A Statistical Approach to Improving Accuracy in Classifier Ensembles},
 institution = {University of Massachusetts Amherst},
 pages= {274},
 month = {September},
 }

@INPROCEEDINGS{Holness2007Direct,
 author = {Gary Holness},
 year = {2007},
 title = {A Direct Measure for the Efficacy of Bayesian Network Structures Learned from Data},
 booktitle = {LNAI 2007},
 address = {Heidelberg, Germany},
 publisher = {Springer Verlag},
 abstract = { Current metrics for evaluating the performance of Bayesian network structure learning includes order statistics of the data likelihood of learned structures, the average data likelihood, and average convergence time. In this work, we define a new metric that directly measures a structure learning algorithm's ability to correctly model causal associations among variables in a data set. By treating membership in a Markov Blanket as a retrieval problem, we use ROC analysis to compute a structure learning algorithm's efficacy in capturing causal associations at varying strengths. Because our metric moves beyond error rate and data-likelihood with a measurement of stability, this is a better characterization of structure learning performance. Because the structure learning problem is NP-hard, practical algorithms are either heuristic or approximate. For this reason, an understanding of a structure learning algorithm's stability and boundary value conditions is necessary. We contribute to state of the art in the data-mining community with a new tool for understanding the behavior of structure learning techniques.}

Abstract:

@TECHREPORT{Holness2007Markov,
 author = {Gary Holness},
 year = {2007},
 title = {Markov Blanket Retrieval: An Approach for Measuring the Efficacy of Bayesian Network Structures Learned from Data},
 number = {QLI-TR-2007-04},
 address = {Newark, DE},
 }

@TECHREPORT{Holness2007Heureka,
 author = {Gary Holness},
 year = {2007},
 title = {Heureka Technologies for Invensys-Pathfinder},
 number = {QLI-TR-2007-09},
 address = {Newark, DE},
 }

@TECHREPORT{Holness2007MarkovChain,
 author = {Gary Holness},
 year = {2007},
 title = {Markov Chain Monte-Carlo Simulation of Incidence of Infection and Recovery in SIR Disease Modeling},
 number = {QLI-TR-2007-10},
 address = {Newark, DE},
 }

@INPROCEEDINGS{sieracki2005Automatic,
 author = {M. Sieracki and E. Riseman and W. Balsh and M. Benfield and A. Hanson and C. Pilskaln and H. Schultz and C. Sieracki and P. Utgoff and M. Blaschko and G. Holness and M. Mattar and D. Lisin and B. Tupper},
 title = {Automatic Classification of Plankton from Digital Images},
 booktitle = {ASLO Aquatic Sciences Meeting},
 year = {2005},
 month = {February},
 address = {Salt Lake City, Utah},
 abstract = {  Marine particles, including plankton and non-living particles, play important roles in ecosystem function and material flux in the oceans. Digital imaging technology used in instruments to study these particles can rapidly produce huge archives of images that require expert interpretation. Automated methods to assist the expert interpret these images are urgently needed. We are building automatic classifier systems to work with the experts to efficiently and accurately classify images of marine particles. We will use images from in-situ camera instruments (e.g. VPR) for zooplankton and marine snow, an imaging-in-flow system (FlowCAM) for phytoplankton, and digital fluorescence microscopy for pico- and nanoplankton. Experiments were conducted using low resolution FlowCAM images of 13 classes of phytoplankton from natural communities, and a variety of image features and classifiers, including classifier ensembles. These preliminary tests yielded classification accuracy of over 70%, compared to published human expert agreement of about 80%. This indicates that automated classification will be practical to automate the majority of images. We intend to develop a probabilistic approach to particle enumeration, and to test the generality of our classifiers across instrument types. }
 }

Abstract:

@INPROCEEDINGS{blaschko2005Automatic,
 author = {M. Blaschko and G. Holness and M. Mattar and D. Lisin and P. Utgoff and A. Hanson and H. Schultz and E. Riseman and M. Sieracki and W. Balch and B. Tupper.},
 title = {Automatic In Situ Identification of Plankton},
 booktitle = {Proceedings of IEEE Workshop on Applications of Computer Vision},
 year = {2005},
 month = {January},
 address = {Breckenridge, CO},
 abstract = {  Earth's oceans are a soup of living micro-organisms known as plankton. As the foundation of the food chain for marine life, plankton are also an integral component of the global carbon cycle which regulates the planet's temperature. In this paper, we present a technique for automatic identification of plankton using a variety of features and classification methods including ensembles. The images were obtained in situ by an instrument known as the Flow Cytometer And Microscope (FlowCAM), that detects particles from a stream of water siphoned directly from the ocean. The images are of necessity of limited resolution, making their identification a rather difficult challenge. We expect that upon completion, our system will become a useful tool for marine biologists to assess the health of the world's oceans.  }
 }

Abstract:

@TECHREPORT{holness2005Model,
 author = {Gary Holness},
 title = {Model Checking a Real-Time Foveate Controller Using Timed Automata},
 booktitle = {TR-05-56},
 institution = {University of Massachusetts Amherst},
 year = {2005},
 month = {January},
 address = {Breckenridge, CO},
 abstract = { 	Research activity in the area of smart spaces strives to endow an
environment with a myriad of sensory-motor and computational devices which, together with, various learning algorithms may discern useful information about activity within the environment.
Computer vision has proved an important sensory mode for uncovering features and dynamics associated with the events that occur. Tracking is an important task in visual sensing. The literature is full of many examples of real-time trackers.

The proliferation of embedded computation grounded in physical systems will continue for the foreseeable future. Helping to increase the correctness of such systems means provable validation of real-time constraints. In this paper we present a system that employs the Timed Automata formalism to verify a foveate controller designed as a processing pipeline.  }
 }

Abstract:

@TECHREPORT{Holness2003Context,
 author = {Gary Holness and Kimberly N. Martin},
 year = {2004},
 title = {Towards a Machine Learning DJ: First Experiments},
 number = {TR-04-01},
 address = {Amherst, MA},
 note = {[paper]}
 }

Abstract:

@misc{Holness2003Context,
 author = {Gary Holness and Rod Grupen and Jack Wileden},
 year = {2003},
 title = {Context Recovery Through Meta-Sensing},
 howpublished = {Synthesis Project, University of Massachusetts-Amherst},
 address = {Amherst, MA},
 note = {[paper]}
 }

Abstract:

@INPROCEEDINGS{Holness2003Context,
 author = {Gary Holness and Deepak Karuppiah and Subramania Uppala and Sai Chandu Ravela and Rod Grupen},
 year = {2001},
 title = {A Service Paradigm for Reconfigurable Agents},
 booktitle = {Proceedngs of the 2nd Workshop on Infrastructure for Agents, MAS, and Scalable MAS},
 location = {Montreal, Canada}
 }

Abstract:

@INPROCEEDINGS{karuppiah2000Software,
 author = {Deepak Karuppiah and Patrick Deegan and Elizeth Araujo and Yunlei Yang and Gary Holness and Zhigang Zhu and Barbara Lerner and Rod Grupen and Ed Riseman},
 year = {2000},
 title = {Software Mode Changes for Continuous Motion Tracking},
 booktitle = {International Workshop on Self-Adaptive Software (IWSAS2000)},
 location = {Oxford, England}
 }

Abstract:

@BOOK{Waldo2000Jini,
 author = {Jim Waldo and The Jini Technology Team},
 year = {2000},
 title = {The Jini Specifications Second Edition},
 institution = {Sun Microsystems Inc},
 publisher = {Addison Wesely}
 pages= {274},
 month = {December}
 }