Penn State Great Valley

Malvern, PA, United States

Penn State Great Valley

Malvern, PA, United States
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News Article | May 10, 2017

The National Association of Professional Women (NAPW) honors Lisa Hannum as a 2017-2018 inductee into its VIP Woman of the Year Circle. She is recognized with this prestigious distinction for leadership in entrepreneurship. NAPW is the nation’s leading networking organization exclusively for professional women, boasting more than 850,000 members, a thriving eChapter and over 200 operating Local Chapters. “I’m pleased to welcome Lisa into this exceptional group of professional women,” said NAPW President Star Jones. “Her knowledge and experience in her industry are valuable assets to her company and community.” Self-motivated and results driven to succeed, Lisa Hannum is a seasoned professional who has constantly set her goals to keep pace with her highest aspirations for personal excellence. Throughout her career, she has exhibited exemplary teamwork, expertise, integrity and dedication. For more than 20 years, Lisa Hannum, Founder of ZLa (pronounced ‘Zee-La’), has worked as a Transformation Professional, providing her expertise when it comes to identifying and evaluating problems, finding creative solutions and implementing necessary changes. From large, global organizations to small startups, Ms. Hannum helps companies find success and reach their goals. To accomplish this, Ms. Hannum, who is Lean Sigma Black Belt certified, uses her expansive skills in management and leadership, including change management, lean process, knowledge management, creative problem solving, talent development and team motivation. She welcomes the challenge of solving “seemingly impossible problems” with a team. “Whether the problem requires ensuring alignment on a strategy or tool selection, taking the work out of a process, making sense out of ambiguous situations, or driving and sustaining changes, I welcome a challenge,” said Ms. Hannum. “And, I like to have fun overcoming it, especially when it includes motivating and engaging a team in the solution.” Ms. Hannum obtained her Lean Black Belt certification from Villanova University. She also holds an M.B.A. and an M.S. in Information Systems from Penn State Great Valley as well as a B.A. in Business Management from Wilkes University. NAPW’s mission is to provide an exclusive, highly advanced networking forum to successful women executives, professionals and entrepreneurs where they can aspire, connect and achieve. Through innovative resources, unique tools and progressive bene ts, professional women interact, exchange ideas, advance their knowledge and empower each other.

Luo R.,University of Delaware | Cannon L.,University of Delaware | Hernandez J.,University of Delaware | Piovoso M.J.,Penn State Great Valley | And 2 more authors.
Journal of Process Control | Year: 2011

Evolution has long been understood as the driving force for many problems of medical interest. The evolution of drug resistance in HIV and bacterial infections is recognized as one of the most significant emerging problems in medicine. In cancer therapy, the evolution of resistance to chemotherapeutic agents is often the differentiating factor between effective therapy and disease progression or death. Interventions to manage the evolution of resistance have, up to this point, been based on steady-state analysis of mutation and selection models. In this paper, we review the mathematical methods applied to studying evolution of resistance in disease. We present a broad review of several classical applications of mathematical modeling of evolution, and review in depth two recent problems which demonstrate the potential for interventions which exploit the dynamic behavior of resistance evolution models. The first problem addresses the problem of sequential treatment failures in HIV; we present a review of our recent publications addressing this problem. The second problem addresses a novel approach to gene therapy for pancreatic cancer treatment, where selection is used to encourage optimal spread of susceptibility genes through a target tumor, which is then eradicated during a second treatment phase. We review the recent in vitro laboratory work on this topic, present a new mathematical model to describe the treatment process, and show why model-based approaches will be necessary to successfully implement this novel and promising approach. © 2010 Elsevier Ltd. All rights reserved.

Barb A.S.,Penn State Great Valley | Shyu C.-R.,University of Missouri
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

This article proposes a methodology to reduce overfitting when ranking high-resolution satellite images by domain semantics. Our approach uses PathFinder Network Scaling ensemble methods. We generate cross-fold co-occurrence matrices for relevance of feature subspaces to each semantic. Each matrix is then reduced using the PathFinder network scaling algorithm. Irrelevant nodes are removed using node strength metrics resulting in an optimized model for ranking by semantic that generalizes better to new images. The experiments show that, when using this approach, the quality of ranking by semantic can be significantly improved. Results show that Mean Average Precision (MAP) of ranking over cross-fold experiments increased by a 13.2% while standard deviation of MAP was reduced by 16.8% relatively to experiments without PathFinder network scaling. © 2012 IEEE.

Barb A.S.,Penn State Great Valley | Shyu C.-R.,University of Missouri
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2010

With recent technological advances, the geospatial industry produces digital image data at an astonishing rate. Such large amounts of data need to be analyzed for visual content in a timely fashion. For in-depth analysis of the geospatial there is a need to find efficient methods to process the visual information into actionable knowledge. One of the most promising methods is to evaluate the relevance of geospatial images to domain-specific visual semantics. Most of existing methods for annotating semantic meaning to geospatial images are trained using binary feedback from users. Such approaches may lead to suboptimal models especially due to the fact that semantic relevance of images is rarely a binary problem. In this paper, we report an algorithm to link low-level image features with high-level visual semantics using graded relevance feedback from image analysts. This linkage is done using flexible possibility functions that mathematically model the existence of visual semantics in new images added to the database. Our experimental results show that our technique improves the knowledge discovery process as evidenced by increased mean average precision of semantic queries. © 2010 IEEE.

Barb A.S.,Penn State Great Valley | Shyu C.-R.,University of Missouri
2011 IEEE 13th International Conference on e-Health Networking, Applications and Services, HEALTHCOM 2011 | Year: 2011

With recent advances in diagnostic medical imaging, huge quantities of medical images are produced and stored in digital image repositories. While these repositories are difficult to be analyzed manually by medical experts, they can be evaluated using computer-based methods to enrich the process of decision making. For example, query by image methods can be used by medical experts for differential diagnosis by displaying previously evaluated cases that contain similar visual patterns. Also, less experienced practitioners can benefit from query-by-semantic methods in training processes especially for difficult-to-interpret cases with multiple pathologies. In this article we develop a methodology for ranking medical images based on Dirichlet process nonparametric distributions. Our approach uses natural groupings of images in a generated feature space to evaluate associative semantic mappings. Relevant semantic mappings are then used to generate additive computer models of semantic understanding of visual patterns found in images. We evaluate the performance of our method using mean average precision and precision-recall charts. © 2011 IEEE.

Wang X.,University of Texas at El Paso | Ceberio M.,University of Texas at El Paso | Virani S.,Penn State Great Valley | Garcia A.,University of Texas at El Paso | Cummins J.,Youngstown State University
Journal of Uncertain Systems | Year: 2013

Being able to assess software quality is essential as software is ubiquitous in every aspect of our day- to-day lives. In this paper, we build upon existing research and metrics for defining software quality and propose a way to automatically assess software quality based on these metrics. In particular, we show that the problem of assessing the quality of software can be viewed as a multi-criteria decision- making (MCDM) problem. In Multi-Criteria Decision Making (MCDM), decisions are based on several criteria that are usually conicting and non-homogenously satisfied. We use non-additive (fuzzy) measures combined with the Choquet integral to solve MCDM problems for they allow to model and aggregate the levels of satisfaction of the several criteria of the problem at hand by considering their relationships. However, in practice, it is difficult to identify such fuzzy measures. An automated process is then necessary and can be used when sample data is available. We propose to automatically assess software by modeling experts' decision process and extracting the fuzzy measure corresponding to their reasoning process: to do this, we use samples of the target experts' decision as seed data to automatically extract the fuzzy measure corresponding to the experts' decision process. In particular, we propose an algorithm to efficiently extract fuzzy measures that is a combination of the Bees algorithm and an interval constraint solver. Our experimental results show that we are able to improve some of the results obtained through previous approaches to automatic software quality assessment that used traditional machine learning techniques. © 2013 World Academic Press, UK. All rights reserved.

Jablokow K.,Penn State Great Valley | Myers M.,SAP America
Proceedings - 5th International Conference on Global Software Engineering, ICGSE 2010 | Year: 2010

The challenges faced in global collaboration are often described in terms of logistical issues (e.g., language, time, distance) and issues related to observed workplace differences, which are frequently attributed to the influence of national culture. In this paper, we suggest that another less visible but equally important factor is at work - namely, cognitive diversity, or differences in the preferred ways in which individuals solve problems. In bringing to light key findings related to this additional factor, we review and integrate core concepts from Hofstede's cultural diversity research and Kirton's cognitive diversity research, respectively. This enables us to explore the potential causes of team conflict from a broader perspective and to create a more comprehensive view of diversity management in general. In addition, we will discuss practical strategic approaches for mitigating both cultural and cognitive differences, making recommendations for their use in the context of global IT teams. © 2010 IEEE.

Barb A.S.,Penn State Great Valley
IMSCI 2013 - 7th International Multi-Conference on Society, Cybernetics and Informatics, Proceedings | Year: 2013

In this article we propose a methodology to evaluate the level of expertise of image analysts when searching domain-specific images by semantics. We apply our methodology to ranking high-resolution satellite images by semantics. Our methodology applies PathFinder Network Scaling methods to create concept maps for representing associations of semantics to regions of a feature space for each image analyst. The relevance of each node in a concept map is evaluated using a hits authority algorithm. The expertise of each image analyst is then evaluated by comparing to ground truth models using the Kendall tau rank correlation coefficient. Our system allows us to identify areas of expert disagreement by evaluating the relative difference individual models place on features as well as recommend areas of that needs to be stressed by novice image analysts.

Reychav I.,Ariel University | Stein E.W.,Penn State Great Valley | Weisberg J.,Bar - Ilan University | Glezer C.,Ariel University
International Journal of Knowledge Management | Year: 2012

This study examines the relationship between creativity and innovation at the individual level and how knowledge sharing mediates the relationship between these two constructs. A survey was conducted that measured individual creativity, innovativeness, and four types of knowledge sharing: explicit knowledge and tacit knowledge (e.g., experience, know-how, and expertise) sharing. It was postulated that the type of knowledge mediates the relationship between creativity and the innovativeness of task performance among systems analysts. The results show that creativity was positively related to task innovativeness. This relationship was mediated negatively by explicit knowledge sharing but positively mediated by tacit knowledge sharing based on know-how among project team members. These results have implications for system development and implementation projects. Copyright © 2012, IGI Global.

Russell D.W.,Penn State Great Valley
Robotics and Computer-Integrated Manufacturing | Year: 2010

This paper describes the on-going design of a factory automation performance and satisfaction metric that is based on variations to the function point analysis (FPA) algorithm used in software engineering and analytical hierarchy process methodology common to systems engineering. The paper asserts that a satisfaction rating can be obtained for completed automation projects based on this tautology. The metric uses five high-level functional ratings, which are subdivided according to complexity criteria. The value of the metric is modified by calculating the effect of 14 appropriate adjustment factors. A level of configurability is added by weighting these factors to suit any specific installation. The scores and configuration data are elicited by averaging results obtained from a multi-part survey administered to four or five company employees selected from a wide range of job titles and responsibilities. A simple implementation of the algorithm was completed and an extension of the work will provide an Internet-based version and a diagnostic tool for company use. © 2010 Elsevier Ltd.

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