Galina Rogova

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Dr. Rogova is a research professor at the State University at Buffalo as well as an independent consultant (DBA Encompass Consulting). She is recognized internationally as an expert in information fusion, machine learning, decision making under uncertainty, and information quality. She has worked on a wide range of defense and non-defense problems such as situation and threat assessment, understanding of volcanic eruption patterns, computer-aided diagnosis, and intelligent transportation systems among others. Her research was funded by multiple government agencies as well as commercial companies. She published numerous papers and co-edited 6 books. She served as a program committee member, special session organizer, and tutorial lecturer for numerous International Conferences on Information Fusion. Dr. Rogova was also a member of organizing committees of multiple NATO Advanced Study Institutes and NATO Advanced Research Workshops on information fusion and decision support.

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Armando Marques-Guedes

UNL - New University of Lisbon

Enrico Cirelli

Università di Bologna

Noam Ebner

Creighton University

Stéphane Péquignot

Ecole Pratique des Hautes Etudes

Simon Springer

The University of Newcastle

Fabrice Lumineau

The University of Hong Kong

Mauro Grondona

University of Genova

Daniel Hershenzon

University of Connecticut

Drazen Pehar

Alexios Arvanitis

University of Crete

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Papers by Galina Rogova

Information Quality in Fusion-Driven Human-Machine Environments

Information fusion and data science, 2019

Effective decision making in complex dynamic situations calls for designing a fusion-based human-... more Effective decision making in complex dynamic situations calls for designing a fusion-based human-machine information system requiring gathering and fusing a large amount of heterogeneous multimedia and multispectral information of variable quality coming from geographically distributed sources. Successful collection and processing of such information strongly depend on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing methods of representing and incorporating information quality into fusion processing is a relatively new and rather difficult problem. The chapter discusses major challenges and suggests some approaches to address this problem.

Automated Roadside Inspection Feasibility Study for Commercial Motor Vehicles

Intelligent Transportation: Realizing the Benefits. Proceedings of the 1996 Annual Meeting of ITS America.ITS America, 1996

If inspected, the average commercial motor vehicle on today's highways stands about a one in ... more If inspected, the average commercial motor vehicle on today's highways stands about a one in three chance of failing an inspection and being placed out-of-service (OOS). Over 80% of these OOS violations are vehicle violations. By far, the largest category of OOS violations is "brakes" accounting for 43% of the total OOS violations. This study concentrates on the vehicle part of the inspection which produces the majority of out-of-service violations when compared to driver violations. The technologies tested allowed for visual imaging technologies, including computer imaging, that report on the mechanical and safety conditions of the underside of a CMV without requiring the inspector to physically crawl under the vehicle. This report summarizes important results of the feasibility study, discusses factors affecting inspection efficiency and discusses a feasible automated inspection concept which could be developed into a future prototype system.

Information Quality in Information Fusion and Decision Making with Applications to Crisis Management

Springer eBooks, 2016

Designing fusion systems for decision support in complex dynamic situations such as crises requir... more Designing fusion systems for decision support in complex dynamic situations such as crises requires fusion of a large amount of multimedia and multispectral information coming from geographically distributed sources to produce estimates about objects and gain knowledge of the entire domain of interest. Information to be fused and made sense of includes but is not limited to data obtained from physical sensors, surveillance reports, human intelligence reports, operational information, and information obtained from social medial, opportunistic sensors and traditional open sources (internet, radio, TV, etc.). Successful processing of this information may also demand information sharing and dissemination, and action cooperation of multiple stakeholders. Decision making in such environment calls for designing a fusion-based human–machine system characterizing constant information exchange between all nodes of the processing. The quality of decision making strongly depends on the success of being aware of, and compensating for, insufficient information quality at each step of information exchange. Designing the methods of representing and incorporating information quality into such processing is a relatively new and a rather difficult problem. The chapter discusses major challenges and suggests some approaches to address this problem.

Crisis Management and Context

Springer eBooks, 2016

Successful management of critical situations created by major natural and man-made activities req... more Successful management of critical situations created by major natural and man-made activities requires monitoring, recognizing, fusing, and making sense of these activities in order to support decision makers in either preventing a crisis or acting effectively to mitigate its adverse impact. Context plays an important role in crisis management since it provides decision makers with important knowledge about current situations and situation dynamics in relation to their goals, functions, and information needs, to enable them to appropriately adapt their decisions and actions. Efficient context exploitation for crisis management requires a clear understanding of what context is, how to represent it and use it. The chapter provides a brief discussion of the key issues of the problem of context definition, representation, discovery, and utilization in crisis management.

Current State of an Intelligent System to Aid in Tephra Layer Correlation

AGUFM, Dec 1, 2007

ABSTRACT We are developing a computer based intelligent system to correlate tephra layers by usin... more ABSTRACT We are developing a computer based intelligent system to correlate tephra layers by using the lithologic, mineralogic, and geochemical characteristics of field samples, to aid geologists in interpreting eruption patterns of volcanic chains and fields. The intelligent system is used to define groups of tephra source vents by utilizing geochemical data, and to correlate tephra layers based on lithostratigraphic characteristics. Understanding the eruption history of a volcano from stratigraphic studies is important for forecasting future eruptive behavior and hazards. In volcanic chains and fields with a complex eruptive history and no central vent, determining the spatio- temporal eruption patterns is difficult. Sedimentologic and chemical variability, and sparse sampling often result in relatively large variances and imprecision in the dataset. Lithostratigraphic and geochemical interpretation also depends on ones&#39; level of expertise and can be subjective. The processing of lithostratigraphic features is conducted by a hybrid classifier, composed of supervised artificial neural networks (ANNs) combined within the framework of the Dempster-Shafer theory of evidence. Since lithostratigraphic features vary with distance from source, hypothetical vent locations are determined by using expert domain knowledge and geostatistical methods. Geochemical data are processed by a suit of fuzzy k- means classifiers. Each fuzzy k-means classifier assigns observations to multiple clusters with various degrees, called membership coefficients. The assignment minimizes a function of the total distance between the centers of clusters and the individual geochemical data patterns weighed by the membership coefficients. Improved clustering results of geochemical data are achieved by the fusion of individual clustering results with an evidential combination method. Lithostratigraphic data from individual tephra beds of the North Mono eruption sequence are used to test the effectiveness of the intelligent system for tephra layer correlation. Geochemical data from tephra bedsets of the Mono and Inyo Craters, CA, are used to test the effectiveness of the intelligent system for eruption sequence correlation. The intelligent system aids correlation by showing matches and disparities between data patterns from different outcrops that may have been overlooked in initial interpretations. Initial results show that the lithostratigraphic classifier is able to accurately differentiate known layers 76% of the time. Output from the lithostratigraphic classifier can furthermore be plotted directly as isopleth maps that can aid in rapid recognition of tephra layers as well as determination of eruption characteristics, e.g. eruption volume, plume height, etc. The intelligent system produces a useful recognition result, while dealing with the uncertainty from sparse data and the imprecise description of layer characteristics.

Learning under uncertainty for interpreting the pattern of volcanic eruptions

International Conference on Information Fusion, Jul 6, 2015

The overall goal of the research presented in this paper is to design an intelligent system to ai... more The overall goal of the research presented in this paper is to design an intelligent system to aid geologists in processing complex rock characteristics for interpreting eruption patterns, and thereby to aid eruption forecasting for volcanic chains and fields. The objective of this paper is to introduce a belief-based partially supervised classification method designed to deal with high uncertainty of geological data. A case study developed to show the feasibility of the presented method for correlation of tephra layers based on geochemical characteristics is also described. This method is not specific to geological data and can be used in other applications.

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A Tephra Database With an Intelligent Correlation System, Mono-Inyo Volcanic Chain, CA

AGU Fall Meeting Abstracts, Dec 1, 2004

ABSTRACT

Intelligent System for Interpreting the Pattern of Volcanic Eruptions

Journal of Advances in Information Fusion, 2008

ABSTRACT

System-Level Use of Contextual Information

Advances in Computer Vision and Pattern Recognition, 2016

A system that exploits information—e.g. to support decision making—can use contextual information... more A system that exploits information—e.g. to support decision making—can use contextual information both in providing expectations and in resolving uncertain inferences. In the latter case, contextual reasoning involves inferring desired information (values of “problem variables”) on the basis of other available information (“context variables”). Relevant contexts are often not self-evident, but must be discovered or selected as a means to problem-solving. Therefore, context exploitation involves (a) predicting the value of contextual information to meet information needs; (b) selecting information types and sources expected to provide information useful in meeting those needs; (c) determining the relevance and quality of acquired information; and (d) applying selected information to a problem at hand. Fusion of contextual information can improve the quality of inferences, but involves concerns about the quality of the contextual information. The availability and quality of predictive models dictate the ways in which contextual information can be used. Many applications are benefitted by inference systems that adaptively discover and exploit context and refine such models to meet evolving information states and information needs.

<title>Feature choice for detection of cancerous masses by constrained optimization</title>

Proceedings of SPIE, May 21, 1999

ABSTRACT

<title>Microcalcification texture analysis in a hybrid system for computer-aided mammography</title>

Proceedings of SPIE, May 21, 1999

Characterization of microcalcifications with high level of confidence is a very challenging probl... more Characterization of microcalcifications with high level of confidence is a very challenging problem since microcalcifications are very small and the difference between benign and malignant clusters is often very subtle. The overall goal of the presented research is to develop a hybrid evidential system for characterization of microcalcifications in order to provide radiologists with a computerized decision aid. The hybrid system intelligently combines a domain knowledge based subsystem with a computer vision subsystem to improve the confidence level of microcalcification characterization. This paper is mainly devoted to the description of the developed computer vision part of the hybrid system. The computer vision subsystem is represented by a hierarchical evidential classifier that computes evidences about the class membership of individual microcalcifications based on their texture and then uses these evidences in a neural network for clusters characterization. The texture of each individual classification is represented by two features: the fractal dimension and a four dimension vector defined by coefficients of the Gabor expansion of a microcalcification image. The results obtained in our experiment prove the feasibility of using this method in the hybrid system.

Evidential Approach to Improved Microcalcification Characterization

Computational imaging and vision, 1998

The overall goal of the research in progress described in this paper is to develop an evidential ... more The overall goal of the research in progress described in this paper is to develop an evidential approach to improved characterization of microcalcifications. Characterization of benign and malignant microcalcifications is very complex and represents a perceptual problem even for an experienced radiologist. Microcalcifications might be very small and the structure of malignant microcalcifications is not much different from that of the benign structures. These perceptual problems result in screening errors which lead either to missed malignant cases or even more often to unnecessary biopsies. All of these factors make the development of an improved automated method for microcalcification characterization a most important and immediate objective.

Information fusion approach to microcalcification characterization

Information Fusion, Jun 1, 2002

The paper presents an information fusion-based approach to one of the most challenging problems i... more The paper presents an information fusion-based approach to one of the most challenging problems in mammogram interpretation: the problem of characterizing mammographic microcalcifications as benign or malignant. There are two categories of methods typically used for designing decision aids for diagnosis of microcalcifications: computer vision methods employing intensity-based features automatically extracted from images and methods using mammogram characteristics considered by human experts. The achieved recognition accuracy of both types of methods is not yet sufficient for them to be utilized in clinical practice. The paper introduces a hybrid system combining decisions of classifiers utilizing both domain knowledge-based and intensity-based features within the framework of the Evidence theory. The system comprises a hierarchical evidential classifier employing a combination of texture features of individual microcalcifications and a neural network employing cluster features observed and described by a radiologist. The results of a pilot study have shown that a false alarm rate of the hybrid system is lower than the false alarm rate of each single classifier used in the combination as well as that of the radiologists participated in the study.

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Decision Making with “Gray Swan” events

“Gray Swan” has been defined by Nassim Taleb as a rare and highly consequential event that, unlik... more “Gray Swan” has been defined by Nassim Taleb as a rare and highly consequential event that, unlike absolutely unforeseen Black Swan events, can be expected. This paper describes an approach to designing a decision support system, which monitors uncertain and ambiguous dynamic environments characterized by both regular and Gray Swan events. It is capable of producing probable explanations of the situations, identifying asymmetric threats, and acting to prevent catastrophic consequences or mitigate their impact while operating under resource and time constraints. The proposed system incorporates decision makers' attitudes toward risk and uncertainty while considering regular and Gray swan events characterized by uncertain catastrophic outcomes. Use case results are presented

Reasoning and Decision Making under Uncertainty and Risk for Situation Management

This paper addresses the problem of situation management in an uncertain and ambiguous environmen... more This paper addresses the problem of situation management in an uncertain and ambiguous environment. It presents an approach to reasoning and decision making for identifying asymmetric threats and preventing or mitigating possible catastrophic consequences under resource constraints. Decision making for action selection incorporates decision makers’ attitude toward risk and uncertainty in the situations involving regular as well as high consequence rare events. The presented approach is based on the OODA loop and implemented within the framework of the Transferable Belief Model, Choquet Expected Utility, Multiple Quantile Model, abductive reasoning utilizing the Belief-based argumentation, and the Anytime decision making. A quality control component is added to the OODA loop to deal with possible insufficient quality of input information and results of processes involved. An example illustrating the approach is presented in this paper.

<title>Constrained-optimization framework for detection of masses</title>

Medical Imaging 1998: Image Processing, Jun 24, 1998

ABSTRACT

On the Integration of Cognitive Work Analysis within a Multisource Information Fusion Development Methodology

Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting, Sep 1, 2004

This research explored the means by which methods in cognitive engineering, namely, work domain a... more This research explored the means by which methods in cognitive engineering, namely, work domain analysis, could be used to provide input to the development of advanced information processing, or multisensor information fusion, algorithms. Specifically, a work domain analysis of an emergency management environment (in a post-earthquake context) was performed, and linked abstraction hierarchy models representing the emergency management and response system, the physical environment (e.g., buildings, transportation systems, civilians), and other goal directed agents (e.g., civilian responders and volunteers) were created. Outputs from that analysis (information requirements) were input to the design of the information processing algorithms, providing guidance as to the nature of information required by decision makers, which could be computed through fusion capabilities. This ongoing work thus presents an example of an integrated cognitive engineering/multisensor fusion methodology.

An Ontological Analysis of Threat and Vulnerability

The overall goal of this paper is to provide a formal ontological analysis of threat. In particul... more The overall goal of this paper is to provide a formal ontological analysis of threat. In particular, this paper discusses the formal ontological structure of threats as integrated wholes possessing three interrelated parts: intentions, capabilities and opportunities, and shows how these elements stand to one another, as well as to states of vulnerability. This discussion offers a means for understanding

Designing ontologies for higher level fusion

Information Fusion, 2009

The purpose of higher level fusion is to produce contextual understanding of the states of the en... more The purpose of higher level fusion is to produce contextual understanding of the states of the environment and prediction of their impact in relation to specific goals of decision makers. One of the main challenges of designing higher level fusion processes is to provide a formal structure of domain-specific types of entities, attributes, situations, and the relations between them for reasoning about situations and threats. This paper presents an attempt at confronting this challenge by describing a process for building formal ontologies that combines a top-down philosophical perspective (from the most abstract levels to domain-specific levels) with a bottom-up application-based perspective (from domain-specific levels to the most abstract levels). The main focus of this paper is to provide a conceptual framework for formally capturing various sorts of complex relation-types, which can serve as a means for a more thorough decomposition of objects, attributes/properties, events, processes, and relations, necessary for higher level fusion processing.

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Action Selection Under Uncertainty and Risk Involving High Consequence Rare Events

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)

This paper describes an approach to decision making for action selection in the presence of high ... more This paper describes an approach to decision making for action selection in the presence of high consequence rare events. A model presented in the paper is an adaptation of the Multiple Quantile Theory that evaluates actions based on Choquet utility while considering decision making attitudes towards gains and losses. In our model, the capacities used for computing Choquet utility are based on the belief structure resulting from situation assessment, which allows for eliminating a priori parameter selection required by the Multiple Quantile Theory. A use case designed to illustrate the feasibility of the model in a simple military scenario is also presented.

Information Quality in Fusion-Driven Human-Machine Environments

Information fusion and data science, 2019

Automated Roadside Inspection Feasibility Study for Commercial Motor Vehicles

Intelligent Transportation: Realizing the Benefits. Proceedings of the 1996 Annual Meeting of ITS America.ITS America, 1996

Information Quality in Information Fusion and Decision Making with Applications to Crisis Management

Springer eBooks, 2016

Crisis Management and Context

Springer eBooks, 2016

Current State of an Intelligent System to Aid in Tephra Layer Correlation

AGUFM, Dec 1, 2007

Learning under uncertainty for interpreting the pattern of volcanic eruptions

International Conference on Information Fusion, Jul 6, 2015

Download

A Tephra Database With an Intelligent Correlation System, Mono-Inyo Volcanic Chain, CA

AGU Fall Meeting Abstracts, Dec 1, 2004

ABSTRACT

Intelligent System for Interpreting the Pattern of Volcanic Eruptions

Journal of Advances in Information Fusion, 2008

ABSTRACT

System-Level Use of Contextual Information

Advances in Computer Vision and Pattern Recognition, 2016

<title>Feature choice for detection of cancerous masses by constrained optimization</title>

Proceedings of SPIE, May 21, 1999

ABSTRACT

<title>Microcalcification texture analysis in a hybrid system for computer-aided mammography</title>

Proceedings of SPIE, May 21, 1999

Evidential Approach to Improved Microcalcification Characterization

Computational imaging and vision, 1998

Information fusion approach to microcalcification characterization

Information Fusion, Jun 1, 2002

Download

Decision Making with “Gray Swan” events

Reasoning and Decision Making under Uncertainty and Risk for Situation Management

<title>Constrained-optimization framework for detection of masses</title>

Medical Imaging 1998: Image Processing, Jun 24, 1998

ABSTRACT

On the Integration of Cognitive Work Analysis within a Multisource Information Fusion Development Methodology

Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting, Sep 1, 2004

An Ontological Analysis of Threat and Vulnerability

Designing ontologies for higher level fusion

Information Fusion, 2009

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Action Selection Under Uncertainty and Risk Involving High Consequence Rare Events

2018 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA)

Galina Rogova

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