Kathryn Blackmond Laskey

• PhD
• Professor at George Mason University

Utilizing groundwater offers a promising solution to alleviate water stress in Ethiopia, providing a dependable and sustainable water source, particularly in regions with limited or unreliable surface water availability. However, effective decision-making regarding well drilling and placement is essential to maximize groundwater resource potential,...

Utilizing groundwater offers a promising solution to alleviate water stress in Ethiopia, providing a dependable and sustainable water source, particularly in regions with limited or unreliable surface water availability. However, effective decision-making regarding well drilling and placement is essential to maximize groundwater resource potential,...

In today’s digital landscape, phishing attacks persist as a formidable challenge, highlighting the need for robust strategies to mitigate individual risk. While advanced machine learning techniques have excelled in identifying those most susceptible to phishing, existing research has primarily focused on refining prediction accuracy rather than lev...

Phishing attacks represent a significant and growing threat in the digital world, affecting individuals and organizations globally. Understanding the various factors that influence susceptibility to phishing is essential for developing more effective strategies to combat this pervasive cybersecurity challenge. Machine learning has become a prevalen...

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use com...

Citation: Li, W.; Finsa, M.M.; Laskey, K.B.; Houser, P.; Douglas-Bate, R. Abstract: Predicting groundwater levels is challenging, especially in regions of water scarcity where data availability is often limited. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. This study uses artific...

In water scarcity regions, using data-driven approaches to predict groundwater level is challenging due to limited data availability. However, these regions have substantial water needs and require cost-effective groundwater utilization strategies. In this study, we use artificial intelligence to predict groundwater levels to provide guidance for d...

Purpose Reducing fatalities and increasing the number of students able to remain safe during an active shooter event is paramount to the health and well-being of schools and communities. Yet, methodological limitations and ethical concerns have restricted prior research on security measures during school shooter lockdown drills. This study aims to...

Does free will exist? It feels that way. We experience choosing freely among different possible actions, and these choices seem to have effects in the world. Yet the mainstream view among scientists is that our choices are entirely a function of neurobiological processes unfolding according to the laws of physics. Our intentions, the argument goes,...

In East Africa, many drought events have occurred over the past few decades. Droughts have resulted in severe food cri-ses, especially for countries relying heavily on agriculture. From the perspective of sustainability, utilizing groundwater for crop irrigation could be an avenue toward resilience to drought. In this study, we aim to use AI to ide...

Objective This paper investigates the impact on emergency hospital services from initiation through recovery of a ransomware attack affecting the emergency department, intensive care unit and supporting laboratory services. Recovery strategies of paying ransom to the attackers with follow-on restoration and in-house full system restoration from bac...

This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is to provide statistical tools for detecting changes in firing patterns with changing stimuli. Our framework is not restricted to the well-understood case of pair interaction...

This article reports on a simulated phishing experiment targeting 6,938 faculty and staff at George Mason University. The three-week phishing campaign employed three types of phishing exploits and examined demographic, linked workstation/network monitoring audit data, and a variety of behavioral and psychological factors measured via pre- and post-...

Space is becoming a more crowded and contested domain, but the techniques used to task the sensors monitoring this environment have not significantly changed since the imple- mentation of James Miller’s marginal analysis technique used in the Special Perturbations (SP) Tasker in 2007. Miller’s heuristic approach to the Sensor Allocation Problem (SA...

Bayesian bandits using Thompson Sampling have seen increasing success in recent years. Yet existing value models (of rewards) are misspecified on many real-world problem. We demonstrate this on the User Experience Optimization (UXO) problem, providing a novel formulation as a restless, sleeping bandit with unobserved confounders plus optional stopp...

Simon's legacies for Mathematics education are briefly explained with examples-.

First-order expressive capabilities allow Bayesian networks (BNs) to model problem domains where the number of entities, their attributes, and their relationships can vary significantly between model instantiations. First-order BNs are well-suited for capturing knowledge representation dependencies, but literature on design patterns specific to fir...

This paper develops arguments for a family of temporal log-linear models to represent spatio-temporal correlations among the spiking events in a group of neurons. The models can represent not just pairwise correlations but also correlations of higher order. Methods are discussed for inferring the existence or absence of correlations and estimating...

After a brief description of the four components of risk literacy and the tools for analyzing risky situations, decision strategies are introduced, These rules, which satisfy tenets of Bounded Rationality, are called fast and frugal trees. Fast and frugal trees serve as efficient heuristics for decision under risk. We describe the construction of f...

Contemporary science and philosophy are dominated by a mechanistic materialist metaphysic that treats consciousness as a derivative aspect of the brain’s physical state, with no independent causal efficacy ascribed to consciousness. Studies suggest there may be negative social consequences to widespread popular belief that our thoughts are passive...

Uncertainty management is a key aspect of any information fu-sion (IF) system. Evaluation of how uncertainty is dealt with withina given IF system is distinct from, although closely related to, evalu-ation of the overall performance of the system. This paper presentsthe Uncertainty Representation and Reasoning Evaluation Frame-work (URREF), which i...

In this paper, the uncertainties that enter through the life-cycle of an information fusion system are exhaustively and explicitly considered and defined. Addressing the factors that influence a fusion system is an essential step required before uncertainty representation and reasoning processes within a fusion system can be evaluated according to...

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variable...

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BNs) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning, and is the logical basis of Probabilistic Web Ontology Language (PR-OWL), a representation language for probabil...

The classical materialist worldview of mainstream cognitive science leads to a conception of our minds as passive spectators watching our bodies execute their built-in programs. Yet this view seems to conflict with our experience of free will. Henry Stapp has long argued that quantum theory provides the basis for a theory of conscious agents posses...

Real world multivariate data mostly contains correlation structure because generally some variables tend to have a similar behavior or some dependency structure. This is due to the nature of data generation process. The variables can contain cross-sectional correlation, correlation between variables at a given time stamp, and temporal correlation,...

We describe algorithms for use by prediction markets in forming a crowd consensus joint probability distribution over thousands of related events. Equivalently, we describe market mechanisms to efficiently crowdsource both structure and parameters of a Bayesian network. Prediction markets are among the most accurate methods to combine forecasts; fo...

Causality is fundamental to agency. Intelligent agents learn about causal relationships by interacting with their environments and use their causal knowledge to choose actions intended to bring about desired outcomes. This paper considers a causal question that is central to the very meaning of agency, that of how a physically embodied agent effect...

We demonstrate that our success in solving a set of increasingly complex challenge problems is associated with an inference enterprise (IE) using inference enterprise models (IEMs). As part of a sponsored research competition, we created a multimodeling inference enterprise modeling (MIEM) process to achieve winning scores on a spectrum of challeng...

A methodology is described for modeling enterprises that use data, tools, people and processes to make mission‐focused inferences. Examples include cyber‐operations centers detecting cyber intrusions, airport security systems detecting attempts to carry prohibited items onto airplanes, or mortgage underwriting offices predicting loan defaults. An i...

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., image understanding, data fusion, medical diagnosis, fraud detection). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks, in which all continuous random variable...

An Artificial Intelligence (AI) system is an autonomous system which emulates human mental and physical activities such as Observe, Orient, Decide, and Act, called the OODA process. An AI system performing the OODA process requires a semantically rich representation to handle a complex real world situation and ability to reason under uncertainty ab...

During the past quarter-century, situation awareness (SAW) has become a critical research theme, because of its importance. Since the concept of SAW was first introduced during World War I, various versions of SAW have been researched and introduced. Predictive Situation Awareness (PSAW) focuses on the ability to predict aspects of a temporally evo...

Multi-Entity Bayesian Network (MEBN) is a knowledge representation formalism combining Bayesian Networks (BN) with First-Order Logic (FOL). MEBN has sufficient expressive power for general-purpose knowledge representation and reasoning. Developing a MEBN model to support a given application is a challenge, requiring definition of entities, relation...

As science reaches further into the cognitive domain, questions once thought firmly outside the realm of science are becoming subjects of scientific inquiry. One of the foremost challenges is the relationship of our thoughts and intentions to the world we study and manipulate. Once thought intractable, this problem seems newly open to scientific di...

Organizations employ a suite of analytical models to solve complex decision problems in their respective domains. The current practice uses different simulation and modeling formalisms and subject matter experts to address parts of a larger problem. There is a realization that complex problems cannot be solved by employing a single analytical metho...

Recent years have witnessed an increasingly mature body of research on the Semantic Web (SW), with new standards being developed and more complex problems being addressed. As complexity increases in SW applications, so does the need to cope with uncertainty. Several approaches to uncertainty representation and reasoning in the SW have emerged. Amon...

Smart manufacturing relies on a combination of different sources providing key information to support diverse activities throughout the manufacturing process. Most smart manufacturing systems focus on activities directly related to the management of robots, conveyor belts, maintenance logs, and others that ensure the process runs smoothly. An initi...

The International Society of Information Fusion (ISIF) Evaluation Techniques for Uncertainty Representation Working Group (ETURWG) investigates the quantification and evaluation of all types of uncertainty regarding the inputs, reasoning and outputs of the information fusion process. The ETURWG is developing an Uncertainty Representation and Reason...

The question addressed in this paper is " what " is to be evaluated by the Uncertainty Representation and Reasoning Evaluation Framework (URREF) ontology. We thus identify the elements composing uncertainty representation and reasoning approaches, which constitute various subjects being assessed. We distinguish between primary evaluation subjects (...

Human behavioral factors are fundamental to understanding, detecting and mitigating insider threats, but to date insufficiently represented in a formal ontology. We report on the design and development of an ontology that emphasizes individual and or-ganizational sociotechnical factors, and incorporates technical indicators from previous work. We c...

Decision making is a big topic in Intelligence, Defense, and Security fields. However, very little work can be found in the literature about ontology languages that simultaneously support decision making under uncertainty, abstractions/generalizations with first-order expressiveness, and forward/backward compatibility with OWL-a standard language f...

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there...

In this paper, the principle taxonomy of the fusion process, the decision loop, is unified with uncertainty quantifi-cation and representation. A typical flow of information in the decision loop takes the form of raw information, uncertainty modelling, combination, and decisions, which corresponds closely with Boyd's Observe, Orient, Decide and Act...

Over the course of Dave Hall’s career, he highlighted various concerns associated with the implementation of data fusion methods. Many of the issues included the role of uncertainty in data fusion, practical implementation of sensor fusion systems, and incorporating new technology into information fusion designs. These thoughts were communicated th...

We describe enhancements underway to our probabilistic argument mapping framework called FUSION. Exploratory modeling in the domain of intelligence analysis has highlighted requirements for additional knowledge representation and reasoning capabilities, particularly regarding argument map nodes that are specified as propositional logic functions of...

Intelligence analysts are tasked to produce well-reasoned, transparent arguments with justified likelihood assessments for plausible outcomes regarding past, present, or future situations. Traditional argument maps help to structure reasoning but afford no computational support for probabilistic judgments. We automatically generate Bayesian network...

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there...

The ubiquity of uncertainty across application domains generates a need for principled support for uncertainty management in semantically aware systems. A probabilistic ontology provides constructs for representing uncertainty in domain ontologies. While the literature has been growing on formalisms for representing uncertainty in ontologies, there...

Predictive Situation Awareness (PSAW) is the ability to estimate and predict aspects of a temporally evolving situation. PSAW systems reason about complex and uncertain situations involving multiple targets observed by multiple sensors at different times. Multi-Entity Bayesian Networks (MEBN) are rich enough to represent and reason about uncertaint...

The International Society of Information Fusion (ISIF) with support of the IEEE Aerospace and Electronics Systems Society (AESS) organized its 18th annual FUSION conference in July 2015 in Washington DC. The host organization was George Mason University???s Center of Excellence in C4I (http:// fusion2015.org/) of which the key members are in Figure...

Each year, natural and anthropogenic crises disrupt the lives of millions of people. Local, national, and international crisis response systems struggle to cope with urgent needs during and immediately after a crisis. The challenges multiply as population grows, density of urban areas increases, and coastal areas become more vulnerable to rising se...

Hybrid Bayesian Networks (HBNs), which contain both discrete and continuous variables, arise naturally in many application areas (e.g., artificial intelligence, data fusion, medical diagnosis, fraud detection, etc). This paper concerns inference in an important subclass of HBNs, the conditional Gaussian (CG) networks. Inference in CG networks can b...

Mathematical and uncertainty modelling is an important component of data fusion (the fusion of unprocessed sensor data) and information fusion (the fusion of processed or interpreted data). If uncertainties in the modelling process are not or are incorrectly accounted for, fusion processes may provide under-or overconfident results, or in some case...

The probabilistic ontology language PR-OWL (Probabilistic OWL) uses Multi-Entity Bayesian Networks (MEBN), an extension of Bayesian networks with first-order logic, to add the ability to deal with uncertainty to OWL, the main language of the Semantic Web. A second version, PR-OWL 2, was proposed to allow the construction of hybrid ontologies, conta...

Markets are a medium for information exchange between buyers and sellers. Prediction markets exploit the information transmission property of markets to improve forecasts of future events. Participants in a prediction market buy and sell assets that pay off if the underlying event occurs. Prices in a prediction market can be interpreted as consensu...

Co‐clustering means simultaneously identifying natural clusters in different kinds of objects. Examples include simultaneously clustering customers and products for a recommender application; simultaneously clustering proteins and molecules in microbiology; or simultaneously clustering documents and words in a text mining application. Important ins...

For many operational information fusion systems, both reliability and credibility are evaluation criteria for collected information. The Uncertainty Representation and Reasoning Evaluation Framework (URREF) is a comprehensive ontology that represents measures of uncertainty. URREF supports standards such as the NATO Standardization Agreement (STANA...

Predictive Situation Awareness (PSAW) emphasizes the ability to make predictions about aspects of a temporally evolving situation. Higher-level fusion to support PSAW requires a semantically rich representation to handle complex real world situations and the ability to reason under uncertainty about the situation. Multi-Entity Bayesian Networks (ME...

In order for the uncertainty representation and reasoning evaluation framework (URREF) ontology for the evaluation of information fusion systems to have maximum value, it must be generally applicable irrespective of the application, uncertainty representation, reasoning scheme or data format. Since the URREF ontology is still an evolving framework,...

Prediction markets have demonstrated their value for aggregating collective expertise. Combinatorial prediction markets allow forecasts not only on base events, but also on conditional and/or Boolean combinations of events. We describe a trade-based combinatorial prediction market asset management system, called Dynamic Asset Cluster (DAC), that im...

In an online prediction market, forecasters who could not see the current state of the market until they made their own separate estimates moved their estimates closer to the market forecast when the current state of the market became known. Their first edits to the market forecast were very similar to the first edits of forecasters who could alway...

In the past decade, Statistical Relational Learning (SRL) has emerged as a new branch of machine learning for representing and learning a joint probability distribution over relational data. Relational representations have the necessary expressive power for important real-world problems, but until recently have not supported uncertainty. Statistica...

A prediction market allows a group of traders to form a consensus probability distribution by entering into agreements that pay off contingent on events of interest. A combinatorial prediction market allows conditional trades or trades on Boolean combinations of events to form a joint distribution over many related events. Sun et al. (2012) showed...

A primary function of mind is to form and manipulate representations to identify and choose survival-enhancing behaviors. Representations are themselves physical systems that can be manipulated to reason about, predict, or plan actions involving the objects they designate. The field of knowledge representation and reasoning (KRR) turns representati...

We present a method to devise, execute, and assess a cyber deception. The aim is to cause an adversary to believe they are under a cyber attack when in fact they are not. Cyber network defense relies on human and computational systems that can reason over multiple individual evidentiary items to detect the presence of meta events, i.e., cyber attac...

The use of ontologies is on the rise, as they facilitate interoperability and provide support for automation. Today, ontologies are popular in areas such as the Semantic Web, Knowledge Engineering, Artificial Intelligence and knowledge management. However, many real world problems in these disciplines are burdened by incomplete information and othe...

Sparse Dictionary Learning has recently become popular for discovering latent components that can be used to reconstruct elements in a dataset. Analysis of sequence data could also benefit from this type of decomposition, but sequence datasets are not natively accepted by the Sparse Dictionary Learning model. A strategy for making sequence data mor...

Prediction markets produce crowdsourced probabilistic forecasts through a market mechanism in which forecasters buy and sell securities that pay off when events occur. Prices in a prediction market can be interpreted as consensus probabilities for the corresponding events. There is strong empirical evidence that aggregate forecasts tend to be more...

An approach focused on inferring the finest possible sub event witnessed by individual photographs in a personal photo stream of an event is presented in this work using image metadata (timestamp, location, and camera parameters), information about the user, ontological event model, mobile device connectivity, web services, and external data source...