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Building spatial process models that capture nonstationary behavior while delivering computationally efficient inference is challenging. Nonstationary spatially varying kernels (see, e.g., Paciorek, 2003) offer flexibility and richness, but…
A range of integrated modeling approaches have been developed to enable a holistic representation of business process logic together with all relevant business rules. These approaches address inherent problems with separate documentation of…
Causal inference, a critical tool for informing business decisions, traditionally relies heavily on structured data. However, in many real-world scenarios, such data can be incomplete or unavailable. This paper presents a framework that…
In this paper we present a proposal for a knowledge-based programming environment. In such an environment, declarative background knowledge, procedures, and concrete data are represented in suitable languages and combined in a flexible…
Ethnomethodological fieldwork has long been acknowledged as a potentially valuable way of informing the design of technology. However, there is relatively little methodological support for this activity, particularly in relation to the…
Most existing evaluations of explainable machine learning (ML) methods rely on simplifying assumptions or proxies that do not reflect real-world use cases; the handful of more robust evaluations on real-world settings have shortcomings in…
This study examines the development of reflective practice among students on a four-year work-based Software Engineering program. Using two established models of reflection - Boud et al.'s Model of Reflective Process and Bain et al.'s 5R…
Context: Empirical studies in software engineering are typically centered on human subjects, ranging from novice to experienced developers. The experience of these individuals is a key context factor that should be properly characterized…
Despite potential benefits in Software Engineering (SE), adoption of software modelling in industry is low. Technical issues such as tool support have gained significant research before, but individual guidance and training have received…
Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances…
Software Engineering and the implementation of software has become a challenging task as many tools, frameworks and languages must be orchestrated into one functioning piece. This complexity increases the need for testing and analysis…
Causal reasoning is essential for business process interventions and improvement, requiring a clear understanding of causal relationships among activity execution times in an event log. Recent work introduced a method for discovering causal…
From a requirements engineering point of view, the elicitation of context-aware functionalities calls for context modeling, an early step that aims at understanding the application contexts and how it may influence user tasks. In practice,…
Although simulation represents a major advance in the understanding of problems in complex systems, the field currently does not has standards in place that would guide the reporting of the data underlying each model, the process for model…
The impact of machine learning models on healthcare will depend on the degree of trust that healthcare professionals place in the predictions made by these models. In this paper, we present a method to provide people with clinical expertise…
The transition to prescriptive maintenance (PsM) in manufacturing is critically constrained by a dependence on predictive models. Such purely predictive models tend to capture statistical associations in the data without identifying the…
An essential characteristic of mature software and system development organizations is the definition and use of explicit process models. For a number of reasons, it can be valuable to produce new process models by tailoring existing…
We consider the problem of modeling, estimating, and controlling the latent state of a spatiotemporally evolving continuous function using very few sensor measurements and actuator locations. Our solution to the problem consists of two…
Empirical software engineering is concerned with the design and analysis of empirical studies that include software products, processes, and resources. Optimization is a form of data analytics in support of human decision-making.…
Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and…