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Vol. 7 (June 17, 2019).

Research

Abstract

Permanent reachability via mobile communications technologies has become a ubiquitous phenomenon. The traditional boundaries between peoples’ different contexts in their lives become blurry and begin to dissolve, if they are not actively maintained. Such boundary management activities allow to individually determine which communication request are considered acceptable in a particular context. Existing research in this field has a used a fixed set of pre-specified contexts to examine boundary management activities and identify different boundary profiles. Based on results from context-aware computing and mental model research, we argue for an open-ended, individual set of contexts to be considered for boundary management. Consequently, we develop an open structure elaboration technique to allow for individual specification of contexts and the information necessary to create a boundary profile, as identified in related work. The method is validated in an exploratory study, which was designed to verify the hypothesis that boundary management should be based on individually specified contexts, and show the feasibility of the proposed method. The results indicatively confirm our assumptions and show that the method can be used to elicit the required information.

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Abstract

Systems evolve with societal, business and technological changes. Because of these changes, socio-technical systems need to adapt to new situations that were unknown at the time of design. Good knowledge of system evolution can help with that adaption.  Although the evolution of software and interactive systems has been broadly debated, little research has been conducted on the specific genre of systems and even less empirical research on the evolution of interactive software has been performed. We propose a three-dimensional framework which consists of what changes during the evolution of training simulators, what are the drivers for those changes and how the changes effect innovation and robustness of the training simulators. By reviewing the literature on training simulators, we argue for this framework. The contribution of the paper is a framework that can be used to carry out empirical studies on evolution of training simulators.

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Abstract

Classifiers are applied in many domains where classification errors have significant implications. However, end-users may not always understand the errors and their impact, as error visualizations are typically designed for experts and for improving classifiers. We discuss the specific needs of classifiers' end-users, and a simplified visualization designed to address them. We evaluate this design with users from three levels of expertise, and compare it with ROC curves and confusion matrices. We identify key difficulties with understanding the classification errors, and how visualizations addressed or aggravated them. The main issues concerned confusions of the actual and predicted classes (e.g., confusion of False Positives and False Negatives). The machine learning terminology, complexity of ROC curves, and symmetry of confusion matrices aggravated the confusions. The end-user-oriented visualization reduced the difficulties by using several visual features to clarify the actual and predicted classes, and more tangible metrics and representation. Our results contribute to supporting end-users' understanding of classification errors, and informed decisions when choosing or tuning classifiers.

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