An Overview of Computational Approaches for Interpretation Analysis

It is said that beauty is in the eye of the beholder. But how exactly can we characterize such discrepancies in interpretation? For example, are there any specific features of an image that makes person A regard an image as beautiful while person B finds the same image displeasing? Such questions ultimately aim at explaining our individual ways of interpretation, an intention that has been of fundamental importance to the social sciences from the beginning. More recently, advances in computer science brought up two related questions: First, can computational tools be adopted for analyzing ways of interpretation? Second, what if the "beholder" is a computer model, i.e., how can we explain a computer model's point of view? Numerous efforts have been made regarding both of these points, while many existing approaches focus on particular aspects and are still rather separate. With this paper, in order to connect these approaches we introduce a theoretical framework for analyzing interpretation, which is applicable to interpretation of both human beings and computer models. We give an overview of relevant computational approaches from various fields, and discuss the most common and promising application areas. The focus of this paper lies on interpretation of text and image data, while many of the presented approaches are applicable to other types of data as well.Comment: Preprint submitted to Digital Signal Processin

Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Methodological issues in cross-cultural research

Regardless of whether the research goal is to establish cultural universals or to identify and explain cross-cultural differences, researchers need measures that are comparable across different cultures when conducting cross-cultural studies. In this chapter, we describe two major strategies for enhancing cross-cultural comparability. First, we discuss a priori methods to ensure the comparability of data in cross-cultural surveys. In particular, we review findings on cross-cultural differences based on the psychology of survey response and provide suggestions on how to deal with these cultural differences in the survey design stage. Second, we discuss post hoc methods to ascertain data comparability and enable comparisons in the presence of threats to equivalence

Process Evaluation of a Dutch Community Intervention to improve Health Related Behaviour in deprived neighbourhoods

Objectives: To assess whether a community intervention on health related behaviour in deprived neighbourhoods was delivered as planned and the extent of exposure to the intervention programme. Methods: Data were gathered throughout the intervention period using minutes of meetings, registration forms and a postal questionnaire among residents in intervention and comparison neighbourhoods. Results: Overall, the intervention was delivered according to the key principles of a "community approach", although community participation could have been improved. Neighbourhood coalitions organized more than 50 health related activities in the neighbourhoods over a two-year period. Most activities were directed at attracting attention, providing information, and increasing awareness and knowledge, and at changing behaviours. Programme awareness and programme participation were 24% respectively 3% among residents in the intervention neighbourhoods. Conclusions: The process evaluation indicated that it was feasible to implement a community intervention according to the key principles of the "community approach" in deprived neighbourhoods. However, it is unlikely that the total package of intervention activities had enough strength and sufficient exposure to attain community-wide health behaviour change

Hybrid intelligent framework for automated medical learning

This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up-to-date medical learning models regarding both the computational and cost the quality of returned solutionspublishedVersio

Sociocultural and psychological determinants in migrants for noncompliance with occlusion therapy for amblyopia

Background: Compliance with occlusion therapy for amblyopia in children is low when their parents have a low level of education, speak Dutch poorly, or originate from another country. We determined how sociocultural and psychological determinants affect compliance. Methods: Included we