This investigation explores relations between 1) a theory of human cognition, called Embodied Cognition, 2) the design of interactive systems and 3) the practice of ‘creative group meetings’ (of which the so-called ‘brainstorm’ is perhaps the best-known example). The investigation is one of Research-through-Design (Overbeeke et al., 2006). This means that, together with students and external stakeholders, I designed two interactive prototypes. Both systems contain a ‘mix’ of both physical and digital forms. Both are designed to be tools in creative meeting sessions, or brainstorms. The tools are meant to form a natural, element in the physical meeting space. The function of these devices is to support the formation of shared insight: that is, the tools should support the process by which participants together, during the activity, get a better grip on the design challenge that they are faced with. Over a series of iterations I reflected on the design process and outcome, and investigated how users interacted with the prototypes.
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Early mobilisation after abdominal surgery is necessary to avoid complications and increase recovery. However, due to a variety of factors, failure of early mobilisation is seen in clinical practice. The aim of this study is to investigate the perspectives of nurses and patients of the Haaglanden Medical Center (HMC) how to increase mobilisation frequency after colorectal surgery in the oncological surgery ward. This explorative study employed qualitative data collection and analysis by means of semi-structured interviews with patients and nurses. Patients were included when they had a colorectal resection, were older than 18 years and spoke Dutch. The interviews were audiotaped and verbatum transcribed. A thematic content analysis was performed. It was concluded that mobilisation can be increased when it is incorporated in daily care activities and family support during visiting hours. Appropriate information about mobilisation and physical activity is needed for nurses, patients and family and the hospital environment should stimulate mobilisation.
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With the proliferation of misinformation on the web, automatic misinformation detection methods are becoming an increasingly important subject of study. Large language models have produced the best results among content-based methods, which rely on the text of the article rather than the metadata or network features. However, finetuning such a model requires significant training data, which has led to the automatic creation of large-scale misinformation detection datasets. In these datasets, articles are not labelled directly. Rather, each news site is labelled for reliability by an established fact-checking organisation and every article is subsequently assigned the corresponding label based on the reliability score of the news source in question. A recent paper has explored the biases present in one such dataset, NELA-GT-2018, and shown that the models are at least partly learning the stylistic and other features of different news sources rather than the features of unreliable news. We confirm a part of their findings. Apart from studying the characteristics and potential biases of the datasets, we also find it important to examine in what way the model architecture influences the results. We therefore explore which text features or combinations of features are learned by models based on contextual word embeddings as opposed to basic bag-of-words models. To elucidate this, we perform extensive error analysis aided by the SHAP post-hoc explanation technique on a debiased portion of the dataset. We validate the explanation technique on our inherently interpretable baseline model.
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From the article: The ethics guidelines put forward by the AI High Level Expert Group (AI-HLEG) present a list of seven key requirements that Human-centered, trustworthy AI systems should meet. These guidelines are useful for the evaluation of AI systems, but can be complemented by applied methods and tools for the development of trustworthy AI systems in practice. In this position paper we propose a framework for translating the AI-HLEG ethics guidelines into the specific context within which an AI system operates. This approach aligns well with a set of Agile principles commonly employed in software engineering. http://ceur-ws.org/Vol-2659/
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Deze quick scan heeft als centrale vraag hoe er in Europese landen wordt omgegaan met arbeidstoeleiding in de aanpak van schulden. De hypothese die onder deze quick scan ligt is dat de aanpak van schulden belemmerd kan worden door het ontbreken van betaald werk (want doorgaans minder inkomsten). In voorliggend document is uitgewerkt wat de quick scan heeft opgeleverd. Door zowel vanuit de arbeidstoeleiding als vanuit de aanpak van schulden te kijken, heeft UWV een breed inzicht verkregen van hetgeen Nederland kan leren uit de manier waarop andere landen omgaan met de samenloop van financiële problemen en werkloosheid. De opbouw van deze quick scan is als volgt. 1 Schets van de samenhang tussen schuldenproblematiek en re-integratie 2 Omvang van de schuldenproblematiek in relatie tot werkloosheid. 3 Kenmerken van Europese stelsels om schulden op te lossen. 4 Hoe wordt re-integratie ingezet in de verschillende landen? 5 Concluderende overweging Bijlage 1 Enquête die is verstuurd om inzichten te verkrijgen. Bijlage 2 Belangrijkste constateringen per land.
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Light enables us to see and perceive our environment but it also initiates effects beyond vision, such as alertness. Literature describes that at least six factors are relevant for initiating effects beyond vision. The exact relationship between these factors and alertness is not yet fully understood. In the current field study, personal lighting conditions of 62 Dutch office workers (aged 49.7 ± 11.4 years) were continuously measured and simultaneously self-reported activities and locations during the day were gathered via diaries. Each office worker participated 10 working days in spring 2017. Personal lighting conditions were interpreted based on four of the six factors (light quantity, spectrum, timing, and duration of light exposure). Large individual differences were found for the daily luminous exposures, illuminances, correlated colour temperatures, and irradiances measured with the blue sensor area of the dosimeter. The average illuminance (over all participants and all days) over the course of the day peaked three times. The analysis of the duration of light exposure demonstrated that the participants were on average only exposed to an illuminance above 1000 lx for 72 minutes per day. The interpretation of personal lighting conditions based on the four factors provides essential information since all of these factors may be relevant for initiating effects beyond vision. The findings in the current paper give first in-depth insight in the possibilities to interpret personal lighting conditions of office workers.
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More people voted in 2024 than any other year in human history, while often relying on the internet for political information. This combination resulted in critical challenges for democracy. To address these concerns, we designed an exhibition that applied interactive experiences to help visitors understand the impact of digitization on democracy. This late-breaking work addresses the research questions: 1) What do participants, exposed to playful interventions, think about these topics? and 2) How do people estimate their skills and knowledge about countering misinformation? We collected data in 5 countries through showcases held within weeks of relevant 2024 elections. During visits, participants completed a survey detailing their experiences and emotional responses. Participants expressed high levels of self-confidence regarding the detection of misinformation and spotting AI-generated content. This paper contributes to addressing digital literacy needs by fostering engaging interactions with AI and politically relevant issues surrounding campaigning and misinformation.
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A common strategy to assign keywords to documents is to select the most appropriate words from the document text. One of the most important criteria for a word to be selected as keyword is its relevance for the text. The tf.idf score of a term is a widely used relevance measure. While easy to compute and giving quite satisfactory results, this measure does not take (semantic) relations between words into account. In this paper we study some alternative relevance measures that do use relations between words. They are computed by defining co-occurrence distributions for words and comparing these distributions with the document and the corpus distribution. We then evaluate keyword extraction algorithms defined by selecting different relevance measures. For two corpora of abstracts with manually assigned keywords, we compare manually extracted keywords with different automatically extracted ones. The results show that using word co-occurrence information can improve precision and recall over tf.idf.
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The problem of single-use personal protective equipment (PPE) made from non-renewable resources polluting the environment encouraged the creation of a circular face mask. The main factors that influence the design of a face mask that protects the user from COVID-19 were investigated. According to the findings, these are filtration, fit performance, and comfortability. Therefore, the two following goals were set for this project, to design a face mask (1) produced in the Netherlands using 50% local circular materials and 50% recyclable materials and (2) that perfectly fits men's and women’s faces
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