Research into automatic text simplification aims to promote access to information for all members of society. To facilitate generalizability, simplification research often abstracts away from specific use cases, and targets a prototypical reader and an underspecified content creator. In this paper, we consider a real-world use case – simplification technology for use in Dutch municipalities – and identify the needs of the content creators and the target audiences in this scenario. The stakeholders envision a system that (a) assists the human writer without taking over the task; (b) provides diverse outputs, tailored for specific target audiences; and (c) explains the suggestions that it outputs. These requirements call for technology that is characterized by modularity, explainability, and variability. We argue that these are important research directions that require further exploration
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Author supplied Business rules play a critical role in an organization’s daily activities. With the increased use of business rules (solutions) the interest in modelling guidelines that address the manageability of business rules has increased as well. However, current research on modelling guidelines is mainly based on a theoretical view of modifications that can occur to a business rule set. Research on actual modifications that occur in practice is limited. The goal of this study is to identify modifications that can occur to a business rule set and underlying business rules. To accomplish this goal we conducted a grounded theory study on 229 rules set, as applied from March 2006 till June 2014, by the National Health Service. In total 3495 modifications have been analysed from which we defined eleven modification categories that can occur to a business rule set. The classification provides a framework for the analysis and design of business rules management architectures.
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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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This Professional Doctorate (PD) project explores the intersection of artistic research, digital heritage, and interactive media, focusing on the reimagining of medieval Persian bestiaries through high dark fantasy and game-making. The research investigates how the process of creation with interactive 3D media can function as a memory practice. At its core, the project treats bestiaries—pre-modern collections of real and imaginary classifications of the world—as a window into West and Central Asian flora, fauna, and the landscape of memory, serving as both repositories of knowledge and imaginative, cosmological accounts of the more-than-human world. As tools for exploring non-human pre-modern agency, bestiaries offer a medium of speculative storytelling, and explicate the unstable nature of memory in diasporic contexts. By integrating these themes into an interactive digital world, the research develops new methodologies for artistic research, treating world-building as a technique of attunement to heritage. Using a practice-based approach, the project aligns with MERIAN’s emphasis on "research in the wild," where artistic and scientific inquiries merge in experimental ways. It engages with hard-core game mechanics, mythopoetic decompressed environmental storytelling, and hand-crafted detailed intentional world-building to offer new ways of interacting with the past that challenges nostalgia and monumentalization. How can a cultural practice do justice to other, more experimental forms of remembering and encountering cultural pasts, particularly those that embrace the interconnections between human and non-human entities? Specifically, how can artistic practice, through the medium of a virtual, bestiary-inspired dark fantasy interactive media, allow for new modes of remembering that resist idealized and monumentalized histories? What forms of inquiry can emerge when technology (3D media, open-world interactive digital media) becomes a tool of attention and a site of experimental attunement to cosmological heritage?
Organisations are increasingly embedding Artificial Intelligence (AI) techniques and tools in their processes. Typical examples are generative AI for images, videos, text, and classification tasks commonly used, for example, in medical applications and industry. One danger of the proliferation of AI systems is the focus on the performance of AI models, neglecting important aspects such as fairness and sustainability. For example, an organisation might be tempted to use a model with better global performance, even if it works poorly for specific vulnerable groups. The same logic can be applied to high-performance models that require a significant amount of energy for training and usage. At the same time, many organisations recognise the need for responsible AI development that balances performance with fairness and sustainability. This KIEM project proposal aims to develop a tool that can be employed by organizations that develop and implement AI systems and aim to do so more responsibly. Through visual aiding and data visualisation, the tool facilitates making these trade-offs. By showing what these values mean in practice, which choices could be made and highlighting the relationship with performance, we aspire to educate users on how the use of different metrics impacts the decisions made by the model and its wider consequences, such as energy consumption or fairness-related harms. This tool is meant to facilitate conversation between developers, product owners and project leaders to assist them in making their choices more explicit and responsible.
