handout van een labtalk waarin de onderzoeker enkele methoden beschrijft rond text mining, story mining: het herkennen van patronen in communicatie met klanten.
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Designing and personalising systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of our paper is to provide a new methodology for determining navigational patterns of behaviour of specific user groups. We consider agricultural users as a specific user group, during the usage of a decision support system supporting cultivar selection - OPTIRas(TM). Combining process mining techniques with insights from decision making theories, we provide a method of analysing logs resulted from usage of decision support systems. For instance, farmers show difficulties in fulfilling the goal of OPTIRas, while other agricultural users seems to manage better. The results of our analysis can be used to support the redesign and personalization of decision support systems.
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Introduction: Many adults do not reach the recommended physical activity (PA) guidelines, which can lead to serious health problems. A promising method to increase PA is the use of smartphone PA applications. However, despite the development and evaluation of multiple PA apps, it remains unclear how to develop and design engaging and effective PA apps. Furthermore, little is known on ways to harness the potential of artificial intelligence for developing personalized apps. In this paper, we describe the design and development of the Playful data-driven Active Urban Living (PAUL): a personalized PA application.Methods: The two-phased development process of the PAUL apps rests on principles from the behavior change model; the Integrate, Design, Assess, and Share (IDEAS) framework; and the behavioral intervention technology (BIT) model. During the first phase, we explored whether location-specific information on performing PA in the built environment is an enhancement to a PA app. During the second phase, the other modules of the app were developed. To this end, we first build the theoretical foundation for the PAUL intervention by performing a literature study. Next, a focus group study was performed to translate the theoretical foundations and the needs and wishes in a set of user requirements. Since the participants indicated the need for reminders at a for-them-relevant moment, we developed a self-learning module for the timing of the reminders. To initialize this module, a data-mining study was performed with historical running data to determine good situations for running.Results: The results of these studies informed the design of a personalized mobile health (mHealth) application for running, walking, and performing strength exercises. The app is implemented as a set of modules based on the persuasive strategies “monitoring of behavior,” “feedback,” “goal setting,” “reminders,” “rewards,” and “providing instruction.” An architecture was set up consisting of a smartphone app for the user, a back-end server for storage and adaptivity, and a research portal to provide access to the research team.Conclusions: The interdisciplinary research encompassing psychology, human movement sciences, computer science, and artificial intelligence has led to a theoretically and empirically driven leisure time PA application. In the current phase, the feasibility of the PAUL app is being assessed.
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The wide diffusion of the "Entrapped Suitors" story-type has often been observed: examples are found in a remarkable number of literatures, ranging from English, French and Greek in the West, to Persian, Arabic and Kashmiri in the East. However, a text of this type that is often overlooked is the Middle Dutch play Een Speel Van Drie Minners ("A Play of Three Suitors"). This is despite the fact that it represents a highly idiosyncratic variation on the story, as it replaces the central moral with something more scabrous. We offer here a comprehensive discussion of this singular text and its narrative form, with an English verse-translation appended.
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E-discovery projects typically start with an assessment of the collected electronic data in order to estimate the risk to prosecute or defend a legal case. This is not a review task but is appropriately called early case assessment, which is better known as exploratory search in the information retrieval community. This paper first describes text mining methodologies that can be used for enhancing exploratory search. Based on these ideas we present a semantic search dashboard that includes entities that are relevant to investigators such as who knew who, what, where and when. We describe how this dashboard can be powered by results from our ongoing research in the “Semantic Search for E-Discovery” project on topic detection and clustering, semantic enrichment of user profiles, email recipient recommendation, expert finding and identity extraction from digital forensic evidence.
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This study furthers game-based learning for circular business model innovation (CBMI), the complex, dynamic process of designing business models according to the circular economy principles. The study explores how game-play in an educational setting affects learning progress on the level of business model elements and from the perspective of six learning categories. We experimented with two student groups using our game education package Re-Organise. All students first studied a reader and a game role description and then filled out a circular business model canvas and a learning reflection. The first group, i.e., the game group, updated the canvas and the reflection in an interactive tutorial after gameplay. The control group submitted their updated canvas and reflection directly after the interactive tutorial without playing the game. The results were analyzed using text-mining and qualitative methods such as word co-occurrence and sentiment polarity. The game group created richer business models (using more waste processing technologies) and reflections with stronger sentiments toward the learning experience. Our detailed study results (i.e., per business model element and learning category) enhance understanding of game-based learning for circular business model innovation while providing directions for improving serious games and accompanying educational packages.
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Youyou et al. showed that from 70 likes the algorithm could predict the personality better than friends, from 150 likes better than family members and from 300 likes even better than the test person himself. However, the machine learning algorithm does not know the person better than the colleagues, the friends or the person themselves. The machine can "only", after sufficient "supervised learning" trials (iterations), determine the correlation between the click behaviour on Facebook and the scored Big5 factors better than individuals. Prediction replaces the Big5 questionnaire. But we are not getting closer to the personality of people than with the Big5 questionnaire. It is argued that - though data mining can help enormously - psychology remains a subject of the narrative in the end.
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Immense beyond imagination, the untamed rainforests of western New Guinea represent a biodiversity hotspot, home to several unique species of flora and fauna. The territory’s astonishing beauty and diversity is underpinned by a stunning array of natural resources. The island is also home to many indigenous communities practicing hundreds of local languages and traditions and depending on their natural environment for maintaining their traditional livelihoods, identity and culture. The territory’s much-contested decolonization process in the 1950-60s led to widespread discontent among indigenous Papuans and gave rise to persistent dissent from Indonesian rule, routinely met with disproportionately violent action by Indonesian security forces. Adding to these longstanding colonial ills and grievances, indigenous Papuan communities also struggle to grapple with inequitable allocation of land and resources, extreme pollution and environmental degradation caused by the mining and palm oil sectors. In the meantime, climate-exacerbated weather events have become more frequent in the region creating new tensions by putting an additional strain on natural resources and thus leading to an increased level of insecurity and inequality. In particular, these challenges have a disproportionate and profound impact on indigenous Papuan women, whose native lands are deeply embedded in their cultural and ethnic identity, and who are dependent on access to land to carry out their prescribed roles. Displacement also puts women at further risk of violence. Adding to sexual violence and displacement experienced by indigenous Papuan women, the loss of traditional lands and resources has been identified as having a singularly negative impact on women as it impedes their empowerment and makes them vulnerable to continued violence. The Papuan experience thus serves as a timely illustration to exemplify how environmental factors, such as resource extraction and climate change, not only amplify vulnerabilities and exacerbate pre-existing inequalities stemming from colonial times, they also give rise to gendered consequences flowing from large-scale degradation and loss of the natural environment.
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