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Democratic deliberation starts at the dinner table: Young Danes and relatable political discussions in their ‘safe gardens’
No full textThe term ‘safe gardens’ (Stald, 2023) indicates a private space with a homely atmosphere of safety, embracing those with a right to be present, excluding ‘threatening’ visitors. But the safe garden is not closed off – you can walk in and out, talk over the hedge, and listen, without being noticed, to what goes on outside the hedge. You can bring in information that you have compiled in other contexts, offline or online. This metaphor describes the reality of being informed and participating citizens for many young Danes. The study behind this paper shows that young Danes are quite interested in being informed about small-scale relatable and big-scale political topics. They know many things about many topics. They trust the democratic system and social institutions. However, they do not perceive everyday exchanges in ‘safe garden’ settings as political debate or consider that local events in their every day lives may also be political.There are many online and offline participation opportunities for young citizens. The findings show that most young Danes exercise a kind of sub-activist agency (Bakardjieva) when they discuss over the dinner table or in other offline private settings with family and friends or when they sign petitions or avoid buying items for political reasons. However, when it comes to participation in public debate, the majority sign off. The arguments for this are fear of not knowing enough, repercussions, sticking out your nose, or lack of motivation (DUF 2024).The research question that frames the objective for the paper is: “What is the discrepancy between the self-perceived lack of political, democratic participation among young Danes, and the meaning of the actual everyday deliberation that takes place in their ‘safe gardens’.The paper draws on findings from a study that investigates young Danes’ experiences with being informed, democratic, digital citizens, including their democratic deliberation and participation. The study comprises interviews (2021) with sixteen 16-to-24-year-old Danes. The findings from this study form the basis for the second study (2024), which comprehends interviews with 20 first-grade high school students, written essays from 75 first and second-grade high school students, and a workshop with the same 75 students. In the workshop, the students created a podcast or rap to motivate younger teens to be informed and speak up. The paper contributes to an important understanding of young citizens’ intrinsic democratic and political potentials in the light of derived democratic normativity and self-perceived lack of political insight and agency. References: Bakardjieva, M. (2009). Subactivism: Lifeworld and Politics in the Age of the Internet. The Information Society, 25(2), 91–104.DUF–Dansk Ungdoms Fællesråd. 2021. Demokratianalysen 2021. Epinion for DUF.Stald, G. (2023). Mobile Democracy: Changing Conditions for Young Danes’ Democratic Information and Participation. Journalism and Media 4: 272–288.<br/
Bias in Danish Medical Notes: Infection Classification of Long Texts Using Transformer and LSTM Architectures Coupled with BERT
No full textMedical notes contain a wealth of information related to diagnosis, prognosis, and overall patient care that can be used to help physicians make informed decisions. However, like any other data sets consisting of data from diverse demographics, they may be biased toward certain subgroups or subpopulations. Consequently, any bias in the data will be reflected in the output of the machine learning models trained on them. In this paper, we investigate the existence of such biases in Danish medical notes related to three types of blood cancer, with the goal of classifying whether the medical notes indicate severe infection. By employing a hierarchical architecture that combines a sequence model (Transformer and LSTM) with a BERT model to classify long notes, we uncover biases related to demographics and cancer types. Furthermore, we observe performance differences between hospitals. These findings underscore the importance of investigating bias in critical settings such as healthcare and the urgency of monitoring and mitigating it when developing AI-based systems
The AI Gap: How Socioeconomic Status Affects Language Technology Interactions
No full textSocioeconomic status (SES) fundamentally influences how people interact with each other and, more recently, with digital technologies like large language models (LLMs). While previous research has highlighted the interaction between SES and language technology, it was limited by reliance on proxy metrics and synthetic data. We survey 1,000 individuals from ‘diverse socioeconomic backgrounds’ about their use of language technologies and generative AI, and collect 6,482 prompts from their previous interactions with LLMs. We find systematic differences across SES groups in language technology usage (i.e., frequency, performed tasks), interaction styles, and topics. Higher SES entail a higher level of abstraction, convey requests more concisely, and topics like ‘inclusivity’ and ‘travel’. Lower SES correlates with higher anthropomorphization of LLMs (using ”hello” and ”thank you”) and more concrete language. Our findings suggest that while generative language technologies are becoming more accessible to everyone, socioeconomic linguistic differences still stratify their use to create a digital divide. These differences underscore the importance of considering SES in developing language technologies to accommodate varying linguistic needs rooted in socioeconomic factors and limit the AI Gap across SES groups
Modelling Recursion and Probabilistic Choice in Guarded Type Theory
Get PDFConstructive type theory combines logic and programming in one language. This is useful both for reasoning about programs written in type theory, as well as for reasoning about other programming languages inside type theory. It is well-known that it is challenging to extend these applications to languages with recursion and computational effects such as probabilistic choice, because these features are not easily represented in constructive type theory. We show how to define and reason about a programming language with probabilistic choice and recursive types, in guarded type theory. We use higher inductive types to represent finite distributions and guarded recursion to model recursion. We define both operational and denotational semantics, as well as a relation between the two. The relation can be used to prove adequacy, but we also show how to use it to reason about programs up to contextual equivalence
data2lang2vec: Data Driven Typological Features Completion
No full textLanguage typology databases enhance multilingual Natural Language Processing (NLP) by improving model adaptability to diverse linguistic structures. The widely-used lang2vec toolkit integrates several such databases, but its coverage remains limited at 28.9%. Previous work on automatically increasing coverage predicts missing values based on features from other languages or focuses on single features; we propose to use textual data for better-informed feature prediction. To this end, we introduce a multi-lingual Part-of-Speech (POS) tagger, achieving over 70% accuracy across 1,749 languages, and experiment with external statistical features and a variety of machine learning algorithms. We also introduce a more realistic evaluation setup, focusing on likely to be missing typology features, and show that our approach outperforms previous work in both setups
Identifying Open Challenges in Language Identification
No full textAutomatic language identification is a core problem of many Natural LanguageProcessing (NLP) pipelines. A wide variety of architectures and benchmarks havebeen proposed with often near-perfect performance. Although previousstudies have focused on certain challenging setups (i.e. cross-domain, shortinputs), a systematic comparison is missing. We propose a benchmark that allows us to test for the effect of input size, training data size, domain, number oflanguages, scripts, and language families on performance. We evaluatefive popular models on this benchmark and identify which open challengesremain for this task as well as which architectures achieve robust performance. Wefind that cross-domain setups are the most challenging (although arguably mostrelevant), and that number of languages, variety in scripts, and variety inlanguage families have only a small impact on performance. We also contributepractical takeaways: training with 1,000 instances per language and a maximuminput length of 100 characters is enough for robust language identification.Based on our findings, we train an accurate (94.41{\%}) multi-domain languageidentification model on 2,034 languages, for which we also provide an analysisof the remaining errors
GreenHouse DT Artifact for Declarative Dynamic Object Reclassification
No full textGreenHouse DT Artifact for Declarative Dynamic Object Reclassification This is the artifact for the "Declarative Dynamic Object Reclassification" paper presented in the European Conference on Object-Oriented Programming (ECOOP) conference in 2025. The project is a greenhouse management system that uses Docker containers to simulate and adapt to environmental changes. It includes components such as a RESTful API, a triplestore database (Fuseki), a time-series database (InfluxDB), and an optional message broker (ActiveMQ). How can it be used? Setup and Deployment: Install dependencies using python3 setup.py. Ensure Docker and docker compose are installed. Configure environment variables in .env files for customization. Testing Run the test script (python3 ecoop-artifact-test.py) to validate system functionality. Tests include adaptation scenarios for plants (e.g., health states) and pumps (e.g., operational states). Real-World Usage Modify .env files to match your environment and security policies. Use the provided API to interact with the system for CRUD operations. Integrate with external components like Apache ActiveMQ
Terra: Enabling Edge-Based Stream Processing for Resource-Constrained Environments
Get PDFThe world is becoming more and more data driven and with the rise of the Internet of Things, more and more data is being generated and transferred every day. The traditional way of data processing by collecting data by default in cloud data centres is becoming unviable due to increased network congestion and demands for privacy-aware processing and real-time responses. The concept of fog and edge computing was introduced to solve these challenges, but these often still require transfer of data from sensor nodes to processing nodes, and the option of processing data on the sensor device itself is still absent. This work introduces Terra, which enables query processing on resource-constrained sensor nodes at the very edge of modern sensor network databases. Integrated into a state-of-the-art network database, this approach significantly minimises network traffic, leading to a big reduction in energy consumption. Through a series of real-world experiments on physical devices, we evaluate Terra’s performance and experimentally derive an energy cost model that verifies Terra’s capabilities. In particular, we show a significant reduction in network transfer and therefore energy cost when pushing aggregate computation onto sensor devices
Skeleton-Based Modeling in Badminton - Unlocking Insights for Stroke Recognition and Forecasting
Get PDFThis thesis uses skeleton poses as the primary modality for analyzing badminton video sequences. The topic is approached through three central computer vision tasks: Action recognition, action forecasting, and 3D shuttle reconstruction. One part of the research explores and develops models that best capture informative motion representations from skeleton sequences to infer the performed stroke. Additional features such as shuttlecock position and player court location are integrated to improve recognition performance. Another focus is on identifying limitations in stroke recognition, such as limited and inconsistent annotation conventions and type imbalance, and exploring potential solutions. Pretraining, especially self-supervised learning, is explored. The models are pretrained using masked autoencoders, reconstructing parts of the skeleton sequences hidden in the input. The findings show that these approaches improve model performance on downstream tasks like stroke recognition. Achieving the best model performance is not the only goal of the thesis. Part of the research tries to identify which elements of a player’s stroke motion carry the most descriptive information on a particular action. Through model inspection, the different phases of the stroke motion and various modalities are examined using ablation and qualitative attention studies to determine which offers the most relevant informa tion to the model. The results are compared to the human perspective of analysts and coaches to gauge how the findings could benefit coaches and players in match preparation. Expanding on this, the thesis investigates the model forecasting capabilities for predicting the next strokes. Usually, in sequence modeling, the next stroke in a rally would be predicted based on the sequence of stroke exchanges up to that point. Here, instead, a model architecture is proposed that learns a stroke representation from the player’s skeleton motion, identity, and shuttle position to condition the prediction probability of the next stroke. The model design reflects the turn-based nature of badminton to capture a basic understanding of the game to make informed predictions. Using 3D information can extract valuable physical and shot statistics while eliminating ambiguities found in 2D image representations. However, the limited availability of 3D badminton data inhibits the development of effective reconstruction models. A physics-based model trained on synthetic 3D shuttlecock trajectories is proposed to overcome this challenge. The developed model, TrajTrans, predicts the initial 3D conditions based on 2D image projections. The results generalize well to real data by implementing shot filtering criteria of the synthetic data that ensure realistic trajectories. Ultimately, the findings contribute to the field of sports analytics by providing foundational knowledge and guidelines that can advance the development of future tools for analysts and coaches
Computer-Supported Hybrid Cooperative Work
Get PDFThis PhD examines the future of work after the COVID-19 pandemic, which introduced abrupt changes in work practices. The impact of long-term remote work extends beyond the lockdown periods and has reshaped work preferences, work-life balance, organizational management, and the integration of technologies that facilitate collaboration across distances. Situated within the field of computer-supported cooperative work (CSCW), this PhD researches contemporary work practices with a focus on what is important in designing cooperative technologies for hybrid work. Hybrid work is defined as cooperation involving both collocated and distributed individuals, supported by multiple physical and digital technologies. To uncover different perspectives of cooperative technologies for hybrid cooperative work, the research employs a mixed-method approach, integrating qualitative insights from literature and ethnographic studies, as well as quantitative data from a survey. This PhD thesis includes five research papers that, through different research lenses, provide distinct conceptualizations of hybrid work. The PhD examines: • The conceptualization and characterization of hybrid cooperative work • The spatial, infrastructural, and organizational challenges that hybrid work practices introduce• Design propositions for computing technologies supporting hybrid cooperative workThis PhD thesis brings together these findings and insights across the included research papers and proposes a framework of dimensional interdependencies in hybrid work. By conceptualizing the spectrum of cooperative work, the PhD thesis identifies unique characteristics of hybrid work, presents design propositions for technologies supporting hybrid practices, and addresses organizational complexities in managing contemporary work environments. The proposed framework unifies these perspectives, emphasizing the interdependent nature of cooperative work, the ecologies of technology artifacts supporting this work, the inherently multiple spatial contexts, and the organizational structuring – dimensions that should all be considered when designing computing technologies for hybrid cooperative work. Keywords: hybrid work, cooperative technology, mixed-method research, conceptual frameworks<br/