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Disabled ecologies:lessons from a conceptually armed study of disability, aquifers, and state-industrial harm
No full textThe Centers and Margins of Modeling Humans in Well-being Technologies
No full textThis paper critically examines the machine learning (ML) modeling of humans in three case studies of well-being technologies. Through a critical technical approach, it examines how these apps were experienced in daily life (technology in use) to surface breakdowns and to identify the assumptions about the “human” body entrenched in the ML models (technology design). To address these issues, this paper applies agential realism to decenter foundational assumptions, such as body regularity and health/illness binaries, and speculates more inclusive design and ML modeling paths that acknowledge irregularity, human-system entanglements, and uncertain transitions. This work is among the first to explore the implications of decentering theories in computational modeling of human bodies and well-being, offering insights for more inclusive technologies and speculations toward posthuman-centered ML modeling
Critical Perspectives on Predictive Policing:Anticipating Proof?
No full textTaking a critical approach, this book advances understanding of the social, legal and ethical aspects of digitalisation in law enforcement and the reliance on data-driven tools to predict and prevent crime. It shows how the proliferation of data analytics challenges citizens’ rights, at a time when what counts as ‘safety’ or ‘policing’ is being fundamentally transformed
Minimizing Combined Sewer Overflows with Online Model-Predictive Reinforcement Learning: A Case Study of the Stormwater Tunnel in Denmark
No full textThis is an artifact that can help reproduce the experimental results represented in the paper "Minimizing Combined Sewer Overflows with Online Model-Predictive Reinforcement Learning". The package contains models such as SWMM model, UPPAAL STRATEGO model, Historical weather data, and python code to run the experiment
Efficient Elicitation of Fictitious Nursing Notes from Volunteer Healthcare Professionals
No full textReliable automatic solutions to extract structured information from free-text nursing notes could bring important efficiency gains in healthcare, but their development is hampered by the sensitivity and limited availability of example data. We describe a method for eliciting fictitious nursing documentation and associated structured documentation from volunteers and a resulting dataset of 397 Danish notes collected and annotated through a custom web application from 98 participating nurses. After some manual refinement, we obtained a high-quality dataset containing nurse notes with relevant entities identified. We describe the implementation and limitations of our approach as well as initial experiments in a named entity tagging setup
Maximum list -colorable induced subgraphs in -free graphs
Get PDFWe show that, for every fixed positive integers and , \textsc{Max-Weight List -Colorable Induced Subgraph} admits a polynomial-time algorithm on -free graphs. This problem is a common generalization of \textsc{Max-Weight Independent Set}, \textsc{Odd Cycle Transversal} and \textsc{List -Coloring}, among others. Our result has several consequences. First, it implies that, for every fixed , assuming , \textsc{Max-Weight List -Colorable Induced Subgraph} is polynomial-time solvable on -free graphs if and only if is an induced subgraph of either or , for some . Second, it makes considerable progress toward a complexity dichotomy for \textsc{Odd Cycle Transversal} on -free graphs, allowing to answer a question of Agrawal, Lima, Lokshtanov, Rz{\k{a}}{\.z}ewski, Saurabh, and Sharma [TALG 2024]. Third, it gives a short and self-contained proof of the known result of Chudnovsky, Hajebi, and Spirkl [Combinatorica 2024] that \textsc{List -Coloring} on -free graphs is polynomial-time solvable for every fixed and . We also consider two natural distance- generalizations of \textsc{Max-Weight Independent Set} and \textsc{List -Coloring} and provide polynomial-time algorithms on -free graphs for every fixed integers , , and
Longest Path Transversals in Claw-Free and -Free Graphs
No full textFor a connected graph G, the longest path transversal number of G, denoted by lpt(G), is the minimum cardinality of a set of vertices that intersects all longest paths in G. It is an open problem whether any graph admits a longest path transversal of constant size. This question remains open even when restricted to claw-free graphs and P5-free graphs. In this work, we investigate these two graph classes. We show that, given a connected graph G, lpt(G)=1 if G is a (P5,H)-free graph, when H is a triangle, a paw, or a diamond. We also provide a complete characterization of the graphs H on at most five vertices for which for any (claw, H)-free graph G it holds that lpt(G)=1. Moreover, in each of these cases, we present a polynomial-time algorithm which finds a vertex in G that belongs to all its longest paths
Three faces of autonomy: Exploring configurations of high autonomy in software project teams
Get PDFThis article seeks to provide deeper insights into the concept of team autonomy within the software industry by investigating the combinations of autonomy and control modes that lead to high perceptions of team autonomy. Additionally, it examines the types of autonomy and control that are most effective for navigating complex environments.The study is grounded in the framework of Complex Adaptive Systems (CAS), integrating interdisciplinary research on autonomy and control to develop a research design. Methodologically, the study employs survey data and qualitative comparative analysis (QCA) to address its research questions.The findings identify three distinct configurations of projects that achieve high team autonomy, demonstrating how the road to high team autonomy can be shaped in various ways in relation to the presence of different modes of control. Using the CAS framework to evaluate these configurations, the third configuration emerges as the most aligned with the framework and empirically the most successful. This configuration is characterized by the absence of control for safeguarding purposes, the presence of control for coordination purposes, and the presence of joint decision-making.The article concludes by discussing the fuzzy and contextual nature of autonomy and its inherent relationship with control. It emphasizes the importance of understanding autonomy within its specific context and highlights the value of applying the CAS framework to grasp the complexity of autonomy-control dynamics. This study contributes to the literature by offering a nuanced perspective on autonomy in teams and its role in addressing the challenges of complexity in projects
Imagining a Soft and Relational Smart Home
Get PDFThis paper presents three scenario-based speculations accompanied by material explorations of soft IoT for smart homes based on humidity data. Cycle Lines tracks and displays weekly patterns of humidity levels shown as colored lines in a woven display, Relationscape, a real-time tracker and knitted display of humidity showing the relation between two different households, and Eco-collective, embroidery IoT that changes color depending on the humidity levels of different objects in the household. Based on first-person engagements with humidity sensors placed in the authors’ homes, they imagine new types of soft IoT devices that sense and display relations to humidity data, suggesting a role for mundane,craft-based IoT for smart homes. They express the relational nature of humidity and how it is tied to well-being in the home, among household members and other human and more-than-human inhabitants, as well as the environmental conditions inside and outside of the home. The soft, craft-based approach to imagining futures of IoT for smart homes has feminist commitments and invites for further problematizing domestic labor practices and craft activism in the domestic context
DECAF: A Dynamically Extensible Corpus Analysis Framework
No full textThe study of generalization in Language Models (LMs) requires controlled experiments that can precisely measure complex linguistic variations between training and testing datasets. We introduce DECAF, a framework that enables the analysis and filtering of linguistically-annotated datasets down to the character level. Rather than creating new resources for each experiment, DECAF starts from datasets with existing linguistic annotations, and leverages them to analyze, filter, and generate highly controlled and reproducible experimental settings targeting specific research questions. We demonstrate DECAF’s functionality by adding 28 morphosyntactic annotation layers to the 115M-word BabyLM corpus and indexing the resulting 1.1B annotations to analyze its internal domain variance, and to create a controlled training data curriculum for a small-scale gender bias study. We release DECAF as an open-source Python library, along with the parsed and indexed version of BabyLM, as resources for future generalization research