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Does Random Movements mean Random Results? Why Asynchrony in Experiments on Body Ownership does not Work as Intended
No full textEffects of embodying virtual avatars are routinely validated experimentally by comparing synchronous and asynchronous movements between virtual and real bodies. This experimental paradigm, however, lacks justification, validation, and standardization. Asynchrony is implemented in numerous ways, such as through delayed, dislocated, or prerecorded movements, and these may impact embodiment and user experience distinctively. An online study () revealed that variations of asynchrony cause disparate responses to embodiment and user experience, with prerecorded movements distorting embodiment the most. A think-aloud study () revealed that asynchronous conditions lead to peculiar and oftentimes negative experiences. Furthermore, asynchronous conditions in some cases maintain, rather than break the body ownership illusion, as participants imitate the virtual body. Our results show that asynchrony in experiments on embodiment entails profound validity issues and should therefore be used with caution.Effects of embodying virtual avatars are routinely validated experimentally by comparing synchronous and asynchronous movements between virtual and real bodies. This experimental paradigm, however, lacks justification, validation, and standardization. Asynchrony is implemented in numerous ways, such as through delayed, dislocated, or prerecorded movements, and these may impact embodiment and user experience distinctively. An online study (N = 202) revealed that variations of asynchrony cause disparate responses to embodiment and user experience, with prerecorded movements distorting embodiment the most. A think-aloud study (N = 16) revealed that asynchronous conditions lead to peculiar and oftentimes negative experiences. Furthermore, asynchronous conditions in some cases maintain, rather than break the body ownership illusion, as participants imitate the virtual body. Our results show that asynchrony in experiments on embodiment entails profound validity issues and should therefore be used with caution
Book review: Algorithms of resistance Bonini T and Treré E (2024) Algorithms of resistance: The everyday fight against platform power . Cambridge, MA: The MIT Press. 256 pp. ISBN 9780262547420 (paperback); ISBN 9780262377492 (ebook)
Get PDFWhen Discourse Stalls:Moving Past Five Semantic Stopsigns about Generative AI in Design Research
Get PDFThis essay examines how Generative AI (GenAI) is rapidly transforming design practices and how discourse often falls into over-simplified narratives that impede meaningful research and practical progress. We identify and deconstruct five prevalent "semantic stopsigns" -- reductive framings about GenAI in design that halt deeper inquiry and limit productive engagement. Reflecting upon two expert workshops at ACM conferences and semi-structured interviews with design practitioners, we analyze how these stopsigns manifest in research and practice. Our analysis develops mid-level knowledge that bridges theoretical discourse and practical implementation, helping designers and researchers interrogate common assumptions about GenAI in their own contexts. By recasting these stopsigns into more nuanced frameworks, we provide the design research community with practical approaches for thinking about and working with these emerging technologies
When the Place of Care Is Hybrid: An Ethnographic Study of Hospital Video Consultations
No full textBased on ethnographic observations of hospital video consultations between clinicians and patients, this article investigates the shifting places of care when consultations are displaced from the clinic. While information and communication technologies allow care across distance, scholars within science and technology studies (STS) and sociology have demonstrated how places still matter to the delivery of healthcare services. However, as healthcare increasingly enters the digital realm, we need a vocabulary and analytics which also address virtual environments' constitutive effects as places of care. This article argues that place must be understood as more than the locations of the consulting parties and be expanded to include the virtual places created during video consultations. Drawing on insights from STS and computer-supported cooperative work, the concept of hybrid place is proposed to capture the spatial hybridity of video consultations when physical locations are virtually connected and projected into virtual environments. The concept becomes a useful heuristic for understanding the spatial reconfiguration of the place of care during video consultations; it brings attention to the multiplicity of places, their connections and their distinct conditions which co-shape the virtual clinical encounter.Based on ethnographic observations of hospital video consultations between clinicians and patients, this article investigates the shifting places of care when consultations are displaced from the clinic. While information and communication technologies allow care across distance, scholars within science and technology studies (STS) and sociology have demonstrated how places still matter to the delivery of healthcare services. However, as healthcare increasingly enters the digital realm, we need a vo-cabulary and analytics which also address virtual environments' constitutive effects as places of care. This article argues that place must be understood as more than the locations of the consulting parties and be expanded to include the virtual places created during video consultations. Drawing on insights from STS and computer-supported cooperative work, the concept of hybrid place is proposed to capture the spatial hybridity of video consultations when physical locations are virtually connected and projected into virtual environments. The concept becomes a useful heuristic for understanding the spatial reconfiguration of the place of care during video consultations; it brings attention to the multiplicity of places, their connections and their distinct conditions which co-shape the virtual clinical encounter
Statistical Assessment of Plans via Probabilistic Optimization of Reliability
Get PDFAutonomous Underwater Vehicles (AUVs) require efficient and reliabletask planning to operate for extended periods without humanintervention. However, current task planning methodologies often relyon deliberately abstract domain models to ensure tractability of the planningtask. This abstraction can lead to unreliable or underperformingplans in real-world execution. In this thesis, I present a novel probabilisticdecision-making model aimed at enhancing the reliability of AUV taskplanning and assisting in plan selection through various techniques. Themodel supports reasoning by inference from past events based on thecurrent state (introspection) as well as reasoning based on past experiencesto retrieve knowledge (retrospection). By evaluating plans andanticipating future events before mission execution (prospection), theproposed framework enhances autonomous decision-making capabilities.The framework comprises three components: Plan Generation, Plan Evaluation,and Plan Execution. Plans can be generated from a knowledgebase, a classical AI planner, or Large Language Models (LLMs).First, I propose a framework that enhances the ability of underwaterrobots to autonomously recover from anomalous situations. This frameworkis built around a knowledge model developed in three stages: first,we create a deterministic knowledge base to describe the health of hardware,software, and environmental components involved in a mission;second, I model these components probabilistically by defining the likelihoodof failures, faults, and fixes; and finally, I combine deterministicand probabilistic knowledge into a minimal ROS package designed todetect failures, isolate underlying faults, propose fixes, and determinethe most likely successful outcome. The approach is motivated by acamera fault scenario and is demonstrated using both a real AUV anda simulated Remotely Operated Vehicle (ROV) experiencing a thrusterfailure.Second, I automate plan generation using a risk-aware planningframework, and a subset of these plans is assessed using various riskmetrics. Traditional planning problems are typically addressed withrisk-neutral optimization, focusing on a single objective such as minimizingtime or energy consumption. However, optimal abstract plans canbecome suboptimal or unreliable during physical execution. To addressthis, I introduce a method that generates diverse high-level plans andevaluates them using risk measures including Variance, Entropy, Valueat Risk (VaR), Conditional Value at Risk (CVaR), and Entropic Valueat Risk (EVaR). These metrics provide a comprehensive assessment ofthe potential risks associated with each plan. The method is evaluatedusing a realistic underwater robot simulation across various scenarios,demonstrating its feasibility and effectiveness. Among these metrics,Variance identifies the minimum-risk plan, while VaR, CVaR, and EVaRoffer more nuanced ranking of the candidate plans. The superiority ofthe approach is demonstrated through two physics-based simulationscenarios: pipeline inspection and subsea infrastructure inspection.A third approach explores plan generation using Large LanguageModels (LLMs) using past incident plans. Testing marine vessel hardwareand software in field trials is essential to avoid technical and environmentalfailures, yet such trials are costly. Generating realistic domaindescriptions based on past missions enhances the value of simulationtesting and reduces the need for expensive real-world trials. In thiswork, I generate scenarios from unstructured Incident Response Plan(IRP) documents using LLMs and convert them into structured planningprograms. The two synthesized marine test-domain datasets containapproximately 90% parsable, 75% solvable, and 57% correct planningprograms. I further evaluate the diversity of the generated plans usingmetrics from the machine learning domain, including L2, Cosine, Wasserstein,and BERTScore. To improve the embedding quality, I fine-tunea pre-trained model, CodeBERT, for PDDL domain inputs. Among allmodels, fine-tuned CodeBERT best captured the diversity of generatedplans (evaluated on 10 samples out of 51), while among pre-trained models,Code-LLaMA showed stronger performance based on the L2 metric.However, these metrics are limited in detecting structural variationsbetween plan elements.This thesis enhances autonomous underwater vehicle task planningby combining these methods to overcome existing limitations and boostthe reliability, variety, and effectiveness of AUV missions
The fundamentals of eye tracking part 4: Tools for conducting an eye tracking study
No full textResearchers using eye tracking are heavily dependent on software and hardware tools to perform their studies, from recording eye tracking data and visualizing it, to processing and analyzing it. This article provides an overview of available tools for research using eye trackers and discusses considerations to make when choosing which tools to adopt for one’s study.Researchers using eye tracking are heavily dependent on software and hardware tools to perform their studies, from recording eye tracking data and visualizing it, to processing and analyzing it. This article provides an overview of available tools for research using eye trackers and discusses considerations to make when choosing which tools to adopt for one’s study
KARRIEREWEGE: A large scale Career Path Prediction Dataset
No full textAccurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce Karrierewege, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in Karrierewege+. This allows for accurate predictions from unstructured data, closely aligning with practical application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a previous benchmark and see increased performance and robustness by synthesizing the data for the free-text use cases
Evaluating Quality of Gaming Narratives Co-created with AI
No full textThis paper proposes a structured methodology to evaluate AI-generated game narratives, leveraging the Delphi study structure with a panel of narrative design experts. Our approach synthesizes story quality dimensions from literature and expert insights, mapping them into the Kano model framework to understand their impact on player satisfaction. The results can inform game developers on prioritizing quality aspects when co-creating game narratives with generative AI
The Complexity of Homomorphism Reconstructibility
No full textRepresenting graphs by their homomorphism counts has led to the beautiful theory of homomorphism indistinguishability in recent years. Moreover, homomorphism counts have promising applications in database theory and machine learning, where one would like to answer queries or classify graphs solely based on the representation of a graph G as a finite vector of homomorphism counts from some fixed finite set of graphs to G. We study the computational complexity of the arguably most fundamental computational problem associated to these representations, the homomorphism reconstructibility problem: given a finite sequence of graphs and a corresponding vector of natural numbers, decide whether there exists a graph G that realises the given vector as the homomorphism counts from the given graphs.We show that this problem yields a natural example of an NP#P-hard problem, which still can be NP-hard when restricted to a fixed number of input graphs of bounded treewidth and a fixed input vector of natural numbers, or alternatively, when restricted to a finite input set of graphs. We further show that, when restricted to a finite input set of graphs and given an upper bound on the order of the graph G as additional input, the problem cannot be NP-hard unless P = NP. For this regime, we obtain partial positive results. We also investigate the problem’s parameterised complexity and provide fpt-algorithms for the case that a single graph is given and that multiple graphs of the same order with subgraph instead of homomorphism counts are given
Quantifiers for Differentiable Logics in Rocq (Extended Abstract)
No full textThe interpretation of logical expressions into loss functions has given rise to so-called differentiable logics. They function as a bridge between formal logic and machine learning, offering a novel approach for property-driven training. The added expressiveness of these logics comes at the price of a more intricate semantics for first-order quantifiers. To ease their integration into machine-learning backends, we explore how to formalize semantics for first-order differentiable logics using the Mathematical Components library in the Rocq proof assistant. We seek to give rigorous semantics for quantifiers, verify their properties with respect to other logical connectives, as well as prove the soundness and completeness of the resulting logics