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Knowing When to Ask: Self-Gated Clarification for Hierarchical Language Agents Collaborative Human-Agent Protocol (CHAP) UXBench: Benchmarking User Experience in AI Assistants Impedance MPC for Physical Human-Robot Interaction: Predictive Disturbance Rejection with Joint-Limit Safety Formalizing all indexed mathematics as a benchmark for general reasoning, with the example of implementing dilatations of categories Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build Learning to Decide with AI Assistance under Human-Alignment Positive Alignment: Artificial Intelligence for Human Flourishing Sycophantic AI makes human interaction feel more effortful and less satisfying over time Exploring Interaction Paradigms for LLM Agents in Scientific Visualization The Alignment Target Problem: Divergent Moral Judgments of Humans, AI Systems, and Their Designers Participatory provenance as representational auditing for AI-mediated public consultation Aligning Human-AI-Interaction Trust for Mental Health Support: Survey and Position for Multi-Stakeholders Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction Multimodal Ambivalence/Hesitancy Recognition in Videos for Personalized Digital Health Interventions The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading Can LLMs Reason About Attention? 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Self-Adaptive Digital Assistance Systems for Work 4.0
Enes Yigitbas, Stefan Sauer, Gregor Engels · 2022-11-30 · via cs.HC updates on arXiv.org

In the era of digital transformation, new technological foundations and possibilities for collaboration, production as well as organization open up many opportunities to work differently in the future. The digitization of workflows results in new forms of working which is denoted by the term Work 4.0. In the context of Work 4.0, digital assistance systems play an important role as they give users additional situation-specific information about a workflow or a product via displays, mobile devices such as tablets and smartphones, or data glasses. Furthermore, such digital assistance systems can be used to provide instructions and technical support in the working process as well as for training purposes. However, existing digital assistance systems are mostly created focusing on the "design for all" paradigm neglecting the situation-specific tasks, skills, preferences, or environments of an individual human worker. To overcome this issue, we present a monitoring and adaptation framework for supporting self-adaptive digital assistance systems for Work 4.0. Our framework supports context monitoring as well as UI adaptation for augmented (AR) and virtual reality (VR)-based digital assistance systems. The benefit of our framework is shown based on exemplary case studies from different domains, e.g. context-aware maintenance application in AR or warehouse management training in VR.