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Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why 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 Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Implementation of Electrical Feedback Technologies in 5 H...
H. Sengul, T. O. Benli · 2016-08-19 · via cs.HC updates on arXiv.org

Most people do not know how much energy they are spending for different purposes and also they are unaware of potential electrical consumption reduction level they could make by changing their consumption behaviors or investing in new efficient technology products. Providing electrical consumption information to household and make the consumption information tangible is very effective strategy in reducing electrical consumption in households. In Home Displays provide consumers real time information on electrical consumption level and related expenditure, with that strategy turning an intangible electric bill into transparent and controllable is possible. We interviewed the participants before the start to our study in order to have knowledge of their income, environmental attitudes and the knowledge level of their electrical consumption conserving activities. Monitoring over 3 to 5 months maximal of five households electrical consumption and comparing that consumption level with the previous year had been realized. Households received advanced real time appliance level feedback. Behavioral change associated electrical consumption reduction levels were identified. It is also realized that setting goals via an electrical energy consumption display have high potential on reducing the total electricity consumption in households. We found 0, 0, 13.21, 3.42, 1.59 % electricity savings on house 1, house 2, house 3, house 4 and house 5 correspondingly. It is suggested that further attention need to be given on potential reduction effect of applying different type of monitor designs and implication of special designed stimulants that can be used all over the house that can alert households with their current consumption level. Also, it is suggested to concentrate on time periods in order to analyze and try to reveal specific time periods for specific consumption behaviors.