Sökning: "interaction- patterns"

Visar resultat 1 - 5 av 669 avhandlingar innehållade orden interaction- patterns.

  1. 1. Shades of Use : The Dynamics of Interaction Design for Sociable Use

    Författare :Mattias Arvola; Kjell Ohlsson; Linköpings universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; interaction design; customer meetings; design studios; domestic environments; Cognitive science; Kognitionsvetenskap;

    Sammanfattning : Computers are used in sociable situations, for example during customer meetings. This is seldom recognized in design, which means that computers often become a hindrance in the meeting. LÄS MER

  3. 2. Designing for Intercorporeality : An Interaction Design Approach to Technology-Supported Movement Learning

    Författare :Laia Turmo Vidal; Annika Waern; Dag Svanæs; Uppsala universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Interaction Design; Movement Learning; Movement Teaching; Biofeedback; Wearables; Constructive Design Research; Research through Design; Practice Design; Soma Design; Technology Probes; Strong Concept; Movement Aesthetics; Människa-dator interaktion; Human-Computer Interaction;

    Sammanfattning : Technology-supported movement learning has emerged as an area with ample possibilities within Human Computer Interaction and Interaction Design, as interactive technology can help people to develop and improve sensorimotor competencies. To date, design research has largely focused on technology development and on supporting individual learning experiences. LÄS MER

  4. 3. Multivariate Networks : Visualization and Interaction Techniques

    Författare :Ilir Jusufi; Andreas Kerren; Jessie Kennedy; Linnéuniversitetet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Information Visualization; Multivariate Networks; Visual Analytics; Exploration; Interaction; Computer Science; Datavetenskap; Information and software visualization; Informations- och programvisualisering;

    Sammanfattning : As more and more data is created each day, researchers from different science domains are trying to make sense of it. A lot of this data, for example our connections to friends on different social networking websites, can be modeled as graphs, where the nodes are actors and the edges are relationships between them. LÄS MER

  5. 4. Multi-Scale Surface Water-Groundwater Interaction : Implications for GroundwaterDischarge Patterns

    Författare :Babak Brian Mojarrad; Anders Wörman; Joakim Riml; Jan Fleckenstein; KTH; []
    Nyckelord :TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; groundwater flow; hyporheic zone; multi-scale modeling; spectral analysis; fragmentation of discharge zones; characteristic parameters; Hydraulik och teknisk hydrologi; Hydraulic and Hydrologic Engineering;

    Sammanfattning : Rivers and aquifers are continuously exchanging water, driven by processes that occur on various temporal and spatial scales, ranging from small streambed features to large geological structures. The interaction between these two components occurs in permeable sediments below the stream channel, called the hyporheic zone. LÄS MER

  7. 5. Visual Analytics for Explainable and Trustworthy Machine Learning

    Författare :Angelos Chatzimparmpas; Andreas Kerren; Rafael M. Martins; Ilir Jusufi; Alex Endert; Linnéuniversitetet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; visualization; interaction; visual analytics; explainable machine learning; XAI; trustworthy machine learning; ensemble learning; dimensionality reduction; supervised learning; unsupervised learning; ML; AI; tabular data; visualisering; interaktion; visuell analys; förklarlig maskininlärning; XAI; pålitlig maskininlärning; ensembleinlärning; dimensionesreducering; övervakad inlärning; oövervakad inlärning; ML; AI; tabelldata; Computer Science; Datavetenskap; Informations- och programvisualisering; Information and software visualization;

    Sammanfattning : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. LÄS MER