139,429 research outputs found

    Typicality degrees to measure relevance of the physiological signals

    Get PDF
    Paper presented at the International Conference on Physiological Computing Systems (PhyCS), Lisbon, Portugal.Physiological measures have a key advantage as they can provide an insight into human feelings that the subjects may not even be consciously aware of. However, modeling user affective states through pysiology still remains with critical questions especially on the relevant physiological measures for real-life emotionally intelligent applications. In this study, we propose the use of typicality degrees defined according to cognitive science and psychology principles to measure the relevance of the physiological features in characterizing user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for modeling user affective states.Physiological measures have a key advantage as they can provide an insight into human feelings that the subjects may not even be consciously aware of. However, modeling user affective states through pysiology still remains with critical questions especially on the relevant physiological measures for real-life emotionally intelligent applications. In this study, we propose the use of typicality degrees defined according to cognitive science and psychology principles to measure the relevance of the physiological features in characterizing user affective states. Thanks to the typicality degrees, we found consistent physiological characteristics for modeling user affective states

    Differentiable User Models

    Full text link
    Probabilistic user modeling is essential for building machine learning systems in the ubiquitous cases with humans in the loop. However, modern advanced user models, often designed as cognitive behavior simulators, are incompatible with modern machine learning pipelines and computationally prohibitive for most practical applications. We address this problem by introducing widely-applicable differentiable surrogates for bypassing this computational bottleneck; the surrogates enable computationally efficient inference with modern cognitive models. We show experimentally that modeling capabilities comparable to the only available solution, existing likelihood-free inference methods, are achievable with a computational cost suitable for online applications. Finally, we demonstrate how AI-assistants can now use cognitive models for online interaction in a menu-search task, which has so far required hours of computation during interaction.Comment: Accepted for publication in The 39th Conference on Uncertainty in Artificial Intelligence (UAI) 202

    The Mediating Role of Real-Time Information Between Location-Based User-Generated Content and Tourist Gift Purchase Intention

    Get PDF
    The global use of Web 2.0 applications has generated enormous volumes of user content. Drawing on cognitive load theory, this study examines unexplored factors that influence gift purchase intention of tourists. The authors identify localization and realtime information for shaping tourists' gift purchase intention, which is facilitated by reduced cognitive overload. Analyzes of the study relies on a sample of 273 foreign tourists in Malaysia. A cross-sectional quantitative study is conducted using partial least square structural equation modeling. Results showed that location-based user-generated content and real-time information significantly affect gift purchase intention of tourists. Moreover, real-time information partially mediates the relationship between location-based user-generated content and gift purchase intention

    Exploring cognitive style and task-specific preferences for process representations

    Get PDF
    Process models describe someone's understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area

    Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

    Full text link
    Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page
    • …
    corecore