1,721,065 research outputs found
A user-centred framework for explainable artificial intelligence in human-robot interaction
No full textAdvancing knowledge of self-care instruments
No full textThere is growing interest in self-care including those theory-based research instruments designed to measure self-care, such as the Self-Care of Heart Failure Index (SCHFI),1 the Self-Care of Chronic Illness Inventory (SC-CII),2 the Self-Care of Diabetes Inventory (SCODI),3 and the Self-Care of Chronic Obstructive Pulmonary Disease Inventory (SC-COPDI).4 All these instruments, and others available on the website https://self-care-measures.com, are based on the Middle-Range Theory of Self-Care of Chronic Illness,5 which provides a theoretical basis on which to develop and validate the self-care instruments of chronic diseases. These self-care instruments have been translated in several languages and their psychometric properties have been assessed in several studies. Among them we can cite the recent article by Bugajski and colleagues (2021)6 that evaluated the validity and reliability of the SC-COPDI in a U.S. sample of people with chronic obstructive pulmonary disease. In spite of the theoretical basis and other examples of psychometric evaluation available in the published literature, it is still common to see publications in which the reliability and validity of self-care instruments were assessed using outdated methods and techniques. For this reason. we want to provide some recommendations on how best to perform the psychometric analysis of the self-care instruments derived from the Middle-Range Theory of Self-care of Chronic illness. We believe that well-conducted validation study will help to advance knowledge of self-care and the self-care instruments used to study self-care. The self-care instruments are important in both research and clinical practice as they can help to identify people at risk of poor self-care who would benefit from specific educational interventions to increase their self-care and improve their clinical outcomes and quality of lif
A method to point out anomalous input-output patterns in a database for training neuro-fuzzy system with a supervised learning rule
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Toward Robots' Behavioral Transparency of Temporal Difference Reinforcement Learning with a Human Teacher
No full textThe high request for autonomous human-robot interaction (HRI), combined with the potential of machine learning (ML) techniques, allow us to deploy ML mechanisms in robot control. However, the use of ML can make robots' behavior unclear to the observer during the learning phase. Recently, transparency in HRI has been investigated to make such interactions more comprehensible. In this work, we propose a model to improve the transparency during reinforcement learning (RL) tasks for HRI scenarios: the model supports transparency by having the robot show nonverbal emotional-behavioral cues. Our model considered human feedback as the reward of the RL algorithm and it presents emotional-behavioral responses based on the progress of the robot learning. The model is managed only by the temporal-difference error. We tested the architecture in a teaching scenario with the iCub humanoid robot. The results highlight that when the robot expresses its emotional-behavioral response, the human teacher is able to understand its learning process better. Furthermore, people prefer to interact with an expressive robot as compared to a mechanical one. Movement-based signals proved to be more effective in revealing the internal state of the robot than facial expressions. In particular, gaze movements were effective in showing the robot's next intentions. In contrast, communicating uncertainty through robot movements sometimes led to action misinterpretation, highlighting the importance of balancing transparency and the legibility of the robot goal. We also found a reliable temporal window in which to register teachers' feedback that can be used by the robot as a reward
Histological analysis of bone healing following alveolar preservation technique by deproteinised bovine bone covered by a xenogenic collagen matrix
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