Search results for "Human-computer interaction"
showing 10 items of 605 documents
Fuzzy Adaptive EKF Motion Control for Nonholonomic and Underactuated Cars with Parametric and non Parametric Uncertainties
2007
A new fuzzy adaptive motion control system including on-line extended Kalman''s filter (EKF) for wheeled underactuated cars with non-holonomic constraints on the motion is presented. The presence of parametric uncertainties in the kinematics and in the dynamics is treated using suitable differential adaptation laws. We merge adaptive control with fuzzy inference system. By using fuzzy system, the parameters of the kinematical controller are functions of the lateral, longitudinal and orientation errors of the motion. In this way we have a robust control system where the dynamics of the motion errors is with lower time response than the adaptive control without fuzzy. Also Lyapunov''s stabili…
Use without training: A case study of evidence-based software design for intuitive use
2019
This paper reviews intuitive software design and outlines the development of an instrument for analysts to evaluate the intuitiveness of software design. Current intuition research outlines three requirements for intuitive use: (a) existing experiential domain knowledge and skills, (b) an unexplainable perception that a novel situation is contextually familiar, and (c) successful application of users’ previously acquired experiential knowledge and skills. A case study illustrates how these requirements can be specified, implemented, and evaluated. Questions to evaluate the characteristics of intuitive design and use resulted in an intuitive use evaluation of 3.2 on a scale of 0–4, indicatin…
Genre-adaptive Semantic Computing and Audio-based Modelling for Music Mood Annotation
2016
This study investigates whether taking genre into account is beneficial for automatic music mood annotation in terms of core affects valence, arousal, and tension, as well as several other mood scales. Novel techniques employing genre-adaptive semantic computing and audio-based modelling are proposed. A technique called the ACTwg employs genre-adaptive semantic computing of mood-related social tags, whereas ACTwg-SLPwg combines semantic computing and audio-based modelling, both in a genre-adaptive manner. The proposed techniques are experimentally evaluated at predicting listener ratings related to a set of 600 popular music tracks spanning multiple genres. The results show that ACTwg outpe…
Entity Recommendation for Everyday Digital Tasks
2021
| openaire: EC/H2020/826266/EU//CO-ADAPT Recommender systems can support everyday digital tasks by retrieving and recommending useful information contextually. This is becoming increasingly relevant in services and operating systems. Previous research often focuses on specific recommendation tasks with data captured from interactions with an individual application. The quality of recommendations is also often evaluated addressing only computational measures of accuracy, without investigating the usefulness of recommendations in realistic tasks. The aim of this work is to synthesize the research in this area through a novel approach by (1) demonstrating comprehensive digital activity monitor…
The risk of sexual-erotic online behavior in adolescents – Which personality factors predict sexting and grooming victimization?
2021
Abstract Information and communication technologies provide new opportunities for adolescents to establish and maintain intimate relationships, as well as exploring their sexuality. However, the young population is particularly vulnerable to becoming victims of violence or online abuse. The aim of this study was to understand to which extent the personality factors (extraversion, narcissism, lack of empathy and disinhibition) are related to sexting and online grooming victimization. The participants were 1763 adolescents between 12 and 16 years (M = 14.56, SD = 1.16, 50.99% girls). A cross-sectional design with self-report data was used, analyzed by structural equation modeling (SEM). The r…
Empirically-derived subgroups of Facebook users and their association with personality characteristics: a Latent Class Analysis
2018
Abstract In recent years, considerable research effort has been directed at the identification of relationships between psychological variables and Facebook usage indicators. However, the identification of homogeneous subgroups of individuals based on similar Facebook usage characteristics still presents a challenge. This study aims: (1) to empirically determine homogeneous groups of Facebook users based on variables regarding their personal experience on Facebook, by using a Latent Class Analysis; and (2) to examine the association between an individual's personality and interpersonal characteristics and the empirically-derived profiles of Facebook usage. Eight hundred and eleven Facebook …
Deep Q-Learning With Q-Matrix Transfer Learning for Novel Fire Evacuation Environment
2021
We focus on the important problem of emergency evacuation, which clearly could benefit from reinforcement learning that has been largely unaddressed. Emergency evacuation is a complex task which is difficult to solve with reinforcement learning, since an emergency situation is highly dynamic, with a lot of changing variables and complex constraints that makes it difficult to train on. In this paper, we propose the first fire evacuation environment to train reinforcement learning agents for evacuation planning. The environment is modelled as a graph capturing the building structure. It consists of realistic features like fire spread, uncertainty and bottlenecks. We have implemented the envir…
Ensemble of Hankel Matrices for Face Emotion Recognition
2015
In this paper, a face emotion is considered as the result of the composition of multiple concurrent signals, each corresponding to the movements of a specific facial muscle. These concurrent signals are represented by means of a set of multi-scale appearance features that might be correlated with one or more concurrent signals. The extraction of these appearance features from a sequence of face images yields to a set of time series. This paper proposes to use the dynamics regulating each appearance feature time series to recognize among different face emotions. To this purpose, an ensemble of Hankel matrices corresponding to the extracted time series is used for emotion classification withi…
Validation of the Virtual Reality Neuroscience Questionnaire: Maximum Duration of Immersive Virtual Reality Sessions Without the Presence of Pertinen…
2019
Research suggests that the duration of a VR session modulates the presence and intensity of VRISE, but there are no suggestions regarding the appropriate maximum duration of VR sessions. The implementation of high-end VR HMDs in conjunction with ergonomic VR software seems to mitigate the presence of VRISE substantially. However, a brief tool does not currently exist to appraise and report both the quality of software features and VRISE intensity quantitatively. The VRNQ was developed to assess the quality of VR software in terms of user experience, game mechanics, in-game assistance, and VRISE. Forty participants aged between 28 and 43 years were recruited (18 gamers and 22 non-gamers) for…
Technological Competence Is a Pre-condition for Effective Implementation of Virtual Reality Head Mounted Displays in Human Neuroscience: A Technologi…
2019
Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of the scientific results. In the current literature review, the technical reasons for the adverse symptomatology are investigated to provide suggestions and technological knowledge for the implementation of VR head-mounted display (HMD) systems in cognitive neuroscience. The technological systematic literature indicated features pertinent to display, sound, motion tracking, navigation, ergonomic interactions, user experience, and computer hardware that should…