Abstract
Emotionally adaptive games are one of the holy grails of modern affective game research. However, current state of the art affective games rely on static game adaptation mechanics that assume a fixed emotional reaction from players every time. Not only this, most commercial titles have no affective adaptation loop whatsoever and their design is based on game design optimizations via typical beta-testing procedures, which falls short of ideal both in the level design and long-term gameplay experience fronts. In this paper, we demonstrate a generalizable approach for building predictive models of players' emotional reactions across different games and game genres. We describe a physiological approach for modelling players' emotional reactions, which relies on features extracted from players' emotional responses to game events, which were collected and extrapolated through their physiological data during actual gameplay sessions. Based on the optimal feature sets found by three feature selection algorithms (best first, sequential feature selection and genetic search), the collected features are used to create computational models of players' emotional reactions on the arousal and valence dimensions of emotion, using several machine learning algorithms. We expect this approach will allow both a more objective and quicker prototyping for digital games, as well as foster a future generation of affective games capable of modelling players' affective profiles over time, thus adapting to their changing preferences and needs.

Abstract
Current approaches to game design improvements rely on gameplay testing, an iterative process following the test, try and fix pattern, relying on target audience feedback via standard questionnaires. Besides being a very time consuming phase, it is also highly subjective. In this paper, we demonstrate a generalizable approach for building predictive models of players' affective reactions across games and genres. Our aim is two-fold: 1) That game developers can use these models to more easily and accurately tune game parameters, allowing improved gaming experiences, and 2) That these models can be used as the basis for parameterisable and adaptive affective gaming. This paper describes our preliminary results regarding a novel, physiological-based method for emotional player profiling, which consists on the following three phases: (i) monitoring players' emotional states and game events, (ii) identifying player's emotional reactions to game events and (iii) individual and cluster-based modelling of players emotional reactions.

Abstract
Current approaches to game design improvements rely on time-consuming gameplay testing processes, which rely on highly subjective feedback from a target audience. In this paper, we propose a generalizable approach for building predictive models of players' emotional reactions across different games and game genres, as well as other forms of digital stimuli. Our input agnostic approach relies on the following steps: (a) collecting players' physiologically-inferred emotional states during actual gameplay sessions, (b) extrapolating the causal relations between changes in players' emotional states and recorded game events, and (c) building hierarchical cluster models of players' emotional reactions that can later be used to infer individual player models via fuzzy cluster membership vectors. We expect this work to benefit game designers by accelerating the affective playtesting process through the offline simulation of players' reactions to game design adaptations, as well as to contribute towards individually-tailored affective gaming.

Abstract

Abstract
The task of moving from one place to another is a difficult challenge that involves obstacle avoidance, staying on street walks, finding doors, knowing the current location and keeping on track through the desired path. Nowadays, navigation systems are widely used to find the correct path, or the quickest, between two places. While assistive technology has contributed to the improvement of the quality of life of people with disabilities, people with visual impairment still face enormous limitations in terms of their mobility. In recent years, several approaches have been made to create systems that allow seamless tracking and navigation both in indoor and outdoor environments. However there is still an enormous lack of availability of information that can be used to assist the navigation of users with visual impairments as well as a lack of sufficient precision in terms of the estimation of the user's location. Blavigator is a navigation system designed to help users with visual impairments. In a known location, the use of object recognition algorithms can provide contextual feedback to the user and even serve as a validator to the positioning module and geographic information system of a navigation system for the visually impaired. This paper proposes a method where the use of computer vision algorithms validate the outputs of the positioning system of the Blavigator prototype. © 2014 Springer International Publishing.

Abstract
This work results from an exhaustive characterization of the practices of E-commerce through the use of a questionnaire to small and medium enterprises of Tras-os-Montes and Alto Douro in Portugal, seeking to identify and promote the use of this technology in order to obtain competitive advantages.