From mode choice to modal diversion: A new behavioural paradigm and an application to the study of the demand for innovative transport services

We analyse past research efforts that focus on modal diversion in the transport sector, as opposed to the classical mode choice concept, showing the added value of this alternative framework that emerges from the existing scientific literature. The modal diversion paradigm is then used to assess the relative importance of the technical performances of transport services on one hand and of the subjective factors of its potential users on the other, when forecasting the use of a new means among a group of white-collars working in a French research institute. We quantitatively show that multimodal habits and cognitive attitudes have an importance that is in general not negligible for this group, compared to that of the transport services performances, even if only these latter are routinely considered by engineers and planners. Beyond this, we find that the role of self-related factors further increased when the group was less familiar with the technological background and the subsequent operation of the new system, such as in the case of demand responsive transport service

Mulsemedia in Telecommunication and Networking Education: A Novel Teaching Approach that Improves the Learning Process

The advent and increased use of new technologies, such as innovative mulsemedia and multi-modal content distribution mechanisms, have brought new challenges and diverse opportunities for technology enhanced learning (TEL). NEWTON is a Horizon 2020 European project that revolutionizes the educational process through innovative TEL methodologies and tools, integrated in a pan-European STEM-related learning network platform. This article focuses on one of these novel TEL methodologies (i.e., mulsemedia) and presents how NEWTON enables mulsemedia- enhanced teaching and learning of STEM subjects, with a particular focus on telecommunication and networking related modules. The article also discusses the very promising results of NEWTON case studies carried out with engineering students across two different universities in Spain and Ireland, respectively. The case studies focused on analyzing the impact on the learning process of the mulsemedia-enhanced teaching in the context of telecommunication and networking modules. The main conclusion of the article is that mulsemedia-enhanced education significantly increases students' learning experience and improves their knowledge gain

Supporting community engagement through teaching, student projects and research

The Education Acts statutory obligations for ITPs are not supported by the Crown funding model. Part of the statutory role of an ITP is “... promotes community learning and by research, particularly applied and technological research ...” [The education act 1989]. In relation to this a 2017 TEC report highlighted impaired business models and an excessive administrative burden as restrictive and impeding success. Further restrictions are seen when considering ITPs attract < 3 % of the available TEC funding for research, and ~ 20 % available TEC funding for teaching, despite having overall student efts of ~ 26 % nationally. An attempt to improve performance and engage through collaboration (community, industry, tertiary) at our institution is proving successful. The cross-disciplinary approach provides students high level experience and the technical stretch needed to be successful engineers, technologists and technicians. This study presents one of the methods we use to collaborate externally through teaching, student projects and research

First impressions: A survey on vision-based apparent personality trait analysis

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft

Time-delay neural network for continuous emotional dimension prediction from facial expression sequences

"(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works."Automatic continuous affective state prediction from naturalistic facial expression is a very challenging research topic but very important in human-computer interaction. One of the main challenges is modeling the dynamics that characterize naturalistic expressions. In this paper, a novel two-stage automatic system is proposed to continuously predict affective dimension values from facial expression videos. In the first stage, traditional regression methods are used to classify each individual video frame, while in the second stage, a Time-Delay Neural Network (TDNN) is proposed to model the temporal relationships between consecutive predictions. The two-stage approach separates the emotional state dynamics modeling from an individual emotional state prediction step based on input features. In doing so, the temporal information used by the TDNN is not biased by the high variability between features of consecutive frames and allows the network to more easily exploit the slow changing dynamics between emotional states. The system was fully tested and evaluated on three different facial expression video datasets. Our experimental results demonstrate that the use of a two-stage approach combined with the TDNN to take into account previously classified frames significantly improves the overall performance of continuous emotional state estimation in naturalistic facial expressions. The proposed approach has won the affect recognition sub-challenge of the third international Audio/Visual Emotion Recognition Challenge (AVEC2013)1