Detecting emotional state of a child in a conversational computer game

The automatic recognition of user’s communicative style within a spoken dialog system framework, including the affective aspects, has received increased attention in the past few years. For dialog systems, it is important to know not only what was said but also how something was communicated, so that the system can engage the user in a richer and more natural interaction. This paper addresses the problem of automatically detecting “frustration”, “politeness”, and “neutral” attitudes from a child’s speech communication cues, elicited in spontaneous dialog interactions with computer characters. Several information sources such as acoustic, lexical, and contextual features, as well as, their combinations are used for this purpose. The study is based on a Wizard-of-Oz dialog corpus of 103 children, 7–14 years of age, playing a voice activated computer game. Three-way classification experiments, as well as, pairwise classification between polite vs. others and frustrated vs. others were performed. Experimental results show that lexical information has more discriminative power than acoustic and contextual cues for detection of politeness, whereas context and acoustic features perform best for frustration detection. Furthermore, the fusion of acoustic, lexical and contextual information provided significantly better classification results. Results also showed that classification performance varies with age and gender. Specifically, for the “politeness” detection task, higher classification accuracy was achieved for females and 10–11 years-olds, compared to males and other age groups, respectively.     

Activity sequence modelling and dynamic clustering for personalized e-learning

Monitoring and interpreting sequential learner activities has the potential to improve adaptivity and personalization within educational environments. We present an approach based on the modeling of learners?? problem solving activity sequences, and on the use of the models in targeted, and ultimately automated clustering, resulting in the discovery of new, semantically meaningful information about the learners. The approach is applicable at different levels: to detect pre-defined, well-established problem solving styles, to identify problem solving styles by analyzing learner behaviour along known learning dimensions, and to semi-automatically discover learning dimensions and concrete problem solving patterns. This article describes the approach itself, demonstrates the feasibility of applying it on real-world data, and discusses aspects of the approach that can be adjusted for different learning contexts. Finally, we address the incorporation of the proposed approach in the adaptation cycle, from data acquisition to adaptive system interventions in the interaction process.     

Multimodal and ontology-based fusion approaches of audio and visual processing for violence detection in movies

In this paper we present our research results towards the detection of violent scenes in movies, employing advanced fusion methodologies, based on learning, knowledge representation and reasoning. Towards this goal, a multi-step approach is followed: initially, automated audio and visual analysis is performed to extract audio and visual cues. Then, two different fusion approaches are deployed: (i) a multimodal one that provides binary decisions on the existence of violence or not, employing machine learning techniques, (ii) an ontological and reasoning one, that combines the audio-visual cues with violence and multimedia ontologies. The latter reasons out not only the existence of violence or not in a video scene, but also the type of violence (fight, screams, gunshots). Both approaches are experimentally tested, validated and compared for the binary decision problem of violence detection. Finally, results for the violence type identification are presented for the ontological fusion approach. For evaluation purposes, a large dataset of real movie data has been populated.     

The welfare costs of non-marginal water pricing: evidence from the water only companies in England and Wales

The evaluation of the economic efficiency of regulatory schemes is essential for regulators and utilities. In this study it is analysed for the first time the welfare costs of non-marginal cost pricing in the water supply in England and Wales, by computing the deadweight loss of the water only companies (WoCs) that existed over the period of 1993–2009. The results indicate that the current price schemes can have substantial efficiency costs. Our estimates show that the loss of efficiency for the WoCs lies between 15 and 60 million GBP over the period 1993–2009. These amounts could have been redistributed either to the companies in terms of profits or to the consumers via price reductions. The methodology and results of this study are of great interest for both regulators and water utilities managers to evaluate the effectiveness of price regulation and make informed decisions.     

A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Human Behaviour Analysis (HBA) is more and more being of interest for computer vision and artificial intelligence researchers. Its main application areas, like Video Surveillance and Ambient-Assisted Living (AAL), have been in great demand in recent years. This paper provides a review on HBA for AAL and ageing in place purposes focusing specially on vision techniques. First, a clearly defined taxonomy is presented in order to classify the reviewed works, which are consequently presented following a bottom-up abstraction and complexity order. At the motion level, pose and gaze estimation as well as basic human movement recognition are covered. Next, the mainly used action and activity recognition approaches are presented with examples of recent research works. Increasing the degree of semantics and the time interval involved in the HBA, finally the behaviour level is reached. Furthermore, useful tools and datasets are analysed in order to provide help for initiating projects.     

Multi‐responsive polymeric microcontainers for potential biomedical applications: synthesis and functionality evaluation

Temperature and pH responsive poly(N‐isopropylacrylamide‐co‐methacrylic acid) (P(NIPAAm‐co‐MAA)) microcontainers with encapsulated magnetic nanoparticles in the shell were prepared by a two‐stage distillation precipitation polymerization. PMAA@Fe₃O₄/P(NIPAAm‐co‐MAA) core–shell nanoparticles were synthesized by the second‐stage polymerization of NIPAAm, MAA and N, N′‐methylenebisacrylamide as crosslinker in the presence of magnetic nanoparticles and PMAA as core. These novel triple‐functional microcontainers were prepared by selective removal of the PMAA core in water. Daunorubicin hydrochloride (DNR) was loaded into the microcontainers and the release profile was studied by UV–visible spectroscopy. The synthesized nanostructures were characterized with transmission and scanning electron microscopy, X‐ray diffraction and Fourier transform infrared spectroscopy. The magnetic properties were evaluated by vibrating sample magnetometry. The shrink and swelling behavior was studied by dynamic light scattering. Copyright © 2012 Society of Chemical Industry     

Validating Operator Event Sequence Diagrams: The case of an automated vehicle to human driver handovers

Predicting what drivers will do as vehicle control is handed over to them from automation is a relatively new challenge for the motor vehicle industry. Operator Event Sequence Diagrams (OESDs) offer a way of modeling the interactions between the driver and vehicle automation in the handover of control. In this paper, two studies are presented in which a range of handover strategies are tested. The anticipated driver strategies were modeled using OESDs to serve as predictions of driver behavior. Drivers were then observed in two separate studies: (1) using a Lower-Fidelity (vehicle seat and controls) simulator and (2) using a Higher-Fidelity (whole vehicle) simulator. Driver behavior during a takeover task was categorized according to the signal detection paradigm into hits, misses, false alarms, and correct rejections. The results showed that for all strategies in both sets of studies, the median criterion for validity was exceeded ( > 0.8), suggesting that OESDs made good predictions of driver behavior during the handover of the vehicle from automation to manual control.     

Serverless streaming for emerging media: towards 5G network-driven cost optimization

Multimedia Tools and Applications - Immersive 3D media is an emerging type of media that captures, encodes and reconstructs the 3D appearance of people and objects, with applications in...