Multimedia clip type: Quality of perception impact on users with and without hearing loss

This paper investigates how variance in multimedia video clip type affects quality of perception (QoP) for users ith and without hearing loss. QoP encompasses not only a user's satisfaction with the quality of a multimedia presentation (subjective), but also his or her ability to analyse, synthesise and assimilate its’ informational content objective). Results show that clip type has a significant impact on the level of deaf information assimilation. Results uggest that certain video content aids deaf information assimilation, for example: those with less textual content. However, it was found that audio / captioned information does not significantly impact user QoP, when Video-textual (VT) information was found to have a significant effect on both hearing and deaf QoP. A positive correlation was found between predicted level of information assimilation and level of enjoyment, independent of hearing level or clip type

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

How level and type of deafness affects user perception of multimedia video clips

Our research investigates the impact that hearing has on the perception of digital video clips, with and without captions, by discussing how hearing loss, captions and deafness type affects user QoP (Quality of Perception). QoP encompasses not only a user's satisfaction with the quality of a multimedia presentation, but also their ability to analyse, synthesise and assimilate informational content of multimedia . Results show that hearing has a significant effect on participants’ ability to assimilate information, independent of video type and use of captions. It is shown that captions do not necessarily provide deaf users with a ‘greater level of information’ from video, but cause a change in user QoP, depending on deafness type, which provides a ‘greater level of context of the video’. It is also shown that post-lingual mild and moderately deaf participants predict less accurately their level of information assimilation than post-lingual profoundly deaf participants, despite residual hearing. A positive correlation was identified between level of enjoyment (LOE) and self-predicted level of information assimilation (PIA), independent of hearing level or hearing type. When this is considered in a QoP quality framework, it puts into question how the user perceives certain factors, such as ‘informative’ and ‘quality’

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

Predicting Causes of Reformulation in Intelligent Assistants

Intelligent assistants (IAs) such as Siri and Cortana conversationally interact with users and execute a wide range of actions (e.g., searching the Web, setting alarms, and chatting). IAs can support these actions through the combination of various components such as automatic speech recognition, natural language understanding, and language generation. However, the complexity of these components hinders developers from determining which component causes an error. To remove this hindrance, we focus on reformulation, which is a useful signal of user dissatisfaction, and propose a method to predict the reformulation causes. We evaluate the method using the user logs of a commercial IA. The experimental results have demonstrated that features designed to detect the error of a specific component improve the performance of reformulation cause detection.Comment: 11 pages, 2 figures, accepted as a long paper for SIGDIAL 201