The AISB’08 Symposium on Multimodal Output Generation (MOG 2008)

Welcome to Aberdeen at the Symposium on Multimodal Output Generation (MOG 2008)! In this volume the papers presented at the MOG 2008 international symposium are collected

Comunicación simétrica y asimétrica en los blogs de divulgación jurídica: entre modalidad epistémica y modalidad deóntica

 This paper aims at investigating some discursive features of blawgs, namely legal blogs in which legal experts disseminate and popularise their expertise. More specifically, it involves a corpus-assisted discourse study of the ways in which situational contexts affect the practices and strategies used to represent, construct and communicate legal knowledge. A comparison is drawn between two corpora representative of two different types of communication: a selection of posts written by legal experts for other experts (symmetrical communication) and posts written by legal experts for laypersons (asymmetrical communication). Combining qualitative and quantitative observations, the analysis shows that, in symmetrical communication, the emphasis is on the blogger’s subjective interpretation of legal texts and on his role as knowledge disseminator, as indicated by the predominance of epistemic modality. In asymmetrical communication, on the other hand, the prevalence of deontic modality shifts the focus on to the reader as addressee of the advice, instructions and information provided by the legal expert

Multispectral object segmentation and retrieval in surveillance video

This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The transferable belief model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenari

Quantifying the Impact of Parameter Tuning on Nature-Inspired Algorithms

The problem of parameterization is often central to the effective deployment of nature-inspired algorithms. However, finding the optimal set of parameter values for a combination of problem instance and solution method is highly challenging, and few concrete guidelines exist on how and when such tuning may be performed. Previous work tends to either focus on a specific algorithm or use benchmark problems, and both of these restrictions limit the applicability of any findings. Here, we examine a number of different algorithms, and study them in a "problem agnostic" fashion (i.e., one that is not tied to specific instances) by considering their performance on fitness landscapes with varying characteristics. Using this approach, we make a number of observations on which algorithms may (or may not) benefit from tuning, and in which specific circumstances.Comment: 8 pages, 7 figures. Accepted at the European Conference on Artificial Life (ECAL) 2013, Taormina, Ital

A comparison of patient testimonials on YouTube of the most common orthodontic treatment modalities: braces, in-office aligners, and direct-to-consumer aligners

Introduction: The objectives of this research was to investigate and compare the educational value of the most popular YouTube orthodontic patient testimonials between braces (B), in- office aligners (IOA), and direct-to-consumer aligners (DTCA), and to classify the emotional response of the viewers through a sentiment analysis of the video comments. Methods: Three different phrases relevant to B, IOA, and DTCA were searched on YouTube. The 20 most popular patient testimonial videos that met the criteria for each group were selected, for a total of 60 videos. Using the YouTube API for each video, 13 video metrics were extracted, an information completeness score (ICS) was assigned, and an analysis of the video comments was performed using sentiment analysis software. Results: The 60 videos included in this study were viewed 34,384,786 times by internet users. Braces videos have significantly more likes, comments, and a higher viewer interaction score than the IOA and DTCA videos. IOA videos had a higher median ICS than B and DTCA videos. Of the 5149 video comments with polarity, 53.6% were positive and 46.4% were negative (P Conclusions: There is high user engagement on YouTube with orthodontic patient testimonials. YouTube users interact with braces patient testimonials the most. YouTube viewers’ comments on orthodontic patient testimonials express more positive sentiment than negative sentiment. There is no significant difference in positive and negative sentiment between the video comments for the three different treatment modalities

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence

The Complexity of Synthesizing Uniform Strategies

We investigate uniformity properties of strategies. These properties involve sets of plays in order to express useful constraints on strategies that are not \mu-calculus definable. Typically, we can state that a strategy is observation-based. We propose a formal language to specify uniformity properties, interpreted over two-player turn-based arenas equipped with a binary relation between plays. This way, we capture e.g. games with winning conditions expressible in epistemic temporal logic, whose underlying equivalence relation between plays reflects the observational capabilities of agents (for example, synchronous perfect recall). Our framework naturally generalizes many other situations from the literature. We establish that the problem of synthesizing strategies under uniformity constraints based on regular binary relations between plays is non-elementary complete.Comment: In Proceedings SR 2013, arXiv:1303.007