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Using Multivariate Statistics

The measurement of meaning

A wide variety of media can be used in learning, including distance learning, such as print, lectures, conference sections, tutors, pictures, video, sound, and computers. Any one instance of distance learning will make choices among these media, perhaps using several.

Multimedia in Learning

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

With digital equipment becoming increasingly networked, either on wired or wireless networks, for personal and professional use alike, distributed software systems have become a crucial element in information and communications technologies. The study of these systems forms the core of the ARLES' work, which is specifically concerned with defining new system software architectures, based on the use of emerging networking technologies. In this context, we concentrate on the study of distributed systems which bring to life the vision of ubiquitous computing systems, also known as ambient intelligence.

반도체 공정 overview

The streaming of gaming content, both passive and interactive, has increased manifolds in recent years. Gaming contents bring with them some peculiarities which are normally not seen in traditional 2D videos, such as the artificial and synthetic nature of contents or repetition of objects in a game. In addition, the perception of gaming content by the user is different from that of traditional 2D videos due to its pecularities and also the fact that users may not often watch such content. Hence, it becomes imperative to evaluate whether the existing video quality models usually designed for traditional 2D videos are applicable to gaming content. In this paper, we evaluate the applicability of the recently standardized bitstream-based video-quality model ITU-T P.1204.3 on gaming content. To analyze the performance of this model, we used 4 different gaming datasets (3 publicly available + 1 internal) not previously used for model training, and compared it with the existing state-of-the-art models. We found that the ITU P.1204.3 model out of the box performs well on these unseen datasets, with an RMSE ranging between 0.38 − 0.45 on the 5-point absolute category rating and Pearson Correlation between 0.85 − 0.93 across all the 4 databases. We further propose a full-HD variant of the P.1204.3 model, since the original model is trained and validated which targets a resolution of 4K/UHD-1. A 50:50 split across all databases is used to train and validate this variant so as to make sure that the proposed model is applicable to various conditions.

/pdf/a-large-scale-evaluation-of-the-bitstream-based-video-37cim7hf9h.pdf

A Large-scale Evaluation of the bitstream-based video-quality model ITU-T P.1204.3 on Gaming Content

In this paper the relation between the quality of a talking head as an output component of a spoken dialog system and the quality of the system itself is investigated. Results show that the quality of the talking head has indeed an important impact on system quality. The quality of the talking head itself is found to be influenced by visual and speech quality and the synchronization of voice and lip movement.

Talking heads for interacting with spoken dialog smart-home systems.

Human perceptions of speaker characteristics, needed to perform automatic predictions from speech features, have generally been collected by conducting demanding in-lab listening tests under controlled conditions. Concurrently, crowdsourcing has emerged as a valuable approach for running user studies through surveys or quantitative ratings. Micro-task crowdsourcing markets enable the completion of small tasks (commonly of minutes or seconds), rewarding users with micro-payments. This paradigm permits effortless collection of user input from a large and diverse pool of participants at low cost. This paper presents different auditory tests for collecting perceptual voice likability ratings employing a common set of 30 male and female voices. These tests are based on direct scaling and on paired-comparisons, and were conducted in the laboratory and via crowdsourcing using micro-tasks. Design considerations are proposed for adapting the laboratory listening tests to a mobile-based crowdsourcing platform to obtain trustworthy listeners’ answers. Our likability scores obtained by the different test approaches are highly correlated. This outcome motivates the use of crowdsourcing for future listening tests investigating e.g. speaker characterization, reducing the efforts involved in engaging participants and administering the tests on-site.

Perceptual Ratings of Voice Likability Collected Through In-Lab Listening Tests vs. Mobile-Based Crowdsourcing.

Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. Information we share online unveil directly or indirectly information about our lifestyle and health situation. Particularly when text input is getting longer or multiple messages can be linked to each other. Those information can be then used to detect possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked against the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German language. We introduce a new dataset which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.

From Witch’s Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa

In this chapter, we discuss the contributions of different modalities to the overall quality of multi-modal interaction. After reviewing some common systematics and findings concerning multi-modality, we present experimental results from several multi-modal scenarios, involving different (human-to-human and human-to-machine) interaction paradigms, different degrees of interactivity, and different (speech, audio, video, touch, gesture) modalities.The results show that the impact of each modality on overall quality in interaction depends heavily onthe scenario and degree of interactivity. Complementary modalities are not considered in this paper, but the models presented allow predicting overall system quality on the basis of individual modality ratings with an appropriate accuracy. These models still have to be validated in order to be used as tools for system developers estimating whether adding modalities will have an impact on the quality experienced by the user.

Sebastian Möller

Papers

A Large-scale Evaluation of the bitstream-based video-quality model ITU-T P.1204.3 on Gaming Content

Talking heads for interacting with spoken dialog smart-home systems.

Perceptual Ratings of Voice Likability Collected Through In-Lab Listening Tests vs. Mobile-Based Crowdsourcing.

From Witch’s Shot to Music Making Bones - Resources for Medical Laymen to Technical Language and Vice Versa

Quality of Experiencing Multi-Modal Interaction