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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

This paper describes the analysis of established and new questionnaires concerning their applicability for the assessment of quality aspects of multimodal systems. To this purpose, an experiment with 27 participants interacting with a a smart-home system via a voice interface, a smartphone-based interface and a multimodal interface, was conducted. Interaction parameters were assessed and related to constructs measured with these questionnaires. The results indicate that some of the questionnaires are suitable for evaluating multimodal interfaces. On the basis of correlations with interaction parameters subscales of these questionnaires can be mapped to quality aspects, such as effectiveness and efficiency. Recommendations are given how to meet two important evaluation requirements, namely which questionnaire to use for comparing two or more systems or system versions and how to identify factors or components in a system that have to be improved. This is another step forward to establish evaluation methods for multimodal systems.

Evaluating multimodal systems: a comparison of established questionnaires and interaction parameters

Seamless use of video telephony in future mobile communication systems creates new challenges for high-quality provisioning. Terminal mobility and dynamically changing network properties will affect the quality of the Internet connection and consequently will impair the Quality of Experience (QoE) of mobile users. To ensure best connected video calls, cross-layer adaptation of the audiovisual transmission to the network circumstances will be required. To successfully adapt the transmission parameters, knowledge about the user perception during mobile video calls is required. This paper addresses this issue and presents a study of quality experience during selected periods of emulated video calls. Perception of dynamic phenomena, such as roaming between network technologies, switching the video and speech codecs, and changing the video bit rate during active calls are in scope of the investigation. The QoE of the analysed material is linked to the underlying transmission properties, which allows to select proper strategies of QoE adaptation during mobile video calls in future wireless networks.

On the way to high-quality video calls in future mobile networks

Publisher Summary This chapter reviews the assessment and evaluation principles applied to multimodal interfaces for intelligent environments. On the basis of a new taxonomy of quality aspects, quantitative metrics are identified that address different aspects of user and system performance, quality, usability, and acceptability. Example applications are presented for multimodal interfaces to domestic devices, focusing on information presentation—for example, via an embodied conversational agent. Furthermore, it is shown which methods are already available and which ones are still missing to support an efficient development of ambient intelligence systems that are well accepted by their users. This chapter provides empirical results and illustrates definitions of factors and aspects as well as information about their relationships. On this common ground, comparable evaluation can be performed, its results can be identified and categorized, and metrics for specific purposes (or missing metrics) can be identified. Because current systems cover a wide range of applications and domains, it is anticipated that an open framework will be needed to enable meaningful evaluation for specific contexts.

Chapter 14 – Evaluation of Multimodal Interfaces for Ambient Intelligence

Subjective quality tests give important information about the user's perception on the quality of an audio or video signal. These tests are helpful but usually lack information about the user's cognitive state. This might be crucial when consuming a service, as lower quality services may lead to a less frequent use. Therefore, physiological measures have become more popular in the domain of quality assessment as they give more detailed insights about e.g. fatigue and attention of a person. In the current study we used spatially degraded videos and obtained 1) standard subjective quality judgments, 2) eye movement parameters and 3) brain activity using electroencephalography (EEG). The results indicate more focused attention on the degraded area once participants found that area, as pupil diameter increases and the proportion of alpha activity decreases. On the basis of the data a model is proposed which predicts the quality scores from the obtained physiological measures.

Using eye-tracking and correlates of brain activity to predict quality scores.

Videotelephony over IP (Internet Protocol) is one of the promising applications which is broadly used in the next-generation network (NGN). To design and assess the quality of experience (QoE), a parametric opinion model for video-telephony applications standardized in ITU-T Rec. G.1070 has been developed to predict the multimedia (speech and video) quality both in a passive and an interactive context. This model defines a multimedia quality integration function which depends on a speech score given by the E-model and a video score predicted by a video quality estimation function. This last function has been validated through subjective experiments for different video codecs and image formats. We present here a new analysis of the video quality estimation function with respect to the influence of the video codec on the basis of a subjective test and propose for the first time a set of parameters for the MPEG2 codec in order to extend the use of the G.1070 model.

Sebastian Möller

Papers

Evaluating multimodal systems: a comparison of established questionnaires and interaction parameters

On the way to high-quality video calls in future mobile networks

Chapter 14 – Evaluation of Multimodal Interfaces for Ambient Intelligence

Using eye-tracking and correlates of brain activity to predict quality scores.

Extension of the G.1070 video quality function for the MPEG2 video codec