FiFi – Robuste Personenerkennung gestengesteuerter Fahrzeuge in der Intralogistik

Die Steuerung des Fahrerlosen Transportfahrzeuges „FiFi“ erfolgt berührungslos durch Gesten- und Personenerkennung basierend auf 3D-Daten der Umgebung. Die genutzten Verfahren zur Personenerkennung führen in einigen Fällen zur Falsch-Erkennung von Personen in Objekten. Das Paper beschreibt die Ursachen der Fehlerkennung und stellt die umgesetzten Lösungsansätze zur Vermeidung vor. Experimente bestätigen, dass die entwickelten Verfahren die Robustheit des Systems erhöhen.

Multilingualism in Finland: A Legal Perspective

Multilingualism is an increasingly frequent societal phenomenon. More and more societies and individuals are, or have become, multilingual. Legislation is an important tool for language policy and, ultimately, language environment. Yet, it seems that little research has been dedicated to multilingualism from a legal framework perspective. The law is, generally speaking, blind to language. This means that the legal framework rarely takes into account the co-existence of several languages in a society other than national languages. In addition, there are altogether relatively few provisions regarding what language shall be used in which contexts. The article focuses on multilingualism in Finland where the cornerstone for the Finnish language policy of the country is laid down in the Constitution. Multilingualism is particularly interesting in a bilingual country Finland that has a long and solid history of language legislation. The country has over a few decades undergone change and rapidly developed into a multilingual country. This article examines whether the Finnish current legislation enables and supports the societal multilingualism or poses restrictions on the parallel use of several languages. Another more fundamental question discussed in this article is if societal multilingualism sets new demands on the national legislation.

Learning Two-Person Interaction Models for Responsive Synthetic Humanoids

Imitation learning is a promising approach for generating life-like behaviors of virtual humans and humanoid robots. So far, however, imitation learning has been mostly restricted to single agent settings where observed motions are adapted to new environment conditions but not to the dynamic behavior of interaction partners. In this paper, we introduce a new imitation learning approach that is based on the simultaneous motion capture of two human interaction partners. From the observed interactions, low-dimensional motion models are extracted and a mapping between these motion models is learned. This interaction model allows the real-time generation of agent behaviors that are responsive to the body movements of an interaction partner. The interaction model can be applied both to the animation of virtual characters as well as to the behavior generation for humanoid robots.

Constructing a user experience user experience-based mobile learning environment

Mobile learning, in the past defined as learning with mobile devices, now refers to any type of learning-on-the-go or learning that takes advantage of mobile technologies. This new definition shifted its focus from the mobility of technology to the mobility of the learner (O'Malley and Stanton 2002; Sharples, Arnedillo-Sanchez et al. 2009). Placing emphasis on the mobile learner’s perspective requires studying “how the mobility of learners augmented by personal and public technology can contribute to the process of gaining new knowledge, skills, and experience” (Sharples, Arnedillo-Sanchez et al. 2009). The demands of an increasingly knowledge based society and the advances in mobile phone technology are combining to spur the growth of mobile learning. Around the world, mobile learning is predicted to be the future of online learning, and is slowly entering the mainstream education. However, for mobile learning to attain its full potential, it is essential to develop more advanced technologies that are tailored to the needs of this new learning environment. A research field that allows putting the development of such technologies onto a solid basis is user experience design, which addresses how to improve usability and therefore user acceptance of a system. Although there is no consensus definition of user experience, simply stated it focuses on how a person feels about using a product, system or service. It is generally agreed that user experience adds subjective attributes and social aspects to a space that has previously concerned itself mainly with ease-of-use. In addition, it can include users’ perceptions of usability and system efficiency. Recent advances in mobile and ubiquitous computing technologies further underline the importance of human-computer interaction and user experience (feelings, motivations, and values) with a system. Today, there are plenty of reports on the limitations of mobile technologies for learning (e.g., small screen size, slow connection), but there is a lack of research on user experience with mobile technologies. This dissertation will fill in this gap by a new approach in building a user experience-based mobile learning environment. The optimized user experience we suggest integrates three priorities, namely a) content, by improving the quality of delivered learning materials, b) the teaching and learning process, by enabling live and synchronous learning, and c) the learners themselves, by enabling a timely detection of their emotional state during mobile learning. In detail, the contributions of this thesis are as follows: • A video codec optimized for screencast videos which achieves an unprecedented compression rate while maintaining a very high video quality, and a novel UI layout for video lectures, which together enable truly mobile access to live lectures. • A new approach in HTTP-based multimedia delivery that exploits the characteristics of live lectures in a mobile context and enables a significantly improved user experience for mobile live lectures. • A non-invasive affective learning model based on multi-modal emotion detection with very high recognition rates, which enables real-time emotion detection and subsequent adaption of the learning environment on mobile devices. The technology resulting from the research presented in this thesis is in daily use at the School of Continuing Education of Shanghai Jiaotong University (SOCE), a blended-learning institution with 35.000 students.