13 research outputs found
New Approach to Teaching Japanese Pronunciation in the Digital Era - Challenges and Practices
Pronunciation has been a black hole in the L2 Japanese classroom on account of a lack of class time, teacher\u2019s confidence, and consciousness of the need to teach pronunciation, among other reasons. The absence of pronunciation instruction is reported to result in fossilized pronunciation errors, communication problems, and learner frustration. With an intention of making a contribution to improve such circumstances, this paper aims at three goals. First, it discusses the importance, necessity, and e ectiveness of teaching prosodic aspects of Japanese pronunciation from an early stage in acquisition. Second, it shows that Japanese prosody is challenging because of its typological rareness, regardless of the L1 backgrounds of learners. Third and finally, it introduces a new approach to teaching L2 pronunciation with the goal of developing L2 comprehensibility by focusing on essential prosodic features, which is followed by discussions on key issues concerning how to implement the new approach both inside and outside the classroom in the digital era
European Approaches to Japanese Language and Linguistics
In this volume European specialists of Japanese language present new and original research into Japanese over
a wide spectrum of topics which include descriptive, sociolinguistic, pragmatic and didactic accounts. The articles share a focus on contemporary issues and adopt new approaches to the study of Japanese that often are specific to European traditions of language study. The articles address an audience that includes both Japanese Studies and Linguistics. They are representative of the wide range of topics that are currently studied in European universities, and they address scholars and students alike
Multimedia Development of English Vocabulary Learning in Primary School
In this paper, we describe a prototype of web-based intelligent handwriting education
system for autonomous learning of Bengali characters. Bengali language is used by more than
211 million people of India and Bangladesh. Due to the socio-economical limitation, all of the
population does not have the chance to go to school. This research project was aimed to develop
an intelligent Bengali handwriting education system. As an intelligent tutor, the system can
automatically check the handwriting errors, such as stroke production errors, stroke sequence
errors, stroke relationship errors and immediately provide a feedback to the students to correct
themselves. Our proposed system can be accessed from smartphone or iPhone that allows
students to do practice their Bengali handwriting at anytime and anywhere. Bengali is a
multi-stroke input characters with extremely long cursive shaped where it has stroke order
variability and stroke direction variability. Due to this structural limitation, recognition speed is
a crucial issue to apply traditional online handwriting recognition algorithm for Bengali
language learning. In this work, we have adopted hierarchical recognition approach to improve
the recognition speed that makes our system adaptable for web-based language learning. We
applied writing speed free recognition methodology together with hierarchical recognition
algorithm. It ensured the learning of all aged population, especially for children and older
national. The experimental results showed that our proposed hierarchical recognition algorithm
can provide higher accuracy than traditional multi-stroke recognition algorithm with more
writing variability
Real-time generation and adaptation of social companion robot behaviors
Social robots will be part of our future homes.
They will assist us in everyday tasks, entertain us, and provide helpful advice.
However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate.
An essential skill of every social robot is verbal and non-verbal communication.
In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine.
Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors.
In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot.
However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems.
This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences.
Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence.
The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning.
Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning.
It provides a higher-level view from the system designer's perspective and guidance from the start to the end.
It illustrates the process of modeling, simulating, and evaluating such adaptation processes.
Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness.
The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes.
They are evaluated in the lab and in in-situ studies.
These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor.
Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein.
Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen.
Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen.
Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation.
Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt.
Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen.
Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen.
So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt.
Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden.
In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen.
Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern.
Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen.
Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt.
Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende.
Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse.
Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten.
Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden.
Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt.
In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an
Proceedings of the 21st International Congress of Aesthetics, Possible Worlds of Contemporary Aesthetics Aesthetics Between History, Geography and Media
The Faculty of Architecture, University of Belgrade and the Society for Aesthetics of Architecture and Visual Arts of Serbia (DEAVUS) are proud to be able to organize the 21st ICA Congress on “Possible Worlds of Contemporary Aesthetics: Aesthetics Between History, Geography and Media”.
We are proud to announce that we received over 500 submissions from 56 countries, which makes this Congress the greatest gathering of aestheticians in this region in the last 40 years.
The ICA 2019 Belgrade aims to map out contemporary aesthetics practices in a vivid dialogue of aestheticians, philosophers, art theorists, architecture theorists, culture theorists, media theorists, artists, media entrepreneurs, architects, cultural activists and researchers in the fields of humanities and social sciences. More precisely, the goal is to map the possible worlds of contemporary aesthetics in Europe, Asia, North and South America, Africa and Australia. The idea is to show, interpret and map the unity and diverseness in aesthetic thought, expression, research, and philosophies on our shared planet. Our goal is to promote a dialogue concerning aesthetics in those parts of the world that have not been involved with the work of the International Association for Aesthetics to this day. Global dialogue, understanding and cooperation are what we aim to achieve.
That said, the 21st ICA is the first Congress to highlight the aesthetic issues of marginalised regions that have not been fully involved in the work of the IAA. This will be accomplished, among others, via thematic round tables discussing contemporary aesthetics in East Africa and South America. Today, aesthetics is recognized as an important philosophical, theoretical and even scientific discipline that aims at interpreting the complexity of phenomena in our contemporary world. People rather talk about possible worlds or possible aesthetic regimes rather than a unique and consistent philosophical, scientific or theoretical discipline
Psychological Engagement in Choice and Judgment Under Risk and Uncertainty
Theories of choice and judgment assume that agents behave rationally, choose the higher expected value option, and evaluate the choice consistently (Expected Utility Theory, Von Neumann, & Morgenstern, 1947). However, researchers in decision-making showed that human behaviour is different in choice and judgement tasks (Slovic & Lichtenstein, 1968; 1971; 1973). In this research, we propose that psychological engagement and control deprivation predict behavioural inconsistencies and utilitarian performance with judgment and choice. Moreover, we explore the influences of engagement and control deprivation on agent’s behaviours, while manipulating content of utility (Kusev et al., 2011, Hertwig & Gigerenzer 1999, Tversky & Khaneman, 1996) and
decision reward (Kusev et al, 2013, Shafir et al., 2002)