Datum und Ort:
23.07.2019 – 25.07.2019 jeweils von 10:00 – 16:00 Uhr
wissens.wert.welt Klagenfurt, Primoschgasse 3, 9020 Klagenfurt
Veranstalter: JOANNEUM RESEARCH ROBOTICS
Anmeldeschluss ist der 30.06.2019
Weitere Infos zum Workshop und zur Anmeldung!
Das Institut ROBOTICS der JOANNEUM RESEARCH Forschungsgesellschaft mbH befasst sich mit zukunftsweisender Robotik und deren Anwendungen. An unserem
Standort im Lakeside Science & Technology Park in Klagenfurt vergeben wir ab
sofort folgende Positionen:
Wir freuen uns auf Ihre aussagekräftige Bewerbung (Motivationsschreiben,
Lebenslauf) per E-Mail an PEMBewerbungen@joanneum.at.
PD Dr. Ralf Klamma | June 17, 2019 | 16:00 | S.2.42
Abstract:
Augmented Reality (AR) is on the way to establishing itself in business and teaching once more. However, there is a lack of uniform guidelines or even standards both in the creation of teaching materials and in the use of AR in teaching. In addition, the industry needs enough well-trained specialists who can implement the established AR concepts, making a transfer from university to industry necessary. Therefore, in this talk we address both challenges in teaching with AR and the special needs of teaching about AR.
As teaching with AR will surely advance human performance and also brings in new perspectives with the communication, coordination and collaboration of AR in supporting human performance. As computer scientists, we have a European, interdisciplinary and application-oriented perspective, as our experience comes from several funded European projects in these areas. We also incorporate new incentives into teaching contexts in our framework, such as gamification, learning analytics and experience capturing. In addition, we refer to international standardization efforts such as IEEE ARLEM.
Teaching about AR adopts a multi-perspective view. First, there is scientific and technological basic knowledge helping to understand the underlying physical and technical principles. Second, there is engineering and design knowledge to master the creation, fabrication, and utilization of AR in many ways. Third, there is the necessary pedagogical knowledge to transform these complex settings in manageable teaching scenarios and processes, e.g. for higher education curricula.
Here, teaching AR can learn from traditions of science and engineering education as well as from more recent knowledge about computer science education. Examples from recent and on-going European projects will illustrate the argumentation.
CV:
Ralf Klamma holds diploma, doctoral and habilitation degrees in computer science from RWTH Aachen University. He leads the research group “advanced community information systems” (ACIS) at the information systems chair, RWTH Aachen University. He is known for his work in major EU projects for Technology Enhanced Learning (PROLEARN, GALA, ROLE, Learning Layers, TELMAP, Tellnet, CUELC, SAGE, BOOST, VIRTUS and WEKIT).
Ralf organized doctoral summer schools & conferences in Technology Enhanced Learning, Web Engineering and Social Network Analysis. He serves as associate editor for Social Network Analysis and Mining (SNAM), Frontiers of AI for Human Learning and Behavior Change and the International Journal on Interaction Design & Architecture(s) (IxD&A).
He was associate editor for IEEE Transactions on Learning Technologies (ToLT). His research interests are community information systems, serious games, augmented reality & wearables, web engineering, social network analysis, requirements engineering and technology enhanced learning.
The review of the TEWI colloquium of Dr. Johanna Pirker from May 16, 2019 comprises the video and slides (below):
Abstract:
AI is used to create parts of our games. It provides intelligent enemy behavior, techniques such as pathfinding or can be used to generate in-game content procedurally. AI can also play our games. The idea to train computers to beat humans in game-like environments such as Jeopardy!, Chess, or soccer is not a new one. But can AI also design our games? The role of Artificial Intelligence in the game development process is constantly expanding. In this talk, Dr. Pirker will talk about the importance of AI in the past, the present, and especially the future of game development.
Bio:
Dr. Johanna Pirker is researcher at the Institute of Interactive Systems and Data Science at Graz University of Technology (TUG). She finished her Master’s Thesis during a research visit at Massachusetts Institute of Technology (MIT) working on collaborative virtual world environments. In 2017, she finished her doctoral dissertation in computer science on motivational environments under the supervision of Christian Gütl (TUG) and John Belcher (MIT). She specialized in games and environments that engage users to learn, train, and work together through motivating tasks. She has long-lasting experience in game design and development, as well as virtual world development and has worked in the video game industry at Electronic Arts. Her research interests include AI, data analysis, immersive environments (VR), games research, gamification strategies, HCI, e-learning, CSE, and IR. She has authored and presented numerous publications in her field and lectured at universities such as Harvard, Berlin Humboldt Universität, or the University of Göttingen. Johanna was listed on the Forbes 30 Under 30 list of science professionals.
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Web: https://sensolligent.com/
Email: want.you@sensolligent.com
The review of the TEWI colloquium of Dr.-Ing. Christian Keimel from May 9, 2019 comprises the video and slides (below):
Abstract: Artificial Intelligence (AI) is nowadays used frequently in many application domains. Although sometimes considered only as an afterthought in the public discussion compared to other domains such as health, transportation, and manufacturing, the media domain is also transformed by AI enabling new opportunities, from content creation e.g. “robojournalism” and individualised content to optimisation of the content production and distribution. Underlaying many of these new opportunities is the use of AI in its current reincarnation as deep learning for understanding the audio-visual content by extracting structured information from the unstructured data, the audio-visual content.
In this talk the current understanding and trends of AI will therefore be discussed, what can be done, what is done, and what challenges remain in the use of AI especially in the context of media applications and services. The talk is not so much focused on the details and fundamentals of deep learning, but rather on a practical perspective on how recent advances in this field can be utilised in use-cases in the media domain, especially with respect to audio-visual content and in the broadcasting domain.
Bio: Christian Keimel received his B.Sc and Dipl.-Ing.(Univ.) in information technology from the Technical University of Munich (TUM) in 2005 and 2007, respectively. In 2014 he received a Dr.-Ing. degree from TUM for his dissertation on the “Design of video quality metrics with multi-way data analysis.” Since 2013 he is with the Institut for Rundfunktechnik (IRT), the research and competence centre of the public service broadcasters of Austria, Germany, and Switzerland, where he leads the machine learning team, working on the applications of machine learning and AI in the broadcasting context. In addition, he is a lecturer at TUM for “Deep Learning for Multimedia”. His current research interests include applications of data-driven models using machine learning particularly deep learning for audio-visual content understanding and distribution optimisation.
The review of the TEWI colloquium of Dr. Stephan Alexander Weiss from May 22, 2019 comprises the slides (below):
Abstract:
Covariance matrices are central to many adaptive filtering and optimisation problems. In practice, they have to be estimated from a finite number of samples; on this, I will review some known results from spectrum estimation and multiple-input multiple-output communications systems, and how properties that are assumed to be inherent in covariance and power spectral densities can easily be lost in the estimation process. I will discuss new results on space-time covariance estimation, and how the estimation from finite sample sets will impact on factorisations such as the eigenvalue decomposition, which is often key to solving the introductory optimisation problems. The purpose of the presentation is to give you some insight into estimating statistics as well as to provide a glimpse on classical signal processing challenges such as the separation of sources from a mixture of signals.
Stephan Weiss. I am a Professor at the University of Strathclyde and head its Centre for Signal & Image Processing. My particular interests are adaptive filtering and array signal processing.
Internationale Karrierechancen und ein Wohlfühlklima mit familiärem Zusammenhalt bei einem der besten Arbeitgeber Österreichs. Für den Standort Althofen in Kärnten suchen wir eine/n
Das erwartet Dich:
Du bist Teil des Information Systems Teams und betreust ausgewählte Applikationen. Als Experte für die von dir betreuten Systeme obliegt es dir, die ideale Lösung für Anforderungen und Störungen zu finden. Im Rahmen von Projekten arbeitest du mit Kollegen und Anwendern an der ständigen Verbesserung unserer Prozesse und Anwendungen.
So tickst Du:
Das bringst Du mit:
Das bieten wir Dir:
Bewirb Dich jetzt mit Deinem Lebenslauf – wir freuen uns auf Dich!
Frau Mirjam Riesser: mirjam.riesser@flex.com
The review of the TEWI colloquium of Prof. Francesco Ricca from May 3, 2019 comprises the video and slides (below):
Abstract:
Answer set programming (ASP) is a prominent knowledge representation and reasoning paradigm that found both industrial and scientific applications. The success of ASP is due to the combination of two factors: a rich modeling language and the availability of efficient ASP implementations. In this talk we trace the history of ASP systems, describing the key evaluation techniques and their implementation in actual tools.
CV:
Francesco Ricca (www.mat.unical.it/ricca) is currently an Associate Professor at the Department of Mathematics and Computer Science of the University of Calabria, Italy. In the same Department he is Coordinator of the Computer Science Courses Council.
He received his Laurea Degree in Computer Science Engineering (2002) and a PhD in Computer Science and Mathematics (2006) from the University of Calabria, Italy, and received the Habilitation for Full Professor in Computer Science (INF/01) in 2017.
He is interested in declarative logic-based languages, consistent query answering, and rule-based reasoning on ontologies and in particular on the issues concerning their practical applications: system design and implementation, and development tools. He is co-author of more than 100 (peer-reviewed) publications including international research journals (30+), encyclopedia chapters, conference proceedings, and workshops of national and international importance. He has served in program committees of international conference and workshop, such as IJCAI, AAAI, KR, ICLP, LPNMR and JELIA, and has been reviewer for AIJ, JAIR, TPLP, JLC, etc. He is Area Editor of Association for Logic Programming newsletters, and member of the Executive Board of the Italian Association for Artificial Intelligence.
Dr. Stephan Alexander Weiss | May 22, 2019 | 11:00 | B02.1.59
Abstract:
Covariance matrices are central to many adaptive filtering and optimisation problems. In practice, they have to be estimated from a finite number of samples; on this, I will review some known results from spectrum estimation and multiple-input multiple-output communications systems, and how properties that are assumed to be inherent in covariance and power spectral densities can easily be lost in the estimation process. I will discuss new results on space-time covariance estimation, and how the estimation from finite sample sets will impact on factorisations such as the eigenvalue decomposition, which is often key to solving the introductory optimisation problems. The purpose of the presentation is to give you some insight into estimating statistics as well as to provide a glimpse on classical signal processing challenges such as the separation of sources from a mixture of signals.
Stephan Weiss. I am a Professor at the University of Strathclyde and head its Centre for Signal & Image Processing. My particular interests are adaptive filtering and array signal processing.
