Dr. Anja Zernig | KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH Villach |
Friday, November 26, 2021 | 10:00 (CET, 09:00 UTC) | Online: https://classroom.aau.at/b/sch-xte-ijl-jdg

Abstract: AI has infected the world. Today, there is a huge hype around Data Science activities all over the world, where one of the biggest challenges for the industry is to deliver financial value quickly but also sustainably. In her talk, she will show some examples on latest Use Cases in the area of Data Science within the semiconductor industry, including technical approaches and practical challenges. Further, she will give some personal insights on important enabling factors that make a Data Science project successful.

Bio: Anja Zernig coordinates Data Science projects at KAI Kompetenzzentrum Automobil- und Industrieelektronik GmbH in Villach, which is a 100% subsidiary of Infineon Technologies Austria AG. Dr. Zernig studied Technical Mathematics at the University of Klagenfurt and received her PhD in 2016. Afterwards, she has been applied as a researcher at KAI, focusing on topics like outlier and anomaly detection, pattern recognition, applied statistical methods and Machine Learning techniques. Since 2019 she is coordinating a team of Data Scientists, involved in various national and international funding projects and acts as a link between the industry and academic collaboration partners. She is supervising researchers and students, dealing with innovative data-analytical concepts within the semiconductor production, testing and optimization and publishes latest scientific insights in different conference and Journal papers. Beside this, Dr. Zernig participates in and supports local Data Science activities, e.g. she is part of the organizing team of the Women in Data Science Villach. In recent times, she is focusing on deployment strategies to guarantee sustainable Machine Learning lifecycles.

Sergey Gorinsky | IMDEA Networks Institute, Madrid |
Friday, November 12, 2021 | 14:00 (CET, 13:00 UTC) | S.0.05

Abstract:
Content delivery networks (CDNs) distribute much of the Internet content by caching and serving the objects requested by users. A major goal of a CDN is to maximize the hit rates of its caches, thereby enabling faster content downloads to the users. Content caching involves two components: an admission algorithm to decide whether to cache an object and an eviction algorithm to decide which object to evict from the cache when it is full. In this paper, we focus on cache admission and propose an algorithm called RL-Cache that uses model-free reinforcement learning (RL) to decide whether or not to admit a requested object into the CDN’s cache. Unlike prior approaches that use a small set of criteria for decision making, RL-Cache weights a large set of features that include the object size, recency, and frequency of access. We develop a publicly available implementation of RL-Cache and perform an evaluation using production traces for the image, video, and web traffic classes from Akamai’s CDN. The evaluation shows that RL-Cache improves the hit rate in comparison with the state of the art and imposes only a modest resource overhead on the CDN servers. Further, RL-Cache is robust enough that it can be trained in one location and executed on request traces of the same or different traffic classes in other locations of the same geographic region.

Bio:
Sergey Gorinsky is a tenured Research Associate Professor at IMDEA Networks Institute in Madrid, Spain. He joined the institute in 2009 and leads the NetEcon (Network Economics) research group there. Dr. Gorinsky received his Ph.D. and M.S. degrees from the University of Texas at Austin, USA in 2003 and 1999 respectively and Engineer degree from Moscow Institute of Electronic Technology, Zelenograd, Russia in 1994. From 2003 to 2009, he served on the tenure-track faculty at Washington University in St. Louis, USA. In 2010-2014, Dr. Gorinsky was a Ramón y Cajal Fellow funded by the Spanish Government. Sergey Gorinsky graduated four Ph.D. students. The areas of his primary research interests are computer networking, distributed systems, and network economics. His work appeared at top conferences and journals such as SIGCOMM, CoNEXT, INFOCOM, Transactions on Networking, and Journal on Selected Areas in Communications. He served as a TPC chair of ICNP 2017 and other conferences, as well as a TPC member for a much broader conference population. Sergey Gorinsky contributed to conference organization in many roles, such as a general chair of SIGCOMM 2018 and ICNP 2020. He also served as an evaluator of research proposals and projects for the European Research Council (ERC StG), European Commission (Horizon 2020, FP7), COST Association, and numerous other funding agencies.

Werner Bailer | Joanneum Research, Graz | Friday, June 25, 2021 | 10:00 (CET, 08:00 UTC) | online

Abstract:

Artificial neural networks have been adopted for a broad range of tasks in multimedia analysis and processing, such as visual and acoustic classification, extraction of multimedia descriptors or image and video coding. The trained neural networks for these applications contain a large number of parameters (weights), resulting in a considerable size. Thus, transferring them to a number of clients using them in applications (e.g., mobile phones, smart cameras) benefits from a compressed representation of neural networks.

MPEG Neural Network Coding and Representation is the first international standard for efficient compression of neural networks (NNs). The standard is designed as a toolbox of compression methods, which can be used to create coding pipelines. It can be either used as an independent coding framework (with its own bitstream format) or together with external neural network formats and frameworks. For providing the highest degree of flexibility, the network compression methods operate per parameter tensor in order to always ensure proper decoding, even if no structure information is provided. The standard contains compression-efficient quantization and an arithmetic coding scheme (DeepCABAC) as core encoding and decoding technologies, as well as neural network parameter pre-processing methods like sparsification, pruning, low-rank decomposition, unification, local scaling and batch norm folding. NNR achieves a compression efficiency of more than 97% for transparent coding cases, i.e. without degrading classification quality, such as top-1 or top-5 accuracies.

This talk presents an overview of the context, technical features and characteristics of NN coding standard, and discusses ongoing topics such as incremental neural network representation.

Bio:

Werner Bailer is a Key Researcher at DIGITAL – Institute for Information and Communication Technologies at JOANNEUM RESEARCH in Graz, Austria. He received a degree in Media Technology and Design in 2002 for his diploma thesis on motion estimation and segmentation for film/video standards conversion. His research interests include audiovisual content analysis, multimedia retrieval and machine learning. He regularly contributes to standardization, among others in MPEG, where he co-chairs the ad-hoc group on neural network compression.

Benedek Kovács PhD | Senior Specialist, Ericsson R&D at Hungary | Friday, May 28, 2021 |

10:30 (CET, 08:30 UTC) |

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Abstract: We overview the edge computing status from a telecommunication networks perspective, give a definition, introduce an example. We discuss the different driving forces in the telecommunication and cloud industry and go thorugh the different solution proposals. We cover the networking, cloud and management aspects and show the different options using the example.

Dragi Kimovski | Alpen-Adria-Universität Klagenfurt | Friday, December 18, 2020 | 11:00 (CET, 10:00 UTC) | online

Abstract: The computing continuum extends the high-performance cloud data centers with energy-efficient and low-latency devices close to the data sources located at the edge of the network. However, the heterogeneity of the computing continuum raises multiple challenges related to application and data management. These include (i) how to efficiently provision compute and storage resources across multiple control domains across the computing continuum, (ii) how to decompose and schedule an application, and (iii) where to store an application source and the related data. To support these decisions, we explore in this thesis, novel approaches for (i) resource characterization and provisioning with detailed performance, mobility, and carbon footprint analysis, (ii) application and data decomposition with increased reliability, and (iii) optimization of application storage repositories. We validate our approaches based on a selection of use case applications with complementary resource requirements across the computing continuum over a real-life evaluation testbed.

Bio: Dragi Kimovski is a postdoctoral researcher with “Zielvereinbarung” at the Institute of Information Technology (ITEC), University of Klagenfurt. He earned his doctoral degree in 2013 from the Technical University of Sofia. He was an assistant professor at the University for Information Science and Technology in Ohrid, and a senior researcher and lecturer at the University of Innsbruck. During his career, he conducted multiple research stays at the University of Michigan, University of Bologna, and University of Granada. He was a work package leader and scientific coordinator in two Horizon 2020 projects (ENTICE and ASPIDE), and coordinated the OeAD AtomicFog project. He co-authored more than 40 articles in international conferences and journals. His research interests include parallel and distributed computing, fog and edge computing, multi-objective optimization, and distributed processing for bioengineering applications.

DI Dr. Verena Radinger-Peer

Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, BOKU Vienna

Friday, October 23rd, 2020 | 9:30  HS10 | via classroom: https://classroom.aau.at/b/szo-n09-fyn-ovf

Abstract: Locating universities in regions where they should spur regional development has been recognized as an important regional political “instrument“ since the middle of the twentieth century. The expected impacts on regional development range from economic, political, demographic, infrastructural, cultural, educational, to social. Thereby various studies confirm that a) the university-region setting is unique and context-specific and b) universities will not spur regional development autonomously or inevitably.

The presentation will show the potentials but also challenges of universities being located in rural regions. This refers to balancing acts between internationality and regional focus, economic performance and societal engagement, curriculum development and demands of the regional/national labor market etc. Particular attention is paid to income students (origin, attraction) as well as the migration of graduates. Hereby the presentations builds on insights from former and ongoing projects with national and international case studies (Germany, The Netherlands). The presentation will conclude with insights on strategies how universities in rural siting regions dealt with this multiple challenges as well as how the regional environment influenced the role the universities resumed.

CV: Verena Radinger-Peer (Dr. nat. techn., Dipl.-Ing.) is trained as spatial and environmental planner and gained her PhD in regional development and economics at the University of Natural Resources and Life Sciences Vienna (BOKU). She was a visiting scholar at the Fraunhofer Institute ISI in Karlsruhe (Germany) as well as at the CHEPS (Center for Higher Education Policy Studies) at the University of Twente (The Netherlands).

Currently she is a FWF Hertha Firnberg Grant holder and investigates the interaction between universities and their surrounding region, with a special focus on knowledge transfer, student intake and mobility, labour market issues as well as regional engagement activities of universities.

Angeliki Katsenou | University of Bristol | September 29, 2020 | 10:00 (CET, 08:00 UTC)

Abstract: Cisco reported in the past reports that the video data share was expected to reach 80% by the year 2023. However, due to the pandemic and recently imposed a remote work lifestyle, this figure is expected to increase even more. Except for the on-demand and conferencing services, the number of users that are generating, storing, and sharing their content usually through either social media platforms or video-sharing platforms is increasing. Meanwhile from the video coding perspective, as video technologies evolve towards improved compression performance, their complexity inversely increases.

A challenge that many video service providers face is the heterogeneity of networks and display devices for streaming, as well as dealing with a wide variety of content with different encoding performance. In the past, a fixed bit rate ladder solution based on a „fitting all“ approach has been employed. However, such a content-tailored solution is highly demanding; the computational and financial cost of constructing the convex hull per video by encoding at all resolutions and quantization levels is huge. In this talk, we present a content-agnostic approach that exploits machine learning to predict the bit rate ladder with only a small number of encodes required.

Bio: Angeliki Katsenou is a Leverhulme Early Career Fellow and is with the Visual Information Lab at the University of Bristol since 2015. She obtained her Ph.D. degree from the Department of Computer Science and Engineering, University of Ioannina, Greece (2014). She received her Diploma in Electrical and Computer Engineering and an M.Sc. degree in Signal and Image Processing from the University of Patras, Greece. She has experience in several FP7 EC-funded and EPSRC projects, such as MSCA-ITN PROVISION and EPSRC Platform Grant EP/M000885/1. Her research interests include perceptual video analysis, video compression, image/video quality, and resource allocation for video communication systems. She has also been involved with conference organization activities and is currently one of the Technical Program Co-Chairs for Picture Coding Symposium (PCS) 2021, Bristol, UK.

The review of the TEWI colloquium of Laura Toni from June 26, 2020 comprises the video and slides (below):

Abstract: A major challenge for the next decade is to design virtual and augmented reality systems (VR at large) for real-world use cases such as healthcare, entertainment, e-education, and high-risk missions. This requires VR systems to operate at scale, in a personalized manner, remaining bandwidth-tolerant whilst meeting quality and latency criteria. One key challenge to reach this goal is to fully understand and anticipate user behaviours in these mixed reality settings.

This can be accomplished only by a fundamental revolution of the network and VR systems that have to put the interactive user at the heart of the system rather than at the end of the chain. With this goal in mind, in this talk, we describe our current researches on user-centric systems. First, we describe our view-port based streaming strategies for 360-degree video. Then, we present more in details our research on of users‘ behaviour analysis, when users interact with the 360-degree content. Specifically, we describe a set of metrics that allows us to identify key behaviours among users and quantify the level of similarity of these behaviours. Specifically, we present our clique-based clustering methodology, information theory and trajectory base in-depth analysis. Finally, we conclude with an overview of the extension of this work to navigation within volumetric video sequences.

Bio: Laura Toni received the M.S. and Ph.D. degrees, both in electrical engineering, from the University of Bologna, Bologna, Italy, in 2005 and 2009, respectively. In 2007, she was a Visiting Scholar at the University of California at San Diego (UCSD), San Diego, CA, USA, and since 2009, she has been a frequent visitor to the UCSD, working on media coding and streaming technologies. Between 2009 and 2011, she was with the Tele-Robotics and Application Department, Italian Institute of Technology, investigating wireless sensor networks for robotics applications. In 2012, she was a Postdoctoral Fellow at UCSD, and between 2013 and 2016, she was a Postdoctoral Fellow in the Signal Processing Laboratory (LTS4) at École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. Since July 2016, she has been a Lecturer in the Electronic and Electrical Engineering Department, University College London (UCL), U.K. Her research mainly involves interactive multimedia systems, decision-making strategies under uncertainty, large-scale signal processing, and communications. She received the UCL Future Leadership Award in 2016, the ACM Best 10% Paper Award in 2013, and the IEEE/IFIP Best Paper Award in 2012.

The review of the TEWI colloquium of Lucia D’Acunto from June 24, 2020 comprises the video and slides (below):

Abstract: In this talk, I will present the recent advancements on 5G for what concerns support for “the media vertical sector”, i.e., use cases involving the transmission of audiovisual content. I will begin by introducing the research that TNO has conducted on this topic in the past few years, starting with the H2020 TRIANGLE project, were we first adapted network orchestration to “communicate” with media orchestration components, such as a DASH Aware Network Element (DANE). Then, I will explain how we created media-specific 5G slices in the context of the H2020 5GINFIRE project, and what benefits media service providers can expect. I will further discuss about the advantages that edge computing offers to video production, based on our results from the H2020 FLAME project. Finally, I will give an overview of the standardization activities around this topic. I will conclude my talk with an outlook on future developments and offer some reflections on what researchers, telecom operators and service providers can expect.

Bio: Lucia D’Acunto received her PhD in 2012 from Delft University of Technology, the Netherlands, with a thesis on video streaming over peer-to-peer networks. She now works as a senior research scientist at TNO, focusing on video distribution and on the impact of future internet architectures (e.g. ICN, SDN and 5G) on it. She has led and is leading various European research projects on these topics, most notably the open call projects from the European Projects TRIANGLE, 5GINFIRE and FLAME. Since 2016, Lucia is an active participant and contributor to the 3GPP SA4 group, which focusses on mobile and 5G standardization for media applications. Lucia also serves in the organizing committees of several international conferences, usually in the roles of program chair or demo chair, and in the program committees. Lucia also regularly advises European operators on network and TV technologies and contributes to 5GPPP and NEM visions on the 5G Media Vertical and pilots. Lucia has published her research in several papers and journals and holds more than 15 patent applications.