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Improve and accelerate how we learn from health data: New approach reduces machine learning time by 60%

Electronic health records, like ELGA in Austria, provide an overview of laboratory results, diagnostics and therapies. Much could be learned from the personal and private data of individuals – with the help of machine learning – for use in the treatment of others. However, the use of the data is a delicate matter, especially when it comes to diseases that carry a stigma. Researchers involved in the EU project “Enabling the Big Data Pipeline Lifecycle on the Computing Continuum (DataCloud)” are working to make new forms of information processing suitable for medical purposes. Dragi Kimovski and his colleagues recently presented their findings in a publication. Read the complete article here.

 

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Second online meeting between Austria and China took place on 21.02.2022

Second online meeting between Austria and China took place on 21.02.2022. Consortium discussed aspects of sustainable transportation networks, #blockchain and new development strategies in line with #UnitedNations #IYBSSD #SDGs

Vignesh V Menon
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Paper accepted: Live-PSTR: Live Per-title Encoding for Ultra HD Adaptive Streaming

2022 NAB Broadcast Engineering and Information Technology (BEIT) Conference

April 24-26, 2022 | Las Vegas, US

Conference Website

Vignesh V Menon (Alpen-Adria-Universität Klagenfurt),  Hadi Amirpour (Alpen-Adria-Universität Klagenfurt), Christian Feldmann (Bitmovin, Klagenfurt),
Adithyan Ilangovan (Bitmovin, Klagenfurt), Martin Smole (Bitmovin, Klagenfurt), Mohammad Ghanbari (School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt).

Abstract:

Current per-title encoding schemes encode the same video content at various bitrates and spatial resolutions to find optimal bitrate-resolution pairs (known as bitrate ladder) for each video content in Video on Demand (VoD) applications. But in live streaming applications, a fixed bitrate ladder is used for simplicity and efficiency to avoid the additional latency to find the optimized bitrate-resolution pairs for every video content. However, an optimized bitrate ladder may result in (i) decreased storage or network resources or/and (ii) increased Quality of Experience (QoE). In this paper, a fast and efficient per-title encoding scheme (Live-PSTR) is proposed tailor-made for live Ultra High Definition (UHD) High Framerate (HFR) streaming. It includes a pre-processing step in which Discrete Cosine Transform (DCT)-energy-based low-complexity spatial and temporal features are used to determine the complexity of each video segment, based on which the optimized encoding resolution and framerate for streaming at every target bitrate is determined. Experimental results show that, on average, Live-PSTR yields bitrate savings of 9.46% and 11.99% to maintain the same PSNR and VMAF scores, respectively compared to the HTTP Live Streaming (HLS) bitrate ladder.

Architecture of Live-PSTR

Prof. Radu Prodan
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Exploit 5G potential: Research team builds “Carinthian Fog”

In Carinthia, researchers find an open test laboratory in the 5G Playground Carinthia, where the possibilities of the new mobile phone technology can be explored. The problem is: 5G enables the fast transmission of large amounts of data, but these also have to be processed. Read the whole interview of Univ.-Prof. DI Dr. Radu Prodan in the latest University Klagenfurt news.

FF4EuroHPC Project “CardioHPC” accepted

The project “CardioHPC” (CardioHPC Improving DL-based Arrhythmia Classification Algorithm and Simulation of Real-Time Heart Monitoring of Thousands of Patients) has been accepted in the “First call for FF4EuroHPC application experiments” (funded under the European Community’s Horizon 2020 Programme). Prof. Prodan will take over the project management in Klagenfurt.

The goal is to conduct an experiment to improve our DL-based arrhythmia classification algorithm and conduct a large-scale demonstration experiment to simulate a monitoring center for automated monitoring and alerting for 10K patients through HPC, focusing on quality and identifying HPC as a key tool for innovation.

Project Partners: The University of Stuttgart, Innovation Dooel, University in Skopje

Project duration: 15 months

ARTICONF Project Meeting in Ohrid, Macedonia

In a hybrid (i.e. online and offline) attendance mode at the project meeting in Ohrid, Macedonia, the ARTICONF team gave a final push to have a unified and integrated ARTICONF toolset for DApp developers. The consortium led by project coordinator Prof. Prodan also outlined a detailed action plan for the remaining six months with regards to exploitation and dissemination of ARTICONF’s latest results and developed technologies.

 

Prof. Radu Prodan
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Radu Prodan @ Horizon Cloud Summit 2021 & Cloud Expo Europe

The Horizon Cloud Summit 2021 – at its second edition – aims to gather innovators and researchers, Cloud adopters, policymakers, and Cloud initiatives and open source projects to shape the EU digital transition.

Radu Prodan held an online presentation: “ARTICONF: A Cloud-agnostic Blockchain-as-a-Service for Social Continuum“ on December 09, 2021.

 

Prof. Radu Prodan
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Radu Prodan gave a keynote at ICAML 2021

Prof. Radu Prodan held a keynote speech about the ARTICONF project at the 3rd International Conference on Applications of AI & Machine Learning (ICAML 2021).

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Internship 2022 at ATHENA

You are a Master Student and want to get to know more about ATHENA in a 3 months ATHENA internship in 2022?

Come and join our team! Apply now.

(Please note: application deadline is 14 December 2021)

 

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Paper in CMC-Computers, Materials & Continua Journal accepted

Title: FSpot: Fast Multi-Objective Heuristic for Efficient Video Encoding Workloads over AWS EC2 Spot Instance Fleet

Authors: Anatoliy Zabrovskiy, Prateek Agrawal, Vladislav Kashansky, Roland Kersche, Christian Timmerer, and Radu Prodan

Abstract: HTTP Adaptive Streaming (HAS) of video content is becoming an undivided part of the Internet and accounts for most of today’s network traffic. Video compression technology plays a vital role in efficiently utilizing network channels, but encoding videos into multiple representations with selected encoding parameters is a significant challenge. However, video encoding is a computationally intensive and time-consuming operation that requires high-performance resources provided by on-premise infrastructures or public clouds. In turn, the public clouds, such as Amazon elastic compute cloud (EC2), provide hundreds of computing instances optimized for different purposes and clients’ budgets. Thus, there is a need for algorithms and methods for optimized computing instance selection for specific tasks such as video encoding and transcoding operations. Additionally, the encoding speed directly depends on the selected encoding parameters and the complexity characteristics of video content. In this paper, we first benchmarked the video encoding performance of Amazon EC2 spot instances using multiple x264 codec encoding parameters and video sequences of varying complexity. Then, we proposed a novel fast approach to optimize Amazon EC2 spot instances and minimize video encoding costs. Furthermore, we evaluated how the optimized selection of EC2 spot instances can affect the encoding cost. The results show that our approach, on average, can reduce the encoding costs by at least 15.8% and up to 47.8% when compared to a random selection of EC2 spot instances.

Keywords: EC2 Spot instance, Encoding time prediction; adaptive streaming; video transcoding; Clustering; HTTP adaptive streaming; MPEG-DASH; Cloud computing; optimization; Pareto front.