Dragi Kimovski
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Paper accepted in the IEEE Computer Magazine: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Title: Decentralized Machine Learning for Intelligent Health Care Systems on the Computing Continuum

Authors: Dragi Kimovski, Sasko Ristov, Radu Prodan

Abstract: The introduction of electronic personal health records (EHR) enables nationwide information exchange and curation among different health care systems. However, the current EHR systems do not provide transparent means for diagnosis support, medical research, or can utilize the omnipresent data produced by the personal medical devices. Besides, the EHR systems are centrally orchestrated, which could potentially lead to a single point of failure. Therefore, in this article, we explore novel approaches for decentralizing machine learning over distributed ledgers to create intelligent EHR systems that can utilize information from personal medical devices for improved knowledge extraction. Consequently, we proposed and evaluated a conceptual EHR to enable anonymous predictive analysis across multiple medical institutions. The evaluation results indicate that the decentralized EHR can be deployed over the computing continuum with reduced machine learning time of up to 60% and consensus latency of below 8 seconds.

5th Klagenfurt Winter Game Jam was a big success!

The 5th Klagenfurt Winter Game Jam took place as a hybrid event on Dec 17-19, 2021. Over 100 participants teamed up over the weekend to create altogether 26 games on the topic of “What’s in the box?”.

The games can be found and played at https://itch.io/jam/5th-winter-game-jam.

 

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.

 

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IEEE VCIP’21 Tutorial: A Journey towards Fully Immersive Media Access

IEEE VCIP 2021
Sunday, December 5, 2021
https://www.vcip2021.org/call-for-tutorials/

Find further info in the blog post here.

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Course “Generative Learning – Deepfakes for Good” in SS 2022 – Prof. Dr. Michael Riegler

ITEC is delighted to announce the next speaker in our guest lecture series – Prof. Dr. Michael Rielger from  SIMULA Research, Norway. The course will take place from May 30 – June 03, 2022.

The course will give an introduction into the topic of generative adversarial networks and synthetic data. Please register at the course 623.714

Further information is available HERE.

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Emmy Award Bitmovin for Innovation at University Klagenfurt

We happily announce, the #Emmy Award Bitmovin (link) for innovation in online TV for @bitmovin arrived at University of Klagenfurt.

 

 

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State of Carinthia Award for Master Thesis in the ARTICONF Project

Alexander Lercher’s Master Thesis titled ‘Context-aware Community Evolution Prediction in Distributed Social Networks‘ under the supervision of Prof. Radu Prodan at ITEC has been awarded outstanding academic thesis and a grant of 600 Euros in the field of digitization for Master’s Category. This work received funding from European Union’s Horizon 2020 research and innovation programme, grant agreement 825134, “Smart Social Media Ecosystem in a Blockchain Federated Environment (ARTICONF)”. The award ceremony will take place on Wednesday, November 24th, 2021 at 11 a.m. in the mirror hall of the Carinthian Provincial Government.

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Farzad Tashtarian to give a talk at IMDEA Networks Institute, Madrid, Spain

Farzad Tashtarian is invited to talk on “LwTE: Light-weight Transcoding at the Edge” at IMDEA Networks Institute, Madrid, Spain.

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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.