Distributed and Parallel Systems

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Reza Farahani gave a tutorial at the 12th European Workshop on Visual Information Processing 2024 in Geneva, Switzerland

Dr. Reza Farahani (University of Klagenfurt, Austria) and Dr. Vignesh V Menon (Fraunhofer HHI, Germany) presented a joint tutorial titled ‘Latency- and Energy-Aware Video Coding and Delivery Streaming Systems’ at the 12th European Workshop on Visual Information Processing (EUVIP 2024) on September 8.

Abstract: This tutorial introduces modern performance and energy-aware video coding and content delivery solutions and tools, focusing on popular video streaming applications, i.e., VoD and live streaming. In this regard, after introducing fundamentals of modern video encoding and networking paradigms, we introduce modern solutions systems, using per-title encoding, per-scene encoding, virtualized and software networks, edge computing, overlay networks such as Content Delivery Networks (CDNs) and/or Peer-to-Peer (P2P) paradigms to provide latency and energy-efficient VoD and live HAS streaming. Furthermore, the tutorial also presents our tools, software, datasets, and testbeds to demonstrate our latest achievements and share practical insights for researchers, engineers, and students who want to improve conversational streaming or even test such techniques for immersive video sequences (e.g., tile-based 360-degree VR) with a focus on latency, economic cost, and energy.

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Paper accepted for 20th International Conference on Network and Service Management (CNSM 2024)

Title: High Complexity and Bad Quality? Efficiency Assessment for Video QoE Prediction Approaches

Authors: Frank Loh, Gülnaziye Bingöl, Reza Farahani, Andrea Pimpinella, Radu Prodan, Luigi Atzori, Tobias Hoßfeld

Venue: 20th International Conference on Network and Service Management (CNSM 2024)

Abstract:  In recent years, video streaming has dominated Internet data traffic, prompting network providers to ensure high-quality streaming experiences to prevent customer churn. However, due to the encryption of streaming traffic, extensive network monitoring by providers is required to predict the streaming quality and improve their services. Several such prediction approaches have been studied in recent years, with a primary focus on the ability to determine key video quality degradation factors, often without considering the required resources or
energy consumption. To address this gap, we consider existing methods to predict key Quality of Experience (QoE) degradation factors from the literature and quantify the data that have to be monitored and processed for video streaming applications. Based on this, we assess the efficiency of different QoE degradation factor prediction approaches and quantify the ratio between efficiency and the achieved prediction quality. In this context, we identify significant disparities in the efficiency, influenced by data requirements and the specific prediction approach, and finally by the resulting quality. Consequently, we provide insights for network providers to choose the most appropriate method tailored to their specific requirements.

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CERCIRAS Summer School @ AAU – 25.-30.08.2024

CERCIRAS (CA19135), short for Connecting Education and Research Communities for an Innovative Resource Aware Society is a 4-year long COST Action, started at the end of September 2020 and now nearing completion. One of the highlights of CERCIRAS has been its yearly Training School, a week-long residential school designed for current and future PhD students with research interests falling within the thematic scope of the Action and open to other profiles, including industry professionals. While perturbed by unfortunate overlap with the outbreak of the COVID pandemic, CERCIRAS managed to execute three consecutive Training Schools successfully: mid-September 2022 in Split (HR); early September 2023 in Riga (LV); and late August 2024 in Klagenfurt (AT). All editions of the CERCIRAS Training School follow a common structure: 4 selected lecture topics, which alternate frontal lessons and assisted hands-on works; 25 to 35 participants from as many participating countries as possible, with rich diversity for provenance, seniority level, and research focus; a rich portfolio of social activities.

The Training School places a considerable burden on the local hosts, which includes securing comfortable and affordable accommodation for a large troop, providing a modern and spacious lecture hall for all lectures and labs, with refreshments for the two daily breaks and nearby canteens for lunch, and arranging exciting options for a whole-afternoon diversion.

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6G Continuum Kick-Off Meeting and Research Collaboration

Dragi Kimovski recently visited Mother Theresa University in Skopje as part of the OeAD 6G Continuum project, which focuses on developing middleware for Artificial Intelligence over 6G networks. This visit is the next step in the collaboration between the involved institutions. During the stay, the kick-off meeting for the 6G Continuum project took place, bringing together researchers from both institutions to discuss the project’s goals and clarify the the research activities in terms of supporting AI traning and inference in the Edge over 6G networks. In addition to the research focus, this visit also helped to strengthen the ongoing partnership between the institutions. They explored new opportunities for collaboration in teaching and research, including student and faculty exchanges through Erasmus and CEEPUS programs.

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Radu Prodan @ 5th edition of the Data Science International Summer School in Bukarest

From July 20-28, 2024, the 5th Edition of the Data Science International Summer School, managed by Bucharest Business School (BBS @ ASE) and in collaboration with  GATE Institute at Sofia University “St. Kliment Ohridski” and the projects enRichMyData , Graph-Massivizer , UPCAST , INTEND , and InterTwino took place in Predeal, Romania. Radu participated as a speaker and mentor and also presented the Graph-Massivizer project.

Prestigious Speakers:

Dan Nicolae (University of Chicago, USA), Razvan Bunescu (University of North Carolina at Charlotte, USA), Anna Fensel Wageningen University & Research, the Netherlands), Radu Prodan (University of Klagenfurt, Austria), Ioan Toma (Onlim GmbH, Austria), Dumitru Roman (SINTEF / University of Oslo, Norway), Jože Rožanec (Qlector, Slovenia), Daniel Thilo Schroeder (SINTEF, Norway), Gabriel Terejanu, PhD (University of North Carolina at Charlotte, USA), Hui Song (SINTEF, Norway), Viktor Sowinski-Mydlarz (London Metropolitan University, UK and GATE Institute, Bulgaria), Roberto Avogadro (SINTEF, Norway), Nikolay Nikolov (SINTEF AS, Norway).

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Horizon Europe project DATAPACT accepted

The EU has approved the DATAPACT project (Datapact: Compliance by Design of Data/AI Operations and Pipelines) application.The project has a total volume of 9,9 Mio. Euros and 19 partners, including ITEC (Radu Prodan).

DataPACT will develop novel tools and methodologies that enable efficient, compliant, ethical, and sustainable data/AI operations and pipelines. DataPACT will deliver a transformative approach where compliance, ethics, and environmental sustainability are not afterthoughts but foundational elements of data/AI operations and pipelines.

 

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Journal article accepted: The Computing Continuum: From IoT to the Cloud

Autohors: Auday Al-Dulaimy, Matthijs Jansen, Bjarne Johansson, Animesh Trivedi, Alexandru Iosup, Mohammad Ashjaei, Antonino Galletta, Dragi Kimovski, Radu Prodan, Konstantinos Tserpes, George Kousiouris, Chris Giannakos, Ivona Brandic, Nawfal Ali, Andre B. Bondi, Alessandro V. Papadopoulos

Journal “Internet of things”: https://link.springer.com/journal/43926

Abstract:

In the era of the IoT revolution, applications are becoming ever more sophisticated and accompanied by diverse functional and non-functional requirements, including those related to computing resources and performance levels. Such requirements make the development and implementation of these applications complex and challenging. Computing models, such as cloud computing, can provide applications with on-demand computation and storage resources to meet their needs. Although cloud computing is a great enabler for IoT and endpoint devices, its limitations make it unsuitable to fulfill all design goals of novel applications and use cases. Instead of only relying on cloud computing, leveraging and integrating resources at different layers (like IoT, edge, and cloud) is necessary to form and utilize a computing continuum.

The layers’ integration in the computing continuum offers a wide range of innovative services, but it introduces new challenges (e.g., monitoring performance and ensuring security) that need to be investigated. A better grasp and more profound understanding of the computing continuum can guide researchers and developers in tackling and overcoming such challenges. Thus, this paper provides a comprehensive and unified view of the computing continuum. The paper discusses computing models in general with a focus on cloud computing, the computing models that emerged beyond the cloud, and the communication technologies that enable computing in the continuum. In addition, two novel reference architectures are presented in this work: one for edge-cloud computing models and the other for edge-cloud communication technologies. We demonstrate real use cases from different application domains (like industry and science) to validate the proposed reference architectures, and we show how these use cases map onto the reference architectures. Finally, the paper highlights key points that express the authors’ vision about efficiently enabling and utilizing the computing continuum in the future.

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Paper accepted in Nature Scientific Data

Title: Cataract-1K Dataset for Deep-Learning-Assisted Analysis of Cataract Surgery Videos

Authors: Negin Ghamsarian, Yosuf El-Shabrawi, Sahar Nasirihaghighi, Doris Putzgruber-Adamitsch, Martin Zinkernagel, Sebastian Wolf, Klaus Schoeffmann, and Raphael Sznitman

Abstract: In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons’ skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. In particular, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations are publicly available in Synapse.

 

The paper is available here: https://doi.org/10.1038/s41597-024-03193-4

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Accepted: A Tutorial on Latency- and Energy-Aware Video Coding and Delivery Streaming Systems

Authors: Reza Farahani (AAU, Austria), and Vignesh V Menon (Fraunhofer HHI, Berlin, Germany)

Venue: The 12th European Workshop on Visual Information Processing (EUVIP 2024)

08-11 September, 2024 in Geneva, Switzerland

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Radu Prodan received Award for Editorial Excellence

Based on the 2023 TPDS editorial data and his excellent performance, Radu Prodan received the 2024 IEEE TPDS Award for Editorial Excellence. His achievement will be recognized by IEEE and his name will appear at the IEEE award website https://next-test.computer.org/digital-library/journals/td/tpds-award-for-editorial-excellence.

Congratulations!