On October 27th, 2021, Negin Ghamsarian successfully defended her thesis on “Deep-Learning-Assisted Analysis of Cataract Surgery VIdeos” under the supervision of Prof. Klaus Schöffmann. The defense was chaired by Prof. Hermann Hellwagner and the examiners were Prof. Henning Müller (University of Applied Sciences Western Switzerland and the University of Geneva) and Prof. Raphael Sznitman (University of Bern). Congratulations to Dr. Ghamsarian for this great achievement!
Title: Monitoring System Architecture for the Multi-Scale Blockchain-based Logistic Network
Authors: Vladislav Kashansky, Radu Prodan, Aso Validi, Cristina Olaverri-Monreal, Gleb Radchenko
Abstract: Contemporary control processes and methods in multi-scale, cyber-physical systems require precise data collection at various levels, timely transmission, and analysis involving large number of computing and storage elements connected within high-performance permissioned consensus networks. For example, in transport networks, resources tend to form multi-scale dynamical systems with diverse operational requirements, including data exchange policies and consensus protocols. Apart from designing complete topology, chaincodes and consensus logic, effective monitoring of the applications and infrastructure of such complex systems remains a research challenge. In this paper, we discuss important aspects of the data-intensive applications monitoring investigated in the frames of the ADAPT project.
We present highlights on the tool-sets, architectures and details on possible optimization approaches for monitoring data collection. We introduce a dynamic multi-scale monitoring system architecture with preliminary workflow model. It allows obtaining effective low-latency publish-subscribe matching of the dynamically varying monitoring tasks and executing machines.
Keywords: Logistics, transportation, decentralization, blockchain, monitoring systems, optimization, data-intensive systems, hybrid systems
Title: Autotuning of exascale applications with anomalies detection
Authors: Dragi Kimovski, Roland Mathá, Gabriel Iuhasz, Fabrizio Marozzo, Dana Petcu, Radu Prodan
Abstract: The execution of complex distributed applications in exascale systems faces many challenges, as it involves empirical evaluation of countless code variations and application run-time parameters over a heterogeneous set of resources. To mitigate these challenges, the research field of autotuning has gained momentum. The autotuning automates identifying the most desirable application implementation in terms of code variations and run-time parameters. However, the complexity and size of the exascale systems make the autotuning process very difficult, especially considering the number of parameter variations that have to be identified. Therefore, we introduce a novel approach for autotuning of exascale applications based on a genetic multi-objective optimization algorithm integrated within the ASPIDE exascale computing framework. The approach considers multi-dimensional search space with support for pluggable objectives functions, including execution time and energy requirements. Furthermore, the autotuner employs a machine learning-based event detection approach to detect events and anomalies during application execution, such as hardware failures or communication bottlenecks.
Keywords: Exascale computing, Autotuning, Events and Anomalies Detection, Multi-objective Optimisation
Univ.-Prof. DI Dr. Radu Prodan was nominated as discussion leader for the defensio of Mrs. Zeinab Bakhshi from Mälardalen University in Stockholm Sweden.
Farzad Tashtarian is invited to talk on “Network-Assisted Video Streaming” at the University of Isfahan, Isfahan, Iran.
Dr. Gerhard Burian and Mag. Vladislav Kashansky participated on behalf of ADAPT collaboration in the international conference: Climate protection: state of play, division of labor, steps forward held at OeNB, Vienna on 07.10.2021.
The first face-to-face DataCloud Meeting took place in Rome, Italy, from October 04-06, 2021. The consortium discussed the architecture and the business cases in preparation for the first project review.
Ekrem Çetinkaya got the Best Doctoral Symposium Paper Award at ACM MMSys 2021 for his paper titled “Machine Learning Based Video Coding Enhancements for HTTP Adaptive Streaming”. More information about the paper can be found HERE.
Congratulations to Natalia Sokolova, who got her journal paper on “Automatic detection of pupil reactions in cataract surgery videos” accepted in the PLOS ONE journal. This work has been (co-)authored by Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer, Stephanie Sarny, Doris Putzgruber-Adamitsch, and Yosuf El-Shabrawi.
The first face-to-face ADAPT Meeting took place on September 30, 2021, in Klagenfurt. The consortium discussed important aspects of systems integration, current achievements and listed down strategies for validating and exploiting ADAPT use cases through real-world testing and external collaboration. The consortium further agreed to continue its ongoing efforts in disseminating scientific and technical results.
