The Klagenfurt University hosted the first ASPIDE technical meeting (30th September – 2nd October), which aims on designing scalable software solutions for exascale computing.
ASPIDE Meeting at Klagenfurt University
2019 ASPIDE Meeting Klagenfurt (Social Event)
Our paper has been accepted for publication at the MMM 2020 Conference on Multimedia Modeling. The work was conducted in the context of the ongoing OVID project.
Authors: Natalia Sokolova, Klaus Schoeffmann, Mario Taschwer (AAU Klagenfurt); Doris
Putzgruber-Adamitsch, Yosuf El-Shabrawi (Klinikum Klagenfurt)
Abstract:
In the field of ophthalmic surgery, many clinicians nowadays record their microscopic procedures with a video camera and use the recorded footage for later purpose, such as forensics, teaching, or training. However, in order to efficiently use the video material after surgery, the video content needs to be analyzed automatically. Important semantic content to be analyzed and indexed in these short videos are operation instruments, since they provide an indication of the corresponding operation phase and surgical action. Related work has already shown that it is possible to accurately detect instruments in cataract surgery videos. However, their underlying dataset (from the CATARACTS challenge) has very good visual quality, which is not reflecting the typical quality of videos acquired in general hospitals. In this paper, we therefore analyze the generalization performance of deep learning models for instrument recognition in terms of dataset change. More precisely, we trained such models as ResNet-50, Inception v3 and NASNet Mobile using a dataset of high visual quality (CATARACT) and test it on another dataset with low visual quality (Cataract-101), and vice versa. Our results show that the generalizability is rather low in general, but clearly worse for the model trained on the high-quality dataset. Another important observation is the fact that the trained models are able to detect similar instruments in the other dataset even if their appearance is different.
@articonf at #ICTPropDay in #Helsinki (towards @DSMeu) pic.twitter.com/SiOqRfw8MA
— Radu Prodan (@RaduProdanAAU) September 20, 2019
The papers titled, ‘ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment’ , and ‘A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications’ presented at Seventh Workshop on Large Scale Distributed Virtual Environments, LSDVE 2019 collocated at Europar 2019.
ARTICONF team also took advantage of the conference to hold a round table inter-cooperative discussion with an another Next Genreation Social Media EU H2020 project HELIOS with Dr. Laura Ricci, Dr. Dana Petcu, and Dr. Zhiming Zhao. Both project teams identified a number of similar research topics exiting within both projects namely #Context #Trust #CommunityDetection, and hence pledged support for improved inter and intra- cooperation.
The presented papers description are as follows:
1. ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment
Authors: Radu Prodan, Nishant Saurabh, Zhiming Zhao and Kate Orton-Johnson
2. A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications
Authors: Zahra Najafabadi Samani, Alexander Lercher, Nishant Saurabh, Radu Prodan
Articonf LSDVE, Euro-Par 2019
The paper “Dynamic Multi-Objective Virtual Machine Placement in Cloud Data Centers” has been accepted for publication at the Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019 to be held at Chalkidiki, Greece (August 28-30, 2019).
Author: Radu Prodan
Abstract: Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. Determining the effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Cloud data centers and depends on how Virtual Machines (VMs) are allocated to physical resources. In this paper, we propose a multi-objective framework for dynamic placement of VMs exploiting live-migration mechanisms which simultaneously optimize the resource wastage, overcommitment ratio and migration cost. The optimization algorithm is based on a novel evolutionary meta-heuristic using an island population model underneath. We implemented and validated our method based on an enhanced version of a well-known simulator.
The results demonstrate that our approach outperforms other related approaches by reducing up to 57% migrations energy consumption while achieving different energy and QoS goals.
The paper “MAPO: A Multi-Objective Model for IoT Application Placement in a Fog Environment” has been accepted for publication at the 9th International Conference on the Internet of Things, IoT 2019 to be held at Bilbao, Spain (October 22-25, 2019).
Authors: Narges Mehran, Dragi Kimovski, Radu Prodan (Alpen-Adria Universität Klagenfurt).
Abstract: The emergence of the Fog computing paradigm that leverages in-network virtualized resources raises important challenges in terms of resource and IoT application management in a heterogeneous environment offering only limited computing resources. In this work, we propose a novel Pareto-based approach for application placement close to the data sources called Multi-objective IoT Application Placement in fOg (MAPO). MAPO models applications based on a finite state machine and uses three convicting optimization objectives, namely completion time, energy consumption, and economic cost, considering both the computation and communication aspects. In contrast to existing solutions that optimize a single objective value, MAPO enables multi-objective energy and cost-aware application placement. To evaluate the quality of the MAPO placements, we created both simulated and real-world testbeds tailored for a set of medical IoT application case studies. Compared to the state-of-the-art approaches, MAPO reduces the economic cost by up to 27%, while decreasing the energy requirements by 23-68%, and optimizes the completion time by up to 7.3 times.
Keywords: Fog computing, IoT application placement, multi-objective optimization, energy consumption
Acknowledgement: Austrian Research Promotion Agency (FFG), project 848448, Tiroler Cloud, funded this work.
Prof. Radu Prodan and Dr. Dragi Kimovski participated in the ASPIDE meeting.
Aspide-Meeting-Madrid-2019
The papers titled, ‘ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment’ , and ‘A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications’ are accepted at Seventh Workshop on Large Scale Distributed Virtual Environments, LSDVE 2019 collocated at Europar 2019. The papers descriptions are as follows:
1. ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment
Authors: Radu Prodan, Nishant Saurabh, Zhiming Zhao and Kate Orton-Johnson
2. A Semantic Model with Self-Adaptive and Autonomous Relevant Technology for Social Media Applications
Authors: Zahra Najafabadi Samani, Alexander Lercher, Nishant Saurabh, Radu Prodan
Prof. Radu Prodan coordinated EU H2020 Project ARTICONF team met at Stavanger and held technical cum plenary discussions between June 4th to June 7th 2019 with a goal to present some exciting development towards next generation social media.
Articonf meeting at Stavanger 2019
Univ.-Prof. DI Dr. Radu Prodan has been invited to be a part of panel discussion on emerging Blockchain and Edge computing technology and paradigms at PAISE (Parallel AI and Systems for the Edge), collocated workshop at the prestigious 33rd IEEE International Parallel & Distributed Processing Symposium, IPDPS 2019 to be held at Rio de Janerio, Brazil (May 20-24 2019).