MMM and VBS 2020 in Daejeon (South Korea)

At the 26th International Conference on MultiMedia Modeling (MMM 2020) in Daejeon, Korea, researchers from ITEC have successfully presented several scientific contributions to the multimedia community. First, Natalia Sokolova presented her first paper on “Evaluating the Generalization Performance of Instrument Classification in Cataract Surgery Videos”. Next, Sabrina Kletz presented her work on “Instrument Recognition in Laparoscopy for Technical Skill Assessment”. Finally, Andreas Leibetseder talked about “GLENDA: Gynecologic Laparoscopy Endometriosis Dataset”.

“Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network” has been accepted

The paper “Deblurring Cataract Surgery Videos Using a Multi-Scale Deconvolutional Neural Network” has been accepted for publication at the “IEEE International Symposium on Biomedical Imaging”, located at Iowa City, Iowa, USA (April 3-7, 2020). This conference is a joint initiative from the IEEE Signal Processing Society (SPS) and the IEEE Engineering in Medicine and Biology Society (EMBS).
Authors: Negin Ghamsarian, Klaus Schoeffmann, Mario Taschwer

Abstract: A common quality impairment observed in surgery videos is blur, caused by object motion or a defocused camera. Degraded image quality hampers the progress of machine-learning-based approaches in learning and recognizing semantic information in surgical video frames like instruments, phases, and surgical actions. This problem can be mitigated by automatically deblurring video frames as a preprocessing method for any subsequent video analysis task. In this paper, we propose and evaluate a multi-scale deconvolutional neural network to deblur cataract surgery videos. Experimental results confirm the effectiveness of the proposed approach in terms of the visual quality of frames as well as PSNR improvement.

Keywords: Video Deblurring, Deconvolutional Neural Networks, Cataract Surgery Videos

Acknowledgment: This work was funded by the FWF Austrian Science Fund under grant P 31486-N31

Natalia Sokolova

ITEC Staff at VBS 2020 in Daejeon (South Korea)

VBS 2020 in Daejeon (South Korea) was an amazing event with a lot of fun! Eleven teams, each consisting of two users (coming from 11 different countries) competed against each other in both a private session for about 5 hours and a public session for almost 3 hours. ITEC did also participate with two teams. In total all teams had to solve 22 challenging video retrieval tasks, issued on a shared dataset consisting of 1000 hours of content (V3C1)! Many thanks go to the VBS teams but also to the VBS organizers as well as the local organizers, who did a great job and made VBS2020 a wonderful and entertaining event!

Prof. Radu Prodan
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Radu Prodan at the PhD defense of Alexey Ilyushkin

200+ excited IT experts at Josef Hammer’s talk on Edge Computing

High-tech meets history. When thousands of international software developers gather at the Vienna Imperial Castle (Hofburg Wien), you can feel that magic is about to happen. Exactly that occurred on November 28 and 29 at this year’s We Are Developers Congress in Vienna.

Josef Hammer - Edge Computing

‘Are you on the Edge? Or still in the Cloud?’ – On one of the three stages, Josef Hammer inspired over 200 IT enthusiasts with a 30-minute talk on Edge Computing and 5G networks. As with the transition from mainframes to desktop computers, in the upcoming years a lot of processing will move from the cloud to the edge of the network, i.e. closer to the user. This will particularly affect areas with high data volume (IoT, AI) and low latency requirements (IoT).

Josef gave a short introduction to this exciting new area and its benefits and use cases, which frameworks and tools developers can use right now, and where we might be headed. Especially the presentation of our 5G Playground Carinthia was curiously followed by the attendees who enjoyed a first glance at the ambitious research projects conducted here.

More information:

https://5gplayground.at/

https://www.wearedevelopers.com/events/congress-vienna/

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M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications has been accepted to the Euromicro PDP’2020

The paper has been accepted (through double-blind peer review) as a regular paper of the Euromicro PDP’2020 conference to be held in Vasteras, Sweden on 11-13 March, 2020.

Title: M3AT: Monitoring Agents Assignment Model for Data-Intensive Applications

Authors: Vladislav Kashansky, Dragi Kimovski, Radu Prodan, Prateek Agrawal, Fabrizio Marozzo, Iuhasz Gabriel, Marek Justyna and Javier Garcia-Blas

Abstract: Nowadays, massive amounts of data are acquired, transferred, and analyzed nearly in real-time by utilizing a large number of computing and storage elements interconnected through high-speed communication networks. However, one issue that still requires research effort is to enable efficient monitoring of applications and infrastructures of such complex systems. In this paper, we introduce a Integer Linear Programming (ILP) model called M3AT for optimised assignment of monitoring agents and aggregators on large-scale computing systems. We identified a set of requirements from three representative data-intensive applications and exploited them to define the model’s input parameters. We evaluated the scalability of M3AT using the Constraint Integer Programing (SCIP) solver with default configuration based on synthetic data sets. Preliminary results show that the model provides optimal assignments for systems composed of up to 200 monitoring agents while keeping the number of aggregators constant and demonstrates variable sensitivity with respect to the scale of monitoring data aggregators and limitation policies imposed.

Keywords: Monitoring systems, high performance computing, aggregation, systems control, data-intensive systems, generalized assignment problem, SCIP optimization suite.

Acknowledgement: This work has received funding from the EC-funded project H2020 FETHPC ASPIDE (Agreement #801091)

3rd Winter Game Jam in Klagenfurt on Dec 20-22, 2019

Save the date! 3rd Winter Game Jam is around the corner: Dec 20-22, 2019! Make sure to get a ticket asap on https://itec.aau.at/gamejam

Thanks to our sponsors Anexia, Alpen-Adria Universität Klagenfurt, Alturos, Bitmovin, Dynatrace, Sensolligent, and Imendo for supporting us!

What is a game jam? The 3rd Winter Game Jam is open to everyone who likes games and wants to create, test and talk about games. Starting on Friday the topic will be revealed to all participants at the same time and random groups will brainstorm games. Then, after the ideas are pitched, team will emerge around ideas and games are to be created. Finally, on Sunday, the projects are presented and can be played and tested.

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Narges Mehran presented her work at the 9th International Conference on the Internet of Things, IoT 2019

Narges Mehran presented the paper  “MAPO: A Multi-Objective Model for IoT Application Placement in a Fog Environment” at the 9th International Conference on the Internet of Things, IoT 2019 in 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 with 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 using three conflicting optimization objectives, completion time, energy consumption, and economic cost, and considering both the computation and communication aspects. In contrast to existing solutions that optimize a single objective, 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 28%, while decreasing the energy requirements by 29-64% on average, and improves the completion time by a factor of six.

Track: IoT Edge and Cloud @IoT’19
Acknowledgement: Austrian Research Promotion Agency (FFG), project 848448, Tiroler Cloud, funded this work.

Prof. Radu Prodan
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Prof. Radu Prodan participated at the PhD examination of Dr. Yang Hu and Dr. Huan Zhou at the University of Amsterdam, Netherlands.

Prof. Radu Prodan participated at the PhD examination of Dr. Yang Hu and Dr. Huan Zhou at the University of Amsterdam, Netherlands.

With: Prof. Peter van Emde Boas, Prof. Cees de Laat, Prof. Henri E. Bal, Dr. Paola Grosso, Dr. Zhiming Zhao, Prof. Rob van Nieuwpoort, Prof. Pieter Adriaans, Dr. Adam S.Z. Belloum

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Sabrina Kletz presented her work at the MIAR Workshop @ MICCAI 2019

Sabrina Kletz presented the paper “Learning the Representation of Instrument Images in Laparoscopy Video” at the MIAR Workshop @ MICCAI 2019 in Shenzhen, China.

Authors: Sabrina Kletz, Klaus Schoeffmann, Heinrich Husslein

Abstract: Automatic recognition of instruments in laparoscopy videos poses many challenges that need to be addressed like identifying multiple instruments appearing in various representations and in different lighting conditions which in turn may be occluded by other instruments, tissue, blood or smoke. Considering these challenges it may be beneficial for recognition approaches that instrument frames are first detected in a sequence of video frames for further investigating only these frames. This pre-recognition step is also relevant for many other classification tasks in laparoscopy videos such as action recognition or adverse event analysis. In this work, we address the task of binary classification to recognize video frames as either instrument or non-instrument images. We examine convolutional neural network models to learn the representation of instrument frames in videos and take a closer look at learned activation patterns. For this task, GoogLeNet together with batch normalization is trained and validated using a publicly available dataset for instrument count classifications. We compare transfer learning with learning from scratch and evaluate on datasets from cholecystectomy and gynecology. The evaluation shows that fine-tuning a pre-trained model on the instrument and non-instrument images is much faster and more stable in learning than training a model from scratch.

Conference: 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), October 13–17, 2018, Shenzhen, China

Track: Medical Imaging and Augmented Reality (MIAR) Workshop @MICCAI