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Paper accepted at IEEE 23rd International Workshop on Multimedia Signal Processing – WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

Title: WISH: User-centric Bitrate Adaptation for HTTP Adaptive Streaming on Mobile Devices

IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP)

October 06-08, Tampere, Finland

Authors: Minh Nguyen (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Ekrem Çetinkaya (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), Hermann Hellwagner (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt), and Christian Timmerer (Christian Doppler Laboratory ATHENA, Alpen-Adria-Universität Klagenfurt)

Abstract: Recently, mobile devices have become paramount in online video streaming. Adaptive bitrate (ABR) algorithms of players responsible for selecting the quality of the videos face critical challenges in providing a high Quality of Experience (QoE) for end users. One open issue is how to ensure the optimal experience for heterogeneous devices in the context of extreme variation of mobile broadband networks. Additionally, end users may have different priorities on video quality and data usage (i.e., the amount of data downloaded to the devices through the mobile networks). A generic mechanism for players that enables specification of various policies to meet end users’ needs is still missing. In this paper, we propose a weighted sum model, namely WISH, that yields high QoE of the video and allows end users to express their preferences among different parameters (i.e., data usage, stall events, and video quality) of video streaming. WISH has been implemented into ExoPlayer, a popular player used in many mobile applications. The experimental results show that WISH improves the QoE by up to 17.6% while saving 36.4% of data usage compared to state-of-the-art ABR algorithms and provides dynamic adaptation to end users’ requirements.

Keywords: ABR Algorithms, HTTP Adaptive Streaming, ITU-T P.1203, WISH

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Successful completion and demo of the Karl Popper Kolleg (KPK) Networked Autonomous Aerial Vehicles (NAV)

Robotics research in Klagenfurt enjoys international success

With a total of 9 contributions at this year’s ICRA, one of the flagship conferences in the field of robotics, the University of Klagenfurt has joined the league of the world’s most important robotics hubs. Among the contributors are the young researchers from the Karl Popper Doktorats- und Wissenschaftskolleg “Networked Autonomous Aerial Vehicles (NAV)”, which is currently celebrating its conclusion with a drone flight demonstration in Klagenfurt. Read more at the University Klagenfurt blog and here.

 

Paper accepted at EuroPar LSDVE Workshop 2021

Title: SMART: a Tool for Trust and Reputation Management in Social Media

Authors: Manuel Herold, Nishant Saurabh, Hamid Mohammadi Fard, Radu Prodan

Abstract: Social media platforms are becoming increasingly popular and essential for next-generation connectivity. However, the emergence of social media also poses critical trust challenges due to the vast amount of created and propagated content. This paper proposes a data-driven tool called SMART for trust and reputation management based on community engagement and rescaled sigmoid model. SMART’s integrated design adopts a set of expert systems with a unique inference logic for trust estimation to compute weighted trust ratings of social media content. SMART further utilizes the trust ratings to compute user reputation and represent them using a sigmoid curve that prevents infinite accumulation of reputation ratings by a user. We demonstrate the SMART tool prototype using a pilot social media application and highlight its user-friendly interfaces for trustworthy content exploration.

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KWF/EFRE project “ONTIS” accepted

The project “ONTIS” (Ontology-based Interoperability of Systems) has been accepted in the EFRE call of KWF (Kärntner Wirtschaftsförderungs Fonds).

The ONTIS project targets the development of methodologies for automatically establishing interoperability between information systems through the combination of ontological expert knowledge and machine learning-based models. With the specific goal of improving the error-prone manual integration of ontological knowledge, ONTIS focuses on applying deep neural networks for processing natural language and visual concepts for automatic semantic annotation.

Project duration: 18 months

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Farzad Tashtarian and Christian Timmerer Co-Chairs of ViSNext 2021 Workshop at the ACM CoNEXT 2021 Conference

ViSNext’21: 1st ACM CoNEXT Workshop on Design, Deployment, and Evaluation of Network-assisted Video Streaming

In recent years, we have witnessed phenomenal growth in live video traffic over the Internet, accelerated by the rise of novel video streaming technologies, advancements in networking paradigms, and our ability to generate, process, and display videos on heterogeneous devices. Regarding the existing constraints and limitations in different components on the video delivery path from the origin server to clients, the network plays an essential role in boosting the perceived Quality of Experience (QoE) by clients. The ViSNext workshop aims to bring together researchers and developers working on all aspects of video streaming, in particular network-assisted concepts backed up by experimental evidence. Read more about the workshop, call for papers at ViSNext2021 and registration here.

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Paper acepted @ GoodIT 2021

The paper “The ADAPT Project: Adaptive and Autonomous Data” has been accepted to appear at the conference ACM International Conference on Information Technology for Social Good (GoodIT 2021) as a regular paper.

Authors: Nishant Saurabh, Vladislav Kashanskii, Radu Prodan, Aso Validi, Christina Olaverri-Monreal

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Master thesis at ITEC wins Dynatrace Outstanding IT-Thesis Award

With his master thesis about “Animating Characters using Deep Learning based Pose Estimation”, Fabian Schober won the “Dynatrace Outstanding IT-Thesis Award” (DO*IT*TA). The award brings attention to extraordinary theses, motivates creativity, and provides insight into modern technologies.
In his thesis, Fabian Schober focuses on animating 2D (video game) characters using the PoseNet pose estimation model. He delivers a proof of concept on how new machine learning technologies can assist in video game development. Read more at the University press release (German only).

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FFG project “Kärntner Fog” accepted

The project “Kärntner Fog” has been accepted in the BRIDGE funding call of FFG.

Abstract: Kärntner Fog aims to contribute with advanced technologies for the distributed optimized provisioning and operation of 5G applications in Austria. For this purpose, it researches and develops a unique infrastructure testbed called the Carinthian Computing Continuum (C3), consisting of heterogeneous Cloud, Fog, and 5G‐Edge devices orchestrated through novel benchmarking, monitoring, analysis, and provisioning services. The project will validate its results using modern virtual reality and smart city use cases in the 5G Playground Carinthia. The results will give companies a competitive technological advantage in exploring 5G‐compliant applications in preparation for the deployment of an Austrian‐wide 5G network by 2025.

Partners:

Alpen-Adria Universität Klagenfurt, ITEC

Fachhochschule Kärnten

siplan gmbh

Project duration: 36 months

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Christian Doppler ‘pilot’ laboratory ATHENA to transition to a regular CD laboratory

Successful review of the first research phase: Christian Doppler ‘pilot’ laboratory ATHENA to transition to a regular CD laboratory two years after launch. Read more about it at ATHENA website and the press release at AAU website.

 

Dragi Kimovski
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Paper accepted in IEEE Transactions on Services Computing (TSC)

The manuscript “Mobility-Aware IoT Application Placement in the Cloud — Edge Continuum” has been accepted for publication in the A* (IF: 5.823) Journal – IEEE Transactions on Services Computing (TSC).

Autors: Dragi Kimovski, Narges Mehran, Christopher Kerth, Radu Prodan

Abstract: The Edge computing extension of the Cloud services towards the network boundaries raises important placement challenges for IoT applications running in a heterogeneous environment with limited computing capacities. Unfortunately, existing works only partially address this challenge by optimizing a single or aggregate objective (e.g., response time), and not considering the edge devices’ mobility and resource constraints. To address this gap, we propose a novel mobility-aware multi-objective IoT application placement (mMAPO) method in the Cloud – Edge Continuum that optimizes completion time, energy consumption, and economic cost as conflicting objectives. mMAPO utilizes a Markov model for predictive analysis of the Edge device mobility and constrains the optimization to devices that do not frequently move through the network. We evaluate the quality of the mMAPO placements using simulation and real-world experimentation on two IoT applications. Compared to related work, mMAPO reduces the economic cost by 28% and decreases the completion time by 80% while maintaining a stable energy consumption.