The enduring popularity of the Pokémon franchise can be explained by a mix of nostalgia, constant innovation, and their appeal to a wide and diverse fan base, as Felix Schniz, game studies scholar and senior scientist at the University of Klagenfurt, explains: Pokémon is a pioneer of this dynamic. The game perfectly shows how a franchise can stay fresh and relevant through the ongoing reinterpretation of its genre dimensions.

Read the whole interview here: Dringel, Severin: “Tag des Pokémon – Warum Pokémon 28 Jahre später immer noch ein Renner ist.” Kleine Zeitung International, 27.02.2024. https://www.kleinezeitung.at/international/18047876/warum-pokemon-auch-nach-zwanzig-jahren-immer-noch-ein-renner-ist

 

Pokemon Ash Transparent Background PNG Image

 

 

 

”Fictional Practices of Spirituality” provides critical insight into the implementation of belief, mysticism, religion, and spirituality into (digital) worlds of fiction. This first volume focuses on interactive, virtual worlds – may that be the digital realms of video games and VR applications or the imaginary spaces of life action role-playing and soul-searching practices. It features analyses of spirituality as gameplay facilitator, sacred spaces and architecture in video game geography, religion in video games and spiritual acts and their dramaturgic function in video games, tabletop, or larp, among other topics. The contributors offer a first-time ever comprehensive overview of play-rites as spiritual incentives and playful spirituality in various medial incarnations.

The anthology was edited by Felix Schniz and Leonardo Marcato. It is now available as a printed copy, or for download via Open Access. Published by transcript 2023.

book: Fictional Practices of Spirituality I

The 15th ACM Multimedia Systems Conference (Technical Demos)

15-18 April, 2024 in Bari, Italy

Authors: Samuel Radler* (AAU, Austria) , Leon Prüller* (AAU, Austria), Emanuele Artioli (AAU, Austria), Farzad Tashtarian (AAU, Austria), and Christian Timmerer (AAU, Austria)

As streaming services become more commonplace, analyzing their behavior effectively under different network conditions is crucial. This is normally quite expensive, requiring multiple players with different bandwidth configurations to be emulated by a powerful local machine or a cloud environment. Furthermore, emulating a realistic network behavior or guaranteeing adherence to a real network trace is challenging. This paper presents PyStream, a simple yet powerful way to emulate a video streaming network, allowing multiple simultaneous tests to run locally. By leveraging a network of Docker containers, many of the implementation challenges are abstracted away, keeping the resulting system easily manageable and upgradeable. We demonstrate how PyStream not only reduces the requirements for testing a video streaming system but also improves the accuracy of the emulations with respect to the current state-of-the-art. On average, PyStream reduces the error between the original network trace and the bandwidth emulated by video players by a factor of 2-3 compared to Wondershaper, a common network traffic shaper in many video streaming evaluation environments. Moreover, PyStream decreases the cost of running experiments compared to existing cloud-based video streaming evaluation environments such as CAdViSE.

 

 

The 15th ACM Multimedia Systems Conference (Open-source Software and Datasets)

15-18 April, 2024 in Bari, Italy

Authors: Farzad Tashtarian∗ (AAU, Austria), Daniele Lorenzi∗ (AAU, Austria), Hadi Amirpour  (AAU, Austria), Samira Afzal  (AAU, Austria), and Christian Timmerer (AAU, Austria)

HTTP Adaptive Streaming (HAS) has emerged as the predominant solution for delivering video content on the Internet. The urgency of the climate crisis has accentuated the demand for investigations into the environmental impact of HAS techniques. In HAS, clients rely on adaptive bitrate (ABR) algorithms to drive the quality selection for video segments. Focusing on maximizing video quality, these algorithms often prioritize maximizing video quality under favorable network conditions, disregarding the impact of energy consumption. To thoroughly investigate the effects of energy consumption, including the impact of bitrate and other video parameters such as resolution and codec, further research is still needed. In this paper, we propose COCONUT, a COntent COnsumption eNergy measUrement daTaset for adaptive video streaming collected through a digital multimeter on various types of client devices, such as laptop and smartphone, streaming MPEG-DASH segments.

5-19 July, 2024, Niagra Falls, Canada

The first workshop on Surpassing Latency Limits in Adaptive Live Video Streaming (LIVES 2024) aims to bring together researchers and developers to satisfy the data-intensive processing requirements and QoE challenges of live video streaming applications through leveraging heuristic and learning-based approaches.

Delivering video content from a video server to viewers over the Internet is time-consuming in the streaming workflow and has to be handled to offer an uninterrupted streaming experience. The end-to-end latency, i.e., from the camera capture to the user device, particularly problematic for live streaming. Some streaming-based applications, such as virtual events, esports, online learning, gaming, webinars, and all-hands meetings, require low latency for their operation. Video streaming is ubiquitous in many applications, devices, and fields. Delivering high Quality-of-Experience (QoE) to the streaming viewers is crucial, while the requirement to process a large amount of data to satisfy such QoE cannot be handled with human-constrained possibilities. Satisfying the requirements of low latency video streaming applications require the streaming workflow to be optimized and streamlined all together, that includes: media provisioning (capturing, encoding, packaging, an ingesting to the origin server), media delivery (from the origin to the CDN and from the CDN to the end users), media playback (end user video player).

Please click here for more information.

On February 1st, 2024, Sahar Nasirihaghighi presented our work on ‘Event Recognition in Laparoscopic Gynecology Videos with Hybrid Transformers’ at this year’s International Conference on Multimedia Modeling (MMM 2024) in Amsterdam, The Netherlands.

Authors: Sahar Nasirihaghighi, Negin Ghamsarian, Heinrich Husslein, Klaus Schoeffmann

Abstract: Analyzing laparoscopic surgery videos presents a complex and multifaceted challenge, with applications including surgical training, intra-operative surgical complication prediction, and post-operative surgical assessment. Identifying crucial events within these videos is a significant prerequisite in a majority of these applications. In this paper, we introduce a comprehensive dataset tailored for relevant event recognition in laparoscopic gynecology videos. Our dataset includes annotations for critical events associated with major intra-operative challenges and post-operative complications. To validate the precision of our annotations, we assess event recognition performance using several CNN-RNN architectures. Furthermore, we introduce and evaluate a hybrid transformer architecture coupled with a customized training-inference framework to recognize four specific events in laparoscopic surgery videos. Leveraging the Transformer networks, our proposed architecture harnesses inter-frame dependencies to counteract the adverse effects of relevant content occlusion, motion blur, and surgical scene variation, thus significantly enhancing event recognition accuracy. Moreover, we present a frame sampling strategy designed to manage variations in surgical scenes and the surgeons’ skill level, resulting in event recognition with high temporal resolution. We empirically demonstrate the superiority of our proposed methodology in event recognition compared to conventional CNN-RNN architectures through a series of extensive experiments.

 

An EU funding programme enabling researchers to set up their own interdisciplinary research networks in Europe and beyond. #COSTactions

Representing Ireland with Prof. Horacio González-Vélez of National College of Ireland at the partner meeting of the Cost Action Cerciras – Connecting Education and Research Communities for an Innovative Resource Aware Society in Montpellier today.

 

 

 

 

 

 

 

 

 

 


Great alignment with several EU skills projects like ARISA – AI Skills, ESSA Software Skills Digital4Business and Digital4Security by facilitating transversal insights.

https://silicon-austria-labs.jobs.personio.de/job/1392891?display=en

We’re seeking a passionate researcher for a PhD role in “Efficient Algorithms and Accelerator Architectures for Distributed Edge AI Systems”. This unique position offers the chance to work under the esteemed supervision of Prof. Radu Prodan (AAU Klagenfurt) and Prof. Marcel Baunach (TU Graz), with my guidance at SAL.

https://www.linkedin.com/feed/update/urn:li:activity:7155106482257068032/

What You Will Do:
– Design & implement innovative distributed AI methods and algorithms.
– Customize these methods for the unique constraints of edge devices and networks.
– Investigate novel accelerator architectures for embedded AI applications.
– Explore quantization methods, with a focus on training and fine-tuning on edge devices.
– Publish research in high-impact journals and present at international conferences.

🎓 Candidate Profile:
– Master’s degree in a relevant field.
– Strong in programming and machine learning.
– Excellent communication skills in English.

🌍 Important Residency Note: Applicants should not have resided or carried out main activities in Austria for more than 12 months in the 3 years immediately before the application deadline.

Apply Now! Ensure to follow the specific application process outlined at Crystalline Program Recruitment (link is in the job description). https://lnkd.in/dBCY2xfe

ACM Mile High Video 2024 (mhv), Denver, Colorado, February 11-14, 2024

Authors: Daniele Lorenzi (Alpen-Adria-Universität Klagenfurt, Austria), Minh Nguyen (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: HTTP Adaptive Streaming (HAS) is the de-facto solution for delivering video content over the Internet. The climate crisis has highlighted the environmental impact of information and communication technologies (ICT) solutions and the need for green solutions to reduce ICT’s carbon footprint. As video streaming dominates Internet traffic, research in this direction is vital now more than ever. HAS relies on Adaptive BitRate (ABR) algorithms, which dynamically choose suitable video representations to accommodate device characteristics and network conditions. ABR algorithms typically prioritize video quality, ignoring the energy impact of their decisions. Consequently, they often select the video representation with the highest bitrate under good network conditions, thereby increasing energy consumption. This is problematic, especially for energy-limited devices, because it affects the device’s battery life and the user experience. To address the aforementioned issues, we propose E-WISH, a novel energy-aware ABR algorithm, which extends the already-existing WISH algorithm to consider energy consumption while selecting the quality for the next video segment. According to the experimental findings, E-WISH shows the ability to improve Quality of Experience (QoE) by up to 52% according to the ITU-T P.1203 model (mode 0) while simultaneously reducing energy consumption by up to 12% with respect to state-of-the-art approaches.

Keywords: HTTP adaptive streaming, Energy, Adaptive Bitrate (ABR), DASH