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Tom Tuček and Felix Schniz visited the Kwadrat youth centre in Klagenfurt

On Friday, July 5 2024, Tom Tuček and Felix Schniz visited the Kwadrat youth centre in Klagenfurt for a workshop on computer role-playing games. Together with a group of highly motivated youngsters (and Kwadrat staff members!), they analysed the opening sequence of the best-selling game Baldur’s Gate 3 together. Afterwards, they introduced the audience to the pen-and-paper roots of modern role-playing games and invited everybody tomjoin a session of the classic Dungeons and Dragons, wonderfully hosted by Tom.

The workshop was well visited and received. Further events to introduce the Klagenfurt youth to the wonders of computer game design are already in the planning.

 

 

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Klaus Schöffmann gave a Keynote Talk about “The Use of AI in Image and Video Retrieval Over the last Decade” at the MVRMLM 2024 Workshop, co-located with ACM ICMR 2024

Together with Cathal Gurrin from DCU, Ireland, on June 14, 2024, Klaus Schöffmann gave a keynote talk about “From Concepts to Embeddings. Charting the Use of AI in Digital Video and Lifelog Search Over the Last Decade” at the International Workshop on Multimodal Video Retrieval and Multimodal Language Modelling (MVRMLM’24), co-located with the ACM ICMR 2024 conference in Phuket, Thailand.

Link: https://mvrmlm2024.ecit.qub.ac.uk

Here is the abstract of the talk:

In the past decade, the field of interactive multimedia retrieval has undergone a transformative evolution driven by the advances in artificial intelligence (AI). This keynote talk will explore the journey from early concept-based retrieval systems to the sophisticated embedding-based techniques that dominate the landscape today. By examining the progression of such AI-driven approaches at both the VBS (Video Browser Showdown) and the LSC (Lifelog Search Challenge), we will highlight the pivotal role of comparative benchmarking in accelerating innovation and establishing performance standards. We will also forward at the potential future developments in interactive multimedia retrieval benchmarking, including emerging trends, the integration of multimodal data, and the future comparative benchmarking challenges within our community.

 

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Paper accepted in IEEE Transactions on Circuits and Systems for Video Technology (IEEE TCSVT)

Title: DeepVCA: Deep Video Complexity Analyzer

Authors: Hadi Amirpour (AAU, Klagenfurt, Austria), Klaus Schoeffmann (AAU, Klagenfurt, Austria), Mohammad Ghanbari (University of Essex, UK), Christian Timmerer (AAU, Klagenfurt, Austria)

Abstract: Video streaming and its applications are growing rapidly, making video optimization a primary target for content providers looking to enhance their services. Enhancing the quality of videos requires the adjustment of different encoding parameters such as bitrate, resolution, and frame rate. To avoid brute force approaches for predicting optimal encoding parameters, video complexity features are typically extracted and utilized. To predict optimal encoding parameters effectively, content providers traditionally use unsupervised feature extraction methods, such as ITU-T’s Spatial Information ( SI ) and Temporal Information ( TI ) to represent the spatial and temporal complexity of video sequences. Recently, Video Complexity Analyzer (VCA) was introduced to extract DCT-based features to represent the complexity of a video sequence (or parts thereof). These unsupervised features, however, cannot accurately predict video encoding parameters. To address this issue, this paper introduces a novel supervised feature extraction method named DeepVCA, which extracts the spatial and temporal complexity of video sequences using deep neural networks. In this approach, the encoding bits required to encode each frame in intra-mode and inter-mode are used as labels for spatial and temporal complexity, respectively. Initially, we benchmark various deep neural network structures to predict spatial complexity. We then leverage the similarity of features used to predict the spatial complexity of the current frame and its previous frame to rapidly predict temporal complexity. This approach is particularly useful as the temporal complexity may depend not only on the differences between two consecutive frames but also on their spatial complexity. Our proposed approach demonstrates significant improvement over unsupervised methods, especially for temporal complexity. As an example application, we verify the effectiveness of these features in predicting the encoding bitrate and encoding time of video sequences, which are crucial tasks in video streaming. The source code and dataset are available at https://github.com/cd-athena/ DeepVCA.

 

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Paper accepted in the Springer Computing Journal

Title: Cloud Storage Tier Optimization through Storage Object Classification

Authors: Akif Quddus Khan, Mihhail Matskin, Radu Prodan, Christoph Bussler, Dumitru Roman, Ahmet Soylu

Abstract: Cloud storage adoption has increased over the years given the high demand for fast processing, low access latency, and ever-increasing amount of data being generated by, e.g., Internet of Things (IoT) applications. In order to meet the users’ demands and provide a cost-effective solution, cloud service providers (CSPs) offer tiered storage; however, keeping the data in one tier is not cost-effective. In this respect, cloud storage tier optimization involves aligning data storage needs with the most suitable and cost-effective storage tier, thus reducing costs while ensuring data availability and meeting performance requirements. Ideally, this process considers the trade-off between performance and cost, as different storage tiers offer different levels of performance and durability. It also encompasses data lifecycle management, where data is automatically moved between tiers based on access patterns, which in turn impacts the storage cost. In this respect, this article explores two novel classification approaches, rule-based and game theory-based, to optimize cloud storage cost by reassigning data between different storage tiers. Four distinct storage tiers are considered: premium, hot, cold, and archive. The viability and potential of the proposed approaches are demonstrated by comparing cost savings and analyzing the computational cost using both fully-synthetic and semi-synthetic datasets with static and dynamic access patterns. The results indicate that the proposed approaches have the potential to significantly reduce cloud storage cost, while being computationally feasible for practical applications. Both approaches are lightweight and industry- and platform-independent.

Computing, https://link.springer.com/journal/607

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Radu Prodan General Chair @ ICONIC 2024

Radu Prodan has been invited and will participate as a general chair at the ICONIC 2024, April 26-27, 2024, at Lovely Professional University, Punjab, India.

The Conference will provide a platform for scientists, researchers, academicians, industrialists, and students to assimilate the knowledge and get the opportunity to discuss and share insights through deep-dive research findings on the recent disruptions and developments in computing. All technical sessions will largely be steering Network Technologies, Artificial Intelligence and ethics, Advances in Computing, Futuristic Trends in Data Science, Security and Privacy, Data Mining and Information Retrieval.

Objectives

  • To provide a platform to facilitate the exchange of knowledge, ideas, and innovations among scientists, researchers, academicians, industrialists, and students.
  • To deliberate and disseminate the recent advancements and challenges in the computing sciences.
  • To enable the delegates to establish research or business relations and find international linkage for future collaborations.
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13th Video Browser Showdown (VBS 2024) held in Amsterdam with 12 International Teams

The 13th Video Browser Showdown (VBS 2024) was held on 29th January, 2024, in Amsterdam, The Netherlands, at the International Conference on Multimedia Modeling (MMM 2024). 12 international teams (from Austria, China, Czech Republic, Germany, Greece, Iceland, Ireland, Italy, Singapore, Switzerland, The Netherlands, Vietnam) competed over about 6 hours for quickly and accurately solving many search tasks of different types (known-item search/KIS, ad-hoc-video search/AVS, question-answering/QA) in three datasets with about 2500 hours of video content. Like in previous years, this large-scale international video retrieval challenge was an exciting event that demonstrated the state-of-the-art performance of interactive video retrieval systems.

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Paper accepted: DIGITWISE: Digital Twin-based Modeling of Adaptive Video Streaming Engagement

ACM MMSys 2024, Bari, Italy, Apr. 15-18, 2024 

Authors: Emanuele Artioli (Alpen-Adria-Universität Klagenfurt, Austria), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: As the popularity of video streaming entertainment continues to grow, understanding how users engage with the content and react to its changes becomes a critical success factor for every stakeholder. User engagement, i.e., the percentage of video the user watches before quitting, is central to customer loyalty, content personalization, ad relevance, and A/B testing. This paper presents DIGITWISE, a digital twin-based approach for modeling adaptive video streaming engagement. Traditional adaptive bitrate (ABR) algorithms assume that all users react similarly to video streaming artifacts and network issues, neglecting individual user sensitivities. DIGITWISE leverages the concept of a digital twin, a digital replica of a physical entity, to model user engagement based on past viewing sessions. The digital twin receives input about streaming events and utilizes supervised machine learning to predict user engagement for a given session. The system model consists of a data processing pipeline, machine learning models acting as digital twins, and a unified model to predict engagement. DIGITWISE employs the XGBoost model in both digital twins and unified models. The proposed architecture demonstrates the importance of personal user sensitivities, reducing user engagement prediction error by up to 5.8% compared to non-user-aware models. Furthermore, DIGITWISE can optimize content provisioning and delivery by identifying the features that maximize engagement, providing an average engagement increase of up to 8.6 %.

Keywords: digital twin, user engagement, xgboost

 

 

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Webinar of Graph-Massivizer @ HiPEAC

Funded by the EU, HiPEAC (High-Performance Edge And Cloud computing) is the premier focal point for networking, dissemination, training, and collaboration activities in Europe for researchers, industry, and policy related to computing systems.

The HiPEAC webinar series allows you to keep up to date on the latest advances in computer architecture and compilation research via online sessions, which can be accessed anywhere.

The Graph-Massivizer Project webinar took place on November 29, 2023. After an introduction by Nuria De Lama,  Radu Prodan presented the background of GraphProcessing, from Euler‘s five-node graphs to the massive graphs of today, and the motivations for the project.

 Project details have been presented by Reza Farahani and Matteo Angelinelli.

WEBINAR is online now: https://www.youtube.com/watch?v=YW7pD6nPMhs

 

 

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Successful Collaboration with Bitmovin Team in APAC

With the current popularity of ECO in the Asia–Pacific (APAC), the Bitmovin team in APAC, led by Adrian Britton, expressed an interest in the energy-aware research initiatives conducted within the GAIA project in Austria. Following an introductory meeting between the APAC team and AAU on October 17, 2023, both teams decided to meet in person on November 21, 2023, to explore the topics further.

The meeting proved to be highly productive, centering around two recent research topics:

– VE-Match: Video Encoding Matching-Based Model in the Cloud and Edge (presented by Samira Afzal & Narges Mehran)

– Energy-aware Spatial and Temporal Resolution Selection for Per-Title (presented by Mohammad Ghasempour & Hadi Amirpour)

Many interesting Q&As appeared during each presentation due to customer and provider requirements and the future insight of climate-friendly video streaming in the Cloud and Edge. The fruitful discussions opened up avenues for future exploration in this dynamic field.

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Paper accepted: Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality @ CoNEXT, Paris December 5-8 2023

The 19th International Conference on emerging Networking EXperiments and Technologies (CoNEXT) Paris, France, December 5-8, 2023

Authors: Leonardo Peroni (IMDEA Networks Institute), Sergey Gorinsky (IMDEA Networks Institute), Farzad Tashtarian (Alpen-Adria-Universität Klagenfurt, Austria), and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria).

Abstract: Quality of Experience (QoE) and QoE models are of an increasing importance to networked systems. The traditional QoE modeling for video streaming applications builds a one-size-fits-all QoE model that underserves atypical viewers who perceive QoE differently. To address the problem of atypical viewers, this paper proposes iQoE (individualized QoE), a method that employs explicit, expressible, and actionable feedback from a viewer to construct a personalized QoE model for this viewer. The iterative iQoE design exercises active learning and combines a novel sampler with a modeler. The chief emphasis of our paper is on making iQoE sample-efficient and accurate.
By leveraging the Microworkers crowdsourcing platform, we conduct studies with 120 subjects who provide 14,400 individual scores. According to the subjective studies, a session of about 22 minutes empowers a viewer to construct a personalized QoE model that, compared to the best of the 10 baseline models, delivers the average accuracy improvement of at least 42% for all viewers and at least 85\% for the atypical viewers. The large-scale simulations based on a new technique of synthetic profiling expand the evaluation scope by exploring iQoE design choices, parameter sensitivity, and generalizability.