Mathias Lux
, ,

First Game Creators Barcamp Held at the University of Klagenfurt

Local game developers and researchers gathered for the inaugural Game Creators Barcamp on Friday, December 6th, at the University of Klagenfurt. The event featured discussions on game development, educational technologies, and industry funding opportunities.

The first Barcamp drew a small but engaged group of participants who explored various game design and development topics. Discussions covered diverse areas, including the story behind the game Electric Alps, potential improvements in language learning through gamification, particularly for teaching irregular German verbs, and an examination of game theory in puzzle game design. The attendees also provided an overview of funding schemes available for game studios. Plans are underway for the next Barcamp, scheduled for spring, hoping to increase participant numbers.

Hadi
,

Patent Approval for “Scalable Per-Title Encoding”

Scalable Per-Title Encoding – US Patent

[PDF]

Hadi Amirpour (Alpen-Adria-Universität Klagenfurt, Austria) and Christian Timmerer (Alpen-Adria-Universität Klagenfurt, Austria)

Abstract: A scalable per-title encoding technique may include detecting scene cuts in an input video received by an encoding network or system, generating segments of the input video, performing per-title encoding of a segment of the input video, training a deep neural network (DNN) for each representation of the segment, thereby generating a trained DNN, compressing the trained DNN, thereby generating a compressed trained DNN, and generating an enhanced bitrate ladder including metadata comprising the compressed trained DNN. In some embodiments, the method may also include generating a base layer bitrate ladder for CPU devices and providing the enhanced bitrate ladder for GPU-available devices.

,

Kseniia and Tom @ CHI PLAY 2024

Kseniia and Tom participated in the annual symposium CHI PLAY (Computer-Human Interaction in Play) from October 14–17/online.
They both took part in the doctoral consortium, where they presented their works and built connections with fellow doctoral students and experienced researchers in the field.
Kseniia presented her topic, “Developing a Virtual-Reality Game for Empathy Enhancement and Perspective-Taking in the Context of Forced Migration Experiences”, and Tom presented his topic, “Enhancing Empathy Through Personalized AI-Driven Experiences and Conversations with Digital Humans in Video Games”.
Proceedings are published under Companion Proceedings of the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY Companion ’24), October 14–17, 2024, Tampere, Finland.

,

Kseniia Harshina @ FROG Conference, Vienna (Future and Reality of Gaming 2024)

Kseniia participated in the annual FROG conference from 11 to 13 October, which had the topic of “Gaming the Apocalypse”. Her talk, “Unraveling the Romanticization of Colonial, Imperial and Authoritarian Narratives in Modern Video Games”, showcased a trend in video games to cutefy serious topics like historical power dynamics through aesthetics, and explained the potential issues caused by this phenomenon.

Her talk can be seen on YouTube (https://www.youtube.com/watch?v=37-sqVJrMoY), and proceedings will follow in the summer of 2025.

,

Keynote talk @ 21st EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous 2024)

Radu Prodan is invited to give a keynote talk at the 21st EAI International Conference on Mobile and Ubiquitous Systems (MobiQuitous 2024), which will be held from November 12-14,2024, in Oslo, Norway.

Abstract:

The presentation starts by reviewing the convergence of two historically disconnected AI branches: symbolic AI of explicit deductive reasoning owning mathematical rigor and interpretability, and connectionist AI of implicit inductive reasoning lacking readability and explainability.
Afterward, it makes a case of neuro-symbolic data processing in knowledge graph representation approached by the Graph-Massivizer Horizon Europe project and applied for anomaly prediction in data centers using graph neural networks. Machine learning-driven graph sampling algorithms support its training and inference on resource-constrained Edge devices. The presentation concludes with an outlook into future projects targeting Edge large language models fine-tuned and contextualized using symbolic knowledge representation for regulatory AI compliance, job market, and medicine.

,

Felix Schniz @ FROG Conference, Vienna (Future and Reality of Gaming 2024)

From 11 to 13 October, ITEC’s Felix Schniz participated in the annual FROG (Future and Reality of Gaming) conference in Vienna. It is Austria’s biggest, (video) game dedicated academic conference, which attracted 50 speakers from 12 countries this year. Under this year’s topic of “Gaming the Apocalypse”, Felix delivered the talk “Scales of Apocalypse: Space and Affect in Dystopian Video Games between Sacred and Profane”.

Also presenting at the conference were AAU’s Kseniia Harshina and the Game Studies and Engineering master students Tim Sanders and Elli Chraibi, showcasing the diverse research interest and academic expertise produced by Game Studies and Engineering staff and students in the field.

The conference proceedings are expected to be published next year Summer.

,

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.

 

 

, ,

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.

 

,

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.

 

,

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