Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies.

SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.

https://www.mdpi.com/2673-2688/5/4/143

The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow.

The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%.

https://www.mdpi.com/2079-9292/14/20/4115

Evaluating Quality of Experience (QoE) is crucial for multimedia services, as it measures user satisfaction with content delivery. This paper presents an objective method for evaluating QoE in adaptive streaming services, using the commercial tool Video-MOS, which allows real-time monitoring and analysis of multimedia content quality across various platforms and networks. The method aims to provide a precise evaluation that incorporates both technical and subjective factors. This approach integrates multiple factors that influence the overall user perception of adaptive streaming quality, offering greater flexibility and performance, which could contribute to more comprehensive future assessment.

The method is validated by a test plan that incorporates a variety of content and scenarios to simulate various network conditions. The results demonstrate the method’s effectiveness in predicting QoE, highlighting the rebuffering frequency as a significant factor. In optimal conditions, specific content types can achieve a QoE score as high as 3.36. Conversely, under unfavorable conditions, the QoE may decrease by up to 1.42 Mean Opinion Score (MOS) points, which represents an 80% reduction from its optimal level.

Although the rebuffering frequency has a substantial influence, a long initial buffer can have an even more negative effect on QoE, particularly under adverse conditions. Furthermore, adaptive streaming technologies, such as Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and Adaptive Bitrate Streaming (ABR) are integral to the assessment process.

https://ieeexplore.ieee.org/document/11151675

During the first day of participation of NEMO project in Tech Expo 2025 some interesting discussions took place. Our colleague Theodore Zacharis from Synelixis participated in a panel that was celebrated at the end of the session. During his intervention, he explained the identity of NEMO project, the living labs and the partner composition of the consortium.

A lot of interesting insights where made on the potential of meta-OS project for the future of edge cloud computing making it simple, smooth and scalable, by ensuring seamless interoperability.

During the participation of NEMO project in Tech Expo Amsterdam, a 8 square booth was built in collaboration with other meta-OS projects such as Nephele, Nebulous, ICOS, Fluidos and aeros. A joint roll-up was created to showcase the potential of the projects but also a compendium of videos where created for the occasion. They all presented each project approach and proposals and their results and findings.

These videos where played in a TV-screen inside the booth so that attendans could engage with the projects and get to know their work better.

As announced in July, NEMO took part this week in Tech Expo Europe in Amsterdam, one of the leading events for emerging digital technologies and future-forward innovation. The event featured a dynamic agenda and NEMO had the opportunity to showcase the project. In this sense, NEMO participated in the event together with other 5 meta-os projects such as NEPHELE, NEBULOUS, ICOS, Fluidos and Aeros. During the event special sessions where organizaed jointly with these other meta-os projects.

The first day, NEMO was in charge of the first session dedicated to explain the usefulness of opensource in software. Philippe Krief, from Eclipse Foudnation, delivered an insightful presentation on “Ecosystem building and Open-source software artifacts from the Meta-OS projects: openness, modularity and extensibility”.

His talk emphasized the strategic role of open-source technologies in fostering innovation, ensuring transparency, and building scalable, sustainable digital ecosystems—core principles that drive the vision behind NEMO’s META Operating System. By kicking off the event, NEMO highlighted its commitment to openness, collaboration, and interoperability in next-generation digital infrastructure. We’re thrilled to have contributed to such a high-impact event and to engage with industry leaders, innovators, and fellow researchers across Europe and beyond.