Today, Prof. Michael Seufert will give a talk on Machine Learning-supported Network Management for User-centric Communication Networks at the IfI Colloquium at the 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Zurich. In his talk, he will discuss how machine learning techniques can be used to optimize and control modern communication networks in order to improve the user experience.

We are pleased to welcome Samuel Hufen as a new research assistant at our chair starting today. We look forward to working together and wish him a great start!

Our research paper titled "To Cap or Not to Cap: Bandwidth Capping Effects on Network Interactions and QoE of Competing Short Video Streams," co-authored by our chair in collaboration with researchers from the 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Würzburg and AT&T Labs in the USA, has been accepted to the 16th ACM Multimedia Systems Conference (MMSys).

The paper "Resource Allocation is All You Need: The Routing and Scheduling Problem in 6TiSCH Networks" has been accepted for presentation at the IEEE Wireless Communications and Networking Conference (WCNC) 2025.
In a collaboration between the Organic Computing and NETCOM research groups, a method was developed to maximize the number of latency-critical data flows in 6TiSCH networks.

Today our paper “Agree to Disagree: Exploring Consensus of XAI Methods for ML-based NIDS” was presented at the 1st Workshop on Network Security Operations (NecSecOr). This paper examines the effectiveness and consensus of various explainable AI (XAI) methods in enhancing the interpretability of machine learning-based Network Intrusion Detection Systems (ML-NIDS), finding that while some methods align closely, others diverge, underscoring the need for careful selection to build trust in real-world cybersecurity applications.

Today our paper “Certainly Uncertain: Demystifying ML Uncertainty for Active Learning in Network Monitoring Tasks” got presented at the 20th International Conference on Network and Service Management (CSNM). This paper explores the use of Active Learning (AL) to enhance Machine Learning (ML) models in network monitoring by incorporating expert input, aiming to increase model trust, adaptability, and performance, with a comprehensive evaluation of uncertainty-based AL approaches across various datasets and scenarios.

Prof. Seufert was ranked in the prestigious Stanford/Elsevier Top 2% Scientists list for the fourth time in a row. This list includes the world's 100,000 highest ranked scientists, as well as the top 2% in each of 174 disciplines. The repeated listing of Prof. Seufert underlines the outstanding research at his chair and the 威尼斯赌博游戏_威尼斯赌博app-【官网】 of Augsburg.

Our latest research paper in ACM TOMM focuses on how video streaming systems can better utilize available bandwidth to provide users with an improved Quality of Experience (QoE).

Our paper “(Not) The Sum of Its Parts: Relating Individual Video and Browsing Stimuli to Web Session QoE” got presented at the 16th International Conference on Quality of Multimedia Experience (QoMEX). This paper investigates the Quality of Experience (QoE) in web sessions that combine both web browsing and video streaming stimuli, addressing the gap in understanding session-level QoE and proposing models to estimate it based on individual stimuli.

Unser Paper ?QoEXplainer: 威尼斯赌博游戏_威尼斯赌博app-【官网】iating Explainable Quality of Experience Models with Large Language Models“ wurde auf der 16th International Conference on Quality of Multimedia Experience (QoMEX) vorgestellt. Das Papier stellt QoEXplainer vor, ein Dashboard, das gro?e Sprachmodelle und die Verwendung von 威尼斯赌博游戏_威尼斯赌博app-【官网】iatoren verwendet, um erkl?rbare, datengesteuerte Quality of Experience (QoE) Modelle zu veranschaulichen und den Benutzern zu helfen, die Beziehungen zwischen den Modellen durch eine interaktive Chatbot-Schnittstelle zu verstehen.

Our paper “Sitting, Chatting, Waiting: Influence of Loading Times on Mobile Instant Messaging QoE” got presented at the 16th International Conference on Quality of Multimedia Experience (QoMEX). The paper examines the relationship between loading times and user experience (QoE) in mobile instant messaging applications and shows that longer loading times reduce user acceptance and satisfaction, although they do not directly influence QoE ratings.

Our paper “HALIDS: a Hardware-Assisted Machine Learning IDS for in-Network Monitoring” was published in the 8th Network Traffic Measurement and Analysis (TMA) Conference.?The paper presents HALIDS, a prototype of a Machine Learning-driven Intrusion Detection System that enables network devices to autonomously make security decisions using in-band and off-band traffic analysis, ultimately aiming to enhance network security through faster processing and intelligent decision-making.