36 citations · 120 across the 25 of their papers we have counts for
9 papers · 1 filter
An Indoor Radio Mapping Dataset Combining 3D Point Clouds and RSSI
Ljupcho Milosheski, Kuon Akiyama, Blaž Bertalanič +2
The growing number of smart devices supporting bandwidth-intensive and latency-sensitive applications, such as real-time video analytics, smart sensing, Extended Reality (XR), etc.…
A Configuration-First Framework for Reproducible, Low-Code Machine Learning: a Localization Use Case
Tim Strnad, Blaž Bertalanič, Carolina Fortuna
As machine learning underpins more critical applications, the value of a reported result depends on whether it can be compared and repeated. In practice, this remains difficult: re…
Label-Free Concept Drift Assessment for Reliable AI in Emerging Wireless Applications
Athanasios Tziouvaras, Carolina Fortuna, George Floros +4
Machine learning models deployed in non-stationary environments degrade silently, since as the input distribution drifts their accuracy decays without an error signal and without l…
Multi-agent Reinforcement Learning-based In-place Scaling Engine for Edge-cloud Systems
Jovan Prodanov, Blaž Bertalanič, Carolina Fortuna +4
Modern edge-cloud systems face challenges in efficiently scaling resources to handle dynamic and unpredictable workloads. Traditional scaling approaches typically rely on static th…
Data Model Design for Explainable Machine Learning-based Electricity Applications
Carolina Fortuna, Gregor Cerar, Blaz Bertalanic +2
The transition from traditional power grids to smart grids, significant increase in the use of renewable energy sources, and soaring electricity prices has triggered a digital tran…
Graph Neural Networks Based Anomalous RSSI Detection
Blaž Bertalanič, Matej Vnučec, Carolina Fortuna
In today's world, modern infrastructures are being equipped with information and communication technologies to create large IoT networks. It is essential to monitor these networks…
MRM3: Machine Readable ML Model Metadata
Andrej Čop, Blaž Bertalanič, Marko Grobelnik +1
As the complexity and number of machine learning (ML) models grows, well-documented ML models are essential for developers and companies to use or adapt them to their specific use…
A Network Science Approach to Granular Time Series Segmentation
Ivana Kesić, Carolina Fortuna, Mihael Mohorčič +1
Time series segmentation assigns a label to each part of a sequence. We formulate dense univariate segmentation as node classification on a graph whose nodes are the original time…
A Representation Learning Approach to Feature Drift Detection in Wireless Networks
Athanasios Tziouvaras, Blaz Bertalanic, George Floros +3
AI is foreseen to be a centerpiece in next generation wireless networks enabling enabling ubiquitous communication as well as new services. However, in real deployment, feature dis…