The MNS group announces the deployment of its automated news clerk, a Slack bot designed to significantly streamline and enhance the publication of news stories on the MNS Hugo website.
We’re delighted to announce that our paper, “Pace: Provable Energy-Aware Configuration for Edge Blockchain Services”, has been accepted to the 24th International Conference on Service-Oriented Computing (ICSOC 2026)! This is fantastic recognition for the hard work put into the research and a great opportunity to share our findings with the international community at such a prominent conference.
Paul Danielse and a student presented CNASIM: A Customizable Simulation Library for Cloud-Native Application Performance Modeling at the International Conference on Computational Science. The conference was held at the DESY particle accelerator in Hamburg.
This years scientific outing happend in Zeist. First on the agenda, getting together and getting up to speed what ideas and project everyone is working on. It was great to hear that there is significant overlap between our groups interests and directions.
This article proposes exploiting active learning (AL) to assist QC experts, reducing their workload by proactively selecting informative data points for labeling. Targeting the data distribution challenge, AL, coupled with imbalance-resilient classifiers, enhances model performance in recognizing erroneous data points.
Data science and machine learning methodologies are essential to address complex scientific challenges across various domains. These advancements generate numerous research assets such as datasets, software tools, and workflows, which are shared within the open science community.
Trustworthy AI-based Performance Diagnosis Systems for Cloud Applications: A Review, ACM Computing Survey, 2025. This article systematically reviews trustworthiness requirements in AI-based performance diagnosis systems. We introduce trustworthiness requirements and extract six key requirements from a technical perspective, including data privacy, fairness, robustness, explainability, efficiency, and human intervention.
Our work evaluates and analyzes the performance and security of the quantum position verification task under real-world constraints, bringing this quantum network application one step closer to practical deployment.
This work was supported by the Dutch National Growth Fund (NGF), as part of the Quantum Delta NL program.
abstract: Preserving privacy in blockchain-based systems is crucial for ensuring anonymity and confidentiality during transactions. While cryptographic solutions can address on-chain privacy concerns, their implementation on blockchains may introduce performance overhead, which remains unclear to researchers and practitioners.
Data quality plays a vital role in scientific research and decision-making across industries. Thus it is crucial to incorporate the data quality control (DQC) process, which comprises various actions and operations to detect and correct data errors.