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.
On Thursday 14 March, the Senior Teaching Qualification certificates were awarded during a festive ceremony. Dr. Zhiming Zhao from MNS together with the other fifteen UvA lecturers received the certificate.
The EU Horizon Europe project OSCARS (Open Science Clusters’ Action for Research & Society) has been successfully kicked off 13/March 2024 in Thessaloniki, Greece. Coordinated by CNRS (French National Centre for Scientific Research), the project aims to bring together European Research Infrastructures (RIs) organized into five “Science Clusters” along the ESFRI thematic research domains1.
The project has 18 partners from 8 countries and will last three years; the consortium includes five clusters of European research infrastructures, including environmental earth science, life science, particle physics, Photon and neutron, and social science.