Related papers: The "I" in FAIR: Translating from Interoperability…
We discuss important aspects of HCI research regarding Research Data Management (RDM) to achieve better publication processes and higher reuse of HCI research results. Various context elements of RDM for HCI are discussed, including…
Designing sustainable systems involves complex interactions between environmental resources, social impacts, and economic issues. In a constrained world, the challenge is to achieve a balanced design across those dimensions while avoiding…
Run-time integrity enforcement in real-time systems presents a fundamental conflict with availability. Existing approaches in real-time systems primarily focus on minimizing the execution-time overhead of monitoring. After a violation is…
Trust between entities in any scenario without a trusted third party is very difficult, and trust is exactly what blockchain aims to bring into the digital world with its basic features. Many applications are moving to blockchain adoption,…
In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. However, in…
This paper presents a philosophical and experimental study of fairness interventions in AI classification, centered on the explainability of corrective methods. We argue that ensuring fairness requires not only satisfying a target…
This document focuses on databases disseminating data on (hazardous) substances found on the North American and the European (EU) market. The goal is to analyse the FAIRness (Findability, Accessibility, Interoperability and Reusability) of…
Precision medicine and health requires the characterization and phenotyping of biological systems and patient datasets using a variety of data formats. This scenario mandates the centralization of various tools and resources in a unified…
With the adoption of Semantic Web technologies, an increasing number of vocabularies and ontologies have been developed in different domains, ranging from Biology to Agronomy or Geosciences. However, many of these ontologies are still…
The integration of machine learning with automated experimentation in self-driving laboratories (SDL) offers a powerful approach to accelerate discovery and optimization tasks in science and engineering applications. When supported by…
A large amount of data has been accumulated. with the development of the Internet industry. Many problems have been exposed with data explosion: 1. The contradiction between data privacy and data collaborations; 2. The contradiction between…
From a data perspective, the materials mechanics field is characterized by sparsity of available data, mainly due to the strong microstructure-sensitivity of properties like strength, fracture toughness, and fatigue limit. This requires…
Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy,…
Machine learning models have shown exceptional prowess in solving complex issues across various domains. However, these models can sometimes exhibit biased decision-making, resulting in unequal treatment of different groups. Despite…
Machines need data and metadata to be machine-actionable and FAIR (findable, accessible, interoperable, reusable) to manage increasing data volumes. Knowledge graphs and ontologies are key to this, but their use is hampered by high access…
In recent years, the importance of well-documented metadata has been discussed increasingly in many research fields. Making all metadata generated during scientific research available in a findable, accessible, interoperable, and reusable…
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research. However, different platforms use different data models…
Users and organizations are generating ever-increasing amounts of private data from a wide range of sources. Incorporating private data is important to personalize open-domain applications such as question-answering, fact-checking, and…
The ``impossibility theorem'' -- which is considered foundational in algorithmic fairness literature -- asserts that there must be trade-offs between common notions of fairness and performance when fitting statistical models, except in two…
Alongside molecular insights into genes and proteins, biological imaging holds great promise for deepening scientific understanding of complex cellular systems and advancing predictive, personalized therapies for human health. To realize…