Related papers: FSS++ Workshop Report: Handling Uncertainty for Da…

This report records and discusses the Third Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE3). The report includes a description of the keynote presentation of the workshop, which served as an overview of…

Data warehousing is continuously gaining importance as organizations are realizing the benefits of decision oriented data bases. However, the stumbling block to this rapid development is data quality issues at various stages of data…

Context: The utility of prediction models in empirical software engineering (ESE) is heavily reliant on the quality of the data used in building those models. Several data quality challenges such as noise, incompleteness, outliers and…

This volume contains the proceedings of the Verification of Scientific Software (VSS 2025) workshop, held on 4 May 2025 at McMaster University, Canada, as part of ETAPS 2025. VSS brings together researchers in software verification and…

Predicting future resource demand in Cloud Computing is essential for optimizing the trade-off between serving customers' requests efficiently and minimizing the provisioning cost. Modelling prediction uncertainty is also desirable to…

Federated learning (FL), aimed at leveraging vast distributed datasets, confronts a crucial challenge: the heterogeneity of data across different silos. While previous studies have explored discrete representations to enhance model…

Driven by the recent advances in smart, miniaturized, and mass produced sensors, networked systems, and high-speed data communication and computing, the ability to collect and process larger volumes of higher veracity real-time data from a…

Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a…

This volume contains the proceedings of the 8th Workshop on Security Issues in Concurrency (SecCo 2010). The workshop was held in Paris, France on August 30th, 2010, as a satellite workshop of CONCUR'10. The aim of the SecCo workshop series…

While coursework provides undergraduate data science students with some relevant analytic skills, many are not given the rich experiences with data and computing they need to be successful in the workplace. Additionally, students often have…

This report summarizes the discussions and recommendations from the NSF Workshop on Algorithm-Hardware Co-design for Medical Applications, held on September 26-27, 2024, in Pittsburgh, PA. The workshop assembled an interdisciplinary cohort…

This volume contains the position papers presented at CSCW 2021 Workshop - Investigating and Mitigating Biases in Crowdsourced Data, held online on 23rd October 2021, at the 24th ACM Conference on Computer-Supported Cooperative Work and…

The International Workshop on Engineering Safety and Security Systems (ESSS) aims at contributing to the challenge of constructing reliable and secure systems. The workshop covers areas such as formal specification, type checking, model…

High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM). Existing DQM schemes often cannot satisfactorily improve ML performance because, by design, they…

The authors of this report met on 28-30 March 2018 at the New Jersey Institute of Technology, Newark, New Jersey, for a 3-day workshop that brought together a group of data providers, expert modelers, and computer and data scientists, in…

Modern nonlinear control theory seeks to endow systems with properties such as stability and safety, and has been deployed successfully across various domains. Despite this success, model uncertainty remains a significant challenge in…

The most successful organizations in the world are data-driven businesses. Data is at the core of the business of many organizations as one of the most important assets, since the decisions they make cannot be better than the data on which…

Recent research has highlighted the importance of data quality in scaling large language models (LLMs). However, automated data quality control faces unique challenges in collaborative settings where sharing is not allowed directly between…

As data is increasingly acknowledged as a highly valuable asset, much effort has been put into investigating inter-organisational data sharing, aiming at utilising the value of formerly unused data. Moreover, most researchers agree, that…

This volume contains the papers presented at the 1st International Workshop on "Decentralized Coordination of Distributed Processes", DCDP 2010, held in Amsterdam, The Netherlands on June 10th, 2010 in conjunction with the 5th International…