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Quantifying the similarity between datasets has widespread applications in statistics and machine learning. The performance of a predictive model on novel datasets, referred to as generalizability, depends on how similar the training and…
This paper discusses the risks and potential impacts of spreadsheet errors in scientific research data in a Neuroscience research centre in the UK. Spreadsheets usage in neuroscience, or indeed any medical discipline, is a largely…
This management paper looks at the real world issues faced by practitioners managing spreadsheets through the production phase of their life cycle. It draws on the commercial experience of several developers working with large corporations,…
When working with distant collaborators on the same documents, one often uses a version control system, which is a program tracking the history of files and helping importing modifications brought by others as patches. The implementation of…
The key contribution of this paper is to propose a classification into two dimensions of the load forecasting studies to decide which forecasting tools to use in which case. This classification aims to provide a synthetic view of the…
End-user programmers create software to solve problems, yet the problem-solving knowledge generated in the process often remains tacit within the software artifact. One approach to exposing this knowledge is to enable the end-user to…
In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings. Improving the calibration of classifiers is crucial for enhancing the…
Toxic comment classification has become an active research field with many recently proposed approaches. However, while these approaches address some of the task's challenges others still remain unsolved and directions for further research…
Recently, detection of label errors and improvement of label quality in datasets for supervised learning tasks has become an increasingly important goal in both research and industry. The consequences of incorrectly annotated data include…
Journal classification into subject categories is an important aspect in scholarly research evaluation as well as in bibliometric analysis. However, this classification is not standardized, resulting in several different journal subject…
Spreadsheets are among the most commonly used file formats for data management, distribution, and analysis. Their widespread employment makes it easy to gather large collections of data, but their flexible canvas-based structure makes…
Automated label generation for clusters of scientific documents is a common task in bibliometric workflows. Traditionally, labels were formed by concatenating distinguishing characteristics of a cluster's documents; while straightforward,…
Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data…
Large Language Models (LLM) have become sophisticated enough that complex computer programs can be created through interpretation of plain English sentences and implemented in a variety of modern languages such as Python, Java Script, C++…
Data pipelines are an integral part of various modern data-driven systems. However, despite their importance, they are often unreliable and deliver poor-quality data. A critical step toward improving this situation is a solid understanding…
The allocation of tasks can be seen as a success-critical management activity in distributed development projects. However, such task allocation is still one of the major challenges in global software development due to an insufficient…
Quantitative program analysis involves computing numerical quantities about individual or collections of program executions. An example of such a computation is quantitative information flow analysis, where one estimates the amount of…
A new taxonomy of issues related to CAD model quality is presented, which distinguishes between explicit and procedural models. For each type of model, morphologic, syntactic, and semantic errors are characterized. The taxonomy was…
Automatically annotating job data with standardized occupations from taxonomies, known as occupation classification, is crucial for labor market analysis. However, this task is often hindered by data scarcity and the challenges of manual…
Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data. To scale and widen the applicability of…