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Understanding human affective states such as emotion and stress is crucial for both practical applications and theoretical research, driving advancements in the field of affective computing. While traditional approaches often rely on generalized models ...

Spatial representation learning (SRL) focuses on developing spatial embeddings from various forms of spatial data, such as points, polylines, polygons, graphs, networks, and images without any additional feature engineering or data conversion step. ...

With the applications of recommendation systems rapidly expanding, an increasing number of studies have focused on every aspect of recommender systems with different data inputs, models, and task settings. Therefore, a flexible library is needed to help ...

As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address their environmental impact. In this study, we estimate the environmental ...

Datasets play a central role in scholarly communications. However, scholarly graphs are often incomplete, particularly due to the lack of connections between publications and datasets. Therefore, the importance of dataset recommendation—identifying ...

One way to increase trust among users towards recommender systems is to provide the recommendation along with a textual explanation. In the literature, extraction-based, generation-based, and, more recently, hybrid solutions based on retrieval-augmented ...

One-class matrix factorization (MF) is an important technique for recommender systems with implicit feedback. In one widely used setting, a regression function is fit in a point-wise manner on observed and some unobserved (user, item) entries. Recently, ...

Recommender systems are prevalent in many applications, but hide risks; issues like bias propagation have been on the focus of related studies in recent years. My own research revolves around tracking bias in the book recommendation domain. Specifically, ...

Quality assessment (QA) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where unpredictable fetal motion can lead to substantial artifacts in the acquired ...$\mathrm{}$

In this paper we present the outcomes of a survey conducted among the research community of a Population Health Management department. The goal is to investigate how (translational) data science applications can be supported in a complex ecosystem ...

Software is ubiquitous in the digital world, and the correct function of software systems is critical for our society, industry, and infrastructure. While testing and static analysis are long-established techniques in software-development ...

Recent efforts to predict stress in the wild using mobile technology have increased; however, the field lacks a common pipeline for assessing the impact of factors such as label encoding and feature selection on prediction performance. This gap hinders ...

The development and roll-out of new Ethernet standards increase the available bandwidths in computer networks. This growth presents significant advantages, enabling novel applications. At the same time, the increase introduces new challenges; ...

Radiomics, the high-throughput extraction of quantitative imaging features from medical images, holds immense potential for advancing precision medicine in oncology and beyond. While radiomics applied to positron emission tomography (PET) imaging ...

• PET Radiomics offers unique insights into tumor biology and treatment responses.
• Despite its potential, PET radiomics faces several challenges and limitations that need to be addressed to facilitate its clinical translation.
• This ...

Model distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the ...

Reproducibility is widely acknowledged as a fundamental principle in scientific research. Currently, the scientific community grapples with numerous challenges associated with reproducibility, often referred to as the “reproducibility crisis”. ...

Mathematical Research Data Initiative (MaRDI) is a consortium of the National Research Data Infrastructure (NFDI) aiming to bring FAIR data practices to mathematical research. In alignment with MaRDI, we have developed a way to preserve software ...

Assuring the correctness of computing systems is fundamental to our society and economy, and formal verification is a class of techniques approaching this issue with mathematical rigor. Researchers have invented numerous algorithms to automatically prove ...

Scientific workflows have established themselves as valuable tools for designing, automating, and sharing scientific experiments and analyses, with the aim of promoting reproducibility and reuse. However, early evidence from a decade-old experiment ...

Reproducibility is essential in scientific experiments to ensure that results can be consistently obtained using new data and methods across studies to answer the same scientific question. It evaluates the validity and robustness of the results. A ...