Quantum computing has been rapidly expanding, and many platforms for writing programs that can be compiled and run on quantum hardware (or simulated) are being developed. As with any compiler, transformation correctness is paramount as the machine ...

Quantum computing has been rapidly expanding, and many platforms for writing programs that can be compiled and run on quantum hardware (or simulated) are being developed. As with any compiler, transformation correctness is paramount as the machine ...

The reproducibility of scientific articles is central to the advancement of science. Despite this importance, evaluating reproducibility remains challenging due to the scarcity of ground truth data. Predictive models can address this limitation by ...

We review the state of open science and the perspectives on open data sharing within the automotive user research community. Openness and transparency are critical not only for judging the quality of empirical research, but also for accelerating ...

Modeling is a critical step in studying epidemics. It allows us to better understand and predict the progression of a disease, design interventions such as vaccination, and assess their impact. Current epidemics are modeled using compartmental and ...

Simulation has become, in many application areas, a sine qua non. Most recently, COVID-19 has underlined the importance of simulation studies and limitations in current practices and methods. We identify four goals of methodological work for addressing ...

Information retrieval systems have been evaluated using the Cranfield paradigm for many years. This paradigm allows a systematic, fair, and reproducible evaluation of different retrieval methods in fixed experimental environments. However, real-world ...

Sharing research artifacts (e.g., software, data, protocols) is an immensely important topic for improving transparency, replicability, and reusability in research, and has recently gained more and more traction in software engineering. For instance, ...

The rise of conversational recommender systems (CRSs) brings the evolution of the recommendation paradigm, which enables users to interact with the system and achieve dynamic recommendations. As one essential branch, multi-turn CRSs, built on the user ...

BEIR is a benchmark dataset originally designed for zero-shot evaluation of retrieval models across 18 different domain/task combinations. In recent years, we have witnessed the growing popularity of models based on representation learning, which ...

The PLAID (Performance-optimized Late Interaction Driver) algorithm for ColBERTv2 uses clustered term representations to retrieve and progressively prune documents for final (exact) document scoring. In this paper, we reproduce and fill in missing gaps ...

Document expansion is a technique that aims to reduce the likelihood of term mismatch by augmenting documents with related terms or queries. Doc2Query minus minus (Doc2Query-) represents an extension to the expansion process that uses a neural model to ...

Despite numerous efforts to predict student dropout in university students, the exact variables influencing dropout remain elusive, as existing studies do not generalise well to other samples. The proposed PhD project uses meta science tools to ...

Why are some research studies easy to reproduce while others are difficult? Casting doubt on the accuracy of scientific work is not fruitful, especially when an individual researcher cannot reproduce the claims made in the paper. There could be many ...

Reproducibility is a fundamental part of scientific progress. Compared to other scientific fields, computational sciences are privileged as experimental setups can be preserved with ease, and regression experiments allow the validation of computational ...

This article investigates challenges and requirements related to the reproducibility of geospatial research using geospatial web-services. Several researchers have identified hinders related to technology on the one hand, as well as challenges ...

Data is a critical resource for Machine Learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that simplifies how data is used by ML tools and frameworks. Croissant makes ...

Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking ...

Data practices shape research and practice on fairness in machine learning (fair ML). Critical data studies offer important reflections and critiques for the responsible advancement of the field by highlighting shortcomings and proposing recommendations ...

A stochastic network simulation is verified when its distribution of outputs is aligned with the ground truth, while tolerating deviations due to variability in real-world measurements and the randomness of a stochastic simulation. However, comparing ...