Related papers: Tidynote: Always-Clear Notebook Authoring

Jupyter has become the go-to platform for developing data applications but data and security concerns, especially when dealing with healthcare, have become paramount for many institutions and applications dealing with sensitive information.…

Reusing and making sense of other scientists' computational notebooks. However, making sense of existing notebooks is a struggle, as these reference notebooks are often exploratory, have messy structures, include multiple alternatives, and…

Deep neural networks deliver state-of-the-art visual recognition, but they rely on large datasets, which are time-consuming to annotate. These datasets are typically annotated in two stages: (1) determining the presence of object classes at…

Recently, tampered text detection has attracted increasing attention due to its essential role in information security. Although existing methods can detect the tampered text region, the interpretation of such detection remains unclear,…

Though recommender systems are defined by personalization, recent work has shown the importance of additional, beyond-accuracy objectives, such as fairness. Because users often expect their recommendations to be purely personalized, these…

As educators push for students to learn science by doing science, there is a need for computational scaffolding to assist students' evaluation of scientific evidence and argument building. In this paper, we present a pilot study of SciNote,…

Manually annotated data is key to developing text-mining and information-extraction algorithms. However, human annotation requires considerable time, effort and expertise. Given the rapid growth of biomedical literature, it is paramount to…

Data documents play a central role in recording, presenting, and disseminating data. Despite the proliferation of applications and systems designed to support the analysis, visualization, and communication of data, writing data documents…

As immersive technologies evolve, immersive computational notebooks offer new opportunities for interacting with code, data, and outputs. However, scaling these environments remains a challenge, particularly when analysts manually arrange…

Explainable AI (XAI) tools represent a turn to more human-centered and human-in-the-loop AI approaches that emphasize user needs and perspectives in machine learning model development workflows. However, while the majority of ML resources…

Information and communications technology and technology-enhanced learning have unquestionably transformed traditional teaching-learning processes and are positioned as key factors to promote quality education, one of the basic sustainable…

A keyboard is the most important input device for a computer. With the development of technology a basic keyboard does not want to remain confined within the basic functionalities of a keyboard, rather it wants to go beyond. There are…

Many important security properties can be formulated in terms of flows of tainted data, and improved taint analysis tools to prevent such flows are of critical need. Most existing taint analyses use whole-program static analysis, leading to…

Explanations in Machine Learning come in many forms, but a consensus regarding their desired properties is yet to emerge. In this paper we introduce a taxonomy and a set of descriptors that can be used to characterise and systematically…

Summarization is a challenging problem, and even more challenging is to manually create, correct, and evaluate the summaries. The severity of the problem grows when the inputs are multi-party dialogues in a meeting setup. To facilitate the…

Knowledge distillation, which involves extracting the "dark knowledge" from a teacher network to guide the learning of a student network, has emerged as an essential technique for model compression and transfer learning. Unlike previous…

Computational notebooks have gained widespread adoption among researchers from academia and industry as they support reproducible science. These notebooks allow users to combine code, text, and visualizations for easy sharing of experiments…

Dynamically typed languages such as Python have become very popular. Among other strengths, Python's dynamic nature and its straightforward linking to native code have made it the de-facto language for many research areas such as Artificial…

The use of lab notebooks for scientific documentation is a ubiquitous part of physics research. However, it is common for undergraduate physics laboratory courses not to emphasize the development of documentation skills, despite the fact…

Computational reproducibility refers to obtaining consistent results when rerunning an experiment. Jupyter Notebook, a web-based computational notebook application, facilitates running, publishing, and sharing computational experiments…