相关论文: NotePlayer: Engaging Jupyter Notebooks for Dynamic…

As scientific work becomes more computational and data intensive, research processes and results become more difficult to interpret and reproduce. In this poster, we show how the Jupyter notebook, a tool originally designed as a free…

Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models. Such notebooks tend to be executed either in the user's own machine or in a cloud environment, having…

Audio-visual learning seeks to enhance the computer's multi-modal perception leveraging the correlation between the auditory and visual modalities. Despite their many useful downstream tasks, such as video retrieval, AR/VR, and…

Datamation is designed to animate an analysis pipeline step by step, which is an intuitive and effective way to interpret the results from data analysis. However, creating a datamation is not easy. A qualified datamation needs to not only…

High-quality and consistent annotations are fundamental to the successful development of robust machine learning models. Traditional data annotation methods are resource-intensive and inefficient, often leading to a reliance on third-party…

Reproducibility of computational studies is a hallmark of scientific methodology. It enables researchers to build with confidence on the methods and findings of others, reuse and extend computational pipelines, and thereby drive scientific…

Keeping abreast of current trends, technologies, and best practices in visualization and data analysis is becoming increasingly difficult, especially for fledgling data scientists. In this paper, we propose Lodestar, an interactive…

We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text annotation tools for NLP and qualitative research abound, few address the complexities of…

In recent years, Jupyter notebooks have grown in popularity in several domains of software engineering, such as data science, machine learning, and computer science education. Their popularity has to do with their rich features for…

Computational notebooks, tools that facilitate storytelling through exploration, data analysis, and information visualization, have become the widely accepted standard in the data science community. These notebooks have been widely adopted…

Citing data and software is a means to give scholarly credit and to facilitate access to research objects. Citation principles encourage authors to provide full descriptions of objects, with stable links, in their papers. As Jupyter…

In recent years, a wide variety of automated machine learning (AutoML) methods have been proposed to search and generate end-to-end learning pipelines. While these techniques facilitate the creation of models for real-world applications,…

AI-powered code assistants, such as Copilot, are quickly becoming a ubiquitous component of contemporary coding contexts. Among these environments, computational notebooks, such as Jupyter, are of particular interest as they provide rich…

Academic presentation videos have become an essential medium for research communication, yet producing them remains highly labor-intensive, often requiring hours of slide design, recording, and editing for a short 2 to 10 minutes video.…

Jupyter notebooks represent a unique format for programming - a combination of code and Markdown with rich formatting, separated into individual cells. We propose to perceive a Jupyter Notebook cell as a simplified and raw version of a…

Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells…

By bringing together code, text, and examples, Jupyter notebooks have become one of the most popular means to produce scientific results in a productive and reproducible way. As many of the notebook authors are experts in their scientific…

Jupyter notebooks enable developers to interleave code snippets with rich-text and in-line visualizations. Data scientists use Jupyter notebook as the de-facto standard for creating and sharing machine-learning based solutions, primarily…

The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate…

Feature extraction is a critical component of many applied data science workflows. In recent years, rapid advances in artificial intelligence and machine learning have led to an explosion of feature extraction tools and services that allow…