SUMMARY: Meta2DB is a curated metagenomic and metadata database that provides structurally consistent microbiome taxonomy feature count tables for 13 897 samples across 84 studies, 23 disease states, and 34 geographical locations. All samples were uniformly processed using a streamlined metagenomic classification pipeline that employs a unique and comprehensive reference database indexed to contain all sequences across all kingdoms of life that were present in the NCBI Nucleotide (nt) database retrieved on 4 January 2023. This pipeline leverages high-performance computing (HPC) resources at Lawrence Livermore National Laboratory and was used to process 50TB of publicly available raw metagenomic sequence data. Extensive metadata curation was carried out through a combination of manual curation and automated parsing, producing a consistent inter-study metadata table specifically structured to facilitate training of ML models for prediction of human health. AVAILABILITY: Data is available at https://gdo-meta2db.llnl.gov/ and https://zenodo.org/records/17315984.

In software development environments, code quality is crucial. This study aims to assist Machine Learning (ML) engineers in enhancing their code by identifying and correcting Data Leakage issues within their models. Data Leakage occurs when information from the test dataset is inadvertently included in the training data when preparing a data science model, resulting in misleading performance evaluations. ML developers must carefully separate their data into training, evaluation, and test sets to avoid introducing Data Leakage into their code. In this paper, we develop a new Visual Studio Code (VS Code) extension, called leakagedetector, that detects Data Leakage - mainly Overlap, Preprocessing and Multi-test leakage - from Jupyter Notebook files. Beyond detection, we included two correction mechanisms: a conventional approach, known as a quick fix, which manually fixes the leakage, and an LLM-driven approach that guides ML developers toward best practices for building ML pipelines. The plugin and its source code are publicly available on GitHub at https://github.com/SE4AIResearch/DataLeakage_JupyterNotebook_Fall2024. The demonstration video can be found on YouTube: https://youtu.be/7YiYVBiID_8. The website can be found at https://leakage-detector.vercel.app/.

Mobile robots are increasingly being deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians encounter these robots without prior understanding of the robots’ behaviour, which can cause discomfort, confusion, and delayed social acceptance. In this research, we explore the common human-robot interaction at a doorway or bottleneck in a structured environment. We designed and evaluated communication cues used by a robot when yielding to a pedestrian in this scenario. We conducted an online user study with 102 participants using videos of a set of robot-to-human yielding cues. Results show that a Robot Retreating cue was the most socially acceptable cue. Repeated measures and Friedman’s ANOVAs on components of social acceptability were statistically significant (p = .01) and had small and medium effect sizes (ηp2= .04, ηp2= .08). The results of this work help guide the development of mobile robots for public spaces.

Dido is an application (and application development environment) in a web page. It is a single web page containing rich structured data, an AJAXy interactive visualizer/editor for that data, and a “metaeditor ” for WYSIWYG editing of the visualizer/editor. Historically, users have been limited to the data schemas, visualizations, and interactions offered by a small number of heavyweight applications. In contrast, Dido encourages and enables the end user to edit (not code) in his or her web browser a distinct ephemeral interaction “wrapper ” for each data collection that is specifically suited to its intended use. Dido’s active document metaphor has been explored before but we show how, given today’s web infrastructure, it can be deployed in a small self-contained HTML document without touching a web client or server. ACM Classification: H5.2 [Information interfaces and presentation]: