Version 2025-10-11 Now works on Igor 9. Tutorial: https://www.bilibili.com/video/BV1oEWtz7ECS/ https://youtu.be/sHM2i9ND3VU Scatter Plot is a useful toolkit based on Igor Pro, designed to enhance data visualization efficiency in atmospheric science. While many existing generalized data visualization software options have been widely used, they often fall short in meeting specific research needs in this field. This gap motivated the development of Scatter Plot. The program incorporates the Deming and York algorithms for linear regression, which account for uncertainties in both the X and Y variables, making it more suitable for atmospheric app
Cheng WU
DataScribe Deep Tabular Learning This repository provides the code, data, and documentation for the paper: Decoding Non-Linearity and Complexity: Deep Tabular Learning Approaches for Materials Science Vahid Attari, Raymundo Arroyave, Department of Materials Science & Engineering, Texas A&M University. DataScribe GUI demo showing encoder-decoder training on HEA tabular data. Watch on YouTube 📖 Overview This project addresses the challenges in tabular materials science data, including extreme skewness, and complex feature interactions. The repository implements several machine learning models, ranging from classical tree-based methods (e.g., XG
Attari, Vahid, Arroyave, Raymundo
This project presents an Adaptive LLM-based system for summarizing multi-source content including text, PDFs, and YouTube videos. It uses content classification and dynamic model selection to improve summarization quality.
Madhu Priya, Sri Lakshmi, Madhumitha, Harsha Vardhan
This is the initial release of the dataset "Dopamine-Triggering Indicators in Children's YouTube Videos". 500 manually annotated videos 250 dopamine-inducing, 250 non-dopamine 250 kids' videos, 250 not-for-kids Annotated equally by male and female annotators See README and CITATION.cff for usage & citation This dataset supports ongoing research on modeling dopamine-triggering patterns in children's media using machine learning.
Sakamuri, Sanjay, Kumaravadivel, Jivanthikaa
The "YouTube Spam Comments Detector" Chrome extension is a powerful tool designed to enhance the YouTube browsing experience by effectively identifying and filtering out spam comments. This user-friendly extension leverages advanced algorithms and machine learning techniques to swiftly analyze and distinguish genuine comments from those that are irrelevant, misleading, or intended to manipulate content visibility.
Madhav Agrawal, Arham Jain
Speakerbox is a library which enables: the creation of an audio-based speaker identification datasets training an audio-based speaker identification transformer model applying a pre-trained audio-based speaker identification to a new audio file and predicting portions of audio as the known speakers This release completes the work for our Journal of Open Source Software (JOSS) paper. The changes from v1.0.0 include: An example video attached to the README which demonstrates how to use this library (on a toy example) -- YouTube Video Link . A more thorough workflow diagram attached to the README which explains how all the components of this lib
Brown, Eva Maxfield, Huynh, To, Weber, Nicholas
This is the code that is used on my paper titled "Bagaimana Masyarakat Menyikapi Pembelajaran Tatap Muka: Analisis Komentar Masyarakat pada Media Sosial Youtube Menggunakan Algoritma Deep Learning Sekuensial dan LDA". This paper is submitted to Jurnal Lingustik Komputasional.
pakfawwaz
Abstract: The role played by YouTube's recommendation algorithm in unwittingly promoting misinformation and conspiracy theories is not entirely understood. Yet, this can have dire real-world consequences, especially when pseudoscientific content is promoted to users at critical times, such as the COVID-19 pandemic. In this paper, we set out to characterize and detect pseudoscientific misinformation on YouTube. We collect 6.6K videos related to COVID-19, the Flat Earth theory, as well as the anti-vaccination and anti-mask movements. Using crowdsourcing, we annotate them as pseudoscience, legitimate science, or irrelevant and train a deep learn
Kostantinos Papadamou, Savvas Zannettou, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, Michael Sirivianos
In this repository we include a package with the latest version of the deep learning model implemented in this work which can be used by anyone who wants to detect inappropriate videos for kids on YouTube. (see https://ojs.aaai.org/index.php/ICWSM/article/view/7320/7174 for the detailed description on the results). Codebase also available on Github . Please appropriately cite the "Disturbed YouTube For Kids: Characterizing And Detecting Inappropriate Videos Targeting Young Children" paper in any publication, of any form and kind, using this software: @inproceedings{papadamou2020disturbedyoutube, title= {{Disturbed YouTube for Kids: Characteri
Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou, Jeremy Blackburn, Nicolas Kourtellis, Ilias Leontiadis, Gianluca Stringhini, Michael Sirivianos
Program yang digunakan dalam pembuatan paper berjudul Bagaimana Masyarakat Menyikapi New Normal: Analisis Komentar Masyarakat pada Media Sosial Youtube Berdasarkan Klasifikasi Multikelas Menggunakan Algoritma Deep Learning Sekuensial.
pakfawwaz
