Pinned Repositories
Adversary-Engagement-Ontology
The adversary engagement ontology for expressing all things cyber denial, deception, and operational narratives.
BINNs
Biologically-informed neural networks
Combating-Human-Trafficking-via-Automatic-OSINT-Collection
Combating Human Trafficking via Automatic OSINT Collection, Validation and Fusion
Drone-Identification-with-mmWave
F23-DSCI6004
Assignments and Aux for Fa23 - DSCI 6004 (Natural Language Processing)
Nepali-Alpaca-ChatGPT
Phorcys-AutoPT-Framework
Capstone project for using Reinforcement Learning to conduct intelligent penetration tests.
rl_for_theranostics
This is the code repository for the data driven modeling for theranostics
SentimentalLIAR
Our Sentimental LIAR dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In our dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, we convert the speaker names to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API . Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then we assign Positive for the sentiment attribute, otherwise Negative is assigned. We also introduced a further extension by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states namely anger, sadness, disgust, fear and joy. The score for each emotion is between the range of 0 and 1. Table I demonstrates a sample record in Sentimental LIAR for a short claim in the LIAR dataset This repository contains the dataset for this paper: https://arxiv.org/abs/2009.01047
TaCo
UNHSAILLab's Repositories
UNHSAILLab/SentimentalLIAR
Our Sentimental LIAR dataset is a modified and further extended version of the LIAR extension introduced by Kirilin et al. In our dataset, the multi-class labeling of LIAR is converted to a binary annotation by changing half-true, false, barely-true and pants-fire labels to False, and the remaining labels to True. Furthermore, we convert the speaker names to numerical IDs in order to avoid bias with regards to the textual representation of names. The binary-label dataset is then extended by adding sentiments derived using the Google NLP API . Sentiment analysis determines the overall attitude of the text (i.e., whether it is positive or negative), and is quantified by a numerical score. If the sentiment score is positive, then we assign Positive for the sentiment attribute, otherwise Negative is assigned. We also introduced a further extension by adding emotion scores extracted using the IBM NLP API for each claim, which determine the detected level of 6 emotional states namely anger, sadness, disgust, fear and joy. The score for each emotion is between the range of 0 and 1. Table I demonstrates a sample record in Sentimental LIAR for a short claim in the LIAR dataset This repository contains the dataset for this paper: https://arxiv.org/abs/2009.01047
UNHSAILLab/TaCo
UNHSAILLab/Adversary-Engagement-Ontology
The adversary engagement ontology for expressing all things cyber denial, deception, and operational narratives.
UNHSAILLab/Combating-Human-Trafficking-via-Automatic-OSINT-Collection
Combating Human Trafficking via Automatic OSINT Collection, Validation and Fusion
UNHSAILLab/Phorcys-AutoPT-Framework
Capstone project for using Reinforcement Learning to conduct intelligent penetration tests.
UNHSAILLab/rl_for_theranostics
This is the code repository for the data driven modeling for theranostics
UNHSAILLab/Drone-Identification-with-mmWave
UNHSAILLab/F23-DSCI6004
Assignments and Aux for Fa23 - DSCI 6004 (Natural Language Processing)
UNHSAILLab/Nepali-Alpaca-ChatGPT
UNHSAILLab/BINNs
Biologically-informed neural networks
UNHSAILLab/S24-AISec
Code and Data for S24 offering of DSCI 6015 - AI & Cybersecurity
UNHSAILLab/ToM_Against_AdvComm
Code for Theory of Mind Defense and Mitigation against Adversarial Communication
UNHSAILLab/tweet-tagging
UNHSAILLab/UCO
This repository is for development of the Unified Cyber Ontology.
UNHSAILLab/aeo-medium-gists
UNHSAILLab/Computational-Physics-UBC
computational physics forked from Research partners at UBC
UNHSAILLab/Fault-Detection-on-Surgical-Stapler-
UNHSAILLab/Multiscale-PBPK-Nanoparticle-Biodistribution-Model
This repository contains all matlab code files relevant to the Physiologically Based Multiscale Pharmacokinetic Model for Determining the Temporal Biodistribution of Targeted Nanoparticles paper
UNHSAILLab/sail-TTS
UNHSAILLab/TTS
🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production