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Found 15 papers, 4 papers with code
Explainable Detection of Sarcasm in Social Media
Sarcasm is a linguistic expression often used to communicate the opposite of what is said, usually something that is very unpleasant with an intention to insult or ridicule.
Sentence Pair Embeddings Based Evaluation Metric for Abstractive and Extractive Summarization
As these sentence pair tasks involve capturing the semantic similarity between a pair of input texts, we leverage these models in our metric computation.
Dominant Set-based Active Learning for Text Classification and its Application to Online Social Media
In this paper, we present a novel pool-based active learning method that can be used for the training of large unlabeled corpus with minimum annotation cost.
Resilience from Diversity: Population-based approach to harden models against adversarial attacks
When CLM is coupled with adversarial training, this defense mechanism achieves state-of-the-art robustness.
Ethical AI for Social Good
The concept of AI for Social Good(AI4SG) is gaining momentum in both information societies and the AI community.
Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media
We show the effectiveness of our approach by achieving state-of-the-art results on multiple datasets from social networking platforms and online media.
A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19
We expect this data set to be useful for many research purposes, including stance detection, evolution and dynamics of opinions regarding this outbreak, and changes in opinions in response to the exogenous shocks such as policy decisions and events.
Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model
Our topic model identifies topics' recurrence over time with a varying time resolution.
Facial Expression Phoenix (FePh): An Annotated Sequenced Dataset for Facial and Emotion-Specified Expressions in Sign Language
Facial expressions are important parts of both gesture and sign language recognition systems.
A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection
Hence, the challenge to develop fraud detection that are accurate and efficient is substantially intensified and, as a consequence, credit card fraud detection has lately become a very active area of research.
Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes
Thus, to test the accurate prediction of Type 2 diabetes, we use the patients' information from an electronic health records company called Practice Fusion, which has about 10, 000 patient records from 2009 to 2012.
Forecasting the Success of Television Series using Machine Learning
The models represent a baseline to understanding the success of a television show and how producers can increase the success of current television shows or utilize this data in the creation of future shows.
DeepFork: Supervised Prediction of Information Diffusion in GitHub
To understand human influence, information spread and evolution of transmitted information among assorted users in GitHub, we developed a deep neural network model: DeepFork, a supervised machine learning based approach that aims to predict information diffusion in complex social networks; considering node as well as topological features.
NL4Py: Agent-Based Modeling in Python with Parallelizable NetLogo Workspaces
External control of agent-based models is vital for complex adaptive systems research.
Multiagent Systems
Evolutionary Model Discovery of Factors for Farm Selection by the Artificial Anasazi
We use evolutionary model discovery to explore plausible farm seeking strategies, extending our previous study by testing four social connectivity strategies, four emotional theory components and five rational mechanisms for a more complex human-like approach towards farm plot selection.
Multiagent Systems
