741 citations · 1.1k across the 12 of their papers we have counts for
14 papers
E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection
Ahmad Mousavi, Yeganeh Abdollahinejad, Roberto Corizzo +2
Detecting multimodal misinformation on social media remains challenging due to inconsistencies between modalities, changes in temporal patterns, and substantial class imbalance. Ma…
Sparse Extended Mean-Variance-CVaR Portfolios with Short-selling
Ahmad Mousavi, Maziar Salahi, Zois Boukouvalas
This paper introduces a novel penalty decomposition algorithm customized for addressing the non-differentiable and nonconvex problem of extended mean-variance-CVaR portfolio optimi…
Using LLMs to discover emerging coded antisemitic hate-speech in extremist social media
Dhanush Kikkisetti, Raza Ul Mustafa, Wendy Melillo +4
Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in bot…
A Semi-Supervised Framework for Misinformation Detection
Yueyang Liu, Zois Boukouvalas, Nathalie Japkowicz
The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause of many kinds of social unrest. Curtailing its prevalence is of great…
Assessing the trade-off between prediction accuracy and interpretability for topic modeling on energetic materials corpora
Monica Puerto, Mason Kellett, Rodanthi Nikopoulou +4
As the amount and variety of energetics research increases, machine aware topic identification is necessary to streamline future research pipelines. The makeup of an automatic topi…
Independent Component Analysis for Trustworthy Cyberspace during High Impact Events: An Application to Covid-19
Zois Boukouvalas, Christine Mallinson, Evan Crothers +5
Social media has become an important communication channel during high impact events, such as the COVID-19 pandemic. As misinformation in social media can rapidly spread, creating…
Deep learning for molecular design - a review of the state of the art
Daniel C. Elton, Zois Boukouvalas, Mark D. Fuge +1
In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, musi…
Using natural language processing techniques to extract information on the properties and functionalities of energetic materials from large text corpora
Daniel C. Elton, Dhruv Turakhia, Nischal Reddy +4
The number of scientific journal articles and reports being published about energetic materials every year is growing exponentially, and therefore extracting relevant information a…
Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Zois Boukouvalas, Daniel C. Elton, Peter W. Chung +1
Due to its high computational speed and accuracy compared to ab-initio quantum chemistry and forcefield modeling, the prediction of molecular properties using machine learning has…
Machine Learning of Energetic Material Properties
Brian C. Barnes, Daniel C. Elton, Zois Boukouvalas +4
In this work, we discuss use of machine learning techniques for rapid prediction of detonation properties including explosive energy, detonation velocity, and detonation pressure.…
Development of ICA and IVA Algorithms with Application to Medical Image Analysis
Zois Boukouvalas
Independent component analysis (ICA) is a widely used BSS method that can uniquely achieve source recovery, subject to only scaling and permutation ambiguities, through the assumpt…
Applying machine learning techniques to predict the properties of energetic materials
Daniel C. Elton, Zois Boukouvalas, Mark S. Butrico +2
We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a sma…
Independent Component Analysis by Entropy Maximization with Kernels
Zois Boukouvalas, Rami Mowakeaa, Geng-Shen Fu +1
Independent component analysis (ICA) is the most popular method for blind source separation (BSS) with a diverse set of applications, such as biomedical signal processing, video an…
Enhancing ICA Performance by Exploiting Sparsity: Application to FMRI Analysis
Zois Boukouvalas, Yuri Levin-Schwartz, Tulay Adali
Independent component analysis (ICA) is a powerful method for blind source separation based on the assumption that sources are statistically independent. Though ICA has proven usef…