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Compressed sensing: a discrete optimization approach
We study the Compressed Sensing (CS) problem, which is the problem of finding the most sparse vector that satisfies a set of linear measurements up...
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Examining ALS: reformed PCA and random forest for effective detection of ALS
ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that...
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TOPCOAT: towards practical two-party Crystals-Dilithium
The development of threshold protocols based on lattice-signature schemes has been of increasing interest in the past several years. The main...
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Explainable decomposition of nested dense subgraphs
Discovering dense regions in a graph is a popular tool for analyzing graphs. While useful, analyzing such decompositions may be difficult without...
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Data reduction in big data: a survey of methods, challenges and future directions
Data reduction plays a pivotal role in managing and analyzing big data, which is characterized by its volume, velocity, variety, veracity, value,...
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Moreau-Yoshida variational transport: a general framework for solving regularized distributional optimization problems
We address a general optimization problem involving the minimization of a composite objective functional defined over a class of probability...
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Multi-task learning and mutual information maximization with crossmodal transformer for multimodal sentiment analysis
The effectiveness of multimodal sentiment analysis hinges on the seamless integration of information from diverse modalities, where the quality of...
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Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis
Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared...
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A Meta-learner approach to multistep-ahead time series prediction
The utilization of machine learning has become ubiquitous in addressing contemporary challenges in data science. Moreover, there has been significant...
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Regional bias in monolingual English language models
In Natural Language Processing (NLP), pre-trained language models (LLMs) are widely employed and refined for various tasks. These models have shown...
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Dynamic event-triggered adaptive control for state-constrained strict-feedback nonlinear systems with guaranteed feasibility conditions
In this paper, a new dynamic event-triggered control solution is presented for state-constrained strict-feedback nonlinear systems. The current...
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Conformal predictions for probabilistically robust scalable machine learning classification
Conformal predictions make it possible to define reliable and robust learning algorithms. But they are essentially a method for evaluating whether an...
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Neural discovery of balance-aware polarized communities
Signed graphs are a model to depict friendly ( positive ) or antagonistic ( negative ) interactions (edges) among users (nodes). 2-Polarized-Communities (
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Permutation-invariant linear classifiers
Invariant concept classes form the backbone of classification algorithms immune to specific data transformations, ensuring consistent predictions...
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Decoding the black box: LIME-assisted understanding of Convolutional Neural Network (CNN) in classification of social media tweets
The rise of social media has brought both opportunities and challenges to the digital age, including the proliferation of online trolls that have...