Academic Articles & Research Papers

Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, s...

Cyber risk and cybersecurity: a systematic review of data availability

Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, th...

Natural Language Processing

In the previous chapters, we took a look at different machine learning algorithms and then we worked on how to work with data for those algorithms. Those data sets were largely tabular, and the individual values were either numerical or cat...

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...

Spectrally-normalized margin bounds for neural networks

This paper presents a margin-based multiclass generalization bound for neural networks that scales with their margin-normalized "spectral complexity": their Lipschitz constant, meaning the product of the spectral norms of the weight matrice...

A neural network approach to ordinal regression

Ordinal regression is an important type of learning, which has properties of both classification and regression. Here we describe an effective approach to adapt a traditional neural network to learn ordinal categories. Our approach is a gen...

Bayesian Nonparametric Federated Learning of Neural Networks

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...

Neural Networks and Computing

This book covers neural networks with special emphasis on advanced learning methodologies and applications. It includes practical issues of weight initializations, stalling of learning, and escape from a local minima, which have not been co...

Heterogeneous Graph Neural Network

Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This...

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...

Understanding Neural Networks as Statistical Tools

Neural networks have received a great deal of attention over the last few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally be...

Intriguing properties of neural networks

Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpr...

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

We introduce a method to train Quantized Neural Networks (QNNs) --- neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At train-time the quantized weights and activations are used for computing ...

Performance Evaluation for Moving Target Tracking Algorithm Based on Orthogonal Test

The performance evaluation of existing moving target tracking algorithms has many drawbacks,such as massive amount of data,redundant tests and insufficient consideration on algorithm performance under multifactor situation.Therefore,this pa...

Sentiment analysis for software engineering

Sentiment analysis has been applied to various software engineering (SE) tasks, such as evaluating app reviews or analyzing developers' emotions in commit messages. Studies indicate that sentiment analysis tools provide unreliable results w...

A campus-scale 5G NR radio propagation dataset integrating drive-test measurements, 3D environmental geometry, and ray tracing.

Multi-source environmental raster dataset of FAO desert locust survey records in Ethiopia (2015–2023) for machine-learning prediction of locust presence.

D-brane and exponential potentials inspire warm inflation and swampland conjecture

This research explores the rapid expansion period of the early universe by applying the Chaplygin gas model, an alternative cosmological framework, to analyze the dynamics of inflationary processes. This study assesses the compatibility of ...

Probability and Statistics for Engineers and Scientists

The new edition of Anthony Hayter's book continues in the same student-oriented vein that has made previous editions successful. Because Tony Hayter teaches and conducts research at a premier engineering school, he is in touch with engineer...

Data resource profile: a nationally representative linked pregnancy cohort in Canada integrating clinical, social, and environmental data

Introduction Perinatal outcomes are shaped by clinical, social, and environmental factors, yet Canada lacks a nationally representative pregnancy cohort capturing these influences at the individual-level. This gap has limited the ability to...