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Academic Articles & Research Papers
Showing 20 of 608 academic articles
John P. Preskill • Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences
(1998)
I assess the potential of quantum computation. Broad and important applications must be found to justify construction of a quantum computer; I review some of the known quantum algorithms and consider the prospects for finding new ones. Quan...
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Open Access
DOI: https://doi.org/10.1098/rspa.1998.0171
Pranabananda Chakraborty • Chapman & Hall/CRC eBooks
(2023)
This chapter demystifies computing systems consisting of multiple CPUs, shared and distributed memory, and different types of I/O in the form of multiprocessors and multicomputers that eventually gave rise to distributed computing systems a...
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DOI: https://doi.org/10.1201/9781003383055-9
Andreas Bolfing • Oxford University Press eBooks
(2020)
Abstract Chapter 5 considers distributed systems by their properties. The first section studies the classification of software systems, which is usually distinguished in centralized, decentralized and distributed systems. It studies the dif...
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DOI: https://doi.org/10.1093/oso/9780198862840.003.0005
Luiz F. R. Monteiro, Yuri Reis Rodrigues, Antônio Carlos Zambroni de Souza • Energies
(2023)
The current energy transition combined with the modernization of power systems has provided meaningful transformations in the transmission, distribution, operation, planning, monitoring, and control of power systems. These advancements are ...
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DOI: https://doi.org/10.3390/en16124556
Feixue Yan, Sheng Wen, Surya Nepal, Cécile L. Paris et al. • International Journal of Intelligent Systems
(2022)
Machine learning (ML) techniques are increasingly important in cybersecurity, as they can quickly analyse and identify different types of threats from millions of events. In spite of the increasing number of possible applications of ML, suc...
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cc-by-nc
DOI: https://doi.org/10.1002/int.23088
Dipankar Dasgupta, Zahid Akhtar, Sajib Sen • The Journal of Defense Modeling and Simulation Applications Methodology Technology
(2020)
Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...
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DOI: https://doi.org/10.1177/1548512920951275
Veronika Fleischmannová • Digital Repository (National Repository of Grey Literature)
(2013)
This master thesis entitled Cybersecurity deals with cybersecuriy issue. The theoretical part defines basic concepts related to cybersecurity and cyber threats classification. The practical part deals with a case study regarding disputes be...
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Anand Handa, Ashu Sharma, Sandeep K. Shukla • Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
(2019)
Machine learning technology has become mainstream in a large number of domains, and cybersecurity applications of machine learning techniques are plenty. Examples include malware analysis, especially for zero‐day malware detection, threat a...
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DOI: https://doi.org/10.1002/widm.1306
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez et al. • Annals of Telecommunications
(2022)
Abstract With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essent...
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DOI: https://doi.org/10.1007/s12243-022-00926-7
Xavier Vasques • Wiley eBooks
(2024)
This book chapter focuses on natural language processing (NLP) and covers various applications such as classifying messages as spam or ham, sentiment analysis, and the use of Bidirectional Encoder Representations from Transformers (BERT). T...
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DOI: https://doi.org/10.1002/9781394220649.ch4
Yang Liu, Meng Zhang • Computational Linguistics
(2017)
Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...
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cc-by-nc-nd
DOI: https://doi.org/10.1162/coli_r_00312
John P Pestian, Henry A. Nasrallah, Paweł Matykiewicz, Aurora J. Bennett et al. • Biomedical Informatics Insights
(2010)
Suicide is the second leading cause of death among 25-34 year olds and the third leading cause of death among 15-25 year olds in the United States. In the Emergency Department, where suicidal patients often present, estimating the risk of r...
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cc-by-nc
DOI: https://doi.org/10.4137/bii.s4706
PMC: 3107011
Santanu Pattanayak • Apress eBooks
(2022)
In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...
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DOI: https://doi.org/10.1007/978-1-4842-8931-0_4
Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
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...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1602.02830
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra • arXiv (Cornell University)
(2015)
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop. It regularises the weights by minimising a compression ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1505.05424
Jianlin Cheng, Zheng Wang, Gianluca Pollastri • International Joint Conference on Neural Networks (IJCNN)
(2008)
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...
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Open Access
DOI: https://doi.org/10.1109/ijcnn.2008.4633963
Abhijit S. Pandya, Robert B. Macy • CRC Press eBooks
(2021)
From the Publisher:
The addition of artificial neural network computing to traditionalpattern recognition has given rise to a new, different, and more powerful methodology that is presented in this interesting book. This is a practical gui...
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DOI: https://doi.org/10.1201/9780138744274
Mikhail Yurochkin, Mayank Agarwal, Ghosh, Soumya, Kristjan H. Greenewald et al. • arXiv (Cornell University)
(2019)
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...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1905.12022
Qeethara Kadhim Al-Shayea (2011)
Artificial neural networks are finding many uses in the medical diagnosis application. The goal of this paper is to evaluate artificial neural network in disease diagnosis. Two cases are studied. The first one is acute nephritis disease; da...
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Octavian-Eugen Ganea, Gary Bécigneul, Thomas Hofmann • arXiv (Cornell University)
(2018)
Hyperbolic spaces have recently gained momentum in the context of machine learning due to their high capacity and tree-likeliness properties. However, the representational power of hyperbolic geometry is not yet on par with Euclidean geomet...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1805.09112