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Academic Articles & Research Papers
Showing 20 of 1,021 academic articles
Jason Edwards, Griffin Weaver • Wiley eBooks
(2024)
Cybersecurity is a critical concern across all sectors in our increasingly digital world. The shift toward digitization, which the global pandemic has further accelerated, has essentially turned cybersecurity into a universal concern. The f...
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Open Access
DOI: https://doi.org/10.1002/9781394250226.ch2
Reeshad Sam Dalal, David Howard, Rebecca J. Bennett, Clay Posey et al. • Journal of Business and Psychology
(2021)
Cybersecurity is an ever-present problem for organizations, but organizational science has barely begun to enter the arena of cybersecurity research. As a result, the "human factor" in cybersecurity research is much less studied than its te...
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DOI: https://doi.org/10.1007/s10869-021-09732-9
PMC: 7861585
Arunesh Sinha, Thanh H. Nguyen, Debarun Kar, Matthew Brown et al. • Journal of Cybersecurity
(2015)
Security is a critical concern around the world. In many domains from cybersecurity to sustainability, limited security resources prevent complete security coverage at all times. Instead, these limited resources must be scheduled (or alloca...
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DOI: https://doi.org/10.1093/cybsec/tyv007
Tomasz Zdzikot • Springer eBooks
(2021)
Abstract The purpose of this chapter is to discuss two basic concepts—cyberspace and cybersecurity. The author describes the genesis of both and attempts to define them. In the introduction, the author briefly addresses the issue of the pro...
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DOI: https://doi.org/10.1007/978-3-030-78551-2_2
Sergeja Slapničar, Tina Vuko, Marko Čular, Matej Drašček • International Journal of Accounting Information Systems
(2022)
The aim of this paper is to analyze the effectiveness of internal audit of cybersecurity. We developed a Cybersecurity Audit Index composed of three dimensions – planning, performing and reporting – to address this question. We hypothesize ...
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DOI: https://doi.org/10.1016/j.accinf.2021.100548
George Kostopoulos • Auerbach Publications eBooks
(2012)
Based on related courses and research on the cyber environment in Europe, the United States, and Asia, Cyberspace and Cybersecurity supplies complete coverage of cyberspace and cybersecurity. It not only emphasizes technologies but also pay...
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DOI: https://doi.org/10.1201/b12526
Esmeralda Kadëna, Marsidi Gupi • Security science journal
(2021)
Technological solutions in the mobile and digital era are becoming more helpful in informing the population, educational systems, monitoring, tracking the individuals, working, and spending time from home. On the other hand, the valuable in...
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DOI: https://doi.org/10.37458/ssj.2.2.3
S. Srisakthi, C. V. Suresh Babu • Advances in business information systems and analytics book series
(2024)
The chapter thoroughly explores the multifaceted cybersecurity landscape's significance in safeguarding business intelligence amid rapid technological evolution. It starts with an overview of cybersecurity, defining its scope, exploring its...
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DOI: https://doi.org/10.4018/979-8-3693-0839-4.ch001
Peratham Wiriyathammabhum, Douglas A. Summers-Stay, Cornelia M. Fermuller, Yiannis Aloimonos • ACM Computing Surveys
(2016)
Integrating computer vision and natural language processing is a novel interdisciplinary field that has received a lot of attention recently. In this survey, we provide a comprehensive introduction of the integration of computer vision and ...
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DOI: https://doi.org/10.1145/3009906
Barry Walsh • Apress eBooks
(2022)
Any book on operationalizing Artificial Intelligence today cannot ignore growth in Natural Language Processing (NLP), with market size projected to be $43b in 2025. Ostensibly the branch of AI concerned with programming computers to underst...
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DOI: https://doi.org/10.1007/978-1-4842-8817-7_10
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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DOI: https://doi.org/10.4137/bii.s4706
PMC: 3107011
Vivi Năstase, Rada F. Mihalcea, Dragomir Radev • Natural Language Engineering
(2015)
Abstract Graphs are a powerful representation formalism that can be applied to a variety of aspects related to language processing. We provide an overview of how Natural Language Processing problems have been projected into the graph framew...
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Open Access
DOI: https://doi.org/10.1017/s1351324915000340
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
George N. Bebis, Michael Georgiopoulos • IEEE Potentials
(1994)
One critical aspect neural network designers face today is choosing an appropriate network size for a given application. Network size involves in the case of layered neural network architectures, the number of layers in a network, the numbe...
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Open Access
DOI: https://doi.org/10.1109/45.329294
Ian Parberry, Michael R. Garey, Albert R. Meyer • The MIT Press eBooks
(1994)
Neural networks usually work adequately on small problems but can run into trouble when they are scaled up to problems involving large amounts of input data. Circuit Complexity and Neural Networks addresses the important question of how wel...
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DOI: https://doi.org/10.7551/mitpress/1836.001.0001
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
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami et al. • ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
(2019)
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...
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Open Access
DOI: https://doi.org/10.1145/3292500.3330961
Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov • arXiv (Cornell University)
(2015)
Deep neural networks currently demonstrate state-of-the-art performance in several domains. At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required b...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1509.06569
Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El‐Yaniv et al. • arXiv (Cornell University)
(2016)
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 ...
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Open Access
DOI: https://doi.org/10.48550/arxiv.1609.07061
Santanu Pattanayak • Apress eBooks
(2017)
Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with T...
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DOI: https://doi.org/10.1007/978-1-4842-3096-1