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Academic Articles & Research Papers
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Hesham El‐Rewini, Theodore Gyle Lewis, Hesham Ali (1994)
1. Resource Allocation: A Classical Problem. 2. The General Model. 3. Optimal Scheduling Algorithms. 4. The Relationship Between Matching and Two-Processor Scheduling. 5. Static Scheduling Heuristics. 6. SPMD Model. 7. Dynamic Task Allocati...
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Mukesh Singhal • Computer
(1989)
The author describes a series of deadlock detection techniques based on centralized, hierarchical, and distributed control organizations. The point of view is that of practical implications. An up-to-date and comprehensive survey of deadloc...
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DOI: https://doi.org/10.1109/2.43525
Shrisha Rao • IEEE Distributed Systems Online
(2008)
Distributed systems: an algorithmic approach is an important addition to the distributed computing literature. The book offers a broad overview of important distributed computing topics, and, where relevant, a touch of networking topics as ...
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DOI: https://doi.org/10.1109/mdso.2008.30
Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang et al. • Engineering
(2018)
Cyberattack forms are complex and varied, and the detection and prediction of dynamic types of attack are always challenging tasks. Research on knowledge graphs is becoming increasingly mature in many fields. At present, it is very signific...
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DOI: https://doi.org/10.1016/j.eng.2018.01.004
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
Mohammad Alauthman, Ammar Almomani, Ahmad Al–Qerem, Mohammad Ali Al Khaldy et al. • Advances in information security, privacy, and ethics book series
(2024)
This chapter explores the impact of quantum computing on cybersecurity, comparing classical and quantum techniques. It provides an overview of the current quantum landscape, focusing on the threats posed by quantum algorithms to traditional...
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DOI: https://doi.org/10.4018/979-8-3693-5330-1.ch004
Chee‐Wooi Ten, Junho Hong, Chen‐Ching Liu • IEEE Transactions on Smart Grid
(2011)
Cybersecurity of the substations in a power system is a major issue as the substations become increasingly dependent on computer and communication networks. This paper is concerned with anomaly detection in the computer network environment ...
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DOI: https://doi.org/10.1109/tsg.2011.2159406
Tim Stevens • Digital War
(2020)
Abstract Cybersecurity protects citizens and society from harm perpetrated through computer networks. Its task is made ever more complex by the diversity of actors—criminals, spies, militaries, hacktivists, firms—operating in global informa...
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DOI: https://doi.org/10.1057/s42984-020-00007-w
Ben D. Sawyer, Peter A. Hancock • Human Factors The Journal of the Human Factors and Ergonomics Society
(2018)
OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...
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Open Access
DOI: https://doi.org/10.1177/0018720818780472
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
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
Stéphane M. Meystre, Peter John Haug • BMC Medical Informatics and Decision Making
(2005)
BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However,...
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DOI: https://doi.org/10.1186/1472-6947-5-30
PMC: 1208893
Joydeep Bhattacharjee • Apress eBooks
(2019)
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...
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DOI: https://doi.org/10.1007/978-1-4842-5121-8_5
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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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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DOI: https://doi.org/10.4137/bii.s4706
PMC: 3107011
Zhiyuan Liu, Yankai Lin, Maosong Sun • Springer eBooks
(2020)
This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for NLP. It also benefit related domains such as machine learning, social network analysis, semantic Web, infor...
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DOI: https://doi.org/10.1007/978-981-15-5573-2
V Ranjit, Suchitra Naik • International Journal of Computing Algorithm
(2023)
A branch of computer science and AI called natural language processing is focused on how computers and human languages interact.In order for computers to interpret written and spoken human language without the use of computer-driven languag...
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DOI: https://doi.org/10.20894/ijcoa.101.012.002.001
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
R. Daniel VanLuchene, ROUFEI SUN • Computer-Aided Civil and Infrastructure Engineering
(1990)
In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...
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DOI: https://doi.org/10.1111/j.1467-8667.1990.tb00377.x