This book includes high-quality papers presented at International Conference on Scientific and Natural Computing (SNC 2021), organized by Department of appliedmathematics, Gautam Buddha University, Greater Noida...
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ISBN:
(数字)9789811615283
ISBN:
(纸本)9789811615276;9789811615306
This book includes high-quality papers presented at International Conference on Scientific and Natural Computing (SNC 2021), organized by Department of appliedmathematics, Gautam Buddha University, Greater Noida in collaboration with IIT Roorkee and Technical University of Ostrava (VSB-TU) and technically sponsored by Soft Computing Research Society of India, held online during 5 – 6 February 2021. The topics include self-organizing migrating algorithm, genetic algorithms, swarm intelligence based techniques, evolutionary computing, fuzzy computing, probabilistic computing, genetic programming, particle swarm optimization, neuro computing, hybrid methods, deep learning, including convolutional neural networks, generative adversarial networks and auto-encoders, bio-inspired systems, data mining, data visualization, intelligent agents, engineering design optimization, multi-objective optimization, fault diagnosis, decision support, robotics, signal or image processing, system identification and modelling, systems integration, time series prediction, virtual reality, vision or pattern recognition, intelligent information retrieval, motion control and power electronics, Internet of Everything (IoE), control systems, and supply chain management.
SIR,-I was pleased to read your editorial "Premature Puff for Smoking Beagles"1 and add more information on Dr Hammond's and the American Cancer Society's attitude toward science, methodology, and hi...
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SIR,-I was pleased to read your editorial "Premature Puff for Smoking Beagles"1 and add more information on Dr Hammond's and the American Cancer Society's attitude toward science, methodology, and his *** University undertook a project early this year, under my direction, with the aim to review some of the crucial data linking smoking to disease. Ten distinguished men and women scientists, from as many leading universities and laboratories, agreed to provide authoritative guidance and to advise us on how to set up conditions that would assure a fair, unbiased and authoritative review of the data. An invitation was also extended to Dr Cuyler Hammond, of the American Cancer Society, to meet with this panel. He was given assurances of all possible safeguards in the use of his data. Dr Hammond's reply was a flat *** since his first survey of smokers and nonsmokers in 1952, the methods by which Dr Hammond obtained data and analysed them have been thoughtfully criticized by some of the world's outstanding statisticians and scientists. The problems created by the objections were never adequately dealt with. Yet there are disturbing possibilities that the association between smoking and lung cancer, presented with such conviction by Dr Hammond, is a spurious byproduct of biased sampling methods. There is an equally disturbing possibility that much of the relationship between smoking and lung cancer in Hammond's data may actually be an expression of occupational exposures hidden within these data and not brought out by adequate analysis. As Dr Hammond continues to produce publication after publication based on these same data, many anomalies of the population studied become apparent. In few measures or observations does this study population resemble the make-up of the population of this country. It is becoming increasingly puzzling who really is represented by that sample collected by volunteers of the American Cancer *** disturbing, howe
A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unifi...
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A unified understanding of terms is essential for every scientific discipline: steganography is no exception. Being divided into several domains (e.g., network and text steganography), it is crucial to provide a unified terminology as well as a taxonomy that is not limited to few applications or areas. A prime attempt towards a unified understanding of terms was conducted in 2015 with the introduction of a pattern-based taxonomy for network steganography. In 2021, the first work towards a pattern-based taxonomy for all domains of steganography was proposed. However, this initial attempt still faced several shortcomings, e.g., remaining inconsistencies and a lack of patterns for several steganography *** the consortium who published the previous studies on steganography patterns, we present the first comprehensive pattern-based taxonomy tailored to fit all known domains of steganography, including smaller and emerging areas, such as filesystem, IoT/CPS, and AI/ML steganography. To make our contribution more effective and promote the use of the taxonomy to advance research, we also provide a unified description method joint with a thorough tutorial on its utilization.
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representa...
Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries, and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.
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