scholarly journals A Taxonomy of Blockchain Consensus Methods

Cryptography ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 32
Author(s):  
Jeff Nijsse ◽  
Alan Litchfield
Keyword(s):  
Fault Tolerance ◽  
Scarce Resource ◽  
Majority Voting ◽  
Consensus Methods ◽  
Consensus Algorithms ◽  
Rational Classification

For a blockchain, consensus is the foundation protocol that enables cryptocurrencies such as Bitcoin to maintain state. Additionally, to ensure safety and liveness for a publicly accessible and verifiable ledger, fault tolerance must be robust. However, there appears to be a degree of misunderstanding about how consensus is applied across blockchains. To assist researchers considering variations between them, this study presents a rational classification of consensus methods applied to current blockchains. The study provides a survey of 19 methods classified by the scarce resource they employ: clock-cycles, bits, tokens, votes, time, and biometrics. Blockchain implementations are split between consensus algorithms requiring proof of resource and those that use majority voting to update the ledger.

The index of ideality of correlation: models of the flash points of ternary mixtures

2020 ◽  
Vol 44 (12) ◽  
pp. 4858-4868 ◽  
Author(s):  
Alla P. Toropova ◽  
Andrey A. Toropov ◽  
Danuta Leszczynska ◽  
Jerzy Leszczynski
Keyword(s):  
Flash Point ◽  
Ternary Mixtures ◽  
Reliable Information ◽  
Correlation Models ◽  
Rational Classification

Reliable information related to the flash point of ternary mixtures assists in the rational classification of different ternary mixtures of liquids.

scholarly journals Vibration-Based Structural Health Monitoring Using Piezoelectric Transducers and Parametric t-SNE

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1716
Author(s):  
David Agis ◽  
Francesc Pozo
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Structural Changes ◽  
Computational Cost ◽  
Principal Component ◽  
Piezoelectric Transducers ◽  
Majority Voting ◽  
Structural Health

In this paper, we evaluate the performance of the so-called parametric t-distributed stochastic neighbor embedding (P-t-SNE), comparing it to the performance of the t-SNE, the non-parametric version. The methodology used in this study is introduced for the detection and classification of structural changes in the field of structural health monitoring. This method is based on the combination of principal component analysis (PCA) and P-t-SNE, and it is applied to an experimental case study of an aluminum plate with four piezoelectric transducers. The basic steps of the detection and classification process are: (i) the raw data are scaled using mean-centered group scaling and then PCA is applied to reduce its dimensionality; (ii) P-t-SNE is applied to represent the scaled and reduced data as 2-dimensional points, defining a cluster for each structural state; and (iii) the current structure to be diagnosed is associated with a cluster employing two strategies: (a) majority voting; and (b) the sum of the inverse distances. The results in the frequency domain manifest the strong performance of P-t-SNE, which is comparable to the performance of t-SNE but outperforms t-SNE in terms of computational cost and runtime. When the method is based on P-t-SNE, the overall accuracy fluctuates between 99.5% and 99.75%.

An Attempt at a Rational Classification of the Principal Accelerators of Vulcanization

1929 ◽  
Vol 2 (3) ◽  
pp. 356-361
Author(s):  
G. Martin ◽  
R. Thiollet
Keyword(s):  
Mechanical Properties ◽  
The Other ◽  
Different Temperatures ◽  
Points Of View ◽  
Time Required ◽  
Rational Classification

Abstract AT the present time there are many accelerators on the market for the vulcanization of rubber, but it is often difficult to choose among them those best suited for a required purpose. Accelerators are often classed as slow medium, rapid and ultra rapid. These brief terms are entirely unsatisfactory for characterizing clearly the properties of these properties, and it frequently happens that two accelerators which have been placed together in one class behave in reality in very different ways and are not entirely replaceable one by the other. The object of this study is to establish a rational classification of the principal accelerators of vulcanization, which is based not only on their activity but also on their other important characteristics. The following points will be considered in their order: (1) The time required for the fixation of mixtures at different temperatures. (2) The time required to bring about vulcanization giving the maxima mechanical properties at different temperatures. (3) Aging. (4) These three points of view will be completed by a study of the plasticizing power and of the influence of different charges on the action of the accelerators.

scholarly journals Optimizing taxonomic classification of marker gene amplicon sequences

2018 ◽  
Author(s):  
Nicholas A Bokulich ◽  
Benjamin D Kaehler ◽  
Jai Ram Rideout ◽  
Matthew Dillon ◽  
Evan Bolyen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sequence Data ◽  
Marker Gene ◽  
Parameter Tuning ◽  
Operating Conditions ◽  
Evaluation Framework ◽  
Taxonomic Classification ◽  
Consensus Methods ◽  
Learning Classifier

Background: Taxonomic classification of marker-gene sequences is an important step in microbiome analysis. Results: We present q2-feature-classifier ( https://github.com/qiime2/q2-feature-classifier ), a QIIME 2 plugin containing several novel machine-learning and alignment-based taxonomy classifiers that meet or exceed the accuracy of existing methods for marker-gene amplicon sequence classification. We evaluated and optimized several commonly used taxonomic classification methods (RDP, BLAST, UCLUST) and several new methods (a scikit-learn naive Bayes machine-learning classifier, and alignment-based taxonomy consensus methods of VSEARCH, BLAST+, and SortMeRNA) for classification of marker-gene amplicon sequence data. Conclusions: Our results illustrate the importance of parameter tuning for optimizing classifier performance, and we make recommendations regarding parameter choices for a range of standard operating conditions. q2-feature-classifier and our evaluation framework, tax-credit, are both free, open-source, BSD-licensed packages available on GitHub.

scholarly journals Classification of the Trap-Neuter-Return Surgery Images of Stray Animals Using Yolo-Based Deep Learning Integrated with a Majority Voting System

2021 ◽  
Vol 11 (18) ◽  
pp. 8578
Author(s):  
Yi-Cheng Huang ◽  
Ting-Hsueh Chuang ◽  
Yeong-Lin Lai
Keyword(s):  
Deep Learning ◽  
Majority Voting ◽  
Effective Solution ◽  
Stray Cats ◽  
Average Accuracy ◽  
Voting System ◽  
The Cost ◽  
Animal Care ◽  
Manual Review

Trap-neuter-return (TNR) has become an effective solution to reduce the prevalence of stray animals. Due to the non-culling policy for stray cats and dogs since 2017, there is a great demand for the sterilization of cats and dogs in Taiwan. In 2020, Heart of Taiwan Animal Care (HOTAC) had more than 32,000 cases of neutered cats and dogs. HOTAC needs to take pictures to record the ears and excised organs of each neutered cat or dog from different veterinary hospitals. The correctness of the archived medical photos and the different shooting and imaging angles from different veterinary hospitals must be carefully reviewed by human professionals. To reduce the cost of manual review, Yolo’s ensemble learning based on deep learning and a majority voting system can effectively identify TNR surgical images, save 80% of the labor force, and its average accuracy (mAP) exceeds 90%. The best feature extraction based on the Yolo model is Yolov4, whose mAP reaches 91.99%, and the result is integrated into the voting classification. Experimental results show that compared with the previous manual work, it can decrease the workload by more than 80%.

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