An Application Study to the Ciphers Weighed in Faithful Transmission

2011 ◽  
Vol 128-129 ◽  
pp. 637-641
Author(s):  
Lan Luo ◽  
Qiong Hai Dai ◽  
Chun Xiang Xu ◽  
Shao Quan Jiang

The cipher algorithms are categorized by block cipher, stream cipher and HASH, and they are weighed in faithful transmission which is known as independent condition. In faithful transmission, the ciphers are studied because of their root cipher. Intelligent application of ciphers is a direction that uses Bayesian model of cognition science. Bayesian inference is a rational engine for solving such problems within a probabilistic framework, and consequently is the heart of most probabilistic models of weighing the ciphers. The approach of this paper is that ciphers, which are considered as a suitable weight cipher to kinds of networks, are ranged based on root ciphers. This paper shows the other kinds of transformation among the different cipher algorithms themselves.

2010 ◽  
Vol 39 ◽  
pp. 436-440
Author(s):  
Zhi Ming Qu

In recent years, much research has been devoted to the refinement of IPv6; on the other hand, few have investigated the confusing unification of interrupts and Internet QoS. In this position paper, it demonstrates the emulation of interrupts. In order to overcome this quagmire, a novel system is presented for the intuitive unification of expert systems and massive multiplayer online role-playing games. It is concluded that erasure coding can be verified to make heterogeneous, interposable, and event-driven, which is proved to be applicable.


2017 ◽  
Vol 27 (03) ◽  
pp. 1850037 ◽  
Author(s):  
Yasir ◽  
Ning Wu ◽  
Xiaoqiang Zhang

This paper proposes compact hardware implementations of 64-bit NESSIE proposed MISTY1 block cipher for area constrained and low power ASIC applications. The architectures comprise only one round MISTY1 block cipher algorithm having optimized FO/FI function by re-utilizing S9/S7 substitution functions. A focus is also made on efficient logic implementations of S9 and S7 substitution functions using common sub-expression elimination (CSE) and parallel AND/XOR gates hierarchy. The proposed architecture 1 generates extended key with independent FI function and is suitable for MISTY1 8-rounds implementation. On the other hand, the proposed architecture 2 uses a single FO/FI function for both MISTY1 round function as well as extended key generation and can be employed for MISTY1 [Formula: see text] rounds. To analyze the performance and covered area for ASICs, Synopsys Design Complier, SMIC 0.18[Formula: see text][Formula: see text]m @ 1.8[Formula: see text]V is used. The hardware constituted 3041 and 2331 NAND gates achieving throughput of 171 and 166 Mbps for 8 rounds implementation of architectures 1 and 2, respectively. Comprehensive analysis of proposed designs is covered in this paper.


2021 ◽  
Author(s):  
Joseph M Barnby ◽  
Nichola Raihani ◽  
Peter Dayan

To benefit from social interactions, people need to predict how their social partners will behave. Such predictions arise through integrating prior expectations with evidence from observations, but where the priors come from and whether they influence the integration is not clear. Furthermore, this process can be affected by factors such as paranoia, in which the tendency to form biased impressions of others is common. Using a modified social value orientation (SVO) task in a large online sample (n=697), we showed that participants used a Bayesian inference process to learn about partners, with priors that were based on their own preferences. Paranoia was associated with preferences for earning more than a partner and less flexible beliefs regarding a partner’s social preferences. Alignment between the preferences of participants and their partners was associated with better predictions and with reduced attributions of harmful intent to partners.


2019 ◽  
Author(s):  
Mark Andrews

The study of memory for texts has had an long tradition of research in psychology. According to most general accounts, the recognition or recall of items in a text is based on querying a memory representation that is built up on the basis of background knowledge. The objective of this paper is to describe and thoroughly test a Bayesian model of these general accounts. In particular, we present a model that describes how we use our background knowledge to form memories in terms of Bayesian inference of statistical patterns in the text, followed by posterior predictive inference of the words that are typical of those inferred patterns. This provides us with precise predictions about which words will be remembered, whether veridically or erroneously, from any given text. We tested these predictions using behavioural data from a memory experiment using a large sample of randomly chosen texts from a representative corpus of British English. The results show that the probability of remembering any given word in the text, whether falsely or veridically, is well predicted by the Bayesian model. Moreover, compared to nontrivial alternative models of text memory, by every measure used in the analyses, the predictions of the Bayesian model were superior, often overwhelmingly so. We conclude that these results provide strong evidence in favour of the Bayesian account of text memory that we have presented in this paper.


2021 ◽  
Author(s):  
Dmytro Perepolkin ◽  
Benjamin Goodrich ◽  
Ullrika Sahlin

This paper extends the application of indirect Bayesian inference to probability distributions defined in terms of quantiles of the observable quantities. Quantile-parameterized distributions are characterized by high shape flexibility and interpretability of its parameters, and are therefore useful for elicitation on observables. To encode uncertainty in the quantiles elicited from experts, we propose a Bayesian model based on the metalog distribution and a version of the Dirichlet prior. The resulting “hybrid” expert elicitation protocol for characterizing uncertainty in parameters using questions about the observable quantities is discussed and contrasted to parametric and predictive elicitation.


2019 ◽  
Vol 491 (4) ◽  
pp. 5238-5247 ◽  
Author(s):  
X Saad-Olivera ◽  
C F Martinez ◽  
A Costa de Souza ◽  
F Roig ◽  
D Nesvorný

ABSTRACT We characterize the radii and masses of the star and planets in the Kepler-59 system, as well as their orbital parameters. The star parameters are determined through a standard spectroscopic analysis, resulting in a mass of $1.359\pm 0.155\, \mathrm{M}_\odot$ and a radius of $1.367\pm 0.078\, \mathrm{R}_\odot$. The obtained planetary radii are $1.5\pm 0.1\, R_\oplus$ for the inner and $2.2\pm 0.1\, R_\oplus$ for the outer planet. The orbital parameters and the planetary masses are determined by the inversion of Transit Timing Variations (TTV) signals. We consider two different data sets: one provided by Holczer et al. (2016), with TTVs only for Kepler-59c, and the other provided by Rowe et al. (2015), with TTVs for both planets. The inversion method applies an algorithm of Bayesian inference (MultiNest) combined with an efficient N-body integrator (Swift). For each of the data set, we found two possible solutions, both having the same probability according to their corresponding Bayesian evidences. All four solutions appear to be indistinguishable within their 2-σ uncertainties. However, statistical analyses show that the solutions from Rowe et al. (2015) data set provide a better characterization. The first solution infers masses of $5.3_{-2.1}^{+4.0}~M_{\mathrm{\oplus }}$ and $4.6_{-2.0}^{+3.6}~M_{\mathrm{\oplus }}$ for the inner and outer planet, respectively, while the second solution gives masses of $3.0^{+0.8}_{-0.8}~M_{\mathrm{\oplus }}$ and $2.6^{+0.9}_{-0.8}~M_{\mathrm{\oplus }}$. These values point to a system with an inner super-Earth and an outer mini-Neptune. A dynamical study shows that the planets have almost co-planar orbits with small eccentricities (e < 0.1), close to the 3:2 mean motion resonance. A stability analysis indicates that this configuration is stable over million years of evolution.


Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 890
Author(s):  
Sergey Oladyshkin ◽  
Farid Mohammadi ◽  
Ilja Kroeker ◽  
Wolfgang Nowak

Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.


2010 ◽  
Vol 1 (25) ◽  
pp. 15-21 ◽  
Author(s):  
Shish Ahmad ◽  
DR. Mohd. Rizwan beg ◽  
Dr. Qamar Abbas ◽  
Jameel Ahmad ◽  
Syed Mohd Atif

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