Reliable Failure Restoration with Bayesian Congestion Aware for Software Defined Networks

SoftwareDefined Networks(SDN)introduced better network management by decoupling control and data plane.However,communication reliability is the desired property in computer networks.The frequency of communication link failure degrades network performance,and service disruptions are likely to occur.Emerging network applications,such as delaysensitive applications,suffer packet loss with higher Round Trip Time(RTT).Several failure recovery schemes have been proposed to address link failure recovery issues in SDN.However,these schemes have various weaknesses,which may not always guarantee service availability.Communication paths differ in their roles;some paths are critical because of the higher frequency usage.Other paths frequently share links between primary and backup.Rerouting the affected flows after failure occurrences without investigating the path roles can lead to post-recovery congestion with packet loss and system throughput.Therefore,there is a lack of studies to incorporate path criticality and residual path capacity to reroute the affected flows in case of link failure.This paper proposed Reliable Failure Restoration with Congestion Aware for SDN to select the reliable backup path that decreases packet loss and RTT,increasing network throughput while minimizing post-recovery congestion.The affected flows are redirected through a path with minimal risk of failure,while Bayesian probability is used to predict post-recovery congestion.Both the former and latter path with a minimal score is chosen.The simulation results improved throughput by(45%),reduced packet losses(87%),and lowered RTT(89%)compared to benchmarking works.

Bayesian Rule Modeling for Interpretable Mortality Classification of COVID-19 Patients

Coronavirus disease 2019(COVID-19)has been termed a“Pandemic Disease”that has infected many people and caused many deaths on a nearly unprecedented level.As more people are infected each day,it continues to pose a serious threat to humanity worldwide.As a result,healthcare systems around the world are facing a shortage of medical space such as wards and sickbeds.In most cases,healthy people experience tolerable symptoms if they are infected.However,in other cases,patients may suffer severe symptoms and require treatment in an intensive care unit.Thus,hospitals should select patients who have a high risk of death and treat them first.To solve this problem,a number of models have been developed for mortality prediction.However,they lack interpretability and generalization.To prepare a model that addresses these issues,we proposed a COVID-19 mortality prediction model that could provide new insights.We identified blood factors that could affect the prediction of COVID-19 mortality.In particular,we focused on dependency reduction using partial correlation and mutual information.Next,we used the Class-Attribute Interdependency Maximization(CAIM)algorithm to bin continuous values.Then,we used Jensen Shannon Divergence(JSD)and Bayesian posterior probability to create less redundant and more accurate rules.We provided a ruleset with its own posterior probability as a result.The extracted rules are in the form of“if antecedent then results,posterior probability(θ)”.If the sample matches the extracted rules,then the result is positive.The average AUC Score was 96.77%for the validation dataset and the F1-score was 92.8%for the test data.Compared to the results of previous studies,it shows good performance in terms of classification performance,generalization,and interpretability.

Application of logistic regression model for hazard assessment of landslides caused by the 2012 Yiliang Ms 5.7 earthquake in Yunnan Province,China

Accurate assessment of seismic landslides hazard is a prerequisite and foundation for postdisaster relief of earthquakes.An Ms 5.7 earthquake occurring on September 7,2012,in Yiliang County,Yunnan Province,China,triggered hundreds of landslides.To explore the characteristics of coseismic landslides caused by this moderate-strong earthquake and their significance in predicting seismic landslides regionally,this study uses an artificial visual interpretation method based on a planet image with 5-m resolution to obtain the information of the coseismic landslides and establishes a coseismic landslide database containing data on 232 landslides.Nine influencing factors of landslides were selected for this study:elevation,relative elevation,slope angle,aspect,slope position,distance to river system,distance to faults,strata,and peak ground acceleration.The real probability of coseismic landslide occurrence is calculated by combining the Bayesian probability and logistic regression model.Based on the coseismic landslides,the probabilities of landslide occurrence under different peak ground acceleration are predicted using a logistic regression model.Finally,the model established in this paper is used to calculate the landslide probability of the Ludian Ms 6.5 earthquake that occurred in August 2014,78.9 km away from the macro-epicenter of the Yiliang earthquake.The probability is verified by the real coseismic landslides of this earthquake,which confirms the reliability of the method presented in this paper.This study proves that the model established according to the seismic landslides triggered by one earthquake has a good effect on the seismic landslides hazard assessment of similar magnitude,and can provide a reference for seismic landslides prediction of moderate-strong earthquakes in this region.

Traffic danger detection by visual attention model of sparse sampling

A method to detect traffic dangers based on visual attention model of sparse sampling was proposed. The hemispherical sparse sampling model was used to decrease the amount of calculation which increases the detection speed. Bayesian probability model and Gaussian kernel function were applied to calculate the saliency of traffic videos. The method of multiscale saliency was used and the final saliency was the average of all scales, which increased the detection rates extraordinarily. The detection results of several typical traffic dangers show that the proposed method has higher detection rates and speed, which meets the requirement of real-time detection of traffic dangers.

Academic Freedom,Feminism and the Probabilistic Conception of Evidence

There is a current debate about the extent to which Academic Freedom should be permitted in our universities.On the one hand,we have traditionalists who maintain that Academic Freedom should be unrestricted:people who have the appropriate qualifications and accomplishments should be allowed to develop theories about how the world is,or ought to be,as they see fit.On the other hand,we have post-traditional philosophers who argue against this degree of Academic Freedom.I consider a conservative version of post-traditional philosophy that permits restrictions on Academic Freedom only if the following conditions are met,Condition 1:The dissemination of the results of a given research project R must cause significant harm to some people,especially to people from oppressed groups.Condition 2:Condition 1 must possess strong empirical support,and which accepts the following assumptions:(1)there is a world of objective facts that is,in principle,discoverable,(2)rational means are the means of discovering it and,(3)rational means requires strong empirical support.I define strong empirical support for an hypothesis h on evidence e in probabilistic terms,as a ratio of posterior to prior probabilities substantially exceeding 1.I now argue in favour of a research policy that accepts unrestricted Academic Freedom.My argument is that there is a formal and general quandary that arises within the standard theory of probability when we apply this account of empirical support to a set of possible causal hypotheses framed in such a way that the“reverse probabilities”,pr(e/h)are 1.I consider various possible ways to escape this quandary,none of which are without difficulties,concluding that a research policy allowing for unrestricted Academic Freedom is probably the best that we can hope for.

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.

Using Weights of Evidence in the Spatial Relation Between Infected Snails and Geographic Factors

Schistosomiasis is a serious public health problem in the middle-lower Yangtze River Basin in China. Study of spatial variation of snail distribution that is related to microgeographic factors can help to choose pertinent measures for snail extinguishment and environment rebuilding. This paper studied the theoretical architecture of weights-of-evidence approach. The case study was made for spatial relation between the occurrence of infected snails and geographic factor combinations in Waijiazhou marshland of Poyang Lake region in China. The multievidence data came from the geographical factor combinations by crossing operation of vegetation coverage grade layer, cattle route distance grade layer, and special environment layer (181 combinations in total) in GIS. The calculation of weight contrast index shows that high vegetation coverage, cattle route distance of <45 meters, and special geographic factor "ground depression" had direct spatial relation with the occurrence of infected snails. The verification by crossing operation in GIS indicated 72.45% of the infected snails concentrated on the areas of positive weight contrast index (sequenced in an order of weight contrast index from high to low), demonstrating the high efficiency of the model established in finding infected snails according to the geographic factor combinations that can be explicitly discerned in the study area.