Exploration of the Impact Mechanism of Government Credibility Based on Variable Screening Method

Government credibility is an important asset of contemporary national governance, an important criterion for evaluating government legitimacy, and a key factor in measuring the effectiveness of government governance. In recent years, researchers’ research on government credibility has mostly focused on exploring theories and mechanisms, with little empirical research on this topic. This article intends to apply variable selection models in the field of social statistics to the issue of government credibility, in order to achieve empirical research on government credibility and explore its core influencing factors from a statistical perspective. Specifically, this article intends to use four regression-analysis-based methods and three random-forest-based methods to study the influencing factors of government credibility in various provinces in China, and compare the performance of these seven variable selection methods in different dimensions. The research results show that there are certain differences in simplicity, accuracy, and variable importance ranking among different variable selection methods, which present different importance in the study of government credibility issues. This study provides a methodological reference for variable selection models in the field of social science research, and also offers a multidimensional comparative perspective for analyzing the influencing factors of government credibility.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

The growth of the internet and technology has had a significant effect on social interactions.False information has become an important research topic due to the massive amount of misinformed content on social networks.It is very easy for any user to spread misinformation through the media.Therefore,misinformation is a problem for professionals,organizers,and societies.Hence,it is essential to observe the credibility and validity of the News articles being shared on social media.The core challenge is to distinguish the difference between accurate and false information.Recent studies focus on News article content,such as News titles and descriptions,which has limited their achievements.However,there are two ordinarily agreed-upon features of misinformation:first,the title and text of an article,and second,the user engagement.In the case of the News context,we extracted different user engagements with articles,for example,tweets,i.e.,read-only,user retweets,likes,and shares.We calculate user credibility and combine it with article content with the user’s context.After combining both features,we used three Natural language processing(NLP)feature extraction techniques,i.e.,Term Frequency-Inverse Document Frequency(TF-IDF),Count-Vectorizer(CV),and Hashing-Vectorizer(HV).Then,we applied different machine learning classifiers to classify misinformation as real or fake.Therefore,we used a Support Vector Machine(SVM),Naive Byes(NB),Random Forest(RF),Decision Tree(DT),Gradient Boosting(GB),and K-Nearest Neighbors(KNN).The proposed method has been tested on a real-world dataset,i.e.,“fakenewsnet”.We refine the fakenewsnet dataset repository according to our required features.The dataset contains 23000+articles with millions of user engagements.The highest accuracy score is 93.4%.The proposed model achieves its highest accuracy using count vector features and a random forest classifier.Our discoveries confirmed that the proposed classifier would effectively classify misinformation in social networks.

A new measure for evaluating spatially related properties of traffic information credibility

With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.

A COMPARISON BETWEEN TWO KINDS OF BMS BASED ON DIFFERENT CREDIBILITY

The paper develops a design of optimal Bonus-Malus System （BMS） based on exact equitable credibility,in which the relative error function is taken as loss function. In BMS,both the frequency and the severity components are considered. This design is compared with traditional BMS derived from classical squared-error loss function.

The Credibility Models under LINEX Loss Functions

LINEX(linear and exponential) loss function is a useful asymmetric loss function. The purpose of using a LINEX loss function in credibility models is to solve the problem of very high premium by suing a symmetric quadratic loss function in most of classical credibility models. The Bayes premium and the credibility premium are derived under LINEX loss function. The consistency of Bayes premium and credibility premium were also checked. Finally, the simulation was introduced to show the differences between the credibility estimator we derived and the classical one.

Optimal Credibility Estimation of Random Parameters in Hierarchical Random Effect Linear Model

In the hierarchical random effect linear model, the Bayes estimator of random parameter are not only dependent on specific prior distribution but also it is difficult to calculate in most cases. This paper derives the distributed-free optimal linear estimator of random parameters in the model by means of the credibility theory method. The estimators the authors derive can be applied in more extensive practical scenarios since they are only dependent on the first two moments of prior parameter rather than on specific prior distribution. Finally, the results are compared with some classical models and a numerical example is given to show the effectiveness of the estimators.

Confucianist Five Constant Virtues and Value Dimension Construction on We Media Credibility

The Confucianism has played a prominent role for thousands of years in boosting the tenacity of the Chinese nation towards innovation and development as well as in promoting and inheriting the traditional Chinese culture. Based on the five constant virtues of Confucianism,this paper analyzes the relationship between "benevolence,righteousness,etiquette,wisdom and credit"and creditability that We Media should construct,and elaborates the construction of value dimension system of We Media's creditability on talents, environments,orientations and innovations from four aspects. Namely,the cognitive and spiritual orientations that reinforce the five constant virtues,the propaganda cultivation that fuses them and the endogenous impetus stimulated by using them as reference.This paper is designed to tell all of the media and media practitioners to constantly remain vigilant of the social responsibilities they are taking and the social value they should create.T hey should take an initiative to guide their serving objects to foster a proper consensus outlook and value and meeting the mass audience's demands and desires on information.

Quantitative credibility evaluation of Global Energy Interconnection data

The development of Global Energy Interconnection(GEI)is essential for supporting a wide range of basic data resources.The Global Energy Interconnection Development and Cooperation Organization has established a comprehensive data center covering six major systems.However,methods for accurately describing and scientifically evaluating the credibility of the massive amount of GEI data remain underdeveloped.To address this lack of such methods,a GEI data credibility quantitative evaluation model is proposed here.An evaluation indicator system is established to evaluate data credibility from multiple perspectives and ensure the comprehensiveness and impartiality of evaluation results.The Cloud Model abandons the hard division of comments to ensure objectivity and accuracy in evaluation results.To evaluate the suitability of the proposed method,a case analysis is conducted,wherein the proposed method demonstrates sufficient validity and feasibility.

Proximity and Framing in News Media： Effects on Credibility, Bias, Recall, and Reader Intentions

A writer＇s decision to localize a news article and the valence of the frame the writer employs can affect readers＇ perceptions of credibility and bias as well as readers＇ factual recall and the likelihood that readers would want to read the newspaper further. A 2 （proximity： local, non-local） ~ 3 （frame valence： positive, neutral, negative） factorial experiment （N = 136） tested the effects of proximity and frame valence on credibility, perceived bias, recall, and reading intentions. Articles that localized enjoyed greater perceptions of credibility but not recall, while articles with either a positive or negative frame yielded greater recall than articles with a neutral frame. Neither factor exhibited a main effect in predicting perceived bias, but their interaction was a significant predictor. In addition, localization and frame valence also influence the likelihood that participants would want to read the source newspaper again. Implications for theory and media practice are discussed.

Study of the Authenticity and Credibility of QQ Chat Records

Digital evidence investigation in QQ software is the current research focus.In order to identify the authenticity and credibility of QQ chat records,the method based on attacking QQ chat records is proposed.Firstly,an evaluation model on checking the authenticity of QQ chat records is proposed.The cases on kinds of changing QQ chat records are discussed.The challenge in identifying the authenticity of QQ chat records is raised.Secondly,case study on attacking QQ chat records is proposed to check the relationship between the authenticity of QQ chat records and the difficulty of changing them.A couple of experiments on changing QQ chat records are finished to find how difficult changing records is and whether there are clues left on changing them or not.Finally,a kind of QQ chat forensic process is presented,which can help for digital evidence investigation in the authenticity and credibility of QQ chat records.The practices in QQ forensic cases show that it works well.

Parallel Machine Scheduling Models with Fuzzy Parameters and Precedence Constraints: A Credibility Approach

A method for modeling the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure is provided. For the given n jobs to be processed on m machines, it is assumed that the processing times and the due dates are nonnegative fuzzy numbers and all the weights are positive, crisp numbers. Based on credibility measure, three parallel machine scheduling problems and a goal-programming model are formulated. Feasible schedules are evaluated not only by their objective values but also by the credibility degree of satisfaction with their precedence constraints. The genetic algorithm is utilized to find the best solutions in a short period of time. An illustrative numerical example is also given. Simulation results show that the proposed models are effective, which can deal with the parallel machine scheduling problems with fuzzy parameters and precedence constraints based on credibility measure.

Agent Belief with Credibility and Its Revision Mechanism

An approach to characterize the credibility of beliefs of an agent is proposed in this paper, which can define the uncertainty of beliefs, calculation rules and inference rules about credibility and a method for belief revision based on abductive reasoning is also given. When an agent receives some new information, if the new information is consistent with the current belief set, then incorporate this new information with an appropriate credibility, otherwise the choice will be different depending on the characters of agents, and the deliberated agents will choose the belief with a better explanation under the current belief set. Removing one belief may cause the removal of those beliefs that, together with others, logically entail the formula to be removed. A method based on abduction is proposed to solve these problems.

Numerical Characteristics of Fuzzy Numbers Based on Credibility Measure

The numerical characteristics of fuzzy numbers include the optimistic value, pessimistic value, expected value, and the variance. We mainly provide the calculation formulae of several numerical characteristics of fuzzy numbers based on credibility measure. Especially, the variance of symmetric fuzzy numbers is formulated, and a super bound for the variance of fuzzy numbers is presented. Meanwhile, some conclusions relative to credibility measure, optimistic and pessimistic values are also given.

Construction of Quality Security and Credibility Mechanism for Chinese Food Enterprises

Credibility deficiency of food enterprises is a major threat for food security in China. Analysis of causes for the credibility deficiency of Chinese food enterprises,and construction of an effective quality security and credibility mechanism are not only the key to constructing a long-term supervision mechanism for food security,but also the important guarantee for coordinating social and economic development.

The Credibility Estimators under MLINEX Loss Function

In this paper, MLINEX loss function was considered to solve the problem of high premium in credibility models. The Bayes premium and credibility premium were obtained under MLINEX loss function by using a symmetric quadratic loss function. A credibility model with multiple contracts was established and the corresponding credibility estimator was derived under MLINEX loss function. For this model the estimations of the structure parameters and a numerical example were also given.

Analysis of the Government Credibility: From the Perspective of Perception and Expectation

Government credibility is a very important issue in today’s society.It seems to have a great relationship with the administrative efficiency of the government.At present,the credibility of the government in the world has generally declined.In this article,I first described the factors affecting the government’s credibility and took the gap between people’s expectations and perception of the government as the main reason for the decline of the government’s credibility.Secondly,I analyze the relationship between credibility and political legitimacy and believe that credibility is the main source of political legitimacy.Finally,based on the actions of governments to improve political legitimacy through various means,I explain their impact on government credibility from the perspectives of expectation and perception.I find that most of these actions will get the opposite of what one wants,increasing the risk of credibility collapse.

Discussions about the differences and credibility on grape wine scoring

The scores of grape wine given by wine critics have differences, therefore, for which its credibility differs. Firstly, our paper analysis differences of overall and individual for the two groups of raters. The analysis method is： first check the score whether meets the normal distribution or not by Chi-square and then test whether the mean of the two group＇s ratings is equal or not, through t-test, to illustrate the differences of the two groups＇ ratings. Secondly this paper respectively characterizes the credibility of each group＇s rating with the variance and gray correlation. Both the results are highly consistent on the credibility, so they can support each other. This score comparison method can be further extended to a similar scoring system.

Accuracy evaluation for in-situ machining reference points binocular measurement based on credibility probability

Passive binocular measurement systems are being increasingly utilized in the in-situ industries of automobiles,aviation,and aerospace,etc.due to their excellent qualities of accuracy,efficiency,and cost performance.Whereas the barrier of evaluating the accuracy of measured objects resulted from the unequal equivalent focal length and quantization of pixels,has limited their further development and application of high requirements for in-situ machining,e.g.,the measurement of machining reference points for the positioning of robotic drilling in aerospace manufacturing.In this paper,an accuracy evaluation method is proposed to address the problem.Firstly,the unequal equivalent focal length is considered to improve the accuracy of 3D reconstruction.Next,the credibility probability model is developed to calculate the probability of the observed error in the public view of the binocular measurement system and indicates the direction of improvement.Finally,the in-situ experiment is carried out to validate the method within the effective public view range of 300 mm×300 mm.The experiment results show that the RMSs of observed errors are superior to 0.035 mm,and the credibility probabilities are all higher than 0.91;the maximum 3D reconstruction accuracy improvement is 60.3%,with the error reduced from 0.078 mm to 0.031 mm.

Adaptive nonlinear Kalman filters based on credibility theory with noise correlation

To solve the divergence problem and overcome the difficulty in guaranteeing filtering accuracy during estimation of the process noise covariance or the measurement noise covariance with traditional new information-based nonlinear filtering methods,we design a new method for estimating noise statistical characteristics of nonlinear systems based on the credibility Kalman Filter(KF)theory considering noise correlation.This method first extends credibility to the Unscented Kalman Filter(UKF)and Extended Kalman Filter(EKF)based on the credibility theory.Further,an optimization model for nonlinear credibility under noise related conditions is established considering noise correlation.A combination of filtering smoothing and credibility iteration formula is used to improve the real-time performance of the nonlinear adaptive credibility KF algorithm,further expanding its application scenarios,and the derivation process of the formula theory is provided.Finally,the performance of the nonlinear credibility filtering algorithm is simulated and analyzed from multiple perspectives,and a comparative analysis conducted on specific experimental data.The simulation and experimental results show that the proposed credibility EKF and credibility UKF algorithms can estimate the noise covariance more accurately and effectively with lower average estimation time than traditional methods,indicating that the proposed algorithm has stable estimation performance and good real-time performance.