Dempster-Shafer Research Articles

Applications of Choquet expected utility to hypothesis testing with incompleteness

The Maximin and Choquet expected utility theories guide decision-making under ambiguity. We apply them to hypothesis testing in incomplete models. We consider a statistical risk function that uses a prior probability to incorporate parameter uncertainty and a belief function to reflect the decision-maker’s willingness to be robust against the model’s incompleteness. We develop a numerical method to implement a test that minimizes the risk function. We also use a sequence of such tests to approximate a minimax optimal test when a nuisance parameter is present under the null hypothesis.

Quantifying Prediction Uncertainty in Regression Using Random Fuzzy Sets: The ENNreg Model

We introduce a neural network model for regression in which prediction uncertainty is quantified by Gaussian random fuzzy numbers (GRFNs), a newly introduced family of random fuzzy subsets of the real line that generalizes both Gaussian random variables and Gaussian possibility distributions. The output GRFN is constructed by combining GRFNs induced by prototypes using a combination operator that generalizes Dempster's rule of Evidence Theory. The three output units indicate the most plausible value of the response variable, variability around this value, and epistemic uncertainty. The network is trained by minimizing a loss function that generalizes the negative log-likelihood. Comparative experiments show that this method is competitive, both in terms of prediction accuracy and calibration error, with state-of-the-art techniques such as random forests or deep learning with Monte Carlo dropout. In addition, the model outputs a predictive belief function that can be shown to be calibrated, in the sense that it allows us to compute conservative prediction intervals with specified belief degree.

A concept evaluation approach based on incomplete information: Considering large-scale criteria and risk attitudes

Concept evaluation plays a pivotal role in complex mechatronic product development, and its results greatly influence the subsequent design and manufacturing. However, the inclusion of large-scale criteria introduces complex calculations and coupling relationships, which will affect the evaluation. Moreover, the limited availability of early design knowledge, risk attitudes and cognitive differences of decision makers (DMs) lead to incompleteness and uncertainty in evaluation information. To address these challenges, a concept evaluation approach based on incomplete information with considering large-scale criteria and risk attitudes is proposed. Initially, an evaluation criteria clustering approach based on interval three-way decision-making is proposed, which considers DMs’ cognitions and risk attitudes. Second, the uncertainty model of evaluation information is established according to the criteria cluster result, and the incomplete evaluation information is fused using the interval Dempster-Shafer evidence theory which takes conflicting evidence into consideration. Third, the optimal concept is selected using the combined criteria weighting approach and the interval acronym in Portuguese for interactive and multi-criteria decision making (I-TODIM). Finally, a pipe inspection robot is utilized as an engineering case to verify the effectiveness of the proposed approach. Approach comparison and sensitivity analysis demonstrate the reliability and robustness of the proposed approach when applied to large-scale criteria concept evaluation.

The construction of teaching quality evaluation system of health management majors in universities based on information fusion technology

Abstract This paper begins with the multi-level processing of instructional multi-source data using information fusion techniques, with varying degrees of abstraction of the raw data for each level of processing. The set of all possible outcomes that an instructional framework can recognize for a given instructional problem is identified through D-S evidence theory. Secondly, the m factors that affect the object of teaching evaluation are grouped into the set of factors affecting the evaluation by fuzzy evaluation method, and their factor set weight values are calculated. Finally, the teaching quality evaluation system was constructed based on the factor set weight values. The results show that the experimental obtained Kendall’s harmony coefficient reaches 0.9028, and the evaluation is reliable. The overall score rate in teaching and assessment evaluation is 0.71 to reach the expected value, which indicates that the teaching quality evaluation index system established this time has relatively good internal consistency. The evaluation index system constructed in this paper makes the classroom teaching evaluation highly operable, which can be accepted by students and teachers in the evaluation process and ensures the accuracy of the evaluation results.

О непонятном в понятии доказательств

The article is devoted to the concept of evidence in criminal proceedings. The author draws attention to the variety of terminology. Tries to find out the reason for the different meanings that are put into the terms “proof” and “evidence”. The question of the necessity of the concept of evidence in the text of the criminal procedure law is considered. To explain the nature of the concept of evidence, the work of processualists who formed this concept before the information model of evidence was strengthened is used. The author comes to the conclusion that despite the development of the theory of evidence and the historical continuity of its provisions, there is still a lot of uncertainty in the concept of evidence.

Enhanced contaminant degradation by FeS under oxic conditions with the coexistence of cobalt and nickel

The process of FeS oxygenation is intrinsically associated with the fate of contaminants in heterogeneous aquifers, whereas the impacts of common heavy metals are poorly understood. In this study, we found that the introduction of Co2+ and Ni2+ (Me2+) resulted in a remarkable 3–10-fold enhancement in the degradation of bisphenol A, which stemmed from the significant increase in the generation of •OH and SO4•−. The improved production of reactive species was primarily attributed to the accelerated regeneration of Fe(II); the introduction of Me2+ manipulated the electronic structure of FeS and consequently facilitated electron transfer from S2- to Fe(III), as revealed by experimental evidence and density functional theory calculations. This study represents the first endeavor to underscore the influence of coexisting heavy metals on contaminant degradation during FeS oxygenation.

Conflicting evidence fusion using a correlation coefficient-based approach in complex network

Dempster–Shafer evidence theory (D–S theory) can effectively deal with uncertain information and it is one of the effective data fusion methods. However, Dempster’s combination rule of D–S theory often produces counter-intuitive fusion results when the handled body of evidence (BOE) is highly conflicting with each other. Therefore, many new methods have been gradually proposed to optimize BOE to avoid the counter-intuitive fusion results. In this work, inspired by the complex network, a body of evidence is compared to a node, therefore multiple nodes composed of the BOEs constitute a complex network structure, and a correlation coefficient is adopted to measure the degree of correlation between two BOEs. The direct and indirect interaction weights of each node are determined through the direct and indirect interactions among the nodes to reflect their importance in the complex network. After that, the total weight of each BOE is calculated through using the direct and indirect weights. Finally, after modifying the original BOE with weight factor, the final result is obtained after information fusion by using Dempster’s combination rule. This work analyses a practical application case based on the proposed evidential-weighting complex networks in D–S theory. The experiment result shows that the complex network optimization algorithm proposed in this work possesses a good convergence and has significantly improved the counter-intuitive fusion results brought about by the highly conflicting evidence with Dempster’s combination rule.

M-MSSEU: source-free domain adaptation for multi-modal stroke lesion segmentation using shadowed sets and evidential uncertainty.

Due to the unavailability of source domain data encountered in unsupervised domain adaptation, there has been an increasing number of studies on source-free domain adaptation (SFDA) in recent years. To better solve the SFDA problem and effectively leverage the multi-modal information in medical images, this paper presents a novel SFDA method for multi-modal stroke lesion segmentation in which evidential deep learning instead of convolutional neural network. Specifically, for multi-modal stroke images, we design a multi-modal opinion fusion module which uses Dempster-Shafer evidence theory for decision fusion of different modalities. Besides, for the SFDA problem, we use the pseudo label learning method, which obtains pseudo labels from the pre-trained source model to perform the adaptation process. To solve the unreliability of pseudo label caused by domain shift, we propose a pseudo label filtering scheme using shadowed sets theory and a pseudo label refining scheme using evidential uncertainty. These two schemes can automatically extract unreliable parts in pseudo labels and jointly improve the quality of pseudo labels with low computational costs. Experiments on two multi-modal stroke lesion datasets demonstrate the superiority of our method over other state-of-the-art SFDA methods.

Structural health monitoring via a group-theoretic WSA for optimal feature selection and data fusion

In this study, two binary versions of the Water Strider Algorithm (WSA) are proposed and applied to optimal feature selection in classification problems. In the new binary versions, the formulations of WSA in continuous space are converted into binary space using group-theoretic operators (in AWSA) and sigmoid function (in BWSA). AWSA, BWSA, genetic algorithm (GA), and binary particle swarm optimization (BPSO) are selected and compared over eighteen well-known datasets from the University of California, Irvine repository. The results of AWSA indicate its satisfactory performance compared to those of other algorithms. Then, they are applied to find optimal features of a structural health monitoring classification problem using two well-known machine learning classifiers, namely k-Nearest Neighbor (KNN) and Naïve Bayes (NB) algorithms. To further improve the accuracy of the classification models, a decision-level data fusion technique is proposed based on the improved Dempster-Shafer theory. It is demonstrated that the AWSA presents superior results compared to the other algorithms and the suggested decision-level data fusion provides a reliable detection of damage.

Research on the development model of fusion of poetry communication and literary creation based on information fusion technology under the perspective of national fusion

Abstract Under the perspective of national integration, the fusion development of poetry communication and literary creation is flourishing. Through analyzing the characteristics of contemporary poetry dissemination and creation, this paper proposes an innovative model for the fusion development of poetry dissemination and literary creation based on information fusion technology. For poetry dissemination of multi-source information data, random set theory is used for representation, information fusion is performed on the representation results of random sets based on D-S evidence theory, and uncertainty is measured by introducing trust functions and multiple basic probability distribution functions synthesized into one basic trust function through orthogonal sum according to synthesis rules. From the viewpoint of dissemination information sources, news and information websites, video websites, podcasts or audiobooks are called the most important information sources for poetry dissemination, with a combined weight of more than 95%. In the case of poetry literature creation under the integration development mode, the periodicity of the works has significantly improved, with an improvement interval of (18.3% and 27.6%). The dissemination degree also increased effectiveness, with a maximum of 30.7% and a minimum of 21.5%. The development model based on information fusion can open up a new path for the fusion development of contemporary poetry communication and literary creation.

Does Hallie See a White Cup an a Desk? A Phenomenological Account of Hallucination Indiscriminability

In this paper, I argue for phenomenology, Husserlian phenomenology to be precise, as providing a solid paradigm on how to determine and assess hallucination. To be more explicit, in the context of my deliberations, I analyze Susanna Schellenberg’s arguments for “phenomenal” evidence and “factive” evidence, as regards her evidential theory of perception. To pinpoint the inadequacies raised in her account of (the hallucinating) Hallie and (the veridically perceiving) Percy sharing any kind of evidence, I propose Edmund Husserl’s epistemic fulfillment as a detailed epistemological analysis of perception, which takes into consideration the latter’s phenomenological complexity.

Evaluate the reliability of information sources using the non-parametric plausibility ReliefF algorithm for multi-source information fusion

How to evaluate the reliability of the information sources is a significant and open issue in the information fusion of Dempster-Shafer evidence theory (DST). We propose a new method, called the non-parametric plausibility ReliefF (NPReliefF) algorithm, to measure the reliability of information sources based on the criterion that sources providing more evidence aligned with “greater similarity within classes than between classes” are more reliable. The proposed method significantly improves the effect of normal BPAs and weakens the impact of abnormal BPAs on the fusion, even when abnormal BPAs are in the majority, thereby maximizing the correctness of the fusion results. Several numerical examples and an application of pattern recognition show the effectiveness and flexibility of the proposed methodology.

GDTRSET: a generalized decision-theoretic rough sets based on evidence theory

GDTRSET: a generalized decision-theoretic rough sets based on evidence theory

Symmetric Renyi-Permutation divergence and conflict management for random permutation set

Recently, a theory called random permutation set (RPS) is proposed, which is the extension of Dempster–Shafer evidence theory. It defines a permutation event space (PES) to represent the order of events and uses the RPS to represent the support of permutation events. In addition, this theory fuses multiple RPSs by developing the left orthogonal sum (LOS) and right orthogonal sum (ROS) algorithms. However, our experiments have found that when two RPSs are highly conflicting, using the LOS or the ROS algorithm to fuse them will generate conflicting results. Therefore, this paper proposes the symmetric Renyi-Permutation (SRP) divergence of the RPS and a LOS-SRP algorithm for conflict management. Furthermore, existing RPS-based reliability assessment methods have the drawbacks of subjectivity and low accuracy, which prompts us to combine SRP divergence with the RPS and propose the RASRP algorithm for reliability assessment. Experimental results show that the RASRP algorithm performs better than the most advanced existing methods.

Application of Information Fusion Technology in Innovative Teaching of Music Theory Courses in Universities

Abstract In this paper, we first investigate the steps to implement a fusion algorithm that determines the quality function through fuzzy theory. Then, we utilize D-S evidence theory for decision-level fusion to identify the target data based on the collected data. The two algorithms, fuzzy theory and D-S evidence theory, are then combined. The affiliation function in fuzzy theory calculates the information collected from the sensors to find out the confidence level and patterns. Finally, the information fusion technology was analyzed in terms of its usage rate in music theory teaching, its impact on piano playing, and its impact on music theory teaching. In terms of utilization rate, 21 student teachers had information fusion technology utilization rate between 50% and 60%, and 21 student teachers had information fusion technology utilization rate between 50% and 60%. In terms of the impact of information fusion technology on the teaching of music theory, a comparative analysis of the pre and post test data T=−6.55 (P<0.0001) showed that the difference in the post test data was significantly higher than that of the pre test. This indicates that the facilitating effect of information fusion technology on music theory teaching is obvious.

Belief updating under ambiguity: A numerical simulation analysis

We compare Bayesian with the two non-Bayesian updating under ambiguity in its model specification and numerical implications. For an ambiguity averse (seeking) decision maker, we find that Gilboa and Schmeidler (GS) updating leads to a lower (higher) certainty equivalent (CE) than Dempster–Shafer (DS) updating and Bayesian updating, and the change of historical information has a bigger (smaller) impact on GS updating than on DS updating. In addition, the results shows that GS updating is more sensitive to the ambiguity attitude than DS updating. The application of numerical analysis in finance especially implies that the sensitivity of price to information depends on the ambiguity attitude of the market.

Development of the Institute of Criminal Procedural Evidence

The fulfillment of the key function of criminal proceedings – the resolution of the case on the merits – is impossible without criminal procedural knowledge of all the circumstances of the crime committed. The foundation of this activity is criminal procedure and evidence, which is a very complex, multi-level system. The foundation of such a system is evidence. At present, there are serious contradictions in how the sources of evidentiary information, evidence of practice, and proof are interrelated. Collection, verification, evaluation and presentation of evidence, both in theory and in practice, often give rise to non-legal, unfair and unjustified criminal procedural decisions in pre-trial proceedings and in court. Despite the plethora if views on the theory of evidence, the lack of consistency in investigative and judicial practice with regard to the evidence suggests that at present there are a large number of unresolved problems in this field as well as on the legislative level. In this regard, it seems necessary to conduct a study on the historical and legal aspects of the institute of evidence and proof in domestic legislation in order to identify the prerequisites of its origin and patterns of development. The conducted research has theoretical and practical significance. First, it contributes to the development of the topic under discussion. Second, the identified regularities allow us to use them to develop recommendations on improving the current criminal procedural legislation. The author concludes that with lapse of time, the concept of evidence has changed: from a means of substantiating guilt in “evidence-argument” terms to an element of the system of evidence including source, content and procedural form. In addition, the criteria for analyzing the development of evidence law have been formulated.

Self-filling evidential clustering for partial multi-view data

Partial multi-view clustering (PMVC) is a hot topic in data mining, where each view suffers from the absence of some data. Although many PMVC algorithms have been presented with appropriate performance, to the best of our knowledge, almost all of them need to be fed with the number of clusters as hyperparameters. Moreover, these existing algorithms only create hard partitions for objects with multiple views, which are often in highly overlapping areas. This ignores ambiguity and uncertainty in object assignment, which likely leads to performance degradation. To address these issues, we propose a novel self-filling evidential clustering algorithm (SFPMEC) for the PMVC problem. Based on the shared coefficient matrix learned from a self-filling process, SFPMEC provides a human-readable chart through which the number of clusters can be easily detected. SFPMEC then derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering and gaining deeper insight into the data structure. Extensive experiments are conducted to demonstrate the benefits of adopting the self-filling process and evidence theory. Besides, our algorithm shows better performance than other state-of-the-art methods on benchmark datasets11The source code can be found in https://drive.google.com/file/d/1KMi0N8cX85YhwiNjetBwA6QeolQP5TnC/view?usp=sharing..

Nonlinear spectrum feature fusion diagnosis method for RV reducer of industrial robots

RV reducers of industrial robots may be possibly subjected to wear and vibration impact and invalidated for keeping the repeatability due to long-time dynamic load in their life cycle. Any failure of RV reducers may decrease the reliability and lead to incredible loss due to the unexpectedly shutdown of the manufacturing system. Failures can cause nonlinear interference when occurring in RV reducers and pose a challenge to RV reducer fault diagnosis. Therefore, it is important to take proper measures to diagnose the failure in RV reducers and reduce the influence of nonlinear interference. Nonlinear spectrum features fusion method is proposed for diagnosing the faults of RV reducers in industrial robots. For the purpose of extracting fault features more effectively, nonlinear output frequency response function is employed to obtain nonlinear frequency spectrum for exactly describing the nonlinear mechanism of faulty phenomenon. Moreover, enhanced evidence theory with similarity measure is proposed to compute the basic probability assignment functions of evidences according to the similarity between different modes for improving the identification accuracy of different faults. RV reducer experiments with different faults are performed to simulate robot operating conditions for obtaining normal and fault data under a variable speed working condition. Among the 180 sets of test data including normal conditions, the diagnostic accuracy of the proposed nonlinear feature fusion method is 92.22% and greatly preferable to that of the ordinary frequency spectrum method. Experimental results demonstrated that the nonlinear spectrum method can effectively extract nonlinear features and the introduction of similarity can effectively improve the diagnostic accuracy in the proposed feature fusion method.

The moderating function of religious belief on the association between sexual dysfunctions and sexual satisfaction among religious Jewish women and men

ABSTRACT The moderating effect of religiousness on the associations between health-associated stressors and well-being is well established in the research literature but has not been examined on the association between sexual dysfunctions and sexual well-being. A sample of adult Jewish women (N = 207) and men (N = 196) responded to an online questionnaire including measures of religious belief and sexual satisfaction. Respondents’ partners assessed sexual desire dysfunction and psychological sexual arousal dysfunction of the respondent. For men and women, desire dysfunction was associated with lower levels of sexual satisfaction. Arousal dysfunction was also associated with sexual satisfaction for women but not for men. In addition, for women, a significant buffering effect of religiousness was found for the associations of both types of sexual dysfunction and sexual satisfaction. In contrast, for men, no buffering effect was found.