Highly conflicting evidence that may lead to the counter-intuitive results is one of the challenges for information fusion in Dempster-Shafer evidence theory. To deal with this issue, evidence conflict is investigated based on belief divergence measuring the discrepancy between evidence. In this paper, the pignistic probability transform belief χ2 divergence, named as BBχ2 divergence, is proposed. By introducing the pignistic probability transform, the proposed BBχ2 divergence can accurately quantify the difference between evidence with the consideration of multi-element sets. Compared with a few belief divergences, the novel divergence has more precision. Based on this advantageous divergence, a new multi-source information fusion method is devised. The proposed method considers both credibility weights and information volume weights to determine the overall weight of each evidence. Eventually, the proposed method is applied in target recognition and fault diagnosis, in which comparative analysis indicates that the proposed method can realize the highest accuracy for managing evidence conflict.

During the COVID-19 pandemic there was a rapid shift to emergency remote teaching practices and online tools for education have already gained further attention. While eLearning initiatives are developed and its implementation at scale are widely discussed, this research focuses on the utilization of data which can be logged in such eLearning systems. We demonstrate the need and potential of utilizing learning logs to create services supporting sustainable quality improvement of education. Learning and Evidence Analytics Framework (LEAF), is the overarching technology framework with affordances to adopt evidence-based practices for education. It aims to promote learning for all by introducing data-driven services for personalized approaches.

The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.

Spectrum sensing is a fundamental function for cognitive radio network to protect transmission of primary system. Cooperative spectrum sensing, which can help increasing sensing performance, is regarded as one of the most promising methods in realizing a reliable cognitive network. In such cooperation system, however the communication resources such as sensing time delay, control channel bandwidth and consumption energy for reporting the cognitive radio node's sensing results to the fusion center may become extremely huge when the number of cognitive users is large. In this paper, we propose an ordered sequential cooperative spectrum sensing scheme in which the local sensing data will be sent according to its reliability order to the fusion center. In proposed scheme, the sequential fusion process is sequentially conducted based on Dempster Shafer theory of evidence's combination of the reported sensing results. Above all, the proposed scheme is highly feasible due to the proposed two ordered sequential reporting methods. From simulation results, it is shown that the proposed technique not only keeps the same sensing performance of non-sequential fusion scheme but also extremely reduces the reporting resource requirements.

We propose a new solvable Markov random field model for Bayesian image processing and give the exact expressions of the marginal likelihood and the restored image by using the multi-dimensional Gaussian formula and the discrete Fourier transform. The proposed Markov random field model includes the conditional autoregressive model and the simultaneous autoregressive model as a special case. The estimates of hyperparameters are obtained by maximizing the marginal likelihood. We study some statistical properties of the solvable Markov random field model. In some numerical experiments for standard images, we show that the proposed Markov random field model is useful for practical applications in image restorations. The investigation of probabilistic information processing by means of a solvable probabilistic model is recently in progress not only for image processing but also for error correcting codes and so on. The solvable probabilistic model gives us some important aspects for the availability of probabilistic computational systems.