Semantic mapping with Bayesian Kernel Inference (BKI) has shown promise in
providing a richer understanding of environments by effectively leveraging
local spatial information. However, existing methods face challenges in
constructing accurate semantic maps or reliable uncertainty maps in
perceptually challenging environments due to unreliable semantic predictions.
To address this issue, we propose an evidential semantic mapping framework,
which integrates the evidential reasoning of Dempster-Shafer Theory of Evidence
(DST) into the entire mapping pipeline by adopting Evidential Deep Learning
(EDL) and Dempster's rule of combination. Additionally, the extended belief is
devised to incorporate local spatial information based on their uncertainty
during the mapping process. Comprehensive experiments across various off-road
datasets demonstrate that our framework enhances the reliability of uncertainty
maps, consistently outperforming existing methods in scenes with high
perceptual uncertainties while showing semantic accuracy comparable to the
best-performing semantic mapping techniques.

通过将证据推理、Evidential Deep Learning（EDL）和 Dempster's combination rule 应用于语义映射，提出了一种证据语义映射框架，以在感知挑战环境下改善不确定性地图的可靠性，并显示与最佳语义映射技术相当的语义准确性。

基于 Dempster-Shafer 证据理论的非道路环境中的不确定性感知语义地图构建

Uncertainty-aware Semantic Mapping in Off-road Environments with  Dempster-Shafer Theory of Evidence

Recently, uncertainty-aware methods have attracted increasing attention in
semi-supervised medical image segmentation. However, current methods usually
suffer from the drawback that it is difficult to balance the computational
cost, estimation accuracy, and theoretical support in a unified framework. To
alleviate this problem, we introduce the Dempster-Shafer Theory of Evidence
(DST) into semi-supervised medical image segmentation, dubbed Evidential
Inference Learning (EVIL). EVIL provides a theoretically guaranteed solution to
infer accurate uncertainty quantification in a single forward pass. Trustworthy
pseudo labels on unlabeled data are generated after uncertainty estimation. The
recently proposed consistency regularization-based training paradigm is adopted
in our framework, which enforces the consistency on the perturbed predictions
to enhance the generalization with few labeled data. Experimental results show
that EVIL achieves competitive performance in comparison with several
state-of-the-art methods on the public dataset.

通过将证据理论引入半监督医学图像分割，以在单次前向传递中推理准确的不确定性量化为目的，提出了命名为 EVIL 的确定性推理学习方法。该方法使用一致性正则化训练范式，通过对扰动预测的一致性进行约束来增强少量标记数据的泛化能力。实验结果表明，EVIL 在公共数据集上与几种最先进的方法相比取得了竞争性的性能。

EVIL: 有据推理学习用于可信任的半监督医学图像分割

EVIL: Evidential Inference Learning for Trustworthy Semi-supervised  Medical Image Segmentation

Expert finding is an information retrieval task concerned with the search for
the most knowledgeable people, in some topic, with basis on documents
describing peoples activities. The task involves taking a user query as input
and returning a list of people sorted by their level of expertise regarding the
user query. This paper introduces a novel approach for combining multiple
estimators of expertise based on a multisensor data fusion framework together
with the Dempster-Shafer theory of evidence and Shannon's entropy. More
specifically, we defined three sensors which detect heterogeneous information
derived from the textual contents, from the graph structure of the citation
patterns for the community of experts, and from profile information about the
academic experts. Given the evidences collected, each sensor may define
different candidates as experts and consequently do not agree in a final
ranking decision. To deal with these conflicts, we applied the Dempster-Shafer
theory of evidence combined with Shannon's Entropy formula to fuse this
information and come up with a more accurate and reliable final ranking list.
Experiments made over two datasets of academic publications from the Computer
Science domain attest for the adequacy of the proposed approach over the
traditional state of the art approaches. We also made experiments against
representative supervised state of the art algorithms. Results revealed that
the proposed method achieved a similar performance when compared to these
supervised techniques, confirming the capabilities of the proposed framework.

该论文介绍了一种基于多传感器数据融合框架与极大熵及 Dempster-Shafer 证据理论相结合的新方法，用于解决查找某个主题下最有知识的人的信息检索任务。它应用于一个学术领域的两个数据集上，证明了该方法的广泛适用性和优越性，可以使它在性能上达到与传统的监督技术相同的水平。