ABSTRACT Reconstructing and processing 3D objects are activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on geometric modelling (a branch of applied mathematics and computational geometry) or machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling or reconstructing the 3D surface's objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and LiDAR techniques, the obtained dataset is close to the original shape of the real objects. Besides, photography and its application based on modern techniques in recent years help us collect data and process the 3D models more precisely. This article proposes a new method for filling holes on the 3D object's surface based on automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on the computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surfaces. Compared to the state‐of‐the‐art (SOTA) methods, our proposed method obtained higher accuracy of the reconstructed 3D objects.

In this paper, we consider the known opinion formation problem from the point of view of computational collective intelligence research, with consideration to some sociological research in the area of social influence. We study two approaches to opinion formation – polarization and persuasion – as viewed from the point of view of our collective model (as opinion integration strategies). We detail both approaches and then conduct simulational experiments to study them both in single and averaged over multiple runs. We found special interest in the polarization strategy, which we want to use in the future as a basis for a new measure of collectives behavior.

Recently online social networks have grown enormously to become an essential marketing tool for many companies. Therefore, identifying the important or central or influential node is a crucial task. Centrality indicates the most central node or most influential node within a network. Centralization has proven to be a vital tool that helps to solve many problems. Besides the existence of numerous centralization measures, there is a lack of requirements for them. The main contribution of this article are postulates for centralization measures that solve this problem by enforcing intuition behind centralization. To prove the legitimacy of these postulates, a few existing measures that have fulfilled all postulates are shown. The proposed postulates are not only helpful during the formation of new measures but they also represent the perfect tool to compare two different measures.

Knowledge diffusion and opinion formation are important research topics in the area of communication in social networks. We present our generic model with interchangeable variants for processing messages from other members of the group. We describe several generic aspects of communication, like learning, persuasion, or polarization, and apply them in our model. We then show how this simple change leads to different results for the communication process of the whole group. We show how the model with interchangeable integration strategies is applicable for modeling various aspects of online social network and real life communication.