Establishing convergent semantics for weighted argumentation graphs is a long-standing fundamental issue. Particularly, it is challenging to develop convergent semantics for weighted bipolar argumentation graphs (wBAG), which include both support and attack relations on weighted arguments. Existing semantics in the literature are not general enough in the sense that they only apply to acyclic graphs or special cyclic cases. In this paper, we provide an elegant solution to this issue by adopting the so-called bilateral gradual semantics, so that the strength of arguments can be defined as the limits of iterative functions that always converge for any wBAG including cyclic ones. A preliminary experimental analysis shows that our semantics appear quite efficient in calculating argument strength. Overall, this paper offers a solid and promising foundation for weighted bipolar argumentation in theoretical and practical aspects.

Three-Dimensional Visual Grounding (3DVG) aims to locate objects in 3D scenes based on natural language queries. Existing methods typically rely on absolute position encoding of global objects to model spatial relationships for target localization. However, this often results in inadequate spatial understanding and redundant or invalid encoding. To address these limitations, we propose the Relative K-object Fusion Perception 3D Visual Grounding (3DRelKG) framework. By dynamically sampling a limited number of scene reference points, our approach enhances the modeling of spatial relationships for localization targets by learning only the positional and relative spherical geometric features of these reference points with respect to scene objects. Additionally, we introduce a heterogeneous feature fusion module, whose core is an information interaction mechanism based on the similarity matrix of heterogeneous features. This approach naturally avoids the problem of unreliable attention interaction weights that arise from directly calculating the similarity between heterogeneous features. Experiments on ScanRefer, SR3D, and NR3D demonstrate that our method outperforms state-of-the-art models, improving accuracy by 2.3%, 4.3%, and 2.1%, respectively, and increasing inference speed by 43.8%.

Abstract argumentation is a reasoning model for evaluating arguments. Recently, gradual semantics has received considerable attention in weighted argumentation, which assigns an acceptability degree to each argument as its strength. In this paper, we aim to enhance gradual semantics by non-reciprocally incorporating the notion of rejectability degree. Such a setting offers a bilateral perspective on argument strength, enabling more comprehensive argument evaluations in practical situations. To this end, we first provide a set of principles for our semantics, taking both the acceptability and rejectability degrees into account, and propose three novel semantics conforming to the above principles. These semantics are defined as the limits of iterative sequences that always converge in any given weighted argumentation system, making them preferable for real-world applications.