Automatic circle detection is one of the most important construction elements to design more complex industrial image tasks such as target detection, manufacture inspection and process control. Most of the literature concerning multi-circle detection can be categorized under deterministic or stochastic perspectives. Deterministic approaches combine geometric and histogram information to identify circle shapes. However, deterministic techniques are unable to detect circles in presence of noise, shape variance, or occlusion. On the other hand, stochastic methodologies such as metaheuristic algorithms have been also proposed as alternatives to deterministic approaches for circle detection. Although these techniques are in general more robust, they allow detecting only one circle per execution, translating the detection problem into a unimodal optimization problem. Under such conditions, it is necessary to execute multiple times the algorithm to identify all the circles contained in the image. In this paper, the multi-circle detection problem has been reformulated as a multimodal optimization problem. The proposed approach adopts the Multimodal Flower Pollination Algorithm (MFPA) to detect all the circular instances contained in the image. Experimental results suggest that the proposed approach significantly improves the multi-circle detection problem.

The beehive sound, a continuous signal produced by bees within the hive, has been found to correlate with different behavioral states of the colony, like being queenless and swarming. We investigated the possibility of identifying foraging spatial cues in this signal. We recorded a colony’s sound while foraging from food sources located at three different distances from the hive, one at a time. The recordings were split into frames to obtain six statistics of their Mel Frequency Cepstral Coefficients. Then, we evaluated different autoencoding networks to obtain a latent space that allowed frames from different foraging distances to be easily differentiable. The high accuracy, silhouette score, and F1 score shown in the obtained latent spaces strongly support our approach for identifying foraging spatial cues in beehive sound activity.

Physical exercise knowledge is usually distributed in flat catalogs, where muscles, equipment and training attributes are stored as independent columns and the dependencies that organize the domain are never made explicit. This work presents a heterogeneous multi-relational Fitness Knowledge Graph built from a public-domain exercise dataset and refined through expert-guided curation: 922 entities and 8598 typed assertions across nine semantic relations, each validated against an explicit domain and range schema before the graph is written. Its organization was assessed through relational representation learning, comparing seven models over thirty independent runs under the same protocol: free node embeddings, DistMult, TransE, ComplEx, a homogeneous Graph Convolutional Network, the same encoder combined with a relation-aware decoder, and a Relational Graph Convolutional Network. Models without propagation reconstruct observed edges more accurately, while propagation yields a markedly larger separation between exercise types, improving similarity scores from 0.652 for DistMult to between 0.810 and 0.968, significant under a paired Wilcoxon test with Holm correction. The advantage does not extend to every criterion: a translational decoder without propagation reaches a Silhouette score indistinguishable from that of the Relational Graph Convolutional Network with less than a third of its parameters. A control experiment removing the two relations derived from the source category field shows that this organization is carried by them, since every type-based metric collapses and the Silhouette score becomes negative for all seven models. The information does not leave the graph, as a parameter-free similarity over the seven remaining relations still recovers exercise type at 0.828 against 0.479 for random neighborhoods, placing the limitation in the training objective rather than in the relational design. Edge reconstruction and semantic organization therefore measure different properties of a representation, and the relational composition of the graph determines which property the representation space reflects.