A dynamic (or temporal) network is a widely used structure that enables understanding dynamic systems by modeling interactions among system components over time. In many real-world cases, however, components (called nodes) and/or interactions (called edges) contain numerous meaningful attributes, leading to the need for a more suitable instrument for representing and analyzing these dynamic and complex systems with multiple attributes: the Dynamic Multivariate Network (DMVN). In this work, we extended LargeNetVis, a visualization system specifically designed for large dynamic networks that focus on network community structure and dynamics, to enable the visual exploration of DMVNs and their communities. The newly introduced visual encodings and interactions allow the visualization of nodes' and edges' attributes at different granularity levels and produce a node tracking capability from both top-down and bottom-up perspectives. With these functionalities, one can track individual nodes across dynamic communities over time. The proposed approach is validated by comparing it with the original LargeNetVis system and conducting a user evaluation involving 37 participants.
Building Information Modeling (BIM) integrates engineering data with 3D geometry, spatial relationships, and other properties to support construction projects throughout their entire lifecycle. Researchers have been focusing on how to automatically generate or update these models from 3D point clouds, considering existing buildings. Most of these studies have focused on recognizing planar structures (such as floors and walls) or cylindrical ones (such as pipelines). Only a few recent works have addressed the detection of steel structural elements, due to their particular geometry. In these approaches, the point cloud of each structural element was manually separated from the point cloud of the entire building. This practice poses a challenge, as manual segmentation of point clouds is a timeconsuming and subjective process. In this paper, we propose a new approach to automatically detect structural steel elements in 3D point clouds of existing buildings projects, without the need for prior segmentation and regardless of orientation. The proposed technique combines geometry processing algorithms with a machine learning strategy. The performance results demonstrate the effectiveness of our approach in detecting the desired elements. These results open up new possibilities for developing automated pipelines aimed at generating highly accurate 3D BIM models.
Generating synthetic data with diffusion models poses a challenge due to the scarcity of data in computer vision. However, its practical application presents challenges, including the need for computationally expensive retraining and the complex, manual effort required to design precise textual instructions. This work approaches these limitations by introducing the ZEDD-G method, a novel zero-shot and prompt-free framework for synthetic data augmentation specifically designed to enhance downstream classification tasks. Our methodology establishes a fully automated pipeline that begins with unsupervised clustering of visually similar images, followed by a multi-image latent guidance mechanism. This mechanism combines visual prompts with direct manipulation of latent features to generate diverse and controlled variations. Evaluated on a demanding classification benchmark, which includes natural and medical images, ZEDD-G demonstrates a substantial impact on performance. For a ResNet-50 model trained from scratch, our method boosts accuracy by an average of over 30 percentage points across diverse datasets. For a pre-trained model, it provides consistent gains of around 2.6 percentage points. These results are competitive with state-of-the-art fine-tuning-based methods, achieved without requiring any model retraining. Thus, ZEDD-G is an efficient method that enables the generation of high-quality synthetic data, thereby improving classification tasks. Our implementation is publicly available at https://github.com/Gardiy/ZEDDG.
Glacial lake outburst floods (GLOFs) pose a significant threat to high-mountain communities and infrastructure, particularly in glacierized regions such as the Cordillera Blanca, Peru. This study presents an automated method for mapping glacial lakes using Sentinel-2 satellite imagery with enhanced band combinations and a segmentation-based foundational model (SAM 2.1). Our method enables consistent multitemporal lake extraction, which successfully mapped 80% of 448 manually identified glacial lakes. We performed a comparative analysis of images taken from May 2016 and May 2024, and identified five lakes potentially vulnerable to GLOFs. Notably, Lake Parón and Lake Piticocha experienced significant surface area expansion of 13.79 and 3.72 hectares, respectively. The other three lakes showed significally expansion despite its smaller size. GLOF simulations incorporating local topography and lake size indicate potential impacts on urban areas (e.g., Caraz city) and agricultural land. These findings highlight the importance of standarized and automated glacial lake monitoring for early risk assessment and disaster preparedness in the face of climate-driven glacial change.
The rapid development of Multimodal Large Language Models (MLLMs) has expanded the possibilities for automatically understanding complex real-world scenarios. However, their ability to interpret social interactions through videos remains underexplored. This study introduces BeSIM, a benchmark designed to evaluate MLLMs' competence in interpreting such interactions among Brazilians, based on the APRACE taxonomy for categorizing key elements of social interaction. A set of 22 videos was collected from YouTube, resulting in 110 multiple-choice questions aligned with these categories. The results show that models such as Gemini 2.5 Pro outperform their performance on generalist benchmarks like Video-MME, reaching up to 90 percent of accuracy on BeSIM. We also conducted a qualitative analysis to discuss possible features to explain failure/success of the models. These findings indicate that, when properly evaluated, MLLMs demonstrate great potential in interpreting human interactions. Code and data available at https://github.com/M4Falcao/BeSIM.
