Abstract
Vivenciar a cidade, enquanto espaço de aprendizagem, implica no seu reconhecimento como uma entidade viva, complexa e comunicativa. É compreendê-la no entrelaçamento de seus vários tempos, espaços e dimensões humanas e não humanas, digitais, biológicas, culturais, tecnológicas, sociais, históricas, econômicas, entre outros. Este artigo apresenta uma prática pedagógica oriunda de tese de doutorado no contexto da educação bilíngue, que problematiza formas de conhecer e produzir conhecimento relacionado ao desenvolvimento do pensamento computacional na cidade. As experiências se desenvolvem na perspectiva da aprendizagem inventiva, abordagem CLIL e Educação OnLIFE. Como método de pesquisa, se apropria do método cartográfico de pesquisa-intervenção para produção e análise de dados. Os resultados baseiam-se em elementos presentes nas epistemologias reticulares e conectivas, na cognição inventiva e nos conceitos de matética e bilinguismo. Tais resultados indicam o pensamento computacional sendo potencializado nas formas de viver a exploração da cidade, contribuindo para sua compreensão interdisciplinar e transversal. Esses resultados apontam a emergência de uma nova política cognitiva em Educação, mais conectada com a vida, implicando repensar o currículo e a formação docente.

Abstract

Abstract
We propose a statistical duality among the preferences and endowments of the agents. Under this duality, the logarithmic prices of random trades among agents in a decentralized economy converge in expectation to the logarithm of the Walrasian equilibrium price in a centralized economy.

Abstract
In football, where minor differences can significantly affect outcomes and performance, automatic video analysis has become a critical tool for analyzing and optimizing team strategies. However, many existing solutions require expensive and complex hardware comprising multiple cameras, sensors, or GPS devices, limiting accessibility for many clubs, particularly those with limited resources. Using images and video from a moving camera can help a wider audience benefit from video analysis, but it introduces new challenges related to motion. To address this, we explore an alternative homography estimation in moving camera scenarios. Homography plays a crucial role in video analysis, but presents challenges when keypoints are sparse, especially in dynamic environments. Existing techniques predominantly rely on visible keypoints and apply homography transformations on a frame-by-frame basis, often lacking temporal consistency and facing challenges in areas with sparse keypoints. This paper explores the use of estimated motion information for homography computation. Our experimental results reveal that integrating motion data directly into homography estimations leads to reduced errors in keypoint-sparse frames, surpassing state-of-the-art methods, filling a current gap in moving camera scenarios.

Abstract
In many data stream applications, there is a normal concept, and the objective is to identify normal and abnormal concepts by training only with normal concept instances. This scenario is known in the literature as one-class learning (OCL) for data streams. In this OCL scenario for data streams, we highlight two main gaps: (i) lack of methods based on graph neural networks (GNNs) and (ii) lack of interpretable methods. We introduce OPENCAST (One-class graPh autoENCoder for dAta STream), a new method for data streams based on OCL and GNNs. Our method learns representations while encapsulating the instances of interest through a hypersphere. OPENCAST learns low-dimensional representations to generate interpretability in the representation learning process. OPENCAST achieved state-of-the-art results for data streams in the OCL scenario, outperforming seven other methods. Furthermore, OPENCAST learns low-dimensional representations, generating interpretability in the representation learning process and results.

Abstract
Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes' underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.