Produção e qualidade do biogás gerado com os dejetos de diferentes espécies animais

Pós-graduação em Zootecnia - FCAV

Comparative wood anatomy of root and stem of Citharexylum myrianthum (Verbenaceae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Diversity, natural history, and geographic distribution of snakes in the Caatinga, Northeastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Association of student position in classroom and school performance

We analyzed whether the position of students in classroom is correlated with academic performance, and which factors might be involved. The sample consisted of 16 classrooms in a school year bimester, each one with around 30 students. Each student's position was registered in classroom maps and the reasons for the students' seat choice were gathered by a questionnaire. School performance and classroom absences of each student were collected directly from the teachers' register notebook. We found that better performances of students at school correlate with lower percentages of absence and choices of seating positions closer to the board. Moreover, the main reason to sit in a front position was motivation for learning. We suggest that school performance is associated to students’ position in the classroom because both are affected by the student motivation for learning. As a consequence, changing students' position in the classroom without increasing their motivation is not likely to improve school performance.