Archival Classification and Knowledge Organization: Theoretical Possibilities for the Archival Field

The main goal of this study is to outline a possible relation between archival classification and knowledge organization theory. In this sense, we seek to contribute to the conceptual classification in Archival Science, since there is a lack of systematization about archival classification; not just classification, but even the study of historical and conceptual aspects of the discipline. In the context of knowledge organization there is a considerable amount of research on how to build classification schemes and indexing systems that can help contribute to and expand archival classification theory. In order to comprehend this vast field of theories and methodologies we construct a parallel comparing the classification concepts in both areas and analyzing these concepts.

Image segmentation through combined methods: watershed transform, unsupervised distance learning and normalized cut

Research on image processing has shown that combining segmentation methods may lead to a solid approach to extract semantic information from different sort of images. Within this context, the Normalized Cut (NCut) is usually used as a final partitioning tool for graphs modeled in some chosen method. This work explores the Watershed Transform as a modeling tool, using different criteria of the hierarchical Watershed to convert an image into an adjacency graph. The Watershed is combined with an unsupervised distance learning step that redistributes the graph weights and redefines the Similarity matrix, before the final segmentation step using NCut. Adopting the Berkeley Segmentation Data Set and Benchmark as a background, our goal is to compare the results obtained for this method with previous work to validate its performance.

The Impact of Teaching Presence in Intensive Online Courses on Perceived Learning and Sense of Community: A Mixed Methods Study

This mixed methods concurrent triangulation design study was predicated upon two models that advocated a connection between teaching presence and perceived learning: the Community of Inquiry Model of Online Learning developed by Garrison, Anderson, and Archer (2000); and the Online Interaction Learning Model by Benbunan-Fich, Hiltz, and Harasim (2005). The objective was to learn how teaching presence impacted students’ perceptions of learning and sense of community in intensive online distance education courses developed and taught by instructors at a regional comprehensive university. In the quantitative phase online surveys collected relevant data from participating students (N = 397) and selected instructional faculty (N = 32) during the second week of a three-week Winter Term. Student information included: demographics such as age, gender, employment status, and distance from campus; perceptions of teaching presence; sense of community; perceived learning; course length; and course type. The students claimed having positive relationships between teaching presence, perceived learning, and sense of community. The instructors showed similar positive relationships with no significant differences when the student and instructor data were compared. The qualitative phase consisted of interviews with 12 instructors who had completed the online survey and replied to all of the open-response questions. The two phases were integrated using a matrix generation, and the analysis allowed for conclusions regarding teaching presence, perceived learning, and sense of community. The findings were equivocal with regard to satisfaction with course length and the relative importance of the teaching presence components. A model was provided depicting relationships between and among teaching presence components, perceived learning, and sense of community in intensive online courses.

Chicana, Indian and Sudanese Perspectives of Teaching in the Academy: Three Levels of Experience in the Teaching-Learning and Social Environment

“Women of color from any culture or country face additional barriers in predominantly white institutions. This panel presents perspectives and experiences of three women from three cultures and three different levels of academia—a Chicana Latino visiting professor, a graduate teaching assistant from India, and a Sudanese graduate research assistant.”

Cocaine effects on mouse incentive-learning and human addiction are linked to alpha 2 subunit-containing GABA(A) receptors

Because GABA(A) receptors containing alpha 2 subunits are highly represented in areas of the brain, such as nucleus accumbens (NAcc), frontal cortex, and amygdala, regions intimately involved in signaling motivation and reward, we hypothesized that manipulations of this receptor subtype would influence processing of rewards. Voltage-clamp recordings from NAcc medium spiny neurons of mice with alpha 2 gene deletion showed reduced synaptic GABA(A) receptor-mediated responses. Behaviorally, the deletion abolished cocaine`s ability to potentiate behaviors conditioned to rewards (conditioned reinforcement), and to support behavioral sensitization. In mice with a point mutation in the benzodiazepine binding pocket of alpha 2-GABA(A) receptors (alpha 2H101R), GABAergic neurotransmission in medium spiny neurons was identical to that of WT (i.e., the mutation was silent), but importantly, receptor function was now facilitated by the atypical benzodiazepine Ro 15-4513 (ethyl 8-amido-5,6-dihydro-5-methyl-6-oxo-4H-imidazo [1,5-a] [1,4] benzodiazepine-3-carboxylate). In alpha 2H101R, but not WT mice, Ro 15-4513 administered directly into the NAcc-stimulated locomotor activity, and when given systemically and repeatedly, induced behavioral sensitization. These data indicate that activation of alpha 2-GABA(A) receptors (most likely in NAcc) is both necessary and sufficient for behavioral sensitization. Consistent with a role of these receptors in addiction, we found specific markers and haplotypes of the GABRA2 gene to be associated with human cocaine addiction.

Rituals and Knowledge Construction: Ethical Dilemmas on Creating Oppositions

What is knowledge construction for? Mesopotamian rituals were practiced in order to grasp the future and guide war strategies. Nowadays, scientific rules are developed to avoid mysticism-constructing more accurate laws to explain the reality. Both rituals and science were, and usually are, grounded in a conception that to know is to decipher the correct meaning behind the expressive relief of the world. Contemporary studies on anthropology have shown that the opposition between nature and culture is the basis of a number of problems in human sciences aiming to comprehend the intricate relation between body and violence and overcome ethical dilemmas.

Rationale and knowledge for the universal implementation of sanitation in areas of social vulnerability

The adoption of principles of equality and universality stipulated in legislation for the sanitation sector requires discussions on innovation. The existing model was able to meet sanitary demands, but was unable to attend all areas causing disparities in vulnerable areas. The universal implementation of sanitation requires identification of the know-how that promotes it and analysis of the model adopted today to establish a new method. Analysis of how different viewpoints on the restructuring process is necessary for the definition of public policy, especially in health, and understanding its complexities and importance in confirming social practices and organizational designs. These are discussed to contribute to universal implementation of sanitation in urban areas by means of a review of the literature and practices in the industry. By way of conclusion, it is considered that accepting a particular concept or idea in sanitation means choosing some effective interventions in the network and on the lives of individual users, and implies a redefinition of the space in which it exercises control and management of sewerage networks, such that connected users are perceived as groups with different interests.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

Awareness and knowledge of HPV, cervical cancer, and vaccines in young women after first delivery in São Paulo, Brazil - a cross-sectional study

Abstract Background The success of HPV vaccination programs will require awareness regarding HPV associated diseases and the benefits of HPV vaccination for the general population. The aim of this study was to assess the level of awareness and knowledge of human papillomavirus (HPV) infection, cervical cancer prevention, vaccines, and factors associated with HPV awareness among young women after birth of the first child. Methods This analysis is part of a cross-sectional study carried out at Hospital Maternidade Leonor Mendes de Barros, a large public maternity hospital in Sao Paulo. Primiparous women (15-24 years) who gave birth in that maternity hospital were included. A questionnaire that included questions concerning knowledge of HPV, cervical cancer, and vaccines was applied. To estimate the association of HPV awareness with selected factors, prevalence ratios (PR) were estimated using a generalized linear model (GLM). Results Three hundred and one primiparous women were included; 37% of them reported that they "had ever heard about HPV", but only 19% and 7%, respectively, knew that HPV is a sexually transmitted infection (STI) and that it can cause cervical cancer. Seventy-four percent of interviewees mentioned the preventive character of vaccines and all participants affirmed that they would accept HPV vaccination after delivery. In the multivariate analysis, only increasing age (P for trend = 0.021) and previous STI (P < 0.001) were factors independently associated with HPV awareness ("had ever heard about HPV"). Conclusions This survey indicated that knowledge about the association between HPV and cervical cancer among primiparous young women is low. Therefore, these young low-income primiparous women could benefit greatly from educational interventions to encourage primary and secondary cervical cancer prevention programs.

Social learning and action understanding in human observers: contributions of sensori-motor constraints and prior information

The aim of this thesis was to investigate the respective contribution of prior information and sensorimotor constraints to action understanding, and to estimate their consequences on the evolution of human social learning. Even though a huge amount of literature is dedicated to the study of action understanding and its role in social learning, these issues are still largely debated. Here, I critically describe two main perspectives. The first perspective interprets faithful social learning as an outcome of a fine-grained representation of others’ actions and intentions that requires sophisticated socio-cognitive skills. In contrast, the second perspective highlights the role of simpler decision heuristics, the recruitment of which is determined by individual and ecological constraints. The present thesis aims to show, through four experimental works, that these two contributions are not mutually exclusive. A first study investigates the role of the inferior frontal cortex (IFC), the anterior intraparietal area (AIP) and the primary somatosensory cortex (S1) in the recognition of other people’s actions, using a transcranial magnetic stimulation adaptation paradigm (TMSA). The second work studies whether, and how, higher-order and lower-order prior information (acquired from the probabilistic sampling of past events vs. derived from an estimation of biomechanical constraints of observed actions) interacts during the prediction of other people’s intentions. Using a single-pulse TMS procedure, the third study investigates whether the interaction between these two classes of priors modulates the motor system activity. The fourth study tests the extent to which behavioral and ecological constraints influence the emergence of faithful social learning strategies at a population level. The collected data contribute to elucidate how higher-order and lower-order prior expectations interact during action prediction, and clarify the neural mechanisms underlying such interaction. Finally, these works provide/open promising perspectives for a better understanding of social learning, with possible extensions to animal models.