Learning in peer-to-peer markets: evidence from Airbnb

Peer-to-peer markets are highly uncertain environments due to the constant presence of shocks. As a consequence, sellers have to constantly adjust to these shocks. Dynamic Pricing is hard, especially for non-professional sellers. We study it in an accommodation rental marketplace, Airbnb. With scraped data from its website, we: 1) describe pricing patterns consistent with learning; 2) estimate a demand model and use it to simulate a dynamic pricing model. We simulate it under three scenarios: a) with learning; b) without learning; c) with full information. We have found that information is an important feature concerning rental markets. Furthermore, we have found that learning is important for hosts to improve their profits.

A Mismatch-Based Model for Memory Reconsolidation and Extinction in Attractor Networks

OSAN, R. , TORT, A. B. L. , AMARAL, O. B. . A mismatch-based model for memory reconsolidation and extinction in attractor networks. Plos One, v. 6, p. e23113, 2011.

Entre o sol e a liberdade impera a violência: formação de redes para a ação a partir da estratégia saúde da família

The research aimed to analyze the feasibility of forming a network of municipal services to prevent and treat child victims of violence from the Basic Health Units in the Family Mossoró / RN. The research is a qualitative approach and was developed in the form of action research. The population was composed of representatives of institutions of the child and primary care professionals. Data were collected through questionnaires, workshops and semi-structured interview. The results were analyzed from data collected through the questionnaire designed to assess the material, lectures, dialogues and assessments with the team and presented in accordance with the findings of the research. The study was approved by the Ethics in Research UFRN with protocol No. 158/2010, CAAE: 0176.0.051.000-10. Situational diagnosis in the participants answered a questionnaire to characterize and then launched the guiding question of the focus group FHS While professional what your perception towards violence against children? It was felt the fear and ignorance on the part of the unit staff on how to prevent and to refer cases of violence against children and adolescents in the area of coverage of the unit and also realized that the professionals felt victims of occupational violence before the violence has reached proportions that the area of the unit. Mind the need to change strategies to work to combat violence, we plan to conduct focus group workshop to complete the questionnaire, training for protection against occupational violence, and meeting with other bodies responsible visor protecting children and adolescents to draw the flowchart of the victims in safety net. The next moment training to fill the notification form professionals were interested in learning and use this tool to combat violence. At the third meeting in Unity, meeting with representatives of the Child Protection Council, the professional unit showed interest in interacting with the agency to expose and combat violence against children and adolescents. Due to difficulties in the physical structure of the unit was not possible to continue the research and planned every moment, and then completed the data collection with interviews with the participating professionals, to assess the meetings. Therefore, it is considered that action research has also achieved its goals because the team was involved in the collective construction of a proposed change in the practices of referral and prevention of violence against children and adolescents. This involvement was favored using the principles Freirian during the course of the study. However, it is assumed that the network was not fully implemented because it is known that it is in a continual process of improvement and must continue evolving with the unit team.

Aquatic weed automatic classification using machine learning techniques

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

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

An Optimum-Path Forest framework for intrusion detection in computer networks

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

Comparing the Teaching-Learning Process With and Without the Use of Computerized Technological Resources

Computerized technological resources have become essential in education, particularly for teaching topics that require the performance of specific tasks. These resources can effectively help the execution of such tasks and the teaching-learning process itself. After the development of a Web site on the topic of nursing staff scheduling, this study aimed at comparing the development of students involved in the teaching-learning process of the previously mentioned topic, with and without the use of computer technology. Two random groups of undergraduate nursing students from a public university in São Paulo state, Brazil, were organized: a case group (used the Web site) and a control group (did not use the Web site). Data were collected from 2003 to 2005 after approval by the Research Ethics Committee. Results showed no significant difference in motivation or knowledge acquisition. A similar performance for the two groups was also verified. Other aspects observed were difficulty in doing the nursing staff scheduling exercise and the students' acknowledgment of the topic's importance for their training and professional lives; easy access was considered to be a positive aspect for maintaining the Web site.

Quality of service control for rt-VBR connections in ATM networks

Nowadays, networks must support applications such as: distance learning, electronic commerce, access to Internet, Intranets and Extranets, voice over IP (Internet Protocol) and many others. These new applications, employing data, voice, and video traffic, require high bandwidth and Quality of Service (QoS). The ATM (Asynchronous Transfer Mode) technology, together with dynamic resource allocation methods, offers network connections that guarantee QoS parameters, such as minimum losses and delays. This paper presents a system that uses Network Management Functions together with dynamic resource allocation for provision of the end-to-end QoS parameters for rt-VBR connections.

Neural networks training using the constructivism paradigms

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Information Visualization to support decision making in the context of distance learning: A study with VLE TelEduc

Digital data sets constitute rich sources of information, which can be extracted and evaluated applying computational tools, for example, those ones for Information Visualization. Web-based applications, such as social network environments, forums and virtual environments for Distance Learning, are good examples for such sources. The great amount of data has direct impact on processing and analysis tasks. This paper presents the computational tool Mapper, defined and implemented to use visual representations - maps, graphics and diagrams - for supporting the decision making process by analyzing data stored in Virtual Learning Environment TelEduc-Unesp. © 2012 IEEE.

Unveiling fieldbus network technology through project-based learning

Fieldbus communications networks are a fundamental part of modern industrial automation technique. This paperwork presents an application of project-based learning (PBL) paradigm to help electrical engineering students grasp the major concepts of fieldbus networks, while attending a one-term long, elective microcontroller course. © 2012 IEEE.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.