Um modelo de negociação automatizada para comercio eletronico

Orientador : Edmundo Roberto Mauro MadeiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Existem poucos modelos de negociação bilateral implementados para aplicações de comércio eletrônico que provêem a negociação entre o comprador e o vendedor. Esta dissertação propõe um protocolo de negociação entre dois participantes e um mecanismo para medir as similaridades entre dois produtos, utilizado para obter o produto mais similar ao que foi requisitado pelo consumidor quando um exato não for encontrado. O protocolo proposto segue o modelo bilateral do OMG. A negociação é tratada pelos agentes móveis (Grasshopper) de compra e de venda, representando respectivamente o comprador e o vendedor. Por sua vez, a negociação do preço é baseada no modelo Kasbah. Os catálogos no modelo foram implementados em XML para prover a interoperabilidade entre diferentes arquiteturas. Apresentamos resultados de diversas simulações com o uso de catálogos em XML. Um protótipo foi desenvolvido para validar o modelo propostoAbstract: Electronic commerce applications are lacking a bilateral negotiation model which provides the bargaining between two participants (supplier and consumer) in order to buy and seU goods. This dissertation presents a negotiation protocol between two participants, as weU as the similarity meas'Ures which were implemented to find a similar product, when a specific one could not be found. The proposed protocol foUows the bilateral model approved by the OMG. The negotiation model is composed of selling and buying Grasshopper mobile agents which negotiate between them in order to get the best deal. The negotiation of the price is based on the K asbah model. The catalogs in the model are implemented in XML to provide the interoperability among different systems. We present several simulation results regarding the use of XML based catalogs. A sim pIe prototype has been implemented to verify the viability of this modelMestradoMestre em Ciência da Computaçã

Exploiting the Use of Convolutional Neural Networks for Localization in Indoor Environments

Indoor localization has been an active research area for the last two decades. A great number of sensors have been applied in the task of localization—some with high computational and energy demands (e.g. laser beams), or with issues related to the coverage area, for example, by making use of images obtained by a network of cameras. A different approach, which presents less energy demands and a wide area of coverage, can be created by means of the signal strength of wireless networks. The open issue with signal strength is its high instability due to interferences, attenuation and fading, which, in general, makes the localization systems to present less than desired accuracy. In this article, we exploit the use of Convolutional Neural Networks (ConvNets) in the task of localization. The main motivation behind the employment of ConvNets is its inherent ability of feature extraction, which we believe can deal better with the noise without a filtering step. We evaluate how ConvNets can be employed and identify the best topologies that lead to the lowest errors

A service-oriented middleware for integrated management of crowdsourced and sensor data streams in disaster management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications

Water level identification with laser sensors, inertial units, and machine learning

Flood risk management usually hinges on accurate water level identification in urban streams such as rivers or creeks. Although research has emphasised the applicability of ultrasonic sensors as a contactless technology for sensor-based water level monitoring, Light Detection and Ranging (LiDAR) sensors are less sensitive to weather conditions that typically happen during flood events, such as dust, fog and rainfall. However, there has been little research on the applicability of LiDAR sensors in this field. No previous literature has analysed the impact of complicating variables on the quality of predictions or evaluated the possible benefits of using a combined approach with Inertial Measurement Units (IMU) and machine learning to produce superior predictions. In this work, we collected a dataset in a laboratory condition synchronising data from a LiDAR, an ultrasonic sensor and an IMU in an experimental device. We controlled the incidence angle, the distance, and the water turbidity to analyse their effect on the predictions. Traditional machine-learning techniques were evaluated as models to combine data from distance and inertial sensors, reducing the error rates compared to individual sensors’ predictions. Results indicated a sharp drop in the mean absolute error, root mean squared error and coefficient of determination for all water turbidity and incidence angles considered, especially when tree-based ensembles were used. The ultrasonic sensor led to improved results for low water turbidity and increased incidence angle, but statistically significant differences were not found in the other cases

Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection

Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models