Artigos
URI permanente para esta coleçãohttps://repositorio.fei.edu.br/handle/FEI/792
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Resultados da Pesquisa
- Distributed user-based collaborative filtering on an opportunistic network(2018-08-09) NUNES, B. L.; GEMMELL, J.; HORVATH, M.; HEIMFARTH, T.© 2018 IEEE.This paper presents a novel collaborative filtering recommender system based on an opportunistic distributed network. Collaborative filtering algorithms are widely used in many online systems. Often, the computation of these recommender systems is performed on a central server, controlled by the provider, requiring constant internet connection for gathering and computing data. However, in many scenarios, such constraints cannot be guaranteed or may not even be desired. This work proposes a recommendation engine where users share information via an opportunistic network independent of a dedicated internet connection. In this framework, each node is responsible for gathering information from nearby nodes and calculating its own recommendations. Using a centralized collaborative filtering recommender as a baseline, we evaluate three simulated scenarios composed by different movement speeds and data exchange parameters. Our results show that in a relatively short time, an opportunistic distributed recommender systems can achieve results similar to a traditional central system. Our analysis shows that the speed at which the opportunistic recommender system stabilizes depends on several factors including density of the users, movement speed and patterns of the users, and transmission strategies.
- Assessing distributed collaborative recommendations in different opportunistic network scenarios(2020-08-01) BARBOSA, L. N.; GEMMELL, J. F.; HORVATH, M.; HEIMFARTH, T.© 2020 Inderscience Enterprises Ltd.Mobile devices are common throughout the world, even in countries with limited internet access and even when natural disasters disrupt access to a centralised infrastructure. This access allows for the exchange of information at an incredible pace and across vast distances. However, this wealth of information can frustrate users as they become inundated with irrelevant or unwanted data. Recommender systems help to alleviate this burden. In this work, we propose a recommender system where users share information via an opportunistic network. Each device is responsible for gathering information from nearby users and computing its own recommendations. An exhaustive empirical evaluation was conducted on two different data sets. Scenarios with different node densities, velocities and data exchange parameters were simulated. Our results show that in a relatively short time when a sufficient number of users are present, an opportunistic distributed recommender system achieves results comparable to that of a centralised architecture.
Artigo A study of a multi-thresholding segmentation algorithm based on bio-inspired metaheuristic and non-extensive tsallis statistics(2019) RODRIGUES, Paulo; BOUZON, M. F.; HORVATH, M.; VARELA, V. P.; LOPES, GuilhermeAn image segmentation process is one of the most important steps in an image recognition or analysis application pipeline. It is a step that splits each image into disjointed regions of interest. It is also a task that is usually performed by biological processes, such as human visual system. Due to the low processing and ease of implementation, one of the most used techniques is the thresholding method, which consists in finding the best cutting thresholds of a probability distribution histogram. However, the higher the number of thresholds, the greater the computational complexity. And there is no consensus on the number of thresholds and the partitioning position as well. This paper presents a study of the number of thresholds for segmenting an image into their regions of interest. For this purpose, the proposed method uses a bio-inspired algorithm based on meta-heuristics, called firefly with a non-extensive Tsallis statistics kernel. Also, the images are pre-filtered with a low-pass filter based on a q-gaussian function. Using a manually segmented database, the results show that there is an inverse correlation between the Fourier spectrum of an image and the number of thresholds which most approximates the image from the used ground truth. This suggests an automatic method for calculating the required number of thresholds.- A strategy based on non-extensive statistics to improve frame-matching algorithms under large viewpoint changes(2019) LOPES, Guilherme; HORVATH, M.; GIRALDI, G. A.; LOPES, Guilherme