Literatura científica selecionada sobre o tema "Dbsan"
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Artigos de revistas sobre o assunto "Dbsan"
Al-Ameri, Mohammed Abdulbasit Ali, Basim Mahmood, Bünyamin Ciylan e Alaa Amged. "Unsupervised Forgery Detection of Documents: A Network-Inspired Approach". Electronics 12, n.º 7 (3 de abril de 2023): 1682. http://dx.doi.org/10.3390/electronics12071682.
Texto completo da fonteRodríguez L., Ingrid, César Honorio J., Julia Ramírez S., Zara León G. e Willman Alarcón G. "Efecto de un anticoccidial natural a base de saponinas de Yucca schidigera y Trigonella foenum-graecum sobre el control de coccidiosis en pollos de carne". Revista de Investigaciones Veterinarias del Perú 30, n.º 3 (10 de outubro de 2019): 1196–206. http://dx.doi.org/10.15381/rivep.v30i3.16597.
Texto completo da fonteLv, Yikun, He Jiang e Pinchen Pan. "NI-DBSCAN: DBSCAN under Non-IID". Journal of Physics: Conference Series 1533 (abril de 2020): 022110. http://dx.doi.org/10.1088/1742-6596/1533/2/022110.
Texto completo da fonteLulli, Alessandro, Matteo Dell'Amico, Pietro Michiardi e Laura Ricci. "NG-DBSCAN". Proceedings of the VLDB Endowment 10, n.º 3 (novembro de 2016): 157–68. http://dx.doi.org/10.14778/3021924.3021932.
Texto completo da fonteGiri, Kinsuk, Tuhin Kr Biswas e Pritisha Sarkar. "ECR-DBSCAN: An improved DBSCAN based on computational geometry". Machine Learning with Applications 6 (dezembro de 2021): 100148. http://dx.doi.org/10.1016/j.mlwa.2021.100148.
Texto completo da fonteFeng, Ling, Kejian Liu, Fuxi Tang e Qingrui Meng. "GO-DBSCAN: Improvements of DBSCAN Algorithm Based on Grid". International Journal of Computer Theory and Engineering 9, n.º 3 (2017): 151–55. http://dx.doi.org/10.7763/ijcte.2017.v9.1129.
Texto completo da fonteSchubert, Erich, Jörg Sander, Martin Ester, Hans Peter Kriegel e Xiaowei Xu. "DBSCAN Revisited, Revisited". ACM Transactions on Database Systems 42, n.º 3 (24 de agosto de 2017): 1–21. http://dx.doi.org/10.1145/3068335.
Texto completo da fonteChen, Guangsheng, Yiqun Cheng e Weipeng Jing. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark". International Journal of High Performance Computing and Networking 13, n.º 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.099265.
Texto completo da fonteJing, Weipeng, Guangsheng Chen e Yiqun Cheng. "DBSCAN-PSM: an improvement method of DBSCAN algorithm on Spark". International Journal of High Performance Computing and Networking 13, n.º 4 (2019): 417. http://dx.doi.org/10.1504/ijhpcn.2019.10020624.
Texto completo da fonteCheng, Dongdong, Cheng Zhang, Ya Li, Shuyin Xia, Guoyin Wang, Jinlong Huang, Sulan Zhang e Jiang Xie. "GB-DBSCAN: A fast granular-ball based DBSCAN clustering algorithm". Information Sciences 674 (julho de 2024): 120731. http://dx.doi.org/10.1016/j.ins.2024.120731.
Texto completo da fonteTeses / dissertações sobre o assunto "Dbsan"
Neto, AntÃnio Cavalcante AraÃjo. "G2P-DBSCAN: Data Partitioning Strategy and Distributed Processing of DBSCAN with MapReduce". Universidade Federal do CearÃ, 2015. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=15592.
Texto completo da fonteClustering is a data mining technique that brings together elements of a data set such so that the elements of a same group are more similar to each other than to those from other groups. This thesis studied the problem of processing the clustering based on density DBSCAN algorithm distributedly through the MapReduce paradigm. In the distributed processing it is important that the partitions are processed have approximately the same size, provided that the total of the processing time is limited by the time the node with a larger amount of data leads to complete the computation of data assigned to it. For this reason we also propose a data set partitioning strategy called G2P, which aims to distribute the data set in a balanced manner between partitions and takes into account the characteristics of DBSCAN algorithm. More Specifically, the G2P strategy uses grid and graph structures to assist in the division of space low density regions. Distributed DBSCAN the algorithm is done processing MapReduce two stages and an intermediate phase that identifies groupings that can were divided into more than one partition, called candidates from merging. The first MapReduce phase applies the algorithm DSBCAN the partitions individually. The second and checks correcting, if necessary, merge candidate clusters. Experiments using data sets demonstrate that true G2P-DBSCAN strategy overcomes the baseline adopted in all the scenarios, both at runtime and quality of obtained partitions.
ClusterizaÃao à uma tÃcnica de mineraÃÃo de dados que agrupa elementos de um conjunto de dados de forma que os elementos que pertencem ao mesmo grupo sÃo mais semelhantes entre si que entre elementos de outros grupos. Nesta dissertaÃÃo nÃs estudamos o problema de processar o algoritmo de clusterizaÃÃo baseado em densidade DBSCAN de maneira distribuÃda atravÃs do paradigma MapReduce. Em processamentos distribuÃdos à importante que as partiÃÃes de dados a serem processadas tenham tamanhos proximadamente iguais, uma vez que o tempo total de processamento à delimitado pelo tempo que o nà com uma maior quantidade de dados leva para finalizar a computaÃÃo dos dados a ele atribuÃdos. Por essa razÃo nÃs tambÃm propomos uma estratÃgia de particionamento de dados, chamada G2P, que busca distribuir o conjunto de dados de forma balanceada entre as partiÃÃes e que leva em consideraÃÃo as caracterÃsticas do algoritmo DBSCAN. Mais especificamente, a estratÃgia G2P usa estruturas de grade e grafo para auxiliar na divisÃo do espaÃo em regiÃes de baixa densidade. Jà o processamento distribuÃdo do algoritmo DBSCAN se dà por meio de duas fases de processamento MapReduce e uma fase intermediÃria que identifica clusters que podem ter sido divididos em mais de uma partiÃÃo, chamados de candidatos à junÃÃo. A primeira fase de MapReduce aplica o algoritmo DSBCAN nas partiÃÃes de dados individualmente, e a segunda verifica e corrige, caso necessÃrio, os clusters candidatos à junÃÃo. Experimentos utilizando dados reais mostram que a estratÃgia G2P-DBSCAN se comporta melhor que a soluÃÃo utilizada para comparaÃÃo em todos os cenÃrios considerados, tanto em tempo de execuÃÃo quanto em qualidade das partiÃÃes obtidas.
Araújo, Neto Antônio Cavalcante. "G2P-DBSCAN: Estratégia de Particionamento de Dados e de Processamento Distribuído fazer DBSCAN com MapReduce". reponame:Repositório Institucional da UFC, 2016. http://www.repositorio.ufc.br/handle/riufc/16372.
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Clustering is a data mining technique that brings together elements of a data set such so that the elements of a same group are more similar to each other than to those from other groups. This thesis studied the problem of processing the clustering based on density DBSCAN algorithm distributedly through the MapReduce paradigm. In the distributed processing it is important that the partitions are processed have approximately the same size, provided that the total of the processing time is limited by the time the node with a larger amount of data leads to complete the computation of data assigned to it. For this reason we also propose a data set partitioning strategy called G2P, which aims to distribute the data set in a balanced manner between partitions and takes into account the characteristics of DBSCAN algorithm. More Specifically, the G2P strategy uses grid and graph structures to assist in the division of space low density regions. Distributed DBSCAN the algorithm is done processing MapReduce two stages and an intermediate phase that identifies groupings that can were divided into more than one partition, called candidates from merging. The first MapReduce phase applies the algorithm DSBCAN the partitions individually. The second and checks correcting, if necessary, merge candidate clusters. Experiments using data sets demonstrate that true G2P-DBSCAN strategy overcomes the baseline adopted in all the scenarios, both at runtime and quality of obtained partitions.
Clusterizaçao é uma técnica de mineração de dados que agrupa elementos de um conjunto de dados de forma que os elementos que pertencem ao mesmo grupo são mais semelhantes entre si que entre elementos de outros grupos. Nesta dissertação nós estudamos o problema de processar o algoritmo de clusterização baseado em densidade DBSCAN de maneira distribuída através do paradigma MapReduce. Em processamentos distribuídos é importante que as partições de dados a serem processadas tenham tamanhos proximadamente iguais, uma vez que o tempo total de processamento é delimitado pelo tempo que o nó com uma maior quantidade de dados leva para finalizar a computação dos dados a ele atribuídos. Por essa razão nós também propomos uma estratégia de particionamento de dados, chamada G2P, que busca distribuir o conjunto de dados de forma balanceada entre as partições e que leva em consideração as características do algoritmo DBSCAN. Mais especificamente, a estratégia G2P usa estruturas de grade e grafo para auxiliar na divisão do espaço em regiões de baixa densidade. Já o processamento distribuído do algoritmo DBSCAN se dá por meio de duas fases de processamento MapReduce e uma fase intermediária que identifica clusters que podem ter sido divididos em mais de uma partição, chamados de candidatos à junção. A primeira fase de MapReduce aplica o algoritmo DSBCAN nas partições de dados individualmente, e a segunda verifica e corrige, caso necessário, os clusters candidatos à junção. Experimentos utilizando dados reais mostram que a estratégia G2P-DBSCAN se comporta melhor que a solução utilizada para comparação em todos os cenários considerados, tanto em tempo de execução quanto em qualidade das partições obtidas.
Mahmod, Shad. "Deinterleaving pulse trains with DBSCAN and FART". Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-379718.
Texto completo da fonteКулік, Євгенія Сергіївна, Евгения Сергеевна Кулик, Євгенія Сергіївна Кулік, Захар Вікторович Козлов, Захар Викторович Козлов e Zakhar Viktorovych Kozlov. "Використання SR-дерев у щільнісному методі кластеризації числових просторів DBSCAN". Thesis, Cумський державний університет, 2016. http://essuir.sumdu.edu.ua/handle/123456789/46528.
Texto completo da fonteKästel, Arne Morten, e Christian Vestergaard. "Comparing performance of K-Means and DBSCAN on customer support queries". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-260252.
Texto completo da fonteI kundtjänst förekommer det ofta upprepningar av frågor samt sådana frågor som inte kräver unika svar. I syfte att öka produktiviteten i kundtjänst funktionens arbete att besvara dessa frågor undersöks metoder för att automatisera en del av arbetet. Vi undersöker olika metoder för klusteranalys, applicerat på existerande korpusar innehållande texter så väl som frågor. Klusteranalysen genomförs i syfte att identifiera dokument som är semantiskt lika, vilket i ett automatiskt system för frågebevarelse skulle kunna användas för att besvara en ny fråga med ett existerande svar. En jämförelse mellan hur K-means och densitetsbaserad metod presterar på tre olika korpusar vars dokumentrepresentationer genererats med BERT genomförs. Vidare diskuteras den digitala transformationsprocessen, varför företag misslyckas avseende implementation samt även möjligheterna för en ny mer iterativ modell.
Legoabe, Reginald Sethole. "An Impact Assessment of the DBSA/ SALGA ICT Internship Programme: A Case Study". Thesis, North-West University (South Africa), 2010. http://hdl.handle.net/10919/71530.
Texto completo da fonteMini-dissertation submitted in partial fullfilment of the requirements for the North-West University Yunibesiti Ya Bokone-Bophirima Noordwest-Universiteit Masters Degree in Business Administration (MBA) Human Resource Management (HRM) North-West University (NWU) Graduate School of Business & Government
Мельникова, П. А. "Поиск аномалий во временных рядах на основе оценивания их параметров". Thesis, ХНУРЕ, 2021. https://openarchive.nure.ua/handle/document/16436.
Texto completo da fonteКрамар, Іван Ігорович. "Кластеризація даних, що збираються з відібраних джерел науково-технічної інформації". Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/36639.
Texto completo da fonteThe aim of the work is to use the clustering of scientific and technical data not only for the visual representation of objects, but also for the recognition of new ones. The purpose of document clustering is to automatically detect groups of semantically similar documents among a given fixed set. Groups are formed only on the basis of pairwise similarity of document descriptions, and no characteristics of these groups are set in advance. Methods for deleting uninformative words are considered: deletion of stop words, stemming, N-diagrams, case reduction. The following methods were used to highlight keywords and classify the results: dictionary, statistical and based on the Y-interpretation of Bradford's law, TF-IDF measure, F-measure and the method of licorice patterns. Python programming language was chosen to implement the system of cluster analysis of scientific and technical data, a high-level, the implementation of the interpreter 2.7. This program code is easier to read, its reuse and maintenance is much easier than using program code in other languages.
Целью работы является применение кластеризации научно-технических данных не только для наглядного представления объектов, но и для распознавания новых. Целью кластеризации документов является автоматическое выявление групп семантически похожих документов среди заданной фиксированной множества. Группы формируются только на основе попарно сходства описаний документов, и никакие характеристики этих групп не задаются заранее. Для удаления неинформативных слов рассмотрены методы: удаление стоп-слов, стемминг, N-диаграммы, приведение регистра. Для выделения ключевых слов и классификации результатов использованы следующие методы: словарный, статистический и построен на основе Y-интерпретации закона Брэдфорда, TF-IDF мера, F-мера и способ лакричным шаблонов. Для реализации системы кластерного анализа научно-технических данных избран высокоуровневый язык программирования Python, реализация интерпретатора 2.7. Данный программный код читается легче, его многократное использование и обслуживание выполняется гораздо проще, чем использование программного кода на других языках.
Kannamareddy, Aruna Sai. "Density and partition based clustering on massive threshold bounded data sets". Kansas State University, 2017. http://hdl.handle.net/2097/35467.
Texto completo da fonteDepartment of Computing and Information Sciences
William H. Hsu
The project explores the possibility of increasing efficiency in the clusters formed out of massive data sets which are formed using threshold blocking algorithm. Clusters thus formed are denser and qualitative. Clusters that are formed out of individual clustering algorithms alone, do not necessarily eliminate outliers and the clusters generated can be complex, or improperly distributed over the data set. The threshold blocking algorithm, a current research paper from Michael Higgins of Statistics Department on other hand, in comparison with existing algorithms performs better in forming the dense and distinctive units with predefined threshold. Developing a hybridized algorithm by implementing the existing clustering algorithms to re-cluster these units thus formed is part of this project. Clustering on the seeds thus formed from threshold blocking Algorithm, eases the task of clustering to the existing algorithm by eliminating the overhead of worrying about the outliers. Also, the clusters thus generated are more representative of the whole. Also, since the threshold blocking algorithm is proven to be fast and efficient, we now can predict a lot more decisions from large data sets in less time. Predicting the similar songs from Million Song Data Set using such a hybridized algorithm is considered as the data set for the evaluation of this goal.
Huo, Shiyin. "Detecting Self-Correlation of Nonlinear, Lognormal, Time-Series Data via DBSCAN Clustering Method, Using Stock Price Data as Example". The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1321989426.
Texto completo da fonteLivros sobre o assunto "Dbsan"
Gibson, Bill. The DBSA macromodel. Halfway House, South Africa: Development Bank of Southern Africa, 1997.
Encontre o texto completo da fonteGrange, A. B. La. DBSA in the southern African development framework. Sandton, Republic of South Africa: Development Bank of Southern Africa, 1985.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. Grub-dban sakya-sri-dznya-na'i gsun 'bum. Kathmandu: Khenpo shedup tenzin and lama thinley namgyal, 1998.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. Collected works sun 'bum of tenth pan-chen chos-kyi-dban-phyug. Delhi: Tashi lhunpo monastery, 1998.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. Lamas of zans-dkar: A collection of manuscript material about the lives of kun-dga'-chos-legs, bla-ma karma, and grub-dban nag-dban-tshe-rin. Gemur, distt. lahul: Tobden tsering, 1985.
Encontre o texto completo da fonteVan der Kooy, R. J. W., ed. An Introduction to economic development in Southern Africa and the role of DBSA. Sandton, Republic of South Africa: Development Bank of Southern Africa, 1985.
Encontre o texto completo da fonteKirsten, Marié. DBSA infrastructure barometer, 2008: Economic and social infrastructure in South Africa : scenarios for the future. Editado por Development Bank of Southern Africa. [Halfway House, Midrand, South Africa]: Development Bank of Southern Africa, 2008.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. Initiation texts (dban dpe) of the bhutanese tradition of the 'brug-pa dkar-brgyud-pa. Rewalsar, distt. mandi, h.p: Zigar drukpa kargyud institute, 1985.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. The collected works (gsun 'bum) of nin-rdzon khri-pa dkon-mchog-don-grub-chos-dban. Bir, h.p: Bir tibetan society, 1985.
Encontre o texto completo da fonteCenter, Buddhist Digital Resource. The autobiography and collected writings of the bhutanese gter-ston yongs-'dzin nag-dban-grags-pa. Thimphu: National library of bhutan, 1985.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Dbsan"
Bhardwaj, Surbhi, e Subrat Kumar Dash. "VDMR-DBSCAN: Varied Density MapReduce DBSCAN". In Big Data Analytics, 134–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27057-9_10.
Texto completo da fonteDierkes, Joel, Daniel Stelter e Christian Braune. "$$\lambda $$-DBSCAN: Augmenting DBSCAN with Prior Knowledge". In Lecture Notes in Computer Science, 107–18. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58553-1_9.
Texto completo da fonteAshour, Wesam, e Saad Sunoallah. "Multi Density DBSCAN". In Lecture Notes in Computer Science, 446–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23878-9_53.
Texto completo da fonteLi, Tianrun, Thomas Heinis e Wayne Luk. "Hashing-Based Approximate DBSCAN". In Advances in Databases and Information Systems, 31–45. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44039-2_3.
Texto completo da fonteKryszkiewicz, Marzena, e Piotr Lasek. "TI-DBSCAN: Clustering with DBSCAN by Means of the Triangle Inequality". In Rough Sets and Current Trends in Computing, 60–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13529-3_8.
Texto completo da fonteNguyen, Thi Quynh, Romain Laborde, Abdelmalek Benzekri, Arnaud Oglaza e Mehdi Mounsif. "AutoRoC-DBSCAN: Automatic Tuning of DBSCAN to Detect Malicious DNS Tunnels". In Communications in Computer and Information Science, 126–44. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23098-1_8.
Texto completo da fonteScicluna, Neil, e Christos-Savvas Bouganis. "FPGA-Based Parallel DBSCAN Architecture". In Lecture Notes in Computer Science, 1–12. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05960-0_1.
Texto completo da fonteBordogna, Gloria, e Dino Ienco. "Fuzzy Core DBScan Clustering Algorithm". In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 100–109. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08852-5_11.
Texto completo da fonteArlia, Domenica, e Massimo Coppola. "Experiments in Parallel Clustering with DBSCAN". In Euro-Par 2001 Parallel Processing, 326–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44681-8_46.
Texto completo da fonteBraune, Christian, Stephan Besecke e Rudolf Kruse. "Density Based Clustering: Alternatives to DBSCAN". In Partitional Clustering Algorithms, 193–213. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09259-1_6.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Dbsan"
Ding, Hu, Fan Yang e Mingyue Wang. "On Metric DBSCAN with Low Doubling Dimension". In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/426.
Texto completo da fontePathak, Sashakt, Arushi Agarwal, Ankita Ankita e Mahendra Kumar Gurve. "Restricted Randomness DBSCAN : A faster DBSCAN Algorithm". In IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474124.3474204.
Texto completo da fonteVentorim, Igor, Diego Luchi e Flávio Varejão. "Um método de amostragem tendenciosa para aplicação do DBSCAN". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/eniac.2020.12129.
Texto completo da fonteSong, Hwanjun, e Jae-Gil Lee. "RP-DBSCAN". In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3196887.
Texto completo da fonteGan, Junhao, e Yufei Tao. "DBSCAN Revisited". In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2723372.2737792.
Texto completo da fonteKisilevich, Slava, Florian Mansmann e Daniel Keim. "P-DBSCAN". In the 1st International Conference and Exhibition. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1823854.1823897.
Texto completo da fonteNagarajan, Vani, e Milind Kulkarni. "RT-DBSCAN: Accelerating DBSCAN using Ray Tracing Hardware". In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2023. http://dx.doi.org/10.1109/ipdps54959.2023.00100.
Texto completo da fontePayghan, Vaibhav Santosh, Miit Prajapati e Abhisha Chauhan. "A Novel Method to Select Hyperparameters of the DBSCAN Algorithm for RADAR Applications". In Symposium on International Automotive Technology. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0030.
Texto completo da fonteBessrour, Malek, Zied Elouedi e Eric Lefevre. "E-DBSCAN: An evidential version of the DBSCAN method". In 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2020. http://dx.doi.org/10.1109/ssci47803.2020.9308578.
Texto completo da fonteTekbir, Mennan, e Songul Albayrak. "Recursive-Partitioned DBSCAN". In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5651189.
Texto completo da fonte