Tema de Projetoojeto

7/24/2019 Tema de Projetoojeto

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Orientador e Co-Orientador (sehouver) Ttulo rea do Conhecimento

19/05/201111:34:35

Inovao consiste em solucionar problemas de maneira elegante (mais simples, criativa,inteligente, sutil, com qualidade), de modo a gerar satisfao e real valor para os usuriose/ou clientes. A inovao pode ser fomentada e estimulada por meio de abordagenscentradas no ser humano, que favoream a prototipao, a experimentao e adiferenciao. Neste contexto, o objetivo desta proposta de pesquisa consiste em introduzirprticas, tcnicas e ferramentas dirigidas inovao no workflow de processos geis degerenciamento e de desenvolvimento de produtos de software.

ANTHONY, S. D. ET AL. Inovao para o Crescimento Ferramentas para incentivar eadministrar a inovao. Harvard Business Review Press. So Paulo: M Books, 2011.BROWN, T. Design thinking: um metodologia poderosa para decretar o fim das velhasideias. Rio de Janeiro: Elsevier, 2010.MOULTRIE, J. ET AL. Innovation spaces: towards a framework for understanding the role ofthe physical environment in innovation. Creative and Innovation Management, v. 16, n. 1, p.53-65, Feb. 2007.SHENHAR, A.; DVIR, D. Gerenciamento de Projetos: A Abordagem Diamante aoCrescimento e Inovao Bem-sucedidos. Harvard Business Review Press. So Paulo: MBooks, 2010.

Ricardo Andr Cavalcante deSouza

Experimentao e introduo deabordagens orientadas inovao no contexto deprocessos geis degerenciamento e dedesenvolvimento de software. Engenharia de Software

19/05/201117:42:28

Estender os resultados obtidos em [1] e investigar o relacionamento entre as Redes NeuraisSem Peso Qunticas [2] e os Autmatos Finitos Qunticos [3]. Em [2] foram apresentadosos modelos anlogos qunticos das redes compostas dos nodos lgicos probabilsticos(qPLN) e dos nodos lgicos probabilsticos multivalorados (qMPLN) [2]. Neste trabalhoespera-se demonstrar uma equivalncia entre variaes dos modelos neurais qunticos evariaes dos modelos de autmatos qunticos, iniciando com a equivalncia entre asqMPLN e os autmatos de Moore-Crutchfield [3].

[1] Marclio C. P. de Souto, Teresa B. Ludermir, Wilson R. de Oliveira. Equivalence betweenRAM-based Neural Networks and Probabilistic Automata. IEEE Trans. on Neural Networks,Estados Unidos, v. 16, n. 4, p. 996-999, 2005.[2] Wilson R. de Oliveira, A. J. Silva, T. Ludermir, W. Galindo, A. Leonel, and J. Pereira.Quantum Logical Neural Network. Brazilian Symposium of Neural Networks, 2008[3] C. Moore, J. Crutchfield, "Quantum automata and quantum grammars", TheoreticalComputer Science, 237 (2000) pp 275-306 Wilson Rosa de Oliveira Jr

Redes Neurais Sem PesoQunticas e AutmatosQunticos

10301011 COMPUTABILIDADEE MODELOS DECOMPUTAO

19/05/201117:45:42

Atualmente as duas principais propostas sobre implementao fs ica de uma RAM qunticaso descritas e investigadas em [1] e em [2], usando sistemas pticos. Em [3] foramapresentados os modelos anlogos qunticos das redes compostas dos nodos lgicosprobabilsticos e dos nodos lgicos probabilsticos multivalorados "implementandos" em umaRAM quntica, mas que foi definida abstrata e no fisicamente. Neste trabalho pretende-seusar a implementao fsica de [1,2] e sugerir outros modelos de redes neurais sem pesomais reais (fisicamente realizadas) e investigar propriedades, aprendizado etc. Pretende-setambm comparar as trs RAMs, verificando primeiro se a proposta em [3] mesmo sendoabstrata - tem as propriedades matemticas dos sistemas fsicos em [1,2].

[1] John C. Howell, John A. Yeazell, Dan VentDan Ventura. Optically simulating a quantumassociative memory. PHYSICAL REVIEW A, VOLUME 62, 042303, 2000.[2] Vittorio Giovannetti, Seth Lloyd, Lorenzo Maccone. Architectures for a quantum randomaccess memory. arXiv:0807.4994v1 (http://arxiv.org/abs/0807.4994v1)[3] W. R. de Oliveira, A. J. Silva, T. Ludermir, W. Galindo, A. Leonel, and J. Pereira.Quantum Logical Neural Network. Brazilian Symposium of Neural Networks, 2008. Wilson Rosa de Oliveira Jr

Simulao ptica de RedesNeurais Sem Peso Qunticas

10302026 MODELOSANALTICOS E DE SIMULAO

19/05/201117:48:16

Em [1] foram apresentados os modelos anlogos qunticos das redes compostas dos nodos

lgicos probabilsticos e dos nodos lgicos probabilsticos multivalorados [2] utilizando-se umcircuito quntico especfico para representar os nodos da rede. Os circuitos utilizados paraimplementar os nodos podem ser aproximados por circuitos DQCk (deterministic quantumcomputation with k pure qubit) [3] porm so mais gerais. Em [3] proposta umaimplementao tica de circuito do tipo DQC1 e realizado um calculo de correlaes no-clssicas geradas por estes circuitos qunticos usando duas medidas: uma chamada dediscord e outra de tangle. Neste trabalho pretendemos investigar as caractersticas dasredes compostas por nodos DQC1 e comparar sua potencialidade com as redes de [1].

[1] W. R. de Oliveira, A. J. Silva, T. Ludermir, W. Galindo, A. Leonel, and J. Pereira.Quantum Logical Neural Network. Brazilian Symposium of Neural Networks, 2008[2] T. B. Ludermir, A.Carvalho, A. P. Braga, and M.C.P. Souto. Weightless neural models: Areview of current and past works. Neural Computing Surveys 2, 41 - 61, 1999.[3]B. P. Lanyon, M. Barbieri, M. P. Almeida, A. G. White. Experimental quantum computingwithout entanglement. arXiv:0807.0668v1 [quant-ph] (http://arxiv.org/abs/0807.0668) Wilson Rosa de Oliveira Jr

Redes Neurais Sem PesoQunticas sem Emaranhamento


19/05/201117:51:37

Estender os resultados obtidos em [1] e investigar o poder computacional e os algoritmos deaprendizado de Redes Neurais compostas de circuitos qunticos, generalizando o estudodas Redes Neurais Sem Peso (que so compostas de circuito booleanos clssicos). Em [1]foram apresentados os modelos anlogos qunticos das redes compostas dos nodoslgicos probabilsticos e dos nodos lgicos probabilsticos multivalorados [2]. Neste trabalhoespera-se desenvolver os anlogos qunticos dos demais modelos de redes neurais sempeso conhecidos na literatura [2,3].

[1] W. R. de Oliveira, A. J. Silva, T. Ludermir, W. Galindo, A. Leonel, and J. Pereira.Quantum Logical Neural Network. Brazilian Symposium of Neural Networks, 2008[2] T. B. Ludermir, A.Carvalho, A. P. Braga, and M.C.P. Souto. Weightless neural models: Areview of current and past works. Neural Computing Surveys 2, 41 - 61, 1999.[3] Geoffrey G. Lockwood, Igor Aleksander. Predicting the behaviour of G-RAM networks.Neural Networks 16 (2003) 91100 Wilson Rosa de Oliveira Jr

Redes Neurais Sem PesoQunticas


19/05/201117:53:33

Estender os resultados obtidos em [1] e desenvolver algoritmos de aprendizagem de RedesNeurais compostas de circuitos qunticos, generalizando o estudo das Redes Neurais SemPeso (que so compostas de circuito booleanos clssicos). Em [1] foram apresentados osmodelos anlogos qunticos das redes compostas dos nodos lgicos probabilsticos e dosnodos lgicos probabilsticos multivalorados [2] e um algoritmo de aprendizado ingnuobaseado no algoritmo clssico. Neste trabalho espera-se desenvolver algoritmos deaprendizado que utilizem mais fortemente a caratersticas qunticas das redes. Entre ospossveis candidatos esto o mtodo do gradiente usando a Geometria Diferencial Discreta[3], Backrpopagation complexo, Quantum Annealing, os Algoritmos Genticos, Algoritmo deGrover , etc

[1] W. R. de Oliveira, A. J. Silva, T. Ludermir, W. Galindo, A. Leonel, and J. Pereira.Quantum Logical Neural Network. Brazilian Symposium of Neural Networks, 2008[2] T. B. Ludermir, A.Carvalho, A. P. Braga, and M.C.P. Souto. Weightless neural models: Areview of current and past works. Neural Computing Surveys 2, 41 - 61, 1999.[3] de Oliveira, W. R. and Smyth, M. B. (2008b). Discrete differential geometry on Evakosgraphmanifolds. Unpublished manuscript. Wilson Rosa de Oliveira Jr

Aprendizado em Neurais SemPeso Qunticas


23/05/201111:43:57

A recente (re)descoberta que, de posse da linguagem da Teoria das Categorias, Fsica(Mecnica Quntica, Teoria da Relatividade), Matemtica (Variedades, Cobordismos,gebras de Hopf), Lgica (Sistemas Dedutivos, Lgica Linear e Intuicionsti ca) eComputao (Lambda-Calculo, Teoria dos Domnios, Computao Quntica) so aspectosde uma mesma realidade, Categorias Simtricas Monoidais [1, Captulos 1 a 3], permitiuavanos nas rea envolvidas e algumas vezes surpreendentes. Existe muito a se explorar eum caminho surpreendente a contribuio que a Computao e seus modelos discretospode dar na busca do que vem a ser o modelo unificador da Mecnica Quntica e a Teoriada Relatividade: Gravidade Quntica. Neste contexto o estudo de modelos de

[1] Coecke, Bob (Ed.). New Structures for Physics, Lecture Notes in Physics, Vol. 813,Springer, 1st Edition, 2011, XVIII, 1000 p. Wilson Rosa de Oliveira Jr

Fudamentos Categricos daComputacao Quantica


27/05/201120:10:50

This research project will investigate the mechanism of innovation generation in theeducational institutions base as a driving factor for the creation of institutions that came froma Triple Helix Model. The goal is to create and characterize innovation scenarios betweenclusters of educational institutions. Thus, parameters and indicators for the creation of thescenarios will be characterized. As a result, we expect to provide an initial set of guidelinesand innovation indicators based on the results of the simulations that driven the generation ofnew triple helix organizations. By the way, for the analysis of the scenarios, where thevariables and the behavior are not well defined, as it is in this case, the scientific communitymakes use of computational models and simulators. For this survey, the cellular automatawill be used as abstract machines of computational simulation. These machines have alreadybeen successfully used for modeling epidemiological systems of several kinds according tothe following authors. As the result of this project: aspects of the application of the epidemiology concepts in thestudy of factors which stimulate the innovative behavior and a study of mathematical-computer models used in the simulation of epidemiological phenomenon when applied in theinnovation process in industry.

ABDALLA et al., Hlice Trplice no Brasil: Um Ensaio Terico acerca dos Benefc ios daEntrada da Universidade nas Parcerias Estatais. October, 2009.DRUKER, Peter Ferdinand. The Discipline of Innovation. Ed. Harvard Business Press,2003. pp. 95-103.ETZKOWITZ, Henry. The Triple Helix of University - Industry Government Implications forPolicy and Evaluation, Nov, 2002, pp. 16.GLADWELL, Malcolm. The Tipping Point. Ed. Back Bay Books, 2000. pp. 280.Michigan Department of Labor & Economic Growth, Bureau of Labor Market Information andStrategic Initiatives. Innovation Indicators Report to the Council for Labor and EconomicGrowth. August, 2007.ROCHA, Elisa Maria Pinto da.; FERREIRA, Marta Arajo Tavares. Analysis of BrazilianTechnological Innovation Indicators: Comparing a Group of Privatized Companies and aGeneral Group of Companies. Ci. Inf., Braslia, v. 30, n. 2, May/August. 2001. pp. 64-69.THE OSLO MANUAL. Guidelines for Collecting and Interpreting Innovation Data. 3 ed.2005. Jones Albuquerque

Applying Cellular Automata toCharacterization an InnovationProcess in EducationalInstituitions


27/05/201120:12:06

One of the key signatures of complex systems is in the fact that many patterns repeatthemselves at different spatial and temporal scales. If we focus on spatial scales for themoment, then simple processes which generate growth at the most local level, when applieduniformly in building up structures from the simplest seed location, generate the same patternat successively larger scales. Such order across scales which is marked by spatial self-similarity is said to be scaling and the structures that result are fractals. One of the bestexamples is the tree-like structure that shows similarity at all scales from the tiniest branchesto the entire network. Dendritic patterns such as rivers and transport systems in cities whichboth grow at the local level to service their surrounding space in the most minimalist fashion,provide excellent examples of growth processes that scale, are never in equilibrium in thetraditional sense, and require the same continual operation of basic local rules or codes tomaintain their structure.There are many models which have been developed to simulate such processes, and thecurrent fascination in urban growth modelling with cellular automata mirrors a concern forgenerating urban patterns from the bottom up. These models are not quite network models inthat they are concerned with generating local development in restricted neighbourhoodsaround already developed sites but the rules that are used routinely and repetitively tend togive rise to structures which in their idealised form are dendritic, reflecting the transport ofenergy between developed cells which together define the growing urban structure or city.In the other hand, a model can be seen as the approximated representation of any realproblem using a determined language (math, logic, geographic, physics, etc.) and respectingone or more theories. Models which represent math language are named mathematic modeland can be represented by a set of equations and/or expressions. Constructing a modelallow us to place the complexity of a real problem within a logic structure which can beanalyzed. Thus, it is possible to evidence the alternatives of decisions and their possibleeffects, indicating relevant data and leading us to informative conclusions. The process ofconstructing models is called modeling. There are several techniques for math andcomputational modeling and lots of them have been used to represent epidemiologicalphenomenon.The main goal of this work is to study, define e development a model to generate scenariosof recommendation in Social Networks based on Complex Systems simulation techiniques. Inthis work, Cellular Automata will be the technique.

ALBUQUERQUE, J. O. ; BOCANEGRA, S. ; Souza, Marco A. A. ; SOUZA, H. P. ; SANTOS,R. S. ; BARBOSA, C. C. G. S. . Xiscanoe - Mathematical and Computational Models forAnalysis the Expansion Process of Schi stosomiasis on the Beaches of Pernambuco - Brazil.Memrias do Instituto Oswaldo Cruz (Impresso), 2010.GLADWELL, Malcolm. The Tipping Point. Ed. Back Bay Books, 2000. pp. 280.LIMA JUNIOR, A. R. ; MATTOS NETO, P. S. G. DE ; ALBUQUERQUE, J. O. ;BOCANEGRA, S. ; Ferreira, Tiago A. E. . Forecasting Chaotic and Non-Linear Time Serieswith Artificial Intelligence and Statistical Measures. In: Aleksandar Lazinica. (Org.).Modelling, Simulation and Optimization. : Rijeka: In-Teh, 2010, v. , p. -.S. Wolfram: A new kind of science. Published by Wolfram Media (2002).Wiliams, H.P. Model building in mathematical programming. John Wiley & Sons, 1997. Jones Albuquerque

Modeling RecommendationClusters in Social Networks usingCellular Automata


27/05/201120:13:14

In the current economic scenario, innovation has been a key factor for companies becomecompetitive and stand out in the market in which they operate. Thus, innovation has becomethe key for the economic development. Not long time ago, we discussed the role of thegovernment and industry in the innovation process. Today, the innovation topic hastranscended economic, social and governmental bounds. As the knowledge is the mainfactor that drives the innovative behavior, is not surprising that it has been the main actor inevidence in the society which we live nowadays. In this context, in addition to the industryand government, today we discuss the role of the universities as the main source ofknowledge and technology transfer in the process of economic development. Thus, with theentrance of the university in the innovative sphere, it is formed an axis composed of threehelices, industry-government-university that interact with each other, generating new andcomplex relationships, placing innovation into an evolutionary context. The Literature namedthis concept Triple Helix. In the other hand, The modeling of Tipping Points in innovationindustry of IT is characterized as a modeling object without math rules. The application ofCellular Automata can provide behavioral scenarios and even scenarios of predictabilities ofbehavior if the rules and simulation environment allow it. This work investigates thispossibility. The Tipping Points theory came up with the idea that the social behavior can bemapped to an epidemic behavior, in other words, events like the ebb and flow of productsales, crime waves, fashion trend adoption must be treated as epidemics.Thus, the main goal of this project is to define and to provide a 3D simulator, based oncellular automata, with the capacity of generating scenarios, resulting from interactionsbetween industry-government-university. As result, such scenarios will show us, whether insome regions, e.g. Brasil, is propitious the birth of innovative projects that came from a TripleHelix Model. As specific results of this project, the following contributions may be listed: (1) Amap of components clusters from Triple Helix indicating the places propitious to haveinnovative projects; (2) A mathematical-computer model used in the simulation ofepidemiological phenomenon in the innovation process in industry; (3) Application of theepidemiology concept in the study of factors which stimulate the innovative behavior.

ABDALLA et al., Hlice Trplice no Brasil: Um Ensaio Terico acerca dos Benefc ios daEntrada da Universidade nas Parcerias Estatais. October, 2009.ALBUQUERQUE, J. O. ; BOCANEGRA, S. ; Souza, Marco A. A. ; SOUZA, H. P. ; SANTOS,R. S. ; BARBOSA, C. C. G. S. . Xiscanoe - Mathematical and Computational Models forAnalysis the Expansion Process of Schi stosomiasis on the Beaches of Pernambuco - Brazil.Memrias do Instituto Oswaldo Cruz (Impresso), 2010.DRUKER, Peter Ferdinand. The Discipline of Innovation. Ed. Harvard Business Press,2003. pp. 95-103.ETZKOWITZ, Henry. The Triple Helix of University - Industry Government Implications forPolicy and Evaluation, Nov, 2002, pp. 16.GLADWELL, Malcolm. The Tipping Point. Ed. Back Bay Books, 2000. pp. 280.LIMA JUNIOR, A. R. ; MATTOS NETO, P. S. G. DE ; ALBUQUERQUE, J. O. ;BOCANEGRA, S. ; Ferreira, Tiago A. E. . Forecasting Chaotic and Non-Linear Time Serieswith Artificial Intelligence and Statistical Measures. In: Aleksandar Lazinica. (Org.).Modelling, Simulation and Optimization. : Rijeka: In-Teh, 2010, v. , p. -.Michigan Department of Labor & Economic Growth, Bureau of Labor Market Information andStrategic Initiatives. Innovation Indicators Report to the Council for Labor and EconomicGrowth. August, 2007.S. Wolfram: A new kind of science. Published by Wolfram Media (2002).ROCHA, Elisa Maria Pinto da.; FERREIRA, Marta Arajo Tavares. Analysis of BrazilianTechnological Innovation Indicators: Comparing a Group of Privatized Companies and aGeneral Group of Companies. Ci. Inf., Braslia, v. 30, n. 2, May/August. 2001. pp. 64-69.THE OSLO MANUAL. Guidelines for Collecting and Interpreting Innovation Data. 3 ed.2005.Wiliams, H.P. Model building in mathematical programming. John Wiley & Sons, 1997. Jones Albuquerque

Modeling The Tipping Point of aInnovation Process using CellularAutomata


27/05/201120:34:38

Transportation Problems can be thought of as either dynamic or static, but not both. Thedifference between a dynamic and a static problem is discussed quite well in literature.The key feature of a dynamic problem is that it has limited access to information about theproblem. In the case of the Vehicle Routing Problem it may know some customer demandsa-priori and be able to plan a route based on this information, however at run time moreinformation will become available changing the problem domain.The distinction has to be made that simply changing customer demands during runtime doesnot imply a dynamic environment unless these demands are kept hidden from the algorithmuntil runtime.These problems can be formulated asShortest Path ProblemVehicle Routing ProblemTravelling Salesman ProblemMulti-commodity freight transportationDynamic Transportation Problems

There exist many techniques for solving these kind of problems. To study practical feasibleones is the goal of this proposal.

1. Euler, L., Solutio problematis ad geometriam situs pertinentis. Commentariiacademiae scientiarum Petropolitanae, 1741: p. 128-140.2. Bianchi, L., Notes on Dynamic Vehicle Routing - The State of the Art. 2000,

IDSIA.3. Dijkstra, E.W., A note on two problems in connexion with graphs. NumerischeMathematik, 1959. 1: p. 269-271.4. Hart, P.E., N.J. Nilsson, and B. Raphael, A Formal Basis for the HeuristicDetermination of Minimum Cost Paths in Graphs. IEEE Trans. on SystemsScience and Cybernetics, 1968. SSC-4(2): p. 100-107.5. Bellman, R., Dynamic Programming. 1957, Princeton, New Jersey: PrincetonUniversity Press.6. Daz, B.D. VRP Web. 2005 [cited; Available from: http://neo.lcc.uma.es/radiaeb/WebVRP/.7. Gambardella, L.M., et al. Ant Colony Optimization for vehicle routing inadvanced logistic systems. in MAS 2003 - International Workshop onModelling and Applied Simulation. 2003. Bergeggi, Italy.8. Reinelt, G. TSPLIB95. [cited; Available from:http://www.iwr.uniheidelberg.de/iwr/comopt/soft/TSPLIB95/TSPLIB95.html.9. Crainic, T.G., A Survey of Optimisation Models for Long-Haul FreightTransportation, in Handbook of Transportation Science, R.W. Hall, Editor.2002, Kluwer.10. Barnhart, C. Recent Research Projects. 2005 [cited;http://web.mit.edu/ctl/www/research/research95-96/re9596_logistics.html].

Jones Albuquerque e SilvanaBocanegra

Modeling Problems in Transportand Logistics


7/24/2019 Tema de Projetoojeto

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Indicao de datae hora TEMA Referncias Bibliogrficas Norteadoras

Orientador e Co-Orientador (sehouver) Ttulo rea do Conhecimento

29/05/201114:43:11

Na literatura, de forma geral, os modelos de previso utilizam apenas a srie sob estudopara realizar a previso, focando apenas nas informaes contidas no conjunto de dados deinteresse. Desde modelos clssicos, como a tcnica estatstica de Box & Jenkins (modeloARIMA) at mtodos hbridos da inteligncia artificial, que combi nam duas ou mais tcnicasinteligentes, como Redes Neurais Artificiais, Algoritmos Genticos, Particle SwarmOptimization, Colnia de Formigas, etc.Contudo, pode-se usar informaes secundrias para aumentar a acurcia da previso.Observando a srie de resduos ou srie de erros, que gerada pela diferena entre osdados reais e a previso de um modelo, pode-se constatar que nem todas informaesforam capturadas pelo preditor. Dessa forma, a Teoria da Perturbao que um mtodomatemtico usado para encontrar uma soluo aproximada para um dado problema podeser aplicada. O intuito desse projeto atravs desse conceito aplicar sucessivas correesna previso original da srie, melhorando a acurcia da previso final.

Paulo Salgado Gomes de Mattos Neto. Teoria da Perturbao em Sistemas HbridosInteligentes para Previso de Sries Temporais. 2008. Dissertao (Mestrado em Cinciasda Computao) - Universidade Federal de Pernambuco, Conselho Nacional deDesenvolvimento Cientfico e Tecnolgico. Co-Orientador: Tiago Alessandro EspinolaFerreira.

G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis:Forecasting and Control, 3rd ed. New Jersey: Prentice Hall, 2008.

Paulo S. G. de Mattos Neto, Aranildo Rodrigues Lima Junior, Tiago A. E. Ferreira. Timeseries forecasting using a perturbative intelligent system. In Proceedings of GECCO'2009.pp.1477-1478

MATTOS, P. S. G. ; Rodrigues, Aranildo L. J. ; FERREIRA, T. A. E. ; CAVALCANTI, G. D.C. . An Intelligent Perturbative Approach for the Time Series Forecasting Problem. In: IEEEWorld Congress On Computational Intelligence, 2010, Barcelona. IEEE World Congress OnComputational Intelligence, 2010.

MATTOS NETO, P. S. G. DE ; LIMA JUNIOR, A. R. ; FERREIRA, T. A. E. ; Vasconcelos,Germano C. . Sistema Hbrido Inteligente Combinado com a Teoria da Perturbao para aPreviso de Sries Temporais. Anais do XXIX Congresso da Sociedade Brasileira deComputao (XXIX CSBC) - VII Encontro Nacional de Inteligncia Artificial (VII ENIA), 2009.p. 759-768.

Tiago Alessandro EspnolaFerreira / Paulo Salgado Gomesde Mattos Neto

Teoria da Perturbao Aplicado aSistemas Inteligentes paraPreviso de Sries Temporais

10303006 - METODOLOGIA ETCNICAS DA COMPUTAO

30/05/201114:07:33

O ramo que tradicionalmente aborda o problema de previso de sries temporais aEstatstica. No entanto, nas ltimas dcadas cada vez mais sistemas de previso sodesenvolvidos baseados em Inteligncia Artificial. O grande diferencial da utilizao de taismtodos a no-linearidade desses modelos, caracterstica importante para realizar uma

previso satisfatria de sries temporais complexas.Um grande desafio na disciplina de sries temporais a previso da volatilidade de ativosfinanceiros. A volatilidade pode ser definida como a varincia de um determinado ativo, ouao num determinado tempo. Quanto maior a flutuao no preo de uma ao, maior suavolatilidade. Desse modo, ter um sistema capaz de prever variaes no mercado pode serde grande valia, tanto para o mundo acadmico com a descrio da dinmica de sistemascomplexos, como para o mercado, j que interessante do ponto de vista prtico.

A idia desse projeto estudar e desenvolver mtodos inteligentes capazes de extrairinformaes e/ou padres temporais e gerar previses, de forma que a taxa de acerto dosistema seja melhorada

Box, Jenkins and Reinsel. Time Series Analysis: Forecasting and Control.Wiley. 2008.

MATTOS, P. S. G. ; Rodrigues, Aranildo L. J. ; FERREIRA, T. A. E. ; CAVALCANTI, G. D.

C. . An Intelligent Perturbative Approach for the Time Series Forecasting Problem. In: IEEEWorld Congress On Computational Intelligence, 2010, Barcelona. IEEE World Congress OnComputational Intelligence, 2010.

MATTOS, P. S. G. ; PETRY, Gustavo Galvo ; Rodrigues, Aranildo L. J. ; FERREIRA, T. A.E. . Combining Artificial Neural Network and Particle Swarm System for Time SeriesForecasting. In: International Joint Conference on Neural Networks 2009, 2009, Atlanta.

Ferreira, Tiago A. E. ; Vasconcelos, Germano C. ; Adeodato, Paulo J. L. . A New IntelligentSystem Methodology for Time Series Forecasting with Artificial Neural Networks. NeuralProcessing Letters, v. 28, p. 113-129, 2008.


Sistemas Inteligentes aplicados aPreviso da Volatilidade doMercado


30/05/201114:23:58

O Processamento de Sinais consiste na anlise e/ou modificao de sinais de forma aextrair informaes dos mesmos e/ou torn-los mais apropriados para alguma aplicaoespecfica.

O processamento de sinais assistido por computador (PSAC) uma forma digital de realiz-lo. O objeto de interesse do PSAC pode incluir sons, imagens, sries temporais, sinais detelecomunicaes, como sinais de rdio e muitos outros.

Exemplos de projetos de processamento de sinais incluem anlise a automtica de imagens(ex.: imagens de satlite, ultrasonografias), a compresso de udio, a anlise de informaomusical e a aplicao de efeitos a vdeos.

1. Understanding Digital Signal Processing; Richard G. Lyons.

2. The Scientist and Engineer's and Guide to Digital Signal Processing; Steven W. Smith.

3. Digital Signal Processing With Examples in Mat lab; Samuel D Stearns. Giordano CabralProcessamento de SinaisAssistido por Computador


30/05/201114:24:52

O Processamento de Sinais consiste na anlise e/ou modificao de sinais de forma aextrair informaes dos mesmos e/ou torn-los mais apropriados para alguma aplicaoespecfica.

O processamento de sinais assistido por computador (PSAC) uma forma digital de realiz-lo. O objeto de interesse do PSAC pode incluir sons, imagens, sries temporais, sinais detelecomunicaes, como sinais de rdio e muitos outros.

Exemplos de projetos de processamento de sinais incluem anlise a automtica de imagens(ex.: imagens de satlite, ultrasonografias), a compresso de udio, a anlise de informaomusical e a aplicao de efeitos a vdeos.

1. Understanding Digital Signal Processing; Richard G. Lyons.

2. The Scientist and Engineer's and Guide to Digital Signal Processing; Steven W. Smith.

3. Digital Signal Processing With Examples in Mat lab; Samuel D Stearns. Giordano CabralProcessamento de SinaisAssistido por Computador


30/05/201115:12:34

A Economia a cincia que tradicionalmente tem a tarefa de desenvolv er abordagens paradescrever a dinmica do mercado de capitais. Entretanto, nas ltimas dcadas um novoramo de pesquisa tem sido desenvolvido, a Econofsica. Esse campo de pesquisainterdisciplinar tem como objetivo o estudo do mercado financeiro e os problemas naeconomia. Para isso, mtodos e teorias baseadas na estatstica, probabilidade, teoria dainformao e fsica estatstica so aplicadas para explicar a dinmica do mercado.Outro atrativo para essa rea pode ser a disponibilidade de grandes quantidades de dadosfinanceiros que as cincias econmicas dispem, facilitando diversas anlises empricas queso bastante utilizadas. Contudo, outras abordagens tambm so bastante encorajadas,como a terica, j que por ser tratar de um novo ramo da cincia, vrias h um vasto campopara o desenvolvimento e proposio de novas teorias. Como o desenvolvimento de agentesinteligentes para simulao de ambientes financeiros e operaes do mercado.Desse modo, esse projeto tem o intuito de desenvolver novas abordagens prticas e/outericas para a anlise do mercado de aes.

R. N. Stanley, H. Eugene; Mantegna. An Introduction to Econophysics: Correlations eComplexity in Finance. Cambridge Univ Press Usa, first edition, 2000.

P. S. G. Mattos, D. Silva, T. Ferreira and G. Cavalcanti. Market Volatility Modelling for ShortTimeWindow. Physica A: Statistical Mechanics and its Applications, 2011.

K. Matia, M. Pal, H. Salunkay and H. E. Stanley. Scale-Dependent Price Fluctuations forthe Indian Stock Market. Europhysics Letters, vol. 66, no. 6, pp. 909914, 2004.

L. Amaral, P. Cizeau, P. Gopikrishnan, Y. Liu, M. Meyer, C.-K. Peng,H. Stanley, Econophysics: can statistical physics contribute to the scienceof economics?, Computer Physics Communications 121-122 (1999) 145 152. Proceedings of the Europhysics Conference on Computational PhysicsCCP 1998.

A. Dragulescu, V. M. Yakovenko, Statistical mechanic s of money, TheEuropean Physical Journal B 17 (2000) 723 729.



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A rea de Computao Musical compreende pesquisa cientfica, tec nolgica e artstica nasreas de composio algortmica, anlise/sntese de som, acstica musical, anlise musicalassistida por computador, ferramentas para a interao musical, composio musicalassistida por computador e multimdia.

Este tema trata do uso de sistemas hbridos inteligentes, aplicados a problemas deComputao Musical.

1. Understanding Music with AI: Perspectives on Music Cognition, Mira Balaban, KernelEbcioglu, Otto E. Laske, The MIT Press, 2004.

2. Hidden Structure: Music Analysis Using Computers. Cope, David. Madison, WI: A-REditions, 2008.

3. The Computer Music Tutorial, Curtis Roads, MIT Press, 1996.

4. Readings in Music and Artificial Intelligence, Harwood Academic publishers, 2000. Giordano CabralSistemas Inteligentes para aCompuo Musical

10303006 METODOLOGIA ETCNICAS DA COMPUTA

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Due to the emergence of the Web and its permanent growth, new database researchsubjects have emerged. One of such subjects is Web Data Integration, which refers toproviding users with a uniform view over a set of heterogeneous, distributed and autonomousWeb data sources. Data Integration settings concern, for instance, mediator-basedintegration systems and P2P ones (named Peer Data Management Systems). In thesesettings, users pose their queries without having to spend time in searching the set ofdistributed data sources. To help matters, research on Data Integration environments hasconsidered the use of semantic knowledge in the form of ontologies and contextualinformation. For instance, ontologies can be used to solve the heterogeneities between thedata sources, while contextual information allows the system to deal with information that isacquired dynamically during the execution of a given query.There is a growing need for data integration in several environments: Web InformationSystems (WISs), Peer Data Management Systems (PDMSs), Enterprise InformationIntegration (EII), Personal Information Management (PIM), to say some. In this sense, even ifa lot of work has already been done it is important to highlight some future (or ongoing)trends in data integration systems (in all mentioned environments): data sources description: are metadata enough? entity resolution: what data objects refer to the same real-world entity? automatic generation and evolution of schema mappings or correspondences: where andwhen must the user intervene? reformulation and query answering: what are the query plans? quality criteria: how to measure data and information quality? How reasoning mechanisms provided by contextual information may enhance DIprocesses?

[Batista et al. 2003] Batista, M. C., Lscio B. F. and Salgado, A. C. Optimizing Access in aData Integration System with Caching and Materialized Data. In Proceedings of 5thInternational Conference on Enterprise Information Systems (ICEIS), Angers, France, 2003.[Halevy et al. 2006] Halevy, A., Rajaraman, A., Ordille, J. Data Integration: the TeenageYears, In Proc. of the 25th International Conference on Very Large Data Bases (VLDB),Seoul, Korea, 2006, p.9-16[Souza et al 2009] Souza D., Arruda T., Salgado A. C., Tedesco P., and Kedad, Z.: UsingSemantics to Enhance Query Reformulation in Dynamic Environments. In: 13th EastEuropean Conference on Advances in Databases and Information Systems (ADBIS09),Riga, Latvia, pp. 78-92 (2009).[Batista 2008] Batista, M.C. Schema Quality Analysis in a Data Integration System. PHDThesis, Federal University of Pernambuco, Brazil, 2008.[Pires 2009] Pires, C. E. 2009. Ontology-based Clustering in a Peer Data ManagementSystem. Ph.D. Thesis, Federal University of Pernambuco (UFPE/CIn). Recife, PE, Brazil. Maria da Conceio Moraes

Batista Data Integration Environments10304002 SISTEMA DECOMPUTAO

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Social machines are defined as tuples of input, output, processes, constraints, state,requests and responses. Apart from defining the machines themselves, there is an algebra todefine a set of connectors and conditionals that can be used to describe the interactionsbetween any number of machines in a multitude of ways, as a means to represent realmachines interacting in the real web, such as Twitter, Twitter running on top of AmazonAWS, mashups built using Twitter and, obviously, other soci almachines.In general, a Social Machine (SM) represents aconnectable entity containing an internal processingunit and a wrapper interface that waits for requestsfrom and replies (with responses) to other socialmachines. Its processing unit receives inputs,produces outputs and has states; and itsconnections define intermittent or permanentrelationships with other SMs, connections which areestablished under specific sets of constraints.In this research we may investigate the needs and use of data in social machines networks inthe Web, in order to propose data management resources to improve the behaviour of thesocial machines.

[Meira et al 2010] Meira, Silvio R. L.; Buregio, Vanilson A. A.; Nascimento, Leandro M.; deFigueiredo, Elaine G. M.; Neto, Misael; Encarnao, Bruno P.; Garcia, Vincius. TheEmergin Web of Social Machines. Available at http://arxiv.org/abs/1010.3045.[Bizer et al. 2009] Christian Bizer, Tom Heath and Tim Berners-Lee: Linked Data - The StorySo Far.International Journal on Semantic Web and Information Systems, Vol. 5(3), 2009, Pages 1-22. DOI:10.4018/jswis.2009081901.[Eller & Touponce 2004] Eller , J. R., Touponce, W. F. Ray Bradbury: the life of fiction. KentState UniversityPress, 2004, p.22.[Fuglsang & Srensen, 2006] Fuglsang, M; Srensen, B. M. Deleuze and the social.Edinburgh UniversityPress, 2006, p. 80.[Google Maps, 2010] Google Maps. Avaiable at http://maps.google.com.br/, Accessed inSeptember, 2010.[Guattari, 1995] Guattari, F. Chaosmosis: an ethico-aesthetic paradigm. Indiana UniversityPress, 1995, p.9.[Halb et al. 2008] W. Halb, Y. Raimond, and M. Hausenblas. Building Linked Data For BothHumans andMachines. In WWW 2008 Workshop: Linked Data on the Web (LDOW2008), Beijing, China,2008.http://bit.ly/b8xGg3[Hayes, 2008] Hayes, B. Cloud computing. In: Communications of the ACM, vol. 51, 2008,p.9.Paper still in ; new versions will be announced at twitter.com/srlm, #SocMac. 19[Hornyak, 2006] Hornyak, T. N. Loving the machine: the art and science of Japanese robots.KodanshaInternational, 2006, p. 21.[Hitzler et al. 2009] P. Hitzler, M. Kroetzsch, S. Rudolph, A. McGettrick, and J. Impagliazzo,Foundations ofSemantic Web Technologies, Chapman and Hall, 2009.[Hwang et al. 2009] Junseok Hwang, Jrn Altmann, Kibae Kim, The structural evolution ofthe Web 2.0

service network, Online Information Review, Vol. 33 Iss: 6, pp. 1040 1057 (2009).[Kroker & Kroker, 2008] Kroker, A.; Kroker, M. Critical digital studies: a reader. University ofToronto Press,2008, p. 454.[Milanovic , 2006] Milanovic, N. Contract-based Web Service Composition. Ph.D. Thesis.Berlin, 2006.[Monroe, 2001] Monroe, R. T. Capturing Software Architecture Design Expertise withArmani, Version 2.3,School of Computer Science - Carnegie Mellon University, Pittsburgh, 2001[Open Dining Network, 2010] Open Dining Network Food Ordering API. Available athttp://bit.ly/b6SjXP,Accessed in September, 2010.[Patton, 2000] Patton, P. Deleuze and the Political. Routledge, 2000, pp. 88-108.[Programmable Web, 2010] ProgrammableWeb - Mashups, APIs, and the Web as Platform.Available athttp://www.programmableweb.com/, Accessed in September 2010.[Roush, 2005] Roush, W. Social Machines - Computing means connecting. MIT TechnologyReview, August,2005.[Turing, 1936] Turing, A. On computable numbers, with an application to theEntscheidungsproblem.Proceedings of the London Mathematical, Vol. 38, pp. 173-198, 1936.[TwitP, 2010] The Twitpocalypse is Near: Will Your Twitter Client Survive? Available athttp://bit.ly/9YLIl7,Accessed in September, 2010.[Twitter, 2010] Twitter - Discover whats happening right now, anywhere in the world.Available athttp://twitter.com/, Accessed in September, 2010.[Yu & Woodard 2009] Yu, S.; Woodard, C. Innovation in the programmable web:Characterizing the mashupecosystem. Service-Oriented Computing - ICSOC 2008 Workshops, pp. 136147, 2009,Springer. (alsoavailable at http://bit.ly/9pLMoI).

Maria da Conceio MoraesBatista

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