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IEEE International Conference on systems, Man, and Cybernetics

作者： Walkiria K. Resende Renato A. Nascimento Carolina R. Xavier Iara F. Lopes Cristianc N. Nobre Department of Computer Science Universidade Federal de Sao Joao del Rei - UFSJ Department of Computer Science Pontificia Universidade Catolica de Minas Gerais PUC/MG Civil Engineering Program COPPE - UFRJ

ISBN: (纸本)9781467317139

In Bio informatics, the prediction of protein function is considered a very important task but also difficult. Using a set of enzymes represented by Hydrolase, Isomerase, Ligase, Lyase, Transferase and Oxidoreductase classes, previously used by Dobson et ah, this paper proposes a self-learning process able to predict their classes, based on their primary and secondary structures, through a Support Vector Machine (SVM) classifier and genetic algorithm. An SVM can he characterized as a supervised machine learning algorithm capable of resolving linear and non-linear classification problems. During the learning process, both the training data and the corresponding output are presented to the SVM to allow its parameters to he adjusted. This study utilized genetic algorithms - optimization heuristics often used to estimate parameters - to adjust the main parameters of the classifier such as kernel function type and parameter C, which provides the relationship between the training error and the margin of separation between classes. In this specific prediction problem, the results indicate that the best function is an RBF where width is 6.1 and C is 6.9. Using these parameters, the classifier obtains an average accuracy of 79.74%.

关键词： Component: Proteins Prediction Support Vector Machine Genetic Algorithms protein structure function Support Vector Network Genetic Algorithm (GA) Classifiers Learning processes secondary structures

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A solution to the state estimation problem of systems with unknown inputs

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Recent Patents on Mechanical engineering 2012年第2期5卷 102-112页

作者： Bolandhemmat, Hamidreza Clark, Christopher Golnaraghi, Farid MicroPilot Inc 72067 Road 8E Sturgeon Rd. Stony Mountain Manitoba R0C 3A0 Canada Computer Science Program Cal Poly State University San Luis Obispo CA 93407 United States Mechatronics Systems Engineering Simon Fraser University Surrey BC Canada

A solution to the state estimation problem of systems with unmeasurable non-zero mean inputs/disturbances, which do not satisfy the disturbance decoupling conditions, is given using the Kalman filtering and Bayesian estimation theory. The proposed estimation algorithm, named Supervisory Kalman Filter (SKF), consists of a Kalman filter with an extra update step which is inspired by the particle filtering technique. The extra step, called supervisory layer, numerically solves the measurement equations for the portion of the state vector that cannot be estimated by the Kalman filter. First, it produces N randomly generated state vectors, the particles, which are distributed based on the Kalman filter's last updated estimate. Then, the estimated measurement vector associated with each particle is compared to the actual measurement vector to identify the particle's probability to be a solution. Finally, a so-called resampling stage is implemented to refine the particles with higher likelihoods. The effectiveness of the SKF is demonstrated by comparing its estimation performance with that of the standard Kalman Filter and the particle filter for a vehicle state estimation problem. The estimation results confirm that the SKF precisely estimates those states of the vehicle that cannot be estimated by either the Kalman filter or the particle filter, regardless of the unknown disturbances from the road. The filtering methodology offered in the article has a potential to improve performance of the systems presented in the patents WO2011115960, WO2010024751, US8073528, and US20110299730. © 2012 Bentham science Publishers.

关键词： State estimation

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Multiobjective optimization for project portfolio selection 12

Multiobjective optimization for project portfolio selection

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14th International Conference on Genetic and Evolutionary Computation Companion, GECCO'12 Companion

作者： De Barros, Márcio O. Costa, Hélio R. Figueiredo, Fábio V. Da Rocha, Ana Regina C. Post-graduate Information Systems Program - PPGI UNIRIO Av. Pasteur 458 Urca - Rio de Janeiro RJ Brazil COPPE UFRJ System Engineering and Computer Science Department Caixa Postal: 68511 CEP: 21945-970 - Rio de Janeiro Brazil CCA-RJ Brazilian Air Force Ponta do Galeão s/no Ilha do Governador CEP: 21941-510 - Rio de Janeiro Brazil

ISBN: (纸本)9781450311786

This paper proposes a multiobjective heuristic search approach to support a project portfolio selection technique on scenarios with a large number of candidate projects. The original formulation for the technique requires analyzing all combinations of candidate projects, which is unfeasible when more than a few alternatives are available. We have used a multiobjective genetic algorithm to partially explore the search space of project combinations and select the most effective ones. We present an experimental study based on four project selection problems that compares the results found by the genetic algorithm to those yielded by a non-systematic search procedure. Results show evidence that the project selection technique can be used in large-scale scenarios and that GA presents better results than simpler search strategy. Copyright is held by the author/owner(s).

关键词： Multiobjective optimization

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Low-frequency noise in "graphene-like" exfoliated thin films of topological insulators

Low-frequency noise in "graphene-like" exfoliated thin films...

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2011 MRS Spring Meeting

作者： Hossain, M.Z. Rumyantsev, S.L. Shahil, K.M.F. Teweldebrhan, D. Shur, M. Balandin, A.A. Nano-Device Laboratory Department of Electrical Engineering and Materials Science and Engineering Program University of California Riverside CA 92521 United States Department of Electrical Computer and Systems Engineering Center for Integrated Electronics Rensselaer Polytechnic Institute Troy NY 12180 United States Ioffe Institute Russian Academy of Sciences St. Petersburg 194021 Russia

ISBN: (纸本)9781605113210

We report results of the study of the low-frequency noise in thin films of bismuth selenide topological insulators, which were mechanically exfoliated from bulk crystals via "graphene-like" procedures. From the resistance dependence on the film thickness, it was established that the surface conduction contributions to electron transport were dominant. It was found that the current fluctuations have the noise spectral density S1 ∞ l/f (where f is the frequency) for the frequency range up to 10 kHz. The obtained noise data are important for transport experiments with topological insulators and for any proposed device applications of these materials. © 2011 Materials Research Society.

关键词： Thin films

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Accelerating the artificial bee colony algorithm by hardware parallel implementations

Accelerating the artificial bee colony algorithm by hardware...

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2012 IEEE 3rd Latin American Symposium on Circuits and systems, LASCAS 2012

作者： Muñoz, Daniel M. Llanos, Carlos H. Coelho, Leandro Dos S. Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781467312080

Artificial bee colony (ABC) is an optimization algorithm inspired on the intelligent behavior of honey bee swarms. It is suitable to be applied when mathematical techniques are impractical or provide suboptimal solutions. As a population-based algorithm, the ABC suffers on large execution times specifically for embedded optimization problems with computational limitations. For that we propose a hardware parallel architecture of the opposition-based ABC algorithm (HPOABC) that facilitates the implementation in Field programmable Gate Arrays (FPGAs). Numerical simulations using four well-known benchmark problems demonstrate that the opposition-based approach allows the algorithm to improve its functionality, preserving the swarm diversity. Additionally, synthesis results point outs that the HPOABC architecture is effectively mapped in hardware and is suitable for embedded applications. © 2012 IEEE.

关键词： Global optimization

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Abstracts from the 3rd International Genomic Medicine Conference (3rd IGMC 2015) : Jeddah, Kingdom of Saudi Arabia. 30 November - 3 December 2015

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BMC genomics 2016年第6期17 Suppl 6卷 487页

作者： Jerry W. Shay Noriko Homma Ruyun Zhou Muhammad Imran Naseer Adeel G. Chaudhary Mohammed Al-Qahtani Nobutaka Hirokawa Maryam Goudarzi Albert J. Fornace Saleh Baeesa Deema Hussain Mohammed Bangash Fahad Alghamdi Hans-Juergen Schulten Angel Carracedo Ishaq Khan Hanadi Qashqari Nawal Madkhali Mohamad Saka Kulvinder S. Saini Awatif Jamal Jaudah Al-Maghrabi Adel Abuzenadah Adeel Chaudhary Mohammed Al Qahtani Ghazi Damanhouri Heba Alkhatabi Anne Goodeve Laura Crookes Nikolas Niksic Nicholas Beauchamp Adel M. Abuzenadah Jim Vaught Bruce Budowle Mourad Assidi Abdelbaset Buhmeida Leena Merdad Sudhir Kumar Sayaka Miura Karen Gomez Mahmood Rasool Ahmed Rebai Sajjad Karim Hend F. Nour Eldin Heba Abusamra Elham M. Alhathli Nada Salem Mohammed H. Al-Qahtani Hossam Faheem Ashok Agarwa Eberhard Nieschlag Joachim Wistuba Oliver S. Damm Mohd A. Beg Taha A. Abdel-Meguid Hisham A. Mosli Osama S. Bajouh Serdar Coskun Muhammad Abu-Elmagd Ashraf Dallol Sahar Hakamy Wejdan Al-Qahtani Asia Al-Harbi Shireen Hussain Burak Ozkosem Rick DuBois Safia S. Messaoudi Maryam T. Dandana Touhami Mahjoub Wassim Y. Almawi S. Abdalla M. Nabil Al-Aama Asmaa Elzawahry Tsuyoshi Takahashi Sachiyo Mimaki Eisaku Furukawa Rie Nakatsuka Isao Kurosaka Takahiko Nishigaki Hiromi Nakamura Satoshi Serada Tetsuji Naka Seiichi Hirota Tatsuhiro Shibata Katsuya Tsuchihara Toshirou Nishida Mamoru Kato Sajid Mehmood Naeem Mahmood Ashraf Awais Asif Muhammad Bilal Malik Siddique Mehmood Aadil Hussain Qazi Mohammad Sajid Jamal Mughees Uddin Siddiqui Mohammad A. Alzohairy Mohammad A. Al Karaawi Taoufik Nedjadi Heba Al-Khattabi Adel Al-Ammari Ahmed Al-Sayyad Hédia Zitouni Nozha Raguema Marwa Ben Ali Wided Malah Raja Lfalah Wassim Almawi Mohammed Elanbari Andrey Ptitsyn Sana Mahjoub Rabeb El Ghali Bechir Achour Nidhal Ben Amor Brahim N’siri Hamid Morjani Esam Azhar Vera Chayeb Maryam Dendena Hedia Zitouni Khedija Zouari-Limayem Bassem Refaat Ahmed M. Ashshi Sarah A. Batwa Hazem Ramadan Amal Awad Ahmed Ateya Adel Galal Ahmed El-Shemi Ahmad Ashshi Mohammed Basalamah Youjin Na Cha University of Texas Southwestern Medical Center Dallas USA Department of Cell Biology and Anatomy Graduate School of Medicine the University of Tokyo Tokyo Japan Center of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Department of Biochemistry and Molecular & Cellular Biology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Oncology Lombardi Comprehensive Cancer Center Georgetown University Washington USA Department of Radiation Medicine Lombardi Comprehensive Cancer Center Georgetown University Washington USA Center of Excellence in Genomic Medicine Research (CEGMR) King Abdulaziz University Jeddah Kingdom of Saudi Arabia King Fahd Medical Research Centre King Abdulaziz University Jeddah Saudi Arabia Centre of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Sheffield Diagnostic Genetic Service Sheffield Children’s NHS Foundation Trust Western Bank Sheffield UK Center of Innovations in Personalized Medicine King Abdulaziz University Jeddah Saudi Arabia President ISBER Editor-in-Chief Biopreservation & Biobanking ᅟ USA Institute of Applied Genetics Department of Molecular and Medical Genetics University of North Texas Health Science Center Fort Worth USA Department of Dental Public Health Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Institute for Genomics and Evolutionary Medicine Temple University Philadelphia USA Center of Excellence in Genomic Medicine King Abdulaziz University Jeddah Kingdom of Saudi Arabia Laboratory of Molecular and Cellular Screening Processes (Bioinformatics Group) Centre of Biotechnology of Sfax Sfax Tunisia Fujitsu Technology Solutions International ᅟ ᅟ Case Western Reserve University School of Medicine and Lerner College of Medicine Cleveland Clinic Foundation Cleveland USA American Center for Reproductive Medicine Andrology Center Cleveland USA Center for Reproductive Medicine and

O1 Regulation of genes by telomere length over long distances Jerry W. Shay O2 The microtubule destabilizer KIF2A regulates the postnatal establishment of neuronal circuits in addition to prenatal cell survival, cell migration, and axon elongation, and its loss leading to malformation of cortical development and severe epilepsy Noriko Homma, Ruyun Zhou, Muhammad Imran Naseer, Adeel G. Chaudhary, Mohammed Al-Qahtani, Nobutaka Hirokawa O3 Integration of metagenomics and metabolomics in gut microbiome research Maryam Goudarzi, Albert J. Fornace Jr. O4 A unique integrated system to discern pathogenesis of central nervous system tumors Saleh Baeesa, Deema Hussain, Mohammed Bangash, Fahad Alghamdi, Hans-Juergen Schulten, Angel Carracedo, Ishaq Khan, Hanadi Qashqari, Nawal Madkhali, Mohamad Saka, Kulvinder S. Saini, Awatif Jamal, Jaudah Al-Maghrabi, Adel Abuzenadah, Adeel Chaudhary, Mohammed Al Qahtani, Ghazi Damanhouri O5 RPL27A is a target of miR-595 and deficiency contributes to ribosomal dysgenesis Heba Alkhatabi O6 Next generation DNA sequencing panels for haemostatic and platelet disorders and for Fanconi anaemia in routine diagnostic service Anne Goodeve, Laura Crookes, Nikolas Niksic, Nicholas Beauchamp O7 Targeted sequencing panels and their utilization in personalized medicine Adel M. Abuzenadah O8 International biobanking in the era of precision medicine Jim Vaught O9 Biobank and biodata for clinical and forensic applications Bruce Budowle, Mourad Assidi, Abdelbaset Buhmeida O10 Tissue microarray technique: a powerful adjunct tool for molecular profiling of solid tumors Jaudah Al-Maghrabi O11 The CEGMR biobanking unit: achievements, challenges and future plans Abdelbaset Buhmeida, Mourad Assidi, Leena Merdad O12 Phylomedicine of tumors Sudhir Kumar, Sayaka Miura, Karen Gomez O13 Clinical implementation of pharmacogenomics for colorectal cancer treatment Angel Carracedo, Mahmood Rasool O14 From association to causality: translation of GWAS findings for genomic me

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PatternLab: From mass spectra to label-free differential shotgun proteomics

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Current Protocols in Bioinformatics 2012年第SUPPL.40期40卷

作者： Carvalho, Paulo C. Fischer, Juliana S.G. Xu, Tao Yates III, John R. Barbosa, Valmir C. Carlos Chagas Institute-Fiocruz Parańa Brazil Department of Cell Biology The Scripps Research Institute La Jolla CA United States Systems Engineering and Computer Science Program COPPE Federal University of Rio de Janeiro Brazil

PatternLab for proteomics is a self-contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine;modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi-labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches;and modules with novel features, such as SEProQ for enabling labelfree quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them. © 2012 by John Wiley & Sons, Inc.

关键词： Dniaf filtering PSMs Protein identification Quantitative proteomics Semi-labeled decoy approach

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Selective gas sensing with a single graphene-on-silicon transistor

Selective gas sensing with a single graphene-on-silicon tran...

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2012 17th IEEE Silicon Nanoelectronics Workshop, SNW 2012

作者： Balandin, A.A. Rumyantsev, S. Liu, G. Shur, M.S. Potyrailo, R.A. Department of Electrical Engineering and Materials Science and Engineering Program Bourns College of Engineering University of California - Riverside Riverside CA 92521 United States Center for Integrated Electronics Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 United States Ioffe Physical-Technical Institute Russian Academy of Sciences St. Petersburg Russia Chemistry and Chemical Engineering GE Global Research Niskayuna NY 12309 United States

ISBN: (纸本)9781467309943

The low-frequency 1/f noise in graphene transistors has been studied extensively owing to the proposed graphene applications in analog devices and communication systems [1-5]. The studies were motivated by the fact that the low-frequency noise can be up-converted by device nonlinearity and contribute to the phase noise of the system. Similarly, the sensor sensitivity is often limited by the electronic low-frequency noise. Therefore, noise is usually considered as one of the main limiting factors for the device or overall system operation. However, the electronic noise spectrum itself can be used as a sensing parameter increasing the sensor sensitivity and selectivity. Here, we show that vapors of different chemicals produce distinguishably different effects on the low-frequency noise spectra of the graphene-on-Si transistor. Our study showed that some gases change the electrical resistance of pristine graphene devices without changing their low-frequency noise spectra while other gases modify the noise spectra by inducing Lorentzian components with distinctive features. The characteristic corner frequency fC of the Lorentzian noise bulges in graphene devices is different for different chemicals and varies from fC=10 - 20 Hz for tetrahydrofuran to f C=1300 - 1600 Hz for chloroform. We tested the selected set of chemicals vapors on different graphene device samples and alternated different vapors for the same samples. The obtained results indicate that 1/f noise in combination with other sensing parameters can allow one to achieve the selective gas sensing with a single pristine graphene transistor. Our method of gas sensing with graphene does not require graphene surface functionalization or fabrication of an array of the devices with each tuned to a certain chemical. The observation of the Lorentzian components in the vapor-exposed graphene can also help in developing an accurate theoretical description of the noise mechanism in graphene. © 2012 IEEE.

关键词： Graphene

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud Computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Applying MVC to Evolutionary Acquisition IRPM

Applying MVC to Evolutionary Acquisition IRPM

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2012 International Conference on Smart Grid systems(ICSGS 2012)

作者： Patrick Letouze Mariwaldo G. Caetano Joāo Y. Ishihara David Prata George Brito UFT-Federal University of Tocantins Brazil:Department of Computer Science UnB-University of Brasilia Brazil:Department of Electrical EngineeringPGEA-Program of Post-Graduation in Electronic and Automation Systems Engineering

This work presents a combination of the Model-View-Controller pattern with the Evolutionary Acquisition Interdisciplinary Research Project Management for web services *** intends to promote an increase in productivity and to facilitate interdisciplinary web services *** consists of an independent database for users' feedback that together with technological opportunities and evolving threats considerations may start a new release of the system allowing a dynamic evolution through acquiring new features or correcting errors,but the new release decision is not *** other words,web service evolution is accomplished adding requirements analysis into the Model-View-Controller pattern,which is incorporated into Interdisciplinary Research Project Management.

关键词： Evolutionary acquisition,IRPM,MVC,project management,software engineering,web services.

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