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International Conference on Complex, Intelligent and Software Intensive systems, CISIS

作者： Diego F. P. De Souza Felipe M.G. Franca Priscila M.V. Lima Syst. Eng. & Comput. Sci. Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro RJ BR System Engineering and Computer Science Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Inst. Tercio Pacitti (NCE) Univ. Fed. do Rio de Janeiro Janeiro Brazil

Music tracking is a useful technique for many music related tasks. Some applications include evaluating musicians performance, automatic score page turning and syncing music lyrics. In this work, we describe a WiSARD-based system for real-time music tracking and evaluate the performance of the corresponding implementation. Given any audio example, the system is capable of recognizing which point of the original signal is currently playing and of dynamically tracking it. In other words, if the music restarts or jumps to another position, the system is capable of following it. This is accomplished thanks to the low complexity training and classifying of the models used, which continually keep track of all possible points of the music, and not only of the current neighboring region. Experiments are provided in order to analyze the performance of the system in three test scenarios: tracking continuous playing, tracking multiple jumps and self predicting errors. The final results demonstrate that the system is efficient even when applied to musics with some level of repetitions and short periods of silence.

关键词： Training Random access memory Music Robot sensing systems Hidden Markov models Neurons

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Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2018年第12期50卷 1754页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Igor Cima Min-Han Tan Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Institute of Bioengineering and Nanotechnology Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation Institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. science Transl. Med. 8, 345ra89, 2016).

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DevCAFE 1.0: A participatory platform for assessing development initiatives in the field

DevCAFE 1.0: A participatory platform for assessing developm...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： Brandie Nonnecke Sanjay Krishnan Jay Patel Mo Zhou Laura Byaruhanga Dorothy Masinde Maria Elena Meneses Alejandro Martin del Campo Camille Crittenden Ken Goldberg CITRIS Connected Communities Initiative UC Berkeley Electrical Engineering and Computer Science UC Berkeley Industrial Engineering and Operations Research UC Berkeley Uganda Program Iowa State University Global Resource Systems Iowa State University Tecnologico de Monterrey CITRIS Connected Communities Initiative UC Berkeley

The design and assessment of development initiatives is increasingly participatory, where decision makers consider feedback from affected populations. While digital data collection facilitates faster and more reliable analysis, existing data collection tools are not optimized for unstructured qualitative (textual) data and peer-to-peer participant collaboration. In this paper, we propose a system called the Development Collaborative Assessment and Feedback Engine version 1.0 (DevCAFE), a customizable participatory assessment platform that collects and integrates quantitative assessment, qualitative feedback and peer-to-peer collaborative filtering. DevCAFE incorporates a library of statistical analyses for researchers to quickly identify quantitative and qualitative trends while collecting field data. DevCAFE can run on any mobile device with a Web-browser and can work with or without Internet connectivity. We present results from two pilot projects: (1) 137 participants evaluating family planning education trainings at three Nutrition Education Centers in rural Uganda, and (2) 4,518 participants evaluating policy priorities for elected leaders in the June 2015 Mexico mid-term elections. DevCAFE collected over 19,000 peer-to-peer ratings of 336 submitted ideas. Feedback gathered through DevCAFE enabled targeted reforms to the family planning efforts in Uganda and the need for increased government attention to public safety in Mexico. Case studies and interactive demos are available at: http://***/devcafe/.

关键词： Data collection Filtering Collaboration Principal component analysis Peer-to-peer computing Internet Visualization

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Monitoring collaboration in software processes using social networks

Monitoring collaboration in software processes using social ...

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20th International Conference on Collaboration and Technology, CRIWG 2014

作者： Costa, Gabriella C. B. Santana, Francisco Magdaleno, Andréa M. Werner, Cláudia M. L. UFRJ - Federal University of Rio de Janeiro COPPE - Systems Engineering and Computer Science Department P.O. Box 68511 21945-970 - Rio de Janeiro - RJ Brazil

ISBN: (纸本)9783319101651

Collaboration monitoring in software process is important to check if the collaboration is indeed happening as planned, but there are few approaches that define how to measure and monitor collaboration. By assessing collaboration during an ongoing process execution, project managers can take corrective actions that might improve the process execution and, consequently, reflect on quality gains of the final product. This research work proposes to evaluate the level of coordination achieved by a running software process through social network analysis metrics. © 2014 Springer International Publishing.

关键词： Social sciences computing

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Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent

Towards autonomous navigation of unsignalized intersections ...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： V. Sezer T. Bandyopadhyay D. Rus E. Frazzoli D. Hsu Department of Mechanical Engineering Istanbul Technical University Istanbul Turkey Tirthankar Bandyopadhyay is with Autonomous Systems Program Commonwealth Scientific and Industrial Research Organization Australia Daniela Rus and Emilio Frazzoli are with the Department of Electri-cal&Engineering and Aeronautics&Astronautic respectively Massachusetts Institute of Technology USA Department of Computer Science National University of Singapore Singapore

In a mixed environment of autonomous driverless vehicles and human driven vehicles operating on the same road, identifying intentions of human drivers and interacting with them in a compliant and responsible manner becomes a challenging problem for the driverless vehicles. In this paper, the problem of vehicle interaction at an intersection merging scenario is formulated as an Intention-Aware motion planning problem using the tools from Mixed Observability Markov Decision Process (MOMDP). We utilize the tools from recent intention aware planning framework to demonstrate a merging behavior in the presence of human drivers by trying to infer and act according to the intentions of the human drivers. A driver behavior model for T-junction intersections is developed in order to calculate the probabilistic state transition functions of the MOMDP model. With proposed solution, it is demonstrated that using intention aware planning improves performance in comparison to present time to merge approach by lowering accident probability and intersection navigation duration. The proposed method is tested on a real autonomous vehicle (AV) in the presence of human driven vehicles to validate our approach.

关键词： Vehicles Navigation Mobile robots Roads Hidden Markov models Uncertainty

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A Parallel Implementation of Data Fusion Algorithm Using Gamma

A Parallel Implementation of Data Fusion Algorithm Using Gam...

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International Symposium on computer Architecture and High Performance Computing Workshops (SBAC-PADW)

作者： Rui R. Mello Rubens H. P. de Almeida Felipe M.G. Franca Gabriel A.L. Paillard Digital Syst. Group Brazilian Navy Res. Insitute Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro RJ Brazil Virtual University Institute Federal University of Ceara Fortaleza CE Brazil

In this paper we carried out designing and implementing of a target tracking data fusion algorithm based on a two stages graph solution using the computational model Gamma (General Abstract Model for Multiset mAnipulation). The proposed solution is the first parallel implementation of the method PPTS (Pairs of Plots in Two Stages). For this, we employed three Gamma implementations, where two of them exploited the resources of a parallel hardware environment, one using the MPI (Message Passing Interface) and the other one GPU (Graphics Processing Unit). Thus, the studied algorithm was evaluated from the parallelism exploited and finally was carried out a performance analysis of this algorithm in the three Gamma implementations used. The aim of this study is to provide an implementation on a real problem using for this the paradigm Gamma, which contributes to the implementations of the Gamma computational model, since it enables the performance analysis of these implementations and provides some suggestions for possible improvements. In addition, this work contributes to the PPTS method since it provides the parallelization of the first stage.

关键词： Sensors Data integration Computational modeling Target tracking Parallel processing Algorithm design and analysis Chemical elements

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Performance Evaluation of Hypervisors for HPC Applications

Performance Evaluation of Hypervisors for HPC Applications

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IEEE International Conference on systems, Man and Cybernetics

作者： David Beserra Felipe Oliveira Jean Araujo Felipe Fernandes Alberto Araújo Patrícia Endo Paulo Maciel Edward David Moreno Graduate Program in Computer Science University of Sergipe Aracaju Brazil Academic Unit of Garanhuns Federal Rural University of Pernambuco Garanhuns Brazil Informatics Center Federal University of Pernambuco Recife Brazil Graduate in Systems and Computing Military Institute of Engineering Rio de Janeiro Brazil University of Pernambuco Caruaru Brazil

ISBN: (纸本)9781479986989

High Performance Computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.

关键词： Virtualization Virtual machine monitors Bandwidth Benchmark testing Random access memory Performance evaluation Kernel

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Using PepExplorer to Filter and Organize De Novo Peptide Sequencing Results

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Current Protocols in Bioinformatics 2015年第1期51卷 13.27.1-13.27.9页

作者： da Veiga Leprevost, Felipe Barbosa, Valmir C. Carvalho, Paulo Costa Computational Mass Spectrometry Group Carlos Chagas Institute–Fiocruz. Curitiba Paraná Brazil Department of Pathology University of Michigan Ann Arbor United States Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

PepExplorer aids in the biological interpretation of de novo sequencing results;this is accomplished by assembling a list of homolog proteins obtained by aligning results from widely adopted de novo sequencing tools against a target-decoy sequence database. Our tool relies on pattern recognition to ensure that the results satisfy a user-given false-discovery rate (FDR). For this, it employs a radial basis function neural network that considers the precursor charge states, de novo sequencing scores, the peptide lengths, and alignment scores. PepExplorer is recommended for studies addressing organisms with no genomic sequence available. PepExplorer is integrated into the PatternLab for proteomics environment, which makes available various tools for downstream data analysis, including the resources for quantitative and differential proteomics. © 2015 by John Wiley & Sons, Inc. © 2015 John Wiley & Sons, Inc.

关键词： de novo sequencing mass spectrometry proteomics

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Author Correction: A systems biology approach uncovers cell-specific gene regulatory effects of genetic associations in multiple sclerosis

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Nature communications 2019年第1期10卷 2956页

作者： Lohith Madireddy Kicheol Kim Xiaoming Jia Adam Santaniello Stacy J. Caillier Bruce A. C. Cree Roland Henry Stephen L. Hauser Jorge R. Oksenberg Sergio E. Baranzini Nikolaos A. Patsopoulos Philip De Jager Chris Cotsapas Matthew R. Lincoln David A. Hafler Steffan D. Bos Hanne F. Harbo Tone Berge Elisabeth G. Celius Ashley Beecham Jacob McCauley Margaret A. Pericak-Vance Till F. M. Andlauer Christiane Gasperi Bernhard Hemmer Lisa F. Barcellos Luisa Bernardinelli Filippo Martinelli-Boneschi David R. Booth Graeme J. Stewart Farren Briggs Jonathan Haines Manuel Comabella Giancarlo Comi Federica Esposito Sandra D’Alfonso Katrina Dedham Pierre Duquette Efthimios Dardiotis Bertrand Fontaine An Goris Bénédicte Dubois Pierre-Antoine Gourraud Georgios Hadjigeorgiou Clive Hawkins Seema Kalra Rogier Hintzen Dana Horakova Noriko Isobe Jun-ichi Kira Michael Khalil Ingrid Kockum Tomas Olsson Christina M. Lill Felix Luessi Frauke Zipp Roland Martin Annette Oturai Aarno Palotie Janna Saarela Adrian Ivinson Bruce V. Taylor Alastair Compston Stephen Sawcer Weill Institute for Neurosciences Department of Neurology University of California San Francisco San Francisco CA 94158 USA Bakar Institute for Computational Health Science. University of California San Francisco San Francisco CA 94158 USA Systems Biology and Computer Science Program Ann Romney Center for Neurological Diseases Department of Neurology and Division of Genetics Department of Medicine Brigham & Women’s Hospital Harvard Medical School Boston MA 02115 USA Broad Institute of Harvard University and Massachusetts Institute of Technology Cambridge MA 02142 USA Department of Neurology Center for Translational and Computational Neuroimmunology and Multiple Sclerosis Center Columbia University Medical Center New York NY 10032 USA Departments of Neurology Yale School of Medicine 300 George St New Haven CT 06511 USA Department of Genetics Yale School of Medicine 300 George St New Haven CT 06511 USA Institute of Clinical Medicine University of Oslo Oslo 0318 Norway Department of Neurology Oslo University Hospital Oslo 0424 Norway Department of Mechanical Electronics and Chemical Engineering Oslo Metropolitan University Oslo 0167 Norway Institute of Health and Society University of Oslo Oslo 0318 Norway John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL 33136 USA Dr. John T. Macdonald Foundation Department of Human Genetics University of Miami Miller School of Medicine Miami FL 33136 USA Department of Neurology Klinikum rechts der Isar School of Medicine Technical University of Munich 81675 Munich Germany Munich Cluster for Systems Neurology (SyNergy) 81377 Munich Germany Division of Epidemiology School of Public HealthUniversity of California 324 Stanley Hall MC#3220 Berkeley CA 94720 USA Section of Biostatistics Neurophyisiology and Psychiatry Unit of medical and genomic statistics Universita of Pavia Pavia 27100 Italy Department of Biomedical Sciences for Health Univers

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Extracting Semantic Information from Patent Claims Using Phrasal Structure Annotations

Extracting Semantic Information from Patent Claims Using Phr...

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Brazilian Conference on Intelligent systems (BRACIS)

作者： Danilo Silva de Carvalho Felipe Maia Galvão França Priscila Machado Vieira Lima Systems Engineering and Computer Science Program-COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Instituto Tércio Pacitti (NCE) Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil

The rapid change of trading values from tangible assets to Intelectual Property has put both businesses and academia in a race to acquire and protect the rights to exploit such property. This is mainly accomplished in the form of patent issuing by the governments, being time consuming and complicated due to the vast amount of documents that need to be analyzed in order to assert the novelty or validity of a patent application. Patent information retrieval research is thus growing quickly to support document analysis across multiple domains and information systems. One of the big challenges in patent analysis is the identification of the elements of innovation (concepts, processes, materials) and the relations between them, in the patent text. This paper presents a method for extracting semantic information from patent claims by using semantic annotations on phrasal structures, abstracting domain ontology information and outputting ontology-friendly structures to achieve generalization. An extraction system built upon the method is briefly evaluated on a document sample from INPI, the Brazilian patent office, a challenging information source.

关键词： Semantics Patents Syntactics Data mining Training Ontologies Testing

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