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Papers by Maria-cristina Marinescu

Research paper thumbnail of MS 13. Implementation of deep learning for image description development

Research paper thumbnail of EpiGraph Internal Structure

Page 1. Carlos III University of Madrid Higher Polytechnic School Computer Science Department Com... more Page 1. Carlos III University of Madrid Higher Polytechnic School Computer Science Department Computer Architecture, Communications and Systems Area Technical Report EpiGraph Internal Structure Gonzalo Martín, Maria ...

Research paper thumbnail of DEArt: Dataset of European Art

Lecture Notes in Computer Science, 2023

Research paper thumbnail of Evaluating the impact of the weather conditions on the influenza propagation

Research Square (Research Square), Mar 19, 2020

Research paper thumbnail of Characterising information loss due to aggregating epidemic model outputs

medRxiv (Cold Spring Harbor Laboratory), Jul 7, 2023

Research paper thumbnail of Learning Singer-Specific Performance Rules

International Journal of Modeling and Optimization, 2012

Research paper thumbnail of From statecharts to ESP

Research paper thumbnail of Evaluation of vaccination strategies for the metropolitan area of Madrid via agent-based simulation

Research paper thumbnail of DEArt: Dataset of European Art

arXiv (Cornell University), Nov 2, 2022

Research paper thumbnail of Evaluating the impact of the weather conditions on the influenza propagation

Research Square (Research Square), Jan 13, 2020

Research paper thumbnail of Jigsaw in the Time of Pandemic

Research paper thumbnail of Data Management in EpiGraph COVID-19 Epidemic Simulator

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Evaluating the spread of Omicron COVID-19 variant in Spain

Future Generation Computer Systems

Research paper thumbnail of Corrigendum: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area

Frontiers in Public Health

Research paper thumbnail of Automated metadata annotation: What is and is not possible with machine learning

Data Intelligence

Automated metadata annotation is only as good as the training set, or rules that are available fo... more Automated metadata annotation is only as good as the training set, or rules that are available for the domain. It's important to learn what type of content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what's popular and what's available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.

Research paper thumbnail of Lindaview: an OBDA-based tool for self-sufficiency assessment

Barcelona Supercomputing Center, May 1, 2021

Research paper thumbnail of Evaluating the spread of Omicron COVID-19 variant in Spain

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

Research paper thumbnail of Evolution of Epidemics

Leveraging social networks for understanding the

Research paper thumbnail of Fusing statecharts and java

ACM Transactions on Embedded Computing Systems, 2013

This article presents FUSE, an approach for modeling and implementing embedded software component... more This article presents FUSE, an approach for modeling and implementing embedded software components which starts from a main-stream programming language and brings some of the key concepts of Statecharts as first-class elements within this language. Our approach provides a unified programming environment which not only preserves some of the advantages of Statecharts' formal foundation but also directly supports features of object-orientation and strong typing. By specifying Statecharts directly in FUSE we eliminate the out-of-synch between the model and the generated code and we allow the tuning and debugging to be done within the same programming model. This article describes the main language constructs of FUSE and presents its semantics by translation into the Java programming language. We conclude by discussing extensions to the base language which enable the efficient static checking of program properties.

Research paper thumbnail of Experimental data of the paper entitled: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area

Experimental data of the paper: Simulation of COVID-19 propagation scenarios in the Madrid metrop... more Experimental data of the paper: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area<br> David E. Singh*,Maria-cristina Marinescu,Miguel Guzman-Merino,Christian Duran,Concepción Delgado-Sanz,<br> Diana Gomez-Barroso,Jesus Carretero<br> Front. Public Health - Infectious Diseases DOI: 10.3389/fpubh.2021.636023 Dataset structure: * Each directory corresponds to each scenario presented in the paper<br> * Each directory contains in each sub-directory each one of the experiments (simulations) run<br> * For each sub-directory the contents are:<br> - citylist.txt -> The cities considered in the simulation<br> - output.txt -> the simulation output<br> - xml directory:<br> - XMLCityConfigFile.xml -> Configuration file with the city list <br> - XMLConfigFile.xml -> Main configuration file<br> - xmlCityName:<br> - distances.dat -> Distance vector with other cities configured in the sim...

Research paper thumbnail of MS 13. Implementation of deep learning for image description development

Research paper thumbnail of EpiGraph Internal Structure

Page 1. Carlos III University of Madrid Higher Polytechnic School Computer Science Department Com... more Page 1. Carlos III University of Madrid Higher Polytechnic School Computer Science Department Computer Architecture, Communications and Systems Area Technical Report EpiGraph Internal Structure Gonzalo Martín, Maria ...

Research paper thumbnail of DEArt: Dataset of European Art

Lecture Notes in Computer Science, 2023

Research paper thumbnail of Evaluating the impact of the weather conditions on the influenza propagation

Research Square (Research Square), Mar 19, 2020

Research paper thumbnail of Characterising information loss due to aggregating epidemic model outputs

medRxiv (Cold Spring Harbor Laboratory), Jul 7, 2023

Research paper thumbnail of Learning Singer-Specific Performance Rules

International Journal of Modeling and Optimization, 2012

Research paper thumbnail of From statecharts to ESP

Research paper thumbnail of Evaluation of vaccination strategies for the metropolitan area of Madrid via agent-based simulation

Research paper thumbnail of DEArt: Dataset of European Art

arXiv (Cornell University), Nov 2, 2022

Research paper thumbnail of Evaluating the impact of the weather conditions on the influenza propagation

Research Square (Research Square), Jan 13, 2020

Research paper thumbnail of Jigsaw in the Time of Pandemic

Research paper thumbnail of Data Management in EpiGraph COVID-19 Epidemic Simulator

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Evaluating the spread of Omicron COVID-19 variant in Spain

Future Generation Computer Systems

Research paper thumbnail of Corrigendum: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area

Frontiers in Public Health

Research paper thumbnail of Automated metadata annotation: What is and is not possible with machine learning

Data Intelligence

Automated metadata annotation is only as good as the training set, or rules that are available fo... more Automated metadata annotation is only as good as the training set, or rules that are available for the domain. It's important to learn what type of content a pre-trained machine learning algorithm has been trained on to understand its limitations and potential biases. Consider what type of content is readily available to train an algorithm—what's popular and what's available. However, scholarly and historical content is often not available in consumable, homogenized, and interoperable formats at the large volume that is required for machine learning. There are exceptions such as science and medicine, where large, well documented collections are available. This paper presents the current state of automated metadata annotation in cultural heritage and research data, discusses challenges identified from use cases, and proposes solutions.

Research paper thumbnail of Lindaview: an OBDA-based tool for self-sufficiency assessment

Barcelona Supercomputing Center, May 1, 2021

Research paper thumbnail of Evaluating the spread of Omicron COVID-19 variant in Spain

2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

Research paper thumbnail of Evolution of Epidemics

Leveraging social networks for understanding the

Research paper thumbnail of Fusing statecharts and java

ACM Transactions on Embedded Computing Systems, 2013

This article presents FUSE, an approach for modeling and implementing embedded software component... more This article presents FUSE, an approach for modeling and implementing embedded software components which starts from a main-stream programming language and brings some of the key concepts of Statecharts as first-class elements within this language. Our approach provides a unified programming environment which not only preserves some of the advantages of Statecharts' formal foundation but also directly supports features of object-orientation and strong typing. By specifying Statecharts directly in FUSE we eliminate the out-of-synch between the model and the generated code and we allow the tuning and debugging to be done within the same programming model. This article describes the main language constructs of FUSE and presents its semantics by translation into the Java programming language. We conclude by discussing extensions to the base language which enable the efficient static checking of program properties.

Research paper thumbnail of Experimental data of the paper entitled: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area

Experimental data of the paper: Simulation of COVID-19 propagation scenarios in the Madrid metrop... more Experimental data of the paper: Simulation of COVID-19 propagation scenarios in the Madrid metropolitan area<br> David E. Singh*,Maria-cristina Marinescu,Miguel Guzman-Merino,Christian Duran,Concepción Delgado-Sanz,<br> Diana Gomez-Barroso,Jesus Carretero<br> Front. Public Health - Infectious Diseases DOI: 10.3389/fpubh.2021.636023 Dataset structure: * Each directory corresponds to each scenario presented in the paper<br> * Each directory contains in each sub-directory each one of the experiments (simulations) run<br> * For each sub-directory the contents are:<br> - citylist.txt -> The cities considered in the simulation<br> - output.txt -> the simulation output<br> - xml directory:<br> - XMLCityConfigFile.xml -> Configuration file with the city list <br> - XMLConfigFile.xml -> Main configuration file<br> - xmlCityName:<br> - distances.dat -> Distance vector with other cities configured in the sim...