RDF description Dr. Federico Castanedo

PostDoc


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Mar. 2011  -  Jun. 2013



[u' @inproceedings{david_ausin_probabilistic_2014, address = {Riva del Garda, Italy,}, title = {A {Probabilistic} {OWL} {Reasoner} for {Intelligent} {Environments}}, volume = {Vol-1259}, isbn = {urn:nbn:de:0074-1259-9}, abstract = {OWL ontologies have gained great popularity as a context modelling tool for intelligent environments due to their expressivity. However, they present some disadvantages when it is necessary to deal with uncertainty, which is common in our daily life and affects the de- cisions that we take. To overcome this drawback, we have developed a novel framework to compute fact probabilities from the axioms in an OWL ontology. This proposal comprises the definition and description of our probabilistic ontology. Our probabilistic ontology extends OWL 2 DL with a new layer to model uncertainty. With this work we aim to overcome OWL limitations to reason with uncertainty, developing a novel framework that can be used in intelligent environments.}, booktitle = {{URSW} 2014 {Uncertainty} {Reasoning} for the {Semantic} {Web}}, author = {David Aus\xedn and Diego L\xf3pez-de-Ipi\xf1a and Federico Castanedo}, month = oct, year = {2014}, note = {00000}, keywords = {OWL, bayesian networks, intelligent environments, uncertainty} }']

[u' @incollection{klein_emergency_2013, series = {Lecture {Notes} in {Computer} {Science}}, title = {Emergency {Event} {Detection} in {Twitter} {Streams} {Based} on {Natural} {Language} {Processing}}, copyright = {\xa92013 Springer International Publishing Switzerland}, isbn = {978-3-319-03175-0 978-3-319-03176-7}, url = {http://link.springer.com/chapter/10.1007/978-3-319-03176-7_31}, abstract = {Real-time social media usage is widely adapted today because it encourages quick spreading of news within social networks. New opportunities arise to use social media feeds to detect emergencies and extract crucial information about that event to support rescue operations. A major challenge for the extraction of emergency event information from applications like Twitter is the big mass of data, inaccurate or lacking metadata and the noisy nature of the post text itself. We propose to filter the real-time media stream by analysing posts seriousity, extract facts through natural language processing and group posts using a novel event identification scheme. Based on a manually tagged social media feed corpus we show that false or missed alarms are limited to posts with highly ambiguous information with less value for the rescue units.}, number = {8276}, urldate = {2014-01-09TZ}, booktitle = {Ubiquitous {Computing} and {Ambient} {Intelligence}. {Context}-{Awareness} and {Context}-{Driven} {Interaction}}, publisher = {Springer International Publishing}, author = {Klein, Bernhard and Castanedo, Federico and Elejalde, I\xf1igo and L\xf3pez-de-Ipi\xf1a, Diego and Nespral, Alejandro Prada}, editor = {Urzaiz, Gabriel and Ochoa, Sergio F. and Bravo, Jos\xe9 and Chen, Liming Luke and Oliveira, Jonice}, month = jan, year = {2013}, keywords = {Artificial Intelligence (incl. Robotics), Computer Communication Networks, Computers and Society, Information Systems Applications (incl. Internet), User Interfaces and Human Computer Interaction, emergency detection, incremental clustering, natural language processing, social media mining, software engineering}, pages = {239--246} }']

[u' @article{castanedo_learning_2013, title = {Learning routines over long-term sensor data using topic models}, copyright = {\xa9 2013 Wiley Publishing Ltd}, issn = {1468-0394}, url = {http://onlinelibrary.wiley.com/doi/10.1111/exsy.12033/abstract}, doi = {10.1111/exsy.12033}, abstract = {Recent advances on sensor network technology provide the infrastructure to create intelligent environments on physical places. One of the main issues of sensor networks is the large amount of data they generate. Therefore, it is necessary to have good data analysis techniques with the aim of learning and discovering what is happening on the monitored environment. The problem becomes even more challenging if this process is performed following an unsupervised way (without having any a priori information) and applied over a long-term timeline with many sensors. In this work, topic models are employed to learn the latent structure and dynamics of sensor network data. Experimental results using two realistic datasets, having over 50 weeks of data, have shown the ability to find routines of activity over sensor network data in office environments.}, language = {en}, urldate = {2013-10-30TZ}, journal = {Expert Systems}, author = {Castanedo, Federico and de-Ipi\xf1a, Diego L\xf3pez- and Aghajan, Hamid K. and Kleihorst, Richard}, year = {2013}, keywords = {Q3, jcr0.769, sensor networks, topic models, unsupervised learning}, pages = {n/a--n/a} }']

[u' @article{hervas_mobile_2013, title = {Mobile {Monitoring} and {Reasoning} {Methods} to {Prevent} {Cardiovascular} {Diseases}}, volume = {13}, issn = {1424-8220}, url = {http://www.mdpi.com/1424-8220/13/5/6524}, doi = {10.3390/s130506524}, number = {5}, urldate = {2013-09-19TZ}, journal = {Sensors}, author = {Herv\xe1s, Ram\xf3n and Fontecha, Jes\xfas and Aus\xedn, David and Castanedo, Federico and Bravo, Jos\xe9 and L\xf3pez-de-Ipi\xf1a, Diego}, month = may, year = {2013}, keywords = {AAL, Ambient Assisted Living, CVD Risk, Mobile Monitoring, Q1, SWRL, Talisengine, java, jcr1.953, ontologies, sensors-basel, talisman+}, pages = {6524--6541} }']

[u' @article{castanedo_review_2013, title = {A {Review} of {Data} {Fusion} {Techniques}}, volume = {2013}, url = {http://www.hindawi.com/journals/tswj/2013/704504/abs/}, doi = {10.1155/2013/704504}, abstract = {The integration of data and knowledge from several sources is known as data fusion. This paper summarizes the state of the data fusion field and describes the most relevant studies. We first enumerate and explain different classification schemes for data fusion. Then, the most common algorithms are reviewed. These methods and algorithms are presented using three different categories: (i) data association, (ii) state estimation, and (iii) decision fusion.}, language = {en}, urldate = {2013-11-05TZ}, journal = {The Scientific World Journal}, author = {Castanedo, Federico}, month = oct, year = {2013}, keywords = {Q1, data fusion, jcr1.730, review} }']

[u' @incollection{fontecha_new_2012, series = {Lecture {Notes} in {Computer} {Science}}, title = {A {New} {Approach} to {Prevent} {Cardiovascular} {Diseases} {Based} on {SCORE} {Charts} through {Reasoning} {Methods} and {Mobile} {Monitoring}}, copyright = {\xa92012 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-35394-9 978-3-642-35395-6}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35395-6_3}, abstract = {Nowadays, vital signs monitoring with mobile devices such as smartphones and tablets is possible through Bluetooth-enabled biometric devices. In this paper, we propose a system to monitor the risk of cardiovascular diseases in Ambient Assisted Living environments through blood pressure monitoring and other clinical factors, using mobile devices and reasoning techniques based on the Systematic Coronary Risk Evaluation Project (SCORE) charts. Mobile applications for patients and doctors, and a reasoning engine based on SWRL rules have been developed.}, number = {7657}, urldate = {2013-09-19}, booktitle = {Ambient {Assisted} {Living} and {Home} {Care}}, publisher = {Springer Berlin Heidelberg}, author = {Fontecha, Jes\xfas and Aus\xedn, David and Castanedo, Federico and L\xf3pez-de-Ipi\xf1a, Diego and Herv\xe1s, Ram\xf3n and Bravo, Jos\xe9}, editor = {Bravo, Jos\xe9 and Herv\xe1s, Ram\xf3n and Rodr\xedguez, Marcela}, month = jan, year = {2012}, keywords = {AI for health, Ambient Assisted Living, CVD Risk, ISI, IWAAL, Mobile Monitoring, OWL API, Reasoning, SWRL, Talisengine, android, java, talisman+}, pages = {17--24} }']

[u' @incollection{ausin_benchmarking_2012, series = {Lecture {Notes} in {Computer} {Science}}, title = {Benchmarking {Results} of {Semantic} {Reasoners} {Applied} to an {Ambient} {Assisted} {Living} {Environment}}, copyright = {\xa92012 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-30778-2 978-3-642-30779-9}, url = {http://link.springer.com/chapter/10.1007/978-3-642-30779-9_45}, abstract = {Ambient Assisted Living (AAL) environments and applications have been receiving a great amount of interest in recent years. The main reason of this interest is the increasing lifetime of elderly people. Our goal is to determine and clarify which combination of semantic reasoners and frameworks is the most suitable for building knowledge-driven smart environments. In particular, we are interested in the combination which provides a better computational performance and reasonable memory requirements. The obtained results show that the fastest choice under the employed dataset is to use the OWL API accessing the Pellet reasoner. On the other hand, the less memory consumption experiment is provided by a combination of HermiT and custom Java rules.}, number = {7251}, urldate = {2013-09-19TZ}, booktitle = {Impact {Analysis} of {Solutions} for {Chronic} {Disease} {Prevention} and {Management}}, publisher = {Springer Berlin Heidelberg}, author = {Aus\xedn, David and Castanedo, Federico and L\xf3pez-de-Ipi\xf1a, Diego}, editor = {Donnelly, Mark and Paggetti, Cristiano and Nugent, Chris and Mokhtari, Mounir}, month = jan, year = {2012}, keywords = {AAL, Ambient Assisted Living, HermiT, ICOST, ISI, OWL API, SWRL, Talisengine, benchmarking, java, jena, ontologies, pellet, reasoners, talisman+}, pages = {282--285} }']

[u' @inproceedings{ausin_measurement_2012, address = {Sof\xeda, Bulgaria}, title = {On the measurement of semantic reasoners in {Ambient} {Assisted} {Living} environments}, doi = {10.1109/IS.2012.6335195}, abstract = {The increasing lifetime and population of elderly people leads to a great amount of interest in Ambient Assisted Living (AAL) environments and applications. An AAL environment could be modeled by ontologies and using a semantic reasoner as a way to infer knowledge about the underlying context. An important question is to clarify the feasibility of employing a semantic reasoner in AAL applications. For this reason, in this work we present our results of a simulated AAL environment using a knowledge driven approach. Our aim is to determine and clarify which combination of semantic reasoners and frameworks is the most convenient for this task. In particular, we are interested in the combination which provides a better computational performance and reasonable memory requirements. The obtained results show that the fastest choice under the employed dataset is to use the OWL API accessing the Pellet reasoner. On other hand, the less used and committed memory consumption experiment is provided by a combination of HermiT and custom Java rules.}, booktitle = {Intelligent {Systems} ({IS}), 2012 6th {IEEE} {International} {Conference}}, author = {Aus\xedn, David and Castanedo, Federico and Lopez-de-Ipina, Diego}, year = {2012}, keywords = {AAL, Ambient Assisted Living, HermiT, IS, IWAAL, OWL, SWRL, Semantic reasoners, Talisengine, ambient assisted living environments, core-c, inference mechanisms, java, jena, ontologies, pellet, talisman+}, pages = {082--087} }']

[u' @incollection{ausin_turambar:_2012, series = {Lecture {Notes} in {Computer} {Science}}, title = {{TURAMBAR}: {An} {Approach} to {Deal} with {Uncertainty} in {Semantic} {Environments}}, copyright = {\xa92012 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-35394-9 978-3-642-35395-6}, shorttitle = {{TURAMBAR}}, url = {http://link.springer.com/chapter/10.1007/978-3-642-35395-6_45}, abstract = {Research community has shown a great interest in OWL ontologies as a context modeling tool for semantic environments. OWL ontologies are characterized by its expressive power and are based on description logics. However, they have limitations when dealing with uncertainty and vagueness knowledge. To overcome these caveats, some approaches have been proposed. This work presents a novel approach to deal with uncertainty in semantic environments, called TURAMBAR.}, number = {7657}, urldate = {2013-09-19TZ}, booktitle = {Ambient {Assisted} {Living} and {Home} {Care}}, publisher = {Springer Berlin Heidelberg}, author = {Aus\xedn, David and Castanedo, Federico and L\xf3pez-de-Ipi\xf1a, Diego}, editor = {Bravo, Jos\xe9 and Herv\xe1s, Ram\xf3n and Rodr\xedguez, Marcela}, month = jan, year = {2012}, keywords = {ISI, IWAAL, OWL, OWL API, Talisengine, bayesian network, java, ontologies, pellet, probability, reasoners, talisman+, uncertainty}, pages = {329--337} }']

[u' @inproceedings{castanedo_building_2011, title = {Building an occupancy model from sensor networks in office environments}, doi = {10.1109/ICDSC.2011.6042929}, abstract = {The work presented here aims to answer this question: Using just binary occupancy sensors is it possible to build a behaviour occupancy model over long-term logged data? Sensor measurements are grouped to form artificial words (activities) and documents (set of activities). The goal is to infer the latent topics which are assumed to be the common routines from the observed data. An unsupervised probabilistic model, namely the Latent Dirichlet Allocation (LDA), is applied to automatically discover the latent topics (routines) in the data. Experimental results using real logged data of 24 weeks from an office building, with different number of topics, are shown. The results show the power of the LDA model in extracting relevant patterns from sensor network data.}, booktitle = {2011 {Fifth} {ACM}/{IEEE} {International} {Conference} on {Distributed} {Smart} {Cameras} ({ICDSC})}, author = {Castanedo, F. and Lopez-de-Ipina, D. and Aghajan, H. and Kleihorst, R.}, year = {2011}, keywords = {Latent Dirichlet Allocation, data mining}, pages = {1--6} }']