@conference {cMayoral21a, title = {Brain structural alterations in cognitively unimpaired individuals with discordant amyloid-β PET and CSF Aβ42 status: findings using Machine Learning}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Mahnaz Shekari and Gemma Salvad{\'o} and Greg Operto and Raffaele Cacciaglia and Carles Falcon and Aida Ni{\~n}erola Baiz{\'a}n and Andr{\'e}s Perissinotti and Carolina Minguillon and Karine Fauria and Ivonne Suridjan and Gwendlyn Kollmorgen and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @conference {cMayoral21, title = {Machine learning on combined neuroimaging and plasma biomarkers for triaging participants of secondary prevention trials in Alzheimer{\textquoteright}s Disease}, booktitle = {Alzheimer{\textquoteright}s Association International Conference}, year = {2021}, month = {07/2021}, author = {Irene Cumplido-Mayoral and Gemma Salvad{\'o} and Mahnaz Shekari and Carles Falcon and Marta Mil{\`a} Alom{\`a} and Aida Ni{\~n}erola Baiz{\'a}n and Jose Luis Molinuevo and Henrik Zetterberg and Kaj Blennow and Marc Suarez Calvet and Ver{\'o}nica Vilaplana and Juan D. Gispert} } @article {aWang19, title = {Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {2019/2/27}, abstract = {

Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.

}, doi = {10.1109/TMI.2019.2901712}, author = {Li Wang and Dong Nie and Guannan Li and Elodie Puybareau and Jose Dolz and Qian Zhang and Fan Wang and Jing Xia and Zhengwang Wu and Jiawei Chen and Kim-HanThung and Toan Duc Bui and Jitae Shin and Guodong Zeng and Guoyan Zheng and Vladimir S. Fonov and Andrew Doyle and Yongchao Xu and Pim Moeskops and Josien Pluim and Christian Desrosiers and Ismail Ben Ayed and Gerard Sanroma and Oualid Benkarim and Adri{\`a} Casamitjana and Ver{\'o}nica Vilaplana and Weili Lin and Gang Li and Dinggang Shen} } @article {aKuijf, title = {Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities; Results of the WMH Segmentation Challenge}, journal = {IEEE Transactions on Medical Imaging}, year = {2019}, month = {03/2019}, abstract = {

Quantification of white matter hyperintensities (WMH) of presumed vascular origin is of key\ importance in many neurological research studies. Advanced\ measurements are obtained from manual segmentations on brain\ MR images, which is a laborious procedure. Automatic WMH\ segmentation methods exist, but a standardized comparison of\ such methods is lacking. We organized a scientific challenge, in\ which developers could evaluate their method on a standardized\ multi-center/-scanner image dataset, giving an objective comparison:\ the WMH Segmentation Challenge (http://wmh.isi.uu.nl/).\ Sixty T1+FLAIR images from three MR scanners were released\ with manual WMH segmentations. A secret test set of 110\ images from five MR scanners was used for evaluation. Methods\ had to be containerized and submitted to the challenge organizers.Five evaluation metrics were used to rank the methods:\ (1) Dice Similarity Coefficient, (2) modified Hausdorff distance\ (95th percentile), (3) absolute percentage volume difference, (4)\ sensitivity for detecting individual lesions, and (5) F1-score for\ individual lesions. Additionally, methods were ranked on their\ inter-scanner robustness.\ Twenty participants submitted their method for evaluation.\ This paper provides a detailed analysis of the results. In brief,there is a cluster of four methods that rank significantly better\ than the other methods. There is one clear winner, which also\ has the best inter-scanner robustness.\ The challenge remains open for future submissions and provides\ a public platform for method evaluation.

}, keywords = {brain, Evaluation and performance, Magnetic resonance imaging (MRI), segmentation}, issn = {0278-0062}, doi = {10.1109/TMI.2019.2905770}, author = {Hugo Kuijf and Matthijs Biesbroek and Jeroen de Bresser and Rutger Heinen and Simon Andermatt and Mariana Bento and Matt Berseth and Mikhail Belyaev and Jorge Cardoso and Adri{\`a} Casamitjana and Louis Collins and Mahsa Dadar and Achileas Georgiou and Mohsen Ghafoorian and Dakai Jin and April Khademi and Jesse Knight and Hongwei Li and Xavier Llado and Miguel Luna and Qaiser Mahmood and Richard McKinley and Alireza Mehrtash and Sebastien Ourselin and Bo-yong Park and Hyunkin Park and Sang Hyun Park and Simon Pezold and Elodie Puybareau and Leticia Rittner and Carole Sudre and Sergi Valverde and Ver{\'o}nica Vilaplana and Rolan Wiest and Yongchao Xu and Ziyue Xu and Guodong Zeng and Jianguo Zhang and Guoyan Zheng and Christoper Chen and Wiesje van der Flier and Frederik Barkhof and Max Viergever and Geert Jan Biessels} } @mastersthesis {xBernal, title = {Predicting emotion in movies: Recurrent and convolutional models applied to videos}, year = {2017}, abstract = {

This Thesis explores di erent approaches using deep learning techniques to predict emotions\ in videos.\ Working with videos implies a huge amount of data including visual frames and acoustic\ samples. The rst step of the project is basically to extract features to represent the videos in\ small sets of arrays. This procedure is done using pre-trained models based on Convolutional\ Networks, the state of the art in visual recognition. Firstly, visual features are extracted using\ 3D convolutions and acoustic features are extracted using VGG19, a pre-trained convolutional\ model for images fi ne-tuned to accept the audio inputs.\ Later, these features are fed into a Recurrent model capable of exploiting the temporal information.\ Emotions are measured in terms of valence and arousal, values between [-1, 1]. Additionally,\ the same techniques are also used to attempt to predict fear scenes. In consequence, this thesis\ deals with both regression and classi cation problems.\ Several architectures and di erent parameters have been tested in order to achieve the best\ performance. Finally, the results will be published in the MediaEval 2017 Challenge and compared\ to the state-of-the-art solutions.

Predicting Emotion in Movies from Xavier Giro-i-Nieto
}, author = {Bernal, Oriol}, editor = {Xavier Gir{\'o}-i-Nieto and Zaharieva, Maia} } @mastersthesis {xCardoner, title = {Predicting Media Interestingness}, year = {2017}, abstract = {

This thesis explores the application of a deep learning approach for the prediction of media\ interestingness. Two di erent models are investigated, one for the prediction of image and one\ for the prediction of video interestingness.\ For the prediction of image interestingness, the ResNet50 network is ne-tuned to obtain\ best results. First, some layers are added. Next, the model is trained and fine-tuned using data\ augmentation, dropout, class weights, and changing other hyper parameters.\ For the prediction of video interestingness, rst, features are extracted with a 3D convolutional\ network. Next a LSTM network is trained and fine-tuned with the features.\ The nal result is a binary label for each image/video: 1 for interesting, 0 for not interesting.\ Additionally, a con fidence value is provided for each prediction. Finally, the Mean Average\ Precision (MAP) is employed as evaluation metric to estimate the quality of the fi nal results.

Predicting Media Interestingness from Xavier Giro-i-Nieto
}, author = {Cardoner, LLuc}, editor = {Zaharieva, Maia and Xavier Gir{\'o}-i-Nieto} } @conference {cMarsden, title = {Dublin City University and Partners{\textquoteright} Participation in the INS and VTT Tracks at TRECVid 2016}, booktitle = {TRECVID Workshop 2016}, year = {2016}, month = {11/2016}, address = {Gaithersburg, MD, USA}, abstract = {

DCU participated with a consortium of colleagues from NUIG and UPC in two tasks,\ INS and VTT. For the INS task we developed a framework consisting of face detection and\ representation and place detection and representation, with a user annotation of top-ranked\ videos. For the VTT task we ran 1,000 concept detectors from the VGG-16 deep CNN on\ 10 keyframes per video and submitted 4 runs for caption re-ranking, based on BM25, Fusion,\ Word2Vec and a fusion of baseline BM25 and Word2Vec. With the same pre-processing for\ caption generation we used an open source image-to-caption CNN-RNN toolkit NeuralTalk2\ to generate a caption for each keyframe and combine them.

}, url = {http://doras.dcu.ie/21484/}, author = {Marsden, Mark and Mohedano, Eva and McGuinness, Kevin and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto and O{\textquoteright}Connor, N. and Zhou, Jiang and Azevedo, Lucas and Daubert, Tobias and Davis, Brian and H{\"u}rlimann, Manuela and Afli, Haithem and Du, Jinhua and Ganguly, Debasis and Li, Wei and Way, Andy and Smeaton, Alan F.} } @conference {cSalvadora, title = {Cultural Event Recognition with Visual ConvNets and Temporal Models}, booktitle = {CVPR ChaLearn Looking at People Workshop 2015}, year = {2015}, month = {06/2015}, abstract = {

This paper presents our contribution to the ChaLearn Challenge 2015 on Cultural Event Classification. The challenge in this task is to automatically classify images from 50 different cultural events. Our solution is based on the combination of visual features extracted from convolutional neural networks with temporal information using a hierarchical classifier scheme. We extract visual features from the last three fully connected layers of both CaffeNet (pretrained with ImageNet) and our fine tuned version for the ChaLearn challenge. We propose a late fusion strategy that trains a separate low-level SVM on each of the extracted neural codes. The class predictions of the low-level SVMs form the input to a higher level SVM, which gives the final event scores. We achieve our best result by adding a temporal refinement step into our classification scheme, which is applied directly to the output of each low-level SVM. Our approach penalizes high classification scores based on visual features when their time stamp does not match well an event-specific temporal distribution learned from the training and validation data. Our system achieved the second best result in the \ ChaLearn Challenge 2015 on Cultural Event Classification with a mean average precision of 0.767 on the test set.

[Preprint in arXiv][Workshop site][Slides on GDrive]

Cultural Event Recognition with Visual ConvNets and Temporal Models from Xavier Giro
}, url = {http://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W09/papers/Salvador_Cultural_Event_Recognition_2015_CVPR_paper.pdf}, author = {Amaia Salvador and Zeppelzauer, Matthias and Manchon-Vizuete, Daniel and Calafell, Andrea and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xCalafell-Oros, title = {Fine-tuning a Convolutional Network for Cultural Event Recognition}, year = {2015}, abstract = {

Advisors: Amaia Salvador (UPC), Matthias Zeppelzauer (FH St P{\"o}lten), Xavier Gir{\'o}-i-Nieto (UPC)

Studies: Bachelor Degree in Audiovisual Systems Engineering at Telecom BCN-ETSETB from the Technical University of Catalonia (UPC)

Grade: A with honors (10/10)

This thesis explores good practices for improving the performance of an existing convnet trained with a dataset of clean data when an additional dataset of noisy data is available. We develop techniques to clean the noisy data with the help of the clean one, a family of solutions that we will refer to as denoising, and then we explore the best sorting of the clean and noisy datasets during the fine-tuning of a convnet. Then we study strategies to select the subset of images of the clean data that will improve the classification performance, a practice we will efer to as fracking. Next, we determine how many layers are actually better to fine-tune in our convnet, given our amount of data. And finally, we compare the classic convnet architecture where a single network is fine-tuned to solve a multi-class problem with the case of fine-tuning a convnet for binary classification for each considered class.

Fine tuning a convolutional network for cultural event recognition from Xavier Giro

2015-TFG-AndreaCalafell-FineTuningConvolutionalNetworkForCulturalEventRecognition from Image Processing Group on Vimeo.

See https://imatge.upc.edu/web/publications/cultural-event-recognition-visual-convnets-and-temporal-models

}, url = {http://hdl.handle.net/2117/78391}, author = {Calafell, Andrea}, editor = {Amaia Salvador and Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @conference {cMcGuinnessa, title = {Insight DCU at TRECVID 2015}, booktitle = {TRECVID 2015 Workshop}, year = {2015}, month = {11/2015}, publisher = {NIST}, organization = {NIST}, address = {Gaithersburg, MD, USA}, abstract = {

Insight-DCU participated in the instance search (INS), semantic indexing (SIN), and localization tasks (LOC) this year.

In the INS task we used deep convolutional network features trained on external data and the query data for this year to train our system. We submitted four runs, three based on convolutional network features, and one based on SIFT/BoW. F A insightdcu 1 was an automatic run using features from the last convolutional layer of a deep network with bag-of-words encoding and achieved 0.123 mAP. F A insightdcu 2 modi ed the previous run to use re-ranking based on an R-CNN model and achieved 0.111 mAP. I A insightdcu 3, our interactive run, achieved 0.269 mAP. Our SIFT-based run F A insightdcu 2 used weak geometric consistency to improve performance over the previous year to 0.187 mAP. Overall we found that using features from the convolutional layers improved performance over features from the fully connected layers used in previous years, and that weak geometric consistency improves performance for local feature ranking.

In the SIN task we again used convolutional network features, this time netuning a network pretrained on external data for the task. We submitted four runs, 2C D A insightdcu.15 1..4 varying the top-level learning algorithm and use of concept co-occurance. 2C D A insightdcu.15 1 used a linear SVM top-level learner, and achieved 0.63 mAP. Exploiting concept co-occurance improved the accuracy of our logistic regression run 2C D A insightdcu.15 3 from 0.058 mAP to 0.6 2C D A insightdcu.15 3.

Our LOC system used training data from IACC.1.B and features similar to our INS run, but using a VLAD encoding instead of a bag-of-words. Unfortunately there was problem with the run that we are still investigating.

Note: UPC and NII participated only in the INS task of this submission.

}, url = {http://www-nlpir.nist.gov/projects/tvpubs/tv.pubs.15.org.html}, author = {Kevin McGuinness and Mohedano, Eva and Amaia Salvador and Zhang, ZhenXing and Marsden, Mark and Wang, Peng and Jargalsaikhan, Iveel and Antony, Joseph and Xavier Gir{\'o}-i-Nieto and Satoh, Shin{\textquoteright}ichi and O{\textquoteright}Connor, N. and Smeaton, Alan F.} } @conference {cLidon, title = {UPC-UB-STP @ MediaEval 2015 Diversity Task: Iterative Reranking of Relevant Images}, booktitle = {MediaEval 2015 Workshop}, year = {2015}, month = {09/2015}, abstract = {

This paper presents the results of the UPC-UB-STP team in the 2015 MediaEval Retrieving Diverse Images Task.The goal of the challenge is to provide a ranked list of Flickr photos for a predefined set of queries. Our approach firstly generates a ranking of images based on a query-independent estimation of its relevance. Only top results are kept and iteratively re-ranked based on their intra-similarity to introduce diversity.

UPC-UB-STP @ MediaEval 2015 diversity task iterative reranking of relevant images from Xavier Giro
}, url = {http://ceur-ws.org/Vol-1436/}, author = {Lidon, Aniol and Bola{\~n}os, Marc and Seidl, Markus and Xavier Gir{\'o}-i-Nieto and Radeva, Petia and Zeppelzauer, Matthias} } @mastersthesis {xTella, title = {Contextless Object Recognition with Shape-enriched SIFT and Bags of Features}, year = {2014}, abstract = {
Final thesis slides (1) from Xavi Gir{\'o}

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Matthias Zeppelzauer\ (TU Wien)

Degree: Telecommunications Engineering (5 years) at\ Telecom BCN-ETSETB\ (UPC)

Currently, there are highly competitive results in the field of object recognition based on the aggregation of point-based features. The aggregation process, typically with an average or max-pooling of the features generates a single vector that represents the image or region that contains the object.

The aggregated point-based features typically describe the texture around the points with descriptors such as SIFT. These descriptors present limitations for wired and textureless objects. A possible solution is the addition of shape-based information. Shape descriptors have been previously used to encode shape information and thus, recognise those types of objects. But generally an alignment step is required in order to match every point from one shape to other ones. The computational cost of the similarity assessment is high.

We purpose to enrich location and texture-based features with shape-based ones. Two main architectures are explored: On the one side, to enrich the SIFT descriptors with shape information before they are aggregated. On the other side, to create the standard Bag of Words histogram and concatenate a shape histogram, classifying them as a single vector.

We evaluate the proposed techniques and the novel features on the Caltech-101 dataset.

Results show that shape features increase the final performance. Our extension of the Bag of Words with a shape-based histogram(BoW+S) results in better performance. However, for a high number of shape features, BoW+S and enriched SIFT architectures tend to converge.

Final grade: A with honors (10/10)

}, keywords = {Bag of Words, Interest Points, object candidates, Object detection, segmentation, shape coding, SIFT, textureless objects, wired objects.}, url = {http://hdl.handle.net/2099.1/22390}, author = {Tella, Marcel}, editor = {Zeppelzauer, Matthias and Xavier Gir{\'o}-i-Nieto} } @conference {cMcGuinness, title = {Insight Centre for Data Analytics (DCU) at TRECVid 2014: Instance Search and Semantic Indexing Tasks}, booktitle = {2014 TRECVID Workshop}, year = {2014}, month = {11/2014}, publisher = {National Institute of Standards and Technology (NIST)}, organization = {National Institute of Standards and Technology (NIST)}, address = {Orlando, Florida (USA)}, abstract = {

Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043.

[2014 TREC Video Retrieval Evaluation Notebook Papers and Slides]

Poster

}, url = {http://hdl.handle.net/2117/24915}, author = {Kevin McGuinness and Mohedano, Eva and Zhang, ZhenXing and Hu, Feiyan and Albatal, Rami and Gurrin, Cathal and O{\textquoteright}Connor, N. and Smeaton, Alan F. and Amaia Salvador and Xavier Gir{\'o}-i-Nieto and Ventura, C.} } @article {aBarkhuus14, title = {New interaction modes for rich panoramic live video experiences}, journal = {Behaviour \& Information Technology}, volume = {33}, year = {2014}, month = {07/2014}, chapter = {859-869}, abstract = {

The possibilities of panoramic video are based on the capabilities of high-resolution digital video streams and higher band- width{\textquoteright}s opportunities to broadcast, stream and transfer large content across platforms. With these opportunities also come challenges such as how to focus on sub-parts of the video stream and interact with the content shown on a large screen. In this paper, we present studies of two different interaction modes with a large-scale panoramic video for live experiences; we focus on interactional challenges and explore if it is (1) possible to develop new interactional methods/ways of approaching this type of high-resolution content and (2) feasible for users to interact with the content in these new ways. We developed prototypes for two different interaction modes: an individual system on a mobile device, either a tablet or a mobile phone, for interacting with the content on the same and a non-touch gesture-based system for the home or small group interaction. We present pilot studies where we explore the possibilities and challenges with these two interaction modes for panoramic content.

}, keywords = {interaction modes, interactive television, panoramic video, pilot studies}, doi = {10.1080/0144929X.2014.914975}, url = {http://www.tandfonline.com/doi/full/10.1080/0144929X.2014.914975}, author = {Barkhuus, Louise and Zoric, Goranka and Engstr{\"o}m, Arvid and Ruiz-Hidalgo, J. and Verzijp, Nico} } @conference {cZoric13, title = {Gesture Interaction with Rich TV Content in the Social Setting}, booktitle = {Exploring and Enhancing the User Experience for Television, Workshop of ACM SIGCHI Conference on Human Factors in Computing Systems, CHI{\textquoteright}13}, year = {2013}, month = {04/2013}, address = {Paris, France}, abstract = {

The appearance of new immersive TV content has increased the interactive possibilities presented to the viewers. Increased interactivity is seen as a valuable feature in viewing richer television content, but new functionalities are limited by what can be done naturally and intuitively using available devices like remote controls. Therefore, new interaction techniques, such as visual gestures control systems, have appeared aiming to enhance the viewers{\textquoteright} viewing experience. In this work we begin uncovering the potential and challenges of gesture interaction with ultra high definition video for people watching TV together. As a first step we have done a study with a group of people interacting with such content using a gesture-based system in the home environment.

}, url = {http://livingroomexperience.wikispaces.com/}, author = {Zoric, Goranka and Engstr{\"o}m, Arvid and Barkhuus, Louise and Ruiz-Hidalgo, J. and Kochale, A.} } @article {aAvrithis03, title = {Unified Access to Heterogeneous Audiovisual Archives}, journal = {Journal of universal computer science}, volume = {9}, number = {6}, year = {2003}, pages = {510{\textendash}519}, abstract = {

In this paper, an integrated information system is presented that offers enhanced search and retrieval capabilities to users of heterogeneous digital audiovisual (a/v) archives. This innovative system exploits the advances in handlings a/v content and related metadata, as introduced by MPEG-4 and worked out by MPEG-7, to offer advanced services characterized by the tri-fold semantic phrasing of the request (query), unified handling and personalized response. The proposed system is targeting the intelligent extraction of semantic information from a/v and text related data taking into account the nature of the queries that users my issue, and the context determined by user profiles. It also provides a personalization process of the response in order to provide end_users with desired information. From a technical point of view, the FAETHON system plays the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to the new MPEG standards.

}, issn = {0948-6968}, doi = {10.3217/jucs-009-06-0510}, url = {http://www.jucs.org/jucs_9_6}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @conference {cAvrithis03, title = {Unified access to heterogeneous audiovisual archives}, booktitle = {International Conference on Knowledge Management}, year = {2003}, pages = {1{\textendash}2}, address = {Graz, Austria}, author = {Avrithis, Y. and Stamou, G. and Wallace, M. and Marqu{\'e}s, F. and Salembier, P. and Xavier Gir{\'o}-i-Nieto and Haas, W. and Vallant, H. and Zufferey, M.} } @conference {cMarcotegui99, title = {A video generation tool allowing friendly user interaction}, booktitle = {1999 IEEE INternational Conference on Image Processing}, year = {1999}, isbn = {0-7803-5470-2}, author = {Marcotegui, B. and Correia, P. and Marqu{\'e}s, F. and Mech, R. and Rosa, R. and Wollborn, M. and Zanoguera, F.} }