• Predicting the success of online petitions leveraging multidimensional time-series

  Proskurnia, Julia; Grabowicz, Przemyslaw; Kobayashi, Ryota; Castillo, Carlos; Cudré-Mauroux, Philippe; Aberer, Karl (ACM Association for Computer Machinery, 2017)

  Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of ...
• FA*IR: a fair top-k ranking algorithm

  Zehlike, Meike; Bonchi, Francesco; Castillo, Carlos; Hajian, Sara; Megahed, Mohamed; Baeza-Yates, Ricardo (ACM Association for Computer Machinery, 2017)

  In this work, we define and solve the Fair Top-k Ranking problem, in which we want to determine a subset of k candidates from a large pool of n>>k candidates, maximizing utility (i.e., select the “best” candidates) subject ...
• Ranking of social media alerts with workload bounds in emergency operation centers

  Purohit, Hemant; Castillo, Carlos; Imran, Muhammad; Pandey, Rahul (Institute of Electrical and Electronics Engineers (IEEE), 2018)

  xtensive research on social media usage during emergencies has shown its value to provide life-saving information, if a mechanism is in place to filter and prioritize messages. Existing ranking systems can provide a ...
• Optimal information transfer in the cortex through synchronization

  Buehlmann, Andres; Deco, Gustavo (Public Library of Science (PLoS), 2010)

  In recent experimental work it has been shown that neuronal interactions are modulated by neuronal synchronization and that this modulation depends on phase shifts in neuronal oscillations. This result suggests that ...
• Functional connectivity dynamics: modeling the switching behavior of the resting state

  Hansen, Enrique C.A.; Battaglia, Demian; Spiegler, Andreas; Deco, Gustavo; Jirsa, Viktor K. (Elsevier, 2015)

  Functional connectivity (FC) sheds light on the interactions between different brain regions. Besides basic research, it is clinically relevant for applications in Alzheimer's disease, schizophrenia, presurgical planning, ...
• Facilitating the manual snnotation of sounds when using large taxonomies

  Favory, Xavier; Fonseca, Eduardo; Font, Frederic; Serra, Xavier (FRUCT, 2018)

  Properly annotated multimedia content is crucial for supporting advances in many Information Retrieval applications. It enables, for instance, the development of automatic tools for the annotation of large and diverse ...
• General-purpose tagging of Freesound audio with audioSet labels: task description, dataset, and baseline

  Fonseca, Eduardo; Plakal, Manoj; Font, Frederic; Ellis, Daniel P. W.; Favory, Xavier; Pons, Jordi; Serra, Xavier (Tampere University of Technology, 2018)

  This paper describes Task 2 of the DCASE 2018 Challenge, titled “General-purpose audio tagging of Freesound content with AudioSet labels”. This task was hosted on the Kaggle platform as “Freesound General-Purpose Audio ...
• Overcoming catastrophic forgetting with hard attention to the task

  Serrà, Joan; Surís, Dídac; Miron, Marius; Karatzoglou, Alexandros (Proceedings of Machine Learning Research, 2018)

  Catastrophic forgetting occurs when a neural network loses the information learned in a previous task after training on subsequent tasks. This problem remains a hurdle for artificial intelligence systems with sequential ...
• Automatic assessment of violin performance using dynamic time warping classification

  Giraldo, Sergio; Ortega, Ariadna; Perez, Alfonso; Ramírez, Rafael; Waddell, George; Williamon, Aaron (Institute of Electrical and Electronics Engineers (IEEE), 2018)

  The automatic assessment of music performance has become an area of special interest due to the increasing amount of technology-enhanced music learning systems. However, in most of these systems the assessment of the musical ...
• Characterization of myocardial motion patterns by unsupervised multiple Kernel learning

  Sánchez Martínez, Sergio; Duchateau, Nicolas Guillem; Erdei, Tamas; Fraser, Alan G.; Bijnens, Bart; Piella Fenoy, Gemma (Elsevier, 2017)

  We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned ...
• Multimodal image registration using Laplacian commutators

  Zimmer, Veronika Anne; González Ballester, Miguel Ángel, 1973-; Piella Fenoy, Gemma (Elsevier, 2019)

  The fusion and combination of images from multiple modalities is important in many applications. Typically, this process consists of the alignment of the images and the combination of the complementary information. In ...
• A Simple fusion of deep and shallow learning for acoustic scene classification

  Fonseca, Eduardo; Gong, Rong; Serra, Xavier (Cyprus University of Technology, 2018)

  In the past, Acoustic Scene Classification systems havebeen based on hand crafting audio features that are input toa classifier. Nowadays, the common trend is to adopt datadriven techniques, e.g., deep learning, where audio ...
• Makam recognition using extended pitch distribution features and multi-layer perceptrons

  Yesiler, Furkan; Bozkurt, Baris; Serra, Xavier (Cyprus University of Technology, 2018)

  This work focuses on automatic makam recognition taskfor Turkish makam music (TMM) using pitch distributionsthat are widely used in mode recognition tasks for variousmusic traditions. Here, we aim to improve the performanceof ...
• Early risk detection of anorexia on social media

  Ramíırez-Cifuentes, Diana; Mayans Yern, Marc; Freire, Ana (Springer, 2018)

  This paper proposes an approach for the early detection of anorexia nervosa (AN) on social media. We present a machine learning approach that processes the texts written by social media users. This method relies on a set ...
• Early prediction of Alzheimer's disease with non-local patch-based longitudinal descriptors

  Sanromà, Gerard; Andrea, Víctor; Benkarim, Oualid M.; Manjón, José V.; Coupé, Pierrick; Camara, Oscar; Piella Fenoy, Gemma; González Ballester, Miguel Ángel, 1973- (Springer, 2017)

  Alzheimer’s disease (AD) is characterized by a progressive decline in the cognitive functions accompanied by an atrophic process which can already be observed in the early stages using magnetic resonance images (MRI). ...
• Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly

  Benkarim, Oualid M.; Hahner, Nadine; Piella Fenoy, Gemma; Gratacós Solsona, Eduard; González Ballester, Miguel Ángel, 1973-; Eixarch, Elisenda; Sanromà, Gerard (Elsevier, 2018)

  Neuroimaging of brain diseases plays a crucial role in understanding brain abnormalities and early diagnosis. Of great importance is the study of brain abnormalities in utero and the assessment of deviations in case of ...
• Beeping socks and chirping arm bands: wearables that foster free play

  Rosales, Andrea; Sayago, Sergio; Blat, Josep (Institute of Electrical and Electronics Engineers (IEEE), 2015)

  Three playful wearable accessories incorporate design principles that promote individuality, natural interaction, ubiquity, and intimacy to encourage spontaneous open-ended social interaction in school-age children. The ...
• Audio-aligned jazz harmony dataset for automatic chord transcription and corpus-based research

  Serra, Xavier (International Society for Music Information Retrieval (ISMIR), 2018)

  In this paper we present a new dataset of time-aligned jazz harmony transcriptions. This dataset is a useful resource for content-based analysis, especially for training and evaluating chord transcription algorithms. Most ...
• Automatic Makam recognition using chroma features

  Demirel, Emir; Bozkurt, Baris; Serra, Xavier (2018)

  This work focuses on the automatic makam recognition task for Turkish Makam Music using chroma features. Chroma features are widely used for music identification and tonal recognition tasks such as key estimation or chord ...
• Brain songs framework used for discovering the relevant timescale of the human brain

  Deco, Gustavo; Cruzat, Josephine; Kringelbach, Morten L. (Nature Research, 2019)

  A key unresolved problem in neuroscience is to determine the relevant timescale for understanding spatiotemporal dynamics across the whole brain. While resting state fMRI reveals networks at an ultraslow timescale (below ...

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