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In this paper, we introduce our recent studies on human perception in audio event classification. In particular, the pre-trained model VGGish is used as feature extractor to process audio data, and DenseNet is trained by and used as feature extractor for o ...
Medical data are often scattered among multiple clinics, hospitals, insurance companies, pharmacies, and research institutions that store and process personal healthcare information. The use of information and communication technologies for health (eHealth ...
Pattern recognition and machine learning research work often contains experimental results on real-world data, which corroborates hypotheses and provides a canvas for the development and comparison of new ideas. Results, in this context, are typically summ ...
This work presents and studies a distributed algorithm for solving optimization problems over networks where agents have individual costs to minimize subject to subspace constraints that require the minimizers across the network to lie in a low-dimensional ...
Advances in data acquisition technologies and supercomputing for large-scale simulations have led to an exponential growth in the volume of spatial data. This growth is accompanied by an increase in data complexity, such as spatial density, but also by mor ...
Advanced technologies based on Internet of Things (IOT) are blazing a trail to effective and efficient management of an overall plant. In this context, manufacturing companies require an innovative strategy to survive in a competitive business environment, ...
Big data and analytics have received great attention from practitioners and academics, nowadays representing a key resource for the renewed interest in artificial intelligence, especially for machine learning techniques. In this article we explore the use ...
Recent studies have shown that the labels collected from crowdworkers can be discriminatory with respect to sensitive attributes such as gender and race. This raises questions about the suitability of using crowdsourced data for further use, such as for tr ...
Decentralized machine learning is a promising emerging paradigm in view of global challenges of data ownership and privacy. We consider learning of linear classification and regression models, in the setting where the training data is decentralized over ma ...
We present novel, low-cost and non-invasive potential diagnostic biomarkers of schizophrenia. They are based on the 'mirrorgame', a coordination task in which two partners are asked to mimic each other's hand movements. In particular, we use the patient's ...
