60 research outputs found
Computational Intelligence and Human- Computer Interaction: Modern Methods and Applications
The present book contains all of the articles that were accepted and published in the Special Issue of MDPI’s journal Mathematics titled "Computational Intelligence and Human–Computer Interaction: Modern Methods and Applications". This Special Issue covered a wide range of topics connected to the theory and application of different computational intelligence techniques to the domain of human–computer interaction, such as automatic speech recognition, speech processing and analysis, virtual reality, emotion-aware applications, digital storytelling, natural language processing, smart cars and devices, and online learning. We hope that this book will be interesting and useful for those working in various areas of artificial intelligence, human–computer interaction, and software engineering as well as for those who are interested in how these domains are connected in real-life situations
Understanding Quantum Technologies 2022
Understanding Quantum Technologies 2022 is a creative-commons ebook that
provides a unique 360 degrees overview of quantum technologies from science and
technology to geopolitical and societal issues. It covers quantum physics
history, quantum physics 101, gate-based quantum computing, quantum computing
engineering (including quantum error corrections and quantum computing
energetics), quantum computing hardware (all qubit types, including quantum
annealing and quantum simulation paradigms, history, science, research,
implementation and vendors), quantum enabling technologies (cryogenics, control
electronics, photonics, components fabs, raw materials), quantum computing
algorithms, software development tools and use cases, unconventional computing
(potential alternatives to quantum and classical computing), quantum
telecommunications and cryptography, quantum sensing, quantum technologies
around the world, quantum technologies societal impact and even quantum fake
sciences. The main audience are computer science engineers, developers and IT
specialists as well as quantum scientists and students who want to acquire a
global view of how quantum technologies work, and particularly quantum
computing. This version is an extensive update to the 2021 edition published in
October 2021.Comment: 1132 pages, 920 figures, Letter forma
History of Construction Cultures Volume 1
History of Construction Cultures Volume 1 contains papers presented at the 7ICCH – Seventh International Congress on Construction History, held at the Lisbon School of Architecture, Portugal, from 12 to 16 July, 2021. The conference has been organized by the Lisbon School of Architecture (FAUL), NOVA School of Social Sciences and Humanities, the Portuguese Society for Construction History Studies and the University of the Azores. The contributions cover the wide interdisciplinary spectrum of Construction History and consist on the most recent advances in theory and practical case studies analysis, following themes such as: - epistemological issues; - building actors; - building materials; - building machines, tools and equipment; - construction processes; - building services and techniques ; -structural theory and analysis ; - political, social and economic aspects; - knowledge transfer and cultural translation of construction cultures. Furthermore, papers presented at thematic sessions aim at covering important problematics, historical periods and different regions of the globe, opening new directions for Construction History research. We are what we build and how we build; thus, the study of Construction History is now more than ever at the centre of current debates as to the shape of a sustainable future for humankind. Therefore, History of Construction Cultures is a critical and indispensable work to expand our understanding of the ways in which everyday building activities have been perceived and experienced in different cultures, from ancient times to our century and all over the world
Recent Advances in Signal Processing
The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity
A Speaker Verification Backend with Robust Performance across Conditions
In this paper, we address the problem of speaker verification in conditions
unseen or unknown during development. A standard method for speaker
verification consists of extracting speaker embeddings with a deep neural
network and processing them through a backend composed of probabilistic linear
discriminant analysis (PLDA) and global logistic regression score calibration.
This method is known to result in systems that work poorly on conditions
different from those used to train the calibration model. We propose to modify
the standard backend, introducing an adaptive calibrator that uses duration and
other automatically extracted side-information to adapt to the conditions of
the inputs. The backend is trained discriminatively to optimize binary
cross-entropy. When trained on a number of diverse datasets that are labeled
only with respect to speaker, the proposed backend consistently and, in some
cases, dramatically improves calibration, compared to the standard PLDA
approach, on a number of held-out datasets, some of which are markedly
different from the training data. Discrimination performance is also
consistently improved. We show that joint training of the PLDA and the adaptive
calibrator is essential -- the same benefits cannot be achieved when freezing
PLDA and fine-tuning the calibrator. To our knowledge, the results in this
paper are the first evidence in the literature that it is possible to develop a
speaker verification system with robust out-of-the-box performance on a large
variety of conditions
Big Data analytics to assess personality based on voice analysis
Trabajo Fin de Grado en IngenierĂa de TecnologĂas y Servicios de
TelecomunicaciĂłnWhen humans speak, the produced series of acoustic signs do not encode only the
linguistic message they wish to communicate, but also several other types of information
about themselves and their states that show glimpses of their personalities and can be
apprehended by judgers. As there is nowadays a trend to film job candidate’s interviews, the
aim of this Thesis is to explore possible correlations between speech features extracted from
interviews and personality characteristics established by experts, and to try to predict in a
candidate the Big Five personality traits: Conscientiousness, Agreeableness, Neuroticism,
Openness to Experience and Extraversion. The features were extracted from a genuine
database of 44 women video recordings acquired in 2020, and 78 in 2019 and before from a
previous study.
Even though many significant correlations were found for each years’ dataset, lots of
them were proven to be inconsistent through both studies. Only extraversion, and openness
in a more limited way, showed a good number of clear correlations. Essentially, extraversion
has been found to be related to the variation in the slope of the pitch (usually at the end of
sentences), which indicates that a more "singing" voice could be associated with a higher
score. In addition, spectral entropy and roll-off measurements have also been found to
indicate that larger changes in the spectrum (which may also be related to more "singing"
voices) could be associated with greater extraversion too.
Regarding predictive modelling algorithms, aimed to estimate personality traits from the
speech features obtained for the study, results were observed to be very limited in terms of
accuracy and RMSE, and also through scatter plots for regression models and confusion
matrixes for classification evaluation. Nevertheless, various results encourage to believe that
there are some predicting capabilities, and extraversion and openness also ended up being
the most predictable personality traits. Better outcomes were achieved when predictions
were performed based on one specific feature instead of all of them or a reduced group, as it
was the case for openness when estimated through linear and logistic regression based on
time over 90% of the variation range of the deltas from the entropy of the spectrum module.
Extraversion too, as it correlates well with features relating variation in F0 decreasing slope
and variations in the spectrum. For the predictions, several machine learning algorithms have
been used, such as linear regression, logistic regression and random forests
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