1,721,142 research outputs found
Multimedia Tools and Applications
No full textMultimedia Tools and Applications publishes original research articles on multimedia development and system support tools, and case studies of multimedia applications. Experimental and survey articles are appropriate for the journal. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed
Journal of Visual Communication and Image Representation
No full textThe Journal of Visual Communication and Image Representation publishes papers on state-of-the-art visual communication and image representation, with emphasis on novel technologies and theoretical work in this multidisciplinary area of pure and applied research. The field of visual communication and image representation is considered in its broadest sense and covers both digital and analog aspects as well as processing and communication in biological visual systems
Large-scale Sporting Events and the Spread of COVID-19 in USA: The Case of the 2021 NFL Super Bowl, in Tampa, FL
No full textAfter that the USA had dealt with a strong wave of COVID-19 infections, driven by the Alpha variant, the NFL Super Bowl event took place: February 7, 2021, in Tampa, Florida. In this context, we have studied the dynamics of the decrease/increase rate of the new daily SARS-COV-2 infections in all the 51 states, before/after that event, investigating the role that this event may have played in the spread of the virus. Using a piecewise linear regression, extending from the end of January 2021 till the end of February 2021, we found that, following a peak of the infections occurred approximately on the mid of January 2021 and a subsequent decreasing infection trend enjoyed by almost all the 51 states, this trend was inverted into an increasing one in 27 of those states (53%), within two weeks since the day of that sport event. Nonetheless, a new counter-inversion of the infections was registered, from an increasing to a decreasing trend, after some few more weeks, thus providing evidence in favor of the hypothesis that a major sport event alone may not have the strength to ignite new, stable and severe surges of COVID-19 infection
A Pedagogy of COVID-19: Facts from 30 Pandemic Months
Full text linkWhile the scientific community, health authorities, international media and the public are still arguing about several characteristics of this virus and the patterns of its spread, after 30 months of pandemics there are now some facts, along with their dynamics, over which a scientific consensus has been finally reached. In science, it is well known that facts and observations should be explained by a hypothesis, which should be tested until it is refuted. This is not, unfortunately, still the case for many of the aforementioned agreed facts. Nonetheless, the time has come for a rapid review of those facts and relative data, which is the specific goal of this short article, while avoiding all the myriads of logical fallacies that have pervaded the universe of discussions about COVID in these months. The undisputed facts we will cite and comment include the following: mechanisms through which the virus spreads (including what is meant with a COVID wave), transmissibility and virulence (i.e. the degree to which this virus sickens and kills), role of meteorological and environmental factors in the transmission, role of control measures and vaccination, role of variants and their evolution, preparedness for pandemics and epidemics, societal impact of COVID (including factors that could explain the variation in infections and mortality across different countries). At the end, it will be evident that, even if many of these facts represent unchallenged and accepted truths, they are not still meaningfully associated with precise causes and clear underlying phenomena on any possible level, including biological, biochemical, bio-statistical, economic and social. And the conclusion is that this just means we need to do more of what we have already done so far
Modeling Patients' Online Medical Conversations: A Granger Causality Approach
No full textUsing AI-derived computerized techniques, we have modeled the large amount of online Reddit conversations exchanged among patients discussing around the prescriptions to take prenatal medical tests (both invasive and non-invasive). Our study has revealed that a patient’s decision to take a specific test (thus possibly suffering medical implications) might significantly have a direct causal influence on her general everyday mood. Preliminary experimental results achieved exploiting the Granger causality analysis technique are discussed at length
Some Reflections on the Potential and Limitations of Deep Learning for Automated Music Generation
No full textDeep Learning and Artificial Intelligence are slowly revolutionizing many fields of applications, having the potential to replace humans in a variety of tasks and jobs. Nevertheless, creativity has always been considered something inherently human: recent research shows that, however, this may not always be the case. From this standpoint, this paper focuses on music and on the recent advancements in deep learning applied to the generation of musical content. We argue that, while those models are able to produce results that could actually be considered music, the role of the human musician still remains preponderant in the production of a musical piece. We here reflect on such limitations, directing our efforts to imagining new tools and instruments that may allow to experience new forms of interaction while supporting novel processes of creativity and music production
Categorical data as a stone guest in a data science project for predicting defective water meters
No full textAfter a one-year long effort of research on the field, we developed a machine
learning-based classifier, tailored to predict whether a mechanical water meter
would fail with passage of time and intensive use as well. A recurrent deep
neural network (RNN) was trained with data extrapolated from 15 million
readings of water consumption, gathered from 1 million meters. The data we used
for training were essentially of two types: continuous vs categorical.
Categorical being a type of data that can take on one of a limited and fixed
number of possible values, on the basis of some qualitative property; while
continuous, in this case, are the values of the measurements. taken at the
meters, of the quantity of consumed water (cubic meters). In this paper, we
want to discuss the fact that while the prediction accuracy of our RNN has
exceeded the 80% on average, based on the use of continuous data, those
performances did not improve, significantly, with the introduction of
categorical information during the training phase. From a specific viewpoint,
this remains an unsolved and critical problem of our research. Yet, if we
reason about this controversial case from a data science perspective, we
realize that we have had a confirmation that accurate machine learning
solutions cannot be built without the participation of domain experts, who can
differentiate on the importance of (the relation between) different types of
data, each with its own sense, validity, and implications. Past all the
original hype, the science of data is thus evolving towards a multifaceted
discipline, where the designitations of data scientist/machine learning expert
and domain expert are symbioti
The Barrier of meaning in archaeological data science
No full textArchaeologists, like other scientists, are experiencing a data-flood in their
discipline, fueled by a surge in computing power and devices that enable the
creation, collection, storage and transfer of an increasingly complex (and
large) amount of data, such as remotely sensed imagery from a multitude of
sources. In this paper, we pose the preliminary question if this increasing
availability of information actually needs new computerized techniques, and
Artificial Intelligence methods, to make new and deeper understanding into
archaeological problems. Simply said, while it is a fact that Deep Learning
(DL) has become prevalent as a type of machine learning design inspired by the
way humans learn, and utilized to perform automatic actions people might
describe as intelligent, we want to anticipate, here, a discussion around the
subject whether machines, trained following this procedure, can extrapolate,
from archaeological data, concepts and meaning in the same way that humans
would do. Even prior to getting to technical results, we will start our
reflection with a very basic concept: Is a collection of satellite images with
notable archaeological sites informative enough to instruct a DL machine to
discover new archaeological sites, as well as other potential locations of
interest? Further, what if similar results could be reached with less
intelligent machines that learn by having people manually program them with
rules? Finally: If with barrier of meaning we refer to the extent to which
human-like understanding can be achieved by a machine, where should be posed
that barrier in the archaeological data science
The Directors' Cut: a Solution to Collaborative Multimedia Management
No full textWeb 2.0 applications allow rich media contents to be exposed and shared by users. Nevertheless, usually, a multimedia is provided as an unicum, made by synchronized media items. Sound tracks, video sequences, captions, cannot be customized “on-the-fly” by users. Managing multimedia in a deep way would meet the expectations of nowadays Web prosumers (i.e. producers and consumers), and it would widen the audience. Describing and synchronizing each medium, as well as specifying different alternative contents for it, are the keystones of multimedia customization and of audience widening. This paper presents a multimedia collaborative system, which provides support to the arrangement of medium into a multi- views composed multimedia. Each prosumer can add medium by juxtaposition or by defining it as an alternative (audio, video, textual) version of an existing one. The implementation of such a system is based on SMIL 3.0 specification but implements a new and compact syntax to let users manipulate the original multimedia synchronization and their alternatives. The proposed approach has been put to test in two different scenarios
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