104,245 research outputs found
Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder
Semantic communications have emerged as a new paradigm for improving
communication efficiency by transmitting the semantic information of a source
message that is most relevant to a desired task at the receiver. Most existing
approaches typically utilize neural networks (NNs) to design end-to-end
semantic communication systems, where NN-based semantic encoders output
continuously distributed signals to be sent directly to the channel in an
analog communication fashion. In this work, we propose a joint
coding-modulation framework for digital semantic communications by using
variational autoencoder (VAE). Our approach learns the transition probability
from source data to discrete constellation symbols, thereby avoiding the
non-differentiability problem of digital modulation. Meanwhile, by jointly
designing the coding and modulation process together, we can match the obtained
modulation strategy with the operating channel condition. We also derive a
matching loss function with information-theoretic meaning for end-to-end
training. Experiments conducted on image semantic communication validate that
our proposed joint coding-modulation framework outperforms separate design of
semantic coding and modulation under various channel conditions, transmission
rates, and modulation orders. Furthermore, its performance gap to analog
semantic communication reduces as the modulation order increases while enjoying
the hardware implementation convenience
XML Matchers: approaches and challenges
Schema Matching, i.e. the process of discovering semantic correspondences
between concepts adopted in different data source schemas, has been a key topic
in Database and Artificial Intelligence research areas for many years. In the
past, it was largely investigated especially for classical database models
(e.g., E/R schemas, relational databases, etc.). However, in the latest years,
the widespread adoption of XML in the most disparate application fields pushed
a growing number of researchers to design XML-specific Schema Matching
approaches, called XML Matchers, aiming at finding semantic matchings between
concepts defined in DTDs and XSDs. XML Matchers do not just take well-known
techniques originally designed for other data models and apply them on
DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical
structure of a DTD/XSD) to improve the performance of the Schema Matching
process. The design of XML Matchers is currently a well-established research
area. The main goal of this paper is to provide a detailed description and
classification of XML Matchers. We first describe to what extent the
specificities of DTDs/XSDs impact on the Schema Matching task. Then we
introduce a template, called XML Matcher Template, that describes the main
components of an XML Matcher, their role and behavior. We illustrate how each
of these components has been implemented in some popular XML Matchers. We
consider our XML Matcher Template as the baseline for objectively comparing
approaches that, at first glance, might appear as unrelated. The introduction
of this template can be useful in the design of future XML Matchers. Finally,
we analyze commercial tools implementing XML Matchers and introduce two
challenging issues strictly related to this topic, namely XML source clustering
and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure
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