2,609 research outputs found
Web-based Geographical Visualization of Container Itineraries
Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair
Data Science and Knowledge Discovery
Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining
Interpreting Deep Learning Features for Myoelectric Control: A Comparison with Handcrafted Features
The research in myoelectric control systems primarily focuses on extracting
discriminative representations from the electromyographic (EMG) signal by
designing handcrafted features. Recently, deep learning techniques have been
applied to the challenging task of EMG-based gesture recognition. The adoption
of these techniques slowly shifts the focus from feature engineering to feature
learning. However, the black-box nature of deep learning makes it hard to
understand the type of information learned by the network and how it relates to
handcrafted features. Additionally, due to the high variability in EMG
recordings between participants, deep features tend to generalize poorly across
subjects using standard training methods. Consequently, this work introduces a
new multi-domain learning algorithm, named ADANN, which significantly enhances
(p=0.00004) inter-subject classification accuracy by an average of 19.40%
compared to standard training. Using ADANN-generated features, the main
contribution of this work is to provide the first topological data analysis of
EMG-based gesture recognition for the characterisation of the information
encoded within a deep network, using handcrafted features as landmarks. This
analysis reveals that handcrafted features and the learned features (in the
earlier layers) both try to discriminate between all gestures, but do not
encode the same information to do so. Furthermore, using convolutional network
visualization techniques reveal that learned features tend to ignore the most
activated channel during gesture contraction, which is in stark contrast with
the prevalence of handcrafted features designed to capture amplitude
information. Overall, this work paves the way for hybrid feature sets by
providing a clear guideline of complementary information encoded within learned
and handcrafted features.Comment: The first two authors shared first authorship. The last three authors
shared senior authorship. 32 page
NONLINEAR APPROACH IN CLASSIFICATION VISUALIZATION AND EVALUATION
In this paper we have proposed the novel methodology to visualize classification scheme in
informatics domain. We have mapped a documents collection of ACM (Association for Computing
Machinery) Digital Library to a sphere surface. Two main stages of visualization processes
complement one another: classification and clusterization. Primarily classified documents were
visualized and their further clusterization by means of keywords was crucial in evaluation process.
For clusters analysis of given visualization maps nonlinear digital filtering techniques were applied.
The clusters of keywords were characterized by a local accuracy. Obtained semantic map was
included to validation process
Generalized Hurst exponent and multifractal function of original and translated texts mapped into frequency and length time series
A nonlinear dynamics approach can be used in order to quantify complexity in
written texts. As a first step, a one-dimensional system is examined : two
written texts by one author (Lewis Carroll) are considered, together with one
translation, into an artificial language, i.e. Esperanto are mapped into time
series. Their corresponding shuffled versions are used for obtaining a "base
line". Two different one-dimensional time series are used here: (i) one based
on word lengths (LTS), (ii) the other on word frequencies (FTS). It is shown
that the generalized Hurst exponent and the derived curves
of the original and translated texts show marked differences. The original
"texts" are far from giving a parabolic function, - in contrast to
the shuffled texts. Moreover, the Esperanto text has more extreme values. This
suggests cascade model-like, with multiscale time asymmetric features as
finally written texts. A discussion of the difference and complementarity of
mapping into a LTS or FTS is presented. The FTS curves are more
opened than the LTS onesComment: preprint for PRE; 2 columns; 10 pages; 6 (multifigures); 3 Tables; 70
reference
Geospatial big data and cartography : research challenges and opportunities for making maps that matter
Geospatial big data present a new set of challenges and opportunities for cartographic researchers in technical, methodological, and artistic realms. New computational and technical paradigms for cartography are accompanying the rise of geospatial big data. Additionally, the art and science of cartography needs to focus its contemporary efforts on work that connects to outside disciplines and is grounded in problems that are important to humankind and its sustainability. Following the development of position papers and a collaborative workshop to craft consensus around key topics, this article presents a new cartographic research agenda focused on making maps that matter using geospatial big data. This agenda provides both long-term challenges that require significant attention as well as short-term opportunities that we believe could be addressed in more concentrated studies.PostprintPeer reviewe
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