pinecone/core-2020-05-10-deduplication huggingface dataset:
User indoor localisation system enhances activity recognition: A proof of concept

Older people would like to live independently in their home as long as possible. They want to reduce the risk of domestic accidents because of polypharmacy, physical weakness and other mental illnesses, which could increase the risks of domestic accidents (i.e. a fall). Changes in the behaviour of healthy older people could be correlated with cognitive disorders; consequently, early intervention could delay the deterioration of the disease. Over the last few years, activity recognition systems have been developed to support the management of senior citizensâ\u80\u99 daily life. In this context, this paper aims to go beyond the state-of-the-art presenting a proof of concept where information on body movement, vital signs and userâ\u80\u99s indoor locations are aggregated to improve the activity recognition task. The presented system has been tested in a realistic environment with three users in order to assess the feasibility of the proposed method. These results encouraged the use of this approach in activity recognition applications; indeed, the overall accuracy values, amongst others, are satisfactory increased (+2.67% DT, +7.39% SVM, +147.37% NN)
A wearable system for stress detection through physiological data analysis

In the last years the impact of stress on the society has been increased, resulting in 77% of people that regularly experiences physical symptoms caused by stress with a negative impact on their personal and professional life, especially in aging working population. This paper aims to demonstrate the feasibility of detection and monitoring of stress, inducted by mental stress tests, through the analysis of physiological data collected by wearable sensors. In fact, the physiological features extracted from heart rate variability and galvanic skin response showed significant differences between stressed and not stressed people. Starting from the physiological data, the work provides also a cluster analysis based on Principal Components (PCs) able to showed a visual discrimination of stressed and relaxed groups. The developed system would support active ageing, monitoring and managing the level of stress in ageing workers and allowing them to reduce the burden of stress related to the workload on the basis of personalized interventions
The Web, the Users, and the MOS: Influence of HTTP/2 on User Experience

This work focuses on the evaluation of Web quality of experience as perceived by actual users and in particular on the impact of HTTP/1 vs HTTP/2. We adopt an experimental methodology that uses real web pages served through a realistic testbed where we control network, protocol, and application configuration. Users are asked to browse such pages and provide their subjective feedback, which we leverage to obtain the Mean Opinion Score (MOS), while the testbed records objective metrics. The collected dataset comprises over 4,000 grades that we explore to tackle the question whether HTTP/2 improves users experience, to what extent, and in which conditions. Findings show that users report marginal differences, with 22%, 52%, 26% of HTTP/2 MOS being better, identical, or worse than HTTP/1, respectively. Even in scenarios that favor HTTP/2, results are not as sharp as expected. This is in contrast with objective metrics, which instead record a positive impact with HTTP/2 usage. This shows the complexity of understanding the web experience and the need to involve actual users in the quality assessment process
Benchmarking Distributed Stream Processing Platforms for IoT
  Applications

Internet of Things (IoT) is a technology paradigm where millions of sensors
monitor, and help inform or manage, physical, envi- ronmental and human systems
in real-time. The inherent closed-loop re- sponsiveness and decision making of
IoT applications makes them ideal candidates for using low latency and scalable
stream processing plat- forms. Distributed Stream Processing Systems (DSPS) are
becoming es- sential components of any IoT stack, but the efficacy and
performance of contemporary DSPS have not been rigorously studied for IoT data
streams and applications. Here, we develop a benchmark suite and per- formance
metrics to evaluate DSPS for streaming IoT applications. The benchmark includes
13 common IoT tasks classified across various func- tional categories and
forming micro-benchmarks, and two IoT applica- tions for statistical
summarization and predictive analytics that leverage various dataflow
compositional features of DSPS. These are coupled with stream workloads sourced
from real IoT observations from smart cities. We validate the IoT benchmark for
the popular Apache Storm DSPS, and present empirical results.Comment: To Appear in VLDB-TPCTC (2016) , 20 pages,Benchmark,IoT,smart cities
  ,velocit
Visual Speech Recognition Using PCA Networks and LSTMs in a Tandem
  GMM-HMM System

Automatic visual speech recognition is an interesting problem in pattern
recognition especially when audio data is noisy or not readily available. It is
also a very challenging task mainly because of the lower amount of information
in the visual articulations compared to the audible utterance. In this work,
principle component analysis is applied to the image patches - extracted from
the video data - to learn the weights of a two-stage convolutional network.
Block histograms are then extracted as the unsupervised learning features.
These features are employed to learn a recurrent neural network with a set of
long short-term memory cells to obtain spatiotemporal features. Finally, the
obtained features are used in a tandem GMM-HMM system for speech recognition.
Our results show that the proposed method has outperformed the baseline
techniques applied to the OuluVS2 audiovisual database for phrase recognition
with the frontal view cross-validation and testing sentence correctness
reaching 79% and 73%, respectively, as compared to the baseline of 74% on
cross-validation
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis

This paper fills a gap in aspect-based sentiment analysis and aims to present
a new method for preparing and analysing texts concerning opinion and
generating user-friendly descriptive reports in natural language. We present a
comprehensive set of techniques derived from Rhetorical Structure Theory and
sentiment analysis to extract aspects from textual opinions and then build an
abstractive summary of a set of opinions. Moreover, we propose aspect-aspect
graphs to evaluate the importance of aspects and to filter out unimportant ones
from the summary. Additionally, the paper presents a prototype solution of data
flow with interesting and valuable results. The proposed method's results
proved the high accuracy of aspect detection when applied to the gold standard
dataset
A Case Study of Crowdsourcing Imagery Coding in Natural Disasters

Crowdsourcing and open licensing allow more people to participate in research and humanitarian activities. Open data, such as geographic information shared through OpenStreetMap and image datasets from disasters, can be useful for disaster response and recovery work. This chapter shares a real-world case study of humanitarian-driven imagery analysis, using open-source crowdsourcing technology. Shared philosophies in open technologies and digital humanities, including remixing and the wisdom of the crowd, are reflected in this case study.This research was funded through the European Commission FP7-ICT project:
Citizen Cyberlab: Technology Enhanced Creative Learning in the field of Citizen Cyberscience
Conflict transformation and history teaching: social psychological theory and its contributions

The aim of this introductory chapter is to render intelligible how history teaching can be enriched with knowledge of social psychological theories that deal with the issue of conflict transformation and partcularly the notions of prejudice reduction and reconciliation. A major aim of history teaching is to engage students with historical texts, establish historical significance, identify continuity and change, analyse cause and consequence, take historical perspectives and understand the ethical dimensions of historical interpretations. Such teaching, enriched with social psychological theory, will enlarge the notion of historical literacy into a study of historical culture and historical consciousness in the classroom so that students become reflective of the role of collective memory and history teaching in processes of conflict transformation and understand the ways in which various forms of historical consciousness relate the past, present and future. This is what the editors of this volume call an interdisciplinary paradigm of transformative history teachin
Operational Modal Analysis in Frequency Domain using Gaussian Mixture Models

Operational Modal Analysis is widely gaining popularity as a means to perform system identification of a structure. Instead of using a detailed experimental setup Operational Modal Analysis relies on measurement of ambient displacements to identify the system. Due to the random nature of ambient excitations and their output responses, various statistical methods have been developed throughout the literature both in the time-domain and the frequency-domain. The most popular of these algorithms rely on the assumption that the structure can be modelled as a multi degree of freedom second order differential system. In this paper we drop the second order differential assumption and treat the identification problem as a curve-fitting problem, by fitting a Gaussian Mixture Model in the frequency domain. We further derive equivalent models for the covariance-driven and the data-driven algorithms. Moreover, we introduce a model comparison criterion to automatically choose the optimum number of Gaussian’s. Later the algorithm is used to predict modal frequencies on a  simulated problem
An n-sided polygonal model to calculate the impact of cyber security
  events

This paper presents a model to represent graphically the impact of cyber
events (e.g., attacks, countermeasures) in a polygonal systems of n-sides. The
approach considers information about all entities composing an information
system (e.g., users, IP addresses, communication protocols, physical and
logical resources, etc.). Every axis is composed of entities that contribute to
the execution of the security event. Each entity has an associated weighting
factor that measures its contribution using a multi-criteria methodology named
CARVER. The graphical representation of cyber events is depicted as straight
lines (one dimension) or polygons (two or more dimensions). Geometrical
operations are used to compute the size (i.e, length, perimeter, surface area)
and thus the impact of each event. As a result, it is possible to identify and
compare the magnitude of cyber events. A case study with multiple security
events is presented as an illustration on how the model is built and computed.Comment: 16 pages, 5 figures, 2 tables, 11th International Conference on Risks
  and Security of Internet and Systems, (CRiSIS 2016), Roscoff, France,
  September 201
Complex Network Tools to Understand the Behavior of Criminality in Urban
  Areas

Complex networks are nowadays employed in several applications. Modeling
urban street networks is one of them, and in particular to analyze criminal
aspects of a city. Several research groups have focused on such application,
but until now, there is a lack of a well-defined methodology for employing
complex networks in a whole crime analysis process, i.e. from data preparation
to a deep analysis of criminal communities. Furthermore, the "toolset"
available for those works is not complete enough, also lacking techniques to
maintain up-to-date, complete crime datasets and proper assessment measures. In
this sense, we propose a threefold methodology for employing complex networks
in the detection of highly criminal areas within a city. Our methodology
comprises three tasks: (i) Mapping of Urban Crimes; (ii) Criminal Community
Identification; and (iii) Crime Analysis. Moreover, it provides a proper set of
assessment measures for analyzing intrinsic criminality of communities,
especially when considering different crime types. We show our methodology by
applying it to a real crime dataset from the city of San Francisco - CA, USA.
The results confirm its effectiveness to identify and analyze high criminality
areas within a city. Hence, our contributions provide a basis for further
developments on complex networks applied to crime analysis.Comment: 7 pages, 2 figures, 14th International Conference on Information
  Technology : New Generation
A Computational Non-Commutative Geometry Program for Disordered
  Topological Insulators

It has been some time since non-commutative geometry was proposed by Jean
Bellissard as a theoretical framework for the investigation of homogeneous
condensed matter systems. Recently, Bellissard's approach has been
enthusiastically adopted in the relatively young field of topological
insulators, where it facilitated many rigorous results concerning the stability
of the topological invariants against disorder. In this work we present a
computational program based on the principles of non-commutative geometry and
showcase several applications to topological insulators. In the first part we
introduce the notion of a homogeneous material and define the class of
disordered crystals together with the classification table which conjectures
all topological phases from this class. We continue with a discussion of
electron dynamics in disordered crystals and we briefly review the theory of
topological invariants in the presence of strong disorder. We show how all
these can be captured in the language of non-commutative geometry using the
concept of non-commutative Brillouin torus, and present a list of known
formulas for various physical response functions. In the second part, we
introduce auxiliary algebras and develop a canonical finite-volume
approximation of the non-commutative Brillouin torus. Explicit numerical
algorithms for computing generic correlation functions are discussed. In the
third part we derive upper bounds on the numerical errors and demonstrate that
the canonical-finite volume approximation converges extremely fast to the
thermodynamic limit. Convergence tests and various applications concludes our
presentation
Improving an Active Shape Model with Random Classiﬁcation Forest for Segmentation of Cervical Vertebrae

X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method
Argumentation Schemes for Events Suggestion in an e-Health Platform

In this work, we propose the introduction of persuasion techniques that guide the users into interacting with the Ambient Assisted Living framework iGenda. It is a cognitive assistant that manages active daily living activities, monitors user's health condition, and creates a social network between users via mobile devices. The objective is to be inserted in a healthcare environment and to provide features like adaptive interfaces, user profiling and machine learning processes that enhance the usage experience. The inclusion of a persuasive architecture (based on argumentation schemes) enables the system to provide recommendations to the users that fit their profile and interests, thus increases the chance of a positive interaction.A. Costa thanks the Fundacao para a Ciencia e a Tecnologia (FCT) the Post-Doc scholarship with the Ref. SFRH/BPD/102696/2014. This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e a Tecnologia within the Project Scope: UID/CEC/00319/2013. It was also supported by the by the projects TIN2015-65515-C4-1-R and TIN2014-55206-R of the Spanish government and by the grant program for the recruitment of doctors for the Spanish system of science and technology (PAID-10-14) of the Universitat Politecnica de Valencia.info:eu-repo/semantics/publishedVersio
Direct and Inverse Variational Problems on Time Scales: A Survey

We deal with direct and inverse problems of the calculus of variations on
arbitrary time scales. Firstly, using the Euler-Lagrange equation and the
strengthened Legendre condition, we give a general form for a variational
functional to attain a local minimum at a given point of the vector space.
Furthermore, we provide a necessary condition for a dynamic
integro-differential equation to be an Euler-Lagrange equation (Helmholtz's
problem of the calculus of variations on time scales). New and interesting
results for the discrete and quantum settings are obtained as particular cases.
Finally, we consider very general problems of the calculus of variations given
by the composition of a certain scalar function with delta and nabla integrals
of a vector valued field.Comment: This is a preprint of a paper whose final and definite form will be
  published in the Springer Volume 'Modeling, Dynamics, Optimization and
  Bioeconomics II', Edited by A. A. Pinto and D. Zilberman (Eds.), Springer
  Proceedings in Mathematics & Statistics. Submitted 03/Sept/2014; Accepted,
  after a revision, 19/Jan/201
Radio Emission from Ultra-Cool Dwarfs

The 2001 discovery of radio emission from ultra-cool dwarfs (UCDs), the very
low-mass stars and brown dwarfs with spectral types of ~M7 and later, revealed
that these objects can generate and dissipate powerful magnetic fields. Radio
observations provide unparalleled insight into UCD magnetism: detections extend
to brown dwarfs with temperatures <1000 K, where no other observational probes
are effective. The data reveal that UCDs can generate strong (kG) fields,
sometimes with a stable dipolar structure; that they can produce and retain
nonthermal plasmas with electron acceleration extending to MeV energies; and
that they can drive auroral current systems resulting in significant
atmospheric energy deposition and powerful, coherent radio bursts. Still to be
understood are the underlying dynamo processes, the precise means by which
particles are accelerated around these objects, the observed diversity of
magnetic phenomenologies, and how all of these factors change as the mass of
the central object approaches that of Jupiter. The answers to these questions
are doubly important because UCDs are both potential exoplanet hosts, as in the
TRAPPIST-1 system, and analogues of extrasolar giant planets themselves.Comment: 19 pages; submitted chapter to the Handbook of Exoplanets, eds. Hans
  J. Deeg and Juan Antonio Belmonte (Springer-Verlag
Rotation of planet-harbouring stars

The rotation rate of a star has important implications for the detectability,
characterisation and stability of any planets that may be orbiting it. This
chapter gives a brief overview of stellar rotation before describing the
methods used to measure the rotation periods of planet host stars, the factors
affecting the evolution of a star's rotation rate, stellar age estimates based
on rotation, and an overview of the observed trends in the rotation properties
of stars with planets.Comment: 16 pages, 4 figures: Invited review to appear in 'Handbook of
  Exoplanets', Springer Reference Works, edited by Hans J. Deeg and Juan
  Antonio Belmont
Future Astrometric Space Missions for Exoplanet Science

High-precision astrometry at the sub-microarcsecond level opens up a window
to study Earth-like planets in the habitable zones of Sun-like stars, and to
determine their masses. It thus promises to play an important role in exoplanet
science in the future. However, such precision can only be acquired from space,
and requires dedicated instrumentation for a sufficient astrometric
calibration. Here we present a series of concepts designed for handling this
task. STARE is a small satellite concept dedicated to finding planets in the
very nearest stellar systems, which offers a low-cost option toward the study
of habitable planets. The NEAT concept is a set of two formation-flying
satellites with the aim to survey the 200 nearest Sun-like stars for Earths in
the habitable zone. Finally, THEIA is a proposal for an ESA M-class mission,
with a single-unit telescope designed for both dark matter studies as well as a
survey for habitable Earth-like planets among the 50 nearest stars. The
concepts illustrate various possible paths and strategies for achieving
exquisite astrometric performance, and thereby addressing key scientific
questions regarding the distribution of habitability and life in the universe.Comment: 12 pages, 3 figures. Invited review to appear in 'Handbook of
  Exoplanets', Springer Reference Works, edited by Hans J. Deeg and Juan
  Antonio Belmont
More on Diophantine sextuples

A rational Diophantine m-tuple is a set of m nonzero rationals such that the
product of any two of them increased by 1 is a perfect square. The first
rational Diophantine quadruple was found by Diophantus, while Euler proved that
there are infinitely many rational Diophantine quintuples. In 1999, Gibbs found
the first example of a rational Diophantine sextuple, and Dujella, Kazalicki,
Mikic and Szikszai recently proved that there exist infinitely many rational
Diophantine sextuples.
  In this paper, generalizing the work of Piezas, we describe a method for
generating new parametric formulas for rational Diophantine sextuples.Comment: to appear in Number Theory - Diophantine problems, uniform
  distribution and applications, Festschrift in honour of Robert F. Tichy's
  60th birthday (C. Elsholtz, P. Grabner, Eds.), Springer-Verlag, Berli
On the monoid generated by a Lucas sequence

A Lucas sequence is a sequence of the general form $v_n = (\phi^n -
\bar{\phi}^n)/(\phi-\bar{\phi})$, where $\phi$ and $\bar{\phi}$ are real
algebraic integers such that $\phi+\bar{\phi}$ and $\phi\bar{\phi}$ are both
rational. Famous examples include the Fibonacci numbers, the Pell numbers, and
the Mersenne numbers. We study the monoid that is generated by such a sequence;
as it turns out, it is almost freely generated. We provide an asymptotic
formula for the number of positive integers $\leq x$ in this monoid, and also
prove Erd\H{o}s-Kac type theorems for the distribution of the number of
factors, with and without multiplicity. While the limiting distribution is
Gaussian if only distinct factors are counted, this is no longer the case when
multiplicities are taken into account
Patterns of primes in arithmetic progressions

After the proof of Zhang about the existence of infinitely many bounded gaps between consecutive primes the author showed the existence of a bounded d such that there are arbitrarily long arithmetic progressions of primes with the property that p′ = p + d is the prime following p for each element of the progression. This was a common generalization of the results of Zhang and Green-Tao. In the present work it is shown that for every m we have a bounded m-tuple of primes such that this configuration (i.e. the integer translates of this m-tuple) appear as arbitrarily long arithmetic progressions in the sequence of all primes. In fact we show that this is true for a positive proportion of all m-tuples. This is a common generalization of the celebrated works of Green-Tao and Maynard/Tao.\ud
\ud
Dedicated to the 60th birthday of Robert F. Tich