pinecone/core-2020-05-10-deduplication huggingface dataset:
Supporting the design of an ambient assisted living system using virtual reality prototypes

APEX, a framework for prototyping ubiquitous environments, is used to design an Ambient Assisted Living (AAL) system to enhance a care home for older people. The environment allows participants in the design process to experience the proposed design and enables developers to explore the design by rapidly developing alternatives. APEX provided the means to explore alternative designs through a virtual environment. It provides a mediating representation (a boundary object) allowing users to be involved in the design process. A group of residents in a city-based care home were involved in the design. The paper describes the design process and lessons learnt for the design of AAL systems.EPSRC - Engineering and Physical Sciences Research Council(EP/G059063/1)Jose C. Campos acknowledges support by the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) within project UID/EEA/50014/2013. José Luís Silva acknowledges
support from project PEST-OE/EEI/LA0009/2015. Michael Harrison was also funded by EPSRC research grant EP/G059063/1: CHI+MED (Computer–Human Interaction for Medical Devices)
Fractal Reconnection in Solar and Stellar Environments

Recent space based observations of the Sun revealed that magnetic
reconnection is ubiquitous in the solar atmosphere, ranging from small scale
reconnection (observed as nanoflares) to large scale one (observed as long
duration flares or giant arcades). Often the magnetic reconnection events are
associated with mass ejections or jets, which seem to be closely related to
multiple plasmoid ejections from fractal current sheet. The bursty radio and
hard X-ray emissions from flares also suggest the fractal reconnection and
associated particle acceleration. We shall discuss recent observations and
theories related to the plasmoid-induced-reconnection and the fractal
reconnection in solar flares, and their implication to reconnection physics and
particle acceleration. Recent findings of many superflares on solar type stars
that has extended the applicability of the fractal reconnection model of solar
flares to much a wider parameter space suitable for stellar flares are also
discussed.Comment: Invited chapter to appear in "Magnetic Reconnection: Concepts and
  Applications", Springer-Verlag, W. D. Gonzalez and E. N. Parker, eds. (2016),
  33 pages, 18 figure
Theory and Applications of Non-Relativistic and Relativistic Turbulent
  Reconnection

Realistic astrophysical environments are turbulent due to the extremely high
Reynolds numbers. Therefore, the theories of reconnection intended for
describing astrophysical reconnection should not ignore the effects of
turbulence on magnetic reconnection. Turbulence is known to change the nature
of many physical processes dramatically and in this review we claim that
magnetic reconnection is not an exception. We stress that not only
astrophysical turbulence is ubiquitous, but also magnetic reconnection itself
induces turbulence. Thus turbulence must be accounted for in any realistic
astrophysical reconnection setup. We argue that due to the similarities of MHD
turbulence in relativistic and non-relativistic cases the theory of magnetic
reconnection developed for the non-relativistic case can be extended to the
relativistic case and we provide numerical simulations that support this
conjecture. We also provide quantitative comparisons of the theoretical
predictions and results of numerical experiments, including the situations when
turbulent reconnection is self-driven, i.e. the turbulence in the system is
generated by the reconnection process itself. We show how turbulent
reconnection entails the violation of magnetic flux freezing, the conclusion
that has really far reaching consequences for many realistically turbulent
astrophysical environments. In addition, we consider observational testing of
turbulent reconnection as well as numerous implications of the theory. The
former includes the Sun and solar wind reconnection, while the latter include
the process of reconnection diffusion induced by turbulent reconnection, the
acceleration of energetic particles, bursts of turbulent reconnection related
to black hole sources as well as gamma ray bursts. Finally, we explain why
turbulent reconnection cannot be explained by turbulent resistivity or derived
through the mean field approach.Comment: 66 pages, 24 figures, a chapter of the book "Magnetic Reconnection -
  Concepts and Applications", editors W. Gonzalez, E. N. Parke
Radiative Magnetic Reconnection in Astrophysics

I review a new rapidly growing area of high-energy plasma astrophysics ---
radiative magnetic reconnection, i.e., a reconnection regime where radiation
reaction influences reconnection dynamics, energetics, and nonthermal particle
acceleration. This influence be may be manifested via a number of
astrophysically important radiative effects, such as radiation-reaction limits
on particle acceleration, radiative cooling, radiative resistivity, braking of
reconnection outflows by radiation drag, radiation pressure, viscosity, and
even pair creation at highest energy densities. Self-consistent inclusion of
these effects in magnetic reconnection theory and modeling calls for serious
modifications to our overall theoretical approach to the problem. In addition,
prompt reconnection-powered radiation often represents our only observational
diagnostic tool for studying remote astrophysical systems; this underscores the
importance of developing predictive modeling capabilities to connect the
underlying physical conditions in a reconnecting system to observable radiative
signatures. This Chapter gives an overview of recent theoretical progress in
developing basic physical understanding of radiative reconnection, with a
special emphasis on astrophysically important radiation mechanisms like
synchrotron, curvature, and inverse-Compton. It also offers a broad review of
key high-energy astrophysical applications of radiative reconnection, such as:
pulsar wind nebulae and magnetospheres, accreting black-hole coronae and jets
in XRBs and AGN, magnetospheres of magnetars, and Gamma-Ray Bursts. Finally,
this Chapter discusses the most critical open questions and outlines the
directions for future research in this exciting new frontier of plasma
astrophysics.Comment: Invited chapter to appear in "Magnetic Reconnection: Concepts and
  Applications", Springer-Verlag, W. D. Gonzalez and E. N. Parker, eds. (2016);
  44 page
Hierarchic interactive path planning in virtual reality

To save time and money while designing new products, industry needs tools to design, test and validate the product using virtual prototypes. These vir- tual prototypes must enable to test the product at all Product Life-cycle Management (PLM) stages. Many operations in PLM involve human manipulation of product com- ponents in cluttered environment (product assembly, disassembly or maintenance). Virtual Reality (VR) enables real operators to perform these tests with virtual proto- types. This work introduces a novel path planning architecture allowing collaboration between a VR user and an automatic path planning system. It is based on an origi- nal environment model including semantic, topological and geometric information, and an automatic path planning process split in two phases: coarse (semantic and topological information) and fine (semantic and geometric information) planning. The collaboration between VR user and automatic path planner is made of 3 main aspects. First, the VR user is guided along a pre-computed path through a haptic device whereas he VR user can go away from the proposed path to explore possible better ways. Second the authority of automatic planning system is balanced to let the user free to explore alternatives (geometric layer). Third the intents of VR user are predicted (on topological layer) to be integrated in the re-planning process. Exper- iments are provided to illustrate the multi-layer representation of the environment, the path planning process, the control sharing and the intent prediction
Social aggravation estimation to seismic hazard using classical fuzzy methods

In the last years, from a disasters perspective, risk has been dimensioned to allow a better management. However, this conceptualization turns out to be limited or constrained, by the generalized use of a fragmented risk scheme, which always consider first, the approach and applicability of each discipline involved. To be congruent with risk definition, it is necessary to consider an integral frame, and social factors must be included. Even those indicators that could tell something about the organizational and institutional capacity to withstand natural hazards, should be invited to the table. In this article, we analyze one of the most important elements in risk formation: the social aggravation, which can be regarded as the convolution of the resilience capacity and social fragility of an urban center. We performed a social aggravation estimation over Barcelona, Spain and Bogota, Colombia considering a particular hazard in the form of seismic activity. The Aggravation coefficient was achieved through a Mamdami fuzzy approach, supported by well-established fuzzy theory, which is characterized by a high expressive power and an intuitive human-like manner.Peer ReviewedPostprint (author's final draft
Real-Time decision support using data mining to predict blood pressure critical events in intensive medicine patients

Patient blood pressure is an important vital signal to the physicians take a decision and to better understand the patient condition. In Intensive Care Units is possible monitoring the blood pressure due the fact of the patient being in continuous monitoring through bedside monitors and the use of sensors. The intensivist only have access to vital signs values when they look to the monitor or consult the values hourly collected. Most important is the sequence of the values collected, i.e., a set of highest or lowest values can signify a critical event and bring future complications to a patient as is Hypotension or Hypertension. This complications can leverage a set of dangerous diseases and side-effects. The main goal of this work is to predict the probability of a patient has a blood pressure critical event in the next hours by combining a set of patient data collected in real-time and using Data Mining classification techniques. As output the models indicate the probability (%) of a patient has a Blood Pressure Critical Event in the next hour. The achieved results showed to be very promising, presenting sensitivity around of 95%
A Real-Time intelligent system for tracking patient condition

Hospitals have multiple data sources, such as embedded systems, monitors and sensors. The number of data available is increasing and the information are used not only to care the patient but also to assist the decision processes. The introduction of intelligent environments in health care institutions has been adopted due their ability to provide useful information for health professionals, either in helping to identify prognosis or also to understand patient condition. Behind of this concept arises this Intelligent System to track patient
condition (e.g. critic events) in health care. This system has the great advantage of being adaptable to the environment and user needs. The system is focused in identifying critic events from data streaming (e.g. vital signs and ventilation) which is particularly valuable for understanding the patient’s condition. This work aims to demonstrate the process of creating an intelligent system capable of operating in a real environment using streaming data provided by ventilators and vital signs monitors. Its development is important to the physician because becomes possible crossing multiple variables in real-time by analyzing if a value is critic or not and if their variation has or not clinical importance
More Sound Static Handling of Java Reflection

Reflection is a highly dynamic language feature that poses grave problems for static analyses. In the Java setting, reflection is ubiquitous in large programs. Any handling of reflection will be approximate, and overestimating its reach in a large codebase can be catastrophic for precision and scalability. We present an approach for handling reflection with improved empirical soundness (as measured against prior approaches and dynamic information) in the context of a points-to analysis. Our approach is based on the combination of string-flow and points-to analysis from past literature augmented with (a) substring analysis and modeling of partial string flow through string builder classes; (b) new techniques for analyzing reflective entities based on information available at their use-sites. The resulting analysis is general, without any need for hand-tuning. In experimental
comparisons with prior approaches, we demonstrate a combination of both improved soundness (recovering the majority of missing call-graph edges) and increased performance
SEIR immune strategy for instance weighted Naive bayes classification

Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods
Enhancing competitive island cooperative neuro - evolution through backpropagation for pattern classification

Cooperative coevolution is a promising method for training neural networks which is also known as cooperative neuro-evolution. Cooperative neuro-evolution has been used for pattern classification, time\ud
series prediction and global optimisation problems. In the past, competitive island based cooperative coevolution has been proposed that employed different instances of problem decomposition methods for competition. Neuro-evolution has limitations in terms of training time although they are known as global search methods. Backpropagation algorithm employs gradient descent which helps in faster convergence which is needed for neuro-evolution. Backpropagation suffers from premature convergence and its combination with neuro-evolution can help eliminate the weakness of both the approaches. In this paper, we propose a competitive island cooperative neuro-evolutionary method that takes advantage of the strengths of gradient descent and neuro-evolution. We use feedforward neural networks on benchmark pattern classification problems to evaluate the performance of the proposed algorithm. The results show\ud
improved performance when compared to related methods
Motor imagery task classification using a signal-dependent orthogonal transform based feature extraction

© Springer International Publishing Switzerland 2015. In this paper, we present the results of classifying electroencephalographic (EEG) signals into four motor imagery tasks using a new method for feature extraction. This method is based on a signal-dependent orthogonal transform, referred to as LP-SVD, defined as the left singular vectors of the LPC filter impulse response matrix. Using a logistic tree based model classifier, the extracted features are mapped into one of four motor imagery movements, namely left hand, right hand, foot, and tongue. The proposed technique-based classification performance was benchmarked against those based on two widely used linear transform for feature extraction methods, namely discrete cosine transform (DCT) and adaptive autoregressive (AAR). By achieving an accuracy of 67.35 %, the LP-SVD based method outperformed the other two by large margins (+25 % compared to DCT and +6 % compared to AAR-based methods)
Secure Multi-Party Computation Based Privacy Preserving Extreme Learning
  Machine Algorithm Over Vertically Distributed Data

Especially in the Big Data era, the usage of different classification methods
is increasing day by day. The success of these classification methods depends
on the effectiveness of learning methods. Extreme learning machine (ELM)
classification algorithm is a relatively new learning method built on
feed-forward neural-network. ELM classification algorithm is a simple and fast
method that can create a model from high-dimensional data sets. Traditional ELM
learning algorithm implicitly assumes complete access to whole data set. This
is a major privacy concern in most of cases. Sharing of private data (i.e.
medical records) is prevented because of security concerns. In this research,
we propose an efficient and secure privacy-preserving learning algorithm for
ELM classification over data that is vertically partitioned among several
parties. The new learning method preserves the privacy on numerical attributes,
builds a classification model without sharing private data without disclosing
the data of each party to others.Comment: 22nd International Conference, ICONIP 201
Hierarchical Data Classification Using Deep Neural Networks

Deep Neural Networks (DNNs) are becoming an increasingly interesting, valuable and efficient machine learning paradigm with implementations in natural language processing, image recognition and hand-written character recognition. Application of deep architectures is increasing in domains that contain feature hierarchies (FHs) i.e. features from higher levels of the hierarchy formed by the composition of lower level features. This is because of a perceived relationship between on the one hand the hierarchical organisation of DNNs, with large numbers of neurons at the bottom layers and increasingly smaller numbers at upper layers, and on the other hand FHs, with comparatively large numbers of low level features resulting in a small number of high level features. However, it is not clear what the relationship between DNNs hierarchies and FHs should be, or whether there even exists one. Nor is it clear whether modelling FHs with a hierarchically organised DNN conveys any benefits over using non-hierarchical neural networks. This study is aimed at exploring these questions and is organized into two parts. Firstly, a taxonomic FH with associated data is generated and a DNN is trained to classify the organisms into various species depending on characteristic features. The second part involves testing the ability of DNNs to identify whether two given organisms are related or not, depending on the sharing of appropriate features in their FHs. The experimental results show that the accuracy of the classification results is reduced with the increase in ‘depth’. Further, improved performance was achieved when every hidden layer has the same number of nodes compared with DNNs with increasingly fewer hidden nodes at higher levels. In other words, our experiments show that the relationship between DNNs and FHs is not simple and may require further extensive experimental research to identify the best DNN architectures when learning FHs
Neuron - synapse level problem decomposition method for cooperative neuro - evolution of feedforward networks for time series prediction\ud
Prediction

A major concern in cooperative coevolution for neuro-evolution is the appropriate problem decomposition method that takes into account the architectural properties of the neural network. Decomposition to the synapse and neuron level has been proposed in the past that have their own strengths and limitations depending on the application problem. In this paper, a new problem decomposition method that combines neuron and synapse level is proposed for feedfoward networks and applied to\ud
time series prediction. The results show that the proposed approach has improved the results in selected benchmark data sets when compared to related methods. It also has promising performance when compared to other computational intelligence methods from the literature
Competitive island - based cooperative coevolution for efficient optimization of large - scale fully - separable continuous functions

In this paper, we investigate the performance of introducing competition in cooperative coevolutionary algorithms to solve large-scale fully-separable continuous optimization problems. It may seem that solving large-scale fully-separable functions is trivial by means of problem decomposition. In principle, due to lack of variable interaction in fully-separable problems, any decomposition is viable. However, the decomposition strategy has shown to have a significant impact on the performance of cooperative coevolution on such functions. Finding an optimal decomposition strategy for solving fully-separable functions is laborious and requires extensive empirical studies. In this paper, we use a competitive two-island cooperative coevolution in which two decomposition strategies compete and collaborate to solve a fully-separable problem. Each problem decomposition has features that may be beneficial at different stages of optimization. Therefore, competition and collaboration of such decomposition strategies may eliminate the need for finding an optimal decomposition. The experimental results in this paper suggest that com-\ud
petition and collaboration of suboptimal decomposition strategies of a fully-separable problem can generate better solutions than the standard cooperative coevolution with standalone decomposition strategies. We\ud
also show that a decomposition strategy that implements competition against itself can also improve the overall optimization performance
Application of simulated annealing to data distribution for all-to-all comparison problems in homogeneous systems

Distributed systems are widely used for solving large-scale and data-intensive computing problems, including all-to-all comparison (ATAC) problems. However, when used for ATAC problems, existing computational frameworks such as Hadoop focus on load balancing for allocating comparison tasks, without careful consideration of data distribution and storage usage. While Hadoop-based solutions provide users with simplicity of implementation, their inherent MapReduce computing pattern does not match the ATAC pattern. This leads to load imbalances and poor data locality when Hadoop's data distribution strategy is used for ATAC problems. Here we present a data distribution strategy which considers data locality, load balancing and storage savings for ATAC computing problems in homogeneous distributed systems. A simulated annealing algorithm is developed for data distribution and task scheduling. Experimental results show a significant performance improvement for our approach over Hadoop-based solutions