56 research outputs found
A Smartphone Health Application To Facilitate Falls Prevention Practices For Older Adults
Falls pose a serious threat to older adults’ health and their quality of life. Web-based technologies such as smartphones have emerged as vital tools for health-related behavioural interventions, but little is known about the potential benefits of a smartphone health application (app) in applying falls prevention practices for older adults. The research presented in this paper sought to answer the question: what are the key features needed in a smartphone health app intended to support falls prevention practices for older adults, increase their autonomy and improve their quality of life? A comprehensive literature review of studies conducted in public health, aged care, mobile health and mobile app design disciplines was undertaken and a conceptual framework for a smartphone app was proposed. The framework depicts the features of a smartphone app that can facilitate the implementation of falls prevention practices, including exercise programs; establishing a healthy diet and falls prevention education. Translation of the conceptual framework into a practical app will reduce falls in older adults, improve their sense of belongingness, and consequently enable better autonomy and quality of life
Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier features
One-class support vector machine (OC-SVM) for a long time has been one of the
most effective anomaly detection methods and extensively adopted in both
research as well as industrial applications. The biggest issue for OC-SVM is
yet the capability to operate with large and high-dimensional datasets due to
optimization complexity. Those problems might be mitigated via dimensionality
reduction techniques such as manifold learning or autoencoder. However,
previous work often treats representation learning and anomaly prediction
separately. In this paper, we propose autoencoder based one-class support
vector machine (AE-1SVM) that brings OC-SVM, with the aid of random Fourier
features to approximate the radial basis kernel, into deep learning context by
combining it with a representation learning architecture and jointly exploit
stochastic gradient descent to obtain end-to-end training. Interestingly, this
also opens up the possible use of gradient-based attribution methods to explain
the decision making for anomaly detection, which has ever been challenging as a
result of the implicit mappings between the input space and the kernel space.
To the best of our knowledge, this is the first work to study the
interpretability of deep learning in anomaly detection. We evaluate our method
on a wide range of unsupervised anomaly detection tasks in which our end-to-end
training architecture achieves a performance significantly better than the
previous work using separate training.Comment: Accepted at European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Databases (ECML-PKDD) 201
Comparison of Network Intrusion Detection Performance Using Feature Representation
P. 463-475Intrusion detection is essential for the security of the components
of any network. For that reason, several strategies can be used in
Intrusion Detection Systems (IDS) to identify the increasing attempts to
gain unauthorized access with malicious purposes including those base
on machine learning. Anomaly detection has been applied successfully to
numerous domains and might help to identify unknown attacks. However,
there are existing issues such as high error rates or large dimensionality
of data that make its deployment di cult in real-life scenarios. Representation
learning allows to estimate new latent features of data in a
low-dimensionality space. In this work, anomaly detection is performed
using a previous feature learning stage in order to compare these methods
for the detection of intrusions in network tra c. For that purpose,
four di erent anomaly detection algorithms are applied to recent network
datasets using two di erent feature learning methods such as principal
component analysis and autoencoders. Several evaluation metrics such
as accuracy, F1 score or ROC curves are used for comparing their performance.
The experimental results show an improvement for two of the
anomaly detection methods using autoencoder and no signi cant variations
for the linear feature transformationS
Deep Learning Application in Security and Privacy - Theory and Practice:A Position Paper
Technology is shaping our lives in a multitude of ways. This is fuelled by a
technology infrastructure, both legacy and state of the art, composed of a
heterogeneous group of hardware, software, services and organisations. Such
infrastructure faces a diverse range of challenges to its operations that
include security, privacy, resilience, and quality of services. Among these,
cybersecurity and privacy are taking the centre-stage, especially since the
General Data Protection Regulation (GDPR) came into effect. Traditional
security and privacy techniques are overstretched and adversarial actors have
evolved to design exploitation techniques that circumvent protection. With the
ever-increasing complexity of technology infrastructure, security and
privacy-preservation specialists have started to look for adaptable and
flexible protection methods that can evolve (potentially autonomously) as the
adversarial actor changes its techniques. For this, Artificial Intelligence
(AI), Machine Learning (ML) and Deep Learning (DL) were put forward as
saviours. In this paper, we look at the promises of AI, ML, and DL stated in
academic and industrial literature and evaluate how realistic they are. We also
put forward potential challenges a DL based security and privacy protection
technique has to overcome. Finally, we conclude the paper with a discussion on
what steps the DL and the security and privacy-preservation community have to
take to ensure that DL is not just going to be hype, but an opportunity to
build a secure, reliable, and trusted technology infrastructure on which we can
rely on for so much in our lives
Multi-messenger observations of a binary neutron star merger
On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta
Facebook support groups for ovarian cancer carers: A qualitative evaluation
© 2019 Association for Information Systems. All rights reserved. A cancer diagnosis takes a great toll on the health of both patients and their carers. Online cancer support groups, including cancer support Facebook groups, have evolved as new sources of support for cancer patients and their carers. However, little is known about how cancer carers make use of such online resources. Most research attention has been paid to Facebook support groups for cancer patients. This research is designed to determine the content of communication in Ovarian Facebook pages, and the impact of those communications on carers of ovarian cancer patients. The study will contribute to knowledge about how cancer patients’ carers use Facebook cancer support groups and the impact of this use on their health and quality of life
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