37,705 research outputs found
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The role of HG in the analysis of temporal iteration and interaural correlation
Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome
The article presents an application of Hidden Markov Models (HMMs) for
pattern recognition on genome sequences. We apply HMM for identifying genes
encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma
brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa
causative agents of sleeping sickness and several diseases in domestic and wild
animals. These parasites have a peculiar strategy to evade the host's immune
system that consists in periodically changing their predominant cellular
surface protein (VSG). The motivation for using patterns recognition methods to
identify these genes, instead of traditional homology based ones, is that the
levels of sequence identity (amino acid and DNA sequence) amongst these genes
is often below of what is considered reliable in these methods. Among pattern
recognition approaches, HMM are particularly suitable to tackle this problem
because they can handle more naturally the determination of gene edges. We
evaluate the performance of the model using different number of states in the
Markov model, as well as several performance metrics. The model is applied
using public genomic data. Our empirical results show that the VSG genes on T.
brucei can be safely identified (high sensitivity and low rate of false
positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications,
Springer. The article contains 23 pages, 4 figures, 8 tables and 51
reference
Keystroke dynamics in the pre-touchscreen era
Biometric authentication seeks to measure an individualās unique physiological attributes for the purpose of identity verification. Conventionally, this task has been realized via analyses of fingerprints or signature iris patterns. However, whilst such methods effectively offer a superior security protocol compared with password-based approaches for example, their substantial infrastructure costs, and intrusive nature, make them undesirable and indeed impractical for many scenarios. An alternative approach seeks to develop similarly robust screening protocols through analysis of typing patterns, formally known as keystroke dynamics. Here, keystroke analysis methodologies can utilize multiple variables, and a range of mathematical techniques, in order to extract individualsā typing signatures. Such variables may include measurement of the period between key presses, and/or releases, or even key-strike pressures. Statistical methods, neural networks, and fuzzy logic have often formed the basis for quantitative analysis on the data gathered, typically from conventional computer keyboards. Extension to more recent technologies such as numerical keypads and touch-screen devices is in its infancy, but obviously important as such devices grow in popularity. Here, we review the state of knowledge pertaining to authentication via conventional keyboards with a view toward indicating how this platform of knowledge can be exploited and extended into the newly emergent type-based technological contexts
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PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures.
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions
D7.2 1st experiment planning and community management
The present deliverable, outlines the overall strategy for approaching the tasks of (a) developing and sustaining an engaged school-based community of ProsocialLearn users; and (b)planning and facilitating small-scale and large-scale school-based evaluation studies of the Prosocial Learn technological solution. It also presents the preliminary work undertaken so far, and details the activities planned for M9-15 with respect to community development and small-scale studies
Towards disappearing user interfaces for ubiquitous computing: human enhancement from sixth sense to super senses
The enhancement of human senses electronically is possible when pervasive computers interact unnoticeably with humans in Ubiquitous Computing. The design of computer user interfaces towards ādisappearingā forces the interaction with humans using a content rather than a menu driven approach, thus the emerging requirement for huge number of non-technical users interfacing intuitively with billions of computers in the Internet of Things is met. Learning to use particular applications in Ubiquitous Computing is either too slow or sometimes impossible so the design of user interfaces must be naturally enough to facilitate intuitive human behaviours. Although humans from different racial, cultural and ethnic backgrounds own the same physiological sensory system, the perception to the same stimuli outside the human bodies can be different. A novel taxonomy for Disappearing User Interfaces (DUIs) to stimulate human senses and to capture human responses is proposed. Furthermore, applications of DUIs are reviewed. DUIs with sensor and data fusion to simulate the Sixth Sense is explored. Enhancement of human senses through DUIs and Context Awareness is discussed as the groundwork enabling smarter wearable devices for interfacing with human emotional memories
The Stylometric Processing of Sensory Open Source Data
This research projectās end goal is on the Lone Wolf Terrorist.
The project uses an exploratory approach to the
self-radicalisation problem by creating a stylistic fingerprint
of a person's personality, or self, from subtle characteristics
hidden in a person's writing style. It separates the identity of
one person from another based on their writing style. It also
separates the writings of suicide attackers from ānormal'
bloggers by critical slowing down; a dynamical property used to
develop early warning signs of tipping points. It identifies
changes in a person's moods, or shifts from one state to another,
that might indicate a tipping point for self-radicalisation.
Research into authorship identity using personality is a
relatively new area in the field of neurolinguistics. There are
very few methods that model how an individual's cognitive
functions present themselves in writing. Here, we develop a
novel algorithm, RPAS, which draws on cognitive functions such as
aging, sensory processing, abstract or concrete thinking through
referential activity emotional experiences, and a person's
internal gender for identity. We use well-known techniques such
as Principal Component Analysis, Linear Discriminant Analysis,
and the Vector Space Method to cluster multiple
anonymous-authored works. Here we use a new approach, using
seriation with noise to separate subtle features in individuals.
We conduct time series analysis using modified variants of 1-lag
autocorrelation and the coefficient of skewness, two statistical
metrics that change near a tipping point, to track serious life
events in an individual through cognitive linguistic markers.
In our journey of discovery, we uncover secrets about the
Elizabethan playwrights hidden for over 400 years. We uncover
markers for depression and anxiety in modern-day writers and
identify linguistic cues for Alzheimer's disease much earlier
than other studies using sensory processing. In using these
techniques on the Lone Wolf, we can separate their writing style
used before their attacks that differs from other writing
Pattern Recognition
Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition
Detecting Abnormal Social Robot Behavior through Emotion Recognition
Sharing characteristics with both the Internet of Things and the Cyber Physical Systems categories, a new type of device has arrived to claim a third category and raise its very own privacy concerns. Social robots are in the market asking consumers to become part of their daily routine and interactions. Ranging in the level and method of communication with the users, all social robots are able to collect, share and analyze a great variety and large volume of personal data.In this thesis, we focus the communityās attention to this emerging area of interest for privacy and security research. We discuss the likely privacy issues, comment on current defense mechanisms that are applicable to this new category of devices, outline new forms of attack that are made possible through social robots, highlight paths that research on consumer perceptions could follow, and propose a system for detecting abnormal social robot behavior based on emotion detection
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