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Over the recent years, there has been a growing interest in developing new research evaluation methods that could go beyond the traditional citation-based metrics. This interest is motivated on one side by the wider availability or even emergence of new information evidencing research performance, such as article downloads, views and Twitter mentions, and on the other side by the continued frustrations...
Facial age estimation is an important and challenging problem in computer vision and pattern recognition. Linear canonical correlation analysis (CCA) has been widely applied owing to low complexity, small and fixed amount of model parameters and good scalability. However, linear CCA based regression gets lower accuracy than its kernel version on the age estimation problem. The inexactness of metric...
A number of image quality assessment (IQA) metrics have been designed in recent years for natural images, leading to a desire to develop IQA approaches for screen content image which is composed of textual as well as pictorial regions and exhibits different visual characteristics from the natural image. In this work, a no reference IQA metric based on convolutional neural network (CNN) is proposed...
In this paper we look into the test methods to evaluate the quality of audio separation algorithms. Specifically we try to correlate the results of listening tests with state-of-the-art objective measures. To this end, the quality of the harmonic signals obtained with two harmonic-percussive separation algorithms was evaluated with BSS_Eval, PEASS and via listening tests. A correlation analysis was...
The volume regulation graph is meant to relate the fundamental determinants of left ventricular (LV) function, namely end-systolic volume (ESV) and end-diastolic volume (EDV). This representation permits evaluation of the LV remodeling process, convenient stratification for clinically relevant covariates, as well as inscription of iso-stroke volume (SV) and iso-ejection fraction (EF) trajectories,...
Normal human brain exhibits approximately bi-fold symmetry with respect to its midsagittal plane (MSP). The objective of this work is to investigate the effect of doubling atlases (i.e., reference images) used in multi-atlas fusion methods by exploiting the inherent bilateral symmetry of human brain. To this end, we perform automated segmentation of 15 subcortical structures using Local Weighted Voting...
Security protocols have been commonly used to protect secure communication in networked systems. It is often assumed that individual wireless nodes or leaders in a system are sincere and use techniques (authentication, permission, etc.) of these protocols to have secure communications. We discover that such protocols may be leaked by a sophisticated collusion attack (a type of attacks in which a node...
Schizophrenia is a mental disorder in which functional and structural brain networks are disrupted. Classical network analysis has been used by many researchers to quantify brain networks and to study the network changes in schizophrenia, but unfortunately metrics used in this classical method highly depend on the networks' density and weight; the comparisons made by this method are biased. The minimum...
In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order...
We propose a novel sequence score to determine to what extent neural activity is consistent with trajectories through latent ensemble states — virtual place fields — in an associated environment. In particular, we show how hidden Markov models (HMMs) can be used to model and analyze sequences of neural activity, and how the resulting joint probability of an observation sequence and an underlying sequence...
In this paper we apply techniques for numerical estimation of system resolution from imaging, to the regression problem of relating biological data to phenotypes. Our approach can be viewed as an extension of Backus-Gilbert theory, which attempts to find the most concentrated estimator that may be reliably computed in an inverse problem. Applied to a regression model, we estimate a minimal combination...
In the dynamic complex networks, the community structure is constantly changing, such as generation, maintaining, merger, die, fusion, etc. These changes will lead to the dynamic evolution of the structure and morphology in an entire community. Current static community division methods can not be well used to analyze the dynamic network evolution. In this paper, based on events in the community evolution,...
In this work, we describe a novel method based on waveform morphology for detecting artifacts in photoplethysmography (PPG) signals and, thus, improve reliability of PPG. By considering inter-individual and measure condition variability, specific parameters are estimated for each record. We introduce a novel metric for comparing pulses, which is the derivative of the correlation coefficient. Then,...
High-density imaging arrays have improved the image quality of diffuse optical tomography (HD-DOT) methods for mapping brain functions. Development of resting state measures of functional connectivity provides a brain assay logistically compatible with bedside imaging in the clinic. In this paper we review these advances and demonstrate the sensitivity of HD-DOT to acute ischemic stroke.
Recently, a mountain of work has been done to evaluate the visual discomfort caused by binocular disparity. In this paper, a subjective experiment was conducted to explore the relationship between fusion time and disparity, stimulus width firstly. Then the fusion time function was built. Finally, another subjective experiment was created to investigate the correlation between fusion time and visual...
In this paper, we introduce graph simplification capabilities of a new tool, CEREBRA, which is used to visualize the 3D network of human brain, extracted from the fMRI data. The nodes of the network are defined as the voxels with the attributes corresponding to the intensity values changing by time and the coordinates in three dimensional Euclidean space. The arc weights are estimated by modeling...
In this short paper we present a socio-technical framework for integrating a security risk escalation maturity model into a security information and event management system. The objective of the framework is to develop the foundations for the next generation socio-technical security information and event management systems (ST-SIEMs) enabling socio-technical security operations centers (ST-SOCs)....
Online monitoring, providing the real-time status information of servers, is indispensable for the management of distributed systems, e.g. failure detection and resource scheduling. The main design challenges for distributed monitoring systems include scalability, fine granularity, reliability and low overheads. And the challenges are growing with the increase of the scales of the distributed systems...
Clustering is a fundamental tool for data analysis. Typically, all attributes of the data are used for clustering. However, if a set of attributes can be divided into meaningful subsets, it may be effective to cluster the data for each subset. In this paper, we propose a method for dividing the set of elements of feature vectors into meaningful subsets. Considering the dependencies between the elements,...
This paper mainly deals with the distributed estimation fusion problem when the correlations are unknown. The local estimates are represented as a set of probability density functions, on which a Riemannian structure endowed with the Fisher metric is built. From the perspective of information geometry, the fused density is formulated as the Fisher barycenter in the space of probability densities and...
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