Evaluating the role of content in subjective video quality assessment.

Video quality as perceived by human observers is the ground truth when Video Quality Assessment (VQA) is in question. It is dependent on many variables, one of them being the content of the video that is being evaluated. Despite the evidence that content has an impact on the quality score the sequence receives from human evaluators, currently available VQA databases mostly comprise of sequences which fail to take this into account. In this paper, we aim to identify and analyze differences between human cognitive, affective, and conative responses to a set of videos commonly used for VQA and a set of videos specifically chosen to include video content which might affect the judgment of evaluators when perceived video quality is in question. Our findings indicate that considerable differences exist between the two sets on selected factors, which leads us to conclude that videos starring a different type of content than the currently employed ones might be more appropriate for VQA.

Echocardiographic parameters as predictors of in-hospital mortality in patients with acute coronary syndrome undergoing percutaneous coronary intervention.

Different ways have been used to stratify risk in acute coronary syndrome (ACS) patients. The aim of the study was to examine the usefulness of echocardiographic parameters as predictors of in-hospital outcome in patients with ACS after percutaneous coronary intervention (PCI). A data of 2030 patients with diagnosis of ACS hospitalized from December 2008 to December 2011 was used to develop a risk model based on echocardiographic parameters using the binary logistic regression. This model was independently evaluated in validation cohort prospectively (954 patients admitted during 2012). In-hospital mortality in derivation cohort was 7.73%, and 6.28% in validation cohort. Developed model has been designed with 4 independent echocardiographic predictors of in-hospital mortality: left ventricular ejection fraction (LVEF RR = 0.892; 95%CI = 0.854-0.932, P < 0.0005), aortic leaflet separation diameter (AOvs RR = 0.131; 95%CI = 0.027-0.627, P = 0.011), right ventricle diameter (RV RR = 2.675; 95%CI = 1.109-6.448, P = 0.028) and right ventricle systolic pressure (RVSP RR = 1.036; 95%CI = 1.000-1.074, P = 0.048). Model has good prognostic accuracy (AUROC = 0.84) and it retains good (AUROC = 0.78) when testing on the validation cohort. Risks for in-hospital mortality after PCI in ACS patients using echocardiographic measurements could be accurately predicted in contemporary practice. Incorporation of such developed model should facilitate research, clinical decisions, and optimizing treatment strategy in selected high risk ACS patients.

MR image based approach for metal artifact reduction in X-ray CT.

For decades, computed tomography (CT) images have been widely used to discover valuable anatomical information. Metallic implants such as dental fillings cause severe streaking artifacts which significantly degrade the quality of CT images. In this paper, we propose a new method for metal-artifact reduction using complementary magnetic resonance (MR) images. The method exploits the possibilities which arise from the use of emergent trimodality systems. The proposed algorithm corrects reconstructed CT images. The projected data which is affected by dental fillings is detected and the missing projections are replaced with data obtained from a corresponding MR image. A simulation study was conducted in order to compare the reconstructed images with images reconstructed through linear interpolation, which is a common metal-artifact reduction technique. The results show that the proposed method is successful in reducing severe metal artifacts without introducing significant amount of secondary artifacts.

Self Paced Deep Learning for Weakly Supervised Object Detection.

In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level ground truth is not available, most of the solutions proposed so far are based on an iterative, Multiple Instance Learning framework in which the current classifier is used to select the highest-confidence boxes in each image, which are treated as pseudo-ground truth in the next training iteration. However, the errors of an immature classifier can make the process drift, usually introducing many of false positives in the training dataset. To alleviate this problem, we propose in this paper a training protocol based on the self-paced learning paradigm. The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training. While in the past few years similar strategies have been adopted for SVMs and other classifiers, we are the first showing that a self-paced approach can be used with deep-network-based classifiers in an end-to-end training pipeline. The method we propose is built on the fully-supervised Fast-RCNN architecture and can be applied to similar architectures which represent the input image as a bag of boxes. We show state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013. On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform even those weakly-supervised approaches which are based on much higher-capacity networks.

PathGame: Crowdsourcing Time-Constrained Human Solutions for the Travelling Salesperson Problem.

Human strategies for solving the travelling salesperson problem (TSP) continue to draw the attention of the researcher community, both to further understanding of human decision-making and inspiration for the design of automated solvers. Online games represent an efficient way of collecting large amounts of human solutions to the TSP, and PathGame is a game focusing on non-Euclideanclosed-form TSP. To capture the instinctive decision-making process of the users, PathGame requires users to solve the problem as quickly as possible, while still favouring more efficient tours. In the initial study presented here, we have used PathGame to collect a dataset of over 16,000 tours, containing over 22,000,000 destinations. Our analysis of the data revealed new insights related to ways in which humans solve TSP and the time it takes them when forced to solve TSPs of large complexity quickly.

Job Satisfaction, Organizational Commitment and Job Involvement: The Mediating Role of Job Involvement.

We conducted an empirical study aimed at identifying and quantifying the relationship between work characteristics, organizational commitment, job satisfaction, job involvement and organizational policies and procedures in the transition economy of Serbia, South Eastern Europe. The study, which included 566 persons, employed by 8 companies, revealed that existing models of work motivation need to be adapted to fit the empirical data, resulting in a revised research model elaborated in the paper. In the proposed model, job involvement partially mediates the effect of job satisfaction on organizational commitment. Job satisfaction in Serbia is affected by work characteristics but, contrary to many studies conducted in developed economies, organizational policies and procedures do not seem significantly affect employee satisfaction.

Neural network approach to background modeling for video object segmentation.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.

Unveiling Spatial Epidemiology of HIV with Mobile Phone Data.

An increasing amount of geo-referenced mobile phone data enables the identification of behavioral patterns, habits and movements of people. With this data, we can extract the knowledge potentially useful for many applications including the one tackled in this study - understanding spatial variation of epidemics. We explored the datasets collected by a cell phone service provider and linked them to spatial HIV prevalence rates estimated from publicly available surveys. For that purpose, 224 features were extracted from mobility and connectivity traces and related to the level of HIV epidemic in 50 Ivory Coast departments. By means of regression models, we evaluated predictive ability of extracted features. Several models predicted HIV prevalence that are highly correlated (>0.7) with actual values. Through contribution analysis we identified key elements that correlate with the rate of infections and could serve as a proxy for epidemic monitoring. Our findings indicate that night connectivity and activity, spatial area covered by users and overall migrations are strongly linked to HIV. By visualizing the communication and mobility flows, we strived to explain the spatial structure of epidemics. We discovered that strong ties and hubs in communication and mobility align with HIV hot spots.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Personalized USB Biosensor Module for Effective ECG Monitoring.

The burden of chronic disease and associated disability present a major threat to financial sustainability of healthcare delivery systems. The need for cost-effective early diagnosis and disease prevention is evident driving the development of personalized home health solutions. The proposed solution presents an easy to use ECG monitoring system. The core hardware component is a biosensor dongle with sensing probes at one end, and micro USB interface at the other end, offering reliable and unobtrusive sensing, preprocessing and storage. An additional component is a smart phone, providing both the biosensor's power supply and an intuitive user application for the real-time data reading. The system usage is simplified, with innovative solutions offering plug and play functionality avoiding additional driver installation. Personalized needs could be met with different sensor combinations enabling adequate monitoring in chronic disease, during physical activity and in the rehabilitation process.

Salient motion features for video quality assessment.

Design of algorithms that are able to estimate video quality as perceived by human observers is of interest for a number of applications. Depending on the video content, the artifacts introduced by the coding process can be more or less pronounced and diversely affect the quality of videos, as estimated by humans. While it is well understood that motion affects both human attention and coding quality, this relationship has only recently started gaining attention among the research community, when video quality assessment (VQA) is concerned. In this paper, the effect of calculating several objective measure features, related to video coding artifacts, separately for salient motion and other regions of the frames of the sequence is examined. In addition, we propose a new scheme for quality assessment of coded video streams, which takes into account salient motion. Standardized procedure has been used to calculate the Mean Opinion Score (MOS), based on experiments conducted with a group of non-expert observers viewing standard definition (SD) sequences. MOS measurements were taken for nine different SD sequences, coded using MPEG-2 at five different bit-rates. Eighteen different published approaches related to measuring the amount of coding artifacts objectively on a single-frame basis were implemented. Additional features describing the intensity of salient motion in the frames, as well as the intensity of coding artifacts in the salient motion regions were proposed. Automatic feature selection was performed to determine the subset of features most correlated to video quality. The results show that salient-motion-related features enhance prediction and indicate that the presence of blocking effect artifacts and blurring in the salient regions and variance and intensity of temporal changes in non-salient regions influence the perceived video quality.