nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Wheezes Using Wavelet Higher Order Spectral Features

Wheezes are musical breath sounds, which usually imply an existing pulmonary obstruction, such as asthma and chronic obstructive pulmonary disease (COPD). Although many studies have addressed the problem of wheeze detection, a limited number of scientific works has focused in the analysis of wheeze characteristics, and in particular, their time-varying nonlinear characteristics. In this study, an effort is made to reveal and statistically analyze the nonlinear characteristics of wheezes and their evolution over time, as they are reflected in the quadratic phase coupling of their harmonics. To this end, the continuous wavelet transform (CWT) is used in combination with third-order spectra to define the analysis domain, where the nonlinear interactions of the harmonics of wheezes and their time variations are revealed by incorporating instantaneous wavelet bispectrum and bicoherence, which provide with the instantaneous biamplitude and biphase curves. Based on this nonlinear information pool, a set of 23 features is proposed for the nonlinear analysis of wheezes. Two complementary perspectives, i.e., general and detailed, related to average performance and to localities, respectively, were used in the construction of the feature set, in order to embed trends and local behaviors, respectively, seen in the nonlinear interaction of the harmonic elements of wheezes over time. The proposed feature set was evaluated on a dataset of wheezes, acquired from adult patients with diagnosed asthma and COPD from a lung sound database. The statistical evaluation of the feature set revealed discrimination ability between the two pathologies for all data subgroupings. In particular, when the total breathing cycle was examined, all 23 features, but one, showed statistically significant difference between the COPD and asthma pathologies, whereas for the subgroupings of inspiratory and expiratory phases, 18 out of 23 and 22 out of 23 features exhibited discrimination power, respectively. This paves the way for the use of the wavelet higher order spectral features as an input vector to an efficient classifier. Apparently, this would integrate the intrinsic characteristics of wheezes within computerized diagnostic tools toward their more efficient evaluation.
A higher-order statistics-based phase identification of three-component seismograms in a redundant wavelet transform domain

An efficient identification of P and S phases of three component seismograms (3-CS) using higher-order statistics (HOS) in a redundant wavelet transform domain (R-WT) is presented in this work. The R-WT peals the 3-CS signal into several frequency layers, where the HOS-based criteria (cross-cumulants, skewness, kurtosis) could reflect efficiently the onset times of the P and S phases of an event, during 3-CS recordings. This is of great importance to seismologists, since, afterwards, the epicenter of an earthquake could be accurately identified. The implementation of this approach on pre-classified 3-CS, recorded during several events from seismological stations was examined. Experiments have shown that a promising and automated estimation of the P and S phase onset times, consistent with that of the analysis, can be achieved even in noisy cases (e.g. additive Gaussian noise), due to properties of HOS.
An orthogonal least squares-based fuzzy filter for real-time analysis of lung sounds

Pathological discontinuous adventitious sounds (DAS) are strongly related with the pulmonary dysfunction. Its clinical use for the interpretation of respiratory malfunction depends on their efficient and objective separation from vesicular sounds (VS). In this paper, an automated approach to the isolation of DAS from VS, based on their nonstationarity, is presented. The proposed scheme uses two fuzzy inference systems (FISs), operating in parallel, to perform the task of adaptive separation, resulting in the orthogonal least squares-based fuzzy filter (OLS-FF). By applying the OLS-FF to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are efficiently separated from VS. The important time domain DAS features, related to diagnostic information, are preserved and their true location and structural morphology are automatically identified. When compared to previous works, the OLS-FF performs quite similarly, but with significantly lower computational load, resulting in a faster real-time clinical screening of DAS.
On modeling children's non-verbal interactions during computer mediated music composition: a case study in primary school peers

The proposed paper aims to model the non-verbal interactions that occur during a computer-mediated music composition process between pupils in primary school within a collaborative framework. The socio-cultural approach, drawn from Vygotsky's theoretical context, is being further developed and some of its aspects are outlined and used as a framework to further understand the collaborative processes that occur during collaborative learning. The latter is achieved by identifying effective peer's verbal and non-verbal interactions in a collaborative computer-based music composition environment (CCMCE).The present paper aims to explore and model children's patterns of body language signs which contribute to measure peer's active and passive collaborative learning in CCMCE. Experimental results from a case study in a primary state school in Greece promise to yield important cues for peer's thinking, concentration and mental processing that will be useful to anyone engaged in examining children's computer-mediated collaborative interactions.
Efficient Heart Sound Segmentation and Extraction Using Ensemble Empirical Mode Decomposition and Kurtosis Features

An efficient heart sound segmentation (HSS) method that automatically detects the location of first ( S1) and second ( S2) heart sound and extracts them from heart auscultatory raw data is presented here. The heart phonocardiogram is analyzed by employing ensemble empirical mode decomposition (EEMD) combined with kurtosis features to locate the presence of S1, S2, and extract them from the recorded data, forming the proposed HSS scheme, namely HSS-EEMD/K. Its performance is evaluated on an experimental dataset of 43 heart sound recordings performed in a real clinical environment, drawn from 11 normal subjects, 16 patients with aortic stenosis, and 16 ones with mitral regurgitation of different degrees of severity, producing 2608 S1 and S2 sequences without and with murmurs, respectively. Experimental results have shown that, overall, the HSS-EEMD/K approach determines the heart sound locations in a percentage of 94.56% and segments heart cycles correctly for the 83.05% of the cases. Moreover, results from a noise stress test with additive Gaussian noise and respiratory noises justify the noise robustness of the HSS-EEMD/K. When compared with four other efficient methods that mainly employ wavelet transform, energy, simplicity, and frequency measures, respectively, using the same experimental database, the HSS-EEMD/K scheme exhibits increased accuracy and prediction power over all others at the level of 7-19% and 4-9%, respectively, both in controls and pathological cases. The promising performance of the HSS-EEMD/K paves the way for further exploitation of the diagnostic value of heart sounds in everyday clinical practice.
Analysis of Fetal Heart Rate in Healthy and Pathological Pregnancies Using Wavelet-based Features

In this paper, a new approach for feature extraction from the Fetal Heart Rate (FHR) signal is introduced. It considers the use of Continuous Wavelet Transform to extract wavelet-based features of FHR signal in order to discriminate the normal from the abnormal cases. The proposed methodology is tested on real data acquired before the beginning or during labor. The results proved the viability of the approach and its potential for further application.
Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis

This paper aims at providing a new feature extraction method for a user-independent emotion recognition system, namely, HAF-HOC, from electroencephalograms (EEGs). A novel filtering procedure, namely, Hybrid Adaptive Filtering (HAF), for an efficient extraction of the emotion-related EEG-characteristics was developed by applying Genetic Algorithms to the Empirical Mode Decomposition-based representation of EEG signals. In addition, Higher Order Crossings (HOCs) analysis was employed for feature extraction realization from the HAF-filtered signals. The introduced HAF-HOC scheme incorporated four different classification methods to accomplish a robust emotion recognition performance. Through a series of facial-expression image projection, as a Mirror Neuron System-based emotion elicitation process, EEG data related to six basic emotions (happiness, surprise, anger, fear, disgust, and sadness) have been acquired from 16 healthy subjects using three EEG channels. Experimental results from the application of the HAF-HOC to the collected EEG data and comparison with previous approaches have shown that the HAF-HOC scheme clearly surpasses the latter in the field of emotion recognition from brain signals for the discrimination of up to six distinct emotions, providing higher classification rates up to 85.17 percent. The promising performance of the HAF-HOC surfaces the value of EEG signals within the endeavor of realizing more pragmatic, affective human-machine interfaces.
Discrimination of single and multiple human transmembrane proteins using kurtosis and morphological analysis

The identification of transmembrane segments in protein sequences is an important issue in the field of bioinformatics. In this study a method is proposed for the discrimination between proteins with single and multiple transmembrane segments, combining chemical and statistical features of the proteins with higher-order statistics and morphological analysis for protein categorisation. The method was tested on human proteins, extracted from public available databases and the results have shown an efficiency of the proposed algorithm to correctly classify the sequences under study into two classes, for a wide range of transmembrane segment lengths. This paves the way for a more efficient analysis of transmembrane proteins taking into account the individual features and patterns occurring within proteins with single and multiple transmembrane segments.
Enhanced inter-subject brain computer interface with associative sensorimotor oscillations

Electroencephalography (EEG) captures electrophysiological signatures of cortical events from the scalp with high-dimensional electrode montages. Usually, excessive sources produce outliers and potentially affect the actual event related sources. Besides, EEG manifests inherent inter-subject variability of the brain dynamics, at the resting state and/or under the performance of task(s), caused probably due to the instantaneous fluctuation of psychophysiological states. A wavelet coherence (WC) analysis for optimally selecting associative inter-subject channels is proposed here and is being used to boost performances of motor imagery (MI)-based inter-subject brain computer interface (BCI). The underlying hypothesis is that optimally associative inter-subject channels can reduce the effects of outliers and, thus, eliminate dissimilar cortical patterns. The proposed approach has been tested on the dataset IVa from BCI competition III, including EEG data acquired from five healthy subjects who were given visual cues to perform 280 trials of MI for the right hand and right foot. Experimental results have shown increased classification accuracy (81.79%) using the WC-based selected 16 channels compared to the one (56.79%) achieved using all the available 118 channels. The associative channels lie mostly around the sensorimotor regions of the brain, reinforced by the previous literature, describing spatial brain dynamics during sensorimotor oscillations. Apparently, the proposed approach paves the way for optimised EEG channel selection that could boost further the efficiency and real-time performance of BCI systems.
Accurate eye center location and tracking using isophote curvature

The ubiquitous application of eye tracking is precluded by the requirement of dedicated and expensive hardware, such as infrared high definition cameras. Therefore, systems based solely on appearance (i.e. not involving active infrared illumination) are being proposed in literature. However, although these systems are able to successfully locate eyes, their accuracy is significantly lower than commercial eye tracking devices. Our aim is to perform very accurate eye center location and tracking, using a simple Web cam. By means of a novel relevance mechanism, the proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve rotational invariance and to keep low computational costs. In this paper we test our approach for accurate eye location and robustness to changes in illumination and pose, using the BioIDand the Yale Face B databases, respectively. We demonstrate that our system can achieve a considerable improvement in accuracy over state of the art techniques.
Robustifying eye center localization by head pose cues

Head pose and eye location estimation are two closely related issues which refer to similar application areas. In recent years, these problems have been studied individually in numerous works in the literature. Previous research shows that cylindrical head models and isophote based schemes provide satisfactory precision in head pose and eye location estimation, respectively. However, the eye locator is not adequate to accurately locate eye in the presence of extreme head poses. Therefore, head pose cues may be suited to enhance the accuracy of eye localization in the presence of severe head poses. In this paper, a hybrid scheme is proposed in which the transformation matrix obtained from the head pose is used to normalize the eye regions and, in turn the transformation matrix generated by the found eye location is used to correct the pose estimation procedure. The scheme is designed to (1) enhance the accuracy of eye location estimations in low resolution videos, (2) to extend the operating range of the eye locator and (3) to improve the accuracy and re-initialization capabilities of the pose tracker. From the experimental results it can be derived that the proposed unified scheme improves the accuracy of eye estimations by 16% to 23%. Further, it considerably extends its operating range by more than 15°, by overcoming the problems introduced by extreme head poses. Finally, the accuracy of the head pose tracker is improved by 12% to 24%.
An autonomous flyer photographer

In this paper we explore the combination of a latest generation mobile device and a micro quadrotor platform to perform indoor autonomous navigation for the purpose of autonomous photography. We use the Yellowstone tablet from Google's Tango project , equipped with onboard, fully integrated sensing platform and with significant computational capability. To the best of our knowledge we are the first to exploit the Google's Tango tablet as source of pose estimate to control the quadrotor's motion. Using the tablet's onboard camera the system is able to detect people and generate a desired pose that the quadrotor will have to reach in order to take a well framed picture of the detected subject. The experimental results and live video demonstrate the capabilities of the autonomous flying robot photographer using the system described throughout this manuscript.
Isocentric color saliency in images

In this paper we propose a novel computational method to infer visual saliency in images. The computational method is based on the idea that salient objects should have local characteristics that are different than the rest of the scene, being edges, color or shape, and that these characteristics can be combined to infer global information. The proposed approach is fast, does not require any learning and the experimentation shows that it can enhance interesting objects in images, improving the state of the art performance on a public dataset.
Accurate Eye Center Location through Invariant Isocentric Patterns

Locating the center of the eyes allows for valuable information to be captured and used in a wide range of applications. Accurate eye center location can be determined using commercial eye-gaze trackers, but additional constraints and expensive hardware make these existing solutions unattractive and impossible to use on standard (i.e., visible wavelength), low-resolution images of eyes. Systems based solely on appearance are proposed in the literature, but their accuracy does not allow us to accurately locate and distinguish eye centers movements in these low-resolution settings. Our aim is to bridge this gap by locating the center of the eye within the area of the pupil on low-resolution images taken from a webcam or a similar device. The proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve in-plane rotational invariance, and to keep low-computational costs. To further gain scale invariance, the approach is applied to a scale space pyramid. In this paper, we extensively test our approach for its robustness to changes in illumination, head pose, scale, occlusion, and eye rotation. We demonstrate that our system can achieve a significant improvement in accuracy over state-of-the-art techniques for eye center location in standard low-resolution imagery.
A non-inertial acceleration suppressor for low cost inertial measurement unit attitude estimation

This paper presents a method to evaluate the attitude of a rigid body under condition of high non-gravitational acceleration. Most of the attitude estimation algorithms based on data from low cost Inertial Measurement Units (IMU), assume that the total acceleration perceived by the accelerometer be gravity or at most small variations of it. When the actual conditions are far away from such assumption, the attitude estimation results in wrong evaluations. We propose a method that uses an external RGB-D camera to measure The non-inertial linear acceleration. Such acceleration is subtracted to the total acceleration reading of the accelerometer in order to obtain a truthful gravity direction that will be fed into the fusion algorithm. Performance of our attitude estimation has been evaluated empirically under non-gravitational acceleration. We compare our results against the output of a commercially avalaible IMU sensor based on a Kalman Filter algorithm as well as the estimation of a recently developed fusion algorithm based on a gradient descent algorithm, showing significant improvement.
Autonomous quadrotor flight using onboard RGB-D visual odometry

In this paper we present a navigation system for Micro Aerial Vehicles (MAV) based on information provided by a visual odometry algorithm processing data from an RGB-D camera. The visual odometry algorithm uses an uncertainty analysis of the depth information to align newly observed features against a global sparse model of previously detected 3D features. The visual odometry provides updates at roughly 30 Hz that is fused at 1 KHz with the inertial sensor data through a Kalman Filter. The high-rate pose estimation is used as feedback for the controller, enabling autonomous flight. We developed a 4DOF path planner and implemented a real-time 3D SLAM where all the system runs on-board. The experimental results and live video demonstrates the autonomous flight and 3D SLAM capabilities of the quadrotor with our system.
Fast visual odometry and mapping from RGB-D data

An RGB-D camera is a sensor which outputs color and depth and information about the scene it observes. In this paper, we present a real-time visual odometry and mapping system for RGB-D cameras. The system runs at frequencies of 30Hz and higher in a single thread on a desktop CPU with no GPU acceleration required. We recover the unconstrained 6-DoF trajectory of a moving camera by aligning sparse features observed in the current RGB-D image against a model of previous features. The model is persistent and dynamically updated from new observations using a Kalman Filter. We formulate a novel uncertainty measure for sparse RGD-B features based on a Gaussian mixture model for the filtering stage. Our registration algorithm is capable of closing small-scale loops in indoor environments online without any additional SLAM back-end techniques.
6-DoF pose localization in 3D point-cloud dense maps using a monocular camera

We present a 6-degree-of-freedom (6-DoF) pose localization method for a monocular camera in a 3D point-cloud dense map prebuilt by depth sensors (e.g., RGB-D sensor, laser scanner, etc.). We employ fast and robust 2D feature detection on the real camera to be matched against features from a virtual view. The virtual view (color and depth images) is constructed by projecting the map's 3D points onto a plane using the previous localized pose of the real camera. 2D-to-3D point correspondences are obtained from the inherent relationship between the real camera's 2D features and their matches on the virtual depth image (projected 3D points). Thus, we can solve the Perspective-n-Point (PnP) problem in order to find the relative pose between the real and virtual cameras. With the help of RANSAC, the projection error is minimized even further. Finally, the real camera's pose is solved with respect to the map by a simple frame transformation. This procedure repeats for each time step (except for the initial case). Our results indicate that a monocular camera alone can be localized within the map in real-time (at QVGA-resolution). Our method differentiates from others in that no chain of poses is needed or kept. Our localization is not susceptible to drift because the history of motion (odometry) is mostly independent over each PnP + RANSAC solution, which throws away past errors. In fact, the previous known pose only acts as a region of interest to associate 2D features on the real image with 3D points in the map. The applications of our proposed method are various, and perhaps it is a solution that has not been attempted before.
Providing feedback on emotional experiences and decision making

We present a novel lifelog system concept created to provide a human user with feedback on their conscious and unconscious emotional reactions and encourage the process of self-reflection by looking into an affective mirror. The emotion of the user is deduced from biometric data and enhanced by affective sound analysis and facial expression recognition of faces of the weares conversational partners. These high-level analyses also offer information about the social context of the user. All of the recorded data is processed using a novel modification of the spreading-activation theory which is used to model human-like associative thinking.
Classification of spontaneous and posed smiles

Automatic detection of affective states from facial images and videos is a very important application in social signal processing. In this study we focus on the expression of happiness, and propose a physiologically inspired approach for detecting whether a smile is spontaneously produced or not. We use distance-based and angular features that characterize movements of facial regions, and train local classifiers for smile classification. Our experiments with different classifiers on two different datasets show that the eyelid region can be very useful in assessing spontaneous versus posed smiles.