nils-herrmann/clean_scirepeval huggingface dataset:
EEG-Based Classification of Music Appraisal Responses Using Time-Frequency Analysis and Familiarity Ratings

A time-windowing feature extraction approach based on time-frequency (TF) analysis is adopted here to investigate the time-course of the discrimination between musical appraisal electroencephalogram (EEG) responses, under the parameter of familiarity. An EEG data set, formed by the responses of nine subjects during music listening, along with self-reported ratings of liking and familiarity, is used. Features are extracted from the beta (13-30 Hz) and gamma (30-49 Hz) EEG bands in time windows of various lengths, by employing three TF distributions (spectrogram, Hilbert-Huang spectrum, and Zhao-Atlas-Marks transform). Subsequently, two classifiers (k-NN and SVM) are used to classify feature vectors in two categories, i.e., "likeâ and "dislike,â under three cases of familiarity, i.e., regardless of familiarity (LD), familiar music (LDF), and unfamiliar music (LDUF). Key findings show that best classification accuracy (CA) is higher and it is achieved earlier in the LDF case {91.02 ± 1.45% (7.5-10.5 s)} as compared to the LDUF case {87.10 ± 1.84% (10-15 s)}. Additionally, best CAs in LDF and LDUF cases are higher as compared to the general LD case {85.28 ± 0.77%}. The latter results, along with neurophysiological correlates, are further discussed in the context of the existing literature on the time-course of music-induced affective responses and the role of familiarity.
Linear discrimination of transmembrane from non-transmembrane segments in proteins using higher-order crossings

Identification of transmembrane segments in protein sequences is an important issue in the field of bioinformatics. In this study, a method is proposed for linear discrimination between transmembrane and non-transmembrane segments, combining chemical and statistical features of the proteins with higher-order crossings analysis for protein segment classification. The method was tested on human proteins extracted from public available databases and the results have shown a remarkable efficiency of the proposed algorithm to correctly classify the sequence segments under study into two linearly separated classes, for a wide range of transmembrane segment lengths
Autoregressive modeling of lung sounds using higher-order statistics: estimation of source and transmission

The use of higher-order statistics in an autoregressive modeling of lung sounds is presented resulting in a characterization of their source and transmission. The lung sound source in the airway is estimated using the prediction error of an all-pole filter based on higher-order statistics (AR-HOS), while the acoustic transmission through the lung parenchyma and chest wall is modeled by the transfer function of the same AR-HOS filter. The parametric bispectrum, using the estimated a/sub i/ coefficients of the AR-HOS model, is also calculated to elucidate the frequency characteristics of the modeled system. The implementation of this approach on pre-classified lung sound segments in known disease conditions, selected from teaching tapes, was examined. Experiments have shown that a reliable and consistent with current knowledge estimation of lung sound characteristics can be achieved using this method, even in the presence of additive Gaussian noise.
On efficiently tracking turn-taking patterns in a CSCL environment using Lempel-Ziv complexity analysis

A complexity-based analysis of the turn-taking sequences produced during peers' computer-mediated collaboration is presented in this paper. The collaborative contributions monitored by the system, namely Lin2k, are mapped to turn-taking sequences, which, in turn, are transformed to symbol-sequences and analyzed for pattern extraction. The use of a normalized complexity measure when applied to collaborative data from the field of environmental engineering reveals peers' tendencies to more complex turn-taking patterns when they receive appropriate feedback from the Lin2k. The efficiency of the proposed complexity analysis in pattern identification and its simplicity in implementation makes it a useful tool for effective tracking of temporal changes in collaborative activity.
Nonlinear separation of crackles and squawks from vesicular sounds using third-order statistics

A combination of nonlinear filtering with third-order statistics is presented, resulting to a modification of the nonlinear digital ST-NST filter, proposed by Arakrwa et al. (1986), for improving its performance in noisy environments. The use of third-order statistics in estimating the AR-model's order and coefficients provides more reliable estimates of the stationary part of the input signal. The implementation of this modified ST-NST filter in fine crackles, coarse crackles and squawks was examined. Experiments have shown that a reliable and accurate separation of these adventitious sounds from vesicular sounds can be achieved, using this method, even in the presence of additive Gaussian noise.
Automatic S-phase arrival determination of seismic signals using nonlinear filtering and higher-order statistics

Automatic seismic P- and S-phases arrival identification has been a challenging scientific goal for the last two decades for seismologists, as it provides them with important seismological information. Based on recent bibliography, various approaches result in efficient P-phase identification, while S- phase identification is still an open problem, since there is an overlap of the S- phase arrival and the P- phase coda. They usually employ simple energy ratio criteria, the linear seismic wave polarity assumption, neural networks or heuristic methods based on seismologists' experience, but they seem to have moderate performance, especially for noisy cases. The proposed method uses a Wavelet Transform-based filtering technique that separates the STationary from the NonSTationary parts of a mixed signal, namely the WTST-NST filter, combined with Higher-Order Statistics (HOS). Initially, the WTST-NST filter is used to remove the undesired P-phase coda (given the P-phase arrival) and the background noise from the seismic data. Then, the HOS are applied on the de-noised signal to detect the S-phase arrival at the location of the maximum value of HOS. The method has been applied on real seismic data recorded in Greece, characterized by human experts as highly noisy cases. Experimental results indicate that the proposed method identifies the S-phase arrival efficiently, since evaluation analysis proves high identification accuracy, when compared to the analysts' findings and due to its low computational complexity, real-time implementation is feasible.
Separation of discontinuous adventitious sounds from vesicular sounds using a wavelet-based filter

The separation of pathological discontinuous adventitious sounds (DAS) from vesicular sounds (VS) is of great importance to the analysis of lung sounds, since DAS are related to certain pulmonary pathologies. An automated way of revealing the diagnostic character of DAS by isolating them from VS, based on their nonstationarity, is presented in this paper. The proposed algorithm combines multiresolution analysis with hard thresholding in order to compose a wavelet transform-based stationary-nonstationary filter (WTST-NST). Applying the WTST-NST filter to fine/coarse crackles and squawks, selected from three lung sound databases, the coherent structure of DAS is revealed and they are separated from VS. When compared to other separation tools, the WTST-NST filter performed more accurately, objectively, and with lower computational cost. Due to its simple implementation it can easily be used in clinical medicine.
Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part II: application results

The application of the wavelet transform-fractal dimension-based (WT-FD) filter of Part I of this paper to real bioacoustic data, which include explosive lung sounds (ELS) and explosive bowel sounds (EBS) recorded from patients with pulmonary or gastrointestinal dysfunction, respectively, is presented in this paper. The objective of the latter is the evaluation of the performance of the WT-FD filter on different types of bioacoustic signals, varying not only in their structural morphology but also in the degree of their noise contamination. As it is thoroughly described in Part I of this paper, the WT-FD filter uses the fractal dimension to form an efficient way of thresholding the WT coefficients at different resolution scales, keeping, thus, only those that can contribute to the accurate reconstruction of the ELS and EBS signals. Quantitative and qualitative analysis of the experimental results show an efficient performance of the WT-FD filter to circumvent the noise presence (100% detectability rate, 100% sensitivity, 100% specificity) by faithfully extracting the authentic structure of ELS and EBS from the background noise. The WT-FD filter does not require any noise reference signal or noise reference templates. The results from a noise stress test (mean cross-correlation index of the original and the estimated signal converging to 100%; mean normalized maximum amplitude error converging to 0.7%) prove its robustness to various noise levels (0-20 dB), enabling its potential use in similar noise cases met in everyday clinical medicine. Furthermore, the efficient performance of the WT-FD filter facilitates the physician to better interpret the auscultation findings. Due to its simplicity and low computational cost, the WT-FD filter can possibly be implemented in a real-time context to serve as a tool for the continuous ELS and EBS screening.
Wavelet-based enhancement of lung and bowel sounds using fractal dimension thresholding-part I: methodology

An efficient method for the enhancement of lung sounds (LS) and bowel sounds (BS), based on wavelet transform (WT), and fractal dimension (FD) analysis is presented in this paper. The proposed method combines multiresolution analysis with FD-based thresholding to compose a WT-FD filter, for enhanced separation of explosive LS (ELS) and BS (EBS) from the background noise. In particular, the WT-FD filter incorporates the WT-based multiresolution decomposition to initially decompose the recorded bioacoustic signal into approximation and detail space in the WT domain. Next, the FD of the derived WT coefficients is estimated within a sliding window and used to infer where the thresholding of the WT coefficients has to happen. This is achieved through a self-adjusted procedure that iteratively "peels" the estimated FD signal and isolates its peaks produced by the WT coefficients corresponding to ELS or EBS. In this way, two new signals are constructed containing the useful and the undesired WT coefficients, respectively. By applying WT-based multiresolution reconstruction to these two signals, a first version of the desired signal and the background noise is provided, accordingly. This procedure is repeated until a stopping criterion is met, finally resulting in efficient separation of the ELS or EBS from the background noise. The proposed WT-FD filter introduces an alternative way to the enhancement of bioacoustic signals, applicable to any separation problem involving nonstationary transient signals mixed with uncorrelated stationary background noise. The results from the application of the WT-FD filter to real bioacoustic data are presented and discussed in an accompanying paper.
A fuzzy logic evaluating system to support Web-based collaboration using collaborative and metacognitive data

A fuzzy logic-based expert system, namely collaboration/reflection-fuzzy inference system (C/R-FIS,) is presented. By means of interconnected fuzzy inference systems (FIS), it automatically evaluates the collaborative activity, during asynchronous, written, Web-based collaboration. This information is used for the provision of enhanced support during the collaboration. The proposed model extents the evaluation system of a Web-based collaborative tool namely Lin2k, which served as a test-bed for the C/R-FIS experimental use. The results proved the potentiality of the proposed model to significantly contribute to the enhancement of the collaborative activity.
Abnormal pattern detection in Wireless Capsule Endoscopy images using nonlinear analysis in RGB color space

In recent years, an innovative method has been developed for the non-invasive observation of the gastrointestinal tract (GT), namely Wireless Capsule Endoscopy (WCE). WCE especially enables a detailed inspection of the entire small bowel and identification of its clinical lesions. However, the foremost disadvantage of this technological breakthrough is the time consuming task of reviewing the vast amount of images produced. To address this, a novel technique for distinguishing pathogenic endoscopic images related to ulcer, the most common disease of GT, is presented here. Towards this direction, the Bidimensional Ensemble Empirical Mode Decomposition was applied to RGB color images of the small bowel acquired by a WCE system in order to extract their Intrinsic Mode Functions (IMFs). The IMFs reveal differences in structure from their finest to their coarsest scale, providing a new analysis domain. Additionally, lacunarity analysis was employed as a method to quantify and extract the texture patterns of the ulcer regions and the normal mucosa, respectively, in order to discriminate the abnormal from the normal images. Experimental results demonstrated promising classification accuracy (>95%), exhibiting a high potential towards WCE-based analysis.
Discrimination of heart sounds using higher-order statistics

A new way of heart sounds discrimination based on higher-order statistics is presented in this paper. The diagnostic character of cardiac auscultation is examined in a new domain, the higher-order spectra domain. The proposed analysis takes into account the nonlinearities of the signal, since it preserves the phase information, and assumes that heart sounds are non-Gaussian processes. In this way, a nonlinear analysis of heart sounds is introduced expanding the process of heart sounds to more accurate estimations of their true character. The proposed analysis was conducted on preclassified signals selected from a teaching tape. The derived results have shown that higher-order statistics define new differences in heart sounds analysis, characterizing efficiently various cardiac pathologies or the severity of cardiac dysfunction associated with the heart sounds.
Enhanced Sign Language Recognition Using Weighted Intrinsic-Mode Entropy and Signer's Level of Deafness

Sign language (SL) forms an important communication canal for the deaf. In this paper, enhanced SL recognition, by relating the individual way of signing with the signer's level of deafness (LoD) through a novel hybrid adaptive weighting (HAW) process applied to surface electromyogram and 3-D accelerometer data, is proposed. Using a LoD-driven genetic algorithm, HAW optimally weights the intrinsic modes of the acquired signals, preparing them for sample entropy (SampEn) estimation that follows. The resulting feature set, namely, weighted intrinsic-mode entropy (IMEn) (wIMEn), aims at increasing the SL-sign-classification accuracy alone or boosted by signer identification and/or signer's LoD-based group identification. The wIMEn was compared with three other feature sets, i.e., time frequency, SampEn, and IMEn, regarding their discrimination ability (both among signers and SL signs). Data from the dominant hand of nine subjects with various LoD were analyzed for the classification of 61 Greek SL (GSL) signs. Experimental results have shown that the introduced wIMEn feature set exhibited higher performance compared to others, both in signer identification and signer's LoD-based group identification and in GSL sign classification. The findings suggest that LoD could be considered in the construction of a signer-independent SL-classification system toward the enhancement of its performance.
Intrinsic mode entropy: An enhanced classification means for automated Greek Sign Language gesture recognition

Sign language forms a communication channel among the deaf; however, automated gesture recognition could further expand their communication with the hearers. In this work, data from three-dimensional accelerometer and five-channel surface electromyogram of the user's dominant forearm are analyzed using intrinsic mode entropy (IMEn) for the automated recognition of Greek Sign Language (GSL) gestures. IMEn was estimated for various window lengths and evaluated by the Mahalanobis distance criterion. Discriminant analysis was used to identify the effective scales of the intrinsic mode functions and the window length for the calculation of the IMEn that contributes to the correct classification of the GSL gestures. Experimental results from the IMEn analysis of GSL gestures corresponding to ten words have shown 100% classification accuracy using IMEn as the only classification feature. This provides a promising bed-set towards the automated GSL gesture recognition.
Intrinsic higher-order correlation and lacunarity analysis for WCE-based ulcer classification

Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data is a burden for the physicians. This limitation was the motive to propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract. Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.
NOESIS: An Enhanced Educational Environment for Kids with Autism Spectrum Disorders

A novel educational environment for kids with autistic spectrum disorders (ASDs), namely NOESIS, is presented in this paper. NOESIS takes into account ASD kidspsila individual characteristics (level of autism, source sensitivity, reaction target, etc), their emotional state (stress level, hyper-/hypo-tension) during their educational procedure, and creativity during guided- and self-activity (e.g., gaming). It adapts to each kidpsilas specific characteristics through system adaptation and self-regulation procedures. Moreover, it provides assistance to the educator for preparation, customization and optimization of the educational material for each kid and provision of enhanced evaluation procedures (scores/tools) via well-managed Web services. Parentspsila updating is also provided via reporting material with learning curve descriptions. Overall, NOESIS contributes to the provision of opportunities to all ASD children to be educated by facilitating access and tuning innovative technology to social needs.
PAI-S/K: A robust automatic seismic P phase arrival identification scheme

The automatic and accurate P phase arrival identification is a fundamental problem for seismologists worldwide. Several approaches have been reported in the literature, but most of them only selectively deal with the problem and are severely affected by noise presence. In this paper, a new approach based on higher-order statistics (HOS) is introduced that overcomes the subjectivity of human intervention and eliminates the noise factor. By using skewness and kurtosis, two algorithms have been formed, namely, Phase Arrival Identification-Skewness/Kurtosis (PAI-S/K), and some advantages have been gained over the usual approaches, resulting in the automatic identification of the transition from Gaussianity to non-Gaussianity that coincides with the onset of the seismic event, despite noise presence. Experimental results on real seismic data, gathered by the Seismological Network of the Department of Geophysics of Aristotle University, demonstrate an excellent performance of the PAI-S/K scheme, regarding both accuracy and noise robustness. The simplicity of the proposed method makes it an attractive candidate for huge seismic data assessment in a real-time context.
Evaluation of surface EMG features for the recognition of American Sign Language gestures

In this work, analysis of the surface electromyogram (sEMG) signal is proposed for the recognition of American sign language (ASL) gestures. To this purpose, sixteen features are extracted from the sEMG signal acquired from the user's forearm, and evaluated by the Mahalanobis distance criterion. Discriminant analysis is used to reduce the number of features used in the classification of the signed ASL gestures. The proposed features are tested against noise resulting in a further reduced set of features, which are evaluated for their discriminant ability. The classification results reveal that 97.7% of the inspected ASL gestures were correctly recognized using sEMG-based features, providing a promising solution to the automatic ASL gesture recognition problem
On Supporting Parkinson's Disease Patients: The i-Prognosis Personalized Game Suite Design Approach

The use of serious games in health care interventions sector has grown rapidly in the last years, however, there is still a gap in the understanding on how these types of interventions are used for the management of the Parkinson Disease (PD), in particular. Targeting intelligent early detection and intervention in PD area, the Personalized Game Suite (PGS) design process approach is presented as part of the H2020 i-PROGNOSIS project that introduces the integration of different serious games in a unified platform (i.e., ExerGames, DietaryGames, EmoGames, and Handwriting/Voice Games). From the methodological point of view, to facilitate the visualization of 14 game-scenarios, the system interface and the PD contexts, the storyboarding technique was adopted here. Overall, the realization of the PGS sets the basis for establishing a holistic framework that could aim at improving motor and non-motor symptoms, in order to inform health care providers and policy makers for its inclusion in routine management for PD.
Classification of Transmembrane Segments in Human Proteins Using Wavelet-based Energy

The special features of transmembrane segments (TMs) as well as the number of TMs within a protein sequence are often associated with properties related to the function or structure of the protein. A classification scheme for the TMs of human proteins is proposed in this work, by defining specific patterns of the total energy of each transmembrane segment after applying the continuous wavelet transform (CWT) to the hydrophobic sequence of the protein. The scheme was applied to proteins of known structure extracted by public available databases and statistical analysis followed in order to reveal possible patterns in the sequence of TMs within a protein. The results show some kind of selectivity in the occurrence of TMs with respect to the proposed types, thus allowing further analysis for the extraction of biological information of the proteins based on the proposed classification.