nils-herrmann/clean_scirepeval huggingface dataset:
Effect of posterized naturalistic stimuli on SSVEP-based BCI

Most visual stimuli used in steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) are simple and elementary. Examples of such stimuli are checkerboard patterns and sinusoidal gratings. These stimuli exhibit distinct contrasts and edges that conform well to the simple cortical cells behavior first observed by Hubel and Weisel. Hence, they are effective in eliciting VEP. On the other hand, the use of naturalistic stimuli is known to advance our understanding of early visual system. However, naturalistic stimuli are spectrally and spatially complex. They may not elicit the optimal VEP and the results obtained may not easily correlate to the stimulus parameters. Hence, we proposed to posterize natural images to generate naturalistic stimuli suitable for SSVEP-BCI. The posterization process considers both the edge and contrast information of the input image. This study elucidates the effect of posterized naturalistic stimuli on SSVEP amplitudes and phases by exploring the relationship between the number of tones of posterized visual stimuli and their effect on the power spectra and phase synchrony of attended stimuli. Results show that posterized visual stimuli at four tone display a significant effect on the dominant frequency response. Our findings suggest the effectiveness of posterized naturalistic stimuli and should advance the use of naturalistic stimuli in SSVEP-BCI.
Expectation-Maximization with Image-Weighted Markov Random Fields to Handle Severe Pathology

This paper describes an automatic tissue segmentation algorithm for brain MRI of children with cerebral palsy (CP) who exhibit severe cortical malformations. Many of the currently popular brain segmentation techniques rely on registered atlas priors and so generalize poorly to severely injured data sets, because of large discrepancies between the target brain and healthy (or injured) atlases. We propose a prior-less approach combined with a modification of the Expectation Maximization (EM)/Markov Random Field (MRF) segmentation by imposing a continuous weighting scheme to penalize intensity discrepancies between pairs of neighbors within each clique neighborhood, to provide robustness to the unique clinical problem of severe anatomical distortion. This approach was applied to gray matter segmentations in 20 3D T1-weighted MRIs, of which 17 were of CP patients exhibiting severe malformation. We compare our adaptive algorithm to the popular 'FreeSurfer', 'NiftySeg', 'FAST' and 'Atropos' segmentations, which collectively are state-of-the-art surface deformation and EM approaches. The algorithm driven approach yielded improved segmentations (DSC 0.66 v 0.44 (FreeSurfer) v 0.60 (NiftySeg with 100% atlas prior relaxation) v 0.59 (FAST) v 0.64 (Atropos)) of the cerebral cortex relative to several ground-truth manual segmentations, when compared to the existing approaches.
Feature and Classifier Selection for Automatic Classification of Lesions in Dynamic Contrast-Enhanced MRI of the Breast

The clinical interpretation of breast MRI remains largely subjective, and the reported findings qualitative. Although the sensitivity of the method for detecting breast cancer is high, its specificity is poor. Computerised interpretation offers the possibility of improving specificity through objective quantitative measurement. This paper reviews the plethora of such features that have been proposed and presents a preliminary study of the most discriminatory features for dynamic contrast-enhanced MRI of the breast. In particular the results of a feature/classifier selection experiment are presented based on 20 lesions (10 malignant and 10 benign) from 20 routine clinical breast MRI examinations. Each lesion was segmented manually by a clinical radiographer and its diagnostic status confirmed by cytopathology or histopathology. The results show that textural and kinetic, rather than morphometric, features are the most important for lesion classification. They also show that the SVM classifier with sigmoid kernel performs better than other well-known classifiers: Fisher's linear discriminant function, Bayes linear classifier, logistic regression, and SVM with other kernels (distance, exponential, and radial).
Analysis of Photon Scattering Trends for Material Classification Using Artificial Neural Network Models

In this project, we concentrate on using the Artificial Neural Network (ANN) approach to analyze the photon scattering trend given by specific materials. The aim of this project is to fully utilize the scatter components of an interrogating gamma-ray radiation beam in order to determine the types of material embedded in sand and later to determine the depth of the material. This is useful in a situation in which the operator has no knowledge of potentially hidden materials. In this paper, the materials that we used were stainless steel, wood and stone. These moderately high density materials are chosen because they have strong scattering components, and provide a good starting point to design our ANN model. Data were acquired using the Monte Carlo N-Particle Code, MCNP5. The source was a collimated pencil-beam projection of 1 MeV energy gamma rays and the beam was projected towards a slab of unknown material that was buried in sand. The scattered photons were collected using a planar surface detector located directly above the sample. In order to execute the ANN model, several feature points were extracted from the frequency domain of the collected signals. For material classification work, the best result was obtained for stone with 86.6% accurate classification while the most accurate buried distance is given by stone and wood, with a mean absolute error of 0.05.
Intelligible Support Vector Machines for Diagnosis of Diabetes Mellitus

Diabetes mellitus is a chronic disease and a major public health challenge worldwide. According to the International Diabetes Federation, there are currently 246 million diabetic people worldwide, and this number is expected to rise to 380 million by 2025. Furthermore, 3.8 million deaths are attributable to diabetes complications each year. It has been shown that 80% of type 2 diabetes complications can be prevented or delayed by early identification of people at risk. In this context, several data mining and machine learning methods have been used for the diagnosis, prognosis, and management of diabetes. In this paper, we propose utilizing support vector machines (SVMs) for the diagnosis of diabetes. In particular, we use an additional explanation module, which turns the “black box” model of an SVM into an intelligible representation of the SVM's diagnostic (classification) decision. Results on a real-life diabetes dataset show that intelligible SVMs provide a promising tool for the prediction of diabetes, where a comprehensible ruleset have been generated, with prediction accuracy of 94%, sensitivity of 93%, and specificity of 94%. Furthermore, the extracted rules are medically sound and agree with the outcome of relevant medical studies.
Template phenology for vegetation models

To assist the representation of phenology in vegetation models we created several templates of phenology-driver relations by characterizing the annual phase differences between phenology and two phenology drivers. We did this using the results of a cross spectral analysis of MODIS EVI with radiation (CPTEC) and with precipitation (TRMM). Four phase ranges were identified for phenology-radiation and three phase ranges for phenology-precipitation. These ranges were classified and mapped together into 12 zones of our study area where particular phase relationships coincided. Around ~25% area was in phase with radiation, with varying phase ranges of precipitation, and ~16 % was in phase with precipitation with varying phase ranges of radiation. For each zone we conceptualized the phenology-driver relationships with phase lagged curves. The phase timing of these plots matched well with average time series plots from the same zones, but more work is needed on the representation of amplitude.
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. It is a novel variation on the nonlocal means (NLM) algorithm. The algorithm, called dynamic nonlocal means (DNLM), exploits the redundancy of information in the temporal sequence of images. Empirical evaluations of the performance of the DNLM algorithm relative to seven other denoising methods-simple Gaussian filtering, the original NLM algorithm, a trivial extension of NLM to include the temporal dimension, bilateral filtering, anisotropic diffusion filtering, wavelet adaptive multiscale products threshold, and traditional wavelet thresholding-are presented. The evaluations include quantitative evaluations using simulated data and real data (20 DCE-MRI data sets from routine clinical breast MRI examinations) as well as qualitative evaluations using the same real data (24 observers: 14 image/signal-processing specialists, 10 clinical breast MRI radiographers). The results of the quantitative evaluation using the simulated data show that the DNLM algorithm consistently yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the quantitative evaluation using the real data provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm yields the smallest MSE between the denoised image and its corresponding original noiseless version. The results of the qualitative evaluation provide evidence, at the ¿ = 0.05 level of significance, that the DNLM algorithm performs visually better than all of the other algorithms. Collectively the qualitative and quantitative results suggest that the DNLM algorithm more effectively attenuates noise in DCE MR images than any of the other algorithms.
XPS and PIXE Analysis of Doped Silica Fibre for Radiation Dosimetry

The material characteristics of doped SiO2 fibre are studied, the electron traps in the product medium creating a situation attractive for their application in thermoluminescence (TL) radiation dosimetry. To date, rather limited research has been conducted towards gaining an essential understanding of the magnitude of TL signal and material characteristics of doped fibres. Characterization is being sought to ensure that the mechanism of TL yield in optical fibres is well understood, allowing a favourable well controlled production situation to be established. The intended end point is to specify dosimeters, not only for clinical dosimetry but also for their application in industrial/energy-industry settings. Investigation of the surface oxidation state of the Ge-doped SiO2 optical preform has been carried out using the X-ray photoelectron spectroscopy technique. In a further development using the fibre forming technology, particle-induced X-ray emission/Rutherford back scattering measurements have been employed to ascertain dopant concentrations of Ge-doped-cladding photonic crystal fibres (PCFs) with a view to improving TL yield. Present results concern uncollapsed and collapsed-hole-PCFs.
An evaluation of four parametric models of contrast enhancement for dynamic magnetic resonance imaging of the breast

This paper presents an empirical evaluation of the goodness-of-fit (GOF) of four parametric models of contrast enhancement for dynamic resonance imaging of the breast: the Tofts, Brix, and Hayton pharmacokinetic models, and a novel empiric model. The goodness-of-fit of each model was evaluated with respect to: (i) two model-fitting algorithms (Levenberg- Marquardt and Nelder-Mead) and two fitting tolerances; and (ii) temporal resolution. In the first case the GOF was measured using data from three dynamic contrast-enhanced (DCE) MRI data sets from routine clinical examinations: one case with benign enhancement, one with malignant enhancement, and one with normal findings. Results are presented for fits to both the whole breast volume and to a selected region of interest. In the second case the GOF was measured by first fitting the models to several temporally sub-sampled versions of a custom high temporal resolution data set (subset of the breast volume containing a malignant lesion), and then comparing the fitted results to the original full temporal resolution data. Our results demonstrate that under the various optimization conditions considered, in general, both the proposed empiric model and the Hayton model fit the data equally well and that both of these models fit the data better than the Tofts and Brix models.
Multi-scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation

This paper addresses the problem of depth estimation from a single still image. Inspired by recent works on multi-scale convolutional neural networks (CNN), we propose a deep model which fuses complementary information derived from multiple CNN side outputs. Different from previous methods, the integration is obtained by means of continuous Conditional Random Fields (CRFs). In particular, we propose two different variations, one based on a cascade of multiple CRFs, the other on a unified graphical model. By designing a novel CNN implementation of mean-field updates for continuous CRFs, we show that both proposed models can be regarded as sequential deep networks and that training can be performed end-to-end. Through extensive experimental evaluation we demonstrate the effectiveness of the proposed approach and establish new state of the art results on publicly available datasets.
DECAF: MEG-Based Multimodal Database for Decoding Affective Physiological Responses

In this work, we present DECAF-a multimodal data set for decoding user physiological responses to affective multimedia content. Different from data sets such as DEAP [15] and MAHNOB-HCI , DECAF contains (1) brain signals acquired using the Magnetoencephalogram (MEG) sensor, which requires little physical contact with the user's scalp and consequently facilitates naturalistic affective response, and (2) explicit and implicit emotional responses of 30 participants to 40 one-minute music video segments used in  and 36 movie clips, thereby enabling comparisons between the EEG versus MEG modalities as well as movie versus music stimuli for affect recognition. In addition to MEG data, DECAF comprises synchronously recorded near-infra-red (NIR) facial videos, horizontal Electrooculogram (hEOG), Electrocardiogram (ECG), and trapezius-Electromyogram (tEMG) peripheral physiological responses. To demonstrate DECAF's utility, we present (i) a detailed analysis of the correlations between participants' self-assessments and their physiological responses and (ii) single-trial classification results for valence, arousal and dominance, with performance evaluation against existing data sets. DECAF also contains time-continuous emotion annotations for movie clips from seven users, which we use to demonstrate dynamic emotion prediction.
Translating Journalists' Requirements into Features for Image Search

This paper illustrates how taking advantage of user studies highlighting the user requirements can lead to the selection of suitable visual features in image search systems. The results of a study to identify pertinent visual features to enhance a text-based press photo search system used by journalists are presented. A requirement was that the visual features should be intuitively understandable by the journalists. This feature selection task is approached by first determining the journalists' photo searching requirements based on a published user study. These requirements are then mapped to suitable visual features. The emphasis was on identifying suitable and intuitive low-level features, as these can be rapidly implemented in the existing text-based image search system. Results demonstrating the use of the selected features are shown.
A Multi-Task Learning Framework for Head Pose Estimation under Target Motion

Recently, head pose estimation (HPE) from low-resolution surveillance data has gained in importance. However, monocular and multi-view HPE approaches still work poorly under target motion, as facial appearance distorts owing to camera perspective and scale changes when a person moves around. To this end, we propose FEGA-MTL, a novel framework based on Multi-Task Learning (MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. Upon partitioning the monitored scene into a dense uniform spatial grid, FEGA-MTL simultaneously clusters grid partitions into regions with similar facial appearance, while learning region-specific head pose classifiers. In the learning phase, guided by two graphs which a-priori model the similarity among (1) grid partitions based on camera geometry and (2) head pose classes, FEGA-MTL derives the optimal scene partitioning and associated pose classifiers. Upon determining the target's position using a person tracker at test time, the corresponding region-specific classifier is invoked for HPE. The FEGA-MTL framework naturally extends to a weakly supervised setting where the target's walking direction is employed as a proxy in lieu of head orientation. Experiments confirm that FEGA-MTL significantly outperforms competing single-task and multi-task learning methods in multi-view settings.
Toward Improved Ranking Metrics

In many computer vision algorithms, a metric or similarity measure is used to determine the distance between two features. The Euclidean or SSD (sum of the squared differences) metric is prevalent and justified from a maximum likelihood perspective when the additive noise distribution is Gaussian. Based on real noise distributions measured from international test sets, we have found that the Gaussian noise distribution assumption is often invalid. This implies that other metrics, which have distributions closer to the real noise distribution, should be used. In this paper, we consider three different applications: content-based retrieval in image databases, stereo matching, and motion tracking. In each of them, we experiment with different modeling functions for the noise distribution and compute the accuracy of the methods using the corresponding distance measures. In our experiments, we compared the SSD metric, the SAD (sum of the absolute differences) metric, the Cauchy metric, and the Kullback relative information. For several algorithms from the research literature which used the SSD or SAD, we showed that greater accuracy could be obtained by using the Cauchy metric instead.
User-centric Affective Video Tagging from MEG and Peripheral Physiological Responses

This paper presents a new multimodal database and the associated results for characterization of affect (valence, arousal and dominance) using the Magneto encephalogram (MEG) brain signals and peripheral physiological signals (horizontal EOG, ECG, trapezius EMG). We attempt single-trial classification of affect in movie and music video clips employing emotional responses extracted from eighteen participants. The main findings of this study are that: (i) the MEG signal effectively encodes affective viewer responses, (ii) clip arousal is better predicted by MEG, while peripheral physiological signals are more effective for predicting valence and (iii) prediction performance is better for movie clips as compared to music video clips.
Multimedia Event Detection Using A Classifier-Specific Intermediate Representation

Multimedia event detection (MED) plays an important role in many applications such as video indexing and retrieval. Current event detection works mainly focus on sports and news event detection or abnormality detection in surveillance videos. Differently, our research aims to detect more complicated and generic events within a longer video sequence. In the past, researchers have proposed using intermediate concept classifiers with concept lexica to help understand the videos. Yet it is difficult to judge how many and what concepts would be sufficient for the particular video analysis task. Additionally, obtaining robust semantic concept classifiers requires a large number of positive training examples, which in turn has high human annotation cost. In this paper, we propose an approach that exploits the external concepts-based videos and event-based videos simultaneously to learn an intermediate representation from video features. Our algorithm integrates the classifier inference and latent intermediate representation into a joint framework. The joint optimization of the intermediate representation and the classifier makes them mutually beneficial and reciprocal. Effectively, the intermediate representation and the classifier are tightly correlated. The classifier dependent intermediate representation not only accurately reflects the task semantics but is also more suitable for the specific classifier. Thus we have created a discriminative semantic analysis framework based on a tightly coupled intermediate representation. Extensive experiments on multimedia event detection using real-world videos demonstrate the effectiveness of the proposed approach.
Inference of personality traits and affect schedule by analysis of spontaneous reactions to affective videos

This paper presents a method for inferring the Positive and Negative Affect Schedule (PANAS) and the BigFive personality traits of 35 participants through the analysis of their implicit responses to 16 emotional videos. The employed modalities to record the implicit responses are (i) EEG, (ii) peripheral physiological signals (ECG, GSR), and (iii) facial landmark trajectories. The predictions of personality traits/PANAS are done using linear regression models that are trained independently on each modality. The main findings of this study are that: (i) PANAS and personality traits of individuals can be predicted based on the users' implicit responses to affective video content, (ii) ECG+GSR signals yield 70%±8% F1-score on the distinction between extroverts/introverts, (iii) EEG signals yield 69%±6% F1-score on the distinction between creative/non creative people, and finally (iv) for the prediction of agreeableness, emotional stability, and baseline affective states we achieved significantly higher than chance-level results.
Wavelet based texture classification

Texture are one of the basic features in visual searching and computational vision. In the literature most of the attention has been focussed on the texture features with minimal consideration of the noise models. In this paper, we investigate the problem of texture classification from a maximum likelihood perspective. We take into account the texture model, the noise distribution, and the inter-dependence of the texture features. Our investigation shows that the real noise distribution is closer to an exponential than a Gaussian distribution, and that the L/sub 1/ metric has a better retrieval rate than L/sub 2/. We also propose the Cauchy metric as an alternative for both the L/sub 1/ and L/sub 2/ metrics. Furthermore, we provide a direct method for deriving an optimal distortion measure from the real noise distribution, which experimentally provides consistently improved results over the other metrics. We conclude with results and discussions on an international texture database.
An overcomplete discrete wavelet transform for video compression

The translated function with any integer multiple of the sampling period is completely represented in the wavelet space by one of the overcomplete discrete wavelet transform (ODWT) members. This theoretical result leads to a new motion estimation and motion compensation scheme working in the wavelet transform domain. Our experiments, performed on real image sequences, show high quality and low bit rate performances. Moreover, by performing the motion estimation in the wavelet space a major reduction of the computational cost is achieved.