dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the research work done in the A4Cloud project 14  built upon the PrimeLife Policy Language (PPL) and extended the identified requirements in order to create a proof of concept for an accountability policy language (A-PPL) for cloud computing contexts. This policy language addresses data handling rules corresponding to cloud provider's privacy policies, data access and usage control rules, retention period and storage location rules, logging, notification, reporting and auditability  [33]. However, those efforts need further work since they have been made before the reform of the European data protection framework with its enhanced transparency requirements in favour of data subjects.
SPECIAL: From enterprise systems to big data and knowledge graphs Enterprise systems have evolved a lot in the meantime. The European Commission has successfully implemented their public sector information strategy. 15 The aim is to provide public information that can be combined with private sector information to form new innovative services and products. The Big Data Europe project (BDE)  16  created an open source platform ready to use for everyone to provide an easy tool for processing analysis of information for the public sector in all seven societal challenges put forward by the Commission: Health, Food, Energy, Transport, Climate, Social sciences and Security. The project quickly found a first challenge. Most of the data found, personal or not, was of high heterogeneity. Within the same societal challenge a wealth of databases in silos was found.
Of course, a deep understanding of the Linked data world is needed to design policy annotated workflows. While the technology stack has not yet arrived in many production systems, it is a rather mature area with a high potential for new use cases. We are now moving from the question of how processing is organised to the question about the semantics for the actual policy annotations. P3P has given some direction with its statement vocabulary. But this was really advertisement driven and is not sufficient for the very generic privacy transparency and data management tool that will be created by SPECIAL. This is why W3C organised a Workshop on Privacy and Linked Data in April 2018  [15]. The workshop assumed that most services today, lack the tools to be good citizens of the Web. Which is related, but not limited, to the work on permissions and on tracking protection.
Tutorial outcomes and conclusions The main objective of the tutorial session was to introduce participants to the transparency requirements of the GDPR and to delve deeper into the possibilities of technical means supporting their realization. This was achieved by an introductory presentation, and discussions during and after this presentation that took place among all attendees. These discussions often evolved around the significance of earlier attempts of creating technical specifications and vocabularies for privacy terms, their usefulness for future endeavours in this field, and initial ideas what could be captured in such vocabularies and taxonomies to enable meaningful and enforceable data handling policies. The discussions showed that the work of the SPECIAL project as well as of the W3C Data Privacy Vocabularies and Controls Community Group are very important first steps into the right direction, while recommendations were made by workshop participants to stick very close to the GDPR and to avoid the pitfalls of former efforts in the area of standardisation.
However, the structural mechanism by which a tandem of WH2 motifs binds two actin monomers and accelerates polymerization and branching is not completely elucidated. Two crystallographic structures of the N-WASP WH2 tandem in complex with actin are available in the Protein Data Bank: a 1:1 actin-domain VC (2VCP  [27] ) and a 2:1 actin-WH2 tandem (3M3N  [26] ). Nevertheless, in both 2VCP and 3M3N structures, we emphasize again that only about 20 residues of each WH2 motif, from the helical N-terminal part to the consensus sequence "LKKV", could be resolved by X-ray experiments (Table  S1 ). It should be noted that the actin dimer in 3M3N complex has an overall longitudinal arrangement similar to that one in actin filament  [26]. This suggests that N-WASP domain V might favor the formation of actin dimers in a longitudinal filament-like conformation, which might accelerate actin polymerization.
Protein-Protein Docking The 527 representative conformations of N-WASP domain V were docked into two crystallographic structures of actin (PDB ID: 2VCP  [27]  and 3M3N  [26] ), using the molecular modeling library PTools  [36]. This toolbox performs rigid-body docking of coarse-grained proteins by multiple energy minimizations, starting from regularly distributed initial positions and orientations of the ligand around the receptor surface. It should be emphasized that no conformational change was allowed during these docking calculations for both protein partners, notably the intrinsically disordered domain V. The energy function minimized here is the physics-based pairwise protein-protein interaction energy SCORPION  [37, 38]. Then, to better discriminate the near-native interface between actin and domain V, the complexes previously generated with PTools were rescored using a knowledge-based scoring function which additionally takes into account three-body interactions.
MD Simulations From the docking results, several probable structures of the actin-domain V complex were selected and submitted to extensive MD simulations performed with the GROMACS software (versions 5. 0. 2 and 2016. 1) [39]. Each selected complex initial conformation was put and solvated in a dodecahedral rhombic box of 14. 0 nm edge, then neutralized by adding 175 sodium and 176 chloride ions to reach the salt concentration of 150 mM. The non-bonded interactions were treated using the smooth PME method  [40]  for the electrostatic terms and a cutoff distance of 1. 2 nm for the van der Waals potentials. The solute and water covalent bond lengths were kept constant using the LINCS  [41]  and SETTLE  [42]  algorithms, respectively, allowing to integrate the equations of motion with a 2 fs time step.
All simulations were run in the NPT ensemble, at T = 310 K and P = 1 bar, using the Nose-Hoover and Parrinello-Rahman algorithms  [43] [44] [45]  with the time coupling constants τ T = 0. 5 ps and τ P = 2. 5 ps. In our previous study of the free state N-WASP domain V, short preliminary MD simulations indicated that the force field AMBER-03w  [46]  combined with the modified water model TIP4P/2005s  [47]  (a combination referred to as A03ws) allowed correctly exploring the protein conformational space. For consistency, we kept this force field for the study of its complex with actin. Each selected complex was submitted to about 350 ns MD simulations within the general conditions previously described. Data collected every 20 ps were kept for subsequent analyses. The latter were made using mostly the GROMACS tools, such as gmx mindist or gmx cluster for computing specific distances or structural clusters, respectively.
Supplementary Materials: The following are available online at http://www. mdpi. com/1422-0067/20/18/4493/ s1. Table  S1 : List of proteins with WH2 motifs which were co-crystallized with actin; Figure  S1 : Alignment of the studied construct sequence with those of N-WASP in 2VCP and 3M3N structures; Figure  S2 : N-WASP domain V residue probabilities to be in α-helix and distributions of gyration of the 7030 conformations in the most probable 1:1 actin-domain V complexes; Table  S2 : Most probable 1:1 actin-domain V encounter complexes in which domain V segment 9-18 is in contact with at least six over nine actin hot-spot residues; Table  S3 : Most probable 1:1 actin-domain V encounter complexes in which domain V segment 37-46 is in contact with at least six over nine actin hot-spot residues; Figure  S3 : N-WASP domain V residue probabilities to be in α-helix and distributions of gyration of the 7540 conformations in the most probable 2:1 actin-domain V complexes; Table  S4 : Most probable 2:1 actin-domain V encounter complexes in which segment 9-18 is in contact with at least six over eight actin hot-spot residues; Table  S5 : Most probable 2:1 actin-domain V encounter complexes in which segment 37-46 is in contact with at least six over eight actin hot-spot residues; Figure  S4  Funding: This research supported by the "IDI 2016" project funded by the IDEX Paris-Saclay (grant number ANR-11-IDEX-0003-02).
Datasets The training of complex machine learning models that comprise large number of variables is particularly cumbersome. Here, model architecture and the selection of relevant data are crucial considerations  [26]. If not chosen properly, the artificial intelligence may produce a model that causes overfitting or underfitting, which often leads to bad predictions of new, unseen, data  [72]. Few datasets that comprise high-quality and uncompressed facial recordings with reference physiological measurements (e. g. , heart rate from an electrocardiograph or pulse rate from a finger oximeter) are currently available. Compression, even at a low factor, has a significant impact on the measurement. Highly compressed video streams lead to low-quality and corrupted PPG signals  [43]. MANHOB-HCI  [73]  is a dataset that contains a large number of facial videos along with electrocardiographic recordings.
The video streams are, however, compressed. PURE, a dataset introduced in  [47]  by Stricker et al. contains 60 one-minute videos along with pulse signals recorded with a finger oximeter. The videos streams have not been compressed. Like PURE, the COHFACE dataset  [74]  also contains one-minute videos along with pulse oximeter recordings. The video streams are, however, highly compressed. More recently, Bobbia et al. proposed the UBFC-RPPG video dataset  [20] , which contains 43 uncompressed videos along with finger oximeter signals. The participants played a time-sensitive mathematical game that supposedly raises their pulse rate. Spetlik et al. proposed ECG-Fitness  [60] , a dataset that contains 204 uncompressed videos. Electrocardiograms were simultaneously recorded. In the context of automatic learning of artificial intelligence models, the databases presented above are limited: only a small volume of data are available and the datasets include notable differences (e.
Modeling iPPG Waveforms We approximated the pulse waveform measured over skin in video streams with curve models fitted to data. To this end, 62 clear PPG waves were extracted from the UBFC-RPPG dataset  [20]  (a typical excerpt is presented in Figure  4a ). Curves generated from periodic and non-periodic models were then fitted to data (Figure  3 ). Models used to approximate PPG waveforms are presented in Table  1. Goodness of fit was evaluated using the fit standard error, also know as the root mean square error (RMSE): RMSE = √ MSE = ∑ n i=1 (y i -ŷi ) 2 n -m. ( 1 ) y is a PPG pulse wave extracted from the UBFC-RPPG dataset, and ŷ is its corresponding fitted-to-data approximation. n corresponds to the number of samples in y, and m is the number of fitted coefficients in the model (nm is the residual degrees of freedom).
As stated in the previous paragraph, only Fourier series and sum of sines models were considered. We computed once more the RMSE between the 2 × 8 average models and the 62 pulse waves extracted from the UBFC-RPPG dataset. Related results are presented in Figure  4c. Independently of the model, n = 2 is the best choice (lowest RMSE). Overall, Fourier series for n = 2 is the method that gives the lowest RMSE. Because the two models are pretty similar (see equations in Table  1 ), the error difference between them for n = 2 is slight (average model RMSE for Fourier series: 0. 12; average model RMSE for sum of sines model: 0. 13). We can also observe that the RMSE increases as we increase n, probably because of overfitting that restricts generalization.
The slope (curve parameters) was randomly selected with a uniform distribution. The results of this procedure are illustrated in Figure  5  for the case of a linear tendency. Figure  5a  shows an excerpt of a raw PPG signal (taken from subject #1, UBFC-RPPG dataset). Figure  5b  depicts a simulated signal (dotted blue line) with its associated trend (dashed crimson line). The resulting signal is presented in a solid black line.
The number of epochs (which is the same as the number of batches because iteration = 1) was set to 5000. We reserved a full batch of data for validation. We used this set to monitor the training process by stopping the procedure with an early-stopping criterion based on overfitting detection. We used categorical cross-entropy as a loss function. In practice, we observed that the training procedure converged well before the 5000th epoch, the decrease in validation loss becoming negligible (Figure  7a ). Validation accuracy was greater than training accuracy, both being greater than 0. 9 (Figure  7b ). Dropout regularization presumably caused this particularity. These metrics are, of course, completely virtual since the model learned only synthetic data. We next present pulse rate estimations that were computed on real PPG videos using the 3D CNN model trained on synthetic data.
Pulse Rate Prediction The learned model produces a prediction for a volume of 25 × 25 × 60 pixels. The synthetic data generator used to train the model does not incorporate a stage ensuring that the frames in the 25 × 25 patches contain pixels that are naturally arranged and ordered like in real video streams. We therefore chose to break the coherent structure of pixels before predicting the pulse rate by shuffling the pixel position. Note that only the green channel  [39]  was processed by the model. Maps of predictions were formed by computing a prediction for each group of pixels in the video stream. The procedure predicts and then shifts the input volume with a constant spatial step of 1 pixel (with overlapping). Typical prediction maps computed from the first 60 frames of subjects #1, #33, and #42 (UBFC-RPPG dataset) are presented in Figure  8.
Results and Discussion The UBFC-RPPG dataset  [20]  was selected to assess the performance of the neural network presented in Section 3. From the initial dataset, we manually removed image sequences in which the participant presented no distinct PPG signal (due particularly to wide head movements) or in case of corrupted ground-truth signals. In total, 1312 pulse rate values from 15 participants were extracted from the initial dataset. The benchmark methods (presented hereafter) operate more efficiently with prior skin detection, robust face tracking, or when pixels of interest are segmented beforehand  [46]. Therefore, and in order to provide fair comparisons, the forehead or the cheeks (when the forehead was covered with hair) were manually selected as regions of interest.
Non-Stationary Signals and Motion The UBFC-RPPG dataset contains videos during which the participants played a time-sensitive mathematical game that supposedly raised their pulse rate. High pulse rate values can effectively be observed on the Bland-Altman plots (Figure  10 ). In addition to natural motion (head movements), the game tends to drastically change the PPG signal frequency over time. These signals are thus predominantly non-stationary. This has not been considered in the method we propose in this study, the synthetic generator (Section 3. 2) producing only stationary signals. Non-stationary PPG signals have an impact on the histogram distribution computed from the prediction maps. Figure  12  presents a typical example: the more a video patch contains a non-stationary PPG signal (Figure  12b , bottom row), the more bins are present in the histogram of predictions (Figure  12c , bottom row), which thus increases entropy and reduces the pulse rate prediction accuracy.
Comparison between signals produced by the synthetic generator and a real PPG signal. (a) Excerpt of subject #1 raw PPG signal taken from the UBFC-RPPG dataset. Pixels from the forehead area (green channel) have been spatially averaged to compute the signal. (b) Dotted blue line: simulated PPG signal (output of Figure 2b). Dashed crimson line: linear trend. Solid black line: signal combined with the trend. (c,d) Signals outputted by the generator with two different noise factors. They were computed using a spatial averaging operation over all the pixels of the synthesized frames. The produced signals are pretty similar to the real PPG signal presented in figure a.
Background: Memory dysfunction is characteristic of aging and often attributed to Alzheimer's disease (AD). An easily administered tool for preliminary assessment of memory function and early AD detection would be integral in improving patient management. Objective: Our primary aim was to utilize machine learning in determining initial viable models to serve as complementary instruments in demonstrating efficacy of the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodicmemory screening and assessing cognitive impairment. Methods: We used an existing dataset subset (n = 18,395) of demographic information, general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adults who took the M-CRT test.
Accordingly, to thoroughly validate the practical clinical utility of MemTrax, individual test performance characteristics and a selected respective array of relevant influencing variables (e. g. , age, medications, symptoms, etc. ) must be considered and appropriately analyzed and modeled concomitantly in aggregate. In this study, we explored an existing dataset consisting of demographic information, answers to general health screening questions (addressing memory, sleep quality, medications, and medical conditions affecting thinking), and test results from a convenience sample of adult individuals who took the MemTrax online Continuous Recognition Tasks (M-CRT) test for episodic-memory screening  [3, 4]. We then performed predictive modeling on these data, using the demographic information and test scores to predict binary classification of the healthrelated questions (yes/no) and general health status.
Data overview The original dataset consisted of 30,435 instances of the M-CRT test conducted online between 9/22/11 and 8/23/2013 as part of the HAPPYneuron program (http://www. happy-neuron. com/) [3]. The study from which these data were provided for our present analysis was previously reviewed and approved by and administered in accord with the ethical standards of the Human Subject Protection Committee of Stanford University. The convenience sample was mix of people (adults) who were participating in this structured program to stimulate cognition. Whereas the sample was not truly representative of the general population, these individuals were generally healthy, though some may have had light cognitive or other impairments. There were 25,146 total users who each took the test between 1 and 24 times.
Additional details of the M-CRT administration and implementation, data reduction, and other data analyses are described elsewhere  [3]. We focused our modeling on the four health-related screening questions and corresponding answers in the dataset. These questions were included in the M-CRT to establish, via self-reporting, whether each test respondent: 1) Has memory problems; 2) Has difficulty sleeping; 3) Is taking any medication; 4) Has any medical conditions that might affect his/her thinking.
Data transformation For our exploration, the data did not require an extensive amount of cleaning beyond the steps described above; but there were some additional items we addressed prior to beginning our analysis. Three attributes in the original dataset had responses in both English and French. Two of these attributes, occupation and employment status, were not used as part of our initial analysis, as they were not deemed relevant to our aims for this study; accordingly, these are not addressed/utilized here. For the third attribute regarding whether the user suffered from memory problems, the dataset was populated with values of "Yes," "No," "Oui," or "Non" (or left blank). Because this translation is unambiguous, we translated the French answers into English prior to completing our analysis.
The original data did not include the user's age; but we were able to derive ages by the user's birthday and date of the respective test, thus creating a numerical attribute representing the user's age on the date the M-CRT test was taken. For precision, age was represented in days rather than years in our analyses and models. Based on the M-CRT test results, two derived features were created for each individual user's overall engagement: one for the percentage of total images shown to which the user registered an active response (keyboard spacebar click) and the other to indicate the percentage of the repeat and initial images (50 total) to which the user responded correctly. Percentage of total images prompting a response (%responses) was calculated using an established  [3, 4]  formula: true positive + (25 -true negative) with this total being divided by 50 representing the total images shown.
Experimental datasets For these preliminary experiments, we created eight versions of the original data, using each of the individual general health questions, as well as various forms of the aggregate score, as the alternating dependent variable. Broadly, each derived dataset served one of two purposes: 1) Prediction of answers to individual general health questions or 2) Prediction of general health status based on HealthQScore. For each of the eight dataset versions, the following M-CRT performance and participant characteristic (demographic) features were used as independent attributes: true positive/negative, %responses/correct, response time true positive, age, sex, and whether the user had consumed alcohol in the preceding 24 h. For predictive modeling, we used the demographic information and test scores to predict binary classification of the health-related questions (yes/no) and general health status (healthy/unhealthy) for the test taker, based on the provided answers to the screening questions.
For each of the general health questions (memory problems, medications, difficulty sleeping, and medical conditions that affect thinking), two variations of each dataset were created, both using the respective general health question attribute as the class label. An instance was part of the positive class if the user answered "Yes" to the question, or it was part of the negative class if the user answered "No". In one variation of each of these datasets, the answers to the other three general health questions were used as independent features, while in the other variation they were not included. This distinction is denoted as 4Q and 1Q, respectively. The four HealthQScore versions of the data differed based on how the data were split for binary classification. The binary classifications of the HealthQScore versions were based on our assumption that the more questions to which the user responded affirmatively, the more likely he or she is at risk for having a cognitive brain health deficit.
Thus, we started our exploratory analysis by examining the most extreme cases only (scores of 0 versus 4 as the negative and positive classes, respectively) to see how well we could differentiate between the two groups. The challenge with this approach was that it only allowed for 1,004 instances to be used for analysis, which may not have been enough to build robust models on this dataset. For this reason, we also added combinations with aggregate scores of 1 and 3 into the negative and positive classes (0 or 1 versus 4; 0 or 1 versus 3 or 4; and 0 versus 3 or 4). All four (4) combinations of these groupings were tested. A summary of all datasets and respective variations is presented in Table  1.