dataesr/scientific-paragraphs-categorization huggingface dataset:
We also provide a Glossary of Codes, which gives the definition of each theme, category, subcategory, and code. An online interactive tool, which enables to visualise the dataset hierarchical structure and codes, completes the description of the dataset. It is accessible at: http://covid19-interventions. com/CCCSLgraph/. We have also established a GitHub repository available at: https://github. com/amel-github/ CCCSL-Codes and provide codes  19  for importing, exploring and visualising the data into R  20. Furthermore, for purposes of transparency of data collection and to motivate collaborative validation process as well as a large use and development of the dataset, an open library is available, that contains all sources used to collect the data: https://www. zotero. org/groups/2488884/cccsl_covid_measure_project (>3,100 data sources are included as of date of submission).
In order to leverage on the potential of crowdsourcing for populating and curating the CCCSL dataset, we have launched a webpage dedicated to this project at: http://covid19-interventions. com/ where contributors can fill up a Google Form at: https://bit. ly/2KsYOTn, if they wish to correct entries, add a measure, and/or provide a feedback. The dataset contains the following fields: ID -Unique identifier for each individually implemented measure. ID is also used in the Google Form to report erroneous entries.
When available, duration of the restriction, as officially announced, is mentioned in this field. Source -Provides the reference for each entry, i. e. URL. Enables to trace back potential changes in the meaning of the label during the translation  21. Enables to access the description of the measure in the source language and/or to access to the information as it was dispatched originally. As of date of submission, the CCCSL dataset included information for 6,068 government interventions, from 56 countries, including 33 European countries, 12 Asian countries, five South American countries, two North American countries, one Oceanian country, three African countries, and the Diamond Princess cruise ship. Regarding the USA, data are available at the state level for 24 states. Figure  1 , Table  1 , and Online-only Table  2  summarize the dataset.
Technical Validation After the initial data entry, the dataset was checked manually by the data curators. For each measure, concordance between L1, L2, L3, and L4 was checked. Moreover, the unique combinations of L1, L2, L3, and L4 were extracted and controlled for consistency. Typographical and coding errors were minimized through a manual process. We initiated a collaborative curation platform relying on internal and external collaborators who exchanged through Slack, GitHub, Skype, and via emails. This extended effort enabled us to correct typographical and coding errors, to remove line breaks, and to homogenize the dataset for universal use in different programming languages. Beyond manual validations, we performed a technical validation step to detect possible duplicates. Using the dplyr package for the R Programming Language  22  , we identified any duplicate entries in the vector composed of country, region, date, and the codes from L1 to L4.
Those entries were flagged as possible duplicates and reviewed by hand by two curators, ensuring that the dataset does not contain duplicated entries. An R script to reproduce this step is provided at: https://github. com/amel-github/CCCSL-Codes. While an important effort has been made for standardizing the records, the four-level-a priori coding scheme originally proposed showed limitations. First, the existing classifications of NPIs are discordant  [23] [24] [25]. We proposed an original classification scheme that best fitted our (emergency) needs and the specificity of the COVID-19 pandemic, but this scheme may be subjected to revisions in the future. Secondly, some NPIs have been uniquely implemented (e. g. the deportation of Chinese workers by the Kazakh government), which complicated the coding and categorisation process.
However, this data cannot be captured for each kind of NPI, e. g. "Increase of healthcare workforce" or "Work safety protocol". We propose an alternative approach that leverages on the theme "Returning to normal life" and record individually all (i) variations in, (ii) conditions of, and (iii) adaptive measures to the gradual lifting of the restrictions (e. g. re-opening of shops > 400 m², re-opening of classes with examination, weddings allowed if the number of attendees is < 100, etc. ). By providing data on each step of the phase-out process, the coding scheme allows therefore to retrieve even more specifically (but indirectly) the "End date" for each NPI (to the best of our knowledge, only the CoronaNet dataset provides a "End date" data element, although as of date of writing, for 30% of the interventions only  26  ).
We plan to maintain the quality level of the dataset with regular updates on the countries currently described. Furthermore, we plan to increase the geographic coverage of the dataset, prioritizing large countries (e. g. China, US states not already covered, and Australia), those with a high number of reported cases (e. g. Vietnam, Iran, Turkey, Russia, Israel, Peru, Chile, Pakistan, Philippines, Saudi Arabia), and those where the epidemic is rising and which may suffer from a data gap (i. e. African and South American countries). The same technical procedures and the classification scheme described above will be applied to any new information to be included in the dataset. Future versions will be subjected to extensive data validation processes. We plan to stabilise the hierarchical coding scheme for NPIs implemented to contain COVID-19 within six months, including measures related to the lifting of the restrictions and adaptive measures that accompany them.
Usage Notes The aim of this work is not only to improve the current knowledge on country-based interventions implemented to mitigate the burden of COVID-19, but also to characterise the political, public health, and economic strategies of the governments worldwide. Combined with publicly available data on the number of confirmed cases, recovered cases, and deaths, the CCCSL dataset makes it possible to assess the effectiveness of the control policies on the COVID-19 epidemic, e. g. the epidemic growth rate or the daily reproduction numbers  12. The standardized coding facilitates an inter-country comparison of government responses. The dataset can further benefit the risk assessment of lifting some restrictions and the development of exit strategies. It can also become an essential data source in the aftermath of the first wave of COVID-19, to guide government control policies anticipating a potential second wave of cases.
Mapping the timeline of government interventions during the epidemic. We propose to visualise the time-series of the dates of implementation of the NPIs recorded in the CCCSL at the level 2 of the hierarchical coding scheme (categories) in the 56 countries using a heat map (Fig. 2 ). In order to highlight country-based differences in the timeline of implementation, we used the epidemic age instead of calendar time. For a given day, t, in a certain country, the epidemic age is defined as the time difference, t-t 0 , measured in days, where t 0 is the first day when the number of confirmed cases was greater or equal to 10. The time-series data of the number of COVID-19 cases was retrieved from the COVID-19 Data Repository by the Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE) at: https://github.
com/CSSEGISandData/COVID-19:https:// github. com/CSSEGISandData/COVID-19. Country-cluster analysis of the government control strategies. In order to partition the countries based on the aggressiveness (number of NPIs) and responsiveness (timeline) of their control strategy, we applied a k-means clustering. We focused on mandatory government interventions (i. e. the theme "Risk communication" was not included) recorded in the CCCSL at the level 2 of the hierarchical coding scheme (categories) that appeared in at least 15 countries, leading to a total number of 40 categories. The clustering algorithm uses the date of implementation of the measures in each country to build a feature vector based on the epidemic age (see above). We considered "anticipatory measures" as those implemented before day when 10 cases were reported; "early measures" as those implemented at the beginning of the epidemic, i.
e. between the day when 10 cases were reported and the day when 200 cases were reported; and "late measures" as those implemented at a later stage of the epidemic, i. e. after the day when 200 cases were reported. The algorithm takes also into account the number of measures implemented at these different stages of the epidemic. The time-series data of the number of COVID-19 cases was retrieved from the COVID-19 Data Repository by the JHU CSSE (https://github. com/ CSSEGISandData/COVID-19: https://github. com/CSSEGISandData/COVID-19). The optimal number of clusters, k, was determined using the elbow method  27. Briefly, this method consists in running k-means clustering on the dataset for a range of values of k (set here from 1 to 15), and for each value of k calculates the sum of squared errors (SSE).
Contextualising the project. During the COVID-19 crisis, other projects have concomitantly tracked data on government policies (interchangeably named NPIs 18 or government(s') responses  26, 28, 29  or government measures  30  or PHSM  31  or policy actions  26  ). We report here on five of them  26, [28] [29] [30] [31]  in order to contextualise our work. The comparison indicates similarities and differences among the NPI trackers and highlights how the CCCSL contributes to the global effort against COVID-19. Supplementary Information 1 outlines the main characteristics of the six datasets (including the CCCSL  18  ). The core value-added of the CCCSL dataset is the remarkable granularity of the data on NPIs (e. g. seven categories of travel restriction are reported, further divided into more than 50 subcategories) and the use of self-explanatory codes, which, completed with the Glossary of Codes, makes the dataset readily intelligible.
As of date of submission, the dataset displays eight themes, 63 categories, >500 subcategories, and >2,000 codes. With regard to the geographic unit, two datasets record data at the country level  28, 29  whereas four record data at a finer administrative scale  18, 26, 30, 31. One dataset uses a binary code (1/0) to assess the presence/absence of the NPIs  28  , another one uses an Likert-like scale to further differentiate the level of implementation  29  , whereas the others use a coding system based on words or short phrases that assign a summative attribute to the data  18, 26, 30, 31. Moreover, the aggregation scheme and, sometimes, the semantic of the NPIs diverge widely between the datasets. For example, the CoronaNet dataset  26  groups school closure together with lockdown measures whereas the CCCSL  18  and the ACAPS  30  datasets classify school closure in the theme "Social distancing".
Acknowledgements D. G. and A. D. N. acknowledge funding from the  Vienna Science and Technology Fund -WWTF  ( VRG16-005 ). S. T. acknowledges funding from the  Austrian Research Promotion Agency FFG  (project number  882184 ) and the  Vienna Science and Technology Fund -WWTF  ( COV20-017 ). The authors acknowledge  William Schueller  for his help in the recruitment of the team of data collectors and  Petar Sekulic  for his advice in the analysis of the dataset. We warmly thank  Caspar Matzhold  and  Michaela Kaleta  for checking and testing our code components.
Code availability A live version of this project is accessible on GitHub at: https://github. com/amel-github/covid19interventionmeasures. The codes used to describe the CCCSL dataset and the codes used to explore the CCCSL dataset are written in R language  19. They are available at: https://github. com/amel-github/CCCSL-Codes. Please refer to the README file in the code release for further instructions.
Early detection of faults in rotating machinery systems is crucial in preventing system failure, increasing safety, and reducing maintenance costs. Current methods of fault detection suffer from the lack of efficient feature extraction method, the need for designating a threshold producing minimal false alarm rates, and the need for expert domain knowledge, which is costly. In this paper, we propose a novel data-driven health division method based on convolutional neural networks using a graphical representation of time series data, called a nested scatter plot. The proposed method trains the model with a small amount of labeled data and does not require a threshold value to predict the health state of rotary machines. Notwithstanding the lack of datasets that show the ground truth of health stages, our experiments with two open datasets of run-to-failure bearing demonstrated that our method is able to detect the early symptoms of bearing wear earlier and more efficiently than other threshold-based health indicator methods.
In this work, we propose a supervised data-driven method for HS division, that avoids usage of a pre-specified HI as well as the employment of pre-specified thresholds. The principle idea presented in this paper involves supervised training of a binary regression model using the early stage (nominal functioning) samples and last stage (near to complete failure) samples from a run-to-failure bearing degradation database. Unlike conventional HI methods, there is no need to set the threshold and it only needs a small number of labels. The laborious task of labeling, which is typically associated with supervised methods, is also avoided as a simple distinction is made between the early and last stages of component degradation containing relatively smaller sets of data to be labeled. The proposed method has two phases.
Phase one consists of transforming the time series data of raw vibration data into a nested-scatter plot image (NSP) for feature extraction. In phase two, these images are used to train a binary regression model using convolutional neural networks (CNN), which are tested on degradation datasets provided by the center of Intelligent Maintenance Systems (IMS), the University of Cincinnati  [7] , and the Fanche-Comte Electronics Mechanics Thermal Science and Optics-Sciences and Technologies Institute (FEMTO)  [8, 9]. The superiority of the proposed method is verified through a comparison with previous detection methods in terms of FPT: RMS in the signal-based method and AE in the unsupervised data-driven method. In addition, the features extracted by CNN are analyzed to verify the reliability of the predicted FPT  [10, 11].
Recently, deep learning and pattern recognition have demonstrated extensive utility in this domain. The data-driven method can be further categorized into supervised  [18]  and unsupervised methods  [19]  in which supervised methods require training sets that are labeled. The labeling of the dataset is a time-consuming process prone to human error. The unsupervised method, on the other hand, does not require manual labeling but tries to detect anomalies that deviate from the nominal behavior. Belmiloud et al. [20]  used wavelet packet decomposition to extract features as the model input and proposed a deep CNN-based method to construct HI. Guo et al. [21]  proposed a CNN-based HI construction method with little prior knowledge to extract features. She et al. [22]  proposed a multi-channel CNN with an exponentially decaying learning rate to construct the HI with the original multi-channel raw vibration signals.
Input In a supervised manner, labeling of the target data is required for supervised learning to distinguish the features of the HS of a machine under different bearing degradation conditions. The training dataset is divided into two HS stages based on the time of acquisition. Figure  3  shows the division of our training dataset. The initial part, corresponding to the nominal functioning in the entire vibration dataset, is labeled as healthy, and the last part of the sample duration, when the bearing is damaged, is labeled as unhealthy. This is based on the assumption that degrading data of rotating machinery are obtained until the point of failure. By the supervised method, it is unnecessary to label all train datasets and analyze the dataset for labeling. To minimize labeling efforts on the entire dataset, the optimal sizes (small set) of the initial and last part, labeling 'healthy' and 'unhealthy', respectively, are evaluated (See Section 5.
Label as healthy Label as unhealthy Once training is over, the performance of the trained CNN-HS is evaluated in the testing phase. The trained CNN-HS was able to classify the NSP of an external test dataset, which was not used in training, but operated under the same conditions as the rest of the training datasets. The binary regression results of the whole run-to-failure are computed, in which there is no need to specify a threshold value. As the trained CNN-HS learns the differences in the features of the NSP for healthy and unhealthy data labeled in part of the training datasets, it can recognize the degradation pattern of all of the data. For HS division, it is important to determine FPT, which is a starting point of the deterioration of bearings, in terms of maintenance. However, it is difficult to determine FPT since the features of the deterioration at FPT are weaker than the features in the unhealthy stage.
Data Description and Implementation The nature of this study needed continuous time series data on machinery that degraded over-time to the point of rolling bearing failure, including any failures in the balls, rings, and cage. The purpose of early fault detection is to detect the degradation as early as possible so that maintenance or prognostic-based decisions can be implemented. FPT is the indicator that detects the start of the degradation. We benchmarked CNN-HS with two widely used datasets: the IMS dataset and the FEMTO dataset. Both bearing datasets were acquired from accelerated degradations of bearings in a run-to-failure manner and are widely used to demonstrate the approaches for fault diagnostics and prognostic methods. The IMS dataset is generated by the NSF I/UCR Center for Intelligent Maintenance Systems (IMS)  [28]  and the experimental test rig and sensor placement are shown in Figure  5.
The IMS dataset is composed of three sets of run-to-failure experiments in which four bearings were installed on a shaft and measured. The test was stopped when the accumulated debris exceeded a certain level and the status of each bearing was recorded. All three sets have a sampling rate of 20 kHz but differ in the data acquisition phase. Only the first set has two-channel (horizontal and vertical) bearing data and was used for our model testing and validation. We transformed the time series data into fixed-size images by using NSP. Figure  6a  shows the spectral density difference in bandwidths through FFT on the IMS dataset and the comparison between healthy and unhealthy data. The spectral density from 400 to 700 Hz of unhealthy data was greater than that of healthy data. In this experiment, the sampling rate was considered by deciding the bandpass filters during the image transformation phase: 400-500 Hz, 500-600 Hz, 600-700 Hz.
as evidence of bearing degradation. Test will stop when the accumulated debris adhered to the magnetic plug exceeds a certain level and cause a switch to turn off. Although it is really hard to know the actual time point of the slight degradation occurring of bearings, the collected debris by magnetic plug is still an effective indication as the evidence of bearing health degradation. The test bearings have 16 rollers in each row, a pitch diameter of 2. 815 in, a roller diameter of 0. 331 in, and a tapered contact angle of 15. 17 deg. A PCB 353B33 high-sensitivity quartz Integrated Circuits Piezoelectric (ICP) accelerometer was installed on each bearing housing. The data sampling rate is 20 kHz, and the data length is 20 480 points. Vibration data were collected every 20 min. Four testings (i.
e. , testings 1, 2, 3, and 4) were implemented in this experiment. For each testing, four new bearings were installed on one shaft, and other experimental conditions (e. g. , radial load on the shaft, alignment, and rotation speed) were kept the same for the four testings. The full life data from five representative bearings (named bearings 1-5, respectively) whose faults include inner race, outer race, and ball defect are used to test the performance of the proposed methods. For more detailed information about this experiment, refer to  [2].
For bearing 1, HMM with DPCA detects the earlier slight degradation (at time point 1760) than HMM with PCA (at time point 1780). This illustrates that DPCA improves the Figure  3  shows the degradation process of bearing 4 of the first dataset, in which a roller element defect occurred at the end of the accelerating process. The degradation data of bearing 4 was used in our experiment to test the capability of our proposed method. The FEMTO dataset has been available to the public since the IEEE PHM 2012 Prognostic Challenge (PHM 2012). The dataset was collected on an accelerated aging platform, PRONOSTIA, as shown in Figure  7   [8, 9]. Asynchronous motor, a shaft, a speed controller, and an assembly of two pulleys are used to change the speed of the rolling bearings. Similarly, the vibration data in the horizontal direction are investigated.
The dataset was composed of 17 run-to-failure data in which a single bearing was tested (two columns of vibration data: horizontal and vertical). The sampling frequency of data acquisition is 25. 6 kHz, which was taken into consideration in our image transformation phase. The 17 datasets are grouped into three sections by operating conditions that differ in rotation speed and radial load. Each dataset has a different run-to-failure time, requiring fault detection methods that are adaptable to time-varying operational conditions and environments. When the amplitude of the vibration data exceeds 20 g, the measurement of the run-to-failure experiments is stopped and the bearing is considered to be defective. Figure  6b  shows the spectral density difference in bandwidths through FFT on the FEMTO dataset and the comparison between healthy and unhealthy data.
The spectral density from 500 to 1200 Hz of unhealthy data was different from that of healthy data. Taking the sampling frequency into consideration, three different bandpass filters were used: 500-800 Hz, 800-900 Hz, 900-1200 Hz. The NSP representation and the CNN-HS were implemented using Python scripts and the Keras library on the TensorFlow framework and were tested on a Linux system. The details of the runtime environment are shown in Table  2. The source code to reproduce our experiments is available at https: //github. com/opensuh/CNNHS/. The proposed method was compared with previously widely used methods, RMS and AE. RMS was implemented simply by calculating the square root value of the addition of the horizontal and vertical vibration data squared. The mean of the calculated values in the interval of 10 s was then used as the RMS value to compare it with other methods.
Evaluation of CNN-HS on IMS Dataset In the IMS dataset, only bearing 4 in the first set was used for analysis since it showed complete failure at the end of the accelerated degradation tests. Both training and testing were done on the dataset of bearing 4 since no other bearing data in the same operational condition was available. The result of our model was then compared with previously widely used methods, RMS and AE in Figure  8. Both methods were implemented using a pre-specified threshold based on statistical properties. Figure  8  shows the HS division in RMS, AE, and CNN-HS. RMS and AE require threshold values for HS division, and the threshold value is represented in the red horizontal line. FPT, determined by a threshold value in RMS and AE, and by the trigger mechanism in CNN-HS, is represented as a vertical red dotted line.