dataesr/scientific-paragraphs-categorization huggingface dataset:
The monitoring was realized by the design and subsequent implementation of an integrated software/hardware system, able to control the movements and deformations of the structure in realtime and to expect them to be at least one month. The integrated system was designed in EasyEDA ® environment. The board was properly designed and integrated with components needed for data processing. Data taken in real-time and data processed using soft-computing techniques were transmitted with 4G LTE internet key to the monitoring platform. The platform was implemented in the WordPress ® environment. At the same time, in order to prevent the disadvantages inherent in programming in WordPress ® , SSL security protocols were integrated, which provided a fundamental level of online security, essential when transmitting sensitive information.
Introduction The current gold standard for the automatic detection of gait events in clinical practice involves the use of ground reaction forces measured by force platforms  (Veilleux et al. , 2016). However, pathological gait is highly variable and the use of gait aids, small steps or foot dragging during swing phase can produce false force thresholds, all of which may reduce the accuracy of the detection. To avoid these issues, gait events are therefore often determined manually, in particular for pediatric gait which is highly variable, however this process is time consuming and its accuracy and reliability strongly depend on operator expertise. To overcome these issues, computational methods of gait event detection, mostly based on kinematic data from opto-electronic systems, have been developed.
Gait data Data from 226 children who had undergone clinical 3D gait analysis were used. All parents provided written informed consent on a form that had been approved by the Brest CHRU. The children had the following pathologies: neurological disorders (65%, 76% of whom had cerebral palsy), neuromuscular disorders (11%), orthopedic disorders (9%) and other types of disorders (15%). All data were recorded using the same motion analysis system (Vicon MX, Oxford Metrics, UK) and four force platforms (Advanced Mechanical Technology, Inc. , Watertown, MA, USA) in the same motion laboratory (Brest CHRU) between 2015 and 2019. The data collected by the 15 infrared cameras (sampling rate 100 Hz or 120 Hz) were synchronized with the ground reaction forces recorded by the force platforms (1000 Hz or 1200 Hz).
The markers were placed according to the protocol by by  Kadaba et al. (1990). Marker trajectories and ground reactions forces were dual-pass filtered with a low-pass Butterworth filter at a cut-off frequency of 6 Hz. All participants walked independently, with no gait aids, at their natural speeds. An average of 5 trials was recorded for each child, yielding 1156 trials in total. All FS and FO events were manually identified by an engineer   (Crowther & Cox (2005) ). Among the test data, 815 FS (446 left FS and 369 right FS) and 815 FO (369 left FO and 446 right FO) had previously been identified using the force platform data and were used as the gold standard method for comparison purposes. FS was determined as the first frame at which the vertical ground reaction force was greater than 10 N.
Network architecture 3D position and velocity of the left and right heel, toe and lateral malleolus markers for each trial were used as input data for the DeepEvent recurrent network. A zero-padding technique was used to obtain the same number of frames for each trial (with a maximum duration of 1536 frames). The neural network consisted of M bidirectional LSTM layers with N hidden units (Supplementary data) and was followed by a dropout layer. Finally, a one-time distributed dense layer was used to detect gait events. The 5 gait events  (no event, left FS, right FS, left FO and right FO)  were encoded using a one-hot encoding scheme. The parameters M and N were empirically chosen to maximize the performance of the network with the validation data set by an empirical approach. A sensitivity analysis was carried out to explain the neural network decisions by measuring the contribution of each input variable to the overall prediction  (Simonyan et al.
, 2013). DeepEvent was implemented using Keras  (Chollet (2015) ) in Python  (Van Rossum & Drake (1995) ), with crossentropy as loss function and Adaptive Moment Estimation (Adam) for optimization on a Dell Workstation 7910 (CPU Xeon e5-2680-v4 2. 4 GHz) with a Nvidia GeForce GTX 1080 Ti. DeepEvent is available on Github (github. com/LempereurMat/deepevent). The contribution of each type of input data to the decision process is presented in figures 1 and 2. The sensitivity analysis showed that DeepEvent used the input data between 0 and 20%, between 50 and 70% and between 90 and 100% of the gait cycle. During these intervals, the contribution of each typeof input data was between 0 and 0. 04. Outside of these intervals, the weights were closer to 0. These timings perfectly matched the definition of the beginning of swing phase at 60% of the gait cycle.
The analysis was sequentially carried out in five stages. First, user review data were crawled from Glassdoor and stored in a database. Second, the raw data were processed in the data pre-processing stage to delete the incomplete data. Third, the words other than noun keyword were eliminated using Standford POS Tagger. Fourth, the noun keywords were then classified into each aspect. Finally, the aspect score was calculated based on the aspect-based sentiment analysis. Results: Result showed that the proposed method managed to turn raw review data into five aspects based on user perception. Conclusion: The study provides information for two parties, job seeker and the company. The analysis of the review could help the job seeker to decide which company that suits his need and ability. For the companies, it can be a great assistance because they will be more aware of their strengths and weaknesses.
The reviews can be very helpful for the readers. They could compare the advantages and disadvantages of each work experience. The background of the readers varies from a job seeker, employee, and even HR Manager. The information extracted from the platform such as Glassdoor was classified as an honest opinion because the employee posted their review without anyone asking. The HR Manager can utilize the review data so that the HR Manager can easily find out the employee's perception. The biggest challenge is a large amount of unstructured review data. Also, the review data from Glassdoor are not freely accessible, so it requires more effort to obtain the large review data. In this study, we focused on Aspect-based Sentiment Analysis. We propose an extension of Liu's Aspect-based Sentiment Analysis methodology to be applied to the crowd sourced employer branding domain, especially on the Glassdoor platform.
The existing works of Aspect-based Sentiment Analysis mostly focus on product review mining. For example, Liu et al  [2]  and Shahkhali et al  [3]  used various camera product reviews, Sindhu  [4]  summarized a lot of product reviews on Amazon, and Shrividya et al  [5]  have considered reviews on iPod product. The users' comment in a product review is usually predictable because the features have already been mentioned, and the review itself is all about whether they like the features or not. Recently, the focus of this Aspect-based Sentiment Analysis research began to use a lot of data on tourism products. Akhtar et al  [6]  used hotel review from TripAdvisor platform to build hotel recommender system, Al-Smadi  [7]  enhanced Aspect-Based Sentiment Analysis from Arabic's hotel only while Garcia-Pablos et al  [8]  use various data from several domains like hotels, restaurants, electronic devices in different languages such as English, Spanish, French and Dutch to classify users' review using unsupervised approaches for Aspect-based Sentiment Analysis.
Marrese-Taylor et al  [1]  stated that there is a difference between usual product review and tourism product review. The users tend to write a longer review of tourism product because they describe their feeling towards it. The users often use complex sentences to describe the tourism product. So, they concluded that a different product produces different reviews, and it has diverse characteristics  [1]. Since product review and tourism product review have become a trend a few years back in Aspect-based Sentiment Analysis research, we decided to do research in crowd sourced employer branding domain. The most important reason to choose crowd sourced employer branding domain is because, to the best of our knowledge, the existing approaches have not addressed the issues in this domain yet. Hence in this study, we proposed an Aspect-based Sentiment Analysis to generate a lot of insights from crowd sourced data in Glassdoor platform, so the company as an employer can easily find out the employee's perception and create employer branding strategies.
A. Data description We collected our study data review at www. glassdoor. com. Glassdoor is a site where current and former employees review companies and their management anonymously  [9]. Fig. 1  shows a screen shot of Glassdoor. com. In this study, we used data from three big technology companies: Amazon, Google, and Apple. The number of reviews from data collection is 27820 reviews, and it consists of seven attributes: company's name, location, dates, job-title, summary, pros, cons, and overall ratings. The first step of the study was to extract three out of seven attributes. They are company's name, summary, and overall rating attributes only. The dataset that has been obtained is saved to the following link http://tiny. cc/reviewdataGD The research procedure is illustrated in Fig. 3.
It consisted of five steps as follows: 1. Crawling companies' review As the latest company's review dataset was not available, then the data from Glassdoor website were crawled using Webharvy Web Scrapper  [10]. Fig. 2  shows a screenshot of Webharvy. It took an input of the URL of the company's webpage on the Glassdoor site, and then chose the attributes which were crawled using a feature in Webharvey. In this study, the company's name, dates, job title, summary, pros, cons, and overall ratings were retrieved. Then, after all the pages had been crawled, the dataset was exported into a CSV file. The same steps were used to retrieve the data from other companies. Therefore, there were three CSV files from the three companies. Later, the CSV files were converted to excel files. Table  2  represents the example of the dataset from each company with seven attributes.
Data pre-processing Data pre-processing is a process to convert raw data into formatted data so that the computer can understand and read the data easily. The general steps of data pre-processing are incomplete data deletion, data selection, data integration and data formatting  [11]. Data pre-processing has to be done because the quality of the dataset determines the result. The main objectives of data pre-processing are summarized as follows: (1) data cleansing; (2) data integration; (3) data transformation; (4) data reduction; (5) data discretization. For data pre-processing in this study, the tool called RapidMiner was used. RapidMiner is an open source software for processing data, text mining, and predictive analysis  [12]. The number of the raw data is not limited in RapidMiner, and the type of the raw data can vary including databases and text  [13].
b. Stop words removal Stop words are commonly known as a list of words that often appear in the sentences. They often appear because stop words include prepositions, articles, and pronouns  [14]. The stop words should be eliminated because they will be taking up space in the database, but they do not provide any useful information. c. Stemming Stemming is the process of mapping and breaking down the form of a word into its basic word form. Simply put, it is the process of changing suffixes words into basic words.
III. RESULTS Table  4  shows the example of the keywords that have high TF in the review dataset from three companies. Thus, these keywords are the words that appear most often in the data set. After the list of keyword was determined, it was then categorized based on five aspects. The five aspects were proposed by Dabirian et al  [19]. He found that those aspects which were often called as employer branding value proposition were the things that employees care most in evaluating places to work. Those five aspects were "work balance", "culture values", "career opportunities", "company benefit" and "management". The score was given for each aspect based on the overall rating value as explained in section II sub section 5. The value was calculated as a positive or negative score. The results were illustrated using bar chart.
Liu stated in  [2]  that there is a possibility that many sentences are not opinionated sentences in the review data so that the precision value is lower than the recall value. IV. DISCUSSION The analysis and summarization of user reviews of three giant companies in Glassdoor are the main focus of this study. The user reviews often provided valuable information about the weaknesses and strengths when working for a particular company. To find out the opinion from the current and former employees, the analysis of the review has been done. Data pre-processing has been done to avoid unwanted data on raw review data. Srividyaet al  [5]  applied POS Tagger to find the noun phrase because aspects are always in the noun phrase form. Thus, we directly did POS Tagger to verify the noun words without considering another POS such as adjectives, adverbs, etc.
V. CONCLUSIONS This study provides a way to extract employee review on predetermined platform called Glassdoor. An Aspectbased Sentiment Analysis managed to calculate the aspect score based on the number of frequent keywords on the review. It was started by crawling the review data from Glassdoor using Webharvy Web Scrapper, categorizing the unprocessed raw data of the user review, and storing them in the database. RapidMiner was used to do preprocessing on raw review data and produced keywords. The keywords were calculated based its term frequency. Next, only nouns were extracted from the group of keywords using Standford POS Tagger. Nouns were then classified based on their aspects. Last, aspect score was counted and displayed in a graphical form.
Design of the study It was an ancillary study from a multicenter (8 centers), randomized controlled trial (#NCT02255370) comparing curcumin to placebo. All adult patients with CD who underwent ileocolonic resection with restoration of bowel continuity were recruited from November 2014 to September 2017. Patients received azathioprine 2. 5 mg/kg once a day within the first 21 days after surgery and were randomized for either oral curcumin (3 g/day) or a placebo. Curcumin, a 95%-pure curcumin preparation, and identical placebo capsules were purchased (Europhartec, Lempdes, France). The primary endpoint was endoscopic POR at 6 months defined by an endoscopic Rutgeerts' score ≥ i2b.
Data managing and statistical analyses Study data were collected and managed using Research Electronic Data Capture (REDCap) tools hosted at Clermont-Ferrand University Hospital  [16]. REDCap is a secure, web-based application designed to support data capture for research studies, providing 1) an intuitive interface for validated data entry; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for importing data from external sources. The statistical analyses were carried out using the statistical software Stata (version 13, StataCorp, College Station, US). All statistical tests were conducted for a two-sided type I error at 0. 05. Continuous variables were described as mean and standard-deviation or median and interquartile range, according to statistical distribution (assumption of normality studied using Shapiro-Wilk test).
Concerning non-repeated data, univariate analyses were performed using Student t-test or Mann-Whitney test when assumptions of t-test were not met (normality, homoscedasticity assessed by the Fisher-Snedecor test) for continuous variables, and Chi-squared or Fisher's exact tests for categorical data. ROC curve analysis was performed to determine the best cut-off value of relative variation of Fcal to predict endoscopic POR. In addition, the optimal threshold of Fcal variation was determined according to usual indexes reported in the available literature (Youden, Liu and efficiency) and to clinical relevance. Sensitivity, specificity and negative and positive predictive values were estimated with 95% confidence intervals. Repeated correlated data (evolution of calprotectin value) were analyzed using random-effects models, useful to study fixed effects (group, time-point evaluation and their interactions), taking into account between and within patient variability (subject as random-effect).
The amount of medical video data that has to be securely stored has been growing exponentially. This rapid expansion is mainly caused by the introduction of higher video resolution such as 4K and 8K to medical devices and the growing usage of telemedicine services, along with a general trend toward increasing transparency with respect to medical treatment, resulting in more and more medical procedures being recorded. Such video data, as medical data, must be maintained for many years, resulting in datasets at the exabytes scale that each hospital must be able to store in the future. Currently, hospitals do not have the required information and communications technology infrastructure to handle such large amounts of data in the long run. In this paper, we discuss the challenges and possible solutions to this problem.
The Pawałowski et al JMIR BIOMEDICAL ENGINEERING XSL • FO RenderX example calculations were performed for Poland. In 2017, there were 951 general hospitals in Poland. On average, there are 8 ORs in a hospital, with 3. 2 surgeries being performed per day in each of them  [10]. Based on this, we can calculate that one hospital recording all surgical procedures produces 189. 8 GB of video data per day and 67. 7 TB per year. Therefore, for the whole of Poland, this adds up to 62. 9 PB per year. If these recordings are treated as medical data, they have to be stored for 20 years since the last visit of the patient. As an example, if surgery of a 20-year-old patient is recorded in 2019 and the patient lives until 80 years old, regularly going back to see the doctors, this recorded surgery will have to be stored until 2099.
Lightweight compression is used during the production process to reduce the data size for transmission or storage while maintaining the quality as close as possible to the original (ie, lossless or visually lossless compression). Compression ratios between 2× and 6× are common, and implementations of encoders and decoders require much less computational resources compared to firm compression. Codecs such as JPEG-2000, JPEG-XS, and VC-2 are part of this group. The H. 265/HEVC video coding standard  [14]  ratified in 2013 represents the state of the art in strong video compression. Compared to H. 264/AVC, H. 265/HEVC is capable of reducing the bit rate by 50% while maintaining the same quality. Compared to uncompressed video, HEVC can achieve compression factors between 250× and 500×, depending on the target bit rate and video resolution, more specifically 249× for full high-definition (1920×1080), 30 frames/s at 3 Mbits/s; 478× for 4K (3840×2160), 60 frames/s at 25 Mbits/s; and 398× for 8K (7680×4320), 60 frames/s at 120 Mbits/s.
Test cases have been defined for various types of video, including HD, UHD, HDR, and 360° video, and investigations on advanced compression tools using a common software platform have been performed  [17]. JVET plans to finalize a new codec standard by the end of 2020 (ITU-T will probably name this codec H. 266). Currently, there are optimized HEVC/H. 265 software encoders available, such as the open-source x265 encoder [18] (in particular, x265 has inherited its basic algorithms from its predecessor x264  [19] , H. 264/AVC) and commercial encoders offered by companies. Independent comparisons have shown that x265 has better compression-quality-speed performance than other industrial encoders  [20] , especially for full high-definition video. Currently, x265 is commonly used as a baseline for benchmarking analyses performed by video codec companies.
RenderX Real-time 4K and 8K hardware encoders are also already available on the market. Nvidia released a new generation of GPUs called Pascal that supports 8K hardware-accelerated HEVC encoding using NVENC technology. Advantech is commercializing the VEGA 3300 Series for 4K/8K HEVC encoding, decoding, and transcoding. NEC also launched its 4K video codec for ultra-low delay applications. Intel with Quick Sync and AMD with VCE technology are quite active in the area of hardware-accelerated HEVC processing as well. There are also companies and universities providing field programmable gate array (FPGA) accelerators for HEVC-based solutions. However, hardware encoders are much more expensive to create and are often much less feature-rich than software encoders. Although firm compression delivers a substantial reduction of bandwidth used, this requires relatively high implementation complexity to achieve.
Codecs are designed to reduce quality degradation with multiple transcoding steps. JPEG XS is a recent promising effort to create a standardized intermediate codec. A strong focus is placed on achieving visually lossless quality with low latency and low-complexity implementations. The standardization efforts also target the integration of file and transmission formats  [21]. The target use cases are video transport over professional video links (SDI, IP, ethernet), real-time video storage, memory buffers, omnidirectional video capture and rendering, and on-sensor compression. In our opinion, considering the large amounts of data that will be produced by the recording of medical procedures, only firm compression should be taken into account. This is why it is crucial that during the standardization of new robust compression codecs, the aspect of quality for medical purposes should be one of the main focus areas.
There are several publications presenting research results on storing compressed medical video sequences. However, it should be stressed that research on medical multimedia data compression is usually performed within narrow medical specializations such as oncology  [22]  (the EUROPATH project), radiology  [22, 23]  (the EURORAD project), ophthalmology  [24] , pediatrics  [25] , surgery  [26] [27] [28] [29] [30] [31] , nursing  [32] , dentistry  [27] , internal medicine  [33] [34] [35] , and emergency medicine  [36] [37] [38]. The effects of this research have influenced the creation and development of standards for storing and compressing medical video sequences  [22]. The most important among them is the digital imaging and communication in medicine (DICOM) standard  [39]. The DICOM standard has become the primary standard used for the storage of compressed surgery multimedia data.
DICOM allows for storing both still images and video sequences  [39] , although it has not been designed to be used for long video sequences. Compression algorithms to be chosen for reliable storage of source data were one of the critical aspects taken into account during the development process of the standard. Currently, most of the codecs specified in the DICOM standard are lossless codecs. Usage of lossless codecs results in low compression ratios that are now achieved when compressing and storing video sequences (eg, in the DICOM format). Therefore, using these codecs entails superfluous requirements on both the archive memory and the throughput of the streaming network. Moreover, it is essential to note that the DICOM standard is continuously under development, and it is likely to include more lossy compression methods in the future.