dataesr/scientific-paragraphs-categorization huggingface dataset:
B. SUBJECTS RECRUITED For the creation of each dataset, industrial operators and skilled artisans consented to be recorded in their actual workplace while wearing the Nansense suit in accordance with the General Data Protection Regulation (GDPR) principles. Firstly, industrial operators from a television plant in Istanbul, Turkey, and an aerospace company in Bucharest, Romania, were captured as they carried out their professional tasks. Four healthy people, three men and one woman, participated in the MoCap recording session at the television plant. Their average age was 31. 5±6. 2 years, their height was 167. 8±4. 6 cm, and their average weight of 65. 3±9. 9 kg. Two male subjects participated in the MoCap session for the recordings in the aerospace company. They had an average age of 50±5 years, a height of 170±2 cm, and a weight of 77±1.
III. DATA PROCESSING AND SEGMENTATION The processing of the MoCap consisted of two steps. To begin, a low pass filter was applied, followed by the correction of incorrect postures caused by electromagnetic interference or sensors drifting when the recording lasted too long, and calibration was required. A low-pass Butterworth filter was applied to the raw MoCap data to eliminate high-frequency noise. To avoid over-smoothing the data, the cut-off frequency was selected using the power spectrum density of the signal. The MoCap system's sensors may drift or be influenced by magnetic disturbances from surrounding metallic objects during the recording process. As a result, occasionally erroneous joint angles were recorded during otherwise precise motion capture. The recordings were adjusted to correct this error using a 3D character animation software.
2 The software was used to adjust the unrealistic movements based on common sense and video feedback. After adjusting and removing noise from the MoCap data, it was segmented by gestures. Firstly, recordings were collected per task, with one recording 2 MotionBuilder, Autodesk Inc. , San Rafael, CA. USA. representing a whole task; however, these recordings were later segmented by gestures. Fig. 7  illustrates an example of how the task of television assembly is segmented, extracting the gestures TVA 1 , TVA 2 , and TVA 3. All the tasks' repetitions in the seven datasets were segmented by gestures or postures for ERGD. A task may contain a single gesture that is performed numerous times, or it may contain additional gestures that are repeated throughout the task. The segmentation of the television assembly and packaging is based on repetitions of the gestures given in Section II-C1.
Only to facilitate the training of the models described in the next sections, the discontinuities of the Euler joint angles present in part of the MoCap files were reduced manually. These discontinuities are dramatic shifts between the values 180 • and -180 • in only certain local joint angles. By examining each MoCap file, it was determined to transform the time series with discontinuities to a data of range [-250 • , 250 • ]. Note that this transformation may not be appropriate for new movements recorded with IMUs. Nonetheless, it was sufficient to eliminate most discontinuities in the datasets presented in this paper. Each transformation was documented so that the transformed data may be inversed to Euler angles. An example of these transformations is represented in Fig. 8. The figure illustrates the MoCap data before and after the modifications, as well as the reconstructed skeleton.
The angles from the arms and forearms and one angle of the Hips were mainly the local angles with discontinuities. The angle of the Hips on the Y axis (pointing up, measuring torso rotation) was the most problematic and prone to drifting. The explanation for this could be related to the sensor's position. If the suit is loose, the sensor can produce inaccurate readings. Another factor is that after the suit is turned on and connected  to the computer for recording, the subjects must move their entire body to ''wake up'' the sensors. This sensor was most likely still in an idle state while performing calibrations. Any MoCap file with a distortion caused by drifting or poor calibration was removed from the datasets. The total size of the seven datasets utilized in the following chapters is 5GB.
IV. ANALYSIS OF THE DATASETS USING ANALYTICAL MODELS Any voluntary movement of the body segments is accomplished via the musculoskeletal system. The musculoskeletal system is an intricate structure comprised of bones, muscles, ligaments, and tendons. Thus, modeling a structure with such complexity is not an easy task. However, even though the musculoskeletal system is primarily responsible for the complexity of locomotion, it can be acceptable to represent human movements using analytical models that include relevant assumptions about body joint associations and their temporal dependencies. The human movements contained in the seven datasets are then analyzed using analytical models based on the Gesture Operational Model. GOM allows quantifying human dexterity based on the learned parameters of its assumptions.
A. THE GESTURE OPERATIONAL MODEL GOM is a mathematical representation of whole-body human movement that takes into account the spatial and temporal dynamics of body joints. The mathematical representation is comprised of a set of models, each of which models a distinct joint motion descriptor using one-shot training with Kalman filters  [16]. The number of models in the equation system of GOM is equal to the number of body joints defined in GOM, multiplied by the number of dimensions the motion descriptor of each joint (e. g. , angle or position) is discomposed (e. g. , X, Y, and Z). For this work, the GOM was trained using motion descriptors from only 19 IMUs (out of 52 available in the datasets) for the modeling. Discarding MoCap data from the fingers and feet to simplify the human motion representation.
Fig. 9  depicts the sensors' placement, labeling, and orientation. Human postures are expressed as 3D Euler joint angles in order to generate poses with subjects of various morphologies. Unlike joint positions, Euler joint angles are unaffected by identity-specific body shape. Moreover, Euler angles can be intuitively interpreted in the analytical model and provide a more clear illustration of how human movements are conducted. Thus, 57 models compose the GOMs used in this paper to analyze the full-body movements of every dataset. The 57 models are created through state-space modeling, where endogenous and exogenous data are included in the second-order model of each motion descriptor. For example, while modeling the angle trajectory of the body joint P t on the  -axis (Px t ), whose movement is decomposed XYZ axes (Px t , Py t , and Pz t ) and has an association with j body parts.
The constant coefficients A and B of the equation system are estimated using Maximum Likelihood Estimation (MLE) via Kalman filtering. GOMs were trained using a reference gesture of each class, which was determined using the Dynamic Time Warping (DTW) algorithm. This algorithm measures the similarity between two time series. Then, the gesture repetition closest to all other gesture repetitions of the same class was chosen for one-shot training using an Intel Core i7-8750H CPU. Next, Section IV-B discusses the simulation performance of the trained GOMs for every gesture in the seven datasets. Metrics and examples of poses generated are provided in the appendix. These metrics are intended to be used as an initial benchmark of the datasets for comparing the simulation or generation performance of other methods that would use the presented datasets.
For the TVA dataset, the most difficult gestures to simulate accurately were TVA 1 and TVA 2 , which corresponded to  VOLUME 11, 2023  gestures in which the operator can move more freely with either the left or right hand to grasp circuit boards or On the other hand, TVA 3 TVA 4 correspond to gestures that were easier to replicate for the operator in each iteration, as the circuit board and drilling were performed similarly in each recorded iteration. GOM provides the best simulation performance for APA, TVP, and ERGD. These gestures and postures had the lowest intra-class variability, given that they were executed in a more controlled environment. In ERGD, for instance, subjects performed various postures in a laboratory while receiving constant instructions on how to execute them. However, as the posture became more complex, such as kneeling with torso and arm movements, the error increased as subjects' movements presented greater variation in how they performed the indicated posture (foot and knee position or arms final position).
Consequently, pedal height and position variations may have caused larger errors in the movement simulation. Likewise, in the motion simulations of GLB, the skilled glassblower progressively adjusted his posture, even for the same repetitive activity, in order to appropriately shape the molten glass. Consequently, the training gestures for each class of the GLB dataset did not adequately represent all gestures from the same class (high intraclass variance), resulting in a drop in simulation accuracy. The most challenging gestures to simulate were those involved in mastic cultivation, as MSC involves gestures in which the farmer moves while kneeling. In the other six datasets, subjects performed the majority of their tasks while standing. The farmer did not keep the same position of the legs while performing the same gestures; he repositioned the legs while kneeling to improve balance in order to reach the tree or objects.
2) SELECTION OF MOST SIGNIFICANT MOTION DESCRIPTORS PER DATASET For selecting the essential inertial sensors to use for the gesture recognition of each professional task, the number of times a motion descriptor (assumption) is statistically significant for all equations that comprise GOM is counted. Then, different combinations of descriptors considered most frequently significant for measuring the arm, spine, and legs were utilized for training in an all-shots approach. Because a single inertial sensor gives three joint angles, all of the sensor's joint angles were used for recognition if at least one was among the joint angles that were more often significant in all gestures of a dataset. For the recognition of human gestures utilizing different sensor combinations, Hidden Markov Models (HMM) were trained using a 10-fold cross-validation.
In order to properly train the HMM, a gesture vocabulary containing the gestures with the most iterations was specified for each dataset. The total number of gesture classes for TVA, APA, and ERGD were four, three, and 28, respectively. The TVP, GLB, and MSC gesture vocabularies contained only gestures with at least seven repetitions. Therefore, their respective gesture vocabularies included five, seven, and six classes of gestures. Regarding SLW, the gesture vocabulary consisted of only three classes of silk weaving on a loom. Despite the differences in loom size, the gestures used to weave on a small, medium, and large loom are similar. Therefore, they were combined into three classes for the gesture recognition problem. The ergodic and left-to-right HMM topologies, along with a different number of hidden states, were evaluated to determine the best settings for the gesture vocabulary defined in each dataset.
The performance metrics utilized were accuracy and F1-score, the last being the harmonic mean of precision and recall. Left-to-right HMM topology produced the best results for all recognition problems. Concerning the number of hidden states, it was defined for the HMMs of TVA and ERGD with seven states, TVP with six states, APA, GLB, and MSW with eight states, and SLW with three states. The sensor configurations that obtained the best recognition results with each dataset's gestures are presented in Table  8 , along with the highest recognition accuracy and F1-score achieved.
The most problematic gesture to model and recognize was APA 2 , which was expected given that its execution varied the most among the three classes (high intra-class variance). The operator did not prepare the material identically for each repetition. In certain repetitions, the operator was slower than usual because he required more time to adjust the pneumatic hammer or to prepare additional rivets. Furthermore, since only one airplane structure was built for this dataset, there is a substantial intra-class variance. There were no repetitions in which the pneumatic hammer was positioned in the same location more than once. Regarding recognizing gestures from traditional crafts, for GLB and MSC, the selected sensors, consisting of 7. 69% of the MoCap data, yielded comparable results to those obtained with data from all sensors.
For SLW, 9. 61% of the MoCap data was selected for the recognition problem, resulting in a performance drop of about 0. 05 in both metrics with respect to the configuration with all 52 sensors. The two-sensor configuration's poor performance for SLW could be attributed to its difficulty in distinguishing movements related to the shoulder (throwing of the shuttle) and the leg, as motion data from the hips and left forearm only were insufficient.
V. CONCLUSION This paper presented seven datasets: TVA, TVP, APA, ERGD, SLW, GLB, and MSC. Most publicly available datasets contain simulated movements performed in a laboratory and related to everyday activities or sports. Therefore, new datasets were created containing gestures performed in professional tasks either from the industry or crafts workshops. These were recorded with actual operators and experts in their real workplace scenarios using an inertial full-body suit of 52 sensors. The aim was to test human motion models with these complex gestures and extract information regarding the dexterity, skill, and know-how related to the adequate use of tangible elements such as materials and tools. Each professional task was segmented by repetitions, and discontinuities      were reduced to improve the modeling of the gestures in the analysis done for this benchmark.
The research leading to these results has received funding from the  CARNOT Projet Fédérateur ''Usine responsable ' ' and the  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 820767 ,  CoLLaboratE  project, Grant No. 822336 ,  Mingei  project,  Craeft , Grant No. 101094349. This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by Article 13/14 of Regulation (EU) 2016/679 (General Data Protection Regulation), and performed in line with the Collaborate and  Mingei  projects.
ACKNOWLEDGMENTS This study is a subproject of the "OPERAM: OPtimising thERapy to prevent Avoidable hospital admissions in Multimorbid older adults" supported by the  European Union 's  Horizon 2020 research and innovation program  under the grant agreement number  634238 , and by the  Swiss State Secretariat for Education, Research and Innovation (SERI)  under contract number  15. 0137. The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the  European Commission  and the  Swiss government. This project was also partially funded by the  Swiss National Scientific Foundation  ( SNSF 320030_188549  and  325130_204361/1 ). OB obtained a  Protected Research Time (PRT) Grant  from the  University of Bern. Open access funding provided by  Universitat Bern.
02° and an acquisition time of 1 second per step. The obtained patterns were refined with the Rietveld method using the software Profex. The microstructure of as-sintered surfaces was investigated by scanning electron microscopy (Tescan Mira 3 SEM) operating in secondary electrons (SE) mode with an acceleration voltage of 20 kV. Before the measurements, all samples were gold-coated to avoid the accumulation of electron charges at the surface under the beam. Samples were then mechanically polished with a Buehler EcoMet 30 polishing machine, under a force of 5 N. Silicon carbide abrasive disks and samples were rotating in the same direction at 150 rpm and 60 rpm, respectively. The final polishing step was performed with a Buehler 40-7220 microCloth and a 1 µm alcohol-based diamond suspension (Presi).
PATIENTS AND METHODS Patient cohorts. To establish a gout PRS, a total of 332,370 individuals of European ancestry from the UK Biobank (project ID 12611) were included in a gout GWAS. This cohort was recruited from the general population of the UK, with detailed phenotypic information available  (15). Detailed information on gout ascertainment and inclusion/exclusion criteria can be found in Supplementary Methods 1, available on the Arthritis & Rheumatology website at https://onlinelibrary. wiley. com/doi/10. 1002/art. 42393, and in Supplementary Methods 2, available for download from the authors' GitHub site at https://github. com/ MerrimanLab/PRS-Project. In total, 7,131 gout patients and 325,239 non-gout controls were included for this analysis. This cohort was genotyped using the Affymetrix UK Biobank Axiom Array, followed by genotype imputation with IMPUTE4 software using the Haplotype Reference Consortium panel and the merged 1000 Genomes plus UK10K reference panels  (15).
These 3 cohorts included 1 West Polynesian cohort and 2 East Polynesian cohorts. The 2 East Polynesian cohorts consisted of individuals recruited in collaboration with the Ng ati Porou Hauora (Health Service) Charitable Trust and individuals recruited elsewhere in Aotearoa New Zealand. Patients in the Ng ati Porou Hauora cohort were recruited from the rohe (area) of the Ng ati Porou iwi (tribe) of the Tair awhiti region (East Coast of the North Island of Aotearoa New Zealand). East and West Polynesian cohorts were defined based on a combination of self-reported ethnicity and genetic principal components analysis of genomewide genotype data  (22) , in which East Polynesian refers to those of Aotearoa New Zealand M aori and Cook Island M aori ancestry (excluding the island of Pukapuka) and West Polynesian refers to those of Samoan, Tongan, Niuean, Tokelauan, and Pukapukan ancestry.
Relevant demographic and clinical characteristics of gout cohorts included in Phenotypic information for all cohorts, excluding the UK Biobank, was obtained from a combination of self-report questionnaires, laboratory measurements, and in-person assessments. Specifically, age at onset was self-reported, and tophaceous disease was defined through a combination of self-report and physical examination for ≥1 subcutaneous tophus by a trained assessor. Any individuals missing data for sex, age, PRS, or both severity phenotypes were excluded from the analysis (Supplementary Tables  1  and 2 , https://onlinelibrary. wiley. com/ doi/10. 1002/art. 42393). Individuals that were related at the first degree or more (defined by KING software kinship coefficient > 0. 177  [23] ) were removed (including duplicate samples across studies).
These cohorts were genotyped using an Illumina CoreExome-24 kit version 1. 0, 1. 1, or 1. 3 array, with a small subset being genotyped on an Illumina OmniExpressExome-8 kit version 1. 3 array. Details on reliability of ascertainment of phenotypic information for all cohorts can be found in Supplementary Methods 1, https://onlinelibrary. wiley. com/doi/10. 1002/art. 42393, and Supplementary Methods 2, https://github. com/MerrimanLab/PRS-Project. GWAS and PRS calculation. A gout GWAS was performed in the UK Biobank cohort using a total of 27,287,012 imputed or genotyped variants. A logistic regression model was produced for each variant, adjusting for age, sex, and the first 40 genetic principal components using PLINK software version 1. 9 build 6. 10 (24). Variants were considered statistically significant at P values less than 5 × 10 -8.
A total of 19 genetic variants at 15 loci were identified as conditionally independent single-nucleotide polymorphisms (SNPs) associated with gout at genome-wide significance in the UK Biobank (Supplementary Table  3  and Supplementary Methods 1, https://onlinelibrary. wiley. com/doi/10. 1002/art. 42393, and Supplementary Methods 2, https://github. com/MerrimanLab/PRS-Project). Three of the 15 loci had >1 independent goutassociated SNP; these were ABCG2 (2 SNPs), SLC2A9/ WDR1 (3 SNPs), and SLC22A11/NRXN2 (2 SNPs). Genotype data for all 19 variants were used to produce a gout PRS in all cohorts. This PRS was calculated for each individual by summing the number of risk alleles multiplied by the respective gout effect size (log odds) for each of the 19 risk alleles. Importantly, these weightings were based on models conditioned on all other PRS SNPs at the same locus, ensuring the weights represented independent effects of SNPs.
Additionally, we produced a second PRS that excluded both variants at the ABCG2 locus. Given that there is uncertainty on the best way to measure gout diagnosis in epidemiologic studies (e. g. , International Classification of Diseases code versus physician diagnosis) but a standard clinical assay for serum urate, we decided to compare our gout PRS to a serum urate PRS. We produced a serum urate PRS that included 82 serum urate-associated variants identified in the European GWAS of serum urate performed by Tin et al  (9)  that were also genotyped in our studied cohorts (Supplementary Methods 1 and Supplementary Table  4 , https://onlinelibrary. wiley. com/doi/10. 1002/art. 42393, and Supplementary Methods 2, https://github. com/MerrimanLab/PRS-Project). We also performed a sensitivity analysis to determine the effect of the weighting method on associations with age at onset in men (Supplementary Methods 1 and 2).
By reproducing our models using the same method, we were able to replicate the results of He et al (5) (Supplementary Methods 2, https://github. com/ MerrimanLab/PRS-Project), suggesting a dominant, West Polynesian ancestry-specific effect of both ABCG2 variants on tophaceous disease. A potential limitation of our case-only study design relates to index event (collider) bias  (34). This occurs when risk factors for incident disease are used in studies of disease progression and severity. This bias results in an induced association between risk factors (both measured and unmeasured) when observational studies are conditioned on a common effect or collider (e. g. , gout). If the risk factor (PRS for gout in our study) causes disease incidence but not progression (i. e. , tophaceous disease), then there is a risk of an induced negative association due to collider bias  (35).
ACKNOWLEDGMENTS We would like to thank  Ria Akuhata-Brown ,  Nancy Aupouri ,  Jill Drake ,  Carol Ford ,  Roddi Laurence ,  Meaghan House , and  Gabrielle Sexton  for assistance in recruitment for the  Genetics of Gout in Aotearoa study. Additionally, we would also like to acknowledge the  European Crystal Network , formed after the first European Crystal Workshop held in Paris, March 2010 ( Prof. Frédéric Lioté ,  Paris , and  Prof. Alexander So , Lausanne, convenors). Acquisition of  data. Sumpter, Takei, Cadzow, Topless, Phipps-Green, Murphy, de Zoysa, Watson, Qasim, Abhishek, Andrés, Cris¸an, Doherty, Jacobsson, M Janssen, TL Jansen, Joosten, Kapetanovic, Lioté, Matsuo, McCarthy, Perez-Ruiz, Riches, Richette, Roddy, Stiburkova, So, Tausche, Torres, Uhlig, Major, Stamp, Dalbeth, Merriman.
) , with an end-to-end optimization of a rate-distortion Lagrangian loss function. This approach allows for the interpolation of the reconstructed attributes continuously across space, and it leads to significant improvement in terms of fidelity reconstruction when compared to state-of-the-art baselines and the latest MPEG G-PCC standard. The authors also show the importance of properly normalizing the coefficients before quantization. Allouche and Mitrea, in their article "Video fingerprinting: Past, present, and future", address a slightly different, but nonetheless very important Research Topic. Video fingerprinting, sometimes also referred to as content-based copy detection or near duplicate detection, addresses the problem of identifying duplicated and/or replicated versions of a given video sequence in a reference dataset.