dataesr/scientific-paragraphs-categorization huggingface dataset:
(2d) The properties of the avatar dataset are evaluated by both computing re-identification and signal retention metrics  19. Importantly, this step allows reiterating phase 2b if the metrics lack sufficient privacy or acceptable statistical conservation. The method is controlled by two types of parameters: 1) parameters affecting the local environment: distance used (e. g. Euclidean or Mahalanobis), number of neighbors ( ), parameters of  multidimensional projection (e. g. standardization, number of dimensions used in neighbor identification and variables custom weighting for projection) and 2) parameters affecting how stochastic an avatar generation can be: the weights distribution law over neighbors (equal or unbalanced contribution) and the percentage of perturbation applied to the avatar for each variable.
After generating the synthetic dataset, multiple metrics are computed. Of the three EDPB criteria 16 , singling out represents the most unfavorable and versatile attack scenario. Herein, we present two metrics addressing the singling-out issue, local cloaking and hidden rate. We place ourselves in the scenario where an attack is based on the full knowledge of both the avatar dataset and the original dataset. The attacker's intention is to reveal the link between sensitive individuals and avatars. In this context, the most likely attack is a distance-based linkage attack  36. For each sensitive individual, the local cloaking metric counts the number of avatars that are more similar (i. e. closer in the multidimensional space) to the original data than the one avatar produced from the data (see supplementary material for details).
Application of the method to biomedical dataset 1: Acquired Immunodeficiency Syndrome (AIDS) clinical trial The AIDS dataset includes 2,139 patients and 26 variables for HIV-infected patients who participated in a clinical trial published in 1996 in the New England Journal of Medicine. The clinical trial had four arms and was analyzed by Hammer et al. (1996)  37. The main endpoints used were survival and a 50% drop in CD4+ cell counts. A synthetic dataset of the clinical trial was generated with the parameter =20. We compared the  multidimensional reduction representation, the survival curve of two treatments and the hazard ratio value computed with original and avatar data. We assessed the previously presented re-identification metrics (local cloaking and hidden rate) to ensure privacy protection of the sensitive data.
To illustrate the stochasticity of the method, we performed 25 avatarizations ( =20) of the dataset, and for each  individual, we looked at the number of times a distance-based linkage attack would have led to a correct re-identification. Then, to evaluate the impact of , we performed survival analyses over 10  avatarizations for each ranging from 4 to 1,200 (to achieve half-size of the dataset). Finally, we  studied the evolution of the data structure with 10 successive avatarizations, where the input dataset at iteration is the output dataset of iteration -1.
Application of the method to biomedical dataset 2: Wisconsin Breast Cancer Diagnosis (WBCD) prediction issue The WBCD dataset comprises 683 observations and 10 variables. It is frequently used for student training purposes and can be downloaded from the University of California Irvine machine-learning repository  38. The outcome corresponds to the tumor severity: benign ( =444) versus malignant  ( =239). The other nine features are built from imaging-specific annotations and are graduated from 1  to 10. Feature selection (F-score computation) and a support vector machine (SVM) were used to predict the severity of a patient's breast cancer diagnosis as per Akay et al. (  2009 )  39. The synthetic dataset was built with =20. We compared multidimensional reduction, F-score  computation (see supplementary materials for details), and classification performance (area under the receiver operating characteristic curve; AUC) in the original and avatar data.
We then looked at previously presented re-identification metrics to ensure the privacy of sensitive data. To illustrate the stochasticity of the method, we performed 25 avatarizations ( =20) of the dataset, and for each  individual, we looked at the number of times a distance-based linkage attack would have led to correct re-identification. Then, to evaluate the impact of k, we computed the AUC over 10 avatarizations for multiple ranging from 4 to 334 (to exceed the size of the smallest group of interest i. e. 239  malignant tumors). Finally, we studied the evolution of the data structure with 10 successive avatarizations, where the input dataset at iteration is the output dataset of iteration -1.
Data availabitity To promote transparency and reproducibility of the results, all avatarisation parameters used, the resulting datasets and analysis codes are available on GitHub (https://github. com/octopize/avatar_paper). The analysis pipeline can be used to evaluate synthetic data in other contexts. For both datasets, we focus on the privacy and the similarity of the results between the original data and the avatar data. Analyses were performed using R (version 4. 1) and Python (version 3. 9).
Avatarization and preservation of the statistical relevance The avatarization process retained the statistical value of the datasets. Figure  2  shows the overlay of the original data (orange) and the avatars (green). For the AIDS (Figure  2a ) and WBCD (Figure  2b ) datasets, the factor analysis of mixed data (FAMD) projection showed that the original data and avatars fully overlapped, including the outliers, and the structural data were maintained. Figure  2c  compares the survival curves calculated with the avatar dataset and the original AIDS dataset. In both treatment arms, the survival curves of the avatars (dotted line) and originals (continuous line) overlapped. Regarding the survival curves, the analysis of the avatar data is leading to the same interpretations as the one obtained with sensitive data.
The main trial results remained unchanged: arm 1 was more effective than arm 0 when comparing CD4 T-cell count over time (cf. original data: ; vs avatar data:  = 0. 49 (95% , 0. 39 -0. 63)  = 9. 19 -09 ; ) (see supplementary table  1  for additional  = 0. 47 (95% , 0. 37 -0. 60)  = 1. 94 -09 comparative statistics). For the WBCD dataset, Figure  2d  shows the F-score comparison for each cancer prediction variable. F-score computations for the avatar (green) and original (orange) datasets were similar. Regarding the F-scores, the predictive model selected the same variables, yielding the same feature importance. These models have comparable prediction performances (original: vs avatar: ; see supplementary table 2  = 99. 46 ( = 0. 25)  = 99. 84 ( = 0. 12) for additional predictive statistics).
Avatarization and assessment of the re-identification risk The leading goal of anonymization methods such as the Avatar method is to prevent re-identification. Figure  3  shows the distribution of the local cloaking metric (3a: AIDS dataset; 3b: WBCD dataset). In panel 3a, the median local cloaking of 12 shows that there is a median of 12 avatars between an original observation of the AIDS dataset and its simulated avatar. Of the local cloaking metrics, 31. 2% were ≤ 5. The hidden rate of 90% means that 10% of the individuals produced the avatar that most resembled them (local cloaking equal to 0). In panel 3b, the median local cloaking was 24, indicating that there is a median of 24 avatars between the original WBCD dataset observations and their simulated avatars. For local cloaking, 16.
3% were lower or equal to 5. The hidden rate was 94%, suggesting strong data protection. In both the AIDS and WBCD datasets, less than 10% of individuals appeared to be unprotected because their avatars showed a local cloaking of 0. Figures  3c  and 3d  present the number of times each sensitive individual observation generated the avatar closest to them over the 25 independent avatarizations. Individuals (AIDS: 28. 2% vs WBCD: 75. 8%) do not generally get local cloakings of 0. For the AIDS dataset, only three individuals (0. 1% of the dataset) had 10 times or more a local cloaking of 0 after 25 avatarizations. For the WBCD dataset, one individual (0. 1% of the dataset) had 10 times or more a local cloaking of 0 after 25 avatarizations (see supplementary materials for additional details). Overall, these metrics for the Avatar method demonstrate that the re-identification risk is quantifiable and provides protection for every single data contributor.
Impact of local model size on avatar generation The number of neighbors is a crucial parameter. For each use case, Figures  4a  and 4b  compare the  FAMD projections of avatars generated with a low (respectively 0. 2% and 6% of the total number  of individuals, light green) and avatars generated with a high (respectively 50%-55% of the total  number of individuals, dark green). The dataset structures were well-conserved for the lowest values  (compared with Figures  2a  and 2b ; =20). The structures and its boundaries faded with the highest  value of. Figures  4c  and 4d  shows the evolution of the major endpoint estimations as a function of   (hazard ratio for AIDS; AUC of cancer prediction for WBCD). The estimations stayed within the confidence interval of the estimations from the original dataset when was between 4 and 750 for the  AIDS dataset and between 4 and 150 for the WBCD dataset.
Avatars of avatars and fade of the original data structure The structural similarity opens multiple possibilities. Among them, avatars can be generated through successive iterations where the input dataset at iteration is the output dataset of iteration -1. Over more iterations, the statistical data structure slowly became compacted. Generations 1-7 retained a large part of the variance of the dataset. After the 10th generation, the variance of the dataset faded. 99. 8% (95% CI, 98. 7-101. 1) of the total variance was retained on the first projection dimension and 75. 0% (95% CI, 69. 6-80. 5) on the second (see supplementary material for all quantitative results). The slight aggregation within each cluster after each avatarization led to an outlier removal and a clustering reinforcement. The Avatar method attracted marginal individuals to denser areas.
DISCUSSION Herein, we presented and evaluated a new method, the Avatar method, to generate synthetic data. This method protects sensitive data from re-identification while retaining the statistical value of the dataset. Evaluation of the method was achieved by comparing results obtained from sensitive data with those obtained from avatar data and resulted in the same interpretation for both datasets. We used a publicly available clinical trial dataset comparing HIV treatments and a breast cancer dataset used to train machine-learning algorithms. The proposed method is patient-centric (i. e. it uses the characteristics of a single patient as the starting point of its statistical modeling). Even if each individual is at the origin of the creation of their avatar, they do not directly contribute to the local modeling of their avatar.
This seemingly paradoxical nature of the method prevents re-identification risks. The method is based on multidimensional projections and a selection of local neighbors in a reduced space. Here, we used factor analysis techniques (e. g. PCAP  31  , FAMD  32  , and MCA 33 ) according to data type, but other methods, such as discriminant analysis  40  , t-SNE  41  , and autoencoder 42 could be considered. The use of multivariate analysis avoids the curse of dimensionality by searching for neighbors in a reduced space, optimizing the computation of the KNN at the same time. Discussing how dimension-reduction methods can be customized to the dataset and usage context of synthetic data is beyond the scope of this study  43. This issue is a balance between computational requirements and the relevance of mathematical modeling, including distance choice, potential loss of information, and noise propagation.
It underlines the central role of factor analysis techniques and dimension-reduction in avatar modeling and computation requirements. An application of the method, using Fourier transform, is presented in another context  44. The parameters have a strong influence on data privacy and quality and need to be adapted given the  data's sensitivity and intended use. We showed that a worthy level of protection can be achieved even with a low value. Using the Avatar method to analyze "second-generation" avatars derived from  avatars slightly lowered the percentage of the dataset variance to protect specific individuals with high uniqueness and intrinsic vulnerability to re-identification (see supplementary materials for details). However, while the synthesized individuals reflect the variability and quality of the original data, as will other simulation methods, the Avatar method allows to generate a synthetic cohort of infinite (lower or greater) size.
For example, this method can be used to compute empirical distributions of any estimates from the original dataset (see supplementary Figure  6  for the hazard ratio and Figure  7  for the F-score). The Avatar method conserves the structure of the original dataset and reaches high signal retention, as do other methods. Where other data simulation methods (e. g. GAN  [20] [21] [22] [23]  ) use a global model to mimic the overall original statistical properties, the Avatar method uses local simulation that facilitates the computation of privacy protection metrics that satisfy EDPB  16. Explainability and accountability are also reinforced at both global and local level, while parameter tuning enables exploration of the method behavior, which may be seen as particularly crucial in health applications  45.
The method is compatible with the use of randomization and generalization methods and can be combined with them in a treatment depending on the intended use of the data. The control of the level of utility and privacy allows for adapting the optimal treatment to the use, particularly in the field of health where the quality of retention of the signal is essential although the data are sensitive. Synthetic data are becoming a key tool in an open-data world and are streamlining making data available to data scientists  50. With the avatar dataset, researchers do not need to expose sensitive patients or risk patients' privacy when publishing their results. This should be a proposed standard in analyzing biomedical data and data in general and has already proven its relevance to promote reproducibility  51.
Feature selection 39,53 F-scores enable measuring the discrimination of two sets with real numbers. Given training vectors of size m, , for = 1, 2,. , m, if the number of positive and negative instances are n+ and n-,    respectively, then the F-score of the i th feature is defined as: 𝐹 𝑖 = (𝑥 𝑖 (+) -𝑥 𝑖 ) 2 +(𝑥 𝑖 (-) -𝑥 𝑖 ) 2 1 𝑛 + -1 𝑘=1 𝑛 + ∑ (𝑥 𝑘,𝑖 (+) -𝑥 𝑖 (+) ) 2 + 1 𝑛 --1 𝑘=1 𝑛 - ∑ (𝑥 𝑘,𝑖 (-) -𝑥 𝑖 (-) ) 2 , where , , are the average of the i th feature of the whole, positive, and negative datasets,     +   respectively; is i th feature of the k th positive instance, k, i, and is the i th feature of the k th  , +  , negative instance. The numerator indicates the discrimination between the positive and negative sets; the denominator indicates the one within each of the two sets. Larger F-scores mean that the feature is more likely to be discriminative.
SUPPLEMENTARY FIGURE 2. Heatmap of conditional probabilities (AIDS dataset). Each case represents the proportion (in percent) of individuals with a local cloaking at {0, 1, 2, 3, 4 or 5 and more} at a second iteration knowing they had a local cloaking at {0, 1, 2, 3, 4 or 5 and more} at a first iteration The avatarizations are considered independent if the probability of a local cloaking of zero equals the conditional probability of a local cloaking of zero in a second avatarization, knowing that the first avatarization had a local cloaking of zero, i. e. P(X = 0) = P(X 2 = 0|X 1 = 0) Using 25 AIDS dataset avatarizations yields P(X = 0) = 0. 06 and P(X 2 = 0|X 1 = 0) = 0. 09 These two probabilities are both low and close; thus, the avatarization can be interpreted as quasi-independent from one simulation to the next.
AIDS dataset degeneration effect We conducted the same experiment as for the WBCD dataset (Supplementary Figure  3 ). Over iterations, the statistical data structure slowly became distorted. Generations 1-7 retained much of the variance of the dataset. After the 10 th generation, the variance architecture of the dataset faded: margins were lost, and the clustering of the dataset was reinforced. 100 value throughout avatarization iterations (iteration 0 is original data).
Statistics distributions We performed 100 avatarizations of the two example datasets with the same parameters (k=20). For the AIDS dataset we focused on the hazard ratio distribution for arms 1, 2, 3 compared to arm 0. For the WBCD dataset, we computed F-score distribution of each variable. Results obtained with original data are then compared to those obtained with avatar data. Regarding AIDS dataset, we obtained a significant effect of the three treatments compared to arm 0. Arm 1 is showing the best performance. For WBCD, F-scores are ranging around the original value and the order of importance is preserved. Such results imply that both strongly discriminating and poorly discriminating variables for prediction retained their properties.
For the WCBD datasets, we trained SVM models (70% training, 30% tests) with five features according to F-score results. Supplementary Table2compares the classification performances for the best original and avatar models. We obtained the same classification performance for both the original and avatar datasets. Evaluation of classification performances, accuracy, specificity, sensitivity, PPV (positive predictive value), NPV (negative predictive value) and AUC (area under the receiver operating characteristics curve), with standard deviation for each best model.
Among the victims of sexual violence, 40% go on to develop PTSD  [3]. Given its prevalence, sexual violence is therefore a major public health problem  [4, 5]  which persists despite enhancement of research in this field (three times as many articles in 2022 than 2010 about "sexual violence" on Google Scholar) or political responses (such as the European program DAPHNE, which aims to reduce gender violence and has a budget of EUR 80 million for the period 2021-2027  [6, 7] ).
Participants and Procedures A total of 225 students (165 (73. 7%) women; M age = 21. 4 years, SD = 3. 2) enrolled on a compulsory module on the prevention of sexual violence at the University of Clermont Auvergne volunteered to take part in the present study. We used cluster randomization to assign them either to a group using case studies or to a group using the Selflife tool. In the case studies group, participants were asked to think about various situations potentially involving sexual violence. They had to say whether sexual violence was indeed involved and think about possible factors/resources in each one. The procedure for using the "Selflife" tool is described below in Section 2. 3 ("Description of Selflife Tool"). At the end of this single-session intervention, each student completed a set of questions collecting their sociodemographic data and their feelings about their participation (i.
Participants and Procedure A sample of 230 French older adults (154 women, 76 men) aged between 55 and 84 (Mage = 66. 61 years, SD = 7. 06) participated in the study. Two eligibility criteria were used in the present study: (1) aged 55 and above, and (2) living in a private home without daily assistance with everyday living tasks (e. g. , grooming, meal preparation), or only household help once or twice a week. The age of 55 is frequently used as the threshold for determining who may be considered an older adult (e. g. ,  [26] ). The data collection was completed in individual paper-based sessions. First, the participants read a brief text explaining what an MTR is, its functionalities, and its potential use to remotely supervise older adults' physical activity. Photos of the MTR accompanied the text because many older adults had never seen an MTR before.
Participants and Procedure A sample of 230 French older adults (154 women, 76 men) aged between 55 and 84 (M age = 66. 61 years, SD = 7. 06) participated in the study. Two eligibility criteria were used in the present study: (1) aged 55 and above, and (2) living in a private home without daily assistance with everyday living tasks (e. g. , grooming, meal preparation), or only household help once or twice a week. The age of 55 is frequently used as the threshold for determining who may be considered an older adult (e. g. ,  [26] ). The data collection was completed in individual paper-based sessions. First, the participants read a brief text explaining what an MTR is, its functionalities, and its potential use to remotely supervise older adults' physical activity. Photos of the MTR accompanied the text because many older adults had never seen an MTR before.
Statistical Analyses All the data analyses were conducted using the JASP software (version 0. 16. 2) with a level of significance defined at p < 0. 05. Preliminary data analyses were performed to detect gross outliers using the Mahalanobis distance (χ 2 (9) = 27. 88, p < 0. 001) at the multivariate level  [30]. Moreover, variables non-normal in distribution were identified with values ≥ |2| for skewness and ≥ |7| for kurtosis  [31]. Following the recommendations  [32, 33] , the model fit was evaluated using several fit indices: the χ 2 /df ratio (a value ≤ 3 is needed), the CFI (Comparative Fit Index; a value ≥ 0. 90 is needed), the TLI (Tucker-Lewis Index; a value ≥ 0. 90 is needed), the RMSEA (Root Mean Square Error of Approximation; a value ≤ 0. 08 is needed), and the SRMR (Standardized Root Mean Square Residual; a value ≤ 0.
Sample For the 2019 Lloyd's Register Foundation World Risk Poll, Gallup recruited national probability-based samples of about 1,000 participants aged 15 + y in 142 countries. Kuwait and Venezuela lacked, respectively, numeracy and income information. The remaining data set included 150,634 participants from 21 low-income, 34 lower-middle income, 42 upper-middle income, and 43 high-income countries. Table  1  shows participant characteristics.
Procedure Gallup conducted surveys in participants' language, faceto-face or by phone. Survey methodology and data are publicly available (http://doi. org/10. 5255/UKDA-SN-8739-1). Ethical approval was obtained at Gallup. Numeracy. Participants answered the following question: ''Do you think that 10% is bigger than 1 out of 10, smaller than 1 out of 10, or the same as 1 out of 10?'' Response options included ''10% is bigger than 1 out of 10,'' ''10% is smaller than 1 out of 10,'' ''10% is the same as 1 out of 10,'' ''I don't know,'' or no answer. Anything other than ''10% is the same as 1 out of 10'' was counted as incorrect and reflecting low numeracy. Single-item measures can provide valid assessments while reducing cost and respondent burden for large surveys. 22 Performance on similar single items is reliably correlated with multi-item numeracy assessments.
To test the model, we collected two sets of cross-sectional survey data (Study B: n = 479; Study C: n = 618) from SMP users studying in two Indian colleges and analysed them through partial least squares structural equation modelling (PLS-SEM). Our findings contribute to the existing literature in four ways. First, by examining the hypothesised association and pathways through which the students' situational stimuli correlate with their students' experienced state (overload), behaviour (SMA), and consequence (RAP), we contribute to the literature on (i) the associations between SMP use and AP  (Evers et al. , 2020; Whelan et al. , 2020a) , (ii) SMP overload  (Whelan et al. , 2020b; Whelan et al. , 2020a) , and (iii) SMA  (Andreassen et al. , 2017; Tandon et al. , 2021a). Additionally, as an overload is an essential element of SMF  (Dhir et al.