dataesr/scientific-paragraphs-categorization huggingface dataset:
These variations would not happen if the Galeria Building was isolated, which indicates the importance of contextualizing climate data during the diagnosis phase. Table  3  indicates air pressure in all facades, considering a point in the center of each opening and the following periods: summer, autumn, and spring (morning) and autumn and winter (afternoon), during occupation (8 AM-7 PM). The maximum differential pressure (P) between windward and leeward windows is 1. 87 Pa (2 nd floor), 1. 59 Pa (6 th floor) and 2. 23 Pa (9 th floor) in the morning, and 1. 09 Pa (2 nd floor), 3. 46 Pa (6 th floor) and 3. 39 Pa (9 th floor) in the afternoon. The air pressure also varies considerably in the horizontal axis, as observed in low-rise buildings (AMERICAN…, 1997). A longitudinal section (Figure  10 ) highlights that in the morning, air flow is little affected on the 2 nd floor, probably due to the channeling effect in Rosario Street (H/W=3) and the descending winds caused by Galeria Building, as described by  Blocken and Carmeliet (2004).
Data collection Patients' medical records were reviewed for the collection of the following variables: age, sex, comorbidities, blood test results, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, Sequential Organ Failure Assessment (SOFA) score, imaging findings, IVIG treatment characteristics (maximum daily dose, duration of treatment) and respiratory support (oxygen therapy, non-invasive mechanical ventilation, invasive mechanical ventilation, prone position ventilation and extracorporeal membrane oxygenation (ECMO)). Laboratory parameters as well as APACHE II and SOFA scores were assessed upon hospital admission.
Statistical analysis Continuous variables were expressed as median (interquartile range (IQR)) and were compared using the ManneWhitney U test. Categorical variables were expressed as numbers (%) and compared using Fisher's exact test or the chi-squared test. Missing data were not imputed in this study. Univariate logistic regression analysis was conducted and applied to evaluate possible predictors of 28day mortality in the matched sample. We included variables associated with the dependent variable in bivariate analysis (p < 0. 1) as well as what made clinical sense, without missing data for multivariate analysis. We estimated the predictors for 28-day mortality by fitting multivariate logistic regression with random intercepts to account for clustering of the inpatient hospitals. Model fit was evaluated by HosmereLemeshow goodness of fit test.
Eighth, due to the unclear reason for the IVIG use in the clinic, Directed Acyclic Graph (DAG) was not used for the selection of relevant variables in the multivariable analysis. We only selected variables with p < 0. 1 that are also important factors in the clinic for the multivariable analyses. This is a limitation of our study. Nineth, different IVIG dosages were not studied due to the difficulty to ascertain the effect of low-vs. high-dose IVIG. Tenth, this study is a retrospective study and survival bias exist in our study. The marginal structural model might be a promising tool to address this bias. However, the data in this study were collected during the early pandemic. There were no medication guidelines, and clinicians prescribed treatment based on their own decisions. Therefore, it is hard to find variables that might prompt patient selection into IVIG treatment, and we matched each patient treated with IVIG with one untreated patient according to the number of days from admission to treatment (or lack of treatment).
A multi-biometric system uses different modalities to identify individuals more accurately. The authors analyse fusion efficiency of a significant number of multi-biometric fusion schemes. To do so, the study applies different functions that are generated using genetic programming (GP) on the 2000 multi-biometric instances produced by the fusion of different biometric matching scores. The functions are represented using a tree of arithmetic operations and are used for fusion at score level. First, genetic programming is implemented on the XM2VTS score database. The GP optimizes the half total error rate of fused matching scores. Then, a comparative study is performed based on our experiments on matching scores of different biometric baseline systems provided by the bio-secure database. This database provides 24 streams that we use to generate 2000 multi-biometric combinations.
However, this cannot affirm whether the enhancement occurs as a result of the fusion or the used baseline systems' performances. Therefore, for a predefined fusion strategy, we can search the best biometric combinations that work better than the fused baseline systems. Here, we propose to combine GP with the proposed CSO algorithm to generate different weighted fusion functions represented in a tree of arithmetic rules. Then, we compare our proposal to the fusion strategies of main proposals in the literature such as the use of norms  [5] , DE, PSO and BSA  [19] [20] [21] 25]. Our first version and application of GP is proposed in  [26]  whose extended experiments of the best trees are conducted on the Biosecure score database  [27]. Then, we perform multiple simulations using GP to get closer to the optimized fitness value, within the defined number of generations, for the maximum number of individuals.
These simulations allow us to select multiple trees that optimize the score fusion. These trees are used to benchmark and analyse fusion on an unlimited number of biometric output combinations that are constructed thanks to the scores provided by the Biosecure score database  [27]. This database includes non-correlated baseline systems of different traits and a quality assessment of the processed signals. The quality assessment must consider the acquired signal and the quality of the biometric system that processes it. The first evolve for each sample, where the second can be evaluated to provide the scalar that indicates its performance and reliability. Therefore, we aim to combine baseline systems weighting based on an evolutionary method with quality assessment computed from the baseline systems quality.
The available quality measure gives a scalar indicator based on different sources of quality  [28]  such as the trait character, imaging and environment. We use the measures, provided by the Biosecure database, to weight the fusion functions. The proposed approach outperforms the baseline biometric subsystems. Then, a comparative study is conducted between the best-computed fusion functions that are obtained from the GP. Then, the statistical analysis of the tested multi-biometric scores fusion is described allowing us to show the ones which reduce the baseline biometric systems' errors for instance the equal error rate (EER) with or without the hybrid cat swarm optimization (HCSO) weighting. In Section 2, we present the score level fusion applied in multi-biometrics. Section 3 describes GP used for generating the proposed functions, the HCSO algorithm applied to compute baseline score weighting and the tested databases.
Modalities Methods References Multi-spectral palmprint Different t-conorm compared to sum, SVM  [8]  Iris, face GA features selectionSVM score fusion  [9]  Speech, lipreading Average  [30]  NIST BSSR1 multi-biometric score database (fingerprint and face), face, iris dataset Dezert-Smarandache theory DSmT  [15]  Palm/phalanges print Sum, product, min, max, hamacher t-norm, frank t-norm, yager's t-norm  [5]  Face, palmprint, signature, speech Sum rule compared to the unimodal system  [31]  Fingerprint, iris, left ear and right ear DE, exponent control, and Kernel mapping  [19]  Fingerprint and voice DE and proportional conflict redistribution  [20]  Face and voice PSO, Dempster Shafer and Belief functions  [21]  Two face databases Quasi-convex optimization  [22]  Iris, finger vein and fingerprint Evolutionary backtracking search optimization algorithm  [25]  Abbreviation: GA, Genetic Algorithm.
only one contains two traits (NIST BSSR1), and the two other databases (FRGC, IRIS dataset) are used for multi-algorithm and multi-sample fusion (Table  2 ). Using multiple databases in multi-biometric fusion analysis can be suitable to compare their performances. However, fusion cannot guarantee performance enhancement. The used modalities that belong to different persons are not dependent. Despite this, multiple works perform their experimental study on combined databases to demonstrate the effectiveness of their fusion proposal. The use of standard multimodal databases remains the best option according to the availability of the chosen modalities.
| MATERIALS AND METHODS Choosing a fusion method in score level is very crucial to enhance the provided multi-biometric system performances. The work of the authors provides performance analysis of different fusion functions (see Table  8 ) that combine elementary operations like sum, product, min and max along many multibiometric scores combinations generated from the score database. To accomplish that, we start by explaining the GP applied previously  [26]  and enhanced to be tested and compared on a new and different database. Then, we define the proposed HCSO to reduce errors of a subset of multi-biometric that we analyse through the analysis performed here. After that, we describe the databases that we use in our experimental study. Finally, we discuss the proposed experiments.
| GENETIC PROGRAMMING We use GP to get a subset of optimal functions among the constructed ones that are represented using a tree structure. This section explains the used GP simulations that conduct us to preliminary results discussed in  [26]. The aim of the authors is to re-explore the proposed optimization and test a diversity of functions that have good performances. The mutation and crossover operations allow exploring search space of different trees by modifying operations on the selected nodes. As we perform tests on a score database, we can vary the number of leaves of the initial population to match the number of input scores of the tested score database. Figure  1  recapitulates the GP parameters. Figure  2  summarizes the applied GP to get the best trees or functions that we use for fusion and test on the score database.
All the population trees are tested using the evaluation protocol offered by the XM2VTS database. As we perform testing on different databases, we give details of the train and test data later. As shown in Figure  2 , in each simulation, we start with the initial population, generated randomly or from the previous simulation by varying the death nodes (between 2 and 8) and the number of nodes that corresponds to a predefined interval for the number of leaves (at least 8 leaves that support the input scores). Afterwards, we evaluate the half total error rate (HTER) of each tree that combines the database scores. Then, according to the fitness of the population or the number of generations, the process continues with tree transformations until it reaches the best population. In our case, we succeed to optimize all the population to get a mixture of functions with an HTER in a reduced interval.
| XM2VTS score database We use the XM2VTS score database  [14] , where the scores are computed based on the publicly available XM2VTS face and speech database. These scores give access to the provided baseline systems with their documentation for further experiments. Publishing score results of these baseline systems is important for researchers to compare them in more detail with their obtained scores. However, fusion, in this context, is less considered as it is more useful to use scores for combining on common settings instead of a simple comparison of outputs. This database provides well-defined protocols (train and test data details are described in Table  4 ) and a set of evaluation tools such as the DET curve, expected performance curve (EPC), and the HTER significance test, which we use in our fitness evaluation as an optimization metric.
The Biosecure score database is a benchmark of qualitydependent, client-specific, and cost-sensitive fusion algorithms. It contains fingerprint, face and Iris modalities acquired with different sensors and scenarios. Baseline systems are evaluated using biometric verification systems cited in Table 5  [27]. The benchmark contains 24 streams of matching scores from two sessions divided into development set and evaluation set as represented in Table  6. The database simulates a real test environment for fusion schemes such as scores of cross-devices, quality evaluation of acquired traits and failures due to sensors or unmeasurable quality. In addition, it provides tools for analysing system performances based on the processed data.
After that, we start our experimental study according to the following steps: 1. First, a set of trees is selected from our previous simulation. The selection was conducted randomly along all generations to get a set of generations according to the HTER. Then we select different trees of each one. 2. We use these trees to fuse matching scores provided by different biometric systems of the Biosecure database. 3. We use the development set to compute the min-max standard deviation applied in normalization. 4. First, we apply a user-specific weighting using the normalized database quality measures giving the sample fidelity to the claimed ID, multiplied by the standard deviation of minmax for each score (computed from the development set). We compute the weight as follows: � 1 2 * À ðx t -x Þ 2 þ À x q -x � 2 � 1 = 2 � -1 * σ min scores;max scores ð4Þ where x t is the template quality (claimed id) and x q is the query quality (true id).
| EXPERIMENTS AND DISCUSSION In the experiments conducted, we get the optimized population illustrated in Figure  5  (the last population). As shown in this figure, the error range [0. 67, 72. 89]% of the initial population is reduced after the first simulation to [0. 3, 0. 87]%, then [0. 1, 0. 34]%. We get the best trees from the generated populations and use them in the next experimental study. We compare our Area Under Curve and HTER achieved in the XM2VTS score database with two evolutionary methods that use the same evaluation metrics (see Table  7 ).
| Experimental study In the experimentation, we aim to test different configurations to analyse the impact of using the same functions and varying the fused uni-biometric scores. The study is essential as it demonstrates that fusion depends on the fusion function and the scores that are fused  [1]. Also, we compare the proposed functions and the HCSO function to other typical operations, the fusion normalization proposed in the literature  [5] , and the applied evolutionary algorithms for fusion weighting  [21, 25]. We use the algorithm presented here: We perform the experiments on the Biosecure score database  [27] , a collection of matching scores computed from the Biosecure biometric databases that proposes a quality evaluation of the captured signals based on the features richness, environmental effects and signal properties.
The quality is a crucial information about the studied fusion schemes to discard undesired influence of high scores provided by poor quality traits. We select a subset of fx10functions that provides low HTER error in the XM2VTS database that is representative of groups of functions with similar HTER. The selected functions are shown in Table  8. Based on these functions, we search the optimized weights using the proposed HCSO algorithm that explores them to find the best one. The experiments are done on MATLAB 20a. The HCSO optimization is performed on two PCs: Intel i5 processor, 8 GB RAM, and Intel i7 processor, 16 GB RAM, and takes three days for 1000 multi-biometric scores using parallel execution with 6 MATLAB workers. Figure  6  illustrates the statistics (MIN, MAX, AVG and STD) of the cited functions that are tested using the evaluation set of the score database.
5% of the 2000 multi-biometric combinations considering only the quality weighting. We compare our results to the recently used Hamacher t-norm and Frank t-norm normalization methods that we implement on the tested data of the Biosecure database. Also, Table  10  gives EER comparison with the state-of-theart results. The HCSO weighting optimization gives the lowest EER average. As shown in Table  10 , only 50% of the tested multi-biometric scores are fused successfully in the best case. Our functions outperform the two best norms that give good results in  [5] , PSO  [21] , and Backtracking search optimization algorithm  [25]. The proposed HCSO algorithm enhances the selected function using the score weighting and reaches more than 87. 5% EER reduction compared to typical operations (see Figure  9 ).
Generally, system failure is caused by system weaknesses to face some special scenarios, which are not considered, like sensor failure or insufficient image quality that affect decisionmaking. Hence, we must deal with these drawbacks to prevent system failure in a real environment. The chosen scores database is built upon some of these scenarios. In the experiments conducted, we test quality weights that we propose based on score quality available in the Biosecure database. We compare it to a normalized score without employing weights. The results are presented along 2000 multi-biometric combinations in Figure  10. As shown, normalization provides the lowest values. However, if we omit zero-values, mostly caused by missing values in the database, we can see that quality weighting provides a steady range and reduces significant EER fluctuation.
However, max peaks are caused by missing quality scalars for some scores in the testing database. Figure  12  compares between the EER of 1000 combinations using user-specific quality weighting and the added HCSO weights. Combining the two quality measures for baseline scores and those related to the signal quality is more performing as demonstrated by these results. In conclusion, searching for the best combination, instead of proving the effectiveness of a unique process, remains   important in future studies. Here, we optimize performances to reach the best multi-biometric systems on two simulations illustrated in Figure  7. This multi-biometric system gives nearby errors for the ten functions on session 1 of the evaluation set. The EER is in the range of [0. 0002 6. 88]%. However, the best functions with small variance (less than 1.
| CONCLUSIONS AND FUTURE WORK Here, the authors propose a new multi-biometric fusion scheme based on optimized and weighted functions. The fusion functions are generated using the GP based on arithmetic operations. The score weighting optimization is performed using the HCSO based on average-inertia weighted CSO and Normal Mutation CSO. The authors conduct the experimentation on the Biosecure score database to compare different combinations of the provided scores and find the best solutions that carry out a fixed range of EER. The proposed hybrid CSO optimizes fusion score weights and reach at least 80% enhancement of the tested multi-biometric combinations. The significance test confirms the improvement of our weighted scores fusion compared to typical fusion operations and weights' optimization using evolutionary algorithms applied on similar databases or approaching modalities.
Data sets Number of match scores per subject Development Evaluation ARTABAZ ET AL. Session 1 Genuine 1 1 Impostor 103*4 206*4 Session 2 Genuine 2 2 Impostor 103*4 126*4 TA B L E 7 Comparison of GP optimization with state-of-the-art results DE [19] DE [20] PSO [21] Our GP XM2VTS AUC 0. 99981 - - 0. 99991 XM2VTS HTER - 1. 119 - 0,1 XM2VTSEER - - 1. 887 0,11 Fct4 S 1 +avg(min((min(S 2 , S 3 ))+S 4 +S 5 ,S 6 ]),min(S 7 ,S 8 )); Fct5 avg(max(max(S 1 , S 2 ),(S 3 +((S 4 *S 5 )-S 6 ))),avg(S 7 ,S 8 )); Fct6 max(avg(S 1 , S 2 ),S 3 )+avg(S 4 ,S 5 )+min(S 6 ,S 7 )+S 8 Fct7 avg(S 1 , S 2 )*S 3 +avg(S 4 ,S 5 )+min(S 6 ,S 7 )+S 8 Fct8 S 1 +avg(max(avg(S 2 +S 3 , S 4 +S 5 ),S 6 ),min(S 7 ,S 8 )); Fct9 avg(avg(min(S 1 , min(max(S 2 ,S 3 ),S 4 )),S 5 ), min(avg(S 6 ,S 7 ), S 8 )); Fct10 avg(avg(S 1 , avg(S 2 ,S 3 )),min(S 4 ,S 5 )+S 6 +S 7 +S 8 ); Our GP outperforms DE and PSO applied on the same database.
TA B L E 8 Non exhaustive list of functions generated on the XM2VTS score database Function Fct1 avg (S 1 ,avg(min(min(S 2 ,S 3 )+S 4 *S 5 ,S 6 ),min(S 7 ,S 8 ))); Fct2 S 1 +avg(min(avg(S 2 +S 3 , min(S 4 ,S 5 )),S 6 ),min(S 7 ,S 8 )); Fct3 S 1 +avg(max(avg(S 2 +S 3 , avg(S 4 ,S 5 )),S 6 ),min(S 7 ,S 8 ));.
00 250. 00 300. 00 80. 00% 70. 00% 60. 00% 50. 00% 40. 00% 30. 00% 20. 00% 10. 00% 0. 00% 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 91 96 First GeneraƟon Last GeneraƟon [26]G U R 4 Impact of mutation and crossover number on fitness average[26]F I G U R E 5 The Fitness of initial and last generations on the XM2VTS score database after only two simulations 334we compute the sum of ranking considering that Session 2 is more important as recommended in the database documentation. We use the sum of these ranks to show the best functions. As a result, Function 3, 8, 5 and 2 whose averages of ranks is less than 5 are the best ones as illustrated in Table9. The HCSO algorithm gives a different ranking by selecting the best function according to the EER and the fused matching scores. In fact, function 5 and 8 remains the most recurrent with functions 1 and 6.
Methodological approach In this paper, farming practices include the ways of farming at both plot and farm scales. The TMR (Technical Management Routes) is a succession of TOs (Technical Operations) applied to a chili-pepper plot. A TO refers to an action or a choice performed by the farmer on the plot. Agroecology-compatible TOs or practices refer to TOs compatible with agroecology. (Non)-environmental-friendly TMRs refer to TMRs compatible with agroecology or not. The global approach, allowing to finely characterize the TMRs, give indications on their sustainability and links to farm characteristics, consisted in four successive steps: i) sampling (a)  (b)  and data collection through field surveys, ii) database and variables construction, including a characterization of their potential environmental impact (in a qualitative manner), iii) statistical analysis including data mining, enabling the description of different clusters of TMRs, and iv) the identification of the types of farms implementing each type TMR.
Sampling and data collection Data collection was carried out through semi-directive interviews with farmers in Spring 2018 and was conducted in three phases. First, 65 farms were chosen for their diversity, not only in their structure (crop types, farm size, land tenure), but also in their localisation throughout the Merguellil plain. A wide range of farm sizes was covered (1-75 ha). Farm selection was actorand expert-based, or by direct observation. Second, a series of semi-directive interviews with farmers were conducted, in order to apprehend the organization and functioning of the farms. These interviews concerned not only crop types and successions, but also labor origin (family or temporary), land tenure (tenants, landlords, sharecroppers), water supply sources Data acquisition on the TMRs applied to chili-pepper was carried out on 45 plots.
Database and variables construction Collected data were transcribed into a table of TMRs, where each TO of the TMR was described by its modalities. The TOs represent the variables used for statistical analyses. The modalities of a TO are the different values it can take. Data were both quantitative and qualitative. The quantitative data were first transformed into qualitative variables, using the "natural occurring division" approach  (Husson et al. , 2017) , based on the analysis of the histograms of the observations, in order to maintain the modalities as observed. Two to four modalities were constructed depending on TOs. To avoid redundant information and characterize the diversity of TMRs, TOs with a single common modality to all farmers were not taken into account in the analyses.