dataesr/scientific-paragraphs-categorization huggingface dataset:
Then creates p clusters (p = no tuples/k value) and chooses the top ranked records (i. e. , the records with the most n-closest) in the sorted list as initial centroids. The remaining (no tuples-p) records are assigned to theirs closest centroids such that every cluster should fulfil k-anonymity property. If the last cluster created contains less than k records, its records are dispersed to other closest clusters. Finally, the clusters are individually anonymized. Aghdam and Sonehara  (Aghdam and Sonehara, 2016)  proposed a bottom up greedy algorithm called Similarity-based Clustering Algorithm (SBCA), to achieve k-anonymity through local recording generalization, for datasets with numerical and categorical attributes without hierarchical taxonomy trees. SBCA starts by sorts the dataset and separates the numerical attributes from categorical one.
The next step is clustering the dataset with respect to k value and anonymizing it. For this, the algorithm chooses the first tuple in sorted dataset and adds to it the k-1 closest tuples to form a cluster which is anonymized through local recording. The tuples belonging to the cluster are, then, delated from sorted dataset. The process is repeated until the total tuples in the sorted dataset is none or less than k. the remaining tuples are suppressed or joined to the closest equivalence classes. Bhaladhare and Jinwala (Bhaladhare and Jinwala, 2016a) proposed two approaches for achieving k-anonymity based on systematic clustering (Approach#1 and Approach#2). Both approaches decompose the original database using a combination of quasiidentifiers (QID) and sensitive information (SA) into separate individual sub-databases then anonymize and publish the generated sub-databases.
The difference between the two is that Approach#1creates unequal combination of QID and SA, and Approach#2 creates equal combination of QID and SA. The two approaches start by calculating the number of clusters which is equal to (no tuples/k value), then generate a sub-database using a combination of QID and SA. In Approach#1 an unequal combination of QID and SA is created. In Approach#2 an equal combination of QID and SA is created. From each generated sub-database, a partition of all records into k groups is created. Then Systematic clustering algorithm  (Kabir et al. , 2011)  is applied to generate the clusters. For this, a record is randomly selected from the first group for the creation of the first cluster. Other records from the first group are selected and added to the closest cluster.
Similarly, the remaining cluster are created by randomly selecting the records from the remaining groups and other records are selected and added to the closest cluster. If some cluster exceeds to the k size, the extra elements should be added in the corresponding closest clusters. Guo et al. (Guo et al. , 2019)  proposed a new concept named Natural Equivalence Class (NEC) and designed a k-anonymization algorithm based on this concept. The idea behind the algorithm is that in raw microdata, there exists naturally equivalence classes i. e. which include all the records with the same quasi-identifier attributes. Those equivalences classes are referred to Natural Equivalence Classes. NEC is used to perform clustering before the anonymization process. The algorithm starts by finding all NECs in the dataset.
A candidate solution of population encoded by a chromosome and contains no fewer than k genes, where each gene indicates the index of a record in the original dataset. The algorithm uses only selection and crossover operations of GA. Mutation is not performed due to the algorithm uses the original record indexes which cannot be altered. Run et al. (Run et al. , 2012)  proposed an hybrid search method based on Tabu Search (TS) and Genetic Algorithm (GA) to achieve k-anonymity. In the proposed method, a TS is embedded into a traditional GA to perform the role of mutation. The objective is to overcome the limitation of ''climbing" ability of traditional TS from a single start point, by implementing local search from multiple start points getting from GA. The proposed algorithm starts with the construction of the solution space lattice (domain generalization hierarchies) representing generalization strategies.
The next step is applying BFO algorithm on the generated population, which consists of three steps such as modified chemotaxis step based on a fractional calculus, reproduction step and elimination dispersal step. These steps are used to generate clusters in the k-anonymized database. Wai and al. (Wai and al. , 2017)  proposed a big data privacy preservation approach based on Hierarchical Particle Swarm Optimization (HPSO). The proposed approach is built upon MapReduce Hadoop infrastructure to address the scalability issues of big data. It consists of two stages; The first stage is HPSO clustering. The algorithm creates a MapReduce job to produce the predefined numbers of intermediate clusters, represented by particles, then a MapReduce job of HPSO clustering is executed on each cluster by iteratively performing Map and Reduce steps until the number of data members in each particle exceed k.
In GWO-CSO algorithm, the update is performed using a modified GWO update by adding a new term corresponding to the update of CSO algorithm. Madan and Goswami  (Madan and Goswami, 2019b ) proposed an anonymity model for data publishing based on K-DDD measure, Dragonfly operators-based Genetic Algorithm called Duplicate-Divergence-Different properties enabled Dragon Genetic (DDDG) algorithm. The first step, called k-DDD anonymization, is the transformation of original database to k-DDD database based on the proposed k-DDD measure. k-DDD measure modifies the original database by creating 'k' number of Duplicate records, 'k' number of Divergence in sensitive attributes and 'k' number of Different service providers in each cluster of the database. The next step is applied D-Genetic algorithm on k-DDD database.
Let D the domain of one numerical/ categorical attribute. The distance between any two values v i and v j 2 D is defined as -Numeric attributes d N v i ; v j À Á ¼ NCP N ¼ v i À v j D j j ð1Þ where |D| is the range of all tuples on D. i. e. , in the whole table. -Categorical attributes d C v i ; v j À Á ¼ NCP C ¼ sizeðcÞ D j j ð2Þ where c is the closest common ancestor of v i and v j in the taxonomy tree and size (c) is the number of leaf nodes in c. |D| is the number of distinct values on D. -Distance between records The distance between two records r 1 and r 2 is the sum of distances between corresponding numeric and categorical quasiidentifiers in the two records. LeQI ¼ fN 1 ; Á Á Á ; N m ; C 1 ; Á Á Á ; C n gt be the quasi-identifiers of dataset T, where N i ði ¼ 1; Á Á Á ; mÞ is numeric attribute, and C j ðj ¼ 1; Á Á Á ; nÞ is categorical attribute.
Then the distance between records r 1 and r 2 is defined as d R r 1 ; r 2 ð Þ¼NCP R ¼ X m i¼1 d N r 1 ½N i ; r 2 ½N i ð Þ þ X n j¼1 d C r 1 ½C j ; r 2 ½C j À Á ð3Þ The cost function is defined by the objective of the clustering problem. Since our objective is to minimize information loss, we use the eq. (  3 ) as a cost function. The pseudo-code of clustering algorithm is summarized in Fig. 2. The clustering algorithm starts by calculating the number of clusters N. The number of clusters is an important criterion as it has an influence on the quality of clustering. The larger the number of clusters the smaller the information loss due to the small NCP value resulting. This is why we have chosen the greatest possible number of clusters i. e. , M=k (where M is the total number of records in the dataset and k is k-anonymity parameter).
Solution encoding After the creation of candidate solutions with clustering algorithm, they must be encoded in order to be used by optimization algorithm i. e. , BHA. As mentioned previously, the population is composed of a set of stars which corresponds to feasible solutions created by the clustering algorithm. To properly represent a solution, each star, in our approach, is encoded 3 by an integer array of one dimension, of size equal to the number of records in dataset, where the ith index indicates the ith record and the ith element indicates the id of the cluster to which the ith record belongs. For example, let X be a candidate solution composed of 15 records and partitioned on 6 clusters. X= {(1,2), (3,5), (4,6,7), (8,9),  (10,12,13), (11,14,15) }. The coding solution of X is X' such as 3 The encoding represents the location of the star X'= 1 1 2 3 2 3 3 4 4 5 6 5 5 6 6 Cluster id 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Record id.
Experimental setup The experimentations were performed on a Desktop PC with Intel Core2Duo 2. 10 GHz CPU and 2 GB of RAM under Windows 7 operating system. The implementations were built and run with Java platform Standard Edition. We use in our experimentation the adult dataset from the UCI machine learning repository, which is considered as a standard benchmark for anonymization. The adult dataset is based on census data and contains a total of 32,561 instances under 15 attributes. Each record describes the personal information of an American and it involves predicting personal income levels as above or below $50 k per year based, on this personal information. Before the experiments, we apply a preprocessing stage on raw dataset to remove duplicate records and records with missing values, and retained only nine of the original attributes, wherein eight attributes 6  are considered as quasi-identifiers and one attribute as sensitive information 7.
Among Quasi-identifiers, age and education were treated as numeric attributes and the remainder attributes as categorical attributes. The size of the resulting dataset is 30,162 records. To evaluate the quality of our algorithm, we conduct three experimentations, and observe quality and scalability of KAB algorithm compared to k-anonymity (implemented with Mondrian Multidimensional  (LeFevre et al. , 2006) ), k-anonymity with BHA and k-anonymity with clustering algorithms. We set, in the three experimentations, the number of stars as 3 and the maximum number of iterations as 10. The parameters used in our evaluation and compared algorithms are described in Table  2.
Scalability and performance In this experiment, we analyze the performance, of the three algorithms, with respect to execution time. To measure the execution time, we ran the experiment 10 times and used the average value to ensure the reliability and consistency of the results. We also used subsets of the Adult dataset with different sizes. The simulation results are depicted in Fig. 8 , Fig. 9 , Fig. 10  and Fig. 11. Fig. 8  reports execution time, of the algorithms, as the value of k increases. We can observe, that the execution time of BHA-based kanonymization and KAB k-anonymization evolve in the same way and, overall, the privacy level does not have much influence on the execution time, which is rather influenced by the number of stars and of iterations. As these are the same regardless of the privacy level, execution time is, almost, constant.
We can note that BHAbased k-anonymization takes less time than KAB-based kanonymization because, unlike BHA, KAB algorithm uses metrics to form the clusters, thus consuming more time. Mondrian multidimensional is the fastest algorithm and has negligible time compared to the other two algorithms. This makes sense, because Mondrian procures lesser time in selecting the records as it does not rely on any heuristics for partitioning. L. Kacha, A. Zitouni and M. Djoudi algorithms follow the same tendencies in the two figures, with smaller values for GCP-based information loss. The information loss of KAB-based anonymization and K-anonymization decreases slowly, compared to BHA-based anonymization, with the increase of data size. As the dataset size grows, the algorithms need to perform less generalization/suppression and then, less changes are made to the original table, which results in decrease information loss.
We can conclude the Classification accuracy of our algorithm which is between 82% and 89%. Concerning execution time, KAB algorithm runs in a reasonable time. Although KAB algorithm is slower than OKA, Mondrian and BHA-based k-anonymity, we think that the execution time is acceptable considering its better performances with respect to other compared utility metrics. To highlight the general behavior of our algorithm, we have summarized all utility metrics, used in our evaluation, with respect to different privacy level and data size. The simulation results are depicted in Fig. 14  and Fig. 15. Fig. 14  and Fig. 15  report summary of data utility of our algorithm, based on GenTotal_IL, GCP, CM and C AVG , according to privacy level and data size, respectively. We can observe that KAB-based kanonymization has a constant C AVG and introduces an almost constant Classification Error (CM), regardless of privacy level or data size.
reports execution time, of the algorithms, as the value of data size increase. It demonstrates that, as in Fig. 8figure, BHAbased k-anonymization and KAB -based k-anonymization follow the same curve, with a better execution time for the former. We can notice that the computing efficiency, of KAB-based kanonymization and BHA-based k-anonymization decreases with the increase of data size. As the dataset size grows, the algorithms need to process more records which results in increasing execution time. Mondrian multidimensional is still the best performer according to execution time, which remains negligible compared to the two other algorithms. Fig. 10and Fig. 11report data utility with respect to information loss, represented by GenTotal-IL and GCP respectively, as the value of data size increases.
This paper aims to quantify the errors in the provided agricultural crop types, estimate the possible error rate in the available dataset, and propose a correction strategy. This quantification could establish a confidence criterion useful for decisions taken on this data or to have a better apprehension of the possible consequences of using this data in learning downstream functions such as classification. We consider two agricultural label errors: crop type mislabels and mis-split crops. To process and correct these errors, we design a two-step methodology. Using class-specific convolutional autoencoders applied to synthetic aperture radar (SAR) time series of free-to-use and temporally dense Sentinel-1 data, we detect out-of-distribution temporal profiles of crop time series, which we categorize as one out of the three following possibilities: crop edge confusion, incorrectly split crop areas, and potentially mislabeled crop.
Such an approach is prevalent in medical applications for novelty detection  [19]. However, having a labeled anomaly dataset is rare, and such a training paradigm is not robust against unexpected anomalies. • semi-supervised anomaly detection  [20, 21] : labels of anomalies and normal instances are still present but in a significant imbalance. In this context, deep autoencoders  [22]  are used. They are unsupervised deep learning models trained with a reconstruction task. In a semi-supervised context, they are trained only on normal observations. Then, deviating instances are used to fit a reconstruction performance threshold above which anomalies can be separated from the norm. However, despite requiring a much lower amount of anomalies than supervised anomaly detection, there is still a need for such labels.
• we combine time series-level and parcel-level analysis to better extract and correct anomalies. We first present our methodology by introducing the context of the illustrating use case and the multi-stage FARMSAR algorithm (Fixing AgRicultural Mislabels using Sentinel-1 time series and AutoencodeRs). We validate the proposed methodology in a controlled environment. We isolate the labels of trustworthy time series within a first half of the dataset and randomly introduce mislabels. We then run our method, and we numerically quantify the mislabel retrieval and correction performance for quantitative analysis of our method's performance. We repeat this process multiple times, with varying amounts of introduced label errors, for statistical relevance. We then apply FARMSAR to the second half of the available dataset, supporting the presented corrections and diagnostics using qualitative validation with Sentinel-2 imagery.
This reporting process is part of a general approach by the European Commission to digitize agricultural information. It involves the creation, among other things, of the Land Parcel Identification System. On the other hand, agricultural labels are crucial for the work of scientists in various fields, including Agronomy, Ecology, and Earth Sciences. In particular, in remote sensing, agricultural labels can help in crop type mapping applications at different scale: local  [5] , national  [33]  or continental  [34]. These applications use trusted ground truths to fit a classification model that predicts an agricultural label for unseen areas. The quality of these predictions is a function of the quality of the training datasets. The presence of label noise in these datasets is directly connected to the creation of these crop surveys.
The Difficulties of Building Agricultural Datasets Both authorities and farmers encounter many difficulties during and after crop survey completion. For instance, Beegle et al. [35]  shows complications regarding the reliability of recall in agricultural data collected from a variety of sources. Further studied by Wollburg et al. [36] , there exists a non-negligible correlation between survey question recall length and measurement errors in agricultural censuses. The authors also detail the impact of these errors on agricultural variables, key to decision making for administrations. On another hand, Tiedeman et al. [13]  shows the impact of data collection methods on crop yield estimations based on Sentinel-2 images analysis, comparing farmer estimated ground truth and true measured ground truth.
Indeed, knowing the impact of errors in annotated databases on learning algorithms is also a topic of interest. Some works have shown that below a certain threshold of errors, and for sufficiently large learning bases, the impact is negligible  [38]. However, this conclusion does not necessarily extend to all scenarios, especially those for which the learning bases are too small, not diversified enough, or when the learning techniques are weakly supervised. For instance, in the context of Land Cover mapping  [38] , a sharp decline in performance appears after 20% of label noise. In another context, in the context of crop type mapping with NDVI time series, Pelletier et al. [14]  shows the impact of errors in agricultural training labels which become non-negligible when reaching more than 20% of label noise.
SAR Temporal Modeling of Crops, a Study Case of Sector BXII, Sevilla Mestre-Quereda et al. [5]  shows the ability of SAR backscatter time series to perform crop type mapping. In particular, Sentinel-1 satellites offer numerous advantages regarding their usage for agricultural monitoring. Their capacity to perform exploitable acquisitions no matter the weather, their high temporal resolution (from 6 to 12 days), and high availability make it an ideal candidate for continuous crop monitoring. This article illustrates our methodology with a dataset of Sentinel-1 acquisitions over agricultural fields of Sector BXII, a farming area located near Sevilla, Spain. Sector BXII is a group of farmers gathered under the concept of community irrigation  [39] , which increases the efficiency of water usage through a bulk supply of water to the agricultural fields from a given source.
These remarks lead us to believe that both of these crops are of the wrong label. When observing the crop highlighted in Figure  4b , it appears heterogeneous in the 22nd of April image, making it a suspected mis-split crop. More suspicious crops are scattered within the agricultural area. However, with more than 3000 crops, it becomes complex and time-consuming to double-check all their labels individually and verify the atomicity of each parcel. Thus, this paper details an automatic correction process of the labels of this dataset with the support of Sentinel-1 time series. The validity of relabels are then assessed quantitatively using controlled degradation of labels of crops we consider as "trustworthy" as well as qualitatively using Sentinel-2 optical imagery.
Convolutional Autoencoder for SAR Time Series In the context of noisy labels, unsupervised algorithms help to gain insights entirely from data without disruption from mislabeled samples. Indeed, considering the tasks of detecting mis-split and mislabeled crops as well as being able to correct mislabels, we need a method that models both dissimilarity, to detect anomalies within a class, and similarity, to correct anomalies. Unsupervised learning methodologies can be used to perform these detections and corrections. In particular, Convolutional Autoencoders (CAE) are used to model a dataset's distribution. CAEs are a Deep Learning model consisting of two components, as shown in Figure  5 ; a convolutional encoder and a decoder:.
This bottlenecking strategy forces the autoencoder to extract discriminative features from the input data and then encode it into a lower-dimension vector. The recreation task that follows the compression can help detect outliers: in-distribution data points will be relatively better reconstructed than anomalies, to the condition that said anomalies are in the minority compared to the norm. Using a threshold over the MSE value of every data point, we can separate the anomalies from the norm. Thus, the reconstruction mean-squared error of a time series and a well-defined thresholding strategy are an efficient anomaly detection criteria. The CAEs architectures are developed using PyTorch 1. 10. 2, and trained on an RTX 5000 with 16 GB of VRAM, alongside 64 GB of RAM and an Intel Xeon W-2255 CPU.
Iterative Training of Class-Expert CAEs The first step of our methodology consists in flagging what we call "suspicious" agricultural pixel-wise time series. The suspiciousness criteria can be defined using an anomaly detection scheme: finding the anomaly within the norm. In the context of multiclass label-ing, we need to account for the presence of a variety of norms. We employ class-expert CAEs: multiple Convolutional Autoencoders, each trained exclusively on time series of their respectively assigned class. For that, we use the original classes of the supplied dataset. Given a survey with n crop types, we then obtain n CAEs, as shown in Figure  7. Each CAE is designed with an embedding dimension of 1 to ensure the strictest bottlenecking possible for improved outlier detection. The training process of CAEs is summarized in Algorithm 1, and training parameters are displayed in Table  3.
CAE ADAM optimizer Learning Rate = 1 × 10 -3 Batch Size = 128 Epochs = 20 For improved detection of suspicious elements, we retrain, from scratch, class-expert CAEs with a refined dataset, where previously suspicious pixel-wise time series have been removed. This iterative process allows different degrees of outliers extraction: from easier to find in the first iterations to more complex in the last. We iterate 10 times over the dataset, performing progressive filtering of suspicious elements. While we could have used a stop criterion, we found an iteration-based solution to be more flexible for the diversities in profile distributions of each class. The decision to identify an input time series as suspicious relies on a vector of meansquared errors (cf. Figure  8 ), one per class-expert CAE.