dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , friend lists) and digital records (e. g. , profile attributes, joined groups and liked pages), which might be unavailable in a real scenario or computationally costly to collect. This personal data as well as writing styles can be modified by the target user to escape inference attacks. In contrast, we infer target user attributes from commenters and Facebook generated data (both called here publication metadata). This metadata is easily available. Since our approach does not need to explore the target user's vicinity network, groups and pages, inference attacks are efficient and can even be launched online. Our work is related to  [23, 5]  where gender and age are inferred from Facebook picture metadata. However, here we do not limit ourselves to specific attributes and our attacks apply to many social media by leveraging commenters' preferences related to pictures, posts, and status updates.
Dividing training datasets Here, we introduce our splitting conditions and their computation. In the following we use term as a shorthand for word/emoji/tag. Criteria for splitting. We can argue that splitting is not beneficial if the commenters use (i) a majority of different terms while commenting on users' publications in different attribute values, or (ii) more frequently the same terms co-occurring more often with similar contexts than dissimilar ones (see Subsection 5. 3). Examples 2 and 3 illustrate cases where splitting the dataset would not be beneficial. Metadata of an image published by male user Generated alt-text: selfie, closeup Comment: great picture However, in the complementary cases, our experiments have shown the neat benefit of splitting for accuracy (see Subsection 5. 3).
For instance, if males and females are commented with the same terms and the contexts of those terms are mostly specific to an attribute value (see Example 1), we can take advantage of this variation and split the dataset to generate vectors that are biased towards that attribute value. We, therefore, propose two conditions to be jointly satisfied in order to split the training dataset. To express these conditions, we define the importance of a set L that contains terms or contexts w as Q(L) = w∈L f req(w) where freq(w) is the number of occurrences of w in the dataset. Condition 1 is satisfied when the set of overlapped terms is more important than the set of non-overlapped terms. Condition 2 is satisfied when the set of non-overlapped contexts is more important than the set of overlapped contexts.
In Example 1, the first condition is satisfied by the overlapped terms (1person and baby), and the second condition is met as the overlapped terms have different contexts (miss you and cray cray). None of the conditions are satisfied in Example 2 and only the first condition is satisfied in Example 3. Therefore, a unique term representation is sufficient in the two latter cases without missing the variations in the contextual meaning of the terms. Dataset dividing algorithm. We label the original training dataset D in such a way that the ith sub-datasets D i contains users labeled with the ith attribute value and words appearing in their publications comments. Algorithm 1 has for inputs D and D i s, and it returns a boolean that is true if D has to be split into sub-datasets D i. We introduce the following notations: 1.
l 1 , l 2 ,. , l k represents the attribute values. If l is gender attribute, then l 1 ="male" and l 2 = "female". 2. D i is the ith sub-dataset containing the set of users with attribute value l i (with i ∈ {1,. , k}). For gender attribute, we obtain D 1 and D 2 as subdatasets annotated by male and female. 3. W i is the set of words in comments of pictures published by users in U i. 4. U T op, and BT op are integer parameters. Step1: for i = 1,. k do U nii ← U nigram(Di, U T op) Bigi ← Bigram(U nii, BT op) Step 2: OT ← k i=1 U nii ; OC ← k i=1 Bigi T ← k i=1 U nii ; C ← k i=1 Bigi N T ← {t ∈ T | T count(t) = 1}; N C ← {c ∈ C | Ccount(c) = 1} if Q(OT) > Q(NT) and Q(OC) < Q(NC) then true; else false;.
As a result, terms appearing in a similar context tend to have a similar distributional vector. Let c = [c -n ,. c -1 , w, c 1 ,. c n ] be the context of w with window from -n to n (n chosen between 1 and 5) and let c be the vector obtained by accumulating word's index vector co-occurring with w in context c. We update the distributional vector w by using RI as follows: w = c∈C(w) -n j n j =0 c j (1) The problem with this approach is that the entire dataset potentially contributes to each term vector representation. Therefore, the vectors are affected by the different attribute values and lose their discriminating power for attribute inference attacks. To remedy this problem, we propose to generate several valuebased vectors for each term  [12]. Values-based random indexing. Despite its simplicity, RI struggles to capture the relation between commenters' words/emojis usage preferences and the owners' publication profile.
Attribute inference attacks We consider an attacker who intends to infer OSN users' attributes from a set of publications P where each publication contains metadata. Thus, the OSN is exposed to potential privacy violations by an attacker (external or OSN user) collecting, storing and analyzing publications metadata from user profiles. Once we learn the vector representations of terms for each attribute value (see Equation  2 ), we compute a vector representation of u m by aggregating all the terms that appear in his/her publications as follows: u m = w∈P (um) w m (3) We introduce a set S of vectors computed by Equation  3. For the target user t, we generate a set of user vectors T = {t 1 , t 2 ,. t k }, where vector t m is obtained as follows: t m = w∈P (t) w m (4) We compute cosine similarity  [20, 22]  between T and S to check which user in S has the most similar vector to the vector of a user in T as follows: (t µ , u µ ) = arg max t∈T u∈S (cosine(t, u)) The arg max function outputs a tuple (t µ , u µ ) and we infer that the target attribute value is l µ.
Dataset As a case study, we concentrate on Facebook. More precisely, we consider photos published by their owners. Compared to other publications (such as post and status update), pictures on Facebook receive an extra comment from Facebook, namely alt-text. The generated alt-text has two advantages. First, it alleviates the image processing tasks. Second, it provides additional information for the attacker. We show the success of our attack on three Facebook sensitive attributes: age, gender and relationship status. The data collection was conducted from October 2019 to April 2020 by utilizing a python crawler for academic research purpose. For the ground truth, we focus on the profiles where these attributes are public, and we collect the required information from the html files of the corresponding images.
For every picture, we extract data such as comments, alt-texts, publication time and attribute's value of the owner. We have collected 9280 users' profiles: 7611 users published their gender, 4604 users shared their relationship status, and 3813 users announced their age. We randomly selected Facebook users to avoid usage bias by region or country. Overall we have collected 399,076 pictures and their 686,859 messages. We have leveraged the available picture metadata (either alt-text, comments or both) in our attack process. In order to get a representative and useful dataset, we perform the following pre-processing steps: -Purifying the conjunction, redundant tag, and text from the generated alttext. -Cleaning the extracted comments by removing stop words and re-formulating flooded characters, misspelled words, and abbreviations.
g. , binary classification) and the corresponding learning objective, n estimators represents the number of trees to fit, and subsample indicates the fraction of observations to be randomly sampled for each tree. We set their default values and evaluate how different values affect our performance. Except for the objective depending on the number of classes, we create an array of different values for each parameter and use a python notebook tool called GridSearchCV to automatically find the best value of that array. For example, we assigned to learning rate the array [0. 01, 0. 03, 0. 05, 0. 07], and use GridSearchCV to find the value giving the best result. As for RI,  [7]  proposes a grid of sample values. We set dimension= 500 and select two non-zero elements of the index vector to {-1, +1}, which maximize the result of our inference attacks.
Inference results For the age inference attack, we consider the following classes: 20 to 25, 25 to 30, 30 to 35, 35 to 40, 40 to 45, and 45 to 50. We chose these age groups to have a compromise between the accuracy of age prediction and the balancing of datasets. The age categories in our dataset reflect, in general, the most active ones on Facebook. We do not consider ages under 20 or over 50 as it is time consuming to collect enough data and keep all age categories balanced. As for relationship status, we collect datasets for single, married and engaged users. Consequently, we consider three classes. Finally, we reduce gender inference attacks to a binary classification problem with female and male classes 3. For age and relationship status, we set the XGBoost classifier objective to multi : sof tprob that gives the probability of each class, while for gender we set it to logistic.
We have used train-test splitting, as it performs faster, and split the entire dataset into the train, validation and test datasets. We have leveraged these datasets for training, parameter pruning and testing our XGBoost classifier, respectively. We have trained word2vec on the same dataset and compared its performance with our approach. Figure  2  shows the AUC result of word2vec and our approach over three different attributes. 2  (b) represents the result of the same attribute inference attack by using our approach, which gets a tremendous boost with a substantial gain in performance. For example, our approach infers the class 35 -40 with 99% AUC, where it was 77%. In addition to AUC, the micro and macro average increased to 98% and 95%, respectively. Figure  2 (c  and d ) display word2vec and our approach performance to relationship status inference attack.
Our approach can accurately infer the relationship status attribute of the target user in comparison to word2vec. The class Engaged obtains 96% AUC in our approach, where word2vec infers this class inadequately (slightly better than random). Lastly, Figure  2 (e  and f ) depict the gender inference attack. Similarly, our approach outstands word2vec model. The attacker can infer the user's gender by using our approach with 98% AUC, where it drops to 70% in word2vec. As mentioned in Subsection 3. 1, we split the dataset only if two conditions are satisfied. To justify this, Table  2  represents some results of RI with splitting when the conditions are not satisfied. From a crawled dataset labeled by gender, we have synthetized two datasets C1 and C2. In C1, the first condition is satisfied and the second one is not satisfied.
Conclusion In this paper, we have presented a new perspective on attribute inference attacks based on reactions to target user publications. We have shown that if a term appears in diverse contexts, it can be represented by different vectors. To capture these diverse contexts, we have divided the dataset based on attribute values. We have also defined some conditions to prevent useless splittings. We have relied on the Random Indexing method to compute the term vectors in each attribute value, as the generated vectors need to be comparable. Based on the intensive analysis of 399,076 pictures and their 686,859 comments on Facebook, we have demonstrated that picture metadata conveys sensitive information and some private attributes such as gender, age category and relationship status are leaked by the variations in commenter's words/emojis usage preferences and picture owner sharing style.
Our attacks are suitable for online execution as they do not require exploring user behavioral data and vicinity networks. They generalize easily to other social media platforms such as Twitter. Even though some Facebook users limit themselves to sharing pictures with friends only, it is often not hard for an attacker to be added to a large list of friends and have access to the picture metadata. As future work, we plan to extend our tool by explainable machine learning techniques  [24, 18]  to offer users several means to reduce attribute inference risks (e. g. , deleting or reducing the influence of leaking terms in comments). We also intend to enlarge the user age boundary and consider users over 50. Furthermore, using recent state-of-the-art tools such as BERT or ELMo for word embeddings would a promising direction to improve our work.
U nigram(D i , U T op) computes U ni i , the set of U T op most frequent terms in D i. 6. Bigram(U ni i , BT op) computes Big i , the set of BT op most frequent terms in D i co-occurring with terms in U ni i. 7. T count(t) = |{i ∈ {1,. k} | t ∈ U ni i }| , (resp. Ccount(c) = |{i ∈ {1,. k} | c ∈ Big i }|)is the number of sets U ni i (resp. Big i ) where a term t (resp. a context c) appears. Suitable values of U T op and BT op will be determined from experiments (see Subsection 5. 2). Dataset dividing algorithm input : D, D1,. , D k output: true iff the dataset D has to be split in D1,. , D k.
5. Algorithm 1 : 51 U nigram(D i , U T op) computes U ni i , the set of U T op most frequent terms in D i. 6. Bigram(U ni i , BT op) computes Big i , the set of BT op most frequent terms in D i co-occurring with terms in U ni i. 7. T count(t) = |{i ∈ {1,. k} | t ∈ U ni i }| , (resp. Ccount(c) = |{i ∈ {1,. k} | c ∈ Big i }|)is the number of sets U ni i (resp. Big i ) where a term t (resp. a context c) appears. Suitable values of U T op and BT op will be determined from experiments (see Subsection 5. 2). Dataset dividing algorithm input : D, D1,. , D k output: true iff the dataset D has to be split in D1,. , D k.
Suzzana Magalhães I moved to Berlin right after the fall of the Berlin wall, and became part of the emerging electronic (club) music scene, and its surrounding culture of re-appropriation of abundant urban spaces, and audiovisual installation art. For several years I have been focusing on installation and performance works using lasers, and LED pixels. My time is divided into musical work at the studio, performing electronic music on stage, developing, preparing and setting up audiovisual installation works, and working at/ for Ableton 2  , the company that has produced the software Live and has a huge impact on how electronic music is composed and presented at the moment. SM: You were part of a scene composed by different types of art emerging at the same time, a historical moment marked by the need for freedom.
SM: Is this about Learning Music, the platform you created that allows one to learn music for free? Because it seems that as in arts, technology may transform the educational system into something more democratic and accessible as well, which is the case of this project, in particular. Could you talk a bit more about the effects Ableton is causing in this field and the motivations that led you to work also with education? RH: It is about Learning Music but also more general about the accessibility of tools. Musical education serves many purposes, but unfortunately is highly undervalued in many school systems. Since Live became some sort of widely accepted platform, it is possible for music teachers to develop classes around it, and in comparison to any classical instrument, the software and necessary hardware is either already part of the children's life or not prohibitively expensive.
Genebanks collect and preserve vast collections of plants and detailed passport information, with the aim of preserving genetic diversity for conservation and breeding. Genetic characterization of such collections has the potential to elucidate the genetic histories of important crops, use marker-trait associations to identify loci controlling traits of interest, search for loci undergoing selection, and contribute to genebank management by identifying taxonomic misassignments and duplicates. We conducted a genomic scan with genotyping by sequencing (GBS) derived single nucleotide polymorphisms (SNPs) of 10,038 pepper (Capsicum spp. ) accessions from worldwide genebanks and investigated the recent history of this iconic staple. Genomic data detected up to 1,618 duplicate accessions within and between genebanks and showed that taxonomic ambiguity and misclassification often involve interspecific hybrids that are difficult to classify morphologically.
Significance This study provides a deep population genomic analysis of 10,000 Capsicum accessions held in genebanks and representing a frame of the global diversity of the genus. By combining single nucleotide polymorphisms (SNPs) based data and passport information, we investigated the genomic diversity and population structure of wild and domesticated peppers, tracing back to routes of evolution and providing a model of Capsicum annuum distribution, which reflects human trade and historical/cultural influences. Our results highlight west-east routes of expansion, shedding light on the links between South and Mesoamerica, Africa, and East/South Asia, the latter two constituting important diversification centers of pepper diversity. Finally, we outline a roadmap for genebank management and future direction for better exploitation of germplasm resources.
Here, we report the sequencing-based genotyping of 10,038 accessions from 14 Capsicum species and subspecies stored in major international genebanks and research institutions. This collection effectively samples the global genetic diversity of pepper spanning the intertropical and temperate regions of the globe. From the genomic inferences on the massive scale of data, we examined the ex situ population structure, providing insight for better management and linking it to the expansion of C. annuum, with a particular focus on the way human trade is reflected in the genetic diversity and differentiation of varieties sampled from different regions. Then, we exploited archives of phenotypic data for scanning loci selected during the evolution of the crop.
Results and Discussion Detection of Redundancy and Species Misassignment in Global Ex Situ Pepper Genebanks. The sharing and inconsistent documentation of germplasm often results in duplicates within and between genebanks that can be hard or impossible to identify and could affect population genomic analyses  (13). To identify tentatively duplicated samples, based on the genotype matrix (26,566 single nucleotide polymorphisms [SNPs]) derived from 10,038 pepper accessions (SI Appendix, Table  S1  and Dataset S1), we estimated identity-by-state (IBS) proportions considering all pairwise combinations of accessions. We added a set of control replicates to the dataset (in this case, 224 replicate samples of C. annuum cv. "CM334") to discover what range of IBS proportions would be assigned to samples known to be identical replicates.
5% at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) to 80. 4% at the Universitat Politècnica de València (UPV) (SI Appendix, Fig. S2A ). A consistent proportion of shared samples was found between World Vegetable Centre (WorldVeg) and IPK (23. 1%), whereas only a few accessions  (8)  were in common among all the five main genebanks (SI Appendix, Fig. S2B ). Overall, duplication occurred mostly within the Annuum clade (C. annuum ∼78%; C. frutescens ∼10%; C. chinense ∼5%) (Dataset S2). These differences presumably reflect variation in both the deduplication practices of genebanks and difficulty with which novel entry accessions can be checked against the current collection. This significant level of duplication should motivate the development of genetic prescreening protocols to be used in genebanks for documenting the potential duplicate samples upon first acquisition.
To correct and impute species assignments  (13) , hierarchical clusters were imputed at a stringent Z-score threshold (Dataset  S3 ). Members of clusters containing multiple species designations were assigned to the majority designation, if the majority assignment made up more than 80% of the cluster, after visual inspection of the principal component analysis (PCA) plots to assure the cluster did not fall within a region of broad overlap between multiple species. In total, 53 clusters included accessions from more than one species or undefined accessions. The largest part of species misassignment occurred within the Annuum clade and between the three C. baccatum groups, including the two botanical varieties and an undefined group. Rather than to misassignment, these outliers could be attributed to allele introgressions due to spontaneous crosses.
While the frequency of accidental hybrids produced in a given genebank will vary with factors such as the multiplication techniques and the crossability between species, these higher heterozygosity levels do suggest the distinct possibility of recent, accidental crossings occurring in genebanks, leading to the creation of intermediate forms. However, many of them were marked as unclassified when they were collected, possibly resulting from natural or ancestral hybridization, and then maintaining a high rate of heterozygosity despite multiplication by selfing. Broad overlap among C. baccatum accessions suggests a certain level of subspecies misassignment occurring within different repositories. C. pubescens and C. chacoense-both consumed to a small degree by humans-form distinct groups, and the data suggest that some wild individuals could arguably be collapsed into C.
4D ). GWAS highlights how a large factor in pepper's initial appeal was certainly its pungency, suggesting putative regions for selection in correspondence of some primary traits that has driven the selection. By confirming established associations, we provide proof of the reliability of genebank records for discovering candidate genes. This suggests a key position of genebanks as a central repository of germplasm and related big data (genomics and phenomics) toward future gene discovery and crop improvement.
Mesoamerica, East/South/Southeast Asia, and Africa are all notable for large proportions of region-unique peppers indicating their status of diversification center. The broad overlapping clines that are formed by each region's peppers suggests that crossing between locally favored varieties and new acquisitions have frequently occurred. As our GWAS and selection scan results confirm, a large factor in pepper's initial appeal was certainly its pungency, especially in nontropical Europe where hot spices were rare and imported black pepper (Piper nigrum) could fetch demanding prices. This study shows how genomics applied to a horticultural crop can offer opportunities for PGR management and provide valuable insight on the conserved germplasm resources. In fact, genetic data stored in genebanks confirm that pepper, thanks to its flexible features (easily preserved and transportable in dried form, needed in moderate quantity to enrich dishes, easy to produce, and wide scale) has been spread along with the very earliest intercontinental traders, being among the very earliest examples of a globally traded, mass-market, consumer-discretionary good.