dataesr/scientific-paragraphs-categorization huggingface dataset:
Remote Control Center is mapped to an external computer, and the drone itself must contain the physical  sensors and drivers: Camera, IR, and Motor Controller. Also, the drone and remote computer must be modeled as communicating over an insecure bus, but the remainder of the architecture and mapping is yet to be determined. For example, moving task Image Processing Unit to the remote computer decreases onboard processing and saves battery power, but broadcast of unencrypted image data is undesired. While our toolkit can perform rapid exploration of multiple architectures, in the interest of space we present two mappings: one with all processing on-board the drone, and another leaving only the minimal tasks on the drone. Figure  3  shows the two proposed mappings. We model that the remote CPU offers more processing power, so it executes tasks faster.
Our solution uses Cryptographic Configurations, a tag indicating various security functions and their overhead. While the implementation details ultimately determine the security of a system, taking security into account in early development phases, without constructing the entire software/system design, would prevent costly late-stage reworking of designs that present vulnerabilities due to early partitioning choices. Even if actual security algorithms will be more complex, basic security primitives and relative computation complexities should be accurate and provide useful feedback to designers when selecting a mapping. An overview of the aspects of security modeling and analysis is shown in Figure  4. Starting with a base partitioning model, our toolkit automatically generates the secured models (which the designer can modify), and then finally translates a final model into a specification handled by a formal verifier -ProVerif  (Blanchet, 2001).
, 2011)  and Direct Anonymouse Attestation  (Smyth et al. , 2015). Once a complete application modeling has been mapped to an architecture, a ProVerif specification can be generated from the partitioning model as shown in Figure  4. Infinite parallel processes are generated for each task, and each task sends and receives data or performs cryptographic operations as shown. In  (Lugou et al. , 2016) , we presented a translation from a software model consisting of states, actions, and cryptographic operations. We leverage this translation by first converting our partitioning model into an equivalent software model representation, and use the existing translation process to generate a ProVerif model. The main ideas of the translation are to convert each functional task into a software-model block, and to transform cryptographic configurations into their corresponding cryptographic primitives.
UMLSec  (Jürjens, 2002)  is a UML profile for expressing security concepts, such as encryption mechanisms and attack scenarios. It provides a modeling framework to define security properties of software components and of their composition within a UML framework. It also features a rather complete framework addressing various stages of model-driven secure software engineering from the specification of security requirements to tests, including logic-based formal verification regarding the composition of software components. However, UMLSec does not take into account the HW/SW Partitioning phase necessary for the design of IoTs.
Unraveling the genetic diversity held in genebanks on a large scale is underway, due to advances in Next-generation sequence (NGS) based technologies that produce highdensity genetic markers for a large number of samples at low cost. Genebank users should be in a position to identify and select germplasm from the global genepool based on a combination of passport, genotypic and phenotypic data. To facilitate this, a new generation of information systems is being designed to efficiently handle data and link it with other external resources such as genome or breeding databases. The Musa Germplasm Information System (MGIS), the database for global ex situ-held banana genetic resources, has been developed to address those needs in a user-friendly way. In developing MGIS, we selected a generic database schema (Chado), the robust content management system Drupal for the user interface, and Tripal, a set of Drupal modules which links the Chado schema to Drupal.
MGIS allows germplasm collection examination, accession browsing, advanced search functions, and germplasm orders. Additionally, we developed unique graphical interfaces to compare accessions and to explore them based on their taxonomic information. Accession-based data has been enriched with publications, genotyping studies and associated genotyping datasets reporting on germplasm use. Finally, an interoperability layer has been implemented to facilitate the link with complementary databases like the Banana Genome Hub and the MusaBase breeding database.
Conversely, crop genomic databases focus on a small number of genotypes restricted to a reference genome sequence for a particular species or crop  (1) (2) (3)  or a collection of reference genomes to facilitate comparative genomic studies  (4) (5) (6). Information on the selected sequenced genotypes is often considered secondary data, and links to the original germplasm are frequently missing or included only in the publication, which can hamper researchers wishing to perform subsequent analyses on the same germplasm. These two types of resources have traditionally been managed with distinct tools and by different communities, although some recent developments are now proposing to create interoperability between those different datasets  (7, 8). Nextgeneration sequencing (NGS) has the potential to change the way scientists deal with genetic resources by unlocking genetic diversity stored in genebanks  (9, 10).
These collections represent a wide range of phenotypic variation and genome constitutions. Unraveling and exploiting genetic diversity in banana germplasm collections is critically important as long-term sustainable crop cultivation depends on this diversity in order to adapt to the everchanging agro-environment. Here, we report on the latest developments of the Musa Germplasm Information System (MGIS). MGIS is the largest database for banana genetic resources and includes passport and characterization data, voucher images and genomic-based marker information generated for a large number of accessions. Data associated with accessions in MGIS will help identify important agricultural traits of banana and thus support targeted analyses and distribution of diverse germplasm to researchers, breeders and farmers.
Germplasm data The MGIS crop-specific database was originally developed with the objective to collect and share publicly all available information on the accessions held by ex situ Musa collections worldwide (Figure  1 ). It contains key information including passport data, botanical classification, morphotaxonomic and phenotypic descriptors, ploidy and digital voucher images. It is compliant with the Multi-Crop Passport Descriptors (MCPD)  (16)  which have been widely used as the international standard to facilitate germplasm passport information exchange. Banana germplasm are identified by accession numbers (i. e. unique identifier in a collection) that will be complemented soon by Digital Object Identifiers (DOI) following the specifications of the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA) (http://www.
Standard Material Transfer Agreement (SMTA) that is generated automatically online and required for exchange of plant material regulated under ITPGRFA (www. fao. org/ plant-treaty/en/). The level of passport data completeness is assessed by the Passport Data Completeness Index (PDCI)  (17) , adapted to the specificities of Musa datasets in MGIS (Supplementary Figure  S1 ) and thus noted as PDCI m (m for Musa). This index is built on the parameters required to calculate the PDCI as implemented in Genesys, but considers eight additional fields such as type of material and previous locations (Supplementary Table  S1 ). The index helps partner genebanks to identify deficiencies in their passport data and to improve associated information. The PDCI m is publicly available on MGIS for the passport data of accessions conserved in the ITC collection, while the indices of data from other collections are only visible to their respective curators as an internal tool for data quality management.
The main target users of the MGIS database are: (i) banana germplasm curators requiring a global system for sharing, comparing and managing of data in their own general users looking for reference information associated with characteristics of cultivars, crop wild relatives or improved varieties at the accession level. MGIS helps users build customized queries, facilitates data export, locates alternative sources of banana germplasm and identifies the most appropriate accessions for users' needs (Figure  2 ). Users can access information regarding the country of origin of the germplasm, its availability, ploidy level, genomic constitution and genetic integrity of accessions verified in the field (18) as well as a set of standardized morphological descriptors with associated voucher images.
Below the tree, a table lists the 105 accessions comprising the panel used for the study, which can be requested through MGIS to perform further GWAS on other traits. In the website administration back-end, we developed an efficient method to generate a list of accession numbers with publications and diversity trees in Newick format (evolution. genetics. washington. edu/phylip/newicktree. html) that will automatically tag any accession with the associated study and redirect them to the detailed study page. As a result, users can compare the genetic profiles of accessions in the same or different studies inserted in the database.
Genomics-based data Since the sequencing of the M. acuminata subsp. malaccensis double haploid 'DH Pahang' genome  (19) , a highquality reference genome sequence with annotated genes is available  (25, 26). NGS technologies have boosted research that helps better understand banana genetic diversity, generating millions of SNP markers computed using bioinformatics methods. Currently, more than one-third of the 1527 accessions of the ITC collection have been analysed by high-throughput genotyping such as GBS or RADSeq methods  (23)  and some studies have been initiated with those datasets to perform trait-gene associations such as GWAS  (24). In order to manage and make available these large datasets to users, we implemented a genotyping module in close interaction with the GIGWA-Genotype Investigator for Genome-Wide Analyses  (27)  project that aims to provide efficient and scalable tools to handle high-throughput genotyping data.
MGIS has been extended with interfaces to query SNP datasets by setting up a dedicated instance of GIGWA that has been integrated with MGIS (Figure  5 ). With this function, users can filter a large quantity of SNP markers and export these in the format of their choice including VCF, GFF, DARwin, IGV  (28)  and Flapjack  (29). The system is compatible with multiple reference genomes or assembly versions, as is the case in banana, with the recent release of an improved assembly of the reference genome  (25).
System architecture MGIS is implemented with the Drupal Content Management System (www. drupal. org/) using the Tripal module  (30, 31)  to work with the standard Chado database schema (32) (Figure  6 ). This solution based on GMOD tools has been adopted and applied to plant genome resources such as Rosacea  (33) , Cotton  (34) , Medicago (35), Arabidopsis (1), Banana (i. e. genome hub)  (26)  and Coffee  (36). This open source community has also recently been extended and documented as a case study to manage plant germplasm and plant breeding data  (8, 37). One reason for Drupal's success is that it is extensible, and that new modules can be developed to meet specific needs. Both shareable and in-house Drupal modules have been developed for MGIS. In order to manage Chado data access levels, modification history and data integrity checks, we developed an open source module called the Chado Controller  (38).
This module relies on another extension that we developed, the Tripal Multi-Chado module, (www. drupal. org/project/tri pal_mc) which also enables the generation of Chado sandboxes for collection updates. The sandboxes are clones of the live database that can be safely altered by germplasm collection curators in order to validate their updates and later propagate them on the live database. An additional inhouse extension called MGIS has been developed in order to solve some MGIS-specific needs such as germplasm requests, comparison, verifications and data visualization (Figure  1 ). To manage faceted searches (i. e. a search query with multiple factors, for example, country of origin, ploidy and exchange availability), we used ElasticSearch (/www. elas tic. co/) associated to the Drupal Search API ElasticSearch module (www.
drupal. org/project/search_api_elasticsearch) coupled with Search Facets module (www. drupal. org/pro ject/faceted_search), which provided filtered accession searches on various criteria such as the collection, country of origin, taxonomy or genotyping study (Figure  2 ). We enabled a feature of ElasticSearch called fuzzy search that allows an approximate text matching in accession name queries. This function is particularly useful in deciphering the many variations of vernacular names in banana. With these functions, MGIS has become a very user-friendly and efficient germplasm search interface. The publication management feature has been implemented using the Biblio module (www. drupal. org/project/ biblio) that we extended to enable mapping with a list of accessions. It provides a comprehensive publication reference with links to article details, authors or Google scholars.
Database interoperability As SNPs are mapped on the reference genome in close proximity to genes of interest, we strengthened the links with the Banana Genome Hub (banana-genome-hub. southgreen. fr) (26) by adopting same style interfaces and making crosslinks. Datasets were also provided to SNiPlay (sniplay. southgreen. fr) (40)  to foster additional analyses. In addition, linking genotyping data with breeding data stored in MusaBase (musabase. org) has been a valuable objective to be able to cross data between breeding trials, Figure  5. Genotyping search page powered by the GIGWA system. SNPs and InDels can be filtered by a wide range of criteria (chromosomes; Minor Allele Frequency (MAF), missing data, genes, gene effect, etc. ) and exported in various formats. Markers can be also located on their gene as provided by the Banana Genome Hub.
pedigree and wild material or landraces distributed by germplasm collections. To facilitate the interoperability, we developed web services compliant with the plant Breeding API, BrAPI (docs. brapi. apiary. io) that are detailed in MGIS at the following address (www. crop-diver sity. org/mgis/brapi/overview). The current implementation as an open source Drupal module (www. drupal. org/pro ject/brapi) enables us to link genebank material in MGIS that are used as parents of crosses and to list their progenies in MusaBase. Finally, once the data has been collected, structured and curated, quality data on passport data for the ITC collection are transferred to Genesys (www. gen esys-pgr. org/wiews/BEL084), the global portal to information on Plant Genetic Resources for Food and Agriculture (PGRFA).
Conclusions and perspectives Partnerships and collaborations are at the heart of the MGIS modus operandi for collecting and exchanging data. By sharing data, germplasm collections help to define the global status of the ex situ banana diversity conserved worldwide and thus support rationalization and gap analyses. The number of participating germplasm collections is expected to grow steadily with the signing of more Data Sharing Agreements (DSA) in which both parties (Bioversity International and partners' collections) engage to deliver the most accurate Musa germplasm information available. Contributing to MGIS with data benefit from a wide range of advanced tools applied to their collection. Efforts are continuously being made to improve MGIS data content. An interdisciplinary team composed of germplasm curators, geneticists, bioinformaticians and computer scientists working with taxonomists (e.
g. MusaNet's taxonomy advisory group) have collaborated in this project to (i) enhance the quality of the data at the collection level leading to the identification of potential inconsistencies in passport data in global banana collections, (ii) implement an adapted graphical interface for the community in MGIS, and (iii) increase the connectivity of MGIS with complementary datasets (e. g. genomics). Regarding the latter, GBS analyses are also an integral part of the research and management of genetic resources. Genomic-based analyses play an increasingly important role in understanding and exploiting the genetic variation of crop diversity maintained in genebanks. And as such can contribute to enhance the productivity, sustainability and resilience of banana cultivars and their agricultural systems.
Finally, although there is an increasing amount of phenotypic information available, one of the key limiting factors in its use has been the lack of standard nomenclature used to describe crop development and agronomic traits. In order to facilitate access to harmonized data held in a range of databases, we will adopt the Crop Ontology scheme/methods  (41)  to manage interoperability between phenotyping experiments on banana germplasm and design interfaces for the management of phenotyping data.
Acknowledgements Over the years, many people contributed directly or indirectly to MGIS developments and we wish to thank the members of the  Global Musa Genetic Resources Network (MusaNet ), specifically to the institutions that provided data and to the  Taxonomic Advisory Group (TAG)  for their commitment. We would like to thank the  MusaNet Documentation Thematic Group (DTG)  members for their feedback on the website and to  Stephen Ficklin  and  Lacey Sanderson  for fruitful interactions regarding Tripal. We are grateful to the  Bill and Melinda Gates Foundation  for supporting us in the development of BrAPI web services and we acknowledge critical interactions with  Lukas Mueller ,  Guillaume Bauchet  and  Nick Morales  at  Boyce Thompson Institute as well as Iain Milne ,  Sebastian Raubach  and  Gordon Stephen  at  The James Hutton Institute.
Scalability. Clearly, the computational complexity increases many-fold while computing meta-path-based similarities. Computing all possible meta-paths on a large-scale graph with millions of vertices (heterogeneous items) can quickly become computationally intractable. Privacy. Heterogeneous recommendations also raise privacy concerns. For example, the new transitive link between Alice and Cecilia (Figure  1(a) ) provides the opportunity for a curious user, say Alice, to discover the straddlers: people like Bob or Cecilia who connect multiple domains. Alice can actually determine the item(s) that allows her to get this recommendation by pretending to be another user and incrementally rating items until she gets the recommendation. This is similar to the privacy risk in statistical database queries where inferences can be derived from combinations of queries  [26].
We illustrate this scalability through our implementation of X-Map on top of Apache Spark  [38]. • We introduce an obfuscation mechanism, based on metapath-based similarities, to guarantee differentially private AlterEgos. We adapt, in addition, a probabilistic technique, inspired by Zhu et al. [39, 40] , to protect the privacy of users in the target domain. Interestingly, we show that, despite these privacy techniques, X-Map outperforms the recommendation accuracy of alternative non-private heterogeneous approaches  [5, 6, 11]. • We deployed an online recommendation platform, using X-Sim on a database of 660K items, to recommend books and movies to users based on their search queries at:.
Collaborative Filtering Collaborative Filtering (CF) algorithms fall mainly in two categories: memory-based  [28, 33]  and model-based  [17, 20, 37]. Memory-based algorithms compute the top-k like-minded users for any given user (say Alice), denoted as the neighbors of Alice, from the training database, and then make recommendations to that user based on the rating history of her neighbors. In contrast to memory-based algorithms, model-based ones first extract some information about users (including Alice) from the database to train a model and then use this model to make recommendations for the users (including Alice). Memory-based algorithms are more flexible in practice compared to model-based ones  [18]. It is usually more difficult to add new incoming data to model-based systems because building a model is often a time-consuming and resource-consuming process.
Differential Privacy Differential privacy  [14]  was initially devised in a context where statistical information about a database is released without revealing information about its individual entries. Differential privacy provides formal privacy guarantees that £ Dictionary for item similarities with j 2: var r; £ Average rating for each user 3: for i in I do 4: £ Average rating for each item 11: for i : item in I do 12: do not depend on an adversary's background knowledge (including access to other databases) or computational power. More specifically, differential privacy is defined as follows. τ j [i] = u∈Y j ∩Y i (r u,j -ru)(ru,i -ru) u∈Y j (r u,j -ru) 2 u∈Y i (r u,i -ru) 2 (3 P red[i] = ri + j∈N i τ (i, j) • (r A,j -rj ) j∈N i |τ (i, j)| ; (4 DEFINITION 1 (Differential Privacy  [14] ).
Clearly, a brute-force scheme considering all possible metapaths would be computationally intractable and not scalable. Assuming m items in the database, the time complexity of such a brute-force scheme (computing similarity for every pair of items) would be O(m 2 ), which is not suitable for big datasets like the Amazon one with millions of items. X-Map uses a layer-based technique to prune the number of meta-paths, thereby leading to O(km) O(m) time complexity where k ≪ m.
X-Sim: A Novel Similarity Metric Consider any two items i and j. We denote by Y i≥ ī the set of users who rated item i higher than or equal to the average rating for i over all the users in the database who rated i. We also denote by Y i< ī as the set of users who rated item i lower than the average rating for i. Additionally, we denote by |Yi| the cardinality of the set Yi. DEFINITION 2 (Weighted Significance). Given any pair of items i and j, we define weighted significance (Si,j) as the number of users who mutually like or dislike this given pair. Formally, we define the weighted significance (Si,j) between i and j as follows. Si ,j = Y i≥ ī ∩ Y j≥ j Mutual like + Y i< ī ∩ Y j< j Mutual dislike Intuitively, a higher significance value implies higher importance of the similarity value. For example, a similarity value of 0.
We first present our similarity-based sensitivity, required for the private approach, along with its correctness proof sketch. We define a rating vector rt i = [rt ai ,. , rt xi , rt yi ] which consists of all the ratings for an item ti ∈ D. Similarly, we define a rating vector r t i for ti ∈ D. Since we use adjusted-cosine for X-Sim, a rating rt xi is the result after subtracting the average rating of user x (rx) from the actual rating provided by x an item i. The similarity-based sensitivity is formulated as follows. THEOREM 2 (Similarity-based sensitivity). Given any score function q : R → R and a dataset D, we formulate the similarity-based sensitivity corresponding to a score function qi(I, tj) for a pair of items ti and tj as: SS(ti, tj) = max{maxu x∈Uij ( rt xi × rt xj r t i × r t j ), maxu x ∈U ij ( rt i • rt j r t i × r t j - rt i • rt j rt i × rt j )} The full proof is provided in our companion technical report  [1]  for interested readers.