dataesr/scientific-paragraphs-categorization huggingface dataset:
In their scheme, the signing key is associated with an access structure while the signature is generated using an attribute set satisfying the access structure. However, ciphertext-policy attribute based signature schemes (CP-ABS) are more appropriate to data sharing than KP-ABS since it enables the encrypting entity to generate an access tree over selected attributes. Ibrahim et al. [52]  have proposed an attribute based authentication scheme. This scheme enables a cloud user to anonymously authenticate with the cloud server. Most of aforementioned attribute based signature schemes have been proposed as an anonymous authentication mechanism in cloud sharing scenarios. Although anonymity is an important feature of attribute based signature mechanism, some malicious users may take advantage of this feature to escape from being traced.
B. System Model Our Ins-PAbAC proposal is defined upon the following nine algorithms. It involves four procedures on the basis of three phases. During the first phase, the system initialisation procedure SYS INIT is executed. The second phase occurs when the data owner wants to share data files with other cloud users, based on both the data storage procedure STORE and the data retrieval procedure BACKUP. While the first and second phases are mandatory for outsourcing data files to the cloud servers, the third phase occurs when there is a need for user's anonymity revocation, relying on the execution of the inspection procedure INSPEC. The SYS INIT procedure consists of three randomized algorithms for the generation of public parameters referred to as setup, the generation of the private and public parameters related to the involved attribute authorities denoted by setup auth , and the keygen algorithm to generate the users' private keys.
C. Security Model In this section, we consider two adversaries for proving the security and privacy properties of our Ins-PAbAC scheme. First, we point out the case of a honest but curious cloud provider. In fact, the cloud server is considered as honest as it provides accurate inputs or outputs, while properly executing any calculations expected from it. However, it is curious as it attempts to gain extra knowledge from the protocol. Consequently, we consider the honest but curious adversary against both the data confidentiality requirement considering adaptive chosen plaintext attacks (IND-CPA) and the computational privacy requirements. Second, we consider the case of malicious users trying to override their rights. That is, malicious users may attempt to deviate from the protocol or to provide invalid inputs.
A. Motivation In order to achieve fine grained and privacy preserving access control to outsourced data in cloud storage, we combine two cryptographic techniques, ciphertext-policy attribute based encryption (CP-ABE) and attribute based signature (ABS). Moreover, ABS ensures the accountability feature which is necessary for imputing malicious acts to individuals. Attribute based encryption is appropriate for cloud data sharing thanks to its functional properties. First, attribute based techniques do not require certificates to certify the users' public keys as in the traditional public key cryptography, thus leading to an easier key management system. Second, the public keys are derived with no need for previous computation of corresponding private keys. For instance, in attribute based cryptography, there are no requirements to generate the private key before the public key unlike traditional public key derivation schemes.
A. Correctness We detail the correctness of our construction, with respect to Theorem 1. Theorem 1. Authorized users can successfully authenticate and decrypt enciphered data files. We recall that cloud users have to collect their certified attributes and the related secret keys from attribute authorities AAs. As such, in Ins-PAbAC, only users, having valid private keys related to their attributes, are able to access data stored in the cloud, once successfully authenticated with the cloud server. This is due to both the correctness of our decryption and signature verification algorithms, asserted by Lemma 1 and Lemma 2 respectively and the compliance of the unforgeability property of the Ins-PAbAC signature scheme, inherited from  [58]  and emphasized by Lemma 3. In the following, we assume that there are κ involved attribute authorities.
D. Privacy Based on an attribute based signature scheme, Ins-PAbAC ensures users' privacy against curious cloud service providers. In our proposed scheme, the requesting data user has to authenticate with the cloud provider. As such, U has to sign a message received from the cloud service provider with respect to the access structure defined by the data owner. The CSP is responsible for verifying the user's access rights without knowing neither his identity nor the attributes used to sign the message. Ins-PAbAC inherits the privacy preserving property from the Waters signature scheme  [58]. Theorem 3. For any adversary A, against the privacy property, our Ins-PAbAC scheme is computationally private according to the Definition 3 with respect to the hardness of the CDH assumption ( Definition 5).
VII. THEORETICAL PERFORMANCE ANALYSIS In this section, we present the computation and storage complexities of our accountable Ins-PAbAC protocol. To this end, we are interested in the computations performed at the data owner side in order to execute the STORE procedure. In addition, we will consider the computation cost related to the execution of the BACKUP procedure by both the user (U) and the cloud service provider (CSP). In the following, we denote by: • E 1 : exponentiation in G 6E 1 + 2E T + O(H ) - 4τ p - E T + 2O(H ) [51] (Delegated decryption) 5E 1 + E T + 3O(H ) - E T + 3O(H ) - 3nτ p + E T [62] (Delegated decryption) (k -m + 2 + r)E 1 + E + 1 tE 1 /(2t + 2)E 1 + τ p 2τ p + E 1 + nE - E + 2O(H ) Ins-PAbAC (n + 1)E + 5nE 1 + τ P (2 + n)τ P + (n + 1)E 1 (4n + 6)E 1 + (2n + κ + 2)τ P + (n + 2)E E 1 + 2E + τ p - The STORE procedure consists of performing the encryption algorithm encdata.
In addition, state of the art schemes use two different access policies for signing (Ψ encrypt ) and encrypting (Ψ sign ). Therefore, the communication overhead between users and attributes authorities grows because they receive two different sets of secret keys for signing and decrypting {sk S j } sign and {sk S j } encrypt , respectively. However, Ins-PAbAC requires only one access policy for both procedures, therefore users receive only one set of secret keys to sign and decrypt data. In Table  III , we reviewed some ABE schemes  [51] ,  [61] ,  [62]  that support decryption delegation. In these schemes, the data user delegates the decryption algorithm execution to an edge server. This latter partially decrypts the ciphertext then returns the result to the user who only executes few mathematical operations to retrieve the plaintext.
B. Computation Cost Evaluation In this section, we discuss the processing time of the different algorithms depending on various parameters. In order to evaluate the processing time of our proposed framework Ins-PAbAC, we have implemented its different procedures such as the system initialisation procedure, the data storage procedure and the data backup procedure. As the system initialisation procedure consists of setup, setup auth and keygen algorithms, we have evaluated their processing cost while considering different security levels (i. e. 80, 112, 128). In cryptography, the brute-force attack consists in checking all possible keys until the correct one is found (i. e; with a key of length S bits, there are 2 S possible keys). S is defined as the security level in symmetric cryptography.
Hence, Figure  4  shows that the encryption algorithm takes 20. 87 ms using 4 attributes. The computation overhead reaches 68. 45 ms using 14 attributes. Hence, the computation cost of the encdata algorithm increases with the augmentation of the number of attributes. As such, Ins-PAbAC presents an interesting processing cost for resource constrained devices while providing fine grained flexible access control. The data backup procedure consists of the two different layers mainly the anonymous user authentication and the data retrieval procedures. We first perform the decryption algorithm decdata while changing the number of involved attributes. As depicted by Figure  4 , the decdata algorithm requires less than 10 ms using 4 attributes. However, its computa- Hence, Ins-PAbAC introduces an attractive computation cost, especially for limited capacities devices.
003 ms considering a security level equal to 128. With the emergence of distributed applications and the enforcement of several laws and regulations aiming at protecting personal data, the main challenging concern is the design of privacy preserving, efficient and secure access control schemes to outsourced data contents on remote distributed servers. This paper presented Ins-PAbAC, an accountable privacy preserving attribute-based framework that relies on attribute based cryptographic techniques for securely sharing outsourced data contents via public cloud servers. The proposed framework ensures the confidentiality of outsourced data in public untrusted cloud servers and defines efficient data sharing in dynamic groups. That is, flexible access control policies are enforced among users belonging to separate groups with different privileges.
Moreover, Ins-PAbAC introduces the accountability feature allowing an inspection authority to reveal the user's identity if needed. In addition, based on formal system and security models, Ins-PAbAC is proven to ensure strong security properties in the random oracle model, namely confidentiality, unforgeability, privacy preservation and anonymity removal. Through experimental evaluation built upon OpenStack Swift testbed, Ins-PAbAC is proven efficient in scalable data sharing, while considering the impact of the cryptographic operations at the client side as well as at the CSP side. As future work, we aim at exploring decryption outsourcing techniques to be applied on Ins-PAbAC in order to achieve a reasonable computation cost at the data user side.
This led to the development of a questionnaire form for data collection and the implementation of a web-based application, called Lesionia. Results. Based on the questionnaire, we developed Lesionia, a digital system for the management and the analysis of clinical and epidemiological data. It consists of a relational database and a web-based user interface (WUI). The database was conceived to be expandable to new collaborators and projects. It allows for data handling from the consented patient interview and sample collection to the samples storage and investigation. The WUI permits data entry, fetching, visualization and analysis. Rigorous controls on data entry were implemented to reduce discrepancies. It also offers a set of analysis tools that range from descriptive statistics to variable correlation analysis.
, 2019)  to disease outbreaks surveillance in Germany  (Krause et al. , 2007). The positive impact of such systems on the management of chronic diseases was also assessed  (Roshanov et al. , 2011; Michael et al. , 2013). For the specific context of epidemiological studies of parasitic diseases,  Gray et al (2009)  presented a DSDM designed to process data collected during field-based surveillance of the transmission of bovine Schistosoma japonicum in China  (Gray et al. , 2009). The DSDM consists in a database with a multi-user interface developed using Microsoft Access, VBA and SQL. The authors demonstrated the relevance of digitizing the data collection and management processes in leveraging the data quality and its impact on the study reliability  (Gray et al. , 2009). Implementing similar DSDM for the collection and the management of CL-related data is of high importance in addressing the under reporting of CLcases worldwide and the mapping of the disease spread and burden.
The present study is focused on the development of a DSDM specific to CL cases, called Lesionia. It was initially developed for the needs of the PEER518 project consortium, but can easily scale-up to other consortia and cutaneous diseases. The project aims at developing speciesspecific point of care diagnostics for CL. These molecular tools will be tested and validated on clinical samples collected in three countries of the MENA region, namely Tunisia, Morocco and Lebanon. The project consortium includes nine institutions based in five countries: Tunisia, Morocco, Lebanon, Mali and the USA. The central node of the consortium is the laboratory of Molecular Epidemiology and Experimental Pathology Applied to Infectious Diseases (MEEPlab) at Institut Pasteur de Tunis (IPT). Lesionia consists of a database and a web-based user interface (WUI).
Data collection In the frame of the PEER518 project, patient recruitment is taking place in five sites located in four countries (Supplementary table  S1 ). Patients are informed and their written consent is obtained. Clinical samples from patients suspected for cutaneous leishmaniasis are collected, anonymously coded then transferred to the MEEP-lab at IPT, along with related data. At the MEEP-lab, species identification is performed or confirmed using different molecular techniques, the results are collected and the samples are stored. Classically, clinicians and practitioners use a simple form that differs from one hospital to another. Prior to implementing the database and in order to harmonize the data collection process within the consortium and enhance data quality, a discussion took place between clinicians, biologists and bioinformaticians.
It aimed at defining the data to be collected from patients and respective samples and how it was to be used. This led to the definition of a questionnaire form (Figure  1 ). It included patient demographic data, residency and travel history, environmental data related to the notion of insect bite and the animals encountered in close proximity, clinical data including previous treatments if any, data on the clinical aspect of the lesion(s) suspected to be CL (CL could have different presentations), the sampling method and the diagnosis results. The latter include direct examination performed at the sampling site and a selection of molecular tests (PCR ITS, qPCR, RPA-LF) that are further performed at the MEEP-lab for species identification. The questionnaire was conceived with respect to the best practices of data collection (Burguess, 2001;  Kelley et al.
, 2003). First, the questions' order and phrasing were established with practitioners to ensure they can fluently go through them during interviews. We verified that interrogators shall understand the questions consistently. Response fields were reduced to checkboxes with pre-coded responses with a supplementary field for a potential non pre-coded response when it applies. In fact, these formats minimize handwriting, and subsequently spelling and transcription errors during data entry into the database. Units were specified whenever necessary and a canonical format was provided for dates. Y/N _ _ / _ _ / _ _ _ _ _ _ D _ _ M _ _ Y _ _ / _ _ / _ _ _ _ _ _ D _ _ M _ _ Y _ _ / _ _ / _ _ _ _ _ _ D _ _ M _ _ Y _ _ / _ _ / _ _ _ _ _ _ D _ _ M _ _ Y.
System architecture The DSDM implemented for the need of the consortium partners was called Lesionia. It contains multiple components as shown in figure  2. First, the database was designed and implemented using MySQL (Supplementary figure  S1 ). It included data collected by the clinicians, data relative to biobanking in the MEEP-lab and data on the different users that shall use the system, their affiliations and functions. Users have unique login and passwords, provided by the system administrator for a secure connection. The back-end communicates with the database which is hosted on a LEMP server -a variation of the LAMP software bundle that uses Nginx rather than Apache, also known as LNMP (Linux, Nginx, MySQL, PhP). The front-end is designed to be a web-based user interface (WUI) accessible from any web browser.
The web user interface (WUI) We developed the WUI using the Shiny framework implemented in R. It is accessible through www. lesionia. pasteur. tn/. It has a homepage that contains the login form and information on the PEER518 project, the consortium and how to contact the system administrator and/or the research team (Figure  3 (a) ). Using a username and a password previously validated by the system manager, users can access four sections (Figure  3 (b) ): (i) Data management, (ii) Data entry, (iii) Data viewer, (iv) Data analysor. The WUI includes a header that contains the application name 'Lesionia', the name of the currently logged in user and a 'logout' button. The 'Lesionia' button allows the user to go back to the homepage from anywhere during the use of the application and thus to switch between sections.
For each section in the questionnaire, a digital form exists in Lesionia. These forms present multiple fields, out of which those that are mandatory are tagged with an asterisk. Missing data fields can be left blank. Default values are beforehand assigned to indicate that these fields correspond to missing data. These default values are "N/A" or "-1" for data of type character or integer, respectively. Missing data with type date are set to 01-01-1900. This is important for the forthcoming data analysis steps and for the assessment of the data collection procedures. All data in the questionnaire form can be entered in the corresponding tabs of Lesionia interface (Figure  3 (c) ). "Data viewer" allows the user to visualize, search and browse all the data within the tables of the database. It also provides statistics on patients recruitment flow, partitions according to nationality, gender and age classes and raw data visualization and download.
The statistical analyses obtained with the "Data analysor" were consistent with the sample distributions, thus confirming that all statistical tests were correctly implemented (Figure  4 ). Lesionia was implemented as an open software accessible through the following link (https://github. com/Harigua/LEISIApp/ ). To make this software further accessible to the scientific community willing to use it as is or customize it, we developed a user guide on how to install it on different operating systems (Linux, Mac and windows). It can either be installed locally as a desktop application or as a web-based application (Supplementary file S1). It presents the advantage of: (i) error checking during data entry, (ii) ability to report discrepancies to the system administrator, (iii) a built-in statistical analysis tool and (iv) data fields are easily expandable as for the case of biological tests, studied species, etc.
, 2009). This system enabled researchers to produce good-quality data while maximizing the rate of digitizing data collection and reducing the costs of data cleaning. It assures quality of the data through a double-entry check and quality control metrics. It included a built-in statistical analysis functionality and an automated code generator. On the other hand, the system presents no interoperability and is restricted to Microsoft Windows operating system. In this specific context, we herein present Lesionia as an open source pilot software for the collection, management and analysis of data relative to patients with cutaneous diseases such as CL. Lesionia has the advantage of allowing multi-users data entry even for a single patient/sample. This is of high importance in smoothing the multi-actors process of data collection from patient recruitment to sample storage and molecular tests performance.
This advantage is valuable for the harmonization of the multicentric process of the experimental detection and identification of the CL causing species. Lesionia is also an ethically-sound application that uses anonymous coded data. Through the double-entry check for ID codes, it guarantees a reliable tracking system of data entries. These IDs also connect the digital entries to the paper forms that are kept by the clinicians. These paper support include patients' personal data and clinical records that are confidential and are not shared through the consortium. Thus, they are not included in the database. These confidential data can only be used by the clinicians in case there is a need to contact patients during outbreaks or other sanitary issues. Lesionia also offers a built-in functionality for real-time data analysis.
Conclusion and future directions Lesionia is an innovative application for clinical and epidemiological data management of CL patients. It offers a multi-site and multi-user web-based application which enhances the efficacy of the data sharing process. It was implemented using interoperable languages which makes it usable on different operating systems. As part of implementing reliable and sustainable data management systems in our lab and institute, Lesionia was also used as part of an institutional clinical investigation project at IPT. It can scale up to a more widely used system in managing and reporting cutaneous diseases in health care centers across the country and the region. Also, it can be reused for other multicentric trials or projects. Positive samples present different individual maps as compared to negative samples with no discriminative power between the different species, which is in concordance with the simulated data conditions.
Acknowledgements The authors acknowledge the contribution of all partners within the PEER518 consortium, namely Pr. Kalthoum Kallel ,  Dr. Aicha Kallel ,  Pr. Slah Bel Haj ,  Dr. Imen Khammeri ,  Dr. Adil El Hamouchi ,  Mr. Othmane Daoui ,  Dr. Pierre Abi Hanna ,  Dr. Oussaima El Dbouni ,  Dr. Bourama Traore ,  Pr. Seydou Doumbia ,  Pr. Rhea N. Coler ,  Pr. Steven Reed , for their recommendations during the setup of the project and the annual meetings.
Traditionally, most algorithmic and software work has focused on Bacteria, while Eukaryotes and Phages/Viruses are often overlooked. Obtaining, mostly for bacteria, metagenomeassembled genomes (MAGs) of good quality is an active research area with these MAGs constituting the sole source of information associated with many non-culturable microorganisms for which no reference genome is available. The second objective dedicated to functional characterisation is classically addressed through mapping genes, or gene fragments, to functional reference databases, considering the community as a 'bag-of-genes'  [9]. However, more modern approaches use MAGs, acknowledging the community consisting of single, isolated, metabolically active units (microbial species), what we termed the 'bag-of-genomes' approach.
Because this approach is relatively novel and technically challeng-Fig. 1. Overview of strategies for functional inference using metagenomics. SSU: Small Sub-Unit. LSU: Large Sub-Unit. DB: database. MAG: Metagenome-Assembled Genome. GSM: Genome-scale Metabolic Model. ing, we describe in this mini-review the current state-of-the-art approaches for MAGs reconstruction in metagenomes and metabolic modelling for communities. We discuss how MAGs can be useful to better understand the metabolism of microbial communities, and what challenges remain in the use of 'bag-of-genomes' approaches to successfully model and compare ecosystem functional predictions and the role of single hubs within these.