dataesr/scientific-paragraphs-categorization huggingface dataset:
(2016)  and conform to the milk microfiltration ontology are shown below. The structure, in terms of columns, of the annotated tables, has been generated automatically by @Web using a selection of relation concepts previously defined in the OTR. In order to homogenise data structuration extracted from heterogeneous sources, the annotator is invited to find in the article the information requested in each column. Once fulfilled, annotated tables which conform to the OTR may be automatically imported into the RDF database thanks to the import @Web module. The first table (Table  1 ) shows one experiment involving five relation concepts in the ontology associated with unit operations commonly used in milk microfiltration (see column Treatment). For example, Experiment 1 is composed of a sequence of five unit operations.
The second unit operation (column named Process step number which equals 2), which is a heat treatment, shows the controlled parameters of treatment duration (15 seconds) and temperature (73°C). The fifth unit operation is the operation of the microfiltration unit, which includes several characteristics of the membrane (e. g. manufacturer, membrane material). An excerpt of the annotated table Dynamics of controlled microfiltration parameters (Table  2 ), which involves one relation concept of the ontology (Fig. 2 ), corresponds to the fifth unit operation (Microfiltration Separation Micelles/Serum proteins) of Experiment 1 (Table  1 ). Values associated with the controlled parameters (e. g. VRF, TMP, Jp) were annotated at different sampling times. Examples of RDF data presented in Table  2  and a SPARQL query to retrieve the subset of columns (Experience_number, ProcessStep_number, Time, VRF) are given in Annex 1.
The complete annotated dataset is available and described in more details in  Buche et al. (2021). To assess the reliability of articles in the domain of milk microfiltration, experts were asked to provide a list of groups of metadata (Fig. 4  and the complete list in  Buche et al. (2021). Each metadatum was associated with an expert opinion (see  Buche et al. (2021) ). The interval scores associated with each article were calculated based on the expert opinions and metadata values registered by annotators. For example, a list of metadata is associated with data from  Jorgensen et al. (2016)  (see Fig. 5 ). The range of reliability scores  ([4. 70, 4. 99] ) indicates that it is considered highly reliable. Based on these scores, @Web querying module computes reliability rank associated with each document.
8% of the Jp of 50-75 kg. h -1. m -  is in this range. From matrix confusions, the overall accuracy of the model may be estimated at about 55% for Jp and TMP when the learning dataset contains missing data, regardless of the reliability of data (Table  6. Including data reliability increases the overall accuracy from 42% to 70% when the learning dataset has complete data (Table  6 ). However, the overall model accuracy for TMP is low (27-28%), due mainly to the existence of a limiting value of Jp for which a wide range of TMP is possible (Fig. 4. 13 p. 126 in Cheyran (  1998 )). As the model learned that nearly all TMP are possible when Jp is high and near its limiting value, it cannot discriminate TMP at high values of Jp. The solution for this issue is to consider the limiting Jp in order to determine the lowest TMP that can reach it.
CONCLUSION AND PERSPECTIVES This study developed a new practical and versatile workflow as a major step in building a decision support system for the agri-food sector. This new workflow combines knowledge and data integration guided by ontology and BNs. Based on expertise, the ontology paradigm is used to build the structure of the BN. The parameters of the model developed were estimated with integrated data from heterogeneous literature sources guided by a domain ontology. The reliability of the sources was included in the learning parameters via reliability indicators. The workflow was assessed for a complex food engineering process: skimmed milk microfiltration. The workflow can be used iteratively in order to enrich it regularly with new data and knowledge to enhance its deduction ability.
An advantage of the approach is that the database connected to the model can be updated in an iterative process without damaging the entire system. This article presents a complete initial iteration for skimmed milk microfiltration. A domain ontology of milk microfiltration was developed to annotate state-of-the-art literature sources, and a list of metadata was developed to assess data source reliability. They were used to build a structured database which was used in part to learn the BN. The Bayesian model was built to predict Jp and TMP process parameters as a function of the membrane technology and associated operating conditions, which is one innovation of this approach. The workflow iteration provided two results: (1) greater model accuracy for Jp when including the reliability of sources and (2) identification of knowledge gaps.
To better describe the observed "plateau" effect of the TMPs with the limiting Jp, dynamic BNs  (Baudrit et al. , 2015)  could be useful for predicting TMP as soon as Jp no longer changes. The database will also be used to assess impacts of process parameters on milk quality parameters, which is another major aspect of process optimisation. A future perspective of this study is to demonstrate that the workflow developed can be adapted easily to predict other performance criteria of microfiltration (including protein transmission) and more generally other processes in the agri-food sector.
), which(Destercke et al. (2013)) describes in detail. Given a scientific article a retrieved from literature databases on the Internet, @Web tool provides an interval score E reflects the a priori (i. e. , avoiding specific examination) reliability of information provided in the article. The score is computed using metadata, and the amplitude of E E information about the consistency of the multiple metadata taken into account. Interested readers by the design of the system are invited to read(Destercke et al. (.
points extracted from the RDF database using SPARQL queries from microfiltration articles, enriched with 33 confidential experiments. The reliability ranks range from 1 ("very reliable") to 16 ("not at all reliable"). A simple way to capture these reliability ranks in the learning dataset is to duplicate a datum (17-[reliability rank]) times (e. g. , duplicate "very reliable" data 16 times). This approach has been applied to the extract of the learning dataset based on reliability ranks whose excerpt is presented in Table4: The first (resp. third) line is duplicated 16 times (resp. 4 times) as the second one is not duplicated. The entire dataset with the experimental data from the literature is available on the INRAE dataverse (MICROFILTRATION learning dataset.
third) line is duplicated 16 times (resp. 4 times) as the second one is not duplicated. The entire dataset with the experimental data from the literature is available on the INRAE dataverse (MICROFILTRATION learning dataset. VRF Memb. Conf. Sys ≤50 (50, 100] Shear stress (100, 150] (150, 200] >200. ≤1. 5 Tubular-UTP 0. 05 0. 15 0. 35 0. 35 0. 15. data.
I. INTRODUCTION Nowadays, a variety of computer-aided modelling techniques, also known as in silico, are developed and intensively applied in drug design, as the first filtering step before the time consuming and expensive conventional experimental testing. The structure-based virtual screening (SB-VS) is one of the most promising in silico approaches in the discovery of hit compounds by docking them in proteins' binding sites. The efficiency of SB-VS can be further improved via post-docking optimization of the protein-ligand complexes. For the needs of post-docking procedures in computeraided drug design projects, the software platform AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening) has been developed  [18]. AMMOS has been elaborated for energy minimization of pre-docked protein-ligand complexes at different levels of protein flexibility.
In this investigation, the recently developed InterCriteria Analysis (ICrA) approach  [1]  is applied to assess the AMMOS software platform outcomes. ICrA, based on the fundamental mathematical formalisms of index matrices and intuitionistic fuzzy sets, has been elaborated to reveal possible relations in the behavior of pairs of criteria when multiple objects are considered. ICrA has gained an increasing scientific interest and has been proven as a reliable decisionmaking technique for solving numerous real world problems in different areas, such as economy and ecology  [6] ,  [7] , medicine and biomedicine  [8] ,  [9] , artificial intelligence and metaheuristics  [10] ,  [11] ,  [12] , etc. This suggests that ICrA might support the decision-making also in in silico studies of complex biomolecular systems.
Recently, the effectiveness of ICrA has been tested for the first time to assess the performance of various scoring functions available in software packages used for molecular docking  [13] ,  [14]. In this study, ICrA is employed to assess the outcomes of different levels of protein flexibility available in AMMOS platform, aiming to reveal potentially new intercriteria relations between results obtained at the stage of post-docking optimization of selected protein-ligand complexes thus helping in selection of appropriate levels of computational complexity in the virtual screening of bioactive compounds.
Selective ERα modulators are applied to prevent or treat ERα-positive breast cancer, among them tamoxifen, raloxifene, fulvestrant, etc. [22]. NA is a major surface tetrameric glycoprotein in influenza A and B viruses  [25] , with the role to facilitate the release of the virions from the infected cell. Among its inhibitors, approved globally or partially, are oseltamivir, zanamivir, peramivir, and laninamivir  [24] , which act by blocking NA activity to prevent the spread of the virions outside of the infected cell. The crystal structures of the two proteins have been downloaded from Protein Data Bank (PDB, https://www. rcsb. org). The selection from the complexes available in PDB has been made based on their resolution and protein structure completeness: PDB ID 3ert, resolution 1. 90 Å has been selected for ERα, and PDB ID 1b9s, resolution 2.
B. Dataset preparation Two focused libraries of small molecules, for each of the target proteins, have been prepared following the steps below: i) ChemBridge diversity set (CDS, https://chembridge. com/) has been used as initial collection of chemical compounds; ii) CDS has been subjected to ADME/Tox filtering by Filter 1. 0. 2 program (OpenEye Scientific Software, https://www. eyesopen. com/) resulting in 37 970 drug-like molecules at the output, further referred as decoys; iii) ten known active ligands (with micromolar to nanomolar binding affinities) have been added for each target protein to the decoys collection, and a single 3D conformer for each compound has been generated in OMEGA 2. 0 program (OpenEye Scientific Software); iv) up to 50 conformers per structure have been generated using Multiconf-DOCK (  [16] , https://dock.
compbio. ucsf. edu/Contributed Code/ multiconfdock. htm); v) the shape complementarity filtering protocol MS-DOCK  [16]  using rigid body docking with DOCK 6. 0 (  [17] , https://dock. compbio. ucsf. edu/DOCK 6/) has been applied to reduce the initial decoys library, considering the binding site properties of the target proteins. The whole procedure of dataset preparation and docking-scoring protocol is described in detail in  [18].
C. Post-docking software platform AMMOS AMMOS integrates automated procedures for efficient computational refinement of proteinligand complexes. Originally developed as a free downloadable standalone software, later on AMMOS has been upgraded to the interactive web server AMMOS2  [19]  (Fig. 1 , http://drugmod. rpbs. univ-paris-diderot. fr/ammos Home. php). Additionally, AMMOS2 allows for inclusion of explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. AMMOS2 provides a comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes allowing the users to perform additional analysis for drug discovery or chemical biology projects throughout ligands ranking by the minimized binding energies. The computational refinement of the protein-ligand complexes operates with five different levels of the protein flexibility (the ligand atoms are always flexible), namely: fully flexible protein (Case 1), flexible side chains only (Case 2), flexible protein within a sphere with user-defined radius (Case 3), flexible side chains only within a sphere with user-defined radius (Case 4), and rigid protein (Case 5).
III. ICRADATA SOFTWARE PACKAGE ICrAData  [15]  is a free access software developed for the needs of ICrA approach implementation. The last downloadable version 2. 5 of the software is available at https://intercriteria. net/wpcontent/uploads/2021/08/ (Last access November 8th, 2021). ICrAData employs Java (versions up to 1. 8) or C++ (versions of the branch 2) to compute and visualize the results from ICrA for particular input data. The ICrAData screen layout includes a section for the input data in table format (on the left), a central section for visualization of the obtained matrices standing for the calculated degrees of agreement and disagreement, and a rightmost panel for graphical representation of the results via the intuitionistic fuzzy triangle. The thresholds for α and β could be modified by the user, their default values being 0.
IV. RESULTS AND DISCUSSION At the step of development and validation  [18] ,  [19] ,  [20] , AMMOS has been tested and validated over several protein-ligand complexes with binding pockets varying in their physicochemical properties and topologies. AMMOS has been shown to improve the enrichment after the docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection. For the target proteins considered in this study, the focused libraries constructed following the steps described in the section Dataset preparation contain about 30% of the initial library for ERα, while regarding NA -about 50%, due to its more open and larger binding pocket. As a result of the shape-based filtering procedure, six of the initially added ten active compounds were retrieved in the resulting focused libraries for each of the proteins of interest, ERα and NA.
For the purposes of this pilot study in terms of investigation of the capacity of ICrA to assess the AMMOS outcomes, only the known active ligands of the target proteins ERα and NA have been used in the analysis. ICrA has been applied on the obtained binding energies of each ligand to the target proteins ERα and NA, respectively from docking with DOCK 6. 0 and from the post-docking optimization performed via AMMOS. In regard to the ICrA terminology, the evaluated criteria are the results (binding energies) obtained after performed docking and post-docking optimization at different levels of protein flexibility in AMMOS, while the objects correspond to the chosen ligands from the evaluated receptor-based database (in the considered case -known active ligands only). The values in green correspond to the pairs of criteria in positive consonance, in the case of ERα those are the results from the different cases of protein flexibility implemented in AMMOS.
I. INTRODUCTION The ever-increasing demand in capacity for mobile communications necessitates new implementation approaches that can significantly boost data rates and the area spectral efficiency (ASE) of mobile networks. One key enabler considered in 5G  [2]  to face this demand is the use of the spectrum beyond the sub-6 GHz frequencies, known as millimeter wave (mmWave). More specifically, 5G is designed to make use of spectrum above 20 GHz, where bandwidth sizes of up to 400 MHz per carrier can be used to offer very high data rates (above 10 Gbps peak rates) and increase the network capacity  [3] ; nevertheless, the sub-6 GHz bands, with up to 100 MHz of bandwidth per carrier, are still needed to ensure wide area coverage and data rates up to a few Gbps  [4]. One of the key difficulties faced by 5G operation on mmWave frequencies is their challenging propagation characteristics: they are subject to high path loss, penetration loss, and diffraction loss, and are thus limited to short distance links, typically of a few hundred meters.
This approach defines the safety policies for actions, controllers, and triggering events. After that, the proposed approach proves logical soundness and completeness by using Prolog. In  [12] ,the authors propose a general framework for the formal depiction of the physical architecture and control software of an IoT-based system. The proposed framework approaches analysis and modeling of the invariant architectural properties, then can add specific properties progressively and check them. The suggested framework is using the Event-B model to experiment and implement. Compared to these works, while there is a lack of research to verify the IoT systems that deal with the physical architecture of IoT layers, our approach introduces a novel formal verification approach based on an Event-B method to describe the physical architecture of IoT layers.
• Gat9:Each controller has data. • Mid10:The controller software is responsible of processing and analysis the data which came from the previous layer, then produce orders to actuators or devices according to specific rules. • Mid11:Any actuator receives data from the controller only. • Mid12:The orders of controllers represent the input of the devices or actuators. • App13:The actuator's output sends a signal to the device to change the state of the device and take action. • App14:The device's output is making action to the environment. the IoT system: Smartwatch(Th1) Wearable ECG Sensor(Th2),Healthcare Cloud(Th3) and Ambulance (Th4). The workflow of the Electrocardiogram(ECG) monitoring according to each layer in our model is: to get the last sensor reading the ECG Analysis"Smartwatch"(Th1)that periodically passing the control to Heart Rate History"Wearable ECG Sensor"(Th2).
Then after returns the control from Heart Rate History to ECG Analysis. If there are signs of a heart attack, then pass the control from ECG Analysis to the Emergency service "Healthcare Cloud"(Th3),which sends the control to the Assistance service"Ambulance"(Th4) to locate the nearest available ambulance. Finally, the control is returned the ECG Analysis. The four layers of IoT architecture are explained in this case study. All four components which represent things are included in the physical layer. The process of passing control from Smartwatch to Wearable ECG Sensor and vice versa and from Smartwatch to Healthcare Cloud are included in the Gateway layer. The middleware layer includes all processes of analysis and processing data. The software that interacts with users by Smartwatch is a part of the application layer.
FORMALIZATION IOT ARCHITECTURE IN EVENT-B In this section, our approach is presented to formalize the physical architecture of IoT layers. The IoT architecture formal model consists of an abstract model and three refinements as illustrated in Fig. 4. We start by modeling the physical layer in the abstract model. After that, we extend it to the first refinement to model the gateway layer, followed by the second refinement to model the middleware layer. Finally, the third refinement will concern the modeling of the application layer. actuators, devices, and data as set name SENSOR, ACTUATOR, DEVICE, DATA. The AXIOMS depicts the properties of the attributes determined in the SETS. In this context, the axm1, axm2, axm3 are added to specify that all the defined sets thing, SENSOR, ACTUATOR respectively are finite.
Second Refinement However, the event computeOrder defines a compute function by using data data_controller that exists in controller set(grd3)and produces orders set act1(see Figure13). An event named controllerToactuator and controllerTodevice they are defined to model the sending of order from the controller to the actuators and devices, respectively. This models the requirements Mid11 and Mid12 see Fig. 14 , Fig. 15. This refinement is extended to formalization developed in the previous refinement for the abstract model and is added to represent the application layer for delivering orders received from the previous layer and changing the state of a device to perform an action in the environment. In other words,the machine Applications layer has one event deviceAction for checking and changing the state of the device set act1 and modeling the requirements App13 and App14 ,See Fig.
Therefore, we suggest to deploy one from these following methods to create a digital identity in the parking system: • Payment card binding: The users have to add their bank cards to the parking application. The PSP does a pre-authorization charge to make sure the payment card used by the user is valid. If so, the PSP will create the digital identity of the user. The digital identity is represented by the payment card number provided by the user. The PSP learns no more information about the user. If the user commits fraud, the PSP will query disclosing the user identity to the bank that issued the payment card. • Mobile number binding: The users have to add their phone number to the parking application. The PSP sends an authorization code to this phone number to make sure the phone number provided by the user is valid.
If the user commits fraud, the PSP will query disclosing the user identity to the organization delivering public digital services in an EU member state. When the digital identity of the user is created, the PSP runs the Register algorithm (see, 1. 3. Register user ). In particular, the PSP generates the user access credential Λ and user secret key sk U. To do so, the PSP will use group signature  [38]. The credential and the secret key are sent to the user's device (see, 1. 4. Send user credentials) where they are securely stored (see, 1. 5. Store credentials and secret key). Furthermore, the secret key is also stored in the PSP Revocation Database (RD) (see, 1. 6. Store secret key). The PSP can use this database to revoke or identify malicious users. The user revocation is possible only in collaboration with the PLT.
2. It outputs the (pk, spar) as a public output and the sk m as the manager's private output. • (sk i , RD) ← KeyGen ← (id i , sk m ): On the input of manager's private key sk m and signer's private identifier id i , the protocol outputs the wBB signature sk i = g 1 skm+id i 1 to the signer and updates the manager's revocation database RD by storing id i. • σ(sk i , m) ← Sign ← (m, id i , sk i ): On the input the signer's private identifier id i , signer's private key sk i , and the message m, the algorithm outputs the signature σ(sk i , m) = (g ′ 1 , sk ′ i , s k i , π), where: -g ′ 1 = g r 1 : The generator raised to a randomly chosen randomizer r ∈ R Z q. sk ′ i = sk r i : The signers' private key raised to the randomizer. -s k i = sk ′ i -idi : The randomized private key raised to the signer identifier.
Searchable Encryption: Outsourced Private Information Retrieval A privacy enhancing technology that can facilitate privacy-preserving data processing is Searchable Encryption (SE), which enables storing a dataset in an encrypted form, while remaining searchable. This process relieves the service provider of the responsibility to maintain and protect the data from data breaches, as well as unauthorized use within the system. Structured Encryption (STE) is a searchable encryption variation that provides balance between efficiency, functionality and security  [35] ,  [41]. Noninteractive STE schemes produce encrypted structures that can be queried using a single message containing a token, whereas in interactive schemes, queries User Signer are performed through an interactive two-party protocol.