Nicole Ngo-Giang-Huong

Dr Avelin F Aghokeng
email: avelin.aghokeng@ird.fr

HIV drug resistance in resource-limited countries: Threat for HIV elimination

2

Nearly 22 million people living with HIV worldwide are now receiving antiretroviral therapy (ART). To achieve the UNAIDS goal of eliminating HIV as a public health threat by 2030, [START_REF] Unaids | Fast-Track -Ending the AIDS epidemic by 2030[END_REF] ART coverage should be maximised, its continued efficacy closely monitored and steps taken to prevent treatment failure. HIV drug resistance (HIVDR) represents one of the major dangers that should be carefully investigated and adequate responses to recognise, adapt treatment and to prevent the development of resistance implemented. [START_REF] Who | HIV DRUG RESISTANCE REPORT[END_REF] The study by Chimukangara and colleagues, published in this issue of EClinical Medicine, investigated trends in the prevalence of pretreatment HIVDR (PDR) in South Africa (SA), between 2000 to 2016. [START_REF] Chimukangara | Trends in Pretreatment HIV-1 Drug Resistance in Antiretroviral Therapy-naive Adults in South Africa, 2000-2016: A Pooled Sequence Analysis[END_REF] The authors report an overall PDR prevalence of 6.9%, and more importantly, a 1.10-fold yearly increase in the odds of PDR. This finding is consistent with other recent reports of an alarming increase in PDR in the Sub-Saharan Africa region. [START_REF] Gupta | HIV-1 drug resistance before initiation or re-initiation of first-line antiretroviral therapy in low-income and middle-income countries: a systematic review and meta-regression analysis[END_REF] These results clearly show that the expansion of ART programs initiated in the 2000s in SA as in many other resourcelimited countries (RLC), fueled the emergence of HIVDR and its subsequent transmission.

The major strengths of the Chimukangara et al. study include the context, SA ART program is the world largest ART program, as well as the large sample size, with the analysis including up to 6880 reverse transcriptase and 6294 protease sequences.

Their results highlight one of the major limitations of the public health approach. Indeed, while this approach has permitted the rapid expansion of ART in RLCs, the associated rapid and ongoing increase of PDR and acquired drug resistance (ADR) in patients on ART, are of critical concern. High levels of ADR favor PDR and recent studies from RLC reported high levels of virological failure, up to 30%, in populations on first-line ART, and rapid development of HIVDR. [START_REF] Aghokeng | Extraordinary heterogeneity of virological outcomes in patients receiving highly antiretroviral therapy and monitored with the World Health Organization public health approach in sub-saharan Africa and southeast Asia[END_REF][START_REF] Boender | Long-term Virological Outcomes of First-Line Antiretroviral Therapy for HIV-1 in Low-and Middle-Income Countries: A Systematic Review and Meta-analysis[END_REF] Low adherence to treatment, inadequate virological monitoring and other program-related factors such as drug stock-outs are associated with virological failure and the emergence and accumulation of resistance mutations. [START_REF] Boender | Accumulation of HIV-1 drug resistance after continued virological failure on first-line ART in adults and children in sub-Saharan Africa[END_REF] In addition, as mentioned by Chimukangara and colleagues "long delays in switching people with virological failure to second-line regimens" increase the probability of transmitting a resistant virus.

As ART coverage in RLCs expands, detecting, combating, and preventing HIVDR and especially PDR, must necessarily become a growing priority. The routine monitoring of viral load (VL) is expected to improve ART outcome and should also support prevention of HIVDR. [START_REF] Raizes | Expansion of Viral Load Testing and the Potential Impact on HIV Drug Resistance[END_REF] In addition, VL monitoring is cost-effective, by preventing premature introduction of more costly second-line regimens, and also by reducing the need of drug resistance testing for second-line ART initiation. Therefore, strengthening the routine use of VL monitoring in RLC is essential. However, as mentioned by Chimukangara and colleagues, SA already has an effective VL monitoring program, which begs the question of what other factors may need to be considered to limit HIVDR ?

The use of drugs with a low genetic barrier to resistance, especially the NNRTIs drugs such as nevirapine and efavirenz, is associated with selection and transmission of NNRTI-resistant viruses. Since 2017, the WHO has recommended non-NNRTI first-line ART regimens in countries where NNRTI PDR exceeds 10%. [START_REF] Who | PRETREATMENT HIV DRUG RESISTANCE[END_REF] The study from Chimukangara and colleagues supports this recommendation in SA.

Also, as we now know that NNRTI PDR inevitably increases in programs using NNRTIs and represents a serious risk for ART outcome, NNRTI-free regimens should be considered in all RLCs. The WHO recommends transition to a dolutegravir (DTG)-based first-line regimen when safe for the patient, but that is not enough. In fact, Chimukangara and colleagues work also highlight the increase of NRTI PDR, especially TDF-associated resistance, which is consistent with recent studies from RLC. [START_REF] Study | Global epidemiology of drug resistance after failure of WHO recommended first-line regimens for adult HIV-1 infection: a multicentre retrospective cohort study[END_REF] A reasonable expectation is that TDF resistance will soon represent a serious threat and a similar scenario cannot be excluded for DTG, even though its barrier to resistance is higher.

Major limitations of the study include the fact that the true ARV status of participants is uncertain, and previous exposure to ARV for some of the study participants cannot be excluded. Also, it is essential to assess PDR in other populations, especially pediatric populations.

In conclusion, combined with similar data from RLCs, the study by Chimukangara and colleagues points to a growing threat of HIVDR and PDR endangering ART efficacy and goals for HIV elimination. It is clear that new strategies are needed to prevent HIVDR in RLC and perhaps it is time to reconsider the public health approach and start moving toward patient-centered strategies for HIV care and treatment in SA and in other RLCs.


Sabrina Belbekhouche
email: belbekhouche@icmpe.cnrs.fr

Noureddine Bousserrhine

Vanessa Alphonse

Benjamin Carbonnier
email: carbonnier@icmpe.cnrs.fr

From beta-cyclodextrin polyelectrolyte to layer-by-layer self-assembly microcapsules: from inhibition of bacterial growth to bactericidal effect

Keywords: particle-templated layer-by-layer assembly, cationic polycyclodextrin, capsule, pathogenic bacteria, antimicrobial activity, polysaccharide

Pathogenic bacteria resistance to antimicrobial agents has emerged as a major health issue. Developing new systems enabling inhibitory and bactericidal effects is crucial.

Herein, layer-by-layer technique (LbL) was used to fabricate microcapsules composed of biopolymers, cationic polycyclodextrin (P(CD + )) and anionic alginate (alg -). This was achieved through successive adsorption of these polyelectrolytes onto calcium carbonate microparticles (CaCO3) used as template. Then, a chelating agent was added for removing the CaCO3 core, leading to microcapsules. Quartz crystal microbalance and zeta potential measurements evidenced the successful alternate adsorption of polyelectrolytes and related charge reversal, respectively, upon the LbL deposition. The shape of the capsules was characterized by Scanning Electron Microscopy (SEM) and fluorescence microscopy. The antibacterial activity of P(CD + ) was first evidenced on two well-known bacteria:

Staphylococcus aureus (Gram-positive) and Escherichia coli (Gram-negative) using well diffusion method on Plate Count Agar (PCA) medium. Then, an growth inhibitory effect on the bacteria growth has been observed for the P(CD + )-based microcapsules during bacterial culture in Luria-Bertani (LB) broth medium. Our results pointed out towards a strong dependence of the extent of inhibition on the number of self-assembled polyelectrolytes' layers and capsules' concentration. The effect increased with both concentration and number

Introduction

Human beings are often impacted by microorganisms such as bacteria, fungi and viruses. [START_REF] Rosenberg | The role of microorganisms in coral health, disease and evolution[END_REF] Furthermore, contamination by microorganisms leads to severe problems in food and pharmaceutical industries where microbes may contaminate raw materials, products and equipments. [START_REF] Marambio-Jones | A review of the antibacterial effects of silver nanomaterials and potential implications for human health and the environment[END_REF] Antimicrobial agents can be regarded as materials that kill or simply inhibit the growth of microorganisms with minimal or no harm to the host. Various mechanisms of microorganism' inhibition have been discussed to date, of which are intimately related to the chemical composition and structure (organic, inorganic, charged…) of the antimicrobials, each exhibiting advantages and limitations. (Batoni, Maisetta, Lisa Brancatisano, Esin, & Campa, 2011) The antimicrobial activity of cationic polymers is generally assigned to the interaction of the positive charge moiety of the polymer with the charged surface of bacteria cell walls. Natural and synthetic nanomaterials have also been widely employed due to their strong antimicrobial properties inherent to their unique properties in terms of adsorption and surface area [START_REF] Chirkov | The Antiviral Activity of Chitosan (Review)[END_REF][START_REF] Wei | Bactericidal Activity of TiO2 Photocatalyst in Aqueous Media: Toward a Solar-Assisted Water Disinfection System[END_REF]. On this basis, the self-assembly of polymers including cationic polyelectrolytes [START_REF] Kiss | Membrane affinity and antibacterial properties of cationic polyelectrolytes with different hydrophobicity[END_REF] or peptides (H. [START_REF] Wang | The efficacy of self-assembled cationic antimicrobial peptide nanoparticles against Cryptococcus neoformans for the treatment of meningitis[END_REF] into nanoantimicrobial agents has been proved efficient for infections treatment [START_REF] Liu | Self-assembled cationic peptide nanoparticles as an efficient antimicrobial agent[END_REF][START_REF] Wang | The efficacy of self-assembled cationic antimicrobial peptide nanoparticles against Cryptococcus neoformans for the treatment of meningitis[END_REF].

Polyelectrolyte capsules are fabricated by successive adsorption of negatively and positively charged polymers onto particulate substrates which serve as sacrificial templates for polyelectrolytes shell that can be easily removed by dissolution providing hollow particles.

This approach offers the possibility to easily introduce multiple functionalities, leading to structured materials with unprecedented properties. For instance, these hollow particles have a great potential for biological applications. [START_REF] Peyratout | Tailor-made polyelectrolyte microcapsules: from multilayers to smart containers[END_REF][START_REF] Shchukina | LbL coated microcapsules for delivering lipidbased drugs[END_REF] The loading and release properties can be easily adjusted by controlling the capsules composition, wall thickness or even and in the case of smart capsules, upon application of an external stimulus including temperature, pH, redox, light… (Sabrina [START_REF] Belbekhouche | Glucosesensitive polyelectrolyte microcapsules based on (alginate/chitosan) pair[END_REF][START_REF] Belbekhouche | Promising sub-100 nm tailor made hollow chitosan/poly (acrylic acid) nanocapsules for antibiotic therapy[END_REF][START_REF] Donath | Hollow polymer shells from biological templates: fabrication and potential applications[END_REF].

Because of their low toxicity toward mammalian cells, intrinsic environmentally-friendly nature and effective antimicrobial properties, biopolymers could be commonly used in consumer products. [START_REF] Cha | Biopolymer-based antimicrobial packaging: a review[END_REF]) Among biopolymers, chitosan has been widely used as a bioactive polysaccharide taking advantage of its inhibitory and bactericidal effect while exhibiting minimal toxicity. [START_REF] Sudarshan | Antibacterial action of chitosan[END_REF]) However, the antibacterial activity of chitosan is promoted only under its cationic form limiting its application to acidic medium [START_REF] Divya | Antimicrobial properties of chitosan nanoparticles: Mode of action and factors affecting activity[END_REF]. The mode of action of chitosan-based antimicrobial agents has been explained through different mechanisms. The first one is based on the electrostatic attraction between polycationic chitosan and the negatively charged membrane of the bacterial cell, ultimately leading to intracellular components disruption. [START_REF] Qi | Preparation and antibacterial activity of chitosan nanoparticles[END_REF] The second one implies inhibition of the enzymatic activity and bacterial growth though chelation of trace metals by chitosan [START_REF] Rabea | Chitosan as antimicrobial agent: applications and mode of action[END_REF]. Antimicrobial efficiency of chitosan and chitosan-based nanostructures is highly influenced by pH. Indeed, because chitosan is soluble in acid pH, its use is restricted to a limited range of pH and application.

In this context, we investigate herein the possibility to use a water soluble cationic polycyclodextrin (PCD + ) bearing permanent quaternary ammonium charges, as a new antimicrobial agent. PCD + will then be applied to the elaboration of microcapsules as a new approach to prevent the growth of two models of pathogenic bacteria, Escherichia coli and Staphylococcus aureus. [START_REF] Goy | A review of the antimicrobial activity of chitosan[END_REF] In the literature, two strategies are described to obtain CD cyclodextrin-containing LbL assemblies. The first one involves the use of charged CDs cyclodextrins (CD), and the LbL assemblies are prepared through electrostatic interactions [START_REF] Sato | Preparation of Polyelectrolyte-Layered Assemblies Containing Cyclodextrin and Their Binding Properties[END_REF], 2004;[START_REF] Yang | Polyelectrolyte and molecular host ion self-assembly to multilayer thin films: An approach to thin film chemical sensors[END_REF]. The second strategy for constructing CD-containing LbL assemblies relies on host-guest complexation between guest molecules and CD acting as the driving force for the assembly build-up. [START_REF] Dubacheva | Unlimited growth of host-guest multilayer films based on functionalized neutral polymers[END_REF][START_REF] Kaftan | Probing Multivalent Host-Guest Interactions between Modified Polymer Layers by Direct Force Measurement[END_REF][START_REF] Suzuki | Construction of positively-charged layered assemblies assisted by cyclodextrin complexation[END_REF][START_REF] Van Der Heyden | Multilayer films based on host-guest interactions between biocompatible polymers[END_REF] It deserves noting that ionic polymers of cyclodextrin have already successfully been used for elaborating multilayer films on flat supports. [START_REF] Jatupol | Layer-by-layer coating of textile with two oppositely charged cyclodextrin polyelectrolytes for extended drug delivery[END_REF][START_REF] Junthip | Cationic β-cyclodextrin polymer applied to a dual cyclodextrin polyelectrolyte multilayer system[END_REF] , [START_REF] Jatupol | Layer-by-layer coating of textile with two oppositely charged cyclodextrin polyelectrolytes for extended drug delivery[END_REF] , [START_REF] Martin | Build-up of an antimicrobial multilayer coating on a textile support based on a methylene blue-poly(cyclodextrin) complex[END_REF]Martin, Tabary, Leclercq, et al., 2013) , (Martin, Tabary, Leclercq, et al., 2013) , [START_REF] Pérez-Anes | Bioinspired Titanium Drug Eluting Platforms Based on a Poly-β-cyclodextrin-Chitosan Layer-by-Layer Self-Assembly Targeting Infections[END_REF] In the literature, relevant works discussed about elegant systems based on cyclodextrin loaded with antimicrobial agents. through Host-guest inclusion complex was involved in these works. [START_REF] Duan | Cyclodextrin solubilization of the antibacterial agents triclosan and triclocarban: formation of aggregates and higher-order complexes[END_REF][START_REF] Wang | Incorporation of the antibacterial agent, miconazole nitrate into a cellulosic fabric grafted with β-cyclodextrin[END_REF] Herein, the dual objective of this work is (1) to demonstrate the possibility of using a cationic polycyclodextrin as an antimicrobial agent instead of the well-known chitosan, and (2) to explore the preparation of capsules based on cationic polycyclodextrin and alginate as well as their antibacterial activity. Both aspects have not yet been investigated in the literature. Indeed, the synthesis of cationic polyclodextrin has been previously reported but its use has been limited to separation sciences [START_REF] Pédehontaa-Hiaa | Layer-by-Layer Assemblies Based on a Cationic β-Cyclodextrin Polymer: Chiral Stationary Phases for Open-Tubular Electrochromatography[END_REF] and no study considered its potential use as an antimicrobial agent by itself.

Herein In the present study, the selected microtemplate is calcium carbonate. This choice is motivated because CaCO3 microparticles can be easily synthesized in a reproducible and size-controlled way,. This materialCaCO3 is they are non-toxic and can be easily dissolved under slightly acidic conditions or by complexation with ethylenediaminetetraacetic acid (EDTA) (Sabrina Belbekhouche, et al., 2018).

The effect of number of polyelectrolyte layers and the concentration of capsules on the pathogenic bacterial growth and viability is investigated. Finally, we We evidence that CD cavities within the LbL assemblies retain their inclusion properties towards tocopherol acetate, an antioxidant selected as a model hydrophobic compound., "Tocopherol acetate" is then used as model of antioxidant, as this class of chemicals may be used to retard oxidation of compounds in foods. This then provides providing evidence for potential dual functionality of the multilayered capsules: antioxidation and antimicrobial.

Experimental section

Materials

These chemicals were purchased and used as received: chitosan (chi + , Sigma-aldrich), alginate (alg -, Sigma-aldrich), acetic acid (Alfa aesar), tocopherol acetate (Sigma Aldrich), calcium chloride (CaCl2, rectapur, 99%), sodium nitrate (rectapur, 99%), sodium 2mercaptoethanesulfonate (Sigma-Aldrich) and sodium acetate (Normapur).

Preparation of cationic cyclodextrin (P(CD + ))

The cationic cyclodextrin (P(CD + )) was prepared by using the procedure developed by [START_REF] Renard | Preparation and characterization of water soluble high molecular weight β-cyclodextrin-epichlorohydrin polymers[END_REF] and [START_REF] Thuaud | Retention behavior and chiral recognition of β-cyclodextrinderivative polymer adsorbed on silica for warfarin, structurally related compounds and Dns-amino acids[END_REF] The cationic cyclodextrin polymer was synthesized through the crosslinking of β-cyclodextrin (β-CD) with epichlorohydrin [START_REF] Renard | Preparation and characterization of water soluble high molecular weight β-cyclodextrin-epichlorohydrin polymers[END_REF] followed by reacting the obtained copolymer with 2,3 epoxypropyltrimethylammonium chloride. [START_REF] Thuaud | Retention behavior and chiral recognition of β-cyclodextrinderivative polymer adsorbed on silica for warfarin, structurally related compounds and Dns-amino acids[END_REF] Briefly, β-CD in NaOH solution (33 %) was allowed to react with epichlorohydrin for 210 min at 50°C. After neutralizing the pH of the reactive mixture, the polymer was concentrated by ultrafiltration to remove salt and further low molecular compounds and further recovered through freeze-drying. The resulting β-CD polymer was chemically modified with quaternary ammonium side groups through reaction with 2,3epoxypropyltrimethylammonium chloride in aqueous basic solution. The resulting modified polymer was purified by a dialysis step using a cellulose dialysis membrane (1 kDa cut off).

The product was isolated via freeze-drying to yield a white solid. The characteristics of the involved polyelectrolytes are given in Table 1. Average molar masses and molar masses distributions were determined by size exclusion chromatography (SEC) coupled online with multi-angle light scattering (MALS) and differential refractive index (DRI) detectors. The MALS apparatus is the DAWN Heleos-II from Wyatt Technology (Ca, USA) filled with a K5 cell and a Ga-As laser (λ= 690 nm). The DRI detector is a Shimadzu RID-10A (Japan).

Columns [Shodex SB OHpak 804 and 806 HQ for alginate, Eprogen CATSEC 100 and 1000 for chitosan, PL aquagel OH 40+30 for the cationic polycyclodextrin] were eluted with 0.1mol.L -1 LiNO3 or acetate buffer 0.1M, respectively at 0.5 mL min -1 . The flow rate was fixed at 1 mL.min -1 . The samples were filtered on 0.45 μm unit filter before injection through a 100 μL full loop. Belbekhouche, et al., 2018;[START_REF] Renard | Preparation and characterization of water soluble high molecular weight β-cyclodextrin-epichlorohydrin polymers[END_REF])

2.3. Elaboration of cationic-polycyclodextrin-based multilayered filmss

The multilayer buildup was monitored by the QCM (Quartz crystal microbalance) technique (Q-Sense E1, Q-Sense, Sweden). Gold coated QCM Sensor Crystal was first flushed with two solvents, water and ethanol respectively. It was then placed into a UV/ozone chamber for a 20 min treatment and rinsed again with ethanol. This first step allows cleaning the gold coated QCM Sensor. Then, the surface was pretreated with a sulfonated thiol solution (5 g.L -1 of a sodium 2-mercaptoethanesulfonate solution prepared in water, during 24 h). This sulfonated thiol derivative is used as a negatively charged anchoring layer enabling the further adsorption of the polycation. After introducing in the QCM flow chamber, the surface was flushed with the buffer for 10 min. The self-assembly of P(CD) + / alg -was performed onto flat gold substrate via alternative contact with the polyelectrolyte solutions for 15 min. A 10 min rinsing step was performed with water between each polyelectrolyte adsorption. Technically, polymer solutions (1 mg.mL -1 ) were injected into the cell with a flow rate of 250 μL.min -1 .

All measurements were performed at 25 °C. This procedure describes the assembly of (P(CD) + / alg -)n (n representing the number of bilayers).

2.4. Elaboration of biopolymer microcapsules based on cationic-polycyclodextrin

The elaboration of the cationic-polycyclodextrin/alginate (P(CD + )/alg -) bilayered capsules consists in the successive deposition of this polyelectrolytes pair onto calcium carbonate microparticles followed by the template removal. More precisely, CaCO3 microparticles were elaborated fabricated according to Volodkin et al. [START_REF] Volodkin | Protein encapsulation via porous CaCO3 microparticles templating[END_REF][START_REF] Volodkin | Matrix polyelectrolyte microcapsules: new system for macromolecule encapsulation[END_REF] Equal volume of 0.33 M calcium chloride (CaCl2) and sodium carbonate (Na2CO3) solutions were mixed under a vigorous stirring for 3 min. This method led to a rapid precipitation of the CaCO3 microparticles. A centrifugation process of the suspension during 10 min allows removal of the residual ions.

Lastly, the resulting particles were rinsed several times with water. Acetone was used to remove the water. Particles were dried under a stream of filtered air. The LbL technique was used to prepare the multilayer films from 1 mg.mL -1 polycation and 1 mg.mL -1 alginate solutions. 2 mg of CaCO3 particles were incubated in 1 mL of water. The first layer of P(CD + )

was deposited onto the surface of the calcium carbonate particles by adding 500 µL of this polycation stock solution to the particles suspension. A stirring time of 15 min is sufficient to allow the polymer adsorption to the particles surface as deduced from the QCM investigations. The final volume was always kept at 1 mL. The particles were then centrifuged

(5 min, 10 000 rpm) and washed three times. The particles were suspended in water and 500 µL of alginate was added to the particles suspension and the mixture was stirred for 15 min for alginate deposition on the previous P(CD + ) layer. Note that the centrifugation method is used to separate the free polyelectrolyte from polyelectrolyte-coated microparticles, then after each polyelectrolyte adsorption step. The particles were then rinsed three times. This overall process describes the assembly of a single bilayer: (cationic-polycyclodextrin /alginate)1 and was repeated to obtain different numbers of assembled bilayers. Finally, the CaCO3 microparticles were removed with 0.2 M EDTA solution. To do this, the coated CaCO3 microparticles were shaken for 1h with the EDTA solution. A dialysis method was applied to purify the capsules for three days using a cellulose dialysis bag (3.5 kDa cut off) against distilled water. Water was refreshed every hour. The procedure is slightly adapted when using chitosan instead of cationic-polycyclodextrin. Indeed, chitosan is solubilized in 10 mM acetate buffer pH 4. Zeta potential measurement was performed on a Zetasizer Nano-ZS (Malvern Instrument) to monitor the adsorption of the polyelectrolytes onto the CaCO3 core (Zetasizer 4700 Malvern Instruments, Brookheaven Instruments Corporation, USA).

Scanning Electron Microscopy (SEM) enables to study the shape of the resulting particles (Leo Elektronenmikroskopie, Oberkoche in Germany). Prior to analysis, the calcium carbonate particles and capsules were covered with palladium. Images of microcapsules were obtained with an optical fluorescence ZEISS microscope equipped with a CCD camera and a fluorescence illuminator and various mirror units. Images were recorded with ZEN software.

A 5 µL drop of capsule suspension was deposited on a cover slip to observe the microcapsules.

Microbial studies

Staphylococcus aureus ATCC6538 (Gram-positive) and Escherichia coli ATCC25922 (Gram-negative) were used. Prior to experiment, bacteria were grown aerobically at 37°C overnight in agitated (150 rpm) liquid Luria-Bertani broth medium (LB, Sigma Aldrich).

Antibacterial activity of polymers and well diffusion method

Antibacterial activities of the studied polymers were evaluated using well diffusion method in Petri dish on solid Plate Count Agar (PCA)). The diameter of inhibition zones (including the diameter of the well) were measured in centimeter (cm). Samples from overnight cultures of each bacteria was diluted in physiological solution (9 g.L -1 NaCl) to obtain a bacterial suspension of the two different strains (OD620nm = 0.5). Note that for enterobacteria as E. coli (Escherichia coli ) and S. aureus (Staphylococcus aureus), 1 OD at 620 nm is equivalent to 8

x 10 8 CFU/mL. [START_REF] Willey | Prescott, Harley, and Klein's microbiology[END_REF] 100 µL of this suspension were inoculated onto the surface of a PCA Petri dish. Four wells (diameter = 6 mm) were done after in the agar surface and were filled with 20 µL of antimicrobial samples (1 mg.mL -1 ). The studied antimicrobial samples were diluted from stock solution of each polymer. The stock solutions are: P(CD) (10 mg.mL -1 ), P(CD + ) (10 mg.mL -1 ), chi + (7 mg.mL -1 ) and alg -(10 mg.mL -1 ).A total of four tests (three replicates of each sample and one filled with physiological solution as reference (9 g.L -

1 NaCl in distillated water)) were was done into each plate. This was used as referenced, i.e.

physiological medium without polymer to clearly evidenced the influence of each of them.

Physiological medium (without polymer) was used as reference to clearly evidence the influence of P(CD), P(CD+), chi + and alg -.. After 24 h of incubation at 37 °C in aerobic conditions, the diameters of inhibition around the well were measured.

Antibacterial effect of (P(CD + )/alg -)n bilayer-based microcapsules: Inhibition of bacterial growth method

Inhibitory impact of microcapsules on growth of E. coli and S. aureus was studied during kinetic growth under shaking in liquid Luria-Bertani broth medium (LB) at 37°C. For the assay, eight parallel wells of a 96-well microplate were used (eight replicates). Microcapsules suspension of each compound described above (section 2.3 4) at various concentrations of 0.02 g.L -1 , 0.06 g.L -1 and 0.1 g.L -1 was added to a same bacterial suspension and then incubated at 37°C. The final volume is 200 µL for each well. The initial optical density of bacterial suspension was 0.05 at 620 nm. Sterile physiological distilled water was mixed with a bacterial suspension as a control bacterial growth test. Bacterial growth was followed periodically by reading the optical density of bacterial suspensions at 620 nm using UV-Visible spectrophotometer micro plate reader. To confirm the results obtained through optical density measurement, bacteria were counted with a Mallassez cell.

Bacterial viability assay: microscopic observation

The effect of microcapsules on bacterial viability was performed assessed using an orange acridine test followed by fluorescence microscopy observation. 10 µL of acridine orange (0.1 % wt) was added to 100 µL of bacterial suspension taken at the end of the antibacterial assay (described in section 2.5.2). A 10 µL drop was then introduced on a cover slip to observe the bacteria. [START_REF] Boulos | LIVE/DEAD® BacLight™: application of a new rapid staining method for direct enumeration of viable and total bacteria in drinking water[END_REF] An optical fluorescence ZEISS microscope equipped with a CCD camera and a fluorescence illuminator and various mirror units were used after staining of bacteria using alcidine orange.

Loading of hydrophobic agent

A solution of tocopherol acetate (5 mg/ml) was added to a suspension of (P(CD + )/alg -)n microcapsules (n = 1 to 4). Then, the suspensions were stirred for 24 h to ensure effective tocopherol acetate complexation. The samples were introduced in a dialysis bag (3.5 kDa cut off) to remove unloaded tocopherol acetate. The loaded microcapsules were subjected to absorbance measurement at 292 nm (Varian, Cary 100).

Methods

Size exclusion chromatography (SEC)

Average molar masses and molar masses distributions were determined by size exclusion chromatography (SEC) coupled online with multi-angle light scattering (MALS) and differential refractive index (DRI) detectors. The MALS apparatus is the DAWN Heleos-II from Wyatt Technology (Ca, USA) filled with a K5 cell and a Ga-As laser (λ= 690 nm). The DRI detector is a Shimadzu RID-10A (Japan). Columns [Shodex SB OHpak 804 and 806 HQ for alginate, Eprogen CATSEC 100 and 1000 for chitosan, PL aquagel OH 40+30 for the cationic polycyclodextrin] were eluted with 0.1mol.L -1 LiNO3 or acetate buffer 0.1M, respectively at 0.5 mL min -1 . The flow rate was fixed at 1 mL.min -1 . The samples were filtered on 0.45 μm unit filter before injection through a 100 μL full loop.

Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D)

A Q-Sense E1 (Q-Sense, Sweden) system employing ATcut gold-coated quartz crystal sensors (Lot-Oriel, France) with a nominal frequency of about 5 MHz was used to monitor in situ formation of the P(CD) + / alg -self-assembly.

Zeta -Potential Measurements

Zeta potential measurement was performed on a Zetasizer Nano-ZS (Malvern Instrument) to monitor the adsorption of the polyelectrolytes onto the CaCO3 core (Zetasizer 4700 Malvern

Instruments, Brookheaven Instruments Corporation, USA).

Scanning Electron Microscopy (SEM)

This technique enables to study the shape of the resulting particles (Leo Elektronenmikroskopie, Oberkoche in Germany). Prior to analysis, the calcium carbonate particles and capsules were covered with palladium.

Fluorescence microscope

Images of microcapsules were obtained with an optical fluorescence ZEISS microscope equipped with a CCD camera and a fluorescence illuminator and various mirror units. Images were recorded with ZEN software. A 5 µL drop of capsule suspension was deposited on a cover slip to observe the microcapsules. This technique was used after staining of bacteria using alcidine orange.

Absorbance measurement.

Absorbance spectra were recorded with a UV-Visible spectrophotometer micro plate reader (bacterial assay) and a Varian Cary 100 (tocophorol acetate)

Results and discussion

Figure 1 illustrates the approach used to elaborate prepare CD-containing microcapsules. The surface of CaCO3 microparticles was modified through alternate deposition of the positively charged cationic polycyclodextrin and a layer of negatively charged alginate. The chemical structures of the P(CD + ), chitosan and alginate are presented in Figure 2. Their characteristics are reported in Table 1. For the alginate, the value is 2.7 10 5 g•mol -1 , the value is 3.8 10 5 g•mol -1 . For the cationic poly(cyclodextrin), the value is 6.5 10 3 g•mol -1 , the value is 2.4 10 4 g•mol -1 and the degree of substitution is 0.40 ±0.05. For the chitosan, the value is 8.5 10 4 g•mol -1 , the value is 2.0 10 5 g•mol -1 . The degree of acetylation of chitosan is 0.14 ±0.02. After the LbL process, the calcium carbonate template is selectively removed to ensure the capsule formation. After each coating step, free or unbound polyelectrolytes are removed by centrifugation to prevent the formation of inter-polyelectrolyte complexes. After the LbL process, the calcium carbonate template is selectively removed to ensure the capsule formation. Herein, we compare the antimicrobial agent performances of the cationic polycyclodextrin to the well-known chitosan as antimicrobial agent. Thus, the antibacterial activities of P(CD + ) and chitosan, under its cationic form, are initially tested in aqueous solutions, to provide comparative data for the intrinsic antibacterial activity of the said polymers. Then, P(CD + ) and chitosan were layer-by-layer assembled with alginate onto CaCO3 core, and the resulting multi-layered capsules were subjected to antibacterial assay.

Characterization of microcapsules based on cationic polycyclodextrin

The assembly process was first monitored on QCM resonators to evidence the mass increase after each deposition step of each polyelectrolyte (figure 3). (P(CD + )/alg -) and (chi + /alg -) have been studied as polyelectrolytes couples. From the QCM frequency shift plots, we observe a decrease of the frequency shift which could be correlated to is in accordance with an increase of mass due to the successive polyelectrolytes adsorption. This result evidences the formation of the corresponding multilayer films.

A) B)

Figure 3. A) QCM result, alternative adsorption of polycation and alginate ((a) P(CD + )/alg - and (b) chi + /alg -) (filled arrow: addition of polycation and unfilled arrow, "*" rinsing with water). B) Dependence of the equilibrium frequency according to the number of (polycation/alg -) bilayer ((a) P(CD + )/alg -and (b) • chi + /alg -).

The final frequency as measured after the adsorption of 7 layers is around -75 Hz when using P(CD + ) as polycation. This value reaches -225 Hz with chi + as polycation.

Figure 3 B presents the equilibrium frequency shift as measured after each polyelectrolyte adsorption. Two growth regimes are evidenced from these plots, an exponential growth and a linear one. The exponential behavior is observed by employing with chitosan as polycation.

This result suggests the ability of the chitosan to diffuse "in" and "out" of multilayer films. [START_REF] Lavalle | Modeling the Buildup of Polyelectrolyte Multilayer Films Having Exponential Growth[END_REF] This is in agreement with the work of Richert et al. [START_REF] Richert | Layer by Layer Buildup of Polysaccharide Films: Physical Chemistry and Cellular Adhesion Aspects[END_REF] who studied chitosan/hyaluronan films. In contrast, the multilayer film elaborated with based on the cationic polycyclodextrin shows a linear build-up process. Using this polycation instead of chitosan may lead to a decrease in the diffusion phenomena and consequently to a linear variation of the thickness with the layers number.

Zeta measurement is one of the most widely used techniques for following the adsorption of polyelectrolytes onto particles. [START_REF] Kirby | Zeta potential of microfluidic substrates: 2. Data for polymers[END_REF] The zeta potential of the modified calcium carbonate microparticles was determined after deposition of each polyelectrolyte (figure 4). The initial surface potential measured for native CaCO3 particles is -10 mV. That is in accordance with a negative surface charge. This offers the possibility to adsorb positively charged polyelectrolyte. An alternation of surface potential from +20 mV or +25 mV for P(CD + ) and chi + respectively, to -25 mV for alginate is observed. This confirms the successful formation of the multilayer films on CaCO3 particles [START_REF] Caruso | Hollow capsule processing through colloidal templating and self-assembly[END_REF] through electrostatic interactions.

Effect of cationic polycyclodextrin particles on pathogenic bacterial growth

Chitosan is known to show excellent bactericidal effects, however it suffers from poor solubility at neutral pH or close to neutrality (pKa of chitosan ~ 6.2 -7.0). It is then crucial to investigate alternative biopolymers being soluble under physiological conditions (pH ~ 7.4).

The studied cationic polycyclodextrin is proposed herein as could then be an excellent candidate.

The impact of cationic polycyclodextrin on the two pathogenic environmental bacteria E. coli and S. aureus, was investigated in agar plates and then in liquid LB medium. E. coli is a

Gram-negative bacterium which could be find in gastrointestinal tract normal flora. S. aureus is a Gram-positive bacterium that can induce skin infection and pus.

Results from antibacterial tests performed on agar plates and using well diffusion method showed no inhibition zone for the unmodified native polycyclodextrin. Interestingly, the modification of the polycyclodextrin (incorporation of cationic charge) leads to an inhibition zone for both bacteria. This could directly be correlated to the incorporation of the quaternary ammoniums which are described as being able to known to be responsible of damage of the microbial membranes and to denature of denaturation of the soluble proteins. This denaturation can disrupt cell functions and induce bacterial death . [START_REF] Kügler | Evidence of a charge-density threshold for optimum efficiency of biocidal cationic surfaces[END_REF] Diameter of inhibition zones were around 1.2 and 1.1 cm ± 0.1 for E. coli and 1.4 and 1.1 cm ± 0.1 for S. aureus with chitosan and cationic polycyclodextrin respectively (Figure 6)

Figure.
6. Diameters of inhibition zones against E. coli and S. aureus when using chitosan (chi + ), the cationic poly(cyclodextrin) (P(CD + )), the poly(cyclodextrin) (P(CD) and the reference (physiological solution).

The results obtained with chitosan, are in agreement with those reported by Li et al. [START_REF] Li | Physicochemical characterization and antibacterial property of chitosan acetates[END_REF] and Du et al. [START_REF] Du | Preparation of water-soluble chitosan from shrimp shell and its antibacterial activity[END_REF] These authors suggested that this polysaccharide usually presents higher antibacterial activities against S. aureus than E. coli.

Compared to chitosan, P(CD + ) shows a lower efficiency on S. aureus (Figure 6) and a same inhibitory effect against E. coli. P(CD + ) provided the same inhibition toward E. coli and S.

aureus. This similar effect on these two bacteria suggests that the structure of the bacterial wall is not involved in inhibitory mechanisms. Like quaternary ammonium-containing Diameter of inhibition zone (cm) materials, P(CD + ) would may cause the denaturation of bacterial cytoplasmic proteins [START_REF] Willey | Prescott, Harley, and Klein's microbiology[END_REF]. It is worth noting that no antimicrobial effect is observed with the alginate.

The bacterial growth kinetics was followed in LB medium (initial bacterial concentration 6.4

x 10 7 cell/mL)) in presence of different composition and concentration of microcapsules. Each experiment was performed in six replicates (Figure 7). Note that no inhibition was observed with bare calcium carbonate particles at the studied particle concentration. The 7 hour (420 min) experimental time for monitoring the growth curve has been selected as it is assumed that the physicochemical conditions of the medium, such as for instance pH, does not significantly change during this period. Therefore, it is expected that the presence of microcapsules is the sole parameter which can impact the behavior of bacteria growth could only be the presence or not of microcapsules.

To investigate the influence of several particles parameters (numbers of layers, concentration of particles...) on the bacteria growth, the percentage of bacterial inhibition at the end of the experiment was estimated (see table 2) through the difference of the number of bacteria per millimeter at the end of the experiment for the particles and the reference essay where bacteria is in presence of sterile physiological distilled water only. Whatever the strain studied, (P(CD + )/alg -)3P(CD + ) particles inhibit the growth rate more than single-layer particles (P(CD + )/alg -)P(CD + ). For instance, at 0.02 g.L -1 the percentage of inhibition for E. coli is 8.9 % and 28.1 % for (P(CD + )/alg -)P(CD + ) and (P(CD + )/alg -)3P(CD + ) respectively. The same trend is observed for S. aureus, i.e. 15.6 % and 42.2 % for (P(CD + )/alg -)P(CD + ) and (P(CD + )/alg -)3P(CD + ) respectively (table 2). The bacterial density reached at the end of the experiment is lower in the presence of capsules. For E. coli the following OD values are measured at the end of the experiment 0.44 (3.6 x 10 8 bacteria per mL), 0.40 (3.2 x 10 8 bacteria per mL) and 0.32(2.6 x10 8 bacteria per mL) for the reference, (P(CD + )/alg -)P(CD + ) and (P(CD + )/alg -)3P(CD + ) respectively. The same observation is made for S. aureus. The obtained OD values at the end of bacterial incubations are 0.45 (3.6 x 10 8 bacteria per mL), 0.38 (3.0 x 10 8 bacteria per mL) and 0.26 (2.1 x 10 8 bacteria per mL) for the reference, (P(CD + )/alg -)P(CD + ) and (P(CD + )/alg -)3P(CD + ) respectively. The comparison of the results obtained for the two strains shows a greater inhibitory impact on S. aureus than on E. coli.

The results also clearly evidence that the increase in the concentration particles (0.02 g.L -1 , 0.06 g.L -1 and 0.1 g.L -1 ) of (P(CD + )/alg -)P(CD + ) and (P(CD + )/alg -)3P(CD + )) particles significantly leads to an enhancement on the inhibition of bacteria growth. For E. coli, the inbition level increases from 8.9 % to 32.6 % and from 28.1 to 53.3 % for (P(CD + )/alg - )P(CD + ) particles and (P(CD + )/alg -)3P(CD + ) particles respectively, due to the increase of the particles concentration (0.02 g.L -1 , 0.06 g.L -1 and 0.1 g.L -1 ). For S. aureus, the inhibition level increases from 15.6 % to 57.8 % and from 42.2 to 57.8 % for (P(CD + )/alg -)P(CD + ) particles and (P(CD + )/alg -)3P(CD + ) particles respectively, due to the increase of the particles concentration.

The difference can be explained by the interaction mechanisms of P(CD + )-particles with the bacterial membrane. Like chitosan, the elaborated P(CD + )-microparticles may induce membrane damage, enzyme inhibition and other protein denaturation. [START_REF] Nair | Role of size scale of ZnO nanoparticles and microparticles on toxicity toward bacteria and osteoblast cancer cells[END_REF] In the future, biochemical analysis and electron microscopy would allow us to know which the dominant inhibition mechanism is. From the above results, we have demonstrated that the microcaspules inhibit significantly the growth of both bacteria. The following question is then raised, are the bacteria alive or not?

Several research works have demonstrated that the color of bacteria stained with acridine orange (AO) allows discriminating between living bacteria and dead bacteria. [START_REF] Francisco | Acridine orange-epifluorescence technique for counting bacteria in natural waters[END_REF] This relationship between their activity and the color of acridine orange-stained bacteria is based upon the in vitro spectrofluorescence of nucleic acids (DNA)

with AO. [START_REF] Rigler | Acridine orange in nucleic acid analysis[END_REF] Indeed, acridine orange is a cell-permeant nucleic acid binding dye and is able to differentially double-stranded (ds) and single-stranded (ss) nucleic acids. AO emits red fluorescence when bound to ssDNA denatured DNA found in dead bacteria and green fluorescence when bound to ds DNA characterizing living bacteria. Herein, a red fluorescence is observed after the bacteria staining (figure 8) evidencing that exposition to capsule (P(CD + )/alg -)P(CD + ) or capsule (P(CD + )/alg -)3P(CD + ) induce the death of bacterial cells. It deserves noting that a green fluorescence is observed for the S. aureus without capsule (negative control, figure 8 A), pointing that the bacteria are alive with no capsule.

Furthermore, the Note that the test implying using the acridine orange is a qualitative test of observations and not a quantitative one. The number of living bacteria in a suspension was then confirmed determined by counting them with a Mallassez cell. [START_REF] Willey | Prescott, Harley, and Klein's microbiology[END_REF] It should be noted here that no No changes in shape of bacteria are observed suggesting that the effect of the microcapsules is not exerted on the level of the bacterial cellular structure. Thus, herein the As quaternary ammonium-containing microparticles in our case would rather induce inhibition and denaturation of bacterial proteins [START_REF] Willey | Prescott, Harley, and Klein's microbiology[END_REF]. The number of bacteria in a suspension was confirmed by counting them with a Mallassez cell. Furthermore, the death of bacteria cells was qualitatively checked. At the end of the experiment, we have inoculated bacterial suspension on PCA solid medium for growth overnight in aerobic conditions at 37 °C. No bacterial colonies were observed which confirms the death of the bacteria due to their contact with microcapsules. increased linearly (from 0 to 0.014 M) as the number of the cationic-poly(cyclodextrin) layers increased (from 1 to 4) for a concentration of microcapsules of 2 mg.mL -1 (Figure 9).

Conclusion

For antibacterial purposes, we first evidenced the possibility to use a water soluble cationic polycyclodextrin bearing permanently charged quaternary ammonium groups rather than the cationic chitosan. This latter has low solubility in water as well as in physiological conditions.

Then, P(CD + )-microcapsules were elaborated prepared through an easy and efficient approach based on template-assisted self-assembly of polyelectrolytes in aqueous solutions without employing neither organic solvents or surfactants. This process, followed by zeta measurements, relied on the capacity of the cationic-polycyclodextrin to be layer-by-layer assembled with alginate onto sacrificial carbonate microparticles. The LbL assembly is based on the electrostatic interactions occurring in aqueous solution as evidenced by zeta measurements. The MEB SEM and fluorescence microscopy results disclosed capsules formation. These polyelectrolytes-coated particles have then been evidenced to inhibit growth and eliminate pathogenic bacteria.

The following conclusions were drawn from this work: (i) cationic polycyclodextrin presents an antibacterial activity and this result which could only be correlated to the incorporation of positive charges in the polycyclodextrin chain, (ii) microcapsules based on the cationic polycyclodextrin inhibited the E. coli and S. aureus growth and (iii) CD cavities within the LbL assemblies retain their inclusion properties towards tocopherol acetate. However, antibacterial activity towards both E. coli (Gram negative bacteria bacterium) and was less than that for S. aureus (Gram positive bacteria bacterium) was similar.

Note that after clearly evidencing the benefit of modifying polymer based on cyclodextrin to bring antibacterial activity, we have also investigated the possibility to still exploit the host-guest complexation in the particle's shell. The studied hydrophobic compound is the tocopherol acetate, an antioxidant molecule. Interestingly, in water, the solubility of the tocopherol acetate increased (from 0 to 0.014 M) linearly as the number of the cationicpoly(cyclodextrin) increased (from 1 to 4) for a concentration of microcapsules of 2 mg.mL -1

This work, following previous researches, offers the possibility to formulate new types of bactericides, i.e. (i) based on cationic poly(cyclodextin) by itself or implied in self-assembly and (ii) capsules (hollow particles).

In a forthcoming study, we will investigate the impact of the degree of substitution of the cationic-polycyclodextrin and take advantage of the cyclodextrin property by considering host-guest interactions for carrying one or multiple hydrophobic antimicrobial, as a penicillin derivative, inside the cyclodextrin cavity. Furthermore, the study will be extended to other bacteria.

Batoni, G., Maisetta, G., Lisa Brancatisano, F., Esin, S., & Campa, M. (2011). Use of antimicrobial peptides against microbial biofilms: advantages and limits. Current medicinal chemistry, 18(2), 256-279.

Figure 1 .

 1 Figure 1. Schematic illustration of the polyelectrolytes adsorption process applied to the elaboration of cationic-polycyclodextrin (P(CD + )) /alginate (alg -) capsules.

Figure 2 .

 2 Figure 2. Polyelectrolytes implied involved in the present study

Figure 4 .Figure 5 .

 45 Figure 4. Zeta potential values for chitosan/alginate or ∆ cationic polycyclodextrin/alginate onto calcium carbonate microparticles.

Figure 7 .

 7 Figure 7. Growth study of S. aureus and E. coli in LB medium inoculated with 6.4 x 10 7 /mL of bacteria in the presence of different concentrations of capsules: i.e. A) 0.02 g.L -1 , B) 0.06 g.L -1 and C) 0.1 g.L -1

Figure 8 :

 8 Figure 8: S. aureus stained with acridine orange after the antibacterial assay, A) without capsules (negative control) and B) in contact with the capsules ((P(CD + )/alg -)3P(CD + )) (magnification: x200)

Figure 9 :

 9 Figure 9 : Variation of the solubility of tocopherol acetate due to the increase of the number cationic-poly(cyclodextrin) layers in the microcapsule. Note that the tocopherol acetate is insoluble in water.

Table 1 :

 1 Average molar masses of the polyelectrolytes used (determined by size exclusion chromatography SEC/MALS/DRI analysis).

	Samples	Abbreviation Mn (g.•mol -1 ) Mw (g.•mol -1 )	Ð*	DS**
	Chitosan	chi +	8.5 x 10 4	2.0 x 10 5	2.3	0.14 ±0.02
						a
	Cationic	P(CD) +	6.5 x 10 3	2.4 x 10 4	3.6 0.40 ±0.05 b
	polycyclodextrin

Table 1 :

 1 Average molar masses of the polyelectrolytes used (determined by size exclusion

	chromatography SEC/MALS/DRI analysis).
	Samples	Abbreviation Mn (g.•mol -1 ) Mw (g.•mol -1 )	Ð*	DS**
	Chitosan	chi +	8.5 x 10 4	2.0 x 10 5	2.3	0.14 ±0.02

a chitosan: (chi + ) cationic poly(cyclodextrin): (PCD + ) alginate : (alg -)

Table 2 .

 2 Impact on E. coli (EC) and S. aureus (SA) growth due to microcapsules based on cationic poly(cyclodextrin) at different concentrations

	I (%) : percentage of inhibition ( = (100 -


Ons Aouedi

Kandaraj Piamrat

Guillaume Muller

Kamal Singh

FLUIDS: Federated Learning with semi-supervised approach for Intrusion Detection System

Keywords: Federated Learning (FL), Semi-Supervised Learning, Deep Learning (DL), Machine Learning (ML), Auto-Encoder (AE), Intrusion Detection System

In this paper, we present FLUIDS, a Federated Learning with semi-sUpervised approach for Intrusion Detection System. FLUIDS formulates the intrusion detection into a semisupervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined in a collaborative way. The combination of federated learning and semi-supervised Learning allows the solution to: better preserve the privacy, improve training and inference efficiency, achieve better accuracy, and be cheaper to deploy.

FLUIDS: Federated Learning with semi-supervised approach for Intrusion Detection System Ons AOUEDI * , Kandaraj PIAMRAT * , Guillaume MULLER + , and Kamal SINGH +

I. INTRODUCTION

Doing Machine Learning (ML) in an IoT context can be difficult, as the need for collecting as much data as possible to train the model collides with the unwelcome necessity to transfer sensitive data [START_REF] Agrawal | Federated learning for intrusion detection system: Concepts, challenges and future directions[END_REF]. In this work, we propose a novel approach, called "FLUIDS", which combines Federated Learning (FL) and Semi-Supervised Learning to solve this dilemma, for Intrusion Detection Systems (IDS). It combines the unsupervised training of an Auto-Encoder (AE) on IoT Gateways (where data have no label) with the supervised training of a classification model on a FL server (where some labels can be determined). This approach does not need to transfer the data, thus better preserves the privacy. Also, it distributes the workload on the devices, thus improving both training and inference efficiency. Finally, by exploiting all the data (labeled & unlabeled), it can achieve better accuracy than models that use only labeled data. It can also prove cheaper, as labels can be costly to obtain while unlabeled data is available in large amounts.

II. METHODOLOGY & ARCHITECTURE

Figure 1 illustrates the architecture of the FLUIDS approach. It consists of IoT devices (bottom), IoT Gateways (middle), and an FL server (top).

The IoT gateways help the IoT devices to connect to the Internet and operate as FL clients and the IoT server acts as the FL server. More specifically, each IoT gateway trains locally an autoencoder model using its unlabeled data in order to learn the representative and low-dimensional features. Then, it sends back the learned model's parameters to the FL server. Unlike the classical FL, in our case, the FL server not only generates a global autoencoder model but it exploits a small amount of This process is repeated various rounds until the desired performance is achieved. It is important to note here, that during step 3, it is not required that all IoT gateways participate in all rounds (e.g., if they are too busy at that time).

III. EXPERIMENT AND RESULTS

In this section, we first present the dataset used in our experiments. Then, we present the implementation parameters used for intrusion detection. Finally, the results are analyzed and discussed.

A. Dataset description

In order to train our semi-supervised FL model, we use the UNSW-NB15 dataset 1 [START_REF] Moustafa | Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)[END_REF] because it is recent and referenced in many existing papers. The training set contains 175,341 and the testing set contains 82,332 (by default) total observations. The simulation of a partially labeled dataset has been done by randomly selecting of a portion of labeled observations and removing their labels.

B. Results

To evaluate the performance of our semi-supervised FL model, we selected the optimal AE's parameters setting in our simulation.

To validate FLUIDS' efficiency, we compared it to four reference ML models: simple classifiers (Decision Trees -DT -and Support Vector -SVM), ensemble learning (Random Forest -RF), neural network classifier (Multi-Layer Perceptron -MLP). By design, these models use only labeled data. Thus, in our setup, we provided them only with the data located on the FL server. Figure 2 shows the experimentation results. It is worth noting that the semi-supervised federated learning model outperforms all the others. For example, the F1-score is increased by 3.68%, 5.46%, 6.21%, 7.55% for MLP, RF, SVM, and DT, respectively. This may be attributed to the fact that the use of unlabeled data in the training process boosts the performance of our model.

IV. CONCLUSION

In this paper, a federated intrusion detection system has been presented. It was trained in a semi-supervised way, using both the labeled and unlabeled data. This model shows better accuracy than traditional supervised models while being cheaper and lighter to train. Also, it is better at protecting sensitive data and hence preserve privacy.

Figure 1 .

 1 Figure 1. Overview of the proposition

  Figure 2. Comparison of FLUIDS performance to identify normal/attack IP flows against supervised models.

	Table I
	IMPLEMENTATION PARAMETERS
	Dataset
	Nb input variables	197
	Nb output variables	1
	Training set	175,341
	Unlabeled set	122,739
	Labeled set	52,602
	Unlabeled ratio Ru	2.33
	Testing set	82,332 (default)
	Deep Learning
	Deep learning tool	PyTorch
	DL algorithms	Auto-Encoder (AE)
		Neural Network (NN)
	AE hidden layers	3
	NN layers	1
	Activation functions	Relu (AE), Sigmoid (NN)
	Optimizer	Adam
	Learning rate	0.0001
	Batch size	64
	Loss functions	Mean Squared Error (AE)
		Binary Cross Entropy (NN)
	Federated Learning
	FL server	1
	Nb clients (gateways)	100
	Clients used in federated updates	10%
	AE epochs (client)	5
	NN epochs (server)	20
	Communication round	6
	1 https://research.unsw.edu.au/projects/unsw-nb15-data-set


Denis Sereno
email: denis.sereno@ird.fr.

Leishmania (Mundinia) spp.: from their description to their emergence as new human and animal Leishmania pathogens

During the last 20 years, Leishmania (Mundinia) spp. have emerged as new causative agents of human and animal leishmaniases. We provide a historical view of these parasites, from their initial description to their emergence as pathogens, to help avoiding future confusion in species assignation of these newly emerging pathogens.

More than one billion people are at risk of contracting visceral (VL) or cutaneous (CL) leishmaniases. Visceral leishmaniasis causes 20,000 to 30,000 deaths annually, and CL affects the quality of life of millions, causing disability and permanent scarring after healing (http://www.who.int/leishmaniasis/en/). The first account of human leishmaniasis dates from the middle ages, but the discovery of Leishmania parasites as causative agents only occurred in modern times. Until recently, it was thought that the etiological agents responsible for leishmaniases belonged almost exclusively to the subgenus Leishmania in the Old and New Worlds and to the subgenus Viannia in the New World. This view is currently challenged [1].

Between 1995 and 2002, human and animal CL and VL caused by noncanonical Leishmania parasites were reported in geographically distant areas (Thailand, Martinique, Switzerland, Australia, and Ghana). In Martinique, reports of cutaneous-like infections date back to 1917, but the etiological agent was not formally characterized until 2001 [2,[START_REF] Desbois | Leishmania (Leishmania) martiniquensis n. sp. (Kinetoplastida: Trypanosomatidae), description of the parasite responsible for cutaneous leishmaniasis in Martinique Island (French West Indies)[END_REF]. A draft genome of the isolate was released in 2013, and the species named L. martiniquensis was included in the Leishmania subgenus [START_REF] Desbois | Leishmania (Leishmania) martiniquensis n. sp. (Kinetoplastida: Trypanosomatidae), description of the parasite responsible for cutaneous leishmaniasis in Martinique Island (French West Indies)[END_REF]. In Thailand, prior to the description of first autochtonous VL cases in 1999, leishmaniases were only diagnosed in travelers returning from the Middle East. In 2008 a new species is suspected of being he ethiological agent of VL cases in Thailand [START_REF] Sukmee | A suspected new species of Leishmania, the causative agent of visceral leishmaniasis in a Thai patient[END_REF]. In West Africa, CL cases caused by L. major have been documented. In Ghana, from 2002 to 2003, 8,876 possible CL cases were reported, and L. major and another Leishmania sp. were identified [START_REF] Kwakye-Nuako | First isolation of a new species of Leishmania responsible for human cutaneous leishmaniasis in Ghana and classification in the Leishmania enriettii complex[END_REF]. Surprisingly, animal cases (horse, cow, and red kangaroo) of CL were detected in North America, Europe, and Australia [START_REF] Rose | Cutaneous leishmaniasis in red kangaroos: isolation and characterisation of the causative organisms[END_REF][START_REF] Reuss | Autochthonous Leishmania siamensis in horse, Florida, USA[END_REF][START_REF] Lobsiger | An autochthonous case of cutaneous bovine leishmaniasis in Switzerland[END_REF]. The enigmatic Leishmania parasite responsible for CL in horses is genetically closed to the suspected new species identified in Thailand. The name "Leishmania siamensis" was used at the time [START_REF] Reuss | Autochthonous Leishmania siamensis in horse, Florida, USA[END_REF], nevetheless given the lack of adherence to the international code for zoological nomenclature, the name "Leishmania siamensis" is considered a nomen nudum. In 2014 clues emerged to incriminate L. martiniquensis in some CL and VL cases reported in Thailand since 1999 [START_REF] Pothirat | First isolation of Leishmania from Northern Thailand: case report, identification as Leishmania martiniquensis and phylogenetic position within the Leishmania enriettii complex[END_REF]. Surprisingly, all these pathogens were genetically related to a parasite previously isolated from a guinea pig in Brazil, L. enriettii [1,[START_REF] Pothirat | First isolation of Leishmania from Northern Thailand: case report, identification as Leishmania martiniquensis and phylogenetic position within the Leishmania enriettii complex[END_REF]. In 2018, the subgenus Mundinia was created [1]. It encompasses Leishmania species responsible for human and animal diseases, previously gathered in the L. enriettii complex, L.

(Mundinia) martiniquensis previously assigned to the subgenus Leishmania [1], and L.

(Mundinia) orientalis, a newly described species responsible for CL in Thailand [1,10]. A schematic timeline the Mundinia subgenus of characterization is given (figure 1).

Strikingly, the last 20 years have seen parasites of the subgenus Mundinia emerging as new human pathogens, causing VL and CL in HIV+ and HIV-patients. These parasites challenge our current knowledge of the worldwide distribution and physiopathology of leishmaniases. To elucidate the underlying factors triggering the worldwide emergence of L. (Mundinia) spp. and to address the future risks for human and animal health, we have to investigate the vectors and hosts involved in the transmission cycles of these pathogens.

Ackwoledgements

We thank anonymous reviewers for their helpful suggestions, corrections and comments on a previous draft of the manuscript.


Rabih Haddad

Eunika Mercier-Laurent

Curriculum Vitae (CVs) Evaluation Using Machine Learning Approach

Keywords: Curriculum Vitae, Data and Text Mining, Natural Language Processing, Classification, Machine Learning, Na¨ıve Bayes Classifier, Support Vector Machine, and Random Forest

Resumes or Curriculum Vitae (CVs) are still an important standard document and a decision element in evaluating the life journeys and human personalities of candidates. Its main role is to detect the eligibility of people who are applying to job vacancies or higher education programs. This research work ambitions in elaborating a system that automates the preselection of eligibility and assessment of candidates in the higher education students' recruitment process. This system will replace the tedious tasks of manual processing of CVs and will provide accurate and effective evaluation results. To achieve this requirement, the system will be implemented using a machine learning approach using different classification algorithms. The evaluation is conducted on the four main knowledge categories that build the CV: personal information, academic background, professional experience, and soft and technical skills. The output of the system will be an indicator to shortlist, discard or request more information to evaluate the candidates' eligibility. Moreover, the scores obtained for each part of the CV will be used to calibrate the indicator in each information category. Consequently, this system boosts the recruitment process of candidates and provide a reasonable decision

Introduction

CVs are still an important standard document and a decision element in evaluating life journeys and human personalities of candidates who are applying to a specific job or pursue an academic program. This work is motivated by real need of automated processing of students applications.

International students who wish to continue their higher studies at the bachelor, or masters, or doctorate level in France should directly obtain their admission from the corresponding institution or by choosing a French institution on Campus France [4]. To apply, candidates should fill out an application form on the institution website. The characteristics of each country and institution shape the admission system, however, there is a big percentage of commonality between these systems as they require the same traditional documentation, evaluation, information: admission and languages proficiency exams, interviews, CVs, transcripts, motivation letters, and recommendations letters. [This is the procedure that is applicable today at EPITA [START_REF]Epita international[END_REF]. ] This is a important challenge for automated processing. In this article, a system that automates the eligibility screening and assessment of applicants in the process of recruiting higher education students will be discussed. This system might replace the tedious tasks of manually processing Curriculum Vitae and provide accurate and efficient assessment results. Considering the various forms of the CV and the needs of extracting information various classification algorithms might be used to handle this exercise. The goal of CV Analysis is to give an outcome at the current stage whether the CV fulfills the criteria to be eligible for a certain academic program. For the current experimentation, the analysis scope is applied to the candidates who are applying to pursue a Masters degree at EPITA [START_REF]Epita masters[END_REF].

The assessment will focus on the four main categories of knowledge that make up the CV: personal information, academic background, work experience, and personal and technical skills. Spoken languages and extracurricular activities are considered as well. The result of the system would be indicative for a shortlist, ignore or request more information to assess the eligibility of some candidates.

Based on what has been elaborated earlier and the availability of an important volume of CVs to be tested in the frame of this research, a Machine Learning approach has been selected. After research and study of the nature of information that are available and the existing algorithm, the below evaluation process will be studied. The evaluation process will be divided into several steps as follows and as in figure 1

Literature Review

The evaluation process that is presented in this paper, is mainly based on data mining and text mining. Data mining is the process of finding discrepancies, patterns, and correlations in large data sets to predict outcomes. With a wide array of technologies, this information can be used to apply in many life sectors. Methods of data mining can be decision trees, associations rules, segmentation algorithms, nearest neighbors' algorithm, and neural networks [START_REF] Dufour | Aix-marseille university[END_REF] In data mining data available is used in databases to identify unknown, important and useful combinations and structures and patterns. The concepts and methods of data mining can be applied in various fields like marketing, medicine, engineering, web mining, etc. Educational data is a new emerging data mining technique that can be applied to many fields including education. This new emerging field, called educational data mining, concerns the development of methods for discovering knowledge from data from educational settings.

One of the researches, which has been already done on CV evaluation using text mining and the related techniques, is mentioned here [START_REF] Piskorski | Ainformation extraction from cv[END_REF]. Many works exist that mention information extraction application on CV, but the evaluation of such CVs is completely absent. For example, in one of the papers that has been recently published in ResearchGate, the study included on how to extract information from conceptual pattern from CV. The aim was to perform only the below tasks out of it:

-Identifying information to be extracted -Preparation of linguistic and ontological resources for applying IE tools in Polish language -Preparing tools to extract relevant information from texts -Applying selected tools on the test document collection -Validation of the results. This might facilitate the task of preparing the CV text prior to being processed and evaluated, however, it will not address the main task of conducting the evaluation. Another work has been done in this regard and presented in the Journal of Critical Reviews [START_REF] Ganesh | A novel job portal with resume evaluation system based on text mining and nlp techniques[END_REF]. It establishes clear and efficient registration procedures by integrating strategies of web dragging, content extraction and regular language management. This makes the registration process basic and successful by offering the possibility of transferring the course material for the required skill during the work. Additionally, it provides a scheduled suggestion for the activity seeker when individual abilities are coordinated with the activity station. This is done using the cooperative separation account, in addition to improving the additional space for specialist collaboration. On the business side, it uses the web crawler to generate a set of business responsibilities and basic requirements. On the activity seeker side, when the CV is published, word separation and letter splitting are stopped. After content segmentation, enrollment is based on training, an understanding of the job, abilities, personality traits and grade frequency. Finally, a framework is proposed that the next age at which the level of education meets the prerequisites of the profession. This approach is a bit general and its output is abstract, making it difficult to give a valid or coherent evaluation indicator.

Hence, in this work, further experimentation aligned with the methodologies is presented in order to get more adjourned results.

Proposed System

As mentioned earlier, a Machine Learning approach is used in this paper to conduct the evaluations of CVs. This approach is be based on the steps that were mentioned in the first section of this article. A detailed elaboration of each step is presented below and the CV evaluation process architecture is presented in figure 2.

System Parameterization

The first step that was conducted is to train the evaluation algorithm using historical CVs and using some existing datasets. It is an essential exercise to guarantee the accuracy of results. This can be done by determining the parameters to evaluate the CV quantitatively. The parameters are considered based on their usability and their helpfulness in providing the quality of the CV. For instance, technologies practiced by the applicant is an important attribute to evaluate in the CV and has a weighted value of 5 points for this type. This step helps to identify the parameters against which the CV is evaluated. The weightings for each of its parameters were given based on its impact on the desirable qualities of the candidate. It also helps to extract only the relevant information from the CV. A decision table as shown in the figure 3 has been established to help in providing the overall score of the CV which can then be processed in the classification algorithms to determine the outcome of the candidate.

Sample Data Collection for training

The process of data collection for training the algorithm is very essential as the trained algorithm can effectively determine the accuracy of the results. Therefore, a relevant data source has been used. Below is the list of data source used for various criteria:

-Technologies known: data source of Stack Overflow [START_REF]Stack overflow nlp[END_REF] questions and answers has been used. There are over a million records of questions and answers covering a wide range of technologies in various text combinations. -Program specific criteria: various data sources are available in Kaggle [6] for each specific program type. Also, a manual insertion of data from historical CVs that were processed earlier at EPITA International Programs. -Extracurricular activity: There is a Kaggle dataset available with over 666 hobbies and manually updating hobbies have not been covered in the above list. -Languages: Only French and English are considered in our case making the related dataset simple. -Type of degrees: The exhaustive list of degree types was obtained from Kaggle and Data World. The abbreviations for the degree types have been manually inserted.

[7]

To train the Natural Language Processing NLP models, relevant data is needed to identify and quantify the parameters. By having comprehensive Datasets like stack-overflow questions and answers, the gaps in the matching patterns are easily identified. This data collection process is repeated for each of the parameters defined previously. The more comprehensive the dataset is the more efficient is the named entity recognition model obtained through NLP training. This step was done by keeping in mind the objective of obtaining accurate models.

These steps are also summarized in figure 4.

Dataset Creation for Testing the Trained Model

This step is to create a CV Dataset. Around 1000 CVs were randomly selected from the database of CVs available at EPITA from candidates applications. The CVs selected were based on the following criteria:

-The candidate whose CV has been selected should have applied for any of the international programs that are available. -The admission process of the file should have been completed; therefore, the final status of the application is already known. -Have a good combination of Accepted and Rejected CV's along with the combination of CVs of different programs that the candidates applied for.

In the previous step, specific parameter dataset has been used to be able to train each model independently. Then the collected CVs are trained in the desired context. The model is trained to learn words in the context of a CV, for example, Tesla is a company and not a physician. For this reason, the training data should always be representative of the data to process. A model trained in romantic novels will likely perform badly on legal text. This also means that in order to know how the model is performing, and whether it's learning the right things, not only training data is needed but we also evaluate data. If the model is tested only on data used in the training, chances are that it might not be generalizing. To train a model from scratch, are at least needed a few hundred examples for both training and evaluation. This step contributes to the evaluation data of the training data provided in step 2.

CV extraction and cleaning data

The main objective of this step is to parse information from a CV using Natural Language Processing and find the keywords, cluster them onto parameters based on their keywords. This information extraction process involves extracting structured specific information from unstructured or semi-structured natural language (in the form of text). The data extracted is then fed to the NLP tools like spaCy [START_REF] Spacy | Industrial-strength nlp[END_REF] for example for text categorization. PDF miner [START_REF]Python[END_REF] has been used to parse the text from PDF files and the parsed result is cleaned by various methods using RegEx patterns to remove unwanted text and special characters and split the text in different lines.

Most text data contains a lot of words that are not actually useful. These words, called stopwords, are useful in human speech, but they do not have much to contribute to data analysis. Hence stop words and lemmatizing should be performed on the selected text. For example, words like connect, connection, connecting, connected, etc. aren't exactly the same, they all have the same essential meaning: connect. The differences in spelling have grammatical functions in spoken language, but for machine processing, those differences can be confusing, so we need a way to change all the words that are forms of the word connect into the word connect itself. Spacy has a built-in way to break a word down to its lemma by calling the method lemma to produce the lemma for each word after analyzing.

Because in CVs data formats that are used are not completely unstructured, it is still quite challenging to take them into the structured format as there is no set in stone rule for writing a CV. As a result, many possible ways of representing qualifications in a CV has been established so far such as chronological CV and functional CV. Beyond these two types, there are many other formats and many people follow their own unique style to make their CV stand out from other ones. Additionally, there is a tendency of adding visual elements to a CV to make it more interesting to visualize. Opposed to many of the visual elements just being there for aesthetic purpose, there are exceptional cases when someone uses visual elements like graphs or charts to represent important information such as their skills because creating and interpreting graphs or charts encourages critical thinking.

As in most of the cases, these graphs are included in image formats, there is no definitive way to process them without using image processing techniques. Hence, these CVs will be kept out of consideration in the frame of this work as it is beyond the scope, therefore PDF Miner [START_REF]Python[END_REF] is used as a tool for extracting information from PDF documents. Unlike other PDF-related tools, it focuses entirely on getting and analyzing text data. It includes a PDF converter that can transform PDF files into text format.

Information Extraction using Natural Language Processing

The cleaned text is passed to Natural Language Processing Algorithm to tokenize the text and identify the phrases and entities in the text. There are various of library options to use and spaCy is chosen as it is designed specifically for this type of applications and it understands a large volume of text. It can be used to build Information Extraction or Natural Language understanding systems or to pre-process text for Deep Learning. The objective is to train the model against reference annotation. Training is an iterative process in which the model's predictions are compared against the reference annotations in order to estimate the gradient of the loss. The gradient of the loss is then used to calculate the gradient of the weights through back-propagation. The gradients indicate how the weight values should be changed so that the model's predictions become more similar to the reference labels over time as shown in figure 5. The main idea is to train and obtain the model for each of the parameters that form a certain CV. For example, it could be applied on the Programming Language, Extra-Curricular Activities, Qualification or Degree, Spoken Language, English Language Test, Program Specific Criteria, etc...The result obtained by processing the CV data of each of the trained model is then passed on to the trained classification model to predict the outcome. For elaboration, an example of applying the model to programming language is presented. To obtain a consistent Named Entity Recognizer model for the programming language, the model is trained with a comprehensive list of language including the various patterns it could appear in (eg: php3.4, php7, Go lang, GoLang). In order to do that a large Dataset is needed based on which the model is trained. Through this research, it was found that Stack Overflow provides a dataset for its question and answers which can be downloaded from Kaggle. Now the dataset is ready spaCy Matcher pattern is used to identify and train the programming languages in each question.

The matching algorithm should follow the steps mentioned in the below figure. It starts by writing exhaustive matcher pattern rules to cover all the criteria for the programming languages, hobbies, languages, degree types in order to obtain the model to be used in the CV evaluation. This algorithm is presented in figure 6. Based on various samples testing with Stack Overflow questions dataset over multiple iterations, an exhaustive list of programming languages (like php, java, python, C), was after obtained as well as patterns across programming language (like php3.4,php7, python3). An interesting scenario for the programming language obtained here was for "GO Lang" since "go" can be a verb in a lot of sentences. The part of speech feature of NLP library is used to help us distinguish the programming language GO from the verb GO. This observation also shows that using NLP along with Regex (Regular Expression) helps efficiently train the algorithm. Once the matcher patterns are defined, labelling is used to evaluate the effectiveness of the pattern. In order to do this, a copy of the Questions Dataset is manually prepared and added in additional column Label. Then this column is manually marked to 1 if a programming language is found for that question and 0 if not. This was done for over 650 records and tested. On running the labelled csv (comma separated values) against the patterns defined, identification of any mistake made in pattern definition was done as well as the pattern to accommodate the changes. The statistical analysis of this model can be determined through the generation of the confusion matrix and classification report. A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. Below is an example of a confusion matrix that has been created for this example.

A classification report is used to measure the quality of predictions from a classification algorithm. How many predictions are True and how many are False. More specifically, True Positives TP, False Positives FP, True Negatives TN and False Negatives FNare used to predict the metrics of a classification report. Figure 7 shows an example of a confusion matrix that has been created for this example. A recall is a measure of the classifier's completeness; the ability of a classifier to correctly find all positive instances. For each class, it is defined as the ratio of true positives to the sum of true positives and false negatives. Said another way, "for all instances that were actually positive, what percent was classified correctly?" The F1 score is a weighted harmonic mean of precision and recall such that the best score is 1.0 and the worst is 0.0. Generally speaking, F1 scores are lower than accuracy measures as they embed precision and recall into their computation. As a rule of thumb, the weighted average of F1 should be used to compare classifier models, not global accuracy.

Support is the number of actual occurrences of the class in the specified Dataset. Imbalanced support in the training data may indicate structural weaknesses in the reported scores of the classifier and could indicate the need for stratified sampling or re-balancing. Support doesn't change between models but instead diagnoses the evaluation process. Moving further after obtaining satisfactory results from the above steps, the programming model is ready to get trained by constructing the model as per the NLP requirement. The obtained trained data is shown in figure 9.

This exercise has been repeated for all parts of the CV (Extra-Curricular activities, English Language Test, Program Specific Criteria...) This step will provide us with a custom Named Entity Recognition model using spaCy statistical modelling for each parameter that will later be used for information extraction (IE) that seeks out and categorizes specified entities in a body or bodies of texts of the CV. The generated model can recognize a wide range of named or numerical entities, which include qualification, extracurricular activities, language, program specific criteria and English language test. The result obtained by processing CV data to each of the trained models is fed in the trained classification model to predict the outcome.

Classification and final Output Prediction

The role of the classification model is to draw some conclusion from observed values. Given one or more inputs a classification model will try to predict the Fig. 9. Trained Data value of one or more outcomes. Outcomes are labels that can be applied to a dataset. For example, when filtering emails "spam" or "not spam", when looking at transaction data, "fraudulent", or "authorized". Similarly, in our case, for a given result set of Programming Languages, Extra-Curricular Activities, Qualification, Spoken Language, English Language Test and Program Specific criteria the classification model should be able to predict the final outcome. It aims to provide pattern recognition based on previously obtained results. Data is segregated in 2 main Datasets: the training test that is used to train the model to recognize the patterns in the given data for suitable predictions and the test set that contains the previously predicted value. The model is trained using several Machine Learning algorithms and the one with the least error will be chosen as a classifier. In order to classify the data obtained after the CV evaluation under the accepted and rejected categories, a sample of over 500 CVs has been manually evaluated based on the parameters mentioned above. An example of the manual attribution on an accepted and refused CV is mentioned in the figures 10, 11 and 12. This step provides three classification models for predictive modeling. In machine learning selecting the best hypothesis for a given data is the most interesting part. The accuracy obtained in general is most for Random Forest classifier. Therefore its result is used to determine the final outcome for the CV. Then it is only needed to provide the parameters weighting to the model to predict the final outcome. In a typical supervised learning workflow, evaluation of various combinations of feature sub-spaces, learning algorithms, and hyper-parameters is done before selecting the model that has a satisfactory performance. As mentioned above, cross-validation is a good way for such an assessment in order to avoid over-fitting to the training data.

Experimentation

This part of the article is dedicated for the experimentation of the CV evaluation. The experimentation will be dedicated to the CV Information Extraction, the training process, and the classification process using 3 different algorithms as follows:

1. Naïve Bayes Classifier 2. Support Vector Machine SVM 3. Random Forest

The classification is done after finalizing the 5 steps that are mentioned in the previous sections as all of them serve to conduct the classification experiment to obtain the desired outcomes.

Naïve Bayes Classifier

Naive Bayes classifiers are a collection of classification algorithms based on Bayes' Theorem [10]. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e., every pair of features being classified is independent each other. The Dataset is required in two parts namely feature matrix and the response vector as explained below:

-Feature matrix: it contains all the vectors(rows) of Dataset in which each vector consists of the value of dependent features. In the above Dataset, features are 'Programming Language', 'Extra-Curricular Activities', 'Language', 'Qualification', 'Work Experience' and 'Test'. -Response Vector: it contains the value of class variable (prediction or output)

for each row of the feature matrix. In the above Dataset, the class variable name is 'Result'.

The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome. Bayes' Theorem predicts the probability of an event occurring given the probability of another event that has already occurred. Bayes' theorem is stated mathematically as the following equation:

where A and B are events and P(B) is different from 0.

-Basically, the probability of event A is to be predicted, given the event B is true. Event B is also termed as evidence. -P(A) is the priori of A (the prior probability, i.e., Probability of event before evidence is seen). The evidence is an attribute value of an unknown instance (here, it is event B). -P(A-B) is a posteriori probability of B, i.e., probability of event after evidence is seen.

With regards to the used Dataset, Bayes' theorem is applied in the following way:

where, y is class variable and X is a dependent feature vector (of size n). where: To add classification, an example of a feature vector and corresponding class variable can be: (refer 1st row of dataset) X = (0(Programming Language), 0(Extra Curricular), 1(Languages) , 1(Program Specific Criteria) ,1(Qualification) ,0(Test) ,1(Work Experience)) y = Yes For simplicity data from each label is assigned to be drawn from a simple Gaussian distribution. The likelihood of the features is assumed to be Gaussian, hence, conditional probability is given by: The used dataset involves predicting the CV result given individual parameters like Technologies, Degree, Language, Extra-Curricular activities and Program Specific criteria. It is therefore a multi-class classification problem. There are around 40 observations with 6 input variables and 1 output variable. A sample of the input variables is listed below:

Using sci-kit learn, it is now possible to train and test the model prediction for Gaussian Naïve Bayes as shown in the figure 13. It was observed that the algorithm generates a Reject response if it matches a similar combination of already existing rejected candidates and Accepted otherwise.

Support Vector Machine(SVM)

The objective of the support vector machine algorithm [START_REF]Towards Data Science. Support vector machine -introduction to machine learning algorithms[END_REF] is to find a hyperplane in an N-dimensional space(N -the number of features) that distinctly classifies the data points as shown in the figure 14. To separate the two classes of data points, there are many possible hyperplanes that could be chosen. The objective of this paper is to find a plane that has the maximum margin, i.e the maximum distance between data points of both classes. Maximizing the margin distance provides some reinforcement so that future data points can be classified with more confidence.

Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. Also, the dimension of the hyperplane depends upon the number of features. If the number of input features is 2, then the hyperplane is just a line. If the number of input features is 3, then the hyperplane becomes a two-dimensional plane. Support vectors are data points that are closer to the hyperplane and influence the position and orientation of the hyperplane. Using these support vectors, the margin of the classifier is maximized. Deleting the support vectors changes the position of the hyperplane. These are the points that help build the SVM.

In the SVM algorithm, maximizing the margin between the data points and the hyperplane is looked for. The loss function that helps maximize the margin is hinge loss.

The cost is 0 if the predicted value and the actual value are of the same sign. If they are not, then calculate the loss value is then calculated. A regularization parameter is added to the cost function. The objective of the regularization parameter is to balance the margin maximization and loss. After adding the regularization parameter, the cost functions looks as below.

Below, the implementation of SVM in python is depicted using the scikit library using Sigmoid Kernel as it was more accurate compared to linear, polynomial linear and the Gaussian kernels:

It was observed that the algorithm generates an Accept response in almost all test cases.

Random Forest Classifier

Random Forest is a supervised learning algorithm [START_REF]Understanding random forests classifiers in python[END_REF]. The "forest" builds, is an ensemble of decision trees, usually trained with the "bagging" method. The general idea of the bagging method is that a combination of learning models increases the overall result.

Random forests also offers a good feature selection indicator. Scikit-learn provides an extra variable with the model, which shows the relative importance or contribution of each feature in the prediction. It automatically computes the relevance score of each feature in the training phase. Then, it scales the relevance down so that the sum of all scores is 1.

This score choose the most important features and drop the least important ones for model building. Random Forest uses GINI importance or mean decrease in impurity (MDI) to calculate the importance of each feature. Gini importance is also known as the total decrease in node impurity. This is how much the model fit or accuracy decreases when you drop a variable. The larger the decrease, the more significant the variable is. Here, the mean decrease is a significant parameter for variable selection. The Gini index can describe the overall explanatory power of the variables. Figure 16 shows the implementation of Random Forest classifier using scikit learn [START_REF]Machine learning in python[END_REF]:

Conclusion and Future Work

As the CV evaluation is a part of a decision support system that we applied to the admission of international students in the French Higher Education systems, the results obtained in the actual classifier is cross-validated with results from the online video interviews results.

: 1 .

 1 System Parameterization 2. Training Data Collection 3. Model Dataset Creation 4. Data Extraction and Cleansing 5. Information Extraction using Natural Language Processing 6. Classification and final Output Prediction

Fig. 1 .

 1 Fig. 1. Evaluation Process

Fig. 2 .

 2 Fig. 2. CV Evaluation Process Architecture

Fig. 3 .

 3 Fig. 3. Decision Table

Fig. 4 .

 4 Fig. 4. Data Collection Samples

Fig. 5 .

 5 Fig. 5. Model Training

Fig. 6 .

 6 Fig. 6. Matcher Algorithm

Fig. 7 .

 7 Fig. 7. Confusion Matrix

Figure 8

 8 Figure8shows a classification report for this example. Precision can be seen as a measure of a classifier's exactness. For each class, it is defined as the ratio of true positives to the sum of true and false positives. Said in another way, "for all instances classified positive, what percent was correct?"

Fig. 8 .

 8 Fig. 8. Classification Report

Fig. 10 .Fig. 12 .

 1012 Fig. 10. Shortlisted CV

Fig. 13 .

 13 Fig. 13. Model Training using Gaussian Naïve Bayes

Fig. 14 .

 14 Fig. 14. Support Vector Machine (SVM)

Fig. 15 .

 15 Fig. 15. Model Training using SVM

Fig. 16 .

 16 Fig. 16. Model Training using Random Forest