Xavier Drevelle
email: xavier.drevelle@invalides.fr

Coralie Villa

Xavier Bonnet

Isabelle Loiret

Pascale Fodé

Hélène Pillet
email: helene.pillet@ensam.eu

Vaulting quantification during level walking of transfemoral amputees

Keywords:

Background: Vaulting is a gait compensatory mechanism used by transfemoral amputees to assist toe clearance during the prosthetic swing phase. It is defined by a plantar flexion of the contralateral ankle during the single-limb support phase. The aim of the study is to propose a method to quantify vaulting of transfemoral amputees. Methods: 17 transfemoral amputees and 28 asymptomatic subjects participated in the data collection. Kinematics and kinetics of the whole body were recorded while subjects were walking on a level surface. Biomechanical gait analysis was focused on a reduced set of parameters linked to the contralateral ankle, the contralateral knee and the trajectory of the center of pressure. The patients were classified in two groups: with or without vaulting using video recordings. Differences between both groups and the control group were analyzed. Findings: A higher generated ankle power was found during the single support phase of the contralateral limb of transfemoral amputees presenting vaulting. These subjects presented also a higher dissipated knee flexion power before the peak in ankle flexion power. The trajectory of the center of pressure was also modified by the vaulting. Interpretation: Vaulting for transfemoral amputees is characterized by a propulsive plantar flexion at the contralateral ankle. Quantifying the ankle flexion power during the contralateral single support phase will help in understanding vaulting.

Introduction

Transfemoral amputees have to pay particular attention to foot clearance during the prosthetic swing phase. Catching of the prosthetic foot and the floor can result in a fall. To reduce the risk of falling, transfemoral amputees develop compensatory strategies which could lead to gait abnormalities, vaulting is one of them. [START_REF] Smith | Atlas of Amputations and Limb Deficiencies[END_REF] described it as "a premature midstance plantar flexion by the sound limb which assists toe clearance of the prosthetic limb by lifting the body". Among possible causes are: an overly long prosthesis, a too posterior alignment of the prosthesis, an excessive prosthetic knee resistance to flexion or a patient habit due to fear of falling [START_REF] Smith | Atlas of Amputations and Limb Deficiencies[END_REF]. It occurs between the initial and the middle of the prosthetic swing phase after body weight transfer to the contralateral limb during the contralateral single support. The plantar flexion of the contralateral ankle helps the patient to lift his whole body. Therefore, it increases the distance between the prosthetic foot extremity and the walking surface to avoid collision with the ground. Long term vaulting could cause overloading of the contralateral forefoot which could lead to pain at the metatarsal head or to the sinus tarsi syndrome. Other biomechanical consequences could be higher energy expenditure compared to asymptomatic subjects [START_REF] Waters | The energy expenditure of normal and pathologic gait[END_REF]) and asymmetric loading distribution with higher ground reaction forces on the intact limb compared to the prosthetic limb [START_REF] Engsberg | Normative ground reaction force data for able-bodied and trans-tibial amputee children during running[END_REF]. It could also lead to an increase of ankle, knee and hip moments at the contralateral limb compared to asymptomatic subjects [START_REF] Nolan | The functional demands on the intact limb during walking for active trans-femoral and trans-tibial amputees[END_REF].

As it is considered as a gait abnormality, clinicians seek to correct vaulting during rehabilitation. Evaluation during a clinical exam is mainly based on naked eye observations. The plantar flexion of the ankle cannot be identified immediately with the naked eyes due to the difficulty to observe a small angle. So the visual criterion is a premature rise of the contralateral heel during the single support, which is caused by vaulting. This qualitative analysis does not allow a quantification of the vaulting. However, a quantitative analysis of this abnormality would be very useful for the understanding of involved mechanisms and the assessment of the efficiency of rehabilitation. Therefore, there is a need to propose one or some relevant and reliable parameter(s) able to quantify vaulting during clinical follow-up of patients with amputation.

Few studies have analyzed vaulting. Subjects with leg length discrepancy have shown an increase of the ankle plantar flexion of the short limb during the single support to ensure toe clearance which could be assumed as vaulting [START_REF] Walsh | Leg length discrepancy-an experimental study of compensatory changes in three dimensions using gait analysis[END_REF]. More recently, ankle flexion power was used to characterize vaulting for children suffering from Charcot-Marie-Tooth disease [START_REF] Ferrarin | Gait pattern classification in children with Charcot-Marie-Tooth disease type 1A[END_REF]. Besides, as vaulting is an abnormality occurring during the single support phase, center of pressure (CoP) displacement under the contralateral foot should be affected. Abnormal CoP displacement patterns have already been reported for both contralateral and prosthetic feet for transfemoral amputees [START_REF] Schmid | Centre of pressure displacements in trans-femoral amputees during gait[END_REF]. However, no correlations were made with gait deviations. To the authors' knowledge, there is no study concerning quantification of vaulting for transfemoral amputees.

The aim of the present study is to quantify vaulting for transfemoral amputees using several gait parameters. Quantitative comparisons between amputees who vault and amputees who did not vault will be made.

Methods

17 subjects with transfemoral amputation participated in data collection (16 male, 1 female; 37 yrs SD 10; height 1.74 m SD 0.09; mass 76 kg SD 10). 16 subjects were fitted with stance and swing phase controlled knees and energy-storing prosthetic foot; 1 subject was equipped with a pneumatic polyaxial knee and an energy-storing prosthetic foot. The mean amputation duration was 10.1 yrs (SD 9). Asymptomatic subjects were recruited to form a control (C) group (17 male, 11 female; age 43 yrs SD 18; height 1.74 m SD 0.10; mass 70 kg SD 14). The protocol was approved by the local ethics committee and written informed consent was obtained from all participants.

The protocol was detailed in a previous study [START_REF] Pillet | Evaluation of force plate-less estimation of the trajectory of the centre of pressure during gait. Comparison of two anthropometric models[END_REF]. All subjects were fitted with 54 reflective markers following a pattern derived from the ISB recommendations [START_REF] Wu | ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion-part I: ankle, hip, and spine. International Society of Biomechanics[END_REF][START_REF] Wu | ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion-part II: shoulder, elbow, wrist and hand[END_REF]. Kinematics of the whole body were recorded at 100 Hz with an 8-camera motion analysis system (Vicon V8i, Vicon Motion Systems, Oxford, UK). Subjects were asked to walk at a comfortable selfselected speed on a level surface instrumented with two force platforms (AMTI, 100 Hz, Advanced Mechanical Technology Inc, Watertown, USA). A trial was considered as successful when each foot was placed centrally on a force plate during stance. Five successful trials were recorded. A personalized inertial model of the body for each subject was built using a static trial and a pair of photographs used to adjust the model's geometry [START_REF] Pillet | Evaluation of force plate-less estimation of the trajectory of the centre of pressure during gait. Comparison of two anthropometric models[END_REF]. The model included feet, shanks, thighs, pelvis, trunk, head, upper arms and forearms.

The spatio-temporal parameters, segmental and articular kinematics and kinetics of the lower limbs were computed from the data of the five successful trials. The biomechanical analysis of vaulting was focused on a reduced set of parameters. It was hypothesized that vaulting is related to motion in the sagittal plane only. Thus, all chosen parameters were computed from the average curves of angles, moments and powers in the sagittal plane. As vaulting is defined by a plantar flexion movement, the minimal flexion angle of the contralateral ankle was computed during the contralateral single support phase (AFlexAng) (Fig. 1). Positive values define dorsal flexion whereas negative values define plantar flexion. As a propulsive movement occurs during vaulting, flexion power at the contralateral ankle was computed as the product between the internal flexion moment and the flexion angular speed at the contralateral ankle [START_REF] Sadeghi | Muscle power compensatory mechanisms in below-knee amputee gait[END_REF]. The power was normalized by subject's mass and was expressed in W•kg -1 . Positive values define a generated power whereas negative values define a dissipated power. The maximum value of the contralateral ankle flexion power during the single support phase (AFlexPwr) of the contralateral limb was computed (Fig. 1) [START_REF] Ferrarin | Gait pattern classification in children with Charcot-Marie-Tooth disease type 1A[END_REF]. In order to analyze the compensatory mechanisms at the contralateral knee, the maximal knee flexion angle (KFlexAng) and the minimal knee flexion power between 0% and 40% of gait cycle (KFlexPwr) were also computed (Fig. 1). As vaulting leads to a premature contralateral heel rise, a modification of the displacement of the CoP can be expected. Therefore, the longitudinal position of the CoP was computed in the contralateral foot anatomical frame (Fig. 2). The origin of the frame was defined by the middle of the first and fifth metatarsal heads (called in the current study "center of the metatarsal heads"). The X-axis was defined by the line joining the calcaneus to the center of the metatarsal heads. Position was normalized by the distance between the calcaneus and the center of the metatarsal heads. The mean position of the CoP during the stance phase along longitudinal axis was computed (CoPX) (Fig. 3). A positive value defined an average position of the CoP in front of the center of the metatarsal heads whereas a negative value defined an average position between the calcaneus and the center of the metatarsal heads (-100% means that CoP is placed at the calcaneus whereas a position equals to 0% means that CoP is placed at the center of the metatarsal heads). A temporal parameter linked to the CoP trajectory was also defined. CoP% was the instant of the gait cycle when CoP longitudinal position became equal to the average CoP position (CoPX) (Fig. 3).

Transfemoral amputees were screened for gait deviations (such as vaulting, hip elevation, circumduction, trunk bending …) through a review of video recordings of front and side views of each trial. In the current study, vaulting was the only gait deviation of interest. According to the expertise of medical experts, amputees were classified in two groups regarding observations of vaulting. Visual criterion was a premature heel rise during the contralateral single stance phase. Transfemoral amputees who appeared to vault on video recordings formed the vaulting (V) group. Patients who did not appear to vault on video recordings constituted the no-vaulting (NV) group. For each parameter, statistical differences between the three groups (C: Control group, V: Vaulting group, NV: Novaulting group) were analyzed using a Mann-Whitney test (α = 0.05).

Results

Clinical observations of vaulting

Resulting from the review of the video recordings, 6 amputees did not appear to vault on video recordings, so they formed the NV group.

Vaulting was suspected in 9 transfemoral amputees who constituted the V group. 2 amputee subjects could not be classified according to the chosen visual criterion.

Biomechanical parameters

The mean values and the standard deviations of each population for all considered parameters are presented in Table 1.

No statistical differences were observed between the C group and the NV group for AFlexAng, AFlexPwr, KFlexAng, KFlexPwr and CoP%. CoPX was significantly lower for the NV group compared to the C group (P = 0.047). This indicates that amputees of the NV group showed an average position of the center of pressure during the stance farther backward in the foot anatomical frame than the control subjects (-29% vs -22%).

The V group showed significantly higher values for AFlexPwr, KFlexPwr, CoPX and CoP% compared to the C group and the NV group (P b 0.001). So amputees who vault presented a higher peak of flexion power during the contralateral single stance phase (AFlexAng = 0.46 W•kg -1 ) and a higher dissipated power at the knee during the early stance (AFlexPwr =-1.59 W•kg -1 ). Their average position of the CoP relatively to the foot frame was farther ahead (CoPX = -11%); furthermore the CoP moved earlier in the front part of the foot during stance phase (CoP% = 20.2% of gait cycle). KFlexAng was significantly higher for V group compared to C group (P b 0.01). On the contrary, values were comparable between the V group and the NV group (P = 0.22). No differences were found between groups regarding AFlexAng.

Review of the two non-classified amputees

The values for the two non-classified transfemoral amputees after reviewing the video recording are presented in Table 1.

Both subjects presented a dorsi-flexion at the contralateral ankle during the contralateral single stance phase (AFlexAng = 3.4°and 1.3°). The ankle flexion powers for both subjects were of the same magnitude as the average value for the C group and the NV group (0.07 W•kg -1 and 0.03 W•kg -1 ). The dissipated knee powers were close to the C group and the NV group. However, higher values for CoPX were found (-3% and 14%) and lower values for CoP% (23% and 24%) compared to the C group and the NV group. CoPX and CoP% parameters for both subjects presented values comparable to the V group.

Discussion

The aim of this study was to quantify vaulting of transfemoral amputees when walking on level surface. We analyzed the differences between asymptomatic subjects and two groups of amputeeswith or without vaulting when categorized by clinical observations.

There was no difference in ankle flexion angle between the groups. The high variability of this parameter due to its sensitivity to anatomical landmark identification jeopardizes its use to discriminate groups. Even if vaulting is characterized by a plantar-flexion movement of the ankle, the assessment of flexion angle is not accurate enough to quantify vaulting. On the contrary, ankle flexion power appears to be a reliable parameter for vaulting quantification. This parameter is directly linked to the concentric work of muscles of the contralateral ankle [START_REF] Sadeghi | Contributions of lower-limb muscle power in gait of people without impairments[END_REF]. Furthermore, all amputees presenting vaulting showed ankle flexion power values higher than 0.15 W•kg -1 whereas values below 0.1 W•kg -1 were observed for other amputees (Fig. 4) suggesting that a threshold value can be defined for a quantitative vaulting detection during clinical follow-up from conventional gait analysis. Furthermore, vaulting quantification could help in improving rehabilitation efficiency.

Significant modifications were also found at the contralateral knee for amputees showing vaulting compared with other groups. The patients of the vaulting group showed higher values of knee flexion during the beginning of the contralateral stance. Higher dissipated power at the knee during the double support in the early stance phase was also found. These results indicate a higher eccentric muscle work at the knee during the double support at the beginning of the stance phase [START_REF] Perry | Gait Analysis: Normal and Pathological Function[END_REF]. These modifications at the knee occurring during the loading response appeared prior to the ankle plantar flexion. The higher flexion and higher eccentric work of the contralateral knee are to prepare the vaulting movement. As already stated, the aim of vaulting is to lift the body up in order to facilitate toe clearance. This lifting is all the more effective if the maximal inclination of the tibia in the sagittal plane is reached earlier in the stance phase of gait. This anticipated progression of the tibia allows both lengthening the soleus muscle to maximize its contraction efficiency and putting the center of mass of the body ahead of the ankle joint to benefit from its plantar flexion motion. The increased knee flexion observed in the vaulting group compared with other groups can be interpreted as a consequence of the faster advancement of the tibia. At the same time, the knee joint must be stabilized by knee extensor muscles inducing an increase of the power dissipated by the joint during loading response [START_REF] Perry | Gait Analysis: Normal and Pathological Function[END_REF].

Results found in the present study agree with the literature. Observations regarding kinematics and kinetics of the contralateral ankle and contralateral knee for the V group compared to the other groups (Fig. 5) were consistent to the results obtained by [START_REF] Ferrarin | Gait pattern classification in children with Charcot-Marie-Tooth disease type 1A[END_REF] on three children suffering from Charcot-Marie-Tooth and presenting vaulting. In spite of a small sample size, the authors reported the same association of important generated power at the contralateral ankle with a higher flexion angle and dissipated power at the contralateral knee during the early stance phase.

Results concerning CoP displacement must be observed with caution. It was hypothesized that vaulting should induce during the contralateral stance an average position of the CoP more towards the front part of the foot and an earlier displacement of the CoP towards the front part of the foot. [START_REF] Schmid | Centre of pressure displacements in trans-femoral amputees during gait[END_REF] have analyzed CoP displacement for 12 transfemoral amputees compared to asymptomatic subjects. Anticipated longitudinal displacement of the CoP was found at the contralateral limb for transfemoral amputees by Schmid. However, they did not link the results with vaulting. It was found in the current study that vaulting is associated to an abnormal CoP displacement due to the premature heel rise. On the contrary, premature heel rise seems not to be sufficient to define vaulting as it is shown by the two non-classified subjects. The review of the video recordings did not allow reaching decision for both subjects. Due to limited video quality, it was not possible for clinicians to identify whether or not there was a premature heel rise since the elevation of the calcaneus was not as distinctive as other amputees who vault. However both patients have been observed with suspicion. Concerning calculated parameters, CoPX and CoP% values for both subjects were close to the V group values indicating that the CoP was more located and moved earlier towards the front part of the foot. At the same time, positive values for AFlexAngand negative values for AFlexPwr, similar to C and NV group average values show that there is a dorsal flexion and dissipated power at the contralateral ankle during Table 1 Values of biomechanical parameters related to vaulting for each group and the two non-classified subjects. (AFlexAng: ankle flexion angle, AFlexPwr: ankle flexion power, KFlexAng: knee flexion angle, KFlexPwr: knee flexion power, CoPX: mean longitudinal position of the CoP in the anatomical foot frame, CoP%: % of gait cycle when CoP moves ahead of its average position). the contralateral single stance phase. Thus, both patients seem to present a premature heel rise without propulsive movement at the ankle and so do not seem to vault. Thus, quantification of vaulting may be beneficial in reaching decisions in such cases.

Another methodological limitation lies in the sample size. The amputees were separated in two subgroups composed each of 9 and 6 subjects. Larger samples will enforce statistical analysis in further works to define threshold values of biomechanical parameters for automatic vaulting detection. However, regarding the literature and difficulties in recruiting transfemoral amputees, the 17 transfemoral amputees who participated in the current study constitute one of the biggest populations of lower-limb amputees.

Conclusions

Vaulting was analyzed for a population of transfemoral amputees using quantitative gait analysis. Transfemoral amputees who vault showed a peak in generated power at the contralateral ankle during the contralateral single stance phase. This parameter seems to be the best quantitative criterion to quantify vaulting and can be easily computed from conventional gait analysis data. Prior to this power peak, adaptations at the contralateral knee occurred which can be interpreted as a preparation of the propulsive movement at the ankle.

Fig. 1 .

 1 Fig. 1. Patterns of contralateral ankle and knee flexion angle and power (solid line: control group; dotted line: transfemoral amputee who vaults). Computed parameters supposed to be linked to vaulting are represented on the graphs.

Fig. 2 .

 2 Fig. 2. Description of the anatomical frame of the foot. Origin is the center of the metatarsal heads. Xf is the vector joining the calcaneus to the middle of the 1st and 5th metatarsal heads. Zf is the vector joining the 1st and 5th metatarsal heads. Yf is obtained from the cross-product of Zf and Xf.

Fig. 3 .

 3 Fig. 3. CoP displacement along the X-axis of the anatomical foot frame vs time (solid line: control group; dotted line: transfemoral amputee subject who vaults). Displacement was normalized with respect to the distance between the calcaneus and the center of the metatarsal heads.

MeanFig. 4 .

 4 Fig. 4. Boxplot of the maximum value of ankle flexion power during the contralateral single stance support for each group.

Acknowledgments

This study was supported by the French National Research Agency, under reference ANR-2010-TECS-020. The authors are deeply grateful to F. Lavaste, N. Martinet, J. Paysant, and N. Rapin for their contribution to the study.


Xiao-Hua Zheng
email: zhengxh@zjut.edu.cn

Fang-Er Yang

Chen Li

Zhan-Ling Chen

Ren-Guo Song

Xiang-Hua Zhang

Microstructure and Mechanical Properties of a-CN x Films Prepared by Bias Voltage Assisted PLD with Carbon Nitride Target

Keywords: Carbon nitride, XPS, Raman, friction and wear, pulsed laser deposition

Amorphous carbon nitride (a-CN x ) films were deposited on silicon substrates using pulsed laser deposition technique (PLD) with a carbon nitride target and a negative bias voltage up to -120 V. The microstructure, chemical composition, bonding configuration and mechanical properties of the films were characterized by using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, nanoindentation and ball-on-disc abrasion test. The results show that the negative bias voltage promotes the formation of sp 3 hybridization bonding and leads to a great improvement of nitrogen content (up to 38 at.%) in the films. With an increasing bias voltage from -40 V to -120 V, the nitrogen content and the fraction of sp 3 hybridization bonding

Introduction

Since Liu and Cohen [1] theoretically predicted that the crystalline phase β-C 3 N 4 could have bulk modulus and hardness comparable to or even higher than those of diamond, CN x films have received considerable interest. According to a large number of publications [2][3][4][5][6][7][8][9], CN x films are proved to be amorphous and the nitrogen content is far from the stoichiometric concentration (~57 at.%). Moreover, the mechanical properties of the films fluctuate in a large scale [10][11][12][13] and the thermal stability is poor due to its low decomposition temperature [14][15][16]. Nowadays, it is a tremendous challenge to fabricate a crystalline film that exhibits superhigh hardness in order to check the correctness of the theoretical prediction with of course tremendous application potential, though a few CN x films of high nitrogen concentration up to ~50 at. % (or N/C ratio ~1.0) have been synthesized by chemical vapor deposition (CVD) [17][18], electrodeposition [19], magnetron sputtering (MS) [20] and pulsed laser deposition (PLD) [21][22][23], etc. The optimal condition for the crystallization of carbon nitride is still to be found.

In recent years, our team has developed a series of pulsed laser deposited CN x films by using a carbon nitride target instead of widely used graphite target. The results show that the nitrogen concentration of the films is greatly increased in comparison with those of conventional films deposited with graphite target, the growth rate of the films is more than twice as high as that of the conventional films and the size of particle lumps on the surface as well as the roughness of the films are reduced. However, the films exhibit a large value of x(sp 2 C-N)/x(sp 3 C-N) (i.e., the mole content ratio of sp 2 C-N bonding to sp 3 C-N bonding) and lower mechanical properties compared to the conventional films. Due to the complexity of bonding configuration in CN x films, it is quiet hard to conclude that a higher level of nitrogen content leads to lower mechanical properties. The importance of bonding configuration should be considered in the elaboration of high performance films.

It is well-known that the DC negative bias voltage technique is widely used in the magnetron sputtering deposition of films [24,25]. The results show that the bias voltage dramatically affects the bonding configuration of carbon with/or nitrogen in the films [26,27].

Consequently, by combining a negative bias voltage and the PLD technique, we can expect to alter efficiently the bonding configuration of the CN x films deposited with carbon nitride target. In the present work, the CN x films were fabricated in the deposition system. The composition, the chemical bonding states and the mechanical properties of the films were investigated by comparison with the films deposited using graphite target with the same process parameters.

Experimental conditions

Preparation of films and the target

CN x films were deposited at room temperature by using a pulsed KrF excimer laser (Lambda Physik COMPex pro-201, wavelength of 248 nm, pulse duration of 25 ns, repetition rate of 10 Hz, and laser flux of ~6.7 J/cm 2 ) with a carbon nitride target and a DC bias voltage value (i.e., V b ) of 0 V, -40 V, -80 V, -120 V, respectively. The negative bias voltage was applied to the sample holder insulated from the grounded chamber and the target. Polished Si<100> wafers were used as substrates and mounted onto the sample holder after being chemically cleaned. During the deposition, the pressure of N 2 (reacting gas, 99.999 % in purity) was set at 5 Pa and the target-to-substrate distance was 45 mm. The target and the substrate were rotated to ensure the uniform erosion of the target and to improve the uniformity of the films. With a deposition duration of 20 minutes, the thickness of the films ranged from 1.1 to1.3 μm. The CN x films for comparison purposes (i.e., the reference films) were deposited by using graphite target (2.15 g cm -3 in density) with the same process parameters as the films deposited with carbon nitride target. The film thickness ranged from 0.3 to 0.5 μm.

The carbon nitride target was also fabricated by PLD technique with a silicon wafer substrate (50.8 mm in diameter and 420 μm in thickness). Its deposition condition was the same as that of the reference films, except for the bias voltage of 0 V and the deposition duration of 520 hours. The thickness, density and the nitrogen concentration of the carbon nitride target are respectively 0.35 mm, 1.63 g cm -3 and 24.5 at.%.

Film characterization

The surface morphology and crystalline state of the films were observed by using scanning electron microscopy (SEM, Hitachi S-4700, with an accelerating voltage of 15 kV) and X-ray diffraction (XRD, Rigaku D/Max 115 2550PC, Cu Kα, at an incidence angle of 3°, with a step of 0.02°). The XPS experiments were carried out with a Kratos AXIS Ultra DLD spectrometer, equipped with standard Al Kα excitation source (hv=1 486.7 eV). The x-ray gun was operated at 300 W and photoelectron spectra were collected under constant pass energy of 20 eV for the analyzer. The energy calibration was referenced to the Cu 2p 3/2 line at 932.67 eV and the Ag 3d 5/2 line at 368.26 eV. After background subtraction of Shirley, the spectra were fitted with Lorentzian-Gaussian functions.

Raman scattering measurements (LabRAM HR UV) were performed by collecting the backscattering radiation at excitation wavelength of 632.8 nm in the wave-number range of 800-2000 cm -1 . After background subtraction by means of linear interpolation, the spectra were fitted with Lorentzian-Gaussian function. Nanoindentation tests were carried out on a Hysitron TI 900 triboindenter with Berkovich indenter and maximum load at 1 mN to evaluate the mechanical properties of the CN x films. Each film was measured 20 times and the mean values of hardness and elastic modulus were calculated. The abrasion tests were carried out on a ball-on-disc tribometer (HT-600). The testing parameters were set as the following: Si 3 N 4 ball (5 mm in diameter), normal load at 0.49 N, sliding velocity at 0.176 m/s, test duration at 15 min, and relative humidity at 60~67%. The average values of the recorded instantaneous friction coefficient were used.

Results and discussion

Composition and chemical structure

The XRD results show that the films are amorphous. The typical surface morphology of the films is shown in Fig. 1. It is similar for all CN x films obtained with different bias voltages.

This could be attributed to the rather weak bombardment effect of the PLD-ed species compared to Argon ions commonly used in ion etching technique. The kinetic energy of the former is about (V b + 20~40) eV and the later is usually greater than 500 eV. Fig. 2 shows the detailed C 1s and N 1s XPS spectra of the films and the reference films as a function of bias voltage. According to some recent publications [28][29][30][31][32], the C 1s spectra (Fig. C). Due to the substitution of nitrogen for carbon in the carbon network, the C-N bonding configurations are presented at higher binding energies due to the higher electronegativity of nitrogen [33]. The contribution at 286.3 eV corresponds to the sp 2 C-N hybridization bonding, while the peak at 287.4 eV is attributed to the sp 3 According to the deconvoluted results from the spectra above, the mole fraction of valence bonds can be determined, and then the ratio of x(sp 2 ) to x(sp 3 ) (calculated from C1s spectrum) and x(sp 2 C-N) to x(sp 3 C-N) (calculated from N1s spectrum) are illustrated as a function of bias voltage in Fig. 3, where x(sp 2 ) is the mole fraction of sp 2 bond, x(sp 3 ) the mole fraction of sp 3 bond, x(sp 2 C-N) the mole fraction of sp 2 C-N bond, and x(sp 3 C-N) the mole fraction of sp 3 C-N bond. From Fig. 3, both the ratio x(sp 2 )/x(sp 3 ) and x(sp 2 C-N)/x(sp 3 C-N) of the films are strongly dependent on bias voltage, and the corresponding minima of 1.78 and 1.07 are obtained at -40V. It is suggested that the films deposited at -40V exhibit the highest mole fractions of both the sp 3 hybridization bonding and the sp 3 C-N bonding. Both minima are obviously lower than those of the reference films (2.43 and 1.36, respectively). This indicates that the use of carbon nitride target can remarkably improve the content of sp 3 hybridization bonding in the films. From Fig. 3, we can also conclude that the bias voltage has an important influence on the bonding structure and the optimum bias voltage is around -40 V for promoting the formation of sp 3 hybridization bonding in the films. Fig. 4 shows the effect of the bias voltage on the nitrogen content of the CN x films. With the increase of bias voltage from 0 V to -120 V, the nitrogen content of the films peaks at -40V (~38 at.%) and is significantly higher than that of the reference films by 9~11 at.%.

Accordingly, we can conclude that the use of carbon nitride target is beneficial to the formation of highly nitrogen-contained CN x films. Due to its relatively low density and heat of evaporation compared to graphite target, the carbon nitride target brings brighter and larger size plume during the ablation and yields a higher concentration of the species (e.g., C + , C 2 + , CN, CN + , C 2 N 2 , N + , N 2 + ) [34][35][36], which provoke more collisions among them. Under the condition without bias voltage, when carbon nitride target is sputtered physically, it is expected to have better composition transfer (therefore nitrogen content) from the target to the film. As a consequence of the chemical sputtering effect, the active carbon-containing species react with nitrogen, leading to the production of some CN radicals and C 2 N 2 which provide additional nitrogen atoms for the construction of CN x film. This is of course helpful for increasing nitrogen content in the film. This mechanism may explain why the nitrogen content in the films deposited with carbon nitride target is higher than that of its target. Now consider the film deposition under a bias voltage. On one hand, bias voltage provokes an increase of the kinetic energy of positively ionized species and consequently an increase of the chemical reactivity of these species. Therefore, the reaction between carbon-containing species and ambient nitrogen becomes easier and more nitrogen atoms can be incorporated into the film.

On the other hand, bias voltage facilitates the refocusing on the film surface, of all positively ionized species (e.g., N + ,N 2 + ,CN + ,C 2 N 2 + ) diverging from the deposition direction due to the collisions among the species. This refocusing will lead to an increase of the nitrogen content of the film. However, under the influence of the bias voltage, the positively ionized species in the plasma are accelerated and driven towards the substrate and induce the actions of bombardment and sputtering on the film surface. At higher bias voltages, more intense bombardment of the species heats the substrate to a higher temperature, which releases the volatile nitrogen-containing molecules (e.g., N 2 , C 2 N 2 ) from the films [14][15][16][17]. Therefore, a further increase in bias voltage results in a reduction of nitrogen incorporation. Moreover, the size of the plume in the normal direction of target surface increases with an increase in bias voltage. Once the bias voltage exceeds a critical threshold (for instance, -550V in this case), the leading portion of the plume will be directly in contact with the films and leads to the destruction of the films by its high temperature.

Compared to the deposition of the reference films, more intense collision during the deposition will result in a decrease of the kinetic energy of the active species. And this will weaken the action of sputtering and bombardment, leading to a lower deposition temperature.

It is well-known that sp 3 hybridization bonding is prone to be formed in the temperature range of 150-400 °C and it is also demonstrated that the fraction of sp 3 hybridization bonding decreases with bias voltage (Fig. 3). Therefore we conclude that the higher fraction of sp 3 bonding of the films can be attributed to a relatively low deposition temperature and to an appropriate kinetic energy with which the deposited species arrive on the film surface. Fig. 5 shows the Raman spectra of the films. The measured spectrum of all films exhibits two asymmetric peaks with Gaussian functions similar to the Raman analysis of amorphous carbon in the range of 800-2000 cm -1 . The peak centered at ~1350 cm -1 is a disordered band (D band) originating from the A 1g breathing modes of sp 2 ring (hexagonal or graphitic) structures or the destruction of carbon networks by nitrogen substitution. The other peak centered at ~1530 cm -1 is a graphitic band (G band) and attributed to the stretching of pairs of sp 2 atoms with vibrations (E 2g modes) [37,38]. After the deconvolution of Raman spectra of the CN x films, the detailed structural information of the films can be understood. Fig. 6 shows the dependence of the I D /I G (i.e., the integral intensity ratio of D band to G band), the full width at half maximum of both D band and G band (i.e., FWHM D and FWHM G ), and the position of both bands with bias voltage. As observed from Fig. 6a, the slight shift of the G and D peak positions towards a higher wave band with the increase in bias voltage of CN x films indicates an increase in the size of C sp 2 domains [39]. Meanwhile, the FWHM G of the films tends to decrease with higher bias voltage and this can be attributed to the increase of the cluster size [40].The I D /I G ratio is a good structural indicator for CN x films, since both I D and I G are sensitive to the size of the micro-domains. In fact, I D and I G decrease as the grain size increases [41]. It can be seen that the I D /I G ratio decreases at first and then increases with increasing bias voltage and the minimum I D /I G ratio of 1.85 is obtained at -40 V. According to A. Alkhawwam et al. [42] and A. C. Ferrari et al. [43], we can conclude that the increase in I D /I G ratio, the shift of the G and D peak positions towards higher frequencies, and the decrease in FWHM G with the increase of bias voltage can be attributed to the decrease in sp 3 bonding fraction and to the increase in size of graphitic cluster. At a bias voltage of -120 V, the high value of I D /I G and FWHM D of the film is attributed to the increasing bond angle disorder and indicate the increased degree of carbon network disorder [44,45]. As for the reference films (Fig. 6b), the higher values of the position of D and G bands and of the I D /I G ratio compared to those of the films deposited with carbon nitride target are mainly attributed to the lower sp 3 bonding fraction.

Mechanical properties

Fig. 7 shows the nanoindentation hardness and elastic modulus of CN x films deposited at different bias voltages. With the increase of bias voltage, the hardness increases initially and then decreases. Based on the fact that both ratio x(sp 2 )/x(sp 3 ) and x(sp 2 C-N)/x(sp 3 C-N) follow exactly the opposite trend, we can conclude that the decrease of the ratio x(sp 2 )/x(sp 3 ) and x(sp 2 C-N)/x(sp 3 C-N) makes a significant contribution to improving the hardness of CN x films [25][26][27]. The correspondence between bonding configuration and nanoindentation hardness can be explained by the formula H~(E b ×a c )/d 2 (the hardness defined by the product of binding energy E b and the covalency a c divided by the square of bond length d) [46]. The short length and tetrahedrally bonded sp 3 hybridization bonding helps to build three-dimensional structure like diamond and makes CN x films more rigid [47]. However, the maximum film hardness of 8.35 GPa obtained at -40 V (5.1 GPa for the reference films) is much lower than the hardness reported in references [11][12][13][48][49][50]. This indicates that these films exhibit a rather low resistance to penetration. Previous studies [10,51] show that the CN x film with a nitrogen concentration between 35-50 at.%, a low density and a polymer-like structure exhibits a rather low hardness and elastic modulus than most films. It is possible that the films deposited at -80V and -120V are similar to these films, because more intense bombardment of species on the film surface leads to a decrease in density and to the structure alteration from compact carbon network to the polymer-like structure. Fig. 8 shows the mean value of friction coefficient of the CN x films against Si 3 N 4 ball in humid air. At the function of bias voltage, the friction coefficient of the films addresses the highest value of 0.28 at -40 V and falls slowly later, but is greater than that of the reference films by 0.02~0.06 typically. We believe that, under the same test conditions and tribological mechanism, the sp 2 hybridization component is easier to be sheared and contributes to low friction coefficient during wear tests, but the sp 3 hybridization component presents an opposite action. When the content of sp 3 hybridization component decreases in the films, the resistance force in sliding tests decreases and the friction coefficient of the films becomes smaller. Therefore, an increase of the ratio x(sp 2 )/x(sp 3 ) of the films leads to a lower friction coefficient. Therefore, the reference films exhibits a low friction coefficient due to the fact that they have a relatively high level of the x(sp 2 )/x(sp 3 ) ratio, compared to the films deposited with carbon nitride target.

Conclusions

The microstructure and mechanical properties of the CN x films prepared by PLD with negative bias voltage and carbon nitride target were studied. The nitrogen content and the fraction of sp 3 hybridization bonding in the films are remarkably improved by using the hybrid deposition system. The nitrogen content as well as the x(sp 2 )/x(sp 3 ) and x(sp 2 C-N)/x(sp 3 C-N) ratio of the films are obviously dependent on the bias voltage value. With the increase of bias voltage from -40 V to -120V, an increased graphitization of the films is observed, accompanied by a decrease of the hardness, the elastic modulus and the friction coefficient of the films. The maximum hardness of 8.35 GPa is obtained with a bias voltage of -40 V.

Figure captions

  2a) have been decomposed with Gaussian-Lorentzian distribution functions into five core level C i (i=1,5): C 1 284.5 ±0.1 eV, C 2 285.3 ±0.2 eV, C 3 286.3 ±0.1 eV, C 4 287.4 ±0.2 eV, and C 5 288.9 ±0.4 eV. The first two peaks are assigned as carbon coordinated carbon bonds (respectively sp 2 C-C and sp 3 C-

  C-N hybridization bonding. Finally, the peak C 5 is a characteristic of the C-O bonding configurations. In Fig.2b, the N 1 peak component (~398.6 ±0.1 eV) has been assigned to a sp 3 C-N hybridization bonding, meanwhile the N 2 peak (~400.0 ±0.1 eV) corresponds to a sp 2 C-N hybridization bonding, and the N 3 peak (~402.1 ±0.2 eV) corresponds to N-O bonding.

Fig. 1 Fig. 2 Fig. 3

 123 Fig.1The SEM image of CN x film deposited at bias voltage of -40 V

Fig. 4 Fig. 5 Fig. 6 Fig. 7 Fig. 8

 45678 Fig. 4 Nitrogen content in the films as a function of bias voltage

  (a) C1s, Carbon nitride target (b) N1s, Carbon nitride target (c) C1s, Graphite target (d) N1s, Graphite target

  Fig. 2

Fig. 6 0Fig. 8

 68 Fig. 6

Acknowledgements

The financial supports from Zhejiang Provincial Natural Science Foundation of China (No.Y4110645) are gratefully acknowledged.


Maximilien Servajean
email: servajean@lirmm.fr

Esther Pacitti
email: pacitti@lirmm.fr

Miguel Liroz-Gistau
email: miguel.lirozgistau@inria.fr

Sihem Amer-Yahia
email: sihem.amer-yahia@imag.fr

Amr El

Exploiting Diversification in Gossip-Based Recommendation

In the context of Web 2.0, the users become massive producers of diverse data that can be stored in a large variety of systems. The fact that the users' data spaces are distributed in many different systems makes data sharing difficult. In this context of large scale distribution of users and data, a general solution to data sharing is offered by distributed search and recommendation. In particular, gossip-based approaches provide scalability, dynamicity, autonomy and decentralized control. Generally, in gossip-based search and recommendation, each user constructs a cluster of "relevant" users that will be employed in the processing of queries. However, considering only relevance introduces a significant amount of redundancy among users. As a result, when a query is submitted, as the user profiles in each user's cluster are quite similar, the probability of retrieving the same set of relevant items increases, and recall results are limited. In this paper, we propose a gossip-based search and recommendation approach that is based on a new clustering score, called usefulness, that combines relevance and diversity, and we present the corresponding new gossip-based clustering algorithm. We validate our proposal with an experimental evaluation using three datasets based on MovieLens, Flickr and LastFM. Compared with state of the art solutions, we obtain major gains with a three order of magnitude recall improvement when using the notion of usefulness regardless of the relevance score between two users used.

Introduction

In the context of Web 2.0, users become massive producers of diverse data (e.g. photos, videos, scientific data) that can be stored in a large variety of systems (e.g. DropBox, Facebook, Flickr, Google+, local computer or smartphone). Users are often willing to share their data with other users in a community of interest. However, the fact that their data spaces are distributed in many different systems makes data sharing especially difficult. For instance, an artist photographer who wants to share her pictures within an online community of photographers may have to log in several different Web applications such as deviantArt, Facebook or Flickr, each with a different interface and account. Similarly, a scientist who needs to search for scientific datasets within an online community of scientists will be faced with the problem that the relevant data is typically distributed in many different labs' servers or scientists' local computers. Furthermore, since this data is hidden to web crawlers, traditional search engines become useless. In order to mitigate this problem, some Web applications allow grouping several accounts and data from different systems (e.g. Facebook enables to regroup DropBox and blogs into a single Facebook account). However, they are limited to a few well-known systems.

In this context of large scale distribution of users and data, a general solution to data sharing is offered by distributed search and recommendation [START_REF] Bai | Collaborative personalized top-k processing[END_REF][START_REF] Carretero | Geology: Modular georecommendation in gossip-based social networks[END_REF]. In this paper, we adopt a peer-to-peer gossip-based approach, because it provides important properties such as scalability, dynamicity, autonomy and decentralized control. Within an online community, each user u is associated to a virtual data space that contains all the data items (stored in different systems) it shares. Given u and a keyword query q, the goal of our search and recommendation approach is to recommend to u items that are relevant with respect to q and that are shared by other users, regardless of the systems that store the items. Then, a recommended item is simply a reference that can be used to retrieve the actual data item. In other words, we combine search and recommendation in the sense that a user u searches relevant items among those recommended by users similar to u.

Distributed search and recommendation has received considerable attention [START_REF] Bai | Collaborative personalized top-k processing[END_REF][START_REF] Carretero | Geology: Modular georecommendation in gossip-based social networks[END_REF][START_REF] Draidi | P2Prec: a social-based P2P recommendation system[END_REF][START_REF] Voulgaris | Epidemic-style management of semantic overlays for content-based searching[END_REF]. However, one open problem is the ability to attain high recall results. A query is generally forwarded only to a subset of users who will be employed to process queries and return recommendations. To compute this subset of users, many solutions cluster relevant user profiles implicitly using gossip protocols. Gossip protocols are known to be highly resilient, scalable and converge quickly [START_REF] Jelasity | T-man: Gossip-based overlay topology management[END_REF], which makes them a good alternative for distributed search and recommendation. A User Network (U-Net in the following) refers to the cluster of relevant users, a user u is aware of by gossiping, using a score (e.g. similarity between u and the users in U-Net). At each gossip round, the most relevant users are kept in U-Net. Since U-Net is used to guide recommendations given a keyword query, the relevance score used in the clustering process plays a very important role to increase the number of relevant items retrieved with respect to the whole set of items (i.e. recall), known as the global corpus.

Relevance scores (e.g. Jaccard, overlap) define how well a user profile v meets the needs of another user u. Most of the existing solutions exploit different kinds of relevance scores to increase recall [2-4, 6, 7]. But recall results remain limited.

The reason why recall remains limited is because using relevance as the clustering score introduces a significant amount of redundant user profiles in U-Net. As a result, when a query is submitted, since many user profiles in U-Net are quite similar (i.e. redundant), and these users are chosen to provide recommendations to answer the query, the probability of retrieving the same set of relevant items increases and recall results remain low. In Information Retrieval, usefulness is used as a way to overcome redundancy between the items of a result list by combining relevance with diversity [START_REF] Angel | Efficient diversity-aware search[END_REF][START_REF] Chen | Less is more: Probabilistic models for retrieving fewer relevant documents[END_REF]. In our context, we claim that usefulness can be used when clustering user profiles in U-Net, instead of just relevance. This way, a more diverse set of results will be returned from different users and the recall would be enhanced.

In this paper, we propose a gossip-based search and recommendation approach based on a new clustering score, called usefulness, that combines relevance and diversity. As we show experimentally, this new score is able to increase significantly the quality of the recommendations returned by the system. However, existing peer-to-peer clustering algorithms are no longer suitable since they are optimized for relevance only. Therefore we also propose a new clustering algorithm especially conceived for usefulness.

In summary, we make the following contributions:

1. We show that usefulness is a good way to increase recall and that it should be expressed as a known probabilistic diversification score [START_REF] Angel | Efficient diversity-aware search[END_REF][START_REF] Chen | Less is more: Probabilistic models for retrieving fewer relevant documents[END_REF]. 2. We propose a clustering algorithm that maintains a useful U-Net over a gossip overlay using the usefulness score. 3. We validate our approach with an experimental evaluation using three different datasets: MovieLens, Flickr and LastFM. We observe that diversification enables a huge increase of recall regardless of the relevance score used. Compared with state of the art solutions, we obtain an excellent gain with recall results up to three times better when using the notion of usefulness.

This paper is organized as follows. Section 2 provides some basic concepts and gives the problem definition. In Section 3, we describe our new clustering score and present in details the new clustering algorithm that maintains U-Net. In Section 4, we provide an experimental evaluation. In Section 5, we compare our contributions with related work. Finally, Section 6 concludes and provides directions for future work.

Basic Concepts and Problem Definition

In this section, we introduce the background necessary to understand the problem we address. In our distributed search and recommendation approach, whenever a user u submits a query q, the system sends q to a subset of users that we call U-Net, and who will return their relevant results to u and will also recursively forward the query to the users in their U-Net until the TTL is reached. To build U-Net, we use a two steps approach. First, based on random gossiping each user u is aware of other peers available on the network. Second, by means of a clustering algorithm, u chooses among these users the best ones to answer u's queries and keep them in U-Net.

More precisely, our peer-to-peer model is expressed based on a graph G = (U, I, E), where U = {u 1 , ..., u n } is the set of users distributed over the network, I = {i 1 , ..., i m } the set of shared data items (in the following, an item refers to a data item), and E = {e 1 , ..., e k } the set of directed edges among users and between users and items. This model is very generic. In our case, users are independent nodes in the network. A node can be a physical computer or a virtual node in a server.

Definition 1 (U-Net). Given a user u, its User Network, or U-Net, refers to the cluster of relevant users u is aware of. There is an edge e(u, v) in the graph between u and a user v, if v is in u's U-Net.

With random gossiping [START_REF] Jelasity | T-man: Gossip-based overlay topology management[END_REF], each user keeps locally a random view of its dynamic acquaintances (or view entries). Each view entry corresponds to a user profile. Periodically, each user chooses randomly a contact (view entry) to gossip with. The two involved users then exchange a subset of each others view (i.e. user profiles), and update their view state. Then, after each gossip exchange, the random view is used to update the U-Net if more relevant profiles are found in the updated view. We use Jaccard as the relevance score to select the best users:

Jaccard(u, v) = |Iu ∩ Iv| / |Iu ∪ Iv| (1)
Where I u and I v are the items shared by user u and v respectively.

Here, we use the vector space model to represent items and user profiles [START_REF] Manning | Introduction to Information Retrieval[END_REF]. Specifically, each item it is modelled as a sparse vector containing only the weights of the keywords k 1 , ..., k z in it. The weight of each keyword is computed using tf × idf . Distributed tf × idf can be easily implemented using gossip protocols. Indeed, the first part of the score, denoted tf, can be computed locally, and the second part, denoted idf, only needs average information (e.g. average number of items per user) to be computed. These averages can be easily computed using gossip protocols [START_REF] Kowalczyk | Towards data mining in large and fully distributed peer-to-peer overlay networks[END_REF]. Due to lack of space, we do not develop this protocol in this paper.

Each user profile is defined based on the items the user shares, I u (i.e. content based recommendation). We choose a relevance score (i.e. Jaccard ) that works well with content-based recommendation, but other relevance measures and profiles definition methods could be used as well.

As mentioned before, whenever a user u submits a keywords query q = k 1 , ..., k w , the query is redirected to all users in the participating users' U-Net recursively, until a predefine upper threshold, T T L (i.e. Time-To-Live). Whenever a user v receives a query, it computes its top-k most relevant items with respect to the query using a specific relevance score (e.g. Jaccard). Then, v returns them to u. A recommended item is defined by its identifier, its tf × idf vector, v's identifier and v's profile. Once u receives the set of recommended items from v 1 , ..., v n with respect to its query q, it ranks them based on their relevance with respect to the query:

Recq = rank (rec 1 q (it1, ...) ∪ ... ∪ rec n q (itp, ...)) (2)
Where rec 1 q (it 1 , ...) is a recommendation (i.e. a set of recommended items) coming from a user v 1 .

To evaluate the quality of search and recommendation, we use the recall measure [START_REF] Baeza-Yates | Modern Information Retrieval[END_REF]. Recall captures the fraction of items that have been successfully recommended: recall = |Iret q | / |Irel q |, where Iret q ∈ I refers to the relevant items recommended with respect to a query q, and Irel q ∈ I refers to all the relevant items with respect to query q.

Problem Definition: Given a user u ∈ U , a query q, I in G, and a gossip based overlay, the goal is to maximize the number of relevant items with respect to q returned to u while minimizing TTL.

Diversified Clustering and Algorithm

In this section, we show that usefulness is an excellent way to increase recall results of gossip-based recommendation, and can be used as a clustering score. In section 3.1, we formally show that to increase recall, usefulness should take into account relevance and diversity. Next, Section 3.2 presents the Useful U-Net clustering algorithm deployed over a gossip-based overlay.

Usefulness Score

The usefulness score should be designed such that it maximizes the probability that a user u can retrieve relevant items given a random query q, known as the coverage probability. In other words, u's neighbors v 1 , ..., v n ∈ G should be chosen such that the number of relevant items (with respect to the queries u will submit) that can be accessed through them is maximized.

Let Q be the set of all possible queries (all the combinations of terms), and P (Q v ) the probability that a user v can return at least one relevant item given a random query q ∈ Q. In the following, we first define the coverage with respect to U-Net u = {v 1 , ..., v n }. Then, based on coverage, we express the usefulness of a user v with respect to the other users in u's U-Net.

Definition 2 (Coverage). Given Q and U-Net u = {v 1 , ..., v n }, the users in u's U-Net. The coverage is the probability that at least one of the user in u's U-Net can return at least one item given a random query q ∈ Q. Coverage is denoted

P (Q v1 ∪ Q v2 ∪ ... ∪ Q vn ).
The user profiles v 1 , ..., v n must be selected such that the coverage probability is maximized. Formula 3 develops the coverage probability with respect to every user in u's U-Net.

P (Qv 1 ∪ ... ∪ Qv n ) = j∈1,...,n (P (Qv j ) -P (Qv j ∩ (Qv j+1 ∪ ... ∪ Qv n ))) (3)
P (Q vj ) -P (Q vj ∩ (Q vj+1 ∪ ... ∪ Q vn ))
represents the coverage added by user v j with respect to the users v j+1 , ..., v n . As a consequence, when j = n, only P (Q vj ) is considered as there is no more user profiles to compare with.

In the following, we define the usefulness of a user profile v i with respect to the coverage probability. Definition 3 (Usefulness). Given u's U-Net, the usefulness of a user profile v j is the probability that it can return relevant items for a random query q, that could not be returned by other users in u's U-Net. In other words, it is defined as follows:

usefulness(vj|vj+1, ..., vn) =P (Qv j ) -P (Qv j ∩ (Qv j+1 ∪ ... ∪ Qv n )) (4)
Formula 4 shows that the usefulness score should consider relevance P (Q vj ) and take into account

P (Q vj ∩ (Q vj+1 ∪ ... ∪ Q vn )
) which corresponds to the redundancy of user profile v j with respect to the other user profiles v j+1 , ..., v n .

In the following, we show that usefulness(v j |v j+1 , ..., v n ) can be expressed into a known probabilistic diversification model [START_REF] Angel | Efficient diversity-aware search[END_REF][START_REF] Chen | Less is more: Probabilistic models for retrieving fewer relevant documents[END_REF]. In Formula 5 we first integrate usefulness (the right hand side of Formula 4) into a conditional probability.

P (Qv j ) -P (Qv j ∩ (Qv j+1 ∪ ... ∪ Qv n )) =P (Qv j ) × (1 -P (Qv j+1 ∪ ... ∪ Qv n |Qv j )) =P (Qv j ) × P ( Qv j+1 ∩ ... ∩ Qvn |Qv j ) (5)
Similar to [START_REF] Angel | Efficient diversity-aware search[END_REF][START_REF] Chen | Less is more: Probabilistic models for retrieving fewer relevant documents[END_REF][START_REF] Carbonell | The use of MMR, diversity-based reranking for reordering documents and producing summaries[END_REF], we assume that the redundancy of a user profile v 1 with another user profile v 2 is independent of its redundancy with other users and we derive Formula 6.

P (Qv j ) × P ( Qv j+1 ∩ ... ∩ Qvn |Qv j ) =P (Qv j ) × i∈j+1,...,n (1 -P (Qv i |Qv j )) (6)
Finally, we observe that the usefulness of a user profile is clearly similar to the probabilistic diversification problem used in [START_REF] Angel | Efficient diversity-aware search[END_REF][START_REF] Chen | Less is more: Probabilistic models for retrieving fewer relevant documents[END_REF] and presented in Formula 7.

usefulness(vj|vj+1, ..., vn) =rel(vj) × i∈j+1,...,n

(1 -red(vj, vi))

where rel(v j ) = P (Q vj ) is the relevance of user profile v j and red(v j , v i ) = P (Q vi |Q vj ) is the redundancy of user profile v j with respect to the other user profile v i .

Useful U-Net Clustering

We now present in details our clustering algorithm that maintains a useful U-Net over a random gossip overlay using the usefulness score.

Given the set of users in the random view, the goal of the clustering algorithm is to compute the usefulness of each user found in the view, with respect to those that were previously added to the U-Net, taking into account relevance and diversity, as defined in Equation 7, and to update the U-Net as consequence.

Based on random gossiping [START_REF] Jelasity | T-man: Gossip-based overlay topology management[END_REF], each user u maintains a set of random view entries corresponding to the users profile u is aware of. Periodically, users gossip, and exchange a random subset of their views entries. After the random gossip merging phase, the clustering algorithm, which corresponds to the Useful U-Net Algorithm depicted in Algorithm 1, is triggered. In fact, taking into account the previous gossip exchange, the algorithm selects the most useful users from the random view considering the useful users previously selected (i.e. from the previous gossip rounds) in the U-Net. The algorithm uses three main data structures: random view, U-Net, and the candidate list. The random view and the U-Net are initialized when u joins the network, and continuously updated as a result of random gossip. The candidates list contains the user profiles that will potentially added to the U-Net and is initialized each time the clustering algorithm is triggered.

In the following we present in more details the Useful U-Net algorithm based on the example of Figure 1. The random view entries correspond to the profiles of users v 1 , v 6 , v 7 , v 8 , v 9 . The previous useful user profiles are v 1 , v 2 , v 3 , v 4 , v 5 and are stored in U-Net. Assuming that the algorithm is executed in u's node, the algorithm input is u's profile, its random view denoted RandomView u and its U-Net denoted U-Net u . The data structure used for U-Net is an array of size N of user profiles, associated to their usefulness score and sorted in decreasing order of usefulness. The output of the algorithm is the updated U-Net. Useful U-Net algorithm has three main parts:

1. The first part (lines 1 to 6) finds the best useful user profile from the random view, and the position i where it should be inserted in the U-Net (recall that the usefulness score of a user depends on its position in the U-Net). As a consequence, the update of the U-Net will only concern the user profiles from position i to N . To find the best useful user from the random view, the algorithm first initializes the candidates list with all users in the random view except those already in the U-Net (line 2). In Figure 1, v 1 is already in the U-Net, so the candidates list is initialized with the users v 6 , v 7 , v 8 , v 9 (1a). For each position i in U-Net, all the usefulness scores of the candidates are computed using Formula 7 taking into account the set of users in the U-Net at positions 1, ..., i -1, and compared with the usefulness score of the user profile in U-Net u [i]. If the best user profile in candidates is more useful than U-Net u [i], then, the algorithm stops iterating (line 6). If there is more than one best user profile, the best user profile is chosen randomly with respect to the set of best user profiles. In Figure 1, v 7 is more useful than v 3 at the third position in u's U-Net because v 3 's usefulness is 0.78 while v 7 's usefulness is 0.89 (1b). If there is no user profile in the candidate list whose profile score is superior to any user profile in the U-Net, position N is reached and the algorithm stops. Only the scores of the user profiles up to position i are definitive. Thus, in our example, the scores of v 4 , v 5 , v 6 , v 8 , v 9 are not definitive because they are either not in the U-Net or after i.

2. The second part (lines 7 to 10) copies and deletes the remaining user profiles (from position i to N ) from the U-Net to the candidates (2a) list because their scores need to be recomputed using Formula 7 and with respect to the best user profile in candidates (computed in part 1). Then, the best user profile is inserted in position i. In the on-going example of Figure 1, the user profiles v 3 , v 4 , v 5 are copied and removed from the U-Net to the candidates list and user profile v 7 is added in the U-Net at position 3 (2a and 2b). 3. Finally, in the last part (lines 11 to 15), the algorithm iteratively computes, for each empty position i in the U-Net (positions emptied in part 2), the scores of the user profiles in the candidates list using Formula 7 and taking into account the set of users in the U-Net at positions 1, ..., i-1 (lines 12 and 13 and step 3a the figure). Then, the most useful candidate is moved to the U-Net at that position (line 15 and step 3b in the figure). The algorithm repeats these steps until all the positions in U-Net are filled out (line 11).

Recall that gossip protocols converge quickly [START_REF] Draidi | P2Prec: a social-based P2P recommendation system[END_REF]. As a consequence the U-Net will also converge quickly and, in general, tends to stabilize. Therefore, the algorithm will stop at step 1b more and more frequently.

Experimental Evaluation

In this section, we provide an experimental evaluation to validate our approach and compare it to other state-of-the-art solutions. We conducted a set of experiments using three datasets which correspond to MovieLens, Flickr and LastFM. In Section 4.1, we introduce the experimental setup of our evaluation. Then, in Section 4.2, we present and discuss the experimental results.

Experimental Setup

We ran our experiments on the PeerSim simulator 5 . We used three different datasets: MovieLens, Flickr and LastFM. MovieLens dataset is composed of users that rated movies. Flickr dataset is composed of users that submitted or added a picture to their favorites. Each user also associates tags to the pictures he/she submits. Finally, LastFM dataset is composed of users who listen and associate tags to artists. Each dataset has different features, in particular users are more or less redundant if the number of items per user is more or less respectively. The characteristics of the datasets are summarized in the following The queries used in the experiments consist of: In MovieLens, for each user, a random subset of movies are shared and the rest are used as the queries to submit. In particular, the words in the title are used as separate keywords. In Flickr and LastFM queries are computed as the random association of several tags submitted by a given user on a given item. An experiment is composed of two parts. First, all users gossip during 400 rounds until convergence. Then, every 20 gossip rounds all users submit one of their queries. The experiment stops at 500 gossip rounds. We measure the average recall results. The recall enables to compute the fraction of items that has been successfully recommended as presented in Section 2. On the MovieLens dataset, the recall value is 1 if the movie has been found and 0 otherwise. On Flickr and LastFM, the recall is the proportion of pictures in the whole dataset that contains all query's keywords that have been returned to the user. On the Flickr and LastFM experiments, we have computed the variance which enables to compute the variability of the recall and is computed as follows: 2 where m is the average recall.

V (X) = 1/N × n i=1 (x i -m)
In our experiments, we use the following relevance scores:

overlap(u, v) = |Iu ∩ Iv| over big(u, v) = |Iu ∩ Iv| + |Iv| Jaccard(u, v) = |Iu ∩ Iv| / |Iu ∪ Iv| cosine(u, v) = Iru × Irv / ||Iru|| × ||Irv||
where I u and I v are the items shared by u and v, respectively, and where Ir u and Ir v are the set of ratings u and v gave to the items they share. We have fixed the U-Net's size to 16 and TTL to 3. Other values have been tested and showed similar results. The size of the random view (5 in our case) is not important as it only modifies the convergence speed.

Experiments

Figures 2d, 2e and 2f

show that all diversification methods enable to increase the recall values compared to undiversified methods. Among them, usefulness obtains the best gain in terms of recall closely followed by xQuad. Finally, MMR shows the worst gain in terms of recall. Indeed, MMR chooses users that minimize the maximum similarity between any two users in u's U-Net. Therefore, it prefers users that are a little bit similar with every user in u's U-Net, and that do not necessarily increase recall results.

Related Work

Distributed recommendation for web data based on collaborative filtering has been recently proposed with promising results. In this section, we compare our recommendation approach with state of the art solutions.

In [START_REF] Loupasakis | eXO: Decentralized autonomous scalable social networking[END_REF], Loupasakis and Ntarmos propose a decentralized approach for social networking with three goals in mind: privacy, scalability with profitability and availability. They propose an architecture based on a DHT for keywords query search. Since, DHTs are better suited for exact-match queries, the author propose to decompose each query into several single word exact-match queries. The main drawback is that responses that have medium scores with respect to each keyword but high scores with respect to all the keywords are likely to be missed.

P2PRec [START_REF] Draidi | P2Prec: a social-based P2P recommendation system[END_REF] has been proposed as a gossip-based search and recommendation solution. The profile of each user u is represented as a set of topics computed based on the items u shares. Then, using gossip protocols, similar users in term of topics, are clustered together and used to guide recommendation as we do. However, since diversity is not taken into account, users within each cluster can be redundant, thus limiting recall results. In [START_REF] Kermarrec | Application of random walks to decentralized recommender systems[END_REF], Kermarrec et al. focus on recommendation and propose to combine gossip algorithms and random walks. First, the users are clustered based on relevance through gossip protocols. A user has knowledge of the items shared by its neighbors. To compute the recommendation, each user runs locally a random walk using a transition similarity matrix. However, computing this matrix and its inverse seems not scalable due to the computational complexity of the algorithm with respect to the size of the neighborhood and the number of items. Also in the context of recommendation, in [START_REF] Kermarrec | Diverging towards the common good: Heterogeneous self-organisation in decentralised recommenders[END_REF] Kermarrec et al. claim that, because of the heterogeneity of the users in the network, a single similarity measure to cluster users is not sufficient to achieve good recall results. Instead they propose that each user employ its own similarity measure to build its view (clustering layer) of the network. Nevertheless, the concept of diversity is different from ours as it represents the usage of various relevance scores depending on each user's profile. As a consequence, each user's cluster may still carry redundant user profiles, because there is no explicit diversification. In [START_REF] Bai | Collaborative personalized top-k processing[END_REF], Bai et al. propose a solution for personalized P2P top-k search in the context of collaborative tagging systems, called P4Q. In this solution, the users are clustered based on relevance through gossip protocols. The users in each cluster are split into two groups: 1) the c closest users from which u replicates all items metadata (i.e. tagging actions) and 2) the n less similar users from which u knows only the profile (i.e. bloom filter ). Still, diversity is not taken into account and users within the clusters are likely to be redundant.

Conclusion and Future Work

In this paper, we proposed a new gossip-based search and recommendation approach with new measures and techniques. We first showed that usefulness, by combining relevance and diversity, is very effective in increasing recall results and can be used as a clustering score. Then, we designed a new clustering algorithm based on usefulness that combines relevance and diversity. We validated our proposal with an experimental evaluation using the MovieLens dataset. Compared with state of the art solutions, we obtain major gains with recall results more than two times better.

In future work we intend to exploit other recommendation scenarios such as multisite recommendation.

Fig. 1 :

 1 Fig. 1: An example of the execution of Useful-Unet.

Algorithm 1 : 4 i++; 5 for

 145 Useful U-Net Input: u profile, U-Netu (array[1..N]), RandomView u Output: U-Netu is updated with respect to the RandomView 1 candidates : unsorted list of user profiles; 2 candidates ← RandomView u -U-Netu; best ← ∅; i ← 0; 3 repeat each cj ∈ candidates do score(cj) ← usefulness(cj,u,U-Netu[1..i -1]) best ← arg max c∈candidates (score(c)); 6 until i=N or score(best) > score(U-Net[i]); 7 if score(best) > score(U-Net[i]) then 8 after ← U-Netu[i..N ]; U-Netu[i] ← best; i++; 9 candidates ← candidates -best; 10 candidates ← after ∪ candidates; U-Netu ← U-Netu -after ; 11 while i < N and candidates = ∅ do 12 for each cj ∈ candidates do 13 score(cj) ← usefulness(cj, u, U-Netu[1..i -1]); 14 best ← arg max c∈candidates (score(c)); U-Netu[i] ← best; 15 candidates ← candidates -best; i++;

Figure 2

 2 Figure 2 presents the results of our experiments. More precisely, Figures 2a, 2b and 2c compare the recall results of the used relevance scores with and without 5 www.peersim.sourceforge.net

  table.

	dataset items # items # users avg items/user
	MovieLens Movies 3, 900	6, 040	166
	Flickr	Pictures 2, 029	2, 000	3.7
	LastFM Artists 23, 346	2, 000	98

Work conducted within the Institut de Biologie Computationnelle and partially funded by the labex NUMEV and the CNRS project Mastodons.

Not surprisingly, diversifying the U-Net enables for all relevance score to significantly increase recall. On the MovieLens dataset, the recall results without diversification range between 0.58 and 0.62 while they range between 0.978 and 0.999 with diversification. On the Flickr dataset, the gains are slightly smaller. Since all users share their own pictures, their profiles are very different and already diversified. Therefore, diversification has less impact on the recall. Finally, the LastFM dataset recall results are up to 3.26 times higher.

In addition to improve the recall, diversified solutions enable to reduce the variance compared to undiversified solutions. For instance, on Flickr, the variance decreases from 0.116 to 0.013 when using Jaccard. This can be explained by the fact that in the undiversified solution, users in U-Net are very similar among them. As a consequence, either all are relevant to the query, and hence they provide a high recall; or none of them is, thus producing a low recall. Diversification enables to increase coverage and therefore, it increases the probability to answer any kind of query.

In addition, we ran these experiments with different sizes of U-Net and values of TTL. For instance, on the MovieLens dataset, with a U-Net of size 5 and a TTL of 2, the recall is in average 2.37 times higher compared to undiversified solutions. Indeed, without diversification, recall values are in average of 0.26 while they reach 0, 61 using usefulness.

We have also compared three different diversification methods. The first is the usefulness score presented in Equation 7. The second method we use is the Maximal Marginal Relevance, known as MMR [START_REF] Carbonell | The use of MMR, diversity-based reranking for reordering documents and producing summaries[END_REF]. MMR chooses users that minimize the maximum similarity between any two users in u's U-Net. Finally, the last method is Explicit Query Aspect Diversification known as xQuad [START_REF] Santos | Explicit search result diversification through sub-queries[END_REF]. xQuad chooses users such that each user v i in u's U-Net is similar to u in a different way. For instance, suppose that u shares items i 1 and i 2 . If v 1 is in u's U-Net and is similar to u because it also shares i 1 , then, xQuad chooses a user v 2 such that v 2 is similar to u because it shares the item i 2 . In this experiment, we use Jaccard as the similarity measure.


Maximilien Servajean
email: servajean@lirmm.fr

Esther Pacitti
email: pacitti@lirmm.fr

Miguel Liroz-Gistau
email: miguel.lirozgistau@inria.fr

Alexis Joly
email: alexis.joly@inria.fr

Julien Champ
email: julien.champ@lirmm.fr

PlantRT : a Distributed and Diversified Recommendation Tool for Citizen Sciences * Plateforme de Recommandation Distribuée

De nombreux domaines scientifiques produisent et consomment des volumes de données considérables (e.g. biologie, astronomie, physique). Dans ce travail, nous nous intéressons au traitement de données de types images, produites à grande échelle par des botanistes dans le cadre de Pl@ntNet 1 . L'objectif de notre prototype, PlantRT, est de permettre l'étude de l'évolution et des corrélations de familles de plantes diverses. Chaque image, ou observation, est ainsi produite de façon personnelle -par chaque citoyen ou botaniste participant au projet -, et peut être stockée sur différents types de sites (e.g. ordinateurs personnels, smartphones, serveurs, clouds). Par ailleurs, l'émergence des systèmes de recommandation distribués favorise le partage, la découverte et la mise en relation de ces données produites par chaque citoyen impliqué. Cet article présente PlantRT un prototype de système de recommandation multi-sites, prenant en compte la diversité des profils des citoyens, ou utilisateurs, et des données, favorisant, par exemple, la découverte de nouvelles espèces de plantes d'une même famille ou de la même zone géographique ainsi que le passage à l'échelle. Nous montrons ici, en particulier, plusieurs cas d'utilisations ainsi que le déploiement de PlantRT en détails.

I. Introduction

Many fields of science are currently massive producers of diverse data items, or items (e.g. images, experimental datasets, plant observations). For instance, in botany, the emergence of citizen sciences has fostered the creation of large and structured communities of nature observers (e.g. e-bird 2 , xeno-canto 3 , Tela Botanica 4 , Pl@ntNet [START_REF] Joly | Interactive plant identification based on social image data[END_REF]) who started to produce outstanding collections of image records (i.e. items). These images are captured by volunteer nature observers, and stored in different and heterogeneous sites (e.g. PCs, Smartphones, Clouds, Serveurs). Building an accurate knowledge of the identity, the geographic distribution and the evolution of living species is essential for a sustainable development of humanity as well as for biodiversity conservation. However, scaling up such collaborative approaches to real-world ecological surveillance systems involving millions of contributors is still challenging.

The emergence of distributed search and recommendation systems favors personalized retrieval of items (e.g. images). In such systems, each user shares a set of items in its local node. Then, given a user u and a query qgenerally keyword based -, the goal is to recommend items in a personalized way. Generally, the recommended items are chosen based only on the query initiator's profile and on the query itself. Diversified search and recommendation is a personalized method [START_REF] Servajean | Profile Diversity in Search and Recommendation[END_REF] that, given a query q and the query initiator u, recommends items taking into account the query, u's profile, and the diversification of both the relevant items and the profiles of the users sharing them. In the context of Pl@ntNet, user profiles are defined based on the observations made by each user. Diversification enables the discovery of items referring to plants from different species within the same family, or concerning the same geographical area. This is very useful for botanists who wish to understand the plants biodiversity.

In [START_REF] Draidi | P2Prec: a Social-Based P2P Recommendation System[END_REF] we proposed a distributed peer-to-peer solution for search and recommendation with no diversification. Here we propose a generic multi-site approach in the sense that sites may be heterogeneous (e.g. PCs, Smatphones, Clouds). As presented, each user has her own profile based on the items she shares. However, for multi-users sites (i.e. Clouds, Servers), we propose to exploit the concept of virtual nodes. That is, in multi-users sites, in addition to the users' profiles, we adopt the approach of clustering them through k-means [START_REF] Amer-Yahia | Efficient network aware search in collaborative tagging sites[END_REF]. Therefore each multi-users site will also be composed of k virtual nodes' profiles (i.e. means). The set of users that are similar with a specific means, defines a virtual node. Virtual nodes are useful to define the logical overlay topology for distributed search and recommendation and may be implemented as a simple thread. In addition, virtual nodes enables to index items in a personalized and efficient manner [START_REF] Amer-Yahia | Efficient network aware search in collaborative tagging sites[END_REF].

In our demo, we show in details how PlantRT was deployed over 10 multi-sites (i.e. Clouds' virtual machines and a PC) using virtual nodes. Our demo uses a dataset provided by Tela Botanica containing more than 10, 000 observations produced by 1, 500 real citizens. We placed items on different sites and we demonstrate the behaviour of PlantRT with different types of queries, submitted by users having various profiles, to show that our distributed recommendation approach provides good results in terms of diversification, recall and performance. For this purpose, we create several scenarios varying the number of users (and virtual nodes), sites and the size of the corpus. In addition, we also demonstrate the efficiency of the distributed TF ×IDF algorithm, necessary to index the items of the distributed corpus using the images meta-data. This indexation is useful to create the inverted lists, thus enabling top-k processing for search and recommendation. To the best of our knowledge, this is the first work that provide a real demonstration of a complete distributed and diversified recommendation tool using real scientific data.

This paper is structured in the following way. First, Section II presents an overview of PlantRT's architecture. Followed by Section III, which presents the details of our distributed TF × IDF solution. Section IV describes the demonstration itself, including the use case and details about the prototype's deployment. Finally, Section V concludes the paper.

II. PlantRT Architecture's Overview

In this section, PlantRT's architecture is discussed. An important element of the prototype is the concept of virtual nodes. That is, each site hosts 1 to n virtual nodes, each referring to a set of 1 to m users having similar profiles, and produced using k-means. Virtual nodes have two benefits. First, they enable to index items in a personalized way [START_REF] Amer-Yahia | Efficient network aware search in collaborative tagging sites[END_REF], thus improving top-k performances. Second, they permit to define the logical overlay topology, noted VN-Network, using gossip protocols. Notice that the VN-Network is used during query processing to guide recommendation among sites.

As presented, PlantRT distributed search and recommendation platform is composed of multi-sites (e.g. server, PC, cloud). Each site hosts 1 to n virtual nodes, each referring to a set of 1 to m users having similar profiles. Each user shares a set of items (i.e. observations images) that are associated to meta-data and used to produce her profile [START_REF] Servajean | Profile Diversity in Search and Recommendation[END_REF]. The meta-data refer to the plant's family, species and genus, the observation's location and a description. Meta-data and users' profiles are presented through a TF × IDF vector, the latter being used by kmeans to produce the virtual nodes. Notice that TF ×IDF is computed in a completely distributed manner, thus avoiding centralization, as explained in Section III.

In our approach, virtual nodes are used both to personalize indexation and to facilitate the distributed overlay construction -users with different profiles will forward their queries through the overlay to different sites.

Thus, each virtual node holds an index (inverted lists) that refers to the items of the related site. Inverted lists associate for each keyword the set of items that contain it. Finally, the site items are ranked in a personalized way with respect to the users' profile of the corresponding virtual node [START_REF] Amer-Yahia | Efficient network aware search in collaborative tagging sites[END_REF]. This enables to improve top-k processing performance over the inverted lists. Notice that virtual nodes of a same site, have access to all the items of that site. Figure 1 presents an example composed of four sites, three being virtual machines in the cloud and one being a local computer. In this example, we focus on the case of the Windows Azure's site where two virtual nodes have been produced; also, the site is associated to a database containing all items shared in it.

Additionally, virtual nodes are connected together through a distributed overlay called VN-network. It enables to connect diverse virtual nodes taking into account profile diversity. That is, a virtual node has its own profile, corresponding to its center (i.e. average of all profiles in the virtual node), computed during the clustering process with k-means. By diversifying the VN-network which is used to guide recommendation, both results diversification and recall levels are increased while latency is minimized [START_REF] Servajean | Exploiting Diversity in Gossip-Based Recommendation[END_REF]. In Figure 1, each virtual node of the Windows Azure's site is connected to two other virtual nodes owned by two different sites. The first one, VN 1 is connected to both Amazon and Google's sites while the second one, VN 2 , is connected to both the local computer and Google's site.

The VN-network overlay is built in two steps: 1) based on random gossiping, each site is aware of remote virtual nodes available on the network, 2) at each gossip exchange, each local virtual node applies a distributed profile diversification clustering algorithm [START_REF] Servajean | Exploiting Diversity in Gossip-Based Recommendation[END_REF], to build its VN-network. Finally, to retrieve items, users submit queries. A query is generally keyword based q = k 1 , ..., k p .Whenever a user u submits a query q, it is forwarded to all nodes in its local virtual node's VN-network. Each virtual node receiving the query repeats the same procedure until the query TTL (i.e. time to live) is reached. Finally, each involved virtual node, including u's one, returns its relevant items, selected within its site, with respect to q and u's profile, by using a specific similarity measure (e.g. Jaccard).

III. PlantRT Distributed TF × IDF Indexing

As presented in Section II, in PlantRT, items refer to images that are associated to textual meta-data. Thus, in order to be indexed in inverted lists, items are represented by a TF × IDF [START_REF] Jones | A statistical interpretation of term specificity and its application in retrieval[END_REF] vector.

In this section, we present in details our distributed solution for TF × IDF computation because it is an original solution for distributed indexation that does not depend on any centralized authority. Recall that TF ×IDF is a score that defines the importance of a term given an item and the global corpus (i.e. set of all items stored in the whole system), which, in our case, is completely distributed. The intuition behind our solution is that we only need average statistics of the global corpus to compute the full score.

More precisely, the TF × IDF score of a given term t is obtained by multiplying the term frequency (T F ) by its inverse document (i.e. item) frequency (IDF ) within the global corpus I. While the first part can be computed locally, the latter needs a distributed protocol since the global corpus is not available. Thus, to compute IDF at each site s, we use a gossip-based protocol. Generally, IDF is computed as follows:

IDF (t, I) = log |I| F t/I (1)
Where F t/D is the number of items in the whole corpus I that contain the term t.

The goal of our distributed approach is to compute the average number of items per site, noted |I|/n, and for each term t, the average number of items that contain it per site, noted

F t/D
n , where n is the total number of sites. Then, we simply exploit the property of convergence of gossip-based protocols applied to P2P average computing [START_REF] Kowalczyk | Towards data mining in large and fully distributed peer-to-peer overlay networks[END_REF]. Based on these averages, each site will be able to compute the IDF vector locally.

IV. PlantRT Demonstration In this section, we show characteristics of PlantRT. First in Section IV-A, we present the use case of our demonstration. Then in section IV-B, we show details of the deployment and of the behavior of the prototype.

A. PlantRT Use Case

Recall that we rely on diversified search and recommendation that takes into account the query, the query initiator's profile, and the diversification of both the relevant items and the profiles of the users sharing them. Here we show the behavior of PlantRT over our distributed platform with different types of queries, submitted by users having various profiles, to show that our distributed recommendation approach provides good results in terms of recall and diversification.

Table I presents the results obtained by executing the query "asteraceae" -which corresponds to a very large family of plants, including the well known daisy -over our whole dataset of 10, 000 observations and 1, 500 users. We chose three users to analyze the returned items.

In Table I, each column refers to the results obtained by a single user. In the first column, corresponding to Élodie Dujardin, results are un-diversified. In the two other columns, corresponding to Marie Dupont and Pierre Durand, results have been obtained using profile diversification. Figure 2a their observations; therefore each line refers to the observed plant's family and species associated to the location of the observation.

Not diversifying the results leads to the first column which only contains very redundant plants observations. In the other hand, diversifying the results enables to retrieve a broader spectrum of plants species. For instance, the first result of Marie Dupont is a plant which species is Cirsium vulgare (Savi) Ten, while the second one corresponds to the species Crepis vesicaria. Similarly, Pierre's first results is Senecio doronicum, while the second one is Echinops ritro. These species can be observed in their respective profile in Figure 2.

We now show how our diversified recommendation method can be applied in the case where a user wishes to retrieve plants observations made in the geographical area where she is. In this case, the query is the geo position and relevant items are chosen taking into account the geographical area around q, its initiator's profile and the diversification of both the relevant items and the profiles of the users sharing them.

For instance, in Figure 3, Pierre Durand went hiking around Montpellier. He is represented by the blue dot in the center of the map, thus referring to the query q = latitude :43.74 , longitude :3.9 . The results of the query are represented by the red pins. They refer to diversified observations made in the area. For instance, in the figure, we zoomed on an observation of a plant from the Rahmnaceae's family.

B. PlantRT Deployment and Robustness

In our demo, we show the diverse steps necessary to deploy PlantRT over a multi-site architecture using virtual nodes. In addition, we show that PlantRT is robust and performs well.

Two main steps are needed to deploy PlantRT. The first step consists in preparing the environment for the prototype; the second one consists in preparing PlantRT to be executed in the previous environment.

First, an environment must be created to host PlantRT. This environment refers to the machine (e.g. local computer, server or a virtual machine in the cloud) and the set of applications needed to host the prototype.

More precisely, two applications must be installed, one being the application server and the other one being a database. . The database is used to store all metadata such as users' profiles or items shared. In our case, we use MongoDB 5 . Once the first step is complete, PlantRT must be configured to be run in this specific environment. The configuration consists in just a few informations: the IP of an existing site to connect to, the maximum number of virtual nodes, the frequency of the gossip protocol and the frequency of the distributed TF × IDF protocol. Once configured, PlantRT can be uploaded in the application server (i.e. tomcat 6 ) and will starts automatically.

During the initialization, several operations are performed by PlantRT automatically. First, the virtual nodes are built and associated to a set of inverted lists. In our case, each set of inverted lists is an instance of Lucene 7 . Then, the user -or administrator in the case of multi-users site -specified an IP address to which PlantRT will connect. The IP refers to an existing remote PlantRT's site s r . Once the connection is established between the two sites, virtual nodes from s r are retrieved to the local site and the gossip protocol is started and executed periodically at a system defined frequency. The gossip view (i.e. random view) are implemented as simple arrays. The last protocol to be initialized and started is TF × IDF distributed protocol since it relies on the gossip view to connect to other sites. The initialization consists in building a TF × IDF vector with the site's local corpus. Once initialized, periodical exchanged are established to compute the TF × IDF vector. Notice that this protocol runs indefinitely to take into account new items added to any sites. After this final step, the PlantRT becomes accessible to the users.

In our demo, we show that virtual nodes and a diversified VN-Network enable to achieve very good results in term of diversification, recall (i.e. the proportion of results answering a query retrieved) and performance. More precisely, with different numbers of sites and virtual nodes, we show that PlantRT reaches centralized-like recall results. Additionally, we have simulated up to 6, 000 nodes still reaching recall results of 99, 9 and very good performance [START_REF] Servajean | Exploiting Diversity in Gossip-Based Recommendation[END_REF].

The distributed TF × IDF protocol shows very good convergence properties. In our demo, we show that only a few exchanges at each site are needed to compute a TF × IDF vector with respect to the global corpus.

V. Conclusion

This paper presents PlantRT a multi-sites diversified search and recommendation tool for citizen sciences, and more precisely, for botanists.

We proposed an original multi-site platform that is built over virtual nodes. This enables to reduce network costs and to improve performances of top-k processing. Our demo is done using real scientific data over a concrete multi-site platform. We show several use cases for diversified search and recommendation. In addition, we show, in details, how we deployed ou platform.

The still evolving source code is available at the following address: http://www2.lirmm.fr/˜servajean/ prototypes/plant-sharing/plant-rt.html.

Figure 1 :

 1 Figure 1: Example of a PlantRT multi-site network with 4 sites.

  Search and recommendation example with profile diversity.

  , 2b and 2c presents the profiles of respectively Élodie Dujardin, Marie Dupont and Pierre Durand. Recall that the users' profiles are derived from

Figure 2 :

 2 Figure 2: Users' profiles.

  5. http://www.mongodb.org 6. http://tomcat.apache.org 7. http://lucene.apache.org

Figure 3 :

 3 Figure 3: PlantRT's example of geo recommendation next to Montpellier, France.

* Work conducted within the Institut de Biologie Computationnelle and partially funded by the labex NUMEV and the CNRS project Mastodons. 1. http://www.plantnet-project.org 2. http://ebird.org 3.