Karim Khan

L Lemaitre

M Boulinguez

A Bastide

Conflict Investigation between Thermal Comfort and Fire Safety in Naturally Ventilated Building Located in Tropical and Hot Climate

Keywords: Fire safety, Thermal Comfort, Smoke extraction, Natural ventilation, Critical Safety Period, Safety design, Hot Climate, Tropical Climate

Safety performance is one of the most important considerations in building design.

Moreover, new environmental building design allows construction to have a sustainable relationship with the outdoor climate, especially in tropical context. This paper aims at evaluating how conception that allows natural ventilation by mean of passive strategies, affects fire safety performance in buildings. A methodology, combining safety and comfort through a global index, is proposed in order to understand how the consideration of fire safety can affect initial passive design. The use of dynamic thermal simulation associated with fire simulation gives the opportunity to quantify fire performance annually.

Sensitivity analysis is performed by using delta moment index. This allows to evaluate the absolute impact of input parameter on both fire safety and thermal comfort outputs. A refined study is then performed by the use of conditional output distribution. It takes into account the relative importance given to fire Safety or thermal comfort concerns. The strong dependence of fire safety, in terms of smoke extraction, on weather conditions, building orientation, occupation profiles and seasons, is demonstrated in naturally ventilated buildings.

Introduction

Insular areas are impacted by energy supply constraints, affect both directly and indirectly the building sector. In Reunion Island, the overall consumption due to construction represents more than 45 % of the overall electric energy consumption [START_REF]Bilan énergétique de La Réunion[END_REF].

Knowing that Reunion Island aims at being autonomous in terms of energy supply by 2030, one could not achieve this goal without thinking about how to build better, in a sustainable way. New construction should hence provide free running building that combine passive design, acceptable thermal comfort, energy efficiency, and last but not least, a high level of safety. Two questions are then arisen: How to integrate safety consideration into passive design process? And why is it important to associate comfort and fire safety? Firstly, it is important to highlight how safety and comfort is commonly covered in Reunion Island. Concerning fire safety, the same standard is prescribed in Reunion as in mainland France. Hence, standards for fire safety is mostly prescriptive [START_REF]Norme française, Sécurité Incendie, Arrêté du[END_REF]- [START_REF] Hoop | Sécurité incendie bâtiment[END_REF]. In others terms, in each situation related with safety, systems and solutions are imposed contrary to objective standards, which are based both on factual measurements and the setting of safety performance goals. These standards are, for most of them, designed for closed building with mechanical air supply and conditioning system. In the case of the extraction of toxic smoke during a fire event, both natural and mechanical exhaust is prescribed by the building regulation. For inlet and outlet related to the extraction of fire smoke, rules are given in terms of dimensions, distances, as well as numbers of openings. Fan characteristics are also given in order to satisfy the standards' requirements. However, the impact of passive ventilation strategies on fire safety design is not taken into account. This is where issues meet. The wind is variable and fluctuate by nature, then natural smoke extraction potential has to be quantify depending on wind variation and building characteristics. This is not the case in French fire regulation. Some research has payed attention to the issue of natural exhaust of smoke for building under natural ventilation [START_REF] Gao | Fire spill plume from a compartment with dual symmetric openings under cross wind[END_REF]- [START_REF] Lai | Determinations of the fire smoke layer height in a naturally ventilated room[END_REF] and outlined the importance of uncertainties of the hypothesis when calculating physical quantities related to fire smoke. Salem [START_REF] Salem | Use of Monte Carlo Simulation to assess uncertainties in fire consequence calculation[END_REF] gives a sense of what could be the impact of uncertainties on the Available Safe Egress Time (ASET) defined by the standard ISO/TR 16738 [START_REF]Fire-safety engineering -Technical information on methods for evaluating behaviour and movement of people[END_REF].The ASET is the time when integrity of occupant is compromised during a fire event ; while the Required Safe Egress Time (RSET), also presented in ISO/TR 16738, is the amount of time required to get out of the building. ASET takes into account toxic and irritating gas as well as radiant and convective temperature effects on Human bodies by setting thresholds. Another study proposed by Kong [START_REF] Kong | Fuzzy Risk Assessment for Life Safety Under Building Fires[END_REF] specifies that uncertainties should not be neglected while elaborating fire scenarios using fire models. Allard et al. [START_REF] Allard | Evaluation of the most influent input variables on quantities of interest in a fire simulation[END_REF]evaluate different parameters, including fire characteristic, that most impact the risks associated to fire using the CFAST [START_REF] Peacock | CFAST-consolidated model of fire growth and smoke transport (version 6) user's guide[END_REF] software. This is one of the multiple cases where fire safety and environmental design are dealt with separately despite of the relation that exits between them [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF]. Thermal comfort is subject to a large number of studies [START_REF] Rupp | A review of human thermal comfort in the built environment[END_REF], [START_REF] Brager | Thermal adaptation in the built environment: a literature review[END_REF]. Several models exist to assess indoor thermal comfort such as analytical models and field models. Fanger [START_REF] Fanger | Thermal comfort. Analysis and applications in environmental engineering[END_REF] introduced Predicted Mean Vote (PMV) in order to evaluate comfort in controlled environments. Climatic diagram [START_REF] Givoni | Man, climate and architecture[END_REF] and adaptive thermal comfort [START_REF] De Dear | Thermal Comfort Inside and Outside Buildings[END_REF]- [START_REF] Humphreys | Updating the adaptive relation between climate and comfort indoors; new insights and an extended database[END_REF] model have been developed for naturally ventilated buildings. Sensitivity analyses have been conducted in order to evaluate the parameters that most affect thermal comfort and energy consumption for energy simulation model [START_REF] Breesch | Building simulation to predict the performances of natural night ventilation: uncertainty and sensitivity analysis[END_REF]- [START_REF] Tian | A review of sensitivity analysis methods in building energy analysis[END_REF]. However, literature review shows a lack of studies combining fire safety and thermal comfort targets, especially for naturally ventilated room in tropical climate.

This study focuses on the effect of the weather as well as the effect of initial design on thermal comfort and fire safety for a naturally ventilated building. Moreover, an investigation on potential conflicts between the two considerations is led. A combined index approach, with a disjunction connective function, is proposed using delta moment measures [START_REF] Borgonovo | A new uncertainty importance measure[END_REF]. Four climate zones, two occupation types, and four building types are considered. One of the objective is to discuss the impact of fire on smoke exhaust performance in naturally ventilated buildings. Another objective is to discuss on the existence of conflicting parameters for comfort and safety. By definition, a contentious parameter has a relative significant impact on both safety and comfort index, but its impact is negative for the one and positive for the other one [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF].

In order to assess thermal comfort, the extended Givoni's zone proposed by Lenoir is considered. Available Safe Egress Time, by means of Fractional Effective Dose related to toxic gases, as well as Critical Velocity, as defined by Chen et al. [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF], are chosen as a fire safety performance measures.

Moreover, another fire safety measure is introduced under the name of Critical Safety Period (CSP). It corresponds to the percentage of time when windy condition can significantly affect smoke extraction. The CSP hence depends of the wind conditions, the orientation of the building, as well as the opening location. The emphasis on passive design interaction with fire safety level is demonstrated. In addition, conditional distribution of the output allows to underscore conflicting parameters by considering uncertainty and setting scenarios. According to [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF], The study of output distribution allows analysts to measure the inputs' influence on given model outputs.

Why integrating fire safety in passive design?

Inlet and outlet position in naturally ventilated room has been studied by Tominaga and Blocken [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF] . They made wind tunnel experiment in order to assess the impact of opening position on flow dispersion. They conclude on the fact that inlet position has a strong impact on contaminant dispersion in naturally cross ventilated buildings. Moreover, depending on the configurations of the openings studied, flow rates varied within a range of 40 %. It is obvious that inlet and outlet position have a strong impact on thermal comfort measures, based on climatic chart, in tropical area since air change rate is one of the most impacting parameter [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF]. Now, thinking about smoke extraction, one can ask if the same opening configuration will be positive for both thermal comfort and smoke extraction efficiency, knowing that safety during a fire event depends on where the flow is coming in [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF]. Smoke extraction and its impact on a building should be introduced.

A suitable definition of smoke can be find in the (National Fire Protection Association) handbook whereby "smoke is defined as airborne solids and liquid particulates and gases evolved when material undergoes pyrolysis or combustion, together with the quantity of air that is entrained or otherwise mixed into mass" [START_REF] Association | NFPA 921, guide for fire and explosion investigations[END_REF]. Since the seventies, fire plume and smoke has been widely studied [START_REF] Zukoski | Entrainment in fire plumes[END_REF]- [START_REF] Heskestad | Fire Plumes, Flame Height, and Air Entrainment[END_REF]. In order to understand fire smoke, Nelsen and Klote present several phenomenon that occurs during fire event [START_REF] Klote | Smoke movement in buildings[END_REF]. According to [START_REF] Klote | Smoke movement in buildings[END_REF] when smoke is produced, it is cooled down: by surrounding air, by thermal exchange with surrounding solid material and by radiant energy lost. As soon as the smoke is cooled down, the same behaviour of a pollutant can be observed. Different forces will impact the smoke movement. The first one is the thermal buoyancy, creates by temperature and density difference between the fire smoke and surrounding air. The second main force that can affect smoke movement is wind force [START_REF] Yi | Study on effect of wind on natural smoke exhaust of enclosure fire with a two-layer zone model[END_REF], [START_REF] Lai | Determinations of the fire smoke layer height in a naturally ventilated room[END_REF], [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF]. Moreover, another factor that can act on smoke displacement is pressure force caused by mechanical ventilation system [START_REF] Pretrel | Doorway Flows Induced by the Combined Effects of Natural and Forced Ventilation in Case of Multi-compartments Large-Scale Fire Experiments[END_REF], [START_REF] Koched | Experimental determination of the discharge flow coefficient at a doorway for fire induced flow in natural and mixed convection[END_REF]. It has been proved that smoke extraction, management and control can have a significant impact on the safety level of a building and on the preservation of human lives [START_REF] Kobes | Building safety and human behaviour in fire: A literature review[END_REF]. SFPE guide pinpoint the fact that during a fire, tenability conditions is compromised by the exposure of radiant and convective heat, inhalation of toxic gases and obscuration cause by smoke [START_REF] Madden | SFPE engineering guide to application of risk assessment in fire protection design[END_REF]. A suitable system of fire smoke control is one of the driving factors that can lead to high safety performance. Identification of physical quantities such as interface height, smoke layer temperature, and mass flow rate through openings, is crucial for quantifying the risk. Hence, the difference between fire induced smoke and isothermal contaminant dispersion study is the strong presence of thermal forces which is one of the two main driving forces of natural ventilation [START_REF] Etheridge | A simple parametric study of ventilation[END_REF]. This makes wind tunnel analysis not adapted to quantify the effect of window position on smoke extraction in naturally ventilated spaces. Some research have been conducted in order to characterize the effect of wind on smoke extraction depending on dual window position. For example, Chen et al. introduced wind effect on smoke extraction by considering Bernoulli approach [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF] with a full mixed indoor ambiance. They demonstrate the existence of a critical velocity which appears when top opening was windward and bottom opening was leeward.

Critical Velocity is the wind velocity at which smoke do not extract thought the windward façade when wind force opposed buoyancy. In this case wind opposed buoyancy and . Yi et al. [START_REF] Yi | Study on effect of wind on natural smoke exhaust of enclosure fire with a two-layer zone model[END_REF] also studied wind impact on smoke extraction considering two zone indoor ambiance and roof outlet. They concluded on the influence of net wind pressure coefficient value on the smoke extraction performance.

In addition, a series of reduced scale experiments have confirmed, by means of observation, the existence of a critical velocity when wind opposed buoyancy. The experimentation is based on Tominaga and Blocken [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF] setup. The chosen geometry represents a generic isolated building with two opposite asymmetric openings placed respectively in the windward and leeward façade. The chosen geometry in one of the five geometry tested by Tominaga and Blocken [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF]. The geometry under study has a top opening on the windward façade and a bottom opening in the leeward one and represents one of the most efficient configuration in terms of passive pollutant dissipation and net flow rate according to Tominaga and Blocken [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF] study. In order to reproduce the effect of hot fire smoke, helium is used based on Vauquelin et al. study [START_REF] Vauquelin | Scaling laws for a buoyant release used to simulate fire-induced smoke in laboratory experiments[END_REF]. The results, in terms of critical velocity observation is presented in Figure 1. All these study shows that wind as both a strong impact on safety level as well as on air quality and comfort. Then it should be necessary:

Wind Wind

Clear layer Smoke layer Mixed ambiance

Fresh air

Fire source model

• on the first hand, to consider fire safety level as a function of time and depending on climatic conditions, and

• on the other hand, to approach the interaction that could exist between sustainable and safety design.

These two points, will surely lead to a better approach when designing or retrofitting a building in a sustainable way for both tropical areas and areas where natural ventilation can be used as passive strategy. Hence, the presented study is held is four steps:

• Step 1 consists in evaluating most impacting parameters for fire safety alone when natural ventilation operates, using CFAST.

• Step 2 consists in combining measures while evaluating the model using sensitivity analysis and disjunction connective function.

• Step 3 is relative to the optimisation using Kernel Density Estimator (KDE) and the use of weighing coefficient.

• Step 4 consists in the research of contentious input parameter, using weighing coefficient.

One of the best way to integrate fire safety in the early sustainable design is to use the same reflexion as in [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF]. Critical Velocity appears when top opening is windward and bottom opening is leeward (Figure 1). It is hence necessary to estimate the amount of time when top opening will be windward thanks to the weather file of a given location and especially the wind rose. This amount of time when critical velocity can appear is called here the Critical Safety Period. The more the CSP will be long during the year, the less safety level will be regarding smoke extraction performance. In this study the CSP and Critical velocity will be used as safety measures. The dependence of safety level on the location will be then demonstrated. All buildings performance measures for both comfort and safety are presented in the next part.

Building performance measures

Thermal comfort measures

The Lenoir extended Givoni's zone is chosen here as a comfort index. The objective here is to show how comfort and safety can interact. Hence, for this study the extended Givoni's zone proposed by Lenoir [START_REF] Lenoir | On Comfort in Tropical Climates. The Design and Operation of Net Zero Energy Buildings[END_REF] is chosen. The percentage of point within the studied zone, for a given period of simulation, represents the measures of comfort knowing that a point correspond to an indoor dry bulb temperature as well as an indoor relative humidity at a given hour of time (Figure 2). This data is given by dynamics thermal simulations. The chosen thermal comfort measure is normalized, the objective for each case is to reach unity. This measure is named as .

Fire safety performance measures

In order to enhance safety level, in accordance with the Available Safe Egress Time (ASET), people have to exit the building as rapidly as possible in the best conditions. In order to achieve such a goal, fire smoke should be defined and quantify the best way as possible. Different type of study can be done to understand the fire smoke impact during fire event. Three types of analyses exist: A reduced scale analyse using scale model [START_REF] Lucchesi | Etude du mouvement d'un fluide à faible masse volumique entre deux compartimentsreliés par une ouverture de type porte: Application à la propagation de la fumée d'incendie[END_REF]- [START_REF] Li | Fine bubble modelling of smoke flows[END_REF], real scale model analyses [START_REF] Yi | Study on effect of wind on natural smoke exhaust of enclosure fire with a two-layer zone model[END_REF], [START_REF] Pretrel | Doorway Flows Induced by the Combined Effects of Natural and Forced Ventilation in Case of Multi-compartments Large-Scale Fire Experiments[END_REF], [START_REF] Koched | Experimental determination of the discharge flow coefficient at a doorway for fire induced flow in natural and mixed convection[END_REF], [START_REF] Dembsey | Compartment fire experiments: Comparison with models[END_REF], [START_REF] Steckler | Fire induced flows through room openings-flow coefficients[END_REF], and numerical simulation using zone model or computational fluid dynamics [START_REF] Kerber | Using FDS to Simulate Smoke Layer Interface Height in a Simple Atrium[END_REF]- [START_REF] Meroney | CFD Simulation of ventilation and smoke movement in a large military firing range[END_REF]. Zone model is used here to identify the quantities that most affect safety measures. Four main fire safety measures will be consider into this article: the Critical Safety Period (CSP), The Fractional Effective Dose of toxic gas [54], the critical velocity, and the same safety index used in [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF] based on Allard et al. work [START_REF] Allard | Evaluation of the most influent input variables on quantities of interest in a fire simulation[END_REF].

Critical Safety Period measure (CSP)

This measure represents the percentage of the time when top inlet is located in the windward part of the building. It hence. Considering a simple volume with wind pressure coefficient on both site as in Figure 3, CSP is when 1 2 " 0.

Critical Velocity measure

Wind CSP appears when 1 2 " 0

The critical velocity measure is based on the correlation proposed by [START_REF] Chen | Wind effects on smoke motion and temperature of ventilation-controlled fire in a two-vent compartment[END_REF]. This correlation is defined as follow (1):

) *+ = , 2 -1 - . / . 0 1 2ℎ ∆ 5 (1)
With . / : the outdoor air temperature, . 0 : the mixed indoor air temperature, 2 : the gravitational constant, ℎ: the height difference between top opening and bottom opening and ∆ 5 = 56 -57 . 56 is the wind pressure coefficient on the windward facade and 57 is the wind pressure coefficient on the leeward facade. The critical velocity index $ & is then defined as the ratio of time, within the CSP, when wind speed is inferior to the Critical Velocity [START_REF]Bilan énergétique de La Réunion[END_REF]. The objective for this measure is to be close to unity. The $ & measure is only evaluated within the CSP.

FED measure

Fractional Effective Dose (FED) for toxic gas is calculated according to the standards ISO/TR 16738. Toxic gas concentration can then be calculated with the following equation

(2):

8 9:; = < < 1 = > ? @ ? A . ∆= B >CD (2)
With = > the time when tenability is compromised due to the toxic component i. and ∆= the time step. The aim here is to calculate FED for carbon monoxide and carbon dioxide which are the two main toxic gas during a fire event. Then global FED is estimated as follow (3):

EFG ?HI>*>?J = KEFG &L M EFG LN O P ) &LN ( 3
)
) &LN is the over ventilation correction which depends on the Q N fraction. The expression of ) &LN is presented in ( 4)

) &LN = exp K0,1903 P % Q N M 2,0004O 7,1 (4)
The safety measure $ 9:; is then equal to the percentage of simulated period during when the tenability is not compromised. Tenability is considered as compromised when 8 9:; is up to a fixed threshold given by the standards ISO/TR 16738. The threshold is set here to 0.3 that correspond to a limit at which 11.4% of the people, affected by fire conditions, will not able to egress. The aim for $ 9:; is also to be near to unity.

Climates description and chosen micro-climate.

Reunion Island (21°16'10.1"S ; 55°30'23.5"E) can be divided into four climate zone according to the PERENE local green buildings guideline [START_REF] Garde | Elaboration of thermal standards for french tropical islands[END_REF]. As a consequence Reunion Island can clearly help in order to evaluate microclimate impact on thermal comfort in naturally ventilated room. Low land of Reunion Island is the most appropriate places to build passive building enhancing natural ventilation. This is the reason why all weather for this study is located in this area (Figure 4). To appreciate environmental conditions for each chosen weather file, relative humidity and dry bulb temperature are plotted on the climatic diagram. In most of cases outdoor conditions is suitable in order to reach thermal comfort conditions according to Givoni's and Lenoir's zones (Figure 5). Ligne-Paradis is the place with the most extreme temperature whereas Gillot's points is more "compacted". It will hence be interesting to evaluate if the four location will have a significant impact on thermal comfort measures.

Building model for fire and energy

Building simulation models can be divided into three categories: zone models, multizone network models, based on nodal approach, and field models. The nodal approach is widely used in building simulation, for both energy and fire models. Nodal models are based on the assumption that zone air temperature, contaminant concentrations and other physical quantities are uniform, and represented by a node.

This assumption, which allows to obtain results without high computational cost, can be discussed in terms of precision. For both fire and advanced thermal models, the studied zone can be divided into two or more nodes (zones) in order to estimate smoke layer or thermal stratification. These models are also known as zone models. Multizone network models allow to connect multiple ambiances, represented by nodes, so as to measure the different exchanges that exist between them. Hence, low computational cost and the modelling procedure allow to easily integrate physical phenomena, which can provide new opportunities not only in fire but also in energy research. The CFAST software [START_REF] Peacock | CFAST-consolidated model of fire growth and smoke transport (version 6) user's guide[END_REF],

developed by the NIST (

Sensitivity analysis

By definition, a sensitivity analysis is global when, on one hand, parameters vary simultaneously and, on the other hand, sensitivity is measured on the overall space of each parameter [START_REF] Saltelli | Sensitivity analysis[END_REF]. Various indicator families exist in order to measure the impact of uncertainties on a given model. For this study, the delta moment of Borgonovo is used [START_REF] Borgonovo | Measuring uncertainty importance: investigation and comparison of alternative approaches[END_REF]. The delta moment importance measures is given as follow (5):

W > = 1 2 F X Y Z[(8 > )\ (5)
Where:

W > is the uncertainty importance of input 8 > ;

[(8 > ) = ]^_ `(a) -_ `|X Y (a)^ca ; F X Y Z[(8 > )\ = ] _ X Y (d > )e]^_ `(a) -_ `|> (a)^cafcd ;
Where _ X Y (d > ) is the density of 8 > .

In other term [(8 > ) represents the output distribution shift when 8 > .is fixed to one of its possible values. [(8 > ) indicates how the view of the decision maker on output distribution will change when 8 > is fixed to a known value. When fixed 8 > value changes the output distribution significantly, then the parameters can be registered among the relevant ones.

At the contrary, if fixing 8 > does not change significantly Y distribution, parameters is considered not impacting.

Another measure is also given by W >g . This measure allows to identify the impact of groups of two parameters on model output. The measure is defined as follow ( 6):

W >g = 1 2 F X Y X h e[(8 > , 8 g )f (6)
Where:

[i8 j , 8 k l = m n_ `(a) -_ `|X Y X h (a)n ca (7)
This study will use sensitivity analysis as a mean of understanding the relationship between smoke extraction during a fire event and thermal comfort. Firstly, sensitivity analysis will focus on smoke extraction performance using CFAST, in order to analyse the influence of inlet position and Window to Wall Ratio (WWR). Secondly, the impact of climate on fire safety is demonstrated thought $ & and $ &%' . Latin Hypercube Sampling (LHS), with a number of 16384 set of parameters, will be used in accordance with Monte Carlo methodology.

Case study and model description

A simple geometry is considered. The objective is to evaluate how fire safety consideration interacts with passive building design strategies. Different building parameters (characteristics) for fire safety and thermal comfort are evaluated. A first "dissociated" fire safety study is conducted in order to evaluate the influence of WWR and inlet position with CFAST. Parameters related to the first study are describes in Table 1.

Two sensitivity analyses are then carried out. The first consists in the evaluation of the impact of several parameters including inlet position, and flow rate but excluding WWR, on the fire safety measure defined in [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF]. The exclusion is due to a CFAST model characteristic which does not allow to simulate wind driven ventilation like in EnergyPlus. Hence, to mimic natural ventilation, the assumption is to model inlet and outlet mechanical vent (Figure 6). Inlet position and flow rate are here considered as input parameters. CFAST does not allow to give length and height of mechanical vents. As a consequence, the influence of WWR is evaluated through another sensitivity analysis with no mechanical vent but just natural vent exhaust (Windows).

Table 2 presents input parameters information for the combined study. It has to be underlined that the two models, i.e. EnergyPlus (E+) and CFAST models, have the same initial parameters in terms of buildings and windows geometry, resulting in four common parameters (Figure 7). is relative to the difference between the middle of the top opening and the middle of the bottom opening. This value is only implemented in the Critical Velocity calculation and not in CFAST or EnergyPlus model for the second analysis. However, the top opening in EnergyPlus is assume to be always in the same façade. This allows to calculate CSP.

Thermal comfort in tropical climate is evaluated. As a result, relative humidity and thermal comfort are model outputs used in order to evaluate comfort performance. The extended Givoni's climatic diagram proposed by Lenoir [START_REF] Lenoir | On Comfort in Tropical Climates. The Design and Operation of Net Zero Energy Buildings[END_REF] allows to plot the data. The simulated room is considered as naturally ventilated. ∆ 5 in ( 1) is calculated thanks to Swami's and Chandra correlation [START_REF] Swami | Correlations for pressure distribution on buildings and calculation of natural-ventilation airflow[END_REF].

All parameters are assumed as uniformly distributed. Weather file, occupation type as well as building type are discrete parameters used in order to build scenarios.

Evaluated building type

The tested building type is based on the commonly building that can be find in Reunion island. Table 3 presents the chosen building composition in terms of wall and roof. This parameter will be a discrete parameter and will allow to analyse building type impact on comfort measures in naturally ventilated building.

Occupation profile

Concerning the occupation, only two option is tested: Office occupancy and home occupancy. These information is taken from EnergyPlus default schedule. The objective here is not to specially represents reality but to analyse qualitatively the impact of a day and night occupancy on the way of optimize a naturally ventilated building This parameter will allow to discuss relative occupancy importance on the designer point of view for free running building which indoor conditions strongly depends on outdoor conditions. The first step of this study is to identify the most impacting parameters during a fire event excluding fire characteristics as an input for sensitivity analysis. Fire size will vary with the floor surface area. For this first study only CFAST will be use. The second approach is to use an index that combines fire safety and comfort, since they can interact while natural ventilation operates [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF]. The disjunction connective function based combined index [START_REF] Lai | Determinations of the fire smoke layer height in a naturally ventilated room[END_REF] is used here to analyse the combination of and $ & :

Methodology

o = p P $ & M q P (8)
Weighing coefficients p and q that range between [0,1] are used to give relative importance on one or the other output measure. As in Juhoor et al. study [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF], in order to keep the new index normalized, we set: q ( K1 pO.To realize the analysis, Python software is used as an interfacing layer and its Salib library is used to perform sensitivity analysis [START_REF] Herman | SALib: An open-source Python library for sensitivity analysis[END_REF]. The objective is to give designers the opportunity to understand how safety and comfort can interact and why it is important to consider fire safety in the early design of passive building providing wind driven natural ventilation.

Results

Most impacting parameter in fire model for naturally ventilated room using

CFAST

The following sensitivity analysis, using delta moment measures, shows how important inlet and outlet position are in terms of interface layer height, upper layer and lower layer temperature. With a simple multizone layer as CFAST the importance of window position as well as flow rate, during a fire event is demonstrated in Figure 10. In this case wind driven ventilation was simulated with mechanical vent given that CFAST does not consider wind driven flow. It appears that inlet position as well as flow rate have a great impact on the safety measure set in [START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF].

A second case is simulated while the windows are opened, allowing natural ventilation.

The result exposed in Figure 11 presents sensitivity measures for the same room but naturally ventilated without wind driven natural ventilation. Impact on SFED is evaluated.

It appears in this case that Window to Wall Ration is by far the most impacting parameters. As a consequence, WWR, Inlet position, as well as flow rate can be considered as the most impacting in naturally ventilated cases. Knowing that those parameters also play a pivotal role contaminant dissipation in cross and naturally ventilated room [START_REF] Tominaga | Wind tunnel analysis of flow and dispersion in crossventilated isolated buildings: Impact of opening positions[END_REF],

one cannot designed passive building without considering smoke extraction performance. It is now important to study the introduced safety index $ & and $ &%' to demonstrate the climatic dependence of fire safety in cross and naturally ventilated room using zonal and nodal models as well as the presented combined index (8).

Comfort and safety interaction through combined Index

In this part the combined index is used to demonstrate the weather importance when studying fire safety in naturally ventilated room by fixing the weighing coefficient p to different values. Figure 12 and Figure 13 shows the delta moment measure associated with the impact of each inputs on global index for summer period and winter period, and for p = 0.1, 0.5 st 0,9.

It can first be observed, difference between parameter impact if we set the study in summer or winter, whatever the value of p. The first conclusion is then that both safety through critical velocity measure $ & and comfort thought are strongly dependent of the location i.e dependent of the weather. Focusing on Figure 12, one can relate that for p = 0.9 (importance given on safety), the most impacting parameter is by far is the weather file.

Hence the strong dependence between safety and climate is highlighted in summer period. When p = 0.1, both weather file and occupation are important. Knowing that natural ventilation systems are usually designed for summer period, designers could not think about safety without adapting opening position as well as WWR to the specificity of the climate. Figure 13 is also very interesting due to its difference with summer period. Indeed we can observe that in winter, the orientation is as impacting as the climatic zone when fixing p = 0.9. When p = 0.1, occupation and weather file are the most impacting parameters. For both summer and winter case, p = 0.5 allows to both take into account safety and comfort. Then for a combined sensitivity analysis, it can be recommended to analyze the results when p = 0.5. This allows to give to the designer a global view of what is really important to consider when realizing of retrofitting a building. The study of p = 0.9 st 0.1 can then be used to find contentious parameters by plotting the conditional distribution of most impacting parameters.

Next part focuses on the study of conditional distribution of the output fixing the most impacting parameter for both cases when p = 0.9 st 0.1.

Conditioned output distribution

This part deals with refined analysis of output distribution and conditional output distribution regarding the most impacting parameters when p = 0.9 st 0.1. In this part, kernel density estimation (KDE) is used in order to plot the output distribution in each case. Firstly, the weather file is fixed in order to observe whether the same climate is favourable to both safety and comfort. Secondly, two climate locations are deeply studied.

For a specific location, as well as for a specific occupation type, global output distributions fixing Window to Wall Ratio to specific range are plotted. Thirdly, the same method is applied to the room's orientation

Output distribution fixing Weather Files

Figure 14 and Figure 15 present kde of the output knowing the location for p = 0.1 st 0.9.

It can be highlight that a huge difference exists between the different locations whether p = 0.1 st 0.9. Moreover, comparing the two graphs, it can be observe that the same location could be very positive when weigh is put on comfort but negative when importance is given to safety. Let's take for example the case of Gillot. Typically, Gillot has a high wind potential (cf. part1) and can thus be favorable when natural ventilation is allowed in building, considering building element allowing to regulate wind speed. Hence for Gillot, the output varies between 0.6 and 1 with two pikes from 0.7 and 0.8 as well as from 0.8 and 1. The two spikes, or kernel, observed are related to the occupation type. Observing now the same location but fixing p ( 0.9, Gillot is the climate with the most negative impact, with a combined index ranging approximately from 0.55 to 0.65. The same trend regarding p value can be observed for each climate location but except for Saint-Joseph. The reason of the observed difference is that Saint-Joseph weather file has an irregularity regarding wind speed and direction.

This study allows to conclude on the weather dependence of fire safety in naturally ventilated room, for the case study, as thermal comfort is link to climate. In the one case, a given weather file can be suitable to reach appropriate thermal comfort level, but in the other case, can be responsible to a poor fire safety level amplitude. Then, indepth study should be realize for each location, each microclimate, in order to not only consider thermal comfort but also in order to take into account fire safety as a key consideration to bioclimatic and sustainable construction. For next discussion, Gillot with office occupancy will be used to understand how most impacting parameters can interact taking different values of p.

Output distribution fixing WWR

Figure 16 and Figure 17 present KDE for the output knowing the location and occupancy for different range of WWR for p = 0.1 st 0.9. When p ( 0.1, and knowing that the study focus on summer, it appears that high range of WWR (between 50% and 60%) is more positive regarding thermal comfort compared to low range of WWR (between 0% and 10%). Indeed, when fixing WWR from 50% to 60%, global index varies from 0.72 to 1 whereas it varies from 0 to 1 with a more dispersed density when ranging from 0% to 10%. However the trend in completely different when the accent is put on safety. Indeed when α=0.9 (Figure 17), two groups of kernel can be identified concerning the conditional output. The results is not as significant as when for α=0.1. Hence there is not high discrepancies when α=0.9 comparing to the case when α=0.1. In order to understand the presence of the two kernel groups, conditional kde is plotted for different range of orientation.

Output distribution fixing the orientation

Figure 18 and Figure 19 show kde for the output knowing the location and occupancy for different range of room orientation for p ( 0.1 st 0.9 . Firsly, Figure 19 allows to understand the presence of two kernel groups observed in Figure 17. Indeed, it can be observed in Figure 19 that for a range of orientation between 150°and

180°, global index is very low compared to the other range. Two groups are also observed in this case. This discrepancies causes the observed groups of densities in Figure 17.

Secondly, it can be concluded that orientation is typically a conflicting parameter when fixing it to some range and more particularly when it is fixed between 150° and 180°.

Indeed, this range of orientation induces high values of global index when α=0.1, but also really poor index when α=0.9. Designers should be careful about the orientation of a room or openings position when thinking about construction that allows natural ventilation if fire safety is also taken into account.

Conclusion

This study has numerically demonstrated the relation between fire safety and thermal comfort in naturally ventilated room using nodal and zonal models and sensitivity analysis by mean of Delta moment introduced by Borgonovo. The advantage of using this methods here is to have an absolute impact of sensitivity measure. A simple case study was taken in order to check fire safety level taking into account a wide range of uncertainties. A combined index methodology was applied to find the relation between thermal comfort and fire safety.

A first dissociated study was conducted in order to show how fire safety level, in terms of Fractional Effective Dose of toxic gas, is sensitive to the inlet position as well as to the The use of kde allows in the last part of the study to highlight the room orientation as a conflicting parameter when fixing its values to a specific range. It is hence important to apply this kind of methodology when allowing natural ventilation inside a building in order to avoid poor safety level that can lead to casualties in case of fire event.

By way of conclusion, this study is the first of its kind that tries to evaluate the link between thermal comfort and fire safety level in passive buildings. The study highlights the strong dependence between fire safety level, using Critical Safety Period and Critical Velocity, and environmental and building variables as location (wind speed and direction), inlet position, orientation and WWR. However the use of Critical Velocity is here a first step and can be questionable since it is based on a macroscopic model that considers a full mixed ambiance inside the room. Further study should be conducted either numerically by mean of KDE or experimentally.

Acknowledgment

This work was financially supported by Intégrale Ingénierie consultancy and developed in the fire and comfort research department. This work cannot be achieved without the experiment led in IUTSI Laboratory along with Olivier Vauquelin's team and Kevin Varrall.

Figure 1 :

 1 Figure 1: Observation of critical velocity on a reduced scale experiment set up in IUSTI Laboratory, for a simple geometry with top opening facing the wind and buoyant injection in the middle of the room. (a) represents typical pattern for a buoyancy dominated flow while (b) shows flow pattern for a wind dominated flow when critical velocity is exceed.

Figure 2 :

 2 Figure 2: Givoni's and lenoir's zones represented on climate chart

Figure 3 :

 3 Figure 3: Description of CSP condition

Figure 4 :

 4 Figure 4: Location of the study

Figure 5 :

 5 Figure 5: Climatic diagram representing Givoni's and Lenoir Zone for four different location in Reunion Island (a) Avirons (b) Gillot (c) Ligne-Paradis (d) Saint-Joseph

Figure 6 :

 6 Figure 6: Inlet and outlet position set up in CFAST

Figure 8 :

 8 Figure 8: Assessed occupancy profile

Figure 9 :

 9 Figure 9: Python process for the proposed optimisation methodology

Figure 10 :Figure 11 :

 1011 Figure 10 : Delta moment values for Fire Safety Study measuring fire model inputs impact on safety measure set in[START_REF] Juhoor | Combined Fire Safety and Comfort Study Using Moment Independent and Variance Based Method[END_REF] (Mechanical ventilation to mimic wind driven natural ventilation)

Figure 12 :

 12 Figure 12: Delta moment for different values of p and for Summer Period

Figure 13 :

 13 Figure 13: Delta moment for different values of p and for Winter Period

Figure 14 :

 14 Figure 14: Kernel density estimation of the output knowing the location for p = 0.1.

Figure 15 :

 15 Figure 15: Kernel density estimation of the output knowing the location for p ( 0.9.

Figure 16 :

 16 Figure 16: Kernel density estimation of the output knowing the location (Gillot) and occupancy (Office) for different range of WWR and for p ( 0.1.

Figure 17 :

 17 Figure 17: Kernel density estimation of the output knowing the location (Gillot) and occupancy (Office) for different range of WWR and for p = 0.9.

Figure 18 :

 18 Figure 18: Kernel density estimation of the output knowing the location (Gillot) and occupancy (Office) for different range of room orientation and for p = 0.1.

Figure 19 :

 19 Figure 19: Kernel density estimation of the output knowing the location (Gillot) and occupancy (Office) for different range of room orientation and for p ( 0.9.

  Window to Wall Ratio (WWR). Using a disjunction function based global index, a global study was realized combining a critical velocity measure as a safety index, and a climatic diagram based comfort index as a thermal comfort measure. It appears that global index is sensitive to the location, the WWR, as well as the occupation when both fixing weighing coefficient α to 0.1 or 0.9. Hence the climate dependence of fire safety level was demonstrated.

  [START_REF] Peacock | CFAST-consolidated model of fire growth and smoke transport (version 6) user's guide[END_REF], allows performing fire simulations so as to assess safety level. CFAST is a two-zone model used to evaluate smoke, species and gas distribution resulting from a fire. The model can also calculate the layers temperatures distribution with respect to the time in the upper and lower layers. CFAST model is based on a set of ordinary differential equations derived from the fundamental laws of mass and energy conservation. Moreover FED and zone temperatures can be calculated with CFAST.Performing whole building energy and thermal simulation, EnergyPlus is capable of simulating thermal building behaviour over a full year, using a weather data file and the building characteristics. Moreover, it allows to estimate pressure coefficient on each façade according to Swami and Chandra correlation. Hence, this tool allows designing complex ventilation network, considering passive design and natural ventilation. In order to quantify thermal comfort, Critical Safety Period and ASET, the combination of EnergyPlus and CFAST is relevant for this study. Therefore, in the case studied, if referring to equation (1), . / is given with the weather file hour by hour, . 0 is calculated thanks to CFAST at mid time of fire event. . 0 is then equal to the mean temperature between upper and lower layers weighed by each layer volume. Finally, ∆ 5 is calculated thanks to EnergyPlus software.

Table 1 :

 1 Parameters and their range for smoke extraction study assessment is to evaluate the ability of the occupants to escape in case of fire. Hence, the first 15 minutes of a fire event are simulated. The room studied has typical single room ratio. The outdoor temperature range is also typical for Reunion Island, with a tropical climate. Considering a naturally ventilated building, indoor temperature and indoor relative humidity are within the range of local weather data. Climate conditions inputs are used within the CFAST software as well as for the critical velocity calculation[START_REF]Norme française, Sécurité Incendie, Arrêté du[END_REF]. All the parameters and the related boundaries are given in Table2. The variable h defined in[START_REF]Bilan énergétique de La Réunion[END_REF]

		If inlet position =
		1 then inlet is in
		the top opening
	Parameters	Distribution	Range	Type
	Room width	Uniform	[4m ; 6m]	Continuous
	Room height	Uniform	[m ; m]	Continuous
	Room length	Uniform	[2.5m ; 3.5m]	Continuous
	Window to wall ratio	Uniform	[0.05m ;0.6m]	Continuous
	Outdoor temperature	Uniform	[15°C ; 35°C]	Continuous
	Indoor temperature	Uniform	[18 °C; 37 °C]	Continuous
	Relative humidity	Uniform	[50% ; 80%]	Continuous
	Inlet Position	Uniform	[1 ; 2 ]	Discrete
	Flow Rate	Uniform	[0ACH; 40ACH]	[ ]

risk

Table 3 :

 3 Tested building type

	Building Type	Concrete	Wood	metal	Concrete + wood

Specific parameters are defined for the Energy Plus and CFAST simulations. Concerning the EnergyPlus specific parameters, the impact of the different components' thickness and the room orientation are evaluated. This represents eight parameters (Figure 7). Only three parameters are specific to the CFAST model: Relative Humidity, Indoor Temperature and Outdoor Temperature (Figure 7). Hence HRR lies between 30 kW and 70 kW which correspond to shelf wood bookcase burning with files on. The fire size is 5% of the floor surface area. The fire reaches its maximal value at the mid-simulation time. In addition, the main objective concerning fire


Hammad Squalli-Houssaini
email: hammad.squalli-houssaini@imt-atlantique.netquang-khanh-ngoc.duong

Marquant Gwenaëlle Ngoc Q K Duong

Claire-Hélène Demarty
email: claire-helene.demarty@technicolor.com

DEEP LEARNING FOR PREDICTING IMAGE MEMORABILITY

Keywords: Image memorability, computational model, deep learning, interestingness, image captioning

Memorability of media content such as images and videos has recently become an important research subject in computer vision. This paper presents our computation model for predicting image memorability, which is based on a deep learning architecture designed for a classification task. We exploit the use of both convolutional neural network (CNN) -based visual features and semantic features related to image captioning for the task. We train and test our model on the large-scale benchmarking memorability dataset: LaMem. Experiment result shows that the proposed computational model obtains better prediction performance than the state of the art, and even outperforms human consistency. We further investigate the genericity of our model on other memorability datasets. Finally, by validating the model on interestingness datasets, we reconfirm the uncorrelation between memorability and interestingness of images.

INTRODUCTION

In our fast moving world, media platforms such as social networks, media advertisement, information retrieval and recommendation systems need a great power to deal with exponentially growing data day after day. Thus the ability to understand the content plays a key role in such media systems to help them optimize their processing. Different concepts such as visual saliency and aesthetics [START_REF] Frintrop | Computational visual attention systems and their cognitive foundations: A survey[END_REF][START_REF] Bhattacharya | A framework for photo-quality assessment and enhancement based on visual aesthetics[END_REF], emotion [START_REF] Rimmele | Emotion enhances the subjective feeling of remembering, despite lower accuracy for contextual details[END_REF], social popularity [START_REF] Khosla | What makes an image popular?[END_REF], interestingness [START_REF] Gygli | The interestingness of images[END_REF][START_REF] Demarty | Predicting interestingness of visual content[END_REF][START_REF] Demarty | Mediaeval 2017 predicting media interestingness task[END_REF], and memorability [START_REF] Brady | Visual long-term memory has a massive storage capacity for object details[END_REF][START_REF] Isola | What makes an image memorable?[END_REF] may intervene in the understanding of content. This paper focuses on memorability of images, an emerging and less investigated concept in multimedia and computer vision.

While image memorability has been only considered recently in computer vision, visual memory has been largely studied in psychology since decades. As examples, with experiments on memory capacity psychologists showed that people can remember thousands of pictures they saw only once, even when exposed to many other similar images afterward [START_REF] Standing | Learning 10000 pictures[END_REF][START_REF] Konkle | Scene memory is more detailed than you think: The role of categories in visual long-term memory[END_REF]. However, different images can be more or less remembered depending on numerous factors concerning both intrisic visual appearance and user's context [START_REF] Brady | Visual long-term memory has a massive storage capacity for object details[END_REF]. It is important to note that even if memorability is likely to be influenced by the user context, there is a general agreement (concensus) between groups of people on judging a certain image [START_REF] Hunt | Distinctiveness and Memory[END_REF]. Such key finding grounds and suggests for further studies in quantifying how memorable a certain image is for a common observer.

In computer vision, researchers have investigated intrinsic and extrinsic characteristics that make an image memorable [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Isola | Understanding the intrinsic memorability of images[END_REF][START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF] and a photograph memorable [START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF]. It reveals that color, simple image features derived from pixel statistics, and object statistics (number of objects, log mean pixel coverage over present object classes, etc.,) do not have strong correlation with memorability. Meanwhile, object and scene semantics offer high correlation with memorability. Other subjective concepts such as aesthetics and interestingness have also been shown to be uncorrelated with memorability. Importantly, experiments in [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] have confirmed a sufficient human consistency while they annotated image memory, and thus prove the feasibility of predicting memorability of images. To support research in the field, several small datasets have been publicly released such as those concerning face photographes [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF], scene categories [START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF][START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF], visualization pictures [START_REF] Michelle | What makes a visualization memorable?[END_REF], and affective impact on image memorability [START_REF] Libkuman | Multidimensional normative ratings for the international affective picture system[END_REF][START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF]. Especially, thanks to some research at MIT, a first large-scale image memorability dataset (LaMem) contaning roughly 60,000 images annotated by crowdsourcing has been published together with a memorability prediction model (MemNet) for benchmarking the task [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF].

In this paper, starting from LaMem, we develop a computational model for predicting image memorability scores and we show that the proposed model obtains better prediction performance than the state-of-the-art MemNet model. It even exceeds human consistency on LaMem. Our computational model differs from existing ones in two main points. First, instead of naturally treating the prediction as a regression problem as in [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], we treat it as a classification problem and aggregate the predicted probabilities of belonging to different classes (corresponding to different degrees of memorability) for the final memorability score. Second, unlike most of existing works, which used either handcrafted low-level image features [START_REF] Isola | What makes an image memorable?[END_REF][START_REF] Celikkale | Predicting memorability of images using attention-driven spatial pooling and image semantics[END_REF], or features extracted from a pre-trained CNN [START_REF] Baveye | Deep learning for image memorability prediction: the emotional bias[END_REF], we propose to exploit additional semantic features related to image captioning to improve the prediction.

The rest of the paper is organized as follows. Section 2 presents the proposed computational model for image memorability prediction. Experimental results are shown in Section 3, including our further study on the genericity of the model and the correlation between the interestingness and memorability concepts. Finally, we conclude in Section 4.

PROPOSED COMPUTATIONAL MODEL

The workflow of the investigated prediction systems, which are based on either a regression model (as a baseline) or on a classification model (proposed), is presented in Fig. 1. The feature extraction step and the proposed classification model will be presented in subsections 2.1 and 2.2, respectively.

Feature extraction

As CNN offers a powerful presentation and has been widely used in the literature for many different tasks, we used the well-known VGG16 network [START_REF] Simonyan | Very deep convolutional networks for large-scale image recognition[END_REF] pre-trained on the Ima-geNet dataset for the extraction of a first image feature. The feature is extracted from the last fully-connected layer before the softmax and has a dimension of 4096.

As scene semantics and high-level visual attributes (such as emotions, actions, movements, appearance of objects, etc.,) have been shown to well characterize the memorability of a photo [START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF][START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF], we further investigated the use of some other semantic features derived from an image captioning (IC) system [START_REF] Kiros | Unifying visual-semantic embeddings with multimodal neural language models[END_REF]. Such an IC model builds an encoder comprising a CNN and a long short-term memory recurrent network (LSTM) for learning a joint image-text embedding. Thus the CNN image feature and the word2vec representation of the image caption are projected on a 2D embedding space which enforces the alignment between an image and its corresponding semantic caption. We extracted such projected CNN feature as logically it should contain some semantic information expected to be relevant for the prediction of image memorability. This feature has a dimension of 1024.

Classification model

In order to treat the prediction as a classification task, we first need to convert the ground-truth memorability scores provided with LaMem into K class labels. To ease the classification step, the score thresholds were chosen so that the resulting classes are balanced in terms of number of samples. As an example, for K = 2 we split LaMem into two classes of roughly 30,000 images each (for K = 4, each class contains roughly 15,000 images). Note that the more classes we have after splitting, the narrower the score range in each class and the smaller the number of images per class. Thus the increase of the number of classes is at the expense of training a good classifier. We tested with different values of K in {2, .., 8} and found that K = 4 offers the best performance.

Our general classification model contains a branch (one or several MLP layers) for the CNNs, a branch for the IC-based semantic features, a merge layer to concatenate both of them followed by some MLP layers with a softmax on top. As the Fig. 1: Workflow of the investigated computational models for image memorability prediction. softmax gives, in addition to the class label, the probabilities p(k|i) ∈ [0, 1] that an input image i belongs to each class k = 1, ..., K, we take into account these values to compute the final predicted memorability score m i , in a final probability aggregation step, as

m i = K k=1 p(k|i)s k , (1)
where s k is a threshold value derived from the ground-truth scores of LaMem that separates class k from class k + 1, and s K = 1. With K = 4, these threshold values are 0.68, 0.77, 0.85, and 1, respectively. We performed some hyper-parameter selection thanks to the use of the Bayesian optimization library Hyperas1 . The selection was done over the following set of parameters: number of neurons per layer (from 32 to 1024, depending on the layers), dropout coefficient, activation function (ReLU, tanh) and optimizer (adam, rmsprop, sgd) so as to maximize the average Spearman correlation coefficient between the predicted scores and the ground-truth scores in a 5-fold validation process. Before that, the number of layers (3), batch size ( 16) and number of epochs [START_REF] Standing | Learning 10000 pictures[END_REF] were manually set once again while targeting the maximisation of the Spearman correlation coef-

EXPERIMENT AND INVESTIGATION

Section 3.1 presents the memorability prediction results on LaMem. We investigate the genericity of the model on other memorability datasets in Section 3.2, and the uncorrelation between memorability and interestingness in Section 3.3.

Prediction results on Lamem dataset

We evaluated our memorability prediction performance on LaMem [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] with the use of different features and different prediction models. The obtained results, measured by the mean Spearman correlation over a 5-fold validation process, are presented in Table 1. As a baseline investigation (see Fig. 1), we tested the use of CNN and IC-based semantic features with different regression methods (support vector regression (SVR), KNN, Decision Tree) where the hyper-parameters for each model were optimized by the Bayesian optimization algorithm implemented in the Hyperopt 2 library. We found that SVR performs better than other regression techniques (thus we mention only SVR in Table 1), and offers similar performance compared to the proposed MLP 2-class model and the state-of-the-art MemNet model. Fig. 3 shows LaMem example images of both some most and least memorable images, together with their predicted scores for our best 4-class model, and their ground-truth values.

As it can be seen, IC-based semantic features, when used with either SVR or MLP, offer better prediction performance than conventional CNN features even though their dimension is 4 times smaller. This reveals that the scene semantics is very important for the task of memorizing images and thus such attributes help better predicting the memorability. Overall, the combination of CNN and IC-based features offers the 2 https://github.com/hyperopt/hyperopt best mean Spearman correlation (0.72), higher than MemNet (0.64) and human consistency (0.68) on Lamem [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF]. This result confirms the complementary between IC-based semantic features and CNNs in predicting image memorability.

Feature

Prediction

Genericity of the model on other memorability datasets

In order to investigate the genericity of our computational model, we tested it on several existing memorability datasets. This also helped us understand more about the differences in content between these datasets. For each of them we computed the Spearman correlation between the provided groundtruth scores and the predicted ones by our model. Example images for each dataset are shown in Fig. 4 and the obtained Spearman correlation values, together with information about the datasets (i.e., number of images in each dataset and type of images), are detailed in Table 2.

As expected, our model generalizes quite good to Isola's [15] and Bylinskii's [START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF] datasets with a Spearman correlation of 0.59 and 0.48, respectively. This can be explained by the fact that these two datasets contain images related to scene categories, a topic quite close to the one of LaMem on which we trained our model as shown in Fig. 4. Our model performs poorly on two datasets containing specific images of human faces [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF], and visualizations [START_REF] Michelle | What makes a visualization memorable?[END_REF]. This is understandable as images in these datasets are very different from those in LaMem, again as presented in Fig. 4.

For datasets con-Dataset's first author Size Type of images Spearman correlation Isola [START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF] 2222 Scene categories 0.59 Bylinskii [START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF] 1754 Scene categories 0.48 Dubey [START_REF] Dubey | What makes an object memorable?[END_REF] 850 Object segmentation 0.36 Cohendet [START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF] 150 Rating of emotions 0.39 Libkuman [START_REF] Libkuman | Multidimensional normative ratings for the international affective picture system[END_REF] 703 Rating of emotions -0.02 Bainbridge [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF] 2222

Face pictures -0.06 Borkin [START_REF] Michelle | What makes a visualization memorable?[END_REF] 410 Visualizations -0,27 LaMem [START_REF] Khosla | Understanding and predicting image memorability at a large scale[END_REF] Isola [START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF] Bylinskii [START_REF] Bylinskii | Intrinsic and extrinsic effects on image memorability[END_REF] Dubey [START_REF] Dubey | What makes an object memorable?[END_REF] Libkuman [START_REF] Libkuman | Multidimensional normative ratings for the international affective picture system[END_REF] Bainbridge [START_REF] Bainbridge | The intrinsic memorability of face photographs[END_REF] Borkin [START_REF] Michelle | What makes a visualization memorable?[END_REF] Fig. 4: Example images from different memorability datasets.

taining images dedicated to the rating of emotions, our model performs reasonably on Cohendet's [START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF] while poorly on Libkuman's [START_REF] Libkuman | Multidimensional normative ratings for the international affective picture system[END_REF] dataset. Actually, the images in [START_REF] Cohendet | Using individual data to characterize emotional user experience and its memorability: Focus on gender factor[END_REF] were also in Libkuman's dataset, however the former was carefully annotated through lab experiments, leading to the conclusion that the ground-truth scores may be more reliable.

Correlation between memorability and interestingness

As memorability and interestingness are two emerging and important concepts for understanding images, we also investigated their correlation by applying our computational model for memorability prediction on existing interestingness datasets and again computing the Spearman correlation between the predicted scores and the ground-truth interestingness scores. The results are shown in Table 3, together with information about the tested interestingness datasets. Note that among these datasets, Flickr dataset was collected from Flickr Interestingness API3 so the collected interestingness scores are somewhat social-driven. Meanwhile other datasets (e.g., MediaEval and Gygli) provides content-driven interestingness as the labels were annotated by human annotators.

Dataset Size Type of images Spearman correlation MediaEval [START_REF] Demarty | Mediaeval 2017 predicting media interestingness task[END_REF] 7396 Movie frames 0.07 Flickr [START_REF] Shen | Technicolor@MediaEval 2016 Predicting Media Interestingness Task[END_REF] 123185 Social network -0.05 Gygli 1 [START_REF] Gygli | The interestingness of images[END_REF] 2688 Scene categories -0.21 Gygli 2 [START_REF] Gygli | The interestingness of images[END_REF][START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF] 2222 Scene categories -0.19 Gygli 3 [5] 3180 Webcam pictures 0.01 3, all Spearman correlation values are very low, meaning that our memorability prediction model can not be used for interestingness prediction, even for datasets [START_REF] Gygli | The interestingness of images[END_REF][START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF] containing the same type of scene category images. This is even more visible when one focuses on Isola's dataset [START_REF] Isola | Pattern Analysis and Machine Intelligence[END_REF] for which ground-truth labels are available for both memorability and interestingness: our model is doing well for memorability prediction (Spearman correlation = 0.59, see Table 2), while it fails to predict interestingess. These results re-confirm the uncorrelation between memorability and interestingness that was already stated in the literature.

CONCLUSION

Focusing on image memorability, an emerging and important concept for high-level understanding of images, we present a deep learning-based computational model for predicting such memorability levels. Experiment results on the benchmarking LaMem dataset prove that the proposed system outperforms both the state of the art and human consistency on the task. We also investigated the genericity of our model on other memorability datasets and further re-confirmed the uncorrelation between image memorability and interestingness, another important concept. As future work, we might want to focus on image editing and investigate how such manipulations affect the image memorability of content.

Fig. 2 :

 2 Fig. 2: Best predicting model in terms of average Spearman correlation over a 5-fold validation process.

Fig. 3 :

 3 Fig. 3: Example images from LaMem together with their ground-truth values (top left corner) and their predicted scores by our best model (top right corner). Predicted score values were normalized between 0 and 1, after probability aggregation.

Table 1 :

 1 Prediction results on LaMem dataset obtained by different methods.

		model	mean Spearman correlation
	CNN	SVR	0.64
	CNN	MLP, 2 classes	0.64
	CNN	MLP, 4 classes	0.65
	IC-based feature	SVR	0.65
	IC-based feature	MLP, 4 classes	0.67
	CNN + IC-based feature	MLP, 4 classes	0.72
	Hybrid-CNN (MemNet)	SVR	0.64

Table 2 :

 2 Prediction results obtained by the proposed computational model on other memorability datasets.

Table 3 :

 3 Prediction results obtained by the proposed computational model on interestingness datasets.As detailed in Table

https://github.com/maxpumperla/hyperas

https://www.flickr.com/services/api/flickr.interestingness.getList.html