Claudia-Lavinia Ignat

Gérald Oster

Meagan Newman

Valerie Shalin

François Charoy

Studying the effect of delay on group performance in collaborative editing

Keywords: Collaborative editing, groupware, delay, usability measurement

Real-time collaborative editing systems such as Google Drive are increasingly common. However, no prior work questioned the maximum acceptable delay for real-time collaboration or the efficacy of compensatory strategies. In this study we examine the performance consequences of simulated network delay on an artificial collaborative document editing task with a time constant and metrics for process and outcome suitable for experimental study. Results suggest that strategy influences task outcome at least as much as delay in the distribution of work in progress. However, a paradoxical interaction between delay and strategy emerged, in which the more generally effective, but highly coupled strategy was also more sensitive to delay.

Introduction

Real time collaborative editing [START_REF] Ellis | Groupware: Some Issues and Experiences[END_REF] allows a group of people to modify a shared document simultaneously. One user's changes appear to other users almost immediately, with very small time intervals of inconsistent document status. Realtime collaborative editing has gained in popularity due to the wide availability of free services such as Google Drive. With several tools to support collaborative editing, the practice is increasingly common, e.g., group note taking during meetings and conferences, and brainstorming activities.

While collaborative editing tools meet technical goals, the requirements for group performance are unclear. One system property of general interest is network delay. Despite several years of continuous increase in network bandwidth, network delay has not decreased. In fact, network delay is presently considered as the constraining factor. This delay is due to the physical communication technology, be it copper wire, optical fiber or radio transmission. Additional delay in real-time collaborative editing can result from underlying architecture or merging algorithms associated with collaborative editing tools.

As in all collaborative work, in collaborative editing, users must "divide, allocate, coordinate, schedule, mesh, and interrelate their contributions" [START_REF] Schmidt | Taking cscw seriously[END_REF]. Unanticipated interactions between subtasks can emerge [START_REF] Simon | The architecture of complexity[END_REF]. Both the avoidance and resolution of such interactions requires communication among the team members [START_REF] Erkens | Coordination processes in computer supported collaborative writing[END_REF]. However, not all tasks are sensitive to delay [START_REF] Dourish | Portholes: supporting awareness in a distributed work group[END_REF] and users might pursue strategies that are not sensitive to delay, or they might adjust their strategies if they are aware of delay [START_REF] Gutwin | Revealing delay in collaborative environments[END_REF].

To our knowledge no prior work questioned the maximum acceptable delay for real-time collaboration or the efficacy of compensatory strategies. In this paper we study the effect of delay on group performance on an artificial collaborative editing task where a group of four participants i) located the release dates for an alphabetized list of movies and ii) re-sorted the list in chronological order. The task is not unlike job shop scheduling [START_REF] Tan | MaryCzerwinski: Using job-shop scheduling tasks for evaluating collocated collaboration[END_REF]. We examine how strategy interacts with delay to affect task outcome and process in this task.

We start by describing the methods we used for designing our experiment. We then continue by presenting our results organised by measures. We next discuss our results and we end the paper by some concluding remarks.

Methods

Participants Eighty students affiliated with a European university participated in this experiment, in mixed gender groups of 4. The participants ranged in age from 21 -27. All participants used French in their daily activities, although they had sufficient working knowledge of English to comprehend the movie titles in the task stimuli. An electronic announcement solicited participation. All participants received a 10 Euro gift certificate for their participation.

Apparatus

The experiment was conducted using four desktop computers in a classroom setting. Participants were separated by partitions and could not directly observe other team members while they worked, although typing activity was audible. The server running the Etherpad application was hosted on an Amazon EC2 instance located in the US East (Northern Virginia) Region. Each desktop ran the Mozilla Firefox web browser executing the Etherpad web client application. Etherpad hosted the task stimuli and a Chat dialogue facility. User operations appeared color-coded in both the text and chat. Etherpad relies on a client-server architecture where each client/user edits a copy of the shared document. When a user performed a modification it was immediately displayed on the local copy of the document and then sent to the server. The server merged the change received from the user with other user changes and then transmitted the updates to the other users. When a user edited a sequence of characters, the first change on the character was immediately sent to the server, while the other changes were sent at once only upon reception of an acknowledgement from the server. With each change sent to the server, it created a new version of the document. Gstreamer software enabled the video recording of user activity. We also instrumented Etherpad to register all user keyboard inputs on the client side and to introduce delays on the server-side. The editor window displayed 50 lines of text. Users editing above the field of view of a collaborator could cause the lines within the collaborators' view to "jump" inexplicably. Such a property is consistent with the inability to view an entire document as it undergoes modification from multiple team members.

Task & Stimuli Participants conducted a 10 minute search and sorting task, starting with an alphabetized list of movies. They used the internet to locate the release year for each movie and sorted the list in chronological order. The list contained 74 movies, extending beyond the window size of the editor.

Procedure The entire procedure was approved by a US University IRB. Participants began the session with informed consent. The present sorting task was second in a three-task series. Scripted instructions (translated into English) for the sorting task follow: "We will provide you with the list of movies. Your task is to search for the release dates of the movies and assemble a single list of movies sorted and labeled with their release dates. You can use the browser for finding the year of release of a movie. The year of release of a movie should be placed before the movie title and the movies should be sorted in an ascending order, starting from the oldest to the newest movie. You will have 10 minutes for finishing the task. Please work as accurately as you can while still being efficient. You are free to coordinate your efforts with your teammates throughout the task using the chat interface at the bottom right side of the screen".

Design The sorting task was conducted with four teams of 4 participants for each level of the continuous independent variable Delay, tested at 0, 4, 6, 8 and 10 seconds in addition to the 100 msec delay inherent to client-server communication. While participants viewed their own document changes in real-time, they viewed other participants' changes according to delay condition. Chat was implemented in real time for all conditions. Delay conditions were tested in random order, and all groups experienced a single level of delay across the three-task session.

Dependant Measures We examined sorting accuracy as an outcome measure. We also examined a set of process measures such as average time per entry and chat behavior, as well as strategies.

-Sorting Accuracy is measured by an insertion sort metric which is the most likely strategy for human sorting [START_REF] Sedgewick | Algorithms[END_REF]. Insertion sort iterates over an input list of elements and generates an output sorted list. At each iteration, an element in the input list is removed and inserted in the proper location within the sorted list, terminating when no more input elements remain. The insertion sort metric quantifies the distance between the input list and the output sorted list. Here the group provides the input list and the output list is the target list of movies, ordered according to their release dates. The distance between an element in the input list and the corresponding element in the sorted list is measured in terms of the number of swaps between adjacent elements required to place the input element properly in the sorted list.

We normalized this distance with the distance in the worst case scenario, i.e. when the input list is sorted in reverse order. We additionally had to accommodate duplicated or missing movies, or movies with incorrect release dates. Therefore we eliminated the duplicated movies and the movies with an incorrect release date from the final list of movies generated by each group. We also eliminated from the output list the missing movies in the input list. The distance computed by the insertion sort metric was adjusted to be proportional to the number of movies that are not duplicated and for which users assigned the correct release dates. The formula that we used for each group score is : 1 -

#Swaps

#SwapsWorstCase × #Movies #Swaps represents the total number of swaps between adjacent movies required using an insert sort method on the group's final list of movies. #SwapsWorstCase represents the total number of swaps between adjacent movies required by an insert method in the worst case, i.e. when the list of movies contains the movies in a descendant order according to the release dates. #Movies represents the number of movies in the final list of movies after a removal of duplicated movies or those with an incorrect release date. Two co-authors independently coded the insertion sort metric in different programming languages with identical results.

-Average Time Per Entry was computed as the period of activity in question divided by the number of characters input. Because the task characteristics potentially changed over the 10 minutes, with the first half corresponding to the identification of movie dates and the second potentially corresponding to the sorting, we also calculated separate average response times for the first and second halves of the session. -Chat behavior was quantified as the number of turns, the number of words, agreement words (yes and OK), group oriented pronouns (You, your, one, us, who, each one, someone, no one, others) and ego oriented pronouns (I, my, me, mine). We examined agreement words, group-oriented pronouns and ego-oriented pronouns as a function of the number of words. -Strategies emerged through detailed analysis and are described below.

Results

We provide results in three subsections, organized by measures. First we examine task strategies. Next we examine task outcome, followed by several measures of task process. For both outcome and process measures, we conduct regression modeling, using Delay condition and Strategy as predictors, and follow up with simple effect analyses by Strategy. We examine additional facets of process in the indicators of coordination as apparent in Chat.

Strategies

As we had no a priori hypotheses about how users would divide up the work, we developed a coding scheme based on a review of the videos, supplemented by the chat discussion. Two strategies emerged:

-Sort at the end (Strategy 0) where sorting starts after all years have been added for all movies. This strategy enables loose coupling among participants at the beginning of the task, but leaves a highly coupled sorting task for end, with no pre-established assignments. -Continuous sorting sorting (Strategy 1) is done immediately after adding a year for a movie. This strategy begins with a highly coupled distribution of work among participants.

Outcome Measure

An insertion sort metric served as the outcome measure. Figure 1 displays the relationship between Delay, Strategy and insertion sort. Strategy alone accounts for insertion sort score, r(18) = .68, adjusted R 2 = .34, F (1, 18) = 10.89, p = .004, b 0 = 44.17 t(18) = 14.54, p < .001, b 1 = 4.53, t(18) = 3.30, p = .004.

We examined the 9 groups who pursued Strategy 1 separately from the 11 groups who pursued Strategy 0. Among the 11 Strategy 0 groups, Delay does not predict insertion sort (r(9) = .11, adjusted R 2 = -.10, F (1, 9) = .12, p = .742). Among the 9 Strategy 1 groups, a linear model for Delay predicts insertion sort score (r(7) = .69, adjusted R 2 = .39, F (1, 7) = 6.21, p = .042). The intercept for the linear model is 66.46, t(7) = 17.69, p = .000 and the unstandardized slope is -1.38, t(7) = -2.49, p = .042. That is, each increment in Delay decrements the outcome measure by 1.38 Insertion Sort score units. A quadratic model does provide a better account for Strategy 1 groups (r(7) = .75, adjusted R 2 = .50, F (1, 7) = 8.98, p = .020). The intercept for the quadratic model is 65.99, t(7) = 21.15, p = .000 and the unstandardized Delay slope is -.15, t(7) = -3.00, p = .020. This model raises the possibility that Delay condition 10 results in qualitatively different behavior than the other conditions.

Process Measures

We examined the average time between task inputs based on client recordings. Software error caused the loss of data for 4 groups. At the group level we used regression analysis to describe our results, treating Delay condition as a continuously valued independent variable. At the participant level, we used a nested ANOVA, using Delay condition as a categorical variable. The nested analysis allowed us to determine whether significant group effects precluded analysis of the Delay main effect. We examined Delay condition and Strategy as independent variables, with tests based on Type III Sums of Squares to account for the unbalanced design and an α = .10 due to the small number of groups [START_REF] Cooke | Team cognition in experienced command-and-control teams[END_REF]. Significant group effects precluded statistical analysis of response time data across the entire experimental session. We proceeded with response time data from the session's first 5 minutes, where group effects were absent.

Group response time over the first 5 minutes accounts for insertion sort score, (r( 14 F (1, 18) = 7.24, p = .015, b 0 = -40.00, t(18) = 8.49, b 1 = 3.83, p = .000, t(18) = 2.69, p = .015). We also examined Strategy and Delay as predictors of chat metrics. Of these analyses, only total words was sensitive to the independent variables. For Strategy 0, Delay predicts total words (r(9) = .64, adjusted R 2 = .34, F (1, 9) = 6.22, p = .034, b 0 = 29.08, t(9) = .79, p = .448, b 1 = 14.32, t(9) = 2.49, p = .034). For Strategy 1, Delay does not predict total words (r(7) = .34, adjusted R 2 = -.01, F (1, 7) = .92, p = .368, b 0 = 123.36, t(7) = .5.39, p = .001, b 1 = -3.23, p = .034, t(7) = -.96, p = .368).

Discussion

An artificial document editing task captures the upper limit of dependency and interactivity in collaborative editing, and permits the measurement of task outcome. Here we return to our original question regarding the relationship between delay and strategy on process and outcome.

The effect of delay depends on strategy. Such an interaction between strategy and experimental manipulation on outcome is consistent with prior studies in game-like motor control environments. However, somewhat counterintuitively, and unlike previous research, the overall superior strategy does not overcome the effect of delay. In fact, the insertion sort score declined with delay for continuous sort, but did not for sort-at-the-end. We suspect that continuous sort entails more coupling, because years must be in place prior to positioning, and because text position is changing frequently throughout the entire task as sorting proceeds. To manage the coupling in continuous sort, we see participants slow down with delay. However, the negative slope on the insertion sort metric for continuous sort relative to the flat slope for sort-at-the-end suggests that the continuous sort strategy is only adaptive within a range of delay. Untested levels of delay could actually result in worse performance for continuous sorting than a sort-atthe-end strategy.

The sort-at-the-end strategy did not encounter coupling until the later phases of task completion. However, the chat metric suggests that sort-at-the-end requires more local coordination as delay increases. Thus the coordination established by formal agreement at the outset in continuous-sort appears to favor efficient communication over the ability to respond to local perturbations. On the other hand, sort-at-the-end appears to favor the ability to respond to local perturbations at the expense of efficient communication.

Performance on a family of related tasks will help to address the relationship between delay and task properties. We have data for the effect of delay on two other artificial editing tasks that vary both the task time constant and the degree of subtask coupling. The analyses presented here suggest the need to add Delay levels, with 2 and 12 second delays and beyond. This will help determine whether the models that relate performance to delay are appropriately linear, or quadratic, with more rapid declines in task performance with delay.

The general effect of delay on an artificial document editing task is to slow the individual participant, which for the present task, decrements the outcome metric. However, similar to game-like tasks (e.g., [START_REF] Gutwin | Revealing delay in collaborative environments[END_REF]), the effect of delay on document editing, as measured by outcome, depends on strategy. A tightly coupled subtask decomposition that enhances outcome in the presence of minimal delay becomes detrimental at higher levels of delay, potentially less effective than a more loosely coupled task decomposition at the beginning of the task. Nevertheless, a loosely coupled strategy at the beginning of the task leaves a poorly coordinated, tightly coupled sorting task to the end of the task, increasing the need for communication and hampering overall performance. Given the time constant of the present task, strategy is at least as important as delay in the distribution of participant inputs to the team.

Fig. 1 .

 1 Fig. 1. Insert Sort Score by Delay, separated by strategy. Strategy 0 groups pursued sorting after finding movie years. Strategy 1 groups pursued continuous sorting.

Fig. 2 .

 2 Fig. 2. Average response time by Delay from the first half of session.

Acknowledgments

The authors are grateful for financial support of the USCoast Inria associated team and of the research program ANR-10-SEGI-010 and for sabbatical support from the Department of Psychology, Wright State University. Many thanks to Olivia Fox for her help on statistical analyses.


H Boyer

A H Fakra

F Miranville

B Malet-Damour

S Guichard

P Lauret

Evolution of CODYRUN from thermal simulation to coupled thermal anddaylight simulation software

Keywords: Daylight simulations, Interior illuminance, Experimental measurements, CODYRUN

CODYRUN is a multi-zone software integrating thermal building simulation, airflow, and pollutant transfer. Described in numerous publications, this softwarewas originally used forthe passive designof buildings, both for research and teaching purposes.In this context, the datatreatedwere mainly concerned with volumes (zones), surfaces andthicknesses (walls andwindows), materials, and systems, with the aim to determine temperatures,heat fluxes,energy consumed, air transfers, and so on.

The questionthus arose as tothe integration ofindoor lighting conditions into the simulation. Hence, previousdata structureshad to beamended to incorporatethe spatial positioning ofentities (walls, windows, and artificial lighting sources) throughvertexes. A set ofprocedureswasalso developed for polygons as well as calculatingnatural and artificial lighting.

The results ofthis new daylighting modulewere thencompared with otherresultsof simulation codes and experimentalcases both in artificialand naturalenvironments. Excellent agreements were obtained, such as the values for luminous efficiencies ina tropical and humid climate.

A simulation exercise was conducted in a classroom located in Reunion Island (French overseas territory in the Indian Ocean), thus confirming the interest for thermal and daylighting designsin lowenergy buildings.

Introduction

Several software packages are available for thermal and airflow simulation in buildings. The most frequently used are ENERGY+ [START_REF]EnergyPlusengineering reference: The reference to energy plus calculation[END_REF], ESP-r [START_REF] Clarke | Energy simulation in building design[END_REF],and TRNSYS [START_REF] Marsh | Calculating variations in the distribution of insolation over building surfaces[END_REF]. These applications allow an increasing number of models to be integrated, such as airflow, pollutant transport, anddaylighting. In thelatter category, we may note E+, ESP-r,or ECOTECT [START_REF] Marsh | Calculating variations in the distribution of insolation over building surfaces[END_REF] software.

After more than 20 years of developing a specific code named CODYRUN, we decided to add a lighting module to our software.This paper therefore provides some details on this evolution and a practical application.

From thermal simulation to daylight model

Developed by the Physics and Mathematical Engineering Laboratory for Energy and Environment at the University of Reunion Island, CODYRUN [START_REF] Boyer | Thermal building simulation and computer generation of nodal models[END_REF][START_REF] Boyer | A multi model approach of thermal building simulation for design and research purposes[END_REF][START_REF] Boyer | Building ventilation: A pressure airflow model computer generation and elements of validation[END_REF][START_REF] Soubdhan | Use of BESTEST procedure to improve a building thermal simulation program[END_REF][START_REF] Lauret | A validation methodology aid for improving a thermal building model: How to account for diffuse radiation in a tropical climate[END_REF][START_REF] Calogine | Identification of parameters in building concentration dispersion model[END_REF][START_REF] Fakra | Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation[END_REF][START_REF] Boyer | Interroom radiative couplings through windows and large openings in buildings: Proposal of a simplified model[END_REF][START_REF] Lucas | Study of moisture in buildings for hot humid climates[END_REF] is a multi-zone software program integrating ventilation and moisture transport transfer in buildings. The software employs a zone approach based on nodal analysisand resolvesa coupled system describing thermal and airflow phenomena. Consequently, the ambient thermal comfort is estimated for buildings' systems. In these energyand comfort calculations, the precise location in spaceof a building's componentsshould be noted, which is not usually helpful, with some models even requiringgeometrical information,such asthe height of openings [START_REF] Boyer | Building ventilation: A pressure airflow model computer generation and elements of validation[END_REF] or solar masks [START_REF] Lauret | A validation methodology aid for improving a thermal building model: How to account for diffuse radiation in a tropical climate[END_REF].

At this step, the datatreatedwere mainly concerned with volumes (zones), surfaces andthicknesses (walls andwindows), materials, and systems, with theaim to determinetemperatures,heat fluxes,energy consumed, air transfers, and so on. Details of the CODYRUN data structures are given in Lucas et al. [START_REF] Lucas | Study of moisture in buildings for hot humid climates[END_REF] although details on the internal organization are not so common, with the exception of ENERGY+ [START_REF]EnergyPlusengineering reference: The reference to energy plus calculation[END_REF], ESP-r [START_REF] Clarke | Energy simulation in building design[END_REF], andCOMFIE [START_REF] Peuportier | Simulation tool with its expert interface for the thermal design of multizone buildings[END_REF].

Initial data structures

As displayed in Figure 1, the building is assembled from various components: zones, walls, windows, heating, ventilation, and air conditioning systems, openings, vents, and so forth. For each of these components, the window description helps users to complete the information for the associated data structures. In the example of a wall, the user must enter its surface, constitution (layers, materials, and thermo-physical properties), and radiative properties. The description of the windows and structures was previously described inBoyer et al. [START_REF] Boyer | Thermal building simulation and computer generation of nodal models[END_REF][START_REF] Boyer | Modelisation methods and data structuration induced for simulation codes[END_REF].

Modified data structures

Taking into account the lighting calculations [START_REF] Fakra | Intégration de modèles d'éclairage à un code de calcul en physique du bâtiment: Modélisation[END_REF],thespatial positioningof the data relating tothe walls, windows,apertures,andartificial illuminationwas required. Previousdata structures describing these elementsthus had to beexpanded to incorporatethe spatial positioning ofthe entities (walls, windows, and artificial lighting sources) throughvertexes. Three types (in terms of C language data structures) were introduced, namely point, polygonand block.

Pointwas linked to the coordinates, polygondescribed a set of points (maximum of eight),and blockcomprised two polygons. The corresponding C structures were thus as follows: To ensure the spatial descriptionof elements,thislast data structurewas incorporated intothatof the componentsdescribed in section 2.1.In the current version of CODYRUN, all of these coordinates areentered individuallyusingthe windowdescriptions. After aspecificsoftware development, thesedetails will soon beretrievable fromGoogleSketchup®andOpenStudio®. Then, newcomponentsfor lightingwere introducedfor artificial lightingandlightpipes.

T_Point { floatx,
Each component of the building is thus viewed as two polygons,each containing a certain number of points. For rectangular glazing, the structure is composed of two rectangular polygons each with four points. The first polygon is the inner face and the second the outer face. Lastly, each of the points of the two polygons is plottedin space using its three coordinates (x,y,z). An example of the four points ina polygon (P1 to P4) is given in Figure 2a for an external opening.

Concerningan artificial lighting source,such as a light bulb, byallowing its assimilation to a point source, the coordinates of a single pointmustbe defined(i.e.,those ofthe point source).

Daylighting models

All availablemodels requirerelative spatial positioningofthe elements(floors,windows,openings, andlight sources) to calculate the receivedlight on each pointinthe working plane (or mesh).

The daylight factor (DF) classical method was used to calculate the diffuse illuminancefor all of the mesh points. This method was elaborated by the British Building Research Establishment (BRE) and published by theChartered Institution of Building Services Engineers (CIBSE) [START_REF]Window design[END_REF]. The diffuse light falling on one point in a roomis commonly considered to be composed of three distinct parts: the sky component (SC), externally reflected component (ERC), and internally reflected component (IRC). These values were calculated for each point of the defined mesh using standard formulas. Thedirect illuminationwas obtainedby projecting theedges of theglazing andthe outer apertures eventually illuminatedby directsolarradiation in order to calculate the indoor sunspot.

Geometrical considerations and polygons

A set ofrelatedC procedures relative to the geometry ofplanar polygonswas incorporated into thecode based on previous research [START_REF] Clarke | Energy simulation in building design[END_REF][START_REF] Siret | A geometrical framework for solving sunlighting problems within CAD systems[END_REF][START_REF] Foley | Fundamentals of interactive computer graphics[END_REF]. Itrelates to,for example,the calculationof surfaceperimetersfor polygons, the distance betweenapoint and a plane, testing the inclusion of apoint in a planeor polygon,orprojectionsrelatingto shadingcalculationsandthe sunspot.

Validation of the model

Numerous validation tests of the CODYRUN code were successfully applied to the software. Apart fromthe daylighting model, the majorityapplied the BESTEST procedure [START_REF] Soubdhan | Use of BESTEST procedure to improve a building thermal simulation program[END_REF] and led to the validation of the outputs.

When dealing with the daylighting simulation model in particular,it was difficult to achieve the prediction accuracy. It is thus necessary to refer to some rigorous and recognized procedures used around the world. However, there is limited documentation on the procedures to follow. In most cases, laboratories implement their own experimental database to serve as a reference for comparisons between model predictions and measurements. The study ofMaamari et al. [START_REF] Maamari | Application of the CIE test cases to assess the accuracy of lighting computer programs[END_REF] responded perfectly to our needs concerning the validation step. Indeed, this theory establishes methods to verify the reliability of simulation codes for indoor daylighting and artificial lighting on the basis of analytical and experimental tests cases. This work was used as a reference in task TC3-33 of the International Commission on Illumination (CIE).

Many other test cases found in scientific literature (publications from the French Scientific and Technical Center for Building, BRE, International Energy Agency's Task 21, and experimental test case from CIBSE, etc.) were applied to the CODYRUN simulation software. An inter-software comparison was also conducted, and finally, database references for the local study in dynamic conditions (from a cell called LGI) were established [START_REF] Fakra | Development of a new model to predict indoor daylighting: Integration in CODYRUN software and validation[END_REF].

Case of a classroom in Reunion Island

A simulation exercisewas conductedon aclassroomin our university. The aimwas to usethe integratedmodelto enable students to understand natural and artificial lightingphenomena through variousdynamic simulations(annual, monthly, and daily) in order to characterizetheindoor lighting conditions and improvethe visual comfort of the room.Some resultsfrom Fakra's study [START_REF] Fakra | Intégration de modèles d'éclairage à un code de calcul en physique du bâtiment: Modélisation[END_REF] are presented inthe following paragraphs.

TD2 classroom (see Figure 2) is located at Saint-Pierre (Latitude: 21°19' South, Longitude: 55°28' East. Altitude: 68 m). The climate is tropical and humid.

The dimensions of the classroom are as follows: 7.2 m in length, 6.74 m in width and 3 m in height, with a totalvolume of 145 m 3 . Two large windows are located on the west side of the classroom and three others on the east side (one small and two large openings). The dimensions of the large openings are 1.1 m high by 1.5 m width, compared with 1.1 m high by 1.2 m width for the smaller window. Three doors, positioned respectively to the south, north, and west of the classroom,each measure 2.1 m high by 1 m width.

The reflection coefficients of the walls, ceiling, and floor were estimated at 80%, 70%, and 60%, respectively. The transmission coefficients for the single-glazed windowswere 85% (given by the manufacturer), while the reflection coefficients of the doorswere estimated at 70%.

Daylight factor spatial distribution

The first issue raisedin this studywasthe need to have access to the luminous fluxonthe classroom's workplane.This distributionis stronglylinked to the positionand dimensions ofthe glazed openings. The most commonly usedfactor to characterizethisdistribution is theDF. The following figurethus shows the obtained values. As expected, the luminous fluxwas found to be higherclose to the windows, tending to decreasetoward the center of the room. We obtainedan average value of4.34% (witha minimum of 0% on the perimeter ofthe surface of ourmeshand a maximum of 15.62% close to the western openings), which is slightlybelow theFrenchstandard.

Monthly average indoor illumination

The next stepof the studywasto examinetheoverall averageilluminanceof the roomthroughoutthe year.This providesan overview of thelighting autonomyof the studied room. Usingan hourly measured meteorological file anda model ofluminous efficiencypresented in [START_REF] Fakra | A simple evaluation of global and diffuse luminous efficacy for all sky conditions in tropical and humid climate[END_REF], thevaluesshown in Figure 4were obtained.

The first observationis thatin a typicalyear,the averageilluminanceinsidethe classroomis approximately 1321lux.The room ismost illuminatedaround the month of November(1541 lux) and leastilluminatedaroundApril(1110lux).Overall,the room can beilluminated by naturallightwithout the needfor artificial lightingduring the day.

Lighting autonomy

The curve in Figure 4 does not provide informationonthe autonomydurationof thelightin the room.Indeed,it is not possible to concludeduring whichpart of the daytheilluminationis sufficient in the room.To ascertain thetemporal availabilityconcerning the quantity ofinterior light, the evolution ofthe firstday of each monthwas observed.Figure 5 depicts theaveragecurves(inklux) available on themesh grid. Regular illumination was observedbetween 8 am and5pmthroughoutthe year.Themaximum value was2089lux(at noonon December 1). From the resultsobtained, wecan deduce thatthe averagefrequency with whichthe room can beilluminated-with naturallight-was90.8%. Only9.2% of the working hoursrequiredartificial lightingthroughout theyear, and thismostoften occurs between8 am and9amor4pmand5 pm.

Conclusion

Our goalof integrating amodelfor lightingcalculationwas reached, withdetailsonthe internal organizationprovidedin this article.Althoughspecific toour simulation software, these detailsare important for understanding thenecessarydata structuration to describea buildingin anysimulation code.

Although it is clear that this type of study can be performedusing other software (like E+), as in previous models (pollutants, airflow, etc.), we believe our own coding for this lighting model to be profitable in terms of its in-depth knowledge and flexibility, as it takes into account certain specificities or local practices.

The results of actual release were satisfactory in terms of their precision and comparability to much more powerful software. They allow us, in the philosophy of CODYRUN, to conduct specific software development dedicated to research and professional audiences. In future research,we aim to explore theinnovativeintegration of specificcomponentsfor lighting(lightpipes, thermotropiclayers, etc.) and interfacingthe software withGoogle Sketchup® and OpenStudio®to improveitsusability among professionals.

Fig. 1 .

 1 Fig. 1. (a) Component assembly of a building; (b) Associated windows and structures

Fig. 2 .

 2 Fig.2. (a) Perspective; (b) Ground plan of the classroom

Fig. 3 .

 3 Fig. 3. (a) Perspective; (b) DF distribution in classroom

Fig. 4 .

 4 Fig. 4. Monthly average indoor illumination

Fig. 5 .

 5 Fig.5. Average annual indoor illumination


Amine Hallili
email: hallili@i3s.unice.fr

Toward an Ontology-Based Chatbot Endowed with Natural Language Processing and Generation

Keywords: Chatbot, Articial Intelligence, Natural Language Processing, Natural Language Generation, Semantic Web

With the last evolution of the web, several new means of communication have showed up. In the commercial domain, chatbot technologies are now considered as essential for providing a wide range of services (e.g. search, FAQ, assistance) to the end-user, and to make a client a faithful customer. In this paper, we propose an on-going work on the denition and implementation of SynchroBot, an ontology-based chatbot that relies on Semantic Web and NLP models and technologies to support user-machine dialogical interaction in the e-commerce domain.

Introduction

During the last decades, our way of consuming information has totally changed with the emergence of new means of communication (e.g. forums, FAQ, social networks, semantic search engines, mobile applications, and text to speech systems) which provide us with dierent possibilities of handling and dealing with information on the web. At the same time, researchers in Natural Language Processing (NLP) and Semantic Web domains have proposed new approaches to model and implement more and more complex systems capable of interpreting natural language, of reasoning, and of assisting end-users (e.g. Chatbots [START_REF] Allen | Toward conversational human-computer interaction[END_REF], Expert Systems [START_REF] Liao | Expert system methodologies and applications -a decade review from 1995 to 2004[END_REF] , multi agent systems [START_REF] Wooldridge | An Introduction to MultiAgent Systems[END_REF], and Question Answering systems [START_REF] Hirschman | Natural language question answering: the view from here[END_REF]). Besides covering both open and close domains (e.g. social, commercial, scientic), such systems aim to be autonomous, self-learning and they can replace humans in performing several tasks. My PhD research proposal, whose preliminary works I present in this paper, focuses on chatbot systems, which we classify in two dierent categories: Question Answering Systems (QA) and Dialog Systems (DS). On the one hand, Question Answering systems aim at nding answers to factual queries in either a Knowledge Base (KB) or raw text and to return them to the user. The answer can be just a textual string (e.g. [START_REF] Cabrio | Natural language interaction with the web of data by mining its textual side[END_REF]) or it can be enriched by other meta-information or well-formed sentences, obtained by applying Natural Language Generation (NLG) techniques (e.g. [START_REF] Augello | A semantic layer on semistructured data sources for intuitive chatbots[END_REF][START_REF] Chai | Natural language assistant: A dialog system for online product recommendation[END_REF]). In spite of their eciency in retrieving the information, such systems lack the capability of handling the links between sequential questions as in a conversation. On the other hand, Dialog Systems aim at keeping in memory the links between consecutive questions in order to ensure a logical conversation mode with the user (e.g. [START_REF] Pakucs | Towards dynamic multi-domain dialogue processing[END_REF][START_REF] Heinroth | Spoken interaction within the computed world: Evaluation of a multitasking adaptive spoken dialogue system[END_REF]). Nevertheless, most of these systems do not rely on robust and exible KBs allowing them to extract information from multiple sources and to reason over the data. The goal of our work is to combine the strenghts of the two categories of systems discussed above, and to propose a dialog system that relies on i) a rich KB for data extraction and reasoning, ii) NLP tools to interpret user's question, and iii) NLG techniques to generate well-formed sentences. The The remainder of this paper is organized as follow: Section 2 presents our preliminary approach and implementation. In Section 3 we describe our ongoing works along with our perspectives for future works.

SynchroBot : A Preliminary Approach

The approach we propose relies on the Semantic Web 1 paradigm, which covers structuring, linking, sharing and reusing data through applications, enterprises and communities. For that, it provides a number of information modeling frameworks e.g. Resource Description Framework (RDF) and RDF Schema (RDFS).

The preliminary approach we propose here toward an ontology-based chatbot covers three aspects, namely i) Knowledge based System ii) Question Interpretation iii) Natural Language Generation. Currently we focus on modeling and implementing an ecient and robust QA system that will be the corner stone for our future Dialog System.

A Knowledge Based System

Our approach relies on the use of exiting tools, resources and information (e.g. FAQ, API, system logs) in order to create a KB in RDF, which means that the data will be represented as triples: <Subject, property, Value>. For example, the following sentence Google sells Nexus 5 can be expressed in RDF as <sbr:Google, sbo:sells, sbr:Nexus_5>. We have created an ontology that 1 www.w3.org/2001/sw/ describes the classes (e.g. Product, Category, Seller, etc.) and properties (e.g. sells, price, locatedIn, etc.) of the KB in the e-commerce domain (the focus of Synchrobot). For instance Google type is sbo:Seller and has the sbo:sells property. Likewise, Nexus_5's type is sbo:Product and has the sbo:locatedIn property. Also, with our ontology we infer, among others, that Nexus 5 is sold by Google (<sbr:Nexus_5, sbo:soldBy, sbr:Google>). Furthermore, every property is annotated in both French and English, by a number of labels that have the same meaning (e.g. sbo:sells will have sell, trade, vend, commercialize, market, etc. as labels), which will be used to match the terms in the question, in order to identify the queried property. The current version of the KB is composed of 500000 product descriptions that we retrieved by using eBay APIs to transform eBay data to RDF.

Question Interpretation

As regards the natural language question interpretation, our approach focuses on textual information as input and relies on the work described in [START_REF] Cabrio | Qakis: an open domain qa system based on relational patterns[END_REF] which requires identifying three aspects i) the Expected Answer Type (EAT), which is the type of the resource that we are looking for, ii) the property, representing the relation linking the entity on which the question is asked to its answer, and iii) the Named Entity (NE) representing the subject of the given question. In this example Who sells Nexus 5?, the EAT is sbo:Seller, the property is sbo:sells and the NE is sbr:Nexus_5.

Named Entity Recognition : To identify the NE, we aim at using natural language processing techniques (e.g. Named Entity detection and linking) to retrieve all possible NEs from the KB by matching the user'question to KB property values (e.g. name, description, etc.). Then, relevant NEs will be used in querying the KB according to the assigned retrieving score which we determine by using the folowing strategy :

In order to assign a score to the quality of the search, our strategy relies on several aspects : First, all KB properties used for the search are ordered depending on their relevance. For instance, matching the user's question to the sbo:hasLegalName property will be more accurate than matching it to the sbo:hasDescription property and so on. Based on that, a relevance coecient is assigned to each property and used in both the score computing and the determination of relevant NEs. Second, a score is assigned to the accuracy of the matching of the user's question and the resources found in the KB. In other word, the bigger the matched words, the higher score will be (an exact match will result of a perfect score and the found resource will be directly used). Finally, we focus on the number of the retrieved resources to determine the precision of our result. This means that the fewer resources we nd, the higher the precision of the search.

Property Detection : We detect the property by matching its annotated labels with the user's question [START_REF] Damljanovic | Natural language interfaces to ontologies: Combining syntactic analysis and ontology-based lookup through the user interaction[END_REF] and following a scoring strategy we pick up the relevant property.

We are also able to recognize questions with two relations as shown below in gure 1. For instance, the following example Give me the address of the Nexus 5's sellers! contains two properties (i.e. sbo:address, sbo:sells) meaning that the question can be divided in two sub-questions: Give me the Nexus 5's sellers! and Give me their addresses!. This can be done thanks to the fact that the properties in our ontology have domain and range that allow the detection of 2relation (n-relations in general). Concretely we aim at constructing a relational graph representing the user's question that contains the identied properties along with the found resources (e.g. Named Entities), while comparing both the NE type and the identied property domain. In the given example, the property sbo:address, with domain sbo:Seller, will have the best score along with the NE sbr:Nexus_5 that have type sbo:Product which diers from sbo:Seller, this leads to the creatation of a relational graph with two nodes which are two properties namely sbo:address and sbo:soldBy and means that the question has more than one relation. These elements (NE, property and EAT) will be used to generate a SPARQL query that retrieves results from the KB.

Note that the system will select the property sbo:soldBy instead of sbo:sells during the process time. This is due to the fact that the system will use inference to pick up more properties to be able to construct a relational graph that represents the user's questio. In our example, only the sbo:soldBy property which is the inversed property of the selected one sbo:sells will give results when creating the relational graph. After detecting relevant properties, we aim at using their respective Domain types. For instance, as shown in gure 1, the following properties' Domains (sbo:Product, sbo:Seller, sbo:Address) will be used along with the score assigned to their respective property. This allows us to sort all the detected EATs before adding them in the generated query that the system use to retrieve results from the KB.

Natural Language Generation

In order to answer questions with a generated sentence, we propose that each property in our ontology will be mapped with a list of generic response patterns. Our challenge is to be able to replace dynamically some particular parts of the pattern to return well-formed answers. For instance, we take the example Give me the price of a Nexus 5! and considering that the identied property sbo:price matches the pattern [ The price of {Product} is {Value} ], so after replacing the {Product} and {Value} parts we can answer that The price of Nexus 5 is 400$. We also use the sbo:mediaType property to show more interesting information to the user after giving the well-formed answer (e.g. image, video, map, etc.). For instance, when a user asks Show me the white version of Nexus 5!, the system infers that the user is more interested in viewing images rather than just textual information, and as a result, images will be displayed in this case.

3 Ongoing and Future Work

This paper sketches out the PhD research project I have recently started, and describes the preliminary steps representing the bases of a number of directions that we consider for future work:

As ongoing and short term works we are planning to improve the NE recognition by using well-known and ecient algorithms (e.g. KNN, Similarity, N-Gram and TF-IDF scoring) in order to gain more precision, to spot complex and ambiguous resources, and to be able to diversify given answers. We have also begun to reuse other famous ontologies that exist in the literature and cover the commercial domain. we have started to use the schema.org [START_REF] Mika | Metadata statistics for a large web corpus[END_REF] ontology but due to its parial coverage, we have decided to use more specic ontology (GoodRelations [START_REF] Hepp | Goodrelations: An ontology for describing products and services oers on the web[END_REF] ontology) which fully covers the commercial domain.

Furthermore, we consider answering n-Relation questions by the construction of a Relational Graph representing the question's NEs and properties. For that, we will study the possibility to generalize the 2-relation question answering approach explained in the previous section, to n-Relation questions. Moreover, we intend to integrate the relational pattern matching module of QAKiS system [START_REF] Cabrio | Natural language interaction with the web of data by mining its textual side[END_REF] that exploits Wikipedia pages to extract lexicalisations of ontological relations.

More specically, we will use website APIs, web services [START_REF] Sonntag | Smartweb handheld -multimodal interaction with ontological knowledge bases and semantic web services[END_REF] and product pages to automatically extract and create generic property and response patterns. This will ensure more precision in detecting properties expressed in the user's question and it will allow to answer questions in dierent ways.

As middle term improvements, we intend to focus our work on the dialog mode part that we will integrate on top of our proposed approach, so that we can propose an approach that ensures our targeted scenario. For that, we will investigate the communicative behavior approaches (e.g. pause, resume, and to switch between interactive tasks [START_REF] Pakucs | Towards dynamic multi-domain dialogue processing[END_REF]), dialog management systems (e.g. [START_REF] Heinroth | Spoken interaction within the computed world: Evaluation of a multitasking adaptive spoken dialogue system[END_REF])

and in particular, the ontology-based dialog systems (e.g. [START_REF] Milward | Ontology-based dialogue systems[END_REF] which correspond perfectly to the kind of system that we want to implement.

  system will ensure the following type of conversation: <User> Give me the price of a Nexus 5! <System> the price of Nexus 5 is 400$ <User> and who sells it? <System> several sellers were found. The main one is Google! Do you want to see other sellers? <User> No, show me the white version, sold by Google and located in France! <System> here are the images of Nexus 5 white version, sold by Google and located in France...

Fig. 1 .

 1 Fig. 1. Relational graph for 2 relations