Abir Mhenni
email: abirmhenni@gmail.com

Estelle Cherrier
email: estelle.cherrier@ensicaen.fr

Christophe Rosenberger
email: christophe.rosenberger@ensicaen.fr

Najoua Essoukri

Ben Amara

User Dependent Template Update for Keystroke Dynamics Recognition

Keywords: Authentication, Password security, Keystroke dynamics, Adaptive strategy, Doddington Zoo, Users classification

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

The security of password based applications is one of major concerns nowadays regarding the proliferation of web and mobile applications. Moreover, these applications are the target of various hackers attacks according to the recent statistics achieved by Hackmageddon [START_REF] Passeri | Information security timelines and statistics[END_REF]. Keystroke dynamics is an emerging solution to reinforce logical access control. It is a behavioral modality that verifies the typing manner of the user in addition to the verification of the syntactic conformity of the password [START_REF] Rybnicek | A roadmap to continuous biometric authentication on mobile devices[END_REF], [START_REF] Gaines | Authentication by keystroke timing: Some preliminary results[END_REF]. The main disadvantage of the keystroke dynamics modality is the variability of the typing manner of users over time [START_REF] Epp | Identifying emotional states using keystroke dynamics[END_REF], [START_REF] Nahin | Identifying emotion by keystroke dynamics and text pattern analysis[END_REF]. In fact, it changes according to several factors like the user's emotional state, their activeness, the password mastery; etc.

Adaptive strategies, are one of the most interesting solutions to remedy to the intra-class variations [START_REF] Didaci | Analysis of unsupervised template update in biometric recognition systems[END_REF], [START_REF] Poh | Critical analysis of adaptive biometric systems[END_REF] for behavioral biometric systems. They consist in updating the biometric reference template describing the typing rhythm of the user at each access verification. These strategies depend generally on five parameters [START_REF] Rattani | Template update methods in adaptive biometric systems: A critical review[END_REF]:

• Reference modeling : which defines the representation of the user's model. It can be represented by a single sample, a gallery or a cluster; • Adaptation criterion : which decides to launch the adaptation process; • Adaptation mode : which can be supervised or semisupervised; • Adaptation periodicity : which can be online or offline;

• Adaptation mechanism : which determines how to apply the modifications to the reference. It can be an additional, replacement or combined mechanism.

These adaptation strategies are promising solutions to intra-class variations for behavioral biometric modality among them the keystroke dynamics one, which we consider in this paper. But, applying the same adaptation mechanism to all users is not the best solution, as the users behaviors are generally different. Doddington Zoo is a concept that ensures users analogy with animal groups [START_REF] Doddington | Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the nist 1998 speaker recognition evaluation[END_REF]. It consists in grouping users according to their behavioral specificities when dealing with the authentication process. For that purpose, we propose a novel adaptation method that is appropriate to the user's typing rhythm. The main contribution of this paper is to propose a user dependent template update strategy based on the Doddington zoo classification. To the best of our knowledge, none work has been done to apply the Doddington zoo concept for updating the reference template of keystroke dynamics data. This methodology is applied on real data coming from two well known datasets in the literature.

The reminder of this paper is organized as follows. In the next section, we present some related works concerning Doddington Zoo categorization. In section III, the proposed adaptive strategy specific to the keystroke dynamics of each user's category is described. Section IV details the experiments and the obtained results. Finally, conclusions and perspectives are drawn in section V. II. RELATED WORK Doddington et al. [9] proposed an animal zoo grouping four animals categories, to model users behaviors:

• Sheep: corresponds to users who are easily recognized;

• Goats: describes users who are particularly difficult to recognize; • Lambs: represents users who are easily imitated;

• Wolves: depicts users who are capable to imitate others easily.

These users classes were differentiated by calculating the False Rejection Rate (FRR) and the False Acceptance Rate (FAR) of each user according to [START_REF] Doddington | Sheep, goats, lambs and wolves: A statistical analysis of speaker performance in the nist 1998 speaker recognition evaluation[END_REF]. Referring to Doddingtons menagerie, sheep are characterized by high genuine (similarity) matching scores whereas goats are characterized by low genuine matching scores. Lambs have similar matching problems as goats, by having high impostor matching scores (FAR).

Besides, sheep generally dominate the population of the zoo, goats as well as lambs constitute only a small fraction of the population. However, the wolves category constitutes a large portion of false rejection and acceptance rates.

Further, Yager and Dunstone [START_REF] Yager | Worms, chameleons, phantoms and doves: New additions to the biometric menagerie[END_REF] distinguished four other animal categories of users by considering simultaneously both the genuine and impostor matching scores, for each claimed identity:

• Chameleons: corresponds to users who are easy to recognize and easy to attack. They are a sub-category of goats and lambs; • Phantoms: depicts the users characterized by rejections of genuine and impostor queries. They are a subcategory of goats class; • Doves: represents the best users because they are easy to recognize and difficult to attack. Doves are a subcategory of sheep class; • Worms: regroup the worst users as they are difficult to recognize and easy to attack. Worms are a sub-category of goats and lambs.

Otherwise, in [START_REF] Houmani | On hunting animals of the biometric menagerie for online signature[END_REF], the authors distinguished between the users' classes using the personal entropy and relative entropy for biometric menagerie of online signature verification. Personal entropy is computed using only genuine data. It serves to differentiate between sheeps and goats class of users. Relative entropy is calculated with both genuine and impostor data. It helps to distinguish lambs class. Once these three groups are specified, it is easy to recognize the other groups, as depicted in Figure 1. In this paper, except the wolves group, we consider all of the zoo groups. The wolves group is eliminated because we are not interested in modeling impostors.

Personal Entropy

Personal

III. USER DEPENDENT ADAPTATION STRATEGY

This paper investigates a novel adaptive strategy that takes into account the specificities of each user to remedy to its intra-class-variations. Figure 2 depicts the proposed authentication process based on the keystroke dynamics modality.

A. Enrollment phase

Two samples are considered initially to register the typing manner of the user. Indeed, for all password-based applications, users are usually asked to type their password and to confirm it when creating an account.

B. Verification phase

During the authentication process, the verification is performed by the K Nearest Neighbor (KNN) classifier based on Hamming, Euclidean, Statistical and Manhattan distances. Afterwards, a vote is ensured by a Genetic Algorithm (GA) to calculate the final score. These distances are chosen because they demonstrated the best performances when compared to other distances as detailed in [START_REF] Mhenni | Towards a secured authentication based on an online double serial adaptive mechanism of users' keystroke dynamics[END_REF].

C. Adaptation phase

We detail here the different components of the adaption strategy.

1) Reference modeling : At the beginning, the reference template is composed of two samples to remedy to the tedious learning phase while aligning with the account creation process for web and mobile applications. After that, each novel query considered in the adaptation phase is added to the user's reference. Then, we obtain a gallery of samples describing the typing rhythm of the user which 2) Adaptation criterion: Different adaptation criteria were proposed in the literature [START_REF] Rattani | Adaptive biometric system based on template update procedures[END_REF], [START_REF] Poh | Model and score adaptation for biometric systems: Coping with device interoperability and changing acquisition conditions[END_REF], [START_REF] Pagano | Context-sensitive self-updating for adaptive face recognition[END_REF]. We are interested in the adapted thresholds criterion that has been proposed in [START_REF] Mhenni | Keystroke template update with adapted thresholds[END_REF]. It has the advantage to use the double threshold verification [START_REF] Rattani | Adaptive biometric system based on template update procedures[END_REF] while maintaining the thresholds user dependent and adapted as time elapses. The adapted thresholds are managed by equation (1).

T i+1 j = T i j -e - µ j σ j (1)
where µ j is the average of the mean vector of the reference of the user j, σ j is the standard deviation of the standard deviation vector of the reference of the user j and T i j is the threshold value specific to user j during session i.

3) Adaptation mode: The adaptation is ensured in a semi-supervised mode thanks to the KNN classifier combined with the GA. If the global score is lower than the adapted thresholds, the query is used to update the reference.

4) Adaptation periodicity:

The adaptation is executed online, immediately after the query acceptation.

5) Adaptation mechanism:

Concerning the adopted mechanism, we combine two existing approaches: the growing window and the sliding window mechanisms [START_REF] Kang | Continual retraining of keystroke dynamics based authenticator[END_REF]. These mechanisms are frequently used for keystroke dynamics modality [START_REF] Giot | Hybrid template update system for unimodal biometric systems[END_REF], [START_REF] Pisani | Adaptive approaches for keystroke dynamics[END_REF]. The growing window mechanism is used to enlarge the size of the reference until the maximum size is reached. The sliding window is afterward considered to maintain a fixed reference size. Hence, the nomenclature is called "double serial mechanism".

D. User classification

During the two first update sessions, we start to classify users into two groups: sheep and goats. We are first interested to only these two groups because we focus on the most representative groups of the Doddington zoo.

Thereby, over the growing phase, we assume that users, whose number of accepted queries has not overcome 3 samples during the update session, are not easily recognized. So, they are classified as goats. The rest of the users, those whose number of accepted queries is greater than 3, are classified as sheep, as they are easy to recognize.

For the sliding window mechanism, the size of the reference is no more significant as the maximum size of the reference is reached. So, we considered the Entropy measure to distinguish between the considered users groups. In fact, it was demonstrated in [START_REF] Houmani | On hunting animals of the biometric menagerie for online signature[END_REF], [START_REF] Morales | Towards predicting good users for biometric recognition based on keystroke dynamics[END_REF] that the higher the user's entropy is, the more the error rates increase. Thereby, both Personal and Relative Entropy are calculated according to equations ( 2) and ( 3) respectively. For this fact, the Personal Entropy of the reference ref j containing N samples of the user j is measured according to equation (2):

Entropy j = - N i=1 ref j (t) (i) log (ref j (t) (i)) (2)
The Relative Entropy is equally calculated according to equation [START_REF] Gaines | Authentication by keystroke timing: Some preliminary results[END_REF], where attaq j is a matrix containing N samples of the keystroke dynamics of multiple users other than the user j:

RelativeEntropy j = 1 2 ( N i=1 ref j (t) (i) log( ref j (t) (i) attaq j (i) ) + N i=1 attaq j (i) log( attaq j (i) ref j (t) (i) ) ) (3)
Therefore, starting from session 4, we use the Entropy to classify users. We initially distinguish the lambs class. Once users of this class are defined, we determine during the following sessions the remaining classes of the zoo. Once session 6 starts, classes of worms, doves, chameleons and phantoms take place and classes of sheep, goats, and lambs disappear.

For each class, we use specific adaptation parameters. Concerning goats and worms classes, which are characterized by a high intra-class variation according to the different conducted experiments, we increased the maximum size of the reference to 15 in order to enrich the description of the keystroke dynamics of the users. The maximum size of phantoms class should be higher because this class is difficult to describe. Regarding the lambs, worms, chameleons and phantoms classes, stricter thresholds are needed to minimize the acceptance of the impostor attacks. These thresholds are generated based on equation (4).

T i+1 j = T i j -e - µ j 2 * σ j (4)
The fixed parameters for each user category are detailed in Table I.

IV. EXPERIMENTS AND RESULTS

The proposed approach was tested on two public databases: WEBGREYC and CMU. WEBGREYC [START_REF] Giot | Web-based benchmark for keystroke dynamics biometric systems: A statistical analysis[END_REF] database, contains 60 samples from 45 users. The CMU database [START_REF] Killourhy | Comparing anomaly detectors for keystroke dynamics[END_REF] includes 400 biometric samples of 50 users.

A. Data stream generation

We managed user samples during the adaptation sessions as follows. Two samples of each user are considered during the enrollment phase in order to create the reference. For Figures 3b and4b.

To illustrate the benefits of the consideration of 7 classes of the Doddington zoo in the proposed approach, we compared it to the same adaptation approach without biometric menagerie and with the consideration of only 3 classes conducted in [START_REF] Mhenni | Adaptive biometric strategy using doddington zoo classification of user's keystroke dynamics[END_REF], namely sheep, goats and lambs. As demonstrated in Tables II and III, the proposed approach show improved performances as it proposes an adaptive strategy that is the most appropriate to the user's specificities. In fact, the considered users' categories encompass a wider variety of users. Hence, the adaptation method acts according to each user's particularities.

V. CONCLUSION

In this paper, we put forward a user specific adaptation strategy based on the Doddington Zoo concept. It consists in applying an adaptive strategy related to each user class. So, regarding users who suffer from a large intra-class variation, we enlarge the reference size to capture more variabilities. Moreover, for users that are more vulnerable to impostor attacks, we apply stricter thresholds to eliminate as much as possible the false accepted queries in our system. The Doddington zoo is a biometric menagerie that applies an analogy between users and animals characteristics, and it was efficient for discrimination between users. A large number of the zoo classes is considered in this work, thus demonstrating enhanced performances. Besides, the proposed approach has the advantage of being conform to the web and mobile applications that generally consider only two password acquisitions (the second is to confirm the firts typed one) when creating a new account. So, we consider only these two samples to create the user's reference.

Figure 1 :

 1 Figure 1: Entropy based classification of the Doddington ZOO animals according to [11].

Figure 2 :

 2 Figure 2: Description of the keystroke authentication process

Table I :

 I Specific parameters according to user's category

	User category Reference size	Thresholds
	Sheep	10	Adapted thresholds
	Goats	15	Adapted thresholds
	Lambs	10	Stricter thresholds
	Worms	15	Stricter thresholds
	Chameleons	10	Stricter thresholds
	Doves	10	Adapted thresholds
	Phantoms	20	Stricter thresholds

Table II :

 II Comparison of the proposed adaptation strategy for WEBGREYC database

	Adaptation strategy	EER	AUC
	Without Doddington menagerie	5.3%	0.02
	Biometric menagerie based on 3 classes 0.8 % 0.003
	Biometric menagerie based on 7 classes 0.2%	0.002
	Table III: Comparison of the proposed adaptation strategy
	for CMU database
	Adaptation strategy	EER	AUC
	Without Doddington menagerie	2.3% 0.004
	Biometric menagerie based on 3 classes 0.3% 0.001
	Biometric menagerie based on 7 classes 0.1% 0.0001

As perspectives to this work, we aim to apply and model impostor attacks to reinforce the security of our authentication system.

each adaptation session, 8 new queries are introduced to the authentication system. These queries are divided into 5 genuine samples and 3 impostor ones. Thus, we considered 12 adaptation sessions for the WEBGREYC database and 80 adaptation sessions for the CMU database.

B. Results and Comparisons

To evaluate the performance of the proposed approach, we consider two evaluation metrics : the Error Equal Rate (EER) and the Area Under Curve (AUC).

The obtained results show an improvement in the performance of the strategy as demonstrated in Figures 3a and4a. Adding doves, phantoms, chameleons and worms classes, improved the EER performances by 0.6% for the WEBGREYC database and by 0.2% for the CMU database. Furthermore, when compared to the same adaptation approach without biometric menagerie, the user specific adaptation approach ensures an improved EER performance of more then 2% for CMU database and 5% for WEBGREYC database.

We also depict the distribution of users categories among all adaptation sessions for the two considered databases. The sheep class includes the majority of users as shown in


Mohamad El-Abed

Mostafa Dafer

Christophe Rosenberger
email: christophe.rosenberger@ensicaen.fr

RHU Keystroke Touchscreen Benchmark

Keywords: Biometrics, Keystroke Dynamics, Benchmark, Performance Evaluation

Biometric systems are currently widely used in many applications to control and verify individual's identity. Keystroke dynamics modality has been shown as a promising solution that would be used in many applications such as e-payment and banking applications. However, such systems suffer from several performance limitations (such as crossdevices problem) that prevent their widespread of use in real applications. The objective of this paper is to provide researchers and developers with a public touchscreen-based benchmark collected using a mobile phone and a tablet (both portrait and landscape orientation each). Such a benchmark can be used to assess keystroke-based matching algorithms. Furthermore, It is mainly developed to measure the robustness of keystroke matching algorithms vis-à-vis cross-devices and orientation variations. An online visualizer for the database is also given to researchers allowing them to visualize the acquired keystroke signals.

I. INTRODUCTION

The use of biometric systems is widely spread on various applications that require individual's authentication [START_REF] Jain | Biometrics: A grand challenge[END_REF]. They are used for controlling borders, managing access to specific places, etc.. Nowadays, many biometric modalities exist and are divided into three categories which are morphological (such as Face [START_REF] Short | Exploiting polarization-state information for cross-spectrum face recognition[END_REF]), behavioral (such as Keystroke [Giot et al., 2009b]) and biological (such as DNA [START_REF] Hashiyada | Development of biometric dna ink for authentication security[END_REF]). Each modality has its own advantages/disadvantages which leads their use to a specific target application and population [START_REF] El-Abed | New Trends and Developments in Biometrics, chapter Evaluation of Biometric Systems[END_REF].

Keystroke dynamics modality has been shown as a promising solution for the banking and e-commerce sectors for the following reasons:

• Invisibility: users are used to type a password to login into information systems.

• Low cost: no additional sensor is required for using keystroke modality, all input devices feature a keyboard.

However, in order to be used in real life applications, the performance evaluation of keystroke matching algorithms should be carefully addressed. As touch-screen keyboards scale according to the screen size and its resolution, touch-screen devices have introduced new challenges that keystroke dynamics researchers and developers need to solve. Physical keyboards had standard sizes and did not need to scale for different computers; but, as we aim to extend the usability of keystroke dynamics solutions for touch-screen devices, we need to address the variability of these dynamically scaled keyboards. Furthermore, the variation of the data collected from different devices (i.e., keyboard size and/or orientation) would deteriorate the overall performance of the matching algorithm, mainly the false recognition rate (FRR). More generally speaking, data collected from a physical keyboard are different from those collected using a touchscreen keyboard, and those collected from different touchscreen keyboards/orientations would be also different, etc. so it is of utmost importance to still be able to verify the user in case the orientation is changed or even if the device is replaced with another of a different size. For all these reasons, it would be important to provide researchers and developers with a significant public benchmark to measure the robustness of their developed algorithms vis-à-vis cross-devices variation. Therefore, the objective of this paper is to provide researchers and developers with a public touchscreen-based benchmark collected using a mobile phone and a tablet (both portrait and landscape orientation each). To the best of our knowledge, it is the first benchmark collected using different devices (mobile and tablet) and orientations (portrait and landscape). An online visualizer for the database is also given to researchers allowing them to visualize the acquired keystroke signals.

The outline of the paper is defined as follows. Section II presents an overview of the existing keystroke dynamics benchmarks. The presentation of the RHU Touchscreen Keystroke benchmark along with the keystroke visualizer is given in Section III. Section IV gives a conclusion and some perspectives of this work.

II. LITERATURE REVIEW

Once a biometric system has the potential to become reliable, different algorithms are developed to enhance the system as best as possible. In order to evaluate and compare biometric systems' algorithms, we need to compute their performance using a predefined protocol; mainly a public benchmark. Two types of benchmarks exist:

• Real Benchmarks

These databases are collected by real contributors and thus are very time and energy consuming.

• Synthetic Benchmarks

These databases are automatically generated using software to simulate real biometric data and thus are very quick and easy to generate. Very few biometric modalities are concerned [START_REF] Cappelli | Synthetic fingerprintdatabase generation[END_REF].

As consumers have started depending more and more on phones and switching from laptops to tablets and hybrids, it was crucial to start new benchmarks specialized in touchscreen devices, especially that touchscreen devices feature portrait and landscape orientations and come in different screen sizes, thus affecting their software keyboards. However, collecting a database is not an easy task because it requires a lot of time, energy, contributors, and sometimes special material.

A lot of the newer benchmarks have respected constraints on the way of creating good behavioral biometrics databases (in terms of number of sessions, duration between each session, number of individuals and so on [START_REF] Cherifi | Performance evaluation of behavioral biometric systems[END_REF]). In this section, we present the list of existing benchmarks from the state-of-the-art. Table I shows the dataset authors, year and availability. As shown in this table, few are the available benchmarks collected using a touchscreen phone. Furthermore, to the best of our knowledge, no touchscreen benchmark exists collected with different devices (touchscreen versus tablet) and orientations (landscape versus portrait). This is what we introduce in the next section which is a benchmark collected using different devices with different orientations. Such a benchmark would be used by researchers/developers during the design and development of usable touchscreen-based keystroke matching algorithms.

Authors

III. RHU KEYSTROKE TOUCHSCREEN BENCHMARK A. Overview of the Android KeyStroke Collecting Tool

It is an Android application that we developed in Java using Android Studio allowing the creation of keystroke dynamics benchmarks using Android devices. A screen capture of this application is presented in Figure 1. We developed this application in order to create our own keystroke dynamics database. The data are stored online by default using an API that we created which allows quick and easy extraction of specific information. The main functionality of the application is as follows:

• Possibility to create new users. All users share the same password which is "rhu.university"; • Possibility to set the user age range, gender, and occupation which are saved to the database; • Possibility for the user to train himself to type the password using a fake account; • Possibility to continue acquisition from users in the form of login, so data can be acquired each time a user logs in normally. • Capturing the typing information of a user. Extracted data is added to the database; it includes:

timing between a key pressure and a key release timing between a key release and a key pressure timing between two key pressures timing between two key releases total time to type the password screen size and orientation

• As in most of static keystroke dynamics studies, typing correction is not allowed, when a user makes a mistake, he has to type the whole password again. • For any keystroke capture, the extracted data features stored in the database are the timing differ-ences between two events of these kinds: press/press, release/release, press/release, release/press, and total time. They are stored in the fields PP, RR, PR, RP, and TT respectively. We have also used the same application on a tablet (to test keyboards of different sizes and orientations).

B. KeyStroke Benchmark

Using the KeyStroke Collecting Tool, we created Keystroke Benchmark which is collected using Nexus 5 touchscreen mobile phone and Samsung Galaxy Note 10.1 2014 tablet. The purpose of this benchmark is twofold, the data provided allows us to analyze the changes in typing patterns of the same person in different orientations of the same device using the same password, it also allows us to analyze the effect of device size on the keystroke dynamics patterns when typing in the same orientation and using the same password.

The database is freely accessible to the community and downloadable through the following link: www.coolestech. com/keystroke-web-visualizer The population composing the database is represented in Figures 2, 3, and4. The hardware used to conduct this benchmark is as follows and presented in Figures 5 and6.

Data representation:

• username: the username of the user • occupation: the occupation of the user

• gender: stores the gender of the user • age: stores the age range of the user (18-30 and 31-60) • PP: timing between two key pressures (ms)

• PR: stores the timing between a key pressure and a key release (ms) • RP: stores the timing between a key release and a key pressure (ms) • RR: stores the timing between two key releases (ms) • TT: the total time of typing the password (ms)

• Screen Orientation (1: portrait, 2: landscape)

• Screen Size (inch)
There are different sizes of touchscreen devices. Our idea is to make a data set of different keyboard sizes and screen orientations publicly available in order to be used as a reference database for testing keystroke dynamics algorithms and facilitate the comparison of previous and future keystroke dynamics authentication methods.

Forty seven (47) individuals have participated in the acquisition process by typing the password "rhu.university". In each session, an individual typed the password in all four configurations: phone/portrait, phone/landscape, tablet/portrait and tablet/landscape. At least 60 acquisitions were collected per session. In general, each user has 20 trials using each device orientation. In total, we have 4155 available acquisitions. All users were able to train themselves on the typing of the password on the keyboard as long as they wanted, because it is not their usual password and they do not have a preexisting typing habit or pattern for it. In addition, touch smartphones' operating systems have different keyboards, and we wanted them to get used to the different keyboard sizes.

C. Keystroke Visualizer

KeyStroke Visualizer, shown in Figure 7, is a web application that we developed allowing the visualization of a keystroke dynamics database created using the KeyStroke Collecting Tool.

We developed this application in order to visualize our own keystroke dynamics database. KeyStroke Visualizer simply connects to the database and automatically collects all information in order to visualize them on-demand.

The functionality of the application is as follows:

• A list of all users is automatically retrieved from the database when the application is started.

• The application provides each user's gender, age range and profession.

• By selecting a user, the application draws all PP, PR, RP, RR and TT (Total Time) graphs. • Each trial is colored differently for easier differentiation between trials.

• Each point value in the chart can be viewed separately by hovering the cursor over it. • Trials can be viewed in mono color (grey).

• Trials can be filtered to view the first 14 trials only for clearer view. • Animation can be applied to the graphs for easier tracing of different trials.

IV. CONCLUSION AND PERSPECTIVES

We have presented in this paper a public benchmark named RHU Keystroke Touchscreen that would be used by researchers and developers to assess the performance of their developed keystroke authentication system. It is mainly developed to measure the robustness of keystroke matching algorithms vis-à-vis cross-devices and orientation variations. To the best of our knowledge, it is the first benchmark collected using different devices (mobile and tablet) and orientations (portrait and landscape).

We have used this benchmark to make a statistical analysis using the Kruskal-Wallis test (KW) [START_REF] Higgins | An introduction to modern nonparametric statistics[END_REF] to check the variation of the collected keystroke features from the different devices (mobile and tablet) and orientations (portrait and landscape). The results from this study recommend removing the Press-Release feature when dealing with touchscreen-based keystroke matching algorithms. The experimental results show significant differences between keystroke features collected using different devices and orientations. Such differences should be taken into consideration when designing touchscreenbased keystroke authentication systems.

As for the perspectives of this work, we aim to use the presented benchmark to develop a novel keystroke matching algorithm that would be robust against cross-devices and orientations problem. It would be also important to compare existing keystroke matching algorithms (mainly those based on data mining approaches) to measure their robustness on the presented variations. Extending the presented benchmark to other devices would be also important to assess the overall performance of keystroke matching algorithms. As a behavior modality, it would be also important to develop a quality index to assess the quality of the acquired keystroke signals to improve the overall performance of keystrokebased matching algorithms.

Figure 1 .

 1 Figure 1. Screenshot of the Android KeyStroke Collecting Tool.

Figure 2 .

 2 Figure 2. Population of RHU Keystroke Touchscreen Benchmark.

Figure 3 .

 3 Figure 3. Distribution of Occupation Presented in RHU Keystroke Touchscreen Benchmark.

Figure 4 .

 4 Figure 4. Trials Distribution on Different Devices and Orientations in RHU Keystroke Touchscreen Benchmark.

Figure 5 .

 5 Figure 5. Samsung GALAXY Note 10.1 (2014 Edition) vs Google Nexus 5 Hardware PhoneArena [Retreived May 17, 2018]

Figure 7 .

 7 Figure 7. Screenshot of the Database Visualization Tool (KeyStroke Online Visualizer)

Table I EXISTING

 I BENCHMARKS' AUTHORS & YEAR, AND AVAILABILITY. N/A*: WE COULD NOT FIND AN ONLINE LINK FOR THE DATASET.

http://mpl.buaa.edu.cn/detail1.html[START_REF] Giot | Web-based benchmark for keystroke dynamics biometric systems: A statistical analysis[END_REF] http://www.epaymentbiometrics.ensicaen.fr/index.php?view=article&id=20El-Abed et al.[2014] http://www.coolestech.com/rhu-keystroke/[START_REF] Tasia | Two novel biometric features in keystroke dynamics authentication systems for touch screen devices[END_REF] http://ty.ncue.edu.tw/N25/data.html[START_REF] Vural | Shared research dataset to support development of keystroke authentication[END_REF] http://clarkson.edu/citer/research/collections/index.html[START_REF] Sun | Shared keystroke dataset for continuous authentication[END_REF] http://cubs.buffalo.edu/research/datasets

N/A* Joyce and Gupta [1990] N/A* Leggett et al. [1991] N/A* Lin [1997] N/A* Monrose and Rubin [1997] N/A* Obaidat and Sadoun [1997] N/A* Gunetti and Picardi [2005] Previously found at: http://www.citefa.gov.ar/si6/k-profiler/dataset/ Rodrigues et al. [2006] N/A* Filho and Freire [2006] http://www.biochaves.com/en/download.htm Hocquet et al. [2007] N/A* Loy et al. [2007] (Queen Mary University) http://www.eecs.qmul.ac.uk/ ∼ ccloy/downloads keystroke100.html Revett et al. [2007] N/A* Hosseinzadeh and Krishnan [2008] N/A* Giot et al. [2009a] http://www.epaymentbiometrics.ensicaen.fr/index.php?view=article&id=19 Killourhy and Maxion [2009] http://www.cs.cmu.edu/ ∼ keystroke/ Li et al. [2011] (Beihang University)

Availability

 [START_REF] Bleha | Computer-access security systems using keystroke dynamics[END_REF]


Harmel Peindy N'dongo

Fabrice Guyon
email: fabrice.guyon@univ-fcomte.fr

Abderrahim Khatyr

Michael Knorr
email: michael.knorr@univ-fcomte.fr

Carsten Strohmann

Harmel N Peindy

Prof G Liliane

Hubert-Pfalzgraf

Dithioether Ligands: Surprising Effect of the Spacer Length on the Dimensionality, Cluster Nuclearity and the Fluorescence Properties of the Metal-Organic Framework

Keywords: Dithioethers, Copper, Fluorescence, Coordination polymers, Metal-metal interactions

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

It is well established that interaction of various nucleophiles (L) such as phosphanes, aniline and pyridyl type ligands with copper(I) halides often affords tetranuclear cubane-like Cu 4 X 4 L 4 clusters by a self-assembly process. [1] The interest in these polymetallic systems, in particular those incorporating the Cu 4 I 4 unit, stems from their remarkable photophysical properties,. [2] Therefore these compounds have also been the object of theoretical studies. [3] Less common is the incorporation of the tetranuclear Cu 4 I 4 core in a metal-organic network. The known examples of 1D to 3D coordination polymers including Cu 4 I 4 cores were assembled by using an alkanedinitrile ligand, [4] difunctional pyridyl ligands, [5] dimethylsufide and diethylsulfide, [6a,6b] a functionalized dithioether ligand, [6c] polydentate thioether macrocycles, [7] and a triselenoether macrocycle. [8] The topology of extended networks based on flexible bidentate thioether ligands has been shown in the case of silver(I) to be very dependent on the spacer between the coordinated work of 2 is built from dimeric Cu 2 I 2 units which are connected by 1,2-bis(phenylthio)ethane bridging ligands. The solid-state luminescence spectrum of 1 exhibits a strong emission around 532 nm, whereas a weak emission centred at 413 nm is observed in the case of 2.

functions. [9] Our interest in the coordination chemistry of bi-and polydentate thioether ligands [10] has led us to investigate the reactivity of the flexible dithioether ligands PhS-(CH 2 ) n -SPh (n = 1-2) towards CuI in order to evaluate the influence of the spacer on the resulting framework.

Results and Discussion

The reaction of CuI with an equimolar amount of bis-(phenylthio)methane in acetonitrile gave colourless crystals of the general formula [Cu 4 I 4 {µ-PhSCH 2 SPh} 2 ] n (1) (Scheme 1). Modification of the molar ratio, for example from 1:1 to 1:2, had no influence on the composition of the resulting material, and only compound 1 was formed. This observation is in contrast to the results reported by Sheldrick et al., who used an alkanedinitrile ligand (L) to obtain the 1:1 and 2:1 coordination polymers, [(CuI) 2 L 2 ] and [(CuI) 4 L 2 ], respectively, from the appropriate molar ratio of reactants. [4] The crystal structure of 1, viewed along the c axis in characterized Cu 4 I 4 units. Two adjacent Cu 4 I 4 cores are linked by two flexible dithioether ligands leading thus to the formation of a 1D necklace structure. Note also that the phenyl substituents adopt a parallel orientation with a short C ipso -C ipso distance of 3.502 Å.

Despite the fact that the phenyl groups of neighbouring ribbons are somewhat interpenetrated, there are no close inter-ribbon interactions between the 1D chains of coordination polymer 1, the separation between the midpoints of two adjacent Cu 4 I 4 units being 11.64 Å (Figure 2). : #1: -x + 2, -y + 1, -z; #2: -x + 2, y, -z + 1/2. The colourless crystals obtained from the reaction between equimolar amounts of CuI and 1,2 bis(phenylthio)ethane in acetonitrile were identified by an X-ray study as the coordination polymer [(CuI) 2 {µ-PhS(CH 2 ) 2 SPh} 2 ] n (2). This framework consists of centrosymmetric Cu 2 (µ 2 -I) 2 rhomboid dimers, each connected to an adjacent unit via one µ 2 -bridging dithioether ligand (Figure 3). Each Cu atom is in a distorted tetrahedral environment, coordinated to the bridging iodido ligand and thioether group of two different ligands.

The 2D network resulting from this coordination mode includes centrosymmetric 24-membered metallomacrocycles constituted from four dithioether ligands, six Cu atoms and two iodido ligands (Figure 4). The average length of the two Cu-S bonds [2.3000(11) and 2.3855( 10 [11c-11d] Our discovery that a subtle steric modification of the dithioether has a major impact on the cluster nuclearity suggests that a small energetic difference exists between the centrosymmetric rhomboid Cu 2 I 2 motif and the cubane-like Cu 4 I 4 cluster. Note that treatment of [Cu(MeCN) 4 ][BF 4 ] with 1,2bis(phenylthio)ethane affords the mononuclear chelate complex [Cu{PhS(CH 2 ) 2 SPh} 2 ][BF 4 ], which has been reported to undergo facile ligand dissociation in solution. [12] The solid-state emission spectra of 1 and 2 are shown in Figure 5. Upon excitation at 360 nm, a strong emission band was observed for the tetranuclear adduct, 1, with a maximum at 532 nm. Emissions in a similar spectral range have been observed for nitrogen-substituted Cu 4 I 4 L 4 clusters and were attributed to an emission from a triplet cluster centred excited state ( 3 CC). [3b,13] In fact, this broad CC excited state emission is mixed in character with equal contributions of iodine to copper charge transfer (XMCT) and copper orbital centred (dǞs) transitions. [3b] In contrast, the emission spectrum recorded for polymeric 2 containing the dinuclear Cu 2 I 2 unit exhibits, under similar experimental conditions, only a very weak emission centred at 413 nm with a shoulder at 438 nm. Although less studied than the tetranuclear systems, photophysical properties of some dinuclear Cu 2 X 2 compounds have been reported in the literature, but are currently limited to those of N-heterocyclic ligands. [3c,14] The observation of a shoulder suggests a mixed emission with different contributions from the XMCT and Cu (dǞs) transitions. To confirm this hypothesis, advanced photophysical studies and ab initio calculations are in progress. Solution measurements carried out in Figure 5. Corrected solid-state luminescence spectra recorded at room temperature for compounds 1 (solid) and 2 (dotted). [15] acetonitrile at room temperature show no luminescence for either 1 or 2. This is probably due to a disassembly of the cluster units by this strongly coordinating solvent.

Conclusions

In summary, this work has shown that the dithioether ligand spacer length has a significant impact on the dimensionality of the resulting coordination polymer and the cluster nuclearity. It therefore determines, indirectly, the luminescence properties of the resulting metal-organic framework. We are currently varying the spacer length and nature of the copper salt systematically in order to establish a correlation between PhS(CH 2 ) n SPh and the motif of the framework. In addition, detailed photophysical studies and theoretical computations on these coordination polymers are underway.

Experimental Section

PhSCH 2 SPh and PhS(CH 2 ) 2 SPh were prepared according to ref. [16] and ref., [17] respectively. The solid-state emission spectra were recorded at room temperature with a Jobin-Yvon Fluorolog-3 spectrometer using a cylindrical 0.5 cm diameter quartz capillary with a scan speed of 1 nm/s. Intensity scales are presented in arbitrary units.

Preparation of 1 and 2:

To a suspension of CuI (191 mg, 1.0 mmol) in acetonitrile (10 mL) was added PhSCH 2 SPh (232 mg, 1.0 mmol). The mixture was heated at reflux for 15 min, then the solution was allowed to reach room temperature. After one day, colourless crystals of 1 formed. Yield 0.22 g, 72 %. C 13 H 12 Cu 2 I 2 S 2 (613.23): calcd. C 25.46, H 1.97; found C 25.89, H 2.05. The reaction of CuI with PhS(CH 2 ) 2 SPh under similar conditions afforded compound 2. Yield 0.33 g, 75 %. C 14 H 14 CuIS 2 (436.81): calcd. C 38.49, H 3.23; found C 38.77, H 3.30.

X-ray Crystallography: Crystal data and experimental details are given in Table 1. Data were collected with a Stoe IPDS diffractometer at 173(2) K. The intensities were determined and corrected with the program INTEGRATE in IPDS. [18] An empirical absorption correction was employed using the FACEIT program in IPDS. [19] All structures were solved by applying direct and Fourier methods. For each structure, the non-hydrogen atoms were refined anisotropically. All hydrogen atoms were placed in geometrically calculated positions, and each was assigned a fixed isotropic displacement parameter based on a riding model. Refinement of the structures was carried out by full-matrix least-squares methods based on F o 2 . All calculations were performed with the WinGX crystallographic software package, using the programs SHELXS-90 [START_REF] Sheldrick | SHELXS-90[END_REF] and SHELXL-97. [START_REF] Sheldrick | SHELXL-97[END_REF] CCDC-623106 and -623107 contain the supplementary data for 1 and 2, respectively. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre via www.ccdc.cam.ac.uk/data_request/cif. Supporting Information (see footnote on the first page of this article): View of the structure and packing of 2 along the b axis and of 1 on the bc plane.

  Scheme 1.

Figure 1 .

 1 Figure 1. (top) View on the 1D chain of 1 along the c axis. H atoms are omitted for clarity. Selected bond lengths [Å]: Cu(1)-S(1) 2.292(2), Cu(2)-S(2) 2.301(2), Cu(1)-Cu(2) 2.7864(14), Cu(1)-Cu(1)#2 2.6173(18), Cu(1)-Cu(2)#2 2.6464(13), Cu(2)-Cu(2)#2 2.663(2), Cu(1)-I(1) 2.6486(15), Cu(1)-I(2) 2.7621(11), Cu(2)-I(2)#2 2.6077(10), Cu(2)-I(2) 2.6825(12), Cu(2)-I(1) 2.7704(11), I(1)-Cu(1)#2 2.6228(12), I(2)-Cu(2)#2 2.6077(11), C(1)-S(2) 1.804(7), C(1)-S(1)#1 1.818(7). (bottom) View of the tetranuclear Cu 4 (µ 3 -I) 4 core of 1. Only the ipso carbon atoms C2 and C9 of the phenyl groups are shown for clarity. Symmetry operations used to generate equivalent atoms: #1: -x + 2, -y + 1, -z; #2: -x + 2, y, -z + 1/2.

  ) Å] is somewhat greater than that in 1. The Cu•••Cu separation of 2.8058(11) Å is much greater than those observed in the above-mentioned Cu 4 I 4 unit of 1. A survey of the literature indicates that Cu•••Cu separations vary in [Cu(µ 2 -I) 2 Cu] compounds. A dithioether-functionalized tetrathiafulvalene Cu complex was reported to have a relatively small Cu•••Cu separation of 2.65 Å.[11a] Distances close to the value in 2 have been reported for [Cu 2 I 2[16]aneS 4 ] n([16]aneS 4 = 1,5,9,13-tetrathiacyclohexadecane) [2.8079(12) Å][7] and [{MeSi(CH 2 SMe) 3 }CuI] 2 [2.862(2) Å].[11b] A far longer

Figure 2 .

 2 Figure 2. View of packing of 1 on the ab plane.

Figure 3 .

 3 Figure 3. View of the packing of 2 on the bc plane.

Figure 4 .

 4 Figure 4. View of the core structure of 2 on the ab plane. H atoms and phenyl groups are omitted for clarity. Selected bond lengths [Å] and angles [°]: Cu(1)-S(1) 2.3000(11), Cu(1)-S(2) 2.3855(10), Cu(1)-I(1) 2.6108(7), Cu(1)-I(1)#3 2.6169(8), Cu(1)-Cu(1)#3 2.8058(10), C(7)-S(1) 1.820(3), C(8)-S(2) 1.816(4); S(1)-Cu(1)-S(2) 109.24(4), S(1)-Cu(1)-I(1) 120.26(3), S(1)-Cu(1)-I(1)#3 105.57(3), S(2)-Cu(1)-I(1)#3 110.00(3), S(2)-Cu(1)-I(1) 96.12(3), I(1)#3-Cu(1)-I(1) 115.08(2), Cu(1)#3-I(1)-Cu(1) 64.92(2), C(7)-S(1)-Cu(1) 110.61(11). Symmetry operations used to generate equivalent atoms: #1: -x + 2, -y + 2, -z; #2: -x + 1, -y + 1, -z; #3: -x + 2, -y + 1, -z.

Table 1 .

 1 Crystal data and structure refinement details for 1 and 2.

	Compound	1	2
	Empirical formula	C 13 H 12 Cu 2 I 2 S 2	C 14 H 14 CuIS 2
	Formula weight	613.13	436.81
	T [K]	173(2)	173(2)
	Crystal system	monoclinic	monoclinic
	Space group	C2/c	P 2 1 /n
	a [Å]	20.857(4)	9.4711(18)
	b [Å]	11.656(2)	7.928(2)
	c [Å]	15.530(3)	19.632(4)
	β [°]	119.42(3)	97.78(2)
	V [Å 3 ]	3288.5(11)	1460.5(6)
	Z	8	4
	ρ calcd. [g/cm 3 ]	2.477	1.987
	µ [mm -1 ]	6.578	3.874
	F(000)	2288	848
	Crystal size [mm]	0.40 ϫ 0.20 ϫ 0.20 0.30 ϫ 0.30 ϫ 0.10
	θ range [°]	2.22 to 25.00	2.28 to 26
	Index ranges	-24 Յ h Յ 24	-11 Յ h Յ 11
		-12 Յ k Յ 13	-9 Յ k Յ 9
		-18 Յ l Յ 18	-24 Յ l Յ 24
	Reflections collected 9932	13463
	Independent reflec-	2871	2782
	tions
	Refinement method		full-matrix least squares
			on F 2
	Data/restraints/pa-	2871/0/172	2782/0/164
	rameters
	Goodness-of-fit on	1.033	1.066
	F 2
	R 1 , wR 2 [I Ͼ 2σ(I)]	0.0463, 0.1404	0.0353, 0.0874
	R 1 , wR 2 [all data]	0.0480, 0.1421	0.0395, 0.0898
	∆ρ(min), ∆ρ(max)	1.119, -1.104	1.060, -0.759
	[e•Å 3 ]

Acknowledgments

Financial support from the Région Franche-Comté for the fluorescence spectrometer is gratefully acknowledged.


Pierre Joubert
email: pierre.joubert@univ-fcomte.fr

Jeanine Bonamy

Laura Gomez

Dionisio Bermejo

N 2 -H 2 isotropic Raman Q-branch linewidths: an Energy-Corrected Sudden scaling law

Keywords: stimulated Raman spectroscopy, collisional line broadening, nitrogen-hydrogen mixture, Energy-Corrected Sudden model

The Energy-Corrected Sudden (ECS) model has been widely developed to calculate rovibrational Raman linewidths as well as the overlap between lines through the off-diagonal elements of the relaxation matrix. The accuracy of the results depends on the precision of the input data on which the ECS parameters are fitted. Starting from recent experimental and theoretical data for nitrogen (N 2 ) Q-branch perturbed by hydrogen from 77 to 580 K, we have determined the ECS parameters for this molecular system and calculated systematically the N 2 -H 2 line broadening coefficients for J = 0 to 20 up to 2000 K, which will be useful for combustion diagnostics purposes.

INTRODUCTION

Stimulated Raman scattering from nitrogen is an improved technique for the purpose of temperature and concentration diagnostics in atmospheric and combustion media. For practical applications, precise studies of the collisional processes involving N 2 as an active molecule are needed, from low temperatures for atmospheric purposes up to very high temperatures in combustions. But the diagnostics is complicated by the phenomenon of 'motional narrowing' that arises in the nitrogen isotropic Raman Q-branch. The Q-branch band shape has to be analyzed through rotational relaxation models such as the Energy-Corrected Sudden (ECS) model. 1,2 The pure nitrogen isotropic Raman linewidths have been measured with high accuracy several years ago, 3,4 as well as those of N 2 -CO 2 5 and N 2 -H 2 O 6 mixtures. Concerning flame thermometry, the line broadening values of nitrogen by hydrogen are also required. [START_REF] Vestin | Dualbroadband rotational CARS thermometry in the product gas of hydrocarbons flames[END_REF] Since very few experimental data were available, self-broadened N 2 linewidths instead of N 2 -H 2 ones were commonly used in rovibrational thermometry. [START_REF] Afzelius | [END_REF] The present work, devoted to N 2 -H 2 , is based on a previous extensive experimental and theoretical study for the collisional line broadening coefficients of this molecular system. [START_REF] Gomez | [END_REF]10 The experimental measurements were obtained from high-resolution stimulated Raman spectra recorded in Madrid at the temperatures of 77, 298, 440, and 580 K. The corresponding theoretical calculations, using an ab initio calculation of the potential energy surface (PES), 11 have been done with the exact close-coupling (CC) method for the three lower temperatures as the computational time remains reasonable until 440 K, and with a coupled-states (CS) treatment at 580 K. The overall agreement 10,12 obtained between experimental and theoretical linewidth values then allows us to build ECS models using three different scaling laws. Finally, the model that gives the best agreement with the input data is used to calculate with reasonable confidence the Q(J) linewidths up to J D 20 and T D 2000 K, which will be essential for combustion purpose.

THE ECS MODEL

The dynamical scaling law, originally proposed by Goldflam et al. 13 to describe the rotational excitation of a simple system, is based on the so-called Infinite Orden Sudden (IOS) approximation. The molecule is assumed not to rotate during the collision, which results in an important simplification of the quantum mechanical equations for molecular scattering. Let us consider here the case of isotropic Raman scattering. For a diatomic molecule, it leads to the following relation between the off-diagonal matrix elements W IOS j i j 0 i T and the basis rate constants Q T :

W IOS j i j 0 i T D [2j i 0 C 1] [2 C 1] j i j i 0 0 0 0 2 Q T 1 1
where is one component of the intermolecular potential developed on Legendre polynomial basis and is a 3-j symbol. In particular, for j 0 i D 0, we have W i0 IOS T D Q T , which represents the transition probability from j i to 0 . Equation (1) means that the entire rotational relaxation matrix is determined by the knowledge of a single column. But the applicability of the IOS approximation is restricted to molecular systems for which the duration of the collision is very short compared to the rotational period of the diatom, such as CO-He for instance. For most other systems, the finite duration of the collision has to be introduced. De Pristo et al. 1 have therefore proposed an adiabatic correction to the IOS scaling law and the resulting equation is:

W ECS j i j 0 i T D [2j i 0 C 1]e E j i> E j i kT 2 j i> ð [2 C 1] j i j i 0 0 0 0 Q T 2 2
where j i> is the largest number of j i and j i 0 , and the adiabaticity factor j is given for a homonuclear molecule by:

j D 1 1 C ω 2 j,j υ 2 c 24v 2 3
In Eqn (3), ω j,j υ is the frequency of the transition to the nearest level (υ D 2 for a homonuclear molecule), c characterizes the interaction length, v is the mean collision velocity of the molecular pair given by the Maxwell-Boltzmann statistics at a given temperature T:

v D 8kT 4
where is the reduced mass of the system. The term 2 c /v 2 D c 2 represents the square of the effective duration of collisions.

The transfer rates Q T may be represented by simple analytical forms. The resulting laws are called ECS-EP, ECS-P, or ECS-E following the choice of the empirical form. Starting from the exponential-polynomial law (EP) proposed by Millot 2 and Bonamy et al. 14

Q T

D A 0 T T 0 N e ˇE kT [ C 1 ] 5
where T 0 is a reference temperature (T 0 D 296 K), one gets the polynomial law (P) by putting ˇD 0 and the exponential law (E) for D 0. Equation (5) shows the four fitting parameters (N, ˇ, , A 0 ). Let us recall that the scaling length C is an additional one. These five parameters have to be determined from linewidth data (experimental and/or theoretical).

RESULTS AND DISCUSSION

The N 2 -Q branch collisional line broadening coefficients have been measured by stimulated Raman loss spectroscopy in Madrid [START_REF] Gomez | [END_REF]15 for a large set of temperatures and rotational quantum numbers, (between 77 and 580 K and for J D 22 for the highest temperature). The corresponding linewidth calculations may be performed using two main ab initio approaches. At high temperature, the semiclassical approach [START_REF] Gomez | [END_REF] is the only tractable one and is commonly used for a large variety of molecular systems. For lower temperatures, exact pure quantum methods such as CC or CS approaches 10,12 are available. All these ab initio models need an adequate description of the molecular interactions such as the PES, which we have recently introduced in pure quantum approaches for the N 2 -H 2 mixture. 11 In Ref. 10, we have presented the new results obtained using the CC method (for T D 77, 298, and 440 K) and the CS method (for T D 580 K). The measured and calculated line broadening coefficients are reported in Table 1. As stated in Ref. 10, we obtain satisfactory agreement between experiment and theory, which confirms that the new PES allows us to reproduce quite well the experimental data and their temperature dependence.

Temperature diagnostics in combustion engines requires the values of the collisional broadening coefficient at temperatures higher than 580 K. Owing to the greatly increasing computing time needed when using pure quantum methods, no evaluation of collisional linewidths is reasonably possible at high temperatures. Apart from the semiclassical model already mentioned, approximate methods like ECS scaling laws have shown their usefulness. In this work, we propose to use the ECS scaling laws to calculate the rovibrationnal linewidths for N 2 perturbed by H 2 in a large temperature range. The first step of the calculation consists in the determination of the parameters (N, ˇ, , A 0 and C ) describing the ECS scaling laws (cf Eqn (5)). Note that only four parameters must be fitted for ECS-E ( D 0) and ECS-P (ˇD 0) laws. To evaluate the temperature power law introduced in the transfer rates Q T (cf Eqn (5)), a grid of values for different temperatures is needed. In this study, four temperatures (77, 298, 440, and 580 K) were investigated. 11,12 Using the calculated line broadening coefficients of Table 1, we have obtained a new set of ECS parameters for the three different laws (ECS-E, ECS-P, and ECS-EP). The fitted parameters are reported in Table 2. The Raman Q-branch line broadening coefficients obtained from the fitted parameters are represented in Figs 1 to 4 for T D 77, 298, 440, and 580 K for ECS-E, ECS-P, and ECS-EP laws. The pure quantum calculations are also reported for comparison.

For all the temperatures studied, we observe good agreement between the pure quantum calculations and the ECS methods. Except for the lowest J values, we can reproduce quite well the J dependence using these three sets of ECS parameters. The ECS-EP law seems to be the most adapted model to describe the temperature dependence Table 1. Comparison between experimental and theoretical values 10,11 of the Q-branch line broadening coefficients of the N 2 -H 2 system for several J values and temperatures. Calculations were made using the close-coupling (CC) or coupled-states (CS) method depending on the considered temperature. 10 All values are in 10

CONCLUSIONS

The ECS dynamical model has been widely used for the purpose of temperature diagnostics in combustion media. Accurate measurements of the collisional linewidths of the Qbranch lines of N 2 in H 2 have been performed by stimulated 10 and those deduced from ECS-E (solid line), ECS-P (dashed line), and ECS-EP (dotted line) laws. 2,14,15 Raman loss spectroscopy from 77 to 580 K. A concomitant quantum mechanical CC study using a new, accurate PES has given very good agreement with experiment. Using

Figure 1 .

 1 Figure 1. Calculated line broadening coefficient of N 2 Q-branch perturbed by H 2 vs the rotational quantum number J at T D 77 K. Comparison between results obtained by pure quantum calculations10 and those deduced from ECS-E (solid line), ECS-P (dashed line), and ECS-EP (dotted line) laws.2,14,15

Figure 2 .Figure 3 .

 23 Figure 2. Same as Fig. 1 but at T D 296 K.

Figure 4 .

 4 Figure 4. Same as Fig. 1 but at T D 580 K. these data, three different sets of ECS parameters (ECS-E, ECS-P, and ECS-EP) have been obtained, allowing linewidths

Figure 5 .

 5 Figure 5. Line broadening coefficient of N 2 Q-branch perturbed by H 2 vs J quantum number and temperature evaluated using the ECS-EP law and the fitting parameters (cf Table2).

Table 2 .

 2 3 cm 1 atm 1 Fitting

		Exp.	CC		Exp.	CC	Exp.	CC	Exp.	CS
	J	77 K	77 K	298 K	298 K	440 K	440 K	580 K	580 K
	0	127.48 (28.5)	121.90	58.2 (12.0)	64.08	45.5 (16.4)	53.49	44.9 (39.3)	47.66
	1	82.64 (16.2)	74.17	49.5 (9.8)	45.85	36.0 (11.4)	-	50.3 (17.6)	-
	2	82.93 (15.4)	85.2		45.4 (4.3)	46.51	36.4 (9.0)	39.60	47.1 (15.0)	35.91
	3	78.11 (19.5)	89.24	50.6 (6.9)	48.29	41.6 (10.9)	-	36.5 (17.2)	-
	4	84.75 (10.8)	92.39	46.5 (3.8)	49.45	39.0 (11.3)	41.48	25.5 (10.3)	36.69
	5	92.69 (12.1)	91.90	44.6 (5.6)	50.13	38.4 (9.0)	-	32.1 (11.8)	-
	6	101.82 (16.1)	92.62	47.8 (4.6)	50.56	37.1 (8.3)	42.31	31.2 (11.2)	37.21
	7	-	92.62	51.1 (5.4)	50.85	40.1 (6.5)	-	36.5 (11.4)	-
	8	-	92.77	46.4 (3.4)	50.97	42.5 (9.0)	42.58	40.2 (10.3)	37.38
	9	-	-		50.5 (4.0)	50.92	38.9 (8.1)	-	37.4 (8.3)	-
	10	-	-		48.6 (4.0)	50.79	37.3 (9.6)	42.62	35.9 (11.8)	37.45
	11	-	-		41.5 (11.2)	50.47	42.6 (6.2)	-	43.3 (10.5)	-
	12	-	-		-	-	43.1 (5.3)	-	38.6 (12.6)	37.44
	13	-	-		-	-	40.7 (9.5)	-	34.7 (14.0)	-
	14	-	-		-	48.87	38.6 (6.7)	41.54	40.1 (9.0)	37.28
	15	-	-		-	-	42.9 (9.3)	-	32.2 (11.1)	-
	16	-	-		-	-	33.4 (6.1)	-	39.7 (10.6)	36.98
	17	-	-		-	-	32.3 (19.6)	-	30.9 (13.4)	-
	18	-	-		-	-	34.2 (6.0)	40.27	26.7 (13.3)	36.48
	19	-	-		-	-	-	-	22.6 (17.7)	-
	20	-	-		-	-	-	-	26.4 (16.1)	35.83
	21	-	-		-	-	-	-	39.9 (9.3)	-
	22	-	-		-	-	-	-	20.1 (15.5)	35.01
		parameters of ECS-E, ECS-P, and
	ECS-EP 1,2,14 for N 2 Q-branch perturbed by H 2 using pure
	quantum calculations of collisional line broadening
	coefficients. 10 A 0 is expressed in 10 3 cm 1 atm 1 ; C in Å; N,
	, and ˇare dimensionless
	N 2 -H 2	A 0	N		ˇ	C
	ECS-E	3.03	1.6181	0	1.4738	0.4647
	ECS-P	72.26	0.7594	1.202	0	0.9291
	ECS-EP	46.595	0.8521	1.028	0.167	0.6511
	of line broadening coefficient with confidence. Systematic
	calculations of the N 2 -H 2 Q-branch collisional linewidth
	perturbed by using the ECS-EP law were then performed,
	and they are reported in Fig. 5 vs the J rotational quantum
	number for temperatures up to 2000 K.


Clement Fauvel
email: clement.fauvel@cea.fr

Kritchai Witheephanich
email: kritchai.witheephanich@cit.ie

Alan Mcgibney
email: alan.mcgibney@cit.ie

Susan Rea
email: susan.rea@cit.ie

Clément Fauvel

Suzanne Lesecq
email: suzanne.lesecq@cea.fr

Generating Models for Model Predictive Control in Buildings

Keywords: building, modelling, energy management system, model predictive control

published or not. The documents may come

Introduction

It is well accepted that energy consumption in buildings accounts for more than 40% of the total primary energy resources throughout the world [START_REF] Ferracuti | Data-driven models for short-term thermal behaviour prediction in real buildings[END_REF]. Therefore it is an essential to enact strategies for energy conservation and energy management in buildings [START_REF] Cigler | Model Predictive Control for Buildings[END_REF][START_REF] Shaikh | A review on optimized control systems for building energy and comfort management of smart sustainable buildings[END_REF]. A promising solution for BEMS [START_REF] Morosan | Building temperature regulation using a distributed model predictive control[END_REF] is Model Predictive Control (MPC) that is able to deal with various objectives at the same time, under constraints. For instance, both comfort and energy can be considered taking into account occupancy schedule, weather forecast, etc. MPC is a model-based control technique: it requires a dynamical discrete-time model of the building under control to predict the behavior in building zones. Although system modelling and parameter identification are mature scientific domains, their application to real buildings is still laboursome, time consuming and error prone [START_REF] Privara | Building modeling as a crucial part for building predictive control[END_REF]. This paper focuses on the development of reliable models that can be used to support the deployment of (Distributive (Di)) MPC applications.

Problem Statement

Consider a multizone building with thermal and comfort coupling between zones, actuators and outdoor conditions such as that depicted in Figure 1. We denote u i (k) as the manipulated variable, w i (k) as the exogenous ones and v i (k) the coupling effects. The modelling problem can be stated as follow: define the function F k, x i , u i , w i , v i , which represents the zone behavior and is accurate, modular and suitable for DiMPC, with respect to the highly coupled interactions.

Method

The measured data is represented by the tuple (z i , u i , w i ) given by:

z i (k) = T z i (k), c z i (k) , u i (k) = u win (k), u pwr (k) , (1)
w i (k) = n occ (k), Q solar (k) v i (k) = T bnd (k), C bnd (k) (2)
where n occ is the number of occupants, Q sol the solar radiation, T bnd and C bnd the temperature and CO2 concentration at the boundaries. Assuming this tuple can be measured for N time samples, it is possible to apply identification techniques to seek a data-driven models. Here, we propose to represent the discrete-time thermal zone behavior by a Brunowski state-space form. The matrices are naturally sparse, which eases the identification process in finding solutions:

A =          0 1 0 0 0 0 0 0 1 0 0 0 a
         , B =          b 11 b 12 b 21 b 22 b 31 b 32 b 41 b 42 b 51 b 52 b 61 b 62          , C =    1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1    (3)
Denote θ = a ij , b ij , x 0 ∈ R n x , β ∈ R n y the vector of parameters to identify with β and x 0 representing an unknown offset and initial state. This vector is computed by resolving the following least-square problem:

θ * = arg min θ∈D N ∑ k=1 y i (k, θ) -z i (k) 2 (4)
subject to the parametrized model:

G k, θ = x i (k + 1) = A θ x i (k) + B u θ u i (k) + B w θ w i (k) + B v θ v i (k), y i (k) = Cx i (k) + β θ (5)
The zone model is given by:

F k, x i , u i , w i , v i = G k, θ * (6)
The quality of the model is evaluated using the following specific index with N p the prediction horizon:

p (t) = t+N p ∑ k=t y t (k) -z t (k) z t (k) 2 (7)

Experimental Results

The case study is the Nimbus building in Cork Institute of Technology (CIT). It has been instrumented using TOPAs platform (see details in [START_REF] Mcgibney | Open BMS-IoT driven architecture for the internet of buildings[END_REF][START_REF] Walker | Application of distributed model predictive approaches to temperature and CO2 concentration control in buildings[END_REF]). We focus on an open-space subdivided in three zones. The thermal and comfort behaviors are modelled using data captured in winter. Figure 2 shows their p index. Models are validated if total of occurences above p = 20% are less than below. For N p = 2 , both thermal and CO2 models are validated; for N p = 5 only the thermal ones is accurate enough for predictive usage. The reliability of the models in real conditions is evaluated by running a DiMPC controller. The reference temperature is T re f = 22 • C and the comfort bounds is chosen as [18

• C; 26 • C].
In Figure 3, we compare the effect of the DiMPC control algorithm against baseline on zone temperatures. Thanks to the proposed modelling procedure, the DiMPC was able to demonstrate energy saving and improved thermal comfort. The same controller is applied during the summer (Figure 4). Weather conditions are warmer, the heating system have been shut down and the windows are the main actuators in this scenario. The temperature remains within the comfort bounds.

Conclusions

This paper has presented an initial analysis of a modeling approach to accurately develop (Di)MPC technology for evalution in real buildings. The solution has been evaluated in a real use case, showing the benifits of DiMPC for energy and comfort management. The approach is generic, in such a way that it is applicable for different types of buildings and building zones.

Figure 1 .

 1 Figure 1. Building with multiple zones (left); zone representation with regulated, manipulated and exogenous variables (right).

5 Figure 2 .

 52 Figure 2. Validation of the modelling for the case-study.

Figure 3 .

 3 Figure 3. Comparison between DiMPC (plain lines) and baseline (dash line) control algorithms during winter: DiMPC is enabled during 14 h then disabled.

  Windows control values in zones

Figure 4 .

 4 Figure 4. Comparison between DiMPC (plain lines) and baseline (dash line) control algorithms during summer: DiMPC is enabled during 40 h.

  11 a 12 a 13 a 14 a 15 a 22 a 23 a 24 a 25 a 26 a 31 a 32 a 33 a 34 a 35 a 36

						16
	0	0	0	0	1	0
	a 21 a

Proceedings 2018, 2, 1137; doi:10.3390/proceedings2151137 www.mdpi.com/journal/proceedings Proceedings 2018, 2, 1137

Funding: This work is part of the TOPAS project (https://www.topas-eeb.eu/), which has received funding from the European Unions Horizon 2020 research and innovation programme under the Grant Agreement No. 676760.


Théo Biasutto-Lervat
email: theo.biasutto-lervat@loria.fr

Slim Ouni
email: slim.ouni@loria.fr

Phoneme-to-Articulatory mapping using bidirectional gated RNN

Keywords: speech production, coarticulation modeling, bidirectional recurrent neural network (BRNN)

Deriving articulatory dynamics from the acoustic speech signal has been addressed in several speech production studies. In this paper, we investigate whether it is possible to predict articulatory dynamics from phonetic information without having the acoustic speech signal. The input data may be considered as not sufficiently rich acoustically, as probably there is no explicit coarticulation information but we expect that the phonetic sequence provides compact yet rich knowledge. Motivated by the recent success of deep learning techniques used in the acoustic-to-articulatory inversion, we have experimented around the bidirectional gated recurrent neural network architectures. We trained these models with an EMA corpus, and have obtained good performances similar to the state-of-theart articulatory inversion from LSF features, but using only the phoneme labels and durations.

Introduction

Recovering the vocal tract shape from speech acoustics could benefit many automatic speech processing system to enrich for instance the acoustic information for synthesis [START_REF] Ling | Integrating articulatory features into hmm-based parametric speech synthesis[END_REF] and recognition [START_REF] King | Speech production knowledge in automatic speech recognition[END_REF]. In fact, articulatory features are more robust than acoustic features as articulatory features vary very slowly when compared with speech acoustic features.

Recently, corpora of synchronized acoustic and articulatory data streams, using electromagnetic articulography (EMA) for instance, have become available making possible to apply machine learning models and algorithms like HMM [START_REF] Hiroya | Estimation of articulatory movements from speech acoustics using an HMM-based speech production model[END_REF][START_REF] Zhang | Acoustic-Articulatory Modeling With the Trajectory HMM[END_REF] or artificial neural networks [START_REF] Uria | Deep architectures for articulatory inversion[END_REF][START_REF] Xie | Deep Neural Network Based Acoustic-to-Articulatory Inversion Using Phone Sequence Information[END_REF]. More recently, acoustic-toarticulatory inversion using bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) provides very good performance [START_REF] Zhu | Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings[END_REF][START_REF] Liu | A deep recurrent approach for acoustic-to-articulatory inversion[END_REF].

In our work, we address the inversion problem differently. One question is whether it is possible to predict articulatory dynamics from the knowledge of the phonetic information only: Does the knowledge of the distribution of the phonemes over time can allow predicting the articulatory dynamics ? So the problem can be seen as a phoneme-to-articulatory inversion.

In the past, this problem has been addressed in a different context, not as an inversion problem, but as a coarticulation modeling problem. There are two standard coarticulation models: (1) the rule-based look-ahead model [START_REF] Öhman | Numerical model of coarticulation[END_REF] , and (2) timelocked model [START_REF] Löfqvist | Speech as audible gestures[END_REF][START_REF] Cohen | Modeling coarticulation in synthetic visual speech[END_REF]. We can also note several studies injecting this phonetic knowledge to help the acoustic-to-articulatory mapping [START_REF] Potard | Incorporation of phonetic constraints in acoustic-to-articulatory inversion[END_REF][START_REF] Ben Youssef | Acousticto-articulatory inversion using speech recognition and trajectory formation based on phoneme hidden Markov models[END_REF][START_REF] Xie | Deep Neural Network Based Acoustic-to-Articulatory Inversion Using Phone Sequence Information[END_REF].

Motivated by the success of BRNNs in the articulatory inversion task, we have explored in this study the ability of such architecture to generate articulatory dynamics from only the phonetic information, i.e. the phoneme label and their respective duration.

In the following sections, we present the RNN architecture used in this study and a simple yet effective training procedure (respectively section 2 and 3). Then we present our phonemeto-articulatory mapping experiments (section 4), and finally we present the results that we discuss in the last section.

Bidirectional Gated RNN

Bidirectional RNN

While feedforward neural networks are universal approximators [START_REF] Hornik | Multilayer feedforward networks are universal approximators[END_REF], recurrent neural networks (RNN) have been shown to be turing complete, and thus should be able to approximate any dynamical system [START_REF] Siegelmann | On the computational power of neural nets[END_REF]. RNNs are able to summarize the input sequence into an internal state using cyclical connection, giving them the ability to learn temporal relationship and correlation between data points.

Formally, a RNN is defined by

ht = H(xt, ht-1; θ) yt = Woutput.ht + boutput (1)
where ht is the network internal state, xt the network input and yt the corresponding output at time t. Woutput is the internal-to-output weight connection, and boutput the associated bias vector. H is the recurrent hidden layer transfer function parametrized by the θ set. However, these recurrent networks are limited to the use of past information, although knowledge of future information could improve the yt prediction. This statement is particularly true when dealing with speech production, for which it is well established that future phoneme influence the production of the current phone through anticipative coarticulation. Bidirectional RNNs (BRNN) [START_REF] Schuster | Bidirectional recurrent neural networks[END_REF] overcomes this limitation using two layers simultaneously trained in positive and negative time direction (see fig. 1).

Extending equation 1 for BRNN gives

← - h t = ← - H(xt, ← - h t+1; ← - θ ) -→ h t = -→ H(xt, -→ h t-1; -→ θ ) yt = Woutput.M( ← - h t, -→ h t) + boutput ( 2
)
where M is the merge function, usually a simple concatenation but literature also exhibits use of element-wise sum or multiplication. ← -. denotes elements related to the backward layer and -→ . to the forward layer.

BRNNs have outperformed unidirectional RNNs in several tasks, such as phoneme classification [START_REF] Graves | Bidirectional lstm networks for improved phoneme classification and recognition[END_REF], neural machine translation [START_REF] Britz | Massive exploration of neural machine translation architectures[END_REF] or speech enhancement [START_REF] Wöllmer | Feature enhancement by bidirectional lstm networks for conversational speech recognition in highly non-stationary noise[END_REF].

Gated RNN

Despite its theoretical abilities, training vanilla RNN to learn long-range dependencies using a gradient descent algorithm is Figure 1: Bidirectional RNN -blue layers are recurrent and hexagons represent the merge strategy still a difficult task due to the vanishing/exploding gradient issue [START_REF] Bengio | Learning long-term dependencies with gradient descent is difficult[END_REF]. Long Short Term Memory (LSTM) network [START_REF] Hochreiter | Long short-term memory[END_REF] gets around this issue by computing increments to the internal state and so encouraging information to stay for much longer, and by adding to each neural unit three gates which act as weight adjusters in function of inputs and hidden states.

For LSTM, H is defined by

ft = σ(W f .[ht-1, xt] + b f ) it = σ(Wi.[ht-1, xt] + bi) ot = σ(Wo.[ht-1, xt] + bo) Ct = ft * Ct -1 + it * tanh(WC .[ht-1, xt] + bC ) ht = ot * tanh(Ct) (3)
where C is the cell memory and f , i and o are respectively the forget, input and output gates. σ is the sigmoid function and various W and b correspond to weight connection matrix and bias vector. Concatenation is denoted by [., .] and * is an element-wise product. Among several variants of LSTM, the Gated Recurrent Unit (GRU) proposed by Cho et al. [START_REF] Cho | Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation[END_REF] has become quite popular and successful. GRU reduces the complexity of LSTM, by removing one gate and the cell memory and so decreasing the number of parameters, which should simplify the training.

For GRU H is defined by

zt = σ(Wz.[ht-1, xt] + bz) rt = σ(Wr.[ht-1, xt] + br) ht = zt * ht-1 + (1 -zt) * tanh(W h .[ht-1 * rt, xt] + b h ) (4
) where z is the update gate and r the reset gate.

Presently, LSTM and variations are well-known for their great performances in language and speech-related tasks for example phoneme classification [START_REF] Graves | Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks[END_REF], machine translation [START_REF] Bahdanau | Neural machine translation by jointly learning to align and translate[END_REF] or language modeling [START_REF] Mulder | A survey on the application of recurrent neural networks to statistical language modeling[END_REF].

Training procedure

Input and output features

We trained the recurrent networks in a framewise regression scheme where inputs and outputs are synchronized, using a pure stochastic gradient descent. Networks have to predict a sequence of articulator position  = (â0, ..., âT ) from a phoneme sequence Φ = (φ0, ..., φT ), where  is as close as possible to the target value A = (a0, ..., aT ).

The target output A is a sequence of n-dimensional vectors representing the stacked spatial coordinates of each articulator, while the input Φ is the encoded phoneme sequence. φt is a one-hot vector representing the articulated phoneme at timestep t. This encoding preserves the duration of each phoneme without having to explicitly feed this information to the network, and can be seen as a multidimensional binary signal synchronized with the articulator trajectories.

Loss function

As usual in regression task, we used the mean squared error as loss function, and defined the error as the Euclidean distance between at and ât:

L(A, Â) = 1 N i j (aij -âij) 2 (5)
with N the sequence size and aij the j-th dimension of ai.

The partial derivatives of the loss function L (equation 5) according to the BRNN's parameters were computed with Backpropagation Through Time (BPTT) [START_REF] Rumelhart | Learning internal representations by error propagation[END_REF], and the network was fully unfolded for each training sequence.

Adam

The optimization method used to train the network was Adam [START_REF] Kingma | Adam: A method for stochastic optimization[END_REF], an adaptive learning rate extension of stochastic gradient descent with many benefits (e.g. appropriate for nonstationary objective and sparse gradients, parameters update invariant to gradient rescaling, intuitive hyper-parameters) and quite popular inside the deep learning community. Adam's authors claim that it combines both the advantages of RM-Sprop [START_REF] Tieleman | RMSprop Gradient Optimization[END_REF] and AdaGrad [START_REF] Duchi | Adaptive Subgradient Methods for Online Learning and Stochastic Optimization[END_REF], two other well-known gradientbased optimization algorithms.

mt = β1.mt-1 + (1 -β1).gt mt = mt/(1 -β t 1 ) vt = β2.vt-1 + (1 -β2).g 2 t vt = vt/(1 -β t 2 ) θt = θt-1 -λ. mt/( √ vt + ) ( 6
)
where λ is the learning rate, gt the gradient of the parameter set θt at step t, and is here for numerical stability. Both m (first moment estimate) and v (second moment estimate) are computed using an exponential moving average (parameters β1 and β2), moreover a bias correction is applied to reduce the importance of the first samples during the moving average, leading to m and v. In our experiments, we keep the recommended parameters λ = 0.001, β1 = 0.9, β2 = 0.999 and = 10 -8 .

Early stopping & Learning decay

We used an early stopping strategy to prevent over-fitting and to speed-up the computation [START_REF] Prechelt | Early stopping but when?[END_REF], which simply consists of stopping the training and rollback to the best model when the validation loss has stopped improving during more than Nstop steps. Finally, a learning rate decay was used to slightly improve our final performances. To properly combine our early stopping method and learning rate decay, we reduce the learning rate only when the validation loss stagnates during N decay consecutive epoch, with obviously N decay < Nstop. The decay is a simple λnew = λ * γ decay with 0 < γ decay < 1. We empirically chose Nstop = 10, N decay = 5 and γ decay = 0.5 to get

Experiments

Training corpus

We obtained the ground-truth trajectories from MNGU0 [START_REF] Richmond | Announcing the electromagnetic articulography (day 1) subset of the mngu0 articulatory corpus[END_REF], an articulatory corpus acquired with a Carstens AG500 electromagnetic articulograph [START_REF] Perkell | Electromagnetic midsagittal articulometer systems for transducing speech articulatory movements[END_REF]. With about one hour of parallel EMA and acoustic data sampled at 200Hz and split among more than 1,300 utterances, MNGU0 is currently the longest and most precise articulatory dataset openly available to our knowledge. All coils are located on the midsagital plane, three along the tongue (on the tip, the body and the tongue dorsum), one on the lower incisive, one on the upper lips and one on the lower lips (fig. 2). This corpus is provided with a phonetic segmentation of all the data. The training phase is performed at each time step on the couple phoneme symbol and articulatory features. When used for inversion, the input of the system is the distribution of the phonetic symbols over time and the output is the articulatory dynamics, as specified in 3.1. For an easier reproducibility of our work and comparison to others methods, we used the training, validation and testing sets proposed by the corpus.

Evaluation metrics

We measured the performances with two well-know metrics, the Root Mean Squared Error (RMSE) and the Pearson correlation ρ. For all dimensions of the stacked spatial coordinates ai, both metrics have been computed and averaged over utterances of the test set. Thus we obtain 12 indices by metrics, one for each position of each coil in the midsagittal plan. The final performance of the system is the mean of all averaged indices. where ei is the prediction at timestep i, ti the associated target, ē is the mean of the predicted values and t the mean of the target values.

RM SE = 1 N i (ei -ti) 2 ρ = i (ei -ē)(ti -t) i (ei -ē) i (ti -t) (7

Performances: LSTM vs. GRU

To compare LSTM and GRU, we experimentally fixed good enough parameters for the network depth and width, i.e. 2 hidden layers and 256 units by a hidden layer (128 forward, 128 backward). This choice has been motivated by its closeness to the network architecture employed in the studies presented by Liu et al. [START_REF] Liu | A deep recurrent approach for acoustic-to-articulatory inversion[END_REF] and by Zhu et al. [START_REF] Zhu | Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings[END_REF] for state-of-the-art acousticto-articulatory inversion. As the training is stochastic and can lead to different results, both LSTM and GRU networks were independently trained and evaluated fifty times using MNGU0. Figure 3 clearly exhibits highly correlated values and good RMSE for each coil. With an average RMSE of 0.6mm, the jaw (LI) and the lips (UL, LL) dynamics are particularly realistic, comparable to state-of-the-art acoustic-to-articulatory inversion. We even reached excellent result for the upper lip (UL) with RMSE around 0.4mm. The tongue control has a median RMSE per coil around 1.2mm. The RMSE difference between both architectures is 0.007mm for the median, and there is a difference of about 0.01mm between the extrema. We consider that the performance is very good. Figure 5 shows an example of an articulatory trajectory inferred using our method in comparison with ground truth. Globally, the trajectories are very similar to the original ones with very good correlation.

GRU performances are slightly better than LSTM, but the gap is insignificant which seems to corroborate the work of Greff et al. on gated recurrent architecture comparison [START_REF] Greff | LSTM: A Search Space Odyssey[END_REF]. A common explanation for this behavior is that for an equivalent number of units, a GRU layer contains fewer parameters, so it should be easier to train and less prone to over-fitting.

Performances: Depth vs. Width

After selecting GRU over LSTM, we explored different network depth and size for this specific unit. Figure 4 exposes the overall performances for networks containing up to 4 layers, where each layer contains 32 to 256 units. Each configuration has been trained twenty times, plain lines represent the median performances, dotted lines the second and third quartile, and triangles the extrema. A first observation is the nice performances of the previous network (2 layers and 128 units by a layer) compared to other architecture. This fact consolidates our conviction in the nearness of coarticulation modeling and articulatory inversion tasks, indeed the complexity of both tasks seems to be quite equivalent for a BRNN. Secondly, the single layer models performs quite badly, maybe because the lack of deepness prevents the model to learn the complex relationship between phonetic sequence and articulatory kinematics. Finally, the four-layered models also perform badly, especially when augmenting the number of units by layer. This phenomenon is certainly explained by the huge numbers of parameters, the lack of data, and/or by a training procedure not adapted for such really deep models.

The lowest RMSE is 0.868mm, with 2 layers of 224 units, which is in the same range as the acoustic-to-articulatory inversion results from LSF features using deep mixture density network [START_REF] Uria | Deep architectures for articulatory inversion[END_REF] (0.885mm) or when using an architecture FBB 1 [START_REF] Zhu | Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings[END_REF] (0.889mm), or FFBB 1 and a more complex training procedure, i.e. greedy layer-wise pretraining with RMSProp followed by a fine-tuning using SGD (0.816mm) [START_REF] Liu | A deep recurrent approach for acoustic-to-articulatory inversion[END_REF]. However, it should be noted that acoustic-to-articulatory mapping from MFCC features has the best performance (0.565mm) [START_REF] Zhu | Articulatory movement prediction using deep bidirectional long short-term memory based recurrent neural networks and word/phone embeddings[END_REF].

Concluding remarks

In this paper, we explored the use of bidirectional gated recurrent neural networks to map phonetic sequence to an articulatory trajectory. We compared two well-known architectures of gated RNN, LSTM and GRU, and concluded that both methods provide similar performance, with a slight advantage to GRU implementation. We also experimented around the depth of the network and the number of units per layer, and observed that two or three layers seems to be optimum.

Using a simple training procedure, we managed to get performances similar to state-of-the-art acoustic-to-articulatory inversion from LSF features (0.868mm vs. 0.816mm). This result suggests that phonetic information (i.e. symbol and duration) contains knowledge rich enough to infer articulatory dynamics, even without the explicit coarticulation information present in the acoustic signal. In fact, this richness is embedded in the phoneme information represented by the manner and 1 F stands for feed-forward and B for bidirectional LSTM, so FBB is a network with one feed-forward layer followed by two layers of BLSTM place of articulation, the phonetic context (the position of the phoneme relatively to the surrounding phonemes), and the duration of each phoneme within this context. This richness seems to model very well the coarticulation phenomena. The success of mapping articulatory dynamics from phonemes suggests that the planning of articulatory movement can be predicted from higher-level information (phonemes) and not necessarily from the lower level information (acoustics).

We are going to investigate further this new manner of driving articulatory dynamics from phonemes, which may make a breakthrough in many fields related to speech production, synthesis and recognition. For instance, it may be interesting to use this technique in audiovisual speech synthesis to animate the tongue from text. Furthermore, we will also consider studying the combination of higher level information with lower-level information to see their impact on predicting articulatory dynamics. We may consider the exploration of different recurrent neural network architectures and training procedures, and assess their repercussion on the quality of the articulatory dynamics.

Figure 2 :

 2 Figure 2: Sensor coil locations for MNGU0

Figure 3 :

 3 Figure 3: Violin plot for RMSE (mm) and Pearson's correlation of 50 independent training using either LSTM or GRU units. Subplots on the right correspond to performances per coils, and subplots on the left correspond to the overall performances. Each dot corresponds to a single training.

Figure 4 :

 4 Figure 4: Overall performances for twenty training when varying the number of units per layer and the number of layers. Plain lines represent the median, dotted lines the second and third quartile, triangles indicate extrema.

Figure 5 :

 5 Figure 5: Tongue trajectory inferred from the sentence "Auctioneers: United Auctions" (mngu0 s1 0220) with the best trained model. Red lines are the ground-truth and black lines are the inference, phonemes are delimited by dotted vertical lines.

Table 1 :

 1 Information about MNGU0: number of sentences, duration and number of phonemes for training, testing and validation sets.

	set	sentences durations #phones
	training	1188	58'8"	42,207
	testing	64	2'43"	2,293
	validation	60	3'18"	1,828
	an acceptable trade-off between performances and computation
	time.

  )

		0.93 0.94	1.2 1.4											LSTM GRU
	RMSE (mm)	0.89 0.90 0.91 0.92	0.6 0.8 1.0
		0.88	0.4
		Overall	T1 x	T1 y	T2 x	T2 y	T3 x	T3 y	LI x	LI y	UL x	UL y	LL x	LL y
		0.89	0.95
	Pearson's correlation	0.85 0.86 0.87 0.88	0.75 0.80 0.85 0.90
			0.70
		Overall	T1 x	T1 y	T2 x	T2 y	T3 x	T3 y	LI x	LI y	UL x	UL y	LL x	LL y

Acknowledgements

This work was supported by PIA2 E-Fran, within the METAL project. Authors would like to thank Korin Richmond for making MNGU0 corpus available and for his advices, and the anonymous reviewers for theirs useful comments.