Nicolas Obin
email: nobin@ircam.fr

Cries and Whispers Classification of Vocal Effort in Expressive Speech

Keywords: speech recognition, vocal effort, voice quality, glottal source, GMM-UBM/SVM

The expansion of the video games industry raises innovative and challenging issues for speech technologies, e.g. the development of automatic content-based speech processing and speech recognition systems in the context of video games postproduction and voice casting. This paper presents a large-scale study on the classification of vocal effort in expressive speech for video games. Changes in vocal effort conduct to substantial modifications in the configuration of voice production mechanisms. In particular, registers of vocal effort affect especially voice quality which reflects qualitative modifications of the source excitation characteristics. This study introduces robust source characteristics to measure various types of voice quality (e.g., breathy, creaky, tense) for the classification of vocal effort into whispered, normal, and shouted speech. The system is evaluated in the real scenario of video games production with the complete speech recordings of a massive role-playing video game. The proposed features significantly improve the classification from 81.1% to 87% over conventional MFCCs. These advancements confirm the role of the source and voice quality for the description of changes in vocal effort.

Introduction

Vocal effort corresponds to the adjustment of vocal intensity of a speaker depending on the communication distance to the listener. Vocal effort may also vary with respect to various causes (e.g., degree of intimacy that relates speaker and listener, emotional state of the speaker). A change in vocal effort causes a change in vocal intensity which conduct to substantial modifications in the configuration of voice production mechanisms. Studies on vocal effort usually assumes 5 configurations of voice production : whispered, soft, normal, loud, and shouted speech 1 .

A large number of studies has been dedicated to the description of the mechanisms involved in the production of whisper [START_REF] Monoson | Quantitative Study of a Whisper[END_REF][START_REF] Solomon | Laryngeal Configuration and Constriction during Two Types of Whispering[END_REF][START_REF] Sundberg | Whispering -A Single-Subject Study of Glottal Configuration and Aerodynamics[END_REF], and shouted [START_REF] Rostolland | Acoustic Features of Shouted Voice[END_REF] speech, and the differences in configuration between soft, normal, and loud speech [START_REF] Holmberg | Glottal Airflow and Transglottal Air Pressure Measurements for Male and Female Speakers in Soft, Normal, and Loud Voice[END_REF]. True whisper (low-effort whisper) refers to the excitation of the vocal tract with half-closed vocal folds. Stage whisper (high-effort whisper) is the simulation of whisper speech by professional actors so the speech is sufficiently loud so that the speech †This study was supported by the European FEDER project VOICE4GAMES.

1. Lombard and stressed speech may also be additional configurations.

is audible by the audience. Stage whisper differs from true whisper with extremely breathy and partially voiced speech [START_REF] Solomon | Laryngeal Configuration and Constriction during Two Types of Whispering[END_REF]. Shouted speech corresponds to a raise in F0 due to the raise of subglottal pressure used to increase vocal intensity [START_REF] Gramming | Relationship between Changes in Voice Pitch and Loudness[END_REF], and non-linearities due to the non-linear interaction of air flow and air vortices near the vocal folds producing additional excitation signals. A raise in vocal effort also affects the characteristics of the vocal tract, from the muscle tension in the vocal tract to the modification of vocal tract resonances. Finally, these modifications can be retrieved from signal analysis and the observation of F0 and spectral characteristics of speech [START_REF] Harwardt | Comparing the Impact of Raised Vocal Effort on Various Spectral Parameters[END_REF] (e.g., spectral slope as an approximation to the glottal source structure). The identification of vocal effort drastically affects the performance of speech recognition systems, from speech segmentation to speaker identification [START_REF] Zhang | Analysis and Classification of Speech Mode : Whispered through Shouted[END_REF][START_REF]Effective Segmentation based on Vocal Effort Change Point Detection[END_REF][START_REF] Zelinka | Impact of Vocal Effort Variability on Automatic Speech Recognition[END_REF]. Vocal effort identification systems has been proposed to adapt speech recognition systems to specific vocal efforts [START_REF] Teager | Evidence from Nonlinear Production Mechanisms in the Vocal Tract[END_REF][START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF][START_REF] Wenndt | A Study on the Classification of Whispered and Normally Phonated Speech[END_REF][START_REF] Ito | Analysis and Recognition of Whispered Speech[END_REF][START_REF] Zhang | Analysis and Classification of Speech Mode : Whispered through Shouted[END_REF][START_REF] Zelinka | Impact of Vocal Effort Variability on Automatic Speech Recognition[END_REF]. In the context of video games production, the classification of vocal effort configuration is critical for audio post-production (e.g., application of specific settings for speech level and multi-band compression depending on the vocal effort in order to simulate the proximity of speaker and listener), and may also helps for automatic voice casting. Additionally, the context of video games raises challenging issues compared to the requirements of conventional vocal effort identification systems, from the large database ( 20.000 to 40.000 audio files and 500 roles), the large range of speech variability of professional actors, including unnatural speech (e.g., cartoons, robots, extraterrestrials) ; the large difference in audio recordings duration from a single filler (e.g., inspirations, screams, ≤ 0.5s.) to complete utterances ( 10s.). This study presents a GMM-UBM/SVM system [START_REF] Charbuillet | GMM-Supervector for Content based Music Similarity[END_REF] and investigates robust source excitation characteristics to measure various types of voice quality [START_REF] Laver | The Phonetic Description of Voice Quality[END_REF] for the classification of vocal effort into whispered/soft, normal, and loud/shouted speech. In particular, this study will exploit recent advances in the robust separation of glottal source and vocal-tract filter [START_REF] Degottex | Glottal Closure Instant Detection from a Glottal Shape Estimate[END_REF][START_REF]Phase Minimization for Glottal Model Estimation[END_REF] for speech recognition systems. The proposed source characteristics are : soft voiced/unvoiced description (VUV) in Melfrequency bands as a measure of breathiness ; the glottis relaxation coefficient (RD) as a measure of tension in the voice ; and the irregularity of the glottal pulse (∆GCI) as a measure of creakiness/breathiness. The proposed source characteristics are compared with conventional features -MFCCs and Teager Energy Operator (TEO) [START_REF] Teager | Evidence from Nonlinear Production Mechanisms in the Vocal Tract[END_REF][START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF] -within a large-scale evaluation in the real scenario of video games production conducted on the complete speech recordings of a massive role-playing video game.

Speech Database

Speech recordings of a video game consist in the interpretation of script lines by professional actors who are directed by an artistic head whose role is to control the expressive content of speech depending on the place of the script within the overall scenario. Script lines may vary from a single sigh to a complete sentence. In role-playing games, the recording covers ten-thousands of speech files that are split into hundreds of roles. The video game used for the study includes around 20.000 French speech recordings and 500 roles (from a single to hundreds of recordings). The duration of speech recordings varies from 0.1 seconds to 20 seconds with a mean duration of 2.5 seconds. Recordings were made in mono 48 kHz/16 bits uncompressed format.

Speech recordings are produced in a studio by professional actors with a varying distance and orientation to the microphone so as to compensate the variations in acoustics due to changes in intended vocal effort (close while whispering, distant while shouting). Additionally, a sound engineer ensures that the speech level is constant through speech recordings so as to provide a homogeneous speech level through the video game. Finally, the situation of professional studio recording of speech for video games exhibits no significant differences of speech level for changes in vocal effort by professional actors (contrary to laboratory recordings [START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF]). Hence, only information provided by changes in the source excitation or the vocal tract resonances can be used for the identification of changes in vocal effort.

The identification of significant changes in vocal effort is critical in the production of video games for the application of specific settings that simulate the perception of proximity to the speaker by the player. For this purpose, the sound engineer is usually in charge for the manual classification of expressive speech recordings into three classes which cover whispered/soft, normal, and loud/shouted speech. In the present study, whispered/soft speech covers sighs, true whisper, stage whisper, stressed whisper (tense whisper typically produced in a life-survival situation in which the conversation intimacy is absolutely required), soft speech, and any situation of intimacy in the speech communication. Loud/shouted speech includes orders, public announcements, exclamations, interjections, stressed-speech, and screams.

Source Characteristics

The objective of this study is to investigate robust speech characteristics which may provide complementary information to the conventional MFCCs. Robust characteristics denote characteristics that are not subject to errors which may be critical for the classification. For instance, F0 and VOICED/UNVOICED FREQUENCY (VUF) were not retained for the comparison due to their sensibility in the context of expressive speechespecially, for the analysis of whispered and shouted speech.

Instead, robust source characteristics are introduced that may relate to various types of voice quality (breathiness, creakiness, tense, stress). For instance, a measure for the description of noisiness in the speech is introduced as a robust reformulation of the VUF into a soft VOICED/UNVOICED DECISION (VUV) within Mel-frequency bands. Additionally, this study will exploit recent advances in the robust separation of glottal source and vocal-tract filter [START_REF] Degottex | Glottal Closure Instant Detection from a Glottal Shape Estimate[END_REF][START_REF]Phase Minimization for Glottal Model Estimation[END_REF] which have been proved to be robust for the analysis of expressive speech. In particular, the glottalsource and vocal-tract separation provides an explicit description of the glottal source characteristics contrary to methods which does not process this separation (e.g., spectral slope). The glottal source characteristics considered are : the glottis relaxation coefficient (RD) as a measure of tension in the voice ; and the irregularity of the glottal pulse (∆GCI) as a measure of creakiness. Finally, TEAGER ENERGY OPERATOR (TEO) based characteristics are also used for comparison which were proved to be extremely relevant for the identification of loud and stressed speech [START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF].

MFCC

13 Mel-Frequency Cepstral Coefficients (MFCC) are extracted after the non-linear compression of amplitude spectrum into 25 Mel-frequency bands. Short-term features were extracted with a 25 ms. hanning moving window of 5ms.

TEO

The Teager Energy Operator has been introduced as a measure to reflect the non-linear airflow structure of speech production that can be observed in emotional and stressed speech [START_REF] Teager | Evidence from Nonlinear Production Mechanisms in the Vocal Tract[END_REF][START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF]. The non-linearities introduced in the source excitation causes the apparition of additional partials and modulations in the spectrum which do not correspond to the usual linear source/filter model. The TEO profile is a constant in the presence of a single and stationary sinusoid. This property can be extended so as to measure separately the degree of nonlinearities within various frequency bands. Further investigations conduct to the introduction of the normalized TEO autocorrelation envelope area which intends to reflect the distribution, the degree of stationarity, and the interactions of partials that are present within a frequency region. The normalized TEO autocorrelation envelope area has been proved to be extremely consistent for the identification of loud and stressed speech [START_REF] Zhou | Nonlinear Feature Based Classification of Speech Under Stress[END_REF]. In the present study, the normalized TEO autocorrelation envelope area was extracted after band-pass filtering of the speech signal into 25 Mel-frequency bands.

V/UV

The frequency distribution of harmonic content is assumed to change significantly with raise in vocal effort and reflect the degree of vibration of the vocal folds, the vocal intensity, and the raise of F0. In particular, a raise in vocal effort may coincide with a raise in the harmonic distribution through high-frequency bands. In this study, a soft Voiced/Unvoiced decision (VUV) [START_REF] Griffin | A New Model-Based Analysis/Synthesis System[END_REF] is introduced as a measure of the harmonic distribution into 25 Mel-frequency bands. For each frequency band, the VUV is measured as :

V U V (i) = P K (i) k=1 |AH (k)| 2 P N (i) n=1 |A(n)| 2 (1)
where : i denotes the i-th Mel frequency band, AH (k) the amplitude of the k-th harmonic [START_REF] Zivanovic | Adaptive Threshold Determination for Spectral Peak Classification[END_REF], and A(n) the amplitude of the n-th frequency bin in the considered frequency band. Hence, VUV is equal to zero when no harmonic content is present in the frequency band, and to one when only harmonic content is present in the frequency band. that the raise of vocal effort conduct to a significant raise of the harmonic content through high frequency bands. For instance, the frequency region upon which no significant harmonic content is present raises from the 9-th frequency band for whispered/Soft speech to the 18-th frequency band for loud/shouted speech.

Voice Quality

Glottal source characteristics are extracted from the robust separation of glottal source and vocal-tract filter [START_REF] Degottex | Glottal Closure Instant Detection from a Glottal Shape Estimate[END_REF][START_REF]Phase Minimization for Glottal Model Estimation[END_REF]. In particular, the glottal shape (LF-RD [START_REF] Fant | The LF-Model Revisited. Transformations and Frequency Domain Analysis[END_REF]) and Glottal Closure Instants (GCI) are determined separately. In this representation, the Liljencrants-Fant (LF) glottal model is described by a single parameter referred as the glottal relaxation coefficient (RD) [START_REF]Phase Minimization for Glottal Model Estimation[END_REF]. Then, the estimated Rds are used to determine the position of the GCIs [START_REF] Degottex | Glottal Closure Instant Detection from a Glottal Shape Estimate[END_REF]. Then, the regularity of the GCIs is determined as :

∆GCI(n) = |∆ 2 log(GCI(n) -GCI(n -1))| (2)
where : ∆ 2 (.) denotes the second derivative operator, and GCI(n) the n-th GCI time position.

These characteristics are referred in this study as voice quality characteristics (VQ) since there are explicit determination of the glottal source characteristics which relate to qualitative voice qualities : RD provides a description of the degree of tension in the voice, and the regularity of the GCIS provides a description of the degree of creakiness and/or breathiness in the voice.

Figure 2 presents the distribution of the VQ characteristics (RD, ∆ GCI) for whispered/soft, normal, and loud/shouted speech. This clearly shows that the raise of vocal effort conduct to a significant differences in the configuration of the glottal source characteristics. In particular, a raise in vocal effort corresponds to a raise in tension of the vocal tract. Moreover, whispered/soft speech exhibits a large dispersion in the glottal pulse regularity which clearly reflects the presence of whispery/breathy/creaky speech. Also, normal speech exhibits a larger dispersion in the glottal pulse regularity than loud/shouted speech which may be due to the presence of intrinsically breathy/creaky speakers in normal speech.

Evaluation

The relevance of the proposed speech characteristics for the classification of vocal effort into whispered/soft, normal, and loud/shouted speech has been conducted within a 5-fold cross-validation. Additional constraints on the design of the cross-validation have been adopted : well-balanced distribution of the vocal effort classes within the train and test sets ; no role overlapping across train and test sets in order to prevent the system to turn into a speaker identification system.

The classification system is based on the GMM-UBM/SVM system [START_REF] Charbuillet | GMM-Supervector for Content based Music Similarity[END_REF] which is a standard for speech recognition [START_REF] Douglas | Speaker Verification Using Adapted Gaussian Mixture Models[END_REF][START_REF] Campbell | Support Vector Machines using GMM Supervectors for Speaker Verification[END_REF]. A UNIVERSAL BACKGROUND MODEL (GMM-UBM) is used to model the acoustic variability in the speech database with a Gaussian Mixture Model (GMM). Then, each utterance is represented as a SUPERVECTOR by MAP adaptation of the GMM-UBM mean vectors [START_REF] Douglas | Speaker Verification Using Adapted Gaussian Mixture Models[END_REF]. Then, SUPERVECTORS are used to determine the parameters of a SUPPORT VECTOR MACHINE classifier which maximize the margin of a highdimensional separation hyperplane for the classification [START_REF] Campbell | Support Vector Machines using GMM Supervectors for Speaker Verification[END_REF].

During the training, each feature set is considered as a separate stream for the determination of the GMM-UBM and the SVM parameters. 64 GMMs with diagonal covariance matrices have been used to determine the parameters of the GMM-UBM, and a SVM with a GMM-supervector Radial Basis Function (RBF) kernel has been determined with various values of the radial bandwidth (from 0.1 to 5). During the classification, the decision is made by fusioning the affinity obtained for each stream using average decision fusion. The performance of the system has been measured with the F-measure metric. Finally, the performance of the system corresponds to the optimal performance obtained for each feature set. The evaluation exhibits that the proposed source characteristics carry complementary information for the classification of vocal effort. The VQ characteristics are proved to be extremely relevant for the classification of vocal effort (from 81.1% to 84.7%), especially for the classification of whispered/soft speech (from 69.4% to 76.9%). Also, the proposed VUV and VQ characteristics significantly outperform the conventional MFCC and TEO characteristics. Finally, the optimal performance is obtained with the complete combination of investigated features (87.0%), which indicates that each feature set carry complementary information for the classification of vocal effort.

Conclusion

In this paper, a large-scale study on the classification of vocal effort in expressive speech was presented. This study introduced robust source characteristics to measure various types of voice quality (e.g., breathy, creaky, tense) for the classification of vocal effort into whispered/soft, normal, and loud/shouted speech. The system is evaluated in the real scenario of video games production with the complete speech recordings of a massive role-playing video game. The proposed features significantly improve the classification from 81.1% to 87% over conventional MFCCs. These advancements confirm the role of the source excitation and voice quality for the description of changes in vocal effort. In further studies, the robustness of vocal effort classification will be assessed with cross video games and cross languages evaluations.

Figure 1 FIGURE 1 -

 11 Figure1presents the distribution of the VUV for whispered/soft, normal, and loud/shouted speech. This clearly shows

Table 1

 1 summarizes the system performance obtained for conventional MFCC, combination of introduced feature sets with MFCC, and the optimal combination of feature sets for the classification of vocal effort.

	MASS EFFECT	WHISPERED	NORMAL	SHOUTED	TOTAL
	MFCC	69.4	82.5	91.0	81.1
	MFCC + TEO	72.5	83.6	91.6	82.5
	MFCC + VUV	75.0	84.3	91.2	83.5
	MFCC + VQ	76.9	84.7	92.0	84.7
	. . .	. . .	. . .	. . .	. . .
	MFCC + TEO	79.1	88.4	93.5	87.0
	+ VUV + VQ

TABLE 1 -

 1 Distribution of the voice quality characteristics (∆ GCI, R d ) for whispered, normal, and shouted speech -from left to right.

	2.5					2.5						2.5
	2					2						2
	Rd					Rd						Rd
	1.5					1.5						1.5
	1					1						1
	0.1	0.2	∆ GCI	0.3	0.4	0.5	0.1	0.2	∆ GCI	0.3	0.4	0.5	0.1	0.2	∆ GCI	0.3	0.4	0.5
	FIGURE 2 -

F-measure obtained for the conventional MFCC, combination of proposed features with MFCC, and optimal configuration for the MASS EFFECT role-playing video game.


Colette Rolland
email: rolland@univ-paris1.fr

Selmin Nurcan
email: nurcan@univ-paris1.fr

Georges Grosz
email: grosz@univ-paris1.fr

Guiding the Participative Design Process

I. Introduction

It is traditional to look to any engineering activity from both the product point of view and the process point of view. The product is the desired result, the process is the route followed to reach the result. Methods were classically focused on the product aspect of systems development and have paid less attention to the description of formally defined ways-of-working. Clearly, there is an important demand on methods and tools where process guidance is offered to provide advice on which activities are appropriate to which situations and how to perform them [START_REF] Rolland | An Approach for Defining Ways-of-Working[END_REF], [START_REF] Rolland | Understanding and Guiding Requirements Engineering Processes[END_REF], [Downson94], [START_REF] Wynekoop | System Development methodologies: unanswered questions and the research-practice gap[END_REF]. We propose a way-of-working which intents to provide such a guidance.

Our approach is composed of three complementary elements :

(1) a set of models used for describing the system to be constructed and the organisation in which it will operate, (2) a way-of-working supporting the usage of concepts, (3) a set of tools supporting the way-of-working. This approach is currently being applied, under the name of EKD (Enterprise Knowledge Development) process, in the context of the ESPRIT project ELEKTRA (This work is partially supported by the ESPRIT project ELEKTRA (N° 22927) founded by the EEC in the context of the Framework 4 programme) [START_REF]ELectrical Enterprise Knowledge for TRansforming Applications[END_REF] for reorganising electricity companies and designing new solutions.

This paper is dedicated to the presentation of the wayof-working along with the tool supporting it. The way-ofworking aims at organising, structuring, the design process. It provides advices on what should be considered during this process, why and how it should be analysed following some relevant techniques. It also suggests which problem should be tackle next and provides some argument to help in the making of the most appropriate design decision. Finally it includes means to support participative design processes including brainstorming, exchange and emergence of ideas. Thank to the tool support, some process automation is possible and tracing facilities emphasise the recording of the rationale and argumentation provided through out the all process.

This paper is organised as follows: Paragraphe 2 presents the EKD way-of-working for participative design. Paragraphe 3 focusses on the guidance offered by the EKD process.

The EKD way of working for participative design

The EKD process is guided

We look to any participative design process as a decision making process i.e. a non deterministic process. The process is performed by responsible agents having the freedom to decide how to proceed according to their evaluation of the situation they are faced to.

However, the participative design process cannot be an ad-hoc and chaotic process. We look to it as a repeatable process made of steps resulting each of the application of the same pattern for decision making. The proposed EKD way-of-working is entirely based on this pattern.

The pattern views a decision as the choice of the way to proceed in a given situation to achieve an intention. In the EKD approach, a decision is contextual i.e. both situation and intention driven. An intention can be fulfilled in different ways depending on the situation being considered.

In order to take this aspect into account, we propose to fully associate the intention and the situation in what we call a context. Therefore, if we visualise the pattern (Figure 1) as having an input, a body and an output, the input is a couple < situation, intention> i.e. a context.

Body input output context product

Figure 1 : the EKD decision making pattern

Any time an EKD engineer is faced to a given situation to which he/she looks with a certain intention/goal in mind, he/she can apply the decision making pattern. The output of the EKD decision making pattern is either a modification of the product or the generation of new contexts.

Participative design requires a complex process to take place. However, it exists some steps during the performance of this process which are grounded on knowledge [START_REF] Rolland | Reusable Process Chunks[END_REF]. There is first, heuristical knowledge which partly constitutes the know-how of EKD engineers. Secondly, an engineer may try to reuse knowledge independent of any particular domain but specific to EKD. Finally, when an engineer has to solve a new design problem, he/she could structure his/her reasoning by looking for alternative ways to solve the problem or by decomposing the problem into smaller problems. This type of knowledge is fully generic and not tailored to EKD.

We identified thus three different types of guiding knowledge : domain specific knowledge, EKD knowledge and generic knowledge. The decision making pattern is taylored to always provide guidance.

Therefore, the body of the decision making pattern is intended to provide guidance on how to proceed to achieve the intention in the given situation. Our approach provides three types of guidance which can be related to the levels of abstraction introduced in figure 2.

The EKD process is incremental and dynamic in nature

The suggested way-of-working makes the EKD process iterative, each step of the process repeating the EKD decision making pattern. As a consequence, the product which is the target of the process (i.e. the new company organisation of its business processes) is incrementally constructed.

In addition, the sequencing of steps is not fixed a priori. Steps are dynamically following one the other. The dynamicity is brought by the decision making pattern which allows the EKD engineers to switch from one context to another depending on new happened situations and changes in his/her intentions.

The EKD process is supported by software tools

The way the EKD environment provides guidance in the performance of the process can be explained using the Dowson's framework [START_REF] Dowson | Consistency Maintenance in Process Sensitive Environments[END_REF] (figure 3).

The framework introduces three interacting domains: process modelling, process performance and process enactment. The process modelling domain captures all activities performed for modelling software development processes : process model definition, process model specialisation, etc.. The process enactment domain encompasses what takes place in a process to support process performance based on the process definitions. The process performance domain is defined as the set of activities conducted by human agents and non human agents (e.g. computer). The process enactment domain supports, controls and monitors the activities of the process performance domain. The process performance domain provides feedback information on the current process performance, to enable process adjustment.

Figure 3 : The three domains of process performance

The process model supporting the EKD way-ofworking comprises three classes of process model fragments; each of them being adapted to the three types of guidance introduced in paragraph 2.1. We call them method chunks, and therefore the participative design environment uses generic method chunks, EKD method chunks and domain specific method chunks. All chunks are stored in the library of the EKD environment and accessible at any point in time of the process performance.

Guidance in EKD process

Generic guidance

The library comprises only one generic guideline that we refer to as the generic method chunk or simply generic chunk. The chunk is applicable in situations where the two other types of guidelines do not hold. The guideline aims to fulfil the goal called "progress". It proposes a help strategy for progressing in the EKD process which offers four options: do, plan and choose, each of them corresponding to a given type of context, executable, plan and choice context, respectively.

-The do option corresponds to a straight forward resolution strategy. It should be chosen when the engineer knows exactly what needs to be done in order to fulfil the context's intention. -The choose option corresponds to a resolution strategy which requires the exploration of alternative paths. It should be selected when the engineer thinks about different alternative ways for progressing with regard to the input context. -The plan option follows a planning strategy. The engineer has in mind a plan for achieving the context's intention and will progress by building a plan of decisions to be made. Parts of the EKD process are dealing with wickled and ill-defined problems for which even the generic guidance provided by these three options might be found too much inflexible. The discovery of goals is an example. As pinpointed in [Potts89, Dardenne93, Anton96], finding goals is very hard and there is not yet, a way of solving this problem which has proven to be efficient. Organising cooperative work sessions and brainstorming are probably the most adapted approaches to deal with this kind of highly creative activity in order to make ideas emerge.

In order to take into account the cooperative work, we complete the generic chunk by a fourth strategy called brainstorm (figure 5). This strategy is supported by the argument "the current situation requires a cooperative brainstorming".

EKD guidance

EKD guidance is based on EKD knowledge. It means knowledge for supporting EKD engineers to specifically undertake the participative design process in an organisation using the EKD models. The EKD knowledge supports for example, the construction of the different models representing the initial enterprise state as well as the future enterprise state of the organisation, the expression of alternative strategies, the evaluation of these strategies, as well as other kinds of activity such as brainstorming, co-operative work, etc.. The EKD knowledge is used within the EKD decision pattern as follows: Assuming that the engineer has chosen the input context, he/she is has to select an EKD method chunk where (1) the situation type matches the input context' situation and (2) the intention of the method chunk matches the input context's intention. This selection is greatly facilitated by the use of a software tool.

We use a matrix presentation to overview the collection of chunks included in the EKD knowledge base. The chunks are the matrix elements. The columns of the matrix are intentions which arise during the EKD process. The rows of the matrix are techniques used in the guidelines. The same technique can be used in different ways in different chunks. For example, brainstorming strategy is a technique which might be used for satisfycing the intention of "Detect goal conflict" and for "Solve goal conflict" as well. The SWOT analysis might be used for satisfycing the intention of "Analyse the context of participative design" and for "Argument alternative design models".

By following the heuristical knowledge embedded in the EKD method chunks, EKD engineers are constantly guided. Part of the solution they have to find is provided by the chunk.

Domain specific guidance

EKD Domain specific guidance is based on EKD Domain specific knowledge. The Domain specific knowledge aims at providing guidance to EKD engineers for solving very well focused problems related to a specific domain. It is grounded on experience based knowledge and suggests to reuse and/or to adapt concrete and already tested solutions which have shown their efficiency and feasibility in different organisational settings of the same domain.

The step starts with the retrieval of the Domain specific chunk matching the input context. If there exists such a matching chunk, the EKD engineer can decide to use it. Because domain specific chunks are defined at the instance level, there is no need for context instantiation (as for EKD method chunks). Then the engineer has just to follow the guideline provided.

Conclusion

The EKD decision making pattern is a reasoning mechanism supporting decision making by providing a library of guidelines. In some cases, the pattern offers a domain specific guidance. This happens when the library contains knowledge about the domain of the project which matches the current context of work. The library contains EKD specific guidelines which are tailored to the way the EKD approach suggests to work with the different EKD models. These guidelines are independent of any particular domain but are based on EKD method knowledge. Finally, if none of the two previous types of guidelines matches the current context of work, the generic guideline may operate. Clearly, the more specific the guidance provided is, the more efficient it is. However, the generic guideline, by offering a general frame for decision making, makes the EKD process entirely based on guidance. Currently, we are implementing all these guidelines in an electronic handbook which will eventually be availaible on the World Wide Web.

To sum-up, the EKD process model suggests an incremental production of the design product through a co-operative work. It has two major advantages: it makes change traceable and it helps participants in the participative design process to share awareness by making the product under construction being discussed, visible and explicit.

Bibliography

[Anton96]: Anton, A., "Goal-Based Requirements Analysis", ICRE '96, IEEE, Colorado Springs, Colorado USA, 1996.

Figure 2 :

 2 Figure 2 : Relationship between the different type of guidance and the abstraction levels Decision making might require emergence of ideas, exploration of choices, argumentation of various alternatives and perhaps deliberation among the stakeholders involved in the process. The generic decision making pattern takes these aspects into account.

Figure 4 :

 4 Figure 4 : The generic method chunk


Selmin Nurcan

Colette Rolland

Meta-modelling for cooperative processes

Cooperative work techniques are becoming very important in organisations as well as in the information systems community. The Computer Supported Cooperative Work (CSCW) discipline makes the assumption that collaborative work can be supported by software tools. This requires among others to develop models able to represent cooperative work processes. This paper presents a meta-modeling framework to deal with a range variety of cooperative work models. A cooperative process meta-model from which models can be instantiated is introduced and exemplified. The meta-model is taylored to support the modeling of both well-structured and ill-defined work processes and their interactions.

Introduction

We are interested in Computer Supported Cooperative Work (CSCW) which examines the possibilities and effects of technological support for humans involved in collaborative group communication and work processes. Organizations are built on the principle that groups of people can carry out tasks which are not feasible individually. Therefore in most applications, well-structured, individually performed procedures coexist with ill-structured tasks which require cooperative work processes and both of them must be managed in the final solution. Cooperative work techniques become very important in organizations as well as in the information systems community. One can note the emergence of cooperative information systems. The development of information systems is itself becoming performed in a cooperative manner [START_REF]Proceedings of the IFIP Conference on Information Systems Development for Decentralized Organizations[END_REF]. In the CREWS 1 project we are developing an approach for supporting cooperative requirements engineering based on scenarios. Therefore, it is necessary to understand the specificities of cooperative work processes in order to take them into account in models and software tools built for supporting their enactment.

Our purpose is to propose a process meta-model, which can deal with both well-defined and wickled work procedures and their interactions, so as to represent a wide range of cooperative work processes.

This paper is organised as follows: In section 2, we introduce computer supported cooperative work and situate workflow with respect to groupware. We shall notice that organizational reasons justify the joint use of these technologies. In section 3, we introduce our needs, in terms of models and ways of working, for modelling and guiding cooperative work processes. In section 4, we present a cooperative process meta-model which provides means to deal with secure and well-structured cooperative work processes and has the flexibility to handle ill-structured cooperative work processes.

Cooperative work

The cooperative work or group work is the object of a multidisciplinary research field called Computer Supported Cooperative Work. The growth of connectivity greatly expands opportunities for office workers to cooperate and work together. Most organizations acknowledge that process simplification and automation are key success factors in the present competitive environment where the watchwords are productivity and quality.

Groupware is defined in [START_REF] Ellis | Groupware: some issues and experiences[END_REF] as follows: "Computer-based systems that support groups of people engaged in a common task (or goal) and that provide an interface to a shared environment". A well-known categorization [START_REF] Ellis | Groupware: some issues and experiences[END_REF][START_REF] Johansen | Leading business teams[END_REF] is the division into synchronous or asynchronous activity and co-located or distributed activity (see figure 1).

Workflow is mainly concerned by scheduling and coordinating work between actors [START_REF] Khoshafian | Collaborative work and work flow in intelligent offices[END_REF][START_REF] Ovum | Workflow Management Software[END_REF]. It is defined for instance in N. Naffah [START_REF] Naffah | Workflow: Etat de l'art et évolution[END_REF] as a «cooperative work involving a number of actors which must realize tasks, in a given time span, according to a predefined procedure and having a global aim». In workflow applications, cooperative work means that several persons are involved in reaching a common goal, but each of them acts individually in a specific step of the work.

Based on the Ellis' definition of group work (involves a common task (or goal) and a shared environment), one might argue that workflow does not fulfill the requirements of the CSCW community because only one person executes his/her own task with his/her own data at a given time. However, taking a general view of the procedure, there is a common goal to reach by a group of people which share the same information.

Workflow is classified by J.Grudin [START_REF] Grudin | Computer-supported cooperative work: History and focus[END_REF] in the distributed asynchronous area of the previous matrix as electronic mail systems. For many people, groupware supports unpredictable and ad-hoc interactions that occur in work group, whereas workflow automates strategies and predefined procedures. However, their global aims are the same: to increase the collective efficiency of groups of people engaged in fulfilling a common goal. Workflow applications have been divided into two different categories depending on the nature of the supported processes [START_REF] Palermo | Workflow software: A primer[END_REF]. The first concerns well-structured and repetitive processes having important coordination and automation needs [START_REF] Nurcan | Une méthode d'analyse et de conception pour les applications workflow[END_REF]. In most current workflow software tools supporting well-structured processes, a procedure is a predefined set of partially ordered tasks. Each task has an assigned role corresponding to a group of actors, and the actor who actually executes the task is chosen from this group.

The second category of workflow applications deals with occasional and ill-structured (ad-hoc) work processes in organizations; a response to a call for tender in a commercial service or problem solving activities are examples of this class. The main characteristic of these applications is the information and knowledge-sharing within the work group more than the ordering of their tasks.

For many organizations, well-structured and ill-structured work processes coexist and must be managed in the final solution [START_REF] Nurcan | Quels modèles choisir pour les applications coopératives mettant en oeuvre les technologies de workflow et de groupware ?[END_REF] [16] [START_REF] Nurcan | Analyse et conception de systèmes d'information coopératifs[END_REF]. The integration aims to make the transition between the different types of group activities transparent. Current workflow products and their underlying control flow models require a strict respect of the predefined procedures. Therefore they cannot be used for ad-hoc workflow applications or deal with the dynamic modifiability of predefined scripts. More and more, users ask for adaptive workflow products and models which can provide the robustness and the security of the predefined scripts and the flexibility of ad-hoc applications.

Providing a single set of concepts to model both aspects of group work processes is our concern is this paper.

Models and way of working

Group work application development starts with the modeling of the process to implement. The implementation of this kind of application requires a preliminary analysis phase before the process may be modeled. For each stage of the work, one has to determine who does what within the task, when, after and before which other task. Information holders, types of handled documents, possible locking points,… etc, have also to be defined.

When the work process is well-structured, the corresponding procedure is a predefined set of partially ordered tasks. Partially ordered means that tasks are not necessarily executed sequentially: loops and parallelism can appear. Each task has an assigned role corresponding to a group of actors, and the actor which shall execute the task shall be chosen among this group.

Finally, the modelling of a procedure (see figure 2) requires the identification of: • event(s) which trigger(s) the procedure,

• tasks which compose it and their relationships with the others: these relationships define sequential, parallel (with rendezvous points) and conditional transitions, and for each task:

• events which trigger its execution, • resources (data+tools) which are necessary for its execution, and • the associated role. We have considered seven models dealing with task-workflow-agent-role representations, respectively OSSAD [3] [4], ICN [5] [7], InConcert [START_REF] Mccarthy | Workflow and transactions in InConcert[END_REF], VPL [START_REF] Swenson | Visual Support for Reengineering Work Process[END_REF], I* [START_REF] Yu | From E-R to "A-R" -Modelling Strategic Actor Relationships for Business Process Reengineering[END_REF], Enterprise Modelling [START_REF] Bubenko | Enterprise Modelling[END_REF][START_REF] Loucopoulos | Enterprise Modelling and the Teleological approach to Requirements Engineering[END_REF] and ITHACA [START_REF] Ang | The ITHACA Office Object Model: Modeling and Implementation, Data Base[END_REF]. This study showed a convergence on a set of concepts such as goal, procedure, task, role, actor, resource, decomposition of tasks, etc. However, an appropriate model for a large variety of cooperative work processes (going from well-structured to ill-structured) must also provide means to represent unstructured activities. We integrated these concepts in one single meta-model that we present in the following section.

A process meta-model for the representation of cooperative work

An approach to generate guidance centered process models has been initially proposed in [START_REF] Rolland | Modeling the Requirements Engineering Process[END_REF] and further developed in [START_REF] Rolland | A Contextual Approach for the Requirements Engineering Process[END_REF][START_REF] Rolland | Modeling the evolution of artifacts[END_REF][START_REF] Rolland | An approach for defining ways-of-working[END_REF]. Authors refer to these models as "ways-ofworking" since they are intended to guide application engineers in their way of working to solve a design problem. We believe that the proposed approach is applicable to any process. However the problem of distributed process guidance has not be tackled in [START_REF] Rolland | An approach for defining ways-of-working[END_REF].

We have extended the process meta-model presented in [START_REF] Rolland | An approach for defining ways-of-working[END_REF] in order to obtain a cooperative process meta-model to be used for any cooperative process.

The cooperative process meta-model

We propose a meta-model as a basis for process model definition. Since a process metamodel carries information about the process model, an instantiation of it shall result in a process model. Our approach introduces three levels of process modelling: -At the lowest level, process traces are recorded.

-At the second level, ways-of-working are defined. A way-of-working is a process model i.e. a description of process. It has a prescriptive purpose and is similar to the concept of plan. A process is then, an instantiation of a process model which is executed.

-The knowledge required to design such models is related to the third level of abstraction and takes the form of a process meta-model. A process meta-model provides a set of generic concepts for describing ways-of-working which are therefore, instances of the process metamodel.

The process meta-model allows us to deal with many different situations in a flexible, decision-oriented manner. Moreover the meta-model can support different levels of granularity in decision making as well as non determinism in process performance. It identifies a decision in context as the basic building block of ways-of-working and permits their grouping into meaningful modules. Parallelism of decisions and ordering constraints are also supported.

The output of a process is a product, it can be requirements specification or a conceptual schema or a loan offer to a client in a bank or messages exchanged between members of a group or a set of business goals.

In the presentation of the cooperative process meta-model we follow a bottom up approach starting with the concept of context, intoducing then the concepts of role, action and product, and ending with an overall view of the concepts progressively introduced.

The concept of context

The central concept of the process meta-model is the one of context which associates a situation with a decision made on it.

A situation is a part of the product it makes sense to take a decision on. Situations can be of various granularity levels; they can be either atomic like an attribute of an object class or they can be coarse-grained like the whole product.

A decision reflects a choice that a user can make at a given moment in the process. A decision refers to an intention. An intention expresses what the user wants to achieve, the goal.

A context is the association of a situation and a decision which can be taken in this situation. A decision is not sufficient in itself, it needs to be associated with the situation in which it applies. A situation can be associated with several decisions. Acting in a context corresponds to a step in the process: in a given situation, and in order to progress in the process, the user has to take a decision (figure 3).

The concept of role

A role is the definition of an organizational intention shared by a collection of users, all of whom have the same privileges and obligations to a set of work processes in an organization. For example, the role of a reservation service clerk, that of an accounts officer, etc.

In procedural workflow applications, tasks are individual and are performed by individual roles. Each task is assigned to a role corresponding to a group of actors (i.e. the collection of the role object).

According to the process meta-model, acting in a context should correspond to a step in the cooperative process. In a given situation, a user has an intention (because of his/her role in this process), and that makes him/her progress in the cooperative process.

To this end, we introduce the concept of role, and then specialise it into individual role and group role (figure 3). For example, the reservation service clerk is an individual role whereas public relations team is a group role. A group role contains several individual roles. We attach the context of the process meta-model to a role. This captures knowledge about which decision can be taken by which role. Therefore, the basic division of responsibility in cooperative processes is imposed on the set of decisions of the meta-model. This helps us in representing co-ordination of roles, providing access control, and in giving more appropriate guidance which is completely tailored to the role.

The different types of contexts

A situation exists at different levels of granularity. Further, decisions have consequences which differ from one granularity level to another. The different contexts are classified (figure 3) according to their consequences in the meta-model into executable contexts, plan contexts, and choice contexts.

Executable context

At the most detailed level, the execution of any process can be seen as a set of transformations performed on the product, each transformation resulting from the execution of a deterministic action. Such an action is a consequence of a decision made in a certain context. This leads to the introduction of the concept of an executable context.

An executable context implements a decision, its intention is realised by an action (figure 4). Therefore, in the meta-model (figure 5), an executable context is associated with an action. An action performs a transformation of the product, it is the implementation of a decision. Performing an action changes the product and may generate a new situation (figure 6) which is itself, subject to new decisions.

The concept of action

We classify actions into two types (figure 5) according to their characteristics: individual action and conversation action.

Performing an individual action or a conversation action does not change the same kind of product. Individual actions perform transformations of artefacts while conversation actions create messages.

Therefore, we classify the concept of product into artefact and message (figure 5). Artefact represents the information system.

We need to represent also the unstructured -conversational-activities of the group work. So, we must be able to keep track of these conversations. We introduce the message concept as the basic component of the conversational activity. A message may concern several artefacts.

The individual action can be complex or simple. A complex individual action is composed of individual actions. A simple individual action performs a tranformation of (changes) an artefact by creating, updating or deleting it. An individual action is performed by an individual role (figure 6). Figure 4 shows an executable context which is applied by an individual action.

We want also to deal with group activities, in the sense that several participants can synchronously act in the same conversational activity by exchanging messages. We represent this type of cooperation by the conversation action.

The conversation action is performed by a group role. It creates several messages, each message being produced by an individual role contained by the previous group role (figure 6).

From any conversation action may emerge new contexts (figure 6). These contexts can be executable and associated to actions, which might be conversational and then, triggers new contexts and so on. This feature enables the cooperative process meta-model to deal with ill-structured cooperative work processes as well-structured cooperative work processes. An example of conversation action is given in section 4.5.

Executable contexts establish situation-based links among contexts, namely correlation links. This is modelled in figure 6 by the loop among contexts through action and situation. The term correlation link refers to the composition of the three following relationships: applied by, changes/creates, and built on.

Choice context

A user may have several alternative ways to fulfil a decision. Therefore, he/she has to select the most appropriate one among the set of possible choices. In order to model such a piece of process knowledge, we use a second specialisation of the concept of context, namely the choice context (figure 7).

A choice context corresponds to a situation which requires the exploration of alternatives in decision making. Each alternative is an approach or a strategy for the resolution of the issue being faced by the user in the current situation. By definition, a choice context offers a choice among a set of strategies, all of them achieving the same purpose. In this sense, one can look upon the choice context as being goal oriented.

There are two major differences between the choice context and the executable context: the first one lies in the absence of any alternatives in the latter and the second is that a choice context has no direct consequence on the product.

In the process meta-model, the various alternatives of a choice context are represented in the alternative relationship (figure 7). They are associated to choice criteria based on arguments.

A choice criterion is a combination of arguments which supports or objects to an alternative of a choice context. It may provide priority rules to select one alternative among several depending on the arguments. Since alternatives of a choice context are also contexts, contexts may share an alternative relationship (figure 7), leading to alternative-based hierarchies of contexts. The alternativebased relationship among contexts allows the refinement of large-grained decisions into more fine-grained ones. This is a means by which the process meta-model handles the granularity problem (figure 8).

The introduction in the process meta-model of alternatives and choice criteria will allow the way-of-working to support the user in exploring possible strategies to resolve an issue and in selecting the most appropriate one. This alternative-based guidance freedom to the user who can make a choice which is not even one of the predefined alternatives proposed by the way-of-working. This feature enables the cooperative process meta-model to deal with exception handling in workflow applications.

Plan context

In order to fulfil an intention associated to a certain situation, a user may be required to take a set of decisions on corresponding situations; he/she has to follow a plan. To this end, a third specialisation of context, namely, plan context is introduced. A plan context is an abstraction mechanism by which a context viewed as a complex issue can be decomposed in a number of sub-issues. Each sub-issue corresponds to a sub-decision working on a subsituation. The decomposition of context is another means provided by the meta-model to solve the granularity problem.

The component contexts can be of any type i.e. executable, choice or plan contexts. For example, for the intention named "Process_Loan_Request" to be fulfilled, the two intentions "Record_Request" and "Evaluate_Request" must be satisfied. This is modelled (figure 9) by a plan context called "<(Request_Message), Process_Loan_Request, Loan_Service_Clerk>" decomposed into two contexts: "<(Request_Message), Record_Request, Loan_Service_Clerk>" executable context and "<(Request_Statement='Recorded'), Estimate_Request, Loan_Service_Clerk)>" choice context.

In the process meta-model the decomposition of a plan context into its more elementary contexts is represented (figure 7) by the relationship precedence graph between context and plan context. The ordering of the contexts, within a plan, is defined by the precedence graph. The nodes of this graph are contexts while the links -called precedence links-define either the possible ordered transitions between contexts or their possible parallel enactment. Based on arguments, a choice criterion may be assigned to a link. The choice criterion defines when the transition can be performed. Flexibility is introduced by allowing several sets of possible parallel or ordered transitions to be defined in the same graph. This feature enables the cooperative process meta-model to deal with well-structured workflow applications which require the use of a model in terms of ordered steps. The precedence graph corresponding to the previous plan context is shown by the figure 10.

Decomposition of contexts can be made iteratively leading to hierarchies of contexts. This hierarchical link is refered to as a decomposition link. Notice that this link corresponds in figure 7 to the composition of the precedence graph relationship with the from and to relationships.

Plan contexts provide a different type of guidance than executable and choice contexts do. They support users in performing long term transactions, providing advice on the ordering of component activities, whereas choice contexts help in making the appropriate choice in the situation in hand and executable contexts tell how to implement the decision taken.

Each type of context influences the on-going process in a different manner: an executable context affects the product and generates a new situation, which itself becomes the subject of decisions; a choice context does not change the product but helps to further the decision making process through the refinement of an intention; a plan context provides the means to manage the complexity of an intention by providing a decomposition mechanism.

Performing decomposition and refinement iteratively allows the users to reach executable intentions and thus, to act on the product.

Conversation action

In this section, we examplify the use of the conversation action for a non structured group activity. Let us take an example from the Air Traffic Control case study and assume that the context C < G1 "minimize risks of accidents", Operationalise G1> requires to call a group of experts (we name it the "risk elucidation group") for a brainstorming session. In other words, the strategy selected for context C is "brainstorm". The guidance provided by this strategy [START_REF] Rolland | Guiding the participative design process[END_REF] suggests:

(1) to define the group role required for this cooperation, (2) to execute a conversation action within the previously defined group role and having the initial input context as situation.

The Risk elucidation group is a group role which contains the following individual stakeholders: Airport manager, ATC center manager, a representative of airlines managers, a representative of pilots, and a local autority.

The executable context is applied by a conversation action leading to the creation of several messages (figure 11). Let assume as an example that the flow of messages is the following: Message 1: (ATC center manager)

Have we got a report about reasons of accidents happened during the five last years in the world ?

Message 2: (Airport manager) No, we don't. But we have some informations about the last three major accidents.

Message 3: (ATC center manager)

So, what about the reasons ? Message 4: (Airport manager) In Strasbourg in France, it was a human error. At Delhi, the reason was twofold; the accident was partly due to the heavy air traffic and partly to a human error, due to his poor knowledge of the english language, the pilot misunderstood the message of the control tower.

In the US, it was a confusion about the airport. The pilot made an error in typing the airport and the computer understood the airport code as Bogota in South America while the aircraft was to land in California.

Message 5: (Representative of airline managers) So if we want to minimize risks of accidents we have to decrease risk of human error.

Message 6: (Pilots representative )

Sometimes what is called human error is not. How to decrease the human error in the accident occurred in US. You must rather review computer systems.

Message 7: (ATC center manager) It's more convenient to talk about Human-Computer interface for this accident. So, our goals are to decrease risk of human error and to review all humancomputer interactions.

Message 8: (Pilots representative)

And what about the accident in Delhi ? The human error was not the unique reason, isn't it ?

Message 9: (ATC center manager)

The number of aircrafts allowed to cross the controlled airspace is too high in Delhi.

Message 10: (Local autority) Precisely, since 2 years local autorities argue that this number must decrease in our city too. People living near the airport are disturbed because of the noise and late/early take offs and landings. In order to minimize risks of accidents we must limit the number of aircrafts allowed to cross the controlled airspace.

As a conclusion of this message exchange, the conversation action generates three emerging contexts : -< message 5, create G2 "decrease risk of human error"> -< message 7, create G3 :"review human-computer interactions"> -< message 10, create G4 :"limit the number of aircrafts allowed to cross the controlled airspace">. The new contexts are inserted in the contexts pile for further processing.

The concept of way-of-working

It should be clear now that due to the meta-model concepts, the basic building block of a way-of working is an instance of context that we call also context. Contexts in the metamodel have hierarchical relationships of two different types, decomposition and refinement.

In the way-of-working, we suggest a grouping of contexts based upon these links. The modules resulting from this grouping are hierarchies of contexts called trees. Finally, a way of working can be composed of several trees. This leads to the final vision of a way-ofworking as a forest trees (figure 12).

Context Tree Forest

composed of

1,N 1,N 1,N 1,N composed of Figure 12
. The way-of-working structure

An example of way of working as a forest of trees

We plan to model the loan process in a bank (figure 13). The working rules are given below: When a customer applies for a loan, the bank clerk in charge of his banking account analyses the loan request according to its nature.

He/she can decide to accept or refuse the request himself/herself, or to ask for a deeper evaluation. In the third case, first a pre-evaluation is made by the financial department (illstructured task carried out synchronously by a group of experts), then the request is examined by the loan manager in order to accept or to refuse it.

The study of the request by the loan service clerk must be validated by the loan manager who has the possibility to either : . accept the loan offer prepared by the loan service clerk, or . ask the loan service clerk to review it, or . ask the financial department for a complete re-evaluation of the loan request.

When the decision is favourable, a proposal of loan is sent to the customer by the clerk's assistant. When the decision is unfavourable, a refusal letter is sent by the same person.

Four different roles are involved in the loan process : -The loan service clerk which is in charge of the client account, -The loan manager, -The work group constitued by the financial manager and three experts in the financial departement, -The clerk's assistant.

The information systems' objects that we defined are: REQUEST, PROPOSAL, CLIENT.

Request and proposal have noticeable states during their life cycle, respectively represented by request_statement and proposal_statement. The approach consists of instantiating the concepts of the meta-model. We have to define the executable, choice and plan contexts, and their decomposition and refinement links.

The loan process is then represented by a forest composed of six trees. Each tree describes a piece of knowledge about the process associated to a given role. The trees describe the process in a workflow style but, in addition, encapsulate guidance to support the participants performing their tasks.

The first tree (figure 14) describes the evaluation of the loan request by the loan service clerk. The first component context of this plan is an executable one associated to an individual action: Create Request. The second component of the plan is a choice context with three alternatives. It provides an alternative-based guidance to the clerk.

The second tree (figure 15) describes the way-of-working for the group work processed by the financial evaluation team and the loan manager. The root of the tree is a plan context which represents the risk evaluation by the financial evaluation team, then the request evaluation by the loan manager. The predefined decomposition of this group work is described in the corresponding precedence graph (figure 16).

The risk evaluation is a group work synchronously processed by a group of experts in the financial department. It is represented by an executable context associated to a conversation action. The evaluation of the request by the loan manager is defined by a choice context with two alternatives, to accept or to refuse the request, each of them being described by an executable context. When the situation corresponds to clerk acceptance, the process can be performed according to tree 5 (figure 19). The first component context of the plan is an executable one and corresponds to the clerk's individual action in order to prepare the loan offer. The second component context of the plan represents the validation of the offer by the loan manager with three alternatives.

Conclusion

In this paper, we have presented a cooperative process meta-model which provides means to deal with secure and rather well-structured processes and provides the flexibility to handle ill-structured processes . It allows us:

• to represent cooperative work processes,

• to integrate conversations between agents,

• to guide and keep track of what happened in cooperative brainstorming sessions,

• to model the emergence of new contexts; all these being made in an homogeneous manner.

An instantiation of the cooperative process meta-model results in a cooperative process model allowing to deal with a large variety of situations in a decision-oriented manner.

The concept of plan context enables the cooperative process meta-model to deal with well-structured cooperative processes which require the use of a control model. In fact, the corresponding precedence graph defines the ordering of the component contexts (the possible ordered transitions between contexts or their possible parallel enactment).

The alternative-based guidance of the choice context leaves freedom to users who can make a choice which is not even one of the predefined alternatives proposed by the way-ofworking. This feature allows the cooperative process meta-model to deal with exception handling in cooperative processes.

The concept of conversation action allows us to represent emergent cooperative activities. It enables the cooperative process meta-model to deal with ill-structured cooperative work processes and the emergent component of globally well-structured cooperative work processes.

Our current work consists of building a cooperative environment which supports the definition of cooperative process models (in terms of ways-of-working) and provides the flexible guidance of groups in well-structured and/or ill-structured cooperative work processes. This environment is an extension of the MENTOR process centred environment [START_REF] Si-Said | MENTOR : A Computer Aided Requirements Engineering Environment[END_REF].

Figure 1 .

 1 Figure 1. Johansen's Space/Time matrix

Figure 2 .

 2 Figure 2. Procedure representation

Figure 3 .

 3 Figure 3. The context is attached to a role

<

  (Request_Statement='Manager_Acceptance'); Prepare_Offer ; Loan_Service_Clerk > (Update Loan Proposal with Proposal_Statement = 'Prepared') < (Situation); Decision ; Role >

Figure 4 .

 4 Figure 4. Example of an executable context

Figure 5 .Figure 6 .

 56 Figure 5. Actions and products that they transform

Figure 7 .

 7 Figure 7. The representation of the concept of context

Figure 8 .

 8 Figure 8. Example of a choice context

Figure 10 .

 10 Figure 10. Example of a precedence graph

<Figure 11 .

 11 Figure 11. An executable context leading to the execution of a conversation action

Figure 13 .

 13 Figure 13. The graphical representation of the Loan Request case study

Figure 16 . 2 Tree 3 ( 3 Figure 17 . 4 Figure 18 .

 1623317418 Figure 16. Precedence graph for process tree 2

< 5 Figure 19 . 6 Figure 20 .

 519620 Figure 19. Way-of-working for Package_Offer_when_accepted_by_the_clerk

This work is partially supported by the european ESPRIT long term research project CREWS (Cooperative Requirements Engineering With Scenarios).


Modification of the magnetic properties of SmCo 5 particles depending on the grinding atmosphere

Introduction

Magnetic nanostructures often exhibit behavior different from bulk materials [1]. Although there are many techniques for fabricating nanostructured materials, mechanical grinding has emerged as a unique method of producing disorder in alloys [2]. Ball milling is a high-energy deformation process that produces dislocations, vacancies, anti-site defects and strains. This technique has been widely developed in the 1960s and was used initially to strengthen the mechanical properties of alloys and to prepare compounds that could not be prepared using usual synthetic methods [3], [4] and [5]. The technique can be defined as a grinding process during which the powder is subjected to compression and traction forces leading to some plastic deformation, welds and fractures of the particles. The continuous repetition of these two latter phenomena (fracture and weld) leads to nanostructured materials.

The intermetallic compound SmCo 5 belongs to a group of magnetic materials with hexagonal structure and a high magneto-crystalline anisotropy. These materials often show a high coercive force when powdered and are suitable for the production of permanent magnets [6]. The use of permanent magnets is of great interest for information storage devices. An increasing demand for miniaturization of the magnetic devices and for high-density storage has motivated research on small-sized particles. However, it is well documented that the magnetic properties are influenced by different characteristics such as size, crystallinity, and morphology of the particles which depend on their preparatory conditions [7] and [8].

The aim of this study was to investigate the influence of grinding on the magnetic properties of SmCo 5 particles. More precisely, we studied the influence of the gaseous atmosphere (Ar or H 2 ) in order to achieve a better control of the magnetic properties when using the mechanical grinding technique. Our goal was to link both the structural characteristics and the morphology of the particles obtained by grinding to their magnetic properties.

Experimental

The intermetallic SmCo 5 was prepared by melting of stoichiometric amounts of the constituent elements under a purified argon atmosphere in an induction levitation furnace. The purity of the starting materials was as follows: Sm, 99.9%; Co, 99.99%. Before milling, the powder was sieved at 100 μm in a dry-box. SmCo 5 particles were obtained by grinding using a Fritsch P5 high-energy apparatus. The ratio weight of ball over weight of sample is 15 and the rotation speed was fixed at 300 rpm. The diameter of the ball used for grinding is 10 mm. The applied pressure of H 2 was 1.1 MPa.

The samples were characterized by conventional X-ray powder diffraction using Cu Kα radiation. The particle size and morphology were investigated by scanning electron microscopy using a JEOL 840 microscope. The granulometer used was a Mastersizer 2000S instrument from Malvern working in a bichromatic diffraction or diffusion mode. The analysis was performed in a liquid medium (i.e. ethanol). Information on the chemical composition was obtained by microprobe-X, using a Cameca SX100 apparatus. The TEM images were obtained with a JEOL 200FX microscope. Samples for dark field images were prepared by dispersion of particles in an EPON resin and cut into pieces of 75 nm thickness. Magnetization measurements were carried out using a MPMS-5S Quantum Design SQUID magnetometer. Particles were dispersed in eicosane in order to prevent any movement of the particles during the measurements.

Results

Structural properties of the particles

All samples were characterized by microprobe-X. The elementary analysis revealed that the samples are homogeneous. The atomic proportion of the initial material corresponds to a 1:5 ratio of Sm and Co atoms, respectively. Fig. 1 shows the X-ray diffraction patterns of the SmCo 5 particles as a function of grinding time under Ar (Fig. 1a) or under H 2 (Fig. 1b). The diffraction patterns of the initial compound exhibit sharp diffraction peaks corresponding to SmCo 5 compound (hexagonal CaCu 5 structure type, space group: P6/mmm). Diffraction patterns of the samples ground under Ar present broadened peaks even after only 20 min of grinding. As the milling time increases, the peaks broaden and, finally an almost amorphous phase is obtained after 2 h. On the other hand, diffraction patterns of the samples obtained under H 2 start to become broadened only after 1 h of grinding. Moreover, the diffraction pattern obtained after the longer grinding time shows peaks of SmH 2+δ (indicated by stars on Fig. 1b) which is indicative of the decomposition of the SmCo 5 compound into samarium hydride and cobalt. To confirm this hypothesis and as Co cannot be detected by XRD with Cu Kα radiation, experiments were performed using Cr Kα radiation (λ=2.28970 Å). The peaks relative to Co have been detected confirming the decomposition of SmCo 5 into SmH 2+δ and Co.

The sizes of the coherent domains of the samples have been evaluated by transmission electronic microscopy in dark field mode. Fig. 2 shows pictures obtained for all samples and inserts correspond to the diffraction patterns. The picture of the starting material is presented in Fig. 2a and corresponds to a monocrystalline particle as confirmed by the diffraction pattern. The pictures of the sample ground during 20 min under Ar or H 2 are shown in Fig. 2b,c, respectively. Although the sample ground under H 2 is still monocrystalline, the sample ground under Ar during the same time presents a totally different pattern. Indeed, Fig. 2b shows a small well defined area of diffraction. The mean size of these areas is 15±5 nm. The images of the samples ground during 1 and 2 h under Ar or H 2 are shown in Fig. 2d-g, respectively. For the same milling time, the shapes of the images are similar whatever the milling atmosphere. These pictures present well defined areas of diffraction with a mean size of 15±5 nm.

Size and morphology of the particles

Table 1 reports the mean size (d 0.5 ) and the span (i.e. the distribution size defined as (d 0.8 -d 0.2 )/d 0.5 ) measured by granulometry for samples obtained under Ar or H 2 . As a general trend, the particle size decreased as the grinding time increased to finally reach the same lower limit either for the samples ground under Ar or H 2 . Starting from a sample sieved at 100 μm, samples ground during 20 min under Ar and H 2 have sizes of 81 and 69 μm, respectively. At 1 or 2 h, the size of the particles is in the range 35-40 μm. Increasing milling time up to 2 h does not lead to a decrease in the particle size. However, it leads to a decrease in the span (e.g. 1.13 after 1 h and 1.08 after 2 h for samples ground under H 2 ).

The morphology of the particles has been studied by SEM. The particles of the starting material present well defined faces with sharp ridges (Fig. 3a). The sample ground during 20 min under H 2 (Fig. 3b) presents, in addition to the previous particles, aggregations of the smallest particles. The latter, however, are present as minor species. The proportions are reversed for the sample ground for 1 h under H 2 (Fig. 3c) whereas the sample ground for 2 h under H 2 is mostly composed of aggregates of particles (Fig. 3d). One feature of the particles ground under H 2 during 20 min is to present fractures as shown in Fig. 4. Such fractures have only been observed for samples ground under H 2 . The samples ground under Ar present a different trend. Indeed, for a grinding time of 20 min, the sample is already mostly composed of aggregates. No major modification in the morphology of the particles is observed for longer grinding times. The main difference between samples ground under Ar or H 2 is observed for a grinding time of 20 min (for comparison, see Fig. 3e).

Magnetic properties

The magnetization versus field curves for the samples ground under Ar or H 2 are shown in Fig. 5a,b, respectively. Values of the coercivity and the saturation magnetization are reported in Table 2. The starting material exhibits a coercivity of 0.4 T and a saturation magnetization of 60 emu•g -1 . The samples ground under Ar present the following trend: for a grinding time of 20 min, the coercivity increases to reach 1.6 T and the saturation magnetization is constant (60 emu•g -1 ). As the grinding time increases to 1 and 2 h, the coercivity decreases, H C =1.0 and 0.6 T, and the saturation magnetization increases, M S =71 and 73 emu•g -1 , respectively. The samples ground under H 2 present a different trend: for a grinding time of 20 min, the coercivity is equal to 0.7 T and the saturation magnetization is equal to 60 emu•g -1 . As the grinding time increases to 1 and 2 h, the coercivity decreases and the saturation magnetization increases (e.g. H C =0.4 and 0.0 T, and M S =71 and 91 emu•g -1 , respectively). If the hysteresis loops of the sample ground during 1 h start to present a large coercivity, a shoulder around ±1.5 T leads to a value of coercivity close to the value observed for the starting material.

Discussion

In the present study, our goal was to control the magnetic properties of SmCo 5 particles using the grinding method. These properties depend greatly on the structural and morphological characteristics of the material. Then, the control of the magnetic properties requires the identification of the relationship between the grinding conditions and the structural and morphological properties. From the results presented in the previous section, we can make the following observations.

When the samples are ground under Ar or H 2 , they are homogeneous and correspond to the phase SmCo 5 at least for a grinding time up to 1 h. For a longer grinding time under H 2 , the X-ray diffraction pattern reveals a decomposition into SmH 2+δ and Co. The grinding under Ar leads to a faster amorphization of the sample in comparison to the grinding under H 2 . It is important to note that the crystallinity of the sample is strongly correlated with the experimental conditions and in particular depends on the size of both the balls and the container (which are constant here). The evolution of the crystallinity of the samples is confirmed by the TEM pictures allowing the determination of the size of the coherent domains of diffraction. It appears that as soon as the amorphization begins, the size of the coherent domains of diffraction reaches a value of 15±5 nm whatever the grinding conditions. It seems that this value corresponds to the lower size limit of the coherent domains of diffraction in the grinding conditions we used.

Whatever the milling atmosphere, the energy of grinding is used to reach amorphization of the sample. Under Ar, there is only one source of energy which comes from the grinding. However, SmCo 5 absorbs hydrogen under only 0.4 MPa at room temperature and this reaction is exothermic [9]. Consequently, under H 2 , there are two sources of energy, one coming from the grinding and the second coming from the formation of the SmCo 5 hydride. This thermal effect could be the origin of the difference of crystallinity of the sample for short grinding time.

The decomposition of the SmCo 5 particles into SmH 2+δ and Co involves the intermediate compound, SmCo 5 H 2.9 . This pathway has been evidenced by the SEM picture showing a straight fracture in the particle (Fig. 4). Indeed, during the grinding, a series of fractures and welds occur. The type of the fracture, brittle or ductile, depends on the nature of the material. Intermetallic compounds like SmCo 5 should present a ductile type behavior. However the fracture observed in the SEM picture is characteristic of a brittle-type behavior indicating the formation of the hydride SmCo 5 H 2.9 .

Moreover, this decomposition is indicative of the relative stability of the hydrides. For SmCo 5 H 2.88 , the enthalpy and entropy, ΔH and ΔS, are equal to -34.95 kJ mol -1 and -0.129 kJ mol -1 K -1 , respectively [9]. The corresponding values for SmH 2 compound are -186.88 kJ mol -1 and -0.115 kJ mol -1 K -1 [10]. If the values of ΔS are close, the ΔH values are different: the ΔH value of SmH 2 is more than five times higher (in absolute value) than the ΔH value of SmCo 5 H 2.9 . It clearly appears that SmH 2+δ is much more stable than SmCo 5 H 2.9 . Moreover, the decomposition of the sample into SmH 2+δ and Co under H 2 after 2 h of grinding is in agreement with reported results for alloys of Sm(Co 1-x Cu x ) 2 which disproportionates at 693 K into SmH 2 , Co and Cu under H 2 without grinding [11].

The magnetic properties observed in both series of compounds are, as expected, strongly linked to the composition, crystallinity and morphology of the particles. For samples ground under Ar, we observed a faster amorphization but no decomposition. This amorphization leads to the creation of defects in the structure. These defects correspond to pinning points of the magnetization that are responsible for the enlargement of the hysteresis loop. For longer grinding times, the sample transforms from a hard magnet (crystalline) to a soft magnet (amorphous) leading to a decrease in the coercivity of the material. Such trends have already been observed for SmCo 5 when grinding under inert atmosphere [7]. However, Leslie-Pelecky and Kirkpatrick observed that the decrease in the coercivity occurs for grinding times longer than 2 h. Moreover, the samples are still crystalline after 2 h of grinding. These differences could come from the grinding conditions. For example, in the reported paper [7], the mill was a Spex 8000 whereas in our case we used a Fritsch P5. Indeed, it is well known that the results obtained by energetic grinding methods depend on numerous parameters like the type of the mill, the number of balls, their diameter, the speed of rotation and so on. These differences in the grinding method lead to different distributions of energy and hence to different results.

For samples ground under H 2 , the magnetic behavior is governed by the decomposition of the material into SmH 2+δ and Co. Indeed, for short grinding times, no major differences in the crystallinity and morphology of the particles are observed implying no important modifications of the magnetic properties. For 1 h of grinding, the hysteresis loop corresponds to the superposition of two behaviors, one coming from the hard magnet SmCo 5 and the second from the soft magnetic Co, the SmH 2+δ compound at 270 K being non-magnetic. After 2 h of grinding, the material consists of SmH 2+δ and Co and the magnetic properties correspond to the soft magnetic Co.

Clearly, this study shows that the magnetic properties depend greatly on the grinding conditions. The grinding method allows modification of the magnetic properties of particles. As a general trend, grinding under Ar or H 2 leads to the same evolution of the magnetic properties starting with an increase in the coercivity due to the creation of defaults and then to a decrease in the coercivity due to the formation of a soft material (amorphous phase or Co metal). However, the increase in the coercivity obtained under Ar is much more important than that obtained under H 2 . Indeed, the grinding under H 2 allows to obtain small particles but does not lead to the formation of the same defaults because of the difference in the distribution of energy. Consequently, grinding under H 2 does not allow to increase considerably the coercivity of the material before its decomposition.

Conclusion

In summary, we synthesized SmCo 5 particles using energetic grinding under two different gaseous atmospheres. Grinding under Ar leads to a faster amorphization. The samples ground under H 2 are less stable and lead to the decomposition of SmCo 5 into SmH 2+δ and Co. In this study, we were able to establish the relationship between structural and morphological properties and the magnetic properties of the compounds. We show that depending on the grinding conditions, the magnetic properties are different. To conclude, the energetic grinding method appears to be a very efficient method to prepare nanostructured material with controllable magnetic properties.

Fig. 1 .

 1 Fig. 1. X-Ray powder diffraction of SmCo 5 particles ground under Ar (a) or H 2 (b). Initial pattern reveals pure SmCO 5 compounds. Star stands for SmH 2+δ compounds.

Fig. 2 .

 2 Fig. 2. TEM pictures of the coherent domains: initial material (a); ground under Ar (b) or H 2 (c) for 20 min; ground under Ar (d) or H 2 (e) for 1 h; ground under Ar (f) or H 2 (g) for 2 h.

Fig. 3 .

 3 Fig. 3. SEM pictures of initial material (a); sample ground under H 2 : 20 min (b); 1 h (c); 2 h (d). For comparison, sample ground for 20 min under Ar (e).

Fig. 4 .

 4 Fig. 4. Evidence of the brittleness of the material ground under H 2 . See the straight fracture.

Fig. 5 .

 5 Fig. 5. Magnetic properties of the samples ground under Ar (a) or H 2 (b).

Table 1 .

 1 Size of SmCo 5 particles ground under Ar or H 2

		Starting	20 min	1 h	2 h
		material Ar H 2 Ar H 2 Ar H 2
	Mean size	125±10 81±4 69±4 35±2 40±2 40±2 35±2
	d 0.5 (μm)
	Span	1.10	1.46 1.22 1.98 1.13 1.72 1.08
	(d 0.8 -d 0.2 )/d 0.5

Table 2 .

 2 Coercivity and saturation magnetization of the samples ground under Ar or H 2

		Starting	20 min 1 h	2 h
		material Ar H 2 Ar H 2 Ar H 2
	H C (T)	0.4	1.6 0.7 1.0 0.4 0.6 0.0
	M S (emu•g -1 ) 60	60 62 71 71 73 91