dataesr/scientific-paragraphs-categorization huggingface dataset:
Recommendations for writing requirements Before going into the details of the structure of requirement rules, lets us first introduce the main features of the TextCoop platform that supports the Dislog language (Saint Dizier, 2012). Dislog (for discourse in logic) has been primarily designed for discourse analysis. This platform has been used to implement the requirement analysis system we present here. Dislog follows the principles of logic based grammars as implemented three decades ago in a series of formalisms, among which, most notably: Metamorphosis Grammars and Definite Clause Grammars. These formalisms were all designed for sentence parsing with an implementation in Prolog via a meta interpreter or a direct translation into Prolog. Various processing strategies have been investigated in particular bottom up parsing, parallel parsing, constraint based parsing and an implementation of the Earley algorithm that merges bottom up analysis with top down predictions.
Once this analysis is realized, the rules and lexical data that follow are implemented on the TextCoop platform. In general, in most types of specifications, requirements are organized by purpose or theme. The organization follows principles given in e. g. (Rossner et al. , 1992). The structure of these specifications is highly hierarchical and very modular, often following authoring and organization principles proper to a company or an organization. Requirements may often be associated with diagrams or pictures, which will not be investigated here. They may even be organized as logigrams. The higher level of these documents often starts with general considerations which are not requirements such as purpose, scope, or context. Then follow definitions, examples, scenarios or schemas. Then we have a series of sections that address, via sets of requirements, the different facets of the problem at stake.
g. 3 cms), which is expressed by a small grammar. -A set of rules (as those above), 9 rules for requirement analysis and about 120 rules for the discourse structures presented in section 6 below. These resources are directly handled by the TextCoop engine that has a dedicated processing strategy. These resources are stored in different modules allowing easy updates. As a result, the system produces annotations of the following form (verbs are also tagged with their morphology: pp = past participle, inf = infinitive). These annotations are produced in a fully automatic way by the system: <requirement> running services must be <verb type = "pp" > restricted </verb> to the strict minimum </requirement> <requirement> equipments must < verb type = " inf " > provide < /verb> acl filtering </requirement> <requirement> physical entities must have at least one Ethernet interface per zone it is connected to (front, back, administrative) </requirement> <requirement> every administrative access must be initiated from a rebound server using a set of predetermined tools </requirement> <requirement> for shared services, a shared information system interface enabler is <verb type = "pp"> required </verb> to route real time charging messages to local systems </requirement> The above rules can be used in a number of applications such as requirement mining (e.
Finally, long-term and large spatial-scale surveys are of prime interest and may allow predictions about the future status of populations in a changing world through the use of species distribution models for instance  (Guisan and Zimmermann 2000). In this study, we attempt to demonstrate how combining PGSs and DMPs provides baseline information for prioritizing and initiating management and conservation plans. We focused on an endangered freshwater fish species (i. e. , the South-west European nase Parachondrostoma toxostoma, Vallot 1837) which is considered vulnerable throughout its restricted native range (i. e. , Southern France,  Crivelli 2006). We used a microsatellite dataset gathered at the river basin scale (i. e. , the Garonne river basin, South-Western France) to (1) describe the genetic diversity and structure of P.
Descriptive genetic analyses The presence/absence of large allele dropouts and null alleles was determined using the software MICRO-CHECKER 2. 3  (Van Oosterhout et al. 2004 ). Departures from Hardy-Weinberg (HW) equilibrium were estimated using the program GENEPOP v4. 0  (Rousset 2008). Levels of significance for HW were adjusted using the false discovery rate (FDR) procedure  (Benjamini and Hochberg 1995). Linkage disequilibrium among loci within sites was tested with the program FSTAT 2. 9. 3. 2  (Goudet 1995). The mean number of alleles per site, the average observed (H o ) and expected (H e ) heterozygosity over loci, as well as H o and H e per loci per site were estimated using ARLEQUIN 3. 5  (Excoffier and Lischer 2010). We used a rarefaction procedure, as implemented in the software ADZE 1.
Population structure A Bayesian model-based clustering approach was used to search for the occurrence of independent genetic groups (i. e. , clusters, K) in our dataset (as implemented in STRUCTURE 2. 3. 3; Pritchard et al. 2000; Falush et al. 2003 Falush et al. , 2007;; Hubisz et al. 2009 ). The burn-in length of the Markov Chain Monte Carlo (MCMC) was set to 50,000 followed by 200,000 iterations. The admixture model and the correlated allele frequencies model were used with priors on population sampling location  (Hubisz et al. 2009). Ten runs were conducted for each K value, with K ranging from 1 to 10. We used CORRSIEVE 1. 6. 2 (Campana et al. 2011)  to combine two approaches aiming at determining K: the DK test  (Evanno et al. 2005 ) and the DF st test  (Campana et al. 2011). To further assess the levels of genetic differentiation among P.
Demographic history inference and current N e estimation We used two different approaches for inferring past changes in the effective population size (i. e. , expansions or contractions) of P. toxostoma. The first method, implemented in the BOTTLENECK v1. 2. 02 software  (Cornuet and Luikart 1996; Piry 1999) , uses summary statistics of the genetic diversity to assess significant deviations from mutation/drift equilibrium. Significant heterozygosity excesses are considered as evidence of recent bottlenecks, whereas significant heterozygosity deficiencies can be interpreted as signals of recent population expansion  (Luikart and Cornuet 1998). We performed analyses considering two different microsatellite evolution models: the stepwise mutation model (SMM) and the twophase model (TPM). For the latter, we set the percentage of multistep mutations at 30%.
We tested the significance of mutation/drift equilibrium deviations for the two models using Wilcoxon's signed rank tests. To account for multiple comparisons, we applied the FDR procedure  (Benjamini and Hochberg 1995). The second method is the full-likelihood Bayesian approach implemented in the program MSVAR 1. 3  (Beaumont 1999; Storz and Beaumont 2002). This coalescent-based method relies on a hierarchical Bayesian model to detect, date, and quantify past demographic changes. The model assumes that a stable, closed population of ancestral size N 1 increased or decreased exponentially to its current size N 0 (i. e. , its current N e ) over a time interval of T a years. This method uses all the information contained in the data and lognormal priors to infer the parameters of the model U = {N 0 , N 1 , T a , h}, where h = 4N 0 l and l is the mutation rate.
Database description We used the surveillance monitoring database of the French National Agency for Water and Aquatic Environments (i. e. , ONEMA) to carry out demographic trend and species distribution analyses. This database includes an extensive spatiotemporal set of monitoring surveys of French freshwater fish populations, representative of all fish assemblages and covering varying degrees of anthropogenic disturbances  (Poulet et al. 2011). Surveys were conducted according to standard electrofishing procedures  (Poulet et al. 2011). We used this database to (1) identify temporal trends in population abundance of P. toxostoma at 12 sampling locations; (2) assess the current spatial distribution of this species in the Garonne river basin; and (3) investigate whether the spatial distribution of this species in the Garonne river basin has declined or expanded over the last three decades.
Temporal trends in abundance From this dataset, we selected all sites belonging to the Garonne river basin that have been sampled and investigated for P. toxostoma abundance for at least 8 years. This resulted in the selection of twelve sites (Table  1 , Fig. 1 ) for which time series ranged between 8 and 18 years and occurred between 1991 and 2010. As sampling procedures were standardized over years, abundances (expressed as the number of individuals per m 2 ) were directly comparable across years. It is noteworthy that (1) this database and the genetic database have been gathered during independent research projects; and (2) P. toxostoma is relatively rare in this area (Fig. S1 ), which both explain why demographic and genetic data are not available for all sites (see Table  1 ). Some sites for which long-term demographic data were available have been unsuccessfully sampled for genetic, and inversely, some sites where genetic data were available had time series that were not long enough to be analyzed (i.
Current spatial distribution and recent distribution changes We used the database described above to assess changes in the spatial distribution of P. toxostoma on the Garonne river basin over two distinct periods, separated by a time span of 10 years  (i. e. , "past period": 1980-1992, and "current period": 2003-2009 ). To account for potential sampling bias when comparing spatial distributions over time based on datasets not originally collected for this purpose  (Shaffer et al. 1998; Shoo et al. 2006) , we modeled the spatial distribution of the species across the French hydrographic network as a function of several climatic and environmental variables. Accurately modeling species distribution requires performing analyses at the entire species range scale, so as to encompass all environmental conditions  (Austin 2007 ).
Descriptive genetic analyses After applying the FDR controlling procedure, no null alleles were detected in our dataset, there were no significant deviations from HW for any loci or any population (Tables  S2  and S3 ), and we failed to detect significant linkage disequilibrium between pairs of loci (Table  S4 ). Overall, genetic diversity estimates were low (Fig. 2A  and B , Table  S3 ). Loci were weakly polymorphic at the basin scale (2-6 alleles per locus), with some loci being monomorphic at the river scale (na = 1; Table  S2 ). Average H e and H o values across loci within rivers were moderately low (H e = 0. 320-0. 450; H o = 0. 315-0. 482), as well as mean number of alleles and allelic richness estimates (AR 8 = 1. 868-2. 536 alleles per river; AR 18 = 2. 147-3. 037; Fig. 2A  and B , Table  S3 ).
Demographic history inference and current N e estimation According to the BOTTLENECK software, and after corrections for multiple tests, there was no significant evidence for demographic changes in the Garonne river basin (Table  S5 ). On the contrary, the MSVAR analyses revealed significant signals of bottleneck in all rivers (Fig. 2C , Table  S6 ). The magnitude of these bottlenecks, as indicated by the median values of the log 10 (N 0 /N 1 ) ratio, ranged between À0. 705 (ELL) and À1. 345 (HER; Fig. 2C , Table  S6 ). Overall, N 0 estimates (i. e. , the current N e of populations) were similar across rivers, with medians ranging from 7 (HER) to 63 individuals (SAL). Concerning ancestral population sizes (N 1 ), median values ranged from 5286 (LOU) to 9155 individuals (HER; Fig. 2C , Table  S6 ).
These bottlenecks were estimated to have occurred between 192 (HER) and 727 years ago (SAL). The MSVAR method has often been considered as more powerful than the BOTTLENECK method  (Williamson-Natesan 2005; Girod et al. 2011) , which   S6 ), the current effective population size (N 0 ; right number in the bubbles, see also Table  S6 ), the time of the beginning of the bottlenecks (in years backward in time; numbers in brackets, see also Table  S6 ), and the magnitude of bottlenecks (i. e. , Log 10 (N 0 /N 1 ): color scale, see also Table  S6 ), and (D) the value of the Mann-Kendall's S statistic (color scale) and the significance of Mann-Kendall trend tests for each time series: Asterisks (*) denote significant (i. e. , P < 0. 05) temporal trends. For all panels, the three-letter code in each bubble corresponds to the river codes (see Table  1 ).
Acknowledgments We are very grateful to all the ONEMA staff who contributed to the elaboration of the demographic monitoring database. Leslie Faggiano ,  Charlotte Evangelista ,  Christine Lauzeral ,  Lo€ ıc Tudesque ,  Roselyne Etienne ,  S ebastien Vill eger , and  South-Western France ONEMA  staff are thanked for their help in the field. We also thank the  CALMIP group , in particular  Boris Dintrans  and  Nicolas Renon. Camille Pag  es and two anonymous referees are also thanked for their constructive and stimulating comments. The authors also thank the "G enopole Toulouse" for help with genotyping. IP is financially supported by a  MESR  (" Minist ere de l'Enseignement Sup erieur et de la Recherche ") PhD scholarship. We are grateful to  Radika Michniewicz  for correcting and editing the English.
INTRODUCTION A Multi-Agent System (MAS) is composed of entities called agents that interact together in order to achieve a common goal. An agent, in general terms, is anything that can be viewed as perceiving its environment through sensors, and acting upon that environment through effectors  (Russell& Norvig, 1995). ComputationalMASs consist entirely of artificial agents executing as software programs and running on a computer system. Some researchers have treated the concept of formal descriptions of MAS, while the aim is to assess their properties and formal specifications. In fact, there are formal descrip tions of both agent paradigms and interaction paradigms; however, some formalisms provide representations of both of them. In the literature, as in Wo oldridge and Jennings  (2005) , formal descriptions are also called theories.
The MAS theories are rules that constitute a basis for speci fication  (Glaser, 2002; Occello, 2002\) , design  (D 'Inverno, Kinny, Luck & Woolridge (1998) ,  Ricordel & Demazeau (2002) ) implementation and verification ofMAS (A rlabrosse,  Gleizes & Occello (2004) , D'inverno, Fisher, Lomuscio, Luck, Rijke, Ryan & Wo olridge (1997),  Picard & Gleizes (2004)  ). There are other methodolo gies and theories dealing with this concept, such as The Multi-Agent Scenario-Based Method  (Moulin & Brassard (1996) ,  Moulin & Cloutier (1994) ) that is intended to be applied in the field of cooperative work Moreover, software agents represent a fundamental way of considering complex dis tributed systems and societies of cooperating autonomous components. When building such systems, special techniques are required for their design and implementation.
Some ques tions arise such as how to conceive and develop adequate collaborative work architectures tak ing into account communication, coordination and production features, and how to evaluate the developed collaborative systems. In this paper, we attempt to answer these questions by putting forward a new conceptual model of an agent called Collaborator Agent (CA) used to develop and evaluate software architectures of collaborative applications. The proposed CA is based on an approach combining astutely two research area: MAS and CSCW  (Ellis, Gibbs & Rein (199 1) ,  Laurillau & Laurence (2002) ). In the first area, a formal model of the MAS  (Ferber approach in Ferber & Muller (1996) ) is used to bring out the features of the agent. The second area provides a useful specification of collaboration based on the communication, coordination and production theories.
These interactions are handled separately from agents' logic using roles. It allows application development based on flexible agents in order to execute automatic tasks. Many research work have also been con ducted in order to implement MAS for robot teleoperation. For example,  Lin, Song and Anderson (2005)  present a distributed hybrid agent-based control (ARC) architecture for multi-robot cooperative tasks. This software architecture also provides an effi cient platform for building up a multi-robot system consisting of heterogeneous robots. In this system, each individual agent is responsible for sensing, actuating, or executing a set of functions, while it can also exchange information with other agents. Furthermore,  Wegner and Anderson (2006)  describe an approach to multi-robot control for search and rescue environments, where robotic agents are implemented using a schema-based architecture with autonomous behaviors.
This approach involves the use of two software agents running on each robot to effectively balance the teleoperated and autono mous components. A mediation agent is used to integrate the commands from a teleoperator with a robot's autonomous processing, while an intervention recognition agent recognizes situations in which an operator should inter vene. Moreover,  Lewis, B. and Tastan, B. and Sukthankar, G. (20 10)  present CoOperator, an agent-based human-robot interface that infers operator distraction and identifies any robots that are not currently being effectively managed. The CoOperator interface is designed to assist the human operator in managing, controlling, and navigating multiple robotic agents through a disaster scenario. In this approach, robots are monitored in order to determine which ones are suffering from operator neglect, by employ ing a Hidden Markov Model in order to infer operator neglect from the robot's observable state.
Agent-Based Collaborative Software Architecture The aim of the proposed model is to assist the collaboration as well as the resolution of conflicts that could result from it. The use of agents enables to create a subtle environment (responsiveness, proactivity) in the goal of a successful execution of tasks, as well as the termination of missions in good conditions. The MAS (CA) presented in this paper is composed of four types of agents: • Agents that choose a mission, called com munication agents; • Agents that select actions to be executed, called coordination agents; • Agents that execute the chosen actions, called the production agents; and • Agents that handle the inter-agents com munication, called collaboration agents. Every instance of these four agents is regrouped under one super agent, called the Collaborator Agent (CA) shown in Figure  2.
In fact, the studies held in the domain of MAS have two objectives. The first is to pres ent concepts, properties and formalisms for the design of complex and distributed systems. The second obj ective is to study multi-agent development platforms that can be used for implementing such systems. In the CSCW domain, the study presented attempts to answer the following questions: What definition can we give for the collaboration term? How can we design the collaborative teleoperation, and what software architecture model should be used? In the MAS domain, the agent formal ism proposed by  Ferber (1997)  as well as the JADE development platform were adopted and used. In the CSCW domain, we adopted the concept of the 3C model for groupware  (Ellis & Wa iner (1994) ): • A Multi-Agent system for collaborative teleoperation: The proposed system has an obj ective of assisting the collaboration process as well as the resolution of conflicts that may result from it.
Global Software Architecture In the ARITI-C system, a generator agent creates the Collaborator agents and handles the global functioning of the system: Event logging, identity verification, etc. As we only have one client to command the robot at one particular instant, it is for the Generator agent to centralize and pass the commands to various users. Finally, a Database Management System (DBMS) handles the information resulting from the collaboration process. Infact,ARITI-C users are network clients for:.
To compose a group, a class called ClientColl maintains all users' information. The first con nected user is the master user, managing group creation and the validation of the missions and actions, which also has a specific interface for group composition. Hence, when the system Pr:ç! : AgentC{cAgentCé 2. receives group's composition by the master user, the communication phase starts: a message is sent to all users connected, and the groups' information is saved in the database: • Communication phase: To enable com munication, the server must wait for all clients to connect. When a client connects, a collaborative agentand a communication agent are created, which will receive us ers' information such as name, id, as well as the agents' address that is in charge of this particular user. Once the entire group is created, the collaboration agent of the master user receives the addresses of other collaboration agents.
However, the choice of the mission can only be done by the master user. The chosen mission is returned to the server and saved in the database: • Coordination phase: The initialization of this step is similar to the communication phase. When a client connects, a coordina tion agent is created and sends its address to the collaboration agent. Once created, the coordination agent is notified by the server for being the "master" agent. Once all the agents expected in the group are identified in the server, the list of missions is sent to each of them. Finally, the master (coordination) agent receives the list of its agents, which enable it to send the list of actions to the client, as well as to send its own address to other coordination agents. M;oo ,. : """" : I AgentC|AgentC} , IAgentCxAgentCy -:ÕOK: Once these agents receive the address of the master agent, the coordination phase can start by sending the list of missions to their corresponding agents.
Analysis and Evaluation for Collaboration We have developed a tool for the analysis and evaluation of the collaboration process in our system based on a Web interface and a MySQL database. This interface also allows the adminis tration of different data used or generated by the MAS. As for example, information concerning users, groups, connections, missions, actions, duration of every collaboration phase, etc. The PHP interface gives various features for database administration, as well as for collaboration's analysis: table modifications, sending emails to users, modifying the missions and actions, regenerating a camera's calibration file, export ing and restoring the database and extracting the statistics by user and by group: • Evaluation objectives: The PHP and the database inARITI-C monitor the system's correct functioning, as well as extract characteristics related to tasks and users: influence of mission parameters on users' performance, influence of the learning process for a single user or a group, influ ence of group's composition, etc.
CONCLUSION In this paper, we presented a conceptual model, the Collaborator agent, intended to design collaborative software architectures based on multi-agent systems. The proposed model is defined as a set of four agents (collaboration, communication, coordination and production) supporting the whole set of collaborative tasks. We discussed implementation and evaluation issues of the Collaborator agent. Our research work is resumed by the presentation of the ARITI-C system as a collaborative teleopera tion Interface via Internet. The design of this system is based on research done in both CSCW and MAS domains. The PHP interface and the database of theARITI-C system were conceived for tracking the functioning of the system, as well as extracting relative characteristics related to missions and users.