A Restful Approach for Managing Citizen profiles Using A Semantic Support

Several steps are missing in the current high-speed race towards the holistic support of citizen needs in the domain of eGovernment. This paper is focused on how to provide support for the citizen profile. This profile, in a wide sense, includes personal information as well documents in possession of the citizen. This also involves the provision of those mechanisms required to publish, access and submit the convenient information to a Public Administration in due curse of a transactional services provided with the last one. Main features of the system are related to interoperability and possibilities for its inclusion in a cost effective manner in already developed platforms. To make that possible, this approach will take full advantage of semantic technologies and the RESTful paradigm to design the entire system. The paper presents the overall system with some notes on the deployment of the solution for its further reuse in similar contexts.

INTRODUCTION
The domain of eGovernment is a quite incipient area or research and development. Even during these last years, a large effort has been devoted to the provision of new solutions and the design of holistic frameworks; a large effort is yet to be devoted. In this road towards a fully transactional environment for services provided by Public Administrations and Agencies (PAs hereafter) there is still some gaps missing to be tackled. And, in order to properly face these challenges, it is required to keep in mind the current needs of citizens and governments such as world-wide interoperability, accessibility, and attention to security concerns.
In particular, this contribution is concern with the provision of support for the management of the profile of citizens. In the globalized context we are involved in current days, it is quite normal to get related with a number of PAs from a number of different scopes. In their regular operations with PAs, citizens are requested to provide with personal information and documents, and, in exchange, they are supplied with a number of documents. It seems clear the need to tackle this issue in a convenient manner in order to avoid unnecessary resubmission of documents and to properly manage the entire pool of documents and personal information.
In a search for a suitable approach, we must keep in mind the definition and the aim of eGovernment. As defined by UN, eGovernment is[1] "the use of information and communication technology (ICT) and its application by the government for the provision of information and basic public services to the people". EU [3] defines eGovernment as "the use of ICT in public administrations combined with organizational changes and new skills in order to improve public services and democratic processes and strengthen support to public policies". From the point of view of the World Bank [2], eGovernment can be understood as "the use by government agencies of information technologies (such as Wide Area Networks, the Internet, and mobile computing) that have the ability to transform relations with citizens, businesses, and other arms of government".
From the review of all of them, it is clear that the aim pursuit in the domain is not merely to replace current fashion systems with similar systems that take advantage of the ICT support. Thus, in the context of this contribution, it is not acceptable to go for a solution based on just allowing citizen to re-submit documents each time they are requested to do so. On the contrary, in the light of this concept for eGovernment, it is mandatory to put the citizen in the centre of processes and redesign, if necessary, all workflows to make easier things for them.
In this line, the motivation for this work comes from the observation of current fashion solutions that, in order to make the most of its potential requires the citizen to provide with personal data and a large range of personal documents. This would be the case of searchers of services, complex services that require citizen to provide data before an actual invocation is completed, or even those services that need before hand information to decide about the eligibility for a certain operation.
And, precisely, one of the burdens of dealing with Public Administration is due to the multiplicity of mechanisms to access data or to login, or the number of times required uploading the same data [6]. To fight back this issue, this paper proposes a mechanism to simplify these operations. The main aim of this contribution is to support in a holistic manner the entire profile of the citizen, including the documents he/she may in possession of. This support must be planned in such manner it would be simple to access for both, the citizen, i.e., the owner of the data, and the PA involved, i.e., the service provider.
The first step is to get a clear idea of what a document is and how it must we managed in the domain of eGovernment (see Section 2). The next step is to set the conditions and requirements for our proposal (see Section 3) and check similar initiatives on the same and related fields (see Section 4). In our case, in order to increase the interoperability of the solution, we take full advantage of semantics. This technology, which can be used for multiple purposes, will be applied to boost the interoperability of the system and to support the recovery of information. Basis of this technology are presented (check Section 5). Additionally, we are interested, not just in provide contents interoperable, but also accessible, in terms of simplicity of use for human users and third party software agents. That is the reason to deploy a simple-to-use and open software architecture. In our case, the RESTful approach (deeply discussed in Section 6) was considered the most convenient solution. An application of the semantic techniques to this particular problem is presented on Section 7. Afterwards, on Section 8, a deeply discussion about how the system works is presented. Information regarding the design and the implementation of the final solution is presented to the reader in order to make possible its replication in further developments (see Section 9). Finally, after the work carried out, some conclusions are presented to the reader on Section 10.

DOCUMENTS IN EGOVERNMENT
The use of documents on public administrations drives the entire workflow on a Public Administration. As stated in [4] , "Government organisation need to manage electronic records as a valuable corporate information resources by constructing interacting systems of software, standards, policies, procedures and interfaces". In fact, in both eGovernment and "regular" Government, documents act as the proof for operations already performed or to be performed and compel PAs and citizen to comply with its contents.
Nowadays, the transition from paper-based administration to a digital model is on the agenda of most countries. The first step is to set the legal framework, also for this subarea of the domain. And this new legal framework will introduce high impact changes on the day-to-day life of the citizen. Using the Spanish regulation [5], that is being published at the moment of writing this document, as a predictor for other legislations, it is possible to outline some relevant advantages for the citizens: • • • • These changes regarding the documents themselves will also imply the generation of a software infrastructure for its management. According to [4], a good electronic recordkeeping requires, among other features: A clear understanding of the nature of electronic records. Ensure the integrity and reliability of electronic records A strategy to ensure that electronic records will remain accessible and usable as long as they are needed.
The considerations for developing a system to manage records or documents in the domain of eGovernment must be consistent with three different levels: At organizational level. All involved PAs in the system must take into account the general philosophy and comply with the guidelines established to ensure the coherence and the good performance of the system.
At the record management level. All records generated in the system must comply with the agreed procedures and the conventions set for this porpoise. At the IT system level. All technical elements involved in this task must be able to work together to deliver the proper service. The must common, and almost compulsory, way to achieve this by means of open technologies and standards.

REQUIREMENTS OF THE SYSTEM
As mentioned in the introduction, currently there is a barrier for the engagement of citizens when they tackle the use of services in the domain: the time-consuming task to upload or to complete its personal profile before they access each platform they may need to interact with. In this context, when we speak about the profile of a citizen we are referring to the sum of the information regarding the citizen itself and the documents he/she actually owns.
In the given context there is a set of premises that we must consider such as interoperability of contents, access to information under secure connections, simplicity of use in the mechanisms and so on.
From the study of the requirements, it is possible to identify some use cases that will describe its functionality from the point of view of the citizen: • • Using these concepts, it is clear that the system must involve two main parts: ( ) • Also it must be provided a software solution that implements all features identified in this section. This implementation must comply with some compulsory features such as openness, simplicity and security. Thus, the use of a RESTful approach was decided (see Sections 6).

RELATED WORK
As previously mentioned, this contribution is not aimed to provide a final solution but to support further projects that may make good use of the possibilities of this work; that would be the case, for example, of project requesting the citizen to provide with external and additional documents. This sort of solution is not very common in independent institutions or small administrative entities due to obvious reasons of cost and actual utility as further projects are required to actually get back the investment for the project. Even there are a number of solutions that actually manage the profile the of citizen in order to fulfil services as expected, e.g., services for paying taxes; there is a lack of projects that are actually focussed on the provision of services related to the profile of the citizen and offer access to third parties.
And this is due, regretfully, to the long-term effort required to develop this sort of project and to achieve the required critic mass of users needed to make worthy the attempt.
Nevertheless, more mature domains have already tackle similar concepts. eLearning is one of those domains where researchers have devoted a large amount of resources to address this feature. In the context of this eTechnology, profile is linked to the metadata description of abilities and capacities of the learner and there are recommendations and standards for its full definition and management [7].
In our scope, anyhow, we can use some basis and concepts already deployed in the frame of other non-highly related projects. Maybe, most relevant initiatives come from the use of metadata to make annotations on documents. In this research line, we can underline some initiatives such as eGif [8] or NZeGIF [9]. These ones are oriented to provide a certain set of metadata that can be used to describe documents belonging to a certain citizen. It is also needed to outline the contributions made by CEN, mainly by mean of the CWA 14860 [10]. This document presents a metadata application profile based on Dublic Core for eGovernment for Europe. Thus, several official recommendations are considered and a mapping between different definitions of metadata is proposed for the shake of interoperability.

SEMANTICS TO DEFINE KNOWLEDGE
At this point, it is required to choose a technology, a tool, that allow us to provide some services required in this application. We would need to provide the system with support to define the information in an interoperable manner, to allow the system to discover new pieces of information from the already provided contents and to reason about the already existent information. It seems clear to us that the most suitable tool in this context is Semantics.
The semantic web has emerged as a new promising technology aimed at addressing information instead of data, i.e., it enables software agents to treat data in a meaningful manner. Making this possible would allow new mechanisms to operate on a higher level of abstraction. Also, by means of this technology, it is possible to express knowledge in a formal and interoperable way. These features will support the provision of the services claimed in this paper.
For the definition of knowledge as it is intended in the context of semantics, ontologies are a key element. In the literature, several definitions or approximations to the concept of ontology are provided. Gruber proposed a quite suitable definition for ontologies [11]: By means of this definition we are addressing an ontology as a support to put in a concrete way abstract information about a certain domain by means of a machineunderstandable data format.
To express an ontology in a formal manner different languages are at our disposal [12]. OWL (Ontology Web Language) [13], is the W3C Recommendation intended to provide a fully functional way to express ontologies. To make different levels of complexity possible, OWL provides different sublanguages with increasing expressivity: OWL Lite, OWL DL and OWL Full. By using OWL, we are addressing a W3C recommendation that can be considered as solid and interoperable support for the provision of this solution.
Currently, there are available for developers, software libraries that support the use of semantic features required for this kind of projects. Nevertheless, we must keep in mind one of weak points of semantics: its immaturity. Even semantics appear several years ago, the software platforms, and even standards themselves, are not in a mature state and problems on software available may impact on the time-to-market of solutions.

THE RESTFUL APPROACH
As mentioned before, in order to deploy a convenient software platform, it is required to make a wise election of an architectural style, i.e., the guidelines and the principles that will rule the actual software architecture for the prototype. In the frame of this work it was decided to make use of the REST (REpresentational State Transfer) philosophy [14]. Systems fitting in this principle are called to be "RESTful" and, taking the client-server model as the basis, it poses some principles: • Resources to be used always must be able to be identified from the outside of the solution. One of the aims of this sort of architectures lays on managing data, i.e., resources, regardless of their storage, origin or status. Thus, it must be provided in a unique manner to establish a link with them. The preferred way is by means of URIs (Uniform Resource Indicator) and IRIs (Internationalized Resource Identifiers). As a result, it is possible to get an unlimited number of resources. • Messages, i.e., commands, must be a set of clear and simple verbs. Contrary to what happen to resources, actions must be limited and well known. Usually, these systems are supposed to be based on CRUD (Create, Read, Update and Delete) systems. Invoking these commands on resource within the system, all required operations should be possible.
On the light of these ideas, it is clear that HTTP is the perfect example for RESTful system. It is possible to use a limited set of commands (addressed in the HTTP1.0 [15] or HTTP1.1[16] specification) on any URL provided by the software agent in use. Anyhow, RESTful systems do not have to be HTTP or even based on HTTP.
The purpose for a RESTful system is to manage all resources in the system by means of this set of limited and well-know verbs. Reader can compare this feature with RPCbased system where the set of operation is freely upgradable and parameters attached do not identify the resource but the properties to be changed.

Figure 1. Excerpt of the ontological model
The change of the status for resources in the RESTful approach is driven by these verbs for actions and never stored on the server that will not have memory about former invocations. This feature drives the design of the servers and makes possible a higher scalability and larger simplicity for these solutions.
In our case, as mentioned before, we will take full advantage of semantics. Thus, instead of providing links to "simple" resources, semantically defined items will be used. Thus, the actual content to be managed by the system belongs to an ontology. This feature enables the system to provide with additional features not present in nonsemantic environments such as reasoning about the outcome of an operation, fully autonomous discovery of next iterations, or smart searches for additional services.

SETTING THE CONTEXT
Taking advantage of semantics is not a trivial task and it requires a large effort. The very first step is the provision of a ontology that can put us in position to express in a explicit manner all the relevant knowledge to characterize the environment of the proposed solution. This task requires a solid methodology that provides us with a systematic and well-documented way to achieve this goal. The very first step is to make clear which are the goals of the provided knowledge support in the system.
To make that explicit the competence questions is the chosen tool. In our case we would need to be able to answer, among others, the following questions: Taking these questions as a starting point and following the steps described by Methontology, an entire ontological system is generated. In due course of this methodology, a number of phases are undertaken: specification, conceptualization, formalization, and implementation. The last phase identified in the methodology, maintenance, is to be performed along the entire life of the system.
The final result is the complete ontological model of the system. This holistic model of the domain is made of different modules provided under the form of an independent ontology. Therefore, different agents for different porpoises can just pick the piece of the ontology related to its goals. Additional, this allows us to reuse pieces of ontology already developed and commonly accepted such as FOAF [22] or the W3C Time ontology [23].
As an excerpt and for the sake of simplicity, the most relevant part of this ontology for our porpoises is presented on Figure 1. In the figure, it is shown how the different classes are related. Besides, the class document includes several sub-classes for national, international, regional and personal documents. Each document included in the system will be translated as a new instance belonging to the appropriated class. Main classes identified within this proposal are: • Citizens. This class models the information regarding citizens, the actual owners of documents in the system. As mentioned previously, FOAF model is used on the characterization of this class. • PA. Public Administrations, as the service providers in this schema, play a main role in this proposal and a complete model of their abilities and capacities must be included. • Document. This is the central element of the ontology and a complete description of this item must be provided. Actually, this class involves information regarding a different number of issues in order to enable to locate the required document using the maximum number of parameters.
These classes defined within the ontology are related through several properties: • issues. It links a document with the PA that issued it.
• owns. It links a document with its owner.
• expires. It states the date of expiration for a document.
• and many others that enables the system to perform inferences and discovery of new information.

SETTING THE INTERACTION MODEL
As mentioned in previous sections, the use of a RESTful approach poses some restrains about how operations can be deployed and which kind of information can be used. From Section 7, a characterization of resources, i.e., documents, PAs, and citizens, is provided. Besides, these resources, as they are defined in terms of an ontological support, can be addressed by means of a link. This is due to the fact that all instances in an ontology are provided with an identifier under the form of a URL.
In order to fully characterize the architecture itself of the system, it must be identified how interactions are conducted and which operations are possible. These operations will be mapped into verbs, using a RESTful terminology, and used with the corresponding resources. In the case of this proposal, we will stick with the HTTP protocol and take advantage of the provided commands. These operations will be used with a concrete meaning. On Table 1 some combinations of verbs and resources are displayed. The reader should note that the same verb or command applied to different resources will have a different meaning. Upon each invocation of the former commands, the server will respond with information expressed in the terms of the ontology included in the system, i.e., a rdf-like file will be generated and sent back to the client. Thus, the agent receiving this response will be able to understand the knowledge implicit in this response and, if the case, take the correspondent decision.
As all the operations possible are defined in terms of the provided ontology it is not required to negotiate about the interface of the service or decide and discover how a particular WS must be addressed.

Resource Meaning
GET Document Recover a particular document within the system POST Citizen Update all the personal data about a citizen PUT Document Add a document to the system DELETE Document Remove a document in the system Table 1.

-Verbs and its meaning in the proposal
The server in this approach will be administrated by a reliable entity, such as a Public Administration or lawyer-like agent provided with the required legal support. The software to fulfil the duties of the client can, in theory, be provided by third parties as far as they stick to the defined interfaces. As already mentioned, it makes no sense to keep this sort of service hidden for citizen or prevent them to know about how their data is actually managed.
Taking into account these main guidelines, functionalities identified in Section 3 must be faced. For each one of them, a number of considerations must be done.

Manage Personal Data
In the provided system, the server entity stores on its internal DBMS all the information regarding the citizen. This information is compliant with the schema defined in the ontology previously addressed in this paper. From a practical point of view, the user must submit a HTTP request using the method POST on a resource "citizen". This resource it is an entity of the class that the server will accept and update the information conveniently on its internal database.

Manage Documents
This is a feature relevant in the system, as it must deal with documents on the behalf of the citizen. This involves tasks related to two different features. The system takes care of the digital version of document regardless of its origin and stores it in the server where the system is currently running. Besides, it manages the knowledge associated to each document. The user must provide this information, as the system is not capable of inferring that information from the file itself. Once all these pieces of information, including the file itself and the related data expressed in terms of the ontology, are provided, the system takes care of them and stores them. Regarding to recovery issue, all this related information expressed with the ontological support is at the disposal of the citizen to find a particular document he/she may be interested at.
Besides of the already mentioned functionalities for managing documents, the system can also provide with enhanced characteristics regarding the use of the information within it. Some advanced features obtained from the application of semantic tools are: • '* -/

Export/Import data
The citizen has the possibility to pack and unpack all the information that comprises his/her personal record. In our case, this will involve the ontology containing his/her personal data and also the files where their documents are stored. To make that possible, the prototype generates a zip file containing the instance of the ontology needed to define entirely the citizen profile and also the documents already uploaded. The reverse operation is also at the disposal of the citizen that, making use of a web form, will be in position to upload a zip file and insert that content on the system as a new instance of his/her records. It is important to note that at this step, the ontology provides a common language to define the contents to be used; therefore, the interoperability of the sogenerated file is guaranteed.

Allow Access
This use case solves a scenario where a citizen wants to put a particular document at the disposal of a certain Public Administration. In this case, the citizen chooses which document a particular Public Administration can access. And, for that single document, it is generated one single URL that drives the access to the previously selected document. In order to guarantee the privacy of the citizen, this URL will be accessible just under certain conditions: • " 56 This functionality shows the balance between the power of the software provided and its integration with external platforms. It would be a better approach for the shake of interoperability to directly allow external software to access documents within the system. Nevertheless that approach would require a larger effort for PAs willing to take part in the proposal. Under this scheme, with no cost for external applications, it is possible for them to take part in the system as they are just expected to access a regular URL where the desired document is hosted.

IMPLEMENTING A PROTOTYPE
In order to test the solution proposed, a prototype was developed. This prototype is composed of two separated software agents. On one hand it is provided the server of the system responsible to fulfil the request of service performed by the citizen and also to conveniently manage the data already uploaded to its pool of contents. On the other hand it is required to provide an implementation of the client capable of invoking the services previously identified. This agent software will act mainly as a front end for the functionalities provided by the server as nearly neither data is stored on it, nor responsibility is put on it.
The implementation of this system must ensure the security of the system from different points of view. To undertake this feature, compulsory due to the nature of the domain, several measures were planned: -Records of documents include a hash field to ensure that information has not been anyhow altered. Thus, agents involved must check the conformance with this code previously to each sensitive operation.
-Citizen must prove their identity using a PKI infrastructure [25]. This is the method to ensure the authenticity of the user and also the manner to avoid the repudiation of the operation. Depending on the country and the available infrastructure, this certification will be provided in a different manner.
-The communication among agents in the system will be conducted always using secure channels using the SSL protocol.
For the shake of simplicity, all these features will be taken for granted and further description of the prototype will be built on the basis of these premises.
This software agent must make good use of the described semantic support and, of course, meet the actual goals of the system as they were planned originally. To this end, Java[24] was the chosen option as the software platform. Due to its support for largescale projects, its community of developers, and support for different and required libraries, this select technology was considered the best option to develop the software prototype.
To manage the semantic contents within the server, Jena [27] was chosen as the library to deal with the involved knowledge. As it was shown previously, we would require to undertake a number of operations on the semantic information within the system. These operations involve the execution of SPARQL queries, adding/removing knowledge to the system, etc. Jena provides support for those operations in a simple manner. In order to illustrate the behaviour of this software tools it is included the snippet required to find out which documents belong to a certain citizen using the suggested tools: // The first step is to load the model of the ontology OntologicalModel individuals = ModelFactory .createOntologyModel(PelletReasonerFactory.THE_SPEC); // Afterwards, the actual contents must be read individuals.getDocumentManager() individuals.read("http://portfolio.det.uvigo.es/ontology.owl"); // The query for the desired knowledge must be prepared // In this case, the query will request documents belonging to a certain citizen String queryString=Portfolio.generateQuery(); Query query = QueryFactory.create(queryString); //The actual query is introduced QueryExecution qe = QueryExecutionFactory.create(query, individuals); //An object to launch the query is created ResultSet rs = ResultSetFactory.copyResults(qe.execSelect()); //And finally, the query is launched and the results copied to another container qe.close(); Reader should take into account that for each document uploaded to the system, it is required to perform two different operations: • Firstly, it is required to add a new piece of ontological knowledge • Later, the document itself must be uploaded to the server to keep a copy of it for its further use.
The latter operation is quite simple, as it is possible to upload it in a simple manner using a HTTP channel. Nevertheless, to perform the second operation it is required to manage the ontology in the system. // Defining the properties for doc1 doc1.addProperty(individuals.getProperty(Document+"Name"), individuals.createTypedLiteral(name, XSDDatatype.XSDstring)); //Further pieces of information can be added in a similar manner // Finally, new contents are put on the ontology in a permanent manner try { FileOutputStream fos = new FileOutputStream("/dataPool/data.owl"); individuals.write(fos, "RDF/XML-ABBREV", "http://portfolio.det.uvigo.es/data.owl"); fos.close(); } catch(IOException e) { e.getMessage(); } Dealing with the communication among agents in the system is quite simple as we realy on the basis of the RESTful paradigm. Therefore, it is only required to open an HTTP connection and compose the correspondent request using the proposed matching of commands and resources.
The implementation of the client agent tackles mainly the provision of a front-end for the invocation of services provided by the server. This client agent is actually responsible only for the presentation of functionalities but nearly no sensitive or resource-demanding operations are required to meet its requirements. For this proof-ofconcept implementation, it was decided to develop an entire application but further implementations of the system could be presented under the form of a simple web site including all services required.
This client was developed under the philosophy of a light-weighted client. Therefore, nearly no logic operation is performed on the client-side. And, also, for the shake of simplicity, semantic operations are also conducted on the sever side. Thus, the client just sends the pieces of information and the server is responsible for constructing semantic queries and analyzing the requested operation.

CONCLUSIONS
eGovernment is getting more mature as more resources are devoted to the domain and more experienced become developers. These new and more complex projects require not just support for final solutions but also supporting projects that may enable the access to these final services. In the frame of these projects, an efficient and simple manner to handle the profile of the citizen is required. And that is the motivation of the proposed system, to overcome some of the burdens attached to projects in both, conventional government and eGovernment: • This sort of support is intended to be used from different projects in order to provide synergies that make this solution actually appealing for both, Public Administrations and citizen. Public Administration will get a simplified mechanism to access secure data. Conversely, citizen will get all their data securely handled, by the entity of his/her election, and ready to be used for any Public Administration. This solution also does not require any special format for documents neither imposes any particular scheme to deal with the documents itself. The Public Administration that will use the document will have full access to the documents, once the citizen agrees on that, to manage it at its own convenience.
The reader should also note that the level of integration of this solution in the internal behaviour of the PA is quite limited. Actually, PAs taking part in this proposal will have access just to raw files, besides all the semantic annotations added, in similar manner to those PAs that just consider an approach closer to the traditional paper-based methodology. And this feature, to certain extension, is on porpoise as we can not expect PAs to directly embrace directly digital procedures. On the contrary, it must be provided an acceptable path from a traditional paper-based administration to more evolved scenario with acceptable costs and procedures in the transition. The presented approach seems to the authors as the best alternative under such conditionings.
From a technological point of view, the use of semantics turned out to be a quite useful support. It provides us with a highly interoperable manner to define knowledge in the system that is actually shared among different entities with quite little linking. Also, it allows us to provide a way to recover information from different sources and to make discoveries about the current status of the citizen and to add new knowledge to the system.
The use of simple software architectures, such as RESTful, proved to meet our requisites and it would require little effort for external platforms to get involved in the solution.
The presented implementation of the platform was intended only as a proof-of-concept for the viability of the proposal. Nevertheless, some problems arose and they should be taken into account for further implementations.
A quite relevant issue involves the non-mature stage of semantic libraries. Even a large community of software developers are currently working in the area, there is no a mature and complete solutions. Besides, the management of such a large amount of semantic information is not currently a simple matter and the times for loading contents on the main memory of the system may grow up too fast for any interactive solution. Also, it must be mentioned that this prototype was build on the top of a OWL ontology; nevertheless, during the development of this proposal, W3C has released the evolution of this recommendation and currently the official recommendation from W3C is to use OWL2 [30].
The actual adoption of the proposed schema by Public Administrations should involve a review of the local/national legislation in order to make sure its compliance with their particular regulation.


Speech Recognition of the letter 'zha' in Tamil Language using HMM

Speech signals of the letter 'zha' in Tamil language of 3 males and 3 females were coded using an improved version of Linear Predictive Coding (LPC). The sampling frequency was at 16 kHz and the bit rate was at 15450 bits per second, where the original bit rate was at 128000 bits per second with the help of wave surfer audio tool. The output LPC cepstrum is implemented in first order three state Hidden Markov Model(HMM) chain.

Introduction
Tamil is one of the oldest and official languages in India. In Tamilnadu it is the prominent and primary language. It is one of the official languages of the union territories of Pondicherry and Andaman & Nicobar Islands. It is one of 23 nationally recognised languages in the Constitution of India. It has official status in Sri Lanka, Malaysia and Singapore. With more than 77 million speakers, Tamil is one of the widely spoken languages of the world. Tamil vowels are classified into short, long (five of each type) and two diphthongs. Consonants are classified into three categories with six in each category: hard, soft (a.k.a nasal), and medium. The classification is based on the place of articulation. In total there are 18 consonants. The vowels and consonants combine to form 216 compound characters. Placing dependent vowel markers on either one side or both sides of the consonant forms the compound characters. There is one more special letter aytham used in classical Tamil and rarely found in modern Tamil. In total there are 247 letters in Tamil alphabet. In these 247 letters Zha is the most significant, because of its usage and pronunciation. Many people will not pronounce the letter 'Zha' properly. There are two letters with same sound as Zha (la, lla), so it is necessary to recognize the letter Zha.

Vowels
There are 12 vowels in Tamil, called uyireluttu (uyir -life, eluttu -letter). These vowels are classified into short (kuril) and long (five of each type) and two diphthongs, /ai/ and /au/, and three "shortened" (kurriyl) vowels. The long vowels are about twice as long as the short vowels. The diphthongss are usually pronounced about 1.5 times as long as the short vowels.

. Consonants
Consonants are known as meyyeluttu (mey-body, eluttu-letters) in Tamil. It is classified into three categories with six in each category: vallinam (hard), mellinam (soft or Nasal) and itayinam (medium).Unlike most Indian languages, Tamil does not distinguish aspirated and unaspirated consonants. In addition, the voicing of plosives is governed by strict rules in centamil (Pure Tamil). Plosives are unvoiced if they occur word-initially or doubled. Elsewhere they are voiced, with a few becoming fricatives intervocalically. Nasals and approximants are always voiced.
As commonplace in languages of India, Tamil is characterised by its use of more than one type of coronal consonants. Retroflex consonants include the retroflex approximant /(H)/ (H) (example Tamil), which among the Dravidian languages is also found in Malayalam (example Kozhikode), disappeared from Kannada in pronunciation at around 1000 AD (the dedicated letter is still found in Unicode), and was never present in Telugu. Dental and alveolar consonants also contrast with each other, a typically Dravidian trait not found in the neighboring Indo-Aryan languages. In spoken Tamil, however, this contrast has been largely lost, and even in literary Tamil, e and d may be seen as allophonic.
A chart of the Tamil consonant phonemes in the International Phonetic Alphabet follows.Phonemes in brackets are voiced equivalents. Both voiceless and voiced forms are represented by the same character in Tamil, and voicing is determined by context. The sounds /f/ and /ʂ/ are peripheral to the phonology of Tamil, being found only in loanwords and frequently replaced by native sounds.

Analysis of 'Zha' using WaveSurfer
WaveSurfer is a simple but powerful interface. The sound can be visualized and analyzed in several ways with the help of this tool. In addition, a spectrum window can be opened using Popup Spectrum Section for analyze Spectrum section plot (Magnitude Vs Frequency). Further the special control windows are available for Waveforms and Spectrograms, which allow the user to make quick modifications such as sound edit, noise elimination etc. The basic document we work with is sound files of 3 male and 3 female speakers with letter 'zha'. The standard speech analysis of the letter 'zha' such as Waveform and Spectrogram are analyzed and the samples are shown in following figures.

. Experimental Results
The variation of the Spectrum section plot in LPC is measures for the letter 'zha' is analyzed with the following parameters and the sample of magnitude Vs frequency plot is shown in figure 3 •

Hidden Markov Model 5.1 HMM Formulation
A first-order N-state Markov chain for N = 3 is shown in Figure 4. The system can be described as being in one of the N distinct states 1,2, . . . , N at any discrete time instant t. We use the state variable qt as the state of the system at discrete time t. The Markov chain is then described by a state transition probability matrix A = [a ij ], where with the following axiomatic constraints: a ij ≥ 0 and (2) 1 aij = 1 for all i.  (1) we have assumed homogeneity of the Markov chain so that the transition probabilities do not depend on time. Assume that at t = 0 the state of the system q 0 is specified by an initial state probability π i , = P (q 0 = i). Then, for any state sequence q = (q 0 , q 1 , q 2 , . . . , q T ), the probability of q being generated by the Markov chain is P (q | A, π) = π q0 a q0q1 a q1q2 …a qT-1qT (4) Suppose now that the state sequence q cannot be readily observed. Instead, we envision each observation O t , say a cepstral vector as mentioned previously, as being produced with the system in state q t , q t The joint probability of 0 and q being produced by the system is simply the product of (4) and (6), written as It then follows that the stochastic process, represented by the observation sequence O, is characterized by which describes the probability of observation being produced by the system without assuming the knowledge of the state sequence in which it was generated. The triple λ = (π, A, B) thus defines an HMM (8). In the following, we shall refer to λ as the model and the model parameter set interchangeably without ambiguity.
The particular formulation of (8) is quite similar to that of the incomplete data problem in statistics. In terms of the physical process of a speech signal, one interpretation that may be helpful for initial understanding of the problem is that a state represents an abstract speech code (such as a phoneme) embedded in a sequence of spectral observations, and because speech is normally produced in a continuous manner, it is often difficult and sometimes unnecessary to determine how and when a state transition (from one abstract speech code to another) is made. Therefore, in (8) we do not assume explicit, definitive observation of the state sequence q, although the Markovian structure of the state sequence is strictly implied. This is called a "Hidden Markov Model".

Statistical Method of HMM
In the development of the HMM methodology, the following problems are of particular interest. First, given the observation sequence O and a model λ, how do we efficiently evaluate the probability of O being produced by the source model λ-that is, P (0⎪λ) Second, given the observation O, how do we solve the inverse problem of estimating the parameters in λ. Although the probability measure of (8) does not depend explicitly on q, the knowledge of the most likely state sequence q that led to the observation (O) is desirable in many applications. The third problem then is how to deduce from observation the most likely state sequence q in a meaningful manner. According to convention, we call these three problems (i) the evaluation problem, (ii) the estimation problem and (iii) the decoding problem. In the following sections, we describe several conventional solutions to these three standard problems.

Evaluation Problem
The main concern in the evaluation problem is computational efficiency. Without complexity constraints, one can simply evaluate P (O⎪λ) directly from the definition of (8). Since the summation in (8) involves N T+1 possible q sequences, the total computational requirements are on the order of 2T. N T+1 operations. The need to compute (8) without the exponential growth of computation, as a function of the sequence length T, is the first challenge for implementation of the HMM technique. Fortunately, using the wellknown forward-backward procedure, this exorbitant computational requirement of the direct summation can be easily alleviated. A forward induction procedure allows evaluation of the probability P (O⎪λ) to be carried out with only a computational requirement linear in the sequence length T and quadratic in the number of states N. To see how this is done, let us define the forward variable α t (i) as α t (i) = P(O l , O 2 . . . , O t , q t = i⎪λ) that is, the probability of the partial observation sequence up to time t and state q t = i at time t. With reference to Figure 5, which shows a trellis structure implementation of the computation of ( ) t i α , we see that the forward variable can be calculated inductively by The desired result is simply . This tremendous reduction in computation makes the HMM method attractive and viable for speech recognizer designs because the evaluation problem can be viewed as one of scoring how well an unknown observation sequence (corresponding to the speech to be recognized) matches a given model (or sequence of models) source, thus providing an efficient mechanism for classification.

Figure 5. A Trellis Structure for the Calculation of the Forward Partial Probabilities
( ) t i α .

Estimation Problem
Given an observation sequence (or a set of sequences) O, the estimation problem involves finding the "right" model parameter values that specify a model most likely to produce the given sequence. In speech recognition, this is often called "training," and the given sequence, on the basis of which we obtain the model parameters, is called the training sequence, even though the formulation here is statistical. In solving the estimation problem, the method of maximum likelihood (ML); that is, we choose λ such that P (O⎪λ), as defined by (8), is maximized for the given training sequence O.

Decoding Problem
As noted previously, we often are interested in uncovering the most likely state sequence that led to the observation sequence O. Although the probability measure of an HMM, by definition, does not explicitly involve the state sequence, it is important in many applications to have the knowledge of the most likely state sequence for several reasons. As an example, if we use the states of a word model to represent the distinct sounds in the word, it may be desirable to know the correspondence between the speech segments and the sounds of the word, because the duration of the individual speech segments provides useful information for speech recognition. As with the second problem, there are several ways to define the decoding objective. The most trivial choice is, following the Bayesian framework, to maximize the (instantaneous) a posteriori probability.

Speech Recognition
The typical use of HMM's in speech recognition is not very different from the traditional pattern matching paradigm. Successful application of HMM methodsusually involves the following steps 1.Define a set of L sound classes for modeling, such as phonemes or words; call the sound classes V= {v l ,v 2 , . . . , v L }. 2. For each class, collect a sizable set (the training set) of labeled utterances that are known to be in the class. 3. Based on each training set, solve the estimation problem to obtain a "best" model λ i for each class v i (i = 1, 2, . . . , L). 4. During recognition, evaluate P (O⎪λ) (i = 1, 2, . . ., L) for the unknown utterance observation and identify the speech that produced O as class v j if 1 ( | ) max ( | ).

Conclusion
It is observed from voice excited LPC with Wavesurfer tool, there is a variation in magnitude of the letter 'Zha' among different people and result is implemented in first order Three-State Markov Chain With Associated Processes. To strengthen the results, more samples could collected from TamilNadu, Srilanka and Malasiya infuture and form a statistical methodology. This statistical methodology is lie in mathematical framework and implementational structure.


Method of multi-source testing information fusion based on bayesian networks

of weapon systems and the conversion of inertial navigation test information, which provides model reference and possible solutions to the Bayesian network based multi-source information fusion methods. Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all fields including engineering, biology and medicine, information and communication technologies and finance. This book is a collection of original contributions to the methodology and applications of Bayesian networks. It contains recent developments in the field and illustrates, on a sample of applications, the power of Bayesian networks in dealing the modeling of complex systems. Readers that are not familiar with this tool, but have some technical background, will find in this book all necessary theoretical and practical information on how to use and implement Bayesian networks in their own work. There is no doubt that this book constitutes a valuable resource for engineers, researchers, students and all those who are interested in discovering and experiencing the potential of this major tool of the century.

Introduction
Large Equipment System Acceptance (such as reliability and accuracy, etc.) is a complicated system engineering. In order to check the performance of large equipment, multi-perspective and more-approach tests are adopted to get a variety of test information. These information are related to many aspects, such as the information in different phases of design, development, pilot, production and application phase, test information of products in different levels (systems, subsystems, components) and the history information of test-related products. These different but still interrelated information have brought a great more reference to analysis and assessment of large-scale equipment, and meanwhile those uncertain information would brought more risk to the assessment of decision-making. How to integrate these information of multiple sources effectively to make an objective evaluation on the performance of large-scale equipments has been a great challenge to the engineering researchers. For example, in assessing the reliability of weapons systems, the cost of system-level testing is often too much which limits the number of test times. In that condition the test information of various equipment and subsystems are urged to be fully utilized. Similarly, in order to improve the practical accuracy of INS (inertial navigation system), a series of checking from the test phase to application phase such as ground calibration tests, vehicle-loaded tests, aircraft-loaded tests and missile-loaded tests, are requisite and the outcome should function to the error coefficients estimation. In the circumstance of sufficient test data, the classical approach of comprehensive assessment to reliability has been widely used; while in contrast when test data are insufficient and moreover they present multi-stage and multi-level properties, the classical approach is in effectiveness challenge. With the development of computer technology and improvement of Bayes methods, especially the emergence of MCMC (Markov chain Monte Carlo) methods and WinBUGS (Bayesian inference Using Gibbs Sampling) software, the Bayesian network is more and more popularized in the application of multi-source information fusion [1~3]. The Bayesian network is a causal network, which is used as an inference engine for the calculation of beliefs or probability of events given the observations of other events in the same network. It does not only make good use of model information and sample data, but also integrates the unknown parameters in the overall distribution of information. Besides, it has overcome defects of traditional static model being incapable of handling emergencies. These flexible, easy to adapt to external changes features can make up for the shortage of insufficient poor quality samples brought to traditional statistical methods, so it is more suitable for prediction and reality reveal to models. The most attractive feature of the Bayesian network is given an observation for one node, the statistical information for all nodes would be updated. This feature is very valuable in the context of model validation, when experimental observations may not be available on the final model output but may be available on one or more intermediate quantities. This paper presents a new approach of information fusion used Bayesian network and is organised as follows. The background of this research, especially for the application in reliability assessment and precision evaluation, is introduced in section 1. In section 2, the fundamental of Bayesian network is stated and how to establish networks for a typical case are then illustrated. In section 3, it is emphasized in utilizing Bayesian networks to integrate multi-source testing information obtained from different layers, states and environments, where the examples of reliability parameters estimation for weapon system and information conversion for inertial navigation system error model are simulated to show the effectiveness of the scheme presented. Finally, some conclusions are given in the end.

Bayesian inference
The basic idea of Bayesian inference is to express the uncertainty of all the unknown parameters of the model by probability distributions [4]. This means that an unknown parameter is modeled as a random parameter beforehand. are in the text the random parameters of our interest is denoted as The observed variables, or the evidence Actually the joint distribution model described in equation (6) consists of network of conditional dependencies between random variables. Such networks are often called Bayesian networks. A Bayesian network can be represented as a directed acyclic graph, in which elliptic nodes correspond to random variables and rectangular nodes represent constants and directed arcs between the nodes describe the dependence between the parameters. Moreover, a solid arrow indicates a stochastic dependence while a hollow arrow indicates a logical function. As an example the graphical representation of the hierarchical model described by equations (3)~(6) is depicted as a Bayesian network in Figure 1.

Implementation of the proposed method
In Bayesian models, where we are interested in the relationships of a large number of variables, Bayesian network becomes an appropriate representation. A Bayesian network is a graphical model that efficiently encodes the joint probability distribution for a large set of variables. However, determining the conditional posterior distributions for the parameters of interest is usually not a simple task in Bayesian networks. To obtain an analytic result for the conditional posterior distribution the denominator of the Bayes formula, which normalizes the conditional posterior distribution to unity, must be evaluated. A proportional result for the posterior distribution can be obtained without resolving the denominator, but the integral for the numerator is only one dimension less. For analytic result, or at least for a good approximation of the result, the integrals have to be determined in a way or another. For simple models the integrals can be evaluated using conventional numerical techniques, but in most applications the Bayesian network contain tens and hundreds of parameters and the analytic evaluation of the integrals by conventional numerical techniques is impossible. Therefore, an MCMC [5] approach is used for obtaining the posterior distribution. In MCMC methods, Monte Carlo estimates of probability density functions and expected values of the desired quantities are obtained using samples generated by a Markov chain whose limiting distribution is the distribution of interest. Thus one can generate samples of multiple random variables from a complicated joint probability density function without explicitly evaluating or inverting the joint cumulative density function. Several schemes such as Metropolis-Hastings algorithm, Gibbs sampling, etc. are available to carry out MCMC simulations [6]. Gibbs sampling is commonly utilized due to its simplicity in the implementation. Let x denote a vector of k random variables x . To sample quantities from the full conditional density of the ith variable, the following relationship is used: Gibbs sampling can then be used to sequentially generate samples from the joint probability density function using the full conditional densities, as below: Step 1: Initialize Step2: Generate Step3: 1 j j   ; Step4: End if j reaches the maximum number of runs, or else, return to step 2.
Gibbs sampling has been shown to have geometric convergence of order N (number of runs) [4]. Exact full conditional densities may not always be available. In such cases, the Gibbs sampling procedure is supplementary to the Metropolis-Hastings algorithm. During each run, the full conditional density function ( )

Testing information fusion using Bayesian networks
Since Bayesian networks can easily establish the uncertainty relationships among parameters and update all the prior distributions of the random variables once new observations come out, it is a effective solution to multi-source information fusion. In this section, two representative applications of Bayesian networks to weapon system reliability evaluation and INS testing information conversion under different circumstance are discussed as illustration. Note that the modeling and simulations in this paper are carried out using the WinBUGS program, and so all the Bayesian networks presented below are depicted in the WinBUGS format. For closer review about the WinBUGS program, see Spiegelhalter et al. [7].

Reliability evaluation of weapon system
Since a great deal of manpower and material resources are requisite in system-level tests to reliability evaluation for such complex weapon system, whereas much more convenience would be obtained if in unit-level test case, the engineering practice usually adopts reliability information of composition units to analyze the reliability of the entire system. These unit-level test information make up for the lack of information on system-level test, and reduce the number of tests in the premise of sustaining its confidence effectively. Obviously, to evaluate weapon system reliability in Bayes method is a kind of information fusion. More clearly, reliability test information about unit and system should be fused into the posterior distribution of system reliability first, and based on it the Bayesian statistical inference could then be carried out. To facilitate following discussion, suppose the weapon system is pass-fail series system.

Reliability analysis of pass-fail unit
The pass-fail unit likelihood function is In the discussion of binomial distribution, the prior distribution of reliability is often in Beta distribution, i.e.
where a and b are auxiliary parameters, Since these auxiliary parameters reflect the full utilization of prior information, selection of a and b are very critical for reliability analysis. Martz et al [8] When m is small, the sampling error may yield negative value in (11) of ( ) a b  , so that it is amended as Once the auxiliary parameters of prior distribution are determined, using Bayes' rule there would be where D is the experimental data, n is the number of tests, f indicates the number of failure. It is obviously that the posterior probability density function The Bayesian analysis method for unit reliability is deduced in above discussion. The following would proceed to system reliability calculation for pass-fail series system.

Series system synthesis
Assume all the reliability tests of system or units considered is in pass-fail type. The series system consists of p units, and denotes i  as the reliability of the constituent units, thus Denote i n as the number of unit tests, i x as the number of success, so the system reliability is Assume i  is independent of each other, and prior distribution of ( ) are the distribution parameters. The posterior probability density function of the system's reliability  is induced as If there are few units in series system, the above formula for the series' system reliability evaluation is feasible; while if the cell number is in great many, the calculations could not be sustainable any more. The system encountered in engineering practice used to be composed in many units, and the information obtained are comprised of unit reliability test information and system reliability information, in this condition the abovementioned method is incapable in handling complex tests information. The following would introduce the reliability analysis method for this kind of complex system using Bayesian network. Assume the distribution of system reliability is also subject to Beta, thus the posterior joint probability density function of unit's reliability i  and system reliability  can be rewritten as: Since the above form of joint distribution is too complex, Bayesian network of the system reliability is established in assistance. In this Bayesian network, MCMC sampling method is employed to update the network graph, hence the analysis to posterior distribution of reliability could be implemented as soon as Markov chain is stabilized. Take the three-numbered pass-fail series as an example, the Bayesian network is developed as below.  , , , R R R R represent the system's reliability and units' reliability;    Table 1. In the sample sequence of system reliability of Figure 3, the Markov chain has been shown fused completely and furthermore reached steady state in sampling 2,000 times. Figure 4 shows the profile of posterior density distribution estimation of reliability which is depicted in consistent with Beta distribution explicitly. Bayesian network integrates test information and prior information about the units and system, and the information in each node is then disseminated to the entire network through the directed link, therefore the integrated inference about the test information is realized. The advantage of this approach is that reliability statistical analysis of the system in it would be more accurate. And furthermore, the percentile information such as the upper bound of reliability are also obtained through MCMC sampling, in result the system reliability analysis is more comprehensive and effective.

Testing information fusion of INS
When the tests are implemented under different technical conditions, the error coefficients of inertial navigation system may have different statistical characteristics. This paper presents a method of multi-source testing information fusion for inertial navigation system based on Bayesian network, which might provide a new idea to the precision evaluation work. Firstly, the testing information of all sorts is interrelated to each other by circumstance-conversion-factor, and then a graphic mapping model is constructed to represent the relationship of all variables by using Bayesian network. With testing information, the post statistical characteristics of variables such as circumstance-conversion-factor can be rapidly inferred by MCMC algorithm applied in Bayesian network, and consequently information conversion of inertial navigation system error model could be carried out between different testing conditions. As the test information are related to the temperature, pressure, humidity and other circumstance factors, a standard state for each type of test should be selected beforehand. In this condition, all the information would be conversed to be the one in the corresponding standard state first, and then conversed in the reference of standard state information.   which way the established Bayesian network for information fusion is depicted as Figure 6.

Inference of circumstance conversion factor
For the sake that the tests of INS could not be a great many, there are only 10 groups of ground calibration and 5 groups of vehicle-loaded estimates to error factor generated through the simulation, see Table 2. Note that a new data generation would accompany a set of mean and standard deviation production.
Ground calibration true distribution of variables in data production 0     Table 3, where the true distribution characteristics (mean and standard deviation) of 1  , 0  and 1  may be computed out from those of other variables.
Obviously, in the case of 15 groups of observational data, Bayesian network has effectively fused the information obtained from calibration tests and vehicle-loaded tests. In comparison with prior distribution, the characteristics of the posterior distribution of all variables whether mean or standard deviation is much closer to those in real situation. Summarized from posterior statistical properties, estimates of the mean for each variable is slightly better than that of standard deviation, while the posterior inference to i shown inferior to that of K and i  . Given the limited sample size, accomplish system-level test data fusion utilizing Bayesian network is still quite effective despite of the errors exist between posterior inference and the true data.   about error coefficient were induced from ground calibration tests (see Table 4). In this condition, we wish to infer the statistical characteristics of error coefficient represents 1   in vehicle-loaded test in the light of circumstance factor K . The links among the variables are depicted in Bayesian network of Figure 15.
Ground calibration tests data true distribution of variables in data generation 0   As long as our purpose is to do the error factor conversion, so we focus on the posterior inference to 1   , K and 0  merely. According to the using means of prior information, two different conversion methods are adopted respectively.

A. Conversion method 1
At first, deal with the posterior statistical characteristics of the variables as the prior information of current coefficient in conversion, by making use of the data fusion results prior to technology improvement merely. Then update Bayesian network as shown in Figure 15 based on the improved calibration test data. Statistical inference results are obtained and displayed in Table 5.  Table 5. Statistical results in use of prior information merely As indicated from Table 5, Circumstance factor of K is hardly changed, mean of error coefficient 0  differs slightly, so there is a big gap between the posterior statistical characteristics and the real states. Although the posterior distribution of the test data is slightly "pulled back" to the real state by the novel test data, the effect is still not very obvious. That's because compared to the prior distribution on one hand, the prior distribution is more certain (standard deviation is small), and on the other the sample information is too limited, so that prior information plays a leading role in the posterior statistical inference. The novel test information is greatly weakened by the prior, which yields inferior posterior inference of 0  . In the presence of large deviations of posterior inference, the statistical results about the conversion value of error factor is not that credible in this occasion.

B. Conversion method 2
Allowing for the impact of prior information to posterior statistical inference, especially in the occasion of small samples, the error coefficient conversion is dealt with improved prior information. These improvements include two aspects. At first, in spite the prior information of error coefficient may vary from the state of current system due to technical progress, the variation is not too much so that the mean of prior distribution could be remained. Second, by increasing the standard deviation of the prior distribution, the prior information could be "fuzzed up" so that "over-conservative" posterior inference from "over-certain" prior characteristic would be avoided; but note that the standard deviation should not be set too large, otherwise the system would tend to non-informative prior and lose the useful information. The statistical inference results from improved prior information are shown in Table 6.
In contrast to the results in Table 5, the posterior statistical inference in Table 5 Table 6. Statistical results in use of improved prior information The error coefficient conversion has been done in two methods from preceding texts analysis. Since very few samples are available, the first method takes more advantage of prior information so that the impact of test information to posterior inference is very weak; while the second method of "fuzzes up" prior information to reduce its impact on posterior inference by increasing the standard deviation, in return the impact of test information is got increased. In the premise of small but capable of accurately reflecting the true statistical characteristics samples, the above examples prove the second method is better in error coefficient conversion than the first. However, when large deviations exist between sample information and the real distribution, the risk of the using the second method increases in accompany; therefore, it is not reasonable to say that the second method is certainly better than the first.

Conclusions
MCMC technology has brought a revolutionary breakthrough to the development and application of Bayes statistical theory. Especially the emergence and further promotion of WinBUGS software, which gets the Bayesian network inference of model parameters out of complicated high-dimensional integral calculations, has routinized the analysis and application of Bayesian network. This paper has discussed the reliability assessment of weapon systems and the conversion of inertial navigation test information, which provides model reference and possible solutions to the Bayesian network based multi-source information fusion methods.


CLD-shaped Brushstrokes in Non-Photorealistic Rendering

Rendering techniques based on a random grid can be improved by adapting brushstrokes to the shape of different areas of the original picture. In this paper, the concept of Coherence Length Diagram is applied to determine the adaptive brushstrokes, in order to simulate an impressionist painting. Some examples are provided to instance the proposed algorithm.

Introduction
Non-photorealistic rendering is focused on developing algorithms for generating or processing images that embody qualities such as the emphasis of certain features, the suppression of details or the use of stylization to suggest specific feelings in the observer. In fact, a wide variety of expressive styles is already existing [1,2,3,4]: among them we can find non-photorealistic rendering styles, inspired by artistic methods such as painting and drawing, useful for digital art. Many rendering techniques simulate the painter's medium, with methods emulating the diffusion of colours through different canvas and paper media to produce watercolour or oil painting effect. Our previous paper [5] describes some non photorealistic rendering algorithms which reproduce different features of impressionist painting. The common framework of such methods consists of two steps: creating a random grid of pixel positions, where to place brushstrokes, and determining the shape and the color of each brushstroke as a function of the color of nearby pixels and of some other parameters. In our previous work the shape was limited to be a circle, a rectangle or a square. The aim of this paper is to shape brushstrokes across the image according to local orientations a painter could manually follow in order to reproduce the texture of the subject. For instance, the upper left area of the picture in fig.1 should be textured with a set of straight vertical strokes, while the lower right area should correspond to angular strokes.
The behaviour of CLD diagrams (see [6], [7], [8]) approximates this requirement, as each pixel position can be associated to a local diagram which reflects both the orientation and the length of the image texture for that point. Such shapes only need to be rescaled and filled with the appropriate RGB color, as in previous algorithms.

CPB Non Photorealistic Rendering
The starting point for CLD shaping of brushstrokes is the simpler Circular Paintbrush (CPB) algorithm, described in [5]: the RGB source image of y x N N × pixels is reppresented by the three-channel brightness function , : For each step k in the sequence , a regularly spaced grid of pixel positions k G is generated as is a random value with a suitable distribution. The random grid k G′ is then computed from k G by adding to each position a displacement in the random grid, a circle of fixed radius r is generated and filled with the solid color given by the color of the corresponding regularly spaced grid pixel in the original image (displaced fill). An alternate solution is using the original color of the same position The previously discussed procedure is iterated several times according to the final desired covering of the canvas. In fact, CPB circles generated at each iterative step can partially or completely overlap. On the other side, some pixels of the output image might not be covered by any circle; in this case, a default color value is assigned, for instance white, the color of the corresponding pixel in the original image or an intermediate value.

The Coherence Length Diagram (CLD)
This section describes a new paint-brush based on CLD image processing. This image analysis method has been discussed in some previous works [7], [8]. Its interesting feature resides in the diagram it generates, which can be regarded as a synthetic way to represent the local texture of the image. Instead of considering the full RGB picture under conversion, the corresponding grayscale image is computed, as CLD is defined on a scalar brightness matrix. So, let us consider and the subscripts 1, 2, 3 correspond to the channels R, G, B. First we compute the average brightness over the whole image which is a function of the distance l . The local CLD, for a given threshold τ , represents the minimum summing distance l such that the corresponding first order moment differs from 0 M by less than τ : This quantity is not defined for all directions of all point: sometimes the edge of the image is reached without entering the desired neighborhood of 0 M . The appearance of a local CLD is represented in fig.2. To see other local behaviour of Coherence Length Diagram, Ref. [6] is quite suitable, as the more general discussion proposed in [7].

Shaping and filling brushstrokes
By connecting all vertices of the CLD in ascending order, a star shaped polygon [9] ) , ( y x P τ is generated, in which the point ) , ( y x belongs to the kernel. With an appropriate scale factor α , such surface can be considered as the brushstroke at the point where the CLD is computed. The above described methods can be used together to obtain an output image containing "well shaped" and "well oriented" brushstrokes, as required at the beginning of this discussion. Instead of generating a set of circles, like in the CPB algorithm, is considered. Clearly, in equations (2) Again, the actual appearance of the output image depends on the sequence of steps k : the latest shapes override the older ones. As stated before, (2) and (3) represent a case with color displacement. The alternate choice would be implemented by filling the generated shapes with the color ) , ( Lower values can be used for 0 d as a conservative measure. , built without and with the displacement filling. Note how the rendering turns out to be different, in particular in depicting the edges of objects. The described CLD paintbrush technique has be organized in a single tool, with the procedures previously discussed in [5]. The package will be proposed in a shortcoming paper devoted to this tool illustration.


Kernel Engineering for Fast and Easy Design of Natural Language Applications

Previous work on the use of Machine Learning for Computational Linguistics has shown that most of the design effort is devoted to feature engineering. Indeed, the latter requires expertise, intuition and deep knowledge about the target problem to convert linguistic objects into attribute-value representations. Kernel Methods (KM) are powerful techniques, which can simplify data modeling by defining abstract representations and implicit feature spaces. More in particular, KM allow for: (a) directly using a similarity function between instances in learning algorithms, thus avoiding explicit feature design; and (b) implicitly defining huge feature spaces, e.g. structures can be represented in the substructure space.

In this tutorial, practical recipes to successfully use KM for target language applications will be presented: first, after an introduction to Support Vector Machines (explained from an application viewpoint), KM theory will be explained in a way that useful practical methods can be derived. Second, basic kernels, such as linear, polynomial, sequence and tree kernels will be presented, by focusing on the implementation, accuracy and efficiency perspectives. KMapplication to typical natural language tasks, e.g. text categorization, question and answer classification, semantic role labeling, textual entailment and so on, will be shown. The aim is to provide practical procedures for the selection and exploitation of the right kernel for the target task. Third, the SVM-Light-TK toolkit, which encodes several kernels in SVMs, will be illustrated along with the associated data structures and its practical use in NL tasks. Finally, the tutorial will illustrate how innovative and effective kernels can be engineered starting from basic kernels and using systematic data transformation. Such know-how allows for a very fast and accurate design of applications even if the underlying language phenomena and properties are still not very well understood, e.g. Arabic SRL or relation extraction between pairs of text fragments.

Linear Classifier
The equation of a hyperplane is is the vector representing the classifying example is the gradient of the hyperplane The classification function is Mapping vectors in a space where they are linearly separable

A mapping example
Given two masses m 1 and m 2 , one is constrained Apply a force f a to the mass m 1 Experiments Features m 1 , m 2 and f a We want to learn a classifier that tells when a mass m 1 will get far away from m 2 The gravitational law is not linear so we need to change space

Novikoff's Theorem
Let S be a non-trivial training-set and let Let us suppose there is a vector and with γ > 0. Then the maximum number of errors of the perceptron is: In each step of perceptron only training data is added with a certain weight So the classification function Note that data only appears in the scalar product

Dual Representation for Learning
as well as the updating function The learning rate only affects the re-scaling of the hyperplane, it does not affect the algorithm, so we can fix We can rewrite the classification function as As well as the updating function

Support Vector Machines
The margin is equal to 2 k w Support Vector Machines

Support Vector Machines
Var 1 Var 2 There is a scale for which k=1.
The problem transforms in:

Optimization Problem
Optimal Hyperplane:

Subject to
The dual problem is simpler

Dual Transformation
To solve the dual problem we need to evaluate: Given the Lagrangian associated with our problem Let us impose the derivatives to 0, with respect to w 

Dual Transformation (cont'd)
and wrt b Then we substituted them in the objective function

Khun-Tucker Theorem
Necessary and sufficient conditions to optimality

Properties coming from constraints
Lagrange constraints: Karush-Kuhn-Tucker constraints Support Vectors have not null To evaluate b, we can apply the following equation

Soft Margin SVMs
slack variables are added Some errors are allowed but they should penalize the objective function i ξ

Soft Margin SVMs
The new constraints are The objective function penalizes the incorrect classified examples C is the trade-off between margin and the error

Dual formulation
By deriving wrt

Soft Margin Support Vector Machines
The algorithm tries to keep ξ i low and maximize the margin NB: The number of error is not directly minimized (NP-complete problem); the distances from the hyperplane are minimized If C→∞, the solution tends to the one of the hard-margin algorithm If C increases the number of error decreases. When C tends to infinite the number of errors must be 0, i.e. the hard-margin formulation

Kernels in Support Vector Machines
In Soft Margin SVMs we maximize: By using kernel functions we rewrite the problem as:

Kernel Function Definition
Kernels are the product of mapping functions such as

The Kernel Gram Matrix
With KM-based learning, the sole information used from the training data set is the Kernel Gram Matrix If the kernel is valid, K is symmetric definite-positive .

Valid Kernels cont'd
If the matrix is positive semi-definite then we can find a mapping φ implementing the kernel function

Mercer's Theorem (finite space)
Let us consider for Takagi factorization of a complex-symmetric matrix, where: Λ is the diagonal matrix of the eigenvalues λ t of K are the eigenvectors, i.e. the columns of V Let us assume lambda values non-negative Therefore , which implies that K is a kernel function

Mercer's Theorem (necessary conditions)
Suppose we have negative eigenvalues λ s and eigenvectors the following point has the following norm: this contradicts the geometry of the space.

Is it a valid kernel?
It may not be a kernel so we can use M´·M

Feature Conjunction (polynomial Kernel)
The initial vectors are mapped in a higher space More expressive, as encodes

Stock+Market vs. Downtown+Market features
We can smartly compute the scalar product as

Lexical Semantic Kernel [CoNLL 2005]
The document similarity is the SK function: where s is any similarity function between words, e.g. WordNet [Basili et al.,2005] similarity or LSA [Cristianini et al., 2002] Good results when training data is small

String Kernel
Given two strings, the number of matches between their substrings is evaluated

Evaluating DP2
Evaluate the weight of the string of size p in case a character will be matched This is done by multiplying the double summation by the number of substrings of size p-1 Evaluating the Predictive DP on strings of size 2 (second row) Let's consider substrings of size 2 and suppose that: we have matched the first "a" we will match the next character that we will add to the two strings We compute the weights of matches above at different string positions with some not-yet known character "?" If the match occurs immediately after "a" the weight will be λ 1+1 x λ 1+1 = λ 4 and we store just λ 2 in the DP entry in ["a","a"]

Evaluating the DP wrt different positions (second row)
If the match for "gatta" occurs after "t" the weight will be λ 1+2 (x λ 2 = λ 5 ) since the substring for it will be with "a☐?" We write such prediction in the entry ["a","t"] Same rationale for a match after the second "t": we have the substring "a☐☐?" (matching with "a?" from "catta") for a weight of λ 3+1 (x λ 2 )

Evaluating the DP wrt different positions (third row)
If the match occurs after "t" of "cata", the weight will be λ 2+1 (x λ 2 = λ 5 ) since it will be with the string "a☐?", with a weight of λ 3 If the match occurs after "t" of both "gatta" and "cata", there are two ways to compose substring of size two: "a☐?" with weight λ 4 or "t?" with weight λ 2 ⇒ the total is λ 2 +λ 4
Between "gatta" and "cata" is λ 7 + λ 5 + λ 4 , i.e the matches of "a☐☐a", "t☐a", "ta" with "a☐a" and "ta"   Fast Evaluation of STK [Moschitti, EACL 2006] where P(n x ) and P(n z ) are the production rules used at nodes n x and n z €

Algorithm Observations
We order the production rules used in T x and T z , at loading time

Observations
We order the production rules used in T x and T z , at loading time If we relax such constraint we get more general substructures [Kashima and Koyanagi, 2002]

Weighting Problems
Both matched pairs give the same contribution.
Gap based weighting is needed.
A novel efficient evaluation has to be defined

Partial Tree Kernel
By adding two decay factors we obtain:

Efficient Evaluation (1)
In [Taylor and Cristianini, 2004 book], sequence kernels with weighted gaps are factorized with respect to different subsequence sizes.
We treat children as sequences and apply the same theory D p

Efficient Evaluation (2)
The complexity of finding the subsequences is Therefore the overall complexity is where ρ is the maximum branching factor (p = ρ)

Tree forests, vector sets
The new SVM-Light-TK toolkit will be released asap

Predicate Argument Classification
In an event: target words describe relation among different entities the participants are often seen as predicate's arguments.

Example:
Paul gives a talk in Rome

Predicate Argument Classification
In an event: target words describe relation among different entities the participants are often seen as predicate's arguments. Example:

Predicate-Argument Feature Representation
Given a sentence, a predicate p:

Bags of words (BOW) and POS-tags (POS)
To save time, apply STK to these trees:

Re-ranking Framework
Local classifier generates the most likely set of hypotheses.
These are used to build annotation pairs, .
positive instances if h i more correct than h j , A binary classifier decides if h i is more accurate than h j .
Each candidate annotation h i is described by a structural representation € h i , h j

Re-ranking framework
Local Model

Syntactic Parsing Re-ranking
Pairs of parse trees (Collins and Duffy, 2002) Re-ranking concept labeling [

Conclusions
Kernel methods and SVMs are useful tools to design language applications Kernel design still requires some level of expertise Engineering approaches to tree kernels Basic Combinations Canonical Mappings, e.g.

Node Marking
Merging of kernels in more complex kernels Easy modeling produces state-of-the-art accuracy in many tasks, RTE, SRL, QC, NER, RE SVM-Light-TK efficient tool to use them

Future (on going work)
Once we have found the right kernel, are we satisfied?
What about knowing the most relevant features?
Can we speed up learning/classification at real-application scenario level?
The answer is reverse kernel engineering: [Pighin&Moschitti, CoNLL2009, EMNLP2009, CoNLL2010] Mine the most relevant fragments according to SVMs gradient Use the linear space Software for reverse kernel engineering available in the next months Thank you


WiNV: A Framework for Web-based Interactive Scalable Network Visualization

In this paper we introduce WiNV - A framework for web-based interactive scalable network visualization. WiNV enables a new class of rich and scalable interactive cross-platform capabilities for visualizing large-scale networks natively in a user's browser. Extensive experiments show that our system can visualize networks that consist of tens of thousands of nodes while maintaining fast, high-quality interaction.

I. INTRODUCTION
This paper presents a new state-of-the-art network visualization framework which supports user interactions with largescale networks in a web browser without the need for plugins or special-purpose runtime systems. Our framework supports the standard visual information browsing functionalities that include overviewing, zooming, and editing. The WiNV framework supports information discovery in two modes; the Standalone Mode and Google Earth Mode. In the Standalone mode, the application runs as a native application. In the Google Earth mode, WiNV visualizes the network on Google Earth enabling the user to get better understanding of widespread networks. Interaction is used to mold the network layout into the user's own mental model and for editing the information being visualized such as device configurations or network layout, if the underlying topology discovery algorithm is unable to discover some connections.

A. Background
Visualization of large scale graphs have been addressed in the research community [1] and [6]; open source community [2], [7], [8]; and industry [9]. However, to our knowledge, most of the tools developed so far are general purpose in such that they are not capable to visualize networks at the detailed level may be required by users. Furthermore, none of these tools is web-based, and thus, they don't benefit from the advantages the web offers. We have developed a web-based extensible framework which enables interactive visualization of networks that may consist of hundreds of thousands of devices natively in a web browser. Our framework was developed initially as a presentation-tier for the Network Management System has been developed in [4] and [5]. In multi-tiered web-based frameworks, presentation tiers (i.e. clients) are classified into either thick or thin client. A thick client typically provides most of the functionality needed for rendering the data in the browser. Thus, most of the processing time is done in the client side and the interaction with the backend is relatively low. In thin-clients, on the other hand, most of the processing is done on the back-end and consequently most of the front-end resources can be used for presentation purposes, which in turn achieves higher scalability. However, this approach requires constant communication with the backend. Web-based presentation-tier applications can also be classified into native or plug-in applications. Examples of plug-ins that are used for browser applications are Adobe Flash and Microsoft Silverlight. The functionality of plug-in-based applications is restricted by the capabilities of the base plug-in. Native applications don't require any application to be preinstalled and, consequently, they can utilize all the features provided by technologies used such as DHTML, JSP, AJAX, and DWR [3]. We chose to develop WiNV as thin-client native application but with the capability of integrating it with other native frameworks such as Google Earth.

B. Architecture
As we stated earlier, the main contribution of this work is a scalable web-based framework for visualizing very large networks while providing smooth interaction and editing capability in a user's web-browser. The framework architecture is shown in Figure 1. To make WiNV flexible and lightweight, the architecture has been designed so that the minimal amount of client's resources are consumed. Furthermore, WiNV can run in any basic browser with no external plug-ins. WiNV supports two modes: Standalone mode and Google Earth mode. In both modes, most of computation including layout, interaction and editing algorithms run on the back-end while the client only renders the network. Network elements such as devices, hosts and links are generated in-the-fly through the AJAX-based DWR technology and don't require any web-page refreshing if any changes are made, giving a very smooth interaction experience. WiNV framework consists of the following tiers: the mouse) such as clicking on a network segment or creating a network connection between two devices.
• Network Tier represents the communication between the presentation and server tiers.
• Server Tier represents the "engine" of the framework. All algorithms used for layout and other computations are loaded within the server tier.
• Persistent Tier is used to interact with the database. All changes made to the network layout by the user are reflected to the persistent tier. The WiNV architecture is modular and provides a set of extendable APIs that can be used by any web-based application to visualize the network. The set of APIs can be also used for plugging in multiple different layout algorithms. These algorithms then can be selected through the user interface. The framework can receive the data from an XML file that contains network representation, or it can be integrated to exchange the date with the middle tier of a web-based application that implements the framework's APIs. The demo phases consist of (1) Rendering -Displaying the network layout based on the initial data coming from the backend or after a change has been made; and (2) Interaction -Capturing the user command and computing modifications to the network. This process includes, for instance, zooming in, or out, changing device and link configurations or editing the topology. If the framework is set up in Google Earth mode, the user can view the network initially in very high level and a detailed layout will be rendered when zooming in the area that has the target network segment. At the very detailed level, the user could view network device interfaces and direction interconnections among them (i.e. physical network topology.) The user can also view VLAN and ISP level configurations and device/link statistics. Figure 2 demonstrates high-level network visualization in Google Earth mode. In Standalone mode, the networks are usually grouped based on their IP prefixes or physical location (i.e IP address longitude and latitude) and each group is displayed as a cloud. When the user clicks on a cloud, it expands and that network is rendered at more detailed level. At the most detailed level, all devices, hosts, and peripherals in that segment are rendered. The number of levels, data refreshing rate, and grouping methods are configurable and can be specified by the user.