Automated tagging of environmental data using a novel SKOS formatted environmental thesaurus Abstract There is increasing need to use the widest range of data to address issues of environmental management and change, which is reflected in increasing emphasis from government funding agencies for better management and access to environmental data. Bringing together different environmental datasets to confidently enable integrated analysis requires reference to common standards and definitions, which are frequently lacking in environmental data, due to the broad subject area and lack of metadata. Automatic inclusion within datasets of controlled vocabulary concepts from publicly available standard vocabularies facilitates accurate annotation and promotes efficiency of metadata creation. To this end, we have developed a thesaurus capable of describing environmental chemistry datasets. We demonstrate a novel method for tagging datasets, via insertion of this thesaurus into a Laboratory Information Management System, enabling automated tagging of data, thus promoting semantic interoperability between tagged data resources. Being web available, and formatted using the Simple Knowledge Organisation System (SKOS) semantic standard, this thesaurus is capable of providing links both to and from other relevant thesauri, thus facilitating a linked data approach. Future developments will see extension of the thesaurus by the user community, in terms of both concepts included and links to externally hosted vocabularies. By employing a Linked Open Data approach, we anticipate that Web-based tools will be able to use concepts from the thesaurus to discover and link data to other information sources, including use in national assessment of the extent and condition of environmental resources. Disparate vocabularies and sparse descriptions present in environmental data are an impediment to gaining greatest value from these data when considering their re-use or integration (Michener et al. 1997) . The concept of Linked Open Data (LOD), first proposed by Tim Berners-Lee, refers to a set of best practices for publishing and connecting structured data on the web. The concept of standardised web-accessible links within data, or documents, can be used to address issues of interoperability, within the field of environmental data, as described here, or any other discipline. The creation of a 'world wide web of data' whereby pieces of data and information are semantically related to other relevant information can greatly enhance the user's ability to derive additional value from a concept with little extra effort (Bizer et al. 2009 ), and can facilitate interoperability both within and across domains. Here, we describe the preliminary steps to implementing these concepts within a national environmental chemistry analysis facility, funded by the UK Natural Environment Research Council (NERC), though the steps and issues discussed will be relevant to any area of research wishing to promote interoperability and re-use in their sector.. There is increasing pressure on publicly funded research institutes to demonstrate value for money in the data they produce and enable others to re-use and add value to these data (e.g. Research Councils UK (RCUK 2014) Common principles on data policy). However, data lacking description of the methodologies used and/or measurements collected hinders this process (Le Duc et al. 2007 ). Inadequate annotation of data has been particularly prevalent within the field of ecology, where documentation of this information is often lacking or an afterthought in many projects (Madin et al. 2007) . The ability to generate metadata during the creation of data, in a pre-defined way would prevent inefficient use of scarce staffing resources for manual documentation work (Batcheller 2008) . Using vocabularies in retrospective creation of metadata (for example, after a project or period of work has come to an end), can be an especially time consuming process with increased risk of errors occurring in metadata and vocabulary tags. Further, early selection of vocabularies intended for use in a project will enable identification of any missing concepts, for which there is no acceptable existing vocabulary term. This allows adequate time to contact relevant vocabulary governance groups to request new concepts, rather than attempting to add new concepts to existing vocabularies at the end of a project, when timescales for completion of work are often compressed, and may be insufficient to allow for requests to external agencies to be processed. Once the desired vocabularies have been selected, development of automated methods for tagging datasets provides the advantages of minimising the time required for tagging and increasing the accuracy with which it is carried out, since it reduces human error (Ailamaki et al. 2010) . Data can also be tagged at point of source i.e. as it is produced, further reducing the likelihood of errors occurring. Deployment of such methodologies to aid in automated tagging of datasets with required information for re-use and integration will be of great benefit to the environmental and ecological communities, but will also ensure that the resources produced are able to be re-purposed by any community wishing to make use of them, without recourse to the original data generators. It is also apparent that many data generators do not fully realise the benefits of using vocabularies to describe the data they produce, and do not therefore utilise vocabularies at all, thus devaluing the datasets produced. By automating the process of tagging using vocabulary concepts, the onus to employ vocabularies is removed from the data generator, which will hopefully increase the use of vocabularies within the research community.. The location, integration and re-use of data have particular scientific value when running meta-analyses which can be a very powerful way of answering complex multi-disciplinary questions (Treseder 2004) . However, this method has been criticised historically for not comparing like with like (Arnqvist and Wooster 1995) . Automated tagging can ensure disparate datasets are semantically comparable and therefore potentially interoperable through the use of Web-accessible controlled vocabularies. Integration between datasets tagged using concepts from the same Web-accessible vocabulary, or described using a vocabulary which has public mappings to another Web-accessible vocabulary, is considerably easier than integrating datasets which do not utilise a vocabulary or are described using a separate vocabulary to which no mappings exist. This can be particularly important where data are being employed for novel purposes, not originally considered by the initial project responsible for generating the data, or in attempting to utilise data from a different discipline e.g. atmospheric scientists wishing to make use of oceanographic data, etc. Discovery of relevant datasets is also facilitated if keywords provided in discovery metadata are selected from defined vocabularies so Web search engines can identify datasets from Web-enabled data catalogues. Use of Web-accessible vocabularies can also reduce the amount of content-level or contextual metadata which must be provided alongside a dataset to permit its re-use, as all the definitions and supplementary information, including semantic relations, on the concepts can easily be read from the Web. This practise has the two-fold benefit of saving data producers' time by reducing the amount of metadata that they are required to produce, and also reducing the volume of metadata required to be processed and maintained per dataset.. The Centre for Ecology and Hydrology (CEH) operates a centralised analytical chemistry facility processing samples from NERC funded researchers and the long-term monitoring activities within CEH. Implementation of a single appropriate vocabulary within this analytical chemistry facility would have the potential to improve re-use, interoperability and discovery of all datasets produced via this laboratory for a wide range of researchers. This is of particular importance, given new drives to make data open and freely available where it has been publicly funded, where the original data generators often do not have knowledge of who has accessed the dataset, thus making unambiguous description of the data essential.. The introduction of a new Laboratory Information Management System (LIMS) within the Analytical Chemistry Group provided the ideal opportunity to experiment with the idea of automatic tagging of data at source using a controlled vocabulary. A Laboratory Information Management System is used to control and manage samples, standards, test results, reports, laboratory staff, instruments, and work flow automation (Skobelev et al. 2011) . If an appropriate vocabulary could be inserted into the LIMS, then any dataset produced would automatically be tagged with concepts from that thesaurus. This would provide the foundation for not only linking the broad range of datasets produced through this facility, but also linking these data with data produced elsewhere that contain comparable vocabulary tags. This foundation could then be used for future development of Linked Open Data where these data can be made discoverable and accessible by incorporating concepts from vocabularies into Web search and delivery tools. Implementation of the approaches described here has enabled accurate semantic interoperability between environmental chemistry datasets tagged using CAST, which has proven invaluable in a current project which aims to link environmental data from across NERC with the intention of being able to quickly assess where, spatially, analytes have been measured, regardless of the individual project or organisation responsible for collection of data. This has only been possible through use of a common vocabulary, which has been mapped to other discipline specific vocabularies. Use of CAST has also promoted re-use of data; well-defined datasets are easier for researcher to subsequently re-use as they are able to quickly understand what has been measured and how data has been generated. Further, Data Centres, such as NERC's Environmental Information Data Centre (EIDC) are increasingly requiring depositors of data to provide more detailed supporting information for datasets -material which can easily be provided using a web-accessible, publicly available vocabulary to describe data. Automation of the tagging process, via a laboratory information management system, has increased efficiency of metadata authoring and reduced the likelihood of errors occurring. By using semantic standards for development of CAST, we have ensured that the thesaurus is fully compatible with Linked Open Data standards. Future developments will see extensions to CAST by the user community, in terms of both concepts included and links to externally hosted vocabularies enabling links to a wide range of publicly funded environmental data. Through use of a Linked Open Data approach, we anticipate that Web-based tools will be able to use CAST concepts to discover and link data to other information sources, including use in national assessment of the extent and condition of environmental resources.

O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O B O O O O B O O O O O O O O B O O O B I O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Automated Adaptor Generation for Behavioral Mismatching Services Based on Pushdown Model Checking In this paper, we introduce an approach of service adaptation for behavior mismatching services using pushdown model checking. This approach uses pushdown systems as model of adaptors so that capturing non-regular behavior in service interactions is possible. Also, the use of pushdown model checking integrates adaptation and verification. This guarantees that an adaptor generated by our approach not only solves behavior mismatches but also satisfies usual verification properties if specified. Unlike conventional approaches, we do not count on specifications of adaptor contracts but take only information from behavior interfaces of services and perform fully automated adaptor generation. Three requirements relating to behavior mismatches, unbounded messages, and branchings are retrieved from behavior interfaces and used to build LTL properties for pushdown model checking. Properties for unbounded messages, i.e., messages sent and received arbitrary multiple times, are especially addressed since it characterizes non-regular behavior in service composition. This paper also shows some experimental results from a prototype tool and provides directions for building BPEL adaptors from behavior interface of generated adaptor. The results show that our approach does solve behavior mismatches and successfully capture non-regular behavior in service composition under the scale of real service applications. key words: service adaptation, behavior mismatch, pushdown model checking, unbounded messages Service composition has been recognized as one of the major issues in service oriented computing (SOC). The basic idea of SOC is to reuse services already developed, especially by other providers. Since existing services are already implemented and tested, reusing them with valid composition should save time and costs in developing new applications. However, direct composition of services is nearly impossible because of incompatibilities/mismatches of interfaces. To perform service composition while mismatches exist, service adaptation [2] provides a promising solution to compose mismatching services. Service adaptation introduces a mediate service called adaptor which coordinates interactions of services. In an adapted system of services, all interactions among services are through the adaptor so that mismatches can be avoid without modifying given services, which provides a non-intrusive way for service composition. Therefore, Services to be composed are treated as black boxes so that the purpose of service reuse can be achieved.. Existing approaches for service adaptation are, to our best knowledge, generally based on a conventional framework of software adaptation [3] . The framework is designed to use behavior interfaces and adaptation contracts to perform automated adaptor generation. More specifically, behavior interfaces requires specifications of behavior interfaces of services represented in labeled transition system (LTS), while adaptation contracts require specifications of mappings of messages to be interacted and the ordering (i.e., expected interactions coordinated by an adaptor) of these mappings represented in LTS. However, given some services that need adaptation, behavior of the adaptor as well as the system behavior may be non-regular and can not be represented in LTS, even if mapping of messages is correctly specified.. In order to tackle non-regular behavior in adaptors, we introduce an approach that uses pushdown automata model for representing behavior of adaptors. The pushdown automata model makes computations with non-regular behavior possible while not being theoretically too complicated. This is reasonable under the view point of software engineering and we still treat services as finite state machines so that our approach is applicable to the systems in conventional framework. Furthermore, our approach uses model checking technique [4] to perform mismatch detection and adaptor generation. The basic idea is to generate Linear Time Logic (LTL) properties corresponding to requirements for a system to be adapted. Thus, adaptation is integrated with verification, i.e. properties related to adaptation and verification can be put together and model checked at the same time. This means a generated adaptor by our approach not only solves behavior mismatches but also guarantees properties such as safety and liveness if specified.. Besides non-regular behavior, our approach also attempts to deal with issues in designing adaptor contracts. In the conventional framework, adaptor contracts include two parts: mapping of transition labels in behavior interfaces, and the execution of interactions corresponding to these mappings. In the former part, mappings are suggestions for solving signature mismatches while the later part is the behavior of an adaptor designed by developers. Generally, both parts have to be specified before performing adaptor generation and it counts on developers to see through behavior interfaces of services to design proper message mappings and behavior of an adaptor. Since determining Copyright c 2012 The Institute of Electronics, Information and Communication Engineers mappings of transition labels usually needs extra information other than behavior interfaces that need additional techniques such as context aware technology, we assume that necessary mappings are already given and leave this part remaining manually specified. On the other hand, executions of interactions can be obtained by analyzing behavior interfaces of services and therefore directly be used to generate adaptors. Some tools have already been developed to interactively support designing adaptor contacts [5] , [6] as LTS.. Unlike these tools, our approach does not generate adaptor contracts but directly generates adaptors from properties obtained from behavior interfaces of services. The properties are automatically retrieved so that fully automated adaptor generation without adaptor contracts is possible. This feature makes our approach especially suitable for adaptation of mobile services being dynamically found and composed. More specifically, by analyzing behavior interfaces of services, we may determine and generate properties for behavior mismatch, unbounded messages, and branchings which reflects requirements implicitly described in behavior interfaces. The first property is the key of solving behavior mismatch and the rest are information about accomplishment of functionalities of services. Among these properties, we are especially interested in unbounded messages since this property characterizes non-regular behavior that we are focusing in this approach.. The rest of this paper is organized in the following structure: Section 2 discusses related work and addresses the position of this paper; Section 3 demonstrates our approach using a motivational example to explain core ideas; Section 4 describes formal definitions of web services and adaptors; Section 5 demonstrates the detail of adaptor generation which is the major part of this paper; Section 6 shows a prototype tool and introduces directions of implementing BPEL adaptors. Section 7 shows some experimental results using a prototype tool and gives some discussions; Finally, Sect. 8 summarizes contributions of this paper and gives some future directions. In this paper, we have proposed an approach for service adaptation. With only specifications of behavior interfaces of services and necessary messages mappings for signature mismatches, our approach automatically generates adaptors that supports non-regular behavior in service composition. The use of pushdown systems successfully captures the essences of adaptors that sent messages are guaranteed being received. The requirement of unbounded messages is especially addressed to characterize the non-regular behavior in service interactions and adaptors generated by our approach is promised to reflect this requirement. We argue that these can not be guaranteed or even concerned in conventional framework where adaptors are represented by finite state machines. Furthermore, applying model checking technique makes service adaptation integrated with verifications so that an adaptor is verified at the same time being generated. This saves much time and cost than doing adaptation and verification separately. Finally, our experimental results showed that our approach is feasible to be applied on realworld web services like BPEL processes. Our approach also has generality of dealing with exclusive branchings and signature mismatches while mapping services are provided in advance.. For future directions, we are working on fulfilling the ability of the prototype tool with automated generation of BPEL adaptors to conduct more real-world experiments, especially with large-scale systems. We also plan to extend our approach on time constraints to tackle real-time issues on service adaptation. As a hot topic in component-based software engineering (CBSE), many approaches are proposed for software adaptation. Most approaches, including ours, focused on solutions for behavior mismatches between abstract behavior interfaces. Brogi et al. proposed a model-based approach [3] which uses Labeled Transition Systems (LTSs) for modeling and calculation of software adaptation. According to our survey, this approach has defined a conventional framework for software adaptation using two basic elements: behavior interfaces and adaptation contracts which are both modeled by LTS. Some work, though using different approaches on computation, is based on this framework. Tivil et al. [8] proposed a computation technique which directly constructs partial behavior of adaptor from corresponding software components, This technique gives more computational efficiency to adaptor generation while incapable of solving reordering mismatches. This technique can also integrate LTL model checking by directly composition with Büchi automata transformed from specified LTL properties. Mateescu et al. [7] used process algebra for modeling behavior, LOTOS for specification of protocols, and CADP toolbox for automated on-the-fly adaptor generation.. Recently, approaches for service adaptation became popular and techniques of software adaptation mentioned above were extended or modified for service composition. Cubo et al. applied the approach of [3] to WF/.NET framework and added verifications in their approach [9] . Mateescu et al. also extended their work in [7] to service adaptation using the model of Symbolic Transition Systems (STSs) [2] . Some other work used their own definitions for adaptation. Nezhad et al. [10] defined their own interfaces including sets of XML data and introduced an algorithm for solving interface mismatches. Mitra et al. [11] used I/O automata with history to support the multiple uses of same messages in service composition. Compare to above work, our approach attempts to capture non-regular behavior in service composition and uses pushdown automata model for representing adaptors. The use of model checking technique integrates adaptation and verification so that generated adaptor is guaranteed to satisfy both behavior mismatch free and safety/liveness properties if specified. Our previous work [12] proposed the first version of our approach that uses Büchi automata model for behavior interfaces of services. The work proposed a property called behavior mismatch free defined from acceptance condition of Büchi automata. To our best knowledge, this work was the first time generation of non-regular behavioral adaptor is tackled.. Another topic in service adaptation is automated generation of adaptation contracts. For web services, it becomes a problem that adaptation contracts have to be manually specified in the conventional framework proposed in [3] while there are mobile services that demand being selected and composed dynamically. Some research about adaptation also tackles this topic in various ways. J.A. Martín and E. Pimental [13] proposed an expert system based approach which combines exploring rules and A * graph search algorithm. Their approach automatically generates adaptation contracts (mainly mappings of labels) having the best score. Other work provides semi-automated way to guide design of adaptation contracts. Nezhad et al. [10] introduced an interactive way for users to specify adaptation contracts related to behavior mismatches on reordering. Cámara et al. developed an integrated tool ITACA [5] to support composition of BPEL services which provides interactive graphical user interface to guide the design of adaptor contracts. Compare to above work, our approach does not generate adaptation contracts but directly generates an adaptor rely on only information from behavior interfaces of services. Assuming signature mismatches are solved and mappings of labels are specified, our approach provides fully automated adaptor generation. This is proposed in our recent work [1] . Furthermore, we especially address property for unbounded messages which characterize non-regular behavior of adaptors.. Though the use of model checking technique is similar to exploring graph structures of behavior interfaces, we argue that the use of model checking brings more advantages since model checking techniques are improved rapidly as well as the feature of performing both service adaptation and verifications at the same time. In this experiment, we use the prototype tool to solve the motivational example shown in Fig. 1 . To show the general- ity of our approach, the example is modified as shown in Fig. 4 which has exclusive branchings and signature mismatches. In order to apply our approach, signature mismatches have to be solved by specifying mappings of messages. Figure 5 shows two mappings of messages represented as two special services. Note that the two services are manually designed according to signature mismatches observed from Fig. 4 . The service in the left merges information of trading order Trade and preferred price Quote from Investor into Transac to send to Online Stock Broker. The service in the right takes transaction result Record from Online Stock Broker and sends out Log to Investor. It also tells Research Department there is nothing to analysis by sending NoRawData. We call these two services mapping services. Mapping services are not necessary to meet the constraint of IA4WS: an IA4WS must have different initial and final states. Furthermore, this example has two exclusive branchings that Investor can decide either requesting analyzed data from Research Department or ask Online Stock Broker to make a transaction. Now we have five services including two mapping services and are ready to apply our approach. First, the behavior interfaces of the five services are encoded in an input file for the prototype tool. Following the steps described in Sect. 6, our tool checks compatibility of the services including mapping services. The signature mismatches are supposed to be solved by providing the two mapping services so the compatibility check should be passed. Then a Promela model is generated to perform detection of behavior mismatches by SPIN. The Promela model describes the five distributing services as finite state machines communicating synchronously, then SPIN reads the model and checks the reachability to the final state (i.e. property of behavior mis- match free) of the system. As described in Sect. 3.1, there are behavior mismatches and the check fails. Therefore we proceed to adaptor generation.. According behavior interfaces of the five services, our tool generates a coordinator for the system and computes the adapted synchronous composition. A pushdown system is generated and output as the input of MOPED model checker. Note that as described in Sect. 6, the pushdown system is modified to support fairness property for transitions. MOPED then checks the system with properties of behavior mismatch free, unbounded messages, and branchings, then successfully returns a counterexample. The counterexample is then read by our tool and an adaptor is generated as shown in Fig. 6 .. Compare to the expected adaptor in Sect. 3.1, the generated adaptor has a branching that has the following behavior:. It is easy to confirm that in this behavior, all messages received by the adaptor are finally sent, which satisfies the basic requirement of an adaptor. Also, the part related to unbounded messages, which emphasized by n and n − 1, is successfully captured in the behavior. It should be noticed that the behavior of the generated adaptor reflects the structures of behavior interfaces of the three services. Therefore, messages RawData and Data are not all packed in one group but separated just as corresponding transitions in the behavior interfaces. Also, the other branching of making a transaction is also correctly generated. Thus, we may conclude that our approach successfully generated an adaptor for the three services not only on the requirement of unbounded messages but also on the requirement of exclusive branchings as well as signature mismatches with mapping services provided. The structure shown in Fig. 7 demonstrates the adapted services coordinated by the adaptor cooperating with mapping services. Note that the adaptation part includes the adaptor and mapping services. We have followed the directions of building a BPEL adaptor in Sect. 6 and built two BPEL processes: BPEL-adaptor and BPEL-stack, which are partly shown in Fig. 8 (a) and Fig. 8 (b) . We also implemented the five services as BPEL processes and constructed a composite application shown in Fig. 8 (c) . The tests in Netbeans IDE were successful. The result shows that for given service protocols such as BPEL processes abstracted into finite state machines, our approach is capable of performing adaptor generation and finally implement a BPEL adaptor.

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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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A quantitative study on the influence of ASON/GMPLS on OPEX Abstract ASON/GMPLS is promoted as one of the key technologies to reduce Operational Expenditures (OPEX) of network operators, since it provides the tools for automating the network operations. This paper gives a detailed model and qualitative analysis of the major OPEX-affecting operation processes in the area of core and metropolitan area transport networks. Moreover, a first quantitative evaluation of the changes in the operational efforts induced by ASON/GMPLS is described. The evaluation shows a significant potential to reduce OPEX, which is to some extent independent of the type of operator. ASON/GMPLS is often promoted as a way to reduce Operational Expenditures (OPEX) of network providers. However, detailed analysis and quantitative evaluation of the changes induced by such technologies is rare. In this paper we quantify the cost reduction potential of ASON/GMPLS. We start with a detailed analysis and modeling of the most technology dependant and OPEX affecting processes within the traditional structure of operators. Then we describe the process changes to be expected by the introduction of ASON/GMPLS [1] . Based on a survey with several operators, a quantitative evaluation, based on a survey with several operators, of these OPEX changes is then provided. Due to a considerable high effort for surveys and interviews with multiple carriers this investigation goes one step beyond the general claims of advantages of ASON/GMPLS. As a result most network operators' processes are similar and can be modelled quite generically. Reductions of OPEX effort for these processes and therefore cost reductions in the order of 50% compared to traditional operations can be identified with ASON/GMPLS. Based on these results the introduction of ASON/GMPLS can generally be recommended to significantly reduce OPEX. This advantage can even be improved, if all network domains and all network layers support interworking ASON/GMPLS control planes and hereby also reduce the operational costs for end-to-end connections across multiple operators' domains.

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A rule-based system for automatic decidability and combinability This paper deals with decision procedures specified by using a superposition calculus which is an inference system at the core of all equational theorem provers. This calculus is refutation complete: it provides a semi-decision procedure that halts on unsatisfiable inputs but may diverge on satisfiable ones. Fortunately, it may also terminate for some theories of interest in verification, and thus it becomes a decision procedure. To reason on the superposition calculus, a schematic superposition calculus has been developed to build the schematic form of the saturations allowing to automatically prove decidability of single theories and of their combinations.This paper presents a rule-based logical framework and a tool implementing a complete many-sorted schematic superposition calculus for arbitrary theories. By providing results for unit theories, arbitrary theories, and also for theories with counting operators, we show that this tool is very useful to derive decidability and combinability of theories of practical interest in verification. Satisfiability procedures modulo background theories such as classical data structures (e.g., lists, records, arrays, . . . ) are at the core of many state-of-the-art verification tools. Designing and implementing satisfiability procedures is a very complex task, where one of the main difficulties consists in proving their soundness.. To overcome this problem, the rewriting approach [1] allows building satisfiability procedures in a flexible way, by using a superposition calculus [2] (also called Paramodulation Calculus in [3] ). In general, a fair and exhaustive application of the rules of this calculus leads to a semi-decision procedure that halts on unsatisfiable inputs (the empty clause is generated) but may diverge on satisfiable ones. Therefore, the superposition calculus provides a decision procedure for the theory of interest if one can show that it terminates on every input made of the (finitely many) axioms and any set of ground literals. The needed termination proof can be done by hand, by analyzing the (finitely many) forms of clauses generated by saturation, but the process is tedious and error-prone. To simplify this process, a schematic superposition calculus has been developed [3] to build the schematic form of the saturations. This schematic superposition calculus is very useful to analyze the behavior of the superposition calculus on a given input theory, as shown in [4] to prove automatically the termination and the combinability of the related decision procedure.. In [5] a schematic superposition calculus for unit theories has been considered. The calculus being defined by an inference system we have proposed a rule-based system to execute it. For the implementation the Maude system has been used because it includes support for unification and narrowing, which are key operations of the calculus of interest, and the Maude metalevel provides a flexible way to control the application of rules and powerful search mechanisms.. In this paper we go further and consider the general schematic superposition calculus for arbitrary theories, not only its restriction to unit ones. The main contribution is the presentation of a rule-based framework and a tool implementing a complete many-sorted schematic superposition calculus for non-unit theories. The tool allows us to automatically check whether the superposition calculus terminates for theories defined by arbitrary clauses. Moreover, the tool can be used to check modular termination when a combination of signature-disjoint theories is considered.. The design of a schematic paramodulation calculus for general clauses is much more involved than for unit clauses. Indeed the first version proposed in [3] contained a flaw, namely a non-termination issue. This issue was addressed by [4] , by considering a new deletion rule specific to non-unit clauses. But that rule did not take into account the constants in the theory signature (such as the constant nil in the signature of the theory of possibly empty lists). One of our contributions is to propose and implement a new schematic paramodulation calculus properly taking these constants into account. Another contribution consists in showing that the schematic superposition calculus halts for the theories in [6] , such as the theory of lists (with and without extensionality), the theory of records, the theory of possibly empty lists and the theory of arrays. Moreover, our implementation of schematic superposition provides a trace of each applied rule which is very useful to validate or invalidate saturation proofs previously described in the literature.. Our tool is also very useful for further investigations related to paramodulation calculi extending the standard paramodulation. We present here the case of a (schematic) paramodulation calculus modulo a simple fragment of arithmetic, called Integer Offsets. This extension allows us to consider classical data structures equipped with counting operators. Our tool produces automatically the schematic saturations of the corresponding theories.. The paper is structured as follows. After introducing preliminary notions and presenting paramodulation calculus in Section 2, Section 3 presents a schematic paramodulation calculus that can be used for proving termination of any fair paramodulation strategies and for checking whether paramodulation calculus decides some unions of finitely presented theories. Section 5 explains how we implement the schematic paramodulation calculus using the Maude system. Then Section 6 reports on our experimentations with the tool, to prove the termination of superposition for theories corresponding to classical data structures such as lists, records and arrays. Section 4 reports on our experiments with the extension to Integer Offsets. Finally, Section 7 concludes and presents future work. This paper has reported on an implemented proof system for designing and verifying decision procedures. The first implementation of this proof system, based on the theoretical studies in [3, 4] has been presented in [5] . This implementation could handle only unit unsorted theories (or, equivalently, defined with only one sort), while in this paper we go further and consider non-unit theories with sorts. Thanks to our tool we can also check whether the considered theory is combinable with the paramodulation calculus.. The correctness of our tool is validated by checking the decidability of classical theories such as the theory of lists, the theory of records, the theory of possibly empty lists and the theory of arrays. For all these theories paramodulation is known to terminate. Our tool computes schematic saturations that allow us to prove termination of paramodulation in an automatic and uniform way. The schematic saturations of some theories we consider were unknown and have been discovered thanks to our tool.. Some automated deduction tools are already implemented in Maude, for instance a Church-Rosser checker [20] , a coherence checker [21] , etc. Our tool is a new contribution to this collection of tools. This environment will help testing new saturation strategies and experimenting new extensions of the original (schematic) superposition calculus. Since schematic superposition is interesting beyond the property of termination, we plan to extend the tool so that one can check deduction completeness and stably infiniteness [4] which are key properties for the combination of decision procedures.. Nowadays, there is a strong interest of having a native polymorphism support in automated reasoners, as advocated in [22] . The combination problem has been initiated for some polymorphic theories [23] . To build decision procedures for polymorphic theories corresponding to data structures, like for instance a polymorphic theory of arrays, it would be very natural to use a superposition-based prover enhanced with polymorphism. Hence, the problem of adding polymorphism to superposition-based provers is a very attractive research direction but it remains unexplored.. We have discussed in this paper a first extension to the case of Integer Offsets. This case has been easily implemented by reusing our tool initially developed for standard schematic paramodulation, and provides us a bunch of new interesting examples of theories with counting operators. The case of Abelian Groups [10] is more involved and would require a substantial amount of work. The reported tool support is a firm basis for further experiments in this direction. This section briefly introduces the Maude language used to implement our schematic paramodulation calculus SPC. Then it describes the main ideas and principles of this implementation.. The tool takes as input a theory and an initial set of constrained clauses. The user has to declare the sorts and the signature of the theory, its set of axioms Ax, and the set G 0 of initial constrained clauses (in separate Maude files with appropriate names). Then a single Maude command saturates the union of Ax and G 0 and displays the saturated set in a pretty way.. With three thousands lines of code instead of one thousand, the tool significantly extends the one for the unit case presented in [5] . For the general case, more rules are implemented, notably the new rule of schematic deletion. The ordering on terms is extended to literals and clauses, and adapted to constrained variables. The tool additionally supports sorts and provides traces of rule applications. It is freely accessible online [13] .

O O O O O O O O O O O O B I O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Building a case for FIPA compliant multiagent approaches for wireless sensor networks There have been some efforts to apply agent based approaches in Wireless Sensor Networks (WSNs) for different application scenarios in recent past. These efforts have met with varying levels of success so far. However, the efforts to assess the suitability of Foundation for Intelligent Physical Agents (FIPA) compliant multiagent solutions for WSNs are almost non-existent. In this paper we present a scenario from an industrial domain and asses the suitability of a FIPA compliant multiagent based solution for it. The scenario is based on a WSN deployment in an underground mine where it could be put to use for several purposes like hazardous gas plume detection and tracking, mine lighting control, Proximate Environment Monitoring (PEM), structural health monitoring of mine structure, to name a few. However, we only consider the PEM usage scenario in this paper. Wireless Sensor Networks (WSNs) are composed of tiny nodes that have sensing, processing, and communication ca-pabilities. Their use is increasing in many spheres of human activity including, but not limited to, environmental monitoring, health-care, natural habitat monitoring, agriculture, military, structural health monitoring, pervasive computing, ambient intelligence etc. One very useful application of WSNs could be for monitoring mine environments, where they could be used as a warning system for the prevention of mining accidents. Each year human lives and machinery are lost or damaged as a result of accidents in mines around the world. Many of these lives could be saved if we make use of modern technology like WSNs as a warning system for the detection of hazardous and explosive gases like carbon monoxide and methane in underground mines. Having said that, it is also very important to choose an appropriate methodology for WSN programming that meets all the requirements posed by such a scenario. Aspects like, how to efficiently distribute commands in the network and how to collect the data of interest back at the base station, which could be fixed or mobile, must be considered. Therefore, first of all, we try to establish what sort of requirements are posed by the underground mining scenario and then we look at the possible approaches to deal with these requirements. In most industrial setups, it is one of the basic requirements, imposed by the industrial safety regulations, that the environment in close proximity to the workers should be made completely safe for them. Normally underground mining environments have many potential dangers for the miners due to the presence of hazardous gases like methane, carbon monoxide, carbon dioxide etc. Therefore, mine safety regimes have to devise ways to ensure miners' safety by providing systems for countering all potential threats. The threats in underground mines could be effectively countered if there is an effective mechanism to identify them first. Additionally, there is the need for monitoring other environmental parameters, such as temperature. The deployed infrastructure (WSNs) should be able to provide that information on a just-in-time (JIT) basis. We call this requirement Proximate Environment Monitoring (PEM) and depict it in Figure 1 . It is evident from the Figure 1 that only the nodes in close proximity to the miners take part in processing their request for environmental monitoring around them. Therefore, PEM tasks should only be disseminated to the proximate nodes of the miners. This proximate set of nodes would keep on changing as the team of miners (workers) moves through a mine. Therefore, any strategy that we use for providing for this PEM requirement should be flexible enough to tackle this important constraint.. In the next section we present three different strategies to provide for PEM requirement in our considered scenario. Our goal is to determine the effectiveness of Foundation for Intelligent Physical Agents (FIPA) [4] compliant multiagent approach with mobile base stations in relation to the other more traditional approaches. In this paper, we presented a scenario from an industrial domain and discussed the suitability of different WSN approaches for meeting requirements of PEM in it. The approaches discussed are fixed base station approach, FIPA compliant multiagent approach and database oriented approaches with mobile base stations. In the mathematical analysis, we used the metric average number of messages transferred per node to compare these approaches. In the mathematical analysis part, we ignored the drawback of fixed base station approach and analyzed it as if it were a simple mobile base station approach. The mathematical analysis showed that the goal of PEM can be more effectively achieved with a FIPA compliant multiagent approach with mobile base stations than with other approaches. However, one potential drawback that could reduce the advantage of multiagent approach over other approaches is the size of the agents. If it increases beyond two messages, then multiagent approach would no longer be as effective as database oriented approaches.

O O O O B O O O O B I I O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The stochastic stability and bifurcation behavior of an Internet congestion control model A stochastic differential equation modeling an Internet congestion control model is investigated. By analyzing the Lyapunov exponent, invariant measure and singular boundary theory, some new criteria ensuring stochastic stability and bifurcation are obtained. Numerical simulation results are given to support the theoretical predictions. Internet congestion control is an algorithm to allocate available resources to competing sources efficiently, so as to avoid congestion collapse. The whole Internet congestion and avoidance mechanism is a combination of the end-to-end TCP congestion control mechanism [1] at the end hosts and the queue management mechanism at the routers. The basis of TCP congestion control lies in the Additive Increase Multiplicative Decrease (AIMD) mechanism that halves the congestion window for every window containing a packet loss, and increases the congestion window by roughly one segment per Round Trip Time (RTT) otherwise [2] . The queue management mechanism is meant to control the congestion level at each router through different kinds of Active Queue Management(AQM) mechanisms, e.g. Drop Tail In [10] , the Internet congestion control model is described by. where W (t) denotes the average of TCP windows size (packets), q(t) is the average of queue length (packets), p(·) is the probability function of a packet mark, N(t) is the number of TCP sessions, C is the queue capacity (packets/s) and R(t) is the Round Trip Time(s) which consists of the propagation delay and queuing delay. When the queuing delay is much smaller than

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Hybrid Model for Cascading Outage in a Power System: A Numerical Study Analysis of cascading outages in power systems is important for understanding why large blackouts emerge and how to prevent them. Cascading outages are complex dynamics of power systems, and one cause of them is the interaction between swing dynamics of synchronous machines and protection operation of relays and circuit breakers. This paper uses hybrid dynamical systems as a mathematical model for cascading outages caused by the interaction. Hybrid dynamical systems can combine families of flows describing swing dynamics with switching rules that are based on protection operation. This paper refers to data on a cascading outage in the September 2003 blackout in Italy and shows a hybrid dynamical system by which propagation of outages reproduced is consistent with the data. This result suggests that hybrid dynamical systems can provide an effective model for the analysis of cascading outages in power systems. key words: power system, cascading outage, modeling, hybrid dynamical system Complex dynamics have recently emerged as a major issue in electric power systems [1] , [2] . Various technological advances and economic reform are known in power system engineering. Examples include development of semiconductor-based power apparatuses, penetration of distributed power sources, and deregulation of power markets. It is widely expected that these advances lead to a robust infrastructure for electricity supply. On the other hand, they make it difficult to grasp and tame dynamics of power systems and possibly cause the dynamics to be unpredictable. Cascading outage is one important example of the dynamics and is the generic mechanism by which outages propagate to cause widespread blackouts of power systems [3] , for example, the August 2003 blackout in North America [4] .. Transient stability of power systems is closely related to the occurrence of cascading outages. Transient stability is concerned with the ability of power system to maintain synchronism when subjected to a severe disturbance [5] , [6] . The stability is mainly governed by swing dynamics of synchronous machines. A swing usually causes overcurrents and heavy power flows. This often results in the outages of transmission and generation facilities by relay operation to protect them. They can cause further swings, thereby inducing the outages of other facilities. The interaction between swing dynamics governing transient stability and protection operation is observed in cascading outages of real power systems, for example, the September 2003 blackout in Italy [4] , [7] . As indicated by [8] - [10] , the interaction involves hybrid modeling, implying that it is modeled with both continuous-and discrete-valued variables.. Extensive studies have been devoted to modeling of cascading outages in power systems. Dobson et al. [3] proposed probabilistic models for cascading outages. Kinney et al. [11] used a simple graph-based model for the analysis of North American power grid. They revealed statistical features of cascading outages. Parrilo et al. [12] and DeMarco [13] analyzed dynamic features of cascading outages using classical models of transient stability. Accurate mathematical models are used in power system analyzers such as EMTP and RTDS that can simulate cascading outages in detail. These models are often specific to particular systems and do not readily reveal dynamical principles, because the models are not amenable to analytical comparative studies. Then it needs to seek a balance between such complexity and simpler phenomenological models.. We develop and analyze a mathematical model of cascading outage in a power system. Following the importance of transient stability, this paper focuses on the interaction between swing dynamics and protection operation. The developed model is a hybrid dynamical system [14] - [16] and consists of families of flows describing swing dynamics and their switching rules that are based on control schemes of protection operation. The model also can be formulated as hybrid automaton [17] which is introduced to model power systems by Hiskens [18] , Fourlas et al. [19] , and Hikihara et al. [20] , [21] . This paper refers to data on a cascading outage in the September 2003 blackout in Italy [7] and shows a hybrid dynamical model by which propagation of outages reproduced is consistent with the data. Numerical simulations of the hybrid dynamical system also show that control schemes of protection operation affect the occurrence of the cascading outage. Preliminary discussions of this paper are in [22] , [23] .. This paper is organized as follows: Sect. 2 reviews data observed in a cascading outage in the September 2003 blackout in Italy [7] . Section 3 reviews a continuous-time model for swing dynamics [24] , [25] and control schemes of overcurrent relay and circuit breaker [26] , thereby derivCopyright c 2009 The Institute of Electronics, Information and Communication Engineers ing a hybrid dynamical system as a mathematical model of the cascading outage. Section 4 performs numerical simulations of the hybrid dynamical system. Section 5 is the conclusion of this paper with a summary and future work.

O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B B I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O B I O O O O O O O B I I O O O O O O O O O O O O O O


A Framework for Computing the Privacy Scores of Users in Online Social Networks A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities.In this article, we approach the privacy issues raised in online social networks from the individual users' viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user's potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility. In recent years, online social networking has moved from niche phenomenon to mass adoption. As we are writing this article, the two largest social-networking Web sites in the U.S., Facebook and MySpace, each already has more than 110 million monthly active users [Owyang 2008 ]. The goal of users upon entering a social network is to contact or be contacted by others, meet new friends or dates, find new jobs, receive or provide recommendations, and much more. Liu and Maes [2005] estimated that well over a million self-descriptive personal profiles are available across different Web-based social networks in the United States. According to Leonard [2004] , already in 2004, seven million people had accounts on Friendster, two million users were registered with MySpace, while a whopping sixteen million users were registered on Tickle for a chance to take a personality test. Facebook, for example, has spread to millions of users [Gross and Acquisti 2005] , that span of various educational institutions, high-school, undergraduate, and graduate students, and faculty members, staff, and alumni-not to mention the vast media attention it has received.. As the number of users of these sites and the number of sites themselves explode, securing individuals' privacy to avoid threats such as identity theft and digital stalking becomes an increasingly important issue. Unfortunately, even experienced users who are aware of their privacy risks are sometimes willing to compromise their privacy in order to improve their digital presence in the virtual world. That is, they prefer being popular and "cool" to being conservative with respect to their privacy settings. They know that loss of control over their personal information poses a long-term threat, but they cannot assess the overall and long-term risk accurately enough to compare it to the shortterm gain. Even worse, setting the privacy controls in online services is often a complicated and time-consuming task that many users feel confused about and usually skip.. Past research on privacy and social networks (e.g., Backstrom et al. [2007] ; Hay et al. [2008] ; Liu and Terzi [2008] ; Ying and Wu [2008] ; Zhou and Pei [2008] ) has mainly focused on corporate-scale privacy concerns, that is, how to share a social network has owned by an organization without revealing the identities of or sensitive relationships among the registered users. Not much attention has been given to the privacy risk for individual users posed by their information-sharing activities.. In this article, we address the privacy issue from the user's perspective: we propose a framework that estimates a privacy score for each user. This score measures the user's potential privacy risk due to his or her online informationsharing behavior. With this score, we can achieve the following.. -Privacy risk monitoring. The score serves as an indicator of the user's potential privacy risk. The system can estimate the sensitivity of each piece of information the user has shared, and send alert to the user if the sensitivity of some information is beyond the predefined threshold. -Privacy setting recommendation. The user can compare his or her privacy score with the rest of the population to know where he or she stands. In the case where the overall privacy score of a user's social graph is lower than that of the user himself or herself, the system can recommend stronger privacy settings based on information from the user's social neighbors. -Social study. As a byproduct, the system can estimate the inherent attitude of each individual. This psychometric measure can help sociologists study the online behavior of users.. The overall objective of our work is to enhance public awareness of privacy, and to reduce the complexity of managing information sharing in social networks.. From the technical point of view, our definition of privacy score satisfies the following intuitive properties: The score increases with (i) the sensitivity of the information being revealed and (ii) the visibility of the revealed information within the network. We develop mathematical models to estimate both the sensitivity and visibility of the information, and we show how to combine these two factors in the calculation of the privacy score.. -Contribution. To the best of our knowledge, we are the first to provide an intuitive and mathematically sound methodology for computing users' privacy scores in online social networks. The two principles stated above are rather general, and many models would be able to satisfy them. In addition, the specific model we propose in this article exhibits two extra advantages: (i) it is container independent, meaning that scores calculated for users belonging to different social networks (e.g., Facebook, LinkedIn, and MySpace) are comparable, and (ii) it fits the real data. Finally, we give algorithms for the computation of privacy scores that scale well and indicative experimental evidence of the efficacy of our framework. Our models draw inspiration from the Item Response Theory (IRT) [Baker and Kim 2004] and Information Propagation (IP) models [Kempe et al. 2003 ]. -Overview of our framework. For a social-network user, j, we compute the privacy score as a combination of the partial privacy scores of each one of his or her profile items, for example, the user's real name, email, hometown, mobile-phone number, relationship to status, sexual orientation, IM screen name, etc. The contribution of each profile item to the total privacy score depends on the sensitivity of the item and the visibility it gets due to j's privacy settings and j's position in the network.. Here, we assume that all N users specify their privacy settings for the same n profile items. These settings are stored in an n × N response matrix R. The profile setting of user j for item i, R (i, j), is an integer value that determines how willing j is to disclose information about i. The higher the value, the more willing j is to disclose information about item i. In general, large values in R imply higher visibility. On the other hand, small values in the privacy settings of an item are an indication of high sensitivity; it is the highly sensitive items that most people try to protect. Therefore, the privacy settings of users for their profile items stored in the response matrix R have lots of valuable information about users' privacy behavior. Our first approach uses exactly this information to compute the privacy score of users. We do so by employing notions from Item Response Theory (IRT) [Baker and Kim 2004] . The position of every user in the social network also affects his or her privacy score. The visibility setting of the profile items is enhanced (or silenced) depending on the user's role in the network. For example, the privacy risk of a completely isolated individual is much lower than the privacy risk of a popular individual, even if both have the same privacy settings in their profiles. In our extended version of privacy-score computation, we take into account the social-network structure and use models and algorithms from information-propagation and viral marketing studies [Kempe et al. 2003 ].. -Remarks. In this article, we do not consider how to conduct inference attacks to derive hidden information about a user based on his or her publicly disclosed data. We deem this inference problem as important, albeit orthogonal, to our work. Some profile items such as hobbies are composite since they may contain many different kinds of sensitive information. We decompose these kinds of items into primitive ones. Again, determining the granularity of the profile items is considered an orthogonal issue to the problem we study here.. Although the privacy scores computed by a single method are all comparable (i.e., they are on the same scale), the scale across different methods varies. Adjusting the scales of measures that have totally different ranges can sometimes be tricky, and crude normalization can lead to misinterpretations of the results. In this article, we do not adjust the scales of different scores. Instead, we emphasize the properties of these scores and the ranking of users with respect to their privacy scores.. -Organization of the material. After the presentation of the related work in Section 2 and the description of the notational conventions in Section 3, we present our definitions of privacy score and present algorithms for computing it. Our initial privacy score definition ignores the structure of the social network; its computation is only based on the privacy settings users associate with their profile items. The models and algorithmic solutions associated with this definition of privacy score are given in Sections 4, 5, 6, and 7. We extend our definition of privacy score to take into account the social-network structure in Section 8. Experimental comparison of our methods is given in Section 9. We conclude the article in Section 10. We have presented models and algorithms for computing the privacy scores of users in online social networks. Our methods take into account the privacy settings of users with respect to their profile items as well as their positions in the social network. Our framework uses notions from item response theory and information-propagation models. We described the mathematical underpinnings of our methods and presented a set of experiments on synthetic and real data that highlight the properties of our models and the current trends in users' behavior. We believe that our framework tackles the issue of privacy in online social networking from a new user-centered perspective and can prove useful in growing users' awareness. The Naive computation can be done efficiently in O (Nn) time. But the disadvantage is that the sensitivity values obtained are significantly biased by the user population contained in R. If the users happen to be quite conservative and they rarely share anything, then the estimated sensitivity values can be very high; otherwise the values can be very low if the users are very extrovert. Therefore, the Naive approach does not exhibit the nice group invariance property. Moreover, as we will show in the experimental section, the probability model defined by Equations (15) and (16), though simple and intuitive, fails to fit the real-world response matrices R (in terms of χ 2 goodness-of-fit). To the best of our knowledge, we are the first to present a framework that formally quantifies the privacy score of online social-network users. None of the previous work on privacy-preserving social-network analysis [Backstrom et al. 2007; Hay et al. 2008; Liu and Terzi 2008; Ying and Wu 2008; Zhou and Pei 2008] has addressed privacy concerns from this perspective. Past work has mostly considered anonymization methods and threats to users' privacy once an anonymized social network is released. What we consider as the most relevant work is that on scoring systems for measuring popularity,. creditworthiness, trustworthiness, and identity verification. We briefly describe these scores here.. -QDOS score. Garlik, a UK-based company, launched a system called QDOS 1 for measuring people's digital presence. The QDOS score is determined by four factors: (1) popularity, that is, who and how many people know you; (2) impact, that is, the extent to which people are influenced by what you say; (3) activity, that is, what you do online; and (4) individuality, that is, how easily you can be located online. Although QDOS can be potentially used to measure one's privacy risk, the primary purpose of this system as of today is the opposite; it encourages people to enhance their digital presence. More importantly, QDOS uses a different mathematical model based on spectral analysis of the input social network. Our model, on the other hand, exploits item response theory and information-propagation models.. -Credit score. A credit score is used to estimate the likelihood that a person will default on a loan. The most famous one, the FICO score, 2 was originally developed by Fair Isaac Corporation in 1956. Nowadays, this ubiquitous three-digit number is used to evaluate the creditworthiness of a person. The credit score is different from our privacy score, not only because it serves different purposes but also because the input data used for estimating the two scores as well as the estimation methods themselves are different.. -Trust score. A trust score is a measure of how much one a member of a group is trusted by the others. There is a large body of applications and research on this topic, see for example, eBay's sellers and buyers rating system, trust management for the Semantic Web [Richardson et al. 2003 ], etc. Trust scores could be used by social-network users to determine who can view their personal information. However, our system is used to quantify the privacy risk after the information has been shared. Ahmad [2006] described a method for managing the release of private information. When a request is received, the information provider calculates a score to serve as a confidence level for authentication and authorization associated with the request. The provider releases the information only when the score is above a predefined threshold.. -Identity score. An identity score 3 is used for tagging and verifying the legitimacy of a person's public identity. It was originally developed by financialservice firms to measure the fraud risk of new customers. Our privacy score is different from an identity score since it serves a different purpose. The purpose of the experimental section is to illustrate the properties of the different methods for computing users' privacy scores and pinpoint their advantages and disadvantages. From the data analysis point-of-view, our experiments with real data show interesting facts about users' behavior.

O O O O O O O O O O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Optical Amplifiers Placement in WDM Mesh Networks for Optical Multicasting Service Support Abstract-The problem of placing optical amplifiers (OAs) in wavelength-routing mesh networks has been studied in the literature in two contexts: network provisioning [1] and connections provisioning [2]. In this paper, we introduce optimal and heuristic solutions for the network provisioning problem. The solution is based on constructing a multicast forest for each multicast connection with the goal of minimizing the total number of OAs needed in the network, hence reducing its cost. The optimal solution is formulated as a mixed integer linear program (MILP). On the other hand, the heuristic solution is obtained by dividing the problem into subproblems and solving them separately while taking the interdependency between these subproblems into consideration. The results obtained from both solutions are compared and they are found to be a good match. R outing and wavelength assignment (RWA) is a fundamental problem in wavelength routing optical networks that has been investigated extensively in the literature [3] . However, most studies concentrate on the classical view of the problem in which the best routing structure and wavelength(s) are computed for all or some sessions while optimizing the network throughput, wavelength usage, or blocking probability. With this classical view, most solutions do not consider practical issues, such as the power loss and noise. The importance of these issues stems from the fact that when a solution may exists for the classical RWA problem, it may not necessarily be feasible in practice.. We focus in this paper on the effect of power loss on RWA under the all optical multicasting (AOM) scenario [4, 5] . In a nutshell, AOM is about supporting multicast service in the optical domain by eliminating any conversion of the transport signal between the electronic and optical domains at the intermediate nodes. To achieve this goal, branching nodes of the multicast structures (called light-trees or lightforests) are equipped with passive optical splitters [6] that are configured to split the power strength of the incoming signal into two or more outgoing links. This operation scheme has the advantages of 1) achieving signal transparency with respect to traffic type, bit rates, and protocol; 2) simplifying the logical network stack structure; and 3) reducing the cost of the switching nodes by eliminating the extra hardware of signal conversion.. A main source of power loss is the attenuation due to propagation in optical fiber, hence called the propagation loss. However, under AOM, the optical signal faces an additional source of power loss due to splitting at the branching nodes of the light-trees/forests. This is called splitting loss. Because of this extra power loss, the traditional optical amplifiers placement (OAP) problem becomes more challenging.. OAP problem is investigated in the literature in two contexts. In the first context, namely, network provisioning, the problem is formulated as a network design problem with the objective of minimizing the network cost. In [1] , we introduced a mixed integer linear program (MILP) that solves the routing (R), wavelength assignment (WA), and optical amplifier placement (OAP) subproblems in an integrated way for AOM traffic. The network cost in [1] is represented as the total power amplification needed in the network.. For the unicast traffic, the authors in [7] addressed the OAP problem in the simpler broadcast-and-select architecture where no routing is performed. The problem was solved using mixed integer non-linear programming (MInLP) with the objective of minimizing the total number of OAs. The work in [8] generalized the problem in [7] by incorporating different layout topologies (stars, trees, and/or rings) and by taking into account the fact that the cost of OAs is location dependent. The proposed solution is based on simulation annealing. While the studies in [1, 7, 8] considered the case of having unequally powered signals at the entry point of the OAs, [9] solved the equal powered signals instance of the problem proposed in [7] using MILP.. The second aspect of the OAP problem, namely, connection provisioning, studies the impact of power constraints on the operation of already provisioned networks. In this context, we proposed an MILP in [2] that provides the optimal RWA solutions for AOM. We also designed a heuristic algorithm that relies on a special link cost function that relates the routing decisions with various power constraints and produces comparable near-optimal solutions. The authors of [10] followed an iterative heuristic approach in which an initial tree is modified by replacing a set of adjacent splitting nodes by a single splitting node. Another heuristic algorithm is proposed in [11] that ensures a minimum signal quality and fairness among all destinations. This is achieved using balanced lighttrees.. The goal of this paper is to investigate the network provisioning aspect of the OAP. Our study takes two directions. In the first direction, we formulate the OAP problem as an MILP with the objective of minimizing the total number of OAs. The MILP solution provides the optimal solution for the problem by solving all the constituent subproblems jointly. However, the MILP formulation cannot provide fast solution for large instances of the problem. Therefore, in the second direction of our study, we solve the problem using a greedy heuristic that provides faster, yet nearoptimal, solutions for the OAP problem. The algorithm is based on formulating link and path cost functions that enable us to base the routing decisions of the sessions on the required number of OAs.. The remainder of the paper is organized as follows. We first define the OAP problem and the system model under OAM in Section III. This is followed by the MILP formulation in Section III. Section III also includes an extension to our original formulation to handle the case of asymmetric splitting loss at the MC nodes. However, this extension results in a non-linear formulation. The heuristic is then introduced in Section IV. Some numerical results are presented in Section V. Finally, we conclude the paper in Section VI. We studied the problem of optical amplifiers placement (OAP) and proposed three solution schemes for it in this paper. The first scheme produces optimal solutions using the MILP formulation. The other two schemes are proposed as two modes of operation for the same heuristic scheme (called the OP algorithm), namely, fixed and adaptive modes. The design of this heuristic consists of several appealing characteristics that make it able to produce near-optimal solutions in an efficient manner. The results also show that the adaptive scheme outperforms the fixed scheme in terms of number of OAs and network resources; yet, it requires extra computation.. The optimal formulation in this paper can be extended to include more practical issues. One extension is to include the case of asymmetric splitting at MC nodes. This extension has been included in Subsection III.D in the paper. However, it has resulted in a nonlinear formulation. A second extension is to take the impact of the noise induced by the amplified spontaneous emission in optical amplifiers, and the need to include regenerators, which will increase the cost of the system. Also, this will introduce another factor of non-linearity in the problem formulation. As part of our future work, we plan to include these factors in the formulation, while exploring the use of linear formulations, which may include approaches to transform the non-linear equations into linear ones.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O