Search

This is a summary of the papers presented at the Active Conceptual Modeling of Learning (ACM-L) Workshop, November 8, 2006, Tucson, Arizona, USA, and several invited papers. [ABSTRACT FROM AUTHOR]

The concept of Active Conceptual Modeling of Learning (ACM-L) has been explored in order to capture content and context changes that permit a comprehensive learning from the past, understanding the present, and forecasting the future. Such capability has not been fully explored and it is not available with today's static oriented database system. The potential of creating a "database of intention" that can have its own aim to understand the intentions of its users and the changes to their environment. This paper explores an architectural approach for the "database of intention" with predictability power. The proposed architecture is presented, illustrated, and discussed. [ABSTRACT FROM AUTHOR]

One distinct characteristics of the context-aware systems is their ability to react and adapt to the evolution of the environment, which is often a result of changes in the values of various (possibly correlated) attributes. Based on these changes, reactive systems typically take corrective actions, e.g., adjusting parameters in order to maintain the desired specifications of the system's state. Pro-active systems, on the other hand, may change the mode of interaction with the environment as well as the desired goals of the system. In this paper we describe our (ECA)2 paradigm for reactive behavior with proactive impact and we present our ongoing work and vision for a system that is capable of context-aware adaptation, while ensuring the maintenance of a set of desired behavioral policies. Our main focus is on developing a formalism that provides tools for expressing normal, as well as defeasible and/or exceptional specification. However, at the same time, we insist on a sound semantics and the capability of answering hypothetical "what-if" queries. Towards this end, we introduce the high-level language $\mathcal L_{\mathcal EAR}$ that can be used to describe the dynamics of the problem domain, specify triggers under the (ECA)2 paradigm, and reason about the consequences of the possible evolutions. [ABSTRACT FROM AUTHOR]

The new field of active conceptual modeling for learning from surprises (ACM-L) may be helpful in preserving life, protecting property, and improving quality of life. The conceptual modeling community has developed sound theory and practices for conceptual modeling that, if properly applied, could help analysts model and predict more accurately. In particular, we need to associate more semantics with links, and we need fully reified high-level objects and relationships that have a clear, formal underlying semantics that follows a natural, ontological approach. We also need to capture more dynamic aspects in our conceptual models to more accurately model complex, dynamic systems. These concepts already exist, and the theory is well developed; what remains is to link them with the ideas needed to predict system evolution, thus enabling risk assessment and response planning. No single researcher or research group will be able to achieve this ambitious vision alone. As a starting point, we recommend that the nascent ACM-L community agree on a common core model that supports all aspects—static and dynamic—needed for active conceptual modeling in support of learning from surprises. A common core will more likely gain the traction needed to sustain the extended ACM-L research effort that will yield the advertised benefits of learning from surprises. [ABSTRACT FROM AUTHOR]

Run-time application environments are affected by the changes in mini-world or technology changes. Large number of applications are process driven. For robust applications that can evolve over time, there is a need for a methodology that implicitly handles changes at various levels from mini-world to run-time environment through a layers of models and systems. In this paper, we present ER* methodology for evolving applications. In the context of this paper, the role of two-way active behaviour and template driven development of applications is presented. This methodology facilitates capturing active behaviour from run-time transactions and provides a means of using this knowledge to guide subsequent application design and its evolution. [ABSTRACT FROM AUTHOR]

Database and information systems technology has substantially changed. Nowadays, content management systems, (information-intensive) web services, collaborating systems, internet databases, OLAP databases etc. have become buzzwords. At the same time, object-relational technology has gained the maturity for being widely applied. Conceptual modelling has not (yet) covered all these novel topics. It has been concentrated for more than two decades around specification of structures. Meanwhile, functionality, interactivity and distribution must be included into conceptual modelling of information systems. Also, some of the open problems that have been already discussed in 1987 [15, 16] still remain to be open. At the same time, novel models such as object-relational models or XML-based models have been developed. They did not overcome all the problems but have been sharpening and extending the variety of open problems. The open problem presented are given for classical areas of database research, i.e., structuring and functionality. The entire are of distribution and interaction is currently an area of very intensive research. The presentation of open problems is combined with the introduction to the achievements of conceptual modelling. The paper develops an approach to conceptual modelling for object-relational, collaborating information systems that support virtual communities of work, integration of information systems, varieties of architecture such as the OLTP-OLAP architecture, varieties of play-out and play-in systems, and data analysis engines. The paper is based on an extended entity-relationship model that covers all structuring facilities of object-relational systems. It uses the theory of media types and storyboards for the specification of interactivity and provides a framework for collaboration. The paper presents 20 open problems that need to be solved for conceptual modelling. The problems are sketched. Main references and the background are given. Additional references can be provided by the author on demand. [ABSTRACT FROM AUTHOR]

Metaphor is a high-level abstract concept that can be an important part of active conceptual modeling. In this paper, we use the extended Unified Modeling Language (UML) for metaphor modeling. We discuss how to create UML diagrams to capture knowledge about metaphors. The metaphor-based processing system on the Semantic Web can support new query/search operations. Such a computer system can be used for a broad spectrum of applications such as predicting surprises (e.g., terrorist attacks) or generating automatically new innovations. [ABSTRACT FROM AUTHOR]

Conceptual changes and historical information have not been emphasized in traditional approaches to conceptual modeling such as the entity-relationship approach. Effective representations for such changes are needed to support robust machine learning and computer-aided organizational learning. However, these aspects have been modeled and studied in other contexts, such as software maintenance, version control, software transformations, etc. This paper reviews some relevant previous results, shows how they have been used to simplify conceptual models to help people make sense out of complex changing situations, and suggests some connections to conceptual models of machine learning. Areas where research is required to support conceptual models for adaptive systems are also explored. These are suggested by studies of the issues surrounding deployment of adaptive systems in mission critical environments. [ABSTRACT FROM AUTHOR]

Terrorist organizations attain their goals by attacking various targets to jeopardize human lives and intimidate governments. As new terrorist attacks almost always aim to break the mold of old plots, tracing the dynamic behaviors of terrorists becomes crucial to national defense. This paper proposes using active modeling in analyzing unconventional attacks in the design of counterterrorism system. The intelligent terrorism detection system not only detects potential threats by monitoring terrorist networks with identified threat patterns, but also continually integrates new threat features in terrorist behaviors and the varying relationships among terrorists. [ABSTRACT FROM AUTHOR]

Reducing the complexity and surprise of emergencies can efficiently alleviate negative impacts on society, either local or global. A good model which can simplify complex events by constructing views to describe changes of entity behaviors and the dynamic and time-varying relationships is the key approach to handle surprise crises. Conventional conceptual modeling has limitations on mapping dynamic real world. Active modeling is suggested as the appropriate way to deal with such surprises. A case study of Hurricane Katrina is used as an example to explain some active modeling concepts including temporal conceptual modeling, multi-perspective modeling, and data, information and knowledge integration. [ABSTRACT FROM AUTHOR]

This paper presents a context-aware text summarizer based on ontologies intended to be used for adapting information to mobile devices. The system generates summaries from texts according to the profile of the user and the context where he/she is at the moment. Context is determined by spatial and temporal localization. Ontologies are used to allow identifying which parts of the texts are related to the user's profile and to the context. Ontologies are structured as hierarchies of concepts and concepts are represented by keywords with a weight associated. [ABSTRACT FROM AUTHOR]

We argue that a key requirement on an information system that can implement an active conceptual model of learning from surprises is the ability to query data that is not query-able by content, especially data streams;we suggest that such data be queried by context. We propose an enhancement of entity-relationship modeling with active constructs in order to permit such streams to have context-based relationships with standard data. We propose a framework wherein the analysis of surprises and the subsequent monitoring of states that are ripe for such events are possible by the use of such contexts. [ABSTRACT FROM AUTHOR]

Information modelling is a collection of dynamic processes, in which its content develops from physical processes to abstract knowledge structures. We study that collection on several levels of abstraction of human cognition and knowledge. These processes can be performed through various approaches, on several levels, and by using several perspectives. We concentrate on active conceptual modelling, which has become important in the science and technology, including educational sciences and learning. It is a process of recognition, finding or creating relevant concepts and conceptual models which describe the UoD, representing the conceptual content of information to be contained in the IS. This characterisation contains the construction of new concepts, too. We study methods for collecting information from various sources in the UoD and accumulating it as possibly actual instances of various types of pre-defined concepts. Some of these instances may be cases of sudden events or processes. They should be recognised as concepts and included in to the conceptual schema. To some extent, some concepts may be constructed which fit to this collected information. During the adaptation process we are applying active conceptual modelling for learning, which organises our conceptual schema in a new way. Learning is a process in which a learner re-organises, removes or refills his knowledge structures by applying his newly organised conceptual schema. [ABSTRACT FROM AUTHOR]

Worldwide globalization increases the complexity of problem solving and decision-making, whatever the endeavor is. This calls for a more accurate and complete understanding of underlying data, processes and events. Data representations have to be as accurate as possible, spanning from the current status of affairs to its past and future statuses, so that it becomes feasible, in particular, to elaborate strategies for the future based on an analysis of past events. Active conceptual modeling is a new framework intended to describe all aspects of a domain. It expands the traditional modeling scope to include, among others, the ability to memorize and use knowledge about the spatial and temporal context of the phenomena of interest, as well as the ability to analyze the same elements under different perspectives. In this paper we show how these advanced modeling features are provided by the MADS conceptual model. [ABSTRACT FROM AUTHOR]

There are four classes of information system that are not well served by current modelling techniques. First, there are systems for which the number of instances for each entity is relatively low resulting in data definition taking a disproportionate amount of effort. Second, there are systems where the storage of data and the retrieval of information must take priority over the full definition of a schema describing that data. Third, there are those that undergo regular structural change and are thus subject to information loss as a result of changes to the schema's information capacity. Finally, there are those systems where the structure of the information is only partially known or for which there are multiple, perhaps contradictory, competing hypotheses as to the underlying structure. This paper presents the Low Instance-to-Entity Ratio (LItER) Model, which attempts to circumvent some of the problems encountered by these types of application. The two-part LItER modelling process possesses an overarching architecture which provides hypothesis, knowledge base and ontology support together with a common conceptual schema. This allows data to be stored immediately and for a more refined conceptual schema to be developed later. It also facilitates later translation to EER, ORM and UML models and the use of (a form of) SQL. Moreover, an additional benefit of the model is that it provides a partial solution to a number of outstanding issues in current conceptual modelling systems. [ABSTRACT FROM AUTHOR]

The SPAWAR Systems Center (SSC San Diego) of the U.S. Navy hosted two workshops on Active Conceptual Modeling of Learning (ACM-L). The first workshop was held at SSC San Diego on May 10-12, 2006 to introduce the Science &Technology (S&T) Initiative and identify a Research and Development agenda for the technology development investigation. Eleven invited researchers in Conceptual Modeling presented position papers on the proposed S&T Initiative. The second workshop was held on November 8, 2006 at the 25th International Conference on Conceptual Modeling, ER 2006, 6-9 November 2006, in Tucson, Arizona. Complementary to the May Workshop, the November workshop was a forum for the international researchers and practitioners to present their papers as a result of a call for papers and to exchange ideas from various perspectives of the subject. This paper describes research issues identified by participants from the two ACM-L workshops. [ABSTRACT FROM AUTHOR]

Many people use the Internet to find pictures of things. When extraneous images appear in response to simple queries on a search engine, the user has a hard time understanding why his seemingly clear request was not properly satisfied. If the computer could only understand what he wanted better, then maybe the results would be more precise. The introduction of an ontology, though hidden from the user, into current image retrieval engines may provide more accurate image responses to his query. The improvement of the results translates into the possibility of offering structured results, to disambiguate queries and to provide more interactivity options to the user, transforming the current string of character based retrieval into a concept based process. Each one of these aspects is presented and examples are used to support our proposals. We equally discuss the notion of picturability and justify our choice to work exclusively with entities that can be directly represented in a picture. Coordinating the use of a lexical ontology (an OWL representation of WordNet) with image processing techniques, we have developed a system that, given an initial query, automatically returns images associated with the query using automatic reformulation (each concepts is represented by its deepest hyponyms from the ontology). We show that picking randomly from this new set of pictures provides an improved representation for the initial, more general query. We also treat the visual aspects of the images for these deepest hyponyms (the leaves of WordNet). The depictions associated to leaf categories are clustered into coherent sets using low-level image features like color and texture. Some limitations (e.g. the quality and coverage of the semantic structure, the impossibility to answer complex queries) of the ontology based retrieval are equally discussed. [ABSTRACT FROM AUTHOR]

Due to the extremely rapid growth of the web, search engines cannot spider all the new pages at the same time or with the same priority. Besides, the search engines index their catalogues with different algorithms, having as a result, different response time in updating their directories. Consequently, the user may lose some useful information resources if he chooses to use only one search service. Thus, the user has to resubmit his queries to more search engines, adding in this way, to a process that is already time-consuming [11]. Using a meta-search engine, additional information is provided, without the need to know the query language for the search services that contribute to the search. However, in this kind of retrieval a large amount of inappropriate and useless information is often returned to the user, especially when requiring images. [ABSTRACT FROM AUTHOR]

Various adaptive hypermedia systems have been proposed to alleviate information overload on the Web by personalising the delivery of information and resources to the user. These systems have however been afflicted with difficulties in the acquisition of an accurate user model, a limited degree of customization offered to the user as well as general lack of user control on and transparency of the systems' adaptive behavior. In this chapter, we argue that the use of rules on top on ontologies can enable adaptive functionality that is transparent and controllable for users. To this end, we present ODAS, a domain ontology for adaptive hypermedia systems, and a model for the specification of ODAS-based adaptation rules. We demonstrate the use of this model by showing how it can be instantiated within a knowledge portal to arrive at rules that exploit ODAS semantics to perform meaningful personalization. [ABSTRACT FROM AUTHOR]

Education of health-related professions such as medicine, nursing, dentistry, pharmacy, etc. represents a major category of adult training. Cognitive flexibility theory which emphasizes a case study based approach involving context-dependent and realistic situations applies directly to health care education. COFALE is an adaptive e-learning environment supporting cognitive flexibility. In this chapter, we present EX-COFALE, an extension to COFALE. COFALE, although offers facilities for adaptive content presentation, adaptive use of pedagogical devices and adaptive communication, it lacks facilities for adaptive student assessment. EX-COFALE remedies this deficiency of COFALE by allowing for automated test creation and assessment based on the students' knowledge information. To this end, COFALE has been modified to allow for representation of associations between test questions and learning concepts. Also, assessment is made at two levels, the concept and the goal level, taking into account the difficulty level of the questions. To achieve the above, a rule-based intelligent component is used. To evaluate EX-COFALE, we implemented a course concerning fundamental aspects of radiation protection, which was used by a number of students in a nursing department and evaluated for usability of the e-learning system and educational content administration. The results are quite promising. [ABSTRACT FROM AUTHOR]

Mobile digital photography has become a ubiquitous service: nearly every mobile phone is equipped with a camera. Huge collections of pictures are created with these phones that are hard to structure in a. usable way. Problems in saving, managing and exchanging digital pictures are becoming typical obstacles that need to be tackled in order to have a usable system. We present PhotoFOAF: a new approach to mobile imaging that exploits the social context of a picture as an important part of the metadata. We present an approach that allows users to insert social metadata in pictures while capturing them. The resulting collections of captured pictures can be managed, searched and exchanged more easily by taking benefit of the personal social relevance of a picture. PhotoFOAF implements an end-to-end process to accomplish this and it exploits, enhances and stimulates social relationships between users during all the different steps. [ABSTRACT FROM AUTHOR]

A global domain Ontology is often proposed as a means to semantically interlink local heterogeneous database resources within a domain. The main advantage is that users need only understand one interface pertaining to the global ontology rather than to understand each of the local database resource interfaces. However, a global domain Ontology may contain considerable complexity, in order to cover increasing numbers of heterogeneous local data resources. Further, some users may not be familiar with the full global model in order to pose and refine their queries under the strict constraints of a single conceptualisation. The development and application of a flexible approach to derive more targeted views, oriented to different types of users and applications, is reported here. The key design challenge tackled here was to derive and constrain user views of the global view, in such a way that the quality of the global view model is maintained in each user view, and that each user view can be accurately mapped to the global view. [ABSTRACT FROM AUTHOR]

There are more and more documents produced and exchanged on both public and private networks. At the same time, the tools proposed to access their content do not fully satisfy the users. Most of them do not really take into account the user's point of view or knowledge. The aim of the works we describe in this chapter is to fill this gap between users and the collections of documents they browse through. Thus, we propose a user-centered model of lexical knowledge as well as related graphical interfaces. The application of this model for access to textual information is realised by the ProxiDocs platform. This tool provides users with interactive maps and hypertexts improved with mark-up directly related to their own choices and preferences. [ABSTRACT FROM AUTHOR]

As the number of available Web services increases there is a growing demand to realise complex business processes by combining and reusing available Web services. In this context, the Ontology Web Language for Services (OWL-S) can be used to specify semantic types of the input and output data of a Web service and its functionality. This paper uses OWL-S to describe Web services and takes advantage of a XML syntax based on the OWL Web Ontology Language to encode OWL domain ontology fragments and SWRL rule fragments as the inputs and outputs of Web services. The approach presented outlines the use of the OWL's XML presentation syntax to obtain Web services that provide reasoning support and easily deal with facts and rules. To validate the proposal, the research has focused on Clinical Practice Guidelines (GLs) related to the biomedical field. This paper highlights the benefits and drawbacks found when applying the approach to obtain Web services that are intended to be used in clinical decision-making and rely on GLs. As an example of use, this paper concentrates on a services-based application for diagnosis and clinical management of Diabetic Retinopathy, where the end-users are health professionals who are not familiarized with Semantic Web technologies. [ABSTRACT FROM AUTHOR]

In medicine many exceptions occur. In medical practice and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a case base is available at the beginning. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements. In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases. [ABSTRACT FROM AUTHOR]

Acquiring case representations from textual sources remains an interesting challenge for CBR research. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Thus there is a need for tools that automatically create knowledge-rich case representations from textual sources without the need to access large volumes of tagged data. Hierarchically structured case representations allow for comparison at different levels of specificity thus resulting in more effective retrieval than can be achieved with a fiat structure. In this paper, we present a novel method for automatically creating, hierarchically structured, knowledge-rich cases from textual reports in the Smart-House domain. Our system, SMART, uses a set of anchors to highlight key phrases in the reports. The key phrases are then used to learn a hierarchically structured case representation onto which reports are mapped to create the corresponding structured cases. SMART does not require large sets of tagged data for learning, and the concepts in the case representation are interpretable, allowing for expert refinement of knowledge. [ABSTRACT FROM AUTHOR]

This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or "chatbot" has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined. [ABSTRACT FROM AUTHOR]

Copyright of Applications & Innovations in Intelligent Systems XV is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Diagnosis of Sinus condition is considered a difficult task in medical clinics due to the similar nature of the symptoms and the complexity of the images (e.g. plane of image, resolution) obtained using either CT-Scan. Discussions with consultant doctors and radiologists working in this area pointed at the need for a computer-based analysis and diagnosis tool that could be used as an aid to experts for diagnosing sinus diseases. There are a number of tools using traditional image processing techniques that are primarily useful for enhancing images. For an integrated system with potential diagnostic abilities artificial neural networks are good candidates that can combine image processing and diagnostic abilities in a single system. This paper presents the background and preliminary results in the development of an automated tool for the analysis and diagnosis of sinus conditions. The data used is in the form of CT scan images of sinus. Technology based on traditional image processing and Artificial Neural Networks (SOM) are explored for image processing and diagnosis. Anonymous CT-images of Sinuses were obtained from a local hospital. Preliminary results show that the proposed system has the potential to be a useful tool for clinicians in the areas of diagnosis and training of junior doctors. [ABSTRACT FROM AUTHOR]

DNA and fingerprint identifications continue to form an integral part of the detection of a wide range of crime types, especially volume crime such as burglary and auto crime. More than ten years ago, researchers first commented on the lack of emphasis on ‘outcome' (i.e. crime detection) related performance indicators for UK police forces. Since then much work has been carried out, mainly by the Association of Chief Police Officers of England & Wales and the Home Office, to produce a framework of forensic science performance indicators that reflect accurately the contribution made by forensic science to crime detection. In this paper, we consider the data currently being collected by five UK police forces that use popular proprietary computer based data collection systems. The accuracy of the data collection has been analysed using a neural network and has identified collection errors in all five forces. These errors are such that they could adversely affect the accuracy and interpretation of the national collection of forensic science data conducted by the Home Office. We propose using this neural network to check the accuracy of data collection and also to provide a ‘front end' collator for national forensic science data returns to the Home Office. Such an approach would improve the accuracy of data collection nationally and also provide some reassurance over the consistency of data recording by individual forces. [ABSTRACT FROM AUTHOR]

This paper deals with data mining applications for the supply chain inventory management. It describes the use of business intelligence (BI) tools, coupled with data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions. The methodology is designed to provide out-of-stock forecasts at the store/product level. The first phase of the modelling process consists of clustering stores in the supply chain based upon aggregate sales patterns. After quality store-cluster models have been constructed, these clusters are used to more accurately make out-of-stock predictions at the store/product level using the decision trees and neural network mining algorithms. The methods for evaluation and accuracy measurement are described. Also, the specialized front-end BI web portal that offers integrated reporting, web analytics, personalization, customization and collaboration is described. [ABSTRACT FROM AUTHOR]

This paper describes the multi-agent system used to control a package of four Uninhabited Air Vehicles (UAVs). The system has recently been used in a series of test flights where the pilot of a fast jet controlled a team of four UAVs (one real, three simulated) carrying out a representative mission. The structure of the system is described and the re-organisation of the agents as the mission progresses is illustrated with an example. The paper concludes by describing the importance of whole system issues and the integration and test cycle for getting AI techniques working and accepted in an application. [ABSTRACT FROM AUTHOR]

This paper documents our ongoing efforts in devising efficient strategies in motion control of the brittle star-typed robot. As part of the control framework, each robotic leg consisting of series of homogenous modules is modeled as a neural network. The modules representative of neurons are interconnected via synaptic weights. The principle operation of the module involves summing the weighted input stimulus and using a sinusoidal activation function to determine the next phase angle. Motion is achieved by propagating phase information from the modules closest to the main body to the remainder of the modules in the leg via the synaptic weights. Genetic algorithm was used to evolve near optimal control parameters. Simulations results indicate that the current neural network inspired control model produces better motion characteristics than the previous cellular automata-based control model as well as addresses other issues such as fault tolerance. [ABSTRACT FROM AUTHOR]

One of the most desired aspects for power suppliers is the acquisition/sell of energy in a future time. This paper presents a study for power supply forecasting of the residential class, based on time series methods and neural networks, considering short and long term forecast, both of great importance for power suppliers in order to define the future power consumption of a given region. [ABSTRACT FROM AUTHOR]

The vast majority of existing Intrusion Detection Systems (IDS) incorporates static knowledge bases, which contain information corresponding to specific attack patterns. Although such knowledge bases can gradually expand, to be able to detect new attacks, this requires the maintenance of an expert. This paper describes a potential application of computationally evolving intelligent behaviour in conjunction with network intrusion detection. Our aim is to develop a standalone Network Intrusion Detection System (NIDS), capable of working in offline and online mode by evolving its structure and parameters in order to prevent both known and novel intrusions. [ABSTRACT FROM AUTHOR]

The RSS news Aggregators is becoming more and more popular among Internet users. These Aggregators download news feeds from online news websites and provide an interface for users to view and organize them. Users can subscribe to numerous feeds. When they add more sources the amount of news feeds becomes more difficult to manage. The users then experience information overload. In order to tackle the RSS overload problem, Fido, an interface agent, is designed to filter news based on user preferences and feedback and presents personalized RSS news items to the users. This paper presents the main features of Fido and design rationale behind it. [ABSTRACT FROM AUTHOR]

This paper describes a method for assignment and deployment of sensors in a virtual environment using constraint programming. We extend an existing model (multiple knapsack problem) to implement this assignment and placement, according to a given set of requirements (modelled as a utility extension). [ABSTRACT FROM AUTHOR]

This paper explores the application of ecosystem simulation to the production of works of generative electronic art. The aim is to demonstrate that virtual ecosystems are capable of producing outcomes that are rich, complex and interesting aesthetically. A number of artworks that employ virtual ecosystems are surveyed. The author argues that the most interesting works of generative art exhibit four basic properties: coherence and unity; multi-scaled temporal complexity; autonomous production of novelty; responsiveness to perturbation. The virtual ecosystem is assessed for its suitability as a medium for constructing generative art in light of these desirable properties. It is concluded that the ecosystem's strengths lie in its exhibition of multi-scaled complexity and its autonomous production of novelty. Whilst an artist may manipulate a simulation to retain visual and sonic coherence, the software also possesses an implicit coherence inherent in its ability to self-organize. Under some circumstances it appears that the weakness of the virtual ecosystem as an artistic medium lies in its unpredictable response to perturbation. Consequently, the paper also explores virtual ecosystems' susceptibility to external control and describes methods that have been employed to adjust the responsiveness of art works that employ them. [ABSTRACT FROM AUTHOR]

This chapter examines the possibilities and challenges that lie ahead for evolutionary music and art. Evolutionary computing methods have enabled new modes of creative expression in the art made by humans. One day, it may be possible for computers to make art autonomously. The idea of machines making art leads to the question: what do we mean by ‘making art' and how do we recognise and acknowledge artistic creativity in general? Two broad categories of human-machine creativity are defined: firstly, machines that make art like, and for, humans; and secondly, machines that make ‘art' that is recognised as creative and novel by other machines or agents. Both these categories are examined from an evolutionary computing perspective. Finding ‘good' art involves searching a phase-space of possibilities beyond astronomical proportions, which makes evolutionary algorithms potentially suitable candidates. However, the problem of developing artistically creative programs is not simply a search problem. The multiple roles of interaction, environment, physics and physicality are examined in the context of generating aesthetic output. A number of ‘open problems' are proposed as grand challenges of investigation for evolutionary music and art. For each problem, the impetus and background are discussed. The paper also looks at theoretical issues that might limit prospects for art made by machines, in particular the role of embodiment, physicality and morphological computation in agent-based and evolutionary models. Finally, the paper looks at artistic challenges for evolutionary music and art systems. [ABSTRACT FROM AUTHOR]

The study presents two simulation models for generic manufacturing systems operated under MRP-push policy and Kanban-pull policy. The models are designed as long run planning tools for improving manufacturing performance. A simulation language for discrete-event simulation based on event graph is used to construct the models for pull, push systems [5]. The models in this paper are totally data driven. Input data includes machines, products, processes, bill of materials, and demand data. An iterative heuristic algorithm is employed to determine the initial model parameters: the number of Kanban for pull systems, and safety stock levels for push systems. Simulation experiments are carried out to demonstrate the effectiveness of the heuristic and the simulation models. The experiments are conducted in an environment involving changes in two operational factors: demand and processing time. The experimental results indicate that for the same throughput, the pull system outperforms the push system in terms of lead time and work in process (WIP) inventory in such environment. [ABSTRACT FROM AUTHOR]

In this paper, a new strict linear matrix inequality (LMI) condition for extended strictly positive real (ESPR) characterization of continuous-time descriptor systems is addressed. Compared with existing nonstrict LMI conditions, the proposed conditions are expressed in terms of definite LMIs with no equality constraint, which are more tractable and reliable in numerical computation. A numerical example is demonstrated to verify the theoretical results obtained in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a fuzzy controller to solve a master-slave chaos synchronization problem. At first, the method of traditional sliding mode control is considered, which utilizes the discontinuous sign function to make the system state reaching a sliding surface. Next, fuzzy rules are determined according to the Lyapunov theorem, and the fuzzy controller is designed for chaos synchronization. Finally, an example of chaos synchronization for an uncertain Duffing-Holmes system is presented to illustrate the validity and feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

The objects of a HLA-based simulation can access model services to update their attributes. However, the grid server may be overloaded and refuse the model service to handle objects accesses. Because these objects have been accessed this model service during last simulation loop and their medium state are stored in this server, this may terminate the simulation. A fault-tolerance mechanism must be introduced into simulations. But the traditional fault-tolerance methods cannot meet the above needs because the transmission latency between a federate and the RTI in grid environment varies from several hundred milliseconds to several seconds. By adding model service URLs to the OMT and expanding the HLA services and model services with some interfaces, this paper proposes a self-adaptive fault-tolerance mechanism of simulations according to the characteristics of federates accessing model services. Benchmark experiments indicate that the expanded HLA/RTI can make simulations self-adaptively run in the grid environment. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach that can provide faster and constant-time record operations with a smaller memory usage while consumes less battery power, using the Record Management System (RMS) of Java Mobile Information Device Profile (MIDP) in small embedded devices. The comparable program segments are presented and analyzed in order to show the efficient use of Java MIDP RMS with or without Record-Enumeration inter-face. The proposed approach in this paper supports the faster access of record traversal that can retrieve and update any records in the MIDP RMS file in constant time, while the previous approach takes O (log n) time. [ABSTRACT FROM AUTHOR]

In an attempt to study the spread of diseases in the National University of Singapore (NUS), a simulation model unique to the NUS environment was developed to evaluate the effectiveness of different intervention policies during the event of a disease outbreak. The system allows the user to introduce a user-defined disease into the NUS population to study its impact. The effectiveness of various intervention policies on selected diseases are evaluated and presented in the paper. [ABSTRACT FROM AUTHOR]

System simulation is one of important tool for risk assessment. In this paper, a new method is presented to deal with credit risk assessment problems for commercial banks based on rare event simulation. The failure probability of repaying loans of listed company is taken as the criterion to measure the level of credit risk. The rare-event concept is adopted to construct the model of credit risk identification in commercial banks, and cross-entropy scheme is designed to implement the rare event simulation, based on which the loss probability can be assessed. Numerical experiments have shown that the method has a strong capability to identify the credit risk for commercial banks and offers a good tool for early warning. [ABSTRACT FROM AUTHOR]

The telecom firms in Korea are taking new step to prepare for the next generation of convergence services, IPTV. In this paper we described our analysis on the effective method for demand forecasting about IPTV broadcasting. We have tried according to 3 types of scenarios based on some aspects of IPTV potential market and made a comparison among the results. The forecasting method used in this paper is the multi generation substitution model with bootstrap ridge regression analysis. [ABSTRACT FROM AUTHOR]

Sate-Dependent Riccati Equation (SDRE) method is applied here to track optimal entry trajectory which can be generated online by On Board Three Dimension Trajectory Generation. In this paper, the design of a longitudinal entry tracking control law based on SDRE was proposed in detail. Since its control value and weigh metric depend on the current state which can be a good and precise compensator to disturbance. Besides, from the design procedure, SDRE method is very easy to implement whether on the mathematical deduction or the online calculation. Numerical simulation for SDRE tracking law was carried out, and compared to traditional tracking method to demonstrate the simplicity, robustness, and doable performance of SDRE controller. [ABSTRACT FROM AUTHOR]

This paper introduces a variable structure technology for the synchronization of chaos between two different hyperchaotic systems with input nonlinearity. Based on Lyapunov stability theory, a sliding mode controller and some generic sufficient conditions for global asymptotic synchronization are designed such that the error dynamics of the hyperchaotic Rössler and hyperchaotic Chen systems satisfy stability in the Lyapunov sense in spit of the input nonlinearity. The Numerical simulation results demonstrate the validity and feasibility of the proposed controller. [ABSTRACT FROM AUTHOR]

The remarkable system of FitzHugh-Nagumo (FHN) neurons in external electrical stimulation is studied from the view of chaos synchronization in this paper. An effective adaptive sliding mode controller is derived to achieve chaos synchronization even when the parameters of the drive and response FHN neurons are fully unknown. An illustrative example is presented for the purpose of verification and illustration. [ABSTRACT FROM AUTHOR]