Online Social Network (OSN) botnet attacks pose a growing threat to the cloud environment and reduce the services’ availability and reliability for users by launching distributed denial of service (DDoS) attacks on crucial servers in the cloud. These attacks involve the deployment of sophisticated botnets that exploit the interconnected nature of social networks to identify targets, exploit vulnerabilities, and launch attacks. The prevalence and impact of these botnet-driven attacks have recently been studied. Although the detection of these botnet attacks is still a challenging process, it remains crucial to gain a comprehensive understanding of and evaluate the best defense strategies against botnet attacks. This evaluation can be further utilized to formulate effective defense plans to mitigate the impact of such botnet attacks. In this paper, we first investigate the properties of OSN botnet attack stages that eventually lead to launching DDoS attacks toward a cloud system. Then, we formalize a defensive model using a sequential game model to analyze both the attacker’s and defenders’ best equilibrium strategies for the proposed botnet attack scenario. Moreover, we formulate optimal strategies for the defender against various attack strategies. Our experiments reveal the best defense strategies against various attack rates to maintain cloud functionality. Finally, we discuss possible countermeasures for these OSN botnet threats.

Privacy breaches have become increasingly prevalent, exposing individuals to significant risks. These breaches can have far-reaching consequences, including identity theft and life-threatening situations. Several studies have analyzed data and privacy breaches and presented detection or prevention techniques to combat these breaches. However, because the number and type of breaches have significantly increased, these studies have become less relevant or outdated. Previous research on data and privacy breaches compared the techniques and results of various studies. However, none comprehensively analyzed the type of information and the level and severity of compromise that occurred after such breaches. In this survey, we examine the fundamental concepts of privacy and security and define the security incidents and data/privacy breaches. We propose a set of criteria to evaluate the published studies on privacy breaches. We thoroughly investigate the problem domains and security-related concerns considering six recent breach cases in Australia, elucidating the critical challenges and issues associated with privacy breaches. We comprehensively review and outline the trends and severity of security incidents and data/privacy breaches from 2020 to 2024. Additionally, we review the current state-of-the-art countermeasures to safeguard against these breaches. Finally, we identify an open research direction to develop an artificial intelligence (AI)-powered security framework. This framework aims to analyze cyber threats, characterize attackers’ behaviors, distinguish between legitimate and illegitimate privacy policies, and restrict access to individuals’ information. Overall, this survey will help organizations to reassess and update their security and privacy measures.

With the rapidly increasing volume of cyber-attacks over the past years due to the new working-from-home paradigm, protecting hosts, networks, and individuals from cyber threats is in higher demand than ever. One promising solution are Provenance-based Intrusion Detection Systems (PIDS), which correlate host-based security logs to generate provenance graphs that describe the causal relationship between system entities. PIDS have shown significant potential in enhancing detection performance and reducing false alarms compared to traditional Intrusion Detection Systems (IDS). Rule-based approaches used in PIDS utilize expert-defined rule sets to identify known malicious patterns in provenance graphs. Although these rule-based techniques have been widely applied, they can only detect known attack patterns, are heavily dependent on the quality of the rules, and creating rules manually is time-consuming. To address these shortcomings, this study proposed two novel techniques: the Multi-level Attack Description Model (MADM) for describing attack patterns at multiple granularity levels and the Provenance-enabled Automated Rule Generation and Matching Framework (PARGMF) to generate rules deterministically and promptly. We evaluated the proposed approaches using the DARPA OpTC dataset, complemented by a practical case study. This case study involved a prototype extension for the CAPEv2 sandbox environment, demonstrating the real-world applicability of our approaches. Our results demonstrate, firstly, that PARGMF generates rules deterministically with an average processing time of only 13.11 s compared to multiple hours or even days for manual rule creation by security experts. Secondly, through generalization of attack descriptions, MADM enhanced the robustness of rules by 21.9% for Behavioural Attack Description (BAD) and 25% for Structural Attack Description (SAD) compared to approaches without generalization. Another added benefit compared to existing approaches is that PARGMF also generates differential graphs to support security experts’ timely validation of security alarms.

With the recent advances of the Internet of Things (IoT), innovative techniques, and concepts have emerged, such as digital twins and industrial 4.0. As one of the essential parts of a digital twin, IoT-based smart predictive maintenance (IoT-SPM) is a key enabling technology for smart manufacturing. This article introduces digital twins and their relationship to IoT-SPM and proposes a reference IoT-SPM, aiming to provide a comprehensive and systematic outlook for the IoT-SPM field. Thus, it can be used as a guide map for interested readers. To give a complete picture of the IoT-SPM ecosystem in industrial 4.0 systems, this article conducts an analysis from multiview perspectives, starting with the architecture, followed by platforms and component. The key components or requirements of an IoT-SPM ecosystem are identified and outlined, including the IoT and cyber–physical system (CPS) as the cornerstone technologies, IoT monitoring data as the base, big data platforms as the backbone, an upgraded computing paradigm as the catalyst, and machine learning-based data analysis as the main processor. This article also focuses on the issues surrounding IoT data when applying analytic models to a real-world industrial IoT system. Then, the current progresses relating IoT and IoT-SPM are depicted, and a research gap on IoT data quality is identified. In particular, regarding the identified IoT data quality problems, this article qualitatively evaluates and discusses the existing solutions. These discussions lead to several open research issues and future directions.

The Industrial Internet of Things (IIoT) enables intelligent predictive maintenance in smart manufacturing by incorporating IoT technologies, Big Data techniques, artificial intelligence, cloud computing, and other ever-developing enabling technologies. Although a large body of research has been conducted on IIoT based predictive maintenance, most work focuses on addressing only a part of the problem. However, predictive maintenance involves an ecosystem from ingesting data from sensors to displaying the results on a dashboard for engineers to visualize. With increasing requirements for real-time responses and privacy, integrating edge computing is no doubt a promising trend. This article proposes a complete and optimized IoT Big Data ecosystem embedded into a three-layer architecture for predictive maintenance applications. The proposed architecture consists of an edge layer, a cloud layer, and an application layer. The proposed edge infrastructure distributes the tasks effectively between the cloud layer and edge layer. On top of the architecture, different layers are integrated seamlessly to address reliability and scalability issues. In addition, an edge computing-assisted autoencoder is introduced and enabled by being deployed in a distributed manner to improve both performance and efficiency. For practical interest, we also provide an application programming interface-oriented implementation guideline for readers. To verify the proposed ecosystem, a real case study from industry is conducted to demonstrate the performance gain of edge computing-based Internet of Things systems in conjunction with the autoencoder-based deep learning technique.

With the rapid growth in the use of various smart digital sensors, the Internet of Things (IoT) is a swiftly growing technology, which has contributed significantly to Industry 4.0 and the promotion of IoT-based smart factories, which gives rise to the new challenges of big data analytics and the implementation of machine learning techniques. This article proposes a practical framework that combines IoT techniques, a data lake, data analysis, and cloud computing for manufacturing equipment health-state monitoring and diagnostics in smart manufacturing. It addresses all the required aspects in the realization of such a system and allows the seamless interchange of data and functionality. Due to the specific characteristics of IoT sensor data (low quality, redundant multisources, partial labeling), we not only provide a promising framework but also give detailed insights and pay considerable attention to data quality issues. In the proposed framework, an ingestion procedure is designed to manage data collection, data security, data transformation and data storage issues. To improve the quality of IoT big data, a high-noise feature filter is proposed for automated preliminary sensor selection to suppress noisy features, followed by a noisy data cleaning module to provide good quality data for unbiased diagnosis modeling. The proposed framework can achieve seamless integration between IoT big data ingestion from the physical factory and machine learning-based data analytics in the virtual systems. It is built on top of the Apache Spark processing engine, being capable of working in both big data and real-time environments. One case study has been conducted based on a four-stage syngas compressor from real industries, which won the Best Industry Application of IoT at the BigInsights Data & AI Innovation Awards. The experimental results demonstrate the effectiveness of both the proposed IoT-architecture and techniques to address the data quality issues.

The emergence of Industry 4.0 and the rapid advances in the Industrial Internet of Things (IIoT) have provided manufacturers with the ability to remotely monitor the process by deploying automatic fault detection in an IoT-based predictive maintenance system. However, the monitoring targets are now manufacturing plant-wide instead of being just a local area. Multiple types of faults are involved and the conventional centralized cloud computing-based IoT solutions always lead to a heavy burden on the network bandwidth due to the large amount of sensor data collected frequently that has to be transmitted to the central server and this leads to poor response time for the monitoring system. To address this problem, this article develops an artificial intelligence-assisted distributed system for manufacturing plant-wide predictive maintenance applications. The developed distributed system relies on the feature selection technique to identity an optimal feature subset for each type of fault and is enabled by deploying each independent model built on the obtained feature subset into different edge nodes. The distributed approach enables the data to be processed near the sensors, requiring less data to be transmitted to the central cloud server reducing network delay and delivering more accurate results. In addition, our proposed feature selection approach is especially designed to accommodate the characteristics of IIoT data such as the lack of labels. The effectiveness of the proposed method is validated using the widely used public Tennessee Eastman dataset.

In recent years, the Internet-of-Things (IoT)-oriented smart manufacturing has become a prominent solution in realizing evolutional digital transformation. Missing data are one of the biggest problems for data preprocessing in an IoT architecture, and it is crucial that missing values are recovered to improve the reliability of monitoring applications. However, due to the high-frequency collection of sensor data, missing data in IoT bring new challenges. Several methods have been developed to recover missing IoT data by utilizing data from sensors that are geographically close to the sensor which is responsible for the missing data, or from sensors which provide data that are highly correlated to the missing data. In IoT systems, because of the transmission of a large volume of data over networks, common mode failures need to be considered where a single event can lead to the loss of data from a large number of sensors. In this situation, it would be infeasible to recover missing data from other sensors. To address this issue, in this article, we focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework using multiple segmented gap iteration called Itr-MS-STLecImp to provide the most appropriate values. The gap is first segmented into several pieces to initialize the missing value imputation process and then, we iteratively run gap reconstruction and gap concatenation to obtain the final imputation results. We validate the proposed approach using sensor data collected from real manufacturing plants in Australia and the comparison results show that the proposed Itr-MS-STLecImp outperforms the state-of-the-art methods in terms of root-mean-square error. Under different gap-length conditions, the proposed approach consistently reduces the error rate more than the baseline algorithm, and the error reduction is greater when the lengths of the gaps increase, indicating that the performance is significantly improved. These analysis results further prove the effectiveness of the multiple segmentation of missing gaps and the iteration operation.

The emergence of the Industrial Internet of Things (IIoT) to enhance manufacturing and industrial processes allows data analysts to address significant problems such as predictive maintenance. For the purpose of accurate data analysis, cleansing noisy sensor data is one of the most fundamental and necessary steps. Without first removing the noise, the anomaly detection techniques are likely to give a large number of false positives. However, using traditional outlier detection methods directly for such analysis are not appropriate as both noise and significant anomalies might exist in the sensor data. This article introduces the new challenges and proposes a novel solution to address the issue of removing noise while preserving anomalies in the IIoT Data. It proposes an approach that measures both the rate of change and deviation to compute the noise score. It employs a sliding window technique to define the analysis unit of the contrast measure which is used in conjunction with statistical techniques. Extensive experiments demonstrate that the proposed approach outperforms the other state-of-the-art noise detection methods, providing a clean data set that preserves the anomalies on which one can effectively apply anomaly detection techniques.

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.

In today’s dynamic ICT environments, the ability to control users’ access to information resources and services has become ever important. On the one hand, it should provide flexibility to adapt to the users’ changing needs, while on the other hand, it should not be compromised. The user is often faced with different contexts and environments that may change the user’s information needs. To allow for this, it is essential to incorporate the dynamically changing context information into the access control policies to reflect different contexts and environments through the use of a new context-aware access control (CAAC) approach with both dynamic associations of user-role and role-permission capabilities. Our proposed CAAC framework differs from the existing access control frameworks in that it supports context-sensitive access control to information resources and dynamically re-evaluates the access control decisions when there are dynamic changes to the context. It uses the dynamic context information to specify the user-role and role-permission assignment policies. We first present a formal policy model for our framework, specifying CAAC policies. Using this model, we then introduce a policy ontology for modeling CAAC policies and a policy enforcement architecture which supports access to resources according to the dynamically changing context information. In addition, we demonstrate the feasibility of our framework by considering (i) the completeness, correctness and consistency of the ontology concepts through application to healthcare scenarios and (ii) the performance and usability testing of the framework when using desktop and mobile-based prototypes.

Traffic flow forecasting is a useful technology applied to solve traffic congestion problems and to improve transportation mobility. Neural networks related approaches have been applied to develop traffic forecasting models for more than two decades. Since neural networks are sensitivity in parameters selection, selecting appropriate modeling configuration is essential to improve the accuracy and efficiency of traffic flow prediction. However, this is usually conducted by the trial-and-error method, which is very time consuming while involving too many design factors. Therefore, this paper utilizes a robust and systematic optimization approach, the Taguchi method, for obtaining the optimized configuration of the proposed exponential smoothing and extreme learning machine forecasting model. The developed model is applied to real-world data collected from freeways and highways in the United Kingdom and is compared with three existing forecasting models. The results indicate that the Taguchi method is efficient and capable for the forecasting model design and the proposed model with the optimized configuration has superior performance in traffic flow forecasting with approximate 91% and 88% accuracy rate in freeway and highway in both peak and nonpeak traffic periods.

In role-based access control, roles are mostly organized in static hierarchies and users are authorized to play such roles in order to exercise the organizational functions. However, some of these roles cannot be organized in the same way in static hierarchies as the authorizations granted to such roles are strictly related to the dynamically changing contextual conditions (e.g., health profile information). Users need to satisfy these conditions in order to exercise the functions of such dynamic contextual roles. While several research works have been done in dynamic activation of static roles, no extensive research has been undertaken in the area of dynamic specification of contextual roles. This article makes a significant research contribution to the dynamic contextual role modeling and activation. We introduce both formal and ontology-based approaches in order to model the dynamic contextual roles and specify the context-aware access control policies by activating such dynamic roles at runtime. These contextual roles are equally important because of the demands of large-scale (pervasive) environments to control context-sensitive access to resources at different granularity levels with low processing overheads. We develop a software prototype to demonstrate the feasibility of our proposal and provide a walkthrough of the whole mechanism. Experimental results demonstrate the satisfactory performance of our proposed approach compared to our previous approach.

Forecasting accuracy is an important issue for successful intelligent traffic management, especially in the domain of traffic efficiency and congestion reduction. The dawning of the big data era brings opportunities to greatly improve prediction accuracy. In this paper, we propose a novel model, stacked autoencoder Levenberg-Marquardt model, which is a type of deep architecture of neural network approach aiming to improve forecasting accuracy. The proposed model is designed using the Taguchi method to develop an optimized structure and to learn traffic flow features through layer-by-layer feature granulation with a greedy layerwise unsupervised learning algorithm. It is applied to real-world data collected from the M6 freeway in the U.K. and is compared with three existing traffic predictors. To the best of our knowledge, this is the first time that an optimized structure of the traffic flow forecasting model with a deep learning approach is presented. The evaluation results demonstrate that the proposed model with an optimized structure has superior performance in traffic flow forecasting.

Development of new products or services requires knowledge and understanding of aesthetic qualities that correlate to perceptual pleasure. As it is not practical to develop a survey to assess aesthetic quality for all objective features of a new product or service, it is necessary to develop a model to predict aesthetic qualities. In this paper, a fuzzy regression method is proposed to predict aesthetic quality from a given set of objective features and to account for uncertainty in human assessment. The proposed method overcomes the shortcoming of statistical regression, which can predict only quality magnitudes but cannot predict quality uncertainty. The proposed method also attempts to improve traditional fuzzy regressions, which simulate a single characteristic with which the estimated uncertainty can only increase with the increasing magnitudes of objective features. The proposed fuzzy regression method uses genetic programming to develop nonlinear structures of the models, and model coefficients are determined by optimizing the fuzzy criteria. Hence, the developed model can be used to fit the nonlinearities of sample magnitudes and uncertainties. The effectiveness and the performance of the proposed method are evaluated by the case study of perceptual images, which are involved with different sampling natures and with different amounts of samples. This case study attempts to address different characteristics of human assessments. The outcomes demonstrate that more robust models can be developed by the proposed fuzzy regression method compared with the recently developed fuzzy regression methods, when the model characteristics and fuzzy criteria are taken into account.

Evolving intelligent system (EIS) is a machine learning algorithm, specifically designed to deal with learning from large data streams. Although the EIS research topic has attracted various contributions over the past decade, the issue of uncertainty, temporal system dynamic, and system order are relatively unexplored by existing studies. A novel EIS, namely evolving type-2 recurrent fuzzy neural network (eT2RFNN) is proposed in this paper. eT2RFNN features a novel recurrent network architecture, possessing double local recurrent connections. It generates a generalized interval type-2 fuzzy rule, where an interval type-2 multivariate Gaussian function constructs the rule premise, and the rule consequent is crafted by the nonlinear wavelet function. eT2RFNN adopts an open structure, where it can start learning process from scratch with an empty rule base. Fuzzy rules can be automatically generated according to degree of nonlinearity data stream conveys. It can performs a rule base simplification procedure by pruning and merging inactive, outdated and overlapping rules. eT2RFNN can deal with the high dimensionality problem, where an online dimensionality reduction method is integrated in the training process. The efficacy of the eT2RFNN has been numerically validated using two real-world data streams, where it provides high predictive accuracy, while retaining low complexity.

Fuzzy regression methods have commonly been used to develop consumer preferences models, which correlate the engineering characteristics with consumer preferences regarding a new product; the consumer preference models provide a platform, whereby product developers can decide the engineering characteristics in order to satisfy consumer preferences prior to developing the products. Recent research shows that these fuzzy regression methods are commonly used to model customer preferences. However, these approaches have a common limitation in that they do not investigate the appropriate polynomial structure, which includes significant regressors with only significant engineering characteristics; also, they cannot generate interaction or high-order regressors in the models. The inclusion of insignificant regressors is not an effective approach when developing the models. Exclusion of significant regressors may affect the generalization capability of the consumer preference models. In this paper, a novel fuzzy modeling method is proposed, namely fuzzy stepwise regression (F-SR), in order to develop a customer preference model which is structured with an appropriate polynomial, which includes only significant regressors. Based on the appropriate polynomial structure, the fuzzy coefficients are determined using the fuzzy least-squares regression. The developed fuzzy regression model attempts to obtain a better generalization capability using a smaller number of regressors. The effectiveness of the F-SR is evaluated based on two design problems, namely a tea maker design and a solder paste dispenser design. Results show that better generalization capabilities can be obtained compared with the fuzzy regression methods commonly used for new product development. In addition, smaller scale consumer preference models with fewer engineering characteristics can be obtained. Hence, a simpler and more effective product development platform can be provided.

Takagi-Sugeno neural fuzzy models (TS-models) have commonly been applied in the development of traffic flow predictors based on traffic flow data captured by the on-road sensors installed along a freeway. However, using all captured traffic flow data is ineffective for the TS-models for traffic flow predictions. Therefore, an appropriate on-road sensor configuration consisting of significant sensors is essential to develop an accurate TS-model for traffic flow forecasting. Although the trial and error method is usually used to determine the appropriate on-road sensor configuration, it is time-consuming and ineffective in trialing all individual configurations. In this paper, a systematic and effective experimental design method involving orthogonal arrays is used to determine appropriate on-road sensor configurations for TS-models. A case study was conducted based on the development of TS-models using traffic flow data captured by on-road sensors installed on a Western Australia freeway. Results show that an appropriate on-road sensor configuration for the TS-model can be developed in a reasonable amount of time when an orthogonal array is used. Also, the developed TS-model can generate accurate traffic flow forecasting.

Building automation is the key to sustainable, safe and comfortable buildings as well as to the integration of buildings into smart grids and with other external applications such as cloud computing. In a typical smart community scenario, various household appliances of multiple residential users are connected via a Home Area Network. HANs are further connected to the local power distribution network via smart meters, forming a LAN, where also renewables communicate. Methods are needed to design and integrate networks with hundred thousands of nodes in a cost-efficient way. The key to providing improved services in building automation is to process complex scenarios in an adequate way. Furthermore, building automation systems must be seen as dependable systems covering both safety and security aspects. The main objective of this Special Section is to bring the ideas of the worldwide research community into a common platform, to present the latest advances and developments.

Smart grids have created an emerging entity of “prosumer” in the energy value network who not only consumes energy but also generates and shares the green energy with the utility grid. Hence, effective management of prosumers has become pivotal to ensure a long-term, sustainable energy-sharing process. Recently, the concept of a Prosumer Community Group (PCG) has emerged as one of the most promising and effective ways to manage prosumers. However, developing sustainable PCGs is challenging. One of the key challenges in this regard is to assess the contribution made by individual prosumers of a PCG, and find a subset of the most influential prosumers whose behavior would facilitate the long-term sustainability of the PCG. In this paper, we have focused on this challenge and proposed an innovative methodology to assess and rank the prosumers, in order to build an influential membership base. We have assessed the long-term and short-term energy behaviors of prosumers based on multiple evaluation criteria and accordingly decided the ranks of the prosumers, whereby the higher ranked prosumers are deemed to be more influential in enhancing the long-term sustenance of the PCG. Furthermore, we have presented simulation results to verify our proposed methodology. The current literature on smart-grid research field has no work investigating this challenge, making our contribution novel.

Neural networks have been applied for short-term traffic flow forecasting with reasonable accuracy. Past traffic flow data, which has been captured by on-road sensors, is used as the inputs of neural networks. The size of this data significantly affects the performance of short-term traffic flow forecasting, as too many inputs result in over-specification of neural networks and too few inputs result in under-learning of neural networks. However, the amount of past traffic flow data input, is usually determined by the trial and error method. In this paper, an experimental design method, namely orthogonal design, is used to determine appropriate amount of past traffic flow data for neural networks for short-term traffic flow forecasting. The effectiveness of the orthogonal design is demonstrated by developing neural networks for short-term traffic flow forecasting based on past traffic flow data captured by on-road sensors located on a freeway in Western Australia.

Smart Grid (SG) achieves bidirectional energy and information flow between the energy user and the utility grid, allowing energy users not only to consume energy, but also to generate the energy and share the excess energy with the utility grid or with other energy consumers. This type of energy user is called the “prosumer”. In current society, a massive number of energy-users have transformed into prosumers due to many reasons such as the strong society attitude with respect to alleviation of negative climate impacts, desires to decrease electricity costs, and various government regulations, including generous feed-in tariff schemes. This leads much attention within the research community on investigating the aspects of prosumers connected to SG. However most researchers find it challenges to find a large dataset of prosumers for performing the experiments. This leads the necessity of identifying the generic prosumers' realistic energy behaviors, and accordingly generates a synthetic dataset. In this research paper, we present prosumers' realistic energy behavior profiles during summer and winter periods in Australia and present its application in generating a synthetic dataset. The new researchers can use the identified energy profiles as a benchmark to generate a synthetic dataset for their experiments.

Cyber-Physical Systems (CPS) provides a smart infrastructure connecting abstract computational artifacts with the physical world. This paper presents some challenges for developing distributed real-time Cyber-Physical Systems. The focus is on one particular challenge, namely event modelling in distributed real-time CPS. A Web-of-Things based CPS framework for event handling and processing is proposed. To illustrate the application of the proposed framework, a case study for achieving demand response in a smart home is provided.

The increasing need for representing information through more complex structures where semantics and relationships among data objects can be more easily expressed has resulted in many semi-structured data sources. Structure comparison among semi-structured data objects can often reveal valuable information, and hence tree mining has gained a considerable amount of interest in areas such as XML mining, Bioinformatics, Web mining etc. We are primarily concerned with the task of mining frequent ordered induced and embedded subtrees from a database of rooted ordered labeled trees. Our previous contributions consist of the efficient Tree Model Guided (TMG) candidate enumeration approach for which we developed a mathematical model that provides an estimate of the worst case complexity for embedded subtree mining. This potentially reveals computationally impractical situations where one would be forced to constrain the mining process in some way so that at least some patterns can be discovered. This motivated our strategy of tackling the complexity of mining embedded subtrees by introducing the Level of Embedding constraint. Thus, when it is too costly to mine all frequent embedded subtrees, one can decrease the level of embedding constraint gradually down to 1, from which all the obtained frequent subtrees are induced subtrees. In this paper we develop alternative implementations and propose two algorithms MB3 -R and iMB3 -R , which achieve better efficiency in terms of time and space. Furthermore, we develop a mathematical model for estimating the worst case complexity for induced subtree mining. It is accompanied with a theoretical analysis of induced-embedded subtree relationships in terms of complexity for frequent subtree mining. Using synthetic and real world data we practically demonstrate the space and time efficiency of our new approach and provide some comparisons to the two well know algorithms for mining induced and embedded subtrees.

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase.

For over two decades, speech control mechanisms have been widely applied in manufacturing systems such as factory automation, warehouse automation, and industrial robotic control for over two decades. To implement speech controls, a commercial speech recognizer is used as the interface between users and the automation system. However, users' commands are often contaminated by environmental noise which degrades the performance of speech recognition for controlling automation systems. This paper presents a multichannel signal enhancement methodology to improve the performance of commercial speech recognizers. The proposed methodology aims to optimize speech recognition accuracy of a commercial speech recognizer in a noisy environment based on a beamformer, which is developed by an intelligent particle swarm optimization. It overcomes the limitation of the existing signal enhancement approaches whereby the parameters inside commercial speech recognizers are required to be tuned, which is impossible in a real-world situation. Also, it overcomes the limitation of the existing optimization algorithm including gradient descent methods, genetic algorithms and classical particle swarm optimization that are unlikely to develop optimal beamformers for maximizing speech recognition accuracy. The performance of the proposed methodology was evaluated by developing beamformers for a commercial speech recognizer, which was implemented on warehouse automation. Results indicate a significant improvement regarding speech recognition accuracy.

This paper proposes a novel neural network (NN) training method that employs the hybrid exponential smoothing method and the Levenberg-Marquardt (LM) algorithm, which aims to improve the generalization capabilities of previously used methods for training NNs for short-term traffic flow forecasting. The approach uses exponential smoothing to preprocess traffic flow data by removing the lumpiness from collected traffic flow data, before employing a variant of the LM algorithm to train the NN weights of an NN model. This approach aids NN training, as the preprocessed traffic flow data are more smooth and continuous than the original unprocessed traffic flow data. The proposed method was evaluated by forecasting short-term traffic flow conditions on the Mitchell freeway in Western Australia. With regard to the generalization capabilities for short-term traffic flow forecasting, the NN models developed using the proposed approach outperform those that are developed based on the alternative tested algorithms, which are particularly designed either for short-term traffic flow forecasting or for enhancing generalization capabilities of NNs.

A writing genre can be thought of as the style in which the writer chooses to present textual content to the reader. We distinguish four main types of essay genres namely Narrative, Persuasive, Descriptive and Expository. An essay's writing genre can be identified by searching for salient features present within those genres using various Natural Language Processing tools such as Named Entity Recognition, Part of Speech tagging and Sentence Parsing. This paper explains the more common writing genres in student essays and describes the method in which essays in the narrative genre are identified.

Cloud computing has change the strategy of the way of providing distributed services for many business and government agents. Cloud computing delivers a scalable and on demand services for most users in different domains. This new technology brings many challenges to service providers and customers especially for users who already own complicated legacy systems. This paper examines challenges related to the concepts of trust, SLA management, and cloud computing. We focus on SLA definition in cloud computing to achieve the aim of presenting a clear structure of SLA for cloud users and improve the way of building trustworthy relationship between service provider and customer. In this paper, we start with the presenting the importance of cloud computing and the need of SLA for cloud computing. Then, survey of cloud computing architecture is provided. Then, we discuss existing frameworks of service level agreements in different domains such as web services and grid computing. The last part of literature review discusses advantages and limitations of performance measurement models in SOA, distributed systems, grid computing, and cloud services. Finally, we summarize and conclude our work.

Ontology evolution becomes an interesting topic in the semantic web field and increasingly getting research momentum. However, there is still a lack of understanding and support in ontology evolution. In this paper, we focus on an approach for ontology evolution of Software Engineering Ontology (SE Ontology) in multi-site software development setting. An integration of agents with semantic web technology and recommender systems to address communication and co-ordination issues is proposed together with a use of social networks for the purpose of ontology evolution.

In this paper, process conditions of epoxy dispensing processes are determined by the proposed genetic algorithm based neural fuzzy networks, which consists of two tasks: a) the approach of neural fuzzy networks, which was shown to be better than the other existing approaches, is proposed to develop models in relating between process parameters and quality characteristics for the epoxy dispensing processes; b) the approach of genetic algorithm is used to determine process parameters with respect to pre-defined quality requirements based on the developed neural fuzzy network models. The results indicate that, based on the proposed genetic algorithm based neural fuzzy network, estimated process parameters can achieve specified requirements of microchip encapsulations with high and robust qualities.

Fuzzy regression is a commonly used approach for modeling manufacturing processes in which the availability of experimental data is limited. Fuzzy regression can address fuzzy nature of experimental data in which fuzziness is not avoidable while carrying experiments. However, fuzzy regression can only address linearity in manufacturing process systems, but nonlinearity, which is unavoidable in the process, cannot be addressed. In this paper, an evolutionary fuzzy regression which integrates the mechanism of a fuzzy regression and genetic programming is proposed to generate manufacturing process models. It intends to overcome the deficiency of the fuzzy regression, which cannot address nonlinearities in manufacturing processes. The evolutionary fuzzy regression uses genetic programming to generate the structural form of the manufacturing process model based on tree representation which can address both linearity and nonlinearities in manufacturing processes. Then it uses a fuzzy regression to determine outliers in experimental data sets. By using experimental data excluding the outliers, the fuzzy regression can determine fuzzy coefficients which indicate the contribution and fuzziness of each term in the structural form of the manufacturing process model. To evaluate the effectiveness of the evolutionary fuzzy regression, a case study regarding modeling of epoxy dispensing process is carried out.

Abstract The recent development of Web‐of‐Things (WoT) and Cyber–Physical Systems (CPS) raises a new requirement of connecting abstract computational artifacts with the physical world. This requires both new theories and engineering practices that model cyber and physical resources in a unified framework, a challenge that few current approaches are able to tackle. The solution must break the boundary between the cyber world and the physical world by providing a unified infrastructure that permits integrated models addressing issues from both worlds simultaneously. This paper proposes a framework to integrate WoT and CPS. A case study is presented to demonstrate the advantage of the framework. Copyright © 2010 John Wiley & Sons, Ltd.

Modeling of manufacturing processes is important because it enables manufacturers to understand the process behavior and determine the optimum operating conditions of the process for a high yield, low cost and robust operation. However, existing techniques in modeling manufacturing processes cannot address the whole common issues in developing models for manufacturing processes: a) manufacturing processes are usually nonlinear in nature; b) a small amount of experimental data is only available for developing manufacturing process models; c) outliers often exist in experimental data; d) explicit models in a polynomial form are often preferred by manufacturing process engineers; and e) models with satisfactory prediction accuracy are required. In this paper, a modeling algorithm, namely, the particle swarm optimization-based fuzzy regression (PSO-FR) approach, is proposed to generate fuzzy nonlinear regression models, which seek to address all of the common issues in developing models for manufacturing processes. The PSO-FR first employs the operations of particle swarm optimization to generate the structures of the process models in nonlinear polynomial form, and then it employs a fuzzy coefficient generator to identify outliers in the original experimental data. Fuzzy coefficients of the process models are determined by the fuzzy coefficient generator in which the experimental data excluding the outliers is used. The effectiveness of the PSO-FR approach is evaluated by modeling the manufacturing process liquid epoxy molding process which is a commonly used technology for microchip encapsulation in electronic packaging. Results were compared with those based on the commonly used modeling methods. It was found that PSO-FR can achieve better goodness-of-fitness than other methods. Also, the prediction accuracy of the model developed based on the PSO-FR is better than the other methods.

In this paper, we propose a service-oriented reliability model that dynamically calculates the reliability of composite web services with rollback recovery based on the real-time reliabilities of the atomic web services of the composition. Our model is a hybrid reliability model based on both path-based and state-based models. Many reliability models assume that failure or error arrival times are exponentially distributed. This is inappropriate for web services as error arrival times are dependent on the operating state including workload of servers where the web service resides. In this manuscript, we modify our previous model (for software based on the Doubly Stochastic Model and Renewal Processes) to evaluate the reliability of atomic web services. In order to fix our idea, we developed the case of one simple web service which contains two states, i.e., idle and active states. In real-world applications, where web services could contain quite a large number of atomic services, the calculus as well as the computing complexity increases greatly. To limit our computing efforts and calculus, we chose the bounded set techniques that we apply using the previously developed stochastic model. As a first type of system combination, we proposed to study a scheme based on combining web services into parallel and serial configurations with centralized coordination. In this case, the broker has an acceptance testing mechanism that examines the results returned from a particular web service. If it was acceptable, then the computation continues to the next web service. Otherwise, it involves rollback and invokes another web service already specified by a checkpoint algorithm. Finally, the acceptance test is conducted using the broker. The broker can be considered as a single point of failure. To increase the reliability of the broker introduced in our systems and mask out errors at the broker level, we suggest a modified general scheme based on Triple modular redundancy and N-version programming. To imitate a real scenario where errors could happen at any stage of our application and improve the quality of Service QoS of the proposed model, we introduce fault-tolerance techniques using an adaption of the recovery block technique.

Many believe that Cloud will reshape the entire ICT industry as a revolution. In this paper, we aim to pinpoint the challenges and issues of Cloud computing. We first discuss two related computing paradigms - Service-Oriented Computing and Grid computing, and their relationships with Cloud computing. We then identify several challenges from the Cloud computing adoption perspective. Last, we will highlight the Cloud interoperability issue that deserves substantial further research and development.

As Wireless Mesh Networks (WMNs) are newly emerging wireless technologies, they are designed to have huge potential for strengthening Internet deployment and access. However, they are far from mature for large-scale deployment in some applications due to the lack of the satisfactory guarantees on security. The main challenges exposed to the security of WMNs come from the facts of the shared nature of the wireless architecture and the lack of globally trusted central authorities. A well-performed security framework for WMNs will contribute to network survivability and strongly support the network growth. A low-computational and scalable key management model for WMNs is proposed in this paper which aims to guarantee well-performed key management services and protection from potential attacks.

A complex fuzzy relation is defined as a fuzzy relation whose membership function takes values in the unit circle on a complex plane. This paper first investigates various operation properties of a complex fuzzy relation. It then defines the distance measure of two complex fuzzy relations that can measure the differences between the grades as well as the phases of two complex fuzzy relations. This distance measure is used to define δ-equalities of complex fuzzy relations that coincide with those of fuzzy relations already defined in the literature if complex fuzzy relations reduce to real-valued fuzzy relations. Two complex fuzzy relations are said to be δ-equal if the distance between them is less than 1-δ. This paper shows how various operations between complex fuzzy relations, including T-norms and S-norms, affect given δ-equalities of complex fuzzy relations. Finally, fuzzy inference is examined in the framework of delta- equalities of complex fuzzy relations.

This paper presents a proposal for the development of an ontology evolution strategy which refines ontological relations in scientific ontologies. In addition to experts' consensus, it is desirable to define ontological relations between any two concepts in a scientific ontology based on scientific evidence. To address this issue, we can relate ontological relations to different research results obtained from various studies. To implement this solution, our envisaged evidence/discovery-based methodology integrates a higher-level ontology (systematic review ontology) into a systematic review agent which employs a Fuzzy Inference System in order to automatically modify ontological relations of a domain ontology based on the evidence received from information resources. The evidence/discovery-based methodology will further use the domain ontology to discover novel connections between distinct literatures, thereby, enrich its conceptualization.

A novel approach, which is based on artificial neural network (ANN) by backpropagation, for fast and trusted herbal ingredient discoveries, is proposed. It is fast, because different ANN modules can be executed in parallel, and the ANN results are trustworthy, because they can be verified by TCM domain experts in real clinical environments. The ANN is able to learn the relationship between herbal ingredients and the set of information given (e.g. symptoms and illnesses). The ANN output is called the relevance index (RI), which conceptually associates two TCM entities (e.g. U and V) in a 2-D or 3-D manner (D for dimension). RI is the quantified P(U∩V) part of P(U ∪ V) = P(U) + P(V) - P(U ∩ V), an IT (information technology) formalism in which P stands for probability. The interpretation of P(U ∩ V) adheres to TCM formalism(s).

Even if genetic programming (GP) has rich literature in development of polynomial models for manufacturing processes, the polynomial models may contain redundant terms which may cause the overfitted models. In other words, those models have good accuracy on training data sets but poor accuracy on untrained data sets. In this paper, a mechanism which aims at avoiding overfitting is proposed based on a statistical method, backward elimination, which intends to eliminate insignificant terms in polynomial models. By modeling a solder paste dispenser for electronic manufacturing, results show that the insignificant terms in the polynomial model can be eliminated by the proposed mechanism. Results also show that the polynomial model generated by the proposed GP can achieve better predictions than the existing methods.

Hypoglycemia is dangerous for Type 1 diabetes mellitus (T1DM) patients. Based on the physiological parameters, we have developed a classification unit with hybridizing the approaches of neural networks and genetic algorithm to identify the presences of hypoglycemic episodes for TIDM patients. The proposed classification unit is built and is validated by using the real T1DM patients' data sets collected from Department of Health, Government of Western Australia. Experimental results show that the proposed neural network based classification unit can achieve more accurate results on both trained and unseen T1DM patients' data sets compared with those developed based on the commonly used classification methods for medical diagnosis including statistical regression, fuzzy regression and genetic programming.

In this paper, a hybrid evolutionary algorithm (HEA) based on the approaches of the evolutionary algorithm and a local search (LS) is proposed to determine the gene signatures for predicting histologic response of chemotherapy on osteosarcoma patients, which is one of the most common malignant bone tumor in children. The HEA consists of a population of individuals but the evolution of individuals is conducted by a LS, rather than the crossover and mutation used in the traditional evolutionary algorithms. The proposed HEA can simultaneously optimize the feature subset and the classifier through a common solution coding mechanism. Experimental results indicate that HEA can obtain more accurate signatures than the other existing approaches in determining chemoresponse for osteosarcoma.

The security of Wireless Sensor Networks (WSNs) has a direct reliance on secure and efficient key management. This leaves key management as a fundamental research topic in the field of WSNs security. Among the proposed key management schemes for WSNs security, LEAP (Localized Encryption and Authentication Protocol) has been regarded as an efficient protocol over the last years. LEAP supports the establishment of four types of keys. The security of these keys is under the assumption that the initial deployment phase is secure and the initial key is erased from sensor nodes after the initialization phase. However, the initial key is used again for node addition after the initialization phase whereas the new node can be compromised before erasing the key. A time-based key management scheme rethought the security of LEAP. We show the deficiency of the time-based key management scheme and proposed a key management scheme for multi-phase WSNs in this paper. The proposed scheme disperses the damage resulting from the disclosure of the initial key. We show it has better resilience and higher key connectivity probability through the analysis.

In affective product design, one of the main goals is to maximize customers' affective satisfaction by optimizing design variables of a new product. To achieve this, a model in relating customers' affective responses and design variables of a new product is required to be developed based on customers' survey data. However, previous research on modelling the relationship between affective response and design variables cannot address the development of explicit models either involving nonlinearity or fuzziness, which exist in customers' survey data. In this paper, an evolutionary fuzzy regression approach is proposed to generate explicit models to represent this nonlinear and fuzzy relationship between affective responses and design variables. In the approach, genetic programming is used to construct branches of a tree representing structures of a model where the nonlinearity of the model can be addressed. Fuzzy coefficients of the model, which is represented by the tree, are determined based on a fuzzy regression algorithm. As a result, the fuzzy nonlinear regression model can be obtained to relate affective responses and design variables.

Cloud services are becoming popular in terms of distributed technology because they allow cloud users to rent well-specified resources of computing, network, and storage infrastructure. Users pay for their use of services without needing to spend massive amounts for integration, maintenance, or management of the IT infrastructure. This creates the need for a reliable measurement methodology of the scalability for this type of new paradigm of services. In this paper, we develop performance metrics to measure and compare the scalability of the resources of virtualization on the cloud data centres. First, we discuss the need for a reliable method to compare the performance of cloud services among a number of various services being offered. Second, we develop a different type of metrics and propose a suitable methodology to measure the scalability using these types of metrics. We focus on the visualization resources such as CPU, storage disk, and network infrastructure. Finally, we compare well-known cloud providers using the proposed approach and conclude the recommendations. This type of research will help cloud consumers, before signing any official contract to use the desired services, to ascertain the ability and capacity of the cloud providers to deliver a particular service.

Large amount of online information is or can be represented using semi-structured documents, such as XML. The information contained in an XML document can be effectively represented using a rooted ordered labeled tree. This has made the frequent patt

Risk assessment in business interactions is carried out to determine beforehand the occurrence of undesirable events and their associated consequences. In the literature, approaches have been proposed by which an interaction initiating agent can ascertain the occurrence of undesirable event/s and determine their consequences in an interaction. But those approaches just consider those events that are related to the performance of the other agent, with whom the interaction initiating agent is forming an interaction. It is possible that there may also be such events that are not dependent on the other agent's performance, but will directly or indirectly have an impact on the successful completion of the business interaction. In this paper, we will highlight the importance of considering such event/s during the process of risk assessment, and propose a methodology by which the interaction initiating agent can determine and quantify their effect on the successful completion of its business interaction.

In a business interaction, transactional risk highlights the uncertainty associated in not achieving the desired outcomes. The assessment of transactional risk gives the interacting user the different levels of failure in achieving its desired outcomes and the consequences that it can experience. In a business interaction, the consequences that can be experienced pertain to the financial resources invested to achieve the desired outcomes. The level of financial loss that could be experienced plays a very important role in the interacting userpsilas decision to form a business interaction. The level of financial loss is dependent on the different types of uncertain events associated with the business interaction. In this paper, we will propose a methodology by which the interacting user in an e-business interaction can capture the different types of uncertainties and ascertain the financial risk that could be experienced from it.

Self-healing key distribution mechanism can be utilized for distributing session keys over an unreliable network. A self-healing key distribution scheme using bilinear pairings is proposed in this paper. As far as we know, it is the first pairing-based authenticated self-healing key distribution scheme. The scheme achieves a number of excellent properties. Firstly, the users can check the integrity and correctness of the ciphertext before carrying out more complex key recovery operations thus fruitless work can be avoided. Secondly, the scheme is collusion-free for any coalition of non-authorized users. Thirdly, the private key has nothing to do with the number of revoked users and can be reused as long as it is not disclosed. Finally, the storage overhead for each user is a constant.

In this paper, we propose an Internet public Web service matching approach that paves the way for (semi-)automatic service mashup. We first provide the overview of the solution, which requires a detailed review of two fundamental models - schema/graph matching and semantic space. Based on the conceptual model and the literature study, the complete service matching approach is then provided with four essential steps - semantic space, parameter tree, similarity measures, and WSDL operation matching. The system demonstration that proves the concept proposed in this approach is finally presented. The solution has the potential to facilitate the Internet services mashup.

We present a suite of key management scheme for heterogeneous sensor networks. In view of different types of communications, a single key can not satisfy various communication requirements. It is necessary to study the establishment and renewal of different types of keys in heterogeneous sensor networks. In this paper, we propose a new key management scheme which can support five types of communications. Our basic scheme is based on a keyed-hash chain approach. A new cluster mechanism is used to improve the probability of key sharing between sensors and their cluster heads. Different from existing schemes where a node capture attack might lead to the disclosure of several key chains, our method can avoid this drawback through not storing network-wide generating keys in low-cost sensors. Only pairwise keys involving the compromised node should be deleted in our scheme. It is motivated by the observation that all the information stored on a sensor may be disclosed once the sensor gets compromised. Through the analysis of both security and performance, we show the scheme meets the security requirements.

As like as wired communication and mobile ad hoc networking, mobile IP communication is also vulnerable to different kinds of attack. Among different kinds of attack Denial-of-Service (DoS) is a great threat for mobile IP communication. In this paper we proposed to imply a lightweight packet filtering technique in different domains and base stations of mobile IP communication. If there is any packet containing spoofed IP address created by DoS attackers, our scheme can detect and then filters the suspected packets. We evaluated the performance of our proposed scheme using ns-2. The results indicate that our proposed scheme can significantly reduce the effect of DoS attacks and improves performance of mobile IP communication.

Abstract Service‐oriented computing (SOC), Grid computing, and Web2.0 computing have increasingly received momentum in both research and industry. In this paper, we propose an abstract Web‐inclusive distributed computing model (WIDCOM) in order to analyse these three important distributed computing paradigms. We then advance the notion of software in existing Grid services as discussed in open Grid services architecture, which provides software middleware or wrappers for accessing hardware resources towards the notion that the resources provided can be hardware, software, or hybrid hardware/software. It also proposes an approach using the integration of Grid service, semantic Grid, and Web2.0 to overcome some of the limitations of the existing Web services architecture, which relies on having the Web version of the RPC mechanism and thus has difficulty in dealing with massive scale of user participation and communication across the Internet. The new approach produces a novel Grid architecture—GRIDSpace. Copyright © 2008 John Wiley & Sons, Ltd.

This paper aims to present an ontology model of software engineering to represent its knowledge. The fundamental knowledge relating to software engineering is well described in the textbook entitled Software Engineering by Sommerville that is now in its eighth edition (2004) and the white paper, Software Engineering Body of Knowledge (SWEBOK), by the IEEE (203) upon which software engineering ontology is based. This paper gives an analysis of what software engineering ontology is, what it consists of, and what it is used for in the form of usage example scenarios. The usage scenarios presented in this paper highlight the characteristics of the software engineering ontology. The software engineering ontology assists in defining information for the exchange of semantic project information and is used as a communication framework. Its users are software engineers sharing domain knowledge as well as instance knowledge of software engineering.

Lots of research have been done within the mental health domain, but exact causes of mental illness are still unknown. Concerningly, the number of people being affected by mental conditions is rapidly increasing and it has been predicted that depression would be the world's leading cause of disability by 2020. Most mental health information is found in electronic form. Application of the cutting-edge information technologies within the mental health domain has the potential to greatly increase the value of the available information. Specifically, ontologies form the basis for collaboration between research teams, for creation of semantic Web services and intelligent multi-agent systems, for intelligent information retrieval, and for automatic data analysis such as data mining. In this paper, we present mental health ontology which can be used to underpin a variety of automatic tasks and positively transform the way information is being managed and used within the mental health domain.

The increasing body of distributed and heterogeneous information and the autonomous, heterogeneous and dynamic nature of information resources are important issues hindering effective and efficient data access, retrieval and knowledge sharing. The importance of ontologies has been recognised within the biomedical domain and work has begun on developing and sharing biomedical ontologies. In this paper, we define ontology and ontology commitments and explain the main characteristics and representations of ontology models. Ontologies are highly expressive knowledge models and as such increase expressiveness and intelligence of a system. We highlight the significance of ontologies in a variety of semi-automatic and automatic tasks, and provide an illustrative example of an ontology-based multi-agent system designed to intelligently retrieve information about human diseases from a number of heterogeneous and dispersed information resources.

Information regarding mental illness is dispersed over various resources but even within a specific resource, such as PubMed, it is difficult to link this information, to share it and find specific information when needed. Specific and targeted searches are very difficult with current search engines as they look for the specific string of letters within the text rather than its meaning. In this paper we present thinking PubMed as a system that results from synergy of ontology and data mining technologies and performs intelligent information searches using the domain ontology. Furthermore, the thinking PubMed analyzes and links the retrieved information, and extracts hidden patterns and knowledge using data mining algorithms. This is a new generation of information-seeking tool where the ontology and data-mining work in concert to increase the value of the available information.

Ontologies and controlled vocabularies are being established by many groups to provide roadmaps through the confused mass of data currently being generated from increasingly large-scale experimental biological experiments. The world of protein chemistry is no exception to this rule, with protein ontology (PO) having lead the field by providing a framework in which individual molecules and complexes can be defined by their structure, function and cellular location. PO performs searches across the protein databases using a standard nomenclature consistent to all entries.

In this paper, we describe a XML based profile for modeling the semantics of real time systems. We aim to use Real Time Markup Language (RTML) to provide a comprehensive description of temporal properties for the use in distributed systems communication. RTML is derived from a number of specifications including OMG UML Profile in Schedulability, Performance, and Time. In this paper, we discuss the technique that was used to develop RTML semantic model and the important concepts in RTML.

In this paper we present an algorithm for mining of unordered embedded subtrees. This is an important problem for association rule mining from semi-structured documents, and it has important applications in many biomedical, Web and scientific domains. The proposed U3 algorithm is an extension of our general tree model guided (TMG) candidate generation framework and it considers both transaction based and occurrence match support. Synthetic and real world data sets are used to experimentally demonstrate the efficiency of our approach to the problem, and the flexibility of our general TMG framework.

An efficient self-healing key distribution scheme with revocation capability is proposed for secure group communication in wireless networks. The scheme bases on vector space secret sharing and one way hash function techniques. Vector space secret sharing helps to realize general monotone decreasing structures for the family of subsets of users that can be revoked instead of a threshold one. One way hash chains contribute to reduce communication overhead. Furthermore, the most prominent characteristic of our scheme is resisting collusion between the new joined users and the revoked users, which is fatal weakness of hash function based self-healing key distribution schemes.

Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly concerned with mining frequent induced and embedded ordered subtrees. Our main contributions are as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation of our Tree Model Guided ( TMG ) candidate generation. TMG is an optimal, nonredundant enumeration strategy that enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this article, we propose two algorithms, MB3-Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint . Our experiments with both synthetic and real datasets against two well-known algorithms for mining induced and embedded subtrees, demonstrate the effectiveness and the efficiency of the proposed techniques.

This paper presents a service oriented architecture (SOA) approach to a distributed framework for reusing, extracting and extending (tailoring) large domain ontologies in the semantic grid environment. The conceptual level of the framework describes how sub-ontologies are tailored while the architectural level of the framework describes the components of the framework that allows the tailoring process happen in the semantic grid environment. A prototype of the framework and a complexity evaluation measure are also provided.

Ontologies are the foundation of the semantic Web (SW) and one of the keys necessary to its success. Conversely, e-Science domains are usually characterized by heterogeneous semistructured data models and formats. In recent years, building ontologies are gaining momentum in the e-science domains to provide explanatory semantics in an agreed, yet shared encodings. Conversely, ontology views hold the promise of; (a) provide a manageable portion of a larger ontology for the localized applications and users, (b) enable precise extraction of sub-ontologies of a larger ontology that commits to the main ontology, (c) enable localized customization and usage of the portion of a larger ontology and (d) enable interoperability between large ontology bases and applications. Therefore, it is interesting to look at ontology views and their desired applications in the context of e-Sciences, as there exists no agreed upon standard, methodology or formalism to specify, define and materialize such ontology views. Thus, in this paper, we elaborate on our own research direction towards proposing a meaningful ontology view formalism and its associated semantics for the e-science domain.

Protein ontology is designed to provide a structured protein data specification for protein data representation. Protein ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for protein structure and function. In paper we summarize the development of PO that we discussed in previous chapters. A large number of diverse bioinformatics sources are available today. The future of biological sciences promises more data. No individual data source will provide us with answers to queries that we need to ask. Instead knowledge has to be composed from multiple data sources to answer the queries. Even though multiple databases may cover same data their focus might be different. Thus, protein ontology project is an ongoing project with creation of every new protein data source. We also outline here in this paper the future work that we will be doing in protein ontology project.

Semi-structured data sources are increasingly in use today because of their capability of representing information through more complex structures where semantics and relationships of data objects are more easily expressed. Extraction of frequent sub-structures from such data has found important applications in areas such as Bioinformatics, XML mining, Web mining, scientific data management etc. This paper is concerned with the task of mining frequent unordered induced subtrees from a database of rooted ordered labeled subtrees. Our previous work in the area of frequent subtree mining is characterized by the efficient tree model guided (TMG) candidate enumeration, where candidate subtrees conform to the data's underlying tree structure. We apply the same approach to the unordered case, motivated by the fact that in many applications of frequent subtree mining the order among siblings is not considered important. The proposed UNI3 algorithm considers both transaction based and occurrence match support. Synthetic and real world data are used to evaluate the time performance of our approach in comparison to the well known algorithms developed for the same problem

To ascertain the possible level of risk in a digital business ecosystem interaction, the initiating agent has to determine beforehand the probability of failure, the possible consequences of failure, and the loss of investment probability to its resources while interacting with the other agent. Out of these three constituents, the initiating agent can determine beforehand the probability of failure in interacting with an agent either by considering its past interaction history with it or by soliciting recommendations from other agents. In both cases, it is imperative for the agent who is either considering its past interaction history, or who is communicating a recommendation about the other agent, to know the accurate level of failure in interacting with the other agent. To achieve this, in this paper we propose a methodology by which the initiating agent of the interaction ascertains the level of failure in the interaction, after interacting with an agent.

Biomedical knowledge of proteomics domain is represented in the protein ontology, whose instantiations, which are undergoing evolution, need a good management and maintenance system. Protein ontology instantiations signify information about proteins that is shared and has evolved to reflect development in protein ontology project and proteomics domain itself. In this paper we explore the development of a conceptual framework for protein ontology instantiations management by using the concepts of trust and reputation in biomedical domain. The developed and engineered ontology approach is trustworthy and facilitates reliable additions and updates to the protein ontology.

Basic bilevel programming deals with hierarchical optimization problems in which the leader at the upper level attempts to optimize his/her objective, subject to a set of constraints and his/her follower's solution, and the follower at the lower level tries to find an optimized strategy according to each of possible decisions made by the leader. Three issues may be involved in a basic bilevel decision problem. One is that bilevel decision making model may involve uncertain parameters which appear either in the objective functions or constraints of the leader or the follower or both. Second, the leader and the follower may have multiple conflict objectives that should be optimized simultaneously. Third, there may have multiple followers in a real decision situation. Following our previous work, this study proposes a set of fuzzy multi-objective multi-follower linear bilevel programming models to describe the three issues. It also develops an approximation branch-and-bound algorithm to solve such kinds of problems.

Due to technological advancement of the Internet, conducting e-commerce transactions have become a part of our daily lives. In a financial interaction to be carried over the digital business ecosystem domain, it is rational for an agent instigating the interaction to analyse beforehand the possible risk in interacting with any other agent. Doing so would give the instigating agent an idea of direction in which its interaction might head and also help it to make an informed decision of its future course of action with that particular agent. For risk analysis, the instigating agent has to determine beforehand the probability of failure and the possible consequences of failure in interacting with an agent. In this paper, we propose such a methodology by which the instigating agent quantifies the probability of failure beforehand in interacting with an agent according to the demand of its future interaction with it.

The M3RT-based real-time traffic pattern detector proposed identifies the Internet traffic pattern on the fly. Firstly it determines if a time series aggregate is stationary. Secondly it confirms if the aggregate exhibits short-range dependence (SRD) or long-range dependence (LRD). Thirdly it detects if the smooth system operation has suddenly become irregular and chaotic. This detection is achieved by computing the instantaneous value of the Holder exponent that has a (0,1) range to accommodate different degrees of fractality. When the Holder exponent has wandered outside the (0,1) region, fractal breakdown has occurred. The capability of detecting such breakdowns by a real-time application enables it to avoid sudden failure. The Intel's VTune Performance Analyzer indicates the proposed model can be deployed in real time effectively. This feature is of importance to the reliability improvement of Web applications which run on the Internet

Digital ecosystems have recently been introduced into the computer and information societies. Digital ecosystem (DES) is the dynamic and synergetic complex of digital communities consisting of interconnected, interrelated and interdependent digital species (DS) situated in a digital environment (DE) that interact as a functional unit and are linked together through actions, information and transaction flows. DES transpose mechanisms from living organisms like autonomy, viability and self-organisation to arrive at novel knowledge and architectures. The proposed DES embraces a number of different technologies such as ontologies, agent-based and self-organizing systems etc. The synergetic effects of these methodologies results in a more efficient, effective, reliable and secure system. DES is still in its early implementation phase. No clear methodology for digital ecosystem design exists yet. In this paper, we propose a methodological framework that consists of five phases and addresses many different aspects of the DES design. In this methodology, we focus on the key factors associated with the DES design such as roles of different digital components within the DES, organisation and collaboration of the digital components, their individual design along with intelligence and security within the DES. More details are introduced at each step and every sequential step takes the DES to a higher level of elaboration. This methodological framework allows better control over the design process and serves as a navigating tool during DES design.

In this article, we introduce trust ontologies. An ontology represents a set of concepts that are commonly shared and agreed to by all parties in a particular domain. Here, we introduce generic and specific trust ontologies. These ontologies include the following: an agent trust ontology and trustworthiness; agents include sellers, service providers, Web sites, brokers, shops, suppliers, buyers, or reviewers. A services trust ontology and trustworthiness assists in measuring the quality of service that agents provide in the service-oriented environment such as sales, orders, track and trace, warehousing, logistics, education, governance, advertising, entertainment, trading, online databases, virtual community services, security, information services, opinions, and e-reviews. A goods or products trust ontology and trustworthiness is useful for measuring the quality of products such as commercial products, information products, entertainment products, or second-hand products. We present a trust ontology that is suitable for all types of agents that exist in the service-oriented environment. As agent trust is measured through the quality of goods and services, we introduce two additional distinct concepts of service trust ontology and product trust ontology. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 519–545, 2007.

This article introduces a new transaction protocol using mobile agents in electronic commerce. The authors first propose a new model for transactions in electronic commerce, mutual authenticated transactions using mobile agents. They then design a new protocol by this model. Furthermore, the authors analyse the new protocol in terms of authentication, construction, and privacy. The aim of the protocol is to guarantee that the customer is committed to the server, and the server is committed to the customer. At the same time, the privacy of the customer is protected.

We present MAGICS, a mobile agent-based system for supporting business-to-consumer electronic commerce (e-commerce) or mobile commerce (m-commerce) applications. To use the system, consumers first provide their buying requirements to a proxy/agent server through a Web browser or a wireless application protocol (WAP) terminal. Having obtained the requirements, mobile agents are generated to carry out tasks for the consumers including getting offers from merchants, evaluating offers, and even completing purchases. In the case of mobile commerce, consumers can generate a mobile agent to conduct a search and evaluation in the digital marketspace before making a purchase in the physical marketplace. To make it possible to choose an offer that best satisfies the consumer's requirement(s), we present a mathematical model for evaluating multiple decision factors. To test the basic functions of the mobile agent-based Internet commerce system (MAGICS), we have built a prototype system. To minimize the average cost of a product (including the cost of sending agents), we have also developed an analytical model that can determine how many agents should be sent to compare prices. Four different price distributions and some real price information are analyzed based on the model. The analysis provides valuable insights into the design of mobile agent-based shopping applications for m-commerce, in particular, and for e-commerce, in general.

Trust and Reputation are vital components for trusted e-business. In the literature however there has been no effort in proposing ontology for trust. The trusted agent in service oriented environment may trust a software agent or human agent or a service or product. Based on this distinction, trust ontology could be proposed for different domains. The trust ontology for the individual domains is proposed and discussed.

Trust and Reputation are vital components for trusted e-business. In this paper, we propose a definition of trust relationship. Additionally we discuss in depth about the concept of trust relationship. A definition for reputation relationship is proposed in this paper. Three inner relationships with in the reputation relationship are detailed and discussed. © 2006 IEEE.

Trust and Reputation are vital components for trusted e-business. In this paper, we propose a definition of trust relationship. Additionally we discuss in depth the concept of trust relationship. A definition for reputation relationship is proposed in this paper. From the analysis of reputation relationship we find that the reputation relationship is a composite relationship and is composed of three inner relationships. We propose the three inner relationships and define them. We then propose the characteristics of the inner relationships within the reputation relationship.

The trusting peer in order to determine the likelihood of the loss in its resources might analyze the Risk before engaging in an interaction with any trusted peer. This likelihood of the loss in the resources is termed as Risk in the interaction. Risk analysis is important in e-commerce transactions because of the vast literature that argues that the decision to buy is based on the Risk-adjusted cost-benefit analysis. If the trusting peer can determine the future Riskiness value or reputation of a trusted peer for the time period of its interaction, before engaging in an activity with it, then it can ease its decision making process of whether to interact with the trusted peer or not. In this paper we present such a novel method which predicts the dynamic nature of Risk and determines the future Riskiness value of the trusted peer, before the interaction starts, thus helping the trusting peer considerably in making its decision. © 2006 IEEE.

Undeniable signatures are the digital signatures that should be verified with the help of the signer. A signer may disavow a genuine document, if the signature is only verifiable with the aid of the signer under the condition that the signer is not honest. Undeniable signatures solve this problem by adding a new feature called the disavowal protocol in addition to the normal components of signature and verification. Disavowal protocol is able to prevent a dishonest signer from disavowing a valid signature. In some situations, an undeniable signature should be converted into a normal digital signature in order that the signature can be universally verified. Blind signatures the digital signatures that help a user to get a signature on a message without revealing the content of the message to a signer. For the blind signatures, if the signer is able to make an agreement with the user, then the underlying signer may include some common information that is known to the user, then such signatures are partially blind signatures. Convertible undeniable partially blind signatures are of the features of undeniable signatures, blind signatures, convertible undeniable signatures, and partially blind signatures. Recently, a convertible undeniable partially blind signature scheme was presented. In this paper, we first analyze a security flaw of the convertible undeniable partially blind signature scheme. To address the security flaw, we present an improvement on the disavowal protocol. The improved scheme can prevent the signer from either proving that a given valid signature as invalid, or cheating the verifier

As there is a lack of central management in an e-commerce interaction carried out based on peer-to-peer architecture, it is obvious for the trusting peer to analyze the risk beforehand that could be involved in dealing with a trusted peer in these types of interactions. Another characteristic of peer-to-peer architecture interactions is that the trusting peer might have to choose a peer to interact with, from a set of possible trusted peers. It can ease its decision making process of choosing a peer to interact with by analyzing the risk that could be involved in dealing with each of the possible trusted peers. In this paper we highlight and propose a solution to this problem by which the trusting peer can decide with which peer to interact with after analyzing the risk that could be associated in dealing with each of them

Biomedical Ontologies are huge. It is not possible for any one person to manage and engineer a complete ontology. They would need the help of Research Assistants and other people to develop and maintain the ontology. In the process of developing and maintaining the ontology the Research Assistants may enter incorrect data, resulting in low quality of the ontology. In this paper we will propose a conceptual framework to solve these ontology management and ontology development issues. There can be N assistants entering data into the ontology. All the data entered initially is stored in an intermediate ontology. The administrator of the ontology has a set of rules, which makes a checklist that checks and validates the data in intermediate ontology for correctness according to the ontology schema. We use the Case Study of Protein Ontology for this proposed approach to develop interfaces for assistants and administrators. The proposed approach can easily be extended to other biomedical ontologies just by tweaking the administrator rule set according to the ontology.

Advances in proteomics and protein expression techniques have lead to the elucidation of large amounts of protein data. Various data mining algorithms and mathematical models provide methods for analyzing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange formats so they can be exchanged across the World Wide Web, and also read into data mining software in a consistent format and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. In this paper we summarize the structure of Protein Ontology we developed earlier, its current applications to various protein families, and its future development.

Recent progress in proteomics, computational biology, and ontology development has presented an opportunity to investigate protein data sources from a unique perspective that is, examining protein data sources through structure and hierarchy of Protein Ontology (PO). Various data mining algorithms and mathematical models provide methods for analyzing protein data sources; however, there are two issues that need to be addressed: (1) the need for standards for defining protein data description and exchange and (2) eliminating errors which arise with the data integration methodologies for complex queries. Protein Ontology is designed to meet these needs by providing a structured protein data specification for Protein Data Representation. Protein Ontology is a standard for representing protein data in a way that helps in defining data integration and data mining models for Protein Structure and Function. We report here our development of PO; a semantic heterogeneity framework based on relationships between PO concepts; and analysis of resultant PO Data of Human Proteins. We also talk in this paper briefly about our ongoing work of designing a trustworthy framework around PO.

The benefits of Service Oriented Architectures (SOAs) start to be recognized and implemented across distributed enterprise businesses. As SOAs promise to accomplish more dynamic and collaborative e-business scenarios, we need to ensure that critical factors such as trust, credibility and quality of service (QoS) are consistently and efficiently measured in such unsupervised environments. We propose a fuzzy logic-based model for the review of service fulfilment after a business interaction and demonstrate its functionalities in an exemplary application. The outcome of our model can then be used to adjust credibility records of third party agents which delivered opinions, to update trustworthiness values in the business partner, or to communicate the trustworthiness of the business partner to interested peer agents.

Due to the increasing dependency on self-describing, schema-based, semi-structured data (e.g. XML), there exists a need to model, design and manipulate semi-structured data and the associated semantics at a higher level of abstraction than at the instance or document level. In this paper, we extend our research and propose to visually model (at the conceptual level) and transform dynamic properties of views in the Layered View Model (LVM) using the eXtensible Semantic (XSemantic) net notation. First, we present the modeling notation and then discuss the declarative transformation to map the dynamic XML view properties to XML query expressions, namely XQuery.

This paper presents an overview of the real time markup language (RTML). RTML is a XML profile which provides the syntactic representation for describing the semantics of real time data for exchange over distributed networked real time systems. For the basis of interoperability, this profile is described in the XML schema language. This paper describes the background of this work and shows how the vocabularies are developed, and how it derives the extensibility of XML schema in aiding the definition of data in real time systems in order to achieve the goal of interoperability.

Software engineering project information is frequently evolving and queried to reflect project development changes in the software requirements or in the design process, to incorporate additional functionality to systems or to allow incremental improvement and the like. Therefore, the project information needs enhancement to ease up-to-date ontological information and to ease communication. Ontologies are widely used for capturing and organising knowledge of a particular domain of interest. We propose the use of software engineering ontology instantiations and enrichment to capture the software engineering project information

Usability of software should measure both user preference and user performance. The notion of usability involves several dimensions. These dimensions include: system feedback, consistency, error prevention, performance/efficiency, user like/dislike, and error recovery. Each of these dimensions are characterized by fuzzy aspects and linguistic terms. This paper develops a model for each of the dimensions using fuzzy-set theory. It then uses the Takagi-Sugeno fuzzy-inference approach for developing an overall measure of usability. Results are presented for several different user interfaces.

Two novel expert dynamic buffer tuners/controllers, namely, the neural network controller (NNC) and the fuzzy logic controller (FLC) are proposed in this paper. They use soft computing techniques to eliminate buffer overflow at the user/server level. As a result they help shorten the end-to-end service roundtrip time (RTT) of the logical Internet transmission control protocol (TCP) channels. The tuners achieve their goal by maintaining the given safety margin Delta around the reference point of the {0,Delta}2objective function. Overflow prevention at the Internet system level, which includes the logical channels and their underlying activities, cannot shorten the service RTT alone. In reality, unpredictable incoming request rates and/or traffic patterns could still cause user-level overflow. The client/server interaction over a logical channel is usually an asymmetric rendezvous, with one server serving many clients. A sudden influx of simultaneous requests from these clients easily inundates the server's buffer, causing overflow. If this occurs only after the system has employed expensive throttling and overflow management resources, the delayed overflow rectification could lead to serious consequences. Therefore, it makes sense to deploy an independent user-level overflow control mechanism to complement the preventative effort by the system. Together they form a unified solution to effectively stifle channel buffer overflow

The extensible markup language (XML) has emerged as the dominant standard in describing and exchanging data among heterogeneous data sources. The increasing presence of large volumes of data appearing in enterprise settings creates the need to investigate XML document warehouses (XDW) as a means of handling and analysing XML data for business intelligence. In our previous work, we proposed a conceptual modelling approach for the design and development of XDWs, with emphasis on capturing data warehouse requirements early in the design stage. To address this issue, in this paper, we explore a requirement engineering (RE) approach, namely the goal-oriented approach. We adopt and extend the notion of this approach and introduce the XDW requirement model. This focuses on deriving dimensions, as opposed to associating organizational objectives to the system functions, which is carried out by the traditional requirement engineering process.

This paper has two original and important proposals: a) the self-similarity (S/sup 2/) traffic filter for real-time applications and b) the R/sup 2/-FLC (reconfigurable real-time fuzzy logic controller) for on-line dynamic buffer size tuning. Inclusion of the S/sup 2/ filter into the extant RTPD (real-time traffic detector) model creates the novel enhanced RTPD (ERTPD) framework. The R/sup 2/-FLC uses the ERPTD to detect the current traffic pattern of a logical channel on the fly and auto-tunes accordingly. The auto-tuning process, which is system reconfiguration in the R/sup 2/-FLC context, neutralizes the ill effects by changing traffic patterns on the accuracy and stability of the dynamic buffer size tuning process.

In this paper we propose software engineering subontology. We called it application-specific ontology, for specific software development. It enables remote team members browsing, searching, sharing, and authoring ontological data under the distributed software engineering projects environment. We transform explicit meaningful human knowledge into application-specific ontology, where knowledge structures and semantics are linked, and we go through a formal hand-shaking agreement establishing process before the semantic contents are updated in ontology repositories. The application-specific ontology is used for communication over project agreement to facilitate better, highly consistent communications and formalized domain knowledge sharing. We assume that object-oriented development is deployed in the distributed projects. The knowledge of object-oriented development formed in the application-specific ontology clarifies the objectoriented development concepts in a machine understandable form. Software agent, for example, can be utilised to extract information.

The growing development of Web based trust and reputation systems in the 21st century will have powerful social and economic impact on all business entities, and will make transparent the quality assessment and customer assurance realities in the distributed Web based service oriented environments. The Web based trust and reputation systems will be the foundation for Web intelligence in the future. Trust and reputation systems help capture business intelligence through establishing customer relationships, learning consumer behavior, capturing market reaction on products and services, disseminating customer feedback, buyers' opinions and end-user recommendations, and revealing dishonest services, unfair trading, biased assessment, discriminatory actions, fraudulent behaviors, and untrue advertising. The continuing development of these technologies will help in the improvement of professional business behavior, sales, reputation of sellers, providers, products and services. However there has been no effort in defining ontology for reputation. In this paper we attempt to define ontology for reputation.

Transportation and warehousing logistics are activities that require strong information systems and computer support. This requirement has grown with the advent of e-commerce. Companies such as FedEx and UPS now allow their customers to track and monitor the fulfillment of their requested services on the Internet. In order for such a system to be effective, the goods need to be handled by the one corporation, with an integrated system. This is rare in the case of small to medium sized enterprise (SME). With the advent of business to business (B2B), and partner to partner (P2P) e-commerce, there has been an increasing tendency for SMEs to set up consortia that represent several players in a given field in order to contend with larger competitors. This paper deals with the concept of an extended logistics enterprise and explores the software engineering issues underlying the development of such complex systems.

The new era of semantic Web has enabled users to extract semantically relevant data from the Web. The backbone of the semantic Web is a shared uniform structure which defines how Web information is split up regardless of the implementation language or the syntax used to represent the data. This structure is known as an ontology. As information on the Web increases significantly in size, Web ontologies also tend to grow bigger, to such an extent that they become too large to be used in their entirety by any single application. This has stimulated our work in the area of sub-ontology extraction where each user may extract optimized sub-ontologies from an existing base ontology. Sub-ontologies are valid independent ontologies, known as materialized ontologies, that are specifically extracted to meet certain needs. Because of the size of the original ontology, the process of repeatedly iterating the millions of nodes and relationships to form an optimized sub-ontology can be very extensive. Therefore we have identified the need for a distributed approach to the extraction process. As ontologies are currently widely used, our proposed approach for distributed ontology extraction will play an important role in improving the efficiency of information retrieval.

Genetic algorithms (GA) have been successfully used in a variety of optimisation problems. They are especially strong in the solution of difficult problems, which cannot be solved or are hard to solve using conventional linear or nonlinear optimisation. One of those problems is the constrained stochastic optimisation (CSO) problem. The central characteristic of these kinds of problems is that some or all variables of the problem are given in the form of random variables. Random variables capture the uncertainties associated with system behaviour. These kinds of variables must be used whenever the problem parameters fluctuate within very large range of values and/or it is difficult to assess their expected values. Problems of this type arise in a variety of engineering fields, in power systems, transport engineering, Internet access, communication networks, etc. In these and many other areas, the system has to be designed for mid to long-term optimum operation forcing the design engineer to use CSO models. Solution of the CSO problem using conventional methods is very complicated. Genetic algorithms offer simple yet accurate solutions using computer efficient techniques. To illustrate the method, the problem of finding the optimum design of an Intranet server is solved.

Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information about the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a generalized radial basis function (GRBF) neural network architecture that generates fuzzy classification rules that could be used for further knowledge discovery. Our proposed techniques were evaluated using protein sequences with ten classes of super-families downloaded from a public domain database, and the results compared favorably with other standard machine learning techniques.

Conventional image restoration techniques are based on some assumptions about a degradation process and the statistics of the additive noise. Logically, a linear model will not be able to perform restoration satisfactorily if a blurring function is strongly nonlinear. This paper aims to develop a technique for nonlinear image restoration using a machine learning approach, where no prior knowledge and assumptions about the blurring process and the additive noise are required. Although some similar learning image restoration methods exist, no one has explored the mechanism to determine why a mapping neural network can be used in modeling the degradation process, and why it works well for some images but does not behave properly for others. In this work, we try to get a better understanding about these. A generic nonlinear image restoration model is considered in this paper. Based on our previous study, a standard radial basis function (RBF) network is employed to realize the functional mapping from the degraded image space to the original image space, which is dynamically structured to ensure good generalization in restoration. The proposed adaptive RBF network is implemented in a dynamic component-based software framework, which can run in either sequential or parallel modes. Primary simulation results indicate that our proposed method perform well in restoring spatially invariant images degraded by nonlinear distortion and noise.

A data warehouse integrates large amounts of extracted and summarized data from multiple sources for direct querying and analysis. While it provides decision makers with easy access to such historical and aggregate data, the real meaning of the data has been ignored. For example, "whether a total sales amount 1,000 items indicates a good or bad sales performance" is still unclear. From the decision makers' point of view, the semantics rather than raw numbers which convey the meaning of the data is very important. In this paper, we explore the use of fuzzy technology to provide this semantics for the summarizations and aggregates developed in data warehousing systems. A three layered data warehouse semantic model, consisting of quantitative (numerical) summarization, qualitative (categorical) summarization, and quantifier summarization, is proposed for capturing and explicating the semantics of warehoused data. Based on the model, several algebraic operators are defined. We also extend the SQL language to allow for flexible queries against such enhanced data warehouses.

A new routing method, known as active routing, has been emerging. This involves using active packets to configure customized network paths. Based on a Markov decision model, this paper presents an active routing service for active networks in general and the next generation network, called ISDN3, in particular. Our aim is to determine the active routing policy so as to minimize the network cost. Theoretical analysis is presented to show the advantages of our proposal as compared with three other approaches.

Abstract XML holds the promise to yield (1) a more precise search by providing additional information in the elements, (2) a better integrated search of documents from heterogeneous sources, (3) a powerful search paradigm using structural as well as content specifications, and (4) data and information exchange to share resources and to support cooperative search. We survey several indexing techniques for XML documents, grouping them into flat‐file, semistructured, and structured indexing paradigms. Searching techniques and supporting techniques for searching are reviewed, including full text search and multistage search. Because searching XML documents can be very flexible, various search result presentations are discussed, as well as database and information retrieval system integration and XML query languages. We also survey various retrieval models, examining how they would be used or extended for retrieving XML documents. To conclude the article, we discuss various open issues that XML poses with respect to information retrieval and database research.

The eXtensible Markup Language (XML) is fast emerging as the dominant standard for describing and interchanging data among various systems and databases on the Internet. It offers the Document Type Definition (DTD) as a formalism for defining the syntax and structure of XML documents. The XML Schema definition language, as a replacement for the DTD, provides more rich facilities for defining and constraining the content of XML documents. However, it does not concentrate on the semantics that underlies these documents, representing a logical data model rather than a conceptual model. To enable efficient business application development in large-scale electronic commerce environments, it is necessary to describe and model real-world data semantics and their complex interrelationships. In this article, we describe a design methodology for XML documents. The aim is to enforce XML conceptual modeling power and bridge the gap between software development and XML document structures. The proposed methodology is comprised of two design levels: the semantic level and the schema level . The first level is based on a semantic network, which provides semantic modeling of XML through four major components: a set of atomic and complex nodes, representing real-world objects; a set of directed edges, representing semantic relationships between the objects; a set of labels denoting different types of semantic relationships, including aggregation, generalization, association , and of-property relationships; and finally a set of constraints defined over nodes and edges to constrain semantic relationships and object domains. The other level of the proposed methodology is concerned with detailed XML schema design, including element/attribute declarations and simple/complex type definitions . The mapping between the two design levels is proposed to transform the XML semantic model into the XML Schema, based on which XML documents can be systematically created, managed, and validated.

The problem of buffer management in an Internet computing environment is concerned with effective determination of an appropriate buffer size so that retransmissions in message passing can be reduced or even avoided. Such retransmissions can cause significant time delay in the message being delivered because of buffer overflow at the reception side. In our previous work, two buffer management approaches were proposed, namely, the P+D and the P+I+D schemes. These two methods are only based on partial information on the system and the rule form lacks adaptive power. This paper aims at developing a more powerful model for adaptive buffer management. Based on the idea of heuristic knowledge, we propose a novel fuzzy model with a modified consequent part of the Takagi-Sugeno type fuzzy inference approach. The use of the prior knowledge In the connectionist fuzzy system improves the reliability of buffer size prediction, and enhances the capability of domain knowledge representation and comprehension. A successive online learning algorithm is outlined using convergence algorithm and gradient descent technique. Simulations of the proposed model confirm that neuro-fuzzy approach is indeed a better adaptive buffer management solution for preventing message loss due to overflow.

This paper presents a new approach to content-based image retrieval by addressing three primary issues: image feature extraction and representation, similarity measure, and search methods. A statistically based feature selection scheme is introduced to guide the selection of the most appropriate image features for dynamic image indexing and similarity measures. In addition, a fractional discrimination function is proposed to enhance image feature points in conjunction with image decomposition and contextual filtering for image classification. Furthermore, a feature component code is used to facilitate the hierarchical search for the best matching, where images are queried by different features or combinations. The experimental results demonstrate the effectiveness of the proposed method.

This paper presents a data warehousing approach to hierarchical multimedia information retrieval. To tackle the key issues such as multimedia data representation, storage, integration, indexing, similarity measures, searching methods and query processing, the proposed algorithms allow one: (1) to extend the concepts of conventional data warehouse and multimedia databases to multimedia data warehouses for effective data representation and storage; (2) to develop a multimedia starflake schema to integrate multiple data streams for hierarchical data representation and indexing; (3) to apply data aggregation techniques for decision support to speed up query processing and searching. In addition, the new system architecture is compared with a conventional database structure. Furthermore, a case study is presented to illustrate the development of a content-based image retrieval system for cyclone pattern recognition. We conclude that the proposed approach can be applied to other multimedia systems with effective data storage, retrieval and integration.

We consider the problem of designing rollback error recovery algorithms for dynamic, wide area distributed systems like the Internet. The characteristics and the scale of such a system complicate the design and performance of the algorithms. Traditional message passing based algorithms incur large overhead, in both the network traffic and message passing delay, in such a wide-area environment. In this paper, we propose a novel approach to designing checkpointing and rollback algorithms using mobile agents as an aid. Using mobile agent leads to a reduction of the total amount of communication and allows us to design algorithms that take the advantage of the most up to date system information for decision making. It also allows us to develop algorithms implementing flexible and adaptive policies. A mobile agent enabled hybrid algorithm combining independent and coordinated checkpointing is proposed. A prototype of the algorithms is developed using IBM's Aglets. Results of performance evaluation are presented and discussed.

The concept of mediation has been successfully applied to the development of distributed information systems, where mediators form the middle tier between client applications and data resources, providing valuable abstraction and integration functions. Using XML, common data models and interfaces can be developed for mediators for deployment in Web -based information systems, making such systems easier to develop, to maintain, and to evolve. This paper discusses the design of such an architecture, and its application in retail inventory control in electronic commerce.

Activity diagrams are a recognized form of modelling real time systems and have been included in the list of UML techniques for modelling the dynamic aspects of object oriented systems. In this paper, we explain how workflow techniques can be used for developing models of larger chunks of systems. These chunks are expressed through the use of worklets. This can then be used as the basis of modelling systems where timeliness is of importance. However, this requires extending workflow ideas to incorporate flexibility, handling of exceptions and adaptability. Extensions of the activity diagrams are then proposed for expressing the models obtained from such workflow techniques. This will allow evolution of real time systems development towards a component based approach.

In an ORC (object oriented real time computing) environment, it is important to reduce message retransmissions during message passing because these retransmissions cause significant time delay, and this makes it difficult to achieve the necessary timeliness. A cause of retransmission is message loss due to buffer overflow at the reception side. Here a P+I+D (P for proportional, I for integral and D for derivative) adaptive buffer control algorithm is proposed to prevent such possible overflow. The I control depends on the Convergence Algorithm (CA), which is a stable and efficient IEPM (Internet End-to-End Performance Measurement) tool that predicts the mean message roundtrip time (RTT) of a communication channel quickly and accurately. The P+I+D algorithm was tested under different conditions in a mobile ORC (MORC) environment where mobile agents collaborate freely over the Internet. The different test results confirm that the proposed P+I+D approach is indeed effective for preventing buffer overflow.

Data mining is the study of how to determine underlying patterns in the data to help make optimal decisions on computers when the database involved is voluminous, hard to characterize accurately and constantly changing. It deploys techniques based on machine learning alongside more conventional methods. These techniques can generate decision or prediction models based on actual historical data. Therefore, they represent true evidence-based decision support. Rainfall prediction is a good problem to solve by data mining techniques. This paper proposes an improved naive Bayes classifier (INCB) technique and explores the use of genetic algorithms (GAs) for the selection of a subset of input features in classification problems. It then carries out a comparison with several other techniques. It compares the following algorithms on real meteorological data in Hong Kong: (1) genetic algorithms with average classification or general classification (GA-AC and GA-C), (2) C4.5 with pruning, and (3) INBC with relative frequency or initial probability density (INBC-RF and INBC-IPD). Two simple schemes are proposed to construct a suitable data set for improving their performance. Scheme I uses all the basic input parameters for rainfall prediction. Scheme II uses the optimal subset of input variables which are selected by a GA. The results show that, among the methods we compared, INBC achieved about a 90% accuracy rate on the rain/no-rain classification problems. This method also attained reasonable performance on rainfall prediction with three-level depth and five-level depth, which are around 65%-70%.

The aim of this project is to improve the fault-tolerant data communication setup that we proposed previously. From the verification tests for this setup, which integrates two basic schemes, we observed that performance could be very sluggish sometimes, due to time constraints imposed by problematic routing among communicating objects and possible message retransmissions. These constraints, if not relaxed, would restrict the possibility of building useful object-based real-time systems over the Internet. The two schemes in the extant setup are namely (a) consecutive message transmissions to improve the communication reliability, and (b) adaptive buffer management to prevent message losses at the reception side due to buffer overflow. Careful analyses of the previous test data have revealed that inclusion of load balancing would make the setup more responsive. With this motivation in mind, a load-balancing model is proposed in this paper. The different experiments in this project involved both local and remote Internet sites. The preliminary test results indicate that the proposed load balancing scheme can indeed enhance system responsiveness by relaxing time constraints. Therefore, further work and deeper investigations in the same direction are worthwhile.

This paper describes a new technique for detecting security breaches in a computer system. For each Unix process, the user credentials, which are user identifiers, determine the process privilege, including whether a process has gained a high privilege, such as that of the superuser. The state transition technique is applied to a suitably defined process state, identified by certain classes of user credential values. A transition takes place when these values change from one class to another. These states are clearly defined, and prohibited state transitions as well as some supporting rules are identified. When many break-ins succeed, either the rules are violated or these prohibited transitions occur, and this implies a violation of system security policy. A specially modified system call, ktrace0, is used by the superuser to monitor the process-state and state transition analysis is applied to the traced information, by the Intrusion Detection System. Tests show that most known security violations belonging to the targeted classes (such as buffer overflow exploits) can be detected (and possibly pre-empted) while the constituent activities are still being processed in the kernel.

The commonly recognized weakness of modern object oriented design and implementation methodologies lies in their superficial treatment of inter-object dynamics. The paper describes a software toolbox called ODYMOT that integrates a number of approaches to the problem of behavior modeling. In order to achieve design flexibility, a two-layer object design architecture is used that blends together object oriented design concepts with those of high level Petri nets. The Petri net representation creates an additional access layer of object architecture, providing meta level object control with the sequencing of method execution. This modeling approach allows one, both mathematically and pragmatically, to achieve a more precise and flexible description and implementation of real time and distributed models.

The proposed fault-tolerant data communication setup has two main features: a consecutive transmission scheme that improves the reliability of message transmission, and an adaptive buffer management scheme that prevents message losses due to buffer overflow. These two features together reduce message retransmissions and produce better channel reliability and system performance. Simulation data confirm that the adaptive buffer management scheme is indeed an effective reliability measure to prevent data overflow.

With the increasing popularity of object-relational technology, it is becoming important to have a methodology which allows designers to exploit the great modelling power of object-oriented conceptual models (OOCMs) and yet which still facilitates implementation on relational database systems. This paper presents a practical solution for the implementation of different types of object-oriented association relationships, which include a wide range of collection types (i.e. sets, lists, arrays and bags), into relational database tables. The implementation strategies raise a number of constraints relating to data integrity in the database system. These constraints are associated with (i) normalisation of the resulting relational tables, and (ii) data integrity after insertion/deletion. In order to ensure completeness of the strategies, some techniques for the implementation of inverse traversal in association relationships are also provided. An example is used throughout this paper to demonstrate and evaluate the proposed method.

The design of the user interface (UI) involves: a) logical design of the UI; and b) perceptual design of the UI. The logical design of a user interface includes both static or data related and dynamic or navigational aspects. The static aspects were discussed previously (E.J. Chang and T.S. Dillon, 1997). An important part of the static design is the determination of abstract user interface (AUI) objects. These abstract interface objects contain all of the information related to carrying out: (i) entry and display of information; (ii) actions that need to be taken by the user to move to another AUI object or window; (iii) actions to initiate or stop an action within the system. They do not however, contain information on the set of preconditions that must hold before a particular action under (ii) or (iii) above can be taken. Nor do they contain information on the set of post conditions that apply after the action is taken. These preconditions and post conditions, when chained together, specify the sequencing of events between the AUI objects. We define the Flow of Interaction Nets (FIN) which provide information related to this sequencing. These nets define the Flow of Interaction between the user and the system.

The paper examines several methods for improving the generalization capability of the BRAINNE technique. These methods deal with proper training of neural networks and improvements in defining bounds for continuous data.

Neural Networks and Symbolic Knowledge-Based Systems each have their strengths and weaknesses. Intelligent Systems that fuse these two paradigms overcome a significant number of the weaknesses of each individual paradigm. There are many different approaches in the literature (including research from the author's own group) to fusing these paradigms. A critical evaluation of these approaches is given within the paper.

EURAL-NETWORK technology has reached a degree of maturity as evidenced by an ever-increasing number of applications. Our experience, however, is that most practitioners of neural networks are familiar with only a handful of cases where neural-network technology has been reduced to practice. The objective of this special issue is presentation of some specific cases of ongoing everyday use of neural networks. Specifically excluded are neural-network applications still in the exploratory stage. While publication of extraordinary exploratory applications papers is within the scope of the IEEE TRANSACTIONS ON NEURAL NETWORKS, this special issue deals only with neural networks used on a regular basis. At minimum, the system must be at the beta test stage. Of the 53 papers received for the special issue, the 14 herein were chosen. In some important cases, papers solicited for submission were unfortunately withheld because developers or licensees wished to not disclose proprietary technology. Nevertheless, the spectrum of the everyday neural-network applications reported herein is a veritable smorgasbord of variety. Applications are reported in telecommunications, control of Publisher Item Identifier S 1045-9227(97)05726-3. steel plants, plasma etching, pattern recognition of cataloged parts, credit card fraud detection, space robot tuning, electric utility load forecasting, railway maintenance, power system security assessment, scanning electron microscope image characterization, cold mill prediction, economic forecasting and, not least, assessment of wine bottle cork quality. This sampling of applications in everyday use is in no way complete. It gives, however, a taste of the impact of neural technology in society. Indeed, impact is the metric by which all technology is ultimately measured.

The main motivation for the present work stems from the wide gap which exists between the research efforts devoted to developing formal descriptions for communication protocols and the effective development methodologies used in industrial implementations. We apply object-oriented (OO) modelling principles to networking protocols, exploring the potential for producing re-useable software modules by discovering the generic underlying class structures and behaviour. Petri nets (PNs) are used for deriving re-useable model elements and the slightly modified TTCN notation is used for message sequence encoding. This demonstrates a formal, practical approach to the development of a protocol implementation through OO modelling. The paper addresses the problem of inter-layer communication among multiple protocol entities (PEs), assuming the standard ISO/OSI Reference Model. A generalised model called the inter-layer communication (ILC) model is proposed. An example of a PE based on the alternating-bit protocol (ABP) is also discussed.

There has been a lack of analytic approaches for verifying and maintaining knowledge bases in expert systems. This article provides a formal description technique for verifying the correctness, consistency, and completeness of production-based systems. It has its foundation on High Level Petri Nets proposed by Liu and Dillon [Proceedings of the International Conference on Modelling and Simulation, Melbourne, Australia, October 1987, pp. 68-73; Int. J. Intell. Syst. 6, 255–276 (1991)], and Liu [“Formal description and verification of expert systems,” Ph.D. Dissertation, Dept. of Computer Science, La Trobe University, Australia, 1992; IEEE Proceedings of the Fifth International Conference on Computing and Information, Sudbury, Ontario, Canada, May 1993]. the approach emphasizes the uses of color tokens to represent the predicate states and the conditional states for the execution of production rules. It enables the detection, location, and identification of a variety of anomalies that could occur in a sequence of inferences. A description of the problems in terms of suitable verification is given. Formal analysis is proposed which is based on reachability markings generated by the transition firings in the Petri network. © 1995 John Wiley & Sons, Inc.

Inductive machine learning has become an important approach to automated knowledge acquisition from databases. The disjunctive normal form (DNF), as the common analytic representation of decision trees and decision tables (rules), provides a basis for formal analysis of uncertainty and complexity in inductive learning. A theory for general decision trees is developed based on C. Shannon's (1949) expansion of the discrete DNF, and a probabilistic induction system PIK is further developed for extracting knowledge from real world data. Then we combine formal and practical approaches to study how data characteristics affect the uncertainty and complexity in inductive learning. Three important data characteristics, namely, disjunctiveness, noise and incompleteness, are studied. The combination of leveled pruning, leveled condensing and resampling estimation turns out to be a very powerful method for dealing with highly disjunctive and inadequate data. Finally the PIK system is compared with other recent inductive learning systems on a number of real world domains.>

In this paper we describe the problem-solving constructs, namely information processing constructs, learning constructs, knowledge representation constructs and computational constructs for building complex symbolic-connectionist systems. These constructs are applicable in particular to complex diagnostic domains and in general to complex data intensive domains.>

The paper presents an algebraic specification of net objects. By net objects we mean those that are defined in object-oriented paradigms and those defined with nested relations in complex database models. An algebraic specification is set up that involves structures of net objects, accesses of net objects, and some features of object-oriented programming, such as multiple inheritance and polymorphism. Objects and their relationships are then characterized formally in the modeling, which utilizes the hierarchical approach in the algebraic theory of abstract data types, and is further developed by adding mechanisms from existing object systems. Categories of net objects are presented with the properties of accesses among them.>

Both exponential and stochastic stabilities of the Hopfield neural network are analyzed. The results are especially useful for analyzing the stabilities of asymmetric neural networks. A constraint on the connection matrix has been found under which the neural network has a unique and exponentially stable equilibrium. Given any connection matrix, this constraint can be satisfied through the adjustment of the gains of the amplifiers and the resistances in the neural net circuit. A one-to-one and smooth map between input currents and the equilibria of the neural network can be set up. The above results can be applied to the master/slave net to prove that the master net can find the best connection matrix for the slave net. For the neural network disturbed by some noise, the stochastic stability of the network is also analyzed. A special asymmetric neural network formed by a closed chain of formal neurons is also studied for its stability and oscillation. Both stable and oscillatory dynamics are obtained in the closed chain network through the adjustment of the gains and resistances of the amplifiers.

The authors develop a theory for general decision tree induction based on both the logical structure of concepts and the probability distribution of examples. The discrete function is the common analytic representation of decision trees and decision tables (rules). One of the most important classes of discrete functions is the disjunctive normal forms (DNF). Disjunctiveness of concepts has a great effect on the accuracy and speed of concept learning. A theory for general decision trees is developed based on Shannon's expansion of the discrete DNF. The function-equivalence, the structural manipulations, and irreducible DNFs and trees are studied. For optimizing decision trees in the context of induction, the functional and structural criteria are investigated.

A major bottleneck in developing knowledge-based systems is the acquisition of knowledge. Machine learning is an area concerned with the automation of this process of knowledge acquisition. Neural networks generally represent their knowledge at the lower level, while knowledge-based systems use higher-level knowledge representations. the method we propose here provides a technique that automatically allows us to extract conjunctive rules from the lower-level representation used by neural networks, the strength of neural networks in dealing with noise has enabled us to produce correct rules in a noisy domain. Thus we propose a method that uses neural networks as the basis for the automation of knowledge acquisition and can be applied to noisy, realworld domains. © 1993 John Wiley & Sons, Inc.

A generic neuro-expert system architecture which can overcome difficulties faced by stand-alone expert systems and artificial neural networks is proposed. It can be applied in various problem domains, such as engineering and fault diagnosis, which require problem decomposition. It is recommended for use in real-time systems. The neuro-expert system architecture can be used at different levels of a power system hierarchy for alarm interpretation and fault diagnosis.>

A method of incorporating real-time semantics in production rules, thus making them suitable for representing knowledge of a real-time expert system, is presented. It is intended that the knowledge representation should allow the incorporation of time-dependent heuristics and dynamic models in the knowledge base. An illustration of this method, the NetManager expert system, is noted.>

Article An example of integrating legal case based reasoning with object-oriented rule-based systems: IKBALS II Share on Authors: George Vossos Database Research Laboratory, Applied Computing Research Institute, La Trobe University, Bundoora Victoria Australia, 3083 Database Research Laboratory, Applied Computing Research Institute, La Trobe University, Bundoora Victoria Australia, 3083View Profile , John Zeleznikow View Profile , Tharam Dillon View Profile , Vivian Vossos View Profile Authors Info & Claims ICAIL '91: Proceedings of the 3rd international conference on Artificial intelligence and lawMay 1991 Pages 31–41https://doi.org/10.1145/112646.112650Online:01 May 1991Publication History 12citation628DownloadsMetricsTotal Citations12Total Downloads628Last 12 Months11Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

A major difficulty that occurs in the construction of large production rule-based expert systems is maintaining the correctness, consistency, and completeness of the knowledge base. A method of transforming the production rules into a numerical petri nets (NPNs) model is proposed. These NPNs are high level nets that are necessary to effectively model production rules. the net model is then analysed by using a computer-aided tool to perform reachability analysis. an algorithm is given to generate the reachability set of the nets. This allows the verification of the correctness, consistency, and completeness of the knowledge base. Examples showing the use of this approach are given.

The authors present a statistical-heuristic feature selection criterion for constructing multibranching decision trees in noisy real-world domains. Real world problems often have multivalued features. To these problems, multibranching decision trees provide a more efficient and more comprehensible solution that binary decision trees. The authors propose a statistical-heuristic criterion, the symmetrical tau and then discuss its consistency with a Bayesian classifier and its built-in statistical test. The combination of a measure of proportional-reduction-in-error and cost-of-complexity heuristic enables the symmetrical tau to be a powerful criterion with many merits, including robustness to noise, fairness to multivalued features, and ability to handle a Boolean combination of logical features, and middle-cut preference. The tau criterion also provides a natural basis for prepruning and dynamic error estimation. Illustrative examples are also presented.>

Semantics is integrated with an object-oriented data model to increase its power of expression, leading to the notions of subsumption of attributes, values, and relationships. In addition, the concept of inheritance is extended to include inheritance of relationships as well as attributes and operations. The specification of attributes, relationships and operations for the hierarchical framework is explained. Multiple relationships between two objects pertaining to the same hierarchy are also allowed. These are important features for object-oriented data models as opposed to object-oriented programming systems where relationships do not play such a central role. These features have been consolidated into a unified model, namely the OSEA (object-oriented semantic model for exception accommodation) model, which also incorporates additional constraints and modeling of exceptions. A prototype system has been developed based on the OSEA model with the view of providing a more complete set of operations for exception handling.>

To produce reliable software, its complexity must be controlled by suitably decomposing the software system into smaller subsystems. A software complexity metric is developed that includes both the internal and external complexity of a module. This allows analysis of a software system during its development and provides a guide to system decomposition. The basis of this complexity metric is in the development of an external complexity measure that characterizes module interaction.>

An experimental method of scheduling the necessary maintenance activities on generator units in a power system is developed. The problem is identified as an integer programming problem, and a method based on the branch-and-bound technique is developed. The maintenance scheduling problem is characterized by a large number of complex constraints. The method presented is capable of taking into account all these constraints and hence, results in a practically implementable solution, if a feasible solution exists. Other features of the method are employment of a number of different objective functions and discovery of a feasible solution if one exists. Furthermore, unlike most present methods, it actually finds the optimal solution. The operation of the method is exemplified by application to a realistic system.