This paper presents a further investigation into computational properties of a novel fuzzy additive spectral clustering method, Fuzzy Additive Spectral clustering (FADDIS), recently introduced by authors. Specifically, we extend our analysis to ‘difficult’ data structures from the recent literature and develop two synthetic data generators simulating affinity data of Gaussian clusters and genuine additive similarity data, with a controlled level of noise. The FADDIS is experimentally verified on these data in comparison with two state-of-the-art fuzzy clustering methods. The claimed ability of FADDIS to help in determining the right number of clusters is experimentally tested, and the role of the pseudo-inverse Laplacian data transformation in this is highlighted. A potentially useful extension of the method to biclustering is introduced.

Regarding that information in broad-coverage knowledge bases, such as thesauri, is usually incomplete, merging information from different sources is an alternative to amplify coverage. We propose a method for the enrichment of a thesaurus with information acquired automatically from dictionaries. First, synonymy pairs are extracted. Then, these pairs are assigned to the most similar candidate synsets. Finally, the remaining pairs are the target of clustering to identify new synsets. After selecting the adequate experimentation settings, this method was applied to enrich a Portuguese thesaurus with synonyms extracted from three dictionaries, which resulted in TRIP, a larger and broader thesaurus with new words and concepts. The steps towards the creation of this new thesaurus and its evaluation are described here.

This paper proposes a new generalization of classical real-valued information systems, that is, interval-valued information systems. By defining an interval-valued dominance relation on a condition attribute, a rough set model and attribute reduction are established over interval-valued information systems. Moreover, several interesting properties are investigated by constructive approach. Furthermore, knowledge reductions of consistent and inconsistent interval-valued dominance decision information systems are studied, respectively. Subsequently, some descriptive theorems of knowledge reduction are presented for interval-valued dominance decision information systems. Finally, the validity of the model and conclusions is verified by numerical example.

The goal of this paper is to highlight the particularities of Worldwide Interoperability for Microwave Access (WiMAX) traffic from a long-range dependence perspective. The application presented in this paper consists in the estimation of the quality of base stations (BS) positioning in the architecture of a WiMAX network. Analysing the traffic of the considered WiMAX network, with an original data mining methodology based on long-range dependence, we determine which BS have a correct position in the topology of the network and which BS have an incorrect position. These BS must be repositioned when the next session of the network maintenance will take place.

This paper presents a multi-level speaker verification system that uses 64 discrete Fourier transform spectrum components as input feature vectors. A speech activity detection technique is used as a pre-processing stage to identify vowel phoneme boundaries within a speech sample. A modified self-organising map (SOM) is then used to filter the speech data by using cluster information extracted from three vowels for a claimed speaker. This SOM filtering stage also provides coarse speaker verification. Finally, a second speaker verification level of three multi-layer perceptron networks classifies the filtered frames provided by the SOMs. These multi-layer perceptrons work as fine-grained vowel-based speaker verifiers. The proposed verification algorithm shows a performance of 94.54% when evaluated using 50 speakers from the Centre for Spoken Language Understanding speaker verification database. In addition, it is shown that the novel discrete Fourier transform spectrum-based linear correlation pre-processing technique, presented here, provides the system with greater robustness against changes in speech volume levels when compared with an equivalent energy frame analysis.

VigilAgent is a methodology for the development of agent-oriented monitoring applications that uses agents as the key abstraction elements of the involved models. It has not been developed from scratch, but it reuses fragments from Prometheus and INGENIAS methodologies for modelling tasks and the ICARO framework for implementation purposes. As VigilAgent intends to automate as much as possible the development process, it exploits.

Model transformation techniques are one of the key aspects of the model-driven development approach. A model-to-model transformation is used to facilitate the interoperability between Prometheus and INGENIAS methodologies. Also, a model-to-text transformation is performed to generate ICARO code from the INGENIAS model. A case study based on access control is used to illustrate the fundamentals of the model-to-model and model-to-text transformations implemented in VigilAgent.

Visualization of static social networks is a mature research field in information visualization. Conventional approaches rely on node-link diagrams that provide a representation of the network topology by representing nodes as points and links between them as lines. However, the increasing availability of longitudinal network data has spurred interest in visualization techniques that go beyond the static node-link representation of a network. In temporal settings, the focus is on the network dynamics at different levels of analysis (e.g. node, communities and whole network). Yet, the development of visualizations that are able to provide actionable insights into different types of changes occurring in the network and their impact on both the neighbourhood and the overall network structure is a challenging task. In such settings, traditional node-link representations can prove to be limited (Yi et al., 2010). Alternative methods, such as matrix graph representations, fail in tasks involving path finding (Ghoniem et al., 2005). This work attempts to overcome these issues by proposing a methodology for tracking the evolution of dynamic social networks, at both the node-level and the community-level, based on the concept of temporal trajectory. We resort to three-order tensors to represent evolving social networks, and we further decompose them using a Tucker3 model. The two most representative components of this model define the 2D space where the trajectories of social entities are projected. To illustrate the proposed methodology, we conduct a case study using a set of temporal self-reported friendship networks.

In this paper, we address the cloud service trustworthiness evaluation problem, which in essence is a multi-attribute decision-making problem, by proposing a novel evaluation model based on the fuzzy gap measurement and the evidential reasoning approach. There are many sources of uncertainties in the process of cloud service trustworthiness evaluation. In addition to the intrinsic uncertainties, cloud service providers face the problem of discrepant evaluation information given by different users from different perspectives. To address these problems, we develop a novel fuzzy gap evaluation approach to assess cloud service trustworthiness and to provide evaluation values from different perspectives. From the evaluation values, the perception–importance, delivery–importance, and perception–delivery gaps are generated. These three gaps reflect the discrepancy evaluation of cloud service trustworthiness in terms of perception utility, delivery utility, and importance utility, respectively. Finally, the gap measurement of each perspective is represented by a belief structure and aggregated using the evidential reasoning approach to generate final evaluation results for informative and robust decision making. From this hybrid two-stage evaluation process, cloud service providers can get improvement suggestions from intermediate information derived from the gap measurement, which is the main advantage of this evaluation process.

Despite increasing deregulation and globalization in financial markets worldwide, banking is still one of the most regulated industries in many countries. The contribution of the present article is to introduce a slacks-based Data Envelopment Analysis assessment framework for assessing bank efficiency and soundness in a risk regulation setting, which is missing from the banking performance literature. Two main sub-processes within the service flow of a typical bank are considered – the primary banking business for making profit and dealing with the compliance requirements of risk regulations. A Data Envelopment Analysis model is applied to measure the performance of the two sub-processes, that is, profit-making efficiency and risk-controlling efficiency. The research framework and models are applied to an empirical study of the banking sector in Taiwan covering the period 2007 – 2010. We demonstrate how to use the empirical results to monitor the efficiency status for individual banks from 1 year to another, providing an early warning for those with low efficiency. Our empirical results show that there is considerable potential for efficiency improvement in Taiwan's banking industry, and the room for risk-controlling efficiency improvement is even larger. The two efficiency estimates are positively correlated with each other, and both have been improved year by year. However, an economic recession can lower efficiency estimates.

Alert intelligent device is an ambient assisted living (AAL) system that allows the evaluation of potentially dangerous situations for elderly people living alone at home. This evaluation is obtained by an ad hoc network of sensor nodes, working in conjunction with an ambient intelligence layer embedded in a personal computer that learns from user behaviour patterns and warns when a detected pattern differs significantly from previously acquired normal patterns. Each new datum read from sensors is processed in the ambient intelligence layer through three processing levels: shallow, intermediate and deep. The shallow processing level focuses on physical data and sensory features. The intermediate level covers information interpretation and its translation into the form required by the third level: the reasoning processing or deep level.

In alert intelligent device, energy is a critical issue, so that sensor devices need to be properly designed and managed to achieve significant energy saving. The use of bed/chair occupancy sensors is mandatory for this kind of ubiquitous computing system. A first way to approach this problem relied on the use of pressure mats, but several environmental drawbacks showed them inappropriate as an efficient and reliable solution for large volume deployments. Moreover, solutions based on force-to-resistor transducers entail power consumption budgets that keep them from being integrated on wireless sensor nodes. In this paper, a force-capacitive transducer based sensor has been proposed, implemented and tested. This sensor is based on electromechanical film (EMFi) transducers, which are able to detect force variations in a quasi-passive way. This kind of transducer behaves as capacitors with variable capacitance depending on the force exerted on its surface. We have developed a new technique to carry out the characterization of these transducers, where the detection of a force change is used to trigger an active mechanism that allows us to measure the weight by means of a novel modelling approach. A low-power wireless sensor node prototype that includes this new transducer has been assembled and tested on a wide range of weights. Occupancy detection achieved by this technology has proven to be successful, increasing the total power consumption of the node by less than 15%, which makes it suitable for implementation.

ZigBee is the primary standard solution for wireless sensor networks, implementing the Ad hoc On-Demand Distance Vector (AODV) protocol in the network layer and supported by the standard IEEE 802.15.4. This study is focused on mesh topologies and the critical problems encountered when AODV is executed in conjunction with the Carrier Sense Multiple Access with Collision Avoidance protocol. These problems are mainly related to the packet overhead required to carry out route creation. To perform preliminary experiments to be able to implement AODV in a real network, a new metric is proposed herein. This metric uses fuzzy logic to help in the decision-making process. The objective of the fuzzy routine is to determine, during the route-discovery process, the best node to forward request/reply packets, with the aim of reducing packet overhead and energy consumption. Moreover, minor changes are also added to the discovery procedure of AODV to improve the performance of the route-creation process. This intelligent version of AODV has provided promising experimental results, greatly reducing the number of packets required, with the consequent energy saving while selecting the best nodes to be part of the routes.

Emotion recognition using physiological signals has gained momentum in the field of human computer–interaction. This work focuses on developing a user-independent emotion recognition system that would classify five emotions (happiness, sadness, fear, surprise and disgust) and neutral state. The various stages such as design of emotion elicitation protocol, data acquisition, pre-processing, feature extraction and classification are discussed. Emotional data were obtained from 30 undergraduate students by using emotional video clips. Power and entropy features were obtained in three ways – by decomposing and reconstructing the signal using empirical mode decomposition, by using a Hilbert–Huang transform and by applying a discrete Fourier transform to the intrinsic mode functions (IMFs). Statistical analysis using analysis of variance indicates significant differences among the six emotional states (p < 0.001). Classification results indicate that applying the discrete Fourier transform instead of the Hilbert transform to the IMFs provides comparatively better accuracy for all the six classes with an overall accuracy of 52%. Although the accuracy is less, it reveals the possibility of developing a system that could identify the six emotional states in a user-independent manner using electrocardiogram signals. The accuracy of the system can be improved by investigating the power and entropy of the individual IMFs.

This paper presents a method for combining domain knowledge and machine learning (CDKML) for classifier generation and online adaptation. The method exploits advantages in domain knowledge and machine learning as complementary information sources. Whereas machine learning may discover patterns in interest domains that are too subtle for humans to detect, domain knowledge may contain information on a domain not present in the available domain dataset. CDKML has three steps. First, prior domain knowledge is enriched with relevant patterns obtained by machine learning to create an initial classifier. Second, genetic algorithms refine the classifier. Third, the classifier is adapted online on the basis of user feedback using the Markov decision process. CDKML was applied in fall detection. Tests showed that the classifiers developed by CDKML have better performance than machine-learning classifiers generated on a training dataset that does not adequately represent all real-life cases of the learned concept. The accuracy of the initial classifier was 10 percentage points higher than the best machine-learning classifier and the refinement added 3 percentage points. The online adaptation improved the accuracy of the refined classifier by an additional 15 percentage points.

Crime attractors are locations (e.g. shopping malls) that attract criminally motivated offenders because of the presence of known criminal opportunities. Although there have been many studies that explore the patterns of crime in and around these locations, there are still many questions that linger. In recent years, there has been a growing interest to develop mathematical models in attempts to help answer questions about various criminological phenomena. In this paper, we are interested in applying a formal methodology to model the relative attractiveness of crime attractor locations based on characteristics of offenders and the crime they committed. To accomplish this task, we adopt fuzzy logic techniques to calculate the attractiveness of crime attractors in three suburban cities in the Metro Vancouver region of British Columbia, Canada. The fuzzy logic techniques provide results comparable with our real-life expectations that offenders do not necessarily commit significant crimes in the immediate neighbourhood of the attractors, but travel towards it, and commit crimes on the way. The results of this study could lead to a variety of crime prevention benefits and urban planning strategies.

Data envelopment analysis (DEA) is a mathematical programming technique that is frequently used for measuring and benchmarking efficiency of the homogenous decision-making units (DMUs). This paper proposes a new use of DEA for customers scoring and particularly their direct mailing modelling. Moreover, because DEA models suffer from some weaknesses, that is, unrealistic weighting scheme of the inputs and outputs and incomplete ranking among efficient DMUs, the present paper compares different ways of solving these problems and concludes that common set of weights method, as a result of some advantages, outperforms other procedures.

This paper proposes a new approach to recognize human actions in 2D sequences, based on real-time visual tracking and simple feature extraction of human activities in video sequences. The proposed method emphasizes the simplicity of the strategies used, in an attempt to describe human actions as precisely as other more sophisticated (and more computationally demanding) methods in the literature. Specifically, we propose three complementary modules for the following: (a) tracking; (b) feature extraction; and (c) action recognition. The first module is based on the hybridization of a particle filter and a local search procedure and makes use of a reduced integral image to speed up the weight computation. The feature extraction module characterizes the silhouette of the tracked person by dividing it into rectangular boxes. Then, the system computes statistics on the evolution of these rectangular boxes over time. Finally, the action recognition module passes these statistics to a support vector machine to classify the actions. Experimental results show that the proposed method works in real-time, and its performance is competitive against other state-of-the-art methods.

Streamflow forecast models are essential ingredients for water resources management. Due to nonlinear nature of streamflow generation, Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbour (K-NN) have turned into popular forecast models. The main objective of this paper was to study the effect of various non-commonly used data, including spatially distributed climatic data, as well as Southern Oscillation Index (SOI), on the improvement of one- to three-month ahead flow forecasts in the Karun basin, Iran. Temperature maps were produced via elevation–temperature relationships, while inverse distance-weighted interpolation was applied to generate precipitation maps. In addition, three different input architectures were constructed for ANNs and ANFIS models using cross-correlation and principal component analysis techniques. Next, the most accurate models with input point data were identified based on the root mean squared error and mean absolute error. Finally, the effect of using spatial climatic data and SOI on the performance of the most accurate models was investigated. Results showed that distributed precipitation data improved the performance of ANN and ANFIS models, while K-NN model accuracy was only marginally affected. Adding SOI as an additional input improved the forecast performance slightly in all lead times.

Among the warehousing activities in distribution centres, order picking is the most time-consuming and labour-intensive. As a result, order picking may become a bottleneck preventing distribution centres from maximizing the effectiveness of their warehousing activities. Although storage location assignment (or product allocation) is a tactical decision, it is especially influential on the effectiveness of order picking. In previous studies, most storage assignment approaches considered the order frequency of individual products rather than that of product groups, which often are purchased together. This paper proposes a new association measure, weighted support count (WSC), based on association rule mining, to represent both the intensity and nature of the relationships between products in a distribution centre. This paper presents two storage assignment heuristics, the modified class-based heuristic (MCBH) and the association seed based heuristic (ASBH), designed to facilitate efficient order picking by applying WSC. The real-world data set of a grocery distribution centre is used to verify the effectiveness of the proposed approaches. From the computational results, MCBH cuts at most 4% from the travel distance for order picking per month, as compared with the traditional class-based approach. Meanwhile, ASBH achieves at most a 13% reduction in travel distance.

Surface electromyogram (sEMG) is a technique in which electrodes are placed on the skin overlying a muscle to detect the electrical activity. Multiple electrical sensors are essential for extracting intrinsic physiological and contextual information from the corresponding sEMG signals. The reason, why more than just one sEMG signal capture has to be used, is as follows: Due to signal propagation inside the human body in terms of an electrical conductor, there cannot be a one-to-one mapping of activities between muscle fibre groups and corresponding sEMG sensing electrodes. Each of such electrodes rather records a composition of many, and widely activity-independent signals, and such kind of raw signal capture cannot be efficiently used for pattern matching due to its linear dependency. On the other hand, Independent component analysis (ICA) provides the perfect answer of separating skin surface recordings into a set of independent muscle actions. Hence, there is a need for a method that indicates the quality of the sensor placements in sEMG. The purpose of this paper is to describe the use of source separation for sEMG using ICA. The actual use in practical sEMG experiments is demonstrated, when the number of recording channels for electrical muscle activities is varied.

Knowledge transfer is a critical factor in ensuring the success of offshore outsourcing software development projects and is, in many cases, neglected. Compared to in-house or co-located projects, however, such globally distributed projects feature far greater complexity. In addition to language barriers, factors such as cultural differences, time zone variance, distinct methods and practices, as well as unique equipment and infrastructure can all lead to problems that negatively impact knowledge transfer, and as a result, a project's overall success. In order to help minimise such risks to knowledge transfer, we conducted a research study based on expert interviews in six projects. Our study used German clients and focused on offshore outsourcing software development projects. We first identified known problems in knowledge transfer that can occur with offshore outsourcing projects. Then we collected best-practice solutions proven to overcome the types of problems described. Afterward, we conducted a follow-up study to evaluate our findings. In this subsequent stage, we presented our findings to a different group of experts in five projects and asked them to evaluate these solutions and recommendations in terms of our original goal, namely to find ways to minimise knowledge-transfer problems in offshore outsourcing software development projects. Thus, the result of our study is a catalog of evaluated solutions and associated recommendations mapped to the identified problem areas.

Identification of transcription factor binding sites still remains a challenging problem even though many computational tools have been proposed in the literature for this specific task. In this study, a method to discover such DNA subsequences, that is, motifs, is proposed. The method uses Gaussian mixture models with expectation-maximization algorithm. In order to show the potential of the proposed method, experiments are conducted by use of data sets extracted from the DNA sequences of various organisms. The proposed method is also compared with four other methods: MEME, MDScan, SOMBRERO and the fuzzy C-means based motif finder. As a result, the proposed method proves itself as a promising tool in identifying over-represented DNA motifs.

The practice of strategy decision making proves that when the management board is strongly limited in its capacity to take rational actions, specifically in the context of great decision complexity and uncertainty, it is considered good practice to refer to experience through reasoning by analogy. In this paper, we would like to concentrate on supporting strategic decisions in small and medium enterprises (SMEs). The complexity of analogy-based reasoning has its roots in an attempt to solve new problems based on past cases from a different domain, while we will focus on case-based approach for a single domain. Additionally, we have chosen case-based reasoning as a suitable decision-making paradigm because it is corresponds to managers’ decision-making behaviour. We present the STRATEGOS case-based reasoning system for supporting strategic decision making by SMEs management boards and then the system evaluation by the dozens of chief executive officers (CEOs) from SMEs is presented. The results of the survey are promising and show the remarkable correspondence of the proposed solution with expectations and strategic behaviour of CEOs.

The security-level detection of a confidential document is a vital task for organizations to protect their confidential information. Diverse classification rules and techniques are being applied by human experts. Increasing number of confidential information in organizations is making difficult to classify all the documents carefully with human effort. The recommended frameworks in this study classify the internal documents of TUBITAK UEKAE (National Research Institute of Electronics and Cryptology of Turkey) by using classification algorithms naïve Bayes, support vector machines (SVMs) and adaptive neuro-fuzzy inference systems (ANFISs). A hybrid approach involving support vector classifiers and adaptive neuro-fuzzy classifiers exposes the most successful accuracy rates of expert system classification. This study also states preprocessing tasks required for document classification with natural language processing. To represent term–document relations, a recommended metric TF-IDF was chosen to construct a weight matrix. Agglutinative nature of Turkish documents is handled by Turkish stemming algorithms. At the end of the article, some experimental results and success metrics are projected with accuracy rates and receiver operating characteristic (ROC) curves.

The selection of skilful players is a complicated process due to the problem criteria consisting of both qualitative and quantitative attributes as well as vague linguistic terms. This study seeks to develop a decision support framework for the selection of candidates eligible to become basketball players through the use of a fuzzy multi-attribute decision making (MADM) algorithm. The proposed model is based on fuzzy analytic hierarchy process (FAHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The model was employed in the Youth and Sports Center of Mugla, Turkey, with the participation of seven junior basketball players aged between 7 and 14. In the present study, physical fitness measurement values and observation values of technical skills were utilized. FAHP was used to determine the weights of the criteria and the observation values of technical skills by decision makers. Physical fitness measurement values were converted to fuzzy values by using a fuzzy set approach. Subsequently, the overall ranking of the candidate players was determined by the TOPSIS method. Results were compared with human experts’ opinions. It is observed that the developed model is more reliable to be used in decision making. The model architecture and experimental results along with illustrative examples are further demonstrated in the study.

Radio frequency identification (RFID) is a powerful technology that enables wireless information storage and control in an economical way. These properties have generated a wide range of applications in different areas. Due to economic and technological constrains, RFID devices are seriously limited, having small or even tiny computational capabilities. This issue is particularly challenging from the security point of view. Security protocols in RFID environments have to deal with strong computational limitations, and classical protocols cannot be used in this context. There have been several attempts to overcome these limitations in the form of new lightweight security protocols designed to be used in very constrained (sometimes called ultra-lightweight) RFID environments. One of these proposals is the David–Prasad ultra-lightweight authentication protocol. This protocol was successfully attacked using a cryptanalysis technique named Tango attack. The capacity of the attack depends on a set of boolean approximations. In this paper, we present an enhanced version of the Tango attack, named Genetic Tango attack, that uses Genetic Programming to design those approximations, easing the generation of automatic cryptanalysis and improving its power compared to a manually designed attack. Experimental results are given to illustrate the effectiveness of this new attack.

Global outsourcing is a growing trend among independent software vendors. In these projects like other distributed work, distances have negative effects on communication and coordination, directly impacting performance. We present a normative model designed to address this issue by improving communication and knowledge exchange. The model consists of six distinct practices and a tool blueprint, each coming with practical guidelines. It is based in part on two case studies of Dutch software vendors who have successfully outsourced part of their activities to an Eastern European outsourcing vendor, and validated by a panel of six experts from industry and the scientific community. It is concluded that knowledge exchange in global software outsourcing is a by-product of efforts to enhance communication and coordination, rather than specific technical solutions. By committing to sharing knowledge, emphasizing transparency and integrating the outsourcing team into their organizations, customers from the product software business can realize the benefits of global outsourcing.

It is well known that the analysis of requirements involves several stakeholders and perspectives. Very often several points of view at different abstraction levels have to be taken into account: all these features make requirements analysis a complex task. Such intrinsic complexity makes it difficult to understand several of the basic concepts that underlie requirements engineering. Actually, there is some confusion – especially in industry – about what really a user requirement is, what are the differences between user requirements and user needs, and what are their relationships with business processes. The paper aims at clarifying the aforementioned issues, by providing a systematic and clear method for establishing requirements hierarchies. The problem of describing requirements hierarchies is tackled using the problem frames concepts and notation. A case study is used throughout the paper to illustrate the proposed approach. The description of requirements at different levels of abstractions and requirements hierarchies are illustrated. The resulting models are coherent with the reference model for requirements specifications and the problem frames. An analysis process that is aware of the differences between user needs and requirements is also provided, to illustrate the process of refining high-level goals into requirements that can be satisfied by a hardware/software machine. The proposed method appears promising to model, study, and evaluate the relationships between business processes and the strategies for achieving business goals based on the usage of information technology.

Manual grading of depression is sometimes difficult due to the subjective signs-symptoms. The aim of this paper is to automate the process of depression grading using a neurofuzzy model (NFM). Two hundred and seventy real-world depression cases are considered in this work. Each case has seven symptoms, which are obtained according to DSM-IV-TR. Each case is graded as ‘mild’ or ‘moderate’. However, in practice, the boundaries of ‘mild’ and ‘moderate’ grading are fuzzy in nature. The paper attempts to solve this fuzzy overlapping zone of these grades. To reduce the number of symptoms, significantly correlated symptoms are mined using a paired t-test. Then, two NFMs have been developed.NFM-1 has been developed with all seven symptoms, while only significantly correlated symptoms have been used to construct the NFM-2 model. Two fuzzy membership functions, such as triangular membership function (TRMF) and Gaussian membership function (GMF) have been considered to note with which better fuzzification could be achieved. The paper concludes that NFM-1 with GMF is the best model with average predicting accuracy of 94.4% and robustness.

In order to explore the most current information and react faster to changing business conditions, organizations consider real-time data warehousing a powerful technique to achieve operational business intelligence (BI). We propose in this paper a novel real-time data warehouse (RTDW) framework based on the virtualization concept. Our approach introduces a conceptual modelling technique, known as ring modelling, for real-time data management and multidimensional analysis. This technique produces a flexible semi-structured data model that accommodates unknown business process data and relationships as they evolve, handles schema changes and aggregate-management efficiently, and scales well with the large size of increasing data volumes. With the help of a telecommunication business example, We evaluated our proposed approach in an extensive experimental study where we compared our approach Ring Model with existing structured multidimensional conceptual models (MCMs), i.e. relational OLAP and multidimensional OLAP, and with semi-structured MCM, i.e. XML Cubes, in terms of scalability, data storage estimations, data updates loading time, and query response times. Our performance results show that encouraging speedups are achieved.

Communication is a key success factor of distributed software projects. Poor communication has been identified as a main obstacle to successful collaboration. Global projects are especially endangered by information gaps between collaborating sites. Different communication styles, technical equipment, and missing awareness of each other can cause severe problems. Knowledge about actual and desired channels, paths, and modes of communication is required for improving communication in a globally distributed project. However, many project participants know little about communication and information flow in their projects. In this contribution, we focus on knowledge about communication and information flow. It is acquired by modelling on-going and desired flows of information, including documented and non-documented channels of information flow. We analyzed a distributed software project from the information flow perspective. Based on the findings, we developed specific techniques to improve information flow in distributed software development according to the FLOW Method. In a second distributed project, we evaluated one of the techniques. We found the FLOW mapping technique to be suitable for effectively spreading knowledge about communication and information flow in global software projects.

Breast cancer is a leading cancer affecting women worldwide. Mammography is a scanning procedure involvingX-rays of the breast. It causes discomfort and may cause high incidence of false negatives. Breast thermography is a new screening method of breast that helps in the early detection of cancer. It is a non-invasive imaging procedure that captures the infrared heat radiating off from the breast surface using an infrared camera. The main objective of this work is to evaluate the use of higher order spectral features extracted from thermograms in classifying normal and abnormal thermograms. For this purpose, we extracted five higher order spectral features and used them in a feed-forward artificial neural network (ANN) classifier and a support vector machine (SVM). Fifty thermograms (25 each of normal and abnormal) were used for analysis.SVM presented a good sensitivity of 76% and specificity of 84%, and theANN classifier demonstrated higher values of sensitivity (92%) and specificity (88%). The proposed system, therefore, shows great promise in automatic classification of normal and abnormal breast thermograms without the need for subjective interpretation.

Data may be afflicted with uncertainty. Uncertain data may be shown by an interval value or in general by a fuzzy set. A number of classification methods have considered uncertainty in features of samples. Some of these classification methods are extended version of the support vector machines (SVMs), such as the Interval-SVM (ISVM), Holder-ISVM and Distance-ISVM, which are used to obtain a classifier for separating samples whose features are interval values. In this paper, we extend the SVM for robust classification of linear/non-linear separable data whose features are fuzzy numbers. The support of such training data is shown by a hypercube. Our proposed method tries to obtain a hyperplane (in the input space or in a high-dimensional feature space) such that the nearest point of the hypercube of each training sample to the hyperplane is separated with the widest symmetric margin. This strategy can reduce the misclassification probability of our proposed method. Our experimental results on six real data sets show that the classification rate of our novel method is better than or equal to the classification rate of the well-known SVM, ISVM, Holder-ISVM and Distance-ISVM for all of these data sets.

Model-based design is a promising technique to improve the quality of software and the efficiency of the software development process. We are investigating how to efficiently model embedded software and its environment to verify the requirements for the system controlled by the software. The software environment consists of mechanical, electrical and other parts; modelling it involves learning how these parts work, deciding what is relevant to model and how to model it. It is not possible to fully automate these steps. There are general guidelines, but given that every modelling problem differs, much is left to the modeller's own preference, background and experience. Still, when the next generation of a system is designed, the new system will have common elements with its previous version. Therefore, lessons learned from the current model could inform future models. We propose a framework for identifying the non-formal elements of knowledge, insights and a model itself, which can support modelling of the next system generation. We will present the application of our framework on an action research case – modelling mechanical parts of a paper-inserting machine.

This paper describes the use of an objective fuzzy approach for fast and accurate vision-based inspection. An inspection problem faced by a Canadian automotive parts manufacturer is being used as a case study. The problem is related to a vision system that is being operated to confirm the placement of metal fastening clips on a structural member that supports a truck dash panel. The manufacturer was interested in identifying the presence or absence of metal clips inserted by a robot arm. It took the manufacturer over 8 months to tune its commercial machine vision system to detect missing clips and yet the accuracy and efficiency of the system are being questioned. Five different universities across Canada have been working in parallel on this problem over a time span of 2 years. To this end, we developed an efficient fuzzy model after trying various statistical approaches. The proposed model properly identifies all the images in a database containing 1910 images. The robustness of the fuzzy model is confirmed by its strong performance on the entire database.

A major drawback associated with the use of classical statistical methods for business failure prediction on top of financial distress is their lack of high accuracy rate. This work analyses the use of the two-stage ensemble of multivariate discriminant analysis (MDA) and logit to improve predictive performance of classical statistical methods. All possible ratios are firstly built from the quantities involved and then the three common filters, that is stepwise MDA, stepwise logit, and t-test, are used to choose another three convenient subsets of ratios. Four principal components spaces (PCSs) are, respectively, produced on the four different feature spaces by using principal components analysis. MDA and logit are used to produce predictions on the four PCSs. After that, two levels of ensemble are implemented: one based on predictions inside each of the same type of model (i.e. MDA or logit) and another based on the former two ensembles and one best model. Each of the eight models is weighted on the base of ranking order information of its predictive accuracy in ensemble by majority voting. MDA and logit and the new challenge model of support vector machine respectively in their best standalone modes are used for comparisons. Empirical results indicate that the two-stage ensemble of MDA and logit compares favourably with the three comparative models and all its component models.

Artificial neural networks (ANNs) are flexible computing tools that have been applied to a wide range of domains with a notable level of accuracy. However, there are multiple choices of ANNs classifiers in the literature that produce dissimilar results. As a consequence of this, the selection of this classifier is crucial for the overall performance of the system. In this work, an integral framework is proposed for the optimization of different ANN classifiers based on statistical hypothesis testing. The framework is tested in a real ballistic scenario. The new quality measures introduced, based on the Student t-test, and employed throughout the framework, ensure the validity of results from a statistical standpoint; they reduce the appearance of experimental errors or the appearance of possible randomness. Results show the relevance of this framework, proving that its application improves the performance and efficiency of multiple classifiers.

This paper aims to develop a model for assessing and valuing the intellectual capital of an organization by gathering data from individual employees. Also, the paper empirically investigates the relationship between intellectual capital and a specific dimension of employees’ performance. Two surveys were conducted on employees of a leading company in the Italian food product market. The sample analysed comprises 1117 observations. A structural equation modelling (SEM) methodology was used to define the measurement model and to determine the relations between intellectual capital and employees’ performance. The model for intellectual capital assessment developed in this paper may integrate already existing intellectual capital frameworks. The three dimensions of intellectual capital identified mutually affect and have a positive effect on employees’ performance. Our findings contribute to the discussion in the promising research stream of intellectual capital management. As limitations, this study focuses on a single company, and it exclusively adopts individual-level perceptual indicators. These elements make it difficult for any inter-company comparison.

In predicting time series, if a trend includes a structural break, then a state space model can be applied to revise the predictive method. Some scholars suggest that restricted damped trend models yield excellent prediction results by automatically revising unforeseen structural break factors in the prediction process. Restricted damped trend models add a smoothed error statistic to a local-level model and use the exponentially weighted moving average (EWMA) method to make corrections. This paper applies the generally weighted moving average (GWMA) concept and method to a restricted damped trend model that changes the smoothed error statistic from the EWMA form to the GWMA form and adds the correction parameter λ, which distinguishes three situations λ < 1 , λ = 1 , and λ > 1 . The original restricted damped trend model applies only to λ = 1 , enabling the model to capture situations in which λ < 1 and λ > 1 increases its generality. This paper also compares the effect of various parameter values on the predictive model and finds the range of parameter settings that optimize the model.

Analysis of scientific data requires accurate regressor algorithms to decrease prediction errors. Lots of machine learning algorithms, that is, neural networks, rule-based algorithms, regression trees and some kinds of lazy learners, are used to realize this need. In recent years, different ensemble regression strategies were improved to obtain enhanced predictors with lower forecasting errors. Ensemble algorithms combine good models that make errors in different parts of analyzed data. There are mainly two approaches in ensemble regression algorithm generation; boosting and bagging. The aim of this article is to evaluate a boosting-based ensemble approach, forward stage-wise additive modelling (FSAM), to improve some widely used base regressors’ prediction ability. We used 10 regression algorithms in four different types to make predictions on 10 diverse data from different scientific areas and we compared the experimental results in terms of correlation coefficient, mean absolute error, and root mean squared error metrics. Furthermore, we made use of scatter plots to demonstrate the effect of ensemble modelling on the prediction accuracies of evaluated algorithms. We empirically obtained that in general FSAM enhances the accuracies of base regressors or it at least maintains the base regressor performance.

Mappings of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance as well as the potential for use in flood forecasting applications. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for flood calculation because the stage, discharge, and other hydrological variables often are functions of many influential variables. Herein, we propose a flood forecasting system with related application, based on ANN. This method offers better performance and efficiency.

This paper explores the effects of the correlation model, the trend model, and the number of training points on the accuracy of Kriging metamodels. Gaussian correlation models are found to be superior to exponential and linear correlation models. No particular trend model is found to be better than the other models. The number of training points used in constructing the Kriging metamodels is observed to change the relative performances of the trend and the correlation functions. The leave-one-out cross-validation error is found to become a better surrogate for the actual error, as the number of training points is increased. Finally, the use of an ensemble of metamodels is discussed and it is found that using an ensemble may improve the accuracy.

While Problem Frames have become a useful approach for requirements analysis, little research has been made to explore how to derive them from a complex problem context. The purpose of this paper is to propose such an approach. The proposed approach consists of three steps to drive the development of Problem Frames. In the first step, business process models are developed to capture the behavioural view of the problem context. In the second step, object analysis models are used to capture the structural view of the problem context. Together, these two views collectively and adequately capture the early context knowledge. These two types of model will then be used in the third step to construct context diagrams and derive Problem Frames. A complex real-world problem – equity trading problem – is used to illustrate this approach.

Genetic algorithms (GAs) and neural networks (NNs) are both inspired by computation in biological systems and many attempts have been made to combine the two methodologies to boost the NNs performance. This paper deals with the evolutionary training of a feedforward NN for both breast cancer detection and recurrence. A multi-layer perceptron (MLP) has been designed for this purpose, using a GA routine to set weights, and a Java implementation of this hybrid model has been made. Four databases concerning cancer detection and recurrence have been used, two databases containing numerical attributes only, one database containing ordinal (categorical) attributes solely and one database with mixed attributes. In comparison to some standard NNs, the performance of this approach using the same databases is shown to be superior. Moreover, this hybrid MLP/GA model is very flexible in terms of providing accurate classification, even with different types of attributes, which is usually found in medical studies.

Many decisions are made in interactive situations in which decision makers interact and may also affect each other's decision outcomes. In order to model decision makings in such interactive situations, this paper incorporates game theory into influence diagrams and presents a new approach, called game theory-based influence diagrams (GIDs). GIDs consider an extra factor, that of the choice of strategies made by other decision makers, to the list of determinants that influence the decision-making process of each decision maker, so that decision makers can make more rational decisions. As a result of integrating influence diagrams and game theory, GIDs benefit from the simplicity and efficiency of influence diagrams for modelling complex decision problems as well as the rationality and suitability of applying game theory for making decisions in dynamic interactive scenarios. This paper also introduces genetic algorithm-based methods to evaluate GIDs. Experimental studies have been performed for validation and evaluation.

Artificial intelligence can be used to recognize and anticipate dynamic situations. Several computational methods based on mathematical tools already exist, but most of the time their implementation is complex and takes a long time to execute.

In this article, we propose another learning and anticipation method in order to assist users in dynamic situations. We call it ‘scenario-based reasoning’ algorithm. It is inspired by case-based reasoning. It works with symbolic data and its aim is to make real-time predictions. To do so, manipulated knowledge is specially designed to limit our solution's complexity and to facilitate learning and anticipation.

This paper presents cluster-based ensemble classifier – an approach toward generating ensemble of classifiers using multiple clusters within classified data. Clustering is incorporated to partition data set into multiple clusters of highly correlated data that are difficult to separate otherwise and different base classifiers are used to learn class boundaries within the clusters. As the different base classifiers engage on different difficult-to-classify subsets of the data, the learning of the base classifiers is more focussed and accurate. A selection rather than fusion approach achieves the final verdict on patterns of unknown classes. The impact of clustering on the learning parameters and accuracy of a number of learning algorithms including neural network, support vector machine, decision tree and k-NN classifier is investigated. A number of benchmark data sets from the UCI machine learning repository were used to evaluate the cluster-based ensemble classifier and the experimental results demonstrate its superiority over bagging and boosting.

This study focuses on the development of a new module for a more accurate determination of geometrical properties of aggregates. A laser-based imaging system has been developed for the shape characterization of aggregates by using various digital image analysis techniques. By using this system it is possible to create a three-dimensional (3D) image form of aggregate particles. The system has been optimized to minimize the possible errors during image capturing and processing. The aggregates were classified according to their shape properties as; round, flat, elongated, angular, sphere, and irregular during test procedures. Geometrical properties of each aggregate group were analysed in 3D spatial domain. 3D shape reconstruction and characterization of the aggregates were realized by using digital image processing and analysis techniques based on the laser imaging system. MatLab® Image Processing Toolbox and Neural Network Toolbox were used to extract typical features of the aggregates and classify them according to their geometrical properties. Among the classifier types, multi-layered perceptron that has two hidden layers revealed the best performance (95.83%). The selection and production of appropriate shaped aggregate for various construction purposes seems to be possible by this developed method.

In the paper, a novel two-level algorithm of time-series change detection is presented. In the first level, to identify non-stationary sequences in a processed signal, preliminary detection of events is performed with a short-term prediction comparison. In the second stage, to confirm the changes detected in the first level, a similarity method aimed at identification of unique changes is employed. The detection of changes in a non-stationary time series is discussed, implemented algorithms are described and the results produced on a sample four financial time series are shown. General conditions for implementing the proposed algorithm as an immune-like event detector are discussed.

This paper describes an approach to human action recognition based on a probabilistic optimization model of body parts using hidden Markov model (HMM). Our method is able to distinguish between similar actions by only considering the body parts having major contribution to the actions, for example, legs for walking, jogging and running; arms for boxing, waving and clapping. We apply HMMs to model the stochastic movement of the body parts for action recognition. The HMM construction uses an ensemble of body-part detectors, followed by grouping of part detections, to perform human identification. Three example-based body-part detectors are trained to detect three components of the human body: the head, legs and arms. These detectors cope with viewpoint changes and self-occlusions through the use of ten sub-classifiers that detect body parts over a specific range of viewpoints. Each sub-classifier is a support vector machine trained on features selected for the discriminative power for each particular part/viewpoint combination. Grouping of these detections is performed using a simple geometric constraint model that yields a viewpoint-invariant human detector. We test our approach on three publicly available action datasets: the KTH dataset, Weizmann dataset and HumanEva dataset. Our results illustrate that with a simple and compact representation we can achieve robust recognition of human actions comparable to the most complex, state-of-the-art methods.

In human behaviour analysis, the visual focus of attention (VFOA) of a person is a very important cue. VFOA detection is difficult, though, especially in a unconstrained and crowded environment, typical of video surveillance scenarios. In this paper, we estimate the VFOA by defining the Subjective View Frustum, which approximates the visual field of a person in a three-dimensional representation of the scene. This opens up to several intriguing behavioural investigations. In particular, we propose the Inter-Relation Pattern Matrix, which suggests possible social interactions between the people present in a scene. Theoretical justifications and experimental results substantiate the validity and the goodness of the analysis performed.

As software complexity grows and clients demand higher quality software, quality requirements can no longer be considered to be of secondary importance. Thus, eliciting, specifying, prioritizing and validating quality requirements is a prerequisite to the development of effective and efficient information systems. Despite the critical importance of quality requirements, there is a considerable gap in the breadth and depth of quality requirements engineering (RE) support in most RE approaches. In practice, it is often the case to have quality requirements considered as an afterthought in the systems development process. While there is a wealth of modelling techniques and tools for functional requirements, there is very limited support for quality requirements in RE. Support for quality requirements is usually ad-hoc, without clear guidelines on how to capture, specify and manage quality requirements and also without proper usage of standardized terminologies based on established quality models such as the ISO/IEC 9126 quality model. In this paper, we discuss a quality-driven RE framework and tool that applies knowledge management techniques and quality ontologies to support RE activities. The ontology implements the quality characteristics and metrics prescribed by the ISO/9126 quality model, providing a common vocabulary to address quality concerns/aspects across RE activities. We empirically validate how the framework and tool can be used to effectively support the requirements elicitation and prioritization activities through a case study addressing the development of an intranet portal project at the University of Manchester.

Shunt active power filters have been widely used for power quality improvement. With the advancement in artificial intelligence techniques, the applications of fuzzy logic-based control systems have increased manifolds. This paper proposes a reduced rule fuzzy logic controller (FLC) in the voltage control loop of a shunt active power filter (APF), which is approximating a conventional large rule FLC. The difference between the controlled outputs of two controllers is compensated by proposed compensating factors. The dynamic response and harmonic compensation performance of proposed 4-rule approximated fuzzy logic controller (AFLC) is compared with 25-rule FLC. A three-phase shunt APF is used for harmonic and reactive power compensation. The proposed scheme is tested with randomly varying single and multiple non-linear loads. The simulation results presented under transient and steady-state conditions confirm that the proposed 4-rule AFLC efficiently approximates the 25-rule FLC. The proposed control methodology takes less computational time and computational memory as the numbers of rules are reduced significantly.

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar-guided genetic programming is described and compared with both classical and recent soft-computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.

Many projects fail because the knowledge learned from them is obtained too late or insufficient (Koenig & Srikantaiah, 2004); ‘knowledge in projects', ‘knowledge about projects', and ‘knowledge from projects' are three types of knowledge that result from project-based work (Love et al., 2005). This study explores the relationships between the system development life cycle (SDLC) of project management, firm-level explicit knowledge of organizational knowledge accumulation (OKA), and implicit knowledge of employee knowledge accumulation (EKA) with respect to knowledge accumulation (KA) and knowledge integration (KI). First, it analyzes the competence of SDLC in Taiwan's IT enterprises by adapting expert interviews, analytic hierarchy process (AHP), and fuzzy rule-based theory. This reveals that system planning (SP) and system analysis (SA) are the most important SDLC phases. Second, based on the above result, this study investigates how the effectiveness of SDLC (ESDLC) correlates with KI, OKA, and EKA. Results indicate that EKA and OKA have obvious mutual influences, and that both show significant impact on ESDLC. Furthermore, KI has positive influence on EKA, but negative influence on OKA.