Computer engineering & Informatics

This paper addresses the issue of software cost estimation through fuzzy decision trees, aiming at acquiring accurate and reliable effort estimates for project resource allocation and control. Two algorithms, namely CHAID and CART, are applied on empirical software cost data recorded in the ISBSG repository. Approximately 1000 project data records are selected for analysis and experimentation, with fuzzy decision trees instances being generated and evaluated based on prediction accuracy. The set of association rules extracted is used for providing mean effort value ranges. The experimental results suggest that the proposed approach may provide accurate cost predictions in terms of effort. In addition, there is strong evidence that the fuzzy transformation of cost drivers contribute to enhancing the estimation process.

Mobile Commerce is an evolving area of e-Commerce, where users can interact with the service providers through a mobile and wireless network, using mobile devices for information retrieval and transaction processing. M-Commerce services and applications can be adopted through different wireless and mobile networks, with the aid of several mobile devices. However, constraints of both mobile networks and devices influence their operational performance; therefore, there is a strong need for taking into consideration those constraints in the design and development phases of m-Commerce services and applications. Another important factor in designing m-Commerce services and applications is the identification of mobile users requirements. Furthermore, m-Commerce services and applications need to be classified based on the functionality they provide to the mobile users. This kind of classification results in two major classes: the directory and the transaction-oriented services and applications. This paper suggests a new approach for designing and developing m-Commerce services and applications. This approach relies on mobile users needs and requirements, the classification of the m-Commerce services and applications, as well as the current technologies for mobile and wireless computing and their constraints.

Feature selection has been recently used in the area of software engineering for improving the accuracy and robustness of software cost models. The idea behind selecting the most informative subset of features from a pool of available cost drivers stems from the hypothesis that reducing the dimensionality of datasets will significantly minimise the complexity and time required to reach to an estimation using a particular modelling technique. This work investigates the appropriateness of attributes, obtained from empirical project databases and aims to reduce the cost drivers used while preserving performance. Finding suitable subset selections that may cater improved predictions may be considered as a pre-processing step of a particular technique employed for cost estimation (filter or wrapper) or an internal (embedded) step to minimise the fitting error. This paper compares nine relatively popular feature selection methods and uses the empirical values of selected attributes recorded in the ISBSG and Desharnais datasets to estimate software development effort.

Near real-time tracking of objects is becoming a critical requirement in transportation business today since competition is increasing and customers are becoming more demanding in obtaining information on services and delivery of products. The technological advancements in the fields of networking (e.g., 3G, HSDPA), near field communications (e.g., RFID) and location identification (e.g., AGPS) provide the potential for the creation of an advanced intelligent tracking platform that identifies and captures an object all across its lifecycle. In the current paper we present such an innovative platform; eTracer which is an end-to-end near realtime tracking platform for objects. In addition, the architecture of eTracer is analyzed and the research issue of reducing the computational intensiveness of processing is also discussed.

The Cultural Journeys in the Information Society is a dynamic hypermedia environment, which proposes the Electronic Roads as a meta-form for exploring cultural information that can form Cultural Journeys. The Electronic Roads meta-form facilitates travelers to explore the information space in a natural and continuous way similar to the exploration of physical roads. This meta-form is advantageous over existing cultural exploration approaches, which usually apply only to discrete information spaces, failing to provide users with continuous explorations and as a result users may lose their intended destinations. The proposed environment provides extendable cultural and historical content for three Mediterranean countries, i.e. Cyprus, Jordan and Egypt. Such a system could be ideal for teaching history and culture; therefore several Learning Activities have been developed in this context. The learning effectiveness of the environment was evaluated in primary schools in Cyprus, where pupils were asked to complete specific Learning Activities by either using the Cultural Journeys in the Information Society environment or a static hypermedia system. The results showed that the pupils found our environment to be easier to use, compared to the static system and that the performance of the pupils that used it was significantly higher.

We are indebted to Professor Keith Hartley and two anonymous referees for their helpful comments. Thanks are also due to Mr. John S. Koufakis, for his valuable assistance concerning the collection of the data series used. The views presented in this paper are strictly personal.

The aim of this paper is to indicate the extent to which the arms race against Turkey, in which Greece and Cyprus have been entangled, imposes a defence expenditure burden that is tough for the two allies to bear. To do so we have resorted to evaluating the optimal military expenditure for the two countries, allied in the context of the Integrated Defence Doctrine, which is compatible with the constraints imposed by the resources of their economies. All experiments and scenarios examined lead to the conclusion that the current defence burden of the two allies seems to be driving their economies beyond capacity limits. The fact remains, however, that under the circumstances, a one-sided disarmament policy like the one currently followed by Greece, is a risky choice given that the long-term armament programmes pursued by Turkey, whose role in this arms race has been proven as leading, leave very small room to the Greek and Cypriot sides to reduce their own defence expenditure.

Heart attacks and devaluations are not predictable and, certainly, are never preannounced". (The usual remark made by government spokesmen shortly after a domestic currency devaluation has taken place.) The contribution that this paper aspires to make is the prediction of an oncoming attack against the domestic currency, something that is expected to increase the possibilities of successful hedging by the authorities. The analysis has focused on the Greek Drachma, which has suffered a series of attacks during the past few years, thus offering a variety of such "shock" incidents accompanied by frequent interventions by the authorities. The prediction exercised here is performed in a discrete dynamics environment, based on the daily fluctuations of the interbank overnight interest rate, using artificial neural networks enhanced by genetic algorithms. The results obtained on the basis of the forecasting performance have been considered most encouraging, in providing a successful prediction of an oncoming attack against the domestic currency.

This paper suggests a new approach for developing m-commerce services and applications based on a scheme that divides m-applications into directory-and transaction-oriented classes, identifies mobile user requirements, and takes into consideration the constraints of current technologies for mobile and wireless computing. The efficacy of the proposed approach in a real scenario is discussed.

This work proposes a new methodology for intelligent classification and retrieval of software components based on user-defined requirements. The classification scheme utilizes a dedicated genetic algorithm which evolves a small number of classifiers by dividing the set of available components stored in a database into certain subsets (clusters). Each classifier thus becomes the leader-representative of its cluster. When a user wishes to trace a component he/she identifies the desired characteristics (component profile) which are then compared with the characteristics of the available classifiers. The closest classifier matching the required characteristics over a user-defined threshold will result in the "winning" set of components belonging to its cluster, which are presented to the user in descending matching fitness. We have validated our methodology over a synthetic dataset of components and the results obtained were very encouraging. Last, we present the web application developed to support the proposed intelligent classification method.

The lack of a software tool to automate the creation of a well-defined, Natural Language Software Requirements Specification (SRS) document is an obstacle to the process of efficient Requirements Engineering (RE). This paper provides an overview of a methodology and a novel software tool that attempt to formalize and automate the RE process, and it expands on the use of the SRS Documentation component of the tool that generates automatically a well-structured Natural Language SRS document.

a) To present a review of ongoing health telematic applications in Cyprus. b) To promote the use of these health telematic applications in the Cyprus region. c) To help in the spin off of other health telematic applications thus enabling the offering of a better health service to the citizens. The health telematics applications include a medical system for emergency telemedicine (AMBULANCE and EMERGENCY-112 projects), a system for the evaluation of the risk of stroke by telemedicine (EROS), a diagnostic telepathology network in gynaecological cancer (TELEGYN), a collaborative virtual medical team for home healthcare of cancer patients (DITIS), and a health telematics training network (HEALTHNET). The paper refers to the set-up and characteristics of these applications and tries to relate them with the health policies that should be applied in Cyprus. It is anticipated that this paper will promote the importance of health telematics applications for Cyprus and increase the awareness ...

Software development organisations are under heavy pressure to complete projects on time, within budget and with the appropriate level of quality, and many questions are asked when a project fails to meet any or all of these requirements. Over the years, much research effort has been spent to find ways to mitigate these failures, the reasons of which come from both within and outside the organisation's control. One possible risk of failure lies in human resource management and, since humans are the main asset of software organisations, getting the right team to do the job is critical. This paper proposes a procedure for software project managers to support their project scheduling and team staffing activities-two areas where human resources directly impact software development projects and management decisions-by adopting a genetic algorithm approach as an optimisation technique to help solve software project scheduling and team staffing problems.

Many parameters affect the cost evolution of software projects. In the area of software cost estimation and project management the main challenge is to understand and quantify the effect of these parameters, or 'cost drivers', on the effort expended to develop software systems. This paper aims at investigating the effect of cost attributes on software development effort using empirical databases of completed projects and building Artificial Neural Network (ANN) models to predict effort. Prediction performance of various ANN models with different combinations of inputs is assessed in an attempt to reduce the models' input dimensions. The latter is performed by using one of the most popular saliency measures of network weights, namely Garson's Algorithm. The proposed methodology provides an insight on the interpretation of ANN which may be used for capturing nonlinear interactions between variables in complex software engineering environments.

This paper deals with the problem of software effort estimation through the use of a new machine learning technique for producing reliable confidence measures in predictions. More specifically, we propose the use of Conformal Predictors (CPs), a novel type of prediction algorithms, as a means for providing effort estimations for software projects in the form of predictive intervals according to a specified confidence level. Our approach is based on the well-known Ridge Regression technique, but instead of the simple effort estimates produced by the original method, it produces predictive intervals that satisfy a given confidence level. The results obtained using the proposed algorithm on the COCOMO, Desharnais and ISBSG datasets suggest a quite successful performance obtaining reliable predictive intervals which are narrow enough to be useful in practice.

This paper addresses the issue of Software Cost Estimation (SCE) providing an alternative approach to modelling and prediction using Artificial Neural Networks (ANN) and Input Sensitivity Analysis (ISA). The overall aim is to identify and investigate the effect of the leading factors in SCE, through ISA. The factors identified decisively influence software effort in the models examined and their ability to provide sufficiently accurate SCEs is examined. ANN of variable topologies are trained to predict effort devoted to software development based on past (finished) projects recorded in two publicly available historical datasets. The main difference with relevant studies is that the proposed approach extracts the most influential cost drivers that describe best the effort devoted to development activities using the weights of the network connections. The approach is validated on known software cost data and the results obtained are assessed and compared. The ANN constructed generalise efficiently the knowledge acquired during training providing accurate effort predictions. The validation process included predictions with only the most highly ranked attributes among the original cost attributes of the datasets and revealed that accuracy performance was maintained at same levels. The results showed that the combination of ANN and ISA is an effective method for evaluating the contribution of cost factors, whereas the subsets of factors selected did not compromise the accuracy of the prediction results.

Artificial Intelligence and Innovations (AIAI) will interest researchers, IT professionals and consultants by examining technologies and applications of demonstrable value. The conference focused on profitable intelligent systems and technologies. AIAI focuses on real world applications; therefore authors should highlight the benefits of AI technology for industry and services. Novel approaches solving business and industrial problems, using AI, will emerge from this conference.

The provision of effective emergency telemedicine and home monitoring solutions are the major fields of interest discussed in this study. Ambulances, Rural Health Centers (RHC) or other remote health location such as Ships navigating in wide seas are common examples of possible emergency sites, while critical care telemetry and telemedicine home follow-ups are important issues of telemonitoring.

This paper examines the use of fuzzy cognitive maps (FCMs) as a technique for modeling political and strategic issues situations and supporting the decision-making process in view of an imminent crisis. Its object domain is soft computing using as its basic elements different methods from the areas of fuzzy logic, cognitive maps, neural networks and genetic algorithms. FCMs, more specifically,