Qualifying input test data for model transformations

Model transformation is a core mechanism for model-driven engineering (MDE). Writing complex model transformations is error-prone, and efficient testing techniques are required as for any complex program development. Testing a model transformation is typically performed by checking the results of the transformation applied to a set of input models. While it is fairly easy to provide some input models, it is difficult to qualify the relevance of these models for testing. In this paper, we propose a set of rules and a framework to assess the quality of given input models for testing a given transformation. Furthermore, the framework identifies missing model elements in input models and assists the user in improving these models.

The 2007 IEEE CEC simulated car racing competition

This paper describes the simulated car racing competition that was arranged as part of the 2007 IEEE Congress on Evolutionary Computation. Both the game that was used as the domain for the competition, the controllers submitted as entries to the competition and its results are presented. With this paper, we hope to provide some insight into the efficacy of various computational intelligence methods on a well-defined game task, as well as an example of one way of running a competition. In the process, we provide a set of reference results for those who wish to use the simplerace game to benchmark their own algorithms. The paper is co-authored by the organizers and participants of the competition.

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Inverse multi-objective robust evolutionary design

In this paper, we present an Inverse Multi-Objective Robust Evolutionary (IMORE) design methodology that handles the presence of uncertainty without making assumptions about the uncertainty structure. We model the clustering of uncertain events in families of nested sets using a multi-level optimization search. To reduce the high computational costs of the proposed methodology we proposed schemes for (1) adapting the step-size in estimating the uncertainty, and (2) trimming down the number of calls to the objective function in the nested search. Both offline and online adaptation strategies are considered in conjunction with the IMORE design algorithm. Design of Experiments (DOE) approaches further reduce the number of objective function calls in the online adaptive IMORE algorithm. Empirical studies conducted on a series of test functions having diverse complexities show that the proposed algorithms converge to a set of Pareto-optimal design solutions with non-dominated nominal and robustness performances efficiently.

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Adaptation and application of multi-objective evolutionary algorithms for rule reduction and parameter tuning of fuzzy rule-based systems

Recently, multi-objective evolutionary algorithms have been applied to improve the difficult tradeoff between interpretability and accuracy of fuzzy rule-based systems. It is known that both requirements are usually contradictory, however, these kinds of algorithms can obtain a set of solutions with different trade-offs. This contribution analyzes different application alternatives in order to attain the desired accuracy/interpr-etability balance by maintaining the improved accuracy that a tuning of membership functions could give but trying to obtain more compact models. In this way, we propose the use of multi-objective evolutionary algorithms as a tool to get almost one improved solution with respect to a classic single objective approach (a solution that could dominate the one obtained by such algorithm in terms of the system error and number of rules). To do that, this work presents and analyzes the application of six different multi-objective evolutionary algorithms to obtain simpler and still accurate linguistic fuzzy models by performing rule selection and a tuning of the membership functions. The results on two different scenarios show that the use of expert knowledge in the algorithm design process significantly improves the search ability of these algorithms and that they are able to improve both objectives together, obtaining more accurate and at the same time simpler models with respect to the single objective based approach.

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E-redesigning of society: towards experiential connectivity of generations in Lithuania

The paper reflects on the unique experience of social and technological development in Lithuania since the regaining of independence as a newly reshaped society constructing a distinctive competitive IST-based model at global level. This has presented Lithuanian pattern of how to integrate different experiences and relations between generations in implementing complex information society approaches. The resulting programme in general is linked to the Lisbon objectives of the European Union. The experience of transitional countries in Europe, each different but facing some common problems, may be useful to developing countries in Africa.

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Quantum Algorithms: Philosophical Lessons

I discuss the philosophical implications that the rising new science of quantum computing may have on the philosophy of computer science. While quantum algorithms leave the notion of Turing-Computability intact, they may re-describe the abstract space of computational complexity theory hence militate against the autonomous character of some of the concepts and categories of computer science.

Semantic analysis of program initialisation in genetic programming

Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.

Methods for quantitative usability requirements: a case study on the development of the user interface of a mobile phone

Quantitative usability requirements are a critical but challenging, and hence an often neglected aspect of a usability engineering process. A case study is described where quantitative usability requirements played a key role in the development of a new user interface of a mobile phone. Within the practical constraints of the project, existing methods for determining usability requirements and evaluating the extent to which these are met, could not be applied as such, therefore tailored methods had to be developed. These methods and their applications are discussed.

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Obtaining linguistic fuzzy rule-based regression models from imprecise data with multiobjective genetic algorithms

Backfitting of fuzzy rules is an Iterative Rule Learning technique for obtaining the knowledge base of a fuzzy rule-based system in regression problems. It consists in fitting one fuzzy rule to the data, and replacing the whole training set by the residual of the approximation. The obtained rule is added to the knowledge base, and the process is repeated until the residual is zero, or near zero. Such a design has been extended to imprecise data for which the observation error is small. Nevertheless, when this error is moderate or high, the learning can stop early. In this kind of algorithms, the specificity of the residual might decrease when a new rule is added. There may happen that the residual grows so wide that it covers the value zero for all points (thus the algorithm stops), but we have not yet extracted all the information available in the dataset. Focusing on this problem, this paper is about datasets with medium to high discrepancies between the observed and the actual values of the variables, such as those containing missing values and coarsely discretized data. We will show that the quality of the iterative learning degrades in this kind of problems, because it does not make full use of all the available information. As an alternative to sequentially obtaining rules, we propose a new multiobjective Genetic Cooperative Competitive Learning (GCCL) algorithm. In our approach, each individual in the population codifies one rule, which competes in the population in terms of maximum coverage and fitting, while the individuals in the population cooperate to form the knowledge base.

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Multimodal support to group dynamics

The complexity of group dynamics occurring in small group interactions often hinders the performance of teams. The availability of rich multimodal information about what is going on during the meeting makes it possible to explore the possibility of providing support to dysfunctional teams from facilitation to training sessions addressing both the individuals and the group as a whole. A necessary step in this direction is that of capturing and understanding group dynamics. In this paper, we discuss a particular scenario, in which meeting participants receive multimedia feedback on their relational behaviour, as a first step towards increasing self-awareness. We describe the background and the motivation for a coding scheme for annotating meeting recordings partially inspired by the Bales’ Interaction Process Analysis. This coding scheme was aimed at identifying suitable observable behavioural sequences. The study is complemented with an experimental investigation on the acceptability of such a service.

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ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach’s efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

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Input validation analysis and testing

This research addresses the problem of statically analyzing input command syntax as defined in interface and requirements specifications and then generating test cases for dynamic input validation testing. The IVAT (Input Validation Analysis and Testing) technique has been developed, a proof-of-concept tool (MICASA) has been implemented, and a case study validation has been performed. Empirical validation on large-scale industrial software (from the Tomahawk Cruise Missile) shows that as compared with senior, experienced analysts and testers, MICASA found more syntactic requirement specification defects, generated test cases with higher syntactic coverage, and found additional defects. The experienced analysts found more semantic defects than MICASA, and the experienced testers’ cases found 7.4 defects per test case as opposed to an average of 4.6 defects found by MICASA test cases. Additionally, the MICASA tool performed at less cost.

Connecting the family with awareness systems

Awareness systems have attracted significant research interest for their potential to support interpersonal relationships. Investigations of awareness systems for the domestic environment have suggested that such systems can help individuals stay in touch with dear friends or family and provide affective benefits to their users. Our research provides empirical evidence to refine and substantiate such suggestions. We report our experience with designing and evaluating the ASTRA awareness system, for connecting households and mobile family members. We introduce the concept of connectedness and its measurement through the Affective Benefits and Costs of communication questionnaire (ABC-Q). We inform results that testify the benefits of sharing experiences at the moment they happen without interrupting potential receivers. Finally, we document the role that lightweight, picture-based communication can play in the range of communication media available.

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Interacting with mobile services: an evaluation of camera-phones and visual tags

We present a study of using camera-phones and visual-tags to access mobile services. Firstly, a user-experience study is described in which participants were both observed learning to interact with a prototype mobile service and interviewed about their experiences. Secondly, a pointing-device task is presented in which quantitative data was gathered regarding the speed and accuracy with which participants aimed and clicked on visual-tags using camera-phones. We found that participants’ attitudes to visual-tag-based applications were broadly positive, although they had several important reservations about camera-phone technology more generally. Data from our pointing-device task demonstrated that novice users were able to aim and click on visual-tags quickly (well under 3 s per pointing-device trial on average) and accurately (almost all meeting our defined speed/accuracy tradeoff of 6% error-rate). Based on our findings, design lessons for camera-phone and visual-tag applications are presented.

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Using trust assumptions with security requirements

Assumptions are frequently made during requirements analysis of a system about the trustworthiness of its various components (including human components). These trust assumptions, whether implicit or explicit, affect the scope of the analysis, derivation of security requirements, and in some cases how functionality is realized. This paper presents trust assumptions in the context of analysis of security requirements. A running example shows how trust assumptions can be used by a requirements engineer to help define and limit the scope of analysis and to document the decisions made during the process. The paper concludes with a case study examining the impact of trust assumptions on software that uses the secure electronic transaction specification.

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Workplace innovation: bridging knowledge and practice

The article draws on a decade of work in the UK by the UK Work Organisation Network (UKWON), and recommends a systematic approach. Taking cases in the National Health Service, the focus is on employee involvement, partnership and the development of social capital. High and low road approaches are compared, in an evaluation of the Improving Working Lives programme.

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Ethical regulations on robotics in Europe

There are only a few ethical regulations that deal explicitly with robots, in contrast to a vast number of regulations, which may be applied. We will focus on ethical issues with regard to “responsibility and autonomous robots”, “machines as a replacement for humans”, and “tele-presence”. Furthermore we will examine examples from special fields of application (medicine and healthcare, armed forces, and entertainment). We do not claim to present a complete list of ethical issue nor of regulations in the field of robotics, but we will demonstrate that there are legal challenges with regard to these issues.

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Scientific models and ethical issues in hybrid bionic systems research

Research on hybrid bionic systems (HBSs) is still in its infancy but promising results have already been achieved in laboratories. Experiments on humans and animals show that artificial devices can be controlled by neural signals. These results suggest that HBS technologies can be employed to restore sensorimotor functionalities in disabled and elderly people. At the same time, HBS research raises ethical concerns related to possible exogenous and endogenous limitations to human autonomy and freedom. The analysis of these concerns requires reflecting on the availability of scientific models accounting for key aspects of sensorimotor coordination and plastic adaptation mechanisms in the brain.

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On modeling software defect repair time

The ability to predict the time required to repair software defects is important for both software quality management and maintenance. Estimated repair times can be used to improve the reliability and time-to-market of software under development. This paper presents an empirical approach to predicting defect repair times by constructing models that use well-established machine learning algorithms and defect data from past software defect reports. We describe, as a case study, the analysis of defect reports collected during the development of a large medical software system. Our predictive models give accuracies as high as 93.44%, despite the limitations of the available data. We present the proposed methodology along with detailed experimental results, which include comparisons with other analytical modeling approaches.

Was kann die Wirtschaftsinformatik dazu beitragen,E-Health voran zu bringen?

Ohne Zusammenfassung

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