Formal verification of multitasking applications based on timed automata model

The aim of this paper is to show, how a multitasking application running under a real-time operating system compliant with an OSEK/VDX standard can be modeled by timed automata. The application under consideration consists of several non-preemptive tasks and interrupt service routines that can be synchronized by events. A model checking tool is used to verify time and logical properties of the proposed model. Use of this methodology is demonstrated on an automated gearbox case study and the result of the worst-case response time verification is compared with the classical method based on the time-demand analysis. It is shown that the model-checking approach provides less pessimistic results due to a more detailed model and exhaustive state-space exploration.

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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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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Formal analysis of human operator behavioural patterns in interactive surveillance systems

An important area of Human Reliability Assessment in interactive systems is the ability to understand the causes of human error and to model their occurrence. This paper investigates a new approach to analysis of task failures based on patterns of operator behaviour, in contrast with more traditional event-based approaches. It considers, as a case study, a formal model of an Air Traffic Control system operator’s task which incorporates a simple model of the high-level cognitive processes involved. The cognitive model is formalised in the CSP process algebra. Various patterns of behaviour that could lead to task failure are described using temporal logic. Then a model-checking technique is used to verify whether the set of selected behavioural patterns is sound and complete with respect to the definition of task failure. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the informal analysis of the problem. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.

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Making SVMs Scalable to Large Data Sets using Hierarchical Cluster Indexing

Support vector machines (SVMs) have been promising methods for classification and regression analysis due to their solid mathematical foundations, which include two desirable properties: margin maximization and nonlinear classification using kernels. However, despite these prominent properties, SVMs are usually not chosen for large-scale data mining problems because their training complexity is highly dependent on the data set size. Unlike traditional pattern recognition and machine learning, real-world data mining applications often involve huge numbers of data records. Thus it is too expensive to perform multiple scans on the entire data set, and it is also infeasible to put the data set in memory. This paper presents a method, Clustering-Based SVM (CB-SVM), that maximizes the SVM performance for very large data sets given a limited amount of resource, e.g., memory. CB-SVM applies a hierarchical micro-clustering algorithm that scans the entire data set only once to provide an SVM with high quality samples. These samples carry statistical summaries of the data and maximize the benefit of learning. Our analyses show that the training complexity of CB-SVM is quadratically dependent on the number of support vectors, which is usually much less than that of the entire data set. Our experiments on synthetic and real-world data sets show that CB-SVM is highly scalable for very large data sets and very accurate in terms of classification. A preliminary version of the paper, “Classifying Large Data Sets Using SVM with Hierarchical Clusters”, by H. Yu, J. Yang, and J. Han, appeared in Proc. 2003 Int. Conf. on Knowledge Discovery in Databases (KDD'03), Washington, DC, August 2003. However, this submission has substantially extended the previous paper and contains new and major-value added technical contribution in comparison with the conference publication.

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An ensemble-based method for linear feature extraction for two-class problems

In this paper we propose three variants of a linear feature extraction technique based on Adaboost for two-class classification problems. Unlike other feature extraction techniques, we do not make any assumptions about the distribution of the data. At each boosting step we select from a pool of linear projections the one that minimizes the weighted error. We propose three different variants of the feature extraction algorithm, depending on the way the pool of individual projections is constructed. Using nine real and two artificial data sets of different original dimensionality and sample size we compare the performance of the three proposed techniques with three classical techniques for linear feature extraction: Fisher linear discriminant analysis (FLD), Nonparametric discriminant analysis (NDA) and a recently proposed feature extraction method for heteroscedastic data based on the Chernoff criterion. Our results show that for data sets of relatively low-original dimensionality FLD appears to be both the most accurate and the most economical feature extraction method (giving just one-dimension in the case of two classes). The techniques based on Adaboost fare better than the classical techniques for data sets of large original dimensionality.

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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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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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Level sets and the representation theorem for intuitionistic fuzzy sets

We discuss the concept of a level set of a fuzzy set and the related ideas of the representation theorem and Zadeh’s extension principle. We then describe the extension of these ideas to the case of interval valued fuzzy sets (IVFS). We then recall the formal equivalence between IVFS and intuitionistic fuzzy sets (IFS). This equivalence allows us to naturally extend the concepts of level sets, representation theorem and extension principle from the domain of IVFS to the domain of IFS. What is important to note here is that in the case of these non-standard fuzzy sets, interval valued and intuitionistic, the number of distinct level sets can be greater then the number of distinct membership grades of the fuzzy set being represented. This is a result of the fact that the distinct level sets are generated by the power set of the membership grades. In particular, the minimum of each subset of membership grades provides a level set. In the case of the standard fuzzy sets the minimum of a subset of membership grades results in one of the elements in the subset. In the case of the non-standard fuzzy sets, the membership grades are not linearly ordered and hence taking the minimum of a subset of these can result in a value that was not one of the members of the subset.

Evolving strategy for a probabilistic game of imperfect information using genetic programming

We provide the complete record of methodology that let us evolve BrilliAnt, the winner of the Ant Wars contest. Ant Wars contestants are virtual ants collecting food on a grid board in the presence of a competing ant. BrilliAnt has been evolved through a competitive one-population coevolution using genetic programming and fitnessless selection. In this paper, we detail the evolutionary setup that lead to BrilliAnt’s emergence, assess its direct and indirect human-competitiveness, and describe the behavioral patterns observed in its strategy.

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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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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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Approximate solutions of moral dilemmas in multiple agent system

Moral dilemmas are one of the major issues of current research in ethical reasoning. In particular, it is well known that admitting moral dilemmas in Standard Deontic Logic generates a family of inconsistencies that are intrinsically unsolvable. Since managing dilemmas means performing preferential reasoning, we argue that one simple approach to both types of problems is by ordering actions. We notice that in general, more than local orderings between two actions, agents have intrinsic preferences based on classification issues, like the action type, and that, once we have discharged the dilemma as it is intrinsically, preferential reasoning is performed by using a second-level choice approach. Decision theory has dealt with the problem of making decisions in presence of conflicting decision criteria, and some researcher has pointed out that this is the case of moral dilemmas as well. In practice, the choice of preferences in presence of conflicting criteria can be seen as a form of preferential-ethical reasoning. Although this is certainly an important topic in multiple agent investigations, it is definitely neglected in the current investigations. It is well known that humans are quite clever in solving moral dilemmas, and the usage they make of preferential reasoning is very complex. In this paper we address the problems of preferential-ethical reasoning in a combinatorial fashion and provide an algorithm for making decisions on moral dilemmas in presence of conflicting decision criteria. We then evaluate the complexity of the algorithm and prove that this approach can be applied in practice.

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

Ohne Zusammenfassung

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Supporting ordering and consistency in a distributed Event Heap for Ubiquitous Computing

The Stanford Event Heap has been shown to provide appropriate support for constructing interactive workspace applications. Given this success it is natural to consider the Event Heap as a platform to support other classes of Ubiquitous Computing applications. In this paper we argue that the distributed, spontaneous nature of these applications places additional demands on the Event Heap that require extensions to both the engineering and API. Suitable extensions are described and their use to support a typical Ubicomp application is discussed.

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