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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Rule cubes for causal investigations

With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today’s automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. Interactive rule cubes, which have been proposed recently, are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues.

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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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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Learning decision tree for ranking

Decision tree is one of the most effective and widely used methods for classification. However, many real-world applications require instances to be ranked by the probability of class membership. The area under the receiver operating characteristics curve, simply AUC, has been recently used as a measure for ranking performance of learning algorithms. In this paper, we present two novel class probability estimation algorithms to improve the ranking performance of decision tree. Instead of estimating the probability of class membership using simple voting at the leaf where the test instance falls into, our algorithms use similarity-weighted voting and naive Bayes. We design empirical experiments to verify that our new algorithms significantly outperform the recent decision tree ranking algorithm C4.4 in terms of AUC.

A novel approach for ranking spelling error corrections for Urdu

This paper presents a scheme for ranking of spelling error corrections for Urdu. Conventionally spell-checking techniques do not provide any explicit ranking mechanism. Ranking is either implicit in the correction algorithm or corrections are not ranked at all. The research presented in this paper shows that for Urdu, phonetic similarity between the corrections and the erroneous word can serve as a useful parameter for ranking the corrections. This combined with a new technique Shapex that uses visual similarity of characters for ranking gives an improvement of 23% in the accuracy of the one-best match compared to the result obtained when the ranking is done on the basis of word frequencies only.

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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Socializing artifacts as a half mirror of the mind

In the near future, our life will normally be surrounded with fairly complicated artifacts, enabled by the autonomous robot and brain–machine interface technologies. In this paper, we argue that what we call the responsibility flaw problem and the inappropriate use problem need to be overcome in order for us to benefit from complicated artifacts. In order to solve these problems, we propose an approach to endowing artifacts with an ability of socially communicating with other agents based on the artifact-as-a-half-mirror metaphor. The idea is to have future artifacts behave according to the hybrid intention composed of the owner’s intention and the social rules. We outline the approach and discuss its feasibility together with preliminary work.

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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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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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Understanding and capturing people’s privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

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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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Rapid prototyping of social group dynamics in multiagent systems

In this article we present an engineering approach for the integration of social group dynamics in the behavior modeling of multiagent systems. To this end, a toolbox was created that brings together several theories from the social sciences, each focusing on different aspects of group dynamics. Due to its modular approach, the toolbox can either be used as a central control component of an application or it can be employed temporarily to rapidly test the feasibility of the incorporated theories for a given application domain. This is exemplified by applying the toolbox to different applications.

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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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Legal safeguards for privacy and data protection in ambient intelligence

To get the maximum benefit from ambient intelligence (AmI), we need to anticipate and react to possible drawbacks and threats emerging from the new technologies in order to devise appropriate safeguards. The SWAMI project took a precautionary approach in its exploration of the privacy risks in AmI and sought ways to reduce them. It constructed four “dark scenarios” showing possible negative implications of AmI, notably for privacy protection. Legal analysis of the depicted futures showed the shortcomings of the current legal framework in being able to provide adequate privacy protection in the AmI environment. In this paper, the authors, building upon their involvement in SWAMI research as well as the further advancement of EU privacy analysis, identify various outstanding issues regarding the legal framework that still need to be resolved in order to deal with AmI in an equitable and efficacious way. This article points out some of the lacunae in the legal framework and postulates several privacy-specific safeguards aimed at overcoming them.

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Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

Protecting business intelligence and customer privacy while outsourcing data mining tasks

Nowadays data mining plays an important role in decision making. Since many organizations do not possess the in-house expertise of data mining, it is beneficial to outsource data mining tasks to external service providers. However, most organizations hesitate to do so due to the concern of loss of business intelligence and customer privacy. In this paper, we present a Bloom filter based solution to enable organizations to outsource their tasks of mining association rules, at the same time, protect their business intelligence and customer privacy. Our approach can achieve high precision in data mining by trading-off the storage requirement. This research was supported by the USA National Science Foundation Grants CCR-0310974 and IIS-0546027.

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Online updating appearance generative mixture model for meanshift tracking

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.

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COMUNICAR: designing a multimedia, context-aware human-machine interface for cars

The European Union co-funded COMUNICAR (communication multimedia unit inside car) project designed and developed an integrated multimedia human–machine interface (HMI) able to manage a wide variety of driver information systems (from entertainment to safety). COMUNICAR proposed an innovative information provision paradigm, in which the on-vehicle HMI is able to tailor the delivery of the information in real time according to the actual driving context and the drivers workload. COMUNICAR adopted a user-centred design process involving an iterative development based on extensive user tests since the early phases of the project. This approach was particularly useful to define and improve the layout of the user interface and specify the rules that decide the scheduling and the modalities of the delivery of the information messages to the driver. This paper introduces the COMUNICAR concept and the user-centred flow of design. Then, a concrete case of user-test driven, iterative improvement of a systems functionality is presented. We also briefly describe two software tools that we have designed to enhance the development process in a user-centred perspective. Finally, the future evolution of the concept of smart and safe information scheduling is sketched and discussed.

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LuxTrace: indoor positioning using building illumination

Tracking location is challenging due to the numerous constraints of practical systems including, but not limited to global cost, device volume and weight, scalability and accuracy; these constraints are typically more severe for systems that should be wearable and used indoors. We investigate the use of wearable solar cells to track changing light conditions (a concept that we named LuxTrace) as a source of user displacement and activity data. We evaluate constraints of this approach and present results from an experimental validation of displacement and activity estimation. The results indicate that a distance estimation accuracy of 21 cm (80% quantile) can be achieved. A simple method to combine LuxTrace with complementary absolute location estimation methods is also presented. We apply carpet-like distributed RFID tags to demonstrate online learning of new lighting environments.

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