Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier

The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-neighbour (EkNN) classifier using a random subspace based ensembling method. Given a set of random subspace EkNN classifier, a PSO is used for obtaining the best parameters of the set of evidential k-nearest-neighbour classifiers, finally these classifiers are combined by the “vote rule”. The performance improvement with respect to the state-of-the-art approaches is validated through experiments with several benchmark datasets.

Classification-based wheel slip detection and detector fusion for mobile robots on outdoor terrain

This paper introduces a signal-recognition based approach for detecting autonomous mobile robot immobilization on outdoor terrain. The technique utilizes a support vector machine classifier to form class boundaries in a feature space composed of statistics related to inertial and (optional) wheel speed measurements. The proposed algorithm is validated using experimental data collected with an autonomous robot operating in an outdoor environment. Additionally, two detector fusion techniques are proposed to combine the outputs of multiple immobilization detectors. One technique is proposed to minimize false immobilization detections. A second technique is proposed to increase overall detection accuracy while maintaining rapid detector response. The two fusion techniques are demonstrated experimentally using the detection algorithm proposed in this work and a dynamic model-based algorithm. It is shown that the proposed techniques can be used to rapidly and robustly detect mobile robot immobilization in outdoor environments, even in the absence of absolute position information.

On Detecting Spatial Outliers

The ever-increasing volume of spatial data has greatly challenged our ability to extract useful but implicit knowledge from them. As an important branch of spatial data mining, spatial outlier detection aims to discover the objects whose non-spatial attribute values are significantly different from the values of their spatial neighbors. These objects, called spatial outliers, may reveal important phenomena in a number of applications including traffic control, satellite image analysis, weather forecast, and medical diagnosis. Most of the existing spatial outlier detection algorithms mainly focus on identifying single attribute outliers and could potentially misclassify normal objects as outliers when their neighborhoods contain real spatial outliers with very large or small attribute values. In addition, many spatial applications contain multiple non-spatial attributes which should be processed altogether to identify outliers. To address these two issues, we formulate the spatial outlier detection problem in a general way, design two robust detection algorithms, one for single attribute and the other for multiple attributes, and analyze their computational complexities. Experiments were conducted on a real-world data set, West Nile virus data, to validate the effectiveness of the proposed algorithms.

On Straight Line Segment Detection

In this paper we propose a comprehensive method for detecting straight line segments in any digital image, accurately controlling both false positive and false negative detections. Based on Helmholtz principle, the proposed method is parameterless. At the core of the work lies a new way to interpret binary sequences in terms of unions of segments, for which a dynamic programming implementation is given. The proposed algorithm is extensively tested on synthetic and real images and compared with the state of the art.

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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Content based similarity of geographic classes organized as partition hierarchies

In this paper we propose a method to measure the semantic similarity of geographic classes organized as partition hierarchies within Naive Geography. The contribution of this work consists in extending and integrating the information content approach, and the method for comparing concept attributes in the ontology management system SymOntos developed at IASI. As a result, this proposal allows us to address both the concept similarity within the partition hierarchy, and the attribute similarity of geographic classes and, therefore, to reduce the gap among the different similarity approaches defined in the literature.

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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Gappy PCA Classification for Occlusion Tolerant 3D Face Detection

This paper presents an innovative approach for the detection of faces in three dimensional scenes. The method is tolerant against partial occlusions produced by the presence of any kind of object. The detection algorithm uses invariant properties of the surfaces to segment salient facial features, namely the eyes and the nose. At least two facial features must be clearly visible in order to perform face detection. Candidate faces are then registered using an ICP (Iterative Correspondent Point) based approach aimed to avoid those samples which belong to the occluding objects. The final face versus non-face discrimination is computed by a Gappy PCA (GPCA) classifier which is able to classify candidate faces using only those regions of the surface which are considered to be non-occluded. The algorithm has been tested using the UND database obtaining 100% of correct detection and only one false alarm. The database has been then processed with an artificial occlusions generator producing realistic acquisitions that emulate unconstrained scenarios. A rate of 89.8% of correct detections shows that 3D data is particularly suited for handling occluding objects. The results have been also verified on a small test set containing real world occlusions obtaining 90.4% of correctly detected faces. The proposed approach can be used to improve the robustness of all those systems requiring a face detection stage in non-controlled scenarios.

Face recognition based on a novel linear discriminant criterion

As an effective technique for feature extraction and pattern classification Fisher linear discriminant (FLD) has been successfully applied in many fields. However, for a task with very high-dimensional data such as face images, conventional FLD technique encounters a fundamental difficulty caused by singular within-class scatter matrix. To avoid the trouble, many improvements on the feature extraction aspect of FLD have been proposed. In contrast, studies on the pattern classification aspect of FLD are quiet few. In this paper, we will focus our attention on the possible improvement on the pattern classification aspect of FLD by presenting a novel linear discriminant criterion called maximum scatter difference (MSD). Theoretical analysis demonstrates that MSD criterion is a generalization of Fisher discriminant criterion, and is the asymptotic form of discriminant criterion: large margin linear projection. The performance of MSD classifier is tested in face recognition. Experiments performed on the ORL, Yale, FERET and AR databases show that MSD classifier can compete with top-performance linear classifiers such as linear support vector machines, and is better than or equivalent to combinations of well known facial feature extraction methods, such as eigenfaces, Fisherfaces, orthogonal complementary space, nullspace, direct linear discriminant analysis, and the nearest neighbor classifier.

Fault detection in autonomous robots based on fault injection and learning

In this paper, we study a new approach to fault detection for autonomous robots. Our hypothesis is that hardware faults change the flow of sensory data and the actions performed by the control program. By detecting these changes, the presence of faults can be inferred. In order to test our hypothesis, we collect data from three different tasks performed by real robots. During a number of training runs, we record sensory data from the robots while they are operating normally and after a fault has been injected. We use back-propagation neural networks to synthesize fault detection components based on the data collected in the training runs. We evaluate the performance of the trained fault detectors in terms of number of false positives and time it takes to detect a fault. The results show that good fault detectors can be obtained. We extend the set of possible faults and go on to show that a single fault detector can be trained to detect several faults in both a robot’s sensors and actuators. We show that fault detectors can be synthesized that are robust to variations in the task, and we show how a fault detector can be trained to allow one robot to detect faults that occur in another robot.

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

Ohne Zusammenfassung

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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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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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Fast Blind Measurement of Blocking Artifacts in both Pixel and DCT Domains

The objective measurement of blocking artifacts plays an important role in the design, optimization, and assessment of image and video compression. In this paper, we propose a novel measurement algorithm for blocking artifacts. Computer simulation results indicate that the proposed method accurately measures the blocking artifacts without using the original image. Moreover, the proposed algorithm can be easily implemented in both pixel and DCT domains.

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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Benchmarking quality measurement

This paper gives a simple benchmarking procedure for companies wishing to develop measures for software quality attributes of software artefacts. The procedure does not require that a proposed measure is a consistent measure of a quality attribute. It requires only that the measure shows agreement most of the time. The procedure provides summary statistics for measures of quality attributes of a software artefact. These statistics can be used to benchmark subjective direct measurement of a quality attribute by a company’s software developers. Each proposed measure is expressed as a set of error rates for measurement on an ordinal scale and these error rates enable simple benchmarking statistics to be derived. The statistics can also be derived for any proposed objective indirect measure or prediction system for the quality attribute. For an objective measure or prediction system to be of value to the company it must be ‘better’ or ‘more objective’ than the organisation’s current measurement or prediction capability; and thus confidence that the benchmark’s objectivity has been surpassed must be demonstrated. By using Bayesian statistical inference, the paper shows how to decide whether a new measure should be considered ‘more objective’ or whether a prediction system’s predictive capability can be considered ‘better’ than the current benchmark. Furthermore, the Bayesian inferential approach is easy to use and provides clear advantages for quantifying and inferring differences in objectivity.

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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