Textual data used to train large language models (LLMs) exhibits multifaceted bias manifestations, encompassing harmful language and skewed demographic distributions. Regulations such as the EU AI Act require identifying and mitigating biases against protected groups in data, with the ultimate goal of preventing unfair model outputs. However, practical guidance and operationalization are lacking. We propose a comprehensive data bias detection and mitigation pipeline comprising four components that address two data bias types, namely representation bias and (explicit) stereotypes for a configurable sensitive attribute. First, we leverage LLM-generated word lists, created according to defined quality criteria, to detect relevant group labels. Second, representation bias is quantified using the Demographic Representation Score. Third, we detect and mitigate stereotypes using sociolinguistically informed filtering. Finally, we mitigate representation bias through Grammar- and Context-Aware Counterfactual Data Augmentation. We conduct a twofold evaluation using gender, religion , and age as examples. First, we evaluate the effectiveness of each individual component on data debiasing through human validation and baseline comparison. The findings demonstrate that we successfully reduce representation bias and (explicit) stereotypes in a text dataset. Second, we evaluate the effect of data debiasing on model bias by benchmarking several models (0.6B-8B parameters) fine-tuned on the debiased text dataset. This evaluation reveals that LLMs fine-tuned on debiased data do not consistently show improved performance on bias benchmarks. These results expose critical gaps in current evaluation methodologies and highlight the need for targeted data interventions to address manifested model bias.

A Triple in knowledge-graph takes a form that consists of head, relation, tail. Triple Classification is used to determine the truth value of an unknown Triple. This is a hard task for 1-to-N relations using the vector-based embedding approach. We propose a new region-based embedding approach using fine-grained type chains. A novel geometric process is presented to extend the vectors of pre-trained entities into n-balls (n-dimensional balls) under the condition that head balls shall contain their tail balls. Our algorithm achieves zero energy cost, therefore, serves as a case study of perfectly imposing tree structures into vector space. An unknown Triple (h,r,x) will be predicted as true, when x’s n-ball is located in the r-subspace of h’s n-ball, following the same construction of known tails of h. The experiments are based on large datasets derived from the benchmark datasets WN11, FB13, and WN18. Our results show that the performance of the new method is related to the length of the type chain and the quality of pre-trained entityembeddings, and that performances of long chains with welltrained entity-embeddings outperform other methods in the literature. Source codes and datasets are located at https: //github.com/GnodIsNait/mushroom.

Assertions are intensively used to facilitate correctness reasoning and error detection in daily programming. However, composing assertions for mobile robotic programs can be painfully inconvenient, because classic Hoare logic lacks the expressing power on spatial knowledge, which is crucial when robots interact with their physical environments. The problem is especially evident for Behavior-Based Robotics (BBR) where the world is not explicitly represented with program variables. In this paper, we propose to incorporate spatial reasoning capability in the assertion framework of Hoare logic. The proposed framework features a two-dimensional region calculus and additional axioms for robot movements. The calculus makes the world representation and the specification of mobile robotic program natural and intuitive, and the axioms enable the reasoning about program correctness. We illustrate the use of the framework with a typical behavior-based robotic program. In addition, we present a runtime error detection and recovery mechanism for BBR programs based on the assertion framework. Preliminary experiments with NAO robots demonstrate the effectiveness.

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach [1] which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily.

BACKGROUND: Biological systems and processes are highly dynamic. To gain insights into their functioning time-resolved measurements are necessary. Time-resolved gene expression data captures temporal behaviour of the genes genome-wide under various biological conditions: in response to stimuli, during cell cycle, differentiation or developmental programs. Dissecting dynamic gene expression patterns from this data may shed light on the functioning of the gene regulatory system. The present approach facilitates this discovery. The fundamental idea behind it is the following: there are change-points (switches) in the gene behaviour separating intervals of increasing and decreasing activity, whereas the intervals may have different durations. Elucidating the switch-points is important for the identification of biologically meanigfull features and patterns of the gene dynamics. RESULTS: We developed a statistical method, called SwitchFinder, for the analysis of time-series data, in particular gene expression data, based on a change-point model. Fitting the model to the gene expression time-courses indicates switch-points between increasing and decreasing activities of each gene. Two types of the model - based on linear and on generalized logistic function - were used to capture the data between the switch-points. Model inference was facilitated with the Bayesian methodology using Markov chain Monte Carlo (MCMC) technique Gibbs sampling. Further on, we introduced features of the switch-points: growth, decay, spike and cleft, which reflect important dynamic aspects. With this, the gene expression profiles are represented in a qualitative manner - as sets of the dynamic features at their onset-times. We developed a Web application of the approach, enabling to put queries to the gene expression time-courses and to deduce groups of genes with common dynamic patterns. SwitchFinder was applied to our original data - the gene expression time-series measured in neuroblastoma cell line upon treatment with all-trans retinoic acid (ATRA). The analysis revealed eight patterns of the gene expression responses to ATRA, indicating the induction of the BMP, WNT, Notch, FGF and NTRK-receptor signaling pathways involved in cell differentiation, as well as the repression of the cell-cycle related genes. CONCLUSIONS: SwitchFinder is a novel approach to the analysis of biological time-series data, supporting inference and interactive exploration of its inherent dynamic patterns, hence facilitating biological discovery process. SwitchFinder is freely available at https://newbioinformatics.eu/switchfinder.

For information systems in which the server must operate on encrypted data (which may be necessary because the service provider cannot be trusted) solutions need to be found that enable fast searches on that data. In this paper we present an approach for encrypted database indexes that enable fast inequality, i.e., range searches, such that also prefix searches on lexicographically ordered but encrypted data are possible. Unlike common techniques that address this issue as well, like hardware-based solutions or order-preserving encryption schemes, our indexes do not require specialized, expensive hardware and use only well-accredited software components; they also do not reveal any information about the encrypted data besides their order. Moreover, when implementing the indexing approach in a commercial software product, multiple application-centric optimization opportunities of the index's performance did emerge, which are also presented in this paper. They include basic performance-increasing measures, pipelined index scans and updates and caching strategies. We further present performance test results proving that our indexing approach shows good performance on substantial amounts of data.

Finding all neighbors of a point inside a given radius is an integral part in many approaches using three-dimensional laser range data. We present novel insights to significantly improve the runtime performance of radius neighbor search using octrees. Our contributions are as follows: (1) We propose an index-based organization of the point cloud such that we can efficiently store start and end indexes of points inside every octant and (2) exploiting this representation, we can use pruning of irrelevant subtrees in the traversal to facilitate highly efficient radius neighbor search. We show significant runtime improvements of our proposed octree representation over state-of-the-art neighbor search implementations on three different urban datasets.

Motivated by the center-surround mechanism in the human visual attention system, we propose to use average contrast maps for the challenge of pedestrian detection in street scenes due to the observation that pedestrians indeed exhibit discriminative contrast texture. Our main contributions are the first to design a local statistical multichannel descriptor to incorporate both color and gradient information. Second, we introduce a multidirection and multiscale contrast scheme based on grid cells to integrate expressive local variations. Contributing to the issue of selecting most discriminative features for assessing and classification, we perform extensive comparisons with respect to statistical descriptors, contrast measurements, and scale structures. By this way, we obtain reasonable results under various configurations. Empirical findings from applying our optimized detector on the INRIA and Caltech pedestrian datasets show that our features yield state-of-the-art performance in pedestrian detection.

Automatic pedestrian detection for advanced driver assistance systems (ADASs) is still a challenging task. Major reasons are dynamic and complex backgrounds in street scenes and variations in clothing or postures of pedestrians. We propose a simple yet effective detector for robust pedestrian detection. Observing that pedestrians usually appear upright in video data, we employ a statistical model of the upright human body in which the head, upper body, and lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular features that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multimodal and multichannel Haar-like features represent characteristic differences between parts of the human body but are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangular features nor does it rely on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the well-established INRIA, Caltech, and KITTI pedestrian data sets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.

We propose a simple yet effective detector for pedestrian detection. The basic idea is to incorporate common sense and everyday knowledge into the design of simple and computationally efficient features. As pedestrians usually appear up-right in image or video data, the problem of pedestrian detection is considerably simpler than general purpose people detection. We therefore employ a statistical model of the up-right human body where the head, the upper body, and the lower body are treated as three distinct components. Our main contribution is to systematically design a pool of rectangular templates that are tailored to this shape model. As we incorporate different kinds of low-level measurements, the resulting multi-modal & multi-channel Haar-like features represent characteristic differences between parts of the human body yet are robust against variations in clothing or environmental settings. Our approach avoids exhaustive searches over all possible configurations of rectangle features and neither relies on random sampling. It thus marks a middle ground among recently published techniques and yields efficient low-dimensional yet highly discriminative features. Experimental results on the INRIA and Caltech pedestrian datasets show that our detector reaches state-of-the-art performance at low computational costs and that our features are robust against occlusions.

Object discovery is the task of detecting unknown objects in images. The task is of large interest in many fields of machine vision, ranging from the automatic analysis of web images to interpreting data of a mobile robot or a driver assistant system. Here, we present a new approach for object discovery, based on findings of the human visual system. Proto-objects are detected with a segmentation module, generating perceptually coherent image regions. In parallel, a saliency system detects regions of interest in images and serves to select segments, depending on their saliency. We obtain very good results on a database of salient objects and on real-world office scenes.

Inspired by the human vision system, in this paper we propose a specifically organized kind of center-surround contrast features and show their suitability for pedestrian detection. These contrasts are computed from a novel combination of both local color and gradient statistics aggregated quickly for arbitrary sized square cells. We exploit our contrast features in a rich multi-scale and -direction fashion between each central cell and its neighbors and boost the significant ones for pedestrian detection. Experimental results on the INRIA and Caltech pedestrian datasets show that our method achieves state-of-the-art performance.

This paper presents a new method for salient object detection based on a sophisticated appearance comparison of multisize superpixels. Those superpixels are modeled by multivariate normal distributions in CIE-Lab color space, which are estimated from the pixels they comprise. This fitting facilitates an efficient application of the Wasserstein distance on the Euclidean norm ( [Formula: see text]) to measure perceptual similarity between elements. Saliency is computed in two ways. On the one hand, we compute global saliency by probabilistically grouping visually similar superpixels into clusters and rate their compactness. On the other hand, we use the same distance measure to determine local center-surround contrasts between superpixels. Then, an innovative locally constrained random walk technique that considers local similarity between elements balances the saliency ratings inside probable objects and background. The results of our experiments show the robustness and efficiency of our approach against 11 recently published state-of-the-art saliency detection methods on five widely used benchmark data sets.

We present a new segment-based method for saliency detection based on multi-size superpixels that combines local and global saliency cues. We extract superpixels at several scales and represent each superpixel with a normal distribution in CIE-Lab space estimated from its associated pixels. Global saliency is computed by grouping similar superpixels to estimate the spatial distribution of colors, while local saliency detection is achieved by determining the center-surround contrast of neighboring superpixels. Both methods rely on the Wasserstein distance on L2norm (W2) to measure perceptual (dis-)similarity between superpixels. Additionally, we propose a Saliency Flow technique to refine the local saliency map. Our approach uses very few empirical parameters and outperforms 6 recent state-of-the-art saliency detection methods in terms of several evaluations on a widely used benchmark.

In this paper, we propose a segment-based object detection approach using laser range data. Our detection approach is built up of three stages: First, a hierarchical segmentation approach generates a hierarchy of coarse-to-fine segments to reduce the impact of over- and under-segmentation in later stages. Next, we employ a learned mixture model to classify all segments. The model combines multiple softmax regression classifiers learned on specific bag-of-word representations using different parameterizations of a descriptor. In the final stage, we filter irrelevant and duplicate detections using a greedy method in consideration of the segment hierarchy. We experimentally evaluate our approach on recently published real-world datasets to detect pedestrians, cars, and cyclists.

The selection of suitable features and their parameters for the classification of three-dimensional laser range data is a crucial issue for high-quality results. In this paper we compare the performance of different histogram descriptors and their parameters on three urban datasets recorded with various sensors-sweeping SICK lasers, tilting SICK lasers and a Velodyne 3D laser range scanner. These descriptors are 1D, 2D, and 3D histograms capturing the distribution of normals or points around a query point. We also propose a novel histogram descriptor, which relies on the spectral values in different scales. We argue that choosing a larger support radius and a z-axis based global reference frame/axis can boost the performance of all kinds of investigated classification models significantly. The 3D histograms relying on the point distribution, normal orientations, or spectral values, turned out to be the best choice for the classification in urban environments.

Recently, several image gradient and edge based features have been introduced. In unison, they all discovered that object shape is a strong cue for recognition and tracking. Generally their basic feature extraction relies on pixel-wise gradient or edge computation using discrete filter masks, while scale invariance is later achieved by higher level operations like accumulating histograms or abstracting edgels to line segments. In this paper we show a novel and fast way to compute region based gradient features which are scale invariant themselves. We developed specialized, quick learnable weak classifiers that are integrated into our adaptively boosted observation model for particle filter based tracking. With an ensemble of region based gradient features this observation model is able to reliably capture the shape of the tracked object. The observation model is adapted to new object and background appearances while tracking. Thus we developed advanced methods to decide when to update the model, or in other words, if the filter is on target or not. We evaluated our approach using the BoBoT1as well as the PROST2datasets.

This paper presents an approach to exploit the richer information of sensor data provided by 3d laser rangefinders for the purpose of person tracking. Introduced is a method to adapt the observation model of a particle filter, to identify partial and full occlusions of a person, to determine the amount of occlusion behind an obstacle, and the occluding obstacle itself. This is done by tracing rays from positions near the person to the sensor and determining whether the ray hits an obstacle. The laser range data is represented using a voxel grid, which facilitates efficient retrieval and data reduction. As our experiments show, our proposed tracking approach is able to reliably keep track of a person in real-time, even when only partially visible, when moving in uneven terrain, or when the person passes closely another person of different size.

In this paper we show that visual landmark generation and redetection is possible with a single feature per frame. The approach is based on the assumption that highly discriminative regions are easily redetectable in subsequent frames as well as in frames visited from different viewpoints. We investigate which feature detectors fit for this purpose and under which conditions the discriminability applies. The approach is tested in a topological localization scenario in which the best feature is tracked over several frames to build landmarks. We show that we can represent a large environment with a few salient landmarks and that a large percentage of these landmarks is robustly redetectable from different viewpoints.

In recent years multiple simultaneous localization and mapping (SLAM) algorithms have been proposed, which address the challenges of 3D environments in combination with six degress of freedom in the robot position. Commonly, solutions based on scan-matching algorithms are applied. In contrast to these approaches, we propose to use a particle filter transferring the concept of the 2D Rao-Blackwellized particle filter SLAM to 3D. As filter input, 3D laser range data and odometry readings are obtained while the robot is in motion. The ground plane is estimated based on previously built map parts, thereby approaching the problem that not all degrees of freedom are covered by the odometry. To gain control of the high memory requirements for the particles' 3D map representations, we introduce a memory efficient search structure and adapt a technique to efficiently organize and share maps between particles. We evaluate our approach based on experimental results obtained by

Segmenting range data into semantic categories has become a more and more active field of research in robotics. In this paper, we advocate to view this task as a problem of fast, large-scale retrieval. Intuitively, given a dataset of millions of labeled scan points and their neighborhoods, we simply search for similar points in the datasets and use the labels of the retrieved ones to predict the labels of a novel point using some local prediction model such as majority vote or logistic regression. However, actually carrying this out requires highly efficient ways of (1) storing millions of scan points in memory and (2) quickly finding similar scan points to a target scan point. In this paper, we propose to address both issues by employing Weiss et al.'s recent spectral hashing. It represents each item in a database by a compact binary code that is constructed so that similar items will have similar binary code words. In turn, similar neighbors have codes within a small Hamming distance of the code for the query. Then, we learn a logistic regression model locally over all points with the same binary code word. Our experiments on real world 3D scans show that the resulting approach, called spectrally hashed logistic regression, can be ultra fast at prediction time and outperforms state-of-the art approaches such as logistic regression and nearest neighbor.

In this paper, we present a visual object tracker for mobile systems that is able to specialize to individual objects during tracking. The core of our method is a novel observation model and the way it is automatically adapted to a changing object and background appearance over time. The model is integrated into the well known Condensation algorithm (SIR filter) for statistical inference, and it consists of a boosted ensemble of simple threshold classifiers built upon center-surround Haar-like features, which the filter continuously updates based on the images perceived. We present optimizations and reasonable approximations to limit the computational costs. Thus, the final algorithms are capable of processing video input at real-time. To experimentally investigate the gain of adapting the observation model we compare two different approaches with a non-adapting version of our observation model: maintaining a single observation model for all particles, and maintaining individual observation models for each particle. In addition, experiments were conducted to compare system performances between the proposed algorithms and two other state of the art Condensation based tracking approaches.

Mobile devices became powerful recently and wireless Internet access is becoming standard. One important class of networked, mobile applications are location based games, making extensive use of device sensors to adapt their application logic and user interface to the numerous, spontaneous and fast changing contexts. To simplify the developers' task of designing adaptable user interfaces, we propose the use of semantic user interface description. By going beyond formbased applications, we argue that the approach comes especially at hand when used in the context of modular reconfigurable mobile games: The interfaces fusion can simplify the generation of complex adaptable game UIs and form an integral aspect of a mobile game development kit.

ISKODOR integrates personal collections, peer search, and centralized search services. User modeling in ISKODOR fills three roles: discovery of sites with suitable information stores, context-based query interpretation, and automatic profile-based filtering of new information. Explanation and control are achieved through graphical depiction of sources, explicit feedback regard ingutility, and explicit control over peer association behavior and information sharing.

MOTIVATION: Computationally, in silico experiments in biology are workflows describing the collaboration of people, data and methods. The Grid and Web services are proposed to be the next generation infrastructure supporting the deployment of bioinformatics workflows. But the growing number of autonomous and heterogeneous services pose challenges to the used middleware w.r.t. composition, i.e. discovery and interoperability of services required within in silico experiments. In the IRIS project, we handle the problem of service interoperability by a semi-automatic procedure for identifying and placing customizable adapters into workflows built by service composition. RESULTS: We show the effectiveness and robustness of the software-aided composition procedure by a case study in the field of life science. In this study we combine different database services with different analysis services with the objective of discovering required adapters. Our experiments show that we can identify relevant adapters with high precision and recall.

Due to a rapidly increasing volume of information and autonomous services, domain experts spend considerable time and effort in correlating and integrating these resources into meaningful business processes. In this paper we present IRIS, a framework that effectively supports users in creating service compositions with the special focus on interoperability. We illustrate our work by means of a scientific business process from the area of life science informatics. For the sake of adaptability and reusability our approach relies on a component-based mediator model. The problem of mediator discovery and composition is addressed by an ontology-based registry approach.

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can determine the positions of the humans in its surrounding. We introduce sample-based joint probabilistic data association filters to track multiple moving objects with a mobile robot. Our technique uses the robot's sensors and a motion model of the objects being tracked. A Bayesian filtering technique is applied to adapt the tracking process to the number of objects in the sensor range of the robot. Our approach to tracking multiple moving objects has been implemented and tested on a real robot. We present experiments illustrating that our approach is able to robustly keep track of multiple persons even in situations in which people are temporarily occluded. The experiments furthermore show that the approach outperforms other techniques developed so far.

One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments and offer various services to humans. For many tasks it is highly desirable that a robot can determine the positions of the humans in its surrounding. In this paper we present a method for tracking multiple moving objects with a mobile robot. We introduce a sample-based variant of joint probabilistic data association filters to track features originating from individual objects and to solve the correspondence problem between the detected features and the filters. In contrast to standard methods, occlusions are handled explicitly during data association. The technique has been implemented and tested on a real robot. Experiments carried out in a typical office environment show that the method is able to track multiple persons even when the trajectories of two people are crossing each other.

The success of an enterprise depends on its ability to effectively adapt to changing external requirements. This is particularly true in the rapidly evolving world of e-commerce, which raises the challenge of building software systems that are flexible enough to support such a dynamic business environment. Previously (O. Stiemerling et al., 1999), we presented the EVOLVE runtime- and tailoring environment as a platform for extensively adaptable autonomous and decentralized systems. The platform provides a high degree of flexibility by maintaining the component structure of such systems. One can (during runtime and in a decentralized fashion), add, exchange or rewire an application's components to meet changing external requirements. While the EVOLVE platform provides the mechanisms to perform these changes, there remains the problem of providing an administration interface to these mechanisms. Like many autonomous and decentralized systems, EVOLVE applications can have great complexity and structural detail. The interface has to present these details in an appropriate and scalable form, permitting the administrator to quickly grasp the overall distribution structure, but also the functional details and fine grained components. The authors argue that 3D visualization and manipulation techniques provide a good basis for the design of administration interfaces for complex, decentralized systems. We support our argument by presenting a 3D interface for the EVOLVE platform and discuss the more general design concepts it embodies. These concepts are relevant and applicable to other autonomous and decentralized systems.

The retrieval performance of content-based image retrieval (CBIR) systems still leaves much to be desired, especially when the system is serving as an interface to an image collection covering many different topics. The problem of missing semantical information about the images leads to great numbers of false matches because of misleading similarities in the visual primitives that are retrieved. This paper introduces an approach called GIVBAC, that tries to reduce the number of false matches in query result sets. It relies heavily on user feedback on retrieval results with regard to user-definable thematic groups. Feedback is used globally, i.e. the feedback of one user has influences on the behaviour of the whole system. The weight of individual votes is a parameter of GIVBAC so that it can be adjusted to the level of trust the user base is given, i.e. higher values in a closed user group and lower values in an open group with anonymous users. The paper presents results of an evaluation comparing the performance of an unmodified CBIR system with a system that is modified so that it uses GIVBAC as an interface to the users.

The integration of extremely heterogeneous geo-scientific data is a challenging problem. To provide a non-redundant maintenance and cooperative utilisation of data by different applications developed within the interdisciplinary geo-scientific research centre at the University of Bonn we elaborated a technology based on GeoToolKit-a component-based software development tool. In the paper we present GeoToolKit extensions required to deal with distributed heterogeneous spatial data sources. They involve metadata-level structures and methods that encourage a convenient navigation in multiple distributed data sources and smart assembling of appropriate data into local task-specific configurations. The proposed extensions can be beneficial for the development of special-purpose applications and rather general data browsers capable to deal with a wide range of geo-scientific data. Also they are likely to be helpful in the creation of geo-scientific warehouses and in the development of exploratory applications which usually need the access to distributed data sources. Finally CORBA-based architectural extensions of GeoToolKit to provide data and operation transfer are presented and evaluated with real geo-scientific applications.

The satisfaction of confidentiality demands is secure logic-based databases requires a distortion of the database’s intended model. This work focuses on distortions incurred by changes in a database’s name-space, i.e., its signature, which are useful

Groupware systems are designed to support the cooperation of people within and between enterprises. The development of groupware using component-based programming techniques has received a lot of attention recently. The commonly expected benefits are reduced development time, lower costs and higher product quality. Aiming at an additional benefit, the work presented in this paper makes use of the concept of software components after development in order to provide tailorability of already-deployed groupware systems. We describe the architecture of the Evolve platform, which permits one to maintain and manipulate nested component structures of distributed groupware systems at run-time. Apart from run-time tailorability, the architecture meets other groupware-specific (or relevant) requirements, namely support for synchronous and asynchronous cooperative tailoring, tailoring at different levels of complexity, and the flexible control of the scope of tailoring activities (i.e. the set of users affected by these activities). Evolve itself is application-independent and works with any set of software components complying with the FlexiBeans component model, which is an adaptation of the JavaBeans model. We demonstrate the object-oriented design behind Evolve with a simple example and give an overview of related and future work.

Describes a semi-automatic method for acquiring SA3D maps, maps that contain hierarchically structured 3D models of static, task relevant objects in the environment. Map acquisition is implemented as a two step process. In the first step, the robot acquires an approximate model that represents regions that might contain objects and indicate possibly occluding objects. This approximate model is then used to compute appropriate locations from where camera images should be taken. Object models are reconstructed interactively through human operators who place wireframe model in the camera images captured by the robot. The method is implemented and validated on an autonomous mobile robot.

This paper describes an interactive tour-guide robot, which was successfully exhibited in a Smithsonian museum. During its two weeks of operation, the robot interacted with thousands of people, traversing more than 44 km at speeds of up to 163 cm/sec. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction. It uses learning pervasively at all levels of the software architecture.

Visualization is an important precondition for successful teleoperation of instructable mobile robots. Data connections with varying and limited bandwidth such as the Internet, however prohibit the continuous transmission of video signals. In this paper we propose a predictive simulation technique which is designed to permit the reliable visualization of the robot's actions over the Internet. It differs from previous approaches in that it includes an odometry and sensor simulation. This simulation of the robot allows the integration of a complete robot control system to reliably predict complex actions of the robot even if large transmission gaps of several seconds occur. We describe an application of the predictive simulation technique to navigation experiments with mobile robots. We present different experiments carried out with a real robot illustrating that the predictive simulation technique provides accurate visualizations of the robot's actions even if transmission gaps of more than ten seconds occur.

Tailorability is generally regarded as a key property of groupware systems due to the dynamics and diversity of cooperative work. This article investigates the use of software components as a generic architectural concept for designing tailorable groupware applications. First, the issues raised by this approach are discussed. The results of a first exploratory, experiment led us to concentrate on the support of distributed tailorable CSCW applications. We discuss the question of an appropriate component model and develop requirements for a platform for the flexible deployment of distributed component-based applications. As a consequence of these reflections, we have developed the FLEXIBEANS component model and the EVOLVE platform whose design principles are described. Furthermore, an application example using the approach to design a tailorable distributed coordination tool is given. Finally, we summarize and propose venues of further research.

Proposes a hybrid approach to the problem of collision avoidance for indoor mobile robots. The /spl mu/DWA (model-based dynamic window approach) integrates sensor data from various sensors with information extracted from a map of the environment, to generate collision-free motion. A novel integration rule ensures that with high likelihood, the robot avoids collisions with obstacles not detectable with its sensors, even if it is uncertain about its position. The approach was implemented and tested extensively as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Here our approach was essential for the success of the entire mission, because a large number of ill-shaped obstacles prohibited the use of purely sensor-based methods for collision avoidance.

Localization is one of the fundamental problems of mobile robots. In order to efficiently perform useful tasks such as office delivery, mobile robots must know their position in their environment. Existing approaches can be distinguished according to the type of localization problem they are designed to solve. Tracking techniques aim at monitoring the robot's position. They assume that the position is initially known and cannot recover from situations in which they lost track of the robot's position. Global localization techniques on the other hand, are able to estimate the robot's position under complete uncertainty. We present the dynamic Markov localization technique as a uniform approach to position estimation, which is able (1) to globally estimate the position of the robot, (2) to efficiently track its position whenever the robot's certainty is high, and (3) to detect and recover from localization failures. The approach has been implemented and intensively tested in real-world environments. We present several experiments illustrating the strength of our method.

Today's geo-information systems are historically grown products which are hardly extensible to meet the requirements imposed by 3D/4D-modeling. The next generation GISs should benefit from modern software engineering technologies. A component-based design encourages a fast "assembly" of applications from high-level software building blocks. Following this approach a complex general-purpose geo-information system can be substituted by a family of specialized subsystems which due to the common design basis are open for mutual data exchange. We introduce GeoToolKit component software intended for the development of 3D/4D geo-scientific applications. We also present our experience in building different types of geo-scientific applications on top of GeoToolKit. We show that common data types inherited by diverse applications from the GeoToolKit spatial class hierarchy create an excellent basis for the database-level integration of heterogeneous geo-scientific data.

Design and evaluation of groupware systems raise questions which do not have to be addressed in the context of single user systems. The designer has to take into account not only the interaction of a single user with the computer, but also the computer-supported interaction of several users with each other. In this article we describe the use of cooperation scenarios in the design and evaluation of an innovative access control system for a concrete groupware application developed in the PoLITeam project. We have used informal textual scenarios to capture a rich description of the particularities of access to cooperatively used documents in three different organizations. Based on these scenarios, we have developed an access control system, which not only allows specification of access rights in advance but also allows involvement of third persons at the actual time of access, using negotiation and notification mechanisms. We describe our evaluation strategy which again employs the cooperation scenarios developed in the empirical phase. After relating our approach to other work, we summarize and discuss our experiences and the advantages (and disadvantages) of using scenarios for the design and evaluation of computer systems cooperative work (CSCW) systems. Finally, we give a brief outlook on future work.

A preliminary example of program development demonstrates that the control-independent implementation of data types can lead to very inefficient code. The concept of a data space, which emphasizes the interaction between data and control structures, is introduced as a formal model of virtual machines. The model consists of three components: a state space X, an information structure F, and a control component p. Formally, X is a set of objects, F a set of functions with common domain X mapping the states to the value sets of certain data types, and p a partial function X → X. Two important points of investigation are the characterization of state space structure and the mechanism of implementation. Good state description is characterized by "completeness" and "orthogonality." Either property plays a prominent role for the concept of implementation which, in turn, is formally defined as a mapping from a "virtual" space to a "real" space, via some "macro" space. These notions are illugtrated by means of several examples from the area of programming languages.