Element distinctness on one-tape Turing machines: a complete solution

We give a complete characterization of the complexity of the element distinctness problem for n elements of bits each on deterministic and nondeterministic one-tape Turing machines. We present an algorithm running in time for deterministic machines and nondeterministic solutions that are of time complexity . For elements of logarithmic size , on nondeterministic machines, these results close the gap between the known lower bound and the previous upper bound . Additional lower bounds are given to show that the upper bounds are optimal for all other possible relations between m and n. The upper bounds employ hashing techniques, while the lower bounds make use of the communication complexity of set disjointness.Received: 23 April 2001, Published online: 2 September 2003Holger Petersen: Supported by Deutsche Akademie der Naturforscher Leopoldina, grant number BMBF-LPD 9901/8-1 of Bundesministerium für Bildung und Forschung.     

Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems

Recent trends in the management of water supply have increased the need for modelling techniques that can provide reliable, efficient, and accurate representation of the complex, non-linear dynamics of water quality within water distribution systems. Statistical models based on artificial neural networks (ANNs) have been found to be highly suited to this application, and offer distinct advantages over more conventional modelling techniques. However, many practitioners utilise somewhat heuristic or ad hoc methods for input variable selection (IVS) during ANN development.This paper describes the application of a newly proposed non-linear IVS algorithm to the development of ANN models to forecast water quality within two water distribution systems. The intention is to reduce the need for arbitrary judgement and extensive trial-and-error during model development. The algorithm utilises the concept of partial mutual information (PMI) to select inputs based on the analysis of relationship strength between inputs and outputs, and between redundant inputs. In comparison with an existing approach, the ANN models developed using the IVS algorithm are found to provide optimal prediction with significantly greater parsimony. Furthermore, the results obtained from the IVS procedure are useful for developing additional insight into the important relationships that exist between water distribution system variables.     

Multifield-graphs: an approach to visualizing correlations in multifield scalar data

We present an approach to visualizing correlations in 3D multifield scalar data. The core of our approach is the computation of correlation fields, which are scalar fields containing the local correlations of subsets of the multiple fields. While the visualization of the correlation fields can be done using standard 3D volume visualization techniques, their huge number makes selection and handling a challenge. We introduce the Multifield-Graph to give an overview of which multiple fields correlate and to show the strength of their correlation. This information guides the selection of informative correlation fields for visualization. We use our approach to visually analyze a number of real and synthetic multifield datasets.     

A survey of approaches for the visual model-driven development of next generation software-intensive systems

Software-intensive systems of the future are expected to be highly distributed and to exhibit adaptive and anticipatory behavior when operating in highly dynamic environments and interfacing with the physical world. Therefore, visual modeling techniques to address these software-intensive systems require a mix of models from a multitude of disciplines such as software engineering, control engineering, and business process engineering. As in this concert of techniques software provides the most flexible element, the integration of these different views can be expected to happen in the software. The software thus includes complex information processing capabilities as well as hard real-time coordination between distributed technical systems and computers.In this article, we identify a number of general requirements for the visual model-driven specification of next generation software-intensive systems. As business process engineering and software engineering are well integrated areas and in order to keep this survey focused, we restrict our attention here to approaches for the visual model-driven development of adaptable software-intensive systems where the integration of software engineering with control engineering concepts and safety issues are important. In this survey, we identify requirements and use them to classify and characterize a number of approaches that can be employed for the development of the considered class of software-intensive systems.     

Linear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Data

One main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster‐based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster‐aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real‐life benchmark high‐dimensional data sets.     

Creativity Bento Box: A Physical Resource Pack to Support Interaction in Virtual Space

The Creativity Bento Box is a physical resource pack, designed to support casual social interaction and break taking in an intensive, computer-mediated social activity. It was developed within the Creativity Greenhouse project, which piloted a mechanism to create research proposals and distribute funding at a distance. This involved facilitated phases of collaboration and competition over multiple days of computer-mediated work, where participants communicate and interact through a virtual world. During the iterative development process, the lack of time for socializing, the intense focus on virtual resources, and a lack of time spent away from the screen were reported as negative issues in feedback from participants. This article reports on the development of the Creativity Bento Box and how it helped to address these issues. By providing physical resources that contrasted with the properties of the virtual world, it supported people to socialize and take breaks from their primary activity, allowed them to include physical space and artifacts in their interactions, and provoked moves away from the otherwise intense focus on the computer. The article reflects on the roles of the Bento Box as a gift, in bridging between physical and virtual contexts, its higher suitability during the earlier phases of ideation and group development, and its perception by participants as something “framed.” Through this, we highlight the underexplored potential of using physical, offline resources as a means to solve difficulties in distanced social interactions.     

Integrating context-free and context-dependent attentional mechanisms for gestural object reference

We present a vision system for human-machine interaction based on a small wearable camera mounted on glasses. The camera views the area in front of the user, especially the hands. To evaluate hand movements for pointing gestures and to recognise object references, an approach to integrating bottom-up generated feature maps and top-down propagated recognition results is introduced. Modules for context-free focus of attention work in parallel with the hand gesture recognition. In contrast to other approaches, the fusion of the two branches is on the sub-symbolic level. This method facilitates both the integration of different modalities and the generation of auditory feedback.Published online: 5 October 2004Robert Rae: Now at PerFact Innovation, Lampingstr. 8, 33615 Bielefeld, Germany     

TORC: test plan optimization by requirements clustering

Acceptance testing is a time-consuming task for complex software systems that have to fulfill a large number of requirements. To reduce this effort, we have developed a widely automated method for deriving test plans from requirements that are expressed in natural language. It consists of three stages: annotation, clustering, and test plan specification. The general idea is to exploit redundancies and implicit relationships in requirements specifications. Multi-viewpoint techniques based on RM-ODP (Reference Model for Open Distributed Processing) are employed for specifying the requirements. We then use linguistic analysis techniques, requirements clustering algorithms, and pattern-based requirements collection to reduce the total effort of testing against the requirements specification. In particular, we use linguistic analysis for extracting and annotating the actor, process and object of a requirements statement. During clustering, a similarity function is computed as a measure for the overlap of requirements. In the test plan specification stage, our approach provides capabilities for semi-automatically deriving test plans and acceptance criteria from the clustered informal textual requirements. Two patterns are applied to compute a suitable order of test activities. The generated test plans consist of a sequence of test steps and asserts that are executed or checked in the given order. We also present the supporting prototype tool TORC, which is available open source. For the evaluation of the approach, we have conducted a case study in the field of acceptance testing of a national electronic identification system. In summary, we report on lessons learned how linguistic analysis and clustering techniques can help testers in understanding the relations between requirements and for improving test planning.