Scale‐aware Structure‐Preserving Texture Filtering

This paper presents a novel method to enhance the performance of structure‐preserving image and texture filtering. With conventional edge‐aware filters, it is often challenging to handle images of high complexity where features of multiple scales coexist. In particular, it is not always easy to find the right balance between removing unimportant details and protecting important features when they come in multiple sizes, shapes, and contrasts. Unlike previous approaches, we address this issue from the perspective of adaptive kernel scales. Relying on patch‐based statistics, our method identifies texture from structure and also finds an optimal per‐pixel smoothing scale. We show that the proposed mechanism helps achieve enhanced image/texture filtering performance in terms of protecting the prominent geometric structures in the image, such as edges and corners, and keeping them sharp even after significant smoothing of the original signal.     

Social media,design and creative citizenship: an introduction

ABSTRACT

This article serves as an introduction to a special issue of the journal Digital Creativity, which addresses the challenges, potentials and meaning of different creative forms of citizenship enacted through social media. The articles in this special issue paint a complex picture of the expressions and meaning and citizenship. This introduction aims to provide a wider context for the contributions to this special issue by reviewing some of the claims made for the creative civic potential of social media and identifying a number of issues and questions that might inform analyses of the subject.     

Reflections on the evolution of implicit Navier–Stokes algorithms

This article gives a historical perspective on the evolution of implicit algorithms for the Navier–Stokes equations that utilize time or Newton linearizations and various forms of approximate factorization including alternating-direction, lower/upper, and symmetric relaxation schemes. A theme of the paper is how progress in implicit Navier–Stokes algorithms has been influenced and enabled by the introduction of characteristic-based upwind approximations, unstructured-grid discretizations, parallelization, and by advances in computer performance and architecture. Historical examples of runtime, problem size, and estimated cost are given for actual and hypothetical Navier–Stokes flow cases from the past 40 years.     

3D model alignment based on minimum projection area

3D model alignment is an important step for applications such as 3D model retrieval and 3D model recognition. In this paper, we propose a novel Minimum Projection Area-based (MPA) alignment method for pose normalization. Our method finds three principal axes to align a model: the first principal axis gives the minimum projection area when we perform an orthographic projection of the model in the direction parallel to this axis, the second axis is perpendicular to the first axis and gives the minimum projection area, and the third axis is the cross product of the first two axes. We devise an optimization method based on Particle Swarm Optimization to efficiently find the axis with minimum projection area. For application in retrieval, we further perform axis ordering and orientation in order to align similar models in similar poses. We have tested MPA on several standard databases which include rigid/non-rigid and open/watertight models. Experimental results demonstrate that MPA has a good performance in finding alignment axes which are parallel to the ideal canonical coordinate frame of models and aligning similar models in similar poses under different conditions such as model variations, noise, and initial poses. In addition, it achieves a better 3D model retrieval performance than several commonly used approaches such as CPCA, NPCA, and PCA.     

Versatile Tuning of Humanoid Agent Activity

In this paper, we present an integration framework for heterogeneous motion generators. The objective is to outline issues that are currently easily solved in professional post-processing systems used in film and game production but which cannot be transposed as is to real-time systems with autonomous agents. We summarise our approach for articulated agent-modelling and their animation by combining heterogeneous motion generators, such as real-time motion capturing, key-framing, inverse kinematics, procedural walking. We propose an agent/action-oriented framework. Activity properties such as action simultaneity and motion blending, spatial coherence, motion-flow update schemes, agent attachments, and location corrections, are the main topics handled by our generic animation framework. Numerous examples throughout the paper illustrate our approach and outline encountered problems and solutions or open research directions.     

Prediction of noisy chaotic time series using an optimal radialbasis function neural network

This paper considers the problem of optimum prediction of noisy chaotic time series using a basis function neural network, in particular, the radial basis function (RBF) network. In the noiseless environment, predicting a chaotic time series is equivalent to approximating a nonlinear function. The optimal generalization is achieved when the number of hidden units of a RBF predictor approaches infinity. When noise exists, it is shown that an optimal RBF predictor should use a finite number of hidden units. To determine the structure of an optimal RBF predictor, we propose a new technique called the cross-validated subspace method to estimate the optimum number of hidden units. While the subspace technique is used to identify a suitable number of hidden units by detecting the dimension of the subspace spanned by the signal eigenvectors, the cross validation method is applied to prevent the problem of overfitting. The effectiveness of this new method is evaluated using simulated noisy chaotic time series as well as real-life oceanic radar signals. Results show that the proposed method can find the correct number of hidden units of an RBF network for an optimal prediction.     

Improving virus protection with an efficient secure architecture with memory encryption, integrity and information leakage protection

Malicious software and other attacks are a major concern in the computing ecosystem and there is a need to go beyond the answers based on untrusted software. Trusted and secure computing can add a new hardware dimension to software protection. Several secure computing hardware architectures using memory encryption and memory integrity checkers have been proposed during the past few years to provide applications with a tamper resistant environment. Some solutions, such as HIDE, have also been proposed to solve the problem of information leakage on the address bus. We propose the CRYPTOPAGE architecture which implements memory encryption, memory integrity protection checking and information leakage protection together with a low performance penalty (3% slowdown on average) by combining the Counter Mode of operation, local authentication values and MERKLE trees. It has also several other security features such as attestation, secure storage for applications and program identification. We present some applications of the CRYPTOPAGE architecture in the computer virology field as a proof of concept of improving security in presence of viruses compared to software only solutions.     

ProteinParser--a community based tool for the generation of a detailed protein consensus and FASTA output

Comparison of bioinformatic data is a common application in the life sciences and beyond. In this communication, a novel Java based software tool, ProteinParser, is outlined. This software tool calculates a detailed consensus, or most common, amino acid at a given position in an aligned protein set, whilst also generating a full consensus protein FASTA output. A second application of this software tool, computing a consensus amino acid given a tolerance threshold, is also demonstrated. The phytase and the common bacterial beta-lactamase proteins are analysed as 'proof of concept' examples. Consensus proteins, as generated by ProteinParser, are regularly utilised in the selection of residues for protein stabilisation mutagenesis; however, this widely applicable software tool will find many alternative applications in areas such as protein homology modelling.     

NodeTrix: a hybrid visualization of social networks

The need to visualize large social networks is growing as hardware capabilities make analyzing large networks feasible and many new data sets become available. Unfortunately, the visualizations in existing systems do not satisfactorily resolve the basic dilemma of being readable both for the global structure of the network and also for detailed analysis of local communities. To address this problem, we present NodeTrix, a hybrid representation for networks that combines the advantages of two traditional representations: node-link diagrams are used to show the global structure of a network, while arbitrary portions of the network can be shown as adjacency matrices to better support the analysis of communities. A key contribution is a set of interaction techniques. These allow analysts to create a NodeTrix visualization by dragging selections to and from node-link and matrix forms, and to flexibly manipulate the NodeTrix representation to explore the dataset and create meaningful summary visualizations of their findings. Finally, we present a case study applying NodeTrix to the analysis of the InfoVis 2004 coauthorship dataset to illustrate the capabilities of NodeTrix as both an exploration tool and an effective means of communicating results.     

Internationale Zusammenarbeit zum Nachweis bestrahlter Lebensmittel

Zusammenfassung Es wird eine Übersicht über die neuesten internationalen Entwicklungen, insbesondere die Programme von ADMIT (FAO/IAEA) und dem BCR (Europäische Gemeinschaft) zum Nachweis einer erfolgten Bestrahlung von Lebensmitteln gegeben. Eine Kontrolle bestrahlter Lebensmittel direkt am Produkt ist wünschenswert, um Kennzeichnung und eventuell bestehende Verbote zu überwachen, aber auch um das Vertrauen des Verbrauchers in eine korrekte Anwendung des Verfahrens zu stärken und sein Recht auf freie Wahl zwischen bestrahlten und unbestrahlten Lebensmitteln zu schützen. Auf internationaler Ebene haben bereits mehrere größere Ringversuche stattgefunden, z. B. Messung der Elektronen-Spin-Resonanz in Knochen von Geflügel, Schwein, Rind, Froschschenkeln und Fisch; Messung der Thermoluminescenz von unlöslichen Mineralien, isoliert aus Gewürzen und Kräutern; die gaschroma- tographische Analyse von Kohlenwasserstoffen und Alkylcyclobutanonen aus der Lipidfraktion von Hähnchen; und die mikrobiologische DEFT/APC-Analyse von Gewürzen. Diese Methoden sollen in naher Zukunft als internationale Standardmethoden etabliert werden.

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International co-operation in the field of detection of irradiated food

A survey over recent international developments to detect the irradiation treatment of foods is given, in particular the programmes of ADMIT (FAO/ IAEA) and of BCR (European Community). The need to detect radiation treatment by analysing the food itself is desirable to check compliance with existing regulations, such as the enforcement of labelling and control of prohibition, to enhance consumer confidence in the correct application of radiation processing, and to protect consumers' freedom of choice between irradiated or unirra-diated food products. Some larger collaborative studies on an international scale have already taken place, e.g. ESR measurements of bones from chicken, pork, beef, frog legs and fish, thermoluminescence of insoluble minerals isolated from herbs and spices, gas Chromatographic analysis of hydrocarbons and alkylcyclobutanones derived from the lipid fraction of chicken and the microbiological DEFT/APC procedure for spices. These methods could soon be implemented in international standard protocols.