Fuzzy versus quantitative association rules: a fair data-driven comparison.

As opposed to quantitative association rule mining, fuzzy association rule mining is said to prevent the overestimation of boundary cases, as can be shown by small examples. Rule mining, however, becomes interesting in large databases, where the problem of boundary cases is less apparent and can be further suppressed by using sensible partitioning methods. A data-driven approach is used to investigate if there is a significant difference between quantitative and fuzzy association rules in large databases. The influence of the choice of a particular triangular norm in this respect is also examined.     

Iterative multi-party agreement negotiation for establishing collaborations

Inter-domain collaborations suffer not only from technological obstacles that hinder interoperability, but also from diverting business objectives of the involved domains. Today, hand-crafted contracts define the terms and conditions for service interactions. Electronic negotiation can serve as the enabler of dynamic interdomain collaborations by providing a large degree of freedom for the automation of agreement formation and electronic contracting. Negotiation by electronic means has been an area of intensive research for many years now. However, most effort was put on the determination of prices and neglected that real-world agreements also consist of complex dependencies of interdependent obligations. In this paper we present agreement negotiation as a tool to establish ad hoc services collaborations. Our novel protocol allows for the discovery of complex agreement options and for the formation of multi-party agreements. The protocol works through an iterative exchange of requirements and offers. A major benefit over existing bilateral negotiation protocols is that our protocol is capable of discovering potential collaborations between different parties. It will leave each negotiating party with a complete agreement document after a successful negotiation. This comprehensive agreement document defines the interdependent obligations between all parties and is well suited for E-Contracting.     

Functional and Molecular Properties of DYT-SGCE Myoclonus-Dystonia Patient-Derived Striatal Medium Spiny Neurons

Myoclonus-dystonia (DYT-SGCE, formerly DYT11) is characterized by alcohol-sensitive, myoclonic-like appearance of fast dystonic movements. It is caused by mutations in the SGCE gene encoding ε-sarcoglycan leading to a dysfunction of this transmembrane protein, alterations in the cerebello-thalamic pathway and impaired striatal plasticity. To elucidate underlying pathogenic mechanisms, we investigated induced pluripotent stem cell (iPSC)-derived striatal medium spiny neurons (MSNs) from two myoclonus-dystonia patients carrying a heterozygous mutation in the SGCE gene (c.298T>G and c.304C>T with protein changes W100G and R102X) in comparison to two matched healthy control lines. Calcium imaging showed significantly elevated basal intracellular Ca²⁺ content and lower frequency of spontaneous Ca²⁺ signals in SGCE MSNs. Blocking of voltage-gated Ca²⁺ channels by verapamil was less efficient in suppressing KCl-induced Ca²⁺ peaks of SGCE MSNs. Ca²⁺ amplitudes upon glycine and acetylcholine applications were increased in SGCE MSNs, but not after GABA or glutamate applications. Expression of voltage-gated Ca²⁺ channels and most ionotropic receptor subunits was not altered. SGCE MSNs showed significantly reduced GABAergic synaptic density. Whole-cell patch-clamp recordings displayed elevated amplitudes of miniature postsynaptic currents and action potentials in SGCE MSNs. Our data contribute to a better understanding of the pathophysiology and the development of novel therapeutic strategies for myoclonus-dystonia.     

Fixing User Authentication for the Internet of Things (IoT)

Over the last few years Internet of Things products have become known for their weak security. News articles regularly describe security vulnerabilities of cars, surveillance cameras, kettles, and other IoT devices. There are, however, recent standardization activities addressing some of these security challenges. This article describes how the work of the IETF and the FIDO Alliance can lead to improved security.     

Paper‐Derived Ferroelectric Ceramics: A Feasibility Study

Preceramic paper may serve as a preform to manufacture single sheet as well as multilayer porous ferroelectric ceramic products. In this article, the authors discuss the formation, microstructure, and properties of preceramic papers highly loaded with BaTiO₃ filler ranging from 70 to 80 vol% and their conversion into ceramic materials. In order to increase the density of the single sheets, post calendering is applied. These sheets are used for the fabrication of multilayer ceramics using warm lamination technique. After binder burnout and sintering up to 1300 °C for 2 h in air, porous paper‐derived multilayer BaTiO₃ is obtained. The effect of ceramic filler content and calendering on the residual porosity in sintered samples is studied. Furthermore, the influence of porosity on the microstructure, mechanical, dielectric, and piezoelectric properties of the sintered BaTiO₃ ceramics is investigated.

     

Fixed-Parameter Algorithms for Cluster Vertex Deletion

We initiate the first systematic study of the NP-hard Cluster Vertex Deletion (CVD) problem (unweighted and weighted) in terms of fixed-parameter algorithmics. In the unweighted case, one searches for a minimum number of vertex deletions to transform a graph into a collection of disjoint cliques. The parameter is the number of vertex deletions. We present efficient fixed-parameter algorithms for CVD applying the fairly new iterative compression technique. Moreover, we study the variant of CVD where the maximum number of cliques to be generated is prespecified. Here, we exploit connections to fixed-parameter algorithms for (weighted) Vertex Cover.     

From embedded sensors to sensorial materials—The road to function scale integration

Ubiquitous computing is about to become part of our everyday lives by integrating hundreds of “invisible” to us computing devices in our environment, so that they can unobtrusively and constantly assist us. This will imply more and smaller “invisible” sensors, homogeneously distributed and at the same time densely packed in host materials, responding to various stimuli and immediately delivering information. In order to reach this aim, the embedded sensors should be integrated within the host material, heading towards sensorial materials. The first step is to omit all parts that are not needed for the sensorial task and to find new solutions for a gentle integration. This is what we call function scale integration. The paper discusses sensor embedding in the human hand as an example of integration in nature, new technological applications and main challenges associated with this approach.     

Slow and fast diffusion effects in image processing

A mathematical model for a nonlinear image multiscale analysis is studied. Processing of an image is based on a solution of the strongly nonlinear parabolic partial differential equation, which can degenerate depending on values of the greylevel intensity function. The governing PDE is a generalization of the regularized (in the sense of Catté, Lions, Morel and Coll) Perona-Malik anisotropic diffusion equation. We present numerical techniques for solving the suggested initial-boundary value problem and also existence and convergence results. Numerical experiments are discussed. Received: 6 May 1998 / Accepted: 27 July 2000     

Generating feature spaces for linear algorithms with regularized sparse kernel slow feature analysis

Without non-linear basis functions many problems can not be solved by linear algorithms. This article proposes a method to automatically construct such basis functions with slow feature analysis (SFA). Non-linear optimization of this unsupervised learning method generates an orthogonal basis on the unknown latent space for a given time series. In contrast to methods like PCA, SFA is thus well suited for techniques that make direct use of the latent space. Real-world time series can be complex, and current SFA algorithms are either not powerful enough or tend to over-fit. We make use of the kernel trick in combination with sparsification to develop a kernelized SFA algorithm which provides a powerful function class for large data sets. Sparsity is achieved by a novel matching pursuit approach that can be applied to other tasks as well. For small data sets, however, the kernel SFA approach leads to over-fitting and numerical instabilities. To enforce a stable solution, we introduce regularization to the SFA objective. We hypothesize that our algorithm generates a feature space that resembles a Fourier basis in the unknown space of latent variables underlying a given real-world time series. We evaluate this hypothesis at the example of a vowel classification task in comparison to sparse kernel PCA. Our results show excellent classification accuracy and demonstrate the superiority of kernel SFA over kernel PCA in encoding latent variables.