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.     

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.

     

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.     

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.     

Automatic data collection for enterprise architecture models

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.     

Consumer credit risk: Individual probability estimates using machine learning

Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods. Since a probability is an expected value, all nonparametric regression approaches which are consistent for the mean are consistent for the probability estimation problem. Among others, random forests (RF), k-nearest neighbors (kNN), and bagged k-nearest neighbors (bNN) belong to this class of consistent nonparametric regression approaches. We apply the machine learning methods and an optimized logistic regression to a large dataset of complete payment histories of short-termed installment credits. We demonstrate probability estimation in Random Jungle, an RF package written in C++ with a generalized framework for fast tree growing, probability estimation, and classification. We also describe an algorithm for tuning the terminal node size for probability estimation. We demonstrate that regression RF outperforms the optimized logistic regression model, kNN, and bNN on the test data of the short-term installment credits.