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.     

LXRα Regulates oxLDL-Induced Trained Immunity in Macrophages

Reprogramming of metabolic pathways in monocytes and macrophages can induce a proatherosclerotic inflammatory memory called trained innate immunity. Here, we have analyzed the role of the Liver X receptor (LXR), a crucial regulator of metabolism and inflammation, in oxidized low-density lipoprotein (oxLDL)-induced trained innate immunity. Human monocytes were incubated with LXR agonists, antagonists, and oxLDL for 24 h. After five days of resting time, cells were restimulated with the TLR-2 agonist Pam3cys. OxLDL priming induced the expression of LXRα but not LXRβ. Pharmacologic LXR activation was enhanced, while LXR inhibition prevented the oxLDL-induced inflammatory response. Furthermore, LXR inhibition blocked the metabolic changes necessary for epigenetic reprogramming associated with trained immunity. In fact, enrichment of activating histone marks at the IL-6 and TNFα promotor was reduced following LXR inhibition. Based on the differential expression of the LXR isoforms, we inhibited LXRα and LXRβ genes using siRNA in THP1 cells. As expected, siRNA-mediated knock-down of LXRα blocked the oxLDL-induced inflammatory response, while knock-down of LXRβ had no effect. We demonstrate a specific and novel role of the LXRα isoform in the regulation of oxLDL-induced trained immunity. Our data reveal important aspects of LXR signaling in innate immunity with relevance to atherosclerosis formation.     

Using enterprise architecture analysis and interview data to estimate service response time

Insights into service response time is important for service-oriented architectures and service management. However, directly measuring the service response time is not always feasible or can be very costly. This paper extends an analytical modeling method which uses enterprise architecture modeling to support the analysis. The extensions consist of (i) a formalization using the Hybrid Probabilistic Relational Model formalism, (ii) an implementation in an analysis tool for enterprise architecture and (iii) a data collection approach using expert assessments collected via interviews and questionnaires. The accuracy and cost effectiveness of the method was tested empirically by comparing it with direct performance measurements of five services of a geographical information system at a Swedish utility company. The tests indicate that the proposed method can be a viable option for rapid service response time estimates when a moderate accuracy within 15% is sufficient.     

Connecting geodesics on smooth surfaces

In this paper, we present a novel method for computing multiple geodesic connections between two arbitrary points on a smooth surface. Our method is based on a homotopy approach that is able to capture the ambiguity of geodesic connections in the presence of positive Gaussian curvature that generates focal curves. Contrary to previous approaches, we exploit focal curves to gain theoretical insights on the number of connecting geodesics and a practical algorithm for collecting these. We consider our method as a contribution to the contemporary debate regarding the calculation of distances in general situations, applying continuous concepts of classical differential geometry which are not immediately transferable in purely discrete settings.