Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

Bayesian Logic Programs

Bayesian networks provide an elegant formalism for representing and reasoning about uncertainty using probability theory. Theyare a probabilistic extension of propositional logic and, hence, inherit some of the limitations of propositional logic, such as the difficulties to represent objects and relations. We introduce a generalization of Bayesian networks, called Bayesian logic programs, to overcome these limitations. In order to represent objects and relations it combines Bayesian networks with definite clause logic by establishing a one-to-one mapping between ground atoms and random variables. We show that Bayesian logic programs combine the advantages of both definite clause logic and Bayesian networks. This includes the separation of quantitative and qualitative aspects of the model. Furthermore, Bayesian logic programs generalize both Bayesian networks as well as logic programs. So, many ideas developedComment: 52 page

Graph Signal Processing: Overview, Challenges and Applications

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing. We then summarize recent developments in developing basic GSP tools, including methods for sampling, filtering or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning. We finish by providing a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE

Effects of inherited structures on inversion tectonics: Examples from the Asturian Basin (NW Iberian Peninsula) interpreted in a Computer Assisted Virtual Environment (CAVE)

Map shows mid-nineteenth century Texas counties, major cities, towns, roads, railroads, and areas of Native American habitation. Includes detailed notes on map. Insets: "Plan of Sabine Lake," "Plan of the Northern Part of Texas," and "Plan of Galveston Bay." Relief shown by hachures. Depths shown by soundings on inset. Scales [ca. 1:2,350,000], [ca. 1: 529,000], [ca. 1:3,800,000], and [ca. 1:887,000]

Transdisciplinarity seen through Information, Communication, Computation, (Inter-)Action and Cognition

Similar to oil that acted as a basic raw material and key driving force of industrial society, information acts as a raw material and principal mover of knowledge society in the knowledge production, propagation and application. New developments in information processing and information communication technologies allow increasingly complex and accurate descriptions, representations and models, which are often multi-parameter, multi-perspective, multi-level and multidimensional. This leads to the necessity of collaborative work between different domains with corresponding specialist competences, sciences and research traditions. We present several major transdisciplinary unification projects for information and knowledge, which proceed on the descriptive, logical and the level of generative mechanisms. Parallel process of boundary crossing and transdisciplinary activity is going on in the applied domains. Technological artifacts are becoming increasingly complex and their design is strongly user-centered, which brings in not only the function and various technological qualities but also other aspects including esthetic, user experience, ethics and sustainability with social and environmental dimensions. When integrating knowledge from a variety of fields, with contributions from different groups of stakeholders, numerous challenges are met in establishing common view and common course of action. In this context, information is our environment, and informational ecology determines both epistemology and spaces for action. We present some insights into the current state of the art of transdisciplinary theory and practice of information studies and informatics. We depict different facets of transdisciplinarity as we see it from our different research fields that include information studies, computability, human-computer interaction, multi-operating-systems environments and philosophy.Comment: Chapter in a forthcoming book: Information Studies and the Quest for Transdisciplinarity - Forthcoming book in World Scientific. Mark Burgin and Wolfgang Hofkirchner, Editor

Building an Infrastructure Level Context Model in Ambient Assisted Living

Ambient Assisted Living (AAL) services provide intelligent and context aware assistance for elderly people in their home environment. This domain puts special requirements on context modeling that are not in the scope of current context modeling approaches. Such an approach has to support all phases of an AAL service, from its specification and development until its operation within the user’s smart home environment. In these phases different types of context models can be identified. We have developed a comprehensive context modeling approach for the development of AAL services. Part of it is the separation of context modeling into infrastructure, service adaptation and end user modeling specific aspects. In this paper we focus on the infrastructure, which includes the context sensors available in the smart home environment. Therein we present our context modeling approach starting from a conceptual context model. We also introduce a context management system based on a metamodel that supports its seamless transition into an operative context model without further implementation

Building an Infrastructure Level Context Model in Ambient Assisted Living

Ambient Assisted Living (AAL) services provide intelligent and context aware assistance for elderly people in their home environment. This domain puts special requirements on context modeling that are not in the scope of current context modeling approaches. Such an approach has to support all phases of an AAL service, from its specification and development until its operation within the user’s smart home environment. In these phases different types of context models can be identified. We have developed a comprehensive context modeling approach for the development of AAL services. Part of it is the separation of context modeling into infrastructure, service adaptation and end user modeling specific aspects. In this paper we focus on the infrastructure, which includes the context sensors available in the smart home environment. Therein we present our context modeling approach starting from a conceptual context model. We also introduce a context management system based on a metamodel that supports its seamless transition into an operative context model without further implementation