Correct-by-Construction Development of Dynamic Topology Control Algorithms

Wireless devices are influencing our everyday lives today and will even more so in the future. A wireless sensor network (WSN) consists of dozens to hundreds of small, cheap, battery-powered, resource-constrained sensor devices (motes) that cooperate to serve a common purpose. These networks are applied in safety- and security-critical areas (e.g., e-health, intrusion detection). The topology of such a system is an attributed graph consisting of nodes representing the devices and edges representing the communication links between devices. Topology control (TC) improves the energy consumption behavior of a WSN by blocking costly links. This allows a mote to reduce its transmission power. A TC algorithm must fulfill important consistency properties (e.g., that the resulting topology is connected). The traditional development process for TC algorithms only considers consistency properties during the initial specification phase. The actual implementation is carried out manually, which is error prone and time consuming. Thus, it is difficult to verify that the implementation fulfills the required consistency properties. The problem becomes even more severe if the development process is iterative. Additionally, many TC algorithms are batch algorithms, which process the entire topology, irrespective of the extent of the topology modifications since the last execution. Therefore, dynamic TC is desirable, which reacts to change events of the topology. In this thesis, we propose a model-driven correct-by-construction methodology for developing dynamic TC algorithms. We model local consistency properties using graph constraints and global consistency properties using second-order logic. Graph transformation rules capture the different types of topology modifications. To specify the control flow of a TC algorithm, we employ the programmed graph transformation language story-driven modeling. We presume that local consistency properties jointly imply the global consistency properties. We ensure the fulfillment of the local consistency properties by synthesizing weakest preconditions for each rule. The synthesized preconditions prohibit the application of a rule if and only if the application would lead to a violation of a consistency property. Still, this restriction is infeasible for topology modifications that need to be executed in any case. Therefore, as a major contribution of this thesis, we propose the anticipation loop synthesis algorithm, which transforms the synthesized preconditions into routines that anticipate all violations of these preconditions. This algorithm also enables the correct-by-construction runtime reconfiguration of adaptive WSNs. We provide tooling for both common evaluation steps. Cobolt allows to evaluate the specified TC algorithms rapidly using the network simulator Simonstrator. cMoflon generates embedded C code for hardware testbeds that build on the sensor operating system Contiki

A sheaf-theoretic approach to pattern matching and related problems

AbstractWe present a general theory of pattern matching by adopting an extensional, geometric view of patterns. Representing the geometry of the pattern via a Grothendieck topology, the extension of the matching relation for a constant target and varying pattern forms a sheaf. We derive a generalized version of the Knuth-Morris-Pratt string-matching algorithm by gradually converting this extensional description into an intensional description, i.e., an algorithm. The generality of this approach is illustrated by briefly considering other applications: Earley's algorithm for parsing, Waltz filtering for scene analysis, matching modulo commutativity, and the n-queens problem

Online Spectral Clustering on Network Streams

Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems may have additional requirements, such as short processing time, scalability in distributed computing environments, and temporal variation tracking. However, to design a spectral clustering method to satisfy these requirements certainly presents non-trivial efforts. There are three major challenges for the new algorithm design. The first challenge is online clustering computation. Most of the existing spectral methods on evolving networks are off-line methods, using standard eigensystem solvers such as the Lanczos method. It needs to recompute solutions from scratch at each time point. The second challenge is the parallelization of algorithms. To parallelize such algorithms is non-trivial since standard eigen solvers are iterative algorithms and the number of iterations can not be predetermined. The third challenge is the very limited existing work. In addition, there exists multiple limitations in the existing method, such as computational inefficiency on large similarity changes, the lack of sound theoretical basis, and the lack of effective way to handle accumulated approximate errors and large data variations over time. In this thesis, we proposed a new online spectral graph clustering approach with a family of three novel spectrum approximation algorithms. Our algorithms incrementally update the eigenpairs in an online manner to improve the computational performance. Our approaches outperformed the existing method in computational efficiency and scalability while retaining competitive or even better clustering accuracy. We derived our spectrum approximation techniques GEPT and EEPT through formal theoretical analysis. The well established matrix perturbation theory forms a solid theoretic foundation for our online clustering method. We facilitated our clustering method with a new metric to track accumulated approximation errors and measure the short-term temporal variation. The metric not only provides a balance between computational efficiency and clustering accuracy, but also offers a useful tool to adapt the online algorithm to the condition of unexpected drastic noise. In addition, we discussed our preliminary work on approximate graph mining with evolutionary process, non-stationary Bayesian Network structure learning from non-stationary time series data, and Bayesian Network structure learning with text priors imposed by non-parametric hierarchical topic modeling

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 24th International Conference on Fundamental Approaches to Software Engineering, FASE 2021, which took place during March 27–April 1, 2021, and was held as part of the Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg but changed to an online format due to the COVID-19 pandemic. The 16 full papers presented in this volume were carefully reviewed and selected from 52 submissions. The book also contains 4 Test-Comp contributions

Inferring phylogenetic trees under the general Markov model via a minimum spanning tree backbone

Phylogenetic trees are models of the evolutionary relationships among species, with species typically placed at the leaves of trees. We address the following problems regarding the calculation of phylogenetic trees. (1) Leaf-labeled phylogenetic trees may not be appropriate models of evolutionary relationships among rapidly evolving pathogens which may contain ancestor-descendant pairs. (2) The models of gene evolution that are widely used unrealistically assume that the base composition of DNA sequences does not evolve. Regarding problem (1) we present a method for inferring generally labeled phylogenetic trees that allow sampled species to be placed at non-leaf nodes of the tree. Regarding problem (2), we present a structural expectation maximization method (SEM-GM) for inferring leaf-labeled phylogenetic trees under the general Markov model (GM) which is the most complex model of DNA substitution that allows the evolution of base composition. In order to improve the scalability of SEM-GM we present a minimum spanning tree (MST) framework called MST-backbone. MST-backbone scales linearly with the number of leaves. However, the unrealistic location of the root as inferred on empirical data suggests that the GM model may be overtrained. MST-backbone was inspired by the topological relationship between MSTs and phylogenetic trees that was introduced by Choi et al. (2011). We discovered that the topological relationship does not necessarily hold if there is no unique MST. We propose so-called vertex-order based MSTs (VMSTs) that guarantee a topological relationship with phylogenetic trees.Phylogenetische Bäume modellieren evolutionäre Beziehungen zwischen Spezies, wobei die Spezies typischerweise an den Blättern der Bäume sitzen. Wir befassen uns mit den folgenden Problemen bei der Berechnung von phylogenetischen Bäumen. (1) Blattmarkierte phylogenetische Bäume sind möglicherweise keine geeigneten Modelle der evolutionären Beziehungen zwischen sich schnell entwickelnden Krankheitserregern, die Vorfahren-Nachfahren-Paare enthalten können. (2) Die weit verbreiteten Modelle der Genevolution gehen unrealistischerweise davon aus, dass sich die Basenzusammensetzung von DNA-Sequenzen nicht ändert. Bezüglich Problem (1) stellen wir eine Methode zur Ableitung von allgemein markierten phylogenetischen Bäumen vor, die es erlaubt, Spezies, für die Proben vorliegen, an inneren des Baumes zu platzieren. Bezüglich Problem (2) stellen wir eine strukturelle Expectation-Maximization-Methode (SEM-GM) zur Ableitung von blattmarkierten phylogenetischen Bäumen unter dem allgemeinen Markov-Modell (GM) vor, das das komplexeste Modell von DNA-Substitution ist und das die Evolution von Basenzusammensetzung erlaubt. Um die Skalierbarkeit von SEM-GM zu verbessern, stellen wir ein Minimale Spannbaum (MST)-Methode vor, die als MST-Backbone bezeichnet wird. MST-Backbone skaliert linear mit der Anzahl der Blätter. Die Tatsache, dass die Lage der Wurzel aus empirischen Daten nicht immer realistisch abgeleitet warden kann, legt jedoch nahe, dass das GM-Modell möglicherweise übertrainiert ist. MST-backbone wurde von einer topologischen Beziehung zwischen minimalen Spannbäumen und phylogenetischen Bäumen inspiriert, die von Choi et al. 2011 eingeführt wurde. Wir entdeckten, dass die topologische Beziehung nicht unbedingt Bestand hat, wenn es keinen eindeutigen minimalen Spannbaum gibt. Wir schlagen so genannte vertex-order-based MSTs (VMSTs) vor, die eine topologische Beziehung zu phylogenetischen Bäumen garantieren

IMPLEMENTATION AND UNIFORM MANAGEMENT OF MODELLING ENTITIES IN A MASSIVELY FEATURE-OBJECT ORIENTED ADVANCED CAD ENVIRONMENT

Today we are spectators of the transition process in computer aided design from traditional geometry based on design systems to advanced computer-based engineering systems. The key is the feature technology that allows both integrating and managing modelling entities in a coherent way. Feature technology is developing rapidly. New research topics and contexts are emerging from time to time. This paper introduces concept, design and technological feature-objects to support operational, structural and morphological modelling of mechanical products. First, the feature-centred approaches to conceptual design are summarized and evaluated. Then an implementation of concept feature-objects and the methodology for using them is presented. The strength of concept feature-objects is in their morphology inclusive nature. They appear as parametrized three-dimensional skeletons providing geometrical representations for the modelled engineering conceptions. A concept feature-object models the physical ports, contact surfaces related to ports, bones between ports, DOF of ports, relevant physical parameters, scientific and empirical descriptions of intentional transformations and environmental effects. Concept feature-objects are related to design feature-objects that, in turn, are constructed of a relevant set of technological feature-entities. Concept feature-objects refer to the configurable and parametrized design feature-objects through an indexing mechanism. The conceptions have been tested during the programming and further development of the authors' PRODES system