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Algorithmics, computation, optimization, complexity, combinatorics and knowledge representation are closely related sub-areas of Theoretical Computer Science. The following summary presents short descriptions of the twelve chapters in this topical ...

The editors of this LNCS volume asked me to speculate on open problems: out of the prominent conjectures in computational complexity, which of them might be true, and why?

I hope the reader is entertained.

Research in combinatorial optimization successfully combines diverse ideas drawn from computer science, mathematics, and operations research. We give a tour of this work, focusing on the early development of the subject and the central role ...

Computational social choice is a research area at the intersection of computer science, mathematics, and economics that is concerned with aggregation of preferences of multiple agents. Typical applications include voting, resource allocation, and ...

Many combinatorial optimization problems such as set cover, clustering, and graph matching have been formulated in geometric settings. We review the progress made in recent years on a number of such geometric optimization problems, with an ...

This is an invitation to the research area of graph drawing. It encompasses basic research such as graph theory, complexity theory, data structures, and graph algorithms as well as applied research such as software libraries, implementations, and ...

Given a graph $$G=(V,E)$$, we may be interested in computing various parameters that are associated with the graph. Such parameters include the average degree, the number of connected components, and the size of a minimum vertex cover. These ...

We propose two classes of dynamic versions of the classical Erdős-Rényi graph: one in which the transition rates are governed by an external regime process, and one in which the transition rates are periodically resampled. For both models we ...

The last decade has seen a large amount of algorithmic work analyzing wireless networks. In this paper we focus on some of the main lessons learned when studying the physical (SINR) wireless model, with a focus on link scheduling, without or with ...

We discuss what green computing algorithmics is, and what a theory of energy as a computational resource isn’t. We then present some open problems in this area, with enough background from the literature to put the open problems in context. This ...

The brain carries out tasks that are very demanding from a computational perspective, apparently powered by a mere 20 W. This fact has intrigued computer scientists for many decades, and is currently drawing many of them to the quest of acquiring ...

Computer chess was originally purposed for insight into the human mind. It became a quest to get the most power out of computer hardware and software. The goal was specialized but the advances spanned multiple areas, from heuristic search to ...

This article gives an overview on knowledge harvesting: automatically constructing large high-quality knowledge bases from Internet sources. The first part reviews key principles and best-practice methods. The second part points out open ...

Language design for simplifying programming, analysis/verification methods and tools for guaranteeing, for example, security and real-time constraints, and validation environments for increasing automation during quality assurance can all be ...

Landin’s seminal paper “The next 700 programming languages” considered programming languages prior to 1966 and speculated on the next 700. Half-a-century on, we cast programming languages in a Darwinian ‘tree of life’ and explore languages, their ...

Our objective is to model and simulate Cyber-Physical Systems (CPS) such as robots, vehicles, and power plants. The structure of CPS models may change during simulation due to the desired operation, due to failure situations or due to changes in ...

In this paper, we present the paradigm of Language-Driven Engineering (LDE), which is characterized by its unique support for division of labour on the basis of Domain-Specific Languages (DSLs) targeting different stakeholders. LDE allows the ...

Deductive software verification aims at formally verifying that all possible behaviors of a given program satisfy formally defined, possibly complex properties, where the verification process is based on logical inference. We follow the trajectory ...

We reflect on current problems and practices in system security, distinguishing between reactive security – which deals with vulnerabilities as they are being exploited – and proactive security – which means to make vulnerabilities un-exploitable ...

Automated formal verification using model checking is a mature field with many tools available. We summarize the recent trends in the design and architecture of model checking tools. An important design goal of modern model checkers is to support ...

This paper presents a retrospective view on probabilistic model checking. We focus on Markov decision processes (MDPs, for short). We survey the basic ingredients of MDP model checking and discuss its enormous developments since the seminal ...

We illustrate the ingredients of the state-of-the-art of model-based approach for the formal design and verification of cyber-physical systems. To capture the interaction between a discrete controller and its continuously evolving environment, we ...

We highlight the contributions made in the field of Statistical Model Checking (SMC) since its inception in 2002. As the formal setting, we use a very general model of Stochastic Systems (an SS is simply a family of time-indexed random variables), ...

The automation of software testing promises to delegate to machines what is otherwise the most labor-intensive and expensive part of software development. The past decade has seen a resurgence in research interest for this problem, bringing about ...

The paper provides an overview of the work performed by the authors since the year 2000 in the field of runtime verification. Runtime verification is the discipline of analyzing program/system executions using rigorous methods. The discipline ...

Model learning is a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, banking cards, network protocols, and control software. The ...