Peter Linz Automata Solution Pdf Download
Anaias Bunz
The resilience of ICT infrastructures is fundamental to the functioning of supply chains. The reliability of these infrastructures increases the reliability of planning for production and supply chains, as well as for customers and demanders. The protection of such systems against threats from cyber space is central to the functioning of a "smart economy", which is based on the principle of "just in time", characterised by very short intermediate storage times and the optimization of supply routes. In the event of a cyber-attack, it is essential to be able to rely on established strategies and processes, effective early detection and adequate decision-making models in order to avoid or reduce disruptions to ICT systems as far as possible.
The SOPHIE project aims at increasing awareness of cyber security issues in the supply chain and incident response, especially for technical and non-technical core staff, as well as supporting and improving relevant processes by implementing suitable tools and reference processes for building resilience. The project has three main objectives:
SOPHIE will use the analysis, modelling and simulation processes in training programmes and cyber security awareness exercises for this purpose. This shall help to reflect the behaviour of users in the case of an emergency, to analyse operational and decision-making processes and to define and validate appropriate response measures as well as to coordinate actors and their responsibilities. In addition, the simulation models also facilitate the identification of critical processes, as well as the recognition of possible resource and capacity bottlenecks, from which relevant opportunities for the tactical optimisation of processes are derived. This contributes to the proactive and reactive handling of cyber-attacks by companies along a supply chain.
The main objective of our development is a toolset for the fast, secure and automated verification of handwritten signatures. Statistical methods and artificial intelligence processes are to be evaluated and used for this purpose.
The goal of the project is to select a sample of existing papers concerning the P/NP problem and to have the arguments contained therein checked by a (machine) proof assistant. The aim is not to find an answer to the open question itself, but to be capable to examine the "mass" of proposed proofs not only manually, but with machine support. If the number of suggested proofs grows faster than scientific peer review can assess the work and identify possible errors, then there is a possibility that the solution is actually found, but is lost in the mass of (incorrect) approaches. This situation should be counteracted with the support of computers, in particular proof assistants.
The publications examined (proof approaches) are selected with respect to the following aspects (i) the rigor of the argumentation, (ii) the proof techniques applied, (iii) "feasibility" of modeling in proof assistants, and (iv) the claimed result of the relation of P to NP (equivalent/indifferent/unprovable).
Expected results: Researchers who deal with the question should be given extended possibilities to subject their ideas and arguments to a mechanical, and thus objective/independent, examination (before publication). Independently of it the P/NP question serves thus as "study object", whose investigation is to advance the possibilities of mechanical proof verification, as an aid, simplification and objectification of scientific Peer reviews.
Cities and their agglomerations are home to a large number of critical infrastructures (CI) that provide essential services in a geographically narrow space and are thus physically and logically dependent on one another. This results in a sensitive network of organizations and connections in which incidents within an infrastructure can have an impact on the entire system. In particular, critical infrastructures in the context of utilities (electricity, gas, water, etc.), communication (ICT), distribution (food, fuel, etc.) and transport (road, rail, etc.) operate extensive networks which have special requirements with regard to security measures. Thus, a detailed risk analysis with a strong focus on the interaction of these networks and on potential cascading effects for the population represents a central aspect for the protection of these critical supply infrastructures, especially when considering the Network and Information Security (NIS) Law. Further, also the so-called "soft targets", i.e. attractive targets in public spaces for terrorist attacks, would have an impact on the above mentioned networks in case of an attack. The goal of the ODYSSEUS project is to create a simulation-based, cross-domain risk model, based on the example of the City of Vienna, which describes the networks of the central supply infrastructures (electricity, gas, water, food and telecommunications, including ICT) as well as the transport networks (road and rail) up to a certain level of abstraction. This level should be kept as low as possible in order to achieve as real a representation as possible (depending on the quantity and quality of data available). Based on this model, potential threats (both natural disasters and man-made incidents) are simulated. In contrast to existing solutions from literature and practice, ODYSSEUS focuses on the dynamic relationships between networks and develops mathematical models from stochastics (e.g. Markov chains, probabilistic automata) for a realistic representation. The central output of ODYSSEUS is a framework that enables a detailed assessment of the effects of threats both on individual critical infrastructures and on possible cascading effects within the entire network of critical supply infrastructures, taking into account the urban population. The simulations describe which potential compensation and displacement mechanisms can be expected within the multi-layered network of supply infrastructures or on public spaces in the event of an incident (intentional, technical or natural hazard). From this knowledge, targeted preventive safety measures can be derived, presented and evaluated in order to minimize the effects in the event of an incident when implemented.
The scientific question in the project concerns machine learning, in particular with regard to the explainability and communicability of recommendations for risk treatment, which are determined or calculated by an artificial intelligence (AI). In this context, the approach considered in the project is a combination of deterministic rules (as in decision trees) with non-rule-based approaches such as regression models. Formally, this involves performing a regression with basis functions generated using fuzzy logic techniques from semantically meaningful defined if-then rules. In other words, the machine learning problem here consists of an optimized selection of if-then rules from a given pool of rules, so that the training data - in this case risk assessments, but alternatively also time series data for the prediction (e.g. via Markov models) of security incidents - are approximated as well as possible (in the sense of metrics or similarity measures to be defined).
Robot ethics demands programmers to write code that is not only functionally correct but also secure and safe to disallow any intended or accidental harm to humans. Hence, programmers bear a responsibility w.r.t. several instances (e.g., system customers, providers, end-users, etc.), for which awareness is required (likewise for questions of liability, which is a complex matter of contemporary research and legislation). SEEROSE aims at achieving usable robotic security by jointly addressing process ethical, psychological, and technical aspects of developing safe and secure robotics systems.
SEEROSE features a Ph.D. project in each key area: The goal of the Ph.D. project DevSafe is to provide techniques and tools to support developers to responsibly develop and evolve safe and secure robotic systems. The goal of the Ph.D. project INBASE-GET is to provide mechanisms for incentivizing developers and robot collaborators to use and follow security precautions out of their own interests. The goal of the Ph.D. project SCoRE is to provide an instrument for the psychological assessment of the core qualifications relevant to robotics engineers. And finally, the goal of the Ph.D. project CERSE is to provide a guideline for the implementation of a process-ethical procedure for distributed assumption of responsibility in safe and secure robotic systems engineering.
Robotic systems increasingly take part in many practices within everyday life. Technological development and innovation transform fields like industrial robotics, medical technology, up to the exploration of space. However, new sets of possibilities come with new forms of responsibilities.
Engineering itself is a process that is (per)formed by many; individuals, teams, systems, norms, cultures and legislations, only to name a few. This PhD project engages with the ethical challenges that arise within safe and secure robotic systems engineering. It investigates the subjectively felt responsibility and explores the perception, governance and distribution of the networking processes of many hybrid actors in multiple heterogeneous fields. A mixed methods approach within the research design of Grounded Theory and Actor-Network Theory enables to identify and follow how responsibilities are organized and shared. The research aims to discuss which new ethical questions emerge and what competencies and strategies of safe and secure robotics engineering are required.
Robotic systems are among the most complex systems that humans built. In fact, they consist of distributed multiple hardware and software components that depend on each other and often such components represent complex systems or subsystems themselves. Maintaining and evolving robotic systems is challenging and each modification poses the risk to introduce vulnerabilities in the implementation or configuration of the robotic system that allow others to attack the robotic system. In this PhD project, we will design techniques and tools to extract detailed information about code changes in robotic systems with a focus on changes that introduce security vulnerabilities. Based on this information, we will investigate algorithms and techniques to analyze and determine the impact of code changes on the safety and security of robotic systems. They will be integrated into recommender systems that guide engineers to detect and fix vulnerabilities, and help them develop safe and secure robotics systems in a responsible way.
7fc3f7cf58