Specification, Testing and Verification of Unconventional Computations using Generalised X-Machines

There are as yet no fully comprehensive techniques for specifying, verifying and testing unconventional computations. In this paper we propose a generally applicable and designer-friendly specification strategy based on a generalised variant of Eilenberg's X-machine model of computation. Our approach, which extends existing approaches to SXM test-based verification, is arguably capable of modelling very general unconventional computations, and would allow implementations to be verified fully against their specifications

Using Isabelle/HOL to verify first-order relativity theory

Logicians at the Rényi Mathematical Institute in Budapest have spent several years developing versions of relativity theory (special, general, and other variants) based wholly on first-order logic, and have argued in favour of the physical decidability, via exploitation of cosmological phenomena, of formally unsolvable questions such as the Halting Problem and the consistency of set theory. As part of a joint project, researchers at Sheffield have recently started generating rigorous machine-verified versions of the Hungarian proofs, so as to demonstrate the soundness of their work. In this paper, we explain the background to the project and demonstrate a first-order proof in Isabelle/HOL of the theorem “no inertial observer can travel faster than light”. This approach to physical theories and physical computability has several pay-offs, because the precision with which physical theories need to be formalised within automated proof systems forces us to recognise subtly hidden assumptions

Comparative Analysis of Statistical Model Checking Tools

Statistical model checking is a powerful and flexible approach for formal verification of computational models like P systems, which can have very large search spaces. Various statistical model checking tools have been developed, but choosing between them and using the most appropriate one requires a significant degree of experience, not only because different tools have different modelling and property specification languages, but also because they may be designed to support only a certain subset of property types. Furthermore, their performance can vary depending on the property types and membrane systems being verified. In this paper we evaluate the performance of various common statistical model checkers against a pool of biological models. Our aim is to help users select the most suitable SMC tools from among the available options, by comparing their modelling and property specification languages, capabilities and performances

Modeling and Simulation of Tawaf and Sa'yee: A Survey of Recent Work in the Field

Between 2002 and 2012 the number of pilgrims taking part in the 5-day hajj (the annual pilgrimage to Mecca) rose dramatically from 1.9m to 3.2m, before stabilizing at around 2 million following the introduction of new quotas in 2013. The gathering together of so many people has obvious crowd-safety implications, ranging from stampedes and protests to pickpocketing and dis- ease control, and there is an obvious need for models and simulations of the relevant crowd behaviours. More- over, the regular occurrence of the event, the size and diversity of the crowds involved, and the amount of freely available information make this an excellent case study for the study of crowd behaviour. We survey recent at- tempts to model the key hajj rituals of tawaf (during which pilgrims collectively circumambulate the Ka’aba seven times) and sa’yee (running or walking seven times between two nearby hills), and highlight ways in which some of the limitations of these studies may be overcome in future work

Kalman filter based prediction and forecasting of cloud server KPIs

Cloud computing depends on the dynamic allocation and release of resources, on demand, to meet heterogeneous computing needs. This is challenging for cloud data centers, which process huge amounts of data characterised by its high volume, velocity, variety and veracity (4Vs model). Managing such a workload is increasingly difficult using state-of-the-art methods for monitoring and adaptation, which typically react to service failures after the fact. To address this, we seek to develop proactive methods for predicting future resource exhaustion and cloud service failures. Our work uses a realistic test bed in the cloud, which is instrumented to monitor and analyze resource usage. In this paper, we employed the optimal Kalman filtering technique to build a predictive and analytic framework for cloud server KPIs, based on historical data. Our k-step-ahead predictions on historical data yielded a prediction accuracy of 95.59%. The information generated from the framework can best be used for optimal resources provisioning, admission control and cloud SLA management

Motion and observation in a single-particle universe

We outline an argument that a single-particle universe can be described, which is observationally indistinguishable from the world we see around us despite the absence of any meaningful frame of reference relative to which motion can be defined. Our argument uses a formal model of spacetime that can be considered either relational or substantivalist depending on ones prefered level of abstraction, suggesting that this long-held distinction is to some extent illusory

Bitcoin Risk Analysis

The surprise advent of the peer-to-peer payment system Bitcoin in 2009 has raised various concerns regarding its relationship to established economic market ideologies. Unlike at currencies, Bitcoin is based on open-source software; it is a secure cryptocurrency, traded as an investment between two individuals over the internet, with no bank involvement. Computationally, this is a very innovative solution, but Bitcoin's popularity has raised a number of security and trust concerns among mainstream economists. With cities and countries, including San Francisco and Germany, using Bitcoin as a unit of account in their financial systems, there is still a lack of understanding and a paucity of models for studying its use, and the role Bitcoin might play in real physical economies. This project tackles these issues by analysing the ramifications of Bitcoin within economic models, by building a computational model of the currency to test its performance in financial market models. The project uses established agent-based modelling techniques to build a decentralised Bitcoin model, which can be `plugged into' existing agent-based models of key economic and financial markets. This allows various metrics to be subjected to critical analysis, gauging the progress of digital economies equipped with Bitcoin usage. This project contributes to the themes of privacy, consent, security and trust in the digital economy and digital technologies, enabling new business models of direct relevance to NEMODE. As computer scientists, we consider Bitcoin from a technical perspective; this contrasts with and complements other current Bitcoin research, and helps document the realizable risks Bitcoin and similar currencies bring to our current economic world. This report outlines a comprehensive collection of risks raised by Bitcoin. Risk management is a discipline that can be used to address the possibility of future threats which may cause harm to the existing systems. Although there has been considerable work on analysing Bitcoin in terms of the potential issues it brings to the economic landscape, this report performs a first ever attempt of identifying the threats and risks posed by the use of Bitcoin from the perspective of computational modeling and engineering. In this project we consider risk at all levels of interaction when Bitcoin is introduced and transferred across the systems. We look at the infrastructure and the computational working of the digital currency to identify the potential risks it brings. Additional information can be seen in our forthcoming companion report on the detailed modeling of Bitcoin

Selection Criteria for Statistical Model Checking

Statistical model checking (SMC) has been used to verify both biological and P systems, but different SMC tools employ different modelling and property specification languages, making it hard to decide which tool is best for which problem. We survey the capabilities of SMC tools and provide experimental results showing their ability to verify patterns against biological models. Our eventual goal is the automation of the SMC selection process