ABSTRACT The formal verification of railway control systems can ensure the safety of complex scheme plans through techniques such as induction‐based model checking. While inductive verification performs well in complex settings, it often produces false positives due to its consideration of transitions from unreachable safe states to unsafe states. Invariants that reduce the state space to an over‐approximation of reachable states, excluding transitions from safe to unsafe states, can help remove these false positives. However, such invariants are difficult to deduce automatically. In previous work, we have demonstrated that reinforcement learning (RL) and descriptive statistics can be used to generate candidate invariants, trivially within small programs, and can be realistically scaled to industrial settings using hardware‐accelerated implementations. This paper extends our hybrid approach that uses General Purpose Graphics Processing Units (GPGPUs) to overcome these challenges while scaling effortlessly horizontally and in a distributed manner. We detail the implementation of a three‐kernel pipeline responsible for consuming streamed data and computing correlation coefficients. Finally, we present a breadth of time samples that highlight the performance and scalability of this approach.

Celestial navigation has fallen into the background in light of newer technologies such as global positioning systems, but research into its core component, star pattern recognition, has remained an active area of study.We examine these methods and the viability of a data-driven approach to detecting and recognising stars within images taken from the Earth's surface.We show that synthetic datasets, necessary due to a lack of labelled real image datasets, are able to appropriately simulate the night sky from a terrestrial perspective and that such an implementation can successfully perform star patter recognition in this domain.In this work we apply three kinds of noise in a parametric fashion; positional noise, false star noise, and dropped star noise.Results show that a pattern mining approach can accurately identify stars from night sky images and our results show the impact of the above noise types on classifier performance.

Image based place recognition has achieved state of the art performance on terrestrial image datasets, however there is very little publicly available research on how these systems perform on waterborne imagery in order to carry out place recognition for autonomous sea vessels. This domain may provide new visual challenges such as water obstruction, distance from shore, lower atmospheric visibility, and camera stability. In this paper, we compare performance and saliency of state of the art place recognition on both terrestrial imagery and waterborne imagery from the Symphony Lake dataset, to see how capable modern pipelines are at adapting to the latter. We utilize convolutional neural network features to highlight salient regions of the candidate image that contributed to its retrieval to gain further insight into what key features are being extracted for each.

Very recent advances in machine learning have expanded deep learning methods to spatially-irregular data domains. Deep learning on graphs in particular has received greater study, providing benefits in numerous fields. In this paper we present a graph-based convolutional autoencoder and assess the contribution of four components towards encoding quality. A graph-based convolution-operator is used to learn localised filtering operations for graph-wise encoding. An evaluation of the proposed method is provided on a topologically-irregular version of MNIST that violates the assumption made by conventional convolutional autoencoder methods of the structure of its input-data.

Expert operative information is a prerequisite for any form of surgical training. However, the shortening of working hours has reduced surgical training time and learning opportunities. As a potential solution to this problem, multimedia programs have been designed to provide computer-based assistance to surgical trainees outside of the operating theatre. Few studies, however, have focused on the interface features of surgical multimedia programs, the successful design of which could be conducive to the evaluation of the effectiveness of learning. This study evaluated a multimedia CD-ROM designed for teaching minor skin surgery. A questionnaire, based on an existing user interface rating tool, was administered to 20 trainees (both junior and senior) in plastic surgery. Findings from the study revealed trainees' high rating of the CD-ROM on a scale of 1–10 (mean = 8); the analysis of which contributes towards an understanding of both the characteristics of the learning material and the learners in the evaluation of the user interface.

We describe a method to analyze materials on a conveyor belt using natural gamma spectra collected with a BGO (Bismuth Germanate) gamma ray detector, which collects emissions from Potassium (K), Uranium (U), and Thorium (Th) in the materials. A statistical model is proposed based on a Poisson process and an approximate maximum likelihood (ML) technique via the expectation-maximization (EM) algorithm is then used to estimate the amount of each of the three elements in the material. A refinement of the statistical model is used to estimate linear drift in the detector.

Taxonomy is the theory and practice of classification and employs laws and principles for classifying objects. This paper defines a taxonomy for specifying complex system requirements and investigates specification approaches. The structure provides wider systems coverage than normally specified for software systems in a number of areas, including non-functional requirements and the operational environment, and includes specification for growth and change. Methods for arriving at the taxonomy are discussed.

Many standards that mandate requirements traceability as well as current literature do not provide a comprehensive model of what information should be captured and used as a part of a traceability scheme. Therefore, the practices and usefulness of traceability vary considerably across systems development efforts, ranging from very simplistic practices just aimed at satisfying the mandates to very comprehensive traceability schemes used as an important tool for managing the systems development process. We present a case study of a systems development organization, employing a comprehensive view of traceability. A model describing the traceability practice in the organization, perceived benefits of such a scheme and lessons learnt from implementing it are presented.

In the development of large-scale, real-time, complex computer intensive systems, it is essential to maintain traceability of requirements to various outputs to ensure that the system meets the current set of requirements. Based on an empirical study in a simulated systems development environment, several major issues that need to be considered in the development of a model of requirements traceability are addressed.< >