Penguins Search Optimisation Algorithm for Association Rules Mining

Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decision-making process. All existing ARM algorithms are time consuming and generate a very large number of association rules with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different datasets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well-known ARM algorithms in both execution time and solution quality

Review of legal frameworks, standards and best practices in verification and assurance for infrastructure inspection robotics

The purpose of this deliverable is to provide a single point of reference on the safety, regulatory and liability issues for operating robots in the European Union. The deliverable describes a state of the art and the well-known normative frameworks for assuring safety on the one hand and examines the regulatory and legal liability issues related to operating robots on the other. We organised the report based on the required structure of the deliverable with taking into consideration the different robots technologies, as recognised at the European Union and international level. This deliverable is closely related to other deliverables which describe the current state of the arts and normative framework from a different point of view. This review report is intended as a guiding document to be used by all project partners. There is currently no single framework to regulate robotics technology in Europe. Different types of robots, depending on where they operate—which Member State and in the air, on land, or in the waters—may be subject to various existing laws or regulations on the international, European Union, Member State levels. The regulations include legal standards and industry guidelines on the robot technologies themselves and on the developers, manufacturers, suppliers, and operators that must be met before these new technologies can be legally and safely deployed. Specific types of robots are subject to different regulatory regimes, and depending on the type of the robot, the applicable regulations may be harmonised across Europe or differ in each Member State. Current liability regimes on the EU and Member State levels govern the situations in which the humans associated with the robots are civilly liable for the damage they cause to property or injuries to persons. The appropriate legal regime could be fault-based, strict liability, or product liability depending on the particular circumstances. While existing laws are sufficient to address liability issues given the current state of the technology, further scientific advances that lead to increasingly sophisticated robots may raise problems on how to appropriately assign responsibility

Runtime Decision Making Under Uncertainty in Autonomous Vehicles

Autonomous vehicles (AV) have the potential of not only increasing the safety, comfort and fuel efficiency in a vehicle but also utilising the road bandwidth more efficiently. This, however, will require us to build an AV control software, capable of coping with multiple sources of uncertainty that are either preexisting or introduced as a result of processing. Such uncertainty can come from many sources like a local or a distant source, for example, the uncertainty about the actual observation of the sensors of the AV or the uncertainty in the environment scenario communicated by peer vehicles respectively. For AV to function safely, this uncertainty needs to be taken into account during the decision making process. In this paper, we provide a generalised method for making safe decisions by estimating and integrating the Model and the Data uncertainties

An overview of the approaches for automotive safety integrity levels allocation

YesISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems, ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges

Exploiting GPU parallelism in improving bees swarm optimization for mining big transactional databases

© 2018 This paper investigates the use of GPU (Graphics Processing Unit) in improving the bees swarm optimization metaheuristic performance for solving the association rule mining problem. Although this metaheuristic proved its effectiveness, it requires huge computational resource when considering big databases for mining. To overcome this limitation, we develop in this paper a GPU-based Bees Swarm Optimization Miner (GBSO-Miner) where the GPU is used as a co-processor to compute the CPU-time intensive steps of the algorithm. Unlike state-of-the-art GPU-based ARM methods, all BSO steps including the determination of search area, the local search, the evaluation, and the dancing are performed on GPU. A mapping method between the data input of each task and the GPU blocks/threads is developed. To demonstrate the effectiveness of the GBSO-Miner framework, intensive experiments have been carried out. The results show that GBSO-Miner outperforms the baseline methods of the literature (GPApriroi, MEGPU, and Dmine) using big textual and graph databases. The results reveal that GBSO-Miner is up to 800 times faster than an optimized CPU-Implementation