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The field of artificial intelligence has gained a significant attention in the media. Some counties claim to be the leaders in the field, other countries claim to be winning in the race for leadership in artificial intelligence. This article conducts a statistical (i.e., bibliometric) analysis of research data records on artificial intelligence by year, country, language, and organisation. The results are clearly in favour of the USA on a national level, and English is clearly the dominant language for disseminating results. But in terms of leading organisation in the field of artificial intelligence creates more confusing result – e.g., between the Chinese Academy of Sciences and the University of California - in the leadership race. The forecasts from this study on future evolution of artificial intelligence is that it is unlikely that (in the next 60 years) AI ‘superintelligence’ would trigger a catastrophic event for humanity.

Fixed edge processing has become a key feature of 5G networks, while playing a key role in reducing latency, improving energy efficiency and introducing flexible compute resource utilization on-demand with added cost savings. Autonomous vehicles are expected to possess significantly more on-board processing capabilities and with improved connectivity. Vehicles continue to be used for a fraction of the day, and as such there is a potential to increase processing capacity by utilizing these resources while vehicles are in short-term and long-term car parks, in roads and at road intersections. Such car parks and road segments can be transformed, through 6G networks, into vehicular fog clusters, or Fogbanks, that can provide processing, storage and sensing capabilities, making use of underutilized vehicular resources. We introduce the Fogbanks concept, outline current research efforts underway in vehicular clouds, and suggest promising directions for 6G in a world where autonomous driving will become commonplace. Moreover, we study the processing allocation problem in cloud-based Fogbank architecture. We solve this problem using Mixed Integer Programming (MILP) to minimize the total power consumption of the proposed architecture, taking into account two allocation strategies, single allocation of tasks and distributed allocation. Finally, we describe additional future directions needed to establish reliability, security, virtualisation, energy efficiency, business models and standardization.

CARMA (Cloud-Assisted Real-time Methods for Autonomy) is a highly innovative and challenging project which aims to develop and test cooperative automated driving technology, based on a distributed control system. The approach is enabled by an ultra-low latency and highly reliable cloud-based infrastructure accessed through 5G. This paper describes the 3-tier distributed computing architecture used in the project comprising the Vehicle, the Edge cloud and the Core. It describes a methodology to test a set of use cases representative of both urban and highway driving and explores the key challenges in such an approach. The first technical challenge is the design of a mobile edge cloud infrastructure that is able to support real-time and safety critical applications. Another non-trivial problem is that of cyber-security for such a real-time cyber physical system. Progress during the first year of this five year project is described.

Culture's role in cognition has long been established, but understanding national culture's role in cyber events currently remains an understudied research area. Michael Minkov observed that culture influences thought; even when people think they are in control, cultural biases are actually controlling their thoughts. Cultural values endure, and because they do, these cultural values leave artifacts can be revealed in thought patterns. This philosophy serves as a foundation for further work in progress on name choices by hackers. In this study, we examined 10 years' worth of Zone-H archives where hacker names that identified with national identities were analysed using Hofstede's cultural framework to determine whether culture may play a role in hacker name and name choices. The findings revealed cultural preferences in several of the six cultural dimensions. In addition, trending the results showed significant findings in two cultural dimensions: (1) masculinity versus femininity and (2) uncertainty avoidance. The results suggest that assumptions about the anonymous nature of the Internet influencing behaviours may not be universally applicable, and that culture should also be considered when evaluating cyber actor behaviours.

Information security is an important aspect of a successful business today. However, financial difficulties and budget cuts create a problem of selecting appropriate security measures and keeping networked systems up and running. Economic models proposed in the literature do not address the challenging problem of security countermeasure selection. We have made a classification of security models, which can be used to harden a system in a cost effective manner based on the methodologies used. In addition, we have specified the challenges of the simplified risk assessment approaches used in the economic models and have made recommendations how the challenges can be addressed in order to support decision makers.

The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a reasonable time. The lack in details leads to disproportional sampling from the posterior distribution. In case of the EEG assessment of brain maturity, BMA results can be biased because of the absence of information about EEG feature importance. In this paper we explore how the posterior information about EEG features can be used in order to reduce a negative impact of disproportional sampling on BMA performance. We use EEG data recorded from sleeping newborns to test the efficiency of the proposed BMA technique., Proceedings of the 9th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED), University of Cambridge, UK, 2010, edited by L. A. Zadeh et al, pp 191 - 195

Recently, more effort has been put into developing interoperable and distributed environments that offer users exceptional opportunities for utilizing resources over the internet. By utilising grids and clouds, resource consumers and providers, they gain significant benefits by either using or purchasing the computer processing capacities and the information provided by data centres. On the other hand, the collective intelligence paradigm is characterized as group based intelligence that emerges from the collaboration of many individuals, who in turn, define a coordinated knowledge model. It is envisaged that such a knowledge model could be of significant advantage if it is incorporated within the grid and cloud community. The dynamic load and access balancing of the grid and cloud data centres and the collective intelligence provides multiple opportunities, involving resource provisioning and development of scalable and heterogeneous applications. The contribution of this paper is that by utilizing grid and cloud resources, internal information stored within a public profile of each participant, resource providers as well as consumers, can lead to an effective mobilization of improved skills of members. We aim to unify the grid and cloud functionality as consumable computational power, for a) discussing the supreme advantages of such on-line resource utilization and provisioning models and b) analyzing the impact of the collective intelligence in the future trends of the aforementioned technologies.

This paper describes an action research project based on the process of designing the visualisation of heart and breath rate data for the interactive artwork Cardiomorphologies. The project aimed to realise the affective goals of the artist as closely as

Learning objects are entities that may be used for learning, education or training. Nowadays they are often stored in Learning Object Repositories (LORs), such as the Ariadne Knowledge Pool System (KPS), Merlot and EdNa. Typically users can search for learning objects in those LORs by filling out an electronic form that enables them to compose Boolean combinations of search criteria. More research is needed on novel access paradigms to enable more effective and flexible access to these repositories. In this paper, we investigate how we can use information visualization techniques for this purpose. We discuss the use of three existing information visualization techniques that we applied to the ARIADNE Knowledge Pool System (KPS). ispartof: pages:465-470 ispartof: Information Visualization (IV04) vol:8 pages:465-470 ispartof: International Conference on Information Visualisation location:London, England date:14 Jul - 16 Jul 2004 status: published