Method for exploratory cluster analysis and visualisation of single-trial ERP ensembles

Background: The validity of ensemble averaging on event-related potential (ERP) data has been questioned, due to its assumption that the ERP is identical across trials. Thus, there is a need for preliminary testing for cluster structure in the data. New method: We propose a complete pipeline for the cluster analysis of ERP data. To increase the signalto-noise (SNR) ratio of the raw single-trials, we used a denoising method based on Empirical Mode Decomposition (EMD). Next, we used a bootstrap-based method to determine the number of clusters, through a measure called the Stability Index (SI). We then used a clustering algorithm based on a Genetic Algorithm (GA)to define initial cluster centroids for subsequent k-means clustering. Finally, we visualised the clustering results through a scheme based on Principal Component Analysis (PCA). Results: After validating the pipeline on simulated data, we tested it on data from two experiments – a P300 speller paradigm on a single subject and a language processing study on 25 subjects. Results revealed evidence for the existence of 6 clusters in one experimental condition from the language processing study. Further, a two-way chi-square test revealed an influence of subject on cluster membership.

Cyber warfare: a multidisciplinary analysis

Cyber warfare is an increasingly important emerging phenomenon in international relations. The focus of this edited volume is on this notion of cyber warfare, meaning interstate cyber aggression, as distinct from cyber-terrorism or cyber-crime. Waging warfare in cyberspace has the capacity to be as devastating as any conventional means of conducting armed conflict. However, while there is a growing amount of literature on the subject within disciplines, there has been very little work done on cyber warfare across disciplines, which necessarily limits our understanding of it. This book is a major multidisciplinary analysis of cyber warfare, featuring contributions by world-leading experts from a mixture of academic and professional backgrounds.

Seafood safety and quality: an analysis of the supply chain in the Sultanate of Oman

The globalization of trade in fish has created many challenges for the developing world specifically with regard to food safety and quality. International organisations have established a good basis for standards in international trade. Whilst these requirements are frequently embraced by the major importers (such as Japan, the EU and the USA), they often impose additional safety requirements and regularly identify batches which fail to meet their strict standards. Creating an effective national seafood control system which meets both the internal national needs as well the requirements for the export market can be challenging. Many countries adopt a dual system where seafood products for the major export markets are subject to tight control whilst the majority of the products (whether for the local market or for more regional trade) are less tightly controlled. With regional liberalization also occurring, deciding on appropriate controls is complex. In the Sultanate of Oman, fisheries production is one of the countries' chief sources of economic revenue after oil production and is a major source of the national food supply. In this paper the structure of the fish supply chain has been analysed and highlighted the different routes operating for the different markets. Although much of the fish are consumed within Oman, there is a major export trade to the local regional markets. Much smaller quantities meet the more stringent standards imposed by the major importing countries and exports to these are limited. The paper has considered the development of the Omani fish control system including the key legislative documents and the administrative structures that have been developed. Establishing modern controls which satisfy the demands of the major importers is possible but places additional costs on businesses. Enhanced controls such as HACCP and other management standards are required but can be difficult to justify when alternative markets do not specify these. These enhanced controls do however provide additional consumer protection and can bring benefits to local consumers. The Omani government is attempting to upgrade the system of controls and has made tremendous progress toward the implementation of HACCP and introducing enhanced management systems into its industrial sector. The existence of strengthened legislative and government support, including subsidies, has encouraged some businesses to implement HACCP. The current control systems have been reviewed and a SWOT analysis approach used to identify key factors for their future development. The study shows that seafood products in the supply chain are often exposed to lengthy handling and distribution process before reaching the consumers, a typical issue faced by many developing countries. As seafood products are often perishable, they safety is compromised if not adequately controlled. The enforcement of current food safety laws in the Sultanate of Oman is shared across various government agencies. Consequently, there is a need to harmonize all regulatory requirements, enhancing the domestic food protection and to continue to work towards a fully risk-based approach in order to compete successfully in the global market.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.