Performance of Kohonen Self-organising Map in Statistical Analysis of Hydrologic Data

The statistical characteristics of a hydrological data for the purposes of decision making in water resource planning and management is only justifiable if the data has the right attributes. This requires that the data being analysed are consistent, free of trend and being part of a stochastic process whose random characteristics is described by an appropriate distribution hypothesis. The data available for statistical analysis had a lot of missing values which could not be ordinarily filled but required a more comprehensive approach to fill these missing values. The KSOM （Kohonen Self-organising Map） was used to fill the missing runoff data from the Jidere-Bonde, Lokoja and Makundi river sites in the Niger basin. Results from the studies have shown that KSOM is the best tool for filling hydrological data with high number of missing values. After the data had been processed, some statistical applications were used to establish the runoff time-series characteristics of the three river sites of the Niger River basin. The results showed good attributes for all three river sites, except that Jidere River＇s data exhibited inconsistency. The presence of trend was also established for all three river sites; Jidere River was modelled based on 3-pararneter lognormal, the other two river sites were modelled based on normal distribution probability. The presence of trend and other attributes require that a more stochastic modelling process be carried out. However, the results established give reference for water resource planning and management.

Artificial Intelligence Self-Organising (AI-SON) Frameworks for 5G-Enabled Networks: A Review

The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.

Links between Arctic sea ice and extreme summer precipitation in China:an alternative view

Potential links between the Arctic sea-ice concentration anomalies and extreme precipitation in China are explored. Associations behind these links can be explained by physical interpretations aided by visualisations of temporarily lagged composites of variables such as atmospheric mean sea level pressure and sea surface temperature. This relatively simple approach is verified by collectively examining already known links between the Arctic sea ice and rainfall in China. For example, similarities in the extreme summer rainfall response to Arctic sea-ice concentration anomalies either in winter （DJF） or in spring （MAM） are highlighted. Furthermore, new links between the Arctic sea ice and the extreme weather in India and Eurasia are proposed. The methodology developed in this study can be further applied to identify other remote impacts of the Arctic sea ice variability.

Homo Faber Scapegoated, or Apocalyptic Artificial Intelligence: Rethinking the Technological Singularity Concept From the Synergetic Historicism Position

The article is focused on discussing a new methodological approach to the study on specifics of transferring human beings to the posthuman cyber society.The approach in question assists in rethinking interconnected problems both of human origins in the universe and mankind’s digital future.And,besides,such an approach allows to deal with self-organising interconversions between the poles of the cardinal dual opposition of the Global Noosphere Brain and the Artificial General Intelligence.Herewith such phenomena of digital social life as Global Digitalisation,Digital Immortality,Mindcloning,and Technological Zombification being the constituents of Technological Singularity Concept,are rethought as paving the way for oncoming Posthuman Digital Era.This concept is evidently exemplified by a bifurcation resulting in two alternatives to be chosen by human beings,to wit,either to be undergone Mindcloning and become digitally immortal or being destroyed by powerful intelligent machines.The investigation in question is based on such a progressive methodology as the Law of Self-Organizing Ideals,as well as on the Method of Dual Oppositions.Rethinking interrelationships between the problem of a sense of social history and the meaning-of-life of local societies members which any intelligent machine is devoid of permits to substantiate specific regularities of Self-Transforming Homo Faber into Homo Digitalis and Technological Zombies ready to be transferred to posthuman cyberspace.

I3:A Self-organising Learning Workflow for Intuitive Integrative Interpretation of Complex Genetic Data

We propose a computational workflow(I3)for intuitive integrative interpretation of complex genetic data mainly building on the self-organising principle.We illustrate the use in interpreting genetics of gene expression and understanding genetic regulators of protein phenotypes,particularly in conjunction with information from human population genetics and/or evolutionary history of human genes.We reveal that loss-of-function intolerant genes tend to be depleted of tissue-sharing genetics of gene expression in brains,and if highly expressed,have broad effects on the protein phenotypes studied.We suggest that this workflow presents a general solution to the challenge of complex genetic data interpretation.I3 is available at http://suprahex.r-forge.r-project.org/I3.html.

Mapping urban building stocks for vulnerability assessment
preliminary results

This paper discusses a methodology to collect building inventory data by combining image processing techniques,field work or tools such as Google Street View and applying statistical inferences.Following the methodology outlined in Marinescu(2002),a family of Gabor filters are first constructed,which are then applied to an optical high-resolution image.The output from the processed image is segmented using Self-Organising Maps.This paper examines the relationship between the segmented areas in the image and the building type distribution within each segmented area,by deriving the distribution from field data.The relationship between the average number of buildings in these cells against the number of grid cells allocated to each segmentation cluster is also investigated.Finally,using these results,the overall building inventory distribution for the whole of the case study site of Pylos is presented.