Cognitive modelling of language acquisition with complex networks

ABSTRACT Cognitive modelling is a well-established computational intelligence tool, which is very useful for studying cognitive phenomena, such as young children's first language acquisition. Specifically, linguistic modelling has recently benefited greatly from complex network theory by modelling large sets of empirical linguistic data as complex networks, thereby illuminating interesting new patterns and trends. In this chapter, we show how simple network analysis techniques can be applied to the study of language acquisition, and we argue that they reveal otherwise hidden information. We also note that a key network parameter -the ranked frequency distribution of the links -provides useful knowledge about the data, even though it had been previously neglected in this domain

Robustness of complex networks to node and cluster damage

Copyright @ 2009 Universtiy of WarwickThe goal of this investigation is to assess the robustness of two popular network structures – random networks and scale-free networks – to node and cluster damage. There is no previous work on the latter. For node damage, we remove nodes iteratively and for cluster damage, we first build a network of clusters and then remove the nodes (clusters)

Community structure detection in the evolution of the United States airport network

This is the post-print version of the Article. Copyright © 2013 World Scientific PublishingThis paper investigates community structure in the US Airport Network as it evolved from 1990 to 2010 by looking at six bi-monthly intervals in 1990, 2000 and 2010, using data obtained from the Bureau of Transportation Statistics of the US Department of Transport. The data contained monthly records of origin-destination pairs of domestic airports and the number of passengers carried. The topological properties and the volume of people traveling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, is applied and reveals a picture of the communities within. The patterns of communities plotted for each bi-monthly interval reveal some interesting seasonal variations of passenger flows and airport clusters that do not occupy a single US region. The long-term evolution of the network between those years is explored and found to have consistently improved its stability. The more recent structure of the network (2010) is compared with migration patterns among the four US macro-regions (West, Midwest, Northeast and South) in order to identify possible relationships and the results highlight a clear overlap between US domestic air travel and migration

Space-independent community structure detection in United States air transportation

This article presents an evolution-based model for the US airport network. The topological properties and the volume of people travelling are both studied in detail, revealing high heterogeneity in space and time. A recently developed community structure detection method, accounting for the spatial nature of these networks, reveals a better picture of the communities within. © 2012 IFAC

A Comparative Study of Defeasible Argumentation and Non-monotonic Fuzzy Reasoning for Elderly Survival Prediction Using Biomarkers

Computational argumentation has been gaining momentum as a solid theoretical research discipline for inference under uncertainty with incomplete and contradicting knowledge. However, its practical counterpart is underdeveloped, with a lack of studies focused on the investigation of its impact in real-world settings and with real knowledge. In this study, computational argumentation is compared against non-monotonic fuzzy reasoning and evaluated in the domain of biological markers for the prediction of mortality in an elderly population. Different non-monotonic argument-based models and fuzzy reasoning models have been designed using an extensive knowledge base gathered from an expert in the field. An analysis of the true positive and false positive rate of the inferences of such models has been performed. Findings indicate a superior inferential capacity of the designed argument-based models

Modelling language acquisition in children using network theory

Research in children’s language acquisition has recently benefited from the application of network theory to large sets of empirical data, which has illuminated interesting patterns and trends. Network theory is an extremely powerful modelling and analysis tool, and its full potential in terms of extracting useful information from raw data has yet to be exploited. In the present paper, we argue that well-established network analysis techniques can, and should be applied to the study of language acquisition, in order to reveal otherwise invisible patterns. We show that a key network parameter – the ranked frequency distribution of the links – provides useful information about the data, even though it had been previously neglected in this domain

Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column

Hazard assessment techniques and multi-sensor fusion are used for intelligent systematic monitoring. Firstly, a hazard identification technique is considered using failure mode and effect analysis and advantages of using a combined hazard technique is discussed. Data sources are identified considering component failures and some sensors associated with potential failure. Possible consequences in a hazardous situation are identified using failure mode and effect analysis to choose suitable safety measures. Failure mode and effect analysis is systematically considers how sequences of events can lead to accidents by looking at components and faults recorded by sensors and anomalies. Data were presented based on their threat levels using a traffic light color code system. Refineries use sensors to observe the process of crude refining and the monitoring system uses real-time data to access information provided by sensors. Understanding hazard assessments, sensor multi-fusion and sensor pattern recognition in a distillation column could help to identify trends, flag major regions of growing malfunction, model risk threat of a crude distillation column and help to systematically make decisions. The decisions could improve design regulations, eliminate anomalies, improve monitoring and reduce threat levels