Cognitive phenomenology : real life

Cognitive phenomenology starts from something that has been obscured in much recent analytic philosophy: the fact that lived conscious experience isn’t just a matter of sensation or feeling, but is also cognitive in character, through and through. This is obviously true of ordinary human perceptual experience, and cognitive phenomenology is also concerned with something more exclusively cognitive, which we may call propositional meaning-experience, e.g. occurrent experience of linguistic representations as meaning something, as this occurs in thinking or reading or hearing others speak.

Distributed parallelization of greedy Mobile Network Optimization algorithms

The Mobile Network Optimization (MNO) technologies have advanced at a tremendous pace in recent years. And the Dynamic Network Optimization (DNO) concept emerged years ago, aimed to continuously optimize the network in response to variations in network traffic and conditions. Yet, DNO development is still at its infancy, mainly hindered by a significant bottleneck of the lengthy optimization runtime. This paper identifies parallelism in greedy MNO algorithms and presents an advanced distributed parallel solution. The solution is designed, implemented and applied to real-life projects whose results yield a significant, highly scalable and nearly linear speedup up to 6.9 and 14.5 on distributed 8-core and 16-core systems respectively. Meanwhile, optimization outputs exhibit self-consistency and high precision compared to their sequential counterpart. This is a milestone in realizing the DNO. Further, the techniques may be applied to similar greedy optimization algorithm based applications.

Asynchronous distributed parallelization of mobile network optimization algorithms

It has been years since the introduction of the Dynamic Network Optimization (DNO) concept, yet the DNO development is still at its infant stage, largely due to a lack of breakthrough in minimizing the lengthy optimization runtime. Our previous work, a distributed parallel solution, has achieved a significant speed gain. To cater for the increased optimization complexity pressed by the uptake of smartphones and tablets, however, this paper examines the potential areas for further improvement and presents a novel asynchronous distributed parallel design that minimizes the inter-process communications. The new approach is implemented and applied to real-life projects whose results demonstrate an augmented acceleration of 7.5 times on a 16-core distributed system compared to 6.1 of our previous solution. Moreover, there is no degradation in the optimization outcome. This is a solid sprint towards the realization of DNO.

The multiple team formation problem using sociometry

The Team Formation problem (TFP) has become a well-known problem in the OR literature over the last few years. In this problem, the allocation of multiple individuals that match a required set of skills as a group must be chosen to maximise one or several social positive attributes. Speci�cally, the aim of the current research is two-fold. First, two new dimensions of the TFP are added by considering multiple projects and fractions of people's dedication. This new problem is named the Multiple Team Formation Problem (MTFP). Second, an optimization model consisting in a quadratic objective function, linear constraints and integer variables is proposed for the problem. The optimization model is solved by three algorithms: a Constraint Programming approach provided by a commercial solver, a Local Search heuristic and a Variable Neighbourhood Search metaheuristic. These three algorithms constitute the first attempt to solve the MTFP, being a variable neighbourhood local search metaheuristic the most effi�cient in almost all cases. Applications of this problem commonly appear in real-life situations, particularly with the current and ongoing development of social network analysis. Therefore, this work opens multiple paths for future research.

Architecture for Living Neuronal Network Control of a mobile robot

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Research is however now ongoing in which biological neural networks are being cultured and trained to act as the brain of an interactive real world robot – thereby either completely replacing or operating in a cooperative fashion with a computer system. Studying such neural systems can give a distinct insight into biological neural structures and therefore such research has immediate medical implications. The principal aims of the present research are to assess the computational and learning capacity of dissociated cultured neuronal networks with a view to advancing network level processing of artificial neural networks. This will be approached by the creation of an artificial hybrid system (animat) involving closed loop control of a mobile robot by a dissociated culture of rat neurons. This paper details the components of the overall animat closed loop system architecture and reports on the evaluation of the results from preliminary real-life and simulated robot experiments.

Controlling a mobile robot with a biological brain

The intelligent controlling mechanism of a typical mobile robot is usually a computer system. Some recent research is ongoing in which biological neurons are being cultured and trained to act as the brain of an interactive real world robot�thereby either completely replacing, or operating in a cooperative fashion with, a computer system. Studying such hybrid systems can provide distinct insights into the operation of biological neural structures, and therefore, such research has immediate medical implications as well as enormous potential in robotics. The main aim of the research is to assess the computational and learning capacity of dissociated cultured neuronal networks. A hybrid system incorporating closed-loop control of a mobile robot by a dissociated culture of neurons has been created. The system is flexible and allows for closed-loop operation, either with hardware robot or its software simulation. The paper provides an overview of the problem area, gives an idea of the breadth of present ongoing research, establises a new system architecture and, as an example, reports on the results of conducted experiments with real-life robots.

Reproductive conflict delays the recovery of an endangered social species

Evolutionary theory predicts that individuals, in order to increase their relative fitness, can evolve behaviours that are detrimental for the group or population. This mismatch is particularly visible in social organisms. Despite its potential to affect the population dynamics of social animals, this principle has not yet been applied to real-life conservation. Social group structure has been argued to stabilize population dynamics due to the buffering effects of nonreproducing subordinates. However, competition for breeding positions in such species can also interfere with the reproduction of breeding pairs. Seychelles magpie robins, Copsychus sechellarum, live in social groups where subordinate individuals do not breed. Analysis of long-term individual-based data and short-term behavioural observations show that subordinates increase the territorial takeover frequency of established breeders. Such takeovers delay offspring production and decrease territory productivity. Individual-based simulations of the Seychelles magpie robin population parameterized with the long-term data show that this process has significantly postponed the recovery of the species from the Critically Endangered status. Social conflict thus can extend the period of high extinction risk, which we show to have population consequences that should be taken into account in management programmes. This is the first quantitative assessment of the effects of social conflict on conservation.

The impact of an intelligent classroom on pupils' interactive behaviour

Purpose – The purpose of this research is to determine whether new intelligent classrooms will affect the behaviour of children in their new learning environments. Design/methodology/approach – A multi-method study approach was used to carry out the research. Behavioural mapping was used to observe and monitor the classroom environment and analyse usage. Two new classrooms designed by INTEGER (Intelligent and Green) in two different UK schools provided the case studies to determine whether intelligent buildings (learning environments) can enhance learning experiences. Findings – Several factors were observed in the learning environments: mobility, flexibility, use of technology, interactions. Relationships among them were found indicating that the new environments have positive impact on pupils' behaviour. Practical implications – A very useful feedback for the Classrooms of the Future initiative will be provided, which can be used as basis for the School of the Future initiative. Originality/value – The behavioural analysis method described in this study will enable an evaluation of the “Schools of the Future” concept, under children's perspective. Using a real life laboratory gives contribution to the education field by rethinking the classroom environment and the way of teaching.

A practical method for testing high-speed networking hardware architectures

This paper deals with the key issues encountered in testing during the development of high-speed networking hardware systems by documenting a practical method for "real-life like" testing. The proposed method is empowered by modern and commonly available Field Programmable Gate Array (FPGA) technology. Innovative application of standard FPGA blocks in combination with reconfigurability are used as a back-bone of the method. A detailed elaboration of the method is given so as to serve as a general reference. The method is fully characterised and compared to alternatives through a case study proving it to be the most efficient and effective one at a reasonable cost.

Cyborg 1.0

I was born human. But this was an accident of fate - a condition merely of time and place. I believe it's something we have the power to change. I will tell you why. In August 1998, a silicon chip was implanted in my arm, allowing a computer to monitor me as I moved through the halls and offices of the Department of Cybernetics at the University of Reading, just west of London, where I've been a professor since 1988. My implant communicated via radio waves with a network of antennas throughout the department that in turn transmitted the signals to a computer programmed to respond to my actions. At the main entrance, a voice box operated by the computer said "Hello" when I entered; the computer detected my progress through the building, opening the door to my lab for me as I approached it and switching on the lights. For the nine days the implant was in place, I performed seemingly magical acts simply by walking in a particular direction. The aim of this experiment was to determine whether information could be transmitted to and from an implant. Not only did we succeed, but the trial demonstrated how the principles behind cybernetics could perform in real-life applications.

Adaptive modelling, estimation and fusion from data: a neurofuzzy approach

In a world of almost permanent and rapidly increasing electronic data availability, techniques of filtering, compressing, and interpreting this data to transform it into valuable and easily comprehensible information is of utmost importance. One key topic in this area is the capability to deduce future system behavior from a given data input. This book brings together for the first time the complete theory of data-based neurofuzzy modelling and the linguistic attributes of fuzzy logic in a single cohesive mathematical framework. After introducing the basic theory of data-based modelling, new concepts including extended additive and multiplicative submodels are developed and their extensions to state estimation and data fusion are derived. All these algorithms are illustrated with benchmark and real-life examples to demonstrate their efficiency. Chris Harris and his group have carried out pioneering work which has tied together the fields of neural networks and linguistic rule-based algortihms. This book is aimed at researchers and scientists in time series modeling, empirical data modeling, knowledge discovery, data mining, and data fusion.

Algorithmic statistical analysis of electrophysiological data for the investigation of structure-activity relationship in single neurons

We are developing computational tools supporting the detailed analysis of the dependence of neural electrophysiological response on dendritic morphology. We approach this problem by combining simulations of faithful models of neurons (experimental real life morphological data with known models of channel kinetics) with algorithmic extraction of morphological and physiological parameters and statistical analysis. In this paper, we present the novel method for an automatic recognition of spike trains in voltage traces, which eliminates the need for human intervention. This enables classification of waveforms with consistent criteria across all the analyzed traces and so it amounts to reduction of the noise in the data. This method allows for an automatic extraction of relevant physiological parameters necessary for further statistical analysis. In order to illustrate the usefulness of this procedure to analyze voltage traces, we characterized the influence of the somatic current injection level on several electrophysiological parameters in a set of modeled neurons. This application suggests that such an algorithmic processing of physiological data extracts parameters in a suitable form for further investigation of structure-activity relationship in single neurons.

Whether weather affects music

The creative output of composers, writers, and artists is often influenced by their surroundings. To give a literary example, it has been claimed recently that some of the characters in Oliver Twist and A Christmas Carol were based on real-life people who lived near Charles Dickens in London [Richardson, 2012]. Of course, an important part of what we see and hear is not only the people with whom we interact but also our geophysical surroundings. Of all the geophysical phenomena to influence us, the weather is arguably the most significant because we are exposed to it directly and daily. The weather was a great source of inspiration for artists Claude Monet, John Constable, and William Turner, who are known for their scientifically accurate paintings of the skies [e.g., Baker and Thornes, 2006].

Centrality and spectral radius in dynamic communication networks

We explore the influence of the choice of attenuation factor on Katz centrality indices for evolving communication networks. For given snapshots of a network observed over a period of time, recently developed communicability indices aim to identify best broadcasters and listeners in the network. In this article, we looked into the sensitivity of communicability indices on the attenuation factor constraint, in relation to spectral radius (the largest eigenvalue) of the network at any point in time and its computation in the case of large networks. We proposed relaxed communicability measures where the spectral radius bound on attenuation factor is relaxed and the adjacency matrix is normalised in order to maintain the convergence of the measure. Using a vitality based measure of both standard and relaxed communicability indices we looked at the ways of establishing the most important individuals for broadcasting and receiving of messages related to community bridging roles. We illustrated our findings with two examples of real-life networks, MIT reality mining data set of daily communications between 106 individuals during one year and UK Twitter mentions network, direct messages on Twitter between 12.4k individuals during one week.