From the buzzing in Turing’s head to machine intelligence contests

This paper presents an analysis of three major contests for machine intelligence. We conclude that a new era for Turing’s test requires a fillip in the guise of a committed sponsor, not unlike DARPA, funders of the successful 2007 Urban Challenge

The disappearing human–machine divide

In this article a look is taken at some of the different ways in which the human–machine divide is rapidly disappearing. In each case the technical basis is described and then some of the implications are also considered. In particular results from experiments are discussed in terms of their meaning and application possibilities. The article is written from the perspective of scientific experimentation opening up realistic possibilities to be faced in the future, rather than giving conclusive comments. In each case consideration is also given to some of the philosophical questions that arise

A new paradigm for BCI research

A new control paradigm for Brain Computer Interfaces (BCIs) is proposed. BCIs provide a means of communication direct from the brain to a computer that allows individuals with motor disabilities an additional channel of communication and control of their external environment. Traditional BCI control paradigms use motor imagery, frequency rhythm modification or the Event Related Potential (ERP) as a means of extracting a control signal. A new control paradigm for BCIs based on speech imagery is initially proposed. Further to this a unique system for identifying correlations between components of the EEG and target events is proposed and introduced

Closed-loop deep brain stimulation based on a stream-clustering system

Idiopathic Parkinsons disease (PD) is currently the second most important neurodegenerative disease in incidence. Deep brain stimulation (DBS) constitutes a successful and necessary therapy; however, the continuous stimulation it provides can be associated with multiple side effects. DBS uses an implanted pulse generator that delivers, through a set of electrodes, electrical stimulation to the target area, normally the Sub Thalamic Nucleus. Recently, Closed-loop DBS has emerged as a promising new strategy, where the device stimulates only when necessary, thereby reducing any adverse effects. Here, we present a Closed-loop DBS system for PD, which is able to recognize, with 100% accuracy, when the patient is going to enter into the tremor phase, thus allowing the device to stimulate only in such cases. The expert system has been designed and implemented within the data stream mining paradigm, suitable for our scenario since it can cope with continuous data of a theoretical infinite length and with a certain variability, which uses the synchronization among the neural population within the Sub Thalamic Nucleus as the continuous data stream input to the system.Depto. de Psicología Experimental, Procesos Cognitivos y LogopediaDepto. de MedicinaFac. de PsicologíaFac. de MedicinaTRUEpu

Does an individualized back education programme change nurses\u27 knowledge and practice about back injury prevention

Back injury has predominantly been a problem which has affected a large cross-section of nursing staff involved with direct patient care. While back injury prevention has been instituted in hospitals for sometime, the percentage of nurses with back injury remains high. Within a major teaching hospital, a ward in which nurses suffered a high rate of back injuries was identified. Through an action research approach the researcher (who worked in the same area as the participants) developed and implemented an individualized back injury prevention programme. The 4 criteria by which the study was measured included, a reduction of back injuries, worth of the programme, behavioural change and cognitive knowledge acquisition. The participants who were involved in the study demonstrated that individual back education has a positive effect upon reducing the injury rate of nurses\u27 back injuries. The study also describes the importance of maintaining good communication skills and co-operation with the people involved or whose behaviour is being changed. Social Learning Theory was the framework from which the design and implementation of teaching was derived

Non-conventional keystroke dynamics for user authentication

This paper introduces an approach for user authentication using free-text keystroke dynamics which incorporates the use of non-conventional keystroke features. Semi-timing features along with editing features are extracted from the user’s typing stream. Decision trees were exploited to classify each of the user’s data. In parallel for comparison, support vector machines (SVMs) were also used for classification in association with an ant colony optimization (ACO) feature selection technique. The results obtained from this study are encouraging as low false accept rates (FAR) and false reject rates (FRR) were achieved in the experimentation phase. This signifies that satisfactory overall system performance was achieved by using the typing attributes in the proposed approach. Thus, the use of non-conventional typing features improves the understanding of human typing behavior and therefore, provides significant contribution to the authentication system

Improving the performance of free-text keystroke dynamics authentication by fusion

Free-text keystroke dynamics is invariably hampered by the huge amount of data needed to train the system. This problem has been addressed in this paper by suggesting a system that combines two methods, both of which provide a reduced training requirement for user authentication using free-text keystrokes. The two methods were fused to achieve error rates lower than those produced by each method separately. Two fusion schemes, namely: decision-level fusion and feature-level fusion, were applied. Feature-level fusion was done by concatenating two sets of features before the learning stage. The two sets of features were: a timing feature set and a non-conventional feature set. Moreover, decision-level fusion was used to merge the output of two methods using majority voting. One is Support Vector Machines (SVMs) together with Ant Colony Optimization (ACO) feature selection and the other is decision trees (DTs). Even though the classifiers using the parameters merged at feature level produced low error rates, its results were outperformed by the results achieved by the decision-level fusion scheme. Decision-level fusion was employed to achieve the best performance of 0.00% False Accept Rate (FAR) and 0.00% False Reject Rate (FRR)