Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation

Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the application of cortically coupled computer vision to rapid image search. In RSVP, images are presented to participants in a rapid serial sequence which can evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram (EEG). The contemporary approach to this problem involves supervised spatial filtering techniques which are applied for the purposes of enhancing the discriminative information in the EEG data. In this paper we make two primary contributions to that field: 1) We propose a novel spatial filtering method which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we provide a comprehensive comparison of nine spatial filtering pipelines using three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern (CSP) and three linear classification methods Linear Discriminant Analysis (LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three pipelines without spatial filtering are used as baseline comparison. The Area Under Curve (AUC) is used as an evaluation metric in this paper. The results reveal that MTWLB and xDAWN spatial filtering techniques enhance the classification performance of the pipeline but CSP does not. The results also support the conclusion that LR can be effective for RSVP based BCI if discriminative features are available

Periodic points for good reduction maps on curves

The periodic points of a morphism of good reduction for a smooth projective curve with good reduction over the p-adics form a discrete set. This is used to give an interpretation of the morphic height in terms of asymptotic properties of periodic points, and a morphic analogue of Jensen's formula

Young children suffering, or likely to suffer, significant harm : experiences on entering education

Since 2005, the Centre for Child and Family Research, Loughborough University has been tracing the decision-making process influencing the life pathways of a cohort of very young children who were identified as suffering, or likely to suffer, significant harm before they reached their first birthdays. The overall objective of the research is to collect evidence which supports decisions concerning which children require permanent out of home placements (such as adoption) and those that can safely remain with their birth parents

Evolving collective behavior in an artificial ecology

Collective behavior refers to coordinated group motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards and food. Each creature has a simple artificial neural network brain that controls movement in different situations. A chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems

Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks

Generative adversarial networks (GANs) are increasingly attracting attention in the computer vision, natural language processing, speech synthesis and similar domains. Arguably the most striking results have been in the area of image synthesis. However, evaluating the performance of GANs is still an open and challenging problem. Existing evaluation metrics primarily measure the dissimilarity between real and generated images using automated statistical methods. They often require large sample sizes for evaluation and do not directly reflect human perception of image quality. In this work, we describe an evaluation metric we call Neuroscore, for evaluating the performance of GANs, that more directly reflects psychoperceptual image quality through the utilization of brain signals. Our results show that Neuroscore has superior performance to the current evaluation metrics in that: (1) It is more consistent with human judgment; (2) The evaluation process needs much smaller numbers of samples; and (3) It is able to rank the quality of images on a per GAN basis. A convolutional neural network (CNN) based neuro-AI interface is proposed to predict Neuroscore from GAN-generated images directly without the need for neural responses. Importantly, we show that including neural responses during the training phase of the network can significantly improve the prediction capability of the proposed model. Materials related to this work are provided at https://github.com/villawang/Neuro-AI-Interface

Between analysis and transformation: technology, methodology and evaluation on the SPLICE project

This paper concerns the ways in which technological change may entail methodological development in e-learning research. The focus of our argument centres on the subject of evaluation in e-learning and how technology can contribute to consensus-building on the value of project outcomes, and the identification of mechanisms behind those outcomes. We argue that a critical approach to the methodology of evaluation which harnesses technology in this way is vital to agile and effective policy and strategy-making in institutions as the challenges of transformation in a rapidly changing educational and technological environment are grappled with. With its focus on mechanisms and multiple stakeholder perspectives, we identify Pawson and Tilley’s ‘Realistic Evaluation’ as an appropriate methodological approach for this purpose, and we report on its use within a JISC-funded project on social software, SPLICE (Social Practices, Learning and Interoperability in Connected Environments). The project created new tools to assist the identification of mechanisms responsible for change to personal and institutional technological practice. These tools included collaborative mind-mapping and focused questioning, and tools for the animated modelling of complex mechanisms. By using these tools, large numbers of project stakeholders could engage in a process where they were encouraged to articulate and share their theories and ideas as to why project outcomes occurred. Using the technology, this process led towards the identification and agreement of common mechanisms which had explanatory power for all stakeholders. In conclusion, we argue that SPLICE has shown the potential of technologically-mediated Realistic Evaluation. Given the technologies we now have, a methodology based on the mass cumulation of stakeholder theories and ideas about mechanisms is feasible. Furthermore, the summative outcomes of such a process are rich in explanatory and predictive power, and therefore useful to the immediate and strategic problems of the sector. Finally, we argue that as well as generating better explanations for phenomena, the evaluation process can itself become transformative for stakeholders