Social and asocial learning in collective action problems:the rise and fall of socially-beneficial behaviour

The allocation of common-pool resources is an important topic in technical and socio-Technical systems, and when left unmanaged, such systems often collapse to highly unequal and unsustainable outcomes. Recent work has highlighted a role for electronic institutions in managing such resources, to ensure socially-beneficial outcomes in the long term. However, open self-organising multi-Agent systems often involve agents that learn behaviours in order to meet their goals. In this paper we explore the interplay between institutional features and forms of social and asocial learning employed by self-interested agents. We show that, while recent results have associated social learning with sustainability, this is sensitive to the form of social learning used. We show that more realistic models that combine social and asocial learning are more likely to lead to unsustainable institutions and anti-social outcomes. However, a key role for pardons in the sanction mechanism of the institution is identified, which allows for tolerance of a range of behaviours associated with ongoing learning, including complacency and exploration

Ageing effect on flicker-induced diameter changes in retinal microvessels of healthy individuals

Purpose: To compare flicker-induced retinal vessel diameter changes in varying age groups with low cardiovascular risk. Methods: Retinal vascular reactivity to flicker light was assessed by means of dynamic retinal vessel analysis in 57 participants aged 19-30 years, 75 participants aged 31-50 years and 62 participants aged 51-70 years participants. Other assessments included carotid intima-media thickness (c-IMT), augmentation index (AIx), blood pressure profiles, blood lipid metabolism markers and Framingham risk scores (FRS). Results: Retinal arterial dilation amplitude (DA) and postflicker percentage constriction (MC%) were significantly decreased in the oldest group compared to the middle-aged (p = 0.028; p = 0.021) and youngest group (p = 0.003; p = 0.026). The arterial constriction slope (SlopeAC) was also decreased in the oldest group compared to the youngest group (p = 0.027). On the venous side, MC% was decreased in the middle-aged and oldest groups in comparison with the youngest group (p = 0.015; p = 0.010, respectively). Additionally, men exhibited increased arterial DA (p = 0.007), and percentage dilation (MD%, p < 0.001) in comparison with women, but only in the youngest age group. Both AIx and c-IMT scores increased with age (both p < 0.001); however, no correlations were found between the observed differences in the measured retinal vascular function and systemic parameters. Conclusion: In individuals with low cardiovascular risk, there are age-related differences in flicker-induced retinal vessel diameter changes throughout the entire functional response curve for arteries and veins. Gender differences mainly affect the arterial dilatory phase and are only present in young individuals

Strafe als gemeinsame Handlung von Eltern und Kind: ein Vorschlag zur Konzeptionalisierung und Rechtfertigung elterlicher Strafakte

Das erste Ziel meiner Argumentation besteht darin zu verdeutlichen, dass sich Fragen der Strafbefugnis, des Strafzwecks und des Strafmaßes elterlicher Strafpraxis nicht einfach als ein Anwendungsbereich klassischer Strafzwecktheorien erklären lassen. Der Strafzweck ist hier ein anderer, die sanktionierten Normen reichen tiefer in das persönliche Verhalten hinein und vor allem ist die Beziehung zwischen Bestrafendem und Bestraftem eine genuin andere als die zwischen staatlichen Organen und mündigen Bürgern. In einem zweiten Schritt werde ich meinen positiven Vorschlag erläutern, elterliche Strafe stattdessen als eine gemeinsame Handlung zu beschreiben und zu rechtfertigen. Dieser Vorschlag setzt an der jüngsten Theoriebildung zu kollektiver Intentionalität an. Interpersonale bzw. elterliche Strafe ist demnach keine Handlung, die ein Subjekt an einem Objekt vornimmt; eine gelungene Strafhandlung ließe sich viel besser, so meine Kernthese, als eine gemeinsame Handlung beschreiben, in der Kind und Erwachsener in beidseitiger intentionaler Übereinstimmung zum Strafakt beitragen. Ausblickend werde ich zeigen, dass diese Sichtweise ein starkes Argument gegen körperliche Strafen impliziert.The first aim of my argument is to clarify why conventional justifications of juridical punishment - and theories of criminal justice in general - are not applicable to the case of parental punishment. The specific function of juridical punishment concerns the compliance of law-abiding citizens. This differs considerably from the function of parental punishment with respect to the purpose of the punitive action and the intimate and responsive relation between parents and child. In a second step, I will come up with a positive proposal to describe and ultimately justify parental punishment in terms of a joint action. This proposal draws on recent writings in the theory of collective action and intentionality. Parental punishment is not to be seen as an act that is committed by a subject (the parents) and suffered by an object (the child); rather, it should be conceptualized as a certain type of joint action that is committed in intentional agreement by both parents and child. In the outlook, I will argue that this perspective implies a strong argument against corporal punishment

Contributors to the aesthetic judgement of 3D virtual sculptures

Aesthetic judgement plays a key role in many aspects of everyday life, judging an object to be aesthetically pleasing often heightens the pleasure and enjoyment derived from that object. One area where this applies is artwork, where most genres and styles of art heavily rely on being considered aesthetically pleasing. It has been shown that an aesthetic judgement of a piece of art combines many different aspects, all contributing to the assessment. Identifying and understanding these aspects for 2D images has been extensively investigated, however, 3D items have not been considered to the same degree. In this paper, we investigate which aspects contribute to the aesthetic judgement of 3D virtual sculptures, using a gamified approach within a custom VR environment. Participants were able to express which aspects contributed to their assessment of the virtual sculptures. We found that some stalwart 2D aspects, such as complexity and order, are not as highly important for 3D items, being replaced by other characteristics such as how dynamic the sculpture appeared

Cross-domain MLP and CNN Transfer Learning for Biological Signal Processing: EEG and EMG

In this work, we show the success of unsupervised transfer learning between Electroencephalographic (brainwave) classification and Electromyographic (muscular wave) domains with both MLP and CNN methods. To achieve this, signals are measured from both the brain and forearm muscles and EMG data is gathered from a 4-class gesture classification experiment via the Myo Armband, and a 3-class mental state EEG dataset is acquired via the Muse EEG Headband. A hyperheuristic multi-objective evolutionary search method is used to find the best network hyperparameters. We then use this optimised topology of deep neural network to classify both EMG and EEG signals, attaining results of 84.76% and 62.37% accuracy, respectively. Next, when pre-trained weights from the EMG classification model are used for initial distribution rather than random weight initialisation for EEG classification, 93.82%(+29.95) accuracy is reached. When EEG pre-trained weights are used for initial weight distribution for EMG, 85.12% (+0.36) accuracy is achieved. When the EMG network attempts to classify EEG, it outperforms the EEG network even without any training (+30.25% to 82.39% at epoch 0), and similarly the EEG network attempting to classify EMG data outperforms the EMG network (+2.38% at epoch 0). All transfer networks achieve higher pre-training abilities, curves, and asymptotes, indicating that knowledge transfer is possible between the two signal domains. In a second experiment with CNN transfer learning, the same datasets are projected as 2D images and the same learning process is carried out. In the CNN experiment, EMG to EEG transfer learning is found to be successful but not vice-versa, although EEG to EMG transfer learning did exhibit a higher starting classification accuracy. The significance of this work is due to the successful transfer of ability between models trained on two different biological signal domains, reducing the need for building more computationally complex models in future research

Synthetic Biological Signals Machine-generated by GPT-2 improve the Classification of EEG and EMG through Data Augmentation

Synthetic data augmentation is of paramount importance for machine learning classification, particularly for biological data, which tend to be high dimensional and with a scarcity of training samples. The applications of robotic control and augmentation in disabled and able-bodied subjects still rely mainly on subject-specific analyses. Those can rarely be generalised to the whole population and appear to over complicate simple action recognition such as grasp and release (standard actions in robotic prosthetics and manipulators). We show for the first time that multiple GPT-2 models can machine-generate synthetic biological signals (EMG and EEG) and improve real data classification. Models trained solely on GPT-2 generated EEG data can classify a real EEG dataset at 74.71% accuracy and models trained on GPT-2 EMG data can classify real EMG data at 78.24% accuracy. Synthetic and calibration data are then introduced within each cross validation fold when benchmarking EEG and EMG models. Results show algorithms are improved when either or both additional data are used. A Random Forest achieves a mean 95.81% (1.46) classification accuracy of EEG data, which increases to 96.69% (1.12) when synthetic GPT-2 EEG signals are introduced during training. Similarly, the Random Forest classifying EMG data increases from 93.62% (0.8) to 93.9% (0.59) when training data is augmented by synthetic EMG signals. Additionally, as predicted, augmentation with synthetic biological signals also increases the classification accuracy of data from new subjects that were not observed during training. A Robotiq 2F-85 Gripper was finally used for real-time gesture-based control, with synthetic EMG data augmentation remarkably improving gesture recognition accuracy, from 68.29% to 89.5%

Look and listen:A multi-modality late fusion approach to scene classification for autonomous machines

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16, 000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach

An architecture for the autonomic curation of crowdsourced knowledge

Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to providing numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality