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Being Responsible in Cybersecurity:A multi-layered perspective
Full text linkThe paper posits that in the increasingly connected digital landscape, there is a growing need to examine the scale and scope of responsible cybersecurity. In an exploratory study that involved qualitative interviews with senior cybersecurity professionals, we identify different layers of responsible cybersecurity that span across techno-centric, human-centric, organizational (intra and inter) and societal perspectives. We present these in an onion-shaped framework and show that collectively these diverse perspectives highlight the linked responsibilities of different stakeholders both within and beyond the organization. The study also finds that senior leadership plays a crucial role in fostering responsible cybersecurity across the different layers. Implications for research and practice are discussed
Auxiliary Generative Adversarial Networks with Iliustration2Vec and Q-Learning based Hyperparameter Optimisation for Anime Image Synthesis
Full text linkHarnessing the power of Generative Adversarial Networks (GANs) for the specialised task of anime face generation, this study introduces enhanced models of Auxiliary Classifier GAN (AC-GAN) and Wasserstein Auxiliary Classifier GAN (WAC-GAN) with modified network architectures and reinforcement learning-based hyperparameter optimisation. These models are uniquely adapted to handle the distinct nuances of anime-style imagery, a domain where conventional GANs often stumble due to complex stylistic variations and a heightened risk of mode collapse. Novel elements of our approach include, (1) modification of existing generator and discriminator architectures of both AC-GAN and WAC-GAN, (2) Q-learning based optimal hyperparameter selection, and (3) Illustration2Vec (I2V)-based automated attribute label extraction. Specifically, the Q-learning method is employed for hyperparameter search which effectively explores the search space of key network configurations by fulfilling the principles of Bellman optimality. Besides that, a deep learning-based 12V's method is utilised to generate attribute class labels and latent vectors to inform the generation process. Furthermore, we augment AC-GAN and WAC-GAN with additional layers to enhance their feature learning and generative capabilities. The insertion of these additional layers is calibrated based on the optimised network learning settings as well as the class labels derived from 12V, to fine-tune model scalability and diversity. Our experimental studies indicate that the conjunction of these techniques has led to a significant improvement in generating high-fidelity anime faces, adeptly handling the diverse and complex attributes inherent in anime-style imagery. The proposed strategies also showcase the potential of our customised AC-GAN and WAC-GAN models to master the nuanced art of anime face generation
Genetic Algorithm with Reinforcement Learning based Parameter Optimisation
Full text linkOptimisation of parameters in Genetic algorithms (GA) can improve the speed and accuracy of the solution produced, but well optimised parameters are dependant on the problem being solved, and the substantial additional cost of spending time pre-computing good parameters can offset the benefit. This research investigates the use of reinforcement learning algorithms to optimise the parameters of the GA during its runtime. Specifically, we propose a variant of the GA method which embeds the Q-learning algorithm to select an optimal mutation rate at each iteration. Evaluating with a set of benchmark functions, the proposed GA model with Q-learning shows promising performance with lower mean scores than those of the original GA for most test functions. In particular, the Q-learning algorithm shows a promising emergent behaviour, i.e. selecting a high mutation rate when the population variance is low to increase swarm and search diversity. Evaluated using diverse unimodal and multimodal numerical optimisation problems, the proposed model outperforms several baseline GAs with a statistical significance
Objective and Subjective Long-term Cognitive Outcomes in COVID-19 Survivors Managed with ECMO: A Case Series
Full text linkCOVID-19 has been associated with significant health complications, including cognitive impairments, particularly among patients requiring intensive care interventions. A subset of these patients, especially those needing extracorporeal membrane oxygenation (ECMO), face heightened vulnerability due to prolonged Intensive Care Unit (ICU) stay and extended ECMO duration, placing them at an increased risk of developing post intensive care syndrome (PICS), a multifaceted condition that affects cognitive and psychological functions among other health-related domains. This study aims to investigate the cognitive screening outcomes and characteristics of cognitive impairments among COVID-19 survivors managed with ECMO, enhancing our understanding of cognitive outcomes in this high-risk group. Eighty-five COVID-19 patients who had been treated with ECMO were contacted after their ICU admission. The Telephone Montreal Cognitive Assessment (T-MoCA) was employed to detect cognitive impairment. Neuropsychological assessment was completed with ten survivors. A case series design was employed to characterise the cognitive profile of these ten COVID-19 survivors. The mean T-MoCA score for the 49 cohort was 16.20 (SD = 2.93), indicating cognitive impairment among COVID-19 survivors managed with ECMO. T-MoCA scores for the ten patients who completed neuropsychological assessments ranged from 10 to 19, with a mean score of 16.2 (SD = 2.94). The case series analysis demonstrated impairments across domains of attention, working memory, processing speed, and memory. Cognitive impairments are evident in COVID-19 survivors managed with ECMO, presenting cognitive profiles similar to those documented in acute respiratory distress syndrome (ARDS) patients (non-COVID-19).Key words: ICU, COVID-19, ECMO, Cognitive Impairment, PICS<br/
Television, Musical Register, and the Franchise: Continuity and Change
Full text linkThis chapter proposes a model for understanding television music within a broader franchise space, exploring how continuity and change operates both within, and across, television series. Specifically, this framework proposes the concept of a musical register for analysing such musical intertextuality in television and beyond. More specific than genre, a musical register serves as a coherent, but ever-developing musical identity for a franchise. Our concept of register identifies musical practice which is flexible enough to evolve over time but remains sufficiently consistent to serve as a musical thread between fragmented elements of a franchise. This adaptable sonic language can develop alongside changing televisual aesthetics, even traversing media boundaries to film and video games, whilst satisfying fan expectations. Our model accounts for musical connections that are broader and more complex than explicit musical recapitulation, but remain distinctive enough to link texts. As franchises continue to be central to mass-market corporate entertainment strategies, this model illuminates how music serves as part of that creative and business agenda, as well as the implications of franchise music for producers, composers and fans. While this approach is applicable to a wide range of franchise contexts, this chapter will use the case study of the Star Trek television series to illustrate our model of a franchise’s musical register. <br/
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