The experience of epilepsy in later life : a qualitative exploration of illness representations

The objective of this study is to explore how older people living with epilepsy appraise their condition through their lived-experience. The common-sense model of illness representations (CSMIR) provides a framework to explain how individuals make sense of and manage health threats. Semi-structured in-depth interviews based on the CSMIR were conducted with ten people with epilepsy who were above the age of 65. The results were analysed using Interpretative Phenomenological Analysis. Three overarching themes emerged from the analysis: ‘the power of epilepsy’, ‘they say you can live a normal life but you can’t’ and ‘attempts to adjust and cope’. Epilepsy was described as a threatening, persistent and unpredictable condition associated with distressing experiences. Participants described a process of balancing negative psychosocial consequences including stigma, loss of control and reliance on other people and medication with parallel co-existing coping strategies. These attempts to manage the condition were characterised by a desire for acceptance and increased awareness of epilepsy, strategies to restore loss of control and strength derived from supportive relationships. We conclude that there is large scope for psychosocial interventions in healthcare provision for this patient group. The role of specialist nursing, relationship-centred models, psychotherapy, educational and self-management programmes is highlighted

Realising intelligent virtual design

This paper presents a vision and focus for the CAD Centre research: the Intelligent Design Assistant (IDA). The vision is based upon the assumption that the human and computer can operate symbiotically, with the computer providing support for the human within the design process. Recently however the focus has been towards the development of integrated design platforms that provide general support irrespective of the domain, to a number of distributed collaborative designers. This is illustrated within the successfully completed Virtual Reality Ship (VRS) virtual platform, and the challenges are discussed further within the NECTISE, SAFEDOR and VIRTUE projects

Realising intelligent virtual design

This paper presents a vision and focus for the CAD Centre research: the Intelligent Design Assistant (IDA). The vision is based upon the assumption that the human and computer can operate symbiotically, with the computer providing support for the human within the design process. Recently however the focus has been towards the development of integrated design platforms that provide general support irrespective of the domain, to a number of distributed collaborative designers. This is illustrated within the successfully completed Virtual Reality Ship (VRS) virtual platform, and the challenges are discussed further within the NECTISE, SAFEDOR and VIRTUE projects

CryptoKnight:generating and modelling compiled cryptographic primitives

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware applicatio