Game based cyber security training: are serious games suitable for cyber security training?

Security research and training is attracting a lot of investment and interest from governments and the private sector. Most efforts have focused on physical security, while cyber security or digital security has been given less importance. With recent high-profile attacks it has become clear that training in cyber security is needed. Serious Games have the capability to be effective tools for public engagement and behavioural change and role play games, are already used by security professionals. Thus cyber security seems especially well-suited to Serious Games. This paper investigates whether games can be effective cyber security training tools. The study is conducted by means of a structured literature review supplemented with a general web search. While there are early positive indications there is not yet enough evidence to draw any definite conclusions. There is a clear gap in target audience with almost all products and studies targeting the general public and very little attention given to IT professionals and managers. The products and studies also mostly work over a short period, while it is known that short-term interventions are not particularly effective at affecting behavioural change

Multiple Action Recognition for Video Games (MARViG)

Action recognition research historically has focused on increasing accuracy on datasets in highly controlled environments. Perfect or near perfect offline action recognition accuracy on scripted datasets has been achieved. The aim of this thesis is to deal with the more complex problem of online action recognition with low latency in real world scenarios. To fulfil this aim two new multi-modal gaming datasets were captured and three novel algorithms for online action recognition were proposed. Two new gaming datasets, G3D and G3Di for real-time action recognition with multiple actions and multi-modal data were captured and publicly released. Furthermore, G3Di was captured using a novel game-sourcing method so the actions are realistic. Three novel algorithms for online action recognition with low latency were proposed. Firstly, Dynamic Feature Selection, which combines the discriminative power of Random Forests for feature selection with an ensemble of AdaBoost classifiers for dynamic classification. Secondly, Clustered Spatio-Temporal Manifolds, which modelled the dynamics of human actions with style invariant action templates that were combined with Dynamic Time Warping for execution rate invariance. Finally, a Hierarchical Transfer Learning framework, comprised of a novel transfer learning algorithm to detect compound actions in addition to hierarchical interaction detection to recognise the actions and interactions of multiple subjects. The proposed algorithms run in real-time with low latency ensuring they are suitable for a wide range of natural user interface applications including gaming. State-of-the art results were achieved for online action recognition. Experimental results indicate higher complexity of the G3Di dataset in comparison to the existing gaming datasets, highlighting the importance of this dataset for designing algorithms suitable for realistic interactive applications. This thesis has advanced the study of realistic action recognition and is expected to serve as a basis for further study within the research community

Hierarchical transfer learning for online recognition of compound actions

Recognising human actions in real-time can provide users with a natural user interface (NUI) enabling a range of innovative and immersive applications. A NUI application should not restrict users’ movements; it should allow users to transition between actions in quick succession, which we term as compound actions. However, the majority of action recognition researchers have focused on individual actions, so their approaches are limited to recognising single actions or multiple actions that are temporally separated. This paper proposes a novel online action recognition method for fast detection of compound actions. A key contribution is our hierarchical body model that can be automatically configured to detect actions based on the low level body parts that are the most discriminative for a particular action. Another key contribution is a transfer learning strategy to allow the tasks of action segmentation and whole body modelling to be performed on a related but simpler dataset, combined with automatic hierarchical body model adaption on a more complex target dataset. Experimental results on a challenging and realistic dataset show an improvement in action recognition performance of 16% due to the introduction of our hierarchical transfer learning. The proposed algorithm is fast with an average latency of just 2 frames (66ms) and outperforms state of the art action recognition algorithms that are capable of fast online action recognition

Going beyond GDP with a parsimonious indicator : inequality-adjusted healthy lifetime income

Per capita GDP has limited use as a well-being indicator because it does not capture many dimensions that imply a good life, such as health and equality of opportunity. However, per capita GDP has the virtues of easy interpretation and can be calculated with manageable data requirements. Against this backdrop, a need exists for a measure of well-being that preserves the advantages of per capita GDP, but also includes health and equality. We propose a new parsimonious indicator to fill this gap and calculate it for 149 countries

Intracellular \u3cem\u3eSalmonella\u3c/em\u3e delivery of an exogenous immunization antigen refocuses CD8 T cells against cancer cells, eliminates pancreatic tumors and forms antitumor immunity

Introduction: Immunotherapies have shown great promise, but are not effective for all tumors types and are effective in less than 3% of patients with pancreatic ductal adenocarcinomas (PDAC). To make an immune treatment that is effective for more cancer patients and those with PDAC specifically, we genetically engineered Salmonella to deliver exogenous antigens directly into the cytoplasm of tumor cells. We hypothesized that intracellular delivery of an exogenous immunization antigen would activate antigen-specific CD8 T cells and reduce tumors in immunized mice. Methods: To test this hypothesis, we administered intracellular delivering (ID) Salmonella that deliver ovalbumin as a model antigen into tumor-bearing, ovalbumin-vaccinated mice. ID Salmonella delivers antigens by autonomously lysing in cells after the induction of cell invasion. Results: We showed that the delivered ovalbumin disperses throughout the cytoplasm of cells in culture and in tumors. This delivery into the cytoplasm is essential for antigen cross-presentation. We showed that co-culture of ovalbumin-recipient cancer cells with ovalbumin-specific CD8 T cells triggered a cytotoxic T cell response. After the adoptive transfer of OT-I CD8 T cells, intracellular delivery of ovalbumin reduced tumor growth and eliminated tumors. This effect was dependent on the presence of the ovalbumin-specific T cells. Following vaccination with the exogenous antigen in mice, intracellular delivery of the antigen cleared 43% of established KPC pancreatic tumors, increased survival, and prevented tumor re-implantation. Discussion: This response in the immunosuppressive KPC model demonstrates the potential to treat tumors that do not respond to checkpoint inhibitors, and the response to re-challenge indicates that new immunity was established against intrinsic tumor antigens. In the clinic, ID Salmonella could be used to deliver a protein antigen from a childhood immunization to refocus pre-existing T cell immunity against tumors. As an off-the-shelf immunotherapy, this bacterial system has the potential to be effective in a broad range of cancer patients

Addressing Resistance to Antibiotics in Pluralist Health Systems

There is growing international concern about the threat to public health of the emergence and spread of bacteria resistant to existing antibiotics. An effective response must invest in both the development of new drugs and measures to slow the emergence of resistance. This paper addresses the former. It focuses on low and middle-income countries with pluralistic health systems, where people obtain much of their antibiotics in unorganised markets. There is evidence that these markets have enabled people to treat many infections and reduce mortality. However, they also encourage overuse of antibiotics and behaviour likely to encourage the emergence of resistance. The paper reviews a number of strategies for improving the use of antibiotics. It concludes that effective strategies need measures to ensure easy access to antibiotics, as well as those aimed at influencing providers and users of these drugs to use them appropriately.Funding for work on this paper was provided by a grant by the UK ESRC to the STEPS Centre and a grant by the UK Department for International Development to the Future Health Systems Consortium

Novel Amdovirus in Gray Foxes

We used viral metagenomics to identify a novel parvovirus in tissues of a gray fox (Urocyon cinereoargenteus). Nearly full genome characterization and phylogenetic analyses showed this parvovirus (provisionally named gray fox amdovirus) to be distantly related to Aleutian mink disease virus, representing the second viral species in the Amdovirus genus

Thermal imaging is a non-invasive alternative to PET-CT for measurement of brown adipose tissue activity in humans

Background Obesity and its metabolic consequences are a major cause of morbidity and mortality. Brown adipose tissue (BAT) utilises glucose and free fatty acids to produce heat, thereby increasing energy expenditure. Effective evaluation of human BAT stimulators is constrained by current standard BAT assessment methods as positron emission tomography-computed tomography (PET-CT) requires exposure to high doses of ionising radiation. Infrared thermography (IRT) is a potential non-invasive, safe alternative, although direct corroboration with PET-CT has not previously been established. Methods IRT and 18F-fluorodeoxyglucose (¹⁸F-FDG) PET-CT data from 8 healthy male participants subjected to water jacket cooling were directly compared. Thermal images (TIs) were geometrically transformed to overlay PET-CT-derived maximum intensity projection (MIP) images from each subject and the areas of greatest intensity of temperature and glucose-uptake within the supraclavicular regions compared. Relationships between supraclavicular temperatures from IRT (TSCR) and the maximum rate of glucose uptake (MR(gluc)) from PET-CT were determined. Results Glucose uptake on MR(gluc)MIP was positively correlated with change in TSCR relative to a reference region (r² = 0.721; p=0.008). Spatial overlap between areas of maximal MR(gluc)MIP and maximal TSCR was 29.5±5.1%. Prolonged cooling to 60 minutes was associated with further TSCR rise compared with cooling to 10 minutes. Conclusions The supraclavicular hotspot identified on IRT closely corresponds to the area of maximal uptake on PET-CT-derived MR(gluc)MIP images. Greater increases in relative TSCR were associated with raised glucose uptake. IRT should now be considered a suitable method for measuring BAT activation, especially in populations where PET-CT is not feasible, practical or repeatable