A learning-based approach to reactive security

Despite the conventional wisdom that proactive security is superior to reactive security, we show that reactive security can be competitive with proactive security as long as the reactive defender learns from past attacks instead of myopically overreacting to the last attack. Our game-theoretic model follows common practice in the security literature by making worst-case assumptions about the attacker: we grant the attacker complete knowledge of the defender’s strategy and do not require the attacker to act rationally. In this model, we bound the competitive ratio between a reactive defense algorithm (which is inspired by online learning theory) and the best fixed proactive defense. Additionally, we show that, unlike proactive defenses, this reactive strategy is robust to a lack of information about the attacker’s incentives and knowledge.

A unifying view of multiple kernel learning

Recent research on multiple kernel learning has lead to a number of approaches for combining kernels in regularized risk minimization. The proposed approaches include different formulations of objectives and varying regularization strategies. In this paper we present a unifying optimization criterion for multiple kernel learning and show how existing formulations are subsumed as special cases. We also derive the criterion’s dual representation, which is suitable for general smooth optimization algorithms. Finally, we evaluate multiple kernel learning in this framework analytically using a Rademacher complexity bound on the generalization error and empirically in a set of experiments.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

Optimal online prediction in adversarial environments

In many prediction problems, including those that arise in computer security and computational finance, the process generating the data is best modelled as an adversary with whom the predictor competes. Even decision problems that are not inherently adversarial can be usefully modeled in this way, since the assumptions are sufficiently weak that effective prediction strategies for adversarial settings are very widely applicable.

A systematic approach to evaluate the process improvement in lean manufacturing organizations

Numerous tools and techniques have been developed to eliminate or reduce waste and carry out Lean concepts in the manufacturing environment. However, in practice, manufacturers encounter difficulties to clearly identify the weaknesses of the existing processes in order to address them by implementing Lean tools. Moreover, selection and implementation of appropriate Lean strategies to address the problems identified is a challenging task. According best of authors‟ knowledge, there is no method available to quantitatively evaluate the cost and benefits of implementing a Lean strategy to address the weaknesses in the manufacturing process. Therefore, benefits of Lean approaches cannot be clearly established. The authors developed a methodology to quantitatively measure the performances of a manufacturing system in detecting the causes of inefficiencies and to select appropriate Lean strategies to address the problems identified. The proposed methodology demonstrates that the Lean strategies should be implemented based on the contexts of the organization and identified problem in order to achieve maximum cost benefits. Finally, a case study has been presented to demonstrate how the procedure developed works in practical situation.

A method for evaluating lean assembly process at design stage

Lean product design has the potential to reduce the overall product development time and cost and can improve the quality of a product. However, it has been found that no or little work has been carried out to provide an integrated framework of "lean design" and to quantitatively evaluate the effectiveness of lean practices/principles in product development process. This research proposed an integrated framework for lean design process and developed a dynamic decision making tool based on Methods Time Measurement (MTM) approach for assessing the impact of lean design on the assembly process. The proposed integrated lean framework demonstrates the lean processes to be followed in the product design and assembly process in order to achieve overall leanness. The decision tool consists of a central database, the lean design guidelines, and MTM analysis. Microsoft Access and C# are utilized to develop the user interface to use the MTM analysis as decision making tool. MTM based dynamic tool is capable of estimating the assembly time, costs of parts and labour of various alternatives of a design and hence is able to achieve optimum design. A case study is conducted to test and validate the functionality of the MTM Analysis as well as to verify the lean guidelines proposed for product development.

Robotics, Vision and Control : Fundamental Algorithms in MATLAB

The practice of robotics and computer vision each involve the application of computational algorithms to data. The research community has developed a very large body of algorithms but for a newcomer to the field this can be quite daunting. For more than 10 years the author has maintained two open-source MATLAB® Toolboxes, one for robotics and one for vision. They provide implementations of many important algorithms and allow users to work with real problems, not just trivial examples. This new book makes the fundamental algorithms of robotics, vision and control accessible to all. It weaves together theory, algorithms and examples in a narrative that covers robotics and computer vision separately and together. Using the latest versions of the Toolboxes the author shows how complex problems can be decomposed and solved using just a few simple lines of code. The topics covered are guided by real problems observed by the author over many years as a practitioner of both robotics and computer vision. It is written in a light but informative style, it is easy to read and absorb, and includes over 1000 MATLAB® and Simulink® examples and figures. The book is a real walk through the fundamentals of mobile robots, navigation, localization, arm-robot kinematics, dynamics and joint level control, then camera models, image processing, feature extraction and multi-view geometry, and finally bringing it all together with an extensive discussion of visual servo systems.

Diversity in action : minority group media and social change

My thesis consists of a creative work plus an exegesis. This exegesis uses case study research to investigate three Brisbane-based media organisations and the role they play in encouraging social inclusion and other positive social change for specific disadvantaged and stigmatised minority groups. Bailey, Cammaerts and Carpentier’s theoretical approach to alternative media forms the basis of this research. Bailey et al. (2008, p. 156) view alternative media organisations as having four important roles, two media-centred and two society-centred, which must all be considered to best understand them: • serving their communities • acting as an alternative to mainstream media discourses • promoting and advocating democratisation in the media and through the media in society • functioning as a crossroads in civil society. The first case study, about community radio station 4RPH (Radio for the Print Handicapped), centres on promoting social inclusion for people with a print disability through access to printed materials (primarily mainstream print media) in an audio format. The station also provides important opportunities for members of this group to produce media and, to a lesser extent, provides disability-specific information and discussions. The second case study, about gay print and online magazine Queensland Pride, focuses on promoting social inclusion and combating the discrimination and repression of people who identify as lesbian, gay, bisexual or transgender. Central issues include the representation (including sexualised representation) of a subculture and niche target market, and the impact of commercialisation on this free publication. The third case study, about community radio station 98.9FM, explores the promotion of social inclusion for peoples whose identity, cultures, issues, politics and contributions are often absent or misrepresented in the mainstream media. This radio station provides “a first level of service” (Meadows & van Vuuren, 1998, p. 104) to these people, but also informs and entertains those in the majority society. The findings of this research suggest that there are two key mechanisms that help these media organisations to effect social change: first, strengthening the minority community and serving its needs, and second, fostering connections with the broader society.

Geometry vs appearance for discriminating between posed and spontaneous emotions

Spontaneous facial expressions differ from posed ones in appearance, timing and accompanying head movements. Still images cannot provide timing or head movement information directly. However, indirectly the distances between key points on a face extracted from a still image using active shape models can capture some movement and pose changes. This information is superposed on information about non-rigid facial movement that is also part of the expression. Does geometric information improve the discrimination between spontaneous and posed facial expressions arising from discrete emotions? We investigate the performance of a machine vision system for discrimination between posed and spontaneous versions of six basic emotions that uses SIFT appearance based features and FAP geometric features. Experimental results on the NVIE database demonstrate that fusion of geometric information leads only to marginal improvement over appearance features. Using fusion features, surprise is the easiest emotion (83.4% accuracy) to be distinguished, while disgust is the most difficult (76.1%). Our results find different important facial regions between discriminating posed versus spontaneous version of one emotion and classifying the same emotion versus other emotions. The distribution of the selected SIFT features shows that mouth is more important for sadness, while nose is more important for surprise, however, both the nose and mouth are important for disgust, fear, and happiness. Eyebrows, eyes, nose and mouth are important for anger.

Learning from episodes of degradation and recovery in variable Australian rangelands

Land-change science emphasizes the intimate linkages between the human and environmental components of land management systems. Recent theoretical developments in drylands identify a small set of key principles that can guide the understanding of these linkages. Using these principles, a detailed study of seven major degradation episodes over the past century in Australian grazed rangelands was reanalyzed to show a common set of events: (i) good climatic and economic conditions for a period, leading to local and regional social responses of increasing stocking rates, setting the preconditions for rapid environmental collapse, followed by (ii) a major drought coupled with a fall in the market making destocking financially unattractive, further exacerbating the pressure on the environment; then (iii) permanent or temporary declines in grazing productivity, depending on follow-up seasons coupled again with market and social conditions. The analysis supports recent theoretical developments but shows that the establishment of environmental knowledge that is strictly local may be insufficient on its own for sustainable management. Learning systems based in a wider community are needed that combine local knowledge, formal research, and institutional support. It also illustrates how natural variability in the state of both ecological and social systems can interact to precipitate nonequilibrial change in each other, so that planning cannot be based only on average conditions. Indeed, it is this variability in both environment and social subsystems that hinders the local learning required to prevent collapse.

Comparison of intranasal and transcutaneous immunization for induction of protective immunity against chlamydia muridarum respiratory tract infection

Chlamydia pneumoniae causes a range of respiratory infections including bronchitis, pharyngitis and pneumonia. Infection has also been implicated in exacerbation/initiation of asthma and chronic obstructive pulmonary disease (COPD) and may play a role in atherosclerosis and Alzheimer's disease. We have used a mouse model of Chlamydia respiratory infection to determine the effectiveness of intranasal (IN) and transcutaneous immunization (TCI) to prevent Chlamydia lung infection. Female BALB/c mice were immunized with chlamydial major outer membrane protein (MOMP) mixed with cholera toxin and CpG oligodeoxynucleotide adjuvants by either the IN or TCI routes. Serum and bronchoalveolar lavage (BAL) were collected for antibody analysis. Mononuclear cells from lung-draining lymph nodes were stimulated in vitro with MOMP and cytokine mRNA production determined by real time PCR. Animals were challenged with live Chlamydia and weighed daily following challenge. At day 10 (the peak of infection) animals were sacrificed and the numbers of recoverable Chlamydia in lungs determined by real time PCR. MOMP-specific antibody-secreting cells in lung tissues were also determined at day 10 post-infection. Both IN and TCI protected animals against weight loss compared to non-immunized controls with both immunized groups gaining weight by day 10-post challenge while controls had lost 6% of body weight. Both immunization protocols induced MOMP-specific IgG in serum and BAL while only IN immunization induced MOMP-specific IgA in BAL. Both immunization routes resulted in high numbers of MOMP-specific antibody-secreting cells in lung tissues (IN > TCI). Following in vitro re-stimulation of lung-draining lymph node cells with MOMP; IFNγ mRNA increased 20-fold in cells from IN immunized animals (compared to non-immunized controls) while IFNγ levels increased 6- to 7-fold in TCI animals. Ten days post challenge non-immunized animals had >7000 IFU in their lungs, IN immunized animals <50 IFU and TCI immunized animals <1500 IFU. Thus, both intranasal and transcutaneous immunization protected mice against respiratory challenge with Chlamydia. The best protection was obtained following IN immunization and correlated with IFNγ production by mononuclear cells in lung-draining LN and MOMP-specific IgA in BAL.

Varenicline, an alpha-4 beta-2 nicotinic acetylcholine receptor partial agonist, selectively decreases ethanol consumption and seeking

Abstract Alcohol dependence is a disease that impacts millions of individuals worldwide. There has been some progress with pharmacotherapy for alcohol-dependent individuals; however, there remains a critical need for the development of novel and additional therapeutic approaches. Alcohol and nicotine are commonly abused together, and there is evidence that neuronal nicotinic acetylcholine receptors (nAChRs) play a role in both alcohol and nicotine dependence. Varenicline, a partial agonist at the alpha4beta2 nAChRs, reduces nicotine intake and was recently approved as a smoking cessation aid. We have investigated the role of varenicline in the modulation of ethanol consumption and seeking using three different animal models of drinking. We show that acute administration of varenicline, in doses reported to reduce nicotine reward, selectively reduced ethanol but not sucrose seeking using an operant self-administration drinking paradigm and also decreased voluntary ethanol but not water consumption in animals chronically exposed to ethanol for 2 months before varenicline treatment. Furthermore, chronic varenicline administration decreased ethanol consumption, which did not result in a rebound increase in ethanol intake when the varenicline was no longer administered. The data suggest that the alpha4beta2 nAChRs may play a role in ethanol-seeking behaviors in animals chronically exposed to ethanol. The selectivity of varenicline in decreasing ethanol consumption combined with its reported safety profile and mild side effects in humans suggest that varenicline may prove to be a treatment for alcohol dependence.

Resource allocation and scheduling of multiple composite web services in cloud computing using cooperative coevolution genetic algorithm

In cloud computing, resource allocation and scheduling of multiple composite web services is an important and challenging problem. This is especially so in a hybrid cloud where there may be some low-cost resources available from private clouds and some high-cost resources from public clouds. Meeting this challenge involves two classical computational problems: one is assigning resources to each of the tasks in the composite web services; the other is scheduling the allocated resources when each resource may be used by multiple tasks at different points of time. In addition, Quality-of-Service (QoS) issues, such as execution time and running costs, must be considered in the resource allocation and scheduling problem. Here we present a Cooperative Coevolutionary Genetic Algorithm (CCGA) to solve the deadline-constrained resource allocation and scheduling problem for multiple composite web services. Experimental results show that our CCGA is both efficient and scalable.