Automated feature engineering for HTTP tunnel detection

Generating discriminative input features is a key requirement for achieving highly accurate classifiers. The process of generating features from raw data is known as feature engineering and it can take significant manual effort. In this paper we propose automated feature engineering to derive a suite of additional features from a given set of basic features with the aim of both improving classifier accuracy through discriminative features, and to assist data scientists through automation. Our implementation is specific to HTTP computer network traffic. To measure the effectiveness of our proposal, we compare the performance of a supervised machine learning classifier built with automated feature engineering versus one using human-guided features. The classifier addresses a problem in computer network security, namely the detection of HTTP tunnels. We use Bro to process network traffic into base features and then apply automated feature engineering to calculate a larger set of derived features. The derived features are calculated without favour to any base feature and include entropy, length and N-grams for all string features, and counts and averages over time for all numeric features. Feature selection is then used to find the most relevant subset of these features. Testing showed that both classifiers achieved a detection rate above 99.93% at a false positive rate below 0.01%. For our datasets, we conclude that automated feature engineering can provide the advantages of increasing classifier development speed and reducing development technical difficulties through the removal of manual feature engineering. These are achieved while also maintaining classification accuracy.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Exploring the economic business case for incorporating biophilic urbanism

Linking sustainability, ecology and economics, this dissertation explored the decision making process for incorporating biophilic urbanism. Biophilic urbanism refers to the use of natural elements, or biophilic elements, as functional design features in urban landscapes to address climate change issues in rapidly growing economies. Informed by five global case studies, this dissertation introduced a novel term, ‘biophilic services’ to describe various characteristics that link the impact of biophilic elements to value. A model is also presented outlining underlying decision making logic for incorporating biophilic urbanism. These findings have immediate and practical implications for integrating biophilic urbanism into the fabric of the built environment.

An introduction to ophthalmic biomaterials and their role in tissue engineering and regenerative medicine

This chapter presents a brief history of the development of ophthalmic biomaterials. Particularities in the development of ophthalmic biomaterials are discussed and some of their historic priorities within the general field of biomaterials are revealed or emphasized. The chapter then discusses the role and integration of ophthalmic biomaterials in tissue engineering and regenerative medicine applications.

Performance modulation of α-MnO2 nanowires by crystal facet engineering

Modulation of material physical and chemical properties through selective surface engineering is currently one of the most active research fields, aimed at optimizing functional performance for applications. The activity of exposed crystal planes determines the catalytic, sensory, photocatalytic, and electrochemical behavior of a material. In the research on nanomagnets, it opens up new perspectives in the fields of nanoelectronics, spintronics, and quantum computation. Herein, we demonstrate controllable magnetic modulation of α-MnO 2 nanowires, which displayed surface ferromagnetism or antiferromagnetism, depending on the exposed plane. First-principles density functional theory calculations confirm that both Mn- and O-terminated α-MnO2(1 1 0) surfaces exhibit ferromagnetic ordering. The investigation of surface-controlled magnetic particles will lead to significant progress in our fundamental understanding of functional aspects of magnetism on the nanoscale, facilitating rational design of nanomagnets. Moreover, we approved that the facet engineering pave the way on designing semiconductors possessing unique properties for novel energy applications, owing to that the bandgap and the electronic transport of the semiconductor can be tailored via exposed surface modulations.

Concise review: Humanized models of tumor immunology in the 21st century: Convergence of cancer research and tissue engineering

Despite positive testing in animal studies, more than 80% of novel drug candidates fail to proof their efficacy when tested in humans. This is primarily due to the use of preclinical models that are not able to recapitulate the physiological or pathological processes in humans. Hence, one of the key challenges in the field of translational medicine is to “make the model organism mouse more human.” To get answers to questions that would be prognostic of outcomes in human medicine, the mouse's genome can be altered in order to create a more permissive host that allows the engraftment of human cell systems. It has been shown in the past that these strategies can improve our understanding of tumor immunology. However, the translational benefits of these platforms have still to be proven. In the 21st century, several research groups and consortia around the world take up the challenge to improve our understanding of how to humanize the animal's genetic code, its cells and, based on tissue engineering principles, its extracellular microenvironment, its tissues, or entire organs with the ultimate goal to foster the translation of new therapeutic strategies from bench to bedside. This article provides an overview of the state of the art of humanized models of tumor immunology and highlights future developments in the field such as the application of tissue engineering and regenerative medicine strategies to further enhance humanized murine model systems.

Investigating limitations of SURF approach for thermal imaging analysis and mapping

Wildlife conservation involves an understanding of a specific animal, its environment and the interaction within a local ecosystem. Unmanned Aerial Vehicles (UAVs) present cost effective, non-intrusive solution for detecting animals over large areas and the use thermal imaging cameras offer the ability detect animals that would otherwise be concealed to visible light cameras. This report examines some of limitations on using SURF for the development of large maps using multiple stills images extracted from the thermal imaging video camera which contain wildlife (eg. Koala in them).

Is economic valuation of ecosystem services useful to decision-makers? Lessons learned from Australian coastal and marine management

Economic valuation of ecosystem services is widely advocated as a useful decision-support tool for ecosystem management. However, the extent to which economic valuation of ecosystem services is actually used or considered useful in decision-making is poorly documented. This literature blindspot is explored with an application to coastal and marine ecosystems management in Australia. Based on a nation-wide survey of eighty-eight decision-makers representing a diversity of management organizations, the perceived usefulness and level of use of ecosystem services economic valuation in support of coastal and marine management are examined. A large majority of decision-makers are found to be familiar with economic valuation and consider it useful - even necessary - in decision-making, although this varies across decision-makers groups. However, most decision-makers never or rarely use it. The perceived level of importance and trust in estimated dollar values differ across ecosystem services, and are especially high for values that relate to commercial activities. A number of factors are also found to influence respondent’s use of economic valuation. Such findings concur with conclusions from other existing works, and are instructive to reflect on the issue of the usefulness of ESV in environmental management decision-making. They also confirm that the survey-based approach developed in this application represents a sound strategy to examine this issue at various scales and management levels.

Synergistic crystal facet engineering and structural control of WO3 films exhibiting unprecedented photoelectrochemical performance

WO3 nanoplate arrays with (002) oriented facets grown on fluorine doped SnO2 (FTO) glass substrates are tailored by tuning the precursor solution via a facile hydrothermal method. A 2-step hydrothermal method leads to the preferential growth of WO3 film with enriched (002) facets, which exhibits extraordinary photoelectrochemical (PEC) performance with a remarkable photocurrent density of 3.7 mA cm–2 at 1.23 V vs. revisable hydrogen electrode (RHE) under AM 1.5 G illumination without the use of any cocatalyst, corresponding to ~93% of the theoretical photocurrent of WO3. Density functional theory (DFT) calculations together with experimental studies reveal that the enhanced photocatalytic activity and better photo-stability of the WO3 films are attributed to the synergistic effect of highly reactive (002) facet and nanoplate structure which facilitates the photo–induced charge carrier separation and suppresses the formation of peroxo-species. Without the use of oxygen evolution cocatalysts, the excellent PEC performance, demonstrated in this work, by simply tuning crystal facets and nanostructure of pristine WO3 films may open up new opportunities in designing high performance photoanodes for PEC water splitting.