High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning

High-dimensional problem domains pose significant challenges for anomaly detection. The presence of irrelevant features can conceal the presence of anomalies. This problem, known as the '. curse of dimensionality', is an obstacle for many anomaly detection techniques. Building a robust anomaly detection model for use in high-dimensional spaces requires the combination of an unsupervised feature extractor and an anomaly detector. While one-class support vector machines are effective at producing decision surfaces from well-behaved feature vectors, they can be inefficient at modelling the variation in large, high-dimensional datasets. Architectures such as deep belief networks (DBNs) are a promising technique for learning robust features. We present a hybrid model where an unsupervised DBN is trained to extract generic underlying features, and a one-class SVM is trained from the features learned by the DBN. Since a linear kernel can be substituted for nonlinear ones in our hybrid model without loss of accuracy, our model is scalable and computationally efficient. The experimental results show that our proposed model yields comparable anomaly detection performance with a deep autoencoder, while reducing its training and testing time by a factor of 3 and 1000, respectively.

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.

Traffic matrix prediction and estimation based on deep learning in large-scale IP backbone networks

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

Forensic challenges in service oriented architectures

Digital forensics relates to the investigation of a crime or other suspect behaviour using digital evidence. Previous work has dealt with the forensic reconstruction of computer-based activity on single hosts, but with the additional complexity involved with a distributed environment, a Web services-centric approach is required. A framework for this type of forensic examination needs to allow for the reconstruction of transactions spanning multiple hosts, platforms and applications. A tool implementing such an approach could be used by an investigator to identify scenarios of Web services being misused, exploited, or otherwise compromised. This information could be used to redesign Web services in order to mitigate identified risks. This paper explores the requirements of a framework for performing effective forensic examinations in a Web services environment. This framework will be necessary in order to develop forensic tools and techniques for use in service oriented architectures.

In search of a hidden long-term isolated sub-chondritic 142Nd/144Nd reservoir in the deep mantle : implications for the Nd isotope systematics of the Earth

Here we search for evidence of the existence of a sub-chondritic 142Nd/144Nd reservoir that balances the Nd isotope chemistry of the Earth relative to chondrites. If present, it may reside in the source region of deeply sourced mantle plume material. We suggest that lavas from Hawai’i with coupled elevations in 186Os/188Os and 187Os/188Os, from Iceland that represent mixing of upper mantle and lower mantle components, and from Gough with sub-chondritic 143Nd/144Nd and high 207Pb/206Pb, are favorable samples that could reflect mantle sources that have interacted with an Early-Enriched Reservoir (EER) with sub-chondritic 142Nd/144Nd. High-precision Nd isotope analyses of basalts from Hawai’i, Iceland and Gough demonstrate no discernable 142Nd/144Nd deviation from terrestrial standards. These data are consistent with previous high-precision Nd isotope analysis of recent mantle-derived samples and demonstrate that no mantle-derived material to date provides evidence for the existence of an EER in the mantle. We then evaluate mass balance in the Earth with respect to both 142Nd/144Nd and 143Nd/144Nd. The Nd isotope systematics of EERs are modeled for different sizes and timing of formation relative to ε143Nd estimates of the reservoirs in the μ142Nd = 0 Earth, where μ142Nd is ((measured 142Nd/144Nd/terrestrial standard 142Nd/144Nd)−1 * 10−6) and the μ142Nd = 0 Earth is the proportion of the silicate Earth with 142Nd/144Nd indistinguishable from the terrestrial standard. The models indicate that it is not possible to balance the Earth with respect to both 142Nd/144Nd and 143Nd/144Nd unless the μ142Nd = 0 Earth has a ε143Nd within error of the present-day Depleted Mid-ocean ridge basalt Mantle source (DMM). The 4567 Myr age 142Nd–143Nd isochron for the Earth intersects μ142Nd = 0 at ε143Nd of +8 ± 2 providing a minimum ε143Nd for the μ142Nd = 0 Earth. The high ε143Nd of the μ142Nd = 0 Earth is confirmed by the Nd isotope systematics of Archean mantle-derived rocks that consistently have positive ε143Nd. If the EER formed early after solar system formation (0–70 Ma) continental crust and DMM can be complementary reservoirs with respect to Nd isotopes, with no requirement for significant additional reservoirs. If the EER formed after 70 Ma then the μ142Nd = 0 Earth must have a bulk ε143Nd more radiogenic than DMM and additional high ε143Nd material is required to balance the Nd isotope systematics of the Earth.

Creating deep and broad change through research and systems approaches in early childhood education for sustainability

The co-authors raise two matters they consider essential for the future development of ECEfS. The first is the need to create deep foundations based in research. At a time of increasing practitioner interest, research in ECEfS is meagre. A robust research community is crucial to support quality in curriculum and pedagogy, and to promote learning and innovation in thinking and practice. The second 'essential' for the expansion and uptake of ECEfS is broad systemic change. All level within the early childhood education system - individual teachers and classrooms, whole centres and schools, professional associations and networks, accreditation and employing authorities, and teacher educators - must work together to create and reinforce the cultural and educational changes required for sustainability. This chapter provides explanations of processes to engender systemic change. It illustrates a systems approach, with reference to a recent study focused on embedding EfS into teacher education. This study emphasises the apparent contradiction that the answer to large-scale reform lies with small-scale reforms that build capacity and make connections.

Mathematically defined tissue engineering scaffold architectures prepared by stereolithography

The technologies employed for the preparation of conventional tissue engineering scaffolds restrict the materials choice and the extent to which the architecture can be designed. Here we show the versatility of stereolithography with respect to materials and freedom of design. Porous scaffolds are designed with computer software and built with either a poly(d,l-lactide)-based resin or a poly(d,l-lactide-co-ε-caprolactone)-based resin. Characterisation of the scaffolds by micro-computed tomography shows excellent reproduction of the designs. The mechanical properties are evaluated in compression, and show good agreement with finite element predictions. The mechanical properties of scaffolds can be controlled by the combination of material and scaffold pore architecture. The presented technology and materials enable an accurate preparation of tissue engineering scaffolds with a large freedom of design, and properties ranging from rigid and strong to highly flexible and elastic.

Micro-CT analysis of tissue engineering scaffold architectures

A novel method was developed for a quantitative assessment of pore interconnectivity using micro-CT data. This method makes use of simulated spherical particles, percolating through the interconnected pore network. For each sphere diameter, the accessible pore volume is calculated. This algorithm was applied to compare pore interconnectivity of two different scaffold architectures; one created by salt-leaching and the other by stereolithography. The algorithm revealed a much higher pore interconnectivity for the latter one.

Mechanical properties of advanced tissue engineering scaffold architectures

In tissue engineering, porous scaffolds are used as a temporal support for tissue regeneration through cell adhesion, proliferation and differentiation. Besides applying a suitable material that is both biocompatible and biodegradable, the architectural design of the porous scaffold can be of essential for successful tissue regeneration. The architecture is of great influence on mechanical properties and transport properties of nutrients and metabolites1.