XML document design via GN-DTD

Designing a well-structured XML document is important for the sake of readability and maintainability. More importantly, this will avoid data redundancies and update anomalies when maintaining a large quantity of XML based documents. In this paper, we propose a method to improve XML structural design by adopting graphical notations for Document Type Definitions (GN-DTD), which is used to describe the structure of an XML document at the schema level. Multiples levels of normal forms for GN-DTD are proposed on the basis of conceptual model approaches and theories of normalization. The normalization rules are applied to transform a poorly designed XML document into a well-designed based on normalized GN-DTD, which is illustrated through examples

COAPEC: Newsletter No. 4

The fourth of an irregular series of Newsletters with brief reports on meetings and projects associated with the NERC’s COAPEC directed programme. Article: Programme News Article: Bjerknes Compensation and the Decadal Variability of Energy Transports in a Coupled Climate Model Article: Probabilistic Attribution of the UK Autumn 2000 Floods using a Forecast Resolution Global Atmospheric Climate Model and Distributed Computing Article: Accuracy of Sea-Ice Observations and Impact on GCM Simulations Article: Use of Spatially Dependent Inverse Analysis Techniques to Close the SOC Flux Climatology Ocean Heat Budget Article: The Hot Summer of 2003 Article: COAPEC Data Article: The PRECIS Regional Modelling System Article: Meetings and Workshop

Anomaly Detection Based on Aggregation of Indicators

Automatic anomaly detection is a major issue in various areas. Beyond mere detection, the identification of the origin of the problem that produced the anomaly is also essential. This paper introduces a general methodology that can assist human operators who aim at classifying monitoring signals. The main idea is to leverage expert knowledge by generating a very large number of indicators. A feature selection method is used to keep only the most discriminant indicators which are used as inputs of a Naive Bayes classifier. The parameters of the classifier have been optimized indirectly by the selection process. Simulated data designed to reproduce some of the anomaly types observed in real world engines.Comment: 23rd annual Belgian-Dutch Conference on Machine Learning (Benelearn 2014), Bruxelles : Belgium (2014

An intelligent information forwarder for healthcare big data systems with distributed wearable sensors

© 2016 IEEE. An increasing number of the elderly population wish to live an independent lifestyle, rather than rely on intrusive care programmes. A big data solution is presented using wearable sensors capable of carrying out continuous monitoring of the elderly, alerting the relevant caregivers when necessary and forwarding pertinent information to a big data system for analysis. A challenge for such a solution is the development of context-awareness through the multidimensional, dynamic and nonlinear sensor readings that have a weak correlation with observable human behaviours and health conditions. To address this challenge, a wearable sensor system with an intelligent data forwarder is discussed in this paper. The forwarder adopts a Hidden Markov Model for human behaviour recognition. Locality sensitive hashing is proposed as an efficient mechanism to learn sensor patterns. A prototype solution is implemented to monitor health conditions of dispersed users. It is shown that the intelligent forwarders can provide the remote sensors with context-awareness. They transmit only important information to the big data server for analytics when certain behaviours happen and avoid overwhelming communication and data storage. The system functions unobtrusively, whilst giving the users peace of mind in the knowledge that their safety is being monitored and analysed

BRUNO: A Deep Recurrent Model for Exchangeable Data

We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations. Our model is provably exchangeable, meaning that the joint distribution over observations is invariant under permutation: this property lies at the heart of Bayesian inference. The model does not require variational approximations to train, and new samples can be generated conditional on previous samples, with cost linear in the size of the conditioning set. The advantages of our architecture are demonstrated on learning tasks that require generalisation from short observed sequences while modelling sequence variability, such as conditional image generation, few-shot learning, and anomaly detection.Comment: NIPS 201

Machine learning approach for detection of nonTor traffic

Intrusion detection has attracted a considerable interest from researchers and industry. After many years of research the community still faces the problem of building reliable and efficient intrusion detection systems (IDS) capable of handling large quantities of data with changing patterns in real time situations. The Tor network is popular in providing privacy and security to end user by anonymizing the identity of internet users connecting through a series of tunnels and nodes. This work identifies two problems; classification of Tor traffic and nonTor traffic to expose the activities within Tor traffic that minimizes the protection of users in using the UNB-CIC Tor Network Traffic dataset and classification of the Tor traffic flow in the network. This paper proposes a hybrid classifier; Artificial Neural Network in conjunction with Correlation feature selection algorithm for dimensionality reduction and improved classification performance. The reliability and efficiency of the propose hybrid classifier is compared with Support Vector Machine and naïve Bayes classifiers in detecting nonTor traffic in UNB-CIC Tor Network Traffic dataset. Experimental results show the hybrid classifier, ANN-CFS proved a better classifier in detecting nonTor traffic and classifying the Tor traffic flow in UNB-CIC Tor Network Traffic dataset

GARDSim - A GPS Receiver Simulation Environment for Integrated Navigation System Development and Analysis

Airservices Australia has recently proposed the use of a Ground-based Regional Augmentation System (GRAS) to improve the safety of using the NAVSTAR Global Positioning System (GPS) in aviation. The GRAS Airborne Receiver Development project (GARD) is being conducted by QUT in conjunction with Airservices Australia and GPSat Systems. The aim of the project is to further enhance the safety and reliability of GPS and GRAS by incorporating smart sensor technology including advanced GPS signal processing and Micro-Electro-Mechanical-Sensor (MEMS) based inertial components. GARDSim is a GPS and GRAS receiver simulation environment which has been developed for algorithm development and analysis in the GARD project. GARDSim is capable of simulating any flight path using a given aeroplane flight model, simulating various GPS, GRAS and inertial system measurements and performing high integrity navigation solutions for the flight. This paper discusses the architecture and capabilities of GARDSim. Simulation results will be presented to demonstrate the usefulness of GARDSim as a simulation environment for algorithm development and evaluation

Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

PICES Press, Vol. 18, No. 1, Winter 2010

•Major Outcomes from the 2009 PICES Annual Meeting: A Note from the Chairman (pp. 1-3, 8) •PICES Science – 2009 (pp. 4-8) •2009 PICES Awards (pp. 9-10) •New Chairmen in PICES (pp. 11-15) •PICES Interns (p. 15) •The State of the Western North Pacific in the First Half of 2009 (pp. 16-17, 27) •The State of the Northeast Pacific in 2009 (pp. 18-19) •The Bering Sea: Current Status and Recent Events (pp. 20-21) •2009 PICES Summer School on “Satellite Oceanography for the Earth Environment” (pp. 22-25) •2009 International Conference on “Marine Bioinvasions” (pp. 26-27) •A New PICES Working Group Holds Workshop and Meeting in Jeju Island (pp. 28-29) •The Second Marine Ecosystem Model Inter-comparison Workshop (pp. 30-32) •ICES/PICES/UNCOVER Symposium on “Rebuilding Depleted Fish Stocks – Biology, Ecology, Social Science and Management Strategies” (pp. 33-35) •2009 North Pacific Synthesis Workshop (pp. 36-37) •2009 PICES Rapid Assessment Survey (pp. 38-40