A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

A decision support system for managing irrigation in agriculture

In this paper, an automatic Smart Irrigation Decision Support System, SIDSS, is proposed to manage irrigation in agriculture. Our system estimates the weekly irrigations needs of a plantation, on the basis of both soil measurements and climatic variables gathered by several autonomous nodes deployed in field. This enables a closed loop control scheme to adapt the decision support system to local perturbations and estimation errors. Two machine learning techniques, PLSR and ANFIS, are proposed as reasoning engine of our SIDSS. Our approach is validated on three commercial plantations of citrus trees located in the South-East of Spain. Performance is tested against decisions taken by a human expert.

The Growth, Learning and Development Study: Summary of Research, Findings and Recommendations.

The Growth, Learning and Development (GLAD) study aimed to examine how a broad range of factors influence child weight during the first year of life. Assessments were undertaken within a multidisciplinary team framework. The sample was drawn from the community and data collection was undertaken in the four Greater Belfast Trusts. Twohundred and thirty-four families took part, each receiving a total of five home visits during which physical growth, oral-motor skills and development were assessed. Psychosocial evaluation examined parent-child interaction, feeding and other parental and child characteristics using quantitative and observational techniques. This paper outlines the main findings and recommendations from the GLAD study.

Risky decision making in younger and older adults:The role of learning.

Are older adults risk seeking or risk averse? Answering this question might depend on both the task used and the analysis performed. By modeling responses to the Balloon Analogue Risk Task (BART), our results illustrate the value of modeling as compared to relying on common analysis techniques. While analysis of overall measures suggested initially that older and younger adults do not differ in their risky decisions, our modeling results indicated that younger adults were at first more willing to take greater risks. Furthermore, older adults may be more cautious when their decision making is based on initial perceptions of risk, rather than learning following some experience with a task.

Early Implementation of Building Information Modeling into a Cold-Formed Steel Company: Providing Novel Project Management Techniques and Solutions to Industry

The ability of building information modeling (BIM) to positively impact projects in the AEC through greater collaboration and integration is widely acknowledged. This paper aims to examine the development of BIM and how it can contribute to the cold-formed steel (CFS) building industry. This is achieved through the adoption of a qualitative methodology encompassing a literature review, exploratory interviews with industry experts, culminating in the development of e-learning material for the sector. In doing so, the research team have collaborated with one of the United Kingdom’s largest cold-formed steel designer/fabricators. By demonstrating the capabilities of BIM software and providing technical and informative videos in its creation, this project has found two key outcomes. Firstly, to provide invaluable assistance in the transition from traditional processes to a fully collaborative 3D BIM as required by the UK Government under the “Government Construction Strategy” by 2016 in all public sector projects. Secondly, to demonstrate BIM’s potential not only within CFS companies, but also within the AEC sector as a whole. As the flexibility, adaptability and interoperability of BIM software is alluded to, the results indicate that the introduction and development of BIM and the underlying ethos suggests that it is a key tool in the development of the industry as a whole.

Learning from time series:Supervised Aggregative Feature Extraction

Many modeling problems require to estimate a scalar output from one or more time series. Such problems are usually tackled by extracting a fixed number of features from the time series (like their statistical moments), with a consequent loss in information that leads to suboptimal predictive models. Moreover, feature extraction techniques usually make assumptions that are not met by real world settings (e.g. uniformly sampled time series of constant length), and fail to deliver a thorough methodology to deal with noisy data. In this paper a methodology based on functional learning is proposed to overcome the aforementioned problems; the proposed Supervised Aggregative Feature Extraction (SAFE) approach allows to derive continuous, smooth estimates of time series data (yielding aggregate local information), while simultaneously estimating a continuous shape function yielding optimal predictions. The SAFE paradigm enjoys several properties like closed form solution, incorporation of first and second order derivative information into the regressor matrix, interpretability of the generated functional predictor and the possibility to exploit Reproducing Kernel Hilbert Spaces setting to yield nonlinear predictive models. Simulation studies are provided to highlight the strengths of the new methodology w.r.t. standard unsupervised feature selection approaches. © 2012 IEEE.

Semi batch learning with store management using enhanced conjugate gradient

This paper explores the performance of sliding-window based training, termed as semi batch, using multilayer perceptron (MLP) neural network in the presence of correlated data. The sliding window training is a form of higher order instantaneous learning strategy without the need of covariance matrix, usually employed for modeling and tracking purposes. Sliding-window framework is implemented to combine the robustness of offline learning algorithms with the ability to track online the underlying process of a function. This paper adopted sliding window training with recent advances in conjugate gradient direction with application of data store management e.g. simple distance measure, angle evaluation and the novel prediction error test. The simulation results show the best convergence performance is gained by using store management techniques. © 2012 Springer-Verlag.

Machine learning for transient discovery in Pan-STARRS1 difference imaging

Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images that are produced by most major ground-based time-domain surveys with large format CCD cameras. This dependence on humans to reject bogus detections is unsustainable for next generation all-sky surveys and significant effort is now being invested to solve the problem computationally. In this paper, we explore a simple machine learning approach to real-bogus classification by constructing a training set from the image data of similar to 32 000 real astrophysical transients and bogus detections from the Pan-STARRS1 Medium Deep Survey. We derive our feature representation from the pixel intensity values of a 20 x 20 pixel stamp around the centre of the candidates. This differs from previous work in that it works directly on the pixels rather than catalogued domain knowledge for feature design or selection. Three machine learning algorithms are trained (artificial neural networks, support vector machines and random forests) and their performances are tested on a held-out subset of 25 per cent of the training data. We find the best results from the random forest classifier and demonstrate that by accepting a false positive rate of 1 per cent, the classifier initially suggests a missed detection rate of around 10 per cent. However, we also find that a combination of bright star variability, nuclear transients and uncertainty in human labelling means that our best estimate of the missed detection rate is approximately 6 per cent.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

A Design & Feasibility Study of the Spaced Learning Programme

Objectives There is evidence from neuroscience, cognitive psychology and educational research that the delivery of a stimulus in a spaced format (over time) rather than a massed format (all at once) leads to more effective learning. This project aimed to pilot spaced learning materials using various spacing lengths for GCSE science by exploring the feasibility of introducing spaced leaning into regular classrooms and by evaluating teacher fidelity to the materials. The spaced learning methods will then be compared with traditional science revision techniques and a programme manual will be produced. Design A feasibility study. Methods A pilot study (4 schools) was carried out to examine the feasibility and teacher fidelity to the materials, using pupil workshops and teacher interviews. A subsequent random assignment experimental study (12 schools) will involve pre and post testing of students on a science attainment measure and a post-test implementation questionnaire. Results The literature review found that longer spacing intervals between repetitions of material (>24 hours) may be optimal for long term memory formation than shorter intervals. A logic model was developed to inform the design of various programme variants for the pilot and experimental study. This paper will report qualitative data from the initial pilot study. Conclusions The paper uses this research project as an example to explain the importance of conducting pilot work and small scale experimental studies to explore the feasibility and inform the design of educational interventions, rather than prematurely moving to RCT type studies.

A Proposed Framework for Simulation Based Learning of Inheritance

Different types of serious games have been used in elucidating computer science areas such as computer games, mobile games, Lego-based games, virtual worlds and webbased games. Different evaluation techniques have been conducted like questionnaires, interviews, discussions and tests. Simulation have been widely used in computer science as a motivational and interactive learning tool. This paper aims to evaluate the possibility of successful implementation of simulation in computer programming modules. A framework is proposed to measure the impact of serious games on enhancing students understanding of key computer science concepts. Experiments will be held on the EEECS of Queen’s University Belfast students to test the framework and attain results.