The use of YouTube Analytics of Video Lecture Capture as a Teaching and Learning Performance Enhancer

Do patterns in the YouTube viewing analytics of Lecture Capture videos point to areas of potential teaching and learning performance enhancement? The goal of this action based research project was to capture and quantitatively analyse the viewing behaviours and patterns of a series of video lecture captures across several computing modules in Queen’s University, Belfast, Northern Ireland. The research sought to establish if a quantitative analysis of viewing behaviours coupled with a qualitative evaluation of the material provided from the students could be correlated to provide generalised patterns that could then be used to understand the learning experience of students during face to face lectures and, thereby, present opportunities to reflectively enhance lecturer performance and the students’ overall learning experience and, ultimately, their level of academic attainment.

Are Computer Scientists Geeks? Gender Differences In Attitudes And Experiences Related To Computing Among Secondary Education Students In Northern Ireland.

The worldwide scarcity of women studying or employed in ICT, or in computing related disciplines, continues to be a topic of concern for industry, the education sector and governments. Within Europe while females make up 46% of the workforce only 17% of IT staff are female. A similar gender divide trend is repeated worldwide, with top technology employers in Silicon Valley, including Facebook, Google, Twitter and Apple reporting that only 30% of the workforce is female (Larson 2014). Previous research into this gender divide suggests that young women in Secondary Education display a more negative attitude towards computing than their male counterparts. It would appear that the negative female perception of computing has led to representatively low numbers of women studying ICT at a tertiary level and consequently an under representation of females within the ICT industry. The aim of this study is to 1) establish a baseline understanding of the attitudes and perceptions of Secondary Education pupils in regard to computing and 2) statistically establish if young females in Secondary Education really do have a more negative attitude towards computing.

Towards Automated Data-Driven Model Creation for Cloud Computing Simulation

The increasing complexity and scale of cloud computing environments due to widespread data centre heterogeneity makes measurement-based evaluations highly difficult to achieve. Therefore the use of simulation tools to support decision making in cloud computing environments to cope with this problem is an increasing trend. However the data required in order to model cloud computing environments with an appropriate degree of accuracy is typically large, very difficult to collect without some form of automation, often not available in a suitable format and a time consuming process if done manually. In this research, an automated method for cloud computing topology definition, data collection and model creation activities is presented, within the context of a suite of tools that have been developed and integrated to support these activities.

Unconventional Computing: A Boolean Chemical Perspective

Elementary computing operations can be arranged within molecules so that problems in chemical, biochemical, and biological situations can be addressed. Problems that are found in small and/or living spaces, where the corresponding semiconductor logic devices cannot operate conveniently, are particularly amenable to this approach. The visualization and monitoring of intracellular species is one such category. Problems in medical diagnostics and therapy form additional categories. Chemists and biologists employ chemical synthesis and molecular biology techniques to build molecular logic devices. The photochemical approach to molecular logic devices is particularly prevalent. The fluorescent photoinduced electron transfer (PET) switching principle is particularly useful for designing logic functions into small molecules.

A Fast Algorithm for Sparse Support Vector Machines for Mobile Computing Applications

This research presents a fast algorithm for projected support vector machines (PSVM) by selecting a basis vector set (BVS) for the kernel-induced feature space, the training points are projected onto the subspace spanned by the selected BVS. A standard linear support vector machine (SVM) is then produced in the subspace with the projected training points. As the dimension of the subspace is determined by the size of the selected basis vector set, the size of the produced SVM expansion can be specified. A two-stage algorithm is derived which selects and refines the basis vector set achieving a locally optimal model. The model expansion coefficients and bias are updated recursively for increase and decrease in the basis set and support vector set. The condition for a point to be classed as outside the current basis vector and selected as a new basis vector is derived and embedded in the recursive procedure. This guarantees the linear independence of the produced basis set. The proposed algorithm is tested and compared with an existing sparse primal SVM (SpSVM) and a standard SVM (LibSVM) on seven public benchmark classification problems. Our new algorithm is designed for use in the application area of human activity recognition using smart devices and embedded sensors where their sometimes limited memory and processing resources must be exploited to the full and the more robust and accurate the classification the more satisfied the user. Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm. This work builds upon a previously published algorithm specifically created for activity recognition within mobile applications for the EU Haptimap project [1]. The algorithms detailed in this paper are more memory and resource efficient making them suitable for use with bigger data sets and more easily trained SVMs.

Targeting FPGA DSP Slices for a Large Integer Multiplier for Integer Based FHE

Homomorphic encryption offers potential for secure cloud computing. However due to the complexity of homomorphic encryption schemes, performance of implemented schemes to date have been unpractical. This work investigates the use of hardware, specifically Field Programmable Gate Array (FPGA) technology, for implementing the building blocks involved in somewhat and fully homomorphic encryption schemes in order to assess the practicality of such schemes. We concentrate on the selection of a suitable multiplication algorithm and hardware architecture for large integer multiplication, one of the main bottlenecks in many homomorphic encryption schemes. We focus on the encryption step of an integer-based fully homomorphic encryption (FHE) scheme. We target the DSP48E1 slices available on Xilinx Virtex 7 FPGAs to ascertain whether the large integer multiplier within the encryption step of a FHE scheme could fit on a single FPGA device. We find that, for toy size parameters for the FHE encryption step, the large integer multiplier fits comfortably within the DSP48E1 slices, greatly improving the practicality of the encryption step compared to a software implementation. As multiplication is an important operation in other FHE schemes, a hardware implementation using this multiplier could also be used to improve performance of these schemes.

Computing probable maximum loss in catastrophe reinsurance portfolios on multi-core and many-core architectures

In the reinsurance market, the risks natural catastrophes pose to portfolios of properties must be quantified, so that they can be priced, and insurance offered. The analysis of such risks at a portfolio level requires a simulation of up to 800 000 trials with an average of 1000 catastrophic events per trial. This is sufficient to capture risk for a global multi-peril reinsurance portfolio covering a range of perils including earthquake, hurricane, tornado, hail, severe thunderstorm, wind storm, storm surge and riverine flooding, and wildfire. Such simulations are both computation and data intensive, making the application of high-performance computing techniques desirable.

In this paper, we explore the design and implementation of portfolio risk analysis on both multi-core and many-core computing platforms. Given a portfolio of property catastrophe insurance treaties, key risk measures, such as probable maximum loss, are computed by taking both primary and secondary uncertainties into account. Primary uncertainty is associated with whether or not an event occurs in a simulated year, while secondary uncertainty captures the uncertainty in the level of loss due to the use of simplified physical models and limitations in the available data. A combination of fast lookup structures, multi-threading and careful hand tuning of numerical operations is required to achieve good performance. Experimental results are reported for multi-core processors and systems using NVIDIA graphics processing unit and Intel Phi many-core accelerators.

Exploiting Significance of Computations for Energy-Constrained Approximate Computing

Approximate execution is a viable technique for environments with energy constraints, provided that applications are given the mechanisms to produce outputs of the highest possible quality within the available energy budget. This paper introduces a framework for energy-constrained execution with controlled and graceful quality loss. A simple programming model allows developers to structure the computation in different tasks, and to express the relative importance of these tasks for the quality of the end result. For non-significant tasks, the developer can also supply less costly, approximate versions. The target energy consumption for a given execution is specified when the application is launched. A significance-aware runtime system employs an application-specific analytical energy model to decide how many cores to use for the execution, the operating frequency for these cores, as well as the degree of task approximation, so as to maximize the quality of the output while meeting the user-specified energy constraints. Evaluation on a dual-socket 16-core Intel platform using 9 benchmark kernels shows that the proposed framework picks the optimal configuration with high accuracy. Also, a comparison with loop perforation (a well-known compile-time approximation technique), shows that the proposed framework results in significantly higher quality for the same energy budget.

Building professionalism and employability skills: embedding employer engagement within first-year computing modules

This paper outlines a means of improving the employability skills of first-year university students through a closely integrated model of employer engagement within computer science modules. The outlined approach illustrates how employability skills, including communication, teamwork and time management skills, can be contextualised in a manner that directly relates to student learning but can still be linked forward into employment. The paper tests the premise that developing employability skills early within the curriculum will result in improved student engagement and learning within later modules. The paper concludes that embedding employer participation within first-year models can help relate a distant notion of employability into something of more immediate relevance in terms of how students can best approach learning. Further, by enhancing employability skills early within the curriculum, it becomes possible to improve academic attainment within later modules.

Bright Molecules for Sensing, Computing and Imaging: A Tale of Two Once-troubled Cities

The circumstances in Colombo, Sri Lanka, and in Belfast, Northern Ireland, which led to a) the generalization of luminescent PET (photoinduced electron transfer) sensing/switching as a design tool, b) the construction of a market-leading blood electrolyte analyzer and c) the invention of molecular logic-based computation as an experimental field, are delineated. Efforts to extend the philosophy of these approaches into issues of small object identification, nanometric mapping, animal visual perception and visual art are also outlined.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Partially Ordered Preferences in Decision Trees: Computing Strategies with Imprecision in Probabilities

Partially ordered preferences generally lead to choices that do not abide by standard expected utility guidelines; often such preferences are revealed by imprecision in probability values. We investigate five criteria for strategy selection in decision trees with imprecision in probabilities: “extensive” Γ-maximin and Γ-maximax, interval dominance, maximality and E-admissibility. We present algorithms that generate strategies for all these criteria; our main contribution is an algorithm for Eadmissibility that runs over admissible strategies rather than over sets of probability distributions.