Evaluating Robustness for 'IPCRESS': Surrey's Text Alignment for Plagiarism Detection---Notebook for PAN at CLEF 2014

This paper briefly describes the approach taken to the subtask of Text Alignment in the Plagiarism Detection track at PAN 14. We have now reimplemented our PAN12 approach in a consistent programmatic manner, courtesy of secured research funding. PAN 14 offers us the first opportunity to evaluate the performance/consistency of this re-implementation. We present results from this re-implementation with respect to various PAN collections

Addressing Issues of Cloud Resilience, Security and Performance through Simple Detection of Co-locating Sibling Virtual Machine Instances

Most current Infrastructure Clouds are built on shared tenancy architectures, with resources shared amongst large numbers of customers. However, multi tenancy can lead to performance issues (so-called “noisy neighbours”) and also brings potential for serious security breaches such as hypervisor breakouts. Consequently, there has been a focus in the literature on identifying co-locating instances that are being affected by noisy neighbours or suggesting that such instances are vulnerable to attack. However, there is limited evidence of any such attacks in the wild. More beneficially, knowing that there is co-location amongst your own Virtual Machine instances (siblings) can help to avoid being your own worst enemy: avoiding your instances acting as your own noisy neighbours, building resilience through ensuring hostbased redundancy, and/or reducing exposure to a single compromised host. In this paper, we propose and demonstrate a test to detect co-locating sibling instances on Xen-based Clouds, as could help address such needs, and evaluate its efficacy on Amazon’s EC2

Are fluoride levels in drinking water associated with hypothyroidism prevalence in England? A large observational study of GP practice data and fluoride levels in drinking water

Background While previous research has suggested that there is an association between fluoride ingestion and the incidence of hypothyroidism, few population level studies have been undertaken. In England, approximately 10% of the population live in areas with community fluoridation schemes and hypothyroidism prevalence can be assessed from general practice data. This observational study examines the association between levels of fluoride in water supplies with practice level hypothyroidism prevalence. Methods We used a cross-sectional study design using secondary data to develop binary logistic regression models of predictive factors for hypothyroidism prevalence at practice level using 2012 data on fluoride levels in drinking water, 2012/2013 Quality and Outcomes Framework (QOF) diagnosed hypothyroidism prevalence data, 2013 General Practitioner registered patient numbers and 2012 practice level Index of Multiple Deprivation scores. Findings We found that higher levels of fluoride in drinking water provide a useful contribution for predicting prevalence of hypothyroidism. We found that practices located in the West Midlands (a wholly fluoridated area) are nearly twice as likely to report high hypothyroidism prevalence in comparison to Greater Manchester (non-fluoridated area). Interpretation In many areas of the world, hypothyroidism is a major health concern and in addition to other factors—such as iodine deficiency—fluoride exposure should be considered as a contributing factor. The findings of the study raise particular concerns about the validity of community fluoridation as a safe public health measure

epcAware: a game-based, energy, performance and cost efficient resource management technique for multi-access edge computing

The Internet of Things (IoT) is producing an extraordinary volume of data daily, and it is possible that the data may become useless while on its way to the cloud for analysis, due to longer distances and delays. Fog/edge computing is a new model for analyzing and acting on time-sensitive data (real-time applications) at the network edge, adjacent to where it is produced. The model sends only selected data to the cloud for analysis and long-term storage. Furthermore, cloud services provided by large companies such as Google, can also be localized to minimize the response time and increase service agility. This could be accomplished through deploying small-scale datacenters (reffered to by name as cloudlets) where essential, closer to customers (IoT devices) and connected to a centrealised cloud through networks - which form a multi-access edge cloud (MEC). The MEC setup involves three different parties, i.e. service providers (IaaS), application providers (SaaS), network providers (NaaS); which might have different goals, therefore, making resource management a defficult job. In the literature, various resource management techniques have been suggested in the context of what kind of services should they host and how the available resources should be allocated to customers’ applications, particularly, if mobility is involved. However, the existing literature considers the resource management problem with respect to a single party. In this paper, we assume resource management with respect to all three parties i.e. IaaS, SaaS, NaaS; and suggest a game theoritic resource management technique that minimises infrastructure energy consumption and costs while ensuring applications performance. Our empirical evaluation, using real workload traces from Google’s cluster, suggests that our approach could reduce up to 11.95% energy consumption, and approximately 17.86% user costs with negligible loss in performance. Moreover, IaaS can reduce up to 20.27% energy bills and NaaS can increase their costs savings up to 18.52% as compared to other methods

A Preliminary Investigation of the Effect of Acceptance and Commitment Therapy on Neural Activation in Clinical Perfectionism

Clinical perfectionism is associated with various cognitive processes including performance monitoring and emotion regulation. This exploratory study analyzed neurological data from a randomized controlled trial for clinical perfectionism that compared acceptance and commitment therapy (ACT) to a waitlist control. The objective was to assess the effect of ACT on neural activation. Twenty-nine participants underwent a functional near-infrared spectroscopy assessment during which they completed behavioral tasks designed to elicit error detection and error generation at pre- and posttreatment. The hemodynamic response function (HRF) in the dorsolateral prefrontal cortex, dorsomedial prefrontal cortex, and right inferior parietal lobe was analyzed using mixed effects models. In all areas, we found reductions or smaller increases in the total HRF for experimental tasks from pre- to posttreatment in the ACT condition compared to the waitlist condition. Decreases in total oxygenated hemoglobin are consistent with diminished recruitment of neurons in response to previously emotionally salient stimuli, possibly representing greater cognitive processing efficiency. Our preliminary findings tentatively support the processes of change posited by the theory underlying ACT and highlight the need for more precise methodology in neurological assessment to adequately evaluate how treatment affects neurological function. Limitations include lack of an active comparison condition and behavioral data

CoLocateMe: Aggregation-based, energy, performance and cost aware VM placement and consolidation in heterogeneous IaaS clouds

In many production clouds, with the notable exception of Google, aggregation-based VM placement policies are used to provision datacenter resources energy and performance efficiently. However, if VMs with similar workloads are placed onto the same machines, they might suffer from contention, particularly, if they are competing for similar resources. High levels of resource contention may degrade VMs performance, and, therefore, could potentially increase users’ costs and infrastructure's energy consumption. Furthermore, segregation-based methods result in stranded resources and, therefore, less economics. The recent industrial interest in segregating workloads opens new directions for research. In this article, we demonstrate how aggregation and segregation-based VM placement policies lead to variabilities in energy efficiency, workload performance, and users’ costs. We, then, propose various approaches to aggregation-based placement and migration. We investigate through a number of experiments, using Microsoft Azure and Google's workload traces for more than twelve thousand hosts and a million VMs, the impact of placement decisions on energy, performance, and costs. Our extensive simulations and empirical evaluation demonstrate that, for certain workloads, aggregation-based allocation and consolidation is ∼9.61% more energy and ∼20.0% more performance efficient than segregation-based policies. Moreover, various aggregation metrics, such as runtimes and workload types, offer variations in energy consumption and performance, therefore, users’ costs