On Instance Weighted Clustering Ensembles

© ESANN, 2023. This is the accepted manuscript version of an article which has been published in final form at: www.esann.org/proceedings/2023Ensemble clustering is a technique which combines multipleclustering results, and instance weighting is a technique which highlightsimportant instances in a dataset. Both techniques are known to enhanceclustering performance and robustness. In this research, ensembles andinstance weighting are integrated with the spectral clustering algorithm.We believe this is the first attempt at creating diversity in the generativemechanism using density based instance weighting for a spectral ensemble.The proposed approach is empirically validated using synthetic datasetscomparing against spectral and a spectral ensemble with random instanceweighting. Results show that using the instance weighted sub-samplingapproach as the generative mechanism for an ensemble of spectral cluster-ing leads to improved clustering performance on datasets with imbalancedclusters.Peer reviewe

Feasibility of a Mitral Annuloplasty With the Capability for Peri- and Postoperative Adjustment

Abstract Surgical repair with implantation of a mitral annuloplasty ring is the gold standard treatment for mitral regurgitation. However, outcomes are variable and recurrent mitral regurgitation is not uncommon. A REshapeable Mitral Annuloplasty DevIce (REMADI) is proposed, which consists of a fully encapsulated low melting temperature alloy. The alloy is solid and rigid at body temperature and provides traction force to shape the annulus. When heated using a noncontact method, the alloy melts and the REMADI becomes malleable. The REMADI is engaged with the mitral valve annulus using anchors which automatically deploy upon contact. A passive beating porcine heart model was used to demonstrate the feasibility of the REMADI device, which was deployed, engaged, and used to reduce the diameter of the mitral valve annulus.Armstrong Trus

Revising Max-min for Scheduling in a Cloud Computing Context

Paper presented at the 2017 IEEE 26th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), Poznan, Poland, 21-23 June 2017. © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Adoption of Cloud Computing is on the rise[1] and many datacenter operators adhere to strict energy efficiency guidelines[2]. In this paper a novel approach to scheduling in a Cloud Computing context is proposed. The algorithm Maxmin Fast Track (MXFT) revises the Max-min algorithm to better support smaller tasks with stricter Service Level Agreements (SLAs), which makes it more relevant to Cloud Computing. MXFT is inspired by queuing in supermarkets, where there is a fast lane for customers with a smaller number of items. The algorithm outperforms Max-min in task execution times and outperforms Min-min in overall makespan. A by-product of investigating this algorithm was the development of simulator called “ScheduleSim”[3] which makes it simpler to prove a scheduling algorithm before committing to a specific scheduling problem in Cloud Computing and therefore might be a useful precursor to experiments using the established simulator CloudSim[4].Final Accepted Versio

The innovation of Multiview3 for development professionals

The Multiview Methodology for Information Systems Development has never been a widely used or mass-market approach. It has always had a small user base, a localised approach to a global issue: coherent IS development. This paper concerns the underreported innovation of the Multiview3 methodology for Information systems analysis, design and development – specifically designed for non-specialists working in developing countries. The innovation emerged from the identification of a methodological ‘gap’ in support for non-specialists struggling with Information Systems problem structuring challenges. The Multiview3 story tells us how IS methodology can be innovated to address the needs of users. This version of Multiview is argued to be theoretically distinct from previous versions in terms of its focus (developing countries) and application (problem solving and co-learning in practice)

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

Urban Aquatic Ecosystems: the good, the bad and the ugly

This editorial was submitted for publication in the journal, Fundamental and Applied Limnology [© E. Schweizerbartsche Verlagsbuchhandlung]. The definitive version is available at: http://dx.doi.org/10.1127/fal/2014/071

Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling

© 2022 Springer Nature Switzerland AG. This is the accepted manuscript version of a conference paper that been published in final form at https://doi.org/10.1007/978-3-031-02462-7_20The performance of cloud computing depends in part on job-scheduling algorithms, but also on the connection structure. Previous work on this structure has mostly looked at fixed and static connections. However, we argue that such static structures cannot be optimal in all situations. We introduce a dynamic hierarchical connection system of sub-schedulers between the scheduler and servers, and use artificial intelligence search algorithms to optimise this structure. Due to its dynamic and flexible nature, this design enables the system to adaptively accommodate heterogeneous jobs and resources to make the most use of resources. Experimental results compare genetic algorithms and simulating annealing for optimising the structure, and demonstrate that a dynamic hierarchical structure can significantly reduce the total makespan (max processing time for given jobs) of the heterogeneous tasks allocated to heterogeneous resources, compared with a one-layer structure. This reduction is particularly pronounced when resources are scarce

Urban Aquatic Ecosystems: the good, the bad and the ugly

Urban Aquatic Ecosystems: the good, the bad and the ugl

Improving the MXFT Scheduling Algorithm for a Cloud Computing Context

© 2019 Inderscience Enterprises Ltd.In this paper, the Max-Min Fast Track (MXFT) scheduling algorithm is improved and compared against a selection of popular algorithms. The improved versions of MXFT are called Min-Min Max-Min Fast Track (MMMXFT) and Clustering Min-Min Max-Min Fast Track (CMMMXFT). The key difference is using Min-Min for the fast track. Experimentation revealed that despite Min-Min’s characteristic of prioritising small tasks at the expense of overall makespan, the overall makespan was not adversely affected and the benefits of prioritising small tasks were identified in MMMXFT. Experiments were conducted by using a simulator with the exception of one real-world experiment. The real-world experiment identified challenges faced by algorithms which rely on accurate execution time prediction.Peer reviewe