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Welcome to the research outputs of the University of Exeter College of Engineering, Mathematics and Physical Sciences, Department of Computer Science

Recent Submissions

  • Optimisation and Landscape Analysis of Computational Biology Models: A Case Study 

    Doherty, K; Alyahya, K; Akman, OE; Fieldsend, JE (Association for Computing Machinery (ACM), 2017-07)
    The parameter explosion problem is a crucial bottleneck in modelling gene regulatory networks (GRNs), limiting the size of models that can be optimised to experimental data. By discretising state, but not time, Boolean ...
  • Clustering of Hyper-heuristic Selections using the Smith-Waterman Algorithm for Off line Learning 

    Yates, W; Keedwell, EC (Association for Computing Machinery (ACM), 2017-07-19)
    Selection hyper-heuristics are methods that are typically used to solve computationally hard optimisation problems (see [1]). A selection hyper-heuristic selects heuristics from a given set of low level heuristics, deciding ...
  • Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution 

    Shaddick, G; Thomas, ML; Jobling, A; Brauer, M; Donkelaar, AV; Burnett, R; Chang, H; Cohen, A; Dingenen, RV; Dora, C; Gumy, S; Liu, Y; Martin, R; Waller, LA; West, J; Zidek, JV; Prüss-Ustün, A (arXiv.org, 2016-09-26)
    Air pollution is a major risk factor for global health, with both ambient and household air pollution contributing substantial components of the overall global disease burden. One of the key drivers of adverse health effects ...
  • Applied Gaussian Process in Optimizing Unburned Carbon Content in Fly Ash for Boiler Combustion 

    Wang, C; Liu, Y; Everson, R; Rahat, AAM; Zheng, S (Hindawi Publishing Corporation, 2017-05-11)
    Recently, Gaussian Process (GP) has attracted generous attention from industry. This article focuses on the application of coal fired boiler combustion and uses GP to design a strategy for reducing Unburned Carbon Content ...
  • Geometric semantic genetic programming for recursive boolean programs 

    Moraglio, A; Krawiec, K (Association for Computing Machinery (ACM), 2017)
    Geometric Semantic Genetic Programming (GSGP) induces a unimodal fitness landscape for any problem that consists in finding a function fitting given input/output examples. Most of the work around GSGP to date has focused ...
  • University Staff Teaching Allocation: Formulating and Optimising a Many-Objective Problem 

    Fieldsend, JE (Association for Computing Machinery (ACM), 2017-07-15)
    The allocation of university staff to teaching exhibits a range of often competing objectives. We illustrate the use of an augmented version of NSGA-III to undertake the seven-objective optimisation of this problem, to fi ...
  • Alternative Infill Strategies for Expensive Multi-Objective Optimisation 

    Rahat, A; Everson, RM; Fieldsend, JE (Association for Computing Machinery (ACM), 2017-07-15)
    Many multi-objective optimisation problems incorporate computationally or financially expensive objective functions. State-of-the-art algorithms therefore construct surrogate model(s) of the parameter space to objective ...
  • Constraint Handling in Efficient Global Optimization 

    Bagheri, S; Konen, W; Allmendinger, R; Branke, J; Deb, K; Fieldsend, JE; Quagliarella, D; Sindhya, K (Association for Computing Machinery (ACM), 2017-07)
    Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization ...
  • On the Exploitation of Search History and Accumulative Sampling in Robust Optimisation 

    Alyahya, K; Doherty, K; Fieldsend, JE; Akman, OE (Association for Computing Machinery (ACM), 2017-07-15)
    Efficient robust optimisation methods exploit the search history when evaluating a new solution by using information from previously visited solutions that fall in the new solution’s uncertainty neighbourhood. We propose ...
  • Investigating Echo-State Networks Dynamics by Means of Recurrence Analysis 

    Bianchi, FM; Livi, L; Alippi, C (Institute of Electrical and Electronics Engineers (IEEE), 2016-12-02)
    In this paper, we elaborate over the well-known interpretability issue in echo-state networks (ESNs). The idea is to investigate the dynamics of reservoir neurons with time-series analysis techniques developed in complex ...
  • One-Class Classifiers Based on Entropic Spanning Graphs 

    Livi, L; Alippi, C (Institute of Electrical and Electronics Engineers (IEEE), 2016-09-28)
    One-class classifiers offer valuable tools to assess the presence of outliers in data. In this paper, we propose a design methodology for one-class classifiers based on entropic spanning graphs. Our approach also takes ...
  • Determination of the Edge of Criticality in Echo State Networks Through Fisher Information Maximization. 

    Livi, L; Bianchi, FM; Alippi, C (Institute of Electrical and Electronics Engineers (IEEE), 2017-01-16)
    It is a widely accepted fact that the computational capability of recurrent neural networks (RNNs) is maximized on the so-called "edge of criticality." Once the network operates in this configuration, it performs efficiently ...
  • Fuzzy identity-based data integrity auditing for reliable cloud storage systems 

    Li, Y; Yu, Y; Min, G; Susilo, W; Ni, J; Choo, K-KR (Institute of Electrical and Electronics Engineers (IEEE), 2017)
    As a core security issue in reliable cloud storage, data integrity has received much attention. Data auditing protocols enable a verifier to efficiently check the integrity of the outsourced data without downloading the ...
  • Distributed feature selection for efficient economic big data analysis 

    Zhao, L; Chen, Z; Hu, Y; Min, G; Jiang, Z (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    With the rapidly increasing popularity of economic activities, a large amount of economic data is being collected. Although such data offers super opportunities for economic analysis, its low-quality, high-dimensionality ...
  • ExCCC-DCN: A Highly Scalable, Cost-Effective and Engergy-Efficient Data Center Stucture 

    Zhang, Z; Deng, Y; Min, G; Xie, J; Huang, S (Institute of Electrical and Electronics Engineers (IEEE), 2016-09-14)
    Over the past decade, many data centers have been constructed around the world due to the explosive growth of data volume and type. The cost and energy consumption have become the most important challenges of building those ...
  • An incrementally scalable and cost-efficient interconnection structure for datacenters 

    Xie, J; Deng, Y; Min, G; Zhou, Y (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    The explosive growth in the volume of data storing and complexity of data processing drive data center networks (DCNs) to become incrementally scalable and cost-efficient while to maintain high network capacity and fault ...
  • Statistical Features-Based Real-Time Detection of Drifted Twitter Spam 

    Chen, C; Wang, Y; Zhang, J; Xiang, Y; Zhou, W; Min, G (Institute of Electrical and Electronics Engineers (IEEE), 2016-10-26)
    Twitter spam has become a critical problem nowadays. Recent works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. In our labeled tweets data ...
  • Lifelogging data validation model for internet of things enabled personalized healthcare 

    Yang, P; Stankevicius, D; Marozas, V; Deng, Z; Liu, E; Lukosevicius, A; Dong, F; Xu, L; Min, G (Institute of Electrical and Electronics Engineers (IEEE), 2016)
    Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life ...
  • An EV Charging Management System Concerning Drivers' Trip Duration and Mobility Uncertainty 

    Cao, Y; Wang, T; Kaiwartya, O; Min, G; Ahmad, N; Abdullah, AH (Institute of Electrical and Electronics Engineers (IEEE), 2016-11-24)
    With continually increased attention on electric vehicles (EVs) due to environment impact, public charging stations (CSs) for EVs will become common. However, due to the limited electricity of battery, EV drivers may ...
  • Vehicular-Publish/Subscribe (V-P/S) communication enabled on-the-move EV charging management. 

    Cao, Y; Miao, Y; Min, G; Wang, T; Zhao, Z; Song, H (Institute of Electrical and Electronics Engineers (IEEE), 2016-12)
    Recently, the charging management for Electric Vehicles (EVs) on-the-move has become an emerging research problem in urban cities. Major technical challenges here involve intelligence for the selection of Charging ...

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