16,061 research outputs found

    Assessing performance of artificial neural networks and re-sampling techniques for healthcare datasets.

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    Re-sampling methods to solve class imbalance problems have shown to improve classification accuracy by mitigating the bias introduced by differences in class size. However, it is possible that a model which uses a specific re-sampling technique prior to Artificial neural networks (ANN) training may not be suitable for aid in classifying varied datasets from the healthcare industry. Five healthcare-related datasets were used across three re-sampling conditions: under-sampling, over-sampling and combi-sampling. Within each condition, different algorithmic approaches were applied to the dataset and the results were statistically analysed for a significant difference in ANN performance. The combi-sampling condition showed that four out of the five datasets did not show significant consistency for the optimal re-sampling technique between the f1-score and Area Under the Receiver Operating Characteristic Curve performance evaluation methods. Contrarily, the over-sampling and under-sampling condition showed all five datasets put forward the same optimal algorithmic approach across performance evaluation methods. Furthermore, the optimal combi-sampling technique (under-, over-sampling and convergence point), were found to be consistent across evaluation measures in only two of the five datasets. This study exemplifies how discrete ANN performances on datasets from the same industry can occur in two ways: how the same re-sampling technique can generate varying ANN performance on different datasets, and how different re-sampling techniques can generate varying ANN performance on the same dataset

    Modelling uncertainties for measurements of the H → γγ Channel with the ATLAS Detector at the LHC

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    The Higgs boson to diphoton (H → γγ) branching ratio is only 0.227 %, but this final state has yielded some of the most precise measurements of the particle. As measurements of the Higgs boson become increasingly precise, greater import is placed on the factors that constitute the uncertainty. Reducing the effects of these uncertainties requires an understanding of their causes. The research presented in this thesis aims to illuminate how uncertainties on simulation modelling are determined and proffers novel techniques in deriving them. The upgrade of the FastCaloSim tool is described, used for simulating events in the ATLAS calorimeter at a rate far exceeding the nominal detector simulation, Geant4. The integration of a method that allows the toolbox to emulate the accordion geometry of the liquid argon calorimeters is detailed. This tool allows for the production of larger samples while using significantly fewer computing resources. A measurement of the total Higgs boson production cross-section multiplied by the diphoton branching ratio (σ × Bγγ) is presented, where this value was determined to be (σ × Bγγ)obs = 127 ± 7 (stat.) ± 7 (syst.) fb, within agreement with the Standard Model prediction. The signal and background shape modelling is described, and the contribution of the background modelling uncertainty to the total uncertainty ranges from 18–2.4 %, depending on the Higgs boson production mechanism. A method for estimating the number of events in a Monte Carlo background sample required to model the shape is detailed. It was found that the size of the nominal γγ background events sample required a multiplicative increase by a factor of 3.60 to adequately model the background with a confidence level of 68 %, or a factor of 7.20 for a confidence level of 95 %. Based on this estimate, 0.5 billion additional simulated events were produced, substantially reducing the background modelling uncertainty. A technique is detailed for emulating the effects of Monte Carlo event generator differences using multivariate reweighting. The technique is used to estimate the event generator uncertainty on the signal modelling of tHqb events, improving the reliability of estimating the tHqb production cross-section. Then this multivariate reweighting technique is used to estimate the generator modelling uncertainties on background V γγ samples for the first time. The estimated uncertainties were found to be covered by the currently assumed background modelling uncertainty

    Gamification in E-Learning: game factors to strengthen specific English pronunciation features in undergraduate students at UPTC Sogamoso

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    Appendix A Characterization survey (104), Appendix B. EFL Students’ questionnaire (109), Appendix C. Characterization survey: data treatment question (113), Appendix D. Informed consent letter, English version (114), Appendix E. Carta de consentimiento informado, versión en español (117), Appendix F. Time Schedule (120), Appendix G. Sample Challenges at Moodle (126), Appendix H. Participants’ questionnaire results (128).La gamificación es un término que suele denotar el uso de componentes del juego en situaciones no relacionadas con el juego en sí para crear experiencias de aprendizaje agradables, divertidas y motivadoras para los estudiantes (Werbach y Hunter, 2012). Por lo tanto, el análisis de los factores básicos de los juegos se convierte en algo esencial a la hora de definir y utilizar la gamificación como estrategia de mediación del inglés como lengua extranjera para fortalecer rasgos específicos de pronunciación en los estudiantes de pregrado de la UPTC Sogamoso. El procedimiento de estudio se basa en la investigación acción mediante la implementación de la estrategia de gamificación para la mediación en la pronunciación del inglés, orientada a treinta estudiantes de diferentes programas de ingeniería, administración y tecnología con niveles heterogéneos de dominio del inglés. Las actividades se centran principalmente en la producción de sonidos, el ritmo, el acento y la entonación, los rasgos de pronunciación segmental y suprasegmental. Los resultados arrojaron una evidente mejora en las características segméntales y suprasegmentales de la percepción en la pronunciación de los participantes así como la contribución del objetivo de los juegos a la instrucción fonética y fonológica, la sensación en el juego a la motivación para mejorar la pronunciación, el reto establecido en los juegos a la actitud positiva de los participantes, y la sociabilidad a la exposición practica de la pronunciación inglesa.Gamification is a relatively new term that often denotes the use of game components in situations unrelated to the game itself to create enjoyable, fun, and motivating learning experiences for students (Werbach and Hunter, 2012). Therefore, analyzing the games' basic factors becomes essential when defining and using gamification as a strategy for English as Foreign Language mediation to strengthen specific pronunciation features in UPTC Sogamoso undergraduate students. The study procedure is based on action research by implementing the gamification strategy for mediation in English pronunciation, oriented to thirty students from different engineering, management, and technology programs at heterogeneous levels of English proficiency. The activities mainly focus on sound production, rhythm, stress, and intonation, segmental and suprasegmental pronunciation features. The results showed an evident improvement in the segmental and suprasegmental features of the participants' pronunciation perception as well as the contribution of game goals to phonetics and phonological instruction, the game sensation to the motivation for pronunciation improvement, the game challenge to the participants' positive attitude, and the sociality to the English pronunciation exposure practice

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    The interpretation of Islam and nationalism by the elite through the English language media in Pakistan.

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    The media is constructed and interpreted through what people 'know'. That knowledge is, forthe most part, created through day to day experiences. In Pakistan, Islam and nationalism aretwo components of this social knowledge which are intrinsically tied to the experiences of thePakistani people. Censorship and selection are means through which this knowledge isarticulated and interpreted.General conceptions of partially shared large scale bodies of knowledge and ideas reinforce,and are reinforced by, general medium of mass communication: the print and electronic media.Focusing on the govermnent, media institutions and Pakistani elites, I describe and analyse thedifferent, sometimes conflicting, interpretations of Islam and Pakistani nationalism manifest inand through media productions presented in Pakistan.The media means many things, not least of which is power. It is the media as a source ofpower that is so frequently controlled, directed and manipulated. The terminology may beslightly different according to the context within which one is talking - propaganda, selection,etc. - but ultimately it comes down to the same thing - censorship. Each of the three groups:government, media institutions and Pakistani elites - have the power to interpret and censormedia content and consideration must be taken of each of the other power holders consequentlyrestricting the power of each group in relation to the other two. The processes of thismanipulation and their consequences form the major themes of this thesis

    Development and evaluation of a treatment package for men with an intellectual disability who sexually offend

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    Sex offending in the general population has been a focus of interest for some time due to the damaging nature of the behaviour, and the need to reduce recidivism. Theoretical and clinical advances (Finke1hor, 1986; HM Prison Service, 1996; Marshall, Anderson, & Fernandez, 1999; Serran & Marshall, 2010) in treatment for sex offenders in the general population have been extended to men with an intellectual disability at risk of sexual offending (Lindsay, 2009). The purpose of this project is to develop and evaluate the SOTSEC-ID version cftrus model. Participants are adult males from 15 different locations across England and Wales, with an intellectual disability or borderline cognitive functioning and who have committed sexual offences. A pilot study clarified assessments and procedures, and individual data over several years is presented. A qualitative study using Interpretive Phenomenological Analysis (JP A) illustrates the 'meaning making' of participants' treatment experience through six major themes. A reliability and validity study assesses the four main quantitative measures, QACSO, SAKA, SOSAS, and VESA, finding limited support for criterion validity for the SOSAS and SAKA, excellent inter-rater reli"ability for all four main measures, and good to excellent inter-rater reliability on all but the SAKA Finally, a quantitative study, in collaboration with the wider SOTSEC-ID group, uses a repeated measures design to compare the QACSO, SOSAS and SAKA across pre-group, post-group and follow. up. Significant main effects and post-hoc comparisons were in the predicted direction for all measures. A range of information on demographic, clinical and criminogenic factors including offending during treatment or follow-up are also presented. A recidivism rate of 12.3% over a year was calculated for the sample. The treatment model and collaborative framework is recommended for wider adoption

    Network Slicing for Industrial IoT and Industrial Wireless Sensor Network: Deep Federated Learning Approach and Its Implementation Challenges

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    5G networks are envisioned to support heterogeneous Industrial IoT (IIoT) and Industrial Wireless Sensor Network (IWSN) applications with a multitude Quality of Service (QoS) requirements. Network slicing is being recognized as a beacon technology that enables multi-service IIoT networks. Motivated by the growing computational capacity of the IIoT and the challenges of meeting QoS, federated reinforcement learning (RL) has become a propitious technique that gives out data collection and computation tasks to distributed network agents. This chapter discuss the new federated learning paradigm and then proposes a Deep Federated RL (DFRL) scheme to provide a federated network resource management for future IIoT networks. Toward this goal, the DFRL learns from Multi-Agent local models and provides them the ability to find optimal action decisions on LoRa parameters that satisfy QoS to IIoT virtual slice. Simulation results prove the effectiveness of the proposed framework compared to the early tools

    Siamese-Based Attention Learning Networks for Robust Visual Object Tracking

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    Tracking with the siamese network has recently gained enormous popularity in visual object tracking by using the template-matching mechanism. However, using only the template-matching process is susceptible to robust target tracking because of its inability to learn better discrimination between target and background. Several attention-learning are introduced to the underlying siamese network to enhance the target feature representation, which helps to improve the discrimination ability of the tracking framework. The attention mechanism is beneficial for focusing on the particular target feature by utilizing relevant weight gain. This chapter presents an in-depth overview and analysis of attention learning-based siamese trackers. We also perform extensive experiments to compare state-of-the-art methods. Furthermore, we also summarize our study by highlighting the key findings to provide insights into future visual object tracking developments
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