Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 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/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Constructing bi-plots for random forest:Tutorial

Current technological developments have allowed for a significant increase and availability of data. Consequently, this has opened enormous opportunities for the machine learning and data science field, translating into the development of new algorithms in a wide range of applications in medical, biomedical, daily-life, and national security areas. Ensemble techniques are among the pillars of the machine learning field, and they can be defined as approaches in which multiple, complex, independent/uncorrelated, predictive models are subsequently combined by either averaging or voting to yield a higher model performance. Random forest (RF), a popular ensemble method, has been successfully applied in various domains due to its ability to build predictive models with high certainty and little necessity of model optimization. RF provides both a predictive model and an estimation of the variable importance. However, the estimation of the variable importance is based on thousands of trees, and therefore, it does not specify which variable is important for which sample group.The present study demonstrates an approach based on the pseudo-sample principle that allows for construction of bi-plots (i.e. spin plots) associated with RF models. The pseudo-sample principle for RF. is explained and demonstrated by using two simulated datasets, and three different types of real data, which include political sciences, food chemistry and the human microbiome data. The pseudo-sample bi plots, associated with RF and its unsupervised version, allow for a versatile visualization of multivariate models, and the variable importance and the relation among them. (c) 2020 Elsevier B.V. All rights reserved.</p

Horizontal network collaboration by entrepreneurial ventures: a supply chain finance perspective

Purpose – The present paper aims at understanding how horizontal network collaborations between small and medium enterprises (SMEs) can be designed and implemented to take advantage of a supply chain finance (SCF) perspective. Design/methodology/approach – This study presents an SCF literature background identifying four literature gaps, and in response to them it adopts an action research approach. The empirical analysis is developed on a network-case study: a horizontal collaboration project between small businesses of the Italian wine industry and their supply chains. Findings – SMEs can play an active role in developing – in terms of design and implementation – their collaborative networks by taking advantage of an SCF perspective for themselves, and their customers, based on the reorganization of relationships interface processes. Taking this perspective can be a concrete and crucial way to sustain the development of SMEs and their supply chains in an actual competitive context. Research limitations/implications – The paper identifies the theoretical gaps in the literature, suggests new research areas that deserve to be more deeply investigated and connects case-related results to the key concepts. The empirical part presents a real case application that proposes a complete roadmap for managers and practitioners who wish to experience similar projects. Practical implications – This network-case study storyline, presenting an overview of ten years of meetings, with related purposes, is suggesting a roadmap for design and implementation of horizontal network as managerial implications. These kinds of active research projects, with a collaborative mixed team of academics and practitioners, and involving a multilayer group of participants, are positive examples for closing the bridge between companies and academia, which enhance this network of small businesses active in trying to improve their competitiveness working together. Originality/value – The value of the paper is to embrace a supply chain-oriented perspective for an SME, independent of the financial system and based on inventory flow management. Very little literature focuses on inventory-based research within the SCF framework, designed for real implementation in horizontal network collaboration by entrepreneurial ventures

Santa Clara Magazine, Volume 33 Number 3, Spring 1991

12 - ABORTION : NO SIMPLE ANSWERS Jesuit theologian Ted Mackin examines pro-life and pro-choice positions and reports that neither side is addressing the issue with total honesty. By Theodore J. Mackin, S.J. 16 - STAY AT HOME MOMS Alumnae discuss why they decided to devote all their time and attention to their families. By Michelle Burget Fletcher \u2778, Brigid Modena Benham \u2781, and Anne Penoyer King \u2769 20 - SCU\u27s WINE FAMILIES Northern California\u27s vineyards are fertile ground for Santa Clara graduates. By Rosina Wilson 28 - WORKING THE SUICIDE HOTLINE Within these walls, no secret is too terrible to share. By Mike Brozda \u2776 32 - IDEALISM AND EDUCATION It was the men and women of ideals, and their ideas, profoundly believed in, that shaped the history of a nation. By Timothy O\u27Keefehttps://scholarcommons.scu.edu/sc_mag/1043/thumbnail.jp

Deep learning for time series classification

Time series analysis is a field of data science which is interested in analyzing sequences of numerical values ordered in time. Time series are particularly interesting because they allow us to visualize and understand the evolution of a process over time. Their analysis can reveal trends, relationships and similarities across the data. There exists numerous fields containing data in the form of time series: health care (electrocardiogram, blood sugar, etc.), activity recognition, remote sensing, finance (stock market price), industry (sensors), etc. Time series classification consists of constructing algorithms dedicated to automatically label time series data. The sequential aspect of time series data requires the development of algorithms that are able to harness this temporal property, thus making the existing off-the-shelf machine learning models for traditional tabular data suboptimal for solving the underlying task. In this context, deep learning has emerged in recent years as one of the most effective methods for tackling the supervised classification task, particularly in the field of computer vision. The main objective of this thesis was to study and develop deep neural networks specifically constructed for the classification of time series data. We thus carried out the first large scale experimental study allowing us to compare the existing deep methods and to position them compared other non-deep learning based state-of-the-art methods. Subsequently, we made numerous contributions in this area, notably in the context of transfer learning, data augmentation, ensembling and adversarial attacks. Finally, we have also proposed a novel architecture, based on the famous Inception network (Google), which ranks among the most efficient to date.Comment: PhD thesi

Dissecting the effect of soil on plant phenology and berry transcriptional plasticity in two Italian grapevine varieties (Vitis vinifera L.)

Grapevine embodies a fascinating species as regards phenotypic plasticity and genotype-per-environment interactions. The terroir, namely the set of agri-environmental factors to which a variety is subjected, can influence the phenotype at the physiological, molecular, and biochemical level, representing an important phenomenon connected to the typicality of productions. We investigated the determinants of plasticity by conducting a field-experiment where all terroir variables, except soil, were kept as constant as possible. We isolated the effect of soils collected from different areas, on phenology, physiology, and transcriptional responses of skin and flesh of a red and a white variety of great economic value: Corvina and Glera. Molecular results, together with physio-phenological parameters, suggest a specific effect of soil on grapevine plastic response, highlighting a higher transcriptional plasticity of Glera in respect to Corvina and a marked response of skin compared to flesh. Using a novel statistical approach, we identified clusters of plastic genes subjected to the specific influence of soil. These findings could represent an issue of applicative value, posing the basis for targeted agricultural practices to enhance the desired characteristics for any soil/cultivar combination, to improve vineyards management for a better resource usage and to valorize vineyards uniqueness maximizing the terroir-effect