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Prof. Theo Lynn
Irish Institute of Digital Business

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Journal article
Published: 27 May 2021 in Sustainability
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The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts.

ACS Style

Theo Lynn; Pierangelo Rosati; Antonia Egli; Stelios Krinidis; Komninos Angelakoglou; Vasileios Sougkakis; Dimitrios Tzovaras; Mohamad Kassem; David Greenwood; Omar Doukari. RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects. Sustainability 2021, 13, 6018 .

AMA Style

Theo Lynn, Pierangelo Rosati, Antonia Egli, Stelios Krinidis, Komninos Angelakoglou, Vasileios Sougkakis, Dimitrios Tzovaras, Mohamad Kassem, David Greenwood, Omar Doukari. RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects. Sustainability. 2021; 13 (11):6018.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Antonia Egli; Stelios Krinidis; Komninos Angelakoglou; Vasileios Sougkakis; Dimitrios Tzovaras; Mohamad Kassem; David Greenwood; Omar Doukari. 2021. "RINNO: Towards an Open Renovation Platform for Integrated Design and Delivery of Deep Renovation Projects." Sustainability 13, no. 11: 6018.

Journal article
Published: 15 April 2021 in Informatics
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Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-Layer Perceptron (MLP) models is the best model to predict the cure class.

ACS Style

Maicon Lino Ferreira da Silva Barros; Geovanne Oliveira Alves; Lubnnia Morais Florêncio Souza; Elisson Da Silva Rocha; João Lorenzato de Oliveira; Theo Lynn; Vanderson Sampaio; Patricia Endo. Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis. Informatics 2021, 8, 27 .

AMA Style

Maicon Lino Ferreira da Silva Barros, Geovanne Oliveira Alves, Lubnnia Morais Florêncio Souza, Elisson Da Silva Rocha, João Lorenzato de Oliveira, Theo Lynn, Vanderson Sampaio, Patricia Endo. Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis. Informatics. 2021; 8 (2):27.

Chicago/Turabian Style

Maicon Lino Ferreira da Silva Barros; Geovanne Oliveira Alves; Lubnnia Morais Florêncio Souza; Elisson Da Silva Rocha; João Lorenzato de Oliveira; Theo Lynn; Vanderson Sampaio; Patricia Endo. 2021. "Benchmarking Machine Learning Models to Assist in the Prognosis of Tuberculosis." Informatics 8, no. 2: 27.

Preprint
Published: 12 April 2021
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Tuberculosis (TB) is an airborne infectious disease caused by organisms in the Mycobacterium tuberculosis (Mtb) complex. In many low and middle-income countries, TB remains a major cause of morbidity and mortality. Once a patient has been diagnosed with TB, it is critical that healthcare workers make the most appropriate treatment decision given the individual conditions of the patient and the likely course of the disease based on medical experience. Depending on the prognosis, delayed or inappropriate treatment can result in unsatisfactory results including the exacerbation of clinical symptoms, poor quality of life, and increased risk of death. This work benchmarks machine learning models to aid TB prognosis using a Brazilian health database of confirmed cases and deaths related to TB in the State of Amazonas. The goal is to predict the probability of death by TB thus aiding the prognosis of TB and associated treatment decision making process. In its original form, the data set comprised 36,228 records and 130 fields but suffered from missing, incomplete, or incorrect data. Following data cleaning and preprocessing, a revised data set was generated comprising 24,015 records and 38 fields, including 22,876 reported cured TB patients and 1,139 deaths by TB. To explore how the data imbalance impacts model performance, two controlled experiments were designed using (1) imbalanced and (2) balanced data sets. The best result is achieved by the Gradient Boosting (GB) model using the balanced data set to predict TB-mortality, and the ensemble model composed by the Random Forest (RF), GB and Multi-layer Perceptron (MLP) models is the best model to predict the cure class.

ACS Style

Maicon Herverton Lino Ferreira Da Silva Barros; Geovanne Oliveira Alves; Lubnnia Morais Florêncio Souza; Élisson Da Silva Rocha; João Fausto Lorenzato de Oliveira; Theo Lynn; Vanderson Sampaio; Patricia Takako Endo. Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis. 2021, 1 .

AMA Style

Maicon Herverton Lino Ferreira Da Silva Barros, Geovanne Oliveira Alves, Lubnnia Morais Florêncio Souza, Élisson Da Silva Rocha, João Fausto Lorenzato de Oliveira, Theo Lynn, Vanderson Sampaio, Patricia Takako Endo. Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis. . 2021; ():1.

Chicago/Turabian Style

Maicon Herverton Lino Ferreira Da Silva Barros; Geovanne Oliveira Alves; Lubnnia Morais Florêncio Souza; Élisson Da Silva Rocha; João Fausto Lorenzato de Oliveira; Theo Lynn; Vanderson Sampaio; Patricia Takako Endo. 2021. "Benchmarking of Machine Learning Models to Assist the Prognosis of Tuberculosis." , no. : 1.

Article
Published: 12 April 2021 in The Journal of Supercomputing
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Software-defined networking and network functions virtualisation are making networks programmable and consequently much more flexible and agile. To meet service-level agreements, achieve greater utilisation of legacy networks, faster service deployment, and reduce expenditure, telecommunications operators are deploying increasingly complex service function chains (SFCs). Notwithstanding the benefits of SFCs, increasing heterogeneity and dynamism from the cloud to the edge introduces significant SFC placement challenges, not least adding or removing network functions while maintaining availability, quality of service, and minimising cost. In this paper, an availability- and energy-aware solution based on reinforcement learning (RL) is proposed for dynamic SFC placement. Two policy-aware RL algorithms, Advantage Actor-Critic (A2C) and Proximal Policy Optimisation (PPO), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa Network, Brazil’s National Teaching and Research Network backbone. The simulation results show that PPO generally outperformed A2C and a greedy approach in terms of both acceptance rate and energy consumption. The biggest difference in the PPO when compared to the other algorithms relates to the SFC availability requirement of 99.965%; the PPO algorithm median acceptance rate is 67.34% better than the A2C algorithm. A2C outperforms PPO only in the scenario where network servers had a greater number of computing resources. In this case, the A2C is 1% better than the PPO.

ACS Style

Guto Leoni Santos; Theo Lynn; Judith Kelner; Patricia Takako Endo. Availability-aware and energy-aware dynamic SFC placement using reinforcement learning. The Journal of Supercomputing 2021, 1 -30.

AMA Style

Guto Leoni Santos, Theo Lynn, Judith Kelner, Patricia Takako Endo. Availability-aware and energy-aware dynamic SFC placement using reinforcement learning. The Journal of Supercomputing. 2021; ():1-30.

Chicago/Turabian Style

Guto Leoni Santos; Theo Lynn; Judith Kelner; Patricia Takako Endo. 2021. "Availability-aware and energy-aware dynamic SFC placement using reinforcement learning." The Journal of Supercomputing , no. : 1-30.

Journal article
Published: 03 April 2021 in Computers in Human Behavior
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The continued proliferation of information technology in all aspects of our lives fosters benefits but also generates risks to individuals’ privacy. In emerging contexts, such as government surveillance technologies, there is a dearth of research investigating the positive and negative drivers of citizens’ acceptance. This is an important gap given the importance of citizen acceptance to the success of these technologies and the need to balance potentially wide-reaching benefits with any dilution of citizen privacy. We conduct a longitudinal examination of the competing influences of positive beliefs and privacy concerns on citizens’ acceptance of a COVID-19 national contact tracing mobile application among 405 Irish citizens. Combining privacy calculus theory with social exchange theory, we find that citizens’ initial acceptance is shaped by their perceptions of health benefits and social influence, with reciprocity exhibiting a sustained influence on acceptance over time and privacy concerns demonstrating a negative, albeit weak influence on willingness to rely on the application. The study offers important empirical and theoretical implications for the privacy literature in the government surveillance, location-based services, and mobile health application contexts, as well as practical implications for governments and developers introducing applications that rely on mass acceptance and reciprocal information disclosure.

ACS Style

Grace Fox; Trevor Clohessy; Lisa van der Werff; Pierangelo Rosati; Theo Lynn. Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Computers in Human Behavior 2021, 121, 106806 .

AMA Style

Grace Fox, Trevor Clohessy, Lisa van der Werff, Pierangelo Rosati, Theo Lynn. Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications. Computers in Human Behavior. 2021; 121 ():106806.

Chicago/Turabian Style

Grace Fox; Trevor Clohessy; Lisa van der Werff; Pierangelo Rosati; Theo Lynn. 2021. "Exploring the competing influences of privacy concerns and positive beliefs on citizen acceptance of contact tracing mobile applications." Computers in Human Behavior 121, no. : 106806.

Journal article
Published: 24 March 2021 in Future Internet
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Simulation has become an indispensable technique for modelling and evaluating the performance of large-scale systems efficiently and at a relatively low cost. ElasticSearch (ES) is one of the most popular open source large-scale distributed data indexing systems worldwide. In this paper, we use the RECAP Discrete Event Simulator (DES) simulator, an extension of CloudSimPlus, to model and evaluate the performance of a real-world cloud-based ES deployment by an Irish small and medium-sized enterprise (SME), Opening.io. Following simulation experiments that explored how much query traffic the existing Opening.io architecture could cater for before performance degradation, a revised architecture was proposed, adding a new virtual machine in order to dissolve the bottleneck. The simulation results suggest that the proposed improved architecture can handle significantly larger query traffic (about 71% more) than the current architecture used by Opening.io. The results also suggest that the RECAP DES simulator is suitable for simulating ES systems and can help companies to understand their infrastructure bottlenecks under various traffic scenarios and inform optimisation and scalability decisions.

ACS Style

Malika Bendechache; Sergej Svorobej; Patricia Endo; Adrian Mihai; Theo Lynn. Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator. Future Internet 2021, 13, 83 .

AMA Style

Malika Bendechache, Sergej Svorobej, Patricia Endo, Adrian Mihai, Theo Lynn. Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator. Future Internet. 2021; 13 (4):83.

Chicago/Turabian Style

Malika Bendechache; Sergej Svorobej; Patricia Endo; Adrian Mihai; Theo Lynn. 2021. "Simulating and Evaluating a Real-World ElasticSearch System Using the RECAP DES Simulator." Future Internet 13, no. 4: 83.

Research article
Published: 08 February 2021 in The European Journal of Finance
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This study investigates the effect of news media coverage on trading activity in, and the liquidity of, target firms’ shares around acquisition announcements. We use the number of articles published in four of the UK's main newspapers as a proxy for media coverage. Our dataset includes 350 UK domestic acquisition deals between 1996 and 2014. The results of our analysis suggest that media coverage is positively associated with target firms’ trading activity and stock liquidity. This is consistent with the media playing a key role in mitigating information asymmetry in the financial markets. This study contributes to the literature on stock market reactions to acquisition announcements by investigating the effect of media coverage on trading activity and stock liquidity beyond the price run-up, and by providing additional insights into the UK market which traditionally attracts less attention than the US market.

ACS Style

Louise Gorman; Theo Lynn; Eleonora Monaco; Riccardo Palumbo; Pierangelo Rosati. The effect of media coverage on target firms’ trading activity and liquidity around domestic acquisition announcements: evidence from UK. The European Journal of Finance 2021, 1 -20.

AMA Style

Louise Gorman, Theo Lynn, Eleonora Monaco, Riccardo Palumbo, Pierangelo Rosati. The effect of media coverage on target firms’ trading activity and liquidity around domestic acquisition announcements: evidence from UK. The European Journal of Finance. 2021; ():1-20.

Chicago/Turabian Style

Louise Gorman; Theo Lynn; Eleonora Monaco; Riccardo Palumbo; Pierangelo Rosati. 2021. "The effect of media coverage on target firms’ trading activity and liquidity around domestic acquisition announcements: evidence from UK." The European Journal of Finance , no. : 1-20.

Articles
Published: 16 December 2020 in European Accounting Review
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As signals of internal control weaknesses, cyber security incidents can represent significant risk factors to the quality of financial reporting. We empirically assess the audit quality implications of data breaches for a large sample of US firms. Using a difference-in-difference approach based on a matched sample of breached and non-breached firms, we find no evidence that cyber-security incidents result in a decline in audit quality. Instead, we observe positive shifts in four widely-used proxies for audit quality. We document that breached firms (i) experience a decrease in abnormal accruals, (ii) are less likely to report small profits or small earnings increases, (iii) are more likely to be issued a going concern report, and (iv) are less likely to restate their financial statements in the two years following a breach. Our results indicate that auditors effectively offset increases in audit risk through additional substantive testing and audit effort. Our evidence supports the view that auditors have increased their audit risk awareness and put in place adequate procedures to deal with the consequences of cyber-security incidents.

ACS Style

Pierangelo Rosati; Fabian Gogolin; Theo Lynn. Cyber-Security Incidents and Audit Quality. European Accounting Review 2020, 1 -28.

AMA Style

Pierangelo Rosati, Fabian Gogolin, Theo Lynn. Cyber-Security Incidents and Audit Quality. European Accounting Review. 2020; ():1-28.

Chicago/Turabian Style

Pierangelo Rosati; Fabian Gogolin; Theo Lynn. 2020. "Cyber-Security Incidents and Audit Quality." European Accounting Review , no. : 1-28.

Research article
Published: 24 November 2020 in Accounting Forum
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Social media is widely used by accounting firms to achieve a variety of business objectives and is a key enabler of non-market strategies. Socio-political involvement (SPI) involves firms taking positions on issues that lack societal consensus, have low information rationality, evolving viewpoints and issue salience, with no clear performance outcomes for firms. SPI may result in firms alienating stakeholders with opposing views, resulting in no or adverse performance outcomes. Accounting firms traditionally may not engage with such issues due to the associated reputational risk. Further, research suggests firm size is one of the most prominent firm-level antecedents of socio-political engagement. As the empirical context for this paper, we choose the Brexit referendum, a significant historical but divisive event with contested social norms. This paper explores the engagement of accounting firms on Twitter with the #Brexit discourse from the referendum announcement to one month after the vote. The objectives of the study are to understand the nature of non-market socio-political engagement by accounting firms in the #Brexit discourse on Twitter, and explore the differences between accounting firms of different size in this context. Our findings suggest that accounting firms engaged in the #Brexit Twitter discourse through a variety of non-market socio-political engagement activities, and that smaller firms tended to engage more than larger firms, most likely reflecting the ideological inclination of firm management. The engagement of accounting firms in socio-political discourse extends our understanding of how accounting firms of all sizes use social media and supports critical accounting and institutional perspectives.

ACS Style

Theo Lynn; Pierangelo Rosati; Brid Murphy. Does size matter? Non-market social and political engagement by accounting firms in the #Brexit discourse on Twitter. Accounting Forum 2020, 45, 58 -84.

AMA Style

Theo Lynn, Pierangelo Rosati, Brid Murphy. Does size matter? Non-market social and political engagement by accounting firms in the #Brexit discourse on Twitter. Accounting Forum. 2020; 45 (1):58-84.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Brid Murphy. 2020. "Does size matter? Non-market social and political engagement by accounting firms in the #Brexit discourse on Twitter." Accounting Forum 45, no. 1: 58-84.

Journal article
Published: 18 November 2020 in International Journal of Environmental Research and Public Health
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Over 2.8 million people die each year from being overweight or obese, a largely preventable disease. Social media has fundamentally changed the way we communicate, collaborate, consume, and create content. The ease with which content can be shared has resulted in a rapid increase in the number of individuals or organisations that seek to influence opinion and the volume of content that they generate. The nutrition and diet domain is not immune to this phenomenon. Unfortunately, from a public health perspective, many of these `influencers’ may be poorly qualified in order to provide nutritional or dietary guidance, and advice given may be without accepted scientific evidence and contrary to public health policy. In this preliminary study, we analyse the `healthy diet’ discourse on Twitter. While using a multi-component analytical approach, we analyse more than 1.2 million English language tweets over a 16-month period in order to identify and characterise the influential actors and discover topics of interest in the discourse. Our analysis suggests that the discourse is dominated by non-health professionals. There is widespread use of bots that pollute the discourse and seek to create a false equivalence on the efficacy of a particular nutritional strategy or diet. Topic modelling suggests a significant focus on diet, nutrition, exercise, weight, disease, and quality of life. Public health policy makers and professional nutritionists need to consider what interventions can be taken in order to counteract the influence of non-professional and bad actors on social media.

ACS Style

Theo Lynn; Pierangelo Rosati; Guto Leoni Santos; Patricia Takako Endo. Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter. International Journal of Environmental Research and Public Health 2020, 17, 8557 .

AMA Style

Theo Lynn, Pierangelo Rosati, Guto Leoni Santos, Patricia Takako Endo. Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter. International Journal of Environmental Research and Public Health. 2020; 17 (22):8557.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Guto Leoni Santos; Patricia Takako Endo. 2020. "Sorting the Healthy Diet Signal from the Social Media Expert Noise: Preliminary Evidence from the Healthy Diet Discourse on Twitter." International Journal of Environmental Research and Public Health 17, no. 22: 8557.

Journal article
Published: 30 October 2020 in Algorithms
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To minimise environmental impact, to avoid regulatory penalties, and to improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time-series models due to its high dimensionality and problem-solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA) and an existing manual technique used at the case site) against three deep-learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and two machine-learning models (Support Vector Regression (SVR) and Random Forest) for short-term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model and then use Diebold–Mariano testing to confirm the results. The results suggests that the legacy approach used at the case site is the worst performing and that the GRU model outperformed all other models tested.

ACS Style

Andrea Maria N. C. Ribeiro; Pedro Rafael X. Do Carmo; Iago Richard Rodrigues; Djamel Sadok; Theo Lynn; Patricia Takako Endo. Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. Algorithms 2020, 13, 274 .

AMA Style

Andrea Maria N. C. Ribeiro, Pedro Rafael X. Do Carmo, Iago Richard Rodrigues, Djamel Sadok, Theo Lynn, Patricia Takako Endo. Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. Algorithms. 2020; 13 (11):274.

Chicago/Turabian Style

Andrea Maria N. C. Ribeiro; Pedro Rafael X. Do Carmo; Iago Richard Rodrigues; Djamel Sadok; Theo Lynn; Patricia Takako Endo. 2020. "Short-Term Firm-Level Energy-Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models." Algorithms 13, no. 11: 274.

Chapter
Published: 14 October 2020 in Data Privacy and Trust in Cloud Computing
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Cloud computing is the dominant paradigm in modern computing, used by billions of Internet users worldwide. It is a market dominated by a small number of hyperscale cloud service providers. The overwhelming majority of cloud customers agree to standard form click-wrap contracts, with no opportunity to negotiate specific terms and conditions. Few cloud customers read the contracts that they agree to. It is clear that contracts in cloud computing are primarily an instrument of control benefiting one side, the cloud service provider. This chapter provides an introduction to the relationship between psychological trust, contracts and contract law. It also offers an overview of the key contract law issues that arise in cloud computing and introduces some emerging paradigms in cloud computing and contracts.

ACS Style

Theo Lynn. Dear Cloud, I Think We Have Trust Issues: Cloud Computing Contracts and Trust. Data Privacy and Trust in Cloud Computing 2020, 21 -42.

AMA Style

Theo Lynn. Dear Cloud, I Think We Have Trust Issues: Cloud Computing Contracts and Trust. Data Privacy and Trust in Cloud Computing. 2020; ():21-42.

Chicago/Turabian Style

Theo Lynn. 2020. "Dear Cloud, I Think We Have Trust Issues: Cloud Computing Contracts and Trust." Data Privacy and Trust in Cloud Computing , no. : 21-42.

Chapter
Published: 14 October 2020 in Data Privacy and Trust in Cloud Computing
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Trust is regularly cited as one the main barriers for increased adoption of cloud computing, however conceptualisations of trust in cloud computing literature can be simplistic. This chapter briefly introduces the trust literature including definitions and antecedents of trust. Following an overview of cloud computing, we discuss some of the cited barriers to trust in cloud computing, and proposed mechanisms for building trust in the cloud. We present a high-level framework for exploring assurance (trust building) and accountability (trust repair) in the cloud and call for a more integrated multi-stakeholder approach to trust research in this multi-faceted context.

ACS Style

Theo Lynn; Lisa Van Der Werff; Grace Fox. Understanding Trust and Cloud Computing: An Integrated Framework for Assurance and Accountability in the Cloud. Data Privacy and Trust in Cloud Computing 2020, 1 -20.

AMA Style

Theo Lynn, Lisa Van Der Werff, Grace Fox. Understanding Trust and Cloud Computing: An Integrated Framework for Assurance and Accountability in the Cloud. Data Privacy and Trust in Cloud Computing. 2020; ():1-20.

Chicago/Turabian Style

Theo Lynn; Lisa Van Der Werff; Grace Fox. 2020. "Understanding Trust and Cloud Computing: An Integrated Framework for Assurance and Accountability in the Cloud." Data Privacy and Trust in Cloud Computing , no. : 1-20.

Preprint
Published: 21 September 2020
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To minimise environmental impact, avoid regulatory penalties, and improve competitiveness, energy-intensive manufacturing firms require accurate forecasts of their energy consumption so that precautionary and mitigation measures can be taken. Deep learning is widely touted as a superior analytical technique to traditional artificial neural networks, machine learning, and other classical time series models due to its high dimensionality and problem solving capabilities. Despite this, research on its application in demand-side energy forecasting is limited. We compare two benchmarks (Autoregressive Integrated Moving Average (ARIMA), and an existing manual technique used at the case site) against three deep learning models (simple Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)) and three machine learning models (Support Vector Regression (SVM), Random Forest, and K-Nearest Neighbors (KNN)) for short term load forecasting (STLF) using data from a Brazilian thermoplastic resin manufacturing plant. We use the grid search method to identify the best configurations for each model, and then use Diebold-Mariano testing to confirm the results. Results suggests that the legacy approach used at the case site is the worst performing, and that the GRU model outperformed all other models tested.

ACS Style

Andrea MariaN.C. Ribeiro; Pedro RafaelX.Do Carmo; Iago Rodrigues; Djamel Sadok; Theo Lynn; Patricia Takako Endo. Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. 2020, 1 .

AMA Style

Andrea MariaN.C. Ribeiro, Pedro RafaelX.Do Carmo, Iago Rodrigues, Djamel Sadok, Theo Lynn, Patricia Takako Endo. Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models. . 2020; ():1.

Chicago/Turabian Style

Andrea MariaN.C. Ribeiro; Pedro RafaelX.Do Carmo; Iago Rodrigues; Djamel Sadok; Theo Lynn; Patricia Takako Endo. 2020. "Short-Term Firm-Level Energy Consumption Forecasting for Energy-Intensive Manufacturing: A Comparison of Machine Learning and Deep Learning Models." , no. : 1.

Journal article
Published: 11 September 2020 in Journal of Open Innovation: Technology, Market, and Complexity
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Together, social media and crowdsourcing can help entrepreneurs to attract external finance and early-stage customers. This paper investigates the characteristics and discourse of an issue-centered public on Twitter organized around the hashtag #crowdfunding through the lens of social network theory. Using a dataset of 2,732,144 tweets published during a calendar year, we use exploratory data analysis to generate insights and hypotheses on who the users in the #crowdfunding network are, what they share, and how they are connected to each other. In order to do so, we adopt a range of descriptive, content, network analytics techniques. The results suggest that platforms, crowdfunders, and other actors who derive income from the crowdfunding economy play a key role in creating the network. Furthermore, latent ties (strangers) play a direct role in disseminating information, investing, and sending signals to platforms that further raises campaign prominence. We also introduce a new type of social tie, the “computer as a social actor”, previously unaddressed in entrepreneurial network literature, which play a role in sending signals to both platforms and networks. Our results suggest that homophily is a key driver for creating network sub-communities built around specific platforms, project types, domains, or geography.

ACS Style

Theo Lynn; Pierangelo Rosati; Binesh Nair; Ciáran Mac An Bhaird. An Exploratory Data Analysis of the #Crowdfunding Network on Twitter. Journal of Open Innovation: Technology, Market, and Complexity 2020, 6, 80 .

AMA Style

Theo Lynn, Pierangelo Rosati, Binesh Nair, Ciáran Mac An Bhaird. An Exploratory Data Analysis of the #Crowdfunding Network on Twitter. Journal of Open Innovation: Technology, Market, and Complexity. 2020; 6 (3):80.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Binesh Nair; Ciáran Mac An Bhaird. 2020. "An Exploratory Data Analysis of the #Crowdfunding Network on Twitter." Journal of Open Innovation: Technology, Market, and Complexity 6, no. 3: 80.

Journal article
Published: 10 September 2020 in Information
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Mobilization theory posits that social media gives a voice to non-traditional actors in socio-political discourse. This study uses network analytics to understand the underlying structure of the Brexit discourse and whether the main sub-networks identify new publics and influencers in political participation, and specifically industry stakeholders. Content analytics and peak detection analysis are used to provide greater explanatory values to the organizing themes for these sub-networks. Our findings suggest that the Brexit discourse on Twitter can be largely explained by calculated publics organized around the two campaigns and political parties. Ad hoc communities were identified based on (i) the media, (ii) geo-location, and (iii) the US presidential election. Other than the media, significant sub-communities did not form around industry as whole or around individual sectors or leaders. Participation by business accounts in the Twitter discourse had limited impact.

ACS Style

Theo Lynn; Pierangelo Rosati; Binesh Nair. Calculated vs. Ad Hoc Publics in the #Brexit Discourse on Twitter and the Role of Business Actors. Information 2020, 11, 435 .

AMA Style

Theo Lynn, Pierangelo Rosati, Binesh Nair. Calculated vs. Ad Hoc Publics in the #Brexit Discourse on Twitter and the Role of Business Actors. Information. 2020; 11 (9):435.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Binesh Nair. 2020. "Calculated vs. Ad Hoc Publics in the #Brexit Discourse on Twitter and the Role of Business Actors." Information 11, no. 9: 435.

Chapter
Published: 28 August 2020 in Data Privacy and Trust in Cloud Computing
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Much of the research on measuring the business value of cloud computing examines cloud computing from the perspective of a centralised commodity-based aggregated conceptualisation of cloud computing, largely based on the NIST reference architecture. Advances in new processor architectures and virtualisation combined with the rise of the Internet of Things are not only changing cloud computing but introducing new computing paradigms from the cloud to the edge. These new paradigms present both opportunities and challenges, not least managing complexity several orders of magnitude greater than today. Yet, academic research on measuring the business value of cloud computing is lagging practice and remains far removed from these innovations. New research is required that explores the relationship between investments in new cloud computing paradigms and business value, and the measurement thereof. This chapter explores a selection of these new paradigms, which may provide fruitful research pathways in the future.

ACS Style

Theo Lynn; Pierangelo Rosati; Grace Fox. Measuring the Business Value of Cloud Computing: Emerging Paradigms and Future Directions for Research. Data Privacy and Trust in Cloud Computing 2020, 107 -122.

AMA Style

Theo Lynn, Pierangelo Rosati, Grace Fox. Measuring the Business Value of Cloud Computing: Emerging Paradigms and Future Directions for Research. Data Privacy and Trust in Cloud Computing. 2020; ():107-122.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Grace Fox. 2020. "Measuring the Business Value of Cloud Computing: Emerging Paradigms and Future Directions for Research." Data Privacy and Trust in Cloud Computing , no. : 107-122.

Journal article
Published: 11 August 2020 in Future Internet
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High performance computing (HPC) is widely recognized as a key enabling technology for advancing scientific progress, industrial competitiveness, national and regional security, and the quality of human life. Notwithstanding this contribution, the large upfront investment and technical expertise required has limited the adoption of HPC to large organizations, government bodies, and third level institutions. Recent advances in cloud computing and telecommunications have the potential to overcome the historical issues associated with HPC through increased flexibility and efficiency, and reduced capital and operational expenditure. This study seeks to advance the literature on technology adoption and assimilation in the under-examined HPC context through a mixed methods approach. Firstly, the determinants of cloud computing adoption for HPC are examined through a survey of 121 HPC decision makers worldwide. Secondly, a modified Delphi method was conducted with 13 experts to identify and prioritize critical issues in the adoption of cloud computing for HPC. Results from the quantitative phase suggest that only organizational and human factors significantly influence cloud computing adoption decisions for HPC. While security was not identified as a significant influencer in adoption decisions, qualitative research findings suggest that data privacy and security issues are an immediate and long-term concern.

ACS Style

Theo Lynn; Grace Fox; Anna Gourinovitch; Pierangelo Rosati. Understanding the Determinants and Future Challenges of Cloud Computing Adoption for High Performance Computing. Future Internet 2020, 12, 135 .

AMA Style

Theo Lynn, Grace Fox, Anna Gourinovitch, Pierangelo Rosati. Understanding the Determinants and Future Challenges of Cloud Computing Adoption for High Performance Computing. Future Internet. 2020; 12 (8):135.

Chicago/Turabian Style

Theo Lynn; Grace Fox; Anna Gourinovitch; Pierangelo Rosati. 2020. "Understanding the Determinants and Future Challenges of Cloud Computing Adoption for High Performance Computing." Future Internet 12, no. 8: 135.

Chapter
Published: 11 August 2020 in Entrepreneurial Finance in Emerging Markets
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Globalisation, technology, the liberalisation of financial markets, and changing consumer behaviour are changing the banking sector irrevocably. Incumbent banks are faced with unprecedented competition from global competitors, large and small, and both from within and outside the financial services sector. Open banking is a business approach in which data, processes, and business functionalities are made available in an ecosystem of banks, customers, and third parties. This chapter explores Banking-as-a-Platform (BaaP), a digital platform strategy for banking. After discussing the key technologies and concepts that underpin BaaP, we present a conceptual reference model for a platform-based bank and discuss the opportunities and challenges for policymakers, banks, FinTech companies, and entrepreneurs in emerging markets.

ACS Style

Theo Lynn; Pierangelo Rosati; Mark Cummins. Exploring Open Banking and Banking-as-a-Platform: Opportunities and Risks for Emerging Markets. Entrepreneurial Finance in Emerging Markets 2020, 319 -334.

AMA Style

Theo Lynn, Pierangelo Rosati, Mark Cummins. Exploring Open Banking and Banking-as-a-Platform: Opportunities and Risks for Emerging Markets. Entrepreneurial Finance in Emerging Markets. 2020; ():319-334.

Chicago/Turabian Style

Theo Lynn; Pierangelo Rosati; Mark Cummins. 2020. "Exploring Open Banking and Banking-as-a-Platform: Opportunities and Risks for Emerging Markets." Entrepreneurial Finance in Emerging Markets , no. : 319-334.

Preprint content
Published: 15 July 2020
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In recent years, there has been significant advancement in resource management mechanisms for cloud computing infrastructure performance in terms of cost, quality of service (QoS) and energy consumption. The emergence of the Internet of Things has led to the development of infrastructure that extends beyond centralised data centers from the cloud to the edge, the so-called cloud-to-thing continuum (C2T). This infrastructure is characterised by extreme heterogeneity, geographic distribution, and complexity, where the key performance indicators (KPIs) for the traditional model of cloud computing may no longer apply in the same way. Existing resource management mechanisms may not be suitable for such complex environments and therefore require thorough testing, validation and evaluation before even being considered for live system implementation. Similarly, previously discounted resource management proposals may be more relevant and worthy of revisiting. Simulation is a widely used technique in the development and evaluation of resource management mechanisms for cloud computing but is a relatively nascent research area for new C2T computing paradigms such as fog and edge computing. We present a methodical literature analysis of C2T resource management research using simulation software tools to assist researchers in identifying suitable methods, algorithms, and simulation approaches for future research. We analyse 35 research articles from a total collection of 317 journal articles published from January 2009 to March 2019. We present our descriptive and synthetic analysis from a variety of perspectives including resource management, C2T layer, and simulation.  

ACS Style

Malika Bendechache; Sergej Svorobej; Patricia Takako Endo; Theo Lynn. Simulating Resource Management across the Cloud-to-Thing Continuum. 2020, 1 .

AMA Style

Malika Bendechache, Sergej Svorobej, Patricia Takako Endo, Theo Lynn. Simulating Resource Management across the Cloud-to-Thing Continuum. . 2020; ():1.

Chicago/Turabian Style

Malika Bendechache; Sergej Svorobej; Patricia Takako Endo; Theo Lynn. 2020. "Simulating Resource Management across the Cloud-to-Thing Continuum." , no. : 1.