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606 results on '"Lynn, Theo"'

3. Digital Towns

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Subjects
digital policy, public policy, urban rural divide, broadband, connectivity, Digital Citizen, technology management, Open access, bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management, bic Book Industry Communication::K Economics, finance, business & management::KJ Business & management::KJQ Business mathematics & systems
Abstract

This open access book explores the digital transformation of small and rural towns, in particular, how to measure the evolution and development of digital towns. In addition to access to resources, competition from urban and global markets, and population trends, rural communities present lesser access and use of digital technologies and have lower digital competencies and skills than their urban counterparts. Consequently, they experience less beneficial outcomes from increased digitalisation than urban areas. This book defines what a digital town is and explores digitalisation from the perspective of the four basic economic sectors in towns - individuals and households, businesses, the public sector, and civil society - and three types of enabling infrastructure - digital connectivity, education, and governance. Particular attention is paid to how digitalisation efforts are measured by intergovernmental and international organisations for each sector and enabling infrastructure. The book concludes with a Digital Town Readiness Framework that offers local communities, policymakers, and scholars an initial set of indicators upon which to develop digital town initiatives, and measure progress. For those ready to embrace the opportunity, this book is a pathfinder on the road to a more equitable and impactful digital society and digital economy.

Published
2022
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4. Kicking Prejudice: Large Language Models for Racism Classification in Soccer Discourse on Social Media

Author

Santos, Guto Leoni, dos Santos, Vitor Gaboardi, Kearns, Colm, Sinclair, Gary, Black, Jack, Doidge, Mark, Fletcher, Thomas, Kilvington, Dan, Endo, Patricia Takako, Liston, Katie, Lynn, Theo, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Guizzardi, Giancarlo, editor, Santoro, Flavia, editor, Mouratidis, Haralambos, editor, and Soffer, Pnina, editor

Published
2024
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5. Identifying Citizen-Related Issues from Social Media Using LLM-Based Data Augmentation

Author

dos Santos, Vitor Gaboardi, Santos, Guto Leoni, Lynn, Theo, Benatallah, Boualem, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Guizzardi, Giancarlo, editor, Santoro, Flavia, editor, Mouratidis, Haralambos, editor, and Soffer, Pnina, editor

Published
2024
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6. Exploring the Performance of Machine Learning Models and Predictive Factors for Fetal Death: Preliminary Results

Author

de Mello, Maria Eduarda Ferro, da Silva Rocha, Élisson, de Morais, Flávio Leandro, de Queiroz Figueiroôa, Barbara, da Silva, Marília Santana, Neto, Waldemar Brandão, Lynn, Theo, Endo, Patricia Takako, Onwubiko, Cyril, editor, Rosati, Pierangelo, editor, Rege, Aunshul, editor, Erola, Arnau, editor, Bellekens, Xavier, editor, Hindy, Hanan, editor, and Jaatun, Martin Gilje, editor

Published
2024
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12. Predicting Short-term Mobile Internet Traffic from Internet Activity using Recurrent Neural Networks

Author

Santos, Guto Leoni, Rosati, Pierangelo, Lynn, Theo, Kelner, Judith, Sadok, Djamel, and Endo, Patricia Takako

Subjects
Computer Science - Networking and Internet Architecture
Abstract

Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures - Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet activity and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error. Both Deep Learning algorithms were effective in modeling Internet activity and seasonality, both within days and across two months. We find variations in performance across clusters within the city. Overall, the LSTM outperformed the GRU in our experiments.

Published
2020

13. The Greatest Teacher, Failure is: Using Reinforcement Learning for SFC Placement Based on Availability and Energy Consumption

Author

Santos, Guto Leoni, Lynn, Theo, Kelner, Judith, and Endo, Patricia Takako

Subjects
Computer Science - Networking and Internet Architecture, Computer Science - Machine Learning
Abstract

Software defined networking (SDN) and network functions virtualisation (NFV) 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 (PPO2), are compared using simulations of a ground truth network topology based on the Rede Nacional de Ensino e Pesquisa (RNP) Network, Brazil's National Teaching and Research Network backbone. The simulation results showed that PPO2 generally outperformed A2C and a greedy approach both in terms of acceptance rate and energy consumption. A2C outperformed PPO2 only in the scenario where network servers had a greater number of computing resources.

Published
2020

15. Digital Education

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, O’Gorman, Colm, Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Published
2022
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17. Digital Technologies and Civil Society

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, O’Gorman, Colm, Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Published
2022
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18. The Digital Citizen

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, O’Gorman, Colm, Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Published
2022
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20. Digital Public Services

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, O’Gorman, Colm, Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Published
2022
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22. Infrastructure for Digital Connectivity

Author

Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, O’Gorman, Colm, Lynn, Theo, Rosati, Pierangelo, Conway, Edel, Curran, Declan, Fox, Grace, and O'Gorman, Colm

Published
2022
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24. Right Scaling for Right Pricing: A Case Study on Total Cost of Ownership Measurement for Cloud Migration

Author

Rosati, Pierangelo, Fowley, Frank, Pahl, Claus, Taibi, Davide, and Lynn, Theo

Subjects
Computer Science - Software Engineering
Abstract

Cloud computing promises traditional enterprises and independent software vendors a myriad of advantages over on-premise installations including cost, operational and organizational efficiencies. The decision to migrate software configured for on-premise delivery to the cloud requires careful technical consideration and planning. In this chapter, we discuss the impact of right-scaling on the cost modelling for migration decision making and price setting of software for commercial resale. An integrated process is presented for measuring total cost of ownership, taking in to account IaaS/PaaS resource consumption based on forecast SaaS usage levels. The process is illustrated with a real world case study.

Published
2019
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33. Simulating Across the Cloud-to-Edge Continuum

Author

Spanopoulos-Karalexidis, Minas, Filelis Papadopoulos, Christos K., Giannoutakis, Konstantinos M., Gravvanis, George A., Tzovaras, Dimitrios, Bendechache, Malika, Svorobej, Sergej, Endo, Patricia Takako, Lynn, Theo, Lynn, Theo, Series Editor, Mooney, John G., Series Editor, Domaschka, Jörg, editor, and Ellis, Keith A., editor

Published
2020
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40. Does telemedicine hold the key for reproductive health care? A quantitative examination of women's intentions toward use and accurate information disclosure.

Author

Fox, Grace, Lynn, Theo, Werff, Lisa, and Kennedy, Jennifer

Subjects
*MEDICAL records, *REPRODUCTIVE health services, *MEDICAL care, *PERCEIVED benefit, *STRUCTURAL equation modeling
Abstract

Objective Study Setting and Design Data Sources and Analytic Sample Principal Findings Conclusions To investigate women's perceptions of telemedicine for reproductive health care services, focusing on how perceived benefits and privacy risks influence their intentions to adopt telemedicine and their willingness to disclose personal health information.A cross‐sectional survey was conducted. The study applied the privacy calculus theory to the context of telemedicine for reproductive health, using adapted, validated variables to develop the survey. Outcome variables included intentions to adopt telemedicine and willingness to disclose accurate personal health information.Data were collected in May and June 2023 using Qualtrics online panel services, targeting women across the United States who had not used telemedicine for reproductive health. The sample comprised 847 women aged 18 and older. Structural equation modeling was employed using AMOS v28.0 to test the hypothesized relationships between perceived benefits, perceived risks, and adoption intentions. The analysis controlled for age, household income, political affiliation, religious views, and prior births.Perceived benefits were positively related to intention to adopt telemedicine for reproductive care (β: 0.600, p < 0.001), and willingness to disclose accurate personal health information (β: 0.453, p < 0.001). Unexpectedly, perceived privacy risks were positively related to adoption intentions (β: 0.128, p < 0.001), but negatively related to willingness to disclose (β: −0.282, p < 0.001). Intentions to adopt were positively associated with willingness to disclose (β: 0.089, p < 0.05). Lastly, older women and women located in states with abortion restrictions expressed lower intentions to adopt. The model explained 40.2% of variance in intention to adopt and 38.3% of variance in willingness to disclose.The study demonstrates the importance of perceived benefits and privacy risks in driving telemedicine adoption and disclosure intentions among women in the reproductive health context. These findings suggest the need for targeted strategies to address privacy concerns and support telemedicine adoption, particularly in restrictive regulatory environments. [ABSTRACT FROM AUTHOR]

Published
2024
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45. RECAP (Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications): The Simulation Approach

Author

Endo, Patricia Takako, Filelis-Papadopoulos, Christos, Svorobej, Sergej, Gourinovitch, Anna, Giannoutakis, Konstantinos, Gravvanis, George, Tzovaras, Dimitrios, Elango, Divyaa Manimaran, Byrne, James, Lynn, Theo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Fazio, Maria, editor, and Zimmermann, Wolf, editor

Published
2020
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50. Digital Sustainability

Author

Lynn, Theo, Rosati, Pierangelo, Kreps, David, and Conboy, Kieran

Subjects
Environmental Sustainability, Greening Operations, Energy Management, Climate Smart Agriculture, Smart Cities, Circular Economy, thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJD Business innovation, thema EDItEUR::K Economics, Finance, Business and Management::KJ Business and Management::KJJ Business and the environment, ‘green’ approaches to business
Abstract

Digitalisation and environmental sustainability are two of the megatrends impacting industry and society. This open access Pivot is a timely exploration of some of the challenges and prospects related to digital sustainability from two main perspectives: how digital technologies can be used and maintained in a way that is environmentally sustainable over the long term (greening of digital technologies), and how digital technologies can be used to address climate change and improve environmental and sustainability outcomes (greening by digital technologies). The chapters included in this book are designed to provide some key definitions and concepts related to digital sustainability and its evolution, and more detailed insights on some of the key priority areas outlined in the European Green Deal, namely energy, mobility, buildings, food and the circular economy. A critical review of these topics will summarise and present different perspectives that challenge old assumptions and highlight emerging trends and possibilities for digital sustainability. Industry and society face significant challenges in the twin transition to digital and green transformation, not least of which is the need to balance investment in digital technologies with environmental sustainability. This open-access book can serve as a primer for scholars, policymakers, and enterprise decision-makers, providing insights on navigating innovation ecosystems to support both green and digital objectives.

Published
2024
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