Computer Engineering Commons^™

Investigation Into Finops Techniques To Optimise Cost In Aws Cloud Deployments, Denis Parker, Kevin Bayliss

Academic Poster Collection

Investigation Into FinOps Techniques To Optimise Cost in AWS Cloud Deployments

The Business-Day Cloud: A Hybrid Kubernetes And Serverless Solution For Sustainable Scaling With Predictable Load Patterns, Brendan Burnside, David White

Academic Poster Collection

The Business-Day Cloud: A Hybrid Kubernetes and Serverless solution for Sustainable Scaling with Predictable Load Patterns

An Evaluation Of Zero Trust Principles In Modern Software Development, Cezar Vararu, David White

Academic Poster Collection

An evaluation of Zero Trust Principles in modern software development

Cost Optimization In Open Telemetry, Niksa Jadric, Cormac Keogh

Academic Poster Collection

Cost Optimization in Open Telemetry

Exploring Rust’S Performance In A Serverless Environment, Saoirse Mullen, Gary Clynch

Academic Poster Collection

Exploring Rust’s Performance in a Serverless Environment

Performance Evaluation Of Zabbix And Azure Monitor In Hybrid It Infrastructure, Ivan Godoy, Cormac Keogh

Academic Poster Collection

Performance Evaluation of Zabbix and Azure Monitor in Hybrid IT Infrastructure

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

Academic Poster Collection

AI-Based Predictive Analytics for Network Operations

Nobocap: Unlocking Mdr/Ivdr Regulations For Innovators In Europe, Graham Gavin, Claire Brougham

Conference Papers

The NoBoCap project (nobocap.eu) is aimed at addressing some of the challenges encountered by both SMEs and Notified Bodies across the EU. It is a multi-organizational consortium including universities, a Notified Body, and Bio-health and Innovation Hubs and Clusters. The NoBoCap project has several work packages focussed on:• Design and delivery of funded short-term courses.• Creating a dedicated NB job board.• Design and delivery of funded university accredited modules.• Design and development of e-tools to support manufacturers.• Develop a community platform to act as a voice for start-ups and SMEs

Power Saving In Open Ran By Using Advanced Cpu Scheduling Algorithm, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN is an emerging wireless technology that is gaining significant attention for its potential to enable flexi- ble, cost-efficient, and interoperable networks. Reducing power utilization in Open RAN, particularly for 5G base stations (gNodeBs) deployed in remote areas, remains a critical challenge due to limited power availability. In our previous work, we developed a CPU scheduling algorithm that optimized core allocation based on load conditions, reducing power utilization for gNodeB in a virtualized Open RAN environment. Extending our previous work, this paper introduces an advanced CPU scheduling for Open RAN designed to reduce power utilization in real hardware Open …

Demonstrating The Impact Of Cpu Scheduling On Power Consumption In Virtualized Open Ran, Saish Urumkar, Sachin Sharma

Conference papers

Open RAN (Open Radio Access Network) is a next- generation wireless network gaining significant research interest globally due to its potential to provide a cost-efficient and scalable solution for growing network demands. Energy efficiency is an important area of focus in Open RAN deployments, as reducing power consumption while maintaining network performance is essential for sustainable wireless communication. This paper demonstrates the impact of CPU (Central Processing Unit) scheduling process priorities on power consumption and network performance in an Open RAN NodeB deployed on a testbed in the USA. The experimental results are demonstrated using two scenarios: (1) CPU Priority-Based …

Multi-Objective Deep Reinforcement Learning For Dynamic Algorithm Selection In Open Ran, Saish Urumkar, Byrav Ramamurthy, Sachin Sharma

Conference papers

Open Radio Access Networks (Open RAN) provide flexible, modular multi-vendor interoperability. Growing mobile data demand requires balancing network performance with power efficiency. Mobile operators need intelligent resource management to achieve Key Performance Indicator (KPI) targets while maintaining operational efficiency. This paper proposes a solution using a multi-objective deep reinforcement learning (MODRL) model deployed on the Open RAN Intelligent Controller (RIC). Three customizable operator profiles (Power Saving, Balanced, and Performance) are used which define specific priority ratios between performance and power saving objectives.

To evaluate, individual algorithms (CPU scheduling and UE connection state switching) are implemented in Open RAN, achieving 5–20%CPU …

Cnns And Transformers For Visual Understanding: From Feature Alignment To Image Captioning, Xuehao Liu

Doctoral

Deep learning has developed rapidly since the introduction of Deep Belief Networks during the past decade. As an area of machine learning, it still has many open challenges. Among these open challenges is the issue of transparency, with deep learning models known as black boxes. Both explainability of a model for understanding the decision making process, and the transparency of the relationship between the input and output are crucial for understanding a model. The understanding of models can build trust between AI systems and humans, verify models behavior, and identify potential biases or errors.

Navigating Human-Robotic Interaction Challenges In Teaching-By-Demonstration, Shakra Mehak

Doctoral

The advancement in interdisciplinary research domains, like robotics and HRI, presents a challenging task. It is an exception rather than the norm for research to extend beyond the boundaries of individual disciplines and encompass the challenges presented by other fields of study. This project is part of ”Collaborative Intelligence for Safety Critical Systems” (CISC), which is a Marie Curie Training Network funded by the European Commission to hire and train researchers with the expertise and skillset necessary to carry out the major tasks required to develop a Collaborative Intelligence system. The program encompasses four overarching themes: Artificial Intelligence (AI), Human …

Personalized Persuasion In The Digital Age: A Data-Driven Approach To Effective Communication, Annye Braca

Doctoral

This thesis investigates the potential of Machine Learning (ML) to personalize persuasive marketing messages. It explores the identification of individuals receptive to specific persuasion techniques based on their psychometric profiles. By developing ML models that incorporate these profiles, the thesis aims to predict the impact of tailored messages and improve the effectiveness of marketing communication.

Machine Learning Applications In Epigenomics And Its Association With Health And Disease, Trevor Doherty

Doctoral

Epigenetic modifications can lead to altered phenotypes without a change in the DNA sequence itself. Disrupted gene expression regulated by epigenetic processes can result in cancers, autoimmune diseases and various other maladies. Machine learning (ML) involves the use of algorithms and models which are trained to learn patterns in data, and has demonstrated remarkable success in solving diverse, complex challenges. Epigenomic studies, such as those that use DNA methylation (DNAm) data, increasingly make use of ML techniques to process extremely high dimensional data obtained from high throughput platforms e.g., DNAm arrays. These datasets suffer from the curse of dimensionality, increased …

Agency, Index & Process: Investigating The Role Of The Artist’S Body In Digital Sculpture Production, Alan Magee

Doctoral

Contemporary Digital Sculpture has emerged out of recent developments in digital art, 3d modelling and virtual modes of production. These advances range from the creation of more powerful software and hardware systems to the to nascent XR sectors, and the potential for materialisation through technologies such as 3d printing, laser-cutting, or CNC1 machining. However, critical discourse remains predominantly focused on the end product, often overlooking the embodied labour processes inherent in its creation. As revealed through indexical traces of the artist’s body, these processes encapsulate the gestures, actions and subjective agency of artistic activity. Consequently, with the limitations of digital …

Enhancing Trust In Ai For Healthcare: A Quantative Evaluation Of Explainable Methods In Clinical Decision Support Systems, Abdul Aziz Noor

Masters

The integration of Artificial Intelligence (AI) into healthcare has revolutionized Clinical Decision Support Systems (CDSS) by enabling sophisticated predictive capabilities. However, the opaque nature of many machine learning models, commonly referred to as "black-box" systems, poses significant challenges to their adoption in critical clinical settings where transparency, interpretability, and trust are paramount. This thesis addresses these challenges by developing a rigorous, mathematically grounded framework to evaluate Explainable AI (XAI) methods and enhance their integration into CDSS.

Power Utilization In Open Ran: Key Findings From A Usa Testbed, Saish Urumkar, Byrav Ramamurthy, Seshu Tirupathi, Sachin Sharma

Articles

Open Radio Access Networks (Open RAN) provide flexible, scalable, and interoperable solutions to address the growing demands of mobile traffic while also aiming to reduce energy consumption. Most prior research on energy-efficient Open RAN has focused on switching techniques such as dynamic cell on/off strategies and adaptive resource allocation, primarily through simulations. This letter investigates Central Processing Unit (CPU) power utilization at the NodeB (base station) level, focusing on User Equipment (UE) connection states by making use of a USA testbed (i.e., POWDER testbed). Two scenarios are considered for the experimental setup: (1) a simulated virtual environment with a single …

The Digital Loophole: Evaluating The Effectiveness Of Child Age Verification Methods On Social Media, Fatmaelzahraa Eltaher, Rahul Gajula, Luis Miralles-Pechuán, Christina Thorpe, Susan Mckeever

Conference papers

Social media platforms are an integral part of daily life for nearly five billion people worldwide. However, the growing presence of underage users on these platforms raises significant concerns regarding children's exposure to harmful content and its impact on their mental health. This paper examines the effectiveness of age verification measures implemented on leading platforms Facebook, YouTube, Instagram, TikTok, Snapchat, and X. We evaluate the age verification processes required for account creation by simulating the registration steps for minors on these platforms. We also compare these methods to best practices in online age assurance in finance, betting and public transportation …

An Evaluation Of Features Extracted From Facial Images In The Context Of Accurate Age Estimation⋆, Malik Awais Khan, Aurelia Power, Peter Corcoran, Christina Thorpe

Conference papers

Age estimation by face image recognition can be used in numerous ways with regression models to manage access control, improve security, and guarantee the protection of children online. The approaches used for predicting age—including data selection, cleaning techniques, feature extraction, algorithm choice, and hyperparameter tuning—often struggles with generalization. Furthermore, a lot of methods neglect to specifically address how extracted face features might be used for prediction. To address the lack of racial diversity we acquired a dataset consisting of different races from literature. We also examined the ability of local, global and hybrid facial features to predict ages. Two variants …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Interpreting Black-Box Time Series Classifiers Using Parameterised Event Primitives, Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio

Conference papers

Amidst the remarkable performance of deep learning models in time series classification, there is a pressing demand for methods that unveil their prediction rationale. Existing feature importance techniques often neglect the temporal nature of time series data, focusing solely on segment importance. Addressing this gap, this paper introduces a local model-agnostic method akin to LIME, which generates neighbouring samples by randomly perturbing segments of the original instance. Subsequently, weights are computed for each neighbouring instance based on its distance from the original, elucidating its influence. Parameterised event primitives (PEPs) are then extracted from these perturbed samples, encompassing increasing and decreasing …

Towards An Iot-Enabled Digital Earth For Sdgs: The Data Quality Challenge, Msb Syed, Paula Kelly, Paul Stacey, Damon Berry

Articles

Digital Earth (DE), a technology offering real-time visualisation of Earth's processes, has shown promising results in aiding decision-making for a sustainable world, raising awareness about individual impacts on our planet, and supporting the United Nations Sustainable Development Goals (UN SDGs) agenda. However, both DE and SDGs face a common obstacle: Data Quality (DQ). This review investigates the challenge of DQ in the context of DE for SDGs and explores how IoT can address this challenge and extend the reach of DE to support SDGs. Furthermore, the study discusses three core aspects; first, the potential of IoT as a data source …

A Global Model-Agnostic Rule-Based Xai Method Based On Parameterized Event Primitives For Time Series Classifiers, Ephrem T. Mekonnen, Luca Longo, Pierpaolo Dondio

Articles

Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps …

Design Considerations For Self-Management Technologies For People Living With Dementia And Informal Carers - Perspectives Of Healthcare Professionals And Charity Workers, Dympna O'Sullivan, Julie Doyle, Orla Moran, Michael Wilson, Siobhan Oneill, Jonathan Turner, Suzanne Smith

Conference papers

Dementia is a neurodegenerative disorder that leads to decline in memory, language, reasoning, and the ability to perform daily activities. It is linked to poorer quality of life for the person with dementia and their informal (unpaid) carers. While early intervention and access to adequate care are critical in slowing dementia's progression and better managing associated symptoms, dementia is frequently only diagnosed at an advanced stage and care is often fragmented. To better understand how to meet the complex needs of persons living with dementia and their informal carers, 10 healthcare professionals and 10 charity workers from relevant community and …

Design Considerations For Self-Management Technologies For People Living With Dementia And Informal Carers – Perspectives Of Healthcare Professionals And Charity Workers, Dympna O'Sullivan

Conference papers

Dementia is a neurodegenerative disorder that leads to decline in memory, language, reasoning, and the ability to perform daily activities. It is linked to poorer quality of life for the person with dementia and their informal (unpaid) carers. While early intervention and access to adequate care are critical in slowing dementia's progression and better managing associated symptoms, dementia is frequently only diagnosed at an advanced stage and care is often fragmented. To better understand how to meet the complex needs of persons living with dementia and their informal carers, 10 healthcare professionals and 10 charity workers from relevant community and …

A Hierarchical Framework For Interpretable, Safe, And Specialised Deep Reinforcement Learning, Ammar Abbas

Doctoral

Safety-critical systems, which are crucial for human safety and the environment, are difficult to control and operate. Traditional controllers need precise models of these complex systems, which is hard to develop. \acrfull{drl} offers a potential solution by learning from interactions rather than detailed models, but it faces limitations such as non-transparent decision-making and an expensive, unsafe learning process. Additionally, a key challenge in DRL is ensuring effective decision-making in rare situations.

This thesis proposes a novel approach called the \acrfull{prop_frame} that enables safe and reliable control of critical systems. SRLA combines probabilistic modelling with reinforcement learning to create an interpretable …

Emerging Cyber Risks & Threats In Healthcare Systems: A Case Study In Resilient Cybersecurity Solutions, Abdiaziz Abdi, Hajar Bennouri, Anthony Keane

Conference Papers

The exponential growth of digitalisation in healthcare and the ongoing threat of cybersecurity breaches are significant and cast a shadow over the industry’s progress. As the cost of data breaches reaches unprecedented levels, reaching an average of US$10.93 million in 2023 alone, and the frequency of attacks escalates, evidenced by a staggering 60% increase in phishing incidents between 2022 and 2023 reported by Smarttech247, Healthcare infrastructure is at a critical intersection. In this paper, we address the complex relationship between harnessing the potential of digital systems to improve and combating the escalating risks posed by cyber threats. The cyberattack on …

Comparing Anova And Powershap Feature Selection Methods Via Shapley Additive Explanations Of Models Of Mental Workload Built With The Theta And Alpha Eeg Band Ratios, Bujar Raufi, Luca Longo

Articles

Background: Creating models to differentiate self-reported mental workload perceptions is challenging and requires machine learning to identify features from EEG signals. EEG band ratios quantify human activity, but limited research on mental workload assessment exists. This study evaluates the use of theta-to-alpha and alpha-to-theta EEG band ratio features to distinguish human self-reported perceptions of mental workload. Methods: In this study, EEG data from 48 participants were analyzed while engaged in resting and task-intensive activities. Multiple mental workload indices were developed using different EEG channel clusters and band ratios. ANOVA’s F-score and PowerSHAP were used to extract the statistical features. At …

Ai And 6g Into The Metaverse: Fundamentals, Challenges And Future Research Trends, Muhammad Zawish, Fayaz Ali Dharejo, Sunder Ali Khowaja, Saleem Raza, Steven Davy, Kapal Dev, Paolo Bellavista

Articles

Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role …