Secure, compliant cloud provisioning is difficult with manual configuration, where misconfigurations, inconsistent security, and limited auditability often arise. Infrastructure-as-Code (IaC) solves this by defining infrastructure declaratively and enabling repeatable, version-controlled deployments. This paper presents an AWS-focused Terraform approach embedding security-by-design controls into automated provisioning, including least-privilege IAM, network segmentation with public and private VPC subnets, bastion-based administrative access, controlled outbound connectivity via a NAT gateway, and centralized logging and encryption baselines. The implementation is evaluated through a comparative study against manual provisioning using the AWS Management Console. Results show Terraform reduces provisioning time by over 75% across complex networking and access-control scenarios, while improving reliability by increasing success rates from 74% to 96%. Connectivity validation confirms th at public resources route traffic through the Internet Gateway, private instances access outbound connectivity only via the NAT gateway, and administrative access to private resources is restricted to the bastion host. Security validation confirms consistent enforcement of baseline controls such as IAM least privilege, subnet isolation, restricted Secure Shell (SSH) ingress, centralized logging, and encryption at rest. These findings demonstrate that Terraform-based Infrastructure-as-Code can simultaneously improve operational efficiency and strengthen security and compliance consistency, offering a practical foundation for repeatable and audit-ready cloud infrastructure deployments, particularly for environments with limited operational overhead.

Optimal tile-based bitrate allocation improves the Quality of Experience (QoE) for adaptive 360° video streaming across multiple clients in heterogeneous network environments; however, it is challenging as it implies accurate viewport prediction, finest tile-based bitrate reservation, and maintaining QoE fairness, particularly under constrained network conditions. This paper proposes a strategy named EDGE360, that employs an edge-driven Multi-Agent Deep Reinforcement Learning (MADRL) solution for rate adaptation to improve the joint QoE in DASH-based rich media content delivery based on adaptive viewport prediction and Video Multi-method Assessment Fusion (VMAF) corresponding tiling granularity selection. Cooperative strategies among agents in the central critic network are crucial for addressing the complexity of network instances at the edge and optimizing media streaming bitrate assignment in multiple-client scenarios. Therefore, EDGE360 aims to implement the Counterfactual Multi-Agent Policy Gradients (COMA) based on 5G network traces to train agents in policies that optimize individual client QoE and fairness among clients, resulting in an improved rich streaming experience. At the edge, a tile-based quality monitor evaluates viewport trajectories, buffer status, and network throughput, employing deep learning to forecast optimal tile bitrate allocation, which is formulated as an MDP and solved with MADRL. Based on extensive experimentation, EDGE360 surpasses state-of-the-art adaptive bitrate algorithms by achieving the highest average reward, outperforming RAPT360, 360SRL, and BOLA360 by 8.12%, 11.86%, and 18.00%, respectively, demonstrating superior convergence and refinement.

Cloud computing has the benefits of scalability and flexibility, yet poses long-term problems of cost estimation, efficient scheduling of resources, and fault tolerance. In this paper, an AI-driven framework is proposed that can reconcile these drawbacks by combining cost prediction, dynamic task scheduling, and fault detection into a user-friendly visualization dashboard. Cost prediction makes use of supervised machine learning algorithms such as Linear Regression, Random Forest, and XGBoost to predict the costs of a task based on synthetic workloads created with iFogSim. The prediction accuracy is also improved after hyperparameter optimization using Optuna. Task scheduling employs Deep Reinforcement Learning (DRL) with a Deep Q-Network (DQN) structure that maximizes job placement on heterogeneous virtual machines (VMs) and has benchmark comparisons with First-Come-First-Serve (FCFS) and Round-Robin schedules. The scheduling logic is trained and tested on the Kaggle Cloud Task Scheduling dataset. The fault detection mechanism uses the Isolation Forest algorithm to detect anomalous system behavior such as CPU usage behavior or long execution time. Evaluation metrics, reward curves, anomaly plots, and interpretability graphs, are displayed as part of a Streamlit-based dashboard on Render. The framework is a modular automation constructed to stage each aspect on demand, making it flexible, reproducible, and resilient in deployment. Experimental results show that such a technique makes cost estimation more accurate, minimizes delays in scheduling, and increases fault tolerance. This makes the proposed framework holistic and practical, since predictive analytics is combined with reinforcement learning along with anomaly detection, to optimise operations in multi-cloud environments. The outcome of this research can be of interest for real-life cloud management applications.

In today’s modern computing infrastructure, cloud computing has emerged as a pivotal paradigm that offers scalability and flexibility to satisfy the demands of a wide variety of specific applications. Maintaining optimal performance and cost-effectiveness inside cloud settings continues to be a significant problem and one of the most important challenges is efficient resource utilisation. A resource utilization prediction system is required to aid the resource allocator in providing optimal resource allocation. Accurate prediction is difficult in such a dynamic resource utilisation. The applications of machine learning techniques are the primary emphasis of this research project which aims to predict resource utilisation in cloud computing systems. The dataset GWA-T-12 Bitbrains have provided the data of timestamp, cpu usage, network transmitted throughput and Microsoft Azure traces has provided the data of cpu usage of a cloud server. To predict VM workloads based on CPU utilization , machine learning models such as Linear Regression, Decision Tree Regression, Gradient Boosting Regression, and Support Vector Regression are used. In addition to these, deep learning models such as Long Short-Term Memory and Bi-directional Long Short-Term Memory have also been evaluated in our approach. Bi-directional Long Short Term Memory approach is considered more effective as compared to other models in terms of CPU Utilisation and Network Transmitted Throughput as its R2 score is close to 1 and hence can produce more accurate results.

Skin detection technology serves multiple purposes across various sectors, including surveillance, criminal justice, and healthcare. This study focuses on skin detection by extracting RGB values of skin tones from facial images of diverse ethnic backgrounds. Utilizing a three-tier OpenCV-based architecture, the approach encompasses skin detection, skin tone identification, and is tested in an application for recommendation of the most fitting makeup foundation shade, brand, and product. The research evaluates three color-space models for their effectiveness in skin and skin tone detection: HSV (Hue, Saturation, Value) with Gaussian blur, HSV alone, and a combination of HSV and YCrCb enhanced with gamma correction and image segmentation. The precision of each color space method was evaluated by measuring the difference between the predicted RGB skin tone values and the actual RGB skin tone values, utilizing the Delta-E metric for comparison. The hybrid model combining HSV and YCrCb color spaces emerged as the most accurate, achieving the lowest Delta-E average value of 16.68, thereby surpassing the other methodologies.

Wildfires rank among the most prevalent natural disasters globally and have emerged as a significant factor in climate change over the past decade. Early detection of wildfires and smoke plumes through satellite imagery is crucial since they are not easily extinguishable which may lead to catastrophic consequences for both wildlife and forest ecosystems. Classic deep-learning models for wildfire and aerosol identification have shown significant progress, but high false-positive rates remain a key limitation. This paper proposes a custom-designed Convolutional Neural Network (CNN) model that aims to improve the identification of wildfires and aerosols, leveraging satellite imagery categorized into cloud, dust, haze, land, seaside, and smoke. Moreover, we considered popular deep learning and transfer learning models, specifically EfficientNet, MobileNetV3, and Inception V3, to identify and distinguish smoke plumes. Hyper-parameter tuning has been performed to achieve more accurate results. The metrics evaluated in this research are accuracy, precision, recall, and f1-score. A comprehensive analysis was performed that aimed to identify the best transfer learning model and the model that closely aligns with the performance of the CNN built. This paper provides valuable insights into the potential use of transfer learning models and of the proposed custom-designed CNN model in early wildfire detection by identifying the smoke plumes and helping in reducing false fire alarms.

Academic Support 360 Framework is a strategy to support the professional development of faculty. Such a strategy can be applied to solving full-time and adjunct faculty issues that relate to pedagogy, systems and processes in Higher Education. The faculty that this framework supports includes professors, lecturers, programme/course directors and classroom assistants. Adjunct faculty are part-time lecturers who complete many of the same tasks as a full-time professor/lecturer whilst also working in industry. For both new full-time and adjunct faculty, understanding pedagogy, systems and processes is a challenge. New faculty may require support around teaching effectively, creating assessments, using the Learning Management System, entering grades into the student management system and so on. For adjunct faculty, the challenge is compounded by their time commitments to both their professional and academic careers. Programme/Course Directors need support in understanding college processes such as course validation, exam boards, reading broadsheets, student requests to defer modules or move courses and more. Classroom assistants require induction, training and support on the Learning Management System, in-class student queries and payroll systems. This research proposes an Academic Support 360 Framework that provides the knowledge, skills and competence for full-time and adjunct faculty to apply pedagogy, use systems and follow processes. The framework is comprised of Induction, Mentoring, Online Resources and Just-in-Time training. This framework provides continual support to faculty which commences before the start of a semester, continues throughout the teaching weeks and is available during the grading process after the end of the semester. The framework shows promise as demonstrated by the increase in the number of users, induction sessions, attendance at weekly Q&A sessions, video views, queries in teams' channels and induction events. This research is useful to academic management that would like to induct and train full-time and adjunct faculty in systems, processes and teaching and learning.

This Research-to-Practice Full Paper looks at the transition from second level education to higher education and the challenges this presents in terms of students getting to know a new learning environment, identifying supports to assist with their learning and even getting to know new friends. This challenge is even more complicated with the move to an online learning environment in response to the COVID19 emergency restrictions. This research introduces a higher education transition framework (called S³F) that provides support and intervention activities to manage students transition from second level education to higher education, to reduce the impact of the online environment on students learning experience and to help to improve student mental health. The S³F framework uses ongoing student Feedback to inform activities across three pillars: Learning Environment Support, Academic Subject Support and Social Support. The research presented in this paper was conducted over the 2020/2021 academic year when 1styear undergraduate Computing students from National College of Ireland, School of Computing participated in an innovative induction programme that consisted of a number of activities and support actions for the entire duration of the academic year that were part of the S³F framework. Students were surveyed during each induction session for live feedback to adapt the activities for the following sessions and to inform staff of other interventions required. Students initially have expressed feelings of nervousness at the start of the first semester however this changed to feelings of excitement midway through the induction programme. Results of the case study demonstrates that the activities and innovative actions introduced as part of S³F framework had a positive impact on student’s transition to higher education, especially around mental health, seen in the retention figures for those students. This paper discusses the results only in terms of students mental health This research is of benefit to higher education management and course directors involved in first year orientation that would like to reduce the impact of the online environment on student’s transition from second level to higher education.

The paper presents results of a case study that aimed to investigate teachers’ opinion regarding the benefits that Augmented Reality and Gamification can bring when educating students with typical development disability and/or intellectual disability. The study concluded that the use of modern technologies in the education of students with intellectual disabilities is extremely effective given that it is used sparingly, adapted to the students’ needs and supported by teachers.

Virtual labs are increasingly used both as an alternative to physical labs or as a complementary technology enhanced (TEL) solution for STEM education. Virtual labs enable students to conduct experiments in a controlled environment at their own pace. However, despite much research on personalisation and adaptation in the TEL area, most virtual labs that have been developed lack personalisation features. This paper presents results from a study with 78 secondary school students, aimed at evaluating an interactive personalised virtual lab called Atomic Structure. The virtual lab integrates personalisation, interactive experimentation, videos, e-assessment and gamification, to provide an engaging environment for learning chemistry concepts related to atoms, isotopes and molecules. The evaluation study followed a multi-dimensional methodology to assess the effectiveness of the virtual lab in terms of knowledge achievement, learner motivation and usability. The results show that the experimental group that learned with the virtual lab achieved statistically significant higher knowledge than the control group that attended a traditional teacher led session. The experimental group also had higher increase than the control group for different motivation dimensions between the pre and post questionnaires. The usability results showed that most students found the virtual lab useful, easy to use and liked/loved its features such as videos, quizzes and interactive atom builder.

Preparing our children for the rapid economic, scientific and technological developments ahead is a top research aspect for many research communities and state institutions. In this context, STEM topics have an important role in the earlier educational stage and more specifically at primary school level. This paper investigates the learning impact of using Final Frontier, an immersive educational video game in a Slovak school for teaching concepts related to the solar system. The experimental study involved 44 children divided in two groups, a control group and an experimental group. The aim of this paper is to present an investigation on the effect of educational game on the learning outcome of the children when the Final Frontier game is used. The results show that Final Frontier game based learning brought better knowledge gain values. In addition, the majority of children perceived learning more entertaining and they believed that the game helped them to learn through problem solv ing tasks and interactive exploration of the planets virtual environment.

Mobile video traffic is rapidly growing, putting significant pressure on current heterogeneous wireless networks. Common data is traditionally requested by multiple users from server or cache devices. This results in the same data being sent across the network multiple times causing unnecessary congestion. The proposed cooperative solution allows neighbouring devices to share content that was previously received with interested peers. In this paper, a Mobile aware Quality-oriented Cooperative Multimedia Delivery Solution is proposed which allows peer devices to identify neighbouring host devices while considering their own mobility. Simulated testing shows that the solution is capable of identifying the most suitable host while travelling at different speeds and maintaining a suitable quality level by adapting the content to meet network conditions. State-of-the-art comparative studies are outperformed by maintaining good quality with increasing speed in a mobile environment.

Teachers are facing many difficulties when trying to improve the motivation, engagement, and learning outcomes of students in Science, Technology, Engineering, and Mathematics (STEM) subjects. Game-based learning helps the students learn in an immersive and engaging environment, attracting them more towards STEM education. This paper introduces a new interactive educational 3D video game called Final Frontier, designed for primary school children. The proposed game design methodology is described and an analysis of a research study conducted in Ireland that investigated learner experience through a survey is presented. Results show that: (1) 92.5% of students have confirmed that the video game helped them to understand better the characteristics of the planets from the Solar system, and (2) 92.6% of students enjoyed the game and appreciated different game features, including the combination between fun and learning aspects which exists in the game.

Although Mathematics is a fundamental subject for many STEM related areas, undergraduate students find Mathematics a challenging and difficult subject, and they face difficulties in developing logical thinking and problem solving skills. This research-to-practice paper introduces Count With Me!, a novel immersive computer-based educational game that teaches counting principles. The paper analyses and discusses the impact of the game on the learning process and knowledge gain. Twenty-four 1styear undergraduate students took part in the case study. Knowledge tests were employed before and after the students interacted with the educational game. Although addition, multiplication, factorial, and permutation topics were already studied by the students in the high school, the pre-test results showed that some students face difficulties with these topics. The post test results analysis showed a statistically significant knowledge improvement and a high student engagement in playing the game and learning about the Mathematics concepts.

Multimedia clips, such as lecture recordings and screencasts, are increasingly used in both formal and informal learning contexts, such as flipped classroom, blended learning, MOOCs and mobile learning. In order to create effective educational multimedia applications, it is increasingly important to understand the factors contributing to the learning performance and learner experience. This paper presents research findings from a subjective study with 60 participants, conducted to investigate the effects of learner's interest, QoE, and EEG-based affective states on learning performance in a multimedia-based mobile learning scenario. The results show that with careful design, similar learning performance and experience can be achieved on both small and large screen mobile devices, such as smartphone and tablet. Moreover, learner's interest and QoE were shown to have a strong effect on learning. While males and females achieved similar learning performance, they presented significant differences in terms of interest, QoE and EEG-based affective states. Moreover, the results show promising potential of using EEG data to automatically detect learner's interest.

Science teachers and researchers believe that students' disengagement from STEM area can be overcome by using interactive and fun-based computer educational games in order to support knowledge acquisition through direct experience. This paper presents a research study on the effectiveness of a new interactive educational 3D video game called Final Frontier. The game supports delivery of scientific knowledge on the Solar system to primary school students. A comprehensive case study that involved 30 children was conducted to evaluate the game. The results confirmed that the game supports high learning achievements through an enjoyable and fun learning environment entirely appreciated by children. The vast majority of the students (93%) have expressed their interest in learning other subjects through such an interactive computer game.

Accurate user-perceived video quality estimation models are increasingly needed with the proliferation of multimedia services. Previous research studies have focused on proposing and evaluating objective Video Quality Assessment (VQA) metrics, without mapping their values to Mean Opinion Scores (MOS). This paper presents a model to compute the estimated user-perceived video quality (EMOS), by combining multiple objective VQA metrics whose continuous values are mapped to discrete scores on the 0 - 5 MOS scale. The results analysis of a subjective video quality assessment study with 60 participants have shown that combining multiple VQA metric mappings can improve the user-perceived quality estimation accuracy up to 98.5%.

Mathematics is a key subject for success in Computer Science and it continues to be a challenging subject. Use of technology has given rise to a new pedagogy called Flip-Classroom (FC). FC involves creating online multimedia content that is utilized out-of-class in conjunction with in-class learning activities such as individual and collaborative problem solving, group-work and class-discussion. An experiment was conducted to investigate the utility of FC pedagogy and its relationship with student learning. FC pedagogy was implemented in a first year "Introduction to Mathematics for Computing" module and was employed for a number of core topics. A traditional lecture approach was utilized for the remaining topics. In-class quiz based assessments, homework assignments and end of semester examinations have been performed in order to assess the learning performance of the students. The results show that on average students performed better in assessments on topics taught through FC pedagogy. For Continuous Assessment (CA) components this increase was 14% and for the terminal exam this increase was 21%. The results indicate that the FC pedagogy may improve learning. Furthermore the students have indicated their preference in favor of FC pedagogy. This study will be of interest to those considering integrating FC pedagogy into teaching Mathematics.

Learner’s motivation is one of the main aspects that need to be addressed for a successful learning process. Consequently, learner motivation assessment and measurement have attracted significant research interest in the e-learning area in general and game-based learning in particular. Traditional methodologies for learner motivation analysis rely on data collected through questionnaires. However, this approach does not fit well in the context of game-based learning, because an out-of-game questionnaire breaks the game user’s flow and immersion. This paper presents a novel electroencephalography (EEG) sensor-based methodology that supports real-time non-disturbing automatic measurement and analysis of learner’s motivation in game-based learning. An evaluation case study with participants playing an educational game was conducted in order to investigate the feasibility of the proposed sensor-based methodology and to compare the proposed methodology with the intrinsic motivation inventory-based questionnaire methodology. The results analysis has shown that the sensor-based methodology outperforms the traditional questionnaire-based methodology.The traditional questionnaire-based methodology is limited to analysing learner’s motivation on short game-playing durations, while losing its feasibility when analysing learner’s overall motivation over a long game-playing duration. Results have shown that learner’s motivation

One of the challenges that mobile services face is the high cost of data delivery over cellular networks. This problem is further aggravated with the use of multimedia type content (understood in the context of this research as video in combination with audio and possible text) that can lead to congestion and higher bills. Although higher cost might not be a problem for everyone, there are people who would not like to pay that much. This paper proposes delivering personalized content that is cost effective for the user considering the user attitude towards risk. A novel model is proposed to assess the user risk attitude by taking into account the user self-assessment, his/her age and gender. Based on this information, a personalized mechanism for delivering adaptive multimedia content can be achieved. The proposed user model was validated through a series of case studies.

This paper addresses the high cost of transmission over mobile networks, when educational content consists of multimedia files. The proposed solution adapts the multimedia content by changing the bit rate. In order to validate the proposed solution, a preliminary study has been performed. The results of the study show that in average a decrease of 79% in size was obtained for the multimedia content involved in the study. This results in a decrease of the price the learner has to pay for downloading the multimedia content. When the subjects are not provided with any information regarding the quality of the video or the cost of transmission the same number of subjects proffered the original version as the ones who prefer the lower quality version. In the case when the size of the multimedia clips, and the cost of delivery over mobile networks are provided, the number of subjects who preferred the adapted versions increased to 95.44%.

Real-time multimedia communications over vehicular ad-hoc networks (VANET) will play an extremely significant role in the next generation intelligent transport systems. In recent years, there has been an upsurge of interest in the quality-oriented adaptive multimedia delivery, including over VANET. In this paper, a hybrid IEEE 802.11p-based multihop network communication solution is presented, which makes use of both in frastructure and ad-hoc modes in order to deliver quality-oriented real-time multimedia content to high-speed vehicles. Simulation-based testing shows how multimedia delivery to vehicles when using the multihop hybrid mechanism achieves significantly higher throughput in comparison to the case when the in frastructure mode is employed and the vehicles communicate directly with the base station. This result is obtained while the average delay and packet loss in the two cases are similar.

New communication technologies can enable Web users to access personalised information "anytime, anywhereö. However, the network environments allowing this "anytime, anywhereö access may have widely varying performance characteristics such as bandwidth, level of congestion, mobility support, and cost of transmission. It is unrealistic to expect that the quality of delivery of the same content can be maintained in this variable environment, but rather an effort must be made to fit the content served to the current delivery conditions, thus ensuring high Quality of Experience (QoE) to the users. This paper introduces an end-user QoE-aware adaptive hypermedia framework that extends the adaptation functionality of adaptive hypermedia systems with a fine-grained content-based adaptation mechanism. The proposed mechanism attempts to take into account multiple factors affecting QoE in relation to the delivery of Web content. Various simulation tests investigate the performance improvements provided by this mechanism, in a home-like, low bit rate operational environment, in terms of access time per page, aggregate access time per browsing session and quantity of transmitted information.

This paper presents the framework for a QoS-aware adaptive Web-based system (QoSAS) that adapts Web content based on both user-perceived-QoS and user interests related to the Web content. The architecture of the QoSAS includes a novel perceived performance model (PPM) that models user-perceived performance features via different QoS metrics and suggests content constraints in order to improve the perceived performance. The PPM stereotype-based mathematical model is presented in detail as well as preliminary simulation test results. These results demonstrate that the PPM helps the QoSAS to improve the user satisfaction.