This paper introduces a no-code platform for modular prompt engineering, designed to democratize access to generative AI for nondevelopers. By integrating advanced technologies such as Node.js, Express, MongoDB, and Azure OpenAI services, the platform provides a robust and flexible environment for creating and managing AI-driven tasks. The intuitive frontend, built with React and TypeScript, enables users with minimal coding expertise to design, execute, and evaluate complex AI workflows. A key feature of the platform is its extensible plugin system, which allows users to easily incorporate additional functionalities to meet their specific needs. This no-code approach empowers a broader audience to harness the power of generative AI, fostering innovation and enabling diverse applications across various fields. By lowering the technical barriers, the platform paves the way for widespread adoption of AI technologies, driving the future of AI-enhanced solutions.

A series of previous Working Groups has met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs). The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work. This Working Group will focus on the role of certification within cloud computing curricula, from the viewpoints of a range of stakeholders: students, graduates, institutions, vendors and other certification providers; and employers. Areas for study will include: the scope of available certifications and their mapping to our KAs and LOs; approaches to integrating certification in academic cloud curricula, and challenges involved in doing so; and perceptions of the stakeholders of the role and value of certifications in evidencing employability. The outcome of the work will include a set of recommendations for best practice.

A series of Working Groups has met at previous ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula, including mapping industry job skills to knowledge areas and those areas to student learning objectives. The importance of industry-standard learning content and certification, produced by cloud vendors and others, was apparent throughout this work. This Working Group has focused on the role of certification within cloud computing curricula, from the viewpoints of a range of stakeholders: students, graduates, institutions, vendors and other certification providers; and employers, with the aim to provide insights and recommendations for educators who are considering whether to integrate cloud certifications into their courses. We reviewed the landscape of certifications provided by the most widely recognised cloud vendors, based on publicly available information and the knowledge embedded within the Working Group through the inclusion of vendor representatives in the membership. An overview is provided of the scope of available certifications and their mapping to our knowledge areas and learning outcomes, and of the influence that standards have or should have on learning design. We then explored the perspectives of stakeholders, through surveys of students on courses with a cloud computing element and of employers who have employed graduates of those courses, drawing conclusions on the awareness of certifications and specific vendors within each of those stakeholder groups, and on differences between the groups on the perceived importance of certifications for employability. Finally we explored approaches to integrating certification in academic cloud curricula, and challenges involved in doing so, through thematic analysis of in-depth interviews with a range of educators who have experience of doing so successfully. A set of recommendations for educators is presented, based on the findings of the Working Group's activities.

0.00001; p = 0.05) for every week that they were enrolled in the study, which equated to a deterioration of 1.5% in FEV 1 annually (0.1 z-score, 95% CI, -0.26 to -0.001; p = 0.05), although there was a significant positive interaction between FEV 1 z-score and total number of weeks of exercise training completed (0.02, 95% CI, 0.01-0.04;p = 0.01).Children who exercised more regularly offset some of the deterioration in FEV 1 z-score that they might otherwise have experienced.Extrapolated data showed that children who attend at least 52 weeks of training over 24 months might expect an improvement of 1.0 in FEV 1 z-score (95% CI, 0.5-2.1;p = 0.01), which equated to an annual improvement of 7.5% in FEV 1 (Figure 1).This effect was not realized in children who did not attend regular exercise sessions.There was also a significant dose-related effect of exercise on FVC and FEF 25-75 .A dose-related effect was not demonstrated for LCI. Figure 1.(abstract 271): Moderate-to vigorous-intensity physical activity profiles of Project Fizzyo participants.

by enabling people of all ages to perform the activities that matter to them now and in the future.In the United Kingdom, PwCF are not routinely offered an occupational therapy review within specialist care, and there is limited knowledge of the impact of CF on their ADLs.Methods: In December 2021, a pilot occupational therapy role was introduced at a single adult center covering a mixed caseload including CF and non-CF respiratory conditions.Referrals were triaged through oral discussions before being accepted.Information was collected on age, sex, presence of CF complications, employment status, and reasons for referral and will continue to be collected for the duration of a 12-month pilot.Results: From December 2021 to March 2022, referrals to occupational therapy were accepted for 19 PwCF (6 male, 13 female; aged 18-69).CFrelated arthritis (CFRA) was a complication that 52% (n = 10) experienced, and 79% (n = 15) had CF-related diabetes (CFRD).None of the PwCF referred were in full-time employment.Reasons for referral were personal ADLs (n = 15), including transfers, mobility, bathing, grooming, toileting, dressing, and managing treatments; instrumental ADLs (n = 11), including meal preparation, laundry, shopping, and driving; leisure (n = 3) including forming new relationships and sports and rest (n = 1).Most referrals indicated difficulties in more than one category.Conclusions: Adults with CF-related complications such as CFRD and CFRA are overrepresented in referrals to occupational therapy within this small sample size.Personal ADLs appeared to be the most commonly reported area of difficulty.Preliminary results suggest that people with CF-related complications may benefit from occupational therapy assessment and intervention to improve ADL performance and overall quality of life.

Three previous Working Groups (WG) have met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula by mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs) and using these as the framework for a repository of learning materials and course exemplars \citefoster2018, foster2019, adams2020. The ongoing value of the work of these WGs will be enhanced by validating the KAs and LOs and their mapping to current job skills and continuing to build a community of educators who will contribute to and benefit from the repository. This WG aims to make a significant contribution to the achievement of both aims. It will focus on planning for a wider, ongoing research study for validation of the mapped KAs and LOs and will create and pilot a model for workshops which will contribute to community building and help to frame the research plan.

Introduction Population health and wellbeing is a priority in the UK, with new initiatives that empower children to live healthier lives. Excess weight has also been associated to worse outcomes during the COVID-19 pandemic period, complicated by reduced activity within the confinements of a home environment and coupled by increased screen time with remote classroom practices. As a result, children and young people now interact with computer interfaces in their home environment for education, gaming and healthcare purposes for prolonged periods and in new ways. Method There is a growing interest in Natural User Interfaces (NUIs) that use natural hand and body gestures to interact with computers. Advances to these technologies mean that they are now more accurate, easier to use and instead of requiring expensive depth cameras, can be operated using simple webcams. In this study, OpenCV library is used to track user movement by calculating the pixel difference between two frames and create a catalogue of exercises. We use PyTorch exercise recognition model to check the status of the user every 8 frames. These are recognised by using Convolutional Neural Networks (CNNs) with static training from datasets and offer users the option to create personalised exercises. Result We present University College London’s (UCL) Motion- Input supporting DirectX: Gestures for at-home exercises. This exercise module can recognise six repetitious static exercises, such as running on the spot, squatting, cycling on an exercise bike, and rowing on a rowing machine using a webcam. This is intended for integrated exercise triggers during gaming in place of a handheld control panel (i.e., jumping to trigger commands), remote coaching for fitness and bespoke treatment plans for physical rehabilitation. Conclusion Webcam-based computer vision exercise catalogues using everyday devices like webcams, hold the potential to encourage healthier and more active behaviours during screen-based activities.

We continuously interact with computerized systems to achieve goals and perform tasks in our personal and professional lives. Therefore, the ability to program such systems is a skill needed by everyone. Consequently, computational thinking skills are essential for everyone, which creates a challenge for the educational system to teach these skills at scale and allow students to practice these skills. To address this challenge, we present a novel approach to providing formative feedback to students on programming assignments. Our approach uses dynamic evaluation to trace intermediate results generated by student’s code and compares them to the reference implementation provided by their teachers. We have implemented this method as a Python library and demonstrate its use to give students relevant feedback on their work while allowing teachers to challenge their students’ computational thinking skills.

Touchless computer interaction has become an important consideration during the COVID-19 pandemic period. Despite progress in machine learning and computer vision that allows for advanced gesture recognition, an integrated collection of such open-source methods and a user-customisable approach to utilising them in a low-cost solution for touchless interaction in existing software is still missing. In this paper, we introduce the MotionInput v2.0 application. This application utilises published open-source libraries and additional gesture definitions developed to take the video stream from a standard RGB webcam as input. It then maps human motion gestures to input operations for existing applications and games. The user can choose their own preferred way of interacting from a series of motion types, including single and bi-modal hand gesturing, full-body repetitive or extremities-based exercises, head and facial movements, eye tracking, and combinations of the above. We also introduce a series of bespoke gesture recognition classifications as DirectInput triggers, including gestures for idle states, auto calibration, depth capture from a 2D RGB webcam stream and tracking of facial motions such as mouth motions, winking, and head direction with rotation. Three use case areas assisted the development of the modules: creativity software, office and clinical software, and gaming software. A collection of open-source libraries has been integrated and provide a layer of modular gesture mapping on top of existing mouse and keyboard controls in Windows via DirectX. With ease of access to webcams integrated into most laptops and desktop computers, touchless computing becomes more available with MotionInput v2.0, in a federated and locally processed method.

Introduction There have been several attempts to integrate touchless interactions with computer interfaces in operating theatres and interventional radiology. Whilst motivated by infection prevention gains, this type of technology is yet to mainstream in healthcare as depth camera are historically expensive. Here we present MotionInput supporting DirectX for desk gestures. The key idea behind this prototype project is to ‘use the tech and tools you already have’, to provide touchless interactive interfaces to existing Windows software. Method This proof-of-concept prototype features Visual Studio based modules that use a regular webcam (e.g., on a laptop) and open-source computer vision libraries to deliver low-latency input on Microsoft Windows 10. The functioning software prototype focuses on several image-processing algorithms, leading to desk-based gestures with in-air pen and gloved hand navigation. Results Discrete hand motions are tracked via x and y coordinates. These coordinates are then mapped and processed by PyDirectInput functions to replicate movements of the mouse or press keys on the keyboard and click with the mouse. This is extended to recognition of 2 fingers that are gloved, which would apply to healthcare scenarios in which clinicians use one hand to navigate clinical applications including an electronic patient record or scan with one hand, whilst maintaining the second hand as free for other tasks. The prototype has been shown to work with UCL’s HoloRepository – an open-source project that enables 3D viewing of CT and MRI DICOM scans of the brain, lungs, chest, abdomen and kidneys. Conclusion Further research is exploring the application of MotionInput to design touchless interactions with computer interfaces in clinical spaces. Coupled with advances in computer vision, we believe that the convenience of use and ease of access of cameras integrated into existing hardware will improve uptake and help bring gesture recognition software into the mainstream.

Three previous Working Groups (WGs) met at ITiCSE conferences to explore ways to help educators incorporate cloud computing into their courses and curricula by mapping industry job skills to knowledge areas (KAs). These WGs identified, organized, and grouped together student learning objectives (LOs) and developed these KAs and LOs in a repository of learning materials and course exemplars. This WG focused on the sustainability of the work of its predecessors through dissemination, community building and validation of the framework of KAs and LOs and its contribution to curriculum development. Firstly, a case study is presented which analyzed the implementation of a new Masters program which was based on the KAs and LOs. It was found that these provide a useful basis for program development and approval and demonstrate that successful program development of this nature can provide a valuable opportunity to communicate the work of the previous WGs. Thereafter, a plan was formulated for dissemination of the work done in order to drive adoption and to encourage instructors with an interest in teaching cloud computing to participate and grow the community. While the strategy included a range of dissemination methods, the importance of interaction with users was a guiding principle. Initial pilots of webinar and workshop activities have been implemented. Approaches to validating that a cloud computing course designed around the KAs and LOs can meet the needs of industry have been outlined with further iterations being considered. A research plan has been designed for a study to be implemented over the coming year in order to perform this validation.

Introduction Daily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This paper describes the protocol for Project Fizzyo, which uses innovative technology and analysis methods to remotely capture longitudinal daily data from physiotherapy treatments to measure adherence and prospectively evaluate associations with clinical outcomes. Methods and analysis A cohort of 145 children and young people with CF aged 6–16 years were recruited. Each participant will record their usual physiotherapy sessions daily for 16 months, using remote monitoring sensors: (1) a bespoke ACT sensor, inserted into their usual ACT device and (2) a Fitbit Alta HR activity tracker. Real-time breath pressure during ACTs, and heart rate and daily step counts (Fitbit) are synced using specific software applications. An interrupted time-series design will facilitate evaluation of ACT interventions (feedback and ACT-driven gaming). Baseline, mid and endpoint assessments of spirometry, exercise capacity and quality of life and longitudinal clinical record data will also be collected. This large dataset will be analysed in R using big data analytics approaches. Distinct ACT and physical activity adherence profiles will be identified, using cluster analysis to define groups of individuals based on measured characteristics and any relationships to clinical profiles assessed. Changes in adherence to physiotherapy over time or in relation to ACT interventions will be quantified and evaluated in relation to clinical outcomes. Ethics and dissemination Ethical approval for this study (IRAS: 228625) was granted by the London-Brighton and Sussex NREC (18/LO/1038). Findings will be disseminated via peer-reviewed publications, at conferences and via CF clinical networks. The statistical code will be published in the Fizzyo GitHub repository and the dataset stored in the Great Ormond Street Hospital Digital Research Environment. Trial registration number ISRCTN51624752; Pre-results.

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that deliver practical and hands-on material relevant to this rapidly evolving field. This is especially relevant as cloud services are quickly becoming the standard computing model for many new computational approaches, including advanced subjects such as machine learning and data science. Since cloud computing influences a number of other traditional computing subjects such as networking and data storage, the ability to incorporate specific components of cloud computing teaching content into a variety of courses has also become important. However, a lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts, continues to be a challenge for instructors. As the volume of teaching material that educators and other providers are willing to share increases, it is important that the content is easily discoverable and accurately catalogued. Using appropriate Learning Objectives (LOs) and tags associated with the correct course material contributes toward a reliable and trustworthy source for faculty. This Working Group (WG) has addressed these challenges by 1) creating a canonical list of LOs, based on the LOs included in the Knowledge Areas (KAs) developed by the ITiCSE working groups in 2018 and 2019; 2) searching for existing teaching material, cataloging the material by LO and then combining the LOs into exemplar modules for some of the more common classes that adopt cloud computing; 3) creating sample syllabi for these exemplars following the ACM Computer Science Curricula 2013 approach; and 4) developing a solution for disseminating the materials and keeping them up to date.

Cloud Computing is a rapidly evolving field that is triggering a wave of innovations in various domains such as machine learning and artificial intelligence. Cloud skills are becoming essential for any technology-related profession. Furthermore, the accelerated adoption of cloud technologies by industry is increasing the demand for cloud-trained professionals. Thus, higher education institutions are offering training opportunities and programs in cloud computing, however, the lack of well-rounded and high-quality curricular materials continues to be a challenge for educators. A 2018 Working Group (WG) created a report that --among other artifacts-- described fourteen Knowledge Areas (KAs), with numerous Learning Objectives (LOs) for each KA, to teach cloud concepts. Expanding on that work, this WG will focus on providing a collection of resources that would eventually constitute a model cloud curriculum. By relying on two particular surveys: one that looks at the existing curricular offerings, and another one that maps knowledge areas to job titles in cloud computing, we plan to provide a dynamic and configurable curricular exemplars repository for the cloud community-at-large, following the popularity of hands-on, project-based learning methodologies as our primary focus.

Background: Physical activity (PA) can improve health outcomes for children and young people with cystic fibrosis (CYPwCF). Longitudinal monitoring of PA can inform personalised guidance and help explain clinical status. Analysis of PA entails identifying time spent in moderate to vigorous PA heart rate (HR) zones daily, which involves estimation of resting HR (RHR). This is complex if HR data are collected remotely from wearable devices. Method: HR data from 62 CYPwCF (6-16yrs) wearing a FitBit (AltaHR) were analysed using 2 methods to find a reliable way of estimating RHR. CYPwCF each provided 252hrs (8am-8pm for 21 days: HR data every 4-10secs, Fig 1). Method A. computed the mean/median of the 5 lowest individual 1 min means daily and B. computed the mean/median of a rolling consecutive 5min window with lowest HR mean. Results: 1,302 days were analysed. All methods produced similar results (Fig 1b, Table1), with Method A:median yielding lowest RHR (range: 49-81 bpm) and A:mean lowest COV overall (range:4-14). Smoothing data did not improve any method. Conclusion: COV<10 is generally accepted for RHR, but in future advanced analytics, focussing on HR data around the wake up time of children may pinpoint RHR more accurately and reduce COV further.

LM ( p = 0.071) increased not statistical significant after treatment.Peak work rate related to body weight remained stable ( p = 0.514). Conclusion:The reduced REE and aerobic EC related to body weight are most likely the results of improvements in PF (reduced hyperinflation).However, peak work rate improved after treatment with ivacaftor.

Cloud computing continues to be an important technology in higher education. This domain is a rapidly evolving space, and continues to gain momentum as a primary infrastructure topology for technological advances across emergent industries. The on-the-cloud paradigm provides numerous affordances and new methods of working in industry, and also for end users within recent fields of study, such as machine learning and artificial intelligence. As we move into the Industry 4.0 era with technology pillars such as the internet of things and cybersecurity, the importance of skillsets for cloud-based services will be an essential attribute for the majority of technology-related professions. Many higher education institutions have focused on offering training opportunities and programs for cloud computing, however, a lack of high-quality, contextualized to industry, curricula materials continues to be a challenge for educators. The purpose of this paper is to report on analyses conducted to categorize cloud computing courses currently taught in the higher education sector and to determine the possibility of moving towards the goal of a model curriculum. Additionally, the paper aims to provide guidance to educators about cloud computing skill-sets sought in the job market, and to report on a community platform designed to host cloud learning resources.

Physical activity (PA) is recommended for all children and young people with CF (CYPwCF), however levels of participation vary between and within individuals. Studies collecting short-term PA data from research trackers are often limited by insufficient wear-time, and fail to reflect variations related to weather/season, weekday/weekend, school holiday, or hospital admission. CYP have reported that comfort, design and feedback features are important, thus commercially available activity trackers with these features may enable better measurement of individual PA patterns over time. Wear-time data were collected from CYPwCF (6-16yrs) asked to wear a Fitbit AltaHR as much as possible during the first 30days of participation in a longitudinal study. From literature, ≥8 recorded daytime hours were considered valid for data analysis with 3-9days providing reliable measurement of PA levels. A total of 1,950days were analysed from 65CYPwCF (median:9.6yrs, 36male). In 30days, CYPwCF contributed mean±SD 16±8days valid for data analysis (range:0-28days). 49/65 (75%) provided >9days (median:21, range:9-28days), 7/65 (11%) provided 3-8days (med:5, range:3-8), and 9/65 (14%) yielded 0-2days, 4/9 due to problems with data synchronising. Days of valid data contributed by participants varied, but 86% easily exceeded traditional thresholds for PA data analysis within 30days. Results suggest that commercially available activity trackers may prove useful in assessing longitudinal patterns of PA in CYPwCF. Further research will continue to evaluate minimal wear-time for accurately and reliably characterising patterns of PA taking account of seasonal and circumstantial changes.