Introductory programming: a systematic literature review

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes

On-Demand Monitoring of Construction Projects through a Game-Like Hybrid Application of BIM and Machine Learning

While unavoidable, inspections, progress monitoring, and comparing as-planned with as-built conditions in construction projects do not readily add tangible intrinsic value to the end-users. In large-scale construction projects, the process of monitoring the implementation of every single part of buildings and reflecting them on the BIM models can become highly labour intensive and error-prone, due to the vast amount of data produced in the form of schedules, reports and photo logs. In order to address the mentioned methodological and technical gap, this paper presents a framework and a proof of concept prototype for on-demand automated simulation of construction projects, integrating some cutting edge IT solutions, namely image processing, machine learning, BIM and Virtual Reality. This study utilised the Unity game engine to integrate data from the original BIM models and the as-built images, which were processed via various computer vision techniques. These methods include object recognition and semantic segmentation for identifying different structural elements through supervised training in order to superimpose the real world images on the as-planned model. The proposed framework leads to an automated update of the 3D virtual environment with states of the construction site. This framework empowers project managers and stockholders with an advanced decision-making tool, highlighting the inconsistencies in an effective manner. This paper contributes to body knowledge by providing a technical exemplar for the integration of ML and image processing approaches with immersive and interactive BIM interfaces, the algorithms and program codes of which can help replicability of these approaches by other scholars

Examining the use of visualisation methods for the design of interactive systems

Human-Computer Interaction (HCI) design has historically involved people from different fields. Designing HCI systems with people of varying background and expertise can bring different perspectives and ideas, but discipline-specific language and design methods can hinder such collaborations. The application of visualisation methods is a way to overcome these challenges, but to date selection tools tend to focus on a facet of HCI design methods and no research has been attempted to assemble a collection of HCI visualisation methods. To fill this gap, this research seeks to establish an inventory of HCI visualisation methods and identify ways of selecting amongst them. Creating the inventory of HCI methods would enable designers to discover and learn about methods that they may not have used before or be familiar with. Categorising the methods provides a structure for new and experienced designers to determine appropriate methods for their design project. The aim of this research is to support designers in the development of Human-Computer Interaction (HCI) systems through better selection and application of visualisation methods. This is achieved through four phases. In the first phase, three case studies are conducted to investigate the challenges and obstacles that influence the choice of a design approach in the development of HCI systems. The findings from the three case studies helped to form the design requirements for a visualisation methods selection and application guide. In the second phase, the Guide is developed. The third phase aims to evaluate the Guide. The Guide is employed in the development of a serious training game to demonstrate its applicability. In the fourth phase, a user study was designed to evaluate the serious training game. Through the evaluation of the serious training game, the Guide is validated. This research has contributed to the knowledge surrounding visualisation tools used in the design of interactive systems. The compilation of HCI visualisation methods establishes an inventory of methods for interaction design. The identification of Selection Approaches brings together the ways in which visualisation methods are organised and grouped. By mapping visualisation methods to Selection Approaches, this study has provided a way for practitioners to select a visualisation method to support their design practice. The development of the Selection Guide provided five filters, which helps designers to identify suitable visualisation methods based on the nature of the design challenge. The development of the Application Guide presented the methodology of each visualisation method in a consistent format. This enables the ease of method comparison and to ensure there is comprehensive information for each method. A user study showing the evaluation of a serious training game is presented. Two learning objectives were identified and mapped to Bloom’s Taxonomy to advocate an approach for like-to-like comparison with future studies

Detecting graves in GPR data: assessing the viability of machine learning for the interpretation of graves in B-scan data using medieval Irish case studies.

As commercial archaeogeophysical survey progressively shifts towards large landscape-scale surveys, small features like graves become more difficult to identify and interpret. In order to increase the rate and confidence of grave identification before excavation using geophysical methods, the accuracy and speed of survey outputs and reporting must be improved. The approach taken in this research was first to consider the survey parameters that govern the effectiveness of the four conventional techniques used in commercial archaeogeophysical evaluations (magnetometry, earth resistance, electromagnetic induction and ground-penetrating radar). Subsequently, in respect of ground-penetrating radar (GPR), this research developed machine learning applications to improve the speed and confidence of detecting inhumation graves. The survey parameters research combined established survey guidelines for the UK, Ireland, and Europe to account for local geology, soils and land cover to provide survey guidance for individual sites via a decision-based application linked to GIS database. To develop two machine learning tools for localising and probability scoring grave-like responses in GPR data, convolutional neural networks and transfer learning were used to analyse radargrams of medieval graves and timeslices of modern proxy clandestine graves. Models were c. 93% accurate at labelling images as containing a grave or no grave and c. 96% accurate in labelling and locating potential graves in radargram images. For timeslices, machine learning models achieved 94% classification accuracy. The >90% accuracy of the machine learning models demonstrates the viability of machine-assisted detection of inhumation graves within GPR data. While the expansion of the training dataset would further improve the accuracy of the proposed methods, the current machine-led interpretation methods provide valuable assistance for human-led interpretation until more data becomes available. The survey guidance tool and the two machine learning applications have been packaged into the Reilig web application toolset, which is freely available

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants