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Visualisation of user stories to UML use case diagram
No full textThe growing usage of Agile methodology in software development projects among industry professionals (software engineers, system analysts, requirement engineers, etc.) and academia (software engineering students) leads to the need for the implementation of UML diagram for requirements modelling. Use case diagram, an example of UML diagram, is a very powerful tool to model the requirements specified by the users while also helping the development teams understand the functionalities and interactions between users and the system. However, there is a lack of a system or tool that can perform the operation to visualise the use case diagram directly from user stories because generating this diagram manually requires a deep understanding of the requirements and effective communications with stakeholders and it consume lots of time while previous studies which relate to this study are unable to fulfil the relationship elements of use case diagram. This study will introduce a method to visualise the use case diagram from structured textual user stories by utilising Natural Language Processing (NLP) and application of logical rules which will be done in four stages, namely Requirement Gathering, Natural Language Processing, Application of Logical Rules and UML Diagram Generation. A tool named Stanford CoreNLP will be used to perform four techniques of NLP: tokenisation, stemming and lemmatisation, POS tagging and dependency parsing to process the textual user stories, followed by applying the logical rules before generating the use case diagram. This study will propose a method to solve the gap, which is the problem with the generation of relationship elements, while contribute a semi-automated approach to generate a use case diagram from user stories
Creating an immersive learning environment for teaching agile scrum and team software process: a framework for software engineering education
No full textMost of the principles and concepts that need to be taught in Software Engineering courses are hard to share the realistic experiences because it is difficult to give the student practical exposure to the insight and processes involved. This paper presents an innovative framework tailored for the establishment of an immersive learning environment within the context of a Software Engineering Project course. The overarching objective is to effectively tackle the inherent challenges associated with teaching intricate software engineering concepts, notably Agile Scrum and Team Software Process (TSPi). Conventional pedagogical approaches often prove inadequate in providing a comprehensive and engaging learning experience for students, educators and stakeholders. In response, our study introduces a pioneering immersive learning approach, offering a robust solution to this educational gap. To gauge the framework's efficacy and pertinence, we conducted online surveys, specifically targeting third-year students enrolled in the Software Engineering Laboratory course and the project stakeholders involved. These surveys were instrumental in collecting valuable feedback on the practicality and impact of our approach in enhancing the teaching and learning processes. This study presents a thorough exposition encompassing the framework's conceptualization, implementation and iterative evolution. Our research outcomes reveal that our immersive learning approach has successfully met the predefined course objectives, effectively addressing the intrinsic challenge of imparting hands-on experiences associated with software engineering principles and concepts. As a significant contribution to the ongoing initiatives aimed at elevating software engineering education, our study underscores the importance of providing students with tangible exposure to vital concepts such as Agile Scrum and TSPi. Moreover, this paper delineates the collaborative journey involved in the creation, execution and refinement of the course framework. Ultimately, our research endeavours to evaluate the degree to which our innovative framework aligns with the objectives established by both students and stakeholders. By doing so, it underscores the transformative potential of our approach in reshaping the landscape of software engineering education, ultimately enhancing its effectiveness and relevance
Ballad
No full textWhile often associated with specific metrical patterns and standard themes, the ballad defies rigid categorisation. This chapter comprises an engaged overview of the form, making a case for the ballad’s centrality within Anglophone culture. Dispensing with traditional classifications, I challenge notions of the form as observed in English since the thirteenth-century. Through close readings of work by canonical figures such as Wordsworth – alongside less prestigious, anonymous ballads – this chapter explores the many and various historical contexts that have shaped, and continue to shape, the form
Reframing English language teaching through engagements with relationality and intersectionality
No full textSir John Ross Bt: the last lord Chancellor of Ireland 1921-1922
No full textSir John Ross was appointed Lord Chancellor of Ireland in 1921, being the last to hold that office with its abolition in 1922. Ross was born and raised in Londonderry, before proceeding to Trinity College, Dublin. Briefly an MP in the 1890s, Ross was chiefly interested in the law. Called to the Irish Bar in 1880, he took silk in 1891. His career as a Chancery barrister, and later a judge, led him to the Irish woolsack. As a result of the Government of Ireland Act 1920, his role as Lord Chancellor was very different to that of his predecessors. However, as Lord Chancellor he took no back seat role, hearing cases in the newly-established High Court of Appeal for Ireland up until his office was abolished. Ross also served as Speaker of the short-lived Senate of Southern Ireland. He retired to Northern Ireland where he died in 1935.<br/
GKM-OD: Gaussian knowledge based modelling for outlier detection
No full textOutlier detection is a critical process in data engineering. Leveraging machine learning techniques for outlier detection enables the handling of large-scale, high-dimensional data, enhancing detection accuracy and efficiency. Traditional methods typically model data directly in the data space. However, these approaches often struggle to accurately distinguish inliers from outliers when dealing with complex data distributions. GMM can flexibly fit complex, multi-peak distributions using multiple Gaussian components and effectively identify outliers through probabilistic modelling. We introduce a novel outlier detection approach, which improves detection efficiency by indirectly modelling data in a latent space using a Gaussian Mixture Model (GMM).This approach aligns with a growing trend in AI, notably advocated by Yann LeCun, that emphasizes decision-making and learning in latent representation spaces, instead of depending on raw token or feature spaces. For this, we design an encoder-decoder neural network with a GMM as the decision layer, enabling effective identification of outliers through probabilistic modelling. Our method not only addresses practical needs in anomaly detection but also contributes to this broader trend of latent space modelling as a step toward more autonomous and generalisable learning systems.Extensive evaluations on public and proprietary datasets demonstrate that our method outperforms existing approaches, including DAGMM and ECOD, highlighting its superiority in accuracy.<br/
Multimodal deep learning model based on ECG and clinical notes for arrhythmia classification
No full textElectrocardiograms (ECGs) are non-invasive tools used to monitor the heart’s electrical activity. It captures the heart’s depolarization and repolarization cycles by measuring the tiny voltage fluctuations on the skin. Analyzing this rhythm requires intensive effort from clinicians. This paper introduce a novel end-to-end multi-modal deep learning model to process 12-lead ECG signals with patient clinical notes to assist cardiologists in classifying eight types of arrhythmia. The ECG signal is processed using a convolutional backbone composed of residual 1D convolutional blocks, a convolutional block attention module, and a bi-directional GRU to capture long-range temporal dependencies. In parallel, 13 clinical features extracted from clinical notes are embedded using an auxiliary multilayer perceptron network. The two modalities are combined through late fusion, followed by a fully connected layer for arrhythmia classification. The model is trained and evaluated on a public dataset. It achieves an accuracy of 92.8% and a macro F1-score of 84.6%, representing +4% increase in accuracy and +9% improvement in macro F1-score compared to processing ECG signals alone. Furthermore, it also outperforms a baseline random forest by a substantial margin. Error analysis shows that the clinical notes help to improve the discriminatory ability of the model for minority and morphologically similar rhythms, particularly Sinus Arrhythmia, Sinus Bradycardia, and Sinus Rhythm. The proposed model demonstrates the clinical potential of fusing waveform morphology with contextual patient data can enable accurate diagnostic tools.<br/
Sustainability obligations and developing countries: any scope for special and differential treatment?
No full textA distinctive feature of the new generation of regional trade agreements signed by developed countries is the inclusion of sustainability obligations. These obligations, whether in the form of clauses or chapters, bind parties to respect internationally recognized core labour standards and protect the environment. While the multilateral forum may be challenging to supporters of ‘trade and . . .’ issues, the regional trade agreements setting appears as the next best avenue to suppress the possibility of incorporating special and differential treatment that upholds equity considerations and guarantees developing countries’ rights to variable geometry in multilateral trade agreements. This chapter explores the role, if any, of special and differential treatment at the three levels of trade governance of sustainability obligations, namely, multilateral, regional/reciprocal, and unilateral
When should a computer decide? Judicial decision-making in the age of automation, algorithms and generative artificial intelligence
No full textThis contribution explores what the activity of judging actually involves and whether it might be replaced by algorithmic technologies, including Large Language Models such as ChatGPT. This involves investigating how algorithmic judging systems operate and might develop, as well as exploring the current limits on using AI in coming to judgment. While it may be accepted that some routine decision can be safely made by machines, others clearly cannot and the focus here is on exploring where and why a decision requires human involvement. This involves considering a range of features centrally involved in judging that may not be capable of being adequately captured by machines. Both the role of judges and wider considerations about the nature and purpose of the legal system are reviewed to support the conclusion that while technology may assist judges, it cannot fully replace them