ION: Navigating the HPC I/O Optimization Journey using Large Language Models

Effectively leveraging the complex software and hardware I/O stacks of HPC systems to deliver needed I/O performance has been a challenging task for domain scientists. To identify and address I/O issues in their applications, scientists largely rely on I/O experts to analyze the recorded I/O traces of their applications and provide insights into the potential issues. However, due to the limited number of I/O experts and the growing demand for data-intensive applications across the wide spectrum of sciences, inaccessibility has become a major bottleneck hindering scientists from maximizing their productivity. Inspired by the recent rapid progress of large language models (LLMs), in this work we propose IO Navigator (ION), an LLM-based framework that takes a recorded I/O trace of an application as input and leverages the in-context learning, chain-of-thought, and code generation capabilities of LLMs to comprehensively analyze the I/O trace and provide diagnosis of potential I/O issues. Similar to an I/O expert, ION provides detailed justifications for the diagnosis and an interactive interface for scientists to ask detailed questions about the diagnosis. We illustrate ION's applicability by assessing it on a set of controlled I/O traces generated with different I/O issues. We also demonstrate that ION can match state-of-the-art I/O optimization tools and provide more insightful and adaptive diagnoses for real applications. We believe ION, with its full capabilities, has the potential to become a powerful tool for scientists to navigate through complex I/O subsystems in the future

Explainable Artificial Intelligence (XAI) from a user perspective- A synthesis of prior literature and problematizing avenues for future research

The final search query for the Systematic Literature Review (SLR) was conducted on 15th July 2022. Initially, we extracted 1707 journal and conference articles from the Scopus and Web of Science databases. Inclusion and exclusion criteria were then applied, and 58 articles were selected for the SLR. The findings show four dimensions that shape the AI explanation, which are format (explanation representation format), completeness (explanation should contain all required information, including the supplementary information), accuracy (information regarding the accuracy of the explanation), and currency (explanation should contain recent information). Moreover, along with the automatic representation of the explanation, the users can request additional information if needed. We have also found five dimensions of XAI effects: trust, transparency, understandability, usability, and fairness. In addition, we investigated current knowledge from selected articles to problematize future research agendas as research questions along with possible research paths. Consequently, a comprehensive framework of XAI and its possible effects on user behavior has been developed

Collaborative trails in e-learning environments

This deliverable focuses on collaboration within groups of learners, and hence collaborative trails. We begin by reviewing the theoretical background to collaborative learning and looking at the kinds of support that computers can give to groups of learners working collaboratively, and then look more deeply at some of the issues in designing environments to support collaborative learning trails and at tools and techniques, including collaborative filtering, that can be used for analysing collaborative trails. We then review the state-of-the-art in supporting collaborative learning in three different areas – experimental academic systems, systems using mobile technology (which are also generally academic), and commercially available systems. The final part of the deliverable presents three scenarios that show where technology that supports groups working collaboratively and producing collaborative trails may be heading in the near future

An evaluation of factors affecting students' use of a web-based engineering resource

The purpose of this work was to investigate the relationship between a number of influential factors, including cognitive style and approach to learning, and students’ processing behaviour during their use of a particular Web-based resource for Electronics and Electrical Engineering undergraduates. This was achieved through the development of a learner profile for each student using Riding’s (1991) Revised Study Process Questionnaire (R-SPQ-2F). The quantitative component of the research was then set against a detailed analysis of students’ processing behaviour using verbal protocol data gathered through individual think-aloud sessions and post-intervention interviews. The results of the quantitative component of the research provided no compelling evidence to suggest that cognitive style was a factor that influenced student performance while using the resource or their perceptions of the package. There was however, some evidence to suggest that the package was more positively received by students who profiled as deep learners than their surface counterparts. The analysis of students’ processing behaviour from their verbal protocols highlighted a number of the resource’s shortcomings, which typically promoted a surface, goal-oriented approach to its content. It also identified problems with the design and structure of the resource, which at times had a deleterious effect on learning. The results also raised questions regarding the efficacy and use of psychometric inventories in this kind of research

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility

Can interfaces and social profiles ‘speak without words’? Social platforms as ideological tools to shape identities and discourses

User agency has been profoundly transformed since all the new digital practices and communicative exchanges are mediated, filtered and re-modelled through digital technologies thanks to the presence of the two potentialities of interactivity and connectivity. Most of the discursive practices represented in social media platforms are focused on processes of self-profiling. Additionally, pre-packaged identities and meanings are produced by multimodal discursive patterns that are generated by social network technologies. The co-deployment of different semiotic resources is regulated by the platform design, which combines multimodal artefacts uploaded by users with those pre-imposed by the interface architecture. So far, digital profiles have been almost exclusively investigated as new multimodal and multimedia digital texts. Our focus, instead, is on technology meant as a further and complex semiotic resource, and its meaning potential gives rise to hidden signs (metadata and algorithms) which are regulated by normative codes. What we are proposing in this theoretical contribution is a tentative framework that is grounded in an integrated view of textuality. Digital meaning is conveyed through texts but also via computational actions that, in turn, are triggered not only by users but also by platform technologies embodied by the interfaces. If we apply a further level of analysis, as suggested by the framework proposed, we realise that users are partially responsible for their identity construction. De facto, algorithmic relations mostly shape their agency, and this implies a new approach to the study of meaning-making processes in digital settings