Abstract
Generative AI presents both challenges and opportunities for educators. This paper explores its potential for automating the creation of programming exercises designed for automated assessment. Traditionally, creating these exercises is a time-intensive and error-prone task that involves developing exercise statements, solutions, and test cases. This ongoing research analyzes the capabilities of the OpenAI GPT API to automatically create these components. An experiment using the OpenAI GPT API to automatically create 120 programming exercises produced interesting results, such as the difficulties encountered in generating valid JSON formats and creating matching test cases for solution code. Learning from this experiment, an enhanced feature was developed to assist teachers in creating programming exercises and was integrated into Agni, a virtual learning environment (VLE). Despite the challenges in generating entirely correct programming exercises, this approach shows potential for reducing the time required to create exercises, thus significantly aiding teachers. The evaluation of this approach, comparing the efficiency and usefulness of using the OpenAI GPT API or authoring the exercises oneself, is in progress. © Yannik Bauer, José Paulo Leal, and Ricardo Queirós;

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Abstract
Semantic measure (SM) algorithms allow software to mimic the human ability of assessing the strength of the semantic relations between elements such as concepts, entities, words, or sentences. SM algorithms are typically evaluated by comparison against gold standard datasets built by human annotators. These datasets are composed of pairs of elements and an averaged numeric rating. Building such datasets usually requires asking human annotators to assign a numeric value to their perception of the strength of the semantic relation between two elements. Large language models (LLMs) have recently been successfully used to perform tasks which previously required human intervention, such as text summarization, essay writing, image description, image synthesis, question answering, and so on. In this paper, we present ongoing research on LLMs capabilities for semantic relations assessment. We queried several LLMs to rate the relationship of pairs of elements from existing semantic measures evaluation datasets, and measured the correlation between the results from the LLMs and gold standard datasets. Furthermore, we performed additional experiments to evaluate which other factors can influence LLMs performance in this task. We present and discuss the results obtained so far. © André Fernandes dos Santos and José Paulo Leal.

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Abstract
Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.