This paper describes our participation in the Shared Task on Software Mentions Disambiguation (SOMD), with a focus on improving relation extraction in scholarly texts through generative Large Language Models (LLMs) using single-choice question-answering. The methodology prioritises the use of in-context learning capabilities of LLMs to extract software-related entities and their descriptive attributes, such as distributive information. Our approach uses Retrieval-Augmented Generation (RAG) techniques and LLMs for Named Entity Recognition (NER) and Attributive NER to identify relationships between extracted software entities, providing a structured solution for analysing software citations in academic literature. The paper provides a detailed description of our approach, demonstrating how using LLMs in a single-choice QA paradigm can greatly enhance IE methodologies. Our participation in the SOMD shared task highlights the importance of precise software citation practices and showcases our system's ability to overcome the challenges of disambiguating and extracting relationships between software mentions. This sets the groundwork for future research and development in this field.

Recognizing software entities such as library names from free-form text is essential to enable many software engineering (SE) technologies, such as traceability link recovery, automated documentation, and API recommendation. While many approaches have been proposed to address this problem, they suffer from small entity vocabularies or noisy training data, hindering their ability to recognize software entities mentioned in sophisticated narratives. To address this challenge, we leverage the Wikipedia taxonomy to develop a comprehensive entity lexicon with 79K unique software entities in 12 fine-grained types, as well as a large labeled dataset of over 1.7M sentences. Then, we propose self-regularization, a noise-robust learning approach, to the training of our software entity recognition (SER) model by accounting for many dropouts. Results show that models trained with self-regularization outperform both their vanilla counterparts and state-of-the-art approaches on our Wikipedia benchmark and two Stack Overflow benchmarks. We release our models, data, and code for future research.

With its large-scale growth, the ways to quickly find existing research on relevant issues have become an urgent demand for researchers. Previous studies, such as SciBERT, have shown that pre-training using domain-specific texts can improve the performance of natural language processing tasks. However, the pre-trained language model for social sciences is not available so far. In light of this, the present research proposes a pre-trained model based on the abstracts published in the Social Science Citation Index (SSCI) journals.

The objective of this chapter is to propose some retrospective analysis of the evolution of programming abstractions, from {\em procedures}, {\em objects}, {\em actors}, {\em components}, {\em services}, up to {\em agents}, %have some compare concepts of software component and of agent (and multi-agent system), %The method chosen is to by replacing them within a general historical perspective. Some common referential with three axes/dimensions is chosen: {\em action selection} at the level of one entity, {\em coupling flexibility} between entities, and {\em abstraction level}. We indeed may observe some continuous quest for higher flexibility (through notions such as {\em late binding}, or {\em reification} of {\em connections}) and higher level of {\em abstraction}. Concepts of components, services and agents have some common objectives (notably, {\em software modularity and reconfigurability}), with multi-agent systems raising further concepts of {\em autonomy} and {\em coordination}. notably through the notion of {\em auto-organization} and the use of {\em knowledge}. We hope that this analysis helps at highlighting some of the basic forces motivating the progress of programming abstractions and therefore that it may provide some seeds for the reflection about future programming abstractions.

It is quite common nowadays that things get so trendy, they eventually loose their meaning. Buzzwords are a plague, where the simple fact that something "it is" does not matter as much as "why is it there?". The same is happening to AI: it is no interest whether it has a real purpose in such system or not, but "hey, it's built on a fancy AI!". Finally, there is nothing intelligent in a machine processing your handwritten manuscript or recognizing the shape of a STOP sign. There is a widespread culture of transforming things, casting a shiny aura on them by changing the words we use to define them.

Technical singularity is defined as a hypothetical future of superhuman machines with a cognitive capability far beyond the capacity of human minds. In the journey toward this potential technology revolution is something that I have been focused on called artificial swarm intelligence. A starling murmuration, something that people have told me is awe-inspiring, is a marvel of nature similar to an army of ants or a swarm of bees. How do all these individual entities organize around a common mission that includes a form of collaboration and unified orchestration as a team? When thinking about swarms of AI bots or even nanobots, the foundational concept we want to define is what exactly AI bot are.