We propose an approach to enhance sentence embeddings by applying generative text models for data augmentation at inference time. Unlike conventional data augmentation that utilises synthetic training data, our approach does not require access to model parameters or the computational resources typically required for fine-tuning state-of-the-art models. Generatively Augmented Sentence Encoding uses diverse linguistic synthetic variants of input texts generated by paraphrasing, summarising, or extracting keywords, followed by pooling the original and synthetic embeddings. Experimental results on the Massive Text Embedding Benchmark for Semantic Textual Similarity (STS) demonstrate performance improvements across a range of embedding models using different generative models for augmentation. We find that generative augmentation leads to larger performance improvements for embedding models with lower baseline performance. These findings suggest that integrating generative augmentation at inference time adds semantic diversity and can enhance the robustness and generalizability of sentence embeddings for embedding models. Our results show that the degree to which generative augmentation can improve STS performance depends not only on the embedding model but also on the dataset. From a broader perspective, the approach allows trading training for inference compute.

In this paper, we aim to address a significant challenge in the field of missing data imputation: identifying and leveraging the interdependencies among features to enhance missing data imputation for tabular data. We introduce a novel framework named the Bipartite and Complete Directed Graph Neural Network (BCGNN). Within BCGNN, observations and features are differentiated as two distinct node types, and the values of observed features are converted into attributed edges linking them. The bipartite segment of our framework inductively learns embedding representations for nodes, efficiently utilizing the comprehensive information encapsulated in the attributed edges. In parallel, the complete directed graph segment adeptly outlines and communicates the complex interdependencies among features. When compared to contemporary leading imputation methodologies, BCGNN consistently outperforms them, achieving a noteworthy average reduction of 15% in mean absolute error for feature imputation tasks under different missing mechanisms. Our extensive experimental investigation confirms that an in-depth grasp of the interdependence structure substantially enhances the model's feature embedding ability. We also highlight the model's superior performance in label prediction tasks involving missing data, and its formidable ability to generalize to unseen data points.

Abstract: This study investigates sentiment patterns within Spanish political party communications on Twitter by employing BETO and RoBERTuito, two pre-trained language models optimized for Spanish text. With a dataset comprising tweets from major Spanish political parties--PSOE, PP, Vox, Podemos, and Ciudadanos--spanning 2019 to 2024, this research analyzes sentiment distributions and explores the relationship between sentiment and party ideology. Results reveal that both models consistently identify a predominant Neutral sentiment across parties, with significant variations in Negative and Positive sentiments that align with ideological distinctions. Vox exhibits higher levels of Negative sentiment, while PSOE demonstrates a relatively high Positive sentiment, supporting the hypothesis that emotional appeals in political messaging reflect ideological stances. This study highlights the utility of pre-trained models in analyzing non-English social media sentiment and underscores the implications of sentiment dynamics in shaping public discourse within a multi-party system. Keywords: Spanish political parties, sentiment analysis, Twitter, BETO, RoBERTuito, political communication, ideology, social media analysis 1. Introduction In the era of digital politics, social media has emerged as a potent platform where public opinion is actively shaped and reflected. For countries like Spain, where a spectrum of political ideologies coexists, understanding the sentiment behind political communications becomes crucial. Sentiment analysis, particularly on platforms like Twitter, serves as a powerful tool to decode public attitudes and the emotional undertones in political party communications (Cambria et al., 2013; Giachanou & Crestani, 2016). By leveraging sentiment analysis, researchers can quantify and interpret political sentiments, thereby offering insights into party strategies and public reactions. In Spain's unique political landscape, where new and traditional parties like Podemos, PSOE, PP, Ciudadanos, and Vox engage vigorously on social media, analyzing sentiment can reveal the underlying strategies each employs. Recent advancements in pre-trained models tailored for the Spanish language, such as BETO and RoBERTuito, offer refined accuracy in detecting nuanced sentiments within Spanish tweets (Pérez et al., 2021).

The Universal Dependencies (UD) project has significantly expanded linguistic coverage across 161 languages, yet Luxembourgish, a West Germanic language spoken by approximately 400,000 people, has remained absent until now. In this paper, we introduce LuxBank, the first UD Treebank for Luxembourgish, addressing the gap in syntactic annotation and analysis for this `low-research' language. We establish formal guidelines for Luxembourgish language annotation, providing the foundation for the first large-scale quantitative analysis of its syntax. LuxBank serves not only as a resource for linguists and language learners but also as a tool for developing spell checkers and grammar checkers, organising existing text archives and even training large language models. By incorporating Luxembourgish into the UD framework, we aim to enhance the understanding of syntactic variation within West Germanic languages and offer a model for documenting smaller, semi-standardised languages. This work positions Luxembourgish as a valuable resource in the broader linguistic and NLP communities, contributing to the study of languages with limited research and resources.

This paper presents the development and evaluation of a medical service robot equipped with 3D LiDAR and advanced localization capabilities for use in hospital environments. The robot employs LiDAR-based Simultaneous Localization and Mapping SLAM to navigate autonomously and interact effectively within complex and dynamic healthcare settings. A comparative analysis with established 3D SLAM technology in Autoware version 1.14.0, under a Linux ROS framework, provided a benchmark for evaluating our system performance. The adaptation of Normal Distribution Transform NDT Matching to indoor navigation allowed for precise real-time mapping and enhanced obstacle avoidance capabilities. Empirical validation was conducted through manual maneuvers in various environments, supplemented by ROS simulations to test the system response to simulated challenges. The findings demonstrate that the robot integration of 3D LiDAR and NDT Matching significantly improves navigation accuracy and operational reliability in a healthcare context. This study highlights the robot ability to perform essential tasks with high efficiency and identifies potential areas for further improvement, particularly in sensor performance under diverse environmental conditions. The successful deployment of this technology in a hospital setting illustrates its potential to support medical staff and contribute to patient care, suggesting a promising direction for future research and development in healthcare robotics.

This chapter is meant to be part of the book "Differential Privacy in Artificial Intelligence: From Theory to Practice" and provides an introduction to Differential Privacy. It starts by illustrating various attempts to protect data privacy, emphasizing where and why they failed, and providing the key desiderata of a robust privacy definition. It then defines the key actors, tasks, and scopes that make up the domain of privacy-preserving data analysis. Following that, it formalizes the definition of Differential Privacy and its inherent properties, including composition, post-processing immunity, and group privacy. The chapter also reviews the basic techniques and mechanisms commonly used to implement Differential Privacy in its pure and approximate forms.

It has been observed that the input space of deep neural network classifiers can exhibit `fragmentation', where the model function rapidly changes class as the input space is traversed. The severity of this fragmentation tends to follow the double descent curve, achieving a maximum at the interpolation regime. We study this phenomenon in the context of image classification and ask whether fragmentation could be predictive of generalization performance. Using a fragmentation-based complexity measure, we show this to be possible by achieving good performance on the PGDL (Predicting Generalization in Deep Learning) benchmark. In addition, we report on new observations related to fragmentation, namely (i) fragmentation is not limited to the input space but occurs in the hidden representations as well, (ii) fragmentation follows the trends in the validation error throughout training, and (iii) fragmentation is not a direct result of increased weight norms. Together, this indicates that fragmentation is a phenomenon worth investigating further when studying the generalization ability of deep neural networks.

Inspired by the success of Geographically Weighted Regression and its accounting for spatial variations, we propose GeogGNN -- A graph neural network model that accounts for geographical latitude and longitudinal points. Using a synthetically generated dataset, we apply the algorithm for a 4-class classification problem in cybersecurity with seemingly realistic geographic coordinates centered in the Gulf Cooperation Council region. We demonstrate that it has higher accuracy than standard neural networks and convolutional neural networks that treat the coordinates as features. Encouraged by the speed-up in model accuracy by the GeogGNN model, we provide a general mathematical result that demonstrates that a geometrically weighted neural network will, in principle, always display higher accuracy in the classification of spatially dependent data by making use of spatial continuity and local averaging features.

Building a corpus become an important topic in natural language processing (NLP) and especially for low resource languages (ex: AD), due to the importance that the corpus plays in the development of several tools, such as: Machine Translation Babaali and Salem [2022], Part of speech tagging Chiche and Yitagesu [2022], Named entities recognition Jarrar et al. [2022], etc. in particular with the emergence of techniques based on statistics, machine learning and deep learning. Who exploits this mass of information to develop, train and evaluate models. However, building a corpus is not an easy task Bakari et al. [2016]; it is extremely time-consuming and requires a lot of work, for the good reason that the volume and quality of the corpus are two important parameters. Despite the recent emergence of techniques that consume fewer resources, such as few-shot learning Tunstall et al. [2022]. Over the last few years, a lot of studies in NLP have focused on languages or variants of languages called low resources Mengoni and Santucci [2023]. This change of direction is mainly due to the emergence of social media such as Facebook, Twitter, RenRen, LinkedIn, Google+, and Tuenti, as a means of communication where people exchange messages and comments.

Natural language processing (NLP) utilizes text data augmentation to overcome sample size constraints. Increasing the sample size is a natural and widely used strategy for alleviating these challenges. In this study, we chose Arabic to increase the sample size and correct grammatical errors. Arabic is considered one of the languages with limited resources for grammatical error correction (GEC). Furthermore, QALB-14 and QALB-15 are the only datasets used in most Arabic grammatical error correction research, with approximately 20,500 parallel examples, which is considered low compared with other languages. Therefore, this study aims to develop an Arabic corpus called "Tibyan" for grammatical error correction using ChatGPT. ChatGPT is used as a data augmenter tool based on a pair of Arabic sentences containing grammatical errors matched with a sentence free of errors extracted from Arabic books, called guide sentences. Multiple steps were involved in establishing our corpus, including the collection and pre-processing of a pair of Arabic texts from various sources, such as books and open-access corpora. We then used ChatGPT to generate a parallel corpus based on the text collected previously, as a guide for generating sentences with multiple types of errors. By engaging linguistic experts to review and validate the automatically generated sentences, we ensured that they were correct and error-free. The corpus was validated and refined iteratively based on feedback provided by linguistic experts to improve its accuracy. Finally, we used the Arabic Error Type Annotation tool (ARETA) to analyze the types of errors in the Tibyan corpus. Our corpus contained 49 of errors, including seven types: orthography, morphology, syntax, semantics, punctuation, merge, and split. The Tibyan corpus contains approximately 600 K tokens.