This paper presents a novel approach to improve global localization and mapping in indoor drone navigation by integrating 5G Time of Arrival (ToA) measurements into ORB-SLAM3, a Simultaneous Localization and Mapping (SLAM) system. By incorporating ToA data from 5G base stations, we align the SLAM's local reference frame with a global coordinate system, enabling accurate and consistent global localization. We extend ORB-SLAM3's optimization pipeline to integrate ToA measurements alongside bias estimation, transforming the inherently local estimation into a globally consistent one. This integration effectively resolves scale ambiguity in monocular SLAM systems and enhances robustness, particularly in challenging scenarios where standard SLAM may fail. Our method is evaluated using five real-world indoor datasets collected with RGB-D cameras and inertial measurement units (IMUs), augmented with simulated 5G ToA measurements at 28 GHz and 78 GHz frequencies using MATLAB and QuaDRiGa. We tested four SLAM configurations: RGB-D, RGB-D-Inertial, Monocular, and Monocular-Inertial. The results demonstrate that while local estimation accuracy remains comparable due to the high precision of RGB-D-based ORB-SLAM3 compared to ToA measurements, the inclusion of ToA measurements facilitates robust global positioning. In scenarios where standard mono-inertial ORB-SLAM3 loses tracking, our approach maintains accurate localization throughout the trajectory.

Humblebragging is a phenomenon where individuals present self-promotional statements under the guise of modesty or complaints. For example, a statement like, "Ugh, I can't believe I got promoted to lead the entire team. So stressful!", subtly highlights an achievement while pretending to be complaining. Detecting humblebragging is important for machines to better understand the nuances of human language, especially in tasks like sentiment analysis and intent recognition. However, this topic has not yet been studied in computational linguistics. For the first time, we introduce the task of automatically detecting humblebragging in text. We formalize the task by proposing a 4-tuple definition of humblebragging and evaluate machine learning, deep learning, and large language models (LLMs) on this task, comparing their performance with humans. We also create and release a dataset called HB24, containing 3,340 humblebrags generated using GPT-4o. Our experiments show that detecting humblebragging is non-trivial, even for humans. Our best model achieves an F1-score of 0.88. This work lays the foundation for further exploration of this nuanced linguistic phenomenon and its integration into broader natural language understanding systems.

The increasing demand for mental health services has highlighted the need for innovative solutions, particularly in the realm of psychological conversational AI, where the availability of sensitive data is scarce. In this work, we explored the development of a system tailored for mental health support with a novel approach to psychological assessment based on explainable emotional profiles in combination with empathetic conversational models, offering a promising tool for augmenting traditional care, particularly where immediate expertise is unavailable. Our work can be divided into two main parts, intrinsecaly connected to each other. First, we present RACLETTE, a conversational system that demonstrates superior emotional accuracy compared to state-of-the-art benchmarks in both understanding users' emotional states and generating empathetic responses during conversations, while progressively building an emotional profile of the user through their interactions. Second, we show how the emotional profiles of a user can be used as interpretable markers for mental health assessment. These profiles can be compared with characteristic emotional patterns associated with different mental disorders, providing a novel approach to preliminary screening and support.

End-to-end encryption (E2EE) has become the gold standard for securing communications, bringing strong confidentiality and privacy guarantees to billions of users worldwide. However, the current push towards widespread integration of artificial intelligence (AI) models, including in E2EE systems, raises some serious security concerns. This work performs a critical examination of the (in)compatibility of AI models and E2EE applications. We explore this on two fronts: (1) the integration of AI "assistants" within E2EE applications, and (2) the use of E2EE data for training AI models. We analyze the potential security implications of each, and identify conflicts with the security guarantees of E2EE. Then, we analyze legal implications of integrating AI models in E2EE applications, given how AI integration can undermine the confidentiality that E2EE promises. Finally, we offer a list of detailed recommendations based on our technical and legal analyses, including: technical design choices that must be prioritized to uphold E2EE security; how service providers must accurately represent E2EE security; and best practices for the default behavior of AI features and for requesting user consent. We hope this paper catalyzes an informed conversation on the tensions that arise between the brisk deployment of AI and the security offered by E2EE, and guides the responsible development of new AI features.

This research investigates the effectiveness of established vulnerability metrics, such as the Common Vulnerability Scoring System (CVSS), in evaluating attacks against Large Language Models (LLMs), with a focus on Adversarial Attacks (AAs). The study explores the influence of both general and specific metric factors in determining vulnerability scores, providing new perspectives on potential enhancements to these metrics. This study adopts a quantitative approach, calculating and comparing the coefficient of variation of vulnerability scores across 56 adversarial attacks on LLMs. The attacks, sourced from various research papers, and obtained through online databases, were evaluated using multiple vulnerability metrics. Scores were determined by averaging the values assessed by three distinct LLMs. The results indicate that existing scoring-systems yield vulnerability scores with minimal variation across different attacks, suggesting that many of the metric factors are inadequate for assessing adversarial attacks on LLMs. This is particularly true for context-specific factors or those with predefined value sets, such as those in CVSS. These findings support the hypothesis that current vulnerability metrics, especially those with rigid values, are limited in evaluating AAs on LLMs, highlighting the need for the development of more flexible, generalized metrics tailored to such attacks. This research offers a fresh analysis of the effectiveness and applicability of established vulnerability metrics, particularly in the context of Adversarial Attacks on Large Language Models, both of which have gained significant attention in recent years. Through extensive testing and calculations, the study underscores the limitations of these metrics and opens up new avenues for improving and refining vulnerability assessment frameworks specifically tailored for LLMs.

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from $\text{A}^*$ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems.

The growing demand for dynamic, user-centric data analysis and visualization is evident across domains like healthcare, finance, and research. Traditional visualization tools often fail to meet individual user needs due to their static and predefined nature. To address this gap, Text2Insight is introduced as an innovative solution that delivers customized data analysis and visualizations based on user-defined natural language requirements. Leveraging a multi-model architecture, Text2Insight transforms user inputs into actionable insights and dynamic visualizations. The methodology begins with analyzing the input dataset to extract structural details such as columns and values. A pre-trained Llama3 model converts the user's natural language query into an SQL query, which is further refined using a Named Entity Recognition (NER) model for accuracy. A chart predictor determines the most suitable visualization type, while the Llama3 model generates insights based on the SQL query's results. The output is a user-friendly and visually informative chart. To enhance analysis capabilities, the system integrates a question-answering model and a predictive model using the BERT framework. These models provide insights into historical data and predict future trends. Performance evaluation of Text2Insight demonstrates its effectiveness, achieving high accuracy (99%), precision (100%), recall (99%), and F1-score (99%), with a BLEU score of 0.5. The question-answering model attained an accuracy of 89% and the predictive model achieved 70% accuracy. These results validate Text2Insight as a robust and viable solution for transforming natural language text into dynamic, user-specific data analysis and visualizations.

Over the past 50 years, predictive machine learning has been a cornerstone for both theorists and practitioners. Predictive tasks like classification and regression have been extensively studied, in both theory and practice, due to their applications to face recognition, autonomous vehicles, fraud detection, recommendation systems, etc. Recently, however, a new paradigm of machine learning has emerged: generation. Unlike predictive models, which focus on making accurate predictions of the true label given examples, generative models aim to create new examples based on observed data. For example, in language modeling, the goal might be to generate coherent text in response to a prompt, while in drug development, one might want to generate candidate molecules. In fact, generative models have already been applied to these tasks and others [Zhao et al., 2023, Jumper et al., 2021]. The vast potential of generative machine learning has spurred a surge of research across diverse fields like natural language processing [Wolf et al., 2020], computer vision [Khan et al., 2022], and computational chemistry/biology [Vanhaelen et al., 2020]. Despite this widespread adoption, the theoretical foundations of generative machine learning lags far behind its predictive counterpart. While prediction has been extensively studied by learning theorists through frameworks like PAC and online learning [Shalev-Shwartz and Ben-David, 2014, Mohri et al., 2012, Cesa-Bianchi and Lugosi, 2006], generative machine learning has, for the most, part

Extending context windows (i.e., Long Context, LC) and using retrievers to selectively access relevant information (i.e., Retrieval-Augmented Generation, RAG) are the two main strategies to enable LLMs to incorporate extremely long external contexts. This paper revisits recent studies on this topic, highlighting their key insights and discrepancies. We then provide a more comprehensive evaluation by filtering out questions answerable without external context, identifying the most effective retrieval methods, and expanding the datasets. We show that LC generally outperforms RAG in question-answering benchmarks, especially for Wikipedia-based questions. Summarization-based retrieval performs comparably to LC, while chunk-based retrieval lags behind. However, RAG has advantages in dialogue-based and general question queries. These insights underscore the trade-offs between RAG and LC strategies, offering guidance for future optimization of LLMs with external knowledge sources. We also provide an in-depth discussion on this topic, highlighting the overlooked importance of context relevance in existing studies.

Digital twin (DT) technology has emerged as a transformative approach to simulate, predict, and optimize the behavior of physical systems, with applications that span manufacturing, healthcare, climate science, and more. However, the development of DT models often faces challenges such as high data requirements, integration complexity, and limited adaptability to dynamic changes in physical systems. This paper presents a new method inspired by dynamic data-driven applications systems (DDDAS), called the dynamic data-driven generative of digital twins framework (DDD-GenDT), which combines the physical system with LLM, allowing LLM to act as DT to interact with the physical system operating status and generate the corresponding physical behaviors. We apply DDD-GenDT to the computer numerical control (CNC) machining process, and we use the spindle current measurement data in the NASA milling wear data set as an example to enable LLMs to forecast the physical behavior from historical data and interact with current observations. Experimental results show that in the zero-shot prediction setting, the LLM-based DT can adapt to the change in the system, and the average RMSE of the GPT-4 prediction is 0.479A, which is 4.79% of the maximum spindle motor current measurement of 10A, with little training data and instructions required. Furthermore, we analyze the performance of DDD-GenDT in this specific application and their potential to construct digital twins. We also discuss the limitations and challenges that may arise in practical implementations.