Algorithmic hate speech detection faces significant challenges due to the diverse definitions and datasets used in research and practice. Social media platforms, legal frameworks, and institutions each apply distinct yet overlapping definitions, complicating classification efforts. This study addresses these challenges by demonstrating that existing datasets and taxonomies can be integrated into a unified model, enhancing prediction performance and reducing reliance on multiple specialized classifiers. The work introduces a universal taxonomy and a hate speech classifier capable of detecting a wide range of definitions within a single framework. Our approach is validated by combining two widely used but differently annotated datasets, showing improved classification performance on an independent test set. This work highlights the potential of dataset and taxonomy integration in advancing hate speech detection, increasing efficiency, and ensuring broader applicability across contexts.

This comprehensive survey examines the integration of knowledge-based approaches into autonomous driving systems, with a focus on trajectory prediction and planning. We systematically review methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into these systems, spanning purely symbolic representations to hybrid neuro-symbolic architectures. In particular, we analyze recent advancements in formal logic and differential logic programming, reinforcement learning frameworks, and emerging techniques that leverage large foundation models and diffusion models for knowledge representation. Organized under a unified literature survey section, our discussion synthesizes the state-of-the-art into a high-level overview, supported by a detailed comparative table that maps key works to their respective methodological categories. This survey not only highlights current trends -- including the growing emphasis on interpretable AI, formal verification in safety-critical systems, and the increased use of generative models in prediction and planning -- but also outlines the challenges and opportunities for developing robust, knowledge-enhanced autonomous driving systems.

In today's digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, \underline{G}raph Information \underline{E}nhanced Deep Neural Ne\underline{t}work Ensemble \underline{A}rchitectur\underline{E} for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks (\textsc{[Bi]RNN}) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel \textit{Propagation-Enhanced Content Embedding} which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models.

Recent advancements in Large Language Models (LLMs) like ChatGPT and GPT-4 have shown remarkable abilities in a wide range of tasks such as summarizing texts and assisting in coding. Scientific research has demonstrated that these models can also play text-adventure games. This study aims to explore whether LLMs can autonomously create text-based games based on anthropological classics, evaluating also their effectiveness in communicating knowledge. To achieve this, the study engaged anthropologists in discussions to gather their expectations and design inputs for an anthropologically themed game. Through iterative processes following the established HCI principle of 'design thinking', the prompts and the conceptual framework for crafting these games were refined. Leveraging GPT3.5, the study created three prototypes of games centered around the seminal anthropological work of the social anthropologist's Bronislaw Malinowski's "Argonauts of the Western Pacific" (1922). Subsequently, evaluations were conducted by inviting senior anthropologists to playtest these games, and based on their inputs, the game designs were refined. The tests revealed promising outcomes but also highlighted key challenges: the models encountered difficulties in providing in-depth thematic understandings, showed suspectibility to misinformation, tended towards monotonic responses after an extended period of play, and struggled to offer detailed biographical information. Despite these limitations, the study's findings open up new research avenues at the crossroads of artificial intelligence, machine learning, LLMs, ethnography, anthropology and human-computer interaction.

Autonomous agents often face the challenge of interpreting uncertain natural language instructions for planning tasks. Representing these instructions as Linear Temporal Logic (LTL) enables planners to synthesize actionable plans. We introduce CoT-TL, a data-efficient in-context learning framework for translating natural language specifications into LTL representations. CoT-TL addresses the limitations of large language models, which typically rely on extensive fine-tuning data, by extending chain-of-thought reasoning and semantic roles to align with the requirements of formal logic creation. This approach enhances the transparency and rationale behind LTL generation, fostering user trust. CoT-TL achieves state-of-the-art accuracy across three diverse datasets in low-data scenarios, outperforming existing methods without fine-tuning or intermediate translations. To improve reliability and minimize hallucinations, we incorporate model checking to validate the syntax of the generated LTL output. We further demonstrate CoT-TL's effectiveness through ablation studies and evaluations on unseen LTL structures and formulas in a new dataset. Finally, we validate CoT-TL's practicality by integrating it into a QuadCopter for multi-step drone planning based on natural language instructions.

The detection of hate speech or toxic content online is a complex and sensitive issue. While the identification itself is highly dependent on the context of the situation, sensitive personal attributes such as age, language, and nationality are rarely available due to privacy concerns. Additionally, platforms struggle with a wide range of local jurisdictions regarding online hate speech and the evaluation of content based on their internal ethical norms. This research presents a novel approach that demonstrates a GDPR-compliant application capable of implementing legal and ethical reasoning into the content moderation process. The application increases the explainability of moderation decisions by utilizing user information. Two use cases fundamental to online communication are presented and implemented using technologies such as GPT-3.5, Solid Pods, and the rule language Prova. The first use case demonstrates the scenario of a platform aiming to protect adolescents from potentially harmful content by limiting the ability to post certain content when minors are present. The second use case aims to identify and counter problematic statements online by providing counter hate speech. The counter hate speech is generated using personal attributes to appeal to the user. This research lays the groundwork for future DSA compliance of online platforms. The work proposes a novel approach to reason within different legal and ethical definitions of hate speech and plan the fitting counter hate speech. Overall, the platform provides a fitted protection to users and a more explainable and individualized response. The hate speech detection service, the chat platform, and the reasoning in Prova are discussed, and the potential benefits for content moderation and algorithmic hate speech detection are outlined. A selection of important aspects for DSA compliance is outlined.

Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum computing to solve real-world problems is still hampered by a number of challenges, including hardware limitations and a relatively under-explored landscape of quantum algorithms, especially when compared to the extensive development of classical computing. The design of quantum circuits, in particular parameterized quantum circuits (PQCs), which contain learnable parameters optimized by classical methods, is a non-trivial and time-consuming task requiring expert knowledge. As a result, research on the automated generation of PQCs, known as quantum architecture search (QAS), has gained considerable interest. QAS focuses on the use of machine learning and optimization-driven techniques to generate PQCs tailored to specific problems and characteristics of quantum hardware. In this paper, we provide an overview of QAS methods by examining relevant research studies in the field. We discuss main challenges in designing and performing an automated search for an optimal PQC, and survey ways to address them to ease future research.

Traffic rules formalization is crucial for verifying the compliance and safety of autonomous vehicles (AVs). However, manual translation of natural language traffic rules as formal specification requires domain knowledge and logic expertise, which limits its adaptation. This paper introduces TR2MTL, a framework that employs large language models (LLMs) to automatically translate traffic rules (TR) into metric temporal logic (MTL). It is envisioned as a human-in-loop system for AV rule formalization. It utilizes a chain-of-thought in-context learning approach to guide the LLM in step-by-step translation and generating valid and grammatically correct MTL formulas. It can be extended to various forms of temporal logic and rules. We evaluated the framework on a challenging dataset of traffic rules we created from various sources and compared it against LLMs using different in-context learning methods. Results show that TR2MTL is domain-agnostic, achieving high accuracy and generalization capability even with a small dataset. Moreover, the method effectively predicts formulas with varying degrees of logical and semantic structure in unstructured traffic rules.

Information Extraction (IE) is a transformative process that converts unstructured text data into a structured format by employing entity and relation extraction (RE) methodologies. The identification of the relation between a pair of entities plays a crucial role within this framework. Despite the existence of various techniques for relation extraction, their efficacy heavily relies on access to labeled data and substantial computational resources. In addressing these challenges, Large Language Models (LLMs) emerge as promising solutions; however, they might return hallucinating responses due to their own training data. To overcome these limitations, Retrieved-Augmented Generation-based Relation Extraction (RAG4RE) in this work is proposed, offering a pathway to enhance the performance of relation extraction tasks. This work evaluated the effectiveness of our RAG4RE approach utilizing different LLMs. Through the utilization of established benchmarks, such as TACRED, TACREV, Re-TACRED, and SemEval RE datasets, our aim is to comprehensively evaluate the efficacy of our RAG4RE approach. In particularly, we leverage prominent LLMs including Flan T5, Llama2, and Mistral in our investigation. The results of our study demonstrate that our RAG4RE approach surpasses performance of traditional RE approaches based solely on LLMs, particularly evident in the TACRED dataset and its variations. Furthermore, our approach exhibits remarkable performance compared to previous RE methodologies across both TACRED and TACREV datasets, underscoring its efficacy and potential for advancing RE tasks in natural language processing.

Information resources such as newspapers have produced unstructured text data in various languages related to the corona outbreak since December 2019. Analyzing these unstructured texts is time-consuming without representing them in a structured format; therefore, representing them in a structured format is crucial. An information extraction pipeline with essential tasks -- named entity tagging and relation extraction -- to accomplish this goal might be applied to these texts. This study proposes a data annotation pipeline to generate training data from corona news articles, including generic and domain-specific entities. Named entity recognition models are trained on this annotated corpus and then evaluated on test sentences manually annotated by domain experts evaluating the performance of a trained model. The code base and demonstration are available at https://github.com/sefeoglu/coronanews-ner.git.