To effectively manage and utilize knowledge graphs, it is crucial to have metrics that can assess the quality of knowledge graphs from various perspectives. While there have been studies on knowledge graph quality metrics, there has been a lack of research on metrics that measure how richly ontologies, which form the backbone of knowledge graphs, are defined or the impact of richly defined ontologies. In this study, we propose a new metric called Class Granularity, which measures how well a knowledge graph is structured in terms of how finely classes with unique characteristics are defined. Furthermore, this research presents potential impact of Class Granularity in knowledge graph's on downstream tasks. In particular, we explore its influence on graph embedding and provide experimental results. Additionally, this research goes beyond traditional Linked Open Data comparison studies, which mainly focus on factors like scale and class distribution, by using Class Granularity to compare four different LOD sources.

Musicians frequently use social media to express their opinions, but they often convey different messages in their music compared to their posts online. Some utilize these platforms to abuse their colleagues, while others use it to show support for political candidates or engage in activism, as seen during the #EndSars protest. There are extensive research done on offensive language detection on social media, the usage of offensive language by musicians has received limited attention. In this study, we introduce VocalTweets, a code-switched and multilingual dataset comprising tweets from 12 prominent Nigerian musicians, labeled with a binary classification method as Normal or Offensive. We trained a model using HuggingFace's base-Twitter-RoBERTa, achieving an F1 score of 74.5. Additionally, we conducted cross-corpus experiments with the OLID dataset to evaluate the generalizability of our dataset.

My eye was caught by a striking photograph in the most recent edition of Charles Arthur's Substack newsletter Social Warming. It shows a narrow street in the aftermath of the "rain bomb" that devastated the region of Valencia in Spain. A year's worth of rain fell in a single day, and in some towns more than 490 litres a square metre fell in eight hours. Water is very heavy, so if there's a gradient it will flow downhill with the kind of force that can pick up a heavy SUV and toss it around like a toy. And if it channels down a narrow urban street, it will throw parked cars around like King Kong in a bad mood.

Ofcom has warned tech firms that content from chatbots impersonating real and fictional people could fall foul of the UK's new digital laws. The communications regulator issued the guidance after it emerged that users on the Character.AI platform had created avatars mimicking the deceased British teenagers Brianna Ghey and Molly Russell. Under pressure from digital safety campaigners to clarify the situation, Ofcom underlined that content created by user-made chatbots would come under the scope of the Online Safety Act. Without naming the US-based artificial intelligence firm Character.AI, Ofcom said a site or app that allowed users to create their own chatbots for other people to interact with would be covered by the act. "This includes services that provide tools for users to create chatbots that mimic the personas of real and fictional people, which can be submitted to a chatbot library for others to interact with," said Ofcom. In an open letter, Ofcom also said any user-to-user site or app – such as a social media platform or messaging app – that enabled people to share content generated by a chatbot on that site with others would also be in scope.

Although misinformation tends to spread online, it can have serious real-world consequences. In order to develop automated tools to detect and mitigate the impact of misinformation, researchers must leverage algorithms that can adapt to the modality (text, images and video), the source, and the content of the false information. However, these characteristics tend to change dynamically across time, making it challenging to develop robust algorithms to fight misinformation spread. Therefore, this paper uses natural language processing to find common characteristics of political misinformation over a twelve year period. The results show that misinformation has increased dramatically in recent years and that it has increasingly started to be shared from sources with primary information modalities of text and images (e.g., Facebook and Instagram), although video sharing sources containing misinformation are starting to increase (e.g., TikTok). Moreover, it was discovered that statements expressing misinformation contain more negative sentiment than accurate information. However, the sentiment associated with both accurate and inaccurate information has trended downward, indicating a generally more negative tone in political statements across time. Finally, recurring misinformation categories were uncovered that occur over multiple years, which may imply that people tend to share inaccurate statements around information they fear or don't understand (Science and Medicine, Crime, Religion), impacts them directly (Policy, Election Integrity, Economic) or Public Figures who are salient in their daily lives. Together, it is hoped that these insights will assist researchers in developing algorithms that are temporally invariant and capable of detecting and mitigating misinformation across time.

Teasers are an effective tool for promoting content in entertainment, commercial and educational fields. However, creating an effective teaser for long videos is challenging for it requires long-range multimodal modeling on the input videos, while necessitating maintaining audiovisual alignments, managing scene changes and preserving factual accuracy for the output teasers. Due to the lack of a publicly-available dataset, progress along this research direction has been hindered. In this work, we present DocumentaryNet, a collection of 1,269 documentaries paired with their teasers, featuring multimodal data streams of video, speech, music, sound effects and narrations. With DocumentaryNet, we propose a new two-stage system for generating teasers from long documentaries. The proposed TeaserGen system first generates the teaser narration from the transcribed narration of the documentary using a pretrained large language model, and then selects the most relevant visual content to accompany the generated narration through language-vision models. For narration-video matching, we explore two approaches: a pretraining-based model using pretrained contrastive language-vision models and a deep sequential model that learns the mapping between the narrations and visuals. Our experimental results show that the pretraining-based approach is more effective at identifying relevant visual content than directly trained deep autoregressive models.

With the recent advance of representation learning algorithms on graphs (e.g., DeepWalk/GraphSage) and natural languages (e.g., Word2Vec/BERT) , the state-of-the art models can even achieve human-level performance over many downstream tasks, particularly for the task of node and sentence classification. However, most algorithms focus on large-scale models for static graphs and text corpus without considering the inherent dynamic characteristics or discovering the reasons behind the changes. This dissertation aims to efficiently model the dynamics in graphs (such as social networks and citation graphs) and understand the changes in texts (specifically news titles and personal biographies). To achieve this goal, we utilize the renowned Personalized PageRank algorithm to create effective dynamic network embeddings for evolving graphs. Our proposed approaches significantly improve the running time and accuracy for both detecting network abnormal intruders and discovering entity meaning shifts over large-scale dynamic graphs. For text changes, we analyze the post-publication changes in news titles to understand the intents behind the edits and discuss the potential impact of titles changes from information integrity perspective. Moreover, we investigate self-presented occupational identities in Twitter users' biographies over five years, investigating job prestige and demographics effects in how people disclose jobs, quantifying over-represented jobs and their transitions over time.

Knowledge representation and reasoning systems represent knowledge as collections of facts and rules. KRRs can represent complex concepts and relations, and they can query and manipulate information in sophisticated ways. Unfortunately, the KRR technology has been hindered by the fact that specifying the requisite knowledge requires skills that most domain experts do not have, and professional knowledge engineers are hard to find. Some recent CNL-based approaches, such as the Knowledge Authoring Logic Machine (KALM), have shown to have very high accuracy compared to others, and a natural question is to what extent the CNL restrictions can be lifted. Besides the CNL restrictions, KALM has limitations in terms of the types of knowledge it can represent. To address these issues, we propose an extension of KALM called KALM for Factual Language (KALMF). KALMF uses a neural parser for natural language, MS, to parse what we call factual English sentences, which require little grammar training to use. Building upon KALMF, we propose KALM for Rules and Actions (KALMR), to represent and reason with rules and actions. Furthermore, we identify the reasons behind the slow speed of KALM and make optimizations to address this issue. Our evaluation using multiple benchmarks shows that our approaches achieve a high level of correctness on fact and query authoring (95%) and on rule authoring (100%). When used for authoring and reasoning with actions, our approach achieves more than 99.3% correctness, demonstrating its effectiveness in enabling more sophisticated knowledge representation and reasoning. We also illustrate the logical reasoning capabilities of our approach by drawing attention to the problems faced by the famous AI, ChatGPT. Finally, the evaluation of the newly proposed speed optimization points not only to a 68% runtime improvement but also yields better accuracy of the overall system.

The mental health of social media users has started more and more to be put at risk by harmful, hateful, and offensive content. In this paper, we propose \textsc{StopHC}, a harmful content detection and mitigation architecture for social media platforms. Our aim with \textsc{StopHC} is to create more secure online environments. Our solution contains two modules, one that employs deep neural network architecture for harmful content detection, and one that uses a network immunization algorithm to block toxic nodes and stop the spread of harmful content. The efficacy of our solution is demonstrated by experiments conducted on two real-world datasets.

The recent introduction of large language models (LLMs) has revolutionized the field of robotics by enabling contextual reasoning and intuitive human-robot interaction in domains as varied as manipulation, locomotion, and self-driving vehicles. When viewed as a stand-alone technology, LLMs are known to be vulnerable to jailbreaking attacks, wherein malicious prompters elicit harmful text by bypassing LLM safety guardrails. To assess the risks of deploying LLMs in robotics, in this paper, we introduce RoboPAIR, the first algorithm designed to jailbreak LLM-controlled robots. Unlike existing, textual attacks on LLM chatbots, RoboPAIR elicits harmful physical actions from LLM-controlled robots, a phenomenon we experimentally demonstrate in three scenarios: (i) a white-box setting, wherein the attacker has full access to the NVIDIA Dolphins self-driving LLM, (ii) a gray-box setting, wherein the attacker has partial access to a Clearpath Robotics Jackal UGV robot equipped with a GPT-4o planner, and (iii) a black-box setting, wherein the attacker has only query access to the GPT-3.5-integrated Unitree Robotics Go2 robot dog. In each scenario and across three new datasets of harmful robotic actions, we demonstrate that RoboPAIR, as well as several static baselines, finds jailbreaks quickly and effectively, often achieving 100% attack success rates. Our results reveal, for the first time, that the risks of jailbroken LLMs extend far beyond text generation, given the distinct possibility that jailbroken robots could cause physical damage in the real world. Indeed, our results on the Unitree Go2 represent the first successful jailbreak of a deployed commercial robotic system. Addressing this emerging vulnerability is critical for ensuring the safe deployment of LLMs in robotics. Additional media is available at: https://robopair.org