The Need for Concerted Efforts for COVID-19 Intelligence Gathering: Medical Espionage and Cyber Crime Trends Analysis to Strengthen the UK's Pandemic Response

An important knowledge gap that needs bridging is the extent to which current natural language processing (NLP) tools, models and resources can be successfully applied to exploring a specific aspect of dark-web data such as COVID-19-related communication. It is imperative to support law enforcement in gathering intelligence on the new COVID-19-related cybercrime strategies in the DW. The research gap around the DW search and textual analysis consists in the lack of robust mechanisms for querying, processing and monitoring trends on the DW. The critical data sets and computational solutions need to be made available for immediate exploitation and further research to strengthen the UK’s pandemic response

From ChatGPT-3 to GPT-4: A Significant Advancement in AI-Driven NLP Tools

Recent improvements in Natural Language Processing (NLP) have led to the creation of powerful language models like Chat Generative Pre-training Transformer (ChatGPT), Google’s BARD, Ernie which has shown to be very good at many different language tasks. But as language tasks get more complicated, having even more advanced NLP tool is essential nowadays. In this study, researchers look at how the latest versions of the GPT language model(GPT-4 & 5) can help with these advancements. The research method for this paper is based on a narrative analysis of the literature, which makes use of secondary data gathered from previously published studies including articles, websites, blogs, and visual and numerical facts etc. Findings of this study revealed that GPT-4 improves the model's training data, the speed with which it can be computed, the flawless answers that it provides with, and its overall performance. This study also shows that GPT-4 does much better than GPT-3.5 at translating languages, answering questions, and figuring out how people feel about things. The study provides a solid basis for building even more advanced NLP tools and programmes like GPT-5. The study will help the AI & LLM researchers, NLP developers and academicians in exploring more into this particular field of study. As this is the first kind of research comparing two NLP tools, therefore researchers suggested going for a quantitative research in the near future to validate the findings of this research

Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning

International audienceAutonomous reinforcement learning agents, like children, do not have access to predefined goals and reward functions. They must discover potential goals, learn their own reward functions and engage in their own learning trajectory. Children, however, benefit from exposure to language, helping to organize and mediate their thought. We propose LE2 (Language Enhanced Exploration), a learning algorithm leveraging intrinsic motivations and natural language (NL) interactions with a descriptive social partner (SP). Using NL descriptions from the SP, it can learn an NL-conditioned reward function to formulate goals for intrinsically motivated goal exploration and learn a goal-conditioned policy. By exploring, collecting descriptions from the SP and jointly learning the reward function and the policy, the agent grounds NL descriptions into real behavioral goals. From simple goals discovered early to more complex goals discovered by experimenting on simpler ones, our agent autonomously builds its own behavioral repertoire. This naturally occurring curriculum is supplemented by an active learning curriculum resulting from the agent's intrinsic motivations. Experiments are presented with a simulated robotic arm that interacts with several objects including tools

Ontology semantic approach to extraction of knowledge from Holy Quran

With the continued demand for Islamic knowledge, which is mainly based on the Quran as a source of knowledge and wisdom, systems that facilitate an easy search of the content of the Quran remain a considerable challenge. Although in recent years there have been tools for Quran search, most of these tools are based on keyword search, meaning that the user needs to know the correct keywords before being able to retrieve the content of al-Quran. In this paper, we propose a system that supports the end user in querying and exploring the Quran ontology. The system comprises user query reformulation against the Quran ontology stored and annotated in the knowledge base. The Quran ontology consists of noun concepts identified in al-Quran, and the relationship that exists between these concepts. The user writes a query in the natural language and the proposed system reformulates the query to match the content found in the knowledge base in order to retrieve the relevant answer. The answer is represented by the Quranic verse related to the user query

Evaluating records for free text content

The PREP team is interested in electronic health records. This project aims to make data in records more usable for researchers. We work on electronic health records with a particular focus on free text (unstructured data) that is not directly amenable to statistical analysis. Our aim is to develop strategies for making available, for research and audit purpose, medical information that is “concealed” from researchers in the free text notes, using primary care electronic patient record (EPR) as an example

e-Social Science and Evidence-Based Policy Assessment : Challenges and Solutions

Peer reviewedPreprin