CybAI news

In the rapidly evolving landscape of AI, organizations across all industries are eager to harness its transformational power. However, successful AI utilization and adoption require more than technological investment - it demands a holistic approach that balances cutting-edge technology with strategic skill development. While data quality and management are undoubtedly top-of-mind for a majority of leaders, the often-overlooked key to AI success lies in the human element: cultivating the right talent and organizational culture to effectively integrate and leverage AI capabilities at scale.Recent research from MIT Sloan Management Review shows that while 94% of C-suite executives plan to increase technology…

[Submitted on 30 Jun 2024 (v1), last revised 21 Nov 2024 (this version, v2)] View a PDF of the paper titled GC-Bench: An Open and Unified Benchmark for Graph Condensation, by Qingyun Sun and 8 other authors View PDF HTML (experimental) Abstract:Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retains the characteristics of the original graph. Despite the proliferation of graph condensation methods developed in recent years, there is no comprehensive evaluation…

arXiv:2411.14560v1 Announce Type: new Abstract: This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of location encoding, which aims to capture location characteristics to enhance GeoAI decision-making capabilities, we improve the GeoAI model by a knowledge driven approach to integrate both first-order and second-order effects of point patterns. This paper investigates how these spatial contexts impact the accuracy of terrain feature predictions. The results show that incorporating spatial point pattern statistics notably enhances model performance by leveraging different representations of spatial relationships. Source link lol

arXiv:2411.14473v1 Announce Type: new Abstract: The exponential growth of text-based data in domains such as healthcare, education, and social sciences has outpaced the capacity of traditional qualitative analysis methods, which are time-intensive and prone to subjectivity. Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools capable of automating and enhancing qualitative analysis. This study systematically maps the literature on the use of LLMs for qualitative research, exploring their application contexts, configurations, methodologies, and evaluation metrics. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes traditionally requiring extensive human input.…

Sponsored Content by Precisely Approximately 80% of data has a location attribute associated with it – and that location data provides a connection with the physical world. For Generali Real Estate*, the addition of alternative forms of data, such as spatial data, created greater context for its data and helped to power highly accurate AI-driven insights for data-driven decision-making. Let’s take a closer look at their journey. Generali Real Estate is one of the world’s leading real estate asset managers. Headquartered in Italy and with operations across Europe, the company has €36.9 billion assets under management (Q2 2024). When Generali…