A critical challenge in Subjective Speech Quality Assessment (SSQA) is enabling models to generalize across diverse and unseen speech domains. General SSQA models evaluate many models in performing ...
Python has become the go-to language for data analysis due to its elegant syntax, rich ecosystem, and abundance of powerful libraries. Data scientists and analysts leverage Python to perform tasks ...
Knowledge bases like Wikidata, Yago, and DBpedia have served as fundamental resources for intelligent applications, but innovation in general-world knowledge base construction has been stagnant over ...
Artificial Intelligence (AI) continues to evolve rapidly, but with that evolution comes a host of technical challenges that need to be overcome for the technology to truly flourish. One of the most ...
In the world of massive-scale cloud infrastructure, even the slightest dip in performance can lead to significant inefficiencies. Imagine a change that causes an application to become 0.05% slower—a ...
In today’s world, Graph similarity computation (GSC) plays an important role in various applications such as code detection, molecular graph similarity, image matching, etc., by evaluating the ...
Sentiment analysis, i.e., determining the emotional tone of a text, has become a crucial tool for researchers, developers, and businesses to comprehend social media trends, consumer feedback, and ...
Time series forecasting has long been integral to finance, healthcare, meteorology, and supply chain management. Its main objective is to predict future data points based on historical observations, ...
Delays or errors in diagnosing pneumoperitoneum, with air outside the intestines within the peritoneal cavity, can severely impact patient survival and health outcomes. In adults, most cases result ...
Accessible mammography datasets and advanced machine-learning methods are key to enhancing computer-aided breast cancer diagnosis. However, limited access to private datasets, selective image sampling ...
Large language models (LLMs) have become foundational in natural language processing, especially in applications where understanding complex text data is critical. These models require vast amounts of ...
Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of large language models (LLMs) by incorporating external knowledge to provide more contextually relevant and accurate ...
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