Abstract: E-commerce platforms face significant challenges in detecting anomalous products, including counterfeit goods and fraudulent listings, which can undermine user trust and platform integrity.
Abstract: Knowledge graph embedding is efficient method for reasoning over known facts and inferring missing links. Existing methods are mainly triplet-based or graph-based. Triplet-based approaches ...
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How Word Embeddings Work in Python RNNs?
Word Embedding (Python) is a technique to convert words into a vector representation. Computers cannot directly understand words/text as they only deal with numbers. So we need to convert words into ...
Google Gemini for Workspace can be exploited to generate email summaries that appear legitimate but include malicious instructions or warnings that direct users to phishing sites without using ...
A new technical paper titled “TROJAN-GUARD: Hardware Trojans Detection Using GNN in RTL Designs” was published by researchers at University of Connecticut and University of Minnesota. “hip ...
ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...
Building a chatbot can feel like an overwhelming task, especially when you’re juggling multiple tools and trying to ensure everything works seamlessly. If you’ve ever found yourself stuck between ...
We are working on a new library, Keras RS (Keras Recommender Systems). Part of this new library will be the Keras 3 multi-backend successor of TFRS' TPUEmbedding. This layer groups multiple embeddings ...
models: Deep learning models developed for surrogating the hydraulic one: contains a base class with common inputs and functions and one for the SWE-GNN and mSWE-GNN models. results: Contains results ...
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