AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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TripleBound is proposed, a hybrid framework for automated monolith-to-microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser-inferred service groups based on package structure, naming conventions, and code location.
This research extends an existing multilingual LLM (Llama-3-8B) to get a better coverage for Sinhala and enhances the LLM tokenizer with Sinhala specific vocabulary and performs continual pre-training on a 10 million sentence Sinhala corpus, resulting in the SinLlama model.
The Colombo Tea Auction (CTA) plays a vital role in determining global tea prices, yet the relationship between local weather conditions and price behavior across different tea catalogues has not been thoroughly explored. In this study, we develop a novel, structured dataset by extracting information from 105 weekly broker reports spanning late 2023 to 2026, and combined with region-specific weather data. Our analysis focuses on four main tea catalogues of Sri Lankan tea: High Grown, Low Grown, Off-Grade, and Dust. To better understand the factors influencing tea prices, we apply Granger causality analysis alongside tree-based machine learning models: Random Forest, XGBoost, LightGBM, and Gradient Boosting. Our results show that while market dynamics are primary drivers, weather conditions also have significant effects. Notably, Low Grown tea shows strong sensitivity to precipitation and sunshine duration (p<0.05) across 1-3-week lags. Off-Grade and Dust catalogues also exhibit significant responses to temperature variations. Catalogue-specific modelling outperformed unified approaches, with LightGBM emerging as the superior model for three out of four catalogues. Overall, this study highlights the importance of considering both localized weather patterns and catalogue-level differences when forecasting tea prices, offering a more precise and practical framework for the tea industry.
A survey and comparative analysis of NLP-based Automatic Deception Detection focusing on the legal domain and the evolution from feature-based machine learning to Large Language Model (LLM) approaches are presented, showing strong domain sensitivity.
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