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Gabriel M. Silva

Publications and source records attributed to Gabriel M. Silva.

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Improved Upper Bounds for Dynamic Bin Packing of General, Unit-Fraction, and Power-Fraction Squares

This paper presents significant upper-bound improvements for dynamic 2D square bin packing, where square items arrive and depart over time and the objective is to minimize the peak number of concurrent active unit bins. In our model, repacking is permitted only within a destination bin upon item arrival; migration between active bins is strictly forbidden. By introducing a streamlined two-list algorithm and proving a tight $5/16$ occupied-area bound for Next-Fit Decreasing Height, we reduce the upper bound on the asymptotic competitive ratio for arbitrary squares from 4.2154 down to 3.918, breaking a longstanding theoretical ceiling. For restricted variants, we establish asymptotic competitive ratios of at most 3.356 for unit-fraction side lengths and 2.211 for power-fraction side lengths.

cs.DS

IDEIA: A Generative AI-Based System for Real-Time Editorial Ideation in Digital Journalism

This paper presents IDEIA (Intelligent Engine for Editorial Ideation and Assistance), a generative AI-powered system designed to optimize the journalistic ideation process by combining real-time trend analysis with automated content suggestion. Developed in collaboration with the Sistema Jornal do Commercio de Comunicação (SJCC), the largest media conglomerate in Brazil's North and Northeast regions, IDEIA integrates the Google Trends API for data-driven topic monitoring and the Google Gemini API for the generation of context-aware headlines and summaries. The system adopts a modular architecture based on Node.js, React, and PostgreSQL, supported by Docker containerization and a CI/CD pipeline using GitHub Actions and Vercel. Empirical results demonstrate a significant reduction in the time and cognitive effort required for editorial planning, with reported gains of up to 70\% in the content ideation stage. This work contributes to the field of computational journalism by showcasing how intelligent automation can enhance productivity while maintaining editorial quality. It also discusses the technical and ethical implications of incorporating generative models into newsroom workflows, highlighting scalability and future applicability across sectors beyond journalism.

cs.HC