arXiv subjects

Linh B. Ngo

Publications and source records attributed to Linh B. Ngo.

3 recordsLinked to original sources

arXiv2025

A Detailed Comparative Analysis of Blockchain Consensus Mechanisms

This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-world blockchain data, the study highlights that PoW offers robust, time-tested security but suffers from high energy consumption, slower throughput, and centralization through mining pools. In contrast, PoS demonstrates improved scalability and efficiency, significantly reduced environmental impact, and more stable transaction fees, however it raises concerns over validator centralization and long-term security maturity. The findings underscore the trade-offs inherent in each mechanism and suggest hybrid designs may combine PoW's security with PoS's efficiency and sustainability. The study aims to inform future blockchain infrastructure development by striking a balance between decentralization, performance, and ecological responsibility.

cs.CR↗

arXiv2025

Evaluating the Limitations of Local LLMs in Solving Complex Programming Challenges

This study examines the performance of today's open-source, locally hosted large-language models (LLMs) in handling complex competitive programming tasks with extended problem descriptions and contexts. Building on the original Framework for AI-driven Code Generation Evaluation (FACE), the authors retrofit the pipeline to work entirely offline through the Ollama runtime, collapsing FACE's sprawling per-problem directory tree into a handful of consolidated JSON files, and adding robust checkpointing so multi-day runs can resume after failures. The enhanced framework generates, submits, and records solutions for the full Kattis corpus of 3,589 problems across eight code-oriented models ranging from 6.7-9 billion parameters. The submission results show that the overall pass@1 accuracy is modest for the local models, with the best models performing at approximately half the acceptance rate of the proprietary models, Gemini 1.5 and ChatGPT-4. These findings expose a persistent gap between private, cost-controlled LLM deployments and state-of-the-art proprietary services, yet also highlight the rapid progress of open models and the practical benefits of an evaluation workflow that organizations can replicate on in-house hardware.

cs.SE↗

arXiv2019

Multi-class Twitter Data Categorization and Geocoding with a Novel Computing Framework

This study details the progress in transportation data analysis with a novel computing framework in keeping with the continuous evolution of the computing technology. The computing framework combines the Labelled Latent Dirichlet Allocation (L-LDA)-incorporated Support Vector Machine (SVM) classifier with the supporting computing strategy on publicly available Twitter data in determining transportation-related events to provide reliable information to travelers. The analytical approach includes analyzing tweets using text classification and geocoding locations based on string similarity. A case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach. Approximately 700,010 tweets are analyzed to extract relevant transportation-related information for one week. The SVM classifier achieves more than 85% accuracy in identifying transportation-related tweets from structured data. To further categorize the transportation-related tweets into sub-classes: incident, congestion, construction, special events, and other events, three supervised classifiers are used: L-LDA, SVM, and L-LDA incorporated SVM. Findings from this study demonstrate that the analytical framework, which uses the L-LDA incorporated SVM, can classify roadway transportation-related data from Twitter with over 98.3% accuracy, which is significantly higher than the accuracies achieved by standalone L-LDA and SVM.

cs.SI↗