Open Access
Article
Article ID: 3166
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by Bowen Li, Bohan Cheng, Patrick D. Taylor, Dale A. Osborne, Fengling Han, Robert Shen, Iqbal Gondal
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 18 December 2024; Accepted: 17 February 2025; Available online: 24 February 2025;
Issue release: 31 March 2025
Abstract Evaluating large numbers of hackathon submissions quickly, fairly, and at scale is a persistent challenge. Existing automated grading systems often struggle with bias, limited scalability, and a lack of transparency. In this paper, we present a novel hybrid evaluation framework that leverages large language models (LLMs) and a weighted scoring mechanism to address these issues. Our approach classifies hackathon submissions using LLMs, converts Jupyter notebooks to markdown for consistent analysis, and integrates multiple evaluation factors—from technical quality to video presentations—into a single, balanced score. Through dynamic prompt engineering and iterative refinement against manually benchmarked evaluations, we mitigate prompt design biases and ensure stable, fair outcomes. We validate our method in a multi-campus GenAI and Cybersecurity hackathon, demonstrating improved scalability, reduced evaluator workload, and transparent feedback. Our results highlight the potential of hybrid AI-driven frameworks to enhance fairness, adaptability, and efficiency in large-scale educational and competitive environments.
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Open Access
Brief Report
Article ID: 2911
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by H. Ali Pacha, W. Aggoune, S. Hamaci, P. Lorenz, A. Ali Pacha
Comput. Telecommun. Eng. 2025, 3(1);   
Received: 30 August 2024; Accepted: 15 October 2024; Available online: 27 February 2025;
Issue release: 31 March 2025
Abstract

For a long time, chaos was considered uncontrollable and unusable. However, over the past thirty years, researchers have formulated equations for certain chaotic phenomena, revealing a deterministic aspect to what initially seems random. The evolution of chaotic systems is characterized by strange attractors, which, despite their complex nature, do not allow precise long-term predictions of system behavior. The Lorenz attractor is the best-known example and was the first to be studied, though many other attractors with unusual shapes have since been discovered. The aim of this work is to perform a spectral analysis of the Lorenz attractor by examining the frequencies present in the time signals generated by the solutions of the Lorenz system of equations. To evaluate the frequency complexity of these signals, the discrete Fast Fourier Transform (FFT) is used to derive their frequency spectrum.

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