Journal of the Brazilian Computer Society

Learning on hierarchical trees with Random Forest

2025-08-26T13:23:42+00:00

Hierarchies, as described in mathematical morphology, represent nested regions of interest and provide mechanisms to create coherent data organization. They facilitate high-level analysis and management of large amounts of data. Represented as hierarchical trees, they have formalisms intersecting with graph theory and generalizable applications. Due to the deterministic algorithms, the multiform representations, and the absence of a direct quality evaluation, it is hard to insert hierarchical information into a learning framework and benefit from the recent advances. Researchers usually tackle this problem by refining the hierarchies for a specific media and assessing their quality for a particular task. The downside of this approach is that it depends on the application, and the formulations limit the generalization to similar data. This work aims to create a learning framework that can operate with hierarchical data and is agnostic to the input and application. The idea is to transform the data into a regular representation required by most learning models while preserving the rich information in the hierarchical structure. The proposed methods use edge-weighted image graphs and hierarchical trees as input, and they evaluate different proposals on the edge detection and segmentation tasks. The learning model is the Random Forest, a fast and scalable method for working with high-dimensional data. Results demonstrate that it is possible to create a learning framework dependent only on the hierarchical data that presents a state-of-the-art performance in multiple tasks.

A Coding-Efficiency Analysis of HEVC Encoder Embedded in High-End Mobile Chipsets

2025-06-06T13:35:56+00:00

High-end mobile devices require dedicated hardware for real-time video encoding and decoding processes. However, the inherent complexity of the video encoding process, combined with the physical limitations imposed by hardware design such as energy consumption, encoding time, memory usage, and heat dissipation, demands the implementation of various constraints and limitations in commercial hardware to simplify and make them feasible for general use. The High Efficiency Video Coding (HEVC) standard is the main targeted video encoder for processing high-resolution videos in high-end chipsets. This paper aims to analyze the HEVC encoder implemented into three commercial chipsets found in high-end smartphones (Apple iPhone 14 Pro, Samsung Galaxy S23 Plus, and Redmi Note 10S) from three major mobile chip manufacturers (Apple, Qualcomm, and MediaTek), considering the impacts of video encoder limitations on encoding efficiency (BD-Rate) and encoding time. The results in this paper may be used as a comparative foundation for hardware designers and future works in the field, as it exposes the encoding efficiency drawbacks and the encoding time gains that commercial chipsets exhibit in their HEVC encoder.

Comparing Explainable AI Techniques In Language Models: A Case Study For Fake News Detection in Portuguese

2025-07-25T20:33:45+00:00

Language models are widely used in natural language processing, but their complexity makes interpretation difficult, limiting their adoption in critical decision-making. This work explores Explainable Artificial Intelligence (XAI) techniques, such as LIME and Integrated Gradients (IG), to understand these models. The study evaluates the effectiveness of BERTimbau in classifying Portuguese news as true or fake, using the FakeRecogna and Fake.Br Corpus datasets. In the experiments, LIME proved to be easier to interpret than IG, and both methods showed limitations when applied to texts, as they focus only on the morphological and lexical levels, ignoring other important levels.

Statistical Invariance vs. AI Safety: Why Prompt Filtering Fails Against Contextual Attacks

2025-07-28T12:52:52+00:00

Large Language Models (LLMs) are increasingly deployed in high-stakes applications, yet their alignment with ethical standards remains fragile and poorly understood. To investigate the probabilistic and dynamic nature of this alignment, we conducted a black-box evaluation of nine widely used LLM platforms, anonymized to emphasize the underlying mechanisms of ethical alignment rather than model benchmarking. We introduce the Semantic Hijacking Method (SHM) as an experimental framework, formally defined and grounded in probabilistic modeling, designed to reveal how ethical alignment can erode gradually, even when all user inputs remain policy-compliant. Across three experimental rounds (324 total executions), SHM achieved a 97.8% success rate in eliciting harmful content, with failure rates progressing from 93.5% (multi-turn conversations) to 100% (both refined sequences and single-turn interactions), demonstrating that vulnerabilities are inherent to semantic processing rather than conversational memory. A qualitative cross-linguistic analysis revealed cultural variations in harmful narratives, with Brazilian Portuguese responses frequently echoing historical and socio-cultural biases, making them more persuasive to local users. Overall, our findings demonstrate that ethical alignment is not a static barrier but a dynamic and fragile property that challenges binary safety metrics. Due to potential risks of misuse, all prompts and outputs are made available exclusively to authorized reviewers under ethical approval, and this publication focuses solely on reporting the research findings.