Jorge Castro-Godínez | Search arXiv

arXiv2026

COSI-Lab: Conference Living Lab for Modeling Multi-Perspective Multimodal Social Intention

COSI-Lab presents a multimodal multi-sensor dataset of a real interdisciplinary scientific workshop with 32 academics at an international conference. It captures ecologically valid social interactions in a weakly scripted setting consisting of two 30-minute mingling sessions with real professional and social consequences. We argue that intelligent systems could better handle subjective perceptions by modeling their multiplicity not as label noise but as an explainable perspective-driven reasoning process. We focus on the Apparent Intent Inference (AII) problem as determined by ex-situ observers and conceptualize intentions to be independent of the future. We contribute a novel annotation process for AII that accounts for a perceiver's own interpretative tendencies, quantitative and qualitative analyses of intent narratives with respect to diversity, plausibility and grounding, benchmark tasks for AII and relevant contextual factors, speech quality audio for all participants as well as privacy-preserving multi-modal environmental and wearable sensor data, enabling lexical and nonverbal behavior analysis; and the coupling of self-reported participant goals (30 minute to 3 hour) with annotated AII (seconds).

cs.HC↗

arXiv2025

A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks

The softmax function is a widely used activation function in the output layers of neural networks, responsible for converting raw scores into class probabilities while introducing essential non-linearity. Implementing Softmax efficiently poses challenges on low-end FPGAs due to limited hardware resources and the computational complexity of exponential and division operations. This work evaluates approximate computing techniques for softmax acceleration using Taylor series and interpolation methods using Look-Up Tables (LUTs). These approximations aim to reduce execution time and resource consumption while maintaining acceptable levels of numerical precision. Our findings show that quadratic interpolation with LUTs yields the lowest numerical error. In contrast, Taylor-based approximations offer significantly better performance in terms of execution time and resource efficiency due to their computational simplicity. When applied to real-world deep learning models such as LeNet-5 and MobileNet v2, the first- and second-order Taylor approximations provided substantial trade-offs between accuracy and resource savings, achieving up to 0.2% accuracy degradation and 14% resource reduction compared to exact implementations. These results highlight the effectiveness of approximate Softmax designs on resource-constrained FPGAs and lay the groundwork for their integration into larger models, including large language models (LLMs).

cs.AR↗