Faculty of Science and Technology

Gonsalves Tad

  (ゴンサルベス タッド)

Profile Information

Affiliation
Professor, Faculty of Science and Technology, Department of Information and Communication Sciences, Sophia University
Degree
博士(工学)(上智大学)

Researcher number
90407338
J-GLOBAL ID
201301073146868965
researchmap Member ID
7000004362

External link

Papers

 141
  • Tandin Wangchuk, Tad Gonsalves
    IEEE Access, 13 103405-103416, 2025  Peer-reviewedCorresponding author
  • Tad Gonsalves, Hu Hang, Yoshimi Hiroyasu
    Lengua y Sociedad, 23(2) 1047-1068, Dec 30, 2024  Peer-reviewedCorresponding author
    La rápida globalización y la creciente necesidad de comunicación interlingüística requieren corpus modernos y en tiempo real para ayudar a los estudiantes de idiomas. Los métodos tradicionales para crear dichos corpus, especialmente en español, son inadecuados debido a su incapacidad para procesar la gran cantidad de datos no estructurados disponibles en internet. En este estudio se exploran las metodologías de inteligencia artificial (IA) para la adquisición automática de documentos en español de la web, preprocesándolos y clasificándolos con el fin de construir un corpus vasto y flexible para el aprendizaje del español. La investigación aplica el rastreo web mediante el framework Scrapy para recopilar datos, que luego se limpian y clasifican utilizando modelos avanzados de procesamiento del lenguaje natural (PLN). En concreto, el estudio emplea el algoritmo BERT (Bidirectional Encoder Representations from Transformers) y su variante mejorada RoBERTa para lograr la clasificación de documentos. Mediante una combinación de técnicas de aumento de datos y modelos de aprendizaje profundo, el estudio logra una alta precisión en la clasificación de texto en español, lo que demuestra el potencial del uso de la IA para superar las limitaciones de los enfoques tradicionales de creación de corpus.
  • Edward Motoaki Wenngren, Tad Gonsalves
    2024 6th International Workshop on Artificial Intelligence and Education (WAIE), 379-383, Sep 28, 2024  Peer-reviewedCorresponding author
  • Qixiang Luan, Tad Gonsalves
    2024 6th International Workshop on Artificial Intelligence and Education (WAIE), 353-357, Sep 28, 2024  Peer-reviewedCorresponding author
  • Thanapat Prapussornchaikul, Tad Gonsalves
    2024 6th International Workshop on Artificial Intelligence and Education (WAIE), 71-75, Sep 28, 2024  Peer-reviewedCorresponding author

Misc.

 2
  • GONSALVES Tad, ITOH Kiyoshi, KAWABATA Ryo
    Technical report of IEICE. KBSE, 103(604) 1-6, Dec, 2004  
    In this paper we propose a composite-server model and make use of the knowledge of the intrinsic composition of its service providing units (personnel or equipment) to derive Qualitative knowledge-based rules for its performance evaluation. The composite server model that takes into account the composite nature of service has wider scope in its applications and can be used to represent a variety of system classes. We use this novel concept in the performance design and improvement of collaborative engineering systems. System modeling is done by Multi-Context Map (MCM) technique. MCM is a descriptive model that expresses the collaborative activity performed through the exchange of token, material and information; bottlenecks primarily arise due to the non-uniformity in the flow of token, material and information. Another source of bottlenecks in collaborative engineering systems is the lack or surplus of service-providing units, known as "Perspectives" in the MCM terminology. Bottlenecks due to inappropriate Perspective allocation are resolved by the Qualitative Reasoning approach. We have found this method successful in the performance design, evaluation and improvement of a practical collaborative engineering system presented at the end of this paper.
  • GONSALVES Tad, ITOH Kiyoshi, KAWABATA Ryo
    IEICE technical report. Artificial intelligence and knowledge-based processing, 103(306) 15-20, Sep 9, 2003  
    This paper discusses the design and implementation of a novel system performance improvement Expert System (ES) with a Qualitative inference engine. The motive for using Qualitative Reasoning is to overcome the computational complexity posed by the triple-input-triple-output contexts interactions in the Multi-Context Map (MCM) queuing network which models the system. The ES analyses the GPSS simulation data of system performance, consults the MCM knowledge base of the system, and with its inference engine driven by qualitative rules draws the parameter-tuning plan to resolve bottlenecks. The ES has been successfully applied in improving a typical benchmarking system in Collaboration Engineering.

Books and Other Publications

 2

Presentations

 63