Pattern discovery and event highlight from heterogenous sources

• Grant number: 20/02241-9
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: March 01, 2020
• End date: November 30, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Principal Investigator: Anderson de Rezende Rocha
• Grantee: José Dorivaldo Nascimento Souza Júnior
• Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events, AP.TEM
• Associated scholarship(s): 22/13061-7 - Pattern discovery and event highlight from heterogeneous sources, BE.EP.DD

Abstract

In this project, we are interested in identifying the most important topics present in a given dataset; finding sub-themes within the data (structures) that are of interest according to a given query; determining whether or not two pieces of information (visual and textual) refer to the same subject; determining involved actors (who is present in the mentioned information pieces); and, ultimately, finding data semantic clusters, in which we aspire to group the different information pieces according to their overall meaning. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

LAVI, BAHRAM; NASCIMENTO, JOSE; ROCHA, ANDERSON; IEEE. SEMI-SUPERVISED FEATURE EMBEDDING FOR DATA SANITIZATION IN REAL-WORLD EVENTS. 2021 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP 2021), v. N/A, p. 5-pg., 2021-01-01. (17/12646-3, 20/02241-9, 18/05668-3)

VEGA-OLIVEROS, DIDIER A.; NASCIMENTO, JOSE; LAVI, BAHRAM; ROCHA, ANDERSON. Real-world-events data sifting through ultra-small labeled datasets and graph fusion. APPLIED SOFT COMPUTING, v. 132, p. 17-pg., 2023-01-01. (17/12646-3, 20/02241-9, 19/26283-5, 18/05668-3)

NASCIMENTO, JOSE; CARDENUTO, JOAO PHILLIPE; YANG, JING; ROCHA, ANDERSON; IEEE. Few-shot Learning for Multi-modal Social Media Event Filtering. 2022 IEEE INTERNATIONAL WORKSHOP ON INFORMATION FORENSICS AND SECURITY (WIFS), v. N/A, p. 6-pg., 2022-01-01. (17/12646-3, 20/02241-9, 20/02211-2, 19/04053-8)