The Second Latin American Workshop on Information Fusion (LAFUSION 2024) is a workshop focusing on the latest research results on the Information Fusion in Latin America. The goal of this workshop is to create a community of Information Fusion researchers in Latin America. Accepted papers will be published in IEEE. Authors of accepted papers are expected to present their work in a plenary session as part of the main workshop program. Proceedings will be indexed in the IEEE Digital Library, SCOPUS, and other prominent digital libraries.
Information fusion is a multidisciplinary field that focuses on combining and integrating information from diverse sources to improve the accuracy, completeness, and reliability of the resulting information. It involves the process of merging data or knowledge from multiple sensors, databases, or information systems to generate a unified and coherent representation of the underlying reality.
The main goal of information fusion is to extract meaningful and actionable insights by leveraging the strengths of individual information sources while compensating for their limitations, uncertainties, or redundancies. It aims to provide a more comprehensive and accurate understanding of a given situation or phenomenon than what can be achieved by using individual sources in isolation.
Information fusion techniques typically involve various processes, including data preprocessing, feature extraction, data association, probabilistic modeling, decision-making, and knowledge representation. These processes may utilize methods from diverse disciplines such as statistics, signal processing, pattern recognition, artificial intelligence, machine learning, and cognitive science.
Applications of information fusion are widespread and can be found in fields such as surveillance and intelligence, remote sensing, robotics, autonomous systems, medical diagnosis, weather forecasting, transportation systems, and cybersecurity. By integrating and interpreting information from multiple sources, information fusion enables improved situational awareness, decision-making, and prediction capabilities, leading to enhanced performance, efficiency, and reliability in complex and uncertain environments. Several Latin American problems could be solved by Information Fusion. We are looking to form a Forum to debate the usage of Information Fusion to produce solutions for the challenges in the region.
LAFUSION will have two types of submissions: (I) tutorial and (II) workshop papers.
Tutorial proposals may cover the areas listed in the general Call for Papers (theory and representation, algorithms, modeling simulation and evaluation and applications), as well as other areas where information fusion can or should be applicable. Examples include but are not limited to fusion of human-produced and unstructured information, big data analytics, information fusion in cloud computing, information fusion applied to cybersecurity, as well as artificial intelligence and machine learning solutions to those.
Proposals should be submitted as PDF files to lafusion2025@labnet.nce.ufrj.br. A proposal needs to contain:
• The title of the proposed tutorial;
• The intended audience and prerequisites for the attendees’ background knowledge;
• A description of the tutorial including the learning objectives and a short summary of the material to be presented;
• Biographical sketch(es) of the instructor(s) including previous lecture and tutorial experience.
For further information, please contact us via lafusion2025@labnet.nce.ufrj.br.
The second type of submission is the workshop papers (2-4 Pages). Authors of accepted positions and regular papers are expected to present their work in a plenary session as part of the main workshop program (hybrid). Accepted regular papers will be published in IEEE. Proceedings will be indexed in the IEEE Digital Library, SCOPUS, and other prominent digital libraries.
Workshop papers must be original (i.e., not previously published) and not currently under review by any other conference or journal. Submissions related to the featured topic are especially welcome, but all other submissions in the scope of Information Fusion are equally welcome. All submissions will be evaluated based on the same criteria. All submitted papers must conform to the IEEE conference template. LAFUSION 2024 will adopt a double-blind review process for regular papers. Authors must make a good faith effort to anonymize their submissions to ensure that their identities are not disclosed to reviewers, and reviewers are discouraged from actively working to uncover author identities. Submitting to arXiv (or similar) is allowed to promote early dissemination, provided cross-citations are not made.
You can submit your paper through: https://openreview.net/group?id=ufrj.br/UFRJ/Lafusion/2024/Workshop
Probability theory, Bayesian inference, argumentation, Dempster-Shafer theory, possibility and fuzzy set theory, rough sets, logic fusion, preference aggregation, decision theory, random sets, finite point processes and others.
Probability theory, Bayesian inference, argumentation, Dempster-Shafer theory, possibility and fuzzy set theory, rough sets, logic fusion, preference aggregation, decision theory, random sets, finite point processes and others.
Cognitive methods, signal processing and localisation, recognition, classification, identification, nonlinear filtering, data association, tracking, prediction, situation/impact assessment, alignment and registration, pattern/behavioural analysis, image fusion, fusion architectures, resource management, machine learning and artificial intelligence, topic modelling, natural language processing, contextual adaptation, anomaly/change detection.
Soft-hard fusion, autonomous systems, defence/security, robotics, intelligent transportation, mining/manufacturing, wireless sensor networks, economics, finance, fintech, environmental monitoring medical care/e-health, bioinformatics, radio astronomy, critical infrastructure protection, condition monitoring precision agriculture, video streaming, streaming and sketching and other emerging applications.
Sequential inference, data mining. graph analysis, ontologies/semantics, modelling/realisation/evaluation, target/sensor modelling, benchmarks/testbeds, trust in fusion systems, computational methods, cloud/edge computing/fusion, fusion performance.
George Mason University, USA
Federal University of Rio de Janeiro, Brazil
Eldorado Institute, Brazil
Federal University of Goiás, Brazil
Brazilian Navy Research Institute, Brazil
Universidade de Lisboa, Portugal
EMBRAER S.A, Brazil
CS Group, France
Polytechnic Institute of Bragança, Portugal
Federal University of Rio de Janeiro, Brazil
Rice University, United States
Independent consultant, USA
ISISTAN Research Institute (UNCPBA/CONICET), Argentina
Federal University of Rio de Janeiro, Brazil
Universidad Nacional de San Agustin de Arequipa, Peru
CEFET-RJ, Brazil
Petrobrás, Brazil
Federal University of Rio de Janeiro, Brazil
Fluminese Federal University, Brazil
Universidad de Alcalá, Spain
Federal University of Rio de Janeiro, Brazil
ISEN, France
EMBRAER S.A, Brazil
Tecnologico de Monterrey, México
Federal University of Rio de Janeiro, Brazil
George Mason University, USA
Army Research Laboratory, USA
University of Udine, Italy
Fluminense Federal University, Brazil
São Paulo State University, Brazil
Federal University of Rio de Janeiro, Brazil
São Paulo State University, Brazil
Brazilian Navy, Brazil
Federal University of Rio de Janeiro, Brazil
Federal University of Rio de Janeiro, Brazil
Federal University of Rio de Janeiro, Brazil
