79 citations · 603 across the 47 of their papers we have counts for
19 papers · 1 filter
Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
Ruixin Song, Gabriel Spadon, Ronald Pelot +2
Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and f…
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
Gabriel Spadon, Jay Kumar, Derek Eden +6
This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS)…
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels
Martha Dais Ferreira, Gabriel Spadon, Amilcar Soares +1
Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a signi…
AdaBest: Minimizing Client Drift in Federated Learning via Adaptive Bias Estimation
Farshid Varno, Marzie Saghayi, Laya Rafiee Sevyeri +3
In Federated Learning (FL), a number of clients or devices collaborate to train a model without sharing their data. Models are optimized locally at each client and further communic…
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Gabriel Spadon, Martha D. Ferreira, Amilcar Soares +1
The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface…
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Gabriel Spadon, Shenda Hong, Bruno Brandoli +3
Time-series forecasting is one of the most active research topics in artificial intelligence. Applications in real-world time series should consider two factors for achieving relia…
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Xiang Jiang, Qicheng Lao, Stan Matwin +1
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution s…
Challenges in Vessel Behavior and Anomaly Detection: From Classical Machine Learning to Deep Learning
Lucas May Petry, Amilcar Soares, Vania Bogorny +2
The global expansion of maritime activities and the development of the Automatic Identification System (AIS) have driven the advances in maritime monitoring systems in the last dec…
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments
Mohammad Etemad, Nader Zare, Mahtab Sarvmaili +3
Unmanned Surface Vehicles technology (USVs) is an exciting topic that essentially deploys an algorithm to safely and efficiently performs a mission. Although reinforcement learning…
Wise Sliding Window Segmentation: A classification-aided approach for trajectory segmentation
Mohammad Etemad, Zahra Etemad, Amilcar Soares +3
Large amounts of mobility data are being generated from many different sources, and several data mining methods have been proposed for this data. One of the most critical steps for…
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
Key features of mental illnesses are reflected in speech. Our research focuses on designing a multimodal deep learning structure that automatically extracts salient features from r…
Unsupervised Behavior Change Detection in Multidimensional Data Streams for Maritime Traffic Monitoring
Lucas May Petry, Amilcar Soares, Vania Bogorny +1
The worldwide growth of maritime traffic and the development of the Automatic Identification System (AIS) has led to advances in monitoring systems for preventing vessel accidents…
Efficient Neural Task Adaptation by Maximum Entropy Initialization
Farshid Varno, Behrouz Haji Soleimani, Marzie Saghayi +2
Transferring knowledge from one neural network to another has been shown to be helpful for learning tasks with few training examples. Prevailing fine-tuning methods could potential…
2-D Embedding of Large and High-dimensional Data with Minimal Memory and Computational Time Requirements
Witold Dzwinel, Rafal Wcislo, Stan Matwin
In the advent of big data era, interactive visualization of large data sets consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+), is an indispensable tool…
Improving the Interpretability of Deep Neural Networks with Knowledge Distillation
Xuan Liu, Xiaoguang Wang, Stan Matwin
Deep Neural Networks have achieved huge success at a wide spectrum of applications from language modeling, computer vision to speech recognition. However, nowadays, good performanc…
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Xiang Jiang, Mohammad Havaei, Gabriel Chartrand +5
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce. We address this problem by…
One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation of Text Data
Ahmad Pesaranghader, Ali Pesaranghader, Stan Matwin +1
Due to recent technical and scientific advances, we have a wealth of information hidden in unstructured text data such as offline/online narratives, research articles, and clinical…
Interpretable Deep Convolutional Neural Networks via Meta-learning
Xuan Liu, Xiaoguang Wang, Stan Matwin
Model interpretability is a requirement in many applications in which crucial decisions are made by users relying on a model's outputs. The recent movement for "algorithmic fairnes…
Reflexive Regular Equivalence for Bipartite Data
Aaron Gerow, Mingyang Zhou, Stan Matwin +1
Bipartite data is common in data engineering and brings unique challenges, particularly when it comes to clustering tasks that impose on strong structural assumptions. This work pr…