Publications (54)
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…
Improving Dribbling, Passing, and Marking Actions in Soccer Simulation 2D Games Using Machine Learning
Nader Zare, Omid Amini, Aref Sayareh +4
The RoboCup competition was started in 1997, and is known as the oldest RoboCup league. The RoboCup 2D Soccer Simulation League is a stochastic, partially observable soccer environ…
SemEval-2020 Task 5: Counterfactual Recognition
Xiaoyu Yang, Stephen Obadinma, Huasha Zhao +3
We present a counterfactual recognition (CR) task, the shared Task 5 of SemEval-2020. Counterfactuals describe potential outcomes (consequents) produced by actions or circumstances…
Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction
Fateha Khanam Bappee, Lucas May Petry, Amilcar Soares +1
Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extens…
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…
Learn Faster and Forget Slower via Fast and Stable Task Adaptation
Farshid Varno, Lucas May Petry, Lisa Di Jorio +1
Training Deep Neural Networks (DNNs) is still highly time-consuming and compute-intensive. It has been shown that adapting a pretrained model may significantly accelerate this proc…
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)…
Give more data, awareness and control to individual citizens, and they will help COVID-19 containment
Mirco Nanni, Gennady Andrienko, Albert-László Barabási +36
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the phase 2 of the pandemic, when…
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…
Beyond Feature Fusion: Contextual Bayesian PEFT for Multimodal Uncertainty Estimation
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
We introduce CoCo-LoRA, a multimodal, uncertainty-aware parameter-efficient fine-tuning method for text prediction tasks accompanied by audio context. Existing PEFT approaches such…
On feature selection and evaluation of transportation mode prediction strategies
Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can s…
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…
Continuous Control with Deep Reinforcement Learning for Autonomous Vessels
Nader Zare, Bruno Brandoli, Mahtab Sarvmaili +2
Maritime autonomous transportation has played a crucial role in the globalization of the world economy. Deep Reinforcement Learning (DRL) has been applied to automatic path plannin…
Marine Mammal Species Classification using Convolutional Neural Networks and a Novel Acoustic Representation
Mark Thomas, Bruce Martin, Katie Kowarski +2
Research into automated systems for detecting and classifying marine mammals in acoustic recordings is expanding internationally due to the necessity to analyze large collections o…
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…
Topic Modelling and Event Identification from Twitter Textual Data
Marina Sokolova, Kanyi Huang, Stan Matwin +6
The tremendous growth of social media content on the Internet has inspired the development of the text analytics to understand and solve real-life problems. Leveraging statistical…
Predicting Crime Using Spatial Features
Fateha Khanam Bappee, Amilcar Soares Junior, Stan Matwin
Our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime. The reverse geocoding technique is applied to ret…
YOURPRIVACYPROTECTOR, A recommender system for privacy settings in social networks
Kambiz Ghazinour, Stan Matwin, Marina Sokolova
Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participan…
A Review of Global Sensitivity Analysis Methods and a comparative case study on Digit Classification
Zahra Sadeghi, Stan Matwin
Global sensitivity analysis (GSA) aims to detect influential input factors that lead a model to arrive at a certain decision and is a significant approach for mitigating the comput…
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…
Maritime Tracking Data Analysis and Integration with AISdb
Gabriel Spadon, Jay Kumar, Jinkun Chen +7
Efficiently handling Automatic Identification System (AIS) data is vital for enhancing maritime safety and navigation, yet is hindered by the system's high volume and error-prone d…
Joint-Centric Dual Contrastive Alignment with Structure-Preserving and Information-Balanced Regularization
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
We propose HILBERT (HIerarchical Long-sequence Balanced Embedding with Reciprocal contrastive Training), a cross-attentive multimodal framework for learning document-level audio-te…
Evolutionary Augmentation Policy Optimization for Self-supervised Learning
Noah Barrett, Zahra Sadeghi, Stan Matwin
Self-supervised Learning (SSL) is a machine learning algorithm for pretraining Deep Neural Networks (DNNs) without requiring manually labeled data. The central idea of this learnin…
COVID-19 Pandemic: Identifying Key Issues using Social Media and Natural Language Processing
Oladapo Oyebode, Chinenye Ndulue, Dinesh Mulchandani +6
The COVID-19 pandemic has affected people's lives in many ways. Social media data can reveal public perceptions and experience with respect to the pandemic, and also reveal factors…
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…
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…
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…
Pyrus Base: An Open Source Python Framework for the RoboCup 2D Soccer Simulation
Nader Zare, Aref Sayareh, Omid Amini +4
Soccer, also known as football in some parts of the world, involves two teams of eleven players whose objective is to score more goals than the opposing team. To simulate this game…
Black Box Explanation by Learning Image Exemplars in the Latent Feature Space
Riccardo Guidotti, Anna Monreale, Stan Matwin +1
We present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent…
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…
Survey of Generative Methods for Social Media Analysis
Stan Matwin, Aristides Milios, PaweÅ PraÅat +2
This survey draws a broad-stroke, panoramic picture of the State of the Art (SoTA) of the research in generative methods for the analysis of social media data. It fills a void, as…
Artificial Intelligence for Emotion-Semantic Trending and People Emotion Detection During COVID-19 Social Isolation
Hamed Jelodar, Rita Orji, Stan Matwin +3
Taking advantage of social media platforms, such as Twitter, this paper provides an effective framework for emotion detection among those who are quarantined. Early detection of em…
CYRUS Soccer Simulation 2D Team Description Paper 2022
Nader Zare, Arad Firouzkouhi, Omid Amini +5
Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each o…
Sanitization of Call Detail Records via Differentially-private Summaries
Mohammad Alaggan, Sébastien Gambs, Stan Matwin +2
In this work, we initiate the study of human mobility from sanitized call detail records (CDRs). Such data can be extremely valuable to solve important societal issues such as the…
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…
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…
Cyrus2D base: Source Code Base for RoboCup 2D Soccer Simulation League
Nader Zare, Omid Amini, Aref Sayareh +5
Soccer Simulation 2D League is one of the major leagues of RoboCup competitions. In a Soccer Simulation 2D (SS2D) game, two teams of 11 players and one coach compete against each o…
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…
Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection
Duong Nguyen, Oliver S. Kirsebom, Fábio Frazão +2
In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty d…
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…
Cross-Modal Bayesian Low-Rank Adaptation for Uncertainty-Aware Multimodal Learning
Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin
Large pre-trained language models are increasingly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT), but existing PEFT methods are typically deterministic a…
Performance of a Deep Neural Network at Detecting North Atlantic Right Whale Upcalls
Oliver S. Kirsebom, Fabio Frazao, Yvan Simard +3
Passive acoustics provides a powerful tool for monitoring the endangered North Atlantic right whale ( ), but robust detection algorithms are needed to handle…
How is Your Mood When Writing Sexist tweets? Detecting the Emotion Type and Intensity of Emotion Using Natural Language Processing Techniques
Sima Sharifirad, Borna Jafarpour, Stan Matwin
Online social platforms have been the battlefield of users with different emotions and attitudes toward each other in recent years. While sexism has been considered as a category o…
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023
Aref Sayareh, Nader Zare, Omid Amini +3
The RoboCup competitions hold various leagues, and the Soccer Simulation 2D League is a major one among them. Soccer Simulation 2D (SS2D) match involves two teams, including 11 pla…
Studying Positive Speech on Twitter
Marina Sokolova, Vera Sazonova, Kanyi Huang +2
We present results of empirical studies on positive speech on Twitter. By positive speech we understand speech that works for the betterment of a given situation, in this case rela…
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…
CYRUS Soccer Simulation 2D Team Description Paper 2021
Nader Zare, Aref Sayareh, Mahtab Sarvmaili +3
In this report, we briefly present the technical procedure and simulation steps for the 2D soccer simulation of team Cyrus. We emphasize on this document on how the prediction of t…
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…
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…
When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP
Sima Sharifirad, Stan Matwin
Sexism is very common in social media and makes the boundaries of freedom tighter for feminist and female users. There is still no comprehensive classification of sexism attracting…
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Xiang Jiang, Erico N de Souza, Ahmad Pesaranghader +3
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing. We propose Trajecto…
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…
Predicting Transportation Modes of GPS Trajectories using Feature Engineering and Noise Removal
Mohammad Etemad, Amilcar Soares Junior, Stan Matwin
Understanding transportation mode from GPS (Global Positioning System) traces is an essential topic in the data mobility domain. In this paper, a framework is proposed to predict t…
Causal Generative Explainers using Counterfactual Inference: A Case Study on the Morpho-MNIST Dataset
Will Taylor-Melanson, Zahra Sadeghi, Stan Matwin
In this paper, we propose leveraging causal generative learning as an interpretable tool for explaining image classifiers. Specifically, we present a generative counterfactual infe…