30 accepted papers
Multi-robot cooperative control has been extensively studied using model-based distributed control methods. However, such control methods rely on sensing and perception modules in a sequential pipeline of design, and the separation of perception and controls may cause processing latency and compound…
Robust and interpretable dementia diagnosis from noisy, non-stationary electroencephalography (EEG) is clinically essential yet remains challenging. To this end, we propose SeeGraph, a Sparse-Explanatory dynamic EEG-graph network that models time-evolving functional connectivity and employs a node-g…
Graph Contrastive Learning (GCL) has proven effective in mitigating data sparsity and enhancing representation learning for recommendation. Yet, most GCL frameworks indiscriminately treat all non-anchor nodes as negatives during contrastive sampling, often leading to the false negative problem where
Achieving visual coordination between egocentric (ego) and exocentric (exo) perspectives is a cornerstone of augmented reality and human-machine collaboration. However, extreme viewpoint shifts and occlusions often cause semantic drift in existing methods, making robust cross-view correspondence a f
In recent years, Contrastive Learning (CL) has garnered significant attention due to its efficacy across various domains, spanning from visual and textual modalities. A fundamental aspect of CL is aligning the representations of anchor instances with relevant positive samples while simultaneously se…
Event argument extraction is one of the subtasks in event extraction, and the current mainstream approaches define it as a span extraction task utilizing the concept of prompt learning. However, upon reproducing the current mainstream work, we identified two issues with event argument span extractio…
The development of EEG decoding algorithms confronts challenges such as data sparsity, subject variability, and the need for precise annotations, all of which are vital for advancing brain-computer interfaces and enhancing the diagnosis of diseases. To address these issues, we propose a novel two-st…
In this paper, we introduce the IMPortance-awaReness maskIng NeTwork (IMPRINT), a novel approach to enhance the robustness of document retrieval systems against query variations, particularly those containing misspellings. Unlike previous models that treat all query components (words/features) equal…
Indoor positioning in environments where GPS cannot be used is a fundamental technology for robot navigation and human-robot interaction. However, existing vision-based localization systems cannot work in low-visibility environments, and existing wireless or acoustic localization systems require spe
This paper introduces the lacrosse mobile manipulator, a robotic system designed to play lacrosse. We focus on the task of ball passing between two robots, where challenges exist, including managing a small ball in the soft lacrosse head and interacting with a fast-moving ball. In this study, we dev
In single Domain Generalization (single-DG), data scarcity in the single source domain hampers the learning for invariant features, leading to overfitting over source domain and poor generalization to unseen target domains. Existing single-DG methods primarily augment the source domain by adversaria
Collecting gold-standard phenotype data via manual extraction is typically labor-intensive and slow, whereas automated computational phenotypes (ACPs) offer a systematic and much faster alternative. However, simply replacing the gold-standard with ACPs, without acknowledging their differences, could…
Graph Neural Networks (GNNs) have emerged as fundamental tools for a wide range of prediction tasks on graph-structured data. Recent studies have drawn analogies between GNN feature propagation and diffusion processes, which can be interpreted as dynamical systems. In this paper, we delve deeper int…
Identifying mild cognitive impairment (MCI) is vital for Alzheimer’s disease prevention. As neurodegenerative diseases progress, synchronous activity in electroencephalography (EEG) - indicating functional connectivity - changes due to neural system deterioration. Thus, developing geometric learning…
Emotion recognition based on electroencephalography (EEG) signals is one of the current research challenges in this field. In order to learn the optimal graph structure information for each subject, we propose a dynamic graph attention neural network model. The model utilizes a graph attention neura…
Data Augmentation (DA) has become a widely adopted strategy for addressing data scarcity in numerous NLP tasks, especially in scenarios with limited resources or imbalanced classes. However, many existing augmentation techniques rely on randomness or additional resources, presenting challenges in bo…
The diagnosis of cognitive impairment (CI), here referred to as mild cognitive impairment (MCI) and probable Alzheimer’s disease (AD), is complicated in practice. Early AD diagnosis using electroencephalography (EEG) has attracted attention due to EEG’s advantages in data accessibility. Because of l…
Click-through rate (CTR) prediction plays important role in the advertisement, recommendation, and retrieval applications. Given the feature set, how to fully utilize the information from the feature set is an active topic in deep CTR model designs. There are several existing deep CTR works focusing…
Dynamic Movement Primitives (DMP) are widely applied in movement representation due to their ability to encode tasks using generalization properties. However, the coupled multiple DMP generalization cannot be directly solved based on the original DMP formula. Prior works provide satisfactory perform
We propose a novel method of registering less-overlap RGB-D scans. Our method learns global information of a scene to construct a panorama, and aligns RGB-D scans to the panorama to perform registration. Different from existing methods that use local feature points to register less-overlap RGB-D sca…
Wearable biofeedback systems (WBS) have been proposed to aid physical rehabilitation of individuals with motor impairments. Due to significant inter- and intra-individual differences, the effectiveness of a given biofeedback strategy may vary for different users and across therapeutic sessions, as a
Given a context knowledge base (KB) and a corresponding question, the Knowledge Base Question Answering task aims to retrieve correct answer entities from this KB. Despite sophisticated retrieval algorithms, the impact of the low-resource (incomplete) KB is not fully exploited, where contributing co…
Model compression techniques are recently gaining explosive attention for obtaining efficient AI models for various real time applications. Channel pruning is one important compression strategy, and widely used in slimming various DNNs. Previous gate-based or importance-based pruning methods aim to…
As humans and mobile robots increasingly coexist in public spaces, their close proximity demands that robots navigate following navigation strategies similar to those exhibited by humans. This could be achieved by learning directly from human demonstration trajectories in a machine learning framewor…
In this paper, we propose an autonomous gait analysis system consisting of a mobile robot and custom-engineered instrumented insoles. The robot is equipped with an on-board RGB-D sensor, the insoles feature inertial sensors and force sensitive resistors. This system is motivated by the need for a ro…
Humans and mobile robots will be increasingly cohabiting in the same environments, which has lead to an increase in studies on human robot interaction (HRI). One important topic in these studies is the development of robot navigation algorithms that are socially compliant to humans navigating in the…
We present field experiment results of ocean plume surveys conducted with a robotic platform. In our experiments, Rhodamine dye was used to generate a chemical plume in a coastal environment, and an unmanned surface vessel equipped with fluorometer sensors was deployed to conduct plume surveys. We p…
Sparse subspace clustering (SSC), as one of the most successful subspace clustering methods, has achieved notable clustering accuracy in computer vision tasks. However, SSC applies only to vector data in Euclidean space. As such, there is still no satisfactory approach to solve subspace clustering…
Due to the faster-is-slower phenomenon in emergency escape, it is desirable to regulate pedestrian flow at the exit or a bottleneck. Modification of pedestrian facilities was previously studied to increase the efficiency and safety by the transportation community. We propose a robot-assisted pedestr…
Using autonomous mobile robots to dynamically track oil plume propagation in ocean environments is challenging. Based on a model of advection-diffusion equation that describes point-source pollution propagation in marine environments, we have previously proposed a model-based estimator-controller de…