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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Li, Yucheng Wang, Yanan Xiong, Mengyuan Chen, Max Yan, Yifan Li, Junxian Wang, Qi Hansen, Preben College of Fashion and Design Donghua University Shanghai China Donghua University Shanghai China Worcester Polytechnic Institute WorcesterMA United States College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou China College of Design and Innovation Tongji University Shanghai China Dept. of Computer and Systems Sciences Stockholm University Kista Sweden

ISBN: (纸本)9798400713958

Compared to the five basic tastes, smell, as an intrinsic element, is crucial in augmenting flavor and dietary experience within Human-Food Interaction. While much research has explored orthonasal olfaction in olfactory displays, the potential of retronasal olfaction in interaction design is always overlooked and less understood. This paper introduces AromaBite, an accessible, edible, bio-compatible and ingestible odor storage and release mechanism that integrates natural eating behaviors, providing dynamic retronasal olfactory experiences. We detail AromaBite’s design parameters and process and technically test its airtightness and durability. We employ time-intensity (TI), confirming that retronasal olfaction significantly enhances flavor perception and providing a design library mapping form factors to guide natural oral interaction. We envision that AromaBite will augment depth and immersive flavor experiences in Virtual Reality, olfactory training, and personalized dining interactions. © 2025 Copyright held by the owner/author(s).

关键词： Flavors

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UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled, and Small Expert-Labeled Tweets for Foodborne Illness Detection

UCE-FID: Using Large Unlabeled, Medium Crowdsourced-Labeled,...

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IEEE International Conference on Big data

作者： Ruofan Hu Dongyu Zhang Dandan Tao Huayi Zhang Hao Feng Elke Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester USA Vanke School of Public Health Tsinghua University Beijing China ByteDance San Jose USA College of Ag&Environ Sciences North Carolina A&T State University Greensboro USA Computer Science/Data Science Program Worcester Polytechnic Institute Worcester USA

Foodborne illnesses significantly impact public health. Deep learning surveillance applications using social media data aim to detect early warning signals. However, labeling foodborne illness-related tweets for model training requires extensive human resources, making it challenging to collect a sufficient number of high-quality labels for tweets within a limited budget. The severe class imbalance resulting from the scarcity of foodborne illness-related tweets among the vast volume of social media further exacerbates the problem. Classifiers trained on a classimbalanced dataset are biased towards the majority class, making accurate detection difficult. To overcome these challenges, we propose EGAL, a deep learning framework for foodborne illness detection that uses small expert-labeled tweets augmented by crowdsourced-labeled and massive unlabeled data. Specifically, by leveraging tweets labeled by experts as a reward set, EGAL learns to assign a weight of zero to incorrectly labeled tweets to mitigate their negative influence. Other tweets receive proportionate weights to counter-balance the unbalanced class distribution. Extensive experiments on real-world TWEET-FID data show that EGAL outperforms strong baseline models across different settings, including varying expert-labeled set sizes and class imbalance ratios. A case study on a multistate outbreak of Salmonella Typhimurium infection linked to packaged salad greens demonstrates how the trained model captures relevant tweets offering valuable outbreak insights. EGAL, funded by the U.S. Department of Agriculture (USDA), has the potential to be deployed for real-time analysis of tweet streaming, contributing to foodborne illness outbreak surveillance efforts.

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Jewani, Yordanos Petry, Michael Sanchez-Arias, Rei Habib, Md Selim Department of Data Science and Business Analytics Florida Polytechnic University LakelandFL33805 United States Department of Aerospace and Geodesy Technical University of Munich Munich80333 Germany Department of Electrical Engineering and Computer Science Florida Institute of Technology FL32901 United States

A supervised machine learning framework is implemented to predict the propagation loss of randomly structured nested hollow-core anti-resonant fiber for the first time. The random forest classifier outperforms other m... 详细信息

ISBN: (纸本)9781957171395

关键词： Prediction models

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

Loss Distillation via Gradient Matching for Point Cloud Comp...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Fangzhou Lin Haotian Liu Haoying Zhou Songlin Hou Kazunori D Yamada Gregory S. Fischer Yanhua Li Haichong K. Zhang Ziming Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Graduate School of Information Sciences Tohoku University Sendai Japan Dell Technologies Hopkinton MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA Department of Electrical & Computer Engineering Worcester Polytechnic Institute Worcester MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/IROS2024-LossDistillationWeightedCD.

关键词： Point cloud compression Training Three-dimensional displays Laser radar Benchmark testing Iterative algorithms Tuning Optimization Intelligent robots Convergence

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Cross-domain NER with Generated Task-Oriented Knowledge: An Empirical Study from Information Density Perspective

Cross-domain NER with Generated Task-Oriented Knowledge: An ...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Zhang, Zhihao Lee, Sophia Yat Mei Wu, Junshuang Zhang, Dong Li, Shoushan Cambria, Erik Zhou, Guodong School of Computer Science & Technology NLP Lab Soochow University China Department of Chinese and Bilingual Studies The Hong Kong Polytechnic University Hong Kong Beijing Jinghang Research Institute of Computing and Communication China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761643

Cross-domain Named Entity Recognition (CDNER) is crucial for Knowledge Graph (KG) construction and natural language processing (NLP), enabling learning from source to target domains with limited data. Previous studies often rely on manually collected entity-relevant sentences from the web or attempt to bridge the gap between tokens and entity labels across domains. These approaches are time-consuming and inefficient, as these data are often weakly correlated with the target task and require extensive pre-training. To address these issues, we propose automatically generating task-oriented knowledge (GTOK) using large language models (LLMs), focusing on the reasoning process of entity extraction. Then, we employ task-oriented pre-training (TOPT) to facilitate domain adaptation. Additionally, current cross-domain NER methods often lack explicit explanations for their effectiveness. Therefore, we introduce the concept of information density to better evaluate the model's effectiveness before performing entity recognition. We conduct systematic experiments and analyses to demonstrate the effectiveness of our proposed approach and the validity of using information density for model evaluation. © 2024 Association for Computational Linguistics.

关键词： Domain Knowledge

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Machine Learning Framework for Loss Range Prediction of Hollow-core Anti-resonant Fibers

Machine Learning Framework for Loss Range Prediction of Holl...

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CLEO: science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Adversarial machine learning

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MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies

arXiv

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arXiv 2024年

作者： Huang, Haojie Liu, Haotian Wang, Dian Walters, Robin Platt, Robert Khoury College of Computer Science Northeastern University United States Worcester Polytechnic Institute United States

Many manipulation tasks require the robot to rearrange objects relative to one another. Such tasks can be described as a sequence of relative poses between parts of a set of rigid bodies. In this work, we propose MATCH POLICY, a simple but novel pipeline for solving high-precision pick and place tasks. Instead of predicting actions directly, our method registers the pick and place targets to the stored demonstrations. This transfers action inference into a point cloud registration task and enables us to realize nontrivial manipulation policies without any training. MATCH POLICY is designed to solve high-precision tasks with a key-frame setting. By leveraging the geometric interaction and the symmetries of the task, it achieves extremely high sample efficiency and generalizability to unseen configurations. We demonstrate its state-of-the-art performance across various tasks on RLbench benchmark compared with several strong baselines and test it on a real robot with six tasks. Videos and code are available on https://***/match page/. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent robots

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Automatic Contact Force-regulated End-effector Using Pneumatic Actuator for Safe Robotic Ultrasound Imaging

Automatic Contact Force-regulated End-effector Using Pneumat...

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International Symposium on Medical Robotics (ISMR)

作者： Wen-Yi Kuo Xihan Ma Dhirajsinh Deshmukh Haichong K. Zhang Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Biomedical Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Worcester Polytechnic Institute Worcester MA USA

Robotic-assisted ultrasound (US) imaging has demonstrated great promise to address issues such as high physical demands and limited scanning reproducibility in traditional freehand US procedures. Yet, using a robot arm to manipulate the US probe brings a fundamental concern about patient safety. This is because an overwhelming force can be easily produced from the robot joint actuation and exerted onto the patient's body when an electrical force-sensing error exists. Therefore, a mechanism that maintains a more compliant contact force, functioning independently from the robot arm, is needed to mitigate this concern. Here, we introduce a robotic US system with a novel automatic contact force-regulated end-effector. A linear pneumatic actuator is embedded in the end-effector to prevent excessive force from being applied to the patient. Such force compliance can be realized without relying on electrical force sensing and robot joint actuation. We tested the proposed system in US imaging tasks under a phantom-based environment. Results show that the contact force has an adjustable range from 4.10 N to 7.53 N, and can be regulated with ± 0.46 N variation using the automatic contact force-regulated end-effector during scanning, which demonstrates potential clinical applicability.

关键词： Pneumatic actuators Ultrasonic imaging Contacts Force Imaging Pneumatic systems Robot sensing systems

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Graph Coordinates and Conventional Neural Networks - An Alternative for Graph Neural Networks

Graph Coordinates and Conventional Neural Networks - An Alte...

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IEEE International Conference on Big data

作者： Zheyi Qin Randy Paffenroth Anura P. Jayasumana Electrical and Computer Engineering Colorado State University Fort Collins USA Department of Data Science Program Worcester Polytechnic Institute Worcester MA USA

Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph’s topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph’s topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall

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MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization for Enhanced Continual Learning

MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpne...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xingyu Li Bo Tang Department of Computer Science Tulane University New Orleans United States Department of Electrical and Computer Engineering Worcester Polytechnic Institute Worcester United States

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Deep neural networks suffer from the catastrophic forgetting problem in the field of continual learning (CL). To address this challenge, we propose MGSER-SAM, a novel memory replay-based algorithm specifically engineered to enhance the generalization capabilities of CL models. We first intergrate the SAM optimizer, a component designed for optimizing flatness, which seamlessly fits into well-known Experience Replay frameworks such as ER and DER++. Then, MGSER-SAM distinctively addresses the complex challenge of reconciling conflicts in weight perturbation directions between ongoing tasks and previously stored memories, which is underexplored in the SAM optimizer. This is effectively accomplished by the strategic integration of soft logits and the alignment of memory gradient directions, where the regularization terms facilitate the concurrent minimization of various training loss terms integral to the CL process. Through rigorous experimental analysis conducted across multiple benchmarks, MGSER-SAM has demonstrated a consistent ability to outperform existing baselines in all three CL scenarios. Comparing to the representative memory replay-based baselines ER and DER++, MGSER-SAM not only improves the testing accuracy by 24.4% and 17.6% respectively, but also achieves the lowest forgetting on each benchmark.

关键词： Continuing education Training Accuracy Perturbation methods Buildings Artificial neural networks Benchmark testing

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