In everyday life we often find ourselves with both hands occupied, e.g. while holding a child in one hand and a dog leash in the other or riding a bicycle. These situations limit our ability to interact with our devic...
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While there has been renewed interest in the use of myoelectric control for general-purpose applications, the burden of training and maintaining robust models still limits its real-world viability. Online unsupervised...
While there has been renewed interest in the use of myoelectric control for general-purpose applications, the burden of training and maintaining robust models still limits its real-world viability. Online unsupervised adaptation has been proposed to solve this issue by updating the model using predicted pseudo-labels in real time during regular device use. Until now, however, these unsupervised strategies have been limited as they rely on the very classifier outputs they are adapting, making them ill-suited when there is a drastic shift in the input space (e.g., after donning and doffing a device) or there is insufficient training data. In such situations, leveraging context (i.e., task-specific information that can help understand or assess a circumstance) could provide additional guidance for adaptation and improve its robustness. Although difficult to extract in traditional prosthesis control use cases without additional sensors, context may be more readily available in other general-purpose applications, such as in human-computerinteraction. In this study, we explore leveraging context, both positive (i.e., reinforcing correct actions) and negative (i.e., correcting poor actions), for conditioning pseudo-label predictions within an adaptive gamified target acquisition setting. The results show that leveraging this additional con-text significantly outperforms the current state-of-the-art high-confidence unsupervised adaptation (p<0.05) using both offline and online performance metrics. This pilot work contributes novel findings and contextual approaches that do not rely on additional sensors, and thus outlines a promising direction of study for myoelectric control as a reliable and effective interaction technique.
Early diagnosis of pediatric adenoidal hypertrophy is crucial for timely identification of respiratory obstructions, thereby preventing declines in sleep quality that can lead to impaired growth, development, and cogn...
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In this paper we describe the need for a framework to support collaborative educational research with game data, then demonstrate a promising solution. We review existing efforts, explore a collection of use cases and...
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ISBN:
(数字)9798350350678
ISBN:
(纸本)9798350350685
In this paper we describe the need for a framework to support collaborative educational research with game data, then demonstrate a promising solution. We review existing efforts, explore a collection of use cases and requirements, then propose a new data architecture with related data standards. The approach provides modularity to the various stages of game data generation and analysis, exposing intermediate transformations and work products. Foregrounding flexibility, each stage of the pipeline generates datasets for use in other tools and workflows. A series of interconnected standards allow for the development of reusable analysis and visualization tools across games, while remaining responsive to the diversity of potential game designs. Finally, we demonstrate the feasibility of the approach through an existing implementation that uses this architecture to process and analyze data from a wide range of games developed by multiple institutions, at scale, supporting a variety of research projects.
This paper investigates three kinds of interactions for a friction based virtual music instrument. The sound synthesis model consists of a bank of mass-spring-dampers individually excited via rubbing. A nonlinear stat...
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Structure from motion (SfM) is a fundamental task in computervision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in ...
Structure from motion (SfM) is a fundamental task in computervision and allows recovering the 3D structure of a stationary scene from an image set. Finding robust and accurate feature matches plays a crucial role in the early stages of SfM. So in this work, we propose a novel method for computing image correspondences based on dense feature matching (DFM) using homographic decomposition: The underlying pipeline provides refinement of existing matches through iterative rematching, detection of occlusions and extrapolation of additional matches in critical image areas between image pairs. Our main contributions are improvements of DFM specifically for SfM, resulting in global refinement and global extrapolation of image correspondences between related views. Furthermore, we propose an iterative version of the Delaunay-triangulation-based outlier detection algorithm for robust processing of repeated image patterns. Through experiments, we demonstrate that the proposed method significantlv improves the reconstruction accuracy.
Tire tracks are crucial evidence for investigating and identifying traffic accident scenes. Intelligent recognition of tire track images enables rapid and precise identification of suspicious vehicles, assisting inves...
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Tire tracks are crucial evidence for investigating and identifying traffic accident scenes. Intelligent recognition of tire track images enables rapid and precise identification of suspicious vehicles, assisting investigators in determining accident causes and liabilities. This enhances both the efficiency and accuracy of case resolution. However, tire track images are complex and diverse, significantly influenced by environmental factors such as lighting and road conditions. Moreover, the limited availability of tire track image samples poses a challenge for training deep learning models in practical applications. To address these challenges, particularly the limited generalization ability of few-shot metric learning models on unseen categories and their poor recognition rates in open-world environments, we propose a novel framework called TireNet for few-shot tire track recognition. First, we adopt a residual network integrated with coordinate attention as the backbone for feature extraction. Secondly, the context-aware features of the support set and the query set are extracted separately through attention-based bi-directional long short-term memory model. Subsequently, the cosine similarity between the features of the support set and the query set is calculated to determine the category of the query image. Finally, to address the class imbalance issue in the tire track image dataset, we utilize an improved Focal Loss for gradient updates. This enables the model to focus more on difficult samples during training, thereby enhancing the training efficiency, particularly in scenarios involving few-shot learning and open-world environments. To validate the proposed method, we constructed a tire track image dataset of 1700 samples, covering various environmental conditions, providing a rich resource for tire track recognition research. Experimental results demonstrate that the proposed method significantly outperforms few-shot image classification and classic machine
Breath has a fundamentally influence on our psychophysiological system. Deep exhalation is beneficial for both meditation and running. Many systems support breathing through visual or auditory guidance. In contrast, o...
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Sound plays a crucial role in shaping diners’ perceptions and enhancing the dining experience. Recognizing this, designers have started to integrate auditory elements into interactive interfaces that connect diners w...
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This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitA...
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