Skip connections (SCs) are commonly employed in neural networks to facilitate gradient-based training and often lead to improved performance in deep learning. To implement SCs, a user writes custom modules along with ...
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This paper explores the role of sparse parameteri-zations on Recurrent Neural Network (RNN) performance using anomaly detection tasks. The findings indicate sparsity plays a significant role in both improving training...
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Depression is a prevalent mental health condition with severe impacts on physical and social health. It is costly and difficult to detect, requiring substantial time from trained mental professionals. To alleviate thi...
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Leveraging AI to analyze key topics on African social media can enhance public governance. Our study analyzes social media discourse within African society on development concerns by (1) evaluating AI techniques for s...
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Networks are rapidly evolving to include more Internet of Things devices as we grow to rely on them for smart home services, health infrastructure, and industrial development. Along with this proliferation, these netw...
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Training neural networks is difficult with small datasets, yet small training data sets are commonly found with machine learning problems in the physical sciences. Prediction accuracy within such sparse data domains c...
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This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team forma...
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Corrosion is a prevalent issue in numerous industrial fields, causing expenses nearing 3 trillion or 4% of the GDP annually with safety threats and environmental pollution. To timely qualify and validate new corrosion...
Human Activity Recognition (HAR) is an important task in ubiquitous computing, with impactful real-world applications. While recent state-of-the-art HAR research has demonstrated impressive performance, some key aspec...
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ISBN:
(纸本)9798350374889
Human Activity Recognition (HAR) is an important task in ubiquitous computing, with impactful real-world applications. While recent state-of-the-art HAR research has demonstrated impressive performance, some key aspects remain under-explored. First, we believe that for optimal performance, HAR models should be both Context-Aware (CA) and personalized. However, prior work has predominantly focused on being Context-Aware (CA), largely ignoring being User-Aware (UA). We argue that learning user-specific differences in performing various activities is as critical as considering user context while performing HAR tasks. Secondly, we believe that the predictions of HAR models should be unified, reliably recognizing the same activity even when performed by different users. As such, the representations utilized by CA and UA models should explicitly place different users performing the same activity closer together. Moreover, identifying the user performing an activity is useful in applications such as thwarting cheating by having another person perform medically-prescribed activities. To bridge this gap, we introduce Contrastive Learning with Auxiliary User Detection for Identifying Activities (CLAUDIA), a novel framework designed to address these issues. Specifically, we expand the contextual scope of the CAHAR task by integrating User Identification (UI) within the CAHAR framework, jointly predicting both CAHAR and UI in a new task called User and Context-Aware HAR (UCA-HAR). This approach enriches personalized and contextual understanding by jointly learning user-invariant and user-specific patterns. Inspired by state-of-the-art designs in the visual domain, we introduce a supervised contrastive loss objective on instance-instance pairs to enhance model efficacy and improve learned feature quality. Through theoretical exposition, empirical analysis of real-world datasets, and rigorous experimentation, we demonstrate the significance of each component of CLAUDIA and discus
Major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) are mental disorders that reduce quality of life. As they are challenging to detect in a timely manner, recent studies explore the mental illne...
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