In this work, a novel operator-based differential evolution (DE) algorithm has been proposed. The proposed approach has been inspired by the internal adaption (environment) of the search space. Therefore, maintaining ...
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Augmented reality (AR) is a recent technology with applications in multiple sectors, including the healthcare industry. AR incorporates (or overlays) digital or computer-generated data such as audio, video, images, an...
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Privacy and security have received significant interest among the research communities Due to the increased exploitation of the Internet of Things (IoT). Since IoT devices gather most of the public data like identity,...
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Age-Related Macular Degeneration (AMD) is one of the most common type of eye ailments which generally affects elderly people of age above 60 years and which damages their central vision. Diabetic Macular Edema (DME) i...
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One in eight women are diagnosed with breast cancer and thirty percent of women not up to date screening recommandations, if diagnosed early effects can be reduced. Breast Cancer is becoming less uncommon. As a result...
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Light Detection and Ranging (LiDAR) technology is one of the integral parts of systems involving connected vehicles. It provides the necessary two-dimensional accuracy to enable proper navigation, object identificatio...
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At present, wireless sensor networks are developing rapidly, but they will also face many challenges. For example, in the deployment problem, many problems such as cost, coverage, connectivity, energy, network life cy...
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Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the preval...
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Unsupervised domain adaptation (UDA) has emerged as a powerful solution for the domain shift problem via transferring the knowledge from a labeled source domain to a shifted unlabeled target domain. Despite the prevalence of UDA for visual applications, it remains relatively less explored for time-series applications. In this work, we propose a novel lightweight contrastive domain adaptation framework called CoTMix for time-series data. Unlike existing approaches that either use statistical distances or adversarial techniques, we leverage contrastive learning solely to mitigate the distribution shift across the different domains. Specifically, we propose a novel temporal mixup strategy to generate two intermediate augmented views for the source and target domains. Subsequently, we leverage contrastive learning to maximize the similarity between each domain and its corresponding augmented view. The generated views consider the temporal dynamics of time-series data during the adaptation process while inheriting the semantics among the two domains. Hence, we gradually push both domains toward a common intermediate space, mitigating the distribution shift across them. Extensive experiments conducted on five real-world time-series datasets show that our approach can significantly outperform all state-of-the-art UDA methods. Impact Statement-Unsupervised domain adaptation (UDA) aims to reduce the gap between two related but shifted domains. CurrentUDAmethods for time-series data are based on adversarial or discrepancy approaches. Thesemethods are complex in training and cannot efficiently address the large domain shift. Therefore, in this work, we propose a time-series UDA framework based purely on contrastive learning, which is simpler in implementation and training. To leverage contrastive learning to mitigate domain shift, we propose a temporal mixup strategy to generate augmentations that are robust to the domain shift and can move both domains towards an intermediate
In natural language processing, an important objective is to perform sentiment analysis, which involves categorizing textual content based on whether it expresses a positive, negative, or neutral sentiment. Sentiment ...
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In transportation systems, a vast volume of traffic data is generated on a daily basis. The contributing factors for this traffic include expanding urban population, aging infrastructure, uncoordinated traffic timings...
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