We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can gene...
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Proteins are complex biological information granules that play a crucial role in various cellular processes within living organisms. Processing 3D protein structures, which are the most informative from the biological...
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By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-mon...
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The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and relia...
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Detection and prediction of failures in Automated Guided Vehicles (AGV) are essential for the uninterrupted operation of production plants. Anomaly detection is usually achieved by comparing expected measurement value...
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In this paper, we propose a heterogeneous federated learning (HFL) system for sparse time series prediction in healthcare, which is a decentralized federated learning algorithm with heterogeneous transfers. We design ...
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Medical internet of things leads to revolutionary improvements in medical services, also known as smart healthcare. With the big healthcare data, data mining and machine learning can assist wellness management and int...
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With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It ...
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With the emergence of AI for good, there has been an increasing interest in building computer vision data-driven deep learning inclusive AI solutions. Sign language Recognition (SLR) has gained attention recently. It is an essential component of a sign-to-text translation system to support the deaf and hard-of-hearing population. This paper presents a computer VISIOn data-driven deep learning framework for Sign Language video Recognition (VisoSLR). VisioSLR provides a precise measurement of translating signs for developing an end-to-end computational translation system. Considering the scarcity of sign language datasets, which hinders the development of an accurate recognition model, we evaluate the performance of our framework by fine-tuning the very well-known YOLO models, which are built from a signs-unrelated collection of images and videos, using a small-sized sign language dataset. Gathering a sign language dataset for signs training would involve an enormous amount of time to collect and annotate videos in different environmental setups and multiple signers, in addition to the training time of a model. Numerical evaluations of VisioSLR show that our framework recognizes signs with a mean average precision of 97.4%, 97.1%, and 95.5% and 11, 12, and 12 milliseconds of recognition time on YOLOv8m, YOLOv9m, and YOLOv11m, respectively.
An Internet-of-Things (IoT) system typically comprises many small computing elements (nodes) that are battery-powered and communicate over a wireless network. These elements monitor properties in the environment and s...
ISBN:
(纸本)9798400700408
An Internet-of-Things (IoT) system typically comprises many small computing elements (nodes) that are battery-powered and communicate over a wireless network. These elements monitor properties in the environment and send the data to client applications via gateways. The wireless networks used by the elements are subject to uncertainties that are difficult to predict upfront, such as dynamic objects (swaying trees, cars,...) and changing weather conditions that may deteriorate the transmissions. To ensure reliable communication over a wireless network of energy-constrained elements, recent research has proposed self-adaptive IoT systems. Such a self-adaptive system equips the network of elements – referred to as the managed system – with a feedback loop – the managing system. The managing system monitors the changing conditions and adapts the transmission settings of the IoT network to ensure the system’s quality goals. Leveraging and consolidating the existing knowledge in this area, we present a pattern that we coined Joint Learning that provides a solution to the decision-making problem of large, distributed self-adaptive IoT systems. With this pattern, elements use a joint learner to make adaptation decisions for individual elements while yielding reliable communication of the overall network. The pattern is applied to two cases to show that the solutions realize the system goals while operating under uncertainties.
ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transforma...
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