data scarcity in low-resource languages can be addressed with word-to-word translations from labeled task data in high-resource languages using bilingual lexicons. However, bilingual lexicons often have limited lexica...
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Blockchain is a distributed database that multiple parties can maintain and share. This new technology is expected to greatly impact the healthcare industry. It can help address various issues related to patient care....
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Recent works often assume that Vision-Language Model (VLM) representations are based on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this information to represent concepts. We pr...
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In today’s corporate landscape, the creation of questionnaires, surveys or evaluation forms for employees is a widespread practice. These tools are regularly used to check various aspects such as motivation, opportun...
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Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence...
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Imposing data-driven with physical laws for user activity prediction could effectively solve various physical problems such as smart care, surveillance, and human-robot. In the growing field of artificial intelligence, the application of activity prediction based on the physical coupled hidden Markov model (CHMM) and tensor theory with physical properties has attracted increasing attentions. However, existing CHMMs usually only consider the time-series characteristic of data, while ignoring physical characteristics of user activity such as periodicity, timing, and correlation. Moreover, they are all matrix-based models, which could not holistically analyze the dependencies among physical states. The aforementioned disadvantages lead to lower prediction accuracy of the CHMM. To remove these disadvantages, three physics-informed tensor-based CHMMs are first constructed by incorporating prior physical knowledge. Then, the corresponding forward-backward algorithms are designed for resolving the evaluation problem of the CHMM. These algorithms could overall model multiple physical features by imposing physics and prior knowledge into the CHMM during training to improve the precision of probabilistic computing. The algorithms reduce the dependence of the model on training data by adding physical features. Finally, the comparative experiments show that our algorithms have better performances than existing prediction methods in precision and efficiency. In addition, further self-comparison experiments verify that our algorithms are effective and practical. Impact Statement-Through the analysis of users' behavior habits, consumption habits, preferences, etc., users? potential needs may be discovered. This discovery could help predict users' activities. If a waiter predicts the user's next activity. He gives her/him unexpected services to meet users' next needs. Obviously, it would significantly improve user satisfaction. In addition, connecting the front and rear products co
An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digi...
An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].
Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an e...
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Stock Portfolio management involves managing the buying, holding and selling decisions for the various stocks in the portfolio. There has been work where Reinforcement Learning (RL) based actor-critic methods like Dee...
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This paper explores the application of state-of-the-art natural language processing (NLP) technologies to improve the user experience in games. Our motivation stems from the realization that a virtual assistant’s inp...
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Efficient operations, within the supply chain are vital for the electronics industry. In this study, we focus on optimizing the supply chain network of a hypothetical company ABC Electronics, an entity crafted for the...
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