Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlog...
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Accurate monitoring of urban waterlogging contributes to the city’s normal operation and the safety of residents’daily ***,due to feedback delays or high costs,existing methods make large-scale,fine-grained waterlogging monitoring impossible.A common method is to forecast the city’s global waterlogging status using its partial waterlogging *** method has two challenges:first,existing predictive algorithms are either driven by knowledge or data alone;and second,the partial waterlogging data is not collected selectively,resulting in poor *** overcome the aforementioned challenges,this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus *** framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and *** predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural *** combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming *** selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget *** experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost,high accuracy,wide coverage,and fine granularity.
Bayesian modelling helps applied researchers to articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers ca...
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Bayesian modelling helps applied researchers to articulate assumptions about their data and develop models tailored for specific applications. Thanks to good methods for approximate posterior inference, researchers can now easily build, use, and revise complicated Bayesian models for large and rich data. These capabilities, however, bring into focus the problem of model criticism. Researchers need tools to diagnose the fitness of their models, to understand where they fall short, and to guide their revision. In this paper, we develop a new method for Bayesian model criticism, the holdout predictive check (HPC). Holdout predictive check are built on posterior predictive check (PPC), a seminal method that checks a model by assessing the posterior predictive distribution on the observed data. However, PPC use the data twice—both to calculate the posterior predictive and to evaluate it—which can lead to uncalibrated p-values. Holdout predictive check, in contrast, compare the posterior predictive distribution to a draw from the population distribution, a heldout dataset. This method blends Bayesian modelling with frequentist assessment. Unlike the PPC, we prove that the HPC is properly calibrated. Empirically, we study HPC on classical regression, a hierarchical model of text data, and factor analysis.
Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activitie...
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Cardiovascular disease remains a major issue for mortality and morbidity, making accurate classification crucial. This paper introduces a novel heart disease classification model utilizing Electrocardiogram (ECG) sign...
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XStorm, an FRP language for small-scale embedded systems, allows us to concisely describe state-dependent behaviors based on the state transition model. However, when we use different sets of peripheral devices depend...
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Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalab...
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Floor localization is crucial for various applications such as emergency response and rescue,indoor positioning,and recommender *** existing floor localization systems have many drawbacks,like low accuracy,poor scalability,and high computational *** this paper,we first frame the problem of floor localization as one of learning node embeddings to predict the floor label of a ***,we introduce FloorLocator,a deep learning-based method for floor localization that integrates efficient spiking neural networks with powerful graph neural *** approach offers high accuracy,easy scalability to new buildings,and computational *** results on using several public datasets demonstrate that FloorLocator outperforms state-of-the-art ***,in building B0,FloorLocator achieved recognition accuracy of 95.9%,exceeding state-of-the-art methods by at least 10%.In building B1,it reached an accuracy of 82.1%,surpassing the latest methods by at least 4%.These results indicate FloorLocator’s superiority in multi-floor building environment localization.
This study proposes a malicious code detection model DTL-MD based on deep transfer learning, which aims to improve the detection accuracy of existing methods in complex malicious code and data scarcity. In the feature...
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Polycystic Ovary Syndrome (PCOS) is a common reproductive and metabolic disorder characterized by an increased number of ovarian follicles. Accurate diagnosis of PCOS requires detailed ultrasound imaging to assess fol...
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User engagement has been improved by using recommender systems, which are essential for giving user recommendations. Matrix factorization (MF), one of the traditional approaches, has shown a promising work in capturin...
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Robust cybersecurity measures are essential to protect complex information systems from a variety of cyber threats, which requires sophisticated security solutions. This paper explores the integration of Large Languag...
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