The movie recommender system is a highly influential and practical tool that assists individuals in efficiently choosing films to watch. Although recommender systems have been extensively used in academic research for...
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to i...
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The nonlinear filtering problem has enduringly been an active research topic in both academia and industry due to its ever-growing theoretical importance and practical *** main objective of nonlinear filtering is to infer the states of a nonlinear dynamical system of interest based on the available noisy measurements. In recent years, the advance of network communication technology has not only popularized the networked systems with apparent advantages in terms of installation,cost and maintenance, but also brought about a series of challenges to the design of nonlinear filtering algorithms, among which the communication constraint has been recognized as a dominating concern. In this context, a great number of investigations have been launched towards the networked nonlinear filtering problem with communication constraints, and many samplebased nonlinear filters have been developed to deal with the highly nonlinear and/or non-Gaussian scenarios. The aim of this paper is to provide a timely survey about the recent advances on the sample-based networked nonlinear filtering problem from the perspective of communication constraints. More specifically, we first review three important families of sample-based filtering methods known as the unscented Kalman filter, particle filter,and maximum correntropy filter. Then, the latest developments are surveyed with stress on the topics regarding incomplete/imperfect information, limited resources and cyber ***, several challenges and open problems are highlighted to shed some lights on the possible trends of future research in this realm.
In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some we...
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In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).
Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are...
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Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic *** disorders are a major global health concern,affecting milli...
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Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves,blood vessels,retinal health,and the impact of diabetes on the optic *** disorders are a major global health concern,affecting millions of people worldwide due to their widespread *** photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic *** a result,accurate fundus detection is essential for early diagnosis and effective treatment,helping to prevent severe complications and improve patient *** address this need,this article introduces a Derivative Model for Fundus Detection using Deep NeuralNetworks(DMFD-DNN)to enhance diagnostic *** selects key features for fundus detection using the least derivative,which identifies features correlating with stored fundus *** filtering relies on the minimum derivative,determined by extracting both similar and varying *** this research,the DNN model was integrated with the derivative *** images were segmented,features were extracted,and the DNN was iteratively trained to identify fundus regions *** goal was to improve the precision of fundoscopic diagnosis by training the DNN incrementally,taking into account the least possible derivative across iterations,and using outputs from previous *** hidden layer of the neural network operates on the most significant derivative,which may reduce precision across *** derivatives are treated as inaccurate,and the model is subsequently trained using selective features and their corresponding *** proposed model outperforms previous techniques in detecting fundus regions,achieving 94.98%accuracy and 91.57%sensitivity,with a minimal error rate of 5.43%.It significantly reduces feature extraction time to 1.462 s and minimizes computational overhead,thereby improving operational efficiency and ***,the propo
As the big data era transforms the information analysis landscape, social network (SN) analytics has emerged as a critical discipline to understand complex relationships and interactions within enormous social systems...
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Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy;and provide ...
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The methodology of this research focuses on analyzing real-world breaches, such as the Star Health Insurance leak, and demonstrating how multi-layered encryption techniques could have mitigated the impact. By splittin...
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Cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, h...
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Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detectio...
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Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection plays a critical role in preventing accidents, enhancing safety, and ultimately saving lives across various sectors and scenarios. This comprehensive review explores the significance of drowsiness detection in various areas of application, transcending the conventional focus solely on driver drowsiness detection. We delve into the current methodologies, challenges, and technological advancements in drowsiness detection schemes, considering diverse contexts such as public transportation, healthcare, workplace safety, and beyond. By examining the multifaceted implications of drowsiness, this work contributes to a holistic understanding of its impact and the crucial role of accurate and real-time detection techniques in enhancing safety and performance. We identified weaknesses in current algorithms and limitations in existing research such as accurate and real-time detection, stable data transmission, and building bias-free systems. Our survey frames existing works and leads to practical recommendations like mitigating the bias issue by using synthetic data, overcoming the hardware limitations with model compression, and leveraging fusion to boost model performance. This is a pioneering work to survey the topic of drowsiness detection in such an entirely and not only focusing on one single aspect. We consider the topic of drowsiness detection as a dynamic and evolving field, presenting numerous opportunities for further exploration. IEEE
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