It is a challenging task to obtain high-quality images in low-light scenarios. While existing low-light image enhancement methods learn the mapping from low-light to clear images, such a straightforward approach lacks...
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This study presents a machine learning-based approach for optimizing Young’s modulus, a critical physical parameter of soft robots. Instead of directly utilizing conventional material property data, the method predic...
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In this paper, we analyze the impact of vaccination on the dynamics of measles transmission using the SEIR mathematical model. We demonstrate that high vaccination coverage significantly reduces disease transmission a...
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Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communica...
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Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise datascience is to enable an effective ‘whole-of-enterprise’ data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where ‘enterprise big tables’ are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables t
In this paper,we focus on the BDS test,which is a nonparametric test of ***,the null hypothesis H0 of it is that {u_(t)} is i.i.d.(independent and identically distributed),where {u_(t)} is a random *** BDS test is wid...
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In this paper,we focus on the BDS test,which is a nonparametric test of ***,the null hypothesis H0 of it is that {u_(t)} is i.i.d.(independent and identically distributed),where {u_(t)} is a random *** BDS test is widely used in economics and finance,but it has a weakness that cannot be ignored:over-rejecting H0 even if the length T of {u_(t)} is as large as(100;2000).To improve the over-rejection problem of BDS test,considering that the correlation integral is the foundation of this test,we not only accurately describe the expectation of the correlation integral under H_(0),but also calculate all terms of the asymptotic variance of the correlation integral whose order is O(T^(-1))and O(T^(-2)),which is essential to improve the finite sample performance of BDS *** on this,we propose a revised BDS(RBDS)test and prove its asymptotic normality under *** RBDS test not only inherits all the advantages of BDS test,but also effectively corrects the over-rejection problem of it,which can be fully confirmed by the simulation results we ***,based on the simulation results,we find that similar to BDS test,RBDS test would also be affected by the parameter estimations of the ARCH-type model,resulting in size distortion,but this phenomenon can be alleviated by the logarithmic transformation preprocessing of the estimate residuals of the ***,through some actual datasets that have been demonstrated to fit well with ARCH-type models,we also compared the performance of BDS test and RBDS test in evaluating the goodness-of-fit of the model in empirical problem,and the results reflect that,under the same condition,the performance of the RBDS test is more encouraging.
Air quality assessment plays a crucial role in environmental governance and public health decision making. Traditional assessment methods have limitations in handling multi source heterogeneous data and complex nonlin...
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Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and *** this paper,a new vital nodes identif...
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Identifying vital nodes is one of the core issues of network science,and is crucial for epidemic prevention and control,network security maintenance,and biomedical research and *** this paper,a new vital nodes identification method,named degree and cycle ratio(DC),is proposed by integrating degree centrality(weightα)and cycle ratio(weight 1-α).The results show that the dynamic observations and weightαare nonlinear and non-monotonicity(i.e.,there exists an optimal valueα^(*)forα),and that DC performs better than a single index in most *** to the value ofα^(*),networks are classified into degree-dominant networks(α^(*)>0.5)and cycle-dominant networks(α^(*)<0.5).Specifically,in most degree-dominant networks(such as Chengdu-BUS,Chongqing-BUS and Beijing-BUS),degree is dominant in the identification of vital nodes,but the identification effect can be improved by adding cycle structure information to the *** most cycle-dominant networks(such as Email,Wiki and Hamsterster),the cycle ratio is dominant in the identification of vital nodes,but the effect can be notably enhanced by additional node degree ***,interestingly,in Lancichinetti-Fortunato-Radicchi(LFR)synthesis networks,the cycle-dominant network is observed.
To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoH...
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To enable precision medicine and remote patient monitoring,internet of healthcare things(IoHT)has gained significant interest as a promising *** the widespread use of IoHT,nonetheless,privacy infringements such as IoHT data leakage have raised serious public *** the other side,blockchain and distributed ledger technologies have demonstrated great potential for enhancing trustworthiness and privacy protection for IoHT *** this survey,a holistic review of existing blockchain-based IoHT systems is conducted to indicate the feasibility of combining blockchain and IoHT in privacy *** addition,various types of privacy challenges in IoHT are identified by examining general data protection regulation(GDPR).More importantly,an associated study of cutting-edge privacy-preserving techniques for the identified IoHT privacy challenges is ***,several challenges in four promising research areas for blockchain-based IoHT systems are pointed out,with the intent of motivating researchers working in these fields to develop possible solutions.
Positive data are very common in many scientific fields and applications;for these data,it is known that estimation and inference based on relative error criterion are superior to that of absolute error *** prediction...
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Positive data are very common in many scientific fields and applications;for these data,it is known that estimation and inference based on relative error criterion are superior to that of absolute error *** prediction problems,conformal prediction provides a useful framework to construct flexible prediction intervals based on hypothesis testing,which has been actively studied in the past *** view of the advantages of the relative error criterion for regression problems with positive responses,in this paper,we combine the relative error criterion(REC)with conformal prediction to develop a novel REC-based predictive inference method to construct prediction intervals for the positive *** proposed method satisfies the finite sample global coverage guarantee and to some extent achieves the local *** conduct extensive simulation studies and two real data analysis to demonstrate the competitiveness of the new proposed method.
An essential component of an Intelligent Transportation System (ITS) is anomaly detection. There is an increasing need for the identification of unusual occurrences in the traffic network due to the yearly growth in v...
An essential component of an Intelligent Transportation System (ITS) is anomaly detection. There is an increasing need for the identification of unusual occurrences in the traffic network due to the yearly growth in vehicle usage and the continual improvement of the traffic networking. Traffic anomaly detection provides sufficient decision-supporting information for road network operators, users, and other stakeholders. It is difficult to find anomalies in large-scale multivariate road traffic data. By bridging the gap between automated and manual traffic analysis methods, visual analytics helps increase process transparency. New rapid and effective processing tools and methodologies are required to swiftly extract and analyze data from the enormous volume of data. datascience approaches can be used to research and pinpoint various kinds of severe attacks and anomalies. Hence, Computational datascience based detection of road traffic Anomalies is presented in this analysis. Thecomputational datascience techniques (CDS) are needed to manage vast volumes of traffic data in a wide range of forms. Finding abnormalities in traffic data that impact traffic efficiency produced the development of computational data *** primary advantage of the CDS approach is the early identification of data anomaly sources to avoid traffic congestion over a long-term.
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