Two States CBR Modeling of Data Source in Dynamic Traffic Monitoring Sensor Networks

Real traffic information was analyzed in the statistical characteristics and approximated as a Gaussian time series. A data source model, called two states constant bit rate (TSCBR), was proposed in dynamic traffic monitoring sensor networks. Analysis of autocorrelation of the models shows that the proposed TSCBR model matches with the statistical characteristics of real data source closely. To further verify the validity of the TSCBR data source model, the performance metrics of power consumption and network lifetime was studied in the evaluation of sensor media access control (SMAC) algorithm. The simulation results show that compared with traditional data source models, TSCBR model can significantly improve accuracy of the algorithm evaluation.

Construction of Mediators for Heterogeneous Data Source Integration Systems

To construct mediators for data integration systems that integrate structured and semi-structured data, and to facilitate the reformulation and decomposition of the query, the presented system uses the XML processing language (XPL) for the mediator. With XPL, it is easy to construct mediators for data integration based on XML, and it can accelerate the work in the mediator.

A Dynamic XML-NS View Based Approach for the Extensible Integration of Web Data Sources

We propose a three-step technique to achieve this purpose. First, we utilize a collection of XML namespaces organized into hierarchical structure as a medium for expressing data semantics. Second, we define the format of resource descriptor for the information source discovery scheme so that we can dynamically register and/or deregister the Web data sources on the fly. Third, we employ an inverted-index mechanism to identify the subset of information sources that are relevant to a particular user query. We describe the design, architecture, and implementation of our approach—IWDS, and illustrate its use through case examples. Key words integration - heterogeneity - Web data source - XML namespace CLC number TP 311.13 Foundation item: Supported by the National Key Technologies R&D Program of China(2002BA103A04)Biography: WU Wei (1975-), male, Ph.D candidate, research direction: information integration, distribute computing

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Detection of Alzheimer’s disease onset using MRI and PET neuroimaging:longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer’s disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer’s disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer’s disease onset.

Prioritization of candidate genes for attention deficit hyperactivity disorder by computational analysis of multiple data sources

Attention deficit hyperactivity disorder(ADHD)is a common,highly heritable psychiatric disorder charac-terized by hyperactivity,inattention and increased im-pulsivity.In recent years,a large number of genetic studies for ADHD have been published and related ge-netic data has been accumulated dramatically.To pro-vide researchers a comprehensive ADHD genetic re-source,we previously developed the first genetic data-base for ADHD(ADHDgene).The abundant genetic data provides novel candidates for further study.Meanwhile,it also brings new challenge for selecting promising candidate genes for replication and verification research.In this study,we surveyed the computational tools for candidate gene prioritization and selected five tools,which integrate multiple data sources for gene prioritiza-tion,to prioritize ADHD candidate genes in ADHDgene.The prioritization analysis resulted in 16 prioritized can-didate genes,which are mainly involved in several major neurotransmitter systems or in nervous system development pathways.Among these genes,nervous system development related genes,especially SNAP25,STX1A and the gene-gene interactions related with each of them deserve further investigations.Our results may provide new insight for further verification study and facilitate the exploration of pathogenesis mechanism of ADHD.

Predicting Stock Price Movement with Multiple Data Sources and Machine Learning Models

Stock price trend prediction is a challenging issue in the financial field.To get improvements in predictive performance,both data and technique are essential.The purpose of this paper is to compare deep learning model(LSTM)with two ensemble models(RF and XGboost)using multiple data.Data is gathered from four stocks of financial sector in China A-share market,and the accuracy and F1-measure are used as performance measure.The data of the past three days is applied to classify the rise and fall trend of price on the next day.The models’performance are tested under different market styles(bull or bear market)and different market activities.The results indicate that under the same conditions,LSTM is the top algorithm followed by RF and XGBoost.For all models applied in this study,prediction performance in bull markets is much better than in bear markets,and the result in active period is better than inactive period by average.It is also found that adding data sources is not always effective in improving forecasting performance,and valuable data sources and proper processing may be more essential than providing a large quantity of data source.

A multi-source data fusion modeling method for debris flow prevention engineering

The Digital Elevation Model(DEM)data of debris flow prevention engineering are the boundary of a debris flow prevention simulation,which provides accurate and reliable DEM data and is a key consideration in debris flow prevention simulations.Thus,this paper proposes a multi-source data fusion method.First,we constructed 3D models of debris flow prevention using virtual reality technology according to the relevant specifications.The 3D spatial data generated by 3D modeling were converted into DEM data for debris flow prevention engineering.Then,the accuracy and applicability of the DEM data were verified by the error analysis testing and fusion testing of the debris flow prevention simulation.Finally,we propose the Levels of Detail algorithm based on the quadtree structure to realize the visualization of a large-scale disaster prevention scene.The test results reveal that the data fusion method controlled the error rate of the DEM data of the debris flow prevention engineering within an allowable range and generated 3D volume data(obj format)to compensate for the deficiency of the DEM data whereby the 3D internal entity space is not expressed.Additionally,the levels of detailed method can dispatch the data of a large-scale debris flow hazard scene in real time to ensure a realistic 3D visualization.In summary,the proposed methods can be applied to the planning of debris flow prevention engineering and to the simulation of the debris flow prevention process.

New application of open source data and Rock Engineering System for debris flow susceptibility analysis

This research describes a quantitative,rapid,and low-cost methodology for debris flow susceptibility evaluation at the basin scale using open-access data and geodatabases.The proposed approach can aid decision makers in land management and territorial planning,by first screening for areas with a higher debris flow susceptibility.Five environmental predisposing factors,namely,bedrock lithology,fracture network,quaternary deposits,slope inclination,and hydrographic network,were selected as independent parameters and their mutual interactions were described and quantified using the Rock Engineering System(RES)methodology.For each parameter,specific indexes were proposed,aiming to provide a final synthetic and representative index of debris flow susceptibility at the basin scale.The methodology was tested in four basins located in the Upper Susa Valley(NW Italian Alps)where debris flow events are the predominant natural hazard.The proposed matrix can represent a useful standardized tool,universally applicable,since it is independent of type and characteristic of the basin.

Development of data acquisition and over-current protection systems for a suppressor-grid current with a neutral-beam ion source

Neutral beam injection is one of the effective auxiliary heating methods in magnetic-confinementfusion experiments. In order to acquire the suppressor-grid current signal and avoid the grid being damaged by overheating, a data acquisition and over-current protection system based on the PXI（PCI e Xtensions for Instrumentation） platform has been developed. The system consists of a current sensor, data acquisition module and over-current protection module. In the data acquisition module,the acquired data of one shot will be transferred in isolation and saved in a data-storage server in a txt file. It can also be recalled using NBWave for future analysis. The over-current protection module contains two modes： remote and local. This gives it the function of setting a threshold voltage remotely and locally, and the forbidden time of over-current protection also can be set by a host PC in remote mode. Experimental results demonstrate that the data acquisition and overcurrent protection system has the advantages of setting forbidden time and isolation transmission.

Exploiting open source omics data to advance pancreas research

The"omics"revolution has transformed the biomedical research landscape by equipping scientists with the ability to interrogate complex biological phenomenon and disease processes at an unprecedented level.The volume of"big"data generated by the different omics studies such as genomics,transcriptomics,proteomics,and metabolomics has led to the concurrent development of computational tools to enable in silico analysis and aid data deconvolution.Considering the intensive resources and high costs required to generate and analyze big data,there has been centralized,collaborative efforts to make the data and analysis tools freely available as"Open Source,"to benefit the wider research community.Pancreatology research studies have contributed to this"big data rush"and have additionally benefitted from utilizing the open source data as evidenced by the increasing number of new research findings and publications that stem from such data.In this review,we briefly introduce the evolution of open source omics data,data types,the"FAIR"guiding principles for data management and reuse,and centralized platforms that enable free and fair data accessibility,availability,and provide tools for omics data analysis.We illustrate,through the case study of our own experience in mining pancreatitis omics data,the power of repurposing open source data to answer translationally relevant questions in pancreas research.

An Eigenspace Method for Detecting Space-Time Disease Clusters with Unknown Population-Data

Space-time disease cluster detection assists in conducting disease surveillance and implementing control strategies.The state-of-the-art method for this kind of problem is the Space-time Scan Statistics(SaTScan)which has limitations for non-traditional/non-clinical data sources due to its parametric model assumptions such as Poisson orGaussian counts.Addressing this problem,an Eigenspace-based method called Multi-EigenSpot has recently been proposed as a nonparametric solution.However,it is based on the population counts data which are not always available in the least developed countries.In addition,the population counts are difficult to approximate for some surveillance data such as emergency department visits and over-the-counter drug sales,where the catchment area for each hospital/pharmacy is undefined.We extend the population-based Multi-EigenSpot method to approximate the potential disease clusters from the observed/reported disease counts only with no need for the population counts.The proposed adaptation uses an estimator of expected disease count that does not depend on the population counts.The proposed method was evaluated on the real-world dataset and the results were compared with the population-based methods:Multi-EigenSpot and SaTScan.The result shows that the proposed adaptation is effective in approximating the important outputs of the population-based methods.

Recent Advances of Deep Learning in Geological Hazard Forecasting

Geological hazard is an adverse geological condition that can cause loss of life and property.Accurate prediction and analysis of geological hazards is an important and challenging task.In the past decade,there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques.In particular,great efforts have been made in applying deep learning to predict geohazards.To understand the recent progress in this field,this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards.

Assessment Method of Offshore Wind Resource Based on Multi-dimenssiional Indexes System

Traditional assessment indexes could not fully describe offshore wind resources,for the meteorological properties of offshore are more complex than onshore.As a result,the uncertainty of offshore wind power projects would be increased and final economic benefits would be affected.Therefore,a study on offshore wind resource assessment is carried out,including three processes of“studying data sources,conducting multidimensional indexes system and proposing an offshore wind resource assessment method based on analytic hierarchy process(AHP).First,measured wind data and two kinds of reanalysis data are used to analyze the characteristics and reliability of data sources.Second,indexes such as effective wind speed occurrence,affluent level occurrence,coefficient of variation,neutral state occurrence have been proposed to depict availability,richness,and stability of offshore wind resources,respectively.Combined with existing parameters(wind power density,dominant wind direction occurrence,water depth,distance to coast),a multidimensional indexes system has been built and on this basis,an offshore wind energy potential assessment method has been proposed.Furthermore,the proposed method is verified by the annual energy production of five offshore wind turbines and practical operating data of four offshore wind farms in China.This study also compares the ranking results of the AHP model to two multi-criteria decision making(MCDM)models including weighted aggregated sum product assessment(WASPAS)and multi-attribute ideal real comparative analysis(MAIRCA).Results show the proposed method gains well in practical engineering applications,where the economic score values have been considered based on the offshore reasonable utilization hours of the whole life cycle in China.

Issues on Application of Portable Pilot Units

Portable Pilot Units(PPU)play an important role in modern pilotage.With more than 20 years’PPU development and practice,a comprehensive data analysis is conducted in this paper.The reliabilities and accuracy of different sensors are compared.Finally,the risk of PPU piloting and the corresponding countermeasures is discussed.

Analysis for integrated energy system:Benchmarking methods and implementation

The selection of suitable models and solutions is a fundamental requirement for con-ducting energy flow analysis in integrated energy systems(IES).However,this task is challenging due to the vast number of existing models and solutions,making it difficult to comprehensively compare scholars'studies with current work.In this paper,we aim to address this issue by presenting a comprehensive overview of mainstream IES models and clarifying their relationships,thereby providing guidance for scholars in selecting appro-priate models.Additionally,we introduce several widely used solvers for solving algebraic and differential equations,along with their detailed implementations in the energy flow analysis of IES.Furthermore,we conduct extensive testing and demonstration of these models and methods in various cases to establish benchmarking datasets.To facilitate reproducibility,verification and comparisons,we provide open‐source access to these datasets,including system data,analysis settings and implementations of the various solvers in the mainstream models.Scholars can utilise the provided datasets to reproduce the results,verify the findings and perform comparative analyses.Moreover,they have the flexibility to customise these settings according to their specific requirements.

Expeditious management plan towards digital earth

The breakthrough developments in geospatial technologies and the increasing availability of spatial data make geoinformation a business and a decisional element to the management.Hence,it is important to have a management plan to factor in practical and feasible data sources,in building geo applications.The authors of this paper are motivated by the fact that right data sources could outclass in-house resources in various application scenarios.This paper outlines pragmatic cases for the tangible benefits of the existing potential data and expeditious patterns for digital earth.This work also proposes‘good-enough’solutions based on the pragmatic cases,available literature,and the 3D city model developed that could be sufficient in contriving the objectives of the common public usage and open business models.To demonstrate this approach,the paper encapsulated the low-cost development of virtual 3D city model using publicly available cadastral data and web services.

Trust Attestation Mechanism for the Sensing Layer Nodes of Internet of Things

The main function of Internet of Things is to collect and transmit data.At present,the data transmission in Internet of Things lacks effective trust attestation mechanism and trust traceability mechanism of data source.To solve the above problems,a trust attestation mechanism for sensing layer nodes is presented.First a trusted group is established,and the node which is going to join the group needs to attest its identity and key attributes to the higher level node.Then the dynamic trust measurement value of the node can be obtained by measuring the node data transmission behavior.Finally the node encapsulates the key attributes and trust measurement value to use short message group signature to attest its trust to the challenger.This mechanism can measure the data sending and receiving behaviors of sensing nodes and track the data source,and it does not expose the privacy information of nodes and the sensing nodes can be traced effectively.The trust measurement for sensing nodes and verification is applicable to Internet of Things and the simulation experiment shows the trust attestation mechanism is flexible,practical and efficient.Besides,it can accurately and quickly identify the malicious nodes at the same time.The impact on the system performance is negligible.