China Argo project:progress in China Argo ocean observations and data applications

This paper reviews the current achievements of the China Argo project. It considers aspects of both the construction of the Argo observing array, float technology, and the quality control and sharing of its data. The developments of associated data products and data applications for use in the fields of ocean, atmosphere, and climate research are discussed, particularly those related to tropical cyclones （typhoons）, ocean circulation, mesoscale eddies, turbulence, oceanic heat/salt storage and transportation, water masses, and operational oceanic/atmospheric/climatic forecasts and predictions. Finaliy, the challenges and opportunities involved in the long-term maintenance and sustained development of the China Argo ocean observation network are outlined. Discussion also focuses on the necessity for increasing the number of floats in the Indian Ocean and for expanding the regional Argo observation network in the South China Sea, together with the importance of promoting the use of Argo data by the maritime countries of Southeast Asia and India.

A Distributed In-Memory Database Solution for Mass Data Applications

In this paper, a Distributed In-Memory Database （DIMDB） system is proposed to improve processing efficiency in mass data applications. The system uses an enhanced language similar to Structured Query Language （SQL） with a key-value storage schema. The design goals of the DIMDB system is described and its system architecture is discussed. Operation flow and the enhanced SOL-like language are also discussed, and experimental results are used to test the validity of the system.

Rough-Set-Based Attribute Dependencies: Foundations and Bio-Medical Data Applications

Theory of rough sets, proposed by Zdzislaw Pawlak in 1982, is a model of approximate reasoning. In applications, rough set methodology focuses on approximate representation of knowledge derivable from data. It leads to significant results in many areas including, for example, finance, industry, multimedia, medicine, and most recently bioinformatics.

Exploration and Practice of Big Data Introductory Courses for Big Data Management and Application Majors

As an introductory course for the emerging major of big data management and application,“Introduction to Big Data”has not yet formed a curriculum standard and implementation plan that is widely accepted and used by everyone.To this end,we discuss some of our explorations and attempts in the construction and teaching process of big data courses for the major of big data management and application from the perspective of course planning,course implementation,and course summary.After interviews with students and feedback from questionnaires,students are highly satisfied with some of the teaching measures and programs currently adopted.

Call for Papers Special Issue on Cloud Computing and Big Data Applications

The publication of Tsinghua Science and Technology was started in 1996. Since then, it has been an international academic journal sponsored by Tsinghua University and published bimonthly. This journal aims at presenting the state-of-art scientific achievements in computer science and other IT fields.

A Short Review of Classification Algorithms Accuracy for Data Prediction in Data Mining Applications

Many business applications rely on their historical data to predict their business future. The marketing products process is one of the core processes for the business. Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time. Moreover, services are considered recently as products. The development of education and health services is depending on historical data. For the more, reducing online social media networks problems and crimes need a significant source of information. Data analysts need to use an efficient classification algorithm to predict the future of such businesses. However, dealing with a huge quantity of data requires great time to process. Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications. The classification technique is one of the most widely used with a variety of algorithms. In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications. A comprehensive analysis is made after delegated reading of 20 papers in the literature. This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education. Results show FFBPN is the most accurate algorithm in the business domain. The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities. Naïve Bayes algorithm is the most accurate to classify agriculture datasets. OneR is the most accurate algorithm to classify instances within the health domain. The C4.5 Decision Tree algorithm is the most accurate to classify students’ records to predict degree completion time.

Improving Performance of Cloud Computing and Big Data Technologies and Applications

Cloud computing technology is changing the development and usage patterns of IT infrastructure and applications. Virtualized and distributed systems as well as unified management and scheduling has greatly im proved computing and storage. Management has become easier, andOAM costs have been significantly reduced. Cloud desktop technology is develop ing rapidly. With this technology, users can flexibly and dynamically use virtual ma chine resources, companies＇ efficiency of using and allocating resources is greatly improved, and information security is ensured. In most existing virtual cloud desk top solutions, computing and storage are bound together, and data is stored as im age files. This limits the flexibility and expandability of systems and is insufficient for meetinz customers＇ requirements in different scenarios.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

Internet of Vehicles in Big Data Era

As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.

Research on the Development and Application of Campus Card in Colleges and Universities

With the rapid development of the construction of smart campus in Colleges and universities and the maturity of related technologies,campus card has become the most frequently used and the most frequently used core component of smart campus.Based on the actual construction of onecard system in domestic universities and the author’s years of experience in campus card management,this paper makes a systematic study on the development of campus card,virtual campus card,big data application,information security and other aspects,with a view to providing effective reference for the construction of campus card in Colleges and universities.

基于BIM技术的铁路车站智能信息管理模式研究

铁路车站内部结构与设施复杂,涉及不同专业之间的配合与协调,从设计、施工到运营全过程中所涉及的工程资料信息复杂繁多。传统铁路车站工程信息多以纸质形式呈现,存在不易拆分、保存与整理费时费力等弊端,难以适应快速发展的铁路现代化建设需求。建筑信息模型(BIM)技术在可视化、参数化等方面的优势突出,将BIM技术引入铁路车站工程信息的综合管理中,可实现铁路车站关键结构的三维可视化建模、工程资料实时更新与调用等功能。同时,通过外接虚拟现实设备,可实现管理人员在全生命周期的任意时段进行场景直观漫游和交互操作,可辅助车站的设计、施工和运营工作,对提高铁路建设管理水平具有重要的现实意义。

Intelligent decision support platform of new energy vehicles

New energy vehicles(NEVs) are gaining wider acceptance as the transportation sector is developing more environmentally friendly and sustainable technology. To solve problems of complex application scenarios and multi-sources heterogenous data for new energy vehicles and weak platform scalability,the framework of an intelligent decision support platform is proposed in this paper. The principle of software and hardware system is introduced. Hadoop is adopted as the software system architecture of the platform. Master-standby redundancy and dual-line redundancy ensure the reliability of the hardware system. In addition, the applications on the intelligent decision support platform in usage patterns recognition, energy consumption, battery state of health and battery safety analysis are also described.

Student Performance Prediction via Attention-Based Multi-Layer Long-Short Term Memory

Online education has attracted a large number of students in recent years, because it breaks through the limitations of time and space and makes high-quality education at your fingertips. The method of predicting student performance is to analyze and predict the student’s final performance by collecting demographic data such as the student’s gender, age, and highest education level, and clickstream data generated when students interact with VLE in different types of specific courses, which are widely used in online education platforms. This article proposes a model to predict student performance via Attention-based Multi-layer LSTM (AML), which combines student demographic data and clickstream data for comprehensive analysis. We hope that we can obtain a higher prediction accuracy as soon as possible to provide timely intervention. The results show that the proposed model can improve the accuracy of 0.52% - 0.85% and the F1 score of 0.89% - 2.30% on the four-class classification task as well as the accuracy of 0.15% - 0.97% and the F1 score of 0.21% - 2.77% on the binary classification task from week 5 to week 25.

BIG DATA:UNLEASHING INFORMATION

At present, it is projected that about 4 zettabytes （or 10^＊＊21 bytes） of digital data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors （i.e., services, manufacturing, construction, agriculture and mining） of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien （2003） forewarned about the data rich, information poor （DRIP） problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed - yet, not necessarily defensible or valid - decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges （identified by the National Academy of Engineering in 2008）, underpin the 10 breakthrough technologies （compiled by the Massachusetts Institute of Technology in 2013） and support the Third Industrial Revolution of mass customization.

Development,application,and prospects for Chinese land observation satellites

The launching of CBERS-01(China Brazil Earth Resource Satellite)in 1999,China’s first land observation satellite,signifies an unprecedented milestone in Chinese satellite remote sensing history.Since then,a large number of applications have been developed that drew upon solely CBERS-01 and other Chinese land observation satellites.The application development evolves from one satellite to multiple satellites,from one series of satellites to multiple series,from scientific research to industrial applications.Six aspects of the Chinese land observation satellite program are discussed in this paper:development status,data sharing and distribution,satellite calibration,industrial data applications,future prospects,and conclusion.

Evaluation of the applicability of climate forecast system reanalysis weather data for hydrologic simulation： A case study in the Bahe River Basin of the Qinling Mountains, China

In recent years, global reanalysis weather data has been widely used in hydrological modeling around the world, but the results of simulations vary greatly. To consider the applicability of Climate Forecast System Reanalysis（CFSR） data in the hydrologic simulation of watersheds, the Bahe River Basin was used as a case study. Two types of weather data（conventional weather data and CFSR weather data） were considered to establish a Soil and Water Assessment Tool（SWAT） model, which was used to simulate runoff from 2001 to 2012 in the basin at annual and monthly scales. The effect of both datasets on the simulation was assessed using regression analysis, Nash-Sutcliffe Efficiency（NSE）, and Percent Bias（PBIAS）. A CFSR weather data correction method was proposed. The main results were as follows.（1） The CFSR climate data was applicable for hydrologic simulation in the Bahe River Basin（R^2 of the simulated results above 0.50, NSE above 0.33, and ｜PBIAS｜ below 14.8. Although the quality of the CFSR weather data is not perfect, it achieved a satisfactory hydrological simulation after rainfall data correction.（2） The simulated streamflow using the CFSR data was higher than the observed streamflow, which was likely because the estimation of daily rainfall data by CFSR weather data resulted in more rainy days and stronger rainfall intensity than was actually observed. Therefore, the data simulated a higher base flow and flood peak discharge in terms of the water balance, except for some individual years.（3） The relation between the CFSR rainfall data（x） and the observed rainfall data（y） could berepresented by a power exponent equation： y=1.4789x0.8875（R2=0.98,P〈0.001）. There was a slight variation between the fitted equations for each station. The equation provides a theoretical basis for the correction of CFSR rainfall data.

Sensor data compression based on MapReduce

A compression algorithm is proposed in this paper for reducing the size of sensor data. By using a dictionary-based lossless compression algorithm, sensor data can be compressed efficiently and interpreted without decompressing. The correlation between redundancy of sensor data and compression ratio is explored. Further, a parallel compression algorithm based on MapReduce [1] is proposed. Meanwhile, data partitioner which plays an important role in performance of MapReduce application is discussed along with performance evaluation criteria proposed in this paper. Experiments demonstrate that random sampler is suitable for highly redundant sensor data and the proposed compression algorithms can compress those highly redundant sensor data efficiently.

A Private User Data Protection Mechanism in TrustZone Architecture Based on Identity Authentication

In Trust Zone architecture, the Trusted Application（TA） in the secure world does not certify the identity of Client Applications（CA） in the normal world that request data access, which represents a user data leakage risk. This paper proposes a private user data protection mechanism in Trust Zone to avoid such risks. We add corresponding modules to both the secure world and the normal world and authenticate the identity of CA to prevent illegal access to private user data. Then we analyze the system security, and perform validity and performance tests.The results show that this method can perform effective identity recognition and control of CA to protect the security of private user data. After adding authentication modules, the data operation time of system increases by about0.16 s, an acceptable price to pay for the improved security.

STEP AP 242 Managed Model-based 3D Engineering:An Application Towards the Automation of Fixture Planning

Fixture design and planning is one of the most important manufacturing activities, playing a pivotal role in deciding the lead time for product development. Fixture design, which affects the part-quality in terms of geometric accuracy and surface finish, can be enhanced by using the product manufacturing information(PMI) stored in the neutral standard for the exchange of product model data(STEP) file, thereby integrating design and manufacturing. The present paper proposes a unique fixture design approach, to extract the geometry information from STEP application protocol(AP) 242 files of computer aided design(CAD) models, for providing automatic suggestions of locator positions and clamping surfaces. Automatic feature extraction software "FiXplan", developed using the programming language C#, is used to extract the part feature, dimension and geometry information. The information from the STEP AP 242 file is deduced using geometric reasoning techniques, which in turn is utilized for fixture planning. The developed software is observed to be adept in identifying the primary, secondary, and tertiary locating faces and locator position configurations of prismatic components. Structural analysis of the prismatic part under different locator positions was performed using commercial finite element method software, ABAQUS, and the optimized locator position was identified on the basis of minimum deformation of the workpiece.The area-ratio(base locator enclosed area(%)/work piece base area(%)) for the ideal locator configuration was observed as 33%. Experiments were conducted on a prismatic workpiece using a specially designed fixture, for different locator configurations. The surface roughness and waviness of the machined surfaces were analysed using an Alicona non-contact optical profilometer. The best surface characteristics were obtained for the surface machined under the ideal locator positions having an area-ratio of 33%, thus validating the predicted numerical results. The efficiency, capability and applicability of the developed software is demonstrated for the finishing operation of a sensor cover – a typical prismatic component having applications in the naval industry, under different locator configurations.The best results were obtained under the proposed ideal locator configuration of area-ratio 33%.

Toward a data analytics -based approach for computing and predicting context- sensitive teaching effectiveness

The traditional student-oriented course evaluation has been the major assessment method on teaching effectiveness worldwide.Useful as it is,it has been widely and continuously criticized for not being a fair,accurate,and reliable measurement.In search of a more objective assessment method on teaching effectiveness that also reflects the impacts of context-based learning,we propose a theoretical approach from a unique perspective that recognizes teaching effectiveness as a result of the interplays between teacher,student,and context.The approach can be used to compute as well as to predict teaching effectiveness using machine and deep learning technologies,which brings strategical benefits to institutional management.In addition,we install into the approach a mechanism using tokens as incentives to assure the quality of subjective data input.The application framework for the approach is proposed leveraging blockchain.Each implementation of the framework by an establishment is a decentralized application that runs on its chosen blockchain.It is envisioned that the implementations together will form a collective ecology on context-based relative teaching effectiveness,which has the potential to fundamentally impact other academic practices besides teaching effectiveness measurement.The theoretical approach provides a common language to delineate teaching effectiveness from the context-based relative perspective and is customizable during implementation.The teaching effectiveness assessment using the approach downplays the roles played by bias(subjectity)and hence is more objective than that by traditional student-oriented course evaluation.