The occurrence of geological hazards is not only affected by natural factors, but is also closely related to human engineering activities. In order to systematically integrate information related to multi-source and heterogeneous geological hazards and to improve the ability of hazard awareness and risk assessment, this paper proposes a multi-level and multiscale geological hazards knowledge graph construction method. The method constructs a multilevel geohazard ontology model containing elements such as hazard types, hazard-causing factors, hazard events, and hazard-bearing bodies, and introduces a multiscale spatio-temporal coding mechanism, integrating geological and human activity data at different spatial and temporal scales, to achieve the structured expression and organization of the hazard knowledge.

Dense time-series remote sensing images have transformed the traditional bitemporal land-cover change detection to continuous monitoring. Previous work mostly employs linear fitting, prediction, or decomposition methods, and the detection accuracy is not high. The latest progress of deep learning (DL) shows its advantages in time-series change monitoring. However, DL models are computationally expensive and require lots of labeled samples, resulting in often employed prediction-threshold-based unsupervised change detection method. However, the determination of a reasonable threshold has always been a big problem. Therefore, we proposed the similarity-measurement-based deep transfer learning for time-series adaptive change detection (SDTL-TSACD) model. First, a standard dynamic time warping (SDTW) distance was proposed and used to cluster large-scale time series into multiple subcategories with high time-series similarity. Second, a time convolutional network (TCN) was used for nonlinear time-series fitting and prediction, and an early stop strategy was used to prevent overfitting. Then, the trained TCN model would be transferred and performed pixel-by-pixel time-series prediction within the same category, and the SDTW was also used to evaluate the prediction accuracy. Finally, the Otsu adaptive threshold was used to detect change points, and the spatial neighbor relationship was used to eliminate the pseudo-change points. Change detection results using 132 benchmark datasets showed that the SDTL-TSACD performed well in both accuracy and efficiency. In addition, the MOD13Q1-EVI images from 2001 to 2020 were used to study the land-cover change of the Loess Plateau, and the SDTL-TSACD also showed a good ability to solve practical problems.

In recent years, advanced smart contract languages (ASCLs) have been proposed to solve the problem of difficult reading, comprehension, and collaboration when writing smart legal contracts among people in different fields. However, this kind of languages are still hard to put into practice due to the lack of an effective conversion method from the ASCLs to executable smart contract programs. Aiming at this problem, we take SPESC as example to explore how to design conversion rules from the contract in it to the target programming language in Solidity, and to propose a three-layer smart contract framework, including advanced smart-contract layer, general smart-contract layer, and executable machine-code layer. These rules provide an approach to convert the definition of SPESC contracting parties into party-contracts on target language, as well as to produce SPESC contract terms into main-contract on target language. Moreover, the proposed framework specifies not only program architecture and storage structure on general smart-contract layer, but also important mechanisms, including personnel management, timing control, exception handling, etc., which can assist programmers to write smart contract programs. Furthermore, taking four SPESC contracts as testing objects, we provide the whole process of converting from SPESC contracts to Solidity programs by the SPESC-Translator, and verify the efficiency and security of the conversion process, including coding, deploying, running, and testing through Ethereum. The instance results show that the conversion rules and the three-layer framework can simplify the writing of smart contracts, standardize the program structure, and help programmers to verify the correctness of the contract programs.

Smart contract is a set of digital executable protocols intended to make contractual clauses partially or fully self-executing, self-enforcing, or both. As the second-generation blockchain technology, smart contracts have greatly enriched the functional expression of blockchain to make application development more convenient. All related data of smart contracts, including program codes, intermediate states, and executed results, will be stored in blockchain to ensure that these data are not tampered with. Also, the consensus protocols of blockchain verify the correctness of the running process by executing the smart contract with the same input at all nodes. Therefore, the blockchain’s security mechanism with tamper-proof and traceable makes it possible for smart contracts to be recognized by law.

Aiming at insufficient situation to express and operate assets in smart contracts, in this article we attempt to add a new asset model into smart contract language (such as SPESC) through combing method of asset's expressions and transactions in real-world contracts. Moreover, a translation mechanism can be set up to accomplish a conversion from the asset model to an executable contract program. On this basis, we propose a new language design toward asset-driven specific smart contracts, called TA-SPESC. This language complies with the structure of real-world contracts and supports a formal definition composed of four modules: Party, asset, term, and contract attribute. This asset model on it can be used to define various types of rights (including the right of ownership, use, possession, usufruct, and disposition of assets), as well as five asset operations (including asset registration, deposit, withdrawal, transfer, and cancellation) to effectively support asset transaction. More important, a series of generation rules are proposed to translate the TA-SPESC contract to an executable contract program. Moreover, taking house rental contract as an example, we provide a TA-SPESC instance and its specific description of translation process according to the generation rules, which supports a semiautomatic generation to executable programs. Finally, the Solidity codes derived from TA-SPESC contracts are run and tested, and the experiment and comparison results indicate that TA-SPESC contracts have high abstraction and low complexity, as well as versatility and convenience of asset transaction, which lead to more reliable software with less errors and fewer misunderstanding.

With the advent of the big data era, information systems have exhibited some new features, including boundary obfuscation, system virtualization, unstructured and diversification of data types, and low coupling among function and data. These features not only lead to a big difference between big data technology (DT) and information technology (IT), but also promote the upgrading and evolution of network security technology. In response to these changes, in this paper we compare the characteristics between IT era and DT era, and then propose four DT security principles: privacy, integrity, traceability, and controllability, as well as active and dynamic defense strategy based on "propagation prediction, audit prediction, dynamic management and control". We further discuss the security challenges faced by DT and the corresponding assurance strategies. On this basis, the big data security technologies can be divided into four levels: elimination, continuation, improvement, and innovation. These technologies are analyzed, combed and explained according to six categories: access control, identification and authentication, data encryption, data privacy, intrusion prevention, security audit and disaster recovery. The results will support the evolution of security technologies in the DT era, the construction of big data platforms, the designation of security assurance strategies, and security technology choices suitable for big data.

SUMMARY Earth observation applications are now facing the challenges of managing and processing massive data sets from multiple sources from large‐scale distributed data centers (DCs). To solve this research problem, this paper presents an infrastructure of multiple data centers (MDC) for managing and processing massive remote sensing images. The proposed system is built on both groups of distributed DCs/clusters, which are equipped with DC or cluster resource manager. Access security and information service are introduced to support this architecture of MDC. We collaboratively organized the algorithm, and data belonged to the MDC in the manner of workflow. In practice, we succeeded in working out the concrete problems regarding procedures in processing applications collaboratively and transfer the massive remote sensing dataset fast and with stable cross‐MDC. On the basis of the previously mentioned research work, we will investigate the platform integration of MDC. Copyright © 2012 John Wiley & Sons, Ltd.

Spatial data processing in general involves large-scale distributed computing resources across multiple data centers and distributed massive data sets. Task scheduling of spatial data processing across distributed data centers brings new research issues and calls for novel scheduling mechanisms and frameworks. This paper analyzes the features of spatial data processing, identifies the research issues of task scheduling in massive spatial data processing, and proposes task model & scheduling mechanisms for task scheduling in spatial data processing.