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This paper takes smart cartography in the context of artificial intelligence as the starting point, analyzes the impact of space-time big data on cartography, points to three possible directions for the evolu‐ tion of the map, explains the connotation of smart cartography and its characteristics of crowd-sourced da‐ ta, user participation, model coupling and visual analysis, discusses in depth the explicit and implicit knowl‐ edge representation of smart cartography, proposes the idea of establishing a map knowledge graph, and outlines a platform development paradigm based on low-code technology. [ABSTRACT FROM AUTHOR]

Considering the unique users of scholars' social networks, this paper proposed a collaborative association model of paper and project data based on scholars' social networks. Firstly, the proposed model used the two-step feature selection method to preprocess the data, removed the irrelevant and redundant features and obtained the effective features that affected the association between the paper and the project. Then it would adopt TVSM to calculate the text similarity between the paper and the project, then formed recommendation sets for different papers/projects. Through the social network (SCHOLAT) data for researchers, the model was implemented and applied to SCHOLAT. The online application situation and user feedback show that the model has good accuracy and practicability. Furthermore, it can more fully explore the potential relationship between the paper and the project, provide users with better academic research management services, and propose a novel research method for analyzing the academic big data. [ABSTRACT FROM AUTHOR]

The Communist Party of China's 19th National Congress Report has clearly set the goal of "smart society" and put forward new requirements for the current development of territorial spatial planning. Nowadays, the empowerment of information technology based on the Internet, big data, cloud computing, etc. is the main driving force and development focus of the current practices of smart territorial spatial planning. How to understand and promote the development of smart society is the foundation of the compilation and implementation of smart territorial spatial planning. This paper emphasizes the importance of human-land relationship and the theory of urban life organism to the planning and governance of territory in a smart society. It points out that the overall conceptualization of smart territorial spatial planning should be transformed from informational empowerment to comprehensive empowerment, which includes technological empowerment and innovative empowerment. The paper constructs a smart territorial spatial planning framework-EPTI-based on the ideas of ecological civilization, people- oriented, technology integration application and institutional innovation, and discusses the paths toward smart compilation and implementation of territorial spatial planning. [ABSTRACT FROM AUTHOR]

In the ship information big data system, aiming at the problems such as long time consuming, distortion of thermal map effect and poor tracking or lag in the roaming of map software when the client requests a large amount of historical data from the server and draws heat map in real time, this paper proposes an improved real-time generation strategy of map map of ship history activity. Based on the thermal map generation method with the idea of pixel clustering, this strategy uses the image enhancement algorithm based on nonlinear transformation and pixel complement method to correct and optimize the display distortion problems existing in the head map. On this basis, the dynamic loading and drawing strategy and multi-thread real-time mapping strategy are proposed to generate the heat map of the ship’s historical activities. Experiments show that the strategy proposed in this paper has been improved in the efficiency of heat map drawing, mapping effect and interactive experience of map software. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence continues to heat up. Large language models have attracted much attention and set off a wave of enthusiasm around the world. The success of artificial intelligence is not essentially a "miracle" of large computing power, but a change in computing models. Firstly, this paper affirms the fundamental role of data in AI, and points out that synthetic data will be the main source of data in the future. Then, this paper reviews the development of computing models, highlights the historic competition between neural network models and Turing models. We points out that the important hallmark of large language models is the emergence of intelligence in machines, emphasizes that the essence of large language models is "compression", and analyzes the reasons for the "illusion" of large language models. Finally, we call on the scientific community to attach importance to large scientific models in "AI for research(AI4R)". [ABSTRACT FROM AUTHOR]

[Purpose/Significance] In recent years, the national leadership has attached great importance to the inheritance and innovation of China's excellent traditional culture, and the concepts of creative transformation and innovative development are constantly evolving. The Ministry of Science and Technology of China, among six departments, jointly formulated the "Guidelines for Promoting the Deep Integration of Culture and Technology", proposing to form a "cultural and technological integration innovation system covering key areas and critical links, to achieve the goal of in-depth integration of culture and technology". Intangible cultural heritage (ICH), as an essential part of China's excellent traditional culture, plays a crucial role in consolidating the sense of the Chinese national community and enhancing cultural self-confidence. This paper discusses the research paths for the creative transformation and innovative development of ICH in the context of culture and technology integration, emphasizing the significant role of modern technology in promoting the transformation and development of ICH. [Method/Process] The paper first interprets the connotation of "creative transformation and innovative development" in ICH, and clarifies the theoretical foundation and guiding principles of "creative transformation and innovative development" in ICH. It then analyzes the significant contributions of "creative transformation and innovative development" in ICH to society, economy, and cultural diversity, as well as the categorized scenarios of technology empowerment in "creative transformation and innovative development" of ICH. It discusses six types of common key technologies that enable technology-driven "creative transformation and innovative development" in ICH, including digital technology, virtual and augmented reality, big data analysis, artificial intelligence, cloud computing, and the Internet of Things. It summarizes a three-tier framework system for technology-driven "creative transformation and innovative development" in ICH, namely the method layer, technology layer, and object layer, forming a progressive relationship from theory to technology and then to specific practices. Finally, by integrating the specific example of the traditional craft of Wuhu iron painting, it demonstrates the facilitating role of modern technology in the "creative transformation and innovative development" of ICH and its impact on the protection, inheritance, and innovative development of ICH. [Results/Conclusions] The findings suggest that technological means can effectively protect and inherit ICH, facilitating its creative transformation and innovative development. However, it also requires careful consideration and precautions against the potential risks and challenges that modern technology poses to ICH projects in terms of intellectual property rights, the digital divide, and the indirect nature of experiences. Looking to the future, with the emergence of more innovative technologies, modern technology will not only help to protect and pass on traditional culture but also give new connotations and expressions to traditional culture, ensuring that its unique value and charm continue to play out in an ever-changing world. [ABSTRACT FROM AUTHOR]

Clinical prediction models can predict individual outcomes or risks based on effect evidence obtained from the population, serving as a powerful tool linking evidence-based medicine and precision medicine. With the continuous advancement of data measurement, storage, interoperability, and analytical techniques, the application prospect for clinical prediction models is becoming increasingly clear. However, it also faces many problems and challenges. This paper aims to provide an overview of the fundamental concepts, application scenarios, current dilemmas, and development opportunities of clinical prediction models [ABSTRACT FROM AUTHOR]

Taking the Chaohu Lake region as the research object, this paper studies the structural characteristics and influencing factors of the rural tourism's source market in the Chaohu Lake region based on the travel notes data of rural tourism from travel websites from 2016 to 2021, employing the combination of statistical analysis and ArcGIS spatial analysis. The results show that: the rural tourism's source market is seasonal with prominent holiday season and weekend effects. April to October is the high season, and November to March is the low season; the geographical concentration index of rural tourism's source market is generally high. The tourist source is characterized by concentrated distribution, with Hefei as the main tourist source, and the average attraction radius of tourists is 219.59km, which belongs to the short-range source market. There are other important influencing factors including leisure time, climate comfort, transportation, and economic development level of tourist source destinations. [ABSTRACT FROM AUTHOR]

Aiming at the difficulties in the quantitative verification and evaluation of underwater countermeasures for our underwater platform and the lack of a guided maneuvering evasive combat model, a prediction model of underwater countermeasures based on big data learning is designed. Firstly, the underwater countermeasure modeling of the underwater platform is carried out, and the large data set of avoidance probability is obtained by several rounds of simulation based on Monte Carlo method. At the same time, in order to solve the problem of poor time efficiency under massive simulation, BP neural network prediction algorithm is proposed for big data learning to provide accurate, fast and visual antagonistic results. The test results show that under the test environment set in this paper, the average prediction error of BP neural network prediction algorithm is 7. 28% respectively, which can effectively predict the avoidance probability of the underwater platform and provide data support for the commander's command decision. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Talent data have become the most important production factor and strategic resource. Building a scientific and technological (S&T) talent database has become an inevitable way to narrow the digital divide and accelerate the digital and intelligent transformation of talent work. Therefore, this study builds an S&T talent database to promote scientific decision-making for talent development, precision in attracting new quality technical talent, reform in evaluating S&T talents, and building talent system for new quality productive forces. [Method/Process] By analyzing the practical requirements and significance of building an S&T talent database, this study first explores and analyzes the intrinsic logic of promoting the development of new quality productive forces through an S&T talent database. It then summarizes the challenges facing the current construction of a S&T talent database, including the scattering and concealment of S&T talent data, the lack of policies and standardized systems for S&T talent data, the inadequate exploration of value-added S&T talent data, the need to expand the application of digital technology in talent work, and the security risks of S&T talent data. In response to these challenges, this paper finally proposes the idea of building an S&T talents database, and introduces the research exploration and application practice on it, including the construction of big data database for S&T talent aimed at the development of new quality productive forces, the development of AI-powered talent data computing engine, research into the system for profiling new quality technical talent, the analysis of talent growth paths for the training of new quality technical talent, the identification method of new quality talented professionals based on big data, the development of an efficient digital platform for talent management, and the development of a strategic analysis platform for technical talent. [Results/Conclusions] The construction of S&T talent database is an objective requirement for the development of the digital era and an inevitable requirement for the formation of new quality productive forces. Building big data for S&T talent, empowering talent workflow with big data and artificial intelligence technology can help empower talent workflow, release the enormous energy contained in digitalization, effectively activate the internal momentum of talented professionals, institutions, society, and government, and then continuously improve the efficiency of talent resource allocation, the operational efficiency of talent work, the overall effectiveness of talent development governance, and promote the development of new quality productive forces. [ABSTRACT FROM AUTHOR]

Due to the lack of tailored security and privacy technology in many fields such as Internet of Things, artificial intelligence and big data, the system based on distributed ledger technology (DLT) can well meet their the security requirements as a decentralized data management method. Consequently, systems based on distributed ledger technology emerge in an endless stream. In order to help subsequent researchers understand the field of distributed ledger technology more comprehensively and quickly, this paper reviewed the research results based on distributed ledger technology in recent years. It analyzed the blockchain in depth from the perspectives of data structure, technical principle, consensus mechanism and performance, and compared and analyzed some recent advances in distributed ledger technology in detail based on the current research status, such as Hashgraph, Holochain and Truechain. Finally, this paper summarized the future development prospect of this field. [ABSTRACT FROM AUTHOR]

Big data, a vital driving force of high-quality economic development in China, improves both the speed and efficiency of information exchange and knowledge dissemination. Existing studies have conducted a series of discussions on the dividend effect of big data development, but few have analyzed it from the perspective of corporate green development. Based on the theoretical analysis of the impact of big data development on the internal mechanisms of corporate green technology innovation and on the regulatory role of intellectual property protection, in light of the exogenous policy shock from the ‘National Big Data Comprehensive Pilot Zones,’ this paper studied the A-share listed companies in Shanghai and Shenzhen by adopting the difference-in-differences model (DID) to evaluate the impact of big data development on corporate green technology innovation and the moderating effect of intellectual property protection between them. The research results showed that the development of big data significantly promoted corporate green technology innovation, and that the conclusion still held after a series of robustness tests. Heterogeneity tests showed that the promoting effect of big data development on corporate green technology innovation was more observable in large-scale enterprises, state-owned businesses, and companies with high-level access to data from local governments. The mechanism test indicated that the development of big data could promote corporate green technology innovation by improving the intensity of regional environmental regulation, easing the financing constraints of enterprises, and improving their human capital level. The moderating effect test showed that intellectual property protection played a significant positive moderating role between big data development and corporate green technology innovation. Accordingly, the government should improve the big data development policies to promote deep integration of big data and the real economy in a consistent manner, reinforce the system design concerning corporate green technology innovation guided by policies of big data development, and combine big data development with green development goals such as achieving peak carbon emissions and carbon neutrality. Meanwhile, policies should be refined based on different types of enterprises in different regions so that a long-term mechanism can be established to support corporate green innovation development. It is also necessary to strengthen the construction of an intellectual property protection system so as to accelerate corporate green technology innovation and green development more effectively. [ABSTRACT FROM AUTHOR]

Firstly, this paper discusses the change in the way of thinking and working of the cartographers and cartographic engineering through data, big data and space-time big data, which promotes cartography to enter a new era of the “fourth scientific paradigm” characterized by “data-intensive computing”. Secondly, this paper analyzes the functions and features of the digital transformation results of 4D products, and the progress in diversification and individuation of the map products, and the “design - compilation-publication” integrated digital transformation of mapping, and the feature of the representative atlases published in 2021. Finally, by giving two specific examples, i.e., the intellectualization of map generalization and digital mapping, this paper reviews four evolutionary stages of map generalization in the era of artificial intelligence and summarizes the difficulties and breakthroughs in key technologies, and provides a profound conclusion of the research status and the issues to be further explored in the domain. [ABSTRACT FROM AUTHOR]

Geographic Information System has made great progress in theory and technology in recent 60 years. The application field of GIS has expanded to all aspects of society, and the social influence is increas‐ able growing. The architecture, development mode and service mode of GIS have undergone profound changes. In order to promote the further development of GISystem, this paper focused on the following three issues based on summarizing the three meanings of “S” in “GIS”, namely “System” “Science” and “Service”, and the fruitful achievements of “GIS” in recent 60 years. The first issue is how to un‐ derstand GISystem. This issue should be analyzed from two aspects, and one is to discuss the connotation of geographic information system through analyzing the three key words (namely system, information and geographic) of GIS. The other one is to analyze the relationship between GIS and map, computer mapping and map database. This paper holds the idea that GIS originates from and goes beyond map, computer map‐ ping and map database. It should be considered that GIS has the characteristics of equipment. The second issue is how does GIS evolve. This issue focuses on the social demand background, technical background and discipline background of the development and evolution from “Geographic Information System” to “Geographic Information Service”. It also focuses on the main manifestations of the development and evo‐ lution of GIS from the aspects of application field, expansion of data resources and functions, architecture, development mode and service mode. The third issue is where will the future development of GIS go. We discussed that the limit application domain expansion of GIS and point out that the future development of GIS must face the urgent needs of national economic construction and national defense construction. Based on the analysis and comparison of three existing GISystem service modes, we consider that the “hybrid” technology system of “grid integration” and “elastic cloud” is the best choice for GISystem service mode. Finally, this paper puts forward six key technical problems that must be solved in the implementation of “hybrid” spatiotemporal big data platform technology based on “grid integration” and “elastic cloud”and designs the application mode of “spatiotemporal big data platform”. [ABSTRACT FROM AUTHOR]

【Objective】The intersection of big data and research on ‘three rural’ issues has created a research field with great potential. By combing the research results in this field, this paper has an in-depth understanding of its research focus and future direction, in order to provide some support for related research.【Method】 The paper used the methods of literature analysis and summary and induction to summarize and discussed the application of big data in the research of ‘three rural issues’ from three aspects: Research on agricultural issues, research on rural issues and research on farmers’ issues.【Result】With the popularization of communication infrastructure, big data such as internet usage data, communication sensor equipment data, geospatial data, organization management and operation data have been applied to the research of ‘three rural’ issues in China. At present, the application of big data in the research of ‘three rural’ in China has been constantly enriched. In the research of agricultural problems, big data has been applied to all links of agricultural production and operation, such as agricultural production monitoring and output improvement, and agricultural product market trend prediction. In the research on rural issues, the application of big data mainly focused on depicting the current situation of rural development, especially the evaluation of rural spatial environment, which can find a large number of rural spatial structures and systems driven by actual social and economic activities. In the research on farmers’ problems, the problem areas applied by big data mainly include the identification of farmers’ spatio-temporal behavior laws and the wealth and well-being of farmers reflected by them. High coverage data reflecting individual behavior, such as mobile phone use and signaling positioning data, has been the main data sources for such research.【Conclusion】The application of big data in the research of ‘three rural’ issues in China shows great development prospects in the development of agricultural industry, farmers’ lives and spatial planning, but there are also challenges in the aspects of data source and quality, method design, integration of traditional governance experience, etc. [ABSTRACT FROM AUTHOR]

With the development of big data and the increase of encryption scenarios, it 's difficult to meet the requirements of encryption performance with only software-run encryption methods, and FPGA/ ASIC encryption systems implemented using Verilog/VHDL methods have poor flexibility and difficult maintenance and upgrades problem. In response to the above problems, this paper designed and implemented an AES algorithm encryption system based on heterogeneous reconfigurable computing, including three mainstream modes of AES algorithm ECB, CBC, CTR, each mode realized 128 bit, 192 bit, 256 bit encryption of three key sizes. This paper realized the hardware acceleration of the module based on FPGA, and realized the dynamic switching of different modes and different bit width encryption modules based on the hardware reconfigurable mechanism. By implementing and verifying the system on Intel Stratix 10, the experimental results show that the throughput rates of AES-ECB, AES-CTR and AES-CBC in the system are 116.43 Gbps,60. 34 Gbps, and 4. 32 Gbps, respectively. The ECB mode compared with Intel Xeon E5-2650 V2 CPU and NVIDIA GeForce GTX 1080 GPU achieves 23.18 times and 1.43 times acceleration ratios respectively. Compared with the method just calling the CPU,the overall system has a computational acceleration ratio of 4.72. [ABSTRACT FROM AUTHOR]

Collaboration recommendation is helpful to the development of scientific research and the transformation of technological achievements. However, the gap between scholars ' academic levels seriously affects the establishment of cooperative relations. This paper made recommendations from three perspectives :the academic level gap between scholars, the topological distance in the collaborative network, and research interests. Firstly, this paper defined three networks, namely scholar-scholar network, scholar-topic network and scholar-level label network, and merged them into a graph of topic-scholar-level label, and set weight to the edges in the graph. Then it turned collaboration recommendation task into a link prediction task. Finally, it employed the biased restart random walk algorithm to calculate the probability of visits among scholars, and recommended the candidate scholars with high visit probability to target scholars. Experiments on three datasets show that the proposed model can improve the precision rate, recall rate and F1 index by 5.4%,2.7% and 3.8% . In addition, the academic levels of target scholars and recommended scholars are more closely matched . [ABSTRACT FROM AUTHOR]

This paper summarizes the achievements of China, s big data industry during the 13th Five・ Year Plan period and the cases of big data technology supporting marine fisheries, ocean transportation and early warning of marine emergencies, as well as the opportunities and challenges faced by the bromine extraction industry, which is an important part of marine chemical industry, in the new stage of economic development・ Based on the profound understanding of the bromine extraction industry and the full knowledge of big data technology to help upgrade and transform the seawater bromine extraction industry, from the perspective of the industry development, the specific entry points based on smart ocean and big data technology to promote the production management level and economic efficiency improvement are proposed ・ That is, to help improve production efficiency and promote energy-saving development of the industry ;to improve equipment operation and maintenance and enhance production safety; to coordinate upstream and downstream demands and optimize supply chain structure. To provide support for the good development of the bromine industry. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] In order to solve the problems of incomplete data chain and insufficient data support decision-making ability in agricultural science and technology management, this paper put forward a set of industrial knowledge service model based on the needs of scientific management in agriculture, and completed the key part of the model, the research and practice on association of scientific data and scientific literature. This study will provide theoretical and practical cases for library and information science (LIS) research institutions to serve agricultural science and technology management and industrial development. [Method/Process] Firstly, this paper reviews the current situation of research into knowledge service and data association. Secondly, based on the analysis of characteristics of industrial requirements and data management life cycle in agricultural science and technology management, the paper constructed a knowledge service model with industrial characteristics. Then, based on the agricultural science data and scientific literature of Sichuan Province, the study designed and constructed an ontology model and system based on industrial elements. [Results/Conclusions] 20 data types, 24 284 scientific data entries and related scientific literature were integrated. And we established multi-source heterogeneous data association in 1 system platform and 1 sub-platform with Chinese and English data. The research results will provide reference for cross-field cooperation between agricultural science and technology management and LIS research, and cross-departmental sharing of scientific data and scientific literature. [ABSTRACT FROM AUTHOR]

Aiming at the problems of parallel support vector machine (SVM) algorithm in big data environment such as redundant data sensitivity, difficulty in parameter selection, and low parallelization efficiency, this paper proposed a parallel SVM algorithm using Relief and bacterial foraging optimization (BFO) algorithm based on MapReduce (RBFO-PSVM). Firstly, the algorithm designed a feature weight calculation strategy (MI-Relief), which used mutual information to improve the weight calculation function of Relief algorithm to eliminate redundant features in the data set and effectively reduce redundant data to support parallelism. Secondly, this paper proposed a hybrid BFO algorithm based on MapReduce (MR-HBFO), which selected the optimal parameters of SVM in parallel, and solved the problem of difficult selection of SVM parameters. Finally, it proposed the kernel clustering strategy (KCS) to reduce the size of the data set involved in parallel training, and proposed a cross-fusion cascaded parallel SVM (CFCPSVM) to improve the cascade SVM (CSVM) feedback mechanism. It trained SVM by combining with the Map Reduce programming framework, and this improved the parallelization efficiency of parallel SVM. Experiments show that the RBFO-PSVM algorithm has a better classification effect on large data sets and is more suitable for large data environments. [ABSTRACT FROM AUTHOR]

In the big data environment, the random forest algorithm suffers from excessive redundancy and irrelevant features, the insufficient spatial information content of feature subspace, and low parallelization efficiency. To resolve these issues, this paper presented PRFGRSAE. Firstly, this algorithm proposed a DRNGRSAE, which filtered redundant and irrelevant features of the feature set and extracted features by stacked auto-encoders to reduce the number of redundant and irrelevant features effectively. Secondly, it proposed a SSLF that combined Latin hypercube sampling and normalized correlation degree, which formed feature subspaces with high spatial expression by performing multi-layer division sampling on the feature set, and ensured the feature subspace information content. Finally, it proposed a reducer allocation strategy DSVLA combining with variable action learning automata, which allocated each cluster to reducers for processing evenly and improved the parallelization efficiency effectively. Experimental results show that compared with IMRF, KSMRF, and GAPRF algorithms, the speedup ratio and accuracy of the PRFGRSAE algorithm are significantly improved. Therefore, the algorithm can obtain higher accuracy and parallel efficiency when applied to process large data, especially for data sets with more features. [ABSTRACT FROM AUTHOR]

In order to solve the problem of extracting temporal, spatial and channel features, this paper proposed an ADGCN for skeleton action recognition. At first, it modeled the skeleton data and fed the movement of joints, bones and joints and bones information into the multi-stream framework of a single stream. And then it transferred the input data to the directed graph convolution network to extract the dependencies between joints and bones, after that, used the space-temporal channel attention network（STCN） to strengthen the key joints in each layer of network temporal, space and channels of information. The last, it calculated the accuracy of action recognition by the weighted average of the information of four streams, output action prediction results. This model trained and verified in two large data sets NTU-RGB+D and Kinectics-Skeleton. Compared with baseline DGNN（directed graph neural network）, the accuracy of this paper on two cross subsets CS and CV is improved by 2.43% and 1.2%, on NTU-RGB+D dataset. On Kinectics-skeleton dataset, the accuracy of top1 and top5 is increased by 0.7% and 0.9%, respectively. The proposed ADGCN can effectively enhance the performance of skeleton action recognition, and the effect is improved on both large data sets. [ABSTRACT FROM AUTHOR]

Aiming at the problems of indigent scalability, poor parameter optimization ability and low efficiency of parallelization in big data density-based clustering algorithm, this paper developed a density-based clustering algorithm MR-GDBIGS based on groups and improved gravitational search. Firstly, it proposed a GSP to divide data and speed up neighborhood search. Secondly, the algorithm designed an IGSA which used the PUF. In addition, it could dynamically select the optimal parameters of local clustering based on IGSA algorithm, which could improve the clustering effect of local clustering. Finally, based on cover-tree and MapReduce, this paper developed a MR-CTMC to get the result of clustering algorithm more quickly, which improved the core clusters merging efficiency of density-based clustering algorithm. The experimental results show that the MRGDBIGS algorithm has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

In the big data environment, the Can-tree based on incremental association rule algorithm has problems such as too much space occupation of the tree structure, the efficiency of frequent pattern mining is poor, and the parallelization performance of MapReduce cluster is insufficient. Aiming at these problems, this paper proposed the MR-PARIRM. Firstly, it designed a RS-SIM to merge similar items in the dataset, and constructed Can-tree based on the merged data, thereby reducing the space occupation of the tree structure. Secondly, this paper proposed an MPS to prune and merge the propagation paths in the tree structure, thereby compressing the frequent pattern search space to speed up frequent item mining. Finally, MRP ARIRM used the DSS to dynamically schedule the computing tasks in the heterogeneous MapReduce cluster, thereby implementing the load balance and effectively improving the parallel computing capabilities of the cluster. The final experimental simulation results show that MR-PARIRM has relatively better performance in the big data environment and is suitable for parallel processing of large-scale data. [ABSTRACT FROM AUTHOR]

The application of the Internet, big data, artificial intelligence and other modern information technology means, and cloud services commercial patterns, can comprehensively enhance the innovation ability of mineral processing and smelting enterprises and realize information sharing.The paper introduced the intelligent push cloud service system and its practical production application.The system establishes the rapid deployment mechanism of cloud service push, pushes the reports such as the operational status and control effect of the equipment and flow sheet in mineral processing and smelting enterprises, and realizes the customized services including content, time and conditions.The intelligent push services push information to varying clients in a targeted and accurate way, so that the clients can conduct timely troubleshooting for equipment and preventive maintenance, learn and control equipment status in real time and achieve the informatization of enterprise running and decision making. [ABSTRACT FROM AUTHOR]

In the large data environment, there are many problems in the parallel deep convolution neural network（DCNN） algorithm, such as excessive data redundancy, slow convolution layer operation and poor convergence of loss function. This paper proposed a parallel deep convolution neural network optimization algorithm based on the Im2 col method. First, the algorithm proposed a parallel feature extraction strategy based on Marr-Hildreth operator to extract target features from data as input of convolution neural network, which can effectively avoid the problem of excessive data redundancy. Secondly, the algorithm designed a parallel model training strategy based on the Im2 col method. The redundant convolution kernel is removed by designing the Mahalanobis distance center value, and the convolution layer operation speed is improved by combining the MapReduce and Im2 col methods. Finally, the algorithm proposed an improved small-batch gradient descent strategy, which eliminates the effect of abnormal data on the batch gradient and solves the problem of poor convergence of the loss function. The experimental results show that IA-PDCNNOA algorithm performs well in deep convolution neural network calculation under large data environment and is suitable for parallel DCNN model training of large datasets. [ABSTRACT FROM AUTHOR]

The optimal allocation of land‐use is an important technical means to solve the contradiction be‐ tween supply and demand of land resources and to coordinate social and economic development with eco‐ logical protection, and it is also one of the key issues that need to be solved for the sustainable use of land resources. With the development of spatial simulation and intelligent optimization technology, the theoretical basis and methodological system of land‐use optimization are constantly updated. Based on bibliometrics analysis, this paper systematically summarized the driving theories and methodology of land‐use optimal al‐ location at home and abroad, focused on the application of spatial simulation models and intelligent optimization models in land‐use optimization problems, discussed and prospected the challenges and the future development of optimal land‐use allocation, and put forward that: The coupling drive of natural and human processes, spatial collaboration of multiple models, embedding big data knowledge mining methods, and the support of digital twin technology, should be the research direction of the next stage in the field of optimal land‐use allocation. [ABSTRACT FROM AUTHOR]

The development of teaching informatization indicates the arrival of big data era of digital teaching resource.Facing on massive digital resources, this study uses distributed processing technology to design a HDFS storage scheme for large file resources and a DFS storage scheme for small file resources. This paper analyzes the working principle of HDFS and DFS in detail, introduces the storage scheme for large files and small file resources, and analyzes the transmission rates of the file, respectively, in teaching practice. It proves the availability of storage scheme, and provides references for other colleges and universities to store digital teaching resources. [ABSTRACT FROM AUTHOR]

With the development of big data, computational power and deep learning, the object detection algorithm based on deep learning becomes the mainstream algorithm in this field, and its performance is far superior to the traditional algorithms. In order to review the development of algorithms, track the latest research results, and promote the research in the field of object detection, this paper summarized the representative object detection algorithms from two dimensions: two-stage /one-stage and anchor-based anchor-free, and realized the rectangular box or approximate rectangular box annotation under these two different classification standards. This paper discussed the process, characteristic, evolution and performance of the algorithms, and made the classification and analysis on the important improved versions. Finally, this paper summarized the advantages and disadvantages, limitations and applicable scenarios the algorithms, and looked forward to the development trend in the field of object detection, and put forward several important research directions in the future. [ABSTRACT FROM AUTHOR]

Objectives: The spatial delimitation of urban area is the foundation for monitoring the urbanization level and determining city scale from the perspective of geographical space.Methods: Based on the requirements of urban area defined in Document No. 51 of the State Council, this paper clarifies the connotations of urban area with further understandings and proposes a process for delimiting urban area in geographical space by connecting map polygons of governmental residents and neighborhood committee, which can reduce the subjectivity and fuzziness in the spatialization of process of urban area. Results: This paper develops the main idea and spatial delimitation about urban area and takes Wuhan as an example to illustrate the details about spatial delimitation of urban areas.Conclusions: This paper mainly discusses how to unify urban delineation under the current technical conditions, and does not mention how to use other auxiliary data, such as the application of multi⁃source big data in urban delineation, because of the unbalanced development of big data infrastructure in different cities. But it does not mean that the application of population big data and urban big data is not important.On the contrary, the application of big data in the work of urban area⁃ delimitation is the next research focus and the future development trend. [ABSTRACT FROM AUTHOR]

Aiming at the problems of sensitivity of initial center, high communication overhead of nodes and low efficiency of cluster in big data clustering algorithm based on partitioning, this paper proposed a partitioning-based clustering algorithm using grid density and locality sensitive hash function based on MapReduce, named PBGDLSH-MR. Firstly, based on the initial data set, it proposed the GDS(grid density strategy) to get the initial clustering center, which avoided the sensitivity of initial center caused by random selection of initial cluster center. Secondly, it proposed the DP-LSH ( data partitioning based on locality sensitive hash functions) to map more closely related data objects into the same subdataset and get data partitions on the map. Meanwhile, it designed a formula SI( similarity improvement) to evaluate the data partitioning results, reduced the communication overhead between nodes. In addition, this paper designed an AGS (adaptive grouping strategy) to handle data skew in data partitions, which improved the cluster efficiency. Finally, based on MapReduce, it mined the cluster centers in parallel to gene rate the final clustering results. The experimental results show that the PBGDLSH-MR has better clustering results and performs better parallelization in big data. [ABSTRACT FROM AUTHOR]

Since the practice of modernization and reform and opening up for more than 40 years, people’s material life has been continuously enriched. Under the wave of big data, people are faced with challenges from all aspects, among which the impact of spiritual life is invisible and imperceptible. On the basis of examining the picture of the “barbaric growth” of spiritual life, this paper analyzes the root cause of the lack of spiritual community: it is actually the worship of capital generated in the process of modern society and extends the alienation of humans, and the failure to rationally deal with the dialectical relationship between technology and human nature. The arrival of the big data wave is both an opportunity and a challenge. To save the wandering spiritual life and cultivate a good spiritual community, it is necessary to use the power of the era of big data to establish a correct concept of science and technology and realize the virtuous cycle of “capital-technology-individual”; it also needs to improve the ecological system of spiritual life in search of individual meaning and group return under the transmission pedigree. [ABSTRACT FROM AUTHOR]

This paper made a comprehensive literature review on the topic of applying machine learning methods to shield tunnelling parameter predictions, stratum predictions ahead of a tunnel face, surface settlements prediction, shield attitude deviation and tool wear predictions. The selections of machine learning methods and the associated input and output parameters were analyzed, and the shortcomings and challenges of existing research were summarized. Some prospects were given, including the model generalization, multi-source heterogeneous data compression and assimilation, data-physics-based intelligent shield tunnelling, big data in shield tunnelling, in order to provide reference and guidance for the future research and engineering practice. [ABSTRACT FROM AUTHOR]

With the advancement of medical technology and big data era, how to protect the privacy of sensitive information in patients ' medical records has become a current research focus issue. In order to protect the privacy of medical big data in the publishing process, this paper proposed an AUR-Tree differential privacy data publishing algorithm. The algorithm used the attribute utility value ranking method to measure the influence degree of the quasi-identity attributes on the sensitive attributes, which was as the measurement basis for iterative segmentation. Then it ad opted top-down iterative segmentation classification tree technology based on generalization, and reasonably allocated the privacy budget through the class arithmetic method to realize the privacy protection in the medical data release process. The experimental results show that the algorithm retains the value of subsequent data mining and greatly improves the security, effectiveness and usability of the data. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] The coronavirus pandemic has caused serious losses to China and other countries. Ignoring the rapid detection of the virus on a large scale and taking timely prevention and control measures to cut off the spread of the virus in the early period of the outbreak is an important reason for the rapid spread of the epidemic. How to realize the rapid detection of coronavirus, relieve the difficulty of virus detection ability, accurately locate the infected person and reduce the scope of infection is the key to the prevention and control of the epidemic. [Method/Process] This paper takes COVID-19 rapid detection and tracing as the starting point, and proposes a scheme to develop a coronavirus rapid detection and tracing platform. We considered the critical role of community closed management and primary health care facilities in the prevention and control of the epidemic. To reduce the flow of people and the risk of population infection, this study integrates biosensors, artificial intelligence, blockchain, and big data technologies, and gives full play to the detection ability of biosensors, the learning ability of artificial intelligence, the information transmission ability of blockchain and the analysis ability of big data. We designed a community-centric solution for a coronavirus rapid detection and tracking platform. [Results/Conclusions] Through the analysis of the solution, the coronavirus rapid detection tracking platform with community focus has the advantages of rapid virus detection, intelligent diagnosis, information sharing, security, traceability, and full-dimensional analysis. With the development of emerging technologies, this study is expected to provide innovative solutions for the intelligent epidemic prevention and control in the world at large. [ABSTRACT FROM AUTHOR]

Among all the clustering problems, the K-means problem is probably the most well-known one. How to obtain a theoretically guaranteed solution of K-means efficiently for the big data can be a key problem. This paper surveyed the progress of this problem. Firstly, this paper defined the K-means problem and introduced relevant backgrounds. Secondly, it introduced separately and described in details the techniques for theoretical guarantee and speed up. Finally, it summarized the main results and forecasted the future directions of K-means algorithms on big data. [ABSTRACT FROM AUTHOR]

In order to understand the development of outlier detection technique detailly, this paper overviewed the outlier detection techniques in recent years. Firstly, it introduced and summarized the definition of outliers, the causes of outlier and classification of the outlier mining algorithms. Next, it overviewed outlier detection methods based on neighbor, based on distributed framework and based on deep learning. Especially, it discussed the most representative methods in outlier detection, and pointed out the merits and drawbacks. In the end, this paper concluded and discussed the future direction of outlier detection techniques. [ABSTRACT FROM AUTHOR]

With the rise of cloud computing, big data and the Internet of Things, network traffic grows at an explosive rate, while the growth rate of network bandwidth cannot match it. In the foreseeable future, the contradiction between increasing network traffic and slow growing network bandwidth will exist for a long time. How to change the current extensive network management means, achieve refined network management, and solve the above problems is the question need to face. This paper presented a state-based view model, described the formal concept of the global state view, and also expanded the extension of the concept of global state view, proposed the concept of state view stack, and expanded the state view of each layer of the defined network. It used the hierarchical Petri net to model and analyze its features, and introduced the global state view construction algorithm in detail. It analyzed the port collision application and constructed the state view of the application. At the same time, this paper took load balancing function plus global state view application as an example to illustrate state-based network management could improve resource utilization. [ABSTRACT FROM AUTHOR]

Feature selection is a common dimension reduction approach for processing high-dimensional big data, but it often involves multiple conflicting feature subsets evaluation objectives which are difficult to balance . To reach a compromise among various feature subset evaluations in feature selection and optimize the performance of subset, this paper proposed a subset evaluation multi-objective optimization based feature selection framework and focused on the application of multi-objective particle swarm optimization( MOPSO) in feature subset evaluation. The framework used sparsity, classification ability and information loss to design multi-objective optimization functions. Then it optimized the weight vectors of the features based on multi-objective optimization algorithm, and selected the " knee" of Pareto solution set as optimal vector. Finally, the framework realized feature selection based on weight vector ranking. This paper designed experiments to compare the performance of MOPSO based feature selection( FS_MOPSO) with four classical methods. The results on several standard data sets show that, FS_MOPSO shows higher classification accuracy in low dimensional space while ensuring less information loss. [ABSTRACT FROM AUTHOR]

In differential privacy histogram publication the privacy budget involves the intensity of noise adding, which has a direct impact on the availability of data publication. Therefore, how to allocate and optimize the privacy budget is a challenge to the histogram publishing algorithm. This paper proposed a histogram publishing algorithm based on adaptive privacy budget allocation strategy, called APB. In order to balance the noise error and reconstruction errorefficiently and get the relations hip between the privacy budget allocation weight and the group size and number of group, APB established an optimization model of privacy budget allocation weight by analyzing the noise introduced before and after the grouping step. In order to boost the accuracy of the released histogram, this paper employed optimization model and greedy grouping to propose an adaptive privacy budget allocation strategy. Experimental results show that the proposed method of histogram publication based on adaptive privacy budget allocation strategy is more available than similar algorithms. [ABSTRACT FROM AUTHOR]

In order to solve the problem that the traditional data management platform is difficult to make management decisions on river system quickly and comprehensively, it is of great significance to construct a big data platform of river system by using big data technology. This paper discussed the big data platform from three aspects of the characteristic of big data of river system, the structure of big data platform of river system and the realization of big data platform of river system. It put forward the platform constructed by Hadoop-based framework, driven by information integration model, river health diagnosis model, risk warning model and decision support model and braced by big data of river system. Generally, the paper provides a new idea for the efficient management of river system to realize the diagnosis and risk warning of river health. [ABSTRACT FROM AUTHOR]

With the development of China’s rail transit system, the transfer problem directly affects the convenience of hundreds of thousands of passengers day after day, and has become an important factor which affects the operation of public transportation systems. The problem of public transportation transfer should be “people-oriented” and analyzed from the time and space mode of passengers' transfer. Based on the smart card data (SCD) of Wuhan City in March 2015, this paper studies the time-space characteristics of passenger transfer, analyzes the current situation of rail transit development, and summarizes the transfor travel mode. The research results are as follows:① In the process of the development of rail transit in Wuhan, the imbalance of rail transit services has arisen and a single-center urban structure has emerged. ② The geographical characteristics of transfer behavior are related to the city’s own geographical pattern, and three factors which influence the transfer behavior are proposed. ③ Using intelligent bus card data, it can effectively identify the social behavior of passengers (transfer behavior) and the time and space mode of passengers traveling. This paper analyzes the spatiotemporal patterns of travel behaviors such as transfer behavior and urban planning and urban space utilization from the perspective of “people-oriented” based on the analysis of big data. [ABSTRACT FROM AUTHOR]

Climate change potentially increases the severity, frequency, and duration of cyanobacterial blooms in freshwater lakes, thereby threatening the ecosystem service functions of these lakes and negatively impacting human health. This paper provides a comprehensive review of the external environmental factors and internal eco-physiological characteristics that may trigger these blooms. In particular, detailed discussions are provided to explain the processes leading to cyanobacterial blooms caused due to rising temperature, CO2 concentration, and extreme climate change. A preliminary research framework to determine the impact of climate change on the blooms, namely, the “mechanism analysis — model construction — simulated prediction — risk assessment” research framework, is established. Strategies for the control and prevention of bacterial blooms are developed. Several key future research themes, including the construction of big data monitoring platforms, mechanism models for development and risk prediction, and formulations of nutrient control standards and prevention policies, are proposed to tackle the current problem. This review provides scientific evidence for the prevention and control of cyanobacterial blooms in lakes. [ABSTRACT FROM AUTHOR]

: In order to monitor illegal land use in real time, video surveillance technology was used to monitor the vulnerable areas of illegal land use. Excavator was one of the most important construction machinery in the engineering construction, an automatic real-time detection of excavator could provide important information for non-contact field monitoring of illegal land. But it was difficult to accurately detect the excavator due to the complex background, uneven illumination and partial occlusion in natural scene, This paper proposed a real-time excavator detection algorithm in natural scene based on the SSD-like (Single Shot Detector). Specifically, the lightweight network DDB (Depthwise Dense Block) was used as the basic network to extract shallow feature and fuse with high-level features in the excavator objection model to enhance the feature representation capability. Meanwhile, the BDM (Bottleneck Down-sampling Module) which was designed based on the lightweight network MobileNetV2 was used as the multi-scale feature extraction network to reduce the parameter quantity and computation. The data sets included 18 537 images of excavators with different shooting angles and natural scenes, 15 009 images were used as training set and 3 528 images were chosen as test set randomly. To enhance the diversity of training data, data set expansion methods such as rotation and image were adopted. Based on the Caffe deep learning framework, the proposed model in this paper was trained with end-to-end approximate joint methods and the model weight was fine-tuned by using SGD (Stochastic Gradient Descent) algorithm. The DDB module of the network was initialized with the weights pre-trained on the PASCAL VOC dataset, and the training time and resources were further reduced by transferring learning. Then the model pre-trained on the large data sets was transplanted to excavator object detection by transfer learning. The proposed method was transplanted and performed on the mainstream Jetson TX1 embedded hardware platform, and experiments on the actual data set of detecting excavator object at different angles of view and natural scenes. Experiment results showed that the parameter quantity and computational complexity of proposed model with BDM was reduced by 68.4% compared to SSD, the mAP (Mean Average Precision) of proposed method reached 90.6% on the testing set, which was 0.4% and 0.4% higher than that of SSD based on VGG16 basic net and MobileNetV2SSD based on MobileNetV2 basic net, respectively. The model size of propose method was 4.2 MB, which was about 1/25 and 1/4 of SSD and mobilenetv2ssd, respectively, and the time-consuming of each frame was 145.2 ms, which was 122.7% and 28.2% faster than SSD and MobileNetV2SSD, respectively. The proposed method not only had better generalization and robustness, but also can be better deployed on the embedded hardware platform which demonstrated the feasibility of real-time monitoring of the excavator at site of illegal land use. [ABSTRACT FROM AUTHOR]

This paper studied the problem of secure subset which most of the existing protocols that solve such problems can only keep the elements of one set private to. Under the semi-honest model, this paper constructed a secure subset protocol by using Bloom filter and Goldwasser-Micah homomorphic encryption algorithm, and proved the security of the protocol by using common simulation examples in secure multi-party computing. It used the Bloom filter to map a data set with a large number or large number field elements into a smaller data set, improved the efficiency and range of the protocol, at the same time, it used the Goldwasser-Micali homomorphic encryption algorithm to ensure the security of the protocol. Most of the relevant researches were based on the difficult problems such that secondary residuals, it is impossible to resist quantum attacks, and the secure subset computation which can resist quantum attacks is a further research direction. [ABSTRACT FROM AUTHOR]

Smart cities cannot be separated from spatiotemporal location big data. This paper summarizes the needs of spatiotemporal location big data for public epidemic events at home and abroad, investigates the specific services that current spatiotemporal location big data can provide in public epidemic events, and discusses the current stage of spatiotemporal location big data services that cannot meet the requirements of public epidemic events. This paper also focuses on how to improve the spatiotemporal location big data services in the next stage to better serve the public epidemic prevention and control, and gives advice on spatio-temporal location service construction at ordinary times and data sharing mechanism in “wartime”. This paper aims to take scientific measures to improve the spatiotemporal big data services, strives to establish a technical support system for public epidemic prevention and control at ordinary times, and introduces a “wartime” spatiotemporal location big data sharing mechanism for epidemic prevention and control. [ABSTRACT FROM AUTHOR]

In order to solve the problems such as sparse data, cold start and lack of diversity of recommendation results in traditional collaborative filtering recommendation algorithms, this paper proposed a collaborative filtering recommendation algorithm that integrated social networking with key users . Based on the score matrix of user projects, the proposed algorithm integrated user social networks to de rive social trust matrix, integrated key user information to obtain key user scoring matrix. And then it used these three matrix data distributions to predict user's target project with different weights score. At the same time, aiming at the massive data problem, this paper used the Spark distributed cluster to realize the parallelization of the algorithm. The experimental results show that the proposed algorithm can effectively alleviate the data sparse problem and improve the data processing speed and recommendation accuracy. [ABSTRACT FROM AUTHOR]

The continuous progress of teaching informatization has improved management efficiency, activated teaching space, promoted teacher-student interaction, and promoted classroom teaching reform. With the rapid popularization of mobile interconnection, artificial intelligence, big data and other technologies, how to construct and perfect teaching informatization in colleges and universities with the rapid development of new technology has become a problem facing most colleges and universities. This paper takes Hangzhou Dianzi University as an example, to analyze and summarize the achievements in the construction of teaching information in recent years, and reflect on the problems existing in the construction process by combining with the actual situation. This paper probes into the ideas and measures of teaching informatization construction in order to promote informatization to serve colleges and universities better and improve the quality of personnel training. [ABSTRACT FROM AUTHOR]

In order to scientifically select the most necessary bus stops to put electronic bus station boards on the premise of limited funds for urban intelligent bus construction, this paper proposes a comprehensive selection index system for electronic bus station boards based on bus big data and GIS. On this basis, a principal component-Two Step clustering combination model is proposed to classify and rank the bus stops. In this paper, the Harbin bus data of 14 days from March 2, 2015 to March 15, 2015 were used for case analysis. A data matching algorithm based on the time difference between IC card and GPS systems without public matching field was proposed. According to the case study results, 1 553 bus stations in Harbin were divided into five categories according to the degree of demand for bus electronic station board. The first category had the highest priority, including 135 stations, but only 8. 69% of the total; the last category had the lowest demand, including 959 stations, accounting for 61. 75% of the total. Firstly, the model constructed in this paper was validated by the indexes of fit and distance of clustering results. In addition, by comparing and analyzing with the actual situation in Harbin, it was proved that the comprehensive selection model of electronic station board put forward in this paper was reasonable, effective and applicable, and can distinguish the level of demand for electronic station board well. The model can provide a scientific and effective method for the construction of bus electronic station board in the case of limited funds for each city. [ABSTRACT FROM AUTHOR]

With the continuous development of big data, federated learning has been widely used in various scenarios to facilitate people’s production and life. However, while this technology brings convenience to people, it also exposes users to the challenge of data leakage. Therefore, data security has become a hot issue in federated learning research. This paper introduced the training process of horizontal and vertical federated learning, and studied potential opponents of these processes and reasons for their attacks, so as to classify and summarize the existing attack methods, such as poisoning attacks, confrontation attacks, and model reverse attacks. Then, it introduced the defense measures against several attack methods in two scenarios, such as gradient sparseness, malicious detection, secret sample alignment, label protection, encryption sharing, and distur-bance sharing. Therefore, these methods not only could ensure the data security of the participants, but also could guarantee the accuracy of the joint model. On this basis, according to the research on the existing technology, it summarized the problems of the existing methods and the future research directions. [ABSTRACT FROM AUTHOR]