13 July 2023, Volume 41 Issue 13

    

Special to S&T Review
Exclusive: Theory and Application of Cyberspace Geography
Reviews
Papers
Science and Technology Humanities

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Special to S&T Review
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  Scientific research and application progress on the International Space Station in 2022

  HAN Lin, WANG Haiming, FAN Weiwei, LI Guopeng, YANG Fan

  Science & Technology Review. 2023, 41(13): 6-13. https://doi.org/10.3981/j.issn.1000-7857.2023.13.001

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  The International Space Station (ISS) continued to play its important role as an on-orbit scientific research and application platform. Comprehensive statistics and text analysis of the ISS experiments databases of the United States and Russia and annual highlights of results report showed the following findings. For the Expedition 66-67 from October 2021 to September 2022, NASA, Roscosmos, ESA, JAXA and CSA sponsored 360 experiments in six categories, including technology development and demonstration, biology and biotechnology, physical science, human research, educational and cultural activities, and earth and space science, of which new experiments accounted for more than 40%. At the same time, new scientific achievements, such as teleoperation by ISS crew to control robotic components on earth, creation and measurements of bubbles of ultracold atoms on-orbit, and observation of a magnetar, attracted widespread attention from the global science community. By focusing on representative new experiments and achievements this review comprehensively reflects the progress in the scientific research and application of ISS in this period, the progress of scientific research and application is comprehensively reflected. ISS is at the peak of scientific research and application output, with highly active research activities and continuous emergence of scientific achievements. In the future, it is expected to produce more new achievements to benefit space and Earth.
Exclusive: Theory and Application of Cyberspace Geography
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  Concepts and methods of cyberspace geographic map

  CHEN Shuai, GUO Qiquan, GAO Chundong, HAO Mengmeng, JIANG Dong, NI Shiyuan, NI Tao

  Science & Technology Review. 2023, 41(13): 14-22. https://doi.org/10.3981/j.issn.1000-7857.2023.13.002

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  Map is an effective tool to recognize real space. Cyberspace also needs abstract, symbolic and numerical maps for visualization and cognition. Based on a comprehensive review of existing theories and methods on cyberspace map and from a perspective of visual representation of cyberspace elements and cyberspace knowledge, the current study proposes a concept and connotation of the cyberspace geographic map by synthesizing the concept of“tupu” in both computer science and geography.. Besides, this study introduces the methods and key technologies for cyberspace geographic map construction, and outlooks the application fields from three aspects: cyberspace resource management, cyberspace behavior cognition, and comprehensive analysis of cyberspace events. Mapping the geographical map of cyberspace can associate cyberspace with real space and clearly express the structure of cyberspace. It is a necessary way to realize the cognition of cyberspace behavior and improve the capability of cybersecurity.
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  A fast representation learning model for large-scale cybersecurity knowledge graphs

  HAN Zhongming, XIONG Zhibing, CHEN Fuyu, YANG Weijie, ZHANG Xun

  Science & Technology Review. 2023, 41(13): 23-31. https://doi.org/10.3981/j.issn.1000-7857.2023.13.003

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  This paper comes up with a fast-training model based on random walk to address the problems of slow training speed for representation learning of large-scale cybersecurity knowledge graph and lack of relational representation of head and tail entities,. The model first performs an initial training representation of the entities of the overall knowledge graph by random walk under relational paths, then, a subject-object embedding is designed to learn the syntactical meaning of the relations in the knowledge graph by combining the relation-specific subject embedding with the relation-specific object embedding. Finally, fast training of the knowledge graph is again assisted by random wandering under relational paths. In this paper, extensive experiments are conducted on several datasets and the results are compared with those using several existing models. The results show that the model proposed in this paper can shorten the training time by 1/3 and improve representation by about 3%, effectively improving the representation learning effect while speeding up the training speed of knowledge graph representation learning.
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  On the technology system of visualization in cyberspace for space-based information systems

  ZHUO Jun, GUO Qiquan, GAO Chundong, HAO Mengmeng, JIANG Dong

  Science & Technology Review. 2023, 41(13): 32-40. https://doi.org/10.3981/j.issn.1000-7857.2023.13.004

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  With the development of information technology, space-based information system has expanded the cyberspace from ground to natural space. As a result, cyberspace is becoming more and more complex. And the problem of network security is also increasingly serious. Therefore, how to display and analyze the situation of space-based information systems in cyberspace comprehensively and deeply has become an urgent problem to be solved. This research analyzes the current situation of space-based information network and summarizes the characteristics of cyberspace. For space-based information systems and based upon the existing theories and methods of cyberspace visualization, this research explores the mapping relationship between cyberspace and geographic space and proposes the content and technical path of cyberspace visualization from three aspects: information elements, topological relations, and security behaviors. Moreover, it constructs a cyberspace geographic graph. This research may provide technical support for cognizing cyberspace situation and maintaining network security of space-based information systems.
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  Survey of vulnerability detection based on graph deep learning

  DONG Jiping, GUO Qiquan, GAO Chundong, HAO Mengmeng, JIANG Dong

  Science & Technology Review. 2023, 41(13): 41-59. https://doi.org/10.3981/j.issn.1000-7857.2023.13.005

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  The recent advances made by graph-based deep learning have demonstrated its great potential in processing non-Euclidean structured data, and a large number of research efforts have attempted to apply graph embeddings or graph neural networks to vulnerability detection. This survey systematically investigates the vulnerability detection based on graph deep learning. Firstly, we summarize the four main stages of the vulnerability detection process, including data set, graph data preparation, graph deep learning model construction, and result evaluation. Then, starting from the effectiveness of graph-based deep learning vulnerability detection, we respectively expound the research results based on code patterns, code similarity and specific application scenarios. Finally, by sorting out and summarizing the existing research works, we analyze the challenges and foresee the trends in this research field.
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  Network vulnerability situation prediction based on spatio-temporal graph convolution

  ZHANG Yingchun, LI Jin, ABDUREYIM Raxidin, ZHANG Xun, HAO Mengmeng, JIANG Dong

  Science & Technology Review. 2023, 41(13): 60-66. https://doi.org/10.3981/j.issn.1000-7857.2023.13.006

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  In view of the increasingly serious problem of network security, geographical space features are added into the prediction process to realize spatio-temporal prediction of network space elements in this study. Considering the research status that network data are often rarely combined with geospatial characteristics in the prediction process of network security elements, network vulnerability detection data with geospatial characteristics are also selected to construct the spatio-temporal data set of network vulnerabilities. By constructing a spatio-temporal graph convolution model combining graph convolution and gated time convolution, the development of network vulnerability situation can be predicted. ARIMA and LSTM temporal prediction models are selected for comparative experiments, and the proposed network vulnerability spatio-temporal graph convolution prediction model shows better prediction effect under MAE, RMSE and MAPE evaluation criteria.
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  A local spatio-temporal graph convolution-based approach and its application to network vulnerability prediction

  ZHANG Xun, ZHANG Chutong, EZIZ Tursun, HAO Mengmeng, ZHANG Yingchun, JIANG Dong

  Science & Technology Review. 2023, 41(13): 67-75. https://doi.org/10.3981/j.issn.1000-7857.2023.13.007

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  To address the shortage of spatio-temporal feature extraction in network security situation prediction, a local spatio-temporal convolution-based network vulnerability prediction method, namely the local spatio-temporal graph convolutional network model, is proposed, and HA, LSTM, SVR and STGCN models are selected for comparison experiments on network vulnerability data. Experimental results show that the model proposed in this paper can effectively improve the accuracy in predicting the time and location of vulnerabilities as well as the type of network vulnerabilities.
Reviews
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  Advances in software defined network security research and key technologies

  ZHAI Yahong, CUI Junwei

  Science & Technology Review. 2023, 41(13): 76-88. https://doi.org/10.3981/j.issn.1000-7857.2023.13.008

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  Software-defined networking(SDN) is a new network architecture that simplifies the development of new applications and services through a centralized software-oriented management approach and has become a research hotspot for the next-generation Internet. To address the security issues in SDN, this paper reviews the existing solutions in terms of the 3-layer architecture of SDN and analyzes the technical challenges faced by SDN security. In particular, it firstly introduces the definition of SDN and the 3-layer architecture then reviews the research advances on security related to SDN. Next, it summarises the security issues and solutions to the application layer, control layer and data layer, respectively. Finally, it provides an outlook on the challenges that SDN security future research may encounter.
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  Shape memory alloys and their applications in non-energetic separating attachment: A review

  WU Xiaojuan, CHEN Wenlong, ZHANG Xiaobing, LIU Lijuan, CHEN Xiaosong, SUO Liang

  Science & Technology Review. 2023, 41(13): 89-99. https://doi.org/10.3981/j.issn.1000-7857.2023.13.009

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  This paper presents a review of the characteristics, types, manufacturing processes and performance control of shape memory alloys (SMAs). The current application in non-energy containing unlocking separation device is also reviewed. Basic working principles of SMA and current series of commonly used alloys are summarized. The main preparation processes of SMA are analyzed, especially the current application situation of spray casting, powder metallurgy and additive manufacturing in preparation of SMA materials. The current research status of SMA performance control based on typical processes such as alloying component design, heat treatment and plastic processing is introduced. Moreover, the current application status of SMA in non-energy containing unlocking separation device is discussed. Finally, key issues that need to be urgently addressed in institutional research and SMA material are prospected from a perspective of application and promotion.
Papers
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  Batch-attention: A method for reconciling overfitting and underfitting in deep learning

  HU Hanqing, LI Zhengxun, WU Zhunan

  Science & Technology Review. 2023, 41(13): 100-108. https://doi.org/10.3981/j.issn.1000-7857.2023.13.010

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  In the process of deep learning network training, most existing methods aim to improve the model effect focus on the network. However, to improve the effect and accuracy of the model it is necessary to pay attention to the characteristics of the data. In this paper, batch-attention, a new training framework for deep learning model, is proposed, which changes the original training method from the data level. It is shown that the method can coordinate overfitting and underfitting of the deep learning model. Experimental comparisons using Resnet34, TNT and efficientnet-b7 on Cifar10 and Cifar100 data sets respectively prove that the batch-attention model has improved both accuracy and F1-score in the test set compared with the benchmark model. In addition, the mechanism of batch-attention is further analyzed in the follow-up experiment.
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  On structure and difference of urban safety production risk based on industries

  CHEN Ning, TANG Junmei, WANG Yao, CHEN An

  Science & Technology Review. 2023, 41(13): 109-117. https://doi.org/10.3981/j.issn.1000-7857.2023.13.011

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  Risk prevention and hidden danger identification are important to urban safety. According to current laws and regulations on safety production and emergency management in China, the responsible subjects of safety management are those departments that are directly involved in risks and hidden dangers. In a comprehensive city there are great differences in the types and distributions of potential risks among industries. These differences should be taken into account so that targeted risk prevention and control measures can be formulated accordingly. This paper studies the distribution differences of potential safety risks in industries, proposes a concept of industry safety risk structure, defines an industry safety risk vector, and gives the quantification indexes of industry risk entropy and risk specificity by means of information entropy theory. Based on the analysis results of tens of thousands of cases it is found that determination of safety risk vector, industry risk entropy, and risk specificity is conducive to the comparative study of safety risks within and between industries and provides a basis for the classified monitoring of industrial safety risks.
Science and Technology Humanities
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  Demarcation of human-machine intelligence

  LIU Wei

  Science & Technology Review. 2023, 41(13): 118-128. https://doi.org/10.3981/j.issn.1000-7857.2023.13.012

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  The victory of Deep Blue against Kasparov in 1997 and the victory of Alpha Dog over Lee Sedol in 2016 set off the trend of artificial intelligence, however, artificial intelligence still has so far a huge gap behind humans in terms of intelligence level and range of capabilities, which hinders its development. The reason is that there is an unbridgeable boundary between human intelligence and machine intelligence. In this context, machines that rely on symbols pointing to objects can only perform formal calculations in a closed environment but cannot realize intentional calculations in an open environment like humans. Therefore, the complementary advantages of man and machine are particularly important. The current relationship between man and machine is mainly a hard function allocation, whereas the relationship between man and machine in the future digital world would more likely be a flexible division of capabilities. This paper discusses in detail the bottlenecks of artificial intelligence, the first principle of intelligence, and other issues, and gives more in-depth thinking about human-machine problem amid these problems. It is proposed that human-machine hybrid intelligence is the future development direction of artificial intelligence.