MANAGEMENT OF SPATIO-TEMPORAL DATA OF CADASTRAL INFORMATION SYSTEM IN CHINA

Cadastral Information System (CIS) is designed for the office automation of cadastral management. With the development of the market economics in China, cadastral management is facing many new problems. The most crucial one is the temporal problem in cadastral management. That is, CIS must consider both spatial data and temporal data. This paper reviews the situation of the current CIS and provides a method to manage the spatiotemporal data of CIS, and takes the CIS for Guangdong Province as an example to explain how to realize it in practice.

Flight parameter calculation method of multi-projectiles using temporal and spatial information constraint

The dynamic parameters of multiple projectiles that are fired using multi-barrel weapons in highfrequency continuous firing modes are important indicators to measure the performance of these weapons.The characteristics of multiple projectiles are high randomness and large numbers launched in a short period of time,making it very difficult to obtain the real dispersion parameters of the projectiles due to the occlusion or coincidence of multiple projectiles.Using six intersecting-screen testing system,in this paper,we propose an association recognition and matching algorithm of multiple projectiles using a temporal and spatial information constraint mechanism.We extract the output signal from each detection screen and then use the wavelet transform to process the output signal.We present a method to identify and extract the time values on which the projectiles pass through the detection screens using the wavelet transform modulus maximum theory.We then use the correlation of the output signals of three parallel detection screens to establish a correlation coefficient recognition constraint function for the multiple projectiles.Based on the premise of linear projectile motion,we establish a temporal and spatial constraint matching model using the projectile’s position coordinates in each detection screen and the projectile’s time constraints within the multiple intersecting-screen geometry.We then determine the time values of the multiple projectiles in each detection screen using an iterative search cycle registration,and finally obtain the flight parameters for the multiple projectiles in the presence of uncertainty.The proposed method and algorithm were verified experimentally and can solve the problem of uncertainty in projectiles flight parameter under different multiple projectile firing states.

Route Temporal⁃Spatial Information Based Residual Neural Networks for Bus Arrival Time Prediction

Bus arrival time prediction contributes to the quality improvement of public transport services.Passengers can arrange departure time effectively if they know the accurate bus arrival time in advance.We proposed a machine⁃learning approach,RTSI⁃ResNet,to forecast the bus arrival time at target stations.The residual neural network framework was employed to model the bus route temporal⁃spatial information.It was found that the bus travel time on a segment between two stations not only had correlation with the preceding buses,but also had common change trends with nearby downstream/upstream segments.Two features about bus travel time and headway were extracted from bus route including target section in both forward and reverse directions to constitute the route temporal⁃spatial information,which reflects the road traffic conditions comprehensively.Experiments on the bus trajectory data of route No.10 in Shenzhen public transport system demonstrated that the proposed RTSI⁃ResNet outperformed other well⁃known methods(e.g.,RNN/LSTM,SVM).Specifically,the advantage was more significant when the distance between bus and the target station was farther.

Shortest path of temporal networks:An information spreading-based approach

The shortest path is a widely studied network science problem and has attracted great attention.Nevertheless,it draws little attention in temporal networks,in which temporal edges determine information dissemination.In this paper,we propose an information spreading-based method to calculate the shortest paths distribution in temporal networks.We verify our method on both artificial and real-world temporal networks and obtain a good agreement.We further generalize our method to identify influential nodes and found an effective method.Finally,we verify the influential nodes identifying method on four networks.

Possible roles of electrical synapse in temporal information processing: A computational study

Temporal information processing in the range of tens to hundreds of milliseconds is critical in many forms of sensory and motor tasks. However, little has been known about the neural mechanisms of temporal information processing. Experimental observations indicate that sensory neurons of the nervous system do not show selective response to temporal properties of external stimuli. On the other hand, temporal selective neurons in the cortex have been reported in many species. Thus, processes which realize the temporal-to-spatial transformation of neuronal activities might be required for temporal information processing. In the present study, we propose a computational model to explore possible roles of electrical synapses in processing the duration of external stimuli. Firstly, we construct a small-scale network with neurons interconnected by electrical synapses in addition to chemical synapses. Basic properties of this small-scale neural network in processing duration information are analyzed. Secondly, a large-scale neural network which is more biologically realistic is further explored. Our results suggest that neural networks with electrical synapses functioning together with chemical synapses can effectively work for the temporal-to-spatial transformation of neuronal activities, and the spatially distributed sequential neural activities can potentially represent temporal information.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

Construction of Smart City Spatio-Temporal Information Cloud Platform in Weifang,China

On the basis of the digital Weifang geospatial framework,Smart Weifang spatio-temporal information cloud platform(WFCP)integrated legal person information,population,place name and address data,macroeconomic data and so on.And it also expanded the data contents,such as the indoor and outdoor data,the overground and underground data,panoramic data and real data.It also introduced the contents of historical geographical information in different periods and real-time location information,address information of sensing equipment,real-time perception and interpreting information.It has overcome the difficulties of real-time access of Internet of Things(IoT)perception,multi-node collaboration,64-bit support,cluster deployment and has the characteristics of spatio-temporal management,ondemand service,large data analysis and micro-service architecture.It built spatio-temporal information big data center and spatio-temporal information cloud platform,realized the convergence and management of the distributed big data,deeply applied for land,transportation,environmental protection,police and subdistrict five areas,by supporting the integrated application of multi-source information and supporting intelligent deep application.In the aspect of hardware environment construction,according to the top-level design and unified arrangement of Smart Weifang,the WFCP was migrated to Weifang cloud computing center,to achieve the on-demand computing resources and dynamic scheduling load-based computing resources,to support the generalizing load map application.

Event temporal relation computation based on machine learning

Temporal relation computation is one of the tasks of the extraction of temporal arguments from event, and it is also the ultimate goal of temporal information processing. However, temporal relation computation based on machine learning requires a lot of hand-marked work, and exploring more features from discourse. A method of two-stage machine learning based on temporal relation computation （TSMLTRC） is proposed in this paper for the shortcomings of current temporal relation computation between two events. The first stage is to get the main temporal attributes of event based on classification learning. The second stage is to compute the event temporal relation in the discourse through employing the result of the first stage as the basic features, and also employing some new linguistic characteristics. Experiments show that, compared with the artificial golden rule, the computational efficiency in the first stage is much higher, and the F1-Score of event temporal relation which is computed through combining multi-features may be increased at 85.8% in the second stage.

Nondeterministic Temporal Relations in Multimedia Data

Synchronization is a important issue in multimedia systems which integrate a variety oftemporally related media objects.One part of synchronization is the representation of tempo-ral information.With the emerging interaetive multimedia,deterministic temporal modelsare replaced by nondeterministic ones with more expressiveness.This paper classifies tempo-ral models by their expressiveness,and evaluates the relevant nondetermi.nistic temporal re-lations in multimedia data.Additionally,an interval-based nondeterministie model based ona complete temporal operator set is proposed,providing high-level abstractions and a highdegree of expressiveness for interactive multimedia systems.

Constructing a raster-based spatio-temporal hierarchical data model for marine fisheries application

Marine information has been increasing quickly. The traditional database technologies have disadvantages in manipulating large amounts of marine information which relates to the position in 3-D with the time. Recently, greater emphasis has been placed on GIS （geographical information system）to deal with the marine information. The GIS has shown great success for terrestrial applications in the last decades, but its use in marine fields has been far more restricted. One of the main reasons is that most of the GIS systems or their data models are designed for land applications. They cannot do well with the nature of the marine environment and for the marine information. And this becomes a fundamental challenge to the traditional GIS and its data structure. This work designed a data model, the raster-based spatio-temporal hierarchical data model （RSHDM）, for the marine information system, or for the knowledge discovery fi＇om spatio-temporal data, which bases itself on the nature of the marine data and overcomes the shortages of the current spatio-temporal models when they are used in the field. As an experiment, the marine fishery data warehouse （FDW） for marine fishery management was set up, which was based on the RSHDM. The experiment proved that the RSHDM can do well with the data and can extract easily the aggregations that the management needs at different levels.

Temporal-spatial Analysis on Urban and Rural Drinking Water Health Monitoring of Tianjin in 2013

Objective Grasp quality and health status of drinking water, and provide scientific basis for decision making of health administrative de- partment. Method According to the Standards for Drinking Water Quality （ GB 5749 -2006）, monitoring results of Tianjin urban and rural drinking water health in 2013 were evaluated, and software SPSS 20.0 and GeoDa was used for temporal-spatial analysis on water quality. Result There were 2 882 copies of monitoring samples in total, in which both finished water and tap water of urban district were qualified, while qualified rates of tap water and secondary water supply from the county were respectively 86.36% and 93.91%, and major exceeding indexes were pH and total number of colonies. Qualified rates of tap water and secondary water supply from the county had difference（x2 = 1 576.875, P 〈0.01 ）. Quality of tap water（ X2 = 5.425, P 〉 0.05） and secondary water supply （X2 = 16.009, P 〉 0.05） was stable at temporal distribution （ January-December）, but spatial distribution of tap water had certain regional difference（x2 = 1 255.802, P 〈0.01 ）. Conclusion General quality situation of Tianjin urban and rural drinking water was better, but qualified rate of water quality in some counties was lower, which had safety risk and threatened the health of residents in the corresponding county. The related departments should enhance the supervision and management of drinking water supply, im- prove supply water quality, strengthen water quality monitoring, and guarantee drinking water safety. Geographic information system can better real- ize visualization of drinking water quality monitoring information.

The survey of touristry information system

In this paper, the writer briefly introduces the concept of touristry information system (TIS), its current research situation, its necessity and urgency of development, analyses the structure, function and technical support of TIS, researches its development procedure and points out its development principles and modes.

基于互信息优化的Option-Critic算法

时序抽象作为分层强化学习的重要研究内容,允许分层强化学习智能体在不同的时间尺度上学习策略,可以有效解决深度强化学习难以处理的稀疏奖励问题。如何端到端地学习到优秀的时序抽象策略一直是分层强化学习研究面临的挑战。Option-Critic(OC)框架在Option框架的基础上,通过策略梯度理论,可以有效解决此问题。然而,在策略学习过程中,OC框架会出现Option内部策略动作分布变得十分相似的退化问题。该退化问题影响了OC框架的实验性能,导致Option的可解释性变差。为了解决上述问题,引入互信息知识作为内部奖励,并提出基于互信息优化的Option-Critic算法(Option-Critic Algorithm with Mutual Information Optimization,MIOOC)。MIOOC算法结合了近端策略Option-Critic(Proximal Policy Option-Critic,PPOC)算法,可以保证下层策略的多样性。为了验证算法的有效性,把MIOOC算法和几种常见的强化学习方法在连续实验环境中进行对比实验。实验结果表明,MIOOC算法可以加快模型学习速度,实验性能更优,Option内部策略更有区分度。

Spatio-temporal epidemic type aftershock sequence model for Tangshan aftershock sequence

Shallow earthquakes usually show obvious spatio-temporal clustering patterns. In this study, several spatio-temporal point process models are applied to investigate the clustering characteristics of the well-known Tangshan sequence based on classical empirical laws and a few assumptions. The relative fit of competing models is compared by Akalke Information Criterion. The spatial clustering pattern is well characterized by the model which gives the best fit to the data. A simulated aftershock sequence is generated by thinning algorithm and compared with the real seismicity.

Investigating Spatio-Temporal Pattern of Relative Risk of Tuberculosis in Kenya Using Bayesian Hierarchical Approaches

Proper understanding of global distribution of infectious diseases is an important part of disease management and policy making. However, data are subject to complexities caused by heterogeneities across host classes and space-time epidemic processes. This paper seeks to suggest or propose Bayesian spatio-temporal model for modeling and mapping tuberculosis relative risks in space and time as well identify risks factors associated with the tuberculosis and counties in Kenya with high tuberculosis relative risks. In this paper, we used spatio-temporal Bayesian hierarchical models to study the pattern of tuberculosis relative risks in Kenya. The Markov Chain Monte Carlo method via WinBUGS and R packages were used for simulations and estimation of the parameter estimates. The best fitting model is selected using the Deviance Information Criterion proposed by Spiegelhalter and colleagues. Among the spatio-temporal models used, the Knorr-Held model with space-time interaction type III and IV fit the data well but type IV appears better than type III. Variation in tuberculosis risk is observed among Kenya counties and clustering among counties with high tuberculosis relative risks. The prevalence of HIV is identified as the determinant of TB. We found clustering and heterogeneity of TB risk among high rate counties and the overall tuberculosis risk is slightly decreasing from 2002-2009. We proposed that the Knorr-Held model with interaction type IV should be used to model and map Kenyan tuberculosis relative risks. Interaction of TB relative risk in space and time increases among rural counties that share boundaries with urban counties with high tuberculosis risk. This is due to the ability of models to borrow strength from neighboring counties, such that nearby counties have similar risk. Although the approaches are less than ideal, we hope that our study provide a useful stepping stone in the development of spatial and spatio-temporal methodology for the statistical analysis of risk from tuberculosis in Kenya.

基于TI-FI-Cat Boost的公司财务困境的集成预测方法

挖掘年度信息披露报告中表外披露的关键信息,对于揭示企业财务困境具有重要的意义。本文选取了2001年-2021年我国A股232家被ST的上市公司和232家正常公司为研究对象,融合年度信息披露报告中的财务文本信息(TI)和财务指标(FI),构建TI-FI-CatBoost集成预测模型,用于预判上市公司的财务困境。研究结果表明:(1)TI-FI-CatBoost模型集成分析了财务文本信息和财务指标,在财务危机预测研究中优于仅使用财务指标的CatBoost模型以及Logistic模型、SVM模型和XGBoost模型;(2)预测财务困境的最佳窗口期为被ST前两年;(3)年度信息披露报告中的财务文本信息对于预警财务困境具有重要的参考价值,中断、原告、锐减、亏损、明显、谨慎、未能、可能、风险、减持等词汇与财务危机显著正相关,成本、控制、规模、资本、缺陷、收益等词汇与财务危机显著负相关。

基于TI指标系列的情报学学科交叉主题研究

学科交叉日益广泛且许多科技前沿及热点涌现于交叉领域，而现有的学科交叉测度指标大都是根据作者、机构或参考文献等共现情况提出的，且大都侧重于研究学科交叉态势。本文运用一种新的测度指标TI来挖掘学科交叉主题，并以情报学为例，通过对TI指标的计算和排序，并基于TI指标提出TI指标系列，结合社会网络分析方法和时序分析方法来分析和研究情报学学科交叉情况。研究表明，TI值可以反映主题词间的学科交叉程度，并与其他指标方法相互补充，成为分析学科交叉的有效指标。

基于TimeDB的时态数据库应用研究

时间是信息的重要属性。信息技术的深入发展,对时态信息处理的要求越来越迫切。时态数据库记录时态数据并反映时态信息。文章论述了时态数据库的发展历程,介绍了TimeDB的工作原理,并结合TimeDB实现职员工资管理系统,最后论述了TimeDB的不足。

北疆地区草地TI-NDVI与NDVImax时空异质性评价

选择草地类型丰富多样的北疆作为研究区。基于MODIS NDVI数据,获取时间累积归一化植被指数TINDVI和年最大NDVI(NDVImax),借助GIS空间分析技术、变异系数(CV)及Mann-Kendall非参数趋势检验等方法,对2000—2019年北疆地区草地覆盖动态变化进行了分析,并探究了TI-NDVI和NDVImax对草地时空异质性的表达能力的比较优势。结果表明:(1)用NDVImax和TI-NDVI表征的北疆草地呈现明显海拔分异。TI-NDVI总体随NDVImax的增大而增大,但NDVImax或TI-NDVI相同的区域,其TI-NDVI或NDVImax却存在较大差异。(2)2000—2019年北疆地区草地TI-NDVI和NDVImax总体呈显著增加趋势(P<0.01),但草地TI-NDVI变化的空间分异与NDVImax明显不同,全区17.55%的草地TI-NDVI变化趋势与NDVImax变化趋势相反。尤其阿尔泰山与伊犁河谷,高覆盖草地分布区的NDVImax与TI-NDVI均呈相反变化趋势。(3)在时间和空间维度上,北疆山区高覆盖草地TI-NDVI的CV均高于NDVImax。TI-NDVI对高覆盖草地的时空异质性反映更敏感,能在一定程度上削弱草地动态评价中NDVI光饱和缺陷的影响。

TimeML在文本时间关系解析中的应用

TimeML是在自然语言文本中标注事件和时间表达的一种规范语言。本文介绍了TimeML标准及其语料库TimeBank，从其起源英文文本事件时间表达标注开始，分析TimeML在文本时间关系解析研究中的发展，并探讨了TimeML在汉语文本时间关系解析研究中的应用。