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.He...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.展开更多
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 characteristic...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.展开更多
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 mac...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.展开更多
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 o...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.展开更多
Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distrib...Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distribution on road networks has enabled us to examine the factors that give rise to the discrepancies and the fundamental spatial causes of traffic congestion. In recent years, mi- cro-perspective, individual, and behavior-based spatial analysis have mushroomed and been facilitated with effective tools such as tem- poral geographic information systems （T-GIS）. It is difficult to study the interrelations between transport and space on the basis of commuting mode choice since the mode choice data are invisible in a specific space such as a particular road network. Therefore, in the field of transport, the classical origin destination （OD） four-stage model （FSM） is usually employed to calculate data when studying commuting mode choice. Based on the relative principles of T-GIS and the platform of ArcGIS, this paper considers Guangzhou as a case study and develops a spatio-temporal tool to examine the daily activities of residents. Meanwhile, the traffic volume distribution in rush hours, which was analyzed according to commuting modes and how they were reflected in the road network, was scrutinized with data extracted from travel diaries. Moreover, efforts were made to explain the relationship between traffic demand and urban spatial structure. Based on the investigation, this research indicates that traffic volumes in divergent groups and on the road networks is driven by： l） the socio-economie characteristics of travelers; 2） a jobs-housing imbalance under suburbanization; 3） differences in the spatial supply of transport modes; 4） the remains of the Danwei （work unit） system and market development in China; and 5） the transition of urban spatial structure and other factors.展开更多
With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of...With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer.This algorithm comprises two branches:one branch consists of a Long and Short-Term Memory Network(LSTM),while the other parallel branch incorporates a one-dimensional Convolutional Neural Network(1DCNN).The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately,which are then concatenated and fed into a fully connected neural network for information fusion.In the LSTM-1DCNN architecture,the 1DCNN branch primarily focuses on extracting spatial features during convolution operations,whereas the LSTM branch mainly captures temporal features.Nine sets of accelerometer data from five publicly available HAR datasets are employed for training and evaluation purposes.The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree,Random Forest,Support Vector Machine,1DCNN,and LSTM on these five public datasets.Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36%to 99.68%,with a mean value of 96.22%and standard deviation of 0.03 across all evaluated metrics on these five public datasets-outperforming other existing HAR algorithms significantly in terms of evaluation metrics used in this study.Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes.Subsequently,the trained HAR algorithm is deployed on Android phones to evaluate its performance.Experimental results demonstrate that the proposed LSTM-1DCNN algorithm achieves an impressive F1-score of 97.67%on our self-built dataset.In conclusion,the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture.展开更多
Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network.However,when dealing with different financial fraud scen...Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network.However,when dealing with different financial fraud scenarios,existing methods face challenges,resulting in difficulty in effectively ensuring financial security.In fraud scenarios,transaction data are generated in real time,in which a strong temporal relationship between multiple fraudulent transactions is observed.Traditional dynamic graph models struggle to effectively balance the temporal features of nodes and spatial structural features,failing to handle different types of nodes in the graph network.In this study,to extract the temporal and structural information,we proposed a dynamic heterogeneous transaction graph embedding(DyHDGE)network based on a dynamic heterogeneous transaction graph,considering both temporal and structural information while incorporating heterogeneous data.To separately extract temporal relationships between transactions and spatial structural relationships between nodes,we used a heterogeneous temporal graph representation learning module and a temporal graph structure information extraction module.Additionally,we designed two loss functions to optimize node feature representations.Extensive experiments demonstrated that the proposed DyHDGE significantly outperformed previous state-of-the-art methods on two simulated datasets of financial fraud scenarios.This capability contributes to enhancing security in financial consumption scenarios.展开更多
Storm surge is one of the most serious oceanic disasters. Accurate and timely numerical prediction is one of the primary measures for disaster control. Traditional storm surge models lack of accuracy and time effects....Storm surge is one of the most serious oceanic disasters. Accurate and timely numerical prediction is one of the primary measures for disaster control. Traditional storm surge models lack of accuracy and time effects. To overcome the disadvantages, in this paper, an analytical cyclone model was first added into the Finite-Volume Coastal Ocean Model （FVCOM） consisting of high resolution, flooding and drying capabilities for 3D storm surge modeling. Then, we integrated MarineTools Pro into a geographic information system （GIS） to supplement the storm surge model. This provided end users with a friendly modeling platform and easy access to geographically referenced data that was required for the model input and output. A temporal GIS tracking analysis module was developed to create a visual path from storm surge numerical results. It was able to track the movement of a storm in space and time. MarineTools Pro＇ capabilities could assist the comprehensive understanding of complex storm events in data visualization, spatial query, and analysis of simulative results in an objective and accurate manner. The tools developed in this study further supported the idea that the coupled system could enhance productivity by providing an efficient operating environ- ment for accurate inversion or storm surge prediction. Finally, this coupled system was used to reconstruct the storm surge generated by Typhoon Agnes （No. 8114） and simulated typhoon induced-wind field and water elevations of Yangtze Estuary and Hangzhou Bay. The simulated results show good correlation with actual surveyed data. The simple operating interface of the coupled system is very convenient for users, who want to learn the usage of the storm surge model, especially for first-time users, which can save their modeling time greatly.展开更多
With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel o...With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.展开更多
文摘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.
基金been supported by Project of the National Natural Science Foundation of China(No.62073256)the Shaanxi Provincial Science and Technology Department(No.2020GY-125)Xi’an Science and Technology Innovation talent service enterprise project(No.2020KJRC0041)。
文摘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.
基金Sponsored by the Transportation Science and Technology Planning Project of Henan Province,China(Grant No.2019G-2-2).
文摘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.
基金Project supported the National Natural Science Foundation of China(Grant No.60975033)the Basic Scientific Research Project of International Centre for Bamboo Rattan(Grant No.1632009006)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘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.
基金Under the auspices of National Natural Science Foundation of China(No.40971098)National High Technology Research and Development Program of China(No.2012AA121402)
文摘Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distribution on road networks has enabled us to examine the factors that give rise to the discrepancies and the fundamental spatial causes of traffic congestion. In recent years, mi- cro-perspective, individual, and behavior-based spatial analysis have mushroomed and been facilitated with effective tools such as tem- poral geographic information systems （T-GIS）. It is difficult to study the interrelations between transport and space on the basis of commuting mode choice since the mode choice data are invisible in a specific space such as a particular road network. Therefore, in the field of transport, the classical origin destination （OD） four-stage model （FSM） is usually employed to calculate data when studying commuting mode choice. Based on the relative principles of T-GIS and the platform of ArcGIS, this paper considers Guangzhou as a case study and develops a spatio-temporal tool to examine the daily activities of residents. Meanwhile, the traffic volume distribution in rush hours, which was analyzed according to commuting modes and how they were reflected in the road network, was scrutinized with data extracted from travel diaries. Moreover, efforts were made to explain the relationship between traffic demand and urban spatial structure. Based on the investigation, this research indicates that traffic volumes in divergent groups and on the road networks is driven by： l） the socio-economie characteristics of travelers; 2） a jobs-housing imbalance under suburbanization; 3） differences in the spatial supply of transport modes; 4） the remains of the Danwei （work unit） system and market development in China; and 5） the transition of urban spatial structure and other factors.
基金supported by the Guangxi University of Science and Technology,Liuzhou,China,sponsored by the Researchers Supporting Project(No.XiaoKeBo21Z27,The Construction of Electronic Information Team supported by Artificial Intelligence Theory and Three-dimensional Visual Technology,Yuesheng Zhao)supported by the 2022 Laboratory Fund Project of the Key Laboratory of Space-Based Integrated Information System(No.SpaceInfoNet20221120,Research on the Key Technologies of Intelligent Spatiotemporal Data Engine Based on Space-Based Information Network,Yuesheng Zhao)supported by the 2023 Guangxi University Young and Middle-Aged Teachers’Basic Scientific Research Ability Improvement Project(No.2023KY0352,Research on the Recognition of Psychological Abnormalities in College Students Based on the Fusion of Pulse and EEG Techniques,Yutong Luo).
文摘With the rapid advancement of wearable devices,Human Activities Recognition(HAR)based on these devices has emerged as a prominent research field.The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer.This algorithm comprises two branches:one branch consists of a Long and Short-Term Memory Network(LSTM),while the other parallel branch incorporates a one-dimensional Convolutional Neural Network(1DCNN).The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately,which are then concatenated and fed into a fully connected neural network for information fusion.In the LSTM-1DCNN architecture,the 1DCNN branch primarily focuses on extracting spatial features during convolution operations,whereas the LSTM branch mainly captures temporal features.Nine sets of accelerometer data from five publicly available HAR datasets are employed for training and evaluation purposes.The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree,Random Forest,Support Vector Machine,1DCNN,and LSTM on these five public datasets.Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36%to 99.68%,with a mean value of 96.22%and standard deviation of 0.03 across all evaluated metrics on these five public datasets-outperforming other existing HAR algorithms significantly in terms of evaluation metrics used in this study.Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes.Subsequently,the trained HAR algorithm is deployed on Android phones to evaluate its performance.Experimental results demonstrate that the proposed LSTM-1DCNN algorithm achieves an impressive F1-score of 97.67%on our self-built dataset.In conclusion,the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture.
基金supported in part by the National Key Research and Development Program of China(2021YFC3300602)the National Natural Science Foundation of China(grant no.72204155)the Natural Science Foundation of Shanghai(grant no.23ZR1423100).
文摘Dynamic graph fraud detection aims to distinguish fraudulent entities that deviate significantly from most benign entities within an ever-changing graph network.However,when dealing with different financial fraud scenarios,existing methods face challenges,resulting in difficulty in effectively ensuring financial security.In fraud scenarios,transaction data are generated in real time,in which a strong temporal relationship between multiple fraudulent transactions is observed.Traditional dynamic graph models struggle to effectively balance the temporal features of nodes and spatial structural features,failing to handle different types of nodes in the graph network.In this study,to extract the temporal and structural information,we proposed a dynamic heterogeneous transaction graph embedding(DyHDGE)network based on a dynamic heterogeneous transaction graph,considering both temporal and structural information while incorporating heterogeneous data.To separately extract temporal relationships between transactions and spatial structural relationships between nodes,we used a heterogeneous temporal graph representation learning module and a temporal graph structure information extraction module.Additionally,we designed two loss functions to optimize node feature representations.Extensive experiments demonstrated that the proposed DyHDGE significantly outperformed previous state-of-the-art methods on two simulated datasets of financial fraud scenarios.This capability contributes to enhancing security in financial consumption scenarios.
文摘Storm surge is one of the most serious oceanic disasters. Accurate and timely numerical prediction is one of the primary measures for disaster control. Traditional storm surge models lack of accuracy and time effects. To overcome the disadvantages, in this paper, an analytical cyclone model was first added into the Finite-Volume Coastal Ocean Model （FVCOM） consisting of high resolution, flooding and drying capabilities for 3D storm surge modeling. Then, we integrated MarineTools Pro into a geographic information system （GIS） to supplement the storm surge model. This provided end users with a friendly modeling platform and easy access to geographically referenced data that was required for the model input and output. A temporal GIS tracking analysis module was developed to create a visual path from storm surge numerical results. It was able to track the movement of a storm in space and time. MarineTools Pro＇ capabilities could assist the comprehensive understanding of complex storm events in data visualization, spatial query, and analysis of simulative results in an objective and accurate manner. The tools developed in this study further supported the idea that the coupled system could enhance productivity by providing an efficient operating environ- ment for accurate inversion or storm surge prediction. Finally, this coupled system was used to reconstruct the storm surge generated by Typhoon Agnes （No. 8114） and simulated typhoon induced-wind field and water elevations of Yangtze Estuary and Hangzhou Bay. The simulated results show good correlation with actual surveyed data. The simple operating interface of the coupled system is very convenient for users, who want to learn the usage of the storm surge model, especially for first-time users, which can save their modeling time greatly.
基金supported by the National Key Project of Scientific and Technical Supporting Programs of China(2014BAK15B01)
文摘With the rapid development of location-based networks, point-of-interest（POI） recommendation has become an important means to help people discover interesting and attractive locations, especially when users travel out of town. However, because users only check-in interaction is highly sparse, which creates a big challenge for POI recommendation. To tackle this challenge, we propose a joint probabilistic generative model called geographical temporal social content popularity（GTSCP） to imitate user check-in activities in a process of decision making, which effectively integrates the geographical influence, temporal effect, social correlation, content information and popularity impact factors to overcome the data sparsity, especially for out-of-town users. Our proposed the GTSCP supports two recommendation scenarios in a joint model, i.e., home-town recommendation and out-of-town recommendation. Experimental results show that GTSCP achieves significantly superior recommendation quality compared to other state-of-the-art POI recommendation techniques.
