Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has recei...Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.展开更多
Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of ...Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.展开更多
As a kind of more and more popular information-sharing platform,Community Question Answering(CQA)portals attract a number of netizens to participate and learn from each other on them.However,some users post misleading...As a kind of more and more popular information-sharing platform,Community Question Answering(CQA)portals attract a number of netizens to participate and learn from each other on them.However,some users post misleading questions and answers to promote a product or service,which can distort the decisions of other users and degrade the credibility of the CQA environment.To address this issue,conventional solutions typically extract user features and classify them to detect CQA spammers.However,they often ignore the rich interaction relations among CQA objects.In this paper,to make up for this limitation,we propose ACSD,an Attributed Heterogeneous Information Network(AHIN)based Community question answering Spammer Detection model.Specifically,the Meta-Path based Neighbors(MPNs)defined in the AHIN are used to leverage the structural information among CQA objects,and meanwhile,a hierarchical attention mechanism is integrated in the ACSD model to assign the weights according to the different levels of importance for objects,MPNs,and meta-paths.We evaluate ACSD in a real-life CQA dataset,and the results demonstrate the effectiveness and advantage of this model on CQA spammer detection.展开更多
基金Our research in this paper was partially supported by JST COI JPMJCE1317.
文摘Many traffic accidents occur in parking lots.One of the serious safety risks is vehicle-pedestrian conflict.Moreover,with the increasing development of automatic driving and parking technology,parking safety has received significant attention from vehicle safety analysts.However,pedestrian protection in parking lots still faces many challenges.For example,the physical structure of a parking lot may be complex,and dead corners would occur when the vehicle density is high.These lead to pedestrians’sudden appearance in the vehicle’s path from an unexpected position,resulting in collision accidents in the parking lot.We advocate that besides vehicular sensing data,high-precision digital map of the parking lot,pedestrians’smart device’s sensing data,and attribute information of pedestrians can be used to detect the position of pedestrians in the parking lot.However,this subject has not been studied and explored in existing studies.Tofill this void,this paper proposes a pedestrian tracking framework integrating multiple information sources to provide pedestrian position and status information for vehicles and protect pedestrians in parking spaces.We also evaluate the proposed method through real-world experiments.The experimental results show that the proposed framework has its advantage in pedestrian attribute information extraction and positioning accuracy.It can also be used for pedestrian tracking in parking spaces.
基金This work was supported by the Key Technologies Research and Development Program of Liaoning Province in China under Grant 2021JH1/10400079the Fundamental Research Funds for the Central Universities under Grant 2217002.
文摘Complex systems in the real world often can be modeled as network structures,and community discovery algorithms for complex networks enable researchers to understand the internal structure and implicit information of networks.Existing community discovery algorithms are usually designed for single-layer networks or single-interaction relationships and do not consider the attribute information of nodes.However,many real-world networks consist of multiple types of nodes and edges,and there may be rich semantic information on nodes and edges.The methods for single-layer networks cannot effectively tackle multi-layer information,multi-relationship information,and attribute information.This paper proposes a community discovery algorithm based on multi-relationship embedding.The proposed algorithm first models the nodes in the network to obtain the embedding matrix for each node relationship type and generates the node embedding matrix for each specific relationship type in the network by node encoder.The node embedding matrix is provided as input for aggregating the node embedding matrix of each specific relationship type using a Graph Convolutional Network(GCN)to obtain the final node embedding matrix.This strategy allows capturing of rich structural and attributes information in multi-relational networks.Experiments were conducted on different datasets with baselines,and the results show that the proposed algorithm obtains significant performance improvement in community discovery,node clustering,and similarity search tasks,and compared to the baseline with the best performance,the proposed algorithm achieves an average improvement of 3.1%on Macro-F1 and 4.7%on Micro-F1,which proves the effectiveness of the proposed algorithm.
基金supported in part by the National Natural Science Foundation of China(Nos.71801123,72371133,62372240,and 71871109)the Key Projects of Natural Science Research in Jiangsu Provincial Colleges and Universities(Nos.22KJA520005 and 20KJA420011)+4 种基金the Planning Fund Project of Humanities and Social Sciences Research of Ministry of Education(No.23YJA870009)the Significant Project of Jiangsu College Philosophy and Social Sciences Research(No.2021SJZDA153)the Qing Lan Project of Jiangsu Province,the Open Project of the Key Laboratory of the Ministry of Education for Computer Network and Information Integration(Southeast University)(No.K93-9-2023-03)the Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201)the Foundation of Yunnan Key Laboratory of Service Computing(No.YNSC24122).
文摘As a kind of more and more popular information-sharing platform,Community Question Answering(CQA)portals attract a number of netizens to participate and learn from each other on them.However,some users post misleading questions and answers to promote a product or service,which can distort the decisions of other users and degrade the credibility of the CQA environment.To address this issue,conventional solutions typically extract user features and classify them to detect CQA spammers.However,they often ignore the rich interaction relations among CQA objects.In this paper,to make up for this limitation,we propose ACSD,an Attributed Heterogeneous Information Network(AHIN)based Community question answering Spammer Detection model.Specifically,the Meta-Path based Neighbors(MPNs)defined in the AHIN are used to leverage the structural information among CQA objects,and meanwhile,a hierarchical attention mechanism is integrated in the ACSD model to assign the weights according to the different levels of importance for objects,MPNs,and meta-paths.We evaluate ACSD in a real-life CQA dataset,and the results demonstrate the effectiveness and advantage of this model on CQA spammer detection.
