【目的/意义】系统分析了2009-2018年信息行为研究领域的学科分布、高被引文献、关键词共现、关键词聚类以及突发词,全面呈现了信息行为研究领域热点演化路径。【方法/过程】运用Citespace V知识图谱软件对通过Web of Science核心数据...【目的/意义】系统分析了2009-2018年信息行为研究领域的学科分布、高被引文献、关键词共现、关键词聚类以及突发词,全面呈现了信息行为研究领域热点演化路径。【方法/过程】运用Citespace V知识图谱软件对通过Web of Science核心数据库检索出的2137篇信息行为研究文献进行了可视化分析。【结果/结论】通过对学科分布分析,发现图书情报学学科在信息行为研究领域中占据主导地位;通过对关键词的共现分析,可以发现,信息搜寻行为在整个信息的生命周期中是比较重要的一个环节。而健康信息行为目前是信息行为领域中最热门的研究话题",微博""社会化媒体""互联网使用"等研究主题则是目前及未来一段时间内信息行为研究领域的前沿性话题。展开更多
Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self...Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.展开更多
Safety production is of great significance to the development of enterprises and society.Accidents often cause great losses because of the particularity environment of electric power.Therefore,it is important to impro...Safety production is of great significance to the development of enterprises and society.Accidents often cause great losses because of the particularity environment of electric power.Therefore,it is important to improve the safety supervision and protection in the electric power environment.In this paper,we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real time.In this method,the human body key points in video frames were first extracted by the high-resolution network,and then classified in real time by spatial-temporal graph convolutional network.Experimental results show that this method can effectively detect illegal actions in the simulated scene.展开更多
It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identificatio...It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.展开更多
为定量梳理社交网络信息对出行行为影响的研究成果,本文基于Web of Science核心数据库与CNKI知网数据库,检索并筛选2010—2022年间133篇英文文献和32篇中文文献。采用知识图谱分析和传统定性文献分析相结合的方法,量化统计文献的年度发...为定量梳理社交网络信息对出行行为影响的研究成果,本文基于Web of Science核心数据库与CNKI知网数据库,检索并筛选2010—2022年间133篇英文文献和32篇中文文献。采用知识图谱分析和传统定性文献分析相结合的方法,量化统计文献的年度发文量、研究热点国家、关键词图谱这3类指标,并从方法模型、社交网络信息行为、社交网络信息对出行决策的影响、社交网络信息对出行活动的影响这4个方面总结现有研究成果。结果表明:数据来源上,现有研究的基础数据尚未实现特征维与决策维融合,需要进一步融合多源数据提升研究结论的鲁棒性;研究方法上,现有研究缺乏分析方法之间的相互支撑,可整合多种研究手段跨学科分析社交网络信息对出行行为的影响;研究内容上,现有研究成果无法全面反映未来出行的发展趋势,且对出行者异质性关注不足,需结合无人驾驶、共享出行等新场景,考虑出行者异质性解析社交网络信息与出行行为之间的联系模式。展开更多
The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cy...The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.展开更多
为全面了解老年人的出行行为特征现有的研究进展,运用知识图谱分析和传统文献研究相结合的方法,通过Web of Science核心合集数据库和CNKI知网数据库,获取了在1993~2020年间出版的老年人出行研究相关中英文文献,分别为303篇和367篇(数据...为全面了解老年人的出行行为特征现有的研究进展,运用知识图谱分析和传统文献研究相结合的方法,通过Web of Science核心合集数据库和CNKI知网数据库,获取了在1993~2020年间出版的老年人出行研究相关中英文文献,分别为303篇和367篇(数据采集的最后时间均为2020年8月16日)。重点探讨了近10年老年人出行研究现状,并利用知识图谱展示研究的发展进程和前沿热点,并基于关键词图谱分析,从数据类型与方法模型、老年人出行行为特征分析、保障或促进老年人出行的策略3个方面总结归纳现有研究成果。结果表明:在数据类型方面,大多采用出行调查数据,对大数据挖掘深度不够,有限的研究使用了开源或多源数据;在分析方法与模型的选取上,多使用描述性统计方法或数理模型,缺乏多学科视角下的综合性分析。在出行行为特征分析方面,关于灵活交通服务和新兴出行方式的研究较少,影响因素的研究多集中于可直接观测的社会人口统计学因素和环境因素,关于心理因素等潜变量因素相对较少。在策略层面上,将其归纳为空间规划、交通规划、政策3个方面,缺少智能化精细化的交通需求管理等方面的策略。未来的研究可以立足于中国国情,从不同社会维度出发,满足老年人的出行需求,通过多样化方法融合多源数据进行分析,全面、准确地描述老年人的出行行为特征,为完善和制定改善老年人出行的人性化策略提供依据。展开更多
文摘【目的/意义】系统分析了2009-2018年信息行为研究领域的学科分布、高被引文献、关键词共现、关键词聚类以及突发词,全面呈现了信息行为研究领域热点演化路径。【方法/过程】运用Citespace V知识图谱软件对通过Web of Science核心数据库检索出的2137篇信息行为研究文献进行了可视化分析。【结果/结论】通过对学科分布分析,发现图书情报学学科在信息行为研究领域中占据主导地位;通过对关键词的共现分析,可以发现,信息搜寻行为在整个信息的生命周期中是比较重要的一个环节。而健康信息行为目前是信息行为领域中最热门的研究话题",微博""社会化媒体""互联网使用"等研究主题则是目前及未来一段时间内信息行为研究领域的前沿性话题。
基金supported in part by the National Natural Science Foundation of China (NSFC,62125106,61860206003,and 62088102)in part by the Ministry of Science and Technology of China (2021ZD0109901)in part by the Provincial Key Research and Development Program of Zhejiang (2021C01016).
文摘Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.
基金the Science and Technology Program of State Grid Corporation of China(No.5211TZ1900S6)。
文摘Safety production is of great significance to the development of enterprises and society.Accidents often cause great losses because of the particularity environment of electric power.Therefore,it is important to improve the safety supervision and protection in the electric power environment.In this paper,we simulate the actual electric power operation scenario by monitoring equipment and propose a real-time detection method of illegal actions based on human body key points to ensure safety behavior in real time.In this method,the human body key points in video frames were first extracted by the high-resolution network,and then classified in real time by spatial-temporal graph convolutional network.Experimental results show that this method can effectively detect illegal actions in the simulated scene.
基金supported by State Grid Corporation of China Project“Research on Coordinated Strategy of Multi-type Controllable Resources Based on Collective Intelligence in an Energy”(5100-202055479A-0-0-00).
文摘It is important to achieve an efficient home energy management system(HEMS)because of its role in promoting energy saving and emission reduction for end-users.Two critical issues in an efficient HEMS are identification of user behavior and energy management strategy.However,current HEMS methods usually assume perfect knowledge of user behavior or ignore the strong correlations of usage habits with different applications.This can lead to an insuffi-cient description of behavior and suboptimal management strategy.To address these gaps,this paper proposes non-intrusive load monitoring(NILM)assisted graph reinforcement learning(GRL)for intelligent HEMS decision making.First,a behavior correlation graph incorporating NILM is introduced to represent the energy consumption behavior of users and a multi-label classification model is used to monitor the loads.Thus,efficient identification of user behavior and description of state transition can be achieved.Second,based on the online updating of the behavior correlation graph,a GRL model is proposed to extract information contained in the graph.Thus,reliable strategy under uncer-tainty of environment and behavior is available.Finally,the experimental results on several datasets verify the effec-tiveness of the proposed model.
文摘为定量梳理社交网络信息对出行行为影响的研究成果,本文基于Web of Science核心数据库与CNKI知网数据库,检索并筛选2010—2022年间133篇英文文献和32篇中文文献。采用知识图谱分析和传统定性文献分析相结合的方法,量化统计文献的年度发文量、研究热点国家、关键词图谱这3类指标,并从方法模型、社交网络信息行为、社交网络信息对出行决策的影响、社交网络信息对出行活动的影响这4个方面总结现有研究成果。结果表明:数据来源上,现有研究的基础数据尚未实现特征维与决策维融合,需要进一步融合多源数据提升研究结论的鲁棒性;研究方法上,现有研究缺乏分析方法之间的相互支撑,可整合多种研究手段跨学科分析社交网络信息对出行行为的影响;研究内容上,现有研究成果无法全面反映未来出行的发展趋势,且对出行者异质性关注不足,需结合无人驾驶、共享出行等新场景,考虑出行者异质性解析社交网络信息与出行行为之间的联系模式。
基金supported by China’s National Key R&D Program,No.2019QY1404the National Natural Science Foundation of China,Grant No.U20A20161,U1836103the Basic Strengthening Program Project,No.2019-JCJQ-ZD-113.
文摘The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.
文摘为全面了解老年人的出行行为特征现有的研究进展,运用知识图谱分析和传统文献研究相结合的方法,通过Web of Science核心合集数据库和CNKI知网数据库,获取了在1993~2020年间出版的老年人出行研究相关中英文文献,分别为303篇和367篇(数据采集的最后时间均为2020年8月16日)。重点探讨了近10年老年人出行研究现状,并利用知识图谱展示研究的发展进程和前沿热点,并基于关键词图谱分析,从数据类型与方法模型、老年人出行行为特征分析、保障或促进老年人出行的策略3个方面总结归纳现有研究成果。结果表明:在数据类型方面,大多采用出行调查数据,对大数据挖掘深度不够,有限的研究使用了开源或多源数据;在分析方法与模型的选取上,多使用描述性统计方法或数理模型,缺乏多学科视角下的综合性分析。在出行行为特征分析方面,关于灵活交通服务和新兴出行方式的研究较少,影响因素的研究多集中于可直接观测的社会人口统计学因素和环境因素,关于心理因素等潜变量因素相对较少。在策略层面上,将其归纳为空间规划、交通规划、政策3个方面,缺少智能化精细化的交通需求管理等方面的策略。未来的研究可以立足于中国国情,从不同社会维度出发,满足老年人的出行需求,通过多样化方法融合多源数据进行分析,全面、准确地描述老年人的出行行为特征,为完善和制定改善老年人出行的人性化策略提供依据。
