研究探讨了对话式阅读中的CROWD提示策略(完成性、回忆性、开放性、问题性、间距性)如何促进4~5岁幼儿心理理论(Theory of Mind, ToM)的发展。首先基于改进的卡西迪分析框架,确立了“心理状态的语言表征”和“错误信念的表征”两大标准...研究探讨了对话式阅读中的CROWD提示策略(完成性、回忆性、开放性、问题性、间距性)如何促进4~5岁幼儿心理理论(Theory of Mind, ToM)的发展。首先基于改进的卡西迪分析框架,确立了“心理状态的语言表征”和“错误信念的表征”两大标准,并以此为依据选取了7本富含心理理论内容的图画书作为分析样本。随后系统地剖析了五种CROWD提示策略在这些图画书阅读中的具体应用路径,通过列举实例说明了每种策略如何引导幼儿识别和推断角色的信念、意图、情绪等心理状态。最后得出结论:CROWD策略通过构建“行为–心理结果”的因果链条、训练双重表征能力、促进反事实推理等方式,能够有效提升幼儿的心理理论能力,并通过认知与情感的双向建构,实现对话式阅读教育效能的最优化。展开更多
In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd dat...In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.展开更多
AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control...AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the study.One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular area.Radial peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device software.Automated parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB software.When the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded disc.RESULTS:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.展开更多
In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psycho...In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.展开更多
Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,...Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.展开更多
A solid electrolyte interphase(SEI)with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal batteries.However,it is difficult to form such a SEI directly from an el...A solid electrolyte interphase(SEI)with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal batteries.However,it is difficult to form such a SEI directly from an electrolyte.In this work,a molecular crowding effect is based on the introduction of Zn(OTF)_(2)and Zn(ClO_(4))_(2)to 2 mol/L ZnSO_(4)electrolytes.Simulations and experiments indicate that the Zn(OTF)_(2)and Zn(ClO_(4))_(2)not only create a molecularly crowded electrolyte environment to promote the interaction of Zn^(2+)and OTF^(-),but also participate in the reduction to construct a robust and high ionic-conductive SEI,thus promoting metal zinc deposition to the(002)crystal surface.With this molecular crowding electrolyte,a high current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn as the anode for over 1000 h.And in a temperature environment of-10℃,a current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn for over 200 h.Zn//Bi_(2)S_(3)/VS4@C cells achieve a CE rate of up to 99.81%over 1000 cycles.Hence,the utilization of a molecular crowding electrolyte is deemed a highly effective approach to fabricating a sophisticated SEI for a zinc anode.展开更多
The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how t...The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.展开更多
Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges i...Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.展开更多
Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoret...Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.展开更多
Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an ima...Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.展开更多
Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting ha...Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.展开更多
By using evacuation simulation technology and taking North China University of Technology as an example,the barrier-free evacuation design scheme for groups with different needs in campus environment was deeply discus...By using evacuation simulation technology and taking North China University of Technology as an example,the barrier-free evacuation design scheme for groups with different needs in campus environment was deeply discussed.Based on the data of building layout,population composition,road system and distribution of shelters in the school,a detailed evacuation model was constructed in the Pathfinder emergency evacuation simulation system.By the simulation during the daytime and at night,the total evacuation time of the whole school,evacuation completion time of each building,selection of evacuation paths and shelter utilization were analyzed in detail.The simulation results show that the distribution of shelters on campus is uneven,and their capacity is limited.As a result,the evacuation paths of the disabled,the elderly and children need to be adjusted frequently,which affects the overall evacuation efficiency.In view of this,the optimization strategies of road renovation and entrances of shelters and buildings were put forward from the perspective of space planning.From the perspective of emergency management,it is suggested to improve the campus evacuation infrastructure and strengthen the evacuation drill for teachers and students.These results provide a solid theoretical support for enhancing the construction of campus barrier-free environment and improving the level of emergency management.展开更多
In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety ris...In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.展开更多
Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data...Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results,ultimately affecting the study’s transparency and integrity.The task of manually annotating extensive datasets demands considerable labor and financial investment,especially when it entails engaging specialized individuals.In our crowd counting study,we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images.By integrating automated annotation tools,we realized substantial time efficiencies,as demonstrated by the remarkable achievement of 858,958 annotations.This underscores the significant contribution of such technologies to the efficiency of the annotation process.展开更多
Background:Crowdfunding has risen rapidly as a way of raising funds to support projects such as art projects,charity projects,and new ventures.It is very important to understand how crowds in the crowdfunding market a...Background:Crowdfunding has risen rapidly as a way of raising funds to support projects such as art projects,charity projects,and new ventures.It is very important to understand how crowds in the crowdfunding market are organized to carry out various activities.This study documents and compares two crowd designs for crowdfunding,namely pure crowds,where all crowd members participate as equals,and hybrid crowds,where crowd members are led by an expert investor.The hybrid design is rarely studied in the crowdfunding literature despite its large presence in equity crowdfunding.Methods:We examine industry practices from various countries in terms of crowd designs,review relevant literature on this topic,and develop a conceptual framework for choosing between pure and hybrid crowds.Results:We identify several inefficiencies of pure crowds in crowdfunding platforms and discuss the advantages of hybrid crowds.We then develop a conceptual framework that illustrates the factors for choosing between pure and hybrid crowds.Finally,we discuss the issue of how to manage and regulate lead investors in hybrid crowds.Conclusions:Pure crowds have several shortcomings that could be mitigated by a hybrid crowd design,especially when the proposed project suffers from greater risks,a high degree of information asymmetry,concerns about information leakage,and a high cost of managing the crowds.But for the hybrid crowd to work well,one must carefully design mechanisms for lead investor selection,compensation,and discipline.Our study contributes to the crowdfunding literature and to crowdfunding practice in multiple ways.展开更多
The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video ana...The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.展开更多
Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)use.Crowd tracking using UAVs is among the most important services provided by a CMA.In thi...Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)use.Crowd tracking using UAVs is among the most important services provided by a CMA.In this paper,we studied the periodic crowd-tracking(PCT)problem.It consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this purpose.The first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications context.This study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of abstraction.To closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs solver.Our main objective was to study the PCT problem fromboth theoretical and numerical viewpoints.To prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation purposes.The empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.展开更多
Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of challenges.With the emergence of crowdsourcing,versatile ...Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of challenges.With the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process.During the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds.This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning processes.In addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.展开更多
文摘研究探讨了对话式阅读中的CROWD提示策略(完成性、回忆性、开放性、问题性、间距性)如何促进4~5岁幼儿心理理论(Theory of Mind, ToM)的发展。首先基于改进的卡西迪分析框架,确立了“心理状态的语言表征”和“错误信念的表征”两大标准,并以此为依据选取了7本富含心理理论内容的图画书作为分析样本。随后系统地剖析了五种CROWD提示策略在这些图画书阅读中的具体应用路径,通过列举实例说明了每种策略如何引导幼儿识别和推断角色的信念、意图、情绪等心理状态。最后得出结论:CROWD策略通过构建“行为–心理结果”的因果链条、训练双重表征能力、促进反事实推理等方式,能够有效提升幼儿的心理理论能力,并通过认知与情感的双向建构,实现对话式阅读教育效能的最优化。
基金the Humanities and Social Science Fund of the Ministry of Education of China(21YJAZH077)。
文摘In a crowd density estimation dataset,the annotation of crowd locations is an extremely laborious task,and they are not taken into the evaluation metrics.In this paper,we aim to reduce the annotation cost of crowd datasets,and propose a crowd density estimation method based on weakly-supervised learning,in the absence of crowd position supervision information,which directly reduces the number of crowds by using the number of pedestrians in the image as the supervised information.For this purpose,we design a new training method,which exploits the correlation between global and local image features by incremental learning to train the network.Specifically,we design a parent-child network(PC-Net)focusing on the global and local image respectively,and propose a linear feature calibration structure to train the PC-Net simultaneously,and the child network learns feature transfer factors and feature bias weights,and uses the transfer factors and bias weights to linearly feature calibrate the features extracted from the Parent network,to improve the convergence of the network by using local features hidden in the crowd images.In addition,we use the pyramid vision transformer as the backbone of the PC-Net to extract crowd features at different levels,and design a global-local feature loss function(L2).We combine it with a crowd counting loss(LC)to enhance the sensitivity of the network to crowd features during the training process,which effectively improves the accuracy of crowd density estimation.The experimental results show that the PC-Net significantly reduces the gap between fullysupervised and weakly-supervised crowd density estimation,and outperforms the comparison methods on five datasets of Shanghai Tech Part A,ShanghaiTech Part B,UCF_CC_50,UCF_QNRF and JHU-CROWD++.
文摘AIM:To compare superficial and deep vascular properties of optic discs between crowded discs and controls using optical coherence tomography angiography(OCT-A).METHODS:Thirty patients with crowded discs,and 47 control subjects were enrolled in the study.One eye of each individual was included and OCT-A scans of optic discs were obtained in a 4.5×4.5 mm^(2) rectangular area.Radial peripapillary capillary(RPC)density,peripapillary retinal nerve fiber layer(pRNFL)thickness,cup volume,rim area,disc area,cup-to-disc(c/d)area ratio,and vertical c/d ratio were obtained automatically using device software.Automated parapapillary choroidal microvasculature(PPCMv)density was calculated using MATLAB software.When the vertical c/d ratio of the optic disc was absent or small cup,it was considered as a crowded disc.RESULTS:The mean signal strength index of OCT-A images was similar between the crowded discs and control eyes(P=0.740).There was no difference in pRNFL between the two groups(P=0.102).There were no differences in RPC density in whole image(P=0.826)and peripapillary region(P=0.923),but inside disc RPC density was higher in crowded optic discs(P=0.003).The PPCMv density in the inner-hemisuperior region was also lower in crowded discs(P=0.026).The pRNFL thickness was positively correlated with peripapillary RPC density(r=0.498,P<0.001).The inside disc RPC density was negatively correlated with c/d area ratio(r=-0.341,P=0.002).CONCLUSION:The higher inside disc RPC density and lower inner-hemisuperior PPCMv density are found in eyes with crowded optic discs.
基金Project supported by the National Natural Science Foundation of China(Grant No.72274208)。
文摘In recent years,attacks against crowded places such as campuses and theaters have had a frequent and negative impact on the security and stability of society.In such an event,the crowd will be subjected to high psychological stress and their emotions will rapidly spread to others.This paper establishes the attack-escape evacuation simulation model(AEES-SFM),based on the social force model,to consider emotion spreading under attack.In this model,(1)the attack-escape driving force is considered for the interaction between an attacker and evacuees and(2)emotion spreading among the evacuees is considered to modify the value of the psychological force.To validate the simulation,several experiments were carried out at a university in China.Comparing the simulation and experimental results,it is found that the simulation results are similar to the experimental results when considering emotion spreading.Therefore,the AEES-SFM is proved to be effective.By comparing the results of the evacuation simulation without emotion spreading,the emotion spreading model reduces the evacuation time and the number of casualties by about 30%,which is closer to the real experimental results.The results are still applicable in the case of a 40-person evacuation.This paper provides theoretical support and practical guidance for campus response to violent attacks.
基金supported by Grant(42271391 and 62006214)from National Natural Science Foundation of Chinaby Grant(8091B022148)from Joint Funds of Equipment Pre-Research and Ministry of Education of China+1 种基金by Grant(2023BIB015)from Special Project of Hubei Key Research and Development Programby Grant(KLIGIP-2021B03)from Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing.
文摘Snake Optimizer(SO)is a novel Meta-heuristic Algorithm(MA)inspired by the mating behaviour of snakes,which has achieved success in global numerical optimization problems and practical engineering applications.However,it also has certain drawbacks for the exploration stage and the egg hatch process,resulting in slow convergence speed and inferior solution quality.To address the above issues,a novel multi-strategy improved SO(MISO)with the assistance of population crowding analysis is proposed in this article.In the algorithm,a novel multi-strategy operator is designed for the exploration stage,which not only focuses on using the information of better performing individuals to improve the quality of solution,but also focuses on maintaining population diversity.To boost the efficiency of the egg hatch process,the multi-strategy egg hatch process is proposed to regenerate individuals according to the results of the population crowding analysis.In addition,a local search method is employed to further enhance the convergence speed and the local search capability.MISO is first compared with three sets of algorithms in the CEC2020 benchmark functions,including SO with its two recently discussed variants,ten advanced MAs,and six powerful CEC competition algorithms.The performance of MISO is then verified on five practical engineering design problems.The experimental results show that MISO provides a promising performance for the above optimization cases in terms of convergence speed and solution quality.
基金supported by Jilin Provincial Natural Fund(No.20230101205JC)Chongqing Natural Science Foundation(Nos.cstc2022jcyj-msxmX0184 and CSTB2022NSCQ-MSX0241)the International Cooperation Foundation(No.20220402026GH)of Science and Technology Department of Jilin Province。
文摘A solid electrolyte interphase(SEI)with a robust mechanical property and a high ionic conductivity is imperative for high-performance zinc metal batteries.However,it is difficult to form such a SEI directly from an electrolyte.In this work,a molecular crowding effect is based on the introduction of Zn(OTF)_(2)and Zn(ClO_(4))_(2)to 2 mol/L ZnSO_(4)electrolytes.Simulations and experiments indicate that the Zn(OTF)_(2)and Zn(ClO_(4))_(2)not only create a molecularly crowded electrolyte environment to promote the interaction of Zn^(2+)and OTF^(-),but also participate in the reduction to construct a robust and high ionic-conductive SEI,thus promoting metal zinc deposition to the(002)crystal surface.With this molecular crowding electrolyte,a high current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn as the anode for over 1000 h.And in a temperature environment of-10℃,a current density of 1 mA/cm^(2)can be obtained by assembling symmetric batteries with Zn for over 200 h.Zn//Bi_(2)S_(3)/VS4@C cells achieve a CE rate of up to 99.81%over 1000 cycles.Hence,the utilization of a molecular crowding electrolyte is deemed a highly effective approach to fabricating a sophisticated SEI for a zinc anode.
基金supported in part by the National Key Research and Development Program of China under Grant 2020YFB1005900the National Natural Science Foundation of China(NSFC)under Grant 62102232,62122042,61971269Natural Science Foundation of Shandong province under Grant ZR2021QF064.
文摘The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
基金Double First-Class Innovation Research Project for People’s Public Security University of China(2023SYL08).
文摘Crowd counting is a promising hotspot of computer vision involving crowd intelligence analysis,achieving tremendous success recently with the development of deep learning.However,there have been stillmany challenges including crowd multi-scale variations and high network complexity,etc.To tackle these issues,a lightweight Resconnection multi-branch network(LRMBNet)for highly accurate crowd counting and localization is proposed.Specifically,using improved ShuffleNet V2 as the backbone,a lightweight shallow extractor has been designed by employing the channel compression mechanism to reduce enormously the number of network parameters.A light multi-branch structure with different expansion rate convolutions is demonstrated to extract multi-scale features and enlarged receptive fields,where the information transmission and fusion of diverse scale features is enhanced via residual concatenation.In addition,a compound loss function is introduced for training themethod to improve global context information correlation.The proposed method is evaluated on the SHHA,SHHB,UCF-QNRF and UCF_CC_50 public datasets.The accuracy is better than those of many advanced approaches,while the number of parameters is smaller.The experimental results show that the proposed method achieves a good tradeoff between the complexity and accuracy of crowd counting,indicating a lightweight and high-precision method for crowd counting.
文摘Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route selection.The proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.
基金funded by Naif Arab University for Security Sciences under grant No.NAUSS-23-R10.
文摘Estimation of crowd count is becoming crucial nowadays,as it can help in security surveillance,crowd monitoring,and management for different events.It is challenging to determine the approximate crowd size from an image of the crowd’s density.Therefore in this research study,we proposed a multi-headed convolutional neural network architecture-based model for crowd counting,where we divided our proposed model into two main components:(i)the convolutional neural network,which extracts the feature across the whole image that is given to it as an input,and(ii)the multi-headed layers,which make it easier to evaluate density maps to estimate the number of people in the input image and determine their number in the crowd.We employed the available public benchmark crowd-counting datasets UCF CC 50 and ShanghaiTech parts A and B for model training and testing to validate the model’s performance.To analyze the results,we used two metrics Mean Absolute Error(MAE)and Mean Square Error(MSE),and compared the results of the proposed systems with the state-of-art models of crowd counting.The results show the superiority of the proposed system.
基金supported by Research Project Support Program for Excellence Institute(2022,ESL)in Incheon National University.
文摘Quantifying the number of individuals in images or videos to estimate crowd density is a challenging yet crucial task with significant implications for fields such as urban planning and public safety.Crowd counting has attracted considerable attention in the field of computer vision,leading to the development of numerous advanced models and methodologies.These approaches vary in terms of supervision techniques,network architectures,and model complexity.Currently,most crowd counting methods rely on fully supervised learning,which has proven to be effective.However,this approach presents challenges in real-world scenarios,where labeled data and ground-truth annotations are often scarce.As a result,there is an increasing need to explore unsupervised and semi-supervised methods to effectively address crowd counting tasks in practical applications.This paper offers a comprehensive review of crowd counting models,with a particular focus on semi-supervised and unsupervised approaches based on their supervision paradigms.We summarize and critically analyze the key methods in these two categories,highlighting their strengths and limitations.Furthermore,we provide a comparative analysis of prominent crowd counting methods using widely adopted benchmark datasets.We believe that this survey will offer valuable insights and guide future advancements in crowd counting technology.
基金Innovation and Entrepreneurship Training Project for College Students in Beijing(10805136024-XN139-100)Scientific Research Foundation of North China University of Technology(11005136024XN147-56)Yuxiu Innovation Project of North China University of Technology(2024NCUTYXCX214).
文摘By using evacuation simulation technology and taking North China University of Technology as an example,the barrier-free evacuation design scheme for groups with different needs in campus environment was deeply discussed.Based on the data of building layout,population composition,road system and distribution of shelters in the school,a detailed evacuation model was constructed in the Pathfinder emergency evacuation simulation system.By the simulation during the daytime and at night,the total evacuation time of the whole school,evacuation completion time of each building,selection of evacuation paths and shelter utilization were analyzed in detail.The simulation results show that the distribution of shelters on campus is uneven,and their capacity is limited.As a result,the evacuation paths of the disabled,the elderly and children need to be adjusted frequently,which affects the overall evacuation efficiency.In view of this,the optimization strategies of road renovation and entrances of shelters and buildings were put forward from the perspective of space planning.From the perspective of emergency management,it is suggested to improve the campus evacuation infrastructure and strengthen the evacuation drill for teachers and students.These results provide a solid theoretical support for enhancing the construction of campus barrier-free environment and improving the level of emergency management.
文摘In modern society, dense crowd detection technology is particularly important due to the frequent occurrence of crowd scenes such as stations, shopping malls, and event sites, which are often accompanied by safety risks, like stampede accidents. Although many studies have made progress in estimating population density, the ability to accurately identify dense areas in multi-scale scenarios still needs to be improved. To solve this problem, this paper proposed an improved multi-scale dense crowd detection method based on YOLOv5 and improved the DBSCAN clustering algorithm to identify densely crowded areas. Experiments show that the improved multi-scale dense crowd detection method can identify target crowds at multiple scales, and the accuracy of its detection results is around 70%. In addition, by calculating the crowd density under the same scale conditions and visualising the dense areas, we were able to solve the problem of dividing the crowded areas and visualise the dense areas more accurately. These improvements enhanced the applicability and reliability of the model in practical applications and provided strong technical support for security monitoring and management.
基金The authors acknowledge that this research work was supported through project number UCS&T/R&D-09/19–20/17533 titled“An Intelligent Computational Model for Crowd Demonstration and Risk Analysis during Spiritual Events in Haridwar’by the Uttarakhand Council for Science and Technology(UCOST),India.”。
文摘Funding agencies play a pivotal role in bolstering research endeavors by allocating financial resources for data collection and analysis.However,the lack of detailed information regarding the methods employed for data gathering and analysis can obstruct the replication and utilization of the results,ultimately affecting the study’s transparency and integrity.The task of manually annotating extensive datasets demands considerable labor and financial investment,especially when it entails engaging specialized individuals.In our crowd counting study,we employed the web-based annotation tool SuperAnnotate to streamline the human annotation process for a dataset comprising 3,000 images.By integrating automated annotation tools,we realized substantial time efficiencies,as demonstrated by the remarkable achievement of 858,958 annotations.This underscores the significant contribution of such technologies to the efficiency of the annotation process.
文摘Background:Crowdfunding has risen rapidly as a way of raising funds to support projects such as art projects,charity projects,and new ventures.It is very important to understand how crowds in the crowdfunding market are organized to carry out various activities.This study documents and compares two crowd designs for crowdfunding,namely pure crowds,where all crowd members participate as equals,and hybrid crowds,where crowd members are led by an expert investor.The hybrid design is rarely studied in the crowdfunding literature despite its large presence in equity crowdfunding.Methods:We examine industry practices from various countries in terms of crowd designs,review relevant literature on this topic,and develop a conceptual framework for choosing between pure and hybrid crowds.Results:We identify several inefficiencies of pure crowds in crowdfunding platforms and discuss the advantages of hybrid crowds.We then develop a conceptual framework that illustrates the factors for choosing between pure and hybrid crowds.Finally,we discuss the issue of how to manage and regulate lead investors in hybrid crowds.Conclusions:Pure crowds have several shortcomings that could be mitigated by a hybrid crowd design,especially when the proposed project suffers from greater risks,a high degree of information asymmetry,concerns about information leakage,and a high cost of managing the crowds.But for the hybrid crowd to work well,one must carefully design mechanisms for lead investor selection,compensation,and discipline.Our study contributes to the crowdfunding literature and to crowdfunding practice in multiple ways.
基金The authors extend their appreciation to the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research work through the Project Number QURDO001Project title:Intelligent Real-Time Crowd Monitoring System Using Unmanned Aerial Vehicle(UAV)Video and Global Positioning Systems(GPS)Data。
文摘The advent of the COVID-19 pandemic has adversely affected the entire world and has put forth high demand for techniques that remotely manage crowd-related tasks.Video surveillance and crowd management using video analysis techniques have significantly impacted today’s research,and numerous applications have been developed in this domain.This research proposed an anomaly detection technique applied to Umrah videos in Kaaba during the COVID-19 pandemic through sparse crowd analysis.Managing theKaaba rituals is crucial since the crowd gathers from around the world and requires proper analysis during these days of the pandemic.The Umrah videos are analyzed,and a system is devised that can track and monitor the crowd flow in Kaaba.The crowd in these videos is sparse due to the pandemic,and we have developed a technique to track the maximum crowd flow and detect any object(person)moving in the direction unlikely of the major flow.We have detected abnormal movement by creating the histograms for the vertical and horizontal flows and applying thresholds to identify the non-majority flow.Our algorithm aims to analyze the crowd through video surveillance and timely detect any abnormal activity tomaintain a smooth crowd flowinKaaba during the pandemic.
基金supported by the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia under Grant No.MoE-IF-G-20-08.
文摘Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)use.Crowd tracking using UAVs is among the most important services provided by a CMA.In this paper,we studied the periodic crowd-tracking(PCT)problem.It consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this purpose.The first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications context.This study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of abstraction.To closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs solver.Our main objective was to study the PCT problem fromboth theoretical and numerical viewpoints.To prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation purposes.The empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.
基金supported by the National Key Research and Development Program of China(2018AAA0102002)the National Natural Science Foundation of China(62076130,91846104).
文摘Big data have the characteristics of enormous volume,high velocity,diversity,value-sparsity,and uncertainty,which lead the knowledge learning from them full of challenges.With the emergence of crowdsourcing,versatile information can be obtained on-demand so that the wisdom of crowds is easily involved to facilitate the knowledge learning process.During the past thirteen years,researchers in the AI community made great efforts to remove the obstacles in the field of learning from crowds.This concentrated survey paper comprehensively reviews the technical progress in crowdsourcing learning from a systematic perspective that includes three dimensions of data,models,and learning processes.In addition to reviewing existing important work,the paper places a particular emphasis on providing some promising blueprints on each dimension as well as discussing the lessons learned from our past research work,which will light up the way for new researchers and encourage them to pursue new contributions.
