A Weakly-Supervised Crowd Density Estimation Method Based on Two-Stage Linear Feature Calibration

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++.

Automated evaluation of parapapillary choroidal microvasculature in crowded optic discs:a controlled,optical coherence tomography angiography study

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.

Participants Recruitment for Coverage Maximization by Mobility Predicting in Mobile Crowd Sensing

Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.

Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

Abnormal behavior detection is challenging and one of the growing research areas in computer vision.The main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain events.In this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the objective.First stage,OPLKT-EMEHOs algorithm is used to generate the opticalflow from MIIs.Second stage,the MIIs opticalflow is applied as input to 3 layer CNN for detect the abnormal crowd behavior.University of Minnesota(UMN)dataset is used to evaluate the proposed system.The experi-mental result shows that the proposed method provides better classification accu-racy by comparing with the existing methods.Proposed method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population,crowd gatherings are frequently observed in the form of concerts,political,and religious meetings.HAJJ is one of the well-known crowding events that takes place every year in Makkah,Saudi Arabia.Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence(AI)applications.The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification(SSODTL-CD2C)model.The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities.To attain this,SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm(OSSA)with EfficientNet model to derive the feature vectors.At the same time,Stacked Sparse Auto Encoder(SSAE)model is utilized for the classification of crowd densities.Finally,SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism.The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities.The obtained results demonstrated that the proposed SSODTLCD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%.So,the proposed method will be highly helpful in managing HAJJ and other crowded events.

众包在证据合成中的实践应用研究——以Cochrane Crowd公民科学项目中的众包应用为例

[目的/意义]证据生成的及时性对于循证决策至关重要,而目前证据合成的效率通常不能满足决策者的需求。众包被认为是一种可以提高证据合成生产效率的潜在方法。本研究以Cochrane Crowd公民科学项目中的众包应用为例,总结众包在证据合成中的实践应用。[方法/过程]采用文献调研、网络调查、案例分析等方法,从众包者、志愿者、众包任务、Cochrane Crowd平台、质量评估5个维度分析了众包在Cochrane Crowd公民科学项目中的应用机制。[结果/结论]通过设置明确目标、激励措施、清晰任务,提供全面培训和适当的质量控制机制,可以应用众包为证据合成输出高质量结果。为未来针对不同领域证据合成中应用众包以及在证据合成的不同阶段使用众包的进一步研究提供参考。

Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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.

Visual Motion Segmentation in Crowd Videos Based on Spatial-Angular Stacked Sparse Autoencoders

Visual motion segmentation(VMS)is an important and key part of many intelligent crowd systems.It can be used to figure out the flow behavior through a crowd and to spot unusual life-threatening incidents like crowd stampedes and crashes,which pose a serious risk to public safety and have resulted in numerous fatalities over the past few decades.Trajectory clustering has become one of the most popular methods in VMS.However,complex data,such as a large number of samples and parameters,makes it difficult for trajectory clustering to work well with accurate motion segmentation results.This study introduces a spatial-angular stacked sparse autoencoder model(SA-SSAE)with l2-regularization and softmax,a powerful deep learning method for visual motion segmentation to cluster similar motion patterns that belong to the same cluster.The proposed model can extract meaningful high-level features using only spatial-angular features obtained from refined tracklets(a.k.a‘trajectories’).We adopt l2-regularization and sparsity regularization,which can learn sparse representations of features,to guarantee the sparsity of the autoencoders.We employ the softmax layer to map the data points into accurate cluster representations.One of the best advantages of the SA-SSAE framework is it can manage VMS even when individuals move around randomly.This framework helps cluster the motion patterns effectively with higher accuracy.We put forward a new dataset with itsmanual ground truth,including 21 crowd videos.Experiments conducted on two crowd benchmarks demonstrate that the proposed model can more accurately group trajectories than the traditional clustering approaches used in previous studies.The proposed SA-SSAE framework achieved a 0.11 improvement in accuracy and a 0.13 improvement in the F-measure compared with the best current method using the CUHK dataset.

Model considering panic emotion and personality traits for crowd evacuation

Panic is a common emotion when pedestrians are in danger during the actual evacuation, which can affect pedestrians a lot and may lead to fatalities as people are crushed or trampled. However, the systematic studies and quantitative analysis of evacuation panic, such as panic behaviors, panic evolution, and the stress responses of pedestrians with different personality traits to panic emotion are still rare. Here, combined with the theories of OCEAN(openness, conscientiousness,extroversion, agreeableness, neuroticism) model and SIS(susceptible, infected, susceptible) model, an extended cellular automata model is established by the floor field method in order to investigate the dynamics of panic emotion in the crowd and dynamics of pedestrians affected by emotion. In the model, pedestrians are divided into stable pedestrians and sensitive pedestrians according to their different personality traits in response to emotion, and their emotional state can be normal or panic. Besides, emotion contagion, emotion decay, and the influence of emotion on pedestrian movement decision-making are also considered. The simulation results show that evacuation efficiency will be reduced, for panic pedestrians may act maladaptive behaviors, thereby making the crowd more chaotic. The results further suggest that improving pedestrian psychological ability and raising the standard of management can effectively increase evacuation efficiency. And it is necessary to reduce the panic level of group as soon as possible at the beginning of evacuation. We hope this research could provide a new method to analyze crowd evacuation in panic situations.

Smart industrial IoT empowered crowd sensing for safety monitoring in coal mine

The crowd sensing technology can realize the sensing and computing of people,machines,and environment in smart industrial IoT-based coal mine,which provides a solution for safety monitoring through distributed intelligence optimization.However,due to the difficulty of neural network training to achieve global optimality and the fact that traditional LSTM methods do not consider the relationship between adjacent machines,the accuracy of human body position prediction and pressure value prediction is not high.To solve these problems,this paper proposes a smart industrial IoT empowered crowd sensing for safety monitoring in coal mine.First,we propose a Particle Swarm Optimization-Elman Neural Network(PE)algorithm for the mobile human position prediction.Second,we propose an ADI-LSTM neural network prediction algorithm for pressure values of machines supports in underground mines.Among them,our proposed PE algorithm has the lowest average cumulative prediction error,and the trajectory fit rate is improved by 24.1%,13.9%and 8.7%compared with Kalman filtering,Elman and Kalman plus Elman algorithms,respectively.Meanwhile,compared with single-input ARIMA,RNN,LSTM,and GRU,the RMSE values of our proposed ADI-LSTM are reduced by 36.6%,52%,32%,and 13.7%,respectively;and the MAPE values are reduced by 0.0003%,0.9482%,1.1844%,and 0.3620%,respectively.

Robust Counting in Overcrowded Scenes Using Batch-Free Normalized Deep ConvNet

The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.

A Deep Learning-Based Crowd Counting Method and System Implementation on Neural Processing Unit Platform

In this paper, a deep learning-based method is proposed for crowdcountingproblems. Specifically, by utilizing the convolution kernel densitymap, the ground truth is generated dynamically to enhance the featureextractingability of the generator model. Meanwhile, the “cross stage partial”module is integrated into congested scene recognition network (CSRNet) toobtain a lightweight network model. In addition, to compensate for the accuracydrop owing to the lightweight model, we take advantage of “structuredknowledge transfer” to train the model in an end-to-end manner. It aimsto accelerate the fitting speed and enhance the learning ability of the studentmodel. The crowd-counting system solution for edge computing is alsoproposed and implemented on an embedded device equipped with a neuralprocessing unit. Simulations demonstrate the performance improvement ofthe proposed solution in terms of model size, processing speed and accuracy.The performance on the Venice dataset shows that the mean absolute error(MAE) and the root mean squared error (RMSE) of our model drop by32.63% and 39.18% compared with CSRNet. Meanwhile, the performance onthe ShanghaiTech PartB dataset reveals that the MAE and the RMSE of ourmodel are close to those of CSRNet. Therefore, we provide a novel embeddedplatform system scheme for public safety pre-warning applications.

Using NMR-detected hydrogen-deuterium exchange to quantify protein stability in cosolutes,under crowded conditions in vitro and in cells

We review the use of nuclear magnetic resonance(NMR)spectroscopy to assess the exchange of amide protons for deuterons(HDX)in efforts to understand how high concentration of cosolutes,especially macromolecules,affect the equilibrium thermodynamics of protein stability.HDX NMR is the only method that can routinely provide such data at the level of individual amino acids.We begin by discussing the properties of the protein systems required to yield equilibrium thermodynamic data and then review publications using osmolytes,sugars,denaturants,synthetic polymers,proteins,cytoplasm and in cells.

Securing Cloud Computing from Flash Crowd Attack Using Ensemble Intrusion Detection System

Flash Crowd attacks are a form of Distributed Denial of Service(DDoS)attack that is becoming increasingly difficult to detect due to its ability to imitate normal user behavior in Cloud Computing(CC).Botnets are often used by attackers to perform a wide range of DDoS attacks.With advancements in technology,bots are now able to simulate DDoS attacks as flash crowd events,making them difficult to detect.When it comes to application layer DDoS attacks,the Flash Crowd attack that occurs during a Flash Event is viewed as the most intricate issue.This is mainly because it can imitate typical user behavior,leading to a substantial influx of requests that can overwhelm the server by consuming either its network bandwidth or resources.Therefore,identifying these types of attacks on web servers has become crucial,particularly in the CC.In this article,an efficient intrusion detection method is proposed based on White Shark Optimizer and ensemble classifier(Convolutional Neural Network(CNN)and LighGBM).Experiments were conducted using a CICIDS 2017 dataset to evaluate the performance of the proposed method in real-life situations.The proposed IDS achieved superior results,with 95.84%accuracy,96.15%precision,95.54%recall,and 95.84%F1 measure.Flash crowd attacks are challenging to detect,but the proposed IDS has proven its effectiveness in identifying such attacks in CC and holds potential for future improvement.

人群疏散模拟仿真软件系统自主研发与应用——以Pedynamics为例

为更加精准地分析与评估公共场所突发事件下人员疏散的需求,自主研发了新一代人群疏散模拟仿真软件Pedynamics。以某地铁场景为例,比较分析Pedynamics与商业软件Pathfinder和AnyLgoic用于模拟地铁车站不同客流量下的疏散过程,并进行讨论,提出改进人群疏散效率的措施。研究结果表明:Pedynamics软件采用前后端分离的架构进行模块化开发,易于升级维护;后端采用的人群疏散计算模型结合了人群疏散动力学特别是人员有限理性路径选择行为方面的研究成果,能够真实体现行人的疏散行为特征;软件前端的场景预处理界面能够帮助用户简单、快速地基于图纸构建疏散场景,可视化及数据统计模块支持多视角查看模拟结果和疏散数据,提高工程应用的效率。研究结果可为人群疏散风险评估提供技术支持。

改良前方牵引矫治器治疗骨性Ⅲ类错[牙合]伴拥挤的疗效评价

目的:评价改良前方牵引矫治器治疗骨性Ⅲ类错[牙合]伴拥挤的疗效。方法:将40例骨性Ⅲ类错[牙合]患者,按磨牙关系分为2组,每组20例,试验组磨牙中性或远中关系,应用改良前方牵引矫治器;对照组磨牙近中关系,应用传统前方牵引矫治器。2组患者治疗前后分别拍摄头颅侧位片进行比较,采用SPSS 22.0软件包对数据进行统计学分析。结果:2组患者治疗前后角度测量中,SNA、ANB、SN-MP、U4-SN显著增加(P<0.01),SNB显著减小(P<0.01);SN-OL在试验组治疗前后变化有显著差异(P<0.05)。2组患者治疗前后矢状向测量指标中,除对照组上颌牙弓长度外,其余指标均有显著改变(P<0.05);垂直向测量指标中,U1-PP、L1-MP、U4-SN、U6-SN、ANS-Me显著增加(P<0.05);U4-PP、U6-PP在对照组治疗前后的变化有显著差异(P<0.05)。2组患者治疗前后头影测量值变化量与对照组相比,试验组治疗后上颌牙弓长度显著增加,U6位置更偏远中,磨牙关系变化更小(P<0.01)。结论:改良前方牵引矫治器对磨牙中性或远中关系的骨性Ⅲ类错[牙合]伴拥挤患者具有良好的前方牵引和推磨牙向远中的效果。

考虑人群活动的城市场所洪涝灾害风险动态评估——以深圳市为例

为了揭示人群动态活动对城市洪涝灾害风险的影响,本文引入场所视角,建立了考虑人群活动的城市场所洪涝灾害风险评估模型,以深圳市福田区4个典型社区为例分析了工作日和休息日的白天、晚上4个时段下不同类型场所的洪涝灾害风险变化。结果表明:①除危险性以外,频繁的人群活动亦会增加场所的洪涝灾害风险,与人群活动相关的指标权重达0.31;②不同类型场所的洪涝灾害风险差异显著,且随着人群活动动态变化,生活场所平均风险最高,在4个时段波动较小;居住型混合场所洪涝平均风险次之,但晚上时段比白天高25%左右;商业场所、公共服务场所和办公型混合场所平均风险也较高,在工作日白天高于其他时段;绿色休闲场所在4个时段风险都最低;③居住型社区和综合型社区的洪涝风险存在显著差异,居住型社区为持续性高风险社区,其高风险场所占比在4个时段均超过40%,尤其在晚上达60%,而综合型社区风险呈现出工作日白天高于其他时段的特征,其高风险场所比例最高达54%。

群智创新设计研究现状与进展

人工智能时代下,群智创新设计是将创新设计同下一代互联网、人工智能、大数据、区块链等新兴数字技术结合,来解决社会复杂设计问题的综合创新手段。从计算机科学、设计学和社会学的角度出发,通过文献调研的方式,探讨了不同创新阶段的代表性研究、社会创新范式的衍变和群体智慧的发展;分析了群智创新设计的研究现状,系统地提出群智创新设计的体系内容,包括本体—行为—价值三层次理论模型、群智创新主体、群智创新环节、关键技术及研究范围。最后,针对群智计算与群智创新设计的融合发展路径,提出群智创新设计未来的发展趋势。研究对推动人工智能时代下创新设计的发展具有重要意义。

基于行人恐慌情绪解析的改进社会力模型

为深入研究行人恐慌情绪对突发事件下群体疏散效率的影响特性,首先,考虑滞留时间、局部密度、行人与出口的距离、周围人员密度以及行人恐慌情绪传播等因素,解析并构建由自恐慌和恐慌传播2部分组成的行人恐慌情绪量化模型,并将其引入社会力模型中进行改进优化;然后,采用与经典现象对比的方法验证模型的有效性;最后,运用Anylogic软件进行仿真分析。结果表明:改进后的模型能够较好地反映行人在恐慌情形下的疏散运动特性;适度的恐慌情绪,如当恐慌度小于0.3时,可以加快人群的疏散速度;较高的恐慌情绪,如当恐慌值大于0.3时,则会加剧瓶颈现象,出现所谓的“快即是慢”现象,从而降低疏散效率。

人工智能时代的群智创新设计研究

人工智能时代,社会生产方式的进化极大地扩张了设计创新的边界,设计的环境、对象、工具、方法及思维均发生了转变。首先回顾了设计的发展,阐述了人工智能时代下社会创新的新“生产资料”“生产工具”“生产方式”及“生产力”,构建了群智创新设计的方法模型,提出了群智创新设计的研究内容并探讨了群智创新设计的未来发展。群智创新设计范式将设计创新同智能数字技术结合,发挥群体智慧的力量,聚集群体智慧创意流,开创群智创新新模式,为民生、产业和社会创造共赢价值。