Knowledge Learning With Crowdsourcing:A Brief Review and Systematic Perspective

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.

Crowdsourced Sampling of a Composite Random Variable: Analysis, Simulation, and Experimental Test

A composite random variable is a product (or sum of products) of statistically distributed quantities. Such a variable can represent the solution to a multi-factor quantitative problem submitted to a large, diverse, independent, anonymous group of non-expert respondents (the “crowd”). The objective of this research is to examine the statistical distribution of solutions from a large crowd to a quantitative problem involving image analysis and object counting. Theoretical analysis by the author, covering a range of conditions and types of factor variables, predicts that composite random variables are distributed log-normally to an excellent approximation. If the factors in a problem are themselves distributed log-normally, then their product is rigorously log-normal. A crowdsourcing experiment devised by the author and implemented with the assistance of a BBC (British Broadcasting Corporation) television show, yielded a sample of approximately 2000 responses consistent with a log-normal distribution. The sample mean was within ~12% of the true count. However, a Monte Carlo simulation (MCS) of the experiment, employing either normal or log-normal random variables as factors to model the processes by which a crowd of 1 million might arrive at their estimates, resulted in a visually perfect log-normal distribution with a mean response within ~5% of the true count. The results of this research suggest that a well-modeled MCS, by simulating a sample of responses from a large, rational, and incentivized crowd, can provide a more accurate solution to a quantitative problem than might be attainable by direct sampling of a smaller crowd or an uninformed crowd, irrespective of size, that guesses randomly.

转发概率递减的改进Crowds系统

Crowds匿名浏览系统中可以在不影响匿名度水平的前提下,通过递减转发概率减小重路由路径长度,提高系统性能。提出利用路径长度期望值递减规律确定转发概率递减比例系数的方法,仿真实验表明,新方案可以在保持原有匿名度的基础上,有效减小重路由路径长度,提高Crowds系统的通信性能。

Crowds系统中新的前驱攻击模型研究

对前驱攻击模型攻击成功的概率和攻击轮数的原理和机制进行了详细的分析,在此基础上,提出了新的统计前驱攻击模型,该模型在每一轮重路由路径建立完毕后,对路径上所有泄密节点的直接前驱进行统计,减少了攻击的重建路径次数.仿真实验表明,与前驱攻击相比,新的统计前驱攻击可以有效地提高攻击的成功概率.

一种基于Crowds的改进匿名通信系统

提出一种基于Crowds的改进匿名通信系统,通过结合mix匿名通信技术和填充流量技术,使改进的匿名通信系统能抵御两种攻击.此外,对改进的匿名系统在前驱攻击下的匿名性能进行了分析,推导出改进系统在前驱攻击下能提供更好的匿名保护.

Crowds匿名通信系统研究

通信安全是信息传输安全性的重要保障,一般通过信息加密来保障信息传输安全性,但加密无法隐藏通信双方的身份或通信关系,无法完整保护通信安全。匿名通信可以隐藏通信双方的身份或通信关系,从而实现对通信更安全的保护。本文对匿名通信技术做了简要介绍,分析了Crowds匿名通信系统的特点,进行了Crowds匿名通信系统的攻击实验及分析,并就将来该领域的发展趋势作了探讨。

一种改进的Crowds匿名通信协议

Crowds匿名通信系统高效、实用,但存在两个不足之处:中间节点能够知道接收者身份信息且转发路径长度随机。针对上述不足之处,提出了一种改进的匿名通信协议。新协议在Crowds基础上增加了伪接收者和最长最短转发路径。报文中用伪接收者地址代替接收者地址以阻止中间节点获得接收者身份信息;用户设定报文转发路径长度最大最小值实现转发路径长度有界性和满足一定匿名需求。相关分析结果表明,在相同转发路径长度期望值下,新协议能有效增加用户匿名度。同时,对新协议进行了仿真验证。

Pure and hybrid crowds in crowdfunding markets

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.

Lessons in Love——University class attracts huge crowds of curious onlookers

Zhou Dan, an articulate lawyer, led a semi-secret life until recently when he was invited to give a talk to the Homosexual Studies class at Fudan University in Shanghai.

Deep Trajectory Classification Model for Congestion Detection in Human Crowds

In high-density gatherings,crowd disasters frequently occur despite all the safety measures.Timely detection of congestion in human crowds using automated analysis of video footage can prevent crowd disasters.Recent work on the prevention of crowd disasters has been based on manual analysis of video footage.Some methods also measure crowd congestion by estimating crowd density.However,crowd density alone cannot provide reliable information about congestion.This paper proposes a deep learning framework for automated crowd congestion detection that leverages pedestrian trajectories.The proposed framework divided the input video into several temporal segments.We then extracted dense trajectories from each temporal segment and converted these into a spatio-temporal image without losing information.A classification model based on convolutional neural networks was then trained using spatio-temporal images.Next,we generated a score map by encoding each point trajectory with its respective class score.After this,we obtained the congested regions by employing the non-maximum suppression method on the score map.Finally,we demonstrated the proposed framework’s effectiveness by performing a series of experiments on challenging video sequences.

Physics of Traffic and the Crowds Theory： AMultidisciplinary Approach to Mobility Education

Crowding is a reality in which we find ourselves involved daily. The crowd produced in a traffic jam is a dynamic entity inwhich the application of physics, mathematics and biology can provide practical help to understand how and why this problematicsituation occurs and what solutions can be found to resolve it. In the mobility education project ＂SicuraMENTE＂, we carried out anexperiment on the conduct of a crowd by simulating a situation of intense city traffic. Taking a cue from an experimental situationproposed in traffic physics, we have verified that a route with limited access can generate a traffic jam and the crowd of pedestrianscan be mitigated by forcing the crowd to use dedicated streets. Taking the outgoing time of the student crowd from the outlet road,with and without the presence of an obstacle in the middle of the roadway, it was found that the traffic jam is resolved more quicklyin the situation with an obstacle because the flow of people is divided into two separate channels, reducing the probability that twoindividuals are close and that this creates an obstruction （example of counter-intuitive physical principle）. We also verified that thespeed of the elements of the crowd influences the formation of traffic jams, which are on average more likely in the case of higherspeed. These important aspects in the design of road networks and transport infrastructure have made students reason on physics＇topics, but also on the correct conduct in traffic. The multidisciplinary approach in education for safe and sustainable mobility, soinnovative in Italy, turned out to be effective in terms of teaching in the frame of a mix of academic disciplines, in which road safetyeducation has become the context and the goal.

Numerical Simulation of the Flow of Crowds at the Jamarat Bridge during the Annual Hajj Event

The huge number of pilgrims to the holy Mecca in the Hajj needs high awareness of crowd safety management. The stoning of the Jamarat, which is one of the rituals of the Hajj, undergoes the most dangerous crowd movements where fatal accidents occurred. This work investigates some problems related with the crowd dynamics when stoning the Jamarat pillars and gives some solutions. The main idea of this research is to suppose that the crowd dynamics is assimilated to fluid movement under certain conditions. Numerical simulation using a computational fluid dynamics program is used to solve Navier-Stokes equations governing the mechanics of homogeneous and incompressible fluid in a domain similar to the Jamarat Bridge from the entrance to the middle Jamarah. Some solutions are proposed inspired by the flow solutions to better manage crowd movements in the Jamarat Bridge and eventually in other similar dynamics events like sporting events.

Classification-oriented dawid skene model for transferring intelligence from crowds to machines

When a crowdsourcing approach is used to assist the classification of a set of items,the main objective is to classify this set of items by aggregating the worker-provided labels.A secondary objective is to assess the workers’skill levels in this process.A classical model that achieves both objectives is the famous Dawid-Skene model.In this paper,we consider a third objective in this context,namely,to learn a classifier that is capable of labelling future items without further assistance of crowd workers.By extending the DawidSkene model to include the item features into consideration,we develop a Classification-Oriented Dawid Skene(CODS)model,which achieves the three objectives simultaneously.The effectiveness of CODS on this three dimensions of the problem space is demonstrated experimentally.

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.

Investigation on Breakdown Characteristics of Various Surface Terminal Structures for GaN-Based Vertical P-i-N Diodes

GaN-based vertical P-i-N diode with mesa edge terminal structure due to electric field crowding effect, the breakdown voltage of the device is significantly reduced. This work investigates three terminal structures, including deeply etched, bevel, and stepped-mesas terminal structures, to suppress electric field crowding effects at the device and junction edges. Deeply-etched mesa terminal yields a breakdown voltage of 1205 V, i.e., 89% of the ideal voltage. The bevel-mesa terminal achieves about 89% of the ideal breakdown voltage, while the step-mesa terminal is less effective in mitigating electric field crowding, at about 32% of the ideal voltage. This work can provide an important reference for the design of high-power, high-voltage GaN-based P-i-N power devices, finding a terminal protection structure suitable for GaNPiN diodes to further enhance the breakdown performance of the device and to unleash the full potential of GaN semiconductor materials.

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.

A Novel Collaborative Evolutionary Algorithm with Two-Population for Multi-Objective Flexible Job Shop Scheduling

Job shop scheduling(JS)is an important technology for modern manufacturing.Flexible job shop scheduling(FJS)is critical in JS,and it has been widely employed in many industries,including aerospace and energy.FJS enables any machine from a certain set to handle an operation,and this is an NP-hard problem.Furthermore,due to the requirements in real-world cases,multi-objective FJS is increasingly widespread,thus increasing the challenge of solving the FJS problems.As a result,it is necessary to develop a novel method to address this challenge.To achieve this goal,a novel collaborative evolutionary algorithmwith two-population based on Pareto optimality is proposed for FJS,which improves the solutions of FJS by interacting in each generation.In addition,several experimental results have demonstrated that the proposed method is promising and effective for multi-objective FJS,which has discovered some new Pareto solutions in the well-known benchmark problems,and some solutions can dominate the solutions of some other methods.

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%.

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.