Pedestrian evacuation simulation in multi-exit case:An emotion and group dual-driven method

This paper analyzes the characteristics of emotion state and group behavior in the evacuation process.During the emergency evacuation,emotion state and group behavior are interacting with each other,and indivisible.The emotion spread model with the effect of group behavior,and the leader-follower model with the effect of emotion state are proposed.On this basis,exit choice strategies with the effect of emotion state and group behavior are proposed.Fusing emotion spread model,leader-follower model,and exit choice strategies into a cellular automata(CA)-based pedestrian simulation model,we simulate the evacuation process in a multi-exit case.Simulation results indicate that panic emotion and group behavior are two negative influence factors for pedestrian evacuation.Compared with panic emotion or group behavior only,pedestrian evacuation efficiency with the effects of both is lower.

Effect of cognitive behavioral group therapy on rehabilitation of community patients with schizophrenia:A short-term randomized control trial

BACKGROUND The efficacy of cognitive behavioral group therapy(CBGT)for cognitive dysfunction and negative symptoms of schizophrenia is established,but more evidence is required.AIM To assess the effectiveness of CBGT combined with mental health education as a treatment for schizophrenia compared with mental health education alone.METHODS In all,120 schizophrenia out-patients were randomized into CBGT combined with mental health education or single mental health education.The primary outcomes were positive and negative symptoms,cognitive function,excitatory factor,anxiety and depression symptom improvements on the positive and negative syndrome scale score.Secondary outcome measures included social function and drug compliance.RESULTS There were significant differences between CBGT combined with mental health education and single mental health education on measures of positive and negative symptoms,cognitive functions,excitatory factor,anxiety and depression symptoms,and social functions.No other significant difference in outcomes was observed.CONCLUSION CBGT combined with mental health education may be relevant beneficial treatment method in reducing symptoms,cognitive and social functions of patients with schizophrenia.

An Event Alarm System Based on Single and Group Human Behavior Analysis

Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether or not the people need help in a public place. Different from previous work, our work considers not only the behaviors of the target person but also the interaction between him and nearby people. In the paper, we propose an event alarm system which can detect the human behaviors and recognize the happening event through integrating the results generated from the single and group behavior analysis. Several new effective features are proposed in the study. Besides, a mechanism capable of extracting one-to-one and multiple-to-one relations is also developed. Experimental results show that the proposed approach can correctly detect human behaviors and provide the alarm messages when emergency events occur.

Risk of group aggregative behavior during COVID-19 outbreak: A case report

BACKGROUND At the end of 2019,a new epidemic of viral pneumonia emerged in China and was determined to be caused by a novel coronavirus,which was named coronavirus disease 2019(COVID-19)by the World Health Organization.The epidemic quickly spread,causing a worldwide pandemic.Scientists and clinicians across the globe have shifted their research efforts towards understanding the virus itself and its epidemiology.CASE SUMMARY In mid-January 2020,a Chinese family made a visit to a local city,and within the next 2 wk one after another fell ill with COVID-19.At the beginning of their first illness onset,the family had eaten in a restaurant,which led to the subsequent illness onset in another two families.All cases were diagnosed as COVID-19 by real-time fluorescent reverse transcription-polymerase chain reaction.Epidemiological investigation showed that the transmission chain was complete.CONCLUSION This chain of social exposure highlights the danger of group aggregative behavior for spread of COVID-19.

Influence of Data Clouds Fusion From 3D RealTime Vision System on Robotic Group Dead Reckoning in Unknown Terrain

This paper proposes the solution of tasks set required for autonomous robotic group behavior optimization during the mission on a distributed area in a cluttered hazardous terrain.The navigation scheme uses the benefits of the original real-time technical vision system(TVS)based on a dynamic triangulation principle.The method uses TVS output data with fuzzy logic rules processing for resolution stabilization.Based on previous researches,the dynamic communication network model is modified to implement the propagation of information with a feedback method for more stable data exchange inside the robotic group.According to the comparative analysis of approximation methods,in this paper authors are proposing to use two-steps post-processing path planning aiming to get a smooth and energy-saving trajectory.The article provides a wide range of studies and computational experiment results for different scenarios for evaluation of common cloud point influence on robotic motion planning.

The Influence of Familial Reference Groups on Consumer Decisions in Different Culture Context

This paper examines the influence of familial reference groups on individuals＇ product and brand decisions for products across two kinds of culture. The study is addressed through an examination of intergenerational influences across the two different cultures. The results of the study lend support to the original approach and also provide an insight look into how reference group influence may vary depending on different culture context. The paper also provides very useful advice for marketers when they apply western marketing concepts in China market.

A Novel Cultural Crowd Model Toward Cognitive Artificial Intelligence

Existing literature shows cultural crowd management has unforeseen issues due to four dynamic elements;time,capacity,speed,and culture.Crosscultural variations are increasing the complexity level because each mass and event have different characteristics and challenges.However,no prior study has employed the six Hofstede Cultural Dimensions(HCD)for predicting crowd behaviors.This study aims to develop the Cultural Crowd-Artificial Neural Network(CC-ANN)learning model that considers crowd’s HCD to predict their physical(distance and speed)and social(collectivity and cohesion)characteristics.The model was developed towards a cognitive intelligent decision support tool where the predicted characteristics affect the estimated regulation plan’s time and capacity.We designed the experiments as four groups to analyze the proposed model’s outcomes and extract the interrelations between the HCD of crowd’s grouped individuals and their physical and social characteristics.Furthermore,the extracted interrelations were verified with the dataset’s statistical correlation analysis with a P-value<0.05.Results demonstrate that the predicted crowd’s characteristics were positively/negatively affected by their considered cultural features.Similarly,analyzing outcomes identified the most influential HCD for predicting crowd behavior.The results also show that the CC-ANN model improves the prediction and learning performance for the crowd behavior because the achieved accepted level of accuracy does not exceed 10 to 20 epochs in most cases.Moreover,the performance improved by 90%,93%respectively in some cases.Finally,all prediction best cases were related to one or more cultural features with a low error of 0.048,0.117,0.010,and 0.014 mean squared error,indicating a novel cultural learning model.

基于组行为分析检测隐蔽DGA 域名

本文提出FProbe的是一种旨在通过利用共现矩阵和宽松关联规则的检测隐蔽DGA的技术,不但具有基于行为检测的鲁棒性,还具有在低查询率多域名访问的场景下的检测能力。我们利用一种简单的直觉,即DGA查询在时间特征和空间特征方面具有很强的关联性。FProbe利用产品推荐领域和词频共现领域中普遍使用的共现矩阵,进一步使用图卷积的监督方法来聚类受感染的主机,特别是通过该矩阵,我们可以在低查询率的域名请求场景中快速有效地定位被感染的主机,而不是因其低阈值而丢弃该域名,然后我们使用频繁序列树的宽松关联规则对关联域名进行聚类,并使用监督学习用于确定恶意的聚类簇。FProbe在校园网络(峰值负载小时内有4000位活跃用户)中进行了评估,实验结果(准确度为98.4%,平均误报率为0.18%)说明了效率和FProbe的准确性。

Study on single-group dynamics in evacuation considering pre-defined and undeclared leadership

Grouping is a common phenomenon that occurs everywhere.The leader-follower relationship inside groups has often been qualitatively characterized in previous models using simple heuristics.However,a general method is lacking to quantitatively explain leadership in an evacuating group.To understand the evolution of single-group dynamics throughout an evacuation,we developed an extended social force model integrated with a group force.A series of single-group evacuations from a room were simulated.An information-theoretic method,transfer entropy(TE),was applied to detect predefined and undeclared leadership among evacuees.The results showed that the predefined leader was correctly detected by TE,suggesting its capability in measuring leadership based on time series of evacuees’movement information(e.g.,velocity and acceleration).When evacuees were grouped together,TE was higher than when they were alone.Leaders presented a monotonically increasing cumulative influence curve over the investigated period,whereas followers showed a diminishing tendency.We found that leadership emergence correlated with evacuees’spatial positions.The individual located in the foremost part of the group was most likely to become a leader of those in the rear,which concurred with the experimental observations.We observed how a large group split into smaller ones with undeclared leadership during evacuation.These observations were quantitatively verified by TE results.This study provides novel insights into quantifying leadership and understanding single-group dynamics during evacuations.

Social Psychology in the Marketing

Marketing psychology is science which studies socio-economic phenomena, and explain the application of social market, using psychological methods and theoretical, and is in-depth community school of learning; After nearly 20 years development, Chinese marketing psychology still emphasis on Western-style research, and seems to having divorced from a hot issue of market economy in a way yet to be in efforts to be used in marketing among psychology. Through the discussion of the applications of marketing psychology, trying to appeal to all the scholars return to create the purposes of marketing psychology, like other disciplines, like out of development path of striving to become typical combined with Chinese social market economy and psychology.

Forecasting complex group behavior via multiple plan recognition

Group behavior forecasting is an emergent re- search and application field in social computing. Most of the existing group behavior forecasting methods have heavily re- lied on structured data which is usually hard to obtain. To ease the heavy reliance on structured data, in this paper, we pro- pose a computational approach based on the recognition of multiple plans/intentions underlying group behavior. We fur- ther conduct human experiment to empirically evaluate the effectiveness of our proposed approach.

Performance Evaluation of Machine Learning Methods in Cultural Modeling

Cultural modeling （CM） is an emergent and promising research area in social computing. It aims to develop behavioral models of human groups and analyze the impact of culture factors on human group behavior using computational methods. Machine learning methods, in particular classification, play a critical role in such applications. Since various cultural-related data sets possess different characteristics, it is important to gain a computational understanding of performance characteristics of various machine learning methods. In this paper, we investigate the performance of seven representative classification algorithms using a benchmark cultural modeling data set and analyze the experimental results as to group behavior forecasting.

Gregarious pupation act as a defensive mechanism against cannibalism and intraguild predation

Coccinellid pupae use an array of defensive strategies against their natural enemies. This study aims to assess the efficiency of gregarious pupation as a defensive mechanism against intraguild predators and cannibals in coccinellid. The study was designed specifically （i） to determine the natural occurrence of gregarious pupation in the field for different coccinellid species, and （ii） to evaluate the adaptive value of gregarious pupation as a defensive mechanism against 2 types of predators （i.e., cannibals and intraguild predators）. In the field, gregarious pupation consisted of a group of 2-5 pupae. The proportion of gregarious pupation observed varied according to species, the highest rate being observed with Harmonia axyridis Pallas （Coccinellidae; 14.17%）. Gregarious pupation had no impact on the probability that intraguild predators and cannibals locate pupae. Intraguild predation occurred more often in site with gregarious pupation, while cannibalism occurred as often in site with gregarious pupation as in site with isolated pupa. However, for a specific pupa, the mortality rate was higher for isolated pupae than for pupae located in a gregarious pupation site both in the presence of intraguild predators and in the presence of cannibals. The spatial location of pupae within the group had no impact on mortality rate. Since it reduces the risk of predation, it is proposed that gregarious pupation act as a defensive mechanism for 11. axyridis pupae.

Extended knowledge attributed Petri Nets

Choosing the best way for describing physical reality has always been standing in focus of research.Several methodologies have been developed based on classical mathematics,or statistics and also new disciplines-such as soft-computing techniques-appeared.Petri Nets as one of the most naturalistic modeling methodologies are well suited to describe complex process in general.However in some fields of modeling the describing power of basic Petri Nets proved not to be robust enough,therefore several exten-sions were made to the original concept.Colored tokens(Colored Petri Nets),stochastic delayed streaming of mobile entities(Stochastic Petri Nets),object oriented architecture(Object Oriented Petri Nets),numerical(Numerical Petri Nets)and linguistic attributes(Fuzzy Petri Nets)broaden the range of capabilities.In some fields of problem solv-ing,usage of static and mobile knowledge bases is needed:e.g.,flexible manufacturing systems,or intelligent traffic simulation.These problems to be investigated involved new conceptual developments of Petri Nets and led to the introduction of Knowledge Attributed Petri Nets.At the same time distributed control in simulation appeared,intelligent agents supported the connection of mobile knowledge bases and static infer-ence engines in an effective way.The mentioned extensions brought general support in model synthesis,but some unsolved questions remained related to the implementation of intelligent mobile entities.This paper highlights a new level of AI controlled simulation introducing the Extended Knowledge Attributed Petri Nets that offer the capability of easy implementation of mobile inference engines and knowledge base,providing general mobile AI in Petri Nets.