Characteristics Of Human Behavior Research Articles

Research on Human Behavior Feature Recognition and Intelligent Early Warning Methods in Safety Supervision Scene Video based on Yolov7

With the continuous improvement of infrastructure construction in China, the construction of various supporting power facilities is constantly followed up. A large number of ultra-high voltage or high-voltage power facilities, such as high-voltage transmission towers, transformers, or substations, are gradually emerging in populated areas, farmland, or fishponds where people still pass by sometimes. In such circumstances, people who do not have the qualification to contact power facilities at times approach, contact, or even climb these dangerous high-voltage transmission and distribution facilities illegally for various reasons, which not only poses a huge hidden peril or even threat to the safety of people’s lives and property but also cause a huge negative impact on the smooth operation of power infrastructure. To solve this intractable problem, this topic proposes a pedestrian detection and recognition algorithm, which can accurately distinguish between ordinary pedestrians and power grid maintenance inspectors based on YOLOv7, providing a reliable method for timely detection and early warning of pedestrians who may contact power facilities.

대안적 문화체험 모델로서의 지방구술사 기반 관광에 대한 탐색적 연구

As in the industrialization era, capitalism dominates the world, people become interested in experiencing spatio-temporal entities that satisfy all human needs between marketability and publicity, visibility and invisibility, past and present, and authenticity and pseudo-events. This macroscopic shift in the social order has a great influence on people’s cultural experience in the region where they live. The ideological value that enables the convergent production and consumption system can be said to be the convergent self-regulation mechanism of modernism and postmodernism. Oral history research that leads the transboundary cultural experience has recently attracted attention throughout the social sciences that study convergent social order. In particular, local oral history helps us understand the phenomenon of reorganization of time and space that is the basis of human lifestyle in the terms of microscopic life in the community.The oral history of the local community, a combination of objective records and subjective memories, can be an objective and subjective new meta-knowledge that features self-construction of transboundary sign experience in the structure of human-society intimacy. Local history research through personal oral history is a so-called bottom-up historical approach and discusses how human behavioral characteristics change the appearance of social culture itself and how closely related they are to social systems. The development of local tourism based on local oral history as an alternative cultural experience model enables in-depth research on the interaction of the region, society, and culture. It also can serve as a major basis for the development of sustainable and systematic eco-friendly local tourism development.

Application and research on trajectory big data

In the era of big data, the formation of massive trajectory data of moving objects with rich Spatio-temporal feature information can mine data such as human activity patterns and behavioral characteristics through trajectory data processing technology. This paper and analyzes the concept of trajectory data, the application process and the research results. Firstly, this paper briefly describes the trajectory data. Secondly, it outlines the supporting techniques of trajectory data, including data cleaning, integration, and transformation. Next, it introduces the classification steps of trajectory data mining, including using the segmentation method to segment the data, extract features from the segments; besides, it models and studies the predicted questions in this topic. Then it briefly introduces the application method of trajectory data mining in data privacy and the application of trajectory data in the transportation field. Finally, the paper summarizes the challenges and future research directions of trajectory data processing.

The Effect of Human Settlement Pedestrian Environment on Gait of Older People: An Umbrella Review.

Older people are limited by the pedestrian environment in human settlements and are prone to travel difficulties, falls, and stumbles. Furthermore, we still lack systematic knowledge of the pedestrian environment affecting the gait of older people. The purpose of this review is to synthesize current evidence of effective human settlement pedestrian environments interfering with gait in older people. The systematic effects of the human settlement pedestrian environment on gait in older people are discussed. Databases such as Web of Science, Medline (via PubMed), Scopus, and Embase were searched for relevant studies up to June 2022. The literature was screened to extract relevant evidence from the included literature, assess the quality of the evidence, and analyze the systematic effects of the pedestrian environment on gait in older people. From the 4297 studies identified in the initial search, 11 systematic reviews or meta-analysis studies were screened, from which 18 environmental factors and 60 gait changes were extracted. After removing duplicate elements and merging synonymous features, a total of 53 relationships between environmental factors and gait change in older people were extracted: the main human settlement pedestrian environmental factors affecting gait change in older people in existing studies were indoor and outdoor stairs/steps, uneven and irregular ground, obstacles, walking path turns, vibration interventions, mechanical perturbation during gait, and auditory sound cues. Under the influence of these factors, older people may experience changes in the degree of cautiousness and conservatism of gait and stability, and their body posture performance and control, and muscle activation may also be affected. Factors such as ground texture or material, mechanical perturbations during gait, and vibration interventions stimulate older people's understanding and perception of their environment, but there is controversy over the results of specific gait parameters. The results support that human settlements' pedestrian environment affects the gait changes of older people in a positive or negative way. This review may likely contribute evidence-based information to aid communication among practitioners in public health, healthcare, and environmental construction. The above findings are expected to provide useful preference for associated interdisciplinary researchers to understand the interactions among pedestrian environments, human behavior, and physiological characteristics.

Mobile Big Data Analytics for Human Behavior Recognition in Wireless Sensor Network Based on Transfer Learning

Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.

Multisensor Intelligent Fall Perception Algorithm considering Precise Classification of Human Behavior Characteristics

In order to improve the accuracy and efficiency of human motion perception, a multisensor intelligent fall perception algorithm considering the precise classification of human behavior characteristics is proposed. Multisensor devices (smart watches, smart phones) collect data such as acceleration and heart rate of the human body to obtain human behavior data. On the basis of human behavior data collection, the acceleration characteristics of a falling state are extracted, and the SVM method is used to classify human behavior characteristics. Cuckoo search is used to optimize the width of the SVM kernel and improve the accuracy of human behavior recognition. Finally, based on the behavior recognition results, the intelligent perception of human falling behavior is realized through the exercise preparation potential. The experimental results show that the perceptual accuracy of this method is high, which has reached 90%, and the perception efficiency is higher. The minimum perception time is only 0.56 s, which fully verifies the effectiveness of this method. It can be widely used in human‐computer interaction, machine vision, and other fields.

ПСИХОЛОГИЯ В ЮРИДИЧЕСКОМ ОБРАЗОВАНИИ: СОВРЕМЕННЫЕ ВЫЗОВЫ И СОСТОЯНИЕ ПРАКТИКИ

The article discusses the importance of psychology in the training of future specialists in the field of jurisprudence. The content of the discipline “Legal Psychology” is analyzed from the standpoint of theoretical and practical knowledge, the main approaches to the study of the problems of professional formation of personality, stated in the psychology of higher education, are considered. The article is devoted to an urgent topic – the role of psychology in the training of a future specialist in a transforming society. It is noted that the importance of studying the course “Legal Psychology” at universities is primarily due to the fact that psychological science makes a great contribution to solving the multi-profile tasks of strengthening the legal system and implementing the efforts of the state in the fight against offenses, crimes. As is known, the law enforcement activity of most legal professions takes place in the field of public relations and presupposes high emotional tension, self-organization and the development of skills in communication activities. The formation of psychological knowledge among lawyers is the most important task of training a specialist. Being a borderline science between psychology and jurisprudence, legal psychology is designed to provide a future specialist with an understanding of the psychological features of such a socio-cultural phenomenon as law, the socio-psychological essence of legal regulation, psychological features of human behavior in the field of relations regulated by law. It is noted that psychological knowledge will contribute to the development of psychological competence of students on issues most significant in their future professional activities. The author has made an attempt to investigate the mechanisms of organizing the training of students of law schools in psychology. Training is considered as a complex system that affects all key aspects of the educational process of the university. The objectives of the training are outlined, the methodological basis for planning and organizing psychological education of law students under the guidance of a teacher is presented. The principles of teaching in the context of the organization of the educational process are revealed, during the implementation of which the educational context of psychological disciplines is filled with practice-oriented professional content. This is achieved, according to the author of the article, by updating the content of psychological training of future specialists in the field of jurisprudence.

CLOTHES RATIONING DURING WORLD WAR II AND ITS IMPACT ON FASHION

The article deals with rationing clothes during World War II. The hypothesis of the study: rationing is introduced to save resources and curb inflation and becomes a decisive factor shaping fashion along with shortage of goods. The subject of the study is fashion during World War II. The purpose of the study is to identify and compare the features of the rationing of clothing in different countries during the war. The objectives of the study are to consider how the normalized distribution influences the features of wartime fashion, which has been studied mainly on the example of the Soviet Union, Germany, Great Britain, the United States of America, and France. The authors utilize the socio-psychological approach that considers the features of human behavior in relation to apparel in specific historical conditions

Social-Psychological and Legal-Philosophical Characteristics of Personality Anomie

The key to the successful and effective development of any society is the socio-psychological well-being of the population and personality, based on certain legal and philosophical concepts. Modern society experiences a kind of civilizational, philosophical, legal and socio-psychological crisis - the transformation of norms and behavior, against the background of which various forms of deviation are clearly manifested. As a result, in the context of the transformation of society, for a more complete and deeper study of the characteristics of human behavior, it becomes necessary to understand the causes of their occurrence. This methodological position allows first of all describing the mental world of a person who acts in the frames of the certain understanding and perception of social-psychological and legal norms of behavior. In this context, the procedure of psychological and legal-philosophical reconstruction of scientific concept of anomie is used as a methodological tool, which is aimed at analyzing various manifestations of the psyche of the personality and its anomic characteristics.

Criminal law imputation path for biometric information

Abstract Biometric information is the information of human physiological and behavioural characteristics used for automatic identification, state analysis and attribute estimation. It has the characteristics of uniqueness, stability, easy collection and public interest. Illegal acquisition, provision and use of biometric information may constitute an infringement of others’ personal rights, property rights, public order and even national interests. The civil code clearly expresses the importance and protection of biometric information and distinguishes biometric information from general personal information, but the criminal law has not responded to this in a timely manner. Based on the analysis of the minimum uncertainty neural network (MUNN) model, this article discusses the attribution object of biometric information and gives the path of attribution of criminal law under biometric information. The empirical results show that the number of misjudgements, the recognition rate and the number of iterations of the MUNN model proposed in this study are 65, 93.5% and 1501, respectively, and the effects are all optimal. Due to the insufficient response of the criminal law to biometric information, there are differences in main charges and sentences, and the consistency is 50% and 75%, respectively.

Influence of CEO Human Capital and Behavioral Characteristics on Economic Profit of Russian Companies

We investigate how different personal traits of a chief executive officer (CEO) influence value creation in one of the largest emerging capital markets in Russia. Our research model considers several components of human capital of a CEO.
 Moreover, we include CEO’s behavioral biases looking at overconfidence measured by industry adjusted ratio of capital expenditures and narcissism captured by the analysis of CEO’s photos following previous academic research approaches. The CEO power is applied to understand its impact over value creation and possible mitigating effect. Our sample consists of 111 Russian publicly traded companies and 235 CEOs for 8 years (from 2013 to 2020). We apply economic profit criteria to measure corporate performance with economic value added (EVA) which captures the spread between actual return on capital derived from financial reports and overall cost of capital based on the risks of a company collected from Bloomberg .
 We use first-order differences in company’s contribution to EVA after adjustments to the industry and overall market contributions to EVA for the sample. We find empirical evidence that CEO’s human capital affects value creation measured by first-order differences to industry adjusted EVA yearly. Furthermore, the CEO power has positive impact over value creation in Russian corporations while behavioral biases such as overconfidence ad narcissism do not have significant relationship with the changes in EVA.

The stay coefficient: a novel quantification of the relationship between stay time and travel time for urban shopping behavior analysis

ABSTRACT Shoppers typically want to spend an amount of time at a destination that is proportional to the travel time required to arrive there; thus, the travel time can be considered the cost of their trip. This is likely to be the case across regions with different urban structures and cultures. The purpose of this study was therefore to analyze the shopping behaviors contained in travel survey data from three metropolitan areas in Japan to identify common patterns and indicators based on travel time and stay time, thereby obtaining an understanding to inform future trade area analyses. Both the travel time and stay time associated with shopping behavior were found to be log-normally distributed regardless of metropolitan area, and four shopping behavior patterns common among the metropolitan areas were identified. The “stay coefficient” was then defined to express the elasticity of stay time according to travel time, and its values were similar according to shopping behavior pattern regardless of metropolitan area. The stay coefficient proposed in this study can therefore be applied to identify shopping behavior patterns in any urban area based on the relationship between travel time and stay time, realizing a novel approach to the analysis of and marketing for trade areas when planning the construction or renovation of commercial facilities. This approach can help inform the decisions of urban policy makers, marketing advisors, and commercial facility operators, and should be of interest to researchers and practitioners working with geospatial, shopping, and other human behavioral characteristics.

A GIS Approach for Revealing Urban Mobility Hotspots: A Case Study of Amman City in Jordan

Transportation management is an essential aspect of sustainable city development, as transportation networks serve humans in their daily activities. Traffic demand is an important input for transportation planning considering the population growth and travel activities increase, which have produced more traffic congestion throughout the years. Normally, traffic congestions are not uniformly distributed across the city, but rather are concentrated in particular locations. These locations are known as hotspots. We developed a framework for studying urban spatiotemporal characteristics of human mobility behavior within Amman city, where a large amount of trajectory data were processed to extract their pick-up and drop-off points. The available data, which was recorded between May and September 2021, is sorted out in an accumulative way to represent the working days of the week. Travel demand was illustrated through a temporal data representation per day interval as the day was divided into three intervals to further understand mobility behavior throughout the day. Then, the Kernel Density Estimation algorithm (KDE) was applied to derive and categorize different significant hotspots that were visually represented using GIS Software as appropriate spatial clustering tools to produce heatmaps. Finally, the correlation between hotspots and their drop-off points was analyzed to understand the factors influencing the mobility pattern such as day intervals and community structure in terms of residential or commercial. The results enable us to build several strategies and recommendations for transportation planners as well as taxi operators.

Takeoff runway overrun risk assessment in aviation safety based on human pilot behavioral characteristics from real flight data

Aviation safety is an important part of safety science and pilot behavior is a major factor in flight safety analysis. However, when analyzing unsafe events, such as runway overrun, majority of studies do not quantify the impact of pilot behavior on flight safety. The aim of this paper is to discover the pilot behavioral characteristics from actual flight data and apply them to construct a takeoff runway overrun risk assessment model. Concretely, the time series clustering is used to mine the pilot behavioral characteristics based on pilot operating data. Then, the similarity theory is introduced to construct a data-based man–machine-environment system expression including pilot behavioral characteristics to get the takeoff distance for the runway overrun risk assessment. The results of case study showed that 3 types of pilot behavioral characteristics were found in selected fleet, which mainly differ in the pilot reaction time after VR and operation input speed of the control column, and RO is more likely to happen under the combined influence of longer reaction time and slower operation input speed. The proposed method makes full use of real flight data from airlines under different conditions incorporating pilot behavior characteristics and can inform risk analysis of other unsafe events such as hard landing, tail strike, etc.

Bayesian Disturbance Injection: Robust imitation learning of flexible policies for robot manipulation

Humans demonstrate a variety of interesting behavioral characteristics when performing tasks, such as selecting between seemingly equivalent optimal actions, performing recovery actions when deviating from the optimal trajectory, or moderating actions in response to sensed risks. However, imitation learning, which attempts to teach robots to perform these same tasks from observations of human demonstrations, often fails to capture such behavior. Specifically, commonly used learning algorithms embody inherent contradictions between the learning assumptions (e.g., single optimal action) and actual human behavior (e.g., multiple optimal actions), thereby limiting robot generalizability, applicability, and demonstration feasibility. To address this, this paper proposes designing imitation learning algorithms with a focus on utilizing human behavioral characteristics, thereby embodying principles for capturing and exploiting actual demonstrator behavioral characteristics. This paper presents the first imitation learning framework, Bayesian Disturbance Injection (BDI), that typifies human behavioral characteristics by incorporating model flexibility, robustification, and risk sensitivity. Bayesian inference is used to learn flexible non-parametric multi-action policies, while simultaneously robustifying policies by injecting risk-sensitive disturbances to induce human recovery action and ensuring demonstration feasibility. Our method is evaluated through risk-sensitive simulations and real-robot experiments (e.g., table-sweep task, shaft-reach task and shaft-insertion task) using the UR5e 6-DOF robotic arm, to demonstrate the improved characterization of behavior. Results show significant improvement in task performance, through improved flexibility, robustness as well as demonstration feasibility.

Multi‐modal fusion method for human action recognition based on IALC

In occlusion and interaction scenarios, human action recognition (HAR) accuracy is low. To address this issue, this paper proposes a novel multi-modal fusion framework for HAR. In this framework, a module called improved attention long short-term memory (IAL) is proposed, which combines the improved SE-ResNet50 (ISE-ResNet50) with long short-term memory (LSTM). IAL can extract the video sequence features and the skeleton sequence features of human behaviour. To improve the performance of HAR at a high semantic level, the obtained multi-modal sequence features are fed into a couple hidden Markov model (CHMM), and a multi-modal IAL+CHMM method called IALC is developed based on a probability graph model. To test the performance of the proposed method, experiments are conducted on the HMDB51, UCF101, Kinetics 400k, and ActivityNet datasets, and the obtained recognition accuracy are 86.40%, 97.78%, 81.12%, and 69.36% on the four datasets, respectively. The experimental results show that when the environment is complex, the proposed multi-modal fusion method for HAR based on the IALC can achieve more accurate target recognition results.

A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System

To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.

Supply Chain Coordination with Capital-Constrained and Loss Aversion Retailer under Stochastic Demand and Supply

In the face of increasingly complex market environment, supply chain members pay attention to the possibility of obtaining expected profits and various risks while considering profit maximization. The behaviors of decision makers are often influenced by their psychological preferences, and loss aversion is one of the basic characteristics of human economic behavior. We research the supply chain coordination problem with capital constraint and loss-averse retailer in the case of stochastic production and demand. In order to address the loss aversion, a novel utility function is structured according to the theory of mental account. Under wholesale price contract, the optimal expected utility of supply chain under decentralized decision is smaller than that under centralized decision due to the retailer’s constrained capital. Based on that, a model is constructed by making revenue sharing contract act as the dual role of supply chain coordination and delayed payment strategy without seeking external financing or internal financing, and the contract conditions and optimal decisions of the coordinated supply chain are solved. Theoretical analysis indicates that when the contract parameters are properly selected, even though the retailer’s capital is constrained, the revenue sharing contract can not only coordinate the supply chain but also realize the arbitrary distribution of supply chain profits within a certain range. Finally, the change of decision variables and expected utility or profit with contract parameters is analyzed by several numerical examples.

Machine Learning Human Behavior Detection Mechanism Based on Python Architecture

Human behavior is stimulated by the outside world, and the emotional response caused by it is a subjective response expressed by the body. Humans generally behave in common ways, such as lying, sitting, standing, walking, and running. In real life of human beings, there are more and more dangerous behaviors in human beings due to negative emotions in family and work. With the transformation of the information age, human beings can use Industry 4.0 smart devices to realize intelligent behavior monitoring, remote operation, and other means to effectively understand and identify human behavior characteristics. According to the literature survey, researchers at this stage analyze the characteristics of human behavior and cannot achieve the classification learning algorithm of single characteristics and composite characteristics in the process of identifying and judging human behavior. For example, the characteristic analysis of changes in the sitting and sitting process cannot be for classification and identification, and the overall detection rate also needs to be improved. In order to solve this situation, this paper develops an improved machine learning method to identify single and compound features. In this paper, the HATP algorithm is first used for sample collection and learning, which is divided into 12 categories by single and composite features; secondly, the CNN convolutional neural network algorithm dimension, recurrent neural network RNN algorithm, long- and short-term extreme value network LSTM algorithm, and gate control is used. The ring unit GRU algorithm uses the existing algorithm to design the model graph and the existing algorithm for the whole process; thirdly, the machine learning algorithm and the main control algorithm using the proposed fusion feature are used for HATP and human beings under the action of wearable sensors. The output features of each stage of behavior are fused; finally, by using SPSS data analysis and re-optimization of the fusion feature algorithm, the detection mechanism achieves an overall target sample recognition rate of about 83.6%. Finally, the research on the algorithm mechanism of machine learning for human behavior feature classification under the new algorithm is realized.

Analysis and Research on the Characteristics of Historical Villages Based on Spatial Syntax

This study takes Cuiwei village, located in Qianshan District of Zhuhai City, as an example. First, it analyzes the human flow activities and activity size of village road a, the visual agglomeration degree and the spatial characteristics of historical sections. Secondly, select the most representative traditional buildings in the village, namely Sanwang temple and Wechsler mansion. When r=n and radius r=3, observe the changes of two parameters of visual integration and visual depth of the two historical buildings. And with the help of Baidu time machine photos of different periods to observe and record the changes of stores and human behavior characteristics. The results show that at this stage, the coordination of villages is poor, people's recognition in the village space is weak, people can't use local perception to reflect the overall space, there is an obvious interface between the inside and outside of the village, the isolation state is obvious, the local intelligibility is good, but the global intelligibility is poor.