Time-Lapse Detection for Evolution of Trustworthy Network User Operation Behavior Using Bayesian Network

To realize autonomous navigation of intelligent robots in a variety of settings, analysis and classification of places, and the ability to actively collect information are necessary. In this paper, visual data are organized into an orientation histogram to roughly express input images by extracting and cumulating straight lines according to direction angle. In addition, behavioral data are organized into a behavioral histogram by cumulating motions performed to avoid obstacles encountered while the robot is executing specified behavioral patterns. These visual and behavioral data are utilized as input data, and the probability that a place belongs to a specific class is calculated by designating the places already learnt by the robot as categories. The naïve Bayes classification method is employed, in which the probability is calculated that the input data belong to each specific category, and the category with the highest probability is then selected. The location of the robot is classified by merging the probabilities for visual and behavioral data. The experimental results are as follows. First, a comparison of behavioral patterns used by the robot to collect data about a place indicates that a rotational behavior pattern provides the best performance. Second, classification performance is more accurate with two types of input data than with a single type of data.

Work-related stress and burnout among physicians are of increasing relevance. The aim of this study was to investigate work-related behavior and experience patterns and predictors of mental health of physicians working in medical practice in Germany. We surveyed a stratified, random sample of 900 physicians from different specialties. The questionnaire included the standardized instruments Work-related Behavior and Experience Pattern (AVEM) and the Short Form-12 Health Survey (SF-12). Only one third of physicians reported high or very high general satisfaction with their job, but 64% would choose to study medicine again. Only 18% of physicians presented a healthy behavior and experience pattern. Almost 40% presented a pattern of reduced motivation to work, 21% were at risk of overexertion, and 22% at risk for burnout. Willingness to study medicine again, fulfilled job expectations, professional years, marital status, and behavior patterns were significant predictors of mental health and accounted for 35.6% of the variance in mental health scores. Job-related perceptions also had a significant effect on burnout. The strong influence of work-related perceptions suggests a need for realistic expectation management in medical education, as well as support in stress management and coping strategies during medical training.

Prediction of humans’ behaviors during a disaster: The Behavioral Pattern during Disaster Indicator (BPDI)

Twenty-four hour activity budgets and patterns of behavior in captive ocelots ( Leopardus pardalis)

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

Lithium chloride, cholecystokinin and meal patterns: Evidence that cholecystokinin suppresses meal size in rats without causing malaise

Metopic synostosis is a condition in which the metopic suture of the human cranium fuses prematurely and may be related to poor behavioral inhibition leading to behaviors commonly associated with Attention-Deficit Hyperactivity Disorder (ADHD). The purpose of this project was to examine the behavior patterns among children with a history of metopic synostosis. It was hypothesized that children with a history of metopic synostosis would exhibit many of the same behavioral patterns associated with ADHD. It was also hypothesized that children with a history of simple synostosis not involving the metopic suture would not evidence this type of behavioral pattern. In order to test these hypotheses, the behavior of three groups of children was compared including (1) children who had a history of metopic synostosis (M= 7.63 years, SD = 1.92 years), (2) children who had a history of simple craniosynostosis not involving the metopic suture (M= 7.54 years, SD = 1.88 years), and (3) a group of children diagnosed with ADHD (M=7.78 year, SD = 1.87 years). It was found using the Home and School versions of the Attention Deficit Disorders Evaluation Scale (ADDES) that children with a history of metopic synostosis demonstrate significantly more behavioral disturbances than children with a history of simple craniosynostosis not involving the metopic suture. Using the BASC Teacher Rating Form it was found that children with a history of metopic synostosis have a behavior pattern similar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture. Using the BASC Parent Rating Form it was found that children with a history of metopic synostosis have a behavior pattern dissimilar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture.

The possible effect of a specific overt behavior pattern upon serum cholesterol levels, blood coagulation, and incidence of arcus senilis and clinical coronary heart disease was studied in two groups of women randomly chosen for study by lay selectors after careful orientation. Women of group A were selected on the sole basis of their habitual exhibition of a behavior pattern (type A) characterized by excessive competitive "drive," ambitiousness, and immersion in "deadlines." Women of group B were selected by lay selectors on the sole basis of their exhibition of entirely opposite behavioral traits (type B). Each group of subjects included a series of nuns respectively exhibiting the type A and B behavior patterns. The women exhibiting pattern A also exhibited markedly higher serum cholesterol levels, often more rapid blood coagulation, three to five times as much arcus senilis, three to seven times more diastolic hypertension, and a four-fold higher incidence of clinical coronary disease. A history of myocardial infarction or angina pectoris only was found in group A. These striking differences were independent of differences in parental incidence of coronary disease, caloric or fat intake, obesity, physical activity, smoking, or "femininity." The strikingly higher incidence of clinical disease in pattern A women also was not found ascribable per se to differences in heredity, obesity, hypertension, caloric or fat intake, smoking, physical activity, blood coagulation, or serum cholesterol levels. The results closely resemble those previously found in men with similar behavior patterns, and strongly suggest that the type A behavior pattern is itself in some way largely responsible for the observed findings.

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large‐scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long‐time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t‐SNE) algorithm to expose the clustering features of large‐scale cell phone users in the low‐dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

For optimal health promotion in adolescents, it is important to know their typical behavior patterns. The aims of this study were to identify and compare sex-specific health-related behavior patterns of adolescents in Germany. The KiGGS Survey and the MoMo Study assessed the health behavior of 832 boys and 811 girls by means of a questionnaire. The indices physical activity, diet quality and media use were included in cluster analyses for boys and girls separately. For each sex, 4 clusters with similar characteristics on the three included indices were identified. However, cluster size, level of the characteristics and correlates were different in boys and girls: fewer girls showed high activity levels. Overall, physical activity and media use were less pronounced in the behavior pattern of girls. There was a stronger association of behavior patterns with socio-economic status in girls. In boys, behavior patterns were significantly associated with prevalence of overweight. The similar cluster solutions for boys and girls support the assumption that there are typical health-related behavior patterns among German adolescents. This finding might facilitate the precise definition of homogeneous target groups for health promotion programs. Furthermore, it seems that sex-specific foci should be set.

Background: Changing trends in dietary habits, behavioral patterns, and prevalence of overweight/obesity among adolescents is of great concern.Methods: 6469 school children from five districts of Kerala, India were enrolled. Dietary habits and behavioral patterns were collected using a pretested questionnaire. Anthropometric measurements were recorded and compared with regional Indian Academy Pediatrics (IAP) reference charts. Data was computed and analyzed. Results: Mean age: Boys 14.51±0.93, Girls 14.42±1.04 years. Male: Female ratio 0.92:1. Practices like missing breakfast, non-compliance to weekly Iron Folic Acid supplementation and not taking exercise >2 hours/week were more in girls. Increased consumption of junk food, screen time >2 hours/week and not sharing life events with parents were more in boys. Mean anthropometric parameters were <50th percentile of reference charts. 6.1% had low BMI (boys, 4.2% vs. girls,7.6%,), 12.3% were overweight (boys, 14.9% vs. girls,10.2%) and 4.4% had obesity (boys, 4.7% vs. girls, 4.2%). Mean anthropometric parameters were higher in more urbanized districts. Daily consumption of junk food showed significant association with obesity and exercise >2 hours/week with normal BMI.Conclusions: Dietary and behavioral patterns and prevalence of overweight/obesity showed variability with respect to gender and region of residence. 12% were overweight and 4% had obesity. Daily consumption of junk food showed significant association with overweight/obesity and daily exercise >2 hours/week with normal BMI. Health education addressing behavioral change is recommended for better health.

APIB-GAN: A generative adversarial networks based approach for anomaly prediction of internet behavior

Learning from the behavior of the resident is essential in order to adapt the system and to provide intelligent control based on behavior patterns. Different homes have different conditions and habits which have to be taken into account for the intelligent system to be useful. However, even deeply ingrained habits are subject to change over time. Therefore, intelligent system has to respond to changing and diverse environment. In this paper the intelligent lighting control system based on resident’s behavioural patterns is presented that employs artificial neural networks for on-line learning and adaptation. In order to manage constantly growing data and dynamic environment problems, the latter algorithms have been improved with the proposed similarity threshold based data replacement algorithm which has been experimentally tested and compared with alternative algorithms.DOI: http://dx.doi.org/10.5755/j01.eee.21.2.8772

Testing the functional significance of antipredator behaviors is exceedingly difficult given their potential dual functions (i.e., conspecific warning and pursuit deterrence), and this task is made even more difficult when the same term is used to describe different behavioral patterns by different authors. Tail movements and rump patch exposure in ungulates are well‐documented phenomena, but the functional significance of each has remained elusive. I discuss the confusion in the literature regarding the definition of tail movements, redefine two behavioral patterns and three tail positions, and examine their performance in Columbian black‐tailed deer (Odocoileus hemionus columbianus) in response to an approaching human. ‘Tail‐flicking’, the brief swaying of the tail to one side or the other, was most commonly associated with feeding behavior, and ‘tail‐flagging’, the lateral side‐to‐side movement of the tail in rapid succession, was common during both feeding and alert behaviors. Tail‐flicking and tail‐flagging: (i) were performed by both calm/foraging deer and alarmed deer; (ii) probably lack naturally selected antipredator function but may have the benefit of providing public information to conspecifics about potential danger, and (iii) likely lay at opposite ends of a continuum of tail movements that are the result of agitation and motor reflexes involved in locomotion, where flagging indicates greater agitation than flicking. Deer held their tail ‘erect’ during bouts of rapid flight, including stotting and trotting, and erected their tails sooner after flight during more threatening approaches. Tail erection as a behavioral pattern is an antigravity reflex performed when preparing for locomotion and probably lacks antipredator function. However, tail erection likely facilitated the evolution of bright rump patches in ungulates, which were probably shaped by natural selection to serve multiple antipredator or thermoregulatory functions.

Serum lipids and lipoproteins were determined in a blind study of ten men who exhibited an overt behavior pattern (Pattern A) which had earlier been found to be associated with a high prevalence of clinical coronary heart disease, and ten men who showed a converse overt behavior pattern (Pattern B). The men exhibiting Type A Behavior Pattern, were also found to exhibit, in comparison to those with Type B Behavior Pattern, significantly higher serum levels of triglycerides, phospholipids, and cholesterol and of serum beta- and other low-density lipoprotein lipids, and significantly lower α/β lipoprotein cholesterol ratios. These differences were not found ascribable to any differences of diet, weight, or physical activity.