Chapter Four - Pattern in Behavior: The Characterization, Origins, and Evolution of Behavior Patterns

Mining Host Behavior Patterns From Massive Network and Security Logs

Plagiarism has become a major cause of concern that has even spread its root across academic area. Universities are becoming more concerned about it because of growing development of internet especially socio media and thereby increasing opportunity among students to copy and paste the electronic content. Students in today’s digital era follow the trend of exchange and copying of information in order to maintain their socio integrity among their circle without considering its long term negative social impact especially from their career perspective. ‘They feel The more you exchange, the more social you are’. To avoid this kind of plagiarism especially in Universities labs, Hawk Eye an innovative mobile plagiarism detection system was an initiative in this regard. Hawk Eye combination with Cohort Intelligence (CI) represents higher state of vision to see things even with more clarity in ordinary experiences, by using Hawk’s keen and observant eyesight, and CI self-supervising nature. This would also help to take appropriate preventive measures to avoid plagiarism from its root among students. ‘Hawk Eye for Cohort (HEC)’ based on comparative analysis of various algorithms like CI and Genetic Search GA can play an important role in formulation of behavioral distribution patterns of students. CI algorithm deploys its self-supervising mechanism to improvise an individual behavior in a cohort and by observing these behavioral patterns, decisions can be taken by teachers in regard of re-design of appropriate evaluation systems to check and stop plagiarism among students. The final outcome of HEC would be an incrementally learning evaluation systems which would iteratively grow with evolving cohort behavioral patterns with every upcoming batch of students. This evolving behavioral patterns search process can be optimized using GA. HEC really would be a concrete evaluation system for analyzing percentage of plagiarism among students, understanding real time reasons behind the growing percentage and coming up with suitable prevention measures in order to cure plagiarism. The concept of study of cohort behavioral distribution pattern using algorithms like GA and CI for plagiarism detection based on student’s socio thinking using different Cohort Analysis Tools is indeed an entirely new idea which is being discussed in this paper in detail.

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.

ABSTRACT:
 BACKGROUND & OBJECTIVE: Internet has swayed all aspects of human society and the exponential rise in global internet users indicates that internet & Social Networking sites (SNS) have become an essential part of the daily lives of people with potentially addictive effects of its overuse. This may lead to social isolation, depression & professional effects. This behavioral addictive pattern has also been observed in increasing trend among healthcare professionals worldwide. This study aims to assess prevalence of internet addiction and its behavioral patterns (BP) in Pakistani healthcare context, to determine the prevalence and intensity of Internet Addiction (IA) among Medical Doctors.
 METHODOLOGY: A Quantitative; Cross-sectional Survey was conducted at Shaikh Khalifa Bin Zayed/ Azad Kashmir Combined Military Hospital Rawalakot for 2 months.After calculating sample size with 95% Confidence Interval limit,100 medical and dental doctors were selected using convenience sampling. After IRB approval & informed consent data was collected using prevalidated “Young's Internet Addiction Scale”& “Behavioral Patterns scale”. The participants recorded their response on a 5-point Likert scale and dichotomous scale for each scale respectively. Data was summarized using descriptive statistics & inferential statistics in SPSS 23. Addiction was classified into 4 categories. The significant association between IA groups and BP groups was computed by Fisher's exact test with P-value <0.05 as significant. 
 RESULTS: The Response rate was 87% with 54% males and 56% females. The prevalence of internet addiction was 79%(n=69). Out of them 36% (n=31) had mild, 41% (n=36) had moderate addiction while 2% (n=2) had severe addiction. Pattern of internet addiction symptomatology shows that prevalence of IA is higher in excessive use (87.35%) & lack of control (77.01%) while least in anticipation (35.63%) category. Statistically significant difference was seen in behavioral patterns among addicted and nonaddicted medical and dental doctors.
 CONCLUSION: Internet Addiction is a recognizable disorder from the spectrum of Problematic Internet Use. This study reports the prevalence of internet addiction among health care professionals and burden of multiple behavioral patterns in association with IA, which is an emerging mental health concern.

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

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

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.

About one third of the most common cancers could be prevented by the reduction of modifiable behavioral risk factors. We aimed to identify behavioral patterns of risk and protective factors for cancer in Brazil, between 2014 and 2015. Data from Vigitel Survey (n = 95,027 adults aged ≥ 18 years) from all Brazilian capitals and Federal District were used. Thirteen risk (RBF) and protective behavioral factors (PBF) for cancer were investigated. RBF included the consumption of red meat, meat with high-fat content, soft drinks, sweets and abusive alcohol, replacement of lunch/dinner for snacks, television viewing, obesity, and smoking. PBF included the consumption of beans, fruits and vegetables, and physical activity practice. Patterns were identified by principal component analysis and linear regressions models assessed its association with sociodemographic characteristics. Four behavioral patterns for cancer were identified. The ‘healthy behavior pattern’ and the ‘unhealthy food consumption pattern’ were positively associated to females and schooling. The ‘unhealthy behavior pattern’ and the ‘mixed behavior pattern’ were both negatively associated to females, age and schooling. Our data revealed different vulnerable population groups for cancer. Actions for reduction of modifiable behavioral risk factors aiming at cancer prevention should consider distinct approaches by sex, age, and schooling.

The Dwarf Mongoose: A Study of Behavior and Social Structure in Relation to Ecology in a Small, Social Carnivore

Clinical and epidemiologic studies of coronary heart disease (CHD) have from time to time over the last three decades found associations between prevalence of CHD and behavioral attributes and cigarette smoking. The main purpose of this study is reduced to major risk factor of coronary heart disease through prohibition of smoking and control of behavior pattern. The subjects consisted of 120 smokers and 90 nonsmokers who were married men older than 30 years working in officers. The officers were surveyed by means of questionnaire September 26 through October 6, 1989. The Instruments used for this study was a self-administered measurement tool composed of 59 items was made through modifications of Jenkuns Activity Survey (JAS). The Data were analysed by SAS (Statistical Analysis System) program personal computer. The statistical technique used for this study were Frequency, chi 2-test, t-test, ANOVA, Pearson Correlation Coefficient. The 15 items were chosen with items above 0.3 of the factor loading in the factor analysis. In the first factor analysis 19 factors were extracted and accounted for 86% of the total variance. However when the number of factors were limited to 3 in order to derive Jenkins classification, three factors were derived. There names are Job-Involvement, Speed & Impatience, Hard-Driving. Each of them includes 21 items, 21 and 9, respectively. The results of this study were as follow: 1. The score of the smoker group and non-smoker group in Job-Involvement (t = 5.7147, p less than 0.0001), Speed & Impatience (t = 4.6756, p less than .0001), Hard-Driving (t = 8.0822, p less than .0001) and total type A behavior pattern showed statistically significant differences (t = 8.1224, p less than .0001). 2. The score of type A behavior pattern by number of cigarettes smoked daily were not statistically significant differences. 3. The score of type A behavior pattern by duration of smoking were not significant differences. It was concluded that the relationship between smokers and non-smokers of type A behavior pattern was statistically significant difference but number of cigarettes smoked daily and duration of smoking were not significant differences. Therefore this study is needed to adequate nursing intervention of type A behavior pattern in order to elevated to educational effect for prohibition of cigarette smoking.

This is a population-based study based on the 2002 National Survey of Taiwan on Knowledge, Attitude, and Practice of Health Promotion. The objective of this study is to examine health-related behaviors and behavioral patterns among different gender and age groups. A total of 26,755 participants were interviewed, resulting in a response rate of 81.9%. Factor analysis with orthogonal rotation was applied to identify the underlying factor structure for the health-related behaviors, including cigarette smoking, betel nut chewing, alcohol drinking, intake of fruits or vegetables, prevention service utilization, physical activity, and tooth brushing. Protective and risk behavioral patterns were selected consistently among various gender and age subgroups. These two behavior patterns were negatively associated with each other. In younger age groups (age < 55), a risk behavioral pattern was more dominant than a protective behavioral pattern. In the older age group (age >or= 55), the pattern order was reversed. An effective health intervention program should be based on behavioral patterns instead of an individual behavior. Gender and age play an important role in the behavioral patterns and need to be taken into consideration when designing intervention programs.

In distance learning, the professor cannot see that the students are having trouble with a subject, and can fail to perceive the problem in time to intervene. However, in learning management systems (LMS's) a large volume of data regarding online access, participation and progress can be registered and collected allowing analysis based on students' behavioral patterns. As traditional methods have a limited capacity to extract knowledge from big volumes of data, educational data mining (EDM) arises as a tool to help teachers interpreting the behavior of students. The objective of the present article is to describe the application of educational data mining technics aiming to obtain relevant knowledge of students' behavioral patterns in an LMS for an online course, with 1,113 students enrolled. This paper applies two algorithms on educational context, decision tree and clustering, unveiling unknown relevant aspects to professors and managers, such as the most important examinations that contribute to students' approval as well as the most significant attributes to their success.

We humans seem to have an inherent urge to describe and label personalitiesin individuals we meet in everyday life, both professionally and personally.Unfortunately, neither lay descriptors nor psychiatric diagnoses easilycapture the sense we often have of others. In fact, describing andcategorizing personality and personality disorders have been among the weakestlinks in psychiatric nosology since the introduction of the Diagnostic andStatistical Manuals of Mental Disorders (DSMs). Even with the atheoretic,primarily descriptive approaches of DSM-III (published in 1980),DSM-III-R, andDSM-IV,1the section on personality disorders is the most problematic. Still, theinherent attraction of personality labeling is often too great to resist. In his essay, Walling uses the descriptions of Achilles' behaviors andpersonality traits in The Iliad to diagnose him according toDSM-IV criteria. Although the author selects passages from the textthat are consistent with his conclusion that Achilles had antisocialpersonality disorder, we should view this conclusion with great cautionbecause it illustrates some of the pitfalls of personality diagnoses. A personality disorder is an enduring and stable pattern of innerexperience and behavior that deviates markedly from cultural expectations, ispervasive and inflexible, begins in early adolescence or early adulthood, andleads to distress andimpairment.1 Becausepersonality disorders are longitudinal and stable, diagnoses based oncross-sectional examinations are fraught with difficulties. Thus, Achilles mayhave thought and acted as described in The Iliad and noted byWalling, but what do we know about his patterns of behavior throughout hislife? The DSM-IV criteria for antisocial personality disorder requiresymptoms of conduct disorder (similar to adult antisocial behaviors) beforeage 15. Was Achilles a destructive, aggressive, deceitful child or adolescent?Without this information, it is impossible to make an accurate diagnosis. In addition, diagnoses of personality disorders can be made only byexamining the individual's behavior in the context of his culture. AlthoughWalling describes some examples of Achilles' behavior that seem at variancewith his cultural norm, without a fuller knowledge of the culture, suchinterpretations are suspect. An explicit rule for diagnosing personality disorders requires that thetraits and behaviors used to make the diagnosis be due not simply to theeffect of transient stressors or another psychiatric disorder such asdepression, mania, or anxiety disorders. Achilles' behaviors during wartimemay not reflect his typical behaviors at other times. Again, the hallmark ofpersonality and its disorders is the predictable, consistent, enduring patternof traits and behavior, not a series of behaviors during a time of crisis.Similarly, a fixation on honor and revenge and excessive mourning could hardlyqualify as obsessive-compulsive personality traits without further informationabout the culture, his relationship to the deceased, and so forth. Diagnosing personality disorders by literary text is seductivelyinteresting and creative but must be done with caution and a skeptical eye toavoid overgeneralizing from cross-sectional information.

Does attitudinal loyalty influence behavioral loyalty? A theoretical and empirical study

Based on a 9-month observational study, this paper describes variations in the behavior patterns which young children (6 to 70 months) show when they encounter a male observer (25 years old) for the first time on each observation day at a day care center in Japan. It also presents age differences in the variations. An analysis of these differences reveals: (1) young children of 5 age groups use their head, face, hands and arms differently upon encountering the observer; (2) each age group has 1 distinctive F-C (frequent and common) behavior patterns. The analysis suggests: (1) the acquisition of skills of access behavior is not a simple process; (2) young children rather have a shared cultural knowledge with which they interpret the observer's behavioral and interactional patterns within their cognitive framework. Further research is needed on changes of underlying symbolic meaning in the F-C behavior pattern.