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1. Averbeck, J. M. (2010). Irony and language expectancy theory: Evaluations of expectancy violation outcomes. Communication Studies, 61, 356-372. doi: 10.1080/10510971003776147

Averbeck (2010) utilized language expectancy theory to study the intention behind ironic and literal messages in terms of persuasiveness and expectedness. 84 males and 114 female undergraduate students enrolled in communication courses participated in this study. Using a 2X2 scale, one of four conditions was randomly assigned to each participant: ironic compliment, ironic criticism, literal criticism, and literal compliment. They were instructed to view the scenarios from the perspective of a close friend and complete the relational closeness scale regarding that friend. The independent variables were language and message, while the dependent variables were expectedness, behavioral intentions, importance, valence, and attribution. A MANOVA was computed to analyze the hypotheses. The results showed that ironic messages are more unexpected and negative compared to literal ones, criticism showed greater effect on behavioral intentions than compliments, literal and ironic criticisms were more effective than the ironic compliment, and the literal compliment was least effective.

2. Wright, C. N., & Roloff, M. E. (2015). You should just know why I’m upset: Expectancy violation theory and the influence of mind reading expectations (MRE) on responses to relational problems. Communication Research Reports, 32, 10-19. doi: 10.1080/08824096.2014.989969

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Wright and Roloff (2015) studied the relationship between mind reading expectations and problematic interactions using the expectancy violation theory. The study consisted of 33 males along with 73 females in romantic relationships; there was an average age of 20. They were all randomly chosen to explain differet scenarios where the significant other made them feel anger, depression, or disappointment. They did not realize their partners were feeling this way and had these emotions towards them. A six-point scale measured their mind reading expectations and mediation analysis that tested their hypotheses. These results showed a positive relationship between MRE and becoming combative, giving the silent treatment to their partner when they failed to recognize the emotional consequence of their behavior.

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1. INTRODUCTION

1.1 CLOUD COMPUTING

The cloud computing is a model for enabling convenient on demand, network access to
shared pool of resources (network, server, storage, application). The technology of distributed
data processing in which some scalable information resources and capacities are provided as a
service to multiple external customer through internet.
The main aim of cloud computing is move to desktop computer to service oriented
platform. The cloud computing application, data and resources are provide to users as a services
over the web. The service provided may be based on low in cost, massively scalable, on demand
based.

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Figure1: Basic Cloud Storage Model

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1.2 REASON FOR ADAPTING CLOUD COMPUTING

On Demand Service
A consumer can have a provision of computer capabilities, such as a server time and
network storage, as needed automatically without requiring human interaction with each service
provider.
Working from Anywhere
The cloud computing is a model driven methodology that provides configurable
computing resources over the internet service. The cloud resources can be accessed from
anywhere in the world.
Rapid Elasticity and Cost Saving
The cloud capabilities can rapidly scale in and out the resource quickly. The main goal of
cloud can eliminate the capital and operational cost. Because it provides various pooling of
resource.

1.3 USAGE OF CLOUD IN THE HOSPITAL

The hospital environment, have multiple computers that can be used in medical rooms.
Each room needs proper network accessibility, hardware and software which is used to upload,
store and retrieve the patient information or other medical data. The electronic patient health
record contains the overall history of a patient.
Scalability
Real time health records are generated. Each hospital must keep the medical records for
at least 15 years.
Mobility
Cloud can increasing the demand of physicians time. The physicians are needed to access
the patient record in a remote way. So that doctors can access the patient record easily and verify
the patient situation.

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Sharing
Cloud computing can provide the better relationship between the patient and doctor. The
healthcare service provider can access the complete patient information easily through online.
The electronic medical record in used to reduce the repeat diagnostic tests, saving time, memory,
and patient stress.
Many organizations now using daily updatable or changeable data. For keeping data both
security and usability cloud computing provide the environment to store data on different cluster.
Various organizations (e.g., Hospital authorities, industries and government organizations etc)
freeing person specific data, which called as private sensitive information. They provide
information of privacy of persons. The preserving privacy is protecting for individual’s sensitive
information on a public platform. Unluckily de-identification of persons even by neglecting
denotative identity like name, SSN, Voter Id number and license number. Data anonymization is
the best way to preserve privacy over the personal privacy sensitive information. The data
anonymization approach is very efficient technique but if the scalability of the data set like
private sensitive information is increased the anonymization technique fails to preserve privacy.
So scalable big data privacy preservation in cloud can be provided.
The purpose of this project is to develop an environment to provide privacy over the
personable sensitive data. The Major aim of the work is to develop a tool for patients to give
medical care providers more insight into your personal health information. Main aim of privacy
is provide secure data and for external knowledge. This application can helps to view the
patient’s health records only for authorized persons.

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1. More than half of the children don’t complain to the teacher about bullying.
2. Approximately one out of five children bullying reports are filed every day.
3. 10% of students are bullied once in a week.
4. 25% of bullying decreases after bullying prevention programs.
5. The children are bullying on the basis of race, gender, sexuality, religion and disability and many other.
6. 47% of boys bullying starts in 9th standard.
7. Nowadays there are many cases of cyberbullying.
8. About 9 out of 10 students are bullied because of their sexual orientation.
9. This is a myth that girls do less or no bullying. Girls bully in indirect ways like spreading rumors, out casting the children etc.
10. Bullying is commonly done in group.
11. One out of four teacher don’t take bullying seriously, that’s why children often think that seeking help from adults is not helpful.
12. One out of ten students drop out the school because of bullying.
13. In Middle school physical bullying is at peak.
14. There are four categories of bully- verbal, physical, relational and cyberbullying.
15. Girls victims of bullying more likely remain the same and get bullied as they grow younger.
16. Famous sportsman like Michael Phelps was bullied in the school for his big ears.
17. Over 67% of children believes that school don’t or poorly respond to bullying.
18. In high school physical bullying decreases but verbal bullying remains constant throughout the education period.
19. Kids with learning problems are more likely to be bullied.
20. One in every ten children tell to their parents about cyberbullying.
21. Kate Winslet, Lady Gaga also faced bullies in the school.
22. Students who face bully in the school, they are more likely to face bully at the workplace.
23. 160,000 students skip the school because of the fear of bully.
24. In many cases of cyberbullying, they even don’t know who is bullying them.
25. As compare to girls, boys are less likely to report against the bullying.
26. Children with any medical conditions or any facial deformity are more likely to get bullied.
27. Every year about4,400 children commit suicide because of bullying.
28. More than 10% of children are bullied on the daily basis.
29. 10% of bullied children tried to attempt suicide.
30. There are researches that suggests that the bully children have more chances to get involve in crimes in younger age.
31. 41% of children who bullied faces social anxiety.
32. The victims who reply or fight with the bully, most of the times situation get worse in the future for the victims.
33. There are many laws against certain form of bullying like abusive calls or text, assault, theft etc.
34. Children who got bullied most likely to bully others in future.
35. About 44% of people who were bullied experience depression.
36. 83% of bullies who are involve in cyberbullying are also involve in bullying in person.
37. Cyberbullying is highest in high schools.
38. Bully is most common in school, but it can happen in prison, home, family etc.
39. About 74% of LGBT students are bullied verbally.
40. Studies says that 75% of shooting that happen in school is because of bullying.
41. 282,000 children are physically attacked every month by the bullies.
42. Children at the age of three can also be victim of bully.
43. 3.2 million of students are victim of bullying every year.
44. 90% of students are bullied in 4th through 8th standard.
45. Bullied students say that they are bullied most in the stairwell at school and in classroom.
46. Bully leaves very bad impact on mental situation of the victim.
47. 23% of cyberbullied victim notify someone in school.
48. Children who are bullied have nine times more suicidal thoughts.
49. There are many programs like Anti-Bullying Day, Anti-Bullying Week, International STAND UP to Bullying Day etc. are organized against bullying.
50. Researches shows that after changing the school the student remains victim of the bully.