The body of the Indian Woman: A tool of nationalistic discourse Essay

The body of the Indian Woman: A tool of nationalistic discourse The genre of Bollywood film has recently become a popular means of entertainment for the non-resident Indian as well as the western audience. The vibrant color, spontaneous dance numbers, and other alluring factors may have contributed in the popularity of Bollywood films. However, for the NRI, Bollywood films are mean of a connection to the motherland; it brings a sense of nostalgia through cultural and tradition practices. In Chutney Popcorn and Bride and Prejudice, we see how these cultural practices and tradition are preserved by using the woman’s body. A woman’s body is a tool of producing the norms of the Indian national discourses; yet, the woman’s body can be utilized to resist such norms. Norms of rituals, engagement, marriage, procreation, and creation of family are tools that are utilized by Indian society to maintain the heteronormative discourses of the nation. To understand how these film produce and contest such norms, we must look with a critical eye of how the Indian woman’s body is utilize to achieve these goals. Scholars such as Anupama Arora and Christine Geraghty have analyzed Chutney Popcorn and Bride and Prejudice, respectively by viewing the Indian woman’s body as a tool of reproducing and contesting heteronormative discourses of the Indian nation. By following the technique used by Arora and Geraghty, we view these films with a critical eye. First, we must acknowledge that Chutney Popcorn and Bride and Prejudice are different films that tackle similar issues. Chutney Popcorn is an independently made film about a Lesbian NRI living in New York. While Bride and Prejudice is a multimillion dollar film, created by renowned director Gurinder Ch... ...agent of their own will; meaning, they made decisions for themselves without â€Å"falling under pressure.† Reena is the lesbian woman who is the agent of her own will that is not constrained by expectations and culture; however, in the process we see her yearning for acceptance by her mother. Her pregnancy both symbolizes resistance and conformity for pregnancy is a gendered expectation for women; but the fact that she is a lesbian complicates things. Her sexual orientation provides a means of resistance to the idea of a heterosexual family. Lalita on the other hand, follows the norms of Indian culture yet she becomes the agent of her own will by choosing to love Darcy, a white man over Mr. Kholi an American NRI. The ability of both characters to be the agent of their own provides a tool of halting the use of a woman’s body as a tool of promoting oppressive norms.

Causes of Prejudice

Prejudice is an opinion that is not based on actual evidence or experience. In â€Å"Causes of Prejudice,† Vincent Parillo describes the psychological and sociological reasons of prejudice. Among these causes, frustration is defined to produce a prejudicial attitude towards others. Parillo explains in his work that throughout history, minority groups have been used as scapegoats to take the blame for certain events. He clarifies that scapegoating is the act of blaming others for an incident that is not their fault.This idea is also visible in the novel To Kill a Mockingbird by Harper Lee, in the art where a lawyer named Atticus tries to prove an African American innocent who has been falsely charged with raping a white woman. Therefore, both Parillo's â€Å"Causes of Prejudice† and Harper Lee's To Kill a Mockingbird support that frustration is a cause of prejudice because of an increase in aggression towards a scapegoat. To begin with, frustration is caused by relative deprivation, which is the lack of resources in an individual's environment when compared to others.This results in aggression towards a scapegoat in order to relieve this tension. â€Å"Frustrated people ay easily strike out against the perceived cause of their frustration. However, this reaction may not be possible because the true source of the frustration is often too nebulous to be identified or too powerful to act against† (Parillo 583). His view is also seen in To Kill a Mockingbird, when Mayella Ewell claims Tom Robinson has raped her. Atticus tries to prove to the Jury that Tom Robinson in fact did not rape Mayella and that she, a white woman, kissed Tom, a black man.Mayella is an Ewell; a very poor family in the town of Maycomb and therefore she has to live through tough onditions which include living behind the garbage dump, barely having any money to support her dad and seven siblings, as well as being beaten by her own father. Most importantly, she is frustrated t hat she always feels dissatisfied with her life since she was never able to experience any happiness by being isolated from the rest of the world. Therefore, she tries to at least kiss a black man to feel some sense of happiness.When she realizes it is condemned by society, her frustration increases partly because society is not allowing her to have a small amount of happiness, and o she shows her aggression by blaming Tom Robinson for raping her. In addition, aggression resulted from frustration is pinpointed towards scapegoats because they share similar characteristics of being vulnerable to blame. â€Å"The group must be (1) highly visible in physical appearance or observable customs and acations; (2) not strong enough to strike back; (3) situated within easy access of the dominant group .. † (Parillo 584). To Kill a Mockingbird takes place in southern Alabama in the 1930's. This was the time period where prejudice against African Americans was present. Segregation was pre sent because having white skin was een to be better than having black skin. This simple difference in skin color resulted in an unfair treatment of African Americans. Separate bathrooms, drinking fountains, churches, and schools resulted for African Americans and whites. Also, since whites felt that the blacks were inferior to them, they tended to assume all blacks were unintelligent.When Mayella copes with her frustration of being isolated from the rest of the world by blaming Tom Robinson, the court looks at his skin color instead of the evidence given tor this case. Even though Atticus provides plenty ot evidence that roves that it was impossible for Tom to commit the rape of Mayella, Tom is still found guilty because even if Mayella is part of the lower class of Maycomb, she is still a white woman, making her superior to Tom Robinson.It is evident that frustration plays an important role in determining prejudicial attitudes. Both the novel To Kill a Mockingbird by Harper Lee and â€Å"Causes of Prejudice† by Vincent Parillo, agree that frustration is caused by relative deprivation and when aggression forms, the blame is placed on scapegoats. These scapegoats share similar characteristics which allow them to be vulnerable to the blame that falls upon them.

Evaluate the case for cutting public expenditure rather Essay

A fiscal deficit is when a government’s total expenditures exceed the tax revenues that it generates. A budget deficit can be cut by either reducing public expenditure or raising taxes. In this essay, I am going to analyse the benefits and costs of increasing tax rates to reduce fiscal deficits instead of cutting government expenditure. First of all, if the government decides to cut current public expenditure, it will lead to a reduced quantity and quality of public goods and service. For example, closing NHS direct call centres down which results in lower living standard. Moreover as the spending in sectors such as healthcare and education is cut, these services may need to redundant staff to stay within their new budgets. For instance if the NHS’s budget is cut they will lay-off additional staff. Those public sector workers may find it difficult to find a new job in private sector if they are not competitive enough to compete with other people in the labour market, leading to higher unemployment conflicting with the government macroeconomic objective of low unemployment rate. Also higher unemployment will mean less income tax revenue, lower VAT receipts, higher welfare payments, as well as lower standards of living. If the government is to cut capital expenditure this is the type of expenditure that expands LRAS. It might not cause serious problems in short run, however in long run less spending on for example education and healthcare will result in a less educated and skilled workforce and a less healthy workforce. The negative effects of inadequate skilled human capital in the long run include lower productivity which makes the economy less competitive internationally compared with for example Germany. It in turn leads to deterioration on balance of payment, economic stagnant growth and inflationary pressure as labour costs increase. Thirdly, government spending is an injection into the circular flow of income. A decrease in the government spending will incur negative wealth effect and therefore lead to weaker economic growth. In addition, the  government spending is one of the components of aggregate demand, consequently, lower GDP. In a demand-deficient recession, consumption and investment tend to decrease due to lower income and revenue, the (X-M) component tends to level off or worsen in short run, which makes government spending an essential device to stimulate the economy. Therefore a decrease in the government spending will cause an even deeper recession and a larger budget deficit. Last but not least, a decrease in government spending could mean worse income distribution compared with increasing progressive tax. This is because transfer payment forms almost a third of the governments budgets and so by cutting expenditure it is very likely that it will also be cut making the poor poorer and widening the gap. On the other hand, taxes could be increased progressively by for example increasing marginal income taxes so that the people with high income pay more than the poor narrowing the gap between. However, there are also some drawbacks associated with raising taxes. Tax is a form of leakage from the circular flow of income leading to negative multiplier effect. If the government increases income tax rates, it might create disincentives to work. It is because when income tax increases, the opportunity cost for leisure time decreases; and people will have to work longer hours to earn the same disposable income. Some people may therefore prefer claiming Jobseekers’ Allowance instead of working. If the corporation tax is to be increased, there will be disincentive for firms to locate in the UK, leading to less investment and corporation tax revenues. Additionally, an increase in the National Insurance may discourage firms taking more employers as the NI is paid per employee. Secondly, if the government raises higher income by increasing indirect taxes for example VAT, it may also have problems. It shifts the SRAS curve to the left as the cost of production increases. And it may therefore push up the price level and reduce the level of output. Moreover, indirect taxes are regressive taxes, which impose a greater burden relative to the incomes on the poor than on the rich. Thirdly, as the public sector is basically non-profit, their allocation of resources believed to be less efficient than the profit-making private sector firms. Therefore reducing public expenditure may lead to greater efficiency and productivity by for example removing unnecessary layer of management hence more effective communication and better service provided by the public sector. Last but not least, the choices between the two possible ways and their effects depend on the macroeconomic situation- for example the unemployment rate and the size of the public sector. If the size of the public sector is small, the adjustment on government spending might not be very large and the effect on budget deficit wouldn’t be significant. If the unemployment rate is high, for example 26% general rate and 50% youth rate in Spain, making it very hard to raise taxes. Apparently, both reducing government spending and increasing tax rates will lead to a lower AD, but they will have different other effects. Therefore the choice between this two may depend on macroeconomic situation and what the government is focusing on achieving. VICKKIE

Multicultural education Essay

From its early beginnings in the 1960s, multicultural education has since been in a constant state of evolution both in theory and in practice (Gorski & Covert 1996). In the last four decades, it has undergone repeated transformation, focusing and conceptualization as challenges emerge one after the other from a rapidly changing population demographics and a significant growth in diverse multicultural groups. The result is a multitude of conceptualizations reflecting different foci but which basically share the same ideals rooted upon the need for transformation or change. Gorski (2000) defines multicultural education as a â€Å"progressive approach for transforming education that holistically critiques and addresses current shortcomings, failings, and discriminatory practices in education†. These shared ideals that include social justice, equity in educational opportunities, and the dedication to help students reach their full potential as learners and as socially conscious and active individuals provide the basis for understanding multicultural education. It is a process of action, through which adults achieve clarity about their condition in this society and ways to change it (Phillips, 1988). Multicultural education acknowledges that schools, among all other institutions, play a pivotal role in building the foundation and acting as major influencing factor for the transformation of society and the elimination of oppression and injustice. The realities of the times clearly speak for the growing importance and relevance of multicultural education. Cultural diversity in schools is indeed one considerable challenge but like any other, it can be a most welcome opportunity. History has shown us that nations are enriched by the ethnic, cultural, and language diversity among its citizens (Banks, 2001). Schools play a significant part in finding ways to harness and redirect cultural diversity into creating unity and progress in schools and ultimately to society in general. References: Banks, J. A. (April 2001). Diversity within unity: Essential principles for teaching and learning in a multicultural society. New Horizons for Learning. Retrieved on May 28, 2009 from http://www. newhorizons. org/strategies/multicultural/banks. htm Gorski, P. & Covert, B. (1996; 2000). Defining multicultural education. Retrieved on May 28, 2009 from http://www. edchange. org/multicultural/define_old. html Phillips, C. B. (1988). Nurturing diversity for today’s children and tomorrow’s leaders. Young Children: 43(2).

New Hoarding Technique for Handling Disconnection in Mobile

Literature Survey On New Hoarding Technique for Handling Disconnection in Mobile Submitted by Mayur Rajesh Bajaj (IWC2011021) In Partial fulfilment for the award of the degree Of Master of Technology In INFORMATION TECHNOLOGY (Specialization: Wireless Communication and Computing) [pic] Under the Guidance of Dr. Manish Kumar INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD (A University Established under sec. 3 of UGC Act, 1956 vide Notification no. F. 9-4/99-U. 3 Dated 04. 08. 2000 of the Govt. of India) (A Centre of Excellence in Information Technology Established by Govt. of India) Table of Contents [pic] 1.Introduction†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 3 2. Related Work and Motivation 1. Coda: The Pioneering System for Hoarding†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 4 2. Hoarding Based on Data Mining Techniques†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 5 3. Hoarding Techniques Based on Program Trees†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 8 4. Hoarding in a Distributed Environment†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 9 5.Hoarding content for mobile learning†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 10 6. Mobile Clients Through Cooperative Hoarding†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 10 7. Comparative Discussion previous techniques†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 11 3. Problem Definition†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 11 4. New Approach Suggested 1. Zipf’s Law †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. 2 2. Object Hotspot Prediction Model†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢ € ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 13 5. Schedule of Work†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. 13 6. Conclusion†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 13 References†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦ 14 . Introduction Mobile devices are the computers which are having wireless communication capabilities to access global data services from any location while roaming. Now a day’s mobile devices are supporting applications such as multimedia, World Wide Web and other high profile applications which demands continuous connections and Mobile devices are lacking here. However, mobile devices with wireless communication are frequently disconnected from the network due to the cost of wireless communication or the unavailability of the wireless network.Disconnection period of mobile device from its network is called as offline period. Such offline periods may appear for different reasons – intentional (e. g. , the available connection is too expensive for the user) or unintentional (e. g. , lack of infrastructure at a given time and location). During offline periods the user can only access materials located on the device’s local memory. Mobile systems typically have a relatively small amount of memory, which is often not enough to store all the needed data for ongoing activities to continue.In such a case, a decision should be taken on which part of the data has to be cached. Often we cannot count on the user’s own judgement of what he/she will need and prefetch. Rather, in our opinion, some sort of automatic prefetching would be desirable. Uninterrupted operation in offline mode will be in high demand and the mobile computer systems should provide support for it. Seamless disconnection can be achieved by loading the files that a user will access in the future from the network to the local storage. This preparation process for disconnected operation is called hoarding.Few of the parameters which complicate the hoarding process are prediction of future access pattern of the user, handling of hoard miss, limited local hoard memory and unpredictable disconnections and reconnection, activities on hoarded object at other clients, the asymmetry of communications bandwidth in downstream and upstream. An important point is to measure the quality of the hoarding and to try to improve it continuously. An often used metric in the evaluation of caching proxies is the hit ratio. Hit ratio is calculated by dividing the number of by the total number of uploaded predictions.It is a good measure for hoarding systems, though a better measure is the miss ratio – a percentage of accesses for which the cache is ineffective. In this work we have given brief overview of the techniques proposed in earlier days and also given the idea for the new hoarding technique. 2. Related Work and Motivation Before the early 1990’s, there was little research on hoarding. Since then, however, interest has increased dramatically among research scientists and professors around the globe and many techniques have been developed. Here we have listed few of the techniques and also will discuss them in brief. Coda: The Pioneering System for Hoarding †¢ Hoarding Based on Data Mining Techniques ? SEER Hoarding System (inspired by clustering technique) ? Association Rule-Based Techniques ? Hoarding Based on Hyper Graph ? Probability Graph Based Technique †¢ Hoarding Techniques Based on Program Trees †¢ Hoarding in a Distributed Environment †¢ Hoarding content for mobile learning †¢ Mobile Clients Through Cooperative Hoarding 2. 1 Coda Coda is a distributed file system based on client–server architecture, where there are many clients and a comparatively smaller number of servers.It is the first system that enabled users to work in disconnected mode. The concept of hoarding was introduced by the Coda group as a means of enabling disconnected operation. Disconnections in Coda are assumed to occur involuntarily due to network failures or voluntarily due to the detachment of a mobile client from the network. Voluntary and involuntary disconnections are handled the same way. The cache manager of Coda, called Venus, is designed to work in disconnected mode by serving client requests from the cache when the mobile client is detached from the network.Requests to the files that are not in the cache during disconnection are reflected to the client as failures. The hoarding system of Coda lets users select the files that they will hopefully need in the future. This information is used to decide what to load to the local storage. For disconnected operation, files are loaded to the client local storage, because the master copies are kept at stationary servers, there is the notion of replication and how to manage locks on the local copies. When the disconnection is voluntary, Coda handles this case by obtaining exclusive locks to files.However in case of involuntary disconnection, the system should defer the conflicting lock requests for an object to the reconnection time, which may not be predictable. The cache management system of Coda, called Venus, diff ers from the previous ones in that it incorporates user profiles in addition to the recent reference history. Each workstation maintains a list of pathnames, called the hoard database. These pathnames specify objects of interest to the user at the workstation that maintains the hoard database. Users can modify the hoard database via scripts, which are called hoard profiles.Multiple hoard profiles can be defined by the same user and a combination of these profiles can be used to modify the hoard database. Venus provides the user with an option to specify two time points during which all file references will be recorded. Due to the limitations of the mobile cache space, users can also specify priorities to provide the hoarding system with hints about the importance of file objects. Precedence is given to high priority objects during hoarding where the priority of an object is a combination of the user specified priority and a parameter indicating how recently it was accessed.Venus per forms a hierarchical cache management, which means that a directory is not purged unless all the subdirectories are already purged. In summary, the Coda hoarding mechanism is based on a least recently used (LRU) policy plus the user specified profiles to update the hoard data-base, which is used for cache management. It relies on user intervention to determine what to hoard in addition to the objects already maintained by the cache management system. In that respect, it can be classified as semi-automated.Researchers developed more advanced techniques with the aim of minimizing the user intervention in determining the set of objects to be hoarded. These techniques will be discussed in the following sections. 2. 2 Hoarding based on Data mining Techniques Knowing the interested pattern from the large collection of data is the basis of data mining. In the earlier history of hoarding related works researchers have applied many different data mining techniques in this arena of mobile hoa rding. Mainly clustering and association rule mining techniques were adopted from data mining domain. . 2. 1 SEER Hoarding System To automate the hoarding process, author developed a hoarding system called SEER that can make hoarding decisions without user intervention. The basic idea in SEER is to organize users’ activities as projects in order to provide more accurate hoarding decisions. A distance measure needs to be defined in order to apply clustering algorithms to group related files. SEER uses the notion of semantic distance based on the file reference behaviour of the files for which semantic distance needs to be calculated.Once the semantic distance between pairs of files are calculated, a standard clustering algorithm is used to partition the files into clusters. The developers of SEER also employ some filters based on the file type and other conventions introduced by the specific file system they assumed. The basic architecture of the SEER predictive hoarding syste m is provided in figure 1. The observer monitors user behaviour (i. e. , which files are accessed at what time) and feeds the cleaned and formatted access paths to the correlator, which then generates the distances among files in terms of user access behaviour.The distances are called the semantic distance and they are fed to the cluster generator that groups the objects with respect to their distances. The aim of clustering is, given a set of objects and a similarity or distance matrix that describes the pairwise distances or similarities among a set of objects, to group the objects that are close to each other or similar to each other. Calculation of the distances between files is done by looking at the high-level file references, such as open or status inquiry, as opposed to individual reads and writes, which are claimed to obscure the process of distance calculation. pic] Figure 1. Architecture of the SEER Predictive Hoarding System The semantic distance between two file referen ces is based on the number of intervening references to other files in between these two file references. This definition is further enhanced by the notion of lifetime semantic distance. Lifetime semantic distance between an open file A and an open file B is the number of intervening file opens (including the open of B). If the file A is closed before B is opened, then the distance is defined to be zero.The lifetime semantic distance relates two references to different files; however it needs to be somehow converted to a distance measure between two files instead of file references. Geometric mean of the file references is calculated to obtain the distance between the two files. Keeping all pairwise distances takes a lot of space. Therefore, only the distances among the closest files are represented (closest is determined by a parameter K, K closest pairs for each file are considered). The developers of SEER used a variation of an agglomerative (i. e. bottom up) clustering algorithm called k nearest neighbour, which has a low time and space complexity. An agglomerative clustering algorithm first considers individual objects as clusters and tries to combine them to form larger clusters until all the objects are grouped into one single cluster. The algorithm they used is based on merging sub clusters into larger clusters if they share at least kn neighbours. If the two files share less than kn close files but more than kf, then the files in the clusters are replicated to form overlapping clusters instead of being merged.SEER works on top of a user level replication system such as Coda and leaves the hoarding process to the underlying file system after providing the hoard database. The files that are in the same project as the file that is currently in use are included to the set of files to be hoarded. During disconnected operation, hoard misses are calculated to give a feedback to the system. 2. 2. 2 Association Rule-Based Techniques Association rule overview: Let I=i1,i2†¦.. im be a set of literals, called items and D be a set of transactions, such that ?T ? D; T? I. A transaction T contains a set of items X if X? T. An association rule is denoted by an implication of the form X ? Y, where X? I, Y ? I, and X ? Y = NULL. A rule X ? Y is said to hold in the transaction set D with confidence c if c% of the transactions in D that contain X also contain Y. The rule X? Y has support sin the transaction set D if s% of transactions in D contains X? Y. The problem of mining association rules is to find all the association rules that have a support and a confidence greater than user-specified thresholds.The thresholds for confidence and support are called minconf and minsup respectively. In Association Rule Based Technique for hoarding, authors described an application independent and generic technique for determining what should be hoarded prior to disconnection. This method utilizes association rules that are extracted by data mining techni ques for determining the set of items that should be hoarded to a mobile computer prior to disconnection. The proposed method was implemented and tested on synthetic data to estimate its effectiveness.The process of automated hoarding via association rules can be summarized as follows: Step 1: Requests of the client in the current session are used through an inferencing mechanism to construct the candidate set prior to disconnection. Step 2: Candidate set is pruned to form the hoard set. Step 3: Hoard set is loaded to the client cache. The need to have separate steps for constructing the candidate set and the hoard set arises from the fact that users also move from one machine to another that may have lower resources.The construction of the hoard set must adapt to such potential changes. Construction of candidate set: An inferencing mechanism is used to construct the candidate set of data items that are of interest to the client to be disconnected. The candidate set of the client is constructed in two steps; 1. The inferencing mechanism finds the association rules whose heads (i. e. , left hand side) match with the client’s requests in the current session, 2. The tails (i. e. , right hand side) of the matching rules are collected into the candidate set.Construction of Hoard set: The client that issued the hoard request has limited re-sources. The storage resource is of particular importance for hoarding since we have a limited space to load the candidate set. Therefore, the candidate set obtained in the first phase of the hoarding set should shrink to the hoard set so that it fits the client cache. Each data item in the candidate set is associated with a priority. These priorities together with various heuristics must be incorporated for determining the hoard set. The data items are used to sort the rules in descending order of priorities.The hoard set is constructed out of the data items with the highest priority in the candidate set just enough to fil l the cache. 3. Hoarding Based on Hyper Graph Hyper graph based approach presents a kind of low-cost automatic data hoarding technology based on rules and hyper graph model. It first uses data mining technology to extract sequence relevance rules of data from the broadcasting history, and then formulates hyper graph model, sorting the data into clusters through hyper graph partitioning methods and sorting them topologically.Finally, according to the data invalid window and the current visit record, data in corresponding clusters will be collected. Hyper graph model: Hyper graph model is defined as H = (V, E) where V={v1 ,v2 ,†¦ ,vn } is the vertices collection of hyper graph, and E={e1 ,e2 ,†¦ ,em } is super-edge collection of hyper graph (there supposed to be m super-edges in total). Hyper graph is an extension of graph, in which each super-edge can be connected with two or more vertices. Super-edge is the collection of a group of vertices in hyper graph, and superedge ei = {vi1, vi2, †¦ inj} in which vi1,vi2 ,†¦ ,vin ? V . In this model, vertices collection V corresponds to the history of broadcast data, in which each point corresponds to a broadcast data item, and each super-edge corresponds to a sequence model. Sequence model shows the orders of data items. A sequence model in size K can be expressed as p = . Use of hyper graph in hoarding are discussed in paper in details. 4. Probability Graph Based Technique This paper proposed a low-cost automated hoarding for mobile computing.Advantage of this approach is it does not explore application specific heuristics, such as the directory structure or file extension. The property of application independence makes this algorithm applicable to any predicative caching system to address data hoarding. The most distinguished feature of this algorithm is that it uses probability graph to represent data relationships and to update it at the same time when user’s request is processed. Before d isconnection, the cluster algorithm divides data into groups.Then, those groups with the highest priority are selected into hoard set until the cache is filled up. Analysis shows that the overhead of this algorithm is much lower than previous algorithms. Probability Graph: An important parameter used to construct probability graph is look-ahead period. It is a fixed number of file references that defines what it means for one file to be opened ‘soon’ after another. In other words, for a specific file reference, only references within the look-ahead period are considered related. In fact, look-ahead period is an approximate method to avoid traversing the whole trace.Unlike constructing probability graph from local file systems, in the context of mobile data access, data set is dynamically collected from remote data requests. Thus, we implemented a variation of algorithm used to construct probability graph, as illustrated in Figure 2. [pic] Figure 2. Constructing the prob ability graph The basic idea is simple: If a reference to data object A follows the reference to data object B within the look-ahead period, then the weight of directed arc from B to A is added by one. The look-ahead period affects absolute weight of arcs.Larger look-ahead period produces more arcs and larger weight. A ’s dependency to B is represented by the ratio of weight of arc from B to A divided by the total weight of arcs leaving B. Clustering: Before constructing the final hoard set, data objects are clustered into groups based on dependency among data objects. The main objective of the clustering phase is to guarantee closely related data objects are partitioned into the same group. In the successive selecting phase, data objects are selected into hoard set at the unit of group. This design provides more continuity in user operation when disconnected.Selecting Groups: The following four kinds of heuristic information are applicable for calculating priority for a grou p: †¢ Total access time of all data objects; †¢ Average access time of data objects; †¢ Access time of the start data object; †¢ Average access time per byte. 2. Hoarding Techniques Based on Program Trees A hoarding tool based on program execution trees was developed by author running under OS/2 operating system. Their method is based on analyzing program executions to construct a profile for each program depending on the files the program accesses.They proposed a solution to the hoarding problem in case of informed disconnections: the user tells the mobile computer that there is an imminent disconnection to fill the cache intelligently so that the files that will be used in the future are already there in the cache when needed. [pic] Figure 3. Sample program Tree This hoarding mechanism lets the user make the hoarding decision. They present the hoarding options to the user through a graphical user interface and working sets of applications are captured automatic ally. The working sets are detected by logging the user file accesses at the background.During hoarding, this log is analyzed and trees that represent the program executions are constructed. A node denotes a file and a link from a parent to one of its child nodes tells us that either the child is opened by the parent or it is executed by the parent. Roots of the trees are the initial processes. Program trees are constructed for each execution of a program, which captures multiple contexts of executions of the same program. This has the advantage that the whole context is captured from different execution times of the program.Finally, hoarding is performed by taking the union of all the execution trees of a running program. A sample program tree is provided in Figure 3. Due to the storage limitations of mobile computers, the number of trees that can be stored for a program is limited to 15 LRU program trees. Hoarding through program trees can be thought of as a generalization of a pr o-gram execution by looking at the past behaviour. The hoarding mechanism is enhanced by letting the user rule out the data files. Data files are automatically detected using three complementary heuristics: 1.Looking at the filename extensions and observing the filename conventions in OS/2, files can be distinguished as executable, batch files, or data files. 2. Directory inferencing is used as a spatial locality heuristic. The files that differ in the top level directory in their pathnames from the running program are assumed to be data files, but the programs in the same top level directory are assumed to be part of the same program. 3. Modification times of the files are used as the final heuristic to deter-mine the type of a file. Data files are assumed to be modified more recently and frequently than the executables.They devised a parametric model for evaluation, which is based on recency and frequency. 3. Hoarding in a Distributed Environment Another hoarding mechanism, which was presented for specific application in distributed system, assumes a specific architecture, such as infostations where mobile users are connected to the network via wireless local area networks (LANs) that offer a high bandwidth, which is a cheaper option compared to wireless wide area networks (WANs). The hoarding process is handed over to the infostations in that model and it is assumed that what the user wants to access is location-dependent.Hoarding is proposed to fill the gap between the capacity and cost trade-off between wireless WANS and wireless LANs. The infestations do the hoarding and when a request is not found in the infostation, then WAN will be used to get the data item. The hoarding decision is based on the user access patterns coupled with that user’s location information. Items frequently accessed by mobile users are recorded together with spatial information (i. e. , where they were accessed). A region is divided into hoarding areas and each infostation is responsible with one hoarding area. 4. Hoarding content for mobile learningHoarding in the learning context is the process for automatically choosing what part of the overall learning content should be prepared and made available for the next offline period of a learner equipped with a mobile device. We can split the hoarding process into few steps that we will discuss further in more details: 1. Predict the entry point of the current user for his/her next offline learning session. We call it the ‘starting point’. 2. Create a ‘candidate for caching’ set. This set should contain related documents (objects) that the user might access from the starting point we have selected. 3.Prune the set – the objects that probably will not be needed by the user should be excluded from the candidate set, thus making it smaller. This should be done based on user behaviour observations and domain knowledge. 4. Find the priority to all objects still in the hoarding set after pruning. Using all the knowledge available about the user and the current learning domain, every object left in the hoarding set should be assigned a priority value. The priority should mean how important the object is for the next user session and should be higher if we suppose that there is a higher probability that an object will be used sooner. . Sort the objects based on their priority, and produce an ordered list of objects. 6. Cache, starting from the beginning of the list (thus putting in the device cache those objects with higher priority) and continue with the ones with smaller weights until available memory is filled in. 5. Mobile Clients Through Cooperative Hoarding Recent research has shown that mobile users often move in groups. Cooperative hoarding takes advantage of the fact that even when disconnected from the network, clients may still be able to communicate with each other in ad-hoc mode.By performing hoarding cooperatively, clients can share their hoar d content during disconnections to achieve higher data accessibility and reduce the risk of critical cache misses. Two cooperative hoarding schemes, GGH and CAP, have been proposed. GGH improves hoard performance by al-lowing clients to take advantage of what their peers have hoarded when making their own hoarding decisions. On the other hand, CAP selects the best client in the group to Hoard each object to maximise the number of unique objects hoarded and minimise access cost. Simulation results show that compare to existing schemes.Details of GGH and CAP are given in paper. 2. 7 Comparative Discussion previous techniques The hoarding techniques discussed above vary depending on the target system and it is difficult to make an objective comparative evaluation of their effectiveness. We can classify the hoarding techniques as being auto-mated or not. In that respect, being the initial hoarding system, Coda is semiautomated and it needs human intervention for the hoarding decision. T he rest of the hoarding techniques discussed are fully automated; how-ever, user supervision is always desirable to give a final touch to the files to be hoarded.Among the automated hoarding techniques, SEER and program tree-based ones assume a specific operating system and use semantic information about the files, such as the naming conventions, or file reference types and so on to construct the hoard set. However, the ones based on association rule mining and infostation environment do not make any operating system specific assumptions. Therefore, they can be used in generic systems. Coda handles both voluntary and involuntary disconnections well.The infostation-based hoarding approach is also inherently designed for involuntary disconnections, because hoarding is done during the user passing in the range of the infostation area. However, the time of disconnection can be predicted with a certain error bound by considering the direction and the speed of the moving client predicting when the user will go out of range. The program tree-based methods are specifically designed for previously informed disconnections. The scenario assumed in the case of infostations is a distributed wire-less infrastructure, which makes it unique among the hoarding mechanisms.This case is especially important in today’s world where peer-to-peer systems are becoming more and more popular. 3. Problem Definition The New Technique that we have planned to design for hoarding will be used on Mobile Network. Goals that we have set are a. Finding a solution having optimal hit ratio in the hoard at local node. b. Technique should not have greater time complexity because we don’t have much time for performing hoarding operation after the knowledge of disconnection. c. Optimal utilization of hoard memory. d. Support for both intentional and unintentional disconnection. e.Proper handling of conflicts in hoarded objects upon reconnection. However, our priority will be for hit rati o than the other goals that we have set. We will take certain assumptions about for other issues if we find any scope of improvement in hit ratio. 4. New Approach 4. 1 Zipf’s Law It is a mathematical tool to describe the relationship between words in a text and their frequencies. Considering a long text and assigning ranks to all words by the frequencies in this text, the occurrence probability P (i) of the word with rank i satisfies the formula below, which is known as Zipf first law, where C is a constant.P (i) = [pic] †¦. (1) This formula is further extended into a more generalized form, known as Zipf-like law. P (i) = [pic]†¦. (2) Obviously, [pic]†¦. (3) Now According to (2) and (3), we have C[pic] [pic] Our work is to dynamically calculate for different streams and then according to above Formula (2) and (4), the hotspot can be predicted based on the ranking of an object. 4. 2 Object Hotspot Prediction Model 4. 2. 1 Hotspot Classification We classify hotsp ot into two categories: â€Å"permanent hotspot† and â€Å"stage hotspot†. Permanent hotspot is an object which is frequently accessed regularly.Stage hotspot can be further divided into two types: â€Å"cyclical hotspot† and â€Å"sudden hotspot†. Cyclical hotspot is an object which becomes popular periodically. If an object is considered as a focus suddenly, it is a sudden hotspot. 4. 2. 2. Hotspot Identification Hotspots in distributed stream-processing storage systems can be identified via a ranking policy (sorted by access frequencies of objects). In our design, the hotspot objects will be inserted into a hotspot queue. The maximum queue length is determined by the cache size and the average size of hotspot Objects.If an object’s rank is smaller than the maximum hotspot queue length (in this case, the rank is high), it will be considered as â€Å"hotspot† in our system. Otherwise it will be considered as â€Å"non hotspot†. And t he objects in the queue will be handled by hotspot cache strategy. 4. 2. 3 Hotspot Prediction This is our main section of interest, here we will try to determine the prediction model for hoard content with optimal hoard hit ratio. 5. Schedule of Work |Work |Scheduled Period |Remarks | |Studying revious work on Hoarding |July – Aug 2012 |Complete | |Identifying Problem |Sept 2012 |Complete | |Innovating New Approach |Oct 2012 |Ongoing | |Integrating with Mobile Arena as solution to Hoarding |Nov- Dec 2012 |- | |Simulation And Testing |Jan 2013 |- | |Optimization |Feb 2013 |- | |Simulation And Testing |Mar 2013 |- | |Writing Thesis Work / Journal Publication |Apr –May 2013 |- | 6. Conclusion In this literature survey we have discussed previous related work on hoarding. We have also given the requirements for the new technique that is planned to be design.Also we are suggesting a new approach that is coming under the category of Hoarding with Data Mining Techniques. Recen t studies have shown that the use of proposed technique i. e. Zipfs-Like law for caching over the web contents have improved the hit ratio to a greater extent. Here with this work we are expecting improvements in hit ratio of the local hoard. References [1]. James J. Kistler and Mahadev Satyanarayanan. Disconnected Operation in the Coda File System. ACM Transactions on Computer Systems, vol. 10, no. 1, pp. 3–25, 1992. [2]. Mahadev Satyanarayanan. The Evolution of Coda. ACM Transactions on Computer Systems, vol. 20, no. 2, pp. 85–124, 2002 [3]. Geoffrey H. Kuenning and Gerald J. Popek. Automated Hoarding for Mobile Computers.In Proceedings of the 16th ACM Symposium on Operating System Principles (SOSP 1997), October 5–8, St. Malo, France, pp. 264–275, 1997. [4]. Yucel Saygin, Ozgur Ulusoy, and Ahmed K. Elmagarmid. Association Rules for Supporting Hoarding in Mobile Computing Environments. In Proceedings of the 10th IEEE Workshop on Research Issues in Data Engineering (RIDE 2000), February 28–29, San Diego, pp. 71–78, 2000. [5]. Rakesh Agrawal and Ramakrishna Srikant, Fast Algorithms for Mining Association Rules. In Proceedings of the 20th International Conference on Very Large Databases, Chile, 1994. [6]. GUO Peng, Hu Hui, Liu Cheng. The Research of Automatic Data Hoarding Technique Based on Hyper Graph.Information Science and Engineering (ICISE), 1st International Conference, 2009. [7]. Huan Zhou, Yulin Feng, Jing Li. Probability graph based data hoarding for mobile environment. Presented at Information & Software Technology, pp. 35-41, 2003. [8]. Carl Tait, Hui Lei, Swarup Acharya, and Henry Chang. Intelligent File Hoarding for Mobile Computers. In Proceedings of the 1st Annual International Conference on Mobile Computing and Networking (MOBICOM’95), Berkeley, CA, 1995. [9]. Anna Trifonova and Marco Ronchetti. Hoarding content for mobile learning. Journal International Journal of Mobile Communications archive V olume 4 Issue 4, Pages 459-476, 2006. [10]. Kwong Yuen Lai, Zahir Tari, Peter Bertok.Improving Data Accessibility for Mobile Clients through Cooperative Hoarding. Data Engineering, ICDE proceedings 21st international Conference 2005. [11]. G. Zipf, Human Behavior and the Principle of Least Effort. Addison-Wesley, 1949. [12]. Chentao Wu, Xubin He, Shenggang Wan, Qiang Cao and Changsheng Xie. Hotspot Prediction and Cache in Distributed Stream-processing Storage Systems. Performance Computing and Communications Conference (IPCCC) IEEE 28th International, 2009. [13]. Lei Shi, Zhimin Gu, Lin Wei and Yun Shi. An Applicative Study of Zipf’s Law on Web Cache International Journal of Information Technology Vol. 12 No. 4 2006. [14]. Web link: http://en. wikipedia. org/wiki/Zipf%27s_law

Diversity of teachers and learners an asset for learning Essay

Diversity of teachers and learners an asset for learning - Essay Example My teacher would read to us about Santa, we would make Christmas crafts, and we would do a Christmas play. She didn’t teach us about any of the other traditions – only Christmas. Well, this year I had a friend in my class - Yusof. He didn’t celebrate Christmas. He had different traditions and beliefs and instead celebrated other holidays including a big one called Eid Al-Fitr. I remember him crying one class when the teacher made us go around the class and brag about what awesome presents we were getting under the tree that year. When it came to Yusof, despite her knowledge of his religious background, she asked him the same question. He didn’t have an answer and his eyes filled up. He felt isolated and his diversity in tradition and beliefs was never recognized. What I would do and what my teacher did the next year was to talk in general about all of the wonderful traditions that were celebrated in our classroom. We learned more that year about culture and traditions of the world than I have in any other class since. Everyone felt included and our class was able to partake in meaningful learning that applied outside of the classroom. We realized that the world was much more diverse when it came to holidays than we thought and we had a lot of fun doing it. As a teacher I am going to try my best to embrace diversity in every way. This is just one experience but there is diversity in the way we learn, our socio-economic backgrounds, our ethnic backgrounds – The list can go on forever. I am going to give it my all to teach to the individual while creating a cohesive classroom where nobody feels left out and everyone feels like a little part of them as become a process of group learning in a positive way. Diversity is what makes life interesting and I plan to continue to emphasize this in each lesson plan I create! I plan to connect, not disconnect, ALL of my student’s to the

The Negative Effects of Climate Change on Food Security in the Essay - 2

The Negative Effects of Climate Change on Food Security in the Caribbean - Essay Example Acid rain can take away important minerals from trees, plants, and soil (Smith et al., 2002). Without the presence of minerals in the soil, the plants and trees will not be able to grow properly. Based on this context, food security is being threatened because of insufficient supply of foods. Acid rain can cause serious harm to the plants and trees in the forest. In the absence of plants and trees in the forest, climate change such as the increase in the frequency and strength of extreme weather events like droughts, El Niňos, cyclones, heat waves, floods and king tides will become unavoidable (Choi, 2012). Similar to the negative impact of acid rain in the food security of the Caribbean, the presence of droughts, El Niňos, cyclones, heat waves, floods and king tides will also trigger a significant decrease in the country’s available food supply. Climate change is something that is uncontrollable by the humankind. For this reason, the only way to protect the food security of the Caribbean is to encourage its government to import and stock up at least three (3) to six (6) months supply of non-perishable food items. By doing so, the government of the Caribbean can ensure that there will always be food security for its people.