Thursday, November 28, 2019
Scholarship Essay Essays (333 words) - , Term Papers
Scholarship Essay Upon graduating from Frewsburg Central High School, I plan to attend Edinboro University majoring in Computer management information systems. Several factors have led to my decision to pursue a career in the computer field. From the time I was born, I had a debilitating disease known as Juvenile Rheumatoid Arthritis. This disease limits what simple tasks I am physically able to accomplish. I think not being able to participate in normal activities like most children I developed a desire to learn everything I could about new things. Then in first grade, I was introduced to my first computer program, Logo Writer. I automatically wanted to learn everything I could about these fascinating machines. As I grew older I encountered several other models and numerous other programs, all this exposure led me to believe that computers should be a part of my future career. As I went through junior and senior high computers intrigued me even more, and all I wanted to do was figure out how everythi ng worked together both hardware and software. My fascination with computers and my limitation due to my disability has led me to the computer field of study and to Edinboro University. I have been a member of the National Honors Society for three years and have been Vice President for a year. Among my other organizations I am a member of are Student Council and the Frewsburg Leo Club both for four years. Last year I was awarded the Triple ?C? Award, which is given by the State of New York for student who exemplify Courage, Character, and Community Involvement. The scholarship would tremendously help me out financially. I will attend Edinboro University without this scholarship because nothing will hold me back from my dreams and aspirations. However, receiving it would lift a little of the burden of tuition, books, and other costs off my shoulders so I could give that much more attention to achieving my goal of becoming a Computer Systems Analyst. Acceptance Essays
Sunday, November 24, 2019
Where My Mind Wanders essays
Where My Mind Wanders essays My car slows as it approaches a stoplight. I take this opportunity to allow my mind to become engulfed with my surroundings: the bright, fierce red of the traffic light, the brilliant blue sky with its specs clouds, and the mass of hurried people. The four corners of the intersection are filled with people who are preoccupied with their fast-paced lives to notice the little things, such as animals and anxious cars awaiting the traffic light. My thoughts vigorously put all of the information that my mind has gathered from the intersection to order. My mind eagerly involves itself by engulfing my surroundings and giving them some meaning. The office workers, too busy to pay attention to the life that surrounds them catch my eyes first. They seem to be apathetic robots, preprogrammed to start at one location and go to another. They pay little attention to any detail unless it has to do with them. I chuckle at the automated behavior of these robots. Once my mind has come to a conclusion about the workers, this small little squirrel sitting on its hind legs eating a juicy apple catches my attention. Sitting there on the corner of the intersection, minding its own business, the squirrel devours the apple. Once done with its apple the squirrel throws the core to the side and remains there for a bit. My mind temporarily comes out of its lapse and I wonder if this squirrel is analyzing me, too. The neck of the squirrel seems to be a second hand on a clock, tick tock. It moves a bit then stops, then again moves continuing until it can no longer move its neck. A 1996 Ford Mustang next to my car revs the engine, and my mind loses interest in the squirrel and moves to the cars next to and opposite of me. There are two cars, a BMW, and an old pickup truck; the name is not visible. You can see the eagerness of each car; the impatience in these cars is more than of child the day before their birthday. These cars remain perpendicular to the lan...
Thursday, November 21, 2019
Financial Reporting Essay Example | Topics and Well Written Essays - 1500 words
Financial Reporting - Essay Example InterContinental Hotels primarily listed on the London Stock exchange and is a constituent of the FTSE 100 Index. It is secondary listed on the New York Stock Exchange. This report is concerned with the performance of the group in the last Accounting period, 2011. Therefore, performance analysis will compare results of 2011 with those of 2010. We will also analyze segmental information of the group by comparing 2011 results with 2010 results. 1.1 Analysis of segmental information In this analysis, we will consider two segments in IHG which are the business segments and the geographical segments (IHG Annual report 2011). Geographical segments include America, Europe, great China, AMEA and Central. You find that IHGs target market is in the developed markets and the emerging markets. As discussed earlier, IHG has four business segments with regard to the percentage of control. They include the Franchised, managed, owned and leased and central parts of the business. a. Revenue Looking a t the 2011 financial report, there was no significant change in the percentage of revenue contribution by each business segment. The percentage of revenue contribution by franchised part of the business remained the same at 34% compared with year 2010. For the owned and leased segment, revenue contribution dropped by 2% from 35% in 2010 to 33% in 2011. ... From the analysis, most of the revenue has been realized in the franchised part of the business. This is because it majority of the hotels have been franchised, approximately over 3900 hotels. In the case of the geographical segments, the percentage of revenue contribution in America dropped by 3%, that is from 50 percent in 2010 to 47 percent in 2011. In Europe, there was 23 percent revenue contribution and 20 percent in 2010. There was no significant change in AMEA as the percentage of revenue contribution declined by one percent. In 2011, there was 12% revenue contribution and 13% in 2010. The same applies to china as there was little change in revenue contribution percentage. It only increased by 1%, from 11 percent in 2010 to 12 percent in 2011. The central region experienced no changes at all in revenue contribution percentage as it remained at 6 percent. America contributes the highest percentage of revenue since it has over 3.4 million rooms of which 1.3 million rooms are bra nded and has a market share of 64% in the industry. Other geographical segments such as AMEA lie in between. China on the contrary indicates growth opportunity as it seems that the market has not been fully tapped, and no barriers of entry exist (IHG Annual report 2011). In addition, there is a strong economic growth in china. b. Operating profits margin including Exceptional Items In the geographical segment, America has the best performance, then AMEA and Great China. Europe has the lowest performance with regard to operating profits including exceptional items. In 2011, , there was a 9%, 10% and 3% increase in operating profit including exceptional items in America, AMEA and Great China respectively. Europe experienced a drop by 6% that is from 22 percent in 2010 to 16 percent in
Wednesday, November 20, 2019
Career planing Assignment Example | Topics and Well Written Essays - 250 words
Career planing - Assignment Example cognizes the trends in the modern changing world in terms information and technology by constantly researching and acquainting myself with current concepts in mathematics, assessing, evaluating, and applying new strategies and techniques in an appropriate and continuous manner. I want to be involved in action research that will help test the effectiveness of class-specific strategies in order to be used to improve the performance of my students and others by availing them for adoption by other teachers. As a teacher, I will put my skills to task in finding and developing better learning resources and also share them with other educationists in the community. For the fact that my goals revolve around facilitating learning in young people, I will practice a learning lifestyle, share my personal learning with other educationists, and model lifelong learning. In this form of lifestyle, I will be able to gain self-actualization and fulfillment through continuous achievement of performance targets. It will also give me great pleasure to realize that the future is guaranteed of good leadership as a consequence of my
Monday, November 18, 2019
Law Essay Example | Topics and Well Written Essays - 500 words - 2
Law - Essay Example The legal relationship creates rights and obligations between the parties and binds only between those who are privy to the contract, and not other people who are not parties (often described in law books as â€Å"strangers†or by the misnomer â€Å"third parties†) even though those people may be affected by the contract directly or indirectly (p. 15). Usually the agreement will contain a promise or set of promises that each party has made to the other: this is known as bilateral contract because each party promises to do something. For example, X promises to build a house for Y and Y promises to pay X for doing so. Sometimes only one party will make a promise to do something if the other party actually does something stipulated by the former1. For example, X promises to pay $100 if Y completes and returns a marketing questionnaire to X. Such a contract is known as a unilateral contract because the promise is one-sided. Although X promised to pay in the stipulated circumstances, Y is under no obligation to complete and return the marketing questionnaire but if he does the court or arbitral tribunal will recognize a binding agreement that X will pay him $1002. In building projects daring negotiations for the award of a formal contract one sometimes finds so-called letters of intent expressed in terms such as these: â€Å"Please pr oceed with the works and if no formal contract is concluded we will pay you your costs and expenses that you have incurred†(Richard & Stone 2005, p. 115). It is often not appreciated that a letter in such terms can create a unilateral contract which the court will enforce, albeit not the formal contract which the parties had hoped to finalize. And although one often talks of a â€Å"written†or â€Å"formal†contract it is not really the piece of paper which itself is the contract – the piece of paper merely records what the terms of the contract are
Friday, November 15, 2019
Automatic Metadata Harvesting From Digital Content
Automatic Metadata Harvesting From Digital Content MR. RUSHABH D. DOSHI, MR. GIRISH H MULCHANDANI Abstract: Metadata Extraction is one of the predominant research fields in information retrieval. Metadata is used to references information resources. Most metadata extraction systems are still human intensive since they require expert decision to recognize relevant metadata but this is time consuming. However automatic metadata extraction techniques are developed but mostly works with structured format. We proposed a new approach to harvesting metadata from document using NLP. As NLP stands for Natural Language Processing work on natural language that human used in day today life. Keywords: Metadata, Extraction, NLP, Grammars I. Introduction Metadata is data that describes another data Metadata describes an information resource, or helps provide access to an information resource. A collection of such metadata elements may describe one or many information resources. For example, a library catalogue record is a collection of metadata elements, linked to the book or other item in the library collection through the call number. Information stored in the META field of an HTML Web page is metadata, associated with the information resource by being embedded within it. The key purpose of metadata is to facilitate and improve the retrieval of information. At library, college, Metadata can be used to achieve this by identifying the different characteristics of the information resource: the author, subject, title, publisher and so on. Various metadata harvesting techniques is developed to extract the data from digital libraries. NLP is a field of computer science, artificial intelligence and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human computer interaction. Recent research has increasingly focused on unsupervised andsemi-supervisedlearning algorithms. Such algorithms are able to learn from data that has not beenhand-annotatedwith the desired answers, or using a combination of annotated andnon-annotateddata. The goal of NLP evaluation is to measure one or more qualities of an algorithm or a system, in order to determine whether (or to what extent) the system answers the goals of its designers, or meets the needs of its users. II. Method In this paper we proposed automatic metadata harvesting algorithm using natural language (i.e. humans used in day today works). Our technique is rule based. So it does not require any training dataset for it. We harvest metadata based on English Grammar Terms. We identify the possible set of metadata then calculate their frequency then applying weight term based on their position or format that apply to it. The rest of the paper is organized as follows. The next section review some related work regarding to metadata harvesting from digital content. Section gives the detailed description of proposed idea presented here. At last paper is concluded with summary. III. Related Work Existing Metadata harvesting techniques are either machine learning method or ruled based methods. . In machine learning method set of predefined template that contains dataset are given to machine to train machine. Then machine is used to harvest metadata from document based on that dataset. While in rule based method most of techniques set ruled that are used to harvest metadata from documents. In machine learning approach extracted keywords are given to the machine from training documents to learn specific models then that model are applied to new documents to extract keyword from them.Many techniques used machine learning approach such as automatic document metadata extraction using support vector machine . In rule based techniques some predefined rules are given to machine based on that machine harvest metadata from documents. Positions of word in document, specific keyword are used as category of document and etc. are examples rules that are set in various metadata harvest techniques. In some case Metadata classification is based on document types (e.g. purchase order, sales report etc.) and data context (e.g. customer name, order date etc.) [1]. Other statistical methods include word frequency [2], TF*IDF [3], wordco-occurrences[4]. Later on some techniques are used to harvest key phrase based on TF*PDF [5]. Other techniques use TDT (Topic Detection and Tracking) with aging theory to harvest metadata from news website [6]. Some techniques used DDC/RDF editor to define and harvest metadata from document and validate by thirds parties [7]. Several models are developed to harvest metadata from corpus. Now days most of techniques used models that all are depends on corpus. IV. Proposed Theory Our approach focused on harvesting a metadata from document based on English grammar. English grammar has many categories which categorized the word in statement. Grammar categories such as NOUN,VERB, ADJECTIVES, ADVERB, NOUN PHRASE, VERB PHRASE etc. each and every grammar category has a priority in statement. So our approaches to extract out the Metadata extraction based on its priority in grammar. Priority in grammar component is as follows: noun, verb, adjective, adverb, noun phrase V. Proposed Idea Figure-1 Proposed System Architecture Infigure-1we give proposed system architecture. In this architecture we does not stick steps in any order. ArticlePre-processing: articlepre-processingwhich remove irrelevant contents (i.e. tags,header-footerdetails etc.) from documents. POS Taggers: APart-Of-SpeechTagger (POS Tagger) is a piece of software that reads text in some languages and assigns parts of speech to each word (and other token), such as noun, verb, adjective, etc. Stemming: In most cases, morphological variants of words have similar semantic interpretations can be considered as equivalent for the purpose of IR applications. For this reason, a number ofso-calledstemming Algorithms, or stemmers, have been developed, which attempt to reduce a word to its stem or root form. Calculate frequency: Here each termed frequency is calculated i.e. how many occurrence of each term in document. Identify Suitable Metadata: Now metadata is extracted from word set based on their frequency, grammar and their positions. VI. Experiments Results In this study we take a corpus with 100 documents. Documents contain the news article about various categories. Here we first extract the metadata manually from each every documents. Then apply our idea to corpus. We measure our result from following parameter. Precision = No of terms identified correctly by the system / Top N terms out of total terms generated by the system. Recall = Number of keyterms identified correctly by the system / Number of keyterms identified by the authors.F-measure=F=2* ((precision* recall)/ ( precision+ recall)) Table1: Evaluation Results VII. Conclusion Future Works This method based on grammar component Our Aim to use this algorithm to identifying metadata in bigram, trigram tetra gram. This metadata helps us to generate summary of documents. References: [1] Christopher D. Manning, Prabhakar, Raghavan, Hinrich Schtze An Introduction to Information Retrieval book. [2] H. P. Luhn. A Statistical Approach to Mechanized Encoding and Searching of Literary Information. IBM Journal of Research and Development, 1957, 1(4):309-317. [3] G. Salton, C. S. Yang, C. T. Yu. A Theory of Term Importance in Automatic Text Analysis, Journal of the C.Zhang et al American society for Information Science, 1975, 26(1):33-44. [4] Y. Matsuo, M. Ishizuka. Keyword Extraction from a Single Document Using WordCo-ocuurrenceStatistical Information. International Journal on Artificial Intelligence Tools, 2004, 13(1):157-169. [5] Yan Gao Jin Liu, Peixun Ma The HOT keyphrase Extraction based on TF*PDF, IEEE conference, 2011. [6] Canhui Wang, Min Zhang, Liyun Ru, Shaoping Ma An Automatic Online News Topic Keyphrase Extraction System,IEEE conference, 2006. [7] Nor Adnan Yahaya, Rosiza Buang Automated Metadata Extraction from web sources, IEEE conference, 2006. [8] Somchai Chatvienchai Automatic metadata extraction classi_cation of spreadsheet Documents based on layout similarities, IEEE conference, 2005. [9] Dr. Jyoti Pareek, Sonal Jain KeyPhrase Extraction tool (KET) for semantic metadata annotation of Learning Materials, IEEE conference, 2009. [10] Wan Malini Wan Isa, Jamaliah Abdul Hamid, Hamidah Ibrahim, Rusli Abdullah, Mohd. Hasan Selamat, Muhamad Tau_k Abdullah and Nurul Amelina Nasharuddin Metadata Extraction with Cue Model. [11] Zhixin Guo, Hai Jin ARule-basedFramework of Metadata Extraction from Scienti_c Papers, IEEE conference. [12] Ernesto Giralt Hernndez, Joan Marc Piulachs Application of the Dublin Core format for automatic metadata generation and extraction,DC-2005:Proc. International Conference. on Dublin Core and Metadata Applications. [13] Canhui Wang, Min Zhang, Liyun Ru, Shaoping Ma An Automatic Online News Topic Keyphrase Extraction System, IEEE conference. [14] Srinivas Vadrevu, Saravanakumar Nagarajan, Fatih Gelgi, Hasan Davulcu Automated Metadata and Instance Extraction from News Web Sites,IEEE conference.
Wednesday, November 13, 2019
Writing Personal Statements :: College Admissions Essays
Writing Personal Statements   As colleges and universities diminish their reliance on LSAT and GPA numbers in the selection of students to admit, narrative submissions become more significant. The personal statement is the primary way you can make sure the people on the admissions committee are familiar with who you are -- not merely what you have accomplished. Remember that it is an essay you are preparing that should be interesting and revealing about you.     Below are some suggestions you may find useful as you prepare your personal statement. Describing one's self is never an easy endeavor. Do not become frustrated if your first draft (and you should have more than a couple) is less than satisfactory. Be sure and proofread your statement multiple times and have someone else proofread it as well. It is also a very good idea to read it aloud. After the second or third draft, set it aside for a few days and then return to it after the initial efforts.     Certainly you want a polished product: correct grammar, punctuation, diction, and spelling are vital.  In addition you should present the statement in a double spaced format with sufficient margins. The length should be no more than is specified by the school's instructions. If there are no instructions you should write no more than two or three pages (at most). Specificity, accuracy and truthfulness are essential.  Write no more than two pages. Put your name on each page.     Beyond these general observations you should avoid:                  clichà ©s            overuse of thesaurus            use of third person to refer to yourself            a title to your statement            conclusions regarding your abilities or potential            self aggrandizement            whining (i.e., why you got a C in literature 201)            making the statement a resume            procrastination in its preparation            gimmicks such as poetry, quotations, etc.            vague or obscure references            pretentious phrases            ostentatious vocabulary            appearing cynical
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