The Great Gatsby by Fitzgeralds Essay Example For Students

The Great Gatsby by Fitzgeralds Essay In F. Scott Fitzgeralds book The Great Gatsby the narrator, Nick Caraway makes a statement that, in my opinion, reveals to the reader why the book is called The Great Gatsby. Theyre a rotten crowd Nick shouted across the lawn to Gatsby Youre worth the whole damn lot put together. I agree with Nicks statement because Gatsby has outstanding values that differ and set him apart from the other characters in the book. Also the one main feature that really sets him apart from the crowd and makes him worth more than the whole crowd is his ability to set himself goals and achieve them. He had a dream, An American Dream. We will write a custom essay on The Great Gatsby by Fitzgeralds specifically for you for only $16.38 $13.9/page Order now Throughout the book Gatsby is portrayed as a mysterious character. The first impressions of Gatsby, which are conveyed to the reader, are through gossip. This is mainly due to Gatsbys inaccessibility. He surrounds himself with people but never interacts, instead opting to watch other people enjoy themselves. The rumours about Gatsby are abundant and come from the very people who take advantage of his hospitality. Jordan and the two twins, tell Nick at one of Gatsbys parties Somebody told me that he killed a man. There are also disputes about which country he fought for in the war. As one man tells Nick I heard that from a man who knew all about him, grew up with him in Germany. This is opposed by another woman at the party saying, It couldnt be that because he was in the American army during the war. Gatsbys wealth which is also a mystery to begin with, adds to his mysterious reputation. Despite these rumours circulating about Gatsby our first real impression of Gatsby is a positive one, He smiled understandingly- much more than understandingly. It was one of those rare smiles with a quality of eternal reassurance in it, that you may come across four or five times in life. This first impression of Gatsby is important as it shows that he knows how to work with people although he rarely associates with them. Later on in the book we learn that his real name is James Gatz, he was born in North Dakota to a poor family. Whilst serving in the army in World War 1 he met Daisy Fay (now Daisy Buchanan) and fell in love with her. He worked briefly with a millionaire, and became acquainted with the people and customs of high society. This and his love for Daisy inspired Gatsby to devote his life to the acquisition of wealth. For Daisy he would do anything, and he began making his fortune through illegal activities. Daisy, Tom and Jordan are all part of the rotten crowd because they all have much the same questionable values. Daisy for example is selfish; she has an affair with Gatsby whilst married to Tom. She is careless for instance; running over and killing Myrtle Wilson while driving Gatsbys car. She is immature as she doesnt seem to know how to act her age and uses her frail character as an excuse. Finally she is materialistic in that she married Tom Buchannan because he had money even though she promised Gatsby her heart. Jordan is much the same as Daisy as she too is selfish; she left a borrowed car out in the rain and then lied about it. She is a hypocrite; told Nick that she hates careless people when her best friends are the most careless people around. She is materialistic; always wanted to be in the wealthy class but is never accepted by that society. And in Nicks words incurably dishonest, as she lies about nearly everything and most notably she cheated to win her first pro golf tournament. Also Daisy and Jordan represent the high-class/middle-high-class social group. Tom is much the same as Daisy and Jordan as he too is selfish; he had an affair with Myrtle whilst being married to Daisy. .u535a59f2806b75481d8b6371a7738b97 , .u535a59f2806b75481d8b6371a7738b97 .postImageUrl , .u535a59f2806b75481d8b6371a7738b97 .centered-text-area { min-height: 80px; position: relative; } .u535a59f2806b75481d8b6371a7738b97 , .u535a59f2806b75481d8b6371a7738b97:hover , .u535a59f2806b75481d8b6371a7738b97:visited , .u535a59f2806b75481d8b6371a7738b97:active { border:0!important; } .u535a59f2806b75481d8b6371a7738b97 .clearfix:after { content: ""; display: table; clear: both; } .u535a59f2806b75481d8b6371a7738b97 { display: block; transition: background-color 250ms; webkit-transition: background-color 250ms; width: 100%; opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #95A5A6; } .u535a59f2806b75481d8b6371a7738b97:active , .u535a59f2806b75481d8b6371a7738b97:hover { opacity: 1; transition: opacity 250ms; webkit-transition: opacity 250ms; background-color: #2C3E50; } .u535a59f2806b75481d8b6371a7738b97 .centered-text-area { width: 100%; position: relative ; } .u535a59f2806b75481d8b6371a7738b97 .ctaText { border-bottom: 0 solid #fff; color: #2980B9; font-size: 16px; font-weight: bold; margin: 0; padding: 0; text-decoration: underline; } .u535a59f2806b75481d8b6371a7738b97 .postTitle { color: #FFFFFF; font-size: 16px; font-weight: 600; margin: 0; padding: 0; width: 100%; } .u535a59f2806b75481d8b6371a7738b97 .ctaButton { background-color: #7F8C8D!important; color: #2980B9; border: none; border-radius: 3px; box-shadow: none; font-size: 14px; font-weight: bold; line-height: 26px; moz-border-radius: 3px; text-align: center; text-decoration: none; text-shadow: none; width: 80px; min-height: 80px; background: url(https://artscolumbia.org/wp-content/plugins/intelly-related-posts/assets/images/simple-arrow.png)no-repeat; position: absolute; right: 0; top: 0; } .u535a59f2806b75481d8b6371a7738b97:hover .ctaButton { background-color: #34495E!important; } .u535a59f2806b75481d8b6371a7738b97 .centered-text { display: table; height: 80px; padding-left : 18px; top: 0; } .u535a59f2806b75481d8b6371a7738b97 .u535a59f2806b75481d8b6371a7738b97-content { display: table-cell; margin: 0; padding: 0; padding-right: 108px; position: relative; vertical-align: middle; width: 100%; } .u535a59f2806b75481d8b6371a7738b97:after { content: ""; display: block; clear: both; } READ: Great gatsby 4 EssayHe is cruel; to Daisy all the time and he broke Myrtles nose because she disagreed with him. He is materialistic; believes money can solve all of his problems. He is also a hypocrite; he condemns his wife for having an affair with Gatsby but has no worries about continuing his affair with Myrtle. All three of these characters are further shown to belong to the rotten crowd because when Gatsby died not one of them came to his funeral (which shows how selfish they all are especially Daisy as she loved Gatsby at more than one time in her life). The rotten crowd also included all the people that came to Gatsbys parties, except Nick. Although Nick disapproves of Gatsby from beginning to end Gatsby is not part of the rotten crowd. Although some could argue that Gatsby is not worth the whole damn lot put together, as Gatsby has his faults. These faults included his improper greediness; his tasteless pink suits; his connection with Meyer Wolfshiem, the person that had a hand in the fixing of the 1919 world series baseball final; his love of a woman as shallow as Daisy and his pathetic efforts to win her back by showing off what he has rather than who he is. Although it is interesting to note that Nick fell for just a shallow a woman in Jordan Baker. However the truth of the matter is that Gatsbys incorruptible dream is pure and noble and it is that which separates him from the crowd. On the other hand Tom, Daisy and Jordan are all part of the rotten crowd. They are without spiritual values or sympathy. Although in reality Gatsby is nothing more than a common criminal, in Nicks eyes, and in our own, he is perceived to be worth more than the whole damn bunch put together because Gatsby is a man with a dream, An American Dream. Although Gatsby is a criminal he is likeably corrupt and even though Nick says that he disapproved of Gatsby from beginning to end Nick builds quite a strong relationship between himself and Gatsby. Gatsby is also seen as a likeable character because he is not selfish and has that drive that keeps him going for he is always searching for more. For example in the beginning he wanted to obtain wealth, he achieved that and then he wanted more in the form of love; Daisy. Although in the end it is that drive that brings him down. Gatsby lived life to try and catch the attention of one woman, Daisy Fay, and he almost attained that goal but never quite reached it. When the dream was gone, he had nothing left to live for. In conclusion the American Dream that Gatsby had created for himself improved him as a person. Fitzgerald has created Gatsby as a more exciting and mysterious character than any in the story. Gatsby is the only character that had the ability to set himself goals and achieve them. Although this ability brought about his downfall it was the only thing that Gatsby had to live for. For these reasons, Gatsby is worth the whole damn lot put together.

Substitute Folder - Creating a Teacher Packet

Substitute Folder - Creating a Teacher Packet A substitute folder is an essential resource that all teachers should have prepared and clearly labeled on their desk in case they are absent. This folder should provide the substitute with important information to help them teach your students throughout the day. What to Include in Your Substitute Packet The following list includes  general items to include in your substitute teacher packet: Class List:  Provide a class list and place a star next to the students that can be trusted to help the substitute with any questions they may have. Teacher Schedule:  Provide a schedule of any duties that the teacher may have (bus duty, hall duty). Attach a map of the school and mark the spots where theyre assigned to go. Class Schedule/Routine:  Include a copy of the daily routine. Provide information such as how attendance is taken and where it should go, how student work is collected, when students are able to use the restroom, how students are dismissed, etc. Classroom Discipline Plan:  Provide your classroom behavior plan. Inform substitutes to follow your plan and leave you a detailed note if any student has misbehaved. School Policies:  Include a copy of the school behavior plan, what to do in case of an early dismissal, playground rules, lunchroom rules, tardy procedure, computer usage, and rules, etc. Seating Chart:  Provide a copy of the class seating chart clearly labeled with each students name and any important information about each child. Emergency Procedures/Fire Drills:  Include a copy of the schools emergency procedures. Highlight escape  routes and exit doors, so that the substitute will know exactly where to take the children in case of an emergency. Important Student Information:  Provide a list of students food allergies, medical information (such as medicine) and any other special needs. Time Fillers:  Select a few five-minute activities in case the substitute has a few extra minutes to spare. Emergency Lesson Plans:  Select at least a weeks amount of emergency lessons in case you are not able to complete a lesson for them. Include spare worksheets and review sheets with enough copied for the whole class. Colleagues Contact Information:  Include a list of names and numbers of the surrounding classroom teachers and faculty. A Note from the Sub:  Provide a worksheet for the substitute to fill out at the end of the day. Title it A Note From_______ and have the substitute fill in the blanks for the following items: Date:____My day was____The following students were helpful______Some problems I had throughout the day were_____Additional Comments______ Additional Tips Use a three-ring binder with dividers and clearly label each section. Some options for organizing your binder are:Use a divider for each day of the week and place detailed lesson plans and procedure for that day.Use a divider for each essential item and place contents in the appropriate section.Use a divider and color coordinate each component and place contents in each section. Place important items in the front pocket such as office passes, hall passes, lunch tickets, attendance cards, etc.Create a Sub Tub. Place all essential items in a color-coordinated filing tub and leave on your desk each night, just in case.If you know you will be absent then write the daily routine on the front board. This will give the students and the substitute something to refer to.Lock up personal belongings; you do not want the students or substitute having access to your personal information.Clearly, mark the folder and place it on your desk or in an obvious location.

Idioms dont travel well - Emphasis

Idioms dont travel well Idioms dont travel well When writing anything for a global audience, its best to leave idioms out of it. Idioms are groups of words whose meaning is usually metaphorical and cannot necessarily be deduced from looking at each component word. Unsurprisingly, this can make them problematic for anyone who may be attempting to translate word by word. What might you make of these turns of phrase? Spanish: I have an aunt who plays the guitar. (Yo tengo una tà ­a que toca la guitarra.) French: Its the end of the beans! (Cest la fin des haricots!) Arabic: The sky doesnt throw chicks. (El samaa la tohadef katakeet.) Spanish: To leave Guatemala and arrive in worse cornstalks. (Salir de Guatemala y meterse en guatepeor.) [Youll find the answers at the foot of this blog post.] Talking nonsense Not that English idioms are any more sensible. On the face of it, theres no obvious reason why feline nightwear (Its the cats pyjamas, for our readers who speak English as a second language) should indicate the highest of standards. Nor is it clear why a taut top lip (Keep a stiff upper lip) is a desirable feature in the face of adversity. Every country has its own idioms, which tend to reflect back on the culture they come from. Spend long enough studying translations and you might be able to hazard a guess at the meaning of other countries sayings, and sometimes find the odd crossover. Spanish: Everyone has their own way to kill fleas. (Cada quien tiene su manera de matar pulgas.) English: Theres more than one way to skin a cat. German: From a mosquito make an elephant. (Aus einer Mà ¼cke einen Elefanten machen.) English: Make a mountain out of a molehill. The question is: do you want or expect your reader to take that time? Can you be sure they wont dismiss your communication as gibberish rather than work out you dont want them literally to push an envelope, put a project in their bed or extract something from a horses mouth? Dont make work for your reader And much of the time, we dont even know where our own languages idioms came from, or why they mean what they do. We use them based on the context weve heard them in and out of habit. Little wonder, then, that they so easily trip up international readers. In the UK, we may talk of taking a rain check. But how many of us know the expression is borrowed from baseball in the US, where a rain check is the receipt from a ticket, which may be reused if rain prevents play? Lets get literal So lets not beat around the bush. When writing for global audiences, being as literal as possible is the best method by a long chalk. Anything else just wont cut the mustard. Answers: Whats that got to do with the price of tea in China? Thats the last straw! Money doesnt grow on trees. Out of the frying pan and into the fire.

Marketing Research Essay Example | Topics and Well Written Essays - 1000 words - 2

Marketing Research - Essay Example Because of this influence, the demand for such products increases manifold and the retailers can benefit from enhanced volumes of sales. They may also become capable of assessing the market trends and can offer valuable feedback to supplier and manufactures to modify or amend their marketing strategies. Thus it can be stated that endorsement by celebrities can have a strong impact on products and create a healthy demand that in turn will raise sales volumes resulting in increased profits for the retailers. â€Å"Celebrity marketing is also a valuable way to build instant name recognition, attaching yourself to a person who is well-known to achieve awareness that otherwise might have taken years of marketing.†1 Marketing Research is a scientific search for information especially in the areas related to functions of marketing. Marketing research as a separate department can be a source of valuable help to management’s decision making process. Today, the dynamic markets require numerous middlemen between producer and consumer. Each middleman provides flow of communication regarding consumer needs and dealer needs to the manufacturer. To evaluate and understand the size and specialization within the business unit and to determine the intervention of numerous middlemen between producer and consumer a company needs proper marketing research. In this context marketing research plays a very important role in the modern marketing system. Accurate information is a core factor in successful marketing research. There are two types of methods employed in data collection: primary data collection and secondary data collection. Primary data collection is facilitated by collecting information direc tly from the source. There are many tools used like observation, direct interview, telephone interview, mail survey, projective technique, etc used in primary data collection. Interview is

Friends and interaction Statistics Project Example | Topics and Well Written Essays - 2000 words

Friends and interaction - Statistics Project Example actions ranging from home to school ambiance that make a positive impact over their friendly relationship and would further outline some more important elements which assist to sway in assisting or restricting in friendly relationships. Out of many other sources to advance the best relations in home and society, the friendship plays greater role, to attain satisfaction and happiness in the whole life, and to this effect, making friends and building friendly relations is very crucial and critical for the younger aged people. – Epicurus (Welty & Sharp, 1991, p. 65). Source: http://www.cyc-net.org/cyc-online/cycol-0610-overton.html Productive human relations are very vital for the lives leading to happiness. Our friendly terms lead to significant roles–they help us to salvage and command confidence in our lives, that lend us happiness and comfort which enforces us in trying situations, and to authenticate our sense of value and our interests. We may be impressed by a lot of people in different ways yet our friendly relationship with our buddies effects us more vividly. We have recounted narrations of the meaning of friendship which dates back to the time immemorial – means as the history mankind is lengthy that much friendship is lengthy. Cicero writes - now friendship possesses many splendid advantages, but of course the finest thing of all about it is that it sends a ray of hope into the future, and keeps our hearts from faltering or falling by the wayside. For the man who keeps his eye on a true friend keeps it, so to speak, on a model of himself: For this reason, friends are together when they are separated, they are rich when they are poor, strong when they are weak, and a thing even harder to explain – they live on after they have died, so great is the honor that follows them, so vivid the memory, so poignant the sorrow. (Welty & Sharp, 1991, p. 72). Not only the educationists do even the parents perceive the significance of friendly relations with

Intelligent Software Agent

Intelligent Software Agent Chapter 1 Intelligent Software Agent 1.1 Intelligent Agent An Agent can be defined as follows: â€Å"An Agent is a software thing that knows how to do things that you could probably do yourself if you had the time† (Ted Seller of IBM Almaden Research Centre). Another definition is: â€Å"A piece of software which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The software should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended results† (G.W.Lecky Thompson). [1] [2] [3] [4] An Intelligent Agent can be divided into weak and strong notations. Table 1.1 shows the properties for both the notations. Weak notation Strong notation Autonomy Mobility Social ability Benevolence Reactivity Proactivity Rationality Temporal continuity Adaptivity Goal oriented Collaboration Table 1.1 1.1.1 Intelligency Intelligence refers to the ability of the agent to capture and apply domain specific knowledge and processing to solve problems. An Intelligent Agent uses knowledge, information and reasoning to take reasonable actions in pursuit of a goal. It must be able to recognise events, determine the meaning of those events and then take actions on behalf of a user. One central element of intelligent behaviour is the ability to adopt or learn from experience. Any Agent that can learn has an advantage over one that cannot. Adding learning or adaptive behaviour to an intelligent agent elevates it to a higher level of ability. In order to construct an Intelligent Agent, we have to use the following topics of Artificial Intelligence: Knowledge Representation Reasoning Learning [5] 1.1.2 Operation The functionality of a mobile agent is illustrated in 1.1. Computer A and Computer B are connected via a network. In step 1 a mobile Agent is going to be dispatched from Computer A towards Computer B. In the mean time Computer A will suspend its execution. Step 2 shows this mobile Agent is now on network with its state and code. In step 3 this mobile Agent will reach to its destination, computer B, which will resume its execution. [7] 1.1.3 Strengths and Weaknesses Many researchers are now developing methods for improving the technology, with more standardisation and better programming environments that may allow mobile agents to be used in products. It is obvious that the more an application gets intelligent, the more it also gets unpredictable and uncontrollable. The main drawback of mobile agents is the security risk involved in using them. [8] [9] The following table shows the major strengths and weaknesses of Agent technology: Strengths Weakness Overcoming Network Latency Security Reducing Network traffic Performance Asynchronous Execution and Autonomy Lack of Applications Operating in Heterogeneous Environments Limited Exposure Robust and Fault-tolerant Behavior Standardization Table 1.2 1.2 Applications The followings are the major and most widely applicable areas of Mobile Agent: Distributed Computing: Mobile Agents can be applied in a network using free resources for their own computations. Collecting data: A mobile Agent travels around the net. On each computer it processes the data and sends the results back to the central server. Software Distribution and Maintenance: Mobile agents could be used to distribute software in a network environment or to do maintenance tasks. Mobile agents and Bluetooth: Bluetooth is a technology for short range radio communication. Originally, the companies Nokia and Ericsson came up with the idea. Bluetooth has a nominal range of 10 m and 100 m with increased power. [38] Mobile agents as Pets: Mobile agents are the ideal pets. Imagine something like creatures. What if you could have some pets wandering around the internet, choosing where they want to go, leaving you if you dont care about them or coming to you if you handle them nicely? People would buy such things wont they? [38] Mobile agents and offline tasks: 1. Mobile agents could be used for offline tasks in the following way: a- An Agent is sent out over the internet to do some task. b- The Agent performs its task while the home computer is offline. c- The Agent returns with its results. 2. Mobile agents could be used to simulate a factory: a- Machines in factory are agent driven. b- Agents provide realistic data for a simulation, e.g. uptimes and efficiencies. c- Simulation results are used to improve real performance or to plan better production lines. [10[ [11] [12] 1.3 Life Cycle An intelligent and autonomous Agent has properties like Perception, Reasoning  and Action which form the life cycle of an Agent as shown in 1.2. [6] The agent perceives the state of its environment, integrates the perception in its knowledge base that is used to derive the next action which is then executed. This generic cycle is a useful abstraction as it provides a black-box view on the Agent and encapsulates specific aspects. The first step is the Agent initialisation. The Agent will then start to operate and may stop and start again depending upon the environment and the tasks that it tried to accomplish. After the Agent finished all the tasks that are required, it will end at the completing state. [13] Table 1.3 shows these states. Name of Step Description Initialize Performs one-time setup activities. Start Start its job or task. Stop Stops jobs, save intermediate results, joins all threads and stops. Complete Performs one-time termination activities. Table 1.3 1.4 Agent Oriented Programming (AOP) It is a programming technique which deals with objects, which have independent thread of control and can be initiated. We will elaborate on the three main components of the AOP. a- Object: Grouping data and computation together in a single structural unit called an ‘Object. Every Agent looks like an object. b- Independent Thread of control: This means when this developed Agent which is an object, when will be implemented in Boga server, looks like an independent thread. This makes an Agent different from ordinary object. c- Initiation: This deals with the execution plan of an Agent, when implemented, that Agent can be initiated from the server for execution. [14] [15] [16] [17] 1.5 Network paradigms This section illustrates the traditional distributed computing paradigms like Simple Network Management Protocol (SNMP) and Remote Procedure Call (RPC). 1.5.1 SNMP Simple Network Management Protocol is a standard for gathering statistical data about network traffic and the behavior of network components. It is an application layer protocol that sits above TCP/IP stack. It is a set of protocols for managing complex networks. It enables network administrators to manage network performance, find and solve network problems and plan for network growth. It is basically a request or response type of protocol, communicating management information between two types of SNMP entities: Manager (Applications) and Agents. [18] Agents: They are compliant devices; they store data about themselves in Management Information Base (MIB) (Each agent in SNMP maintain a local database of information relevant to network management is known as the Management Information Base) and return this data to the SNMP requesters. An agent has properties like: Implements full SNMP protocol, Stores and retrieves managed data as defined by the Management Information Base and can asynchronously signal an event to the manager. Manager (Application): It issues queries to get information about the status, configuration and performance of external network devices. A manager has the following properties: Implemented as a Network Management Station (the NMS), implements full SNMP Protocol, able to Query Agents, get responses from Agents, set variables in agents and acknowledge asynchronous events from Agents. [18] 1.3 illustrates an interaction between a manager and an Agent. The agent is software that enables a device to respond to manager requests to view or update MIB data and send traps reporting problems or significant events. It receives messages and sends a response back. An Agent does not have to wait for order to act, if a serious problem arises or a significant event occurs, it sends a TRAP (a message that reports a problem or a significant event) to the manager (software in a network management station that enables the station to send requests to view or update MIB variables, and to receive traps from an agent). The Manager software which is in the management station sends message to the Agent and receives a trap and responses. It uses User Data Protocol (UDP, a simple protocol enabling an application to send individual message to other applications. Delivery is not guaranteed, and messages need not be delivered in the same order as they were sent) to carry its messages. Finally, there is one application that enables end user to control the man ager software and view network information. [19] Table 1.4 comprises the Strengths and Weaknesses of SNMP. Strengths Weaknesses Its design and implementation are simple. It may not be suitable for the management of truly large networks because of the performance limitations of polling. Due to its simple design it can be expanded and also the protocol can be updated to meet future needs. It is not well suited for retrieving large volumes of data, such as an entire routing table. All major vendors of internetwork hardware, such as bridges and routers, design their products to support SNMP, making it very easy to implement. Its traps are unacknowledged and most probably not delivered. Not applicable It provides only trivial authentication. Not applicable It does not support explicit actions. Not applicable Its MIB model is limited (does not support management queries based on object types or values). Not applicable It does not support manager-to-manager communications. Not applicable The information it deals with neither detailed nor well-organized enough to deal with the expanding modern networking requirements. Not applicable It uses UDP as a transport protocol. The complex policy updates require a sequence of updates and a reliable transport protocol, such as TCP, allows the policy update to be conducted over a shared state between the managed device and the management station. Table 1.4 1.5.2 RPC A remote procedure call (RPC) is a protocol that allows a computer program running on one host to cause code to be executed on another host without the programmer needing to explicitly code for this. When the code in question is written using object-oriented principles, RPC is sometimes referred to as remote invocation or remote method invocation. It is a popular and powerful technique for constructing distributed, client-server based applications. An RPC is initiated by the caller (client) sending a request message to a remote system (the server) to execute a certain procedure using arguments supplied. A result message is returned to the caller. It is based on extending the notion of conventional or local procedure calling, so that the called procedure need not exist in the same address space as the calling procedure. The two processes may be on the same system, or they may be on different systems with a network connecting them. By using RPC, programmers of distributed applications avoid the details of the interface with the network. The transport independence of RPC isolates the application from the physical and logical elements of the data communications mechanism and allows the application to use a variety of transports. A distributed computing using RPC is illustrated in 1.4. Local procedures are executed on Machine A; the remote procedure is actually executed on Machine B. The program executing on Machine A will wait until Machine B has completed the operation of the remote procedure and then continue with its program logic. The remote procedure may have a return value that continuing program may use immediately. It intercepts calls to a procedure and the following happens: Packages the name of the procedure and arguments to the call and transmits them over network to the remote machine where the RPC server id running. It is called â€Å"Marshalling†. [20] RPC decodes the name of the procedure and the parameters. It makes actual procedure call on server (remote) machine. It packages returned value and output parameters and then transmits it over network back to the machine that made the call. It is called â€Å"Unmarshalling†. [20] 1.6 Comparison between Agent technology and network paradigms Conventional Network Management is based on SNMP and often run in a centralised manner. Although the centralised management approach gives network administrators a flexibility of managing the whole network from a single place, it is prone to information bottleneck and excessive processing load on the manager and heavy usage of network bandwidth. Intelligent Agents for network management tends to monitor and control networked devices on site and consequently save the manager capacity and network bandwidth. The use of Intelligent Agents is due to its major advantages e.g. asynchronous, autonomous and heterogeneous etc. while the other two contemporary technologies i.e. SNMP and RPC are lacking these advantages. The table below shows the comparison between the intelligent agent and its contemporary technologies: Property RPC SNMP Intelligent Agent Communication Synchronous Asynchronous Asynchronous Processing Power Less Autonomy More Autonomous but less than Agent More Autonomous Network support Distributed Centralised Heterogeneous Network Load Management Heavy usage of Network Bandwidth Load on Network traffic and heavy usage of bandwidth Reduce Network traffic and latency Transport Protocol TCP UDP TCP Packet size Network Only address can be sent for request and data on reply Only address can be sent for request and data on reply Code and execution state can be moved around network. (only code in case of weak mobility) Network Monitoring This is not for this purpose Network delays and information bottle neck at centralised management station It gives flexibility to analyse the managed nodes locally Table 1.5 Indeed, Agents, mobile or intelligent, by providing a new paradigm of computer interactions, give new options for developers to design application based on computer connectivity. 20 Chapter 2 Learning Paradigms 2.1 Knowledge Discovery in Databases (KDD) and Information Retrieval (IR) KDD is defined as â€Å"the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data† (Fayyad, Piatetsky-Shapiro and Smith (1996)). A closely related process of IR is defined as â€Å"the methods and processes for searching relevant information out of information systems that contain extremely large numbers of documents† (Rocha (2001)). KDD and IR are, in fact, highly complex processes that are strongly affected by a wide range of factors. These factors include the needs and information seeking characteristics of system users as well as the tools and methods used to search and retrieve the structure and size of the data set or database and the nature of the data itself. The result, of course, was increasing numbers of organizations that possessed very large and continually growing databases but only elementary tools for KD and IR. [21] Two major research areas have been developed in response to this problem: * Data warehousing: It is defined as: â€Å"Collecting and ‘cleaning transactional data to make it available for online analysis and decision support†. (Fayyad 2001, p.30)  · Data Mining: It is defined as: â€Å"The application of specific algorithms to a data set for purpose of extracting data patterns†. (Fayyad p. 28) 2.2 Data Mining Data mining is a statistical term. In Information Technology it is defined as a discovery of useful summaries of data. 2.2.1 Applications of Data Mining The following are examples of the use of data mining technology: Pattern of traveller behavior mined: Manage the sale of discounted seats in planes, rooms in hotels. Diapers and beer: Observation those customers who buy diapers are more likely to buy beer than average allowed supermarkets to place beer and diapers nearby, knowing many customers would walk between them. Placing potato chips between increased sales of all three items. Skycat and Sloan Sky Survey: Clustering sky objects by their radiation levels in different bands allowed astronomers to distinguish between galaxies, nearby stars, and many other kinds of celestial objects. Comparison of genotype of people: With/without a condition allowed the discovery of a set of genes that together account for many case of diabetes. This sort of mining will become much more important as the human genome is constructed. [22] [23] [24] 2.2.2 Communities of Data Mining As data mining has become recognised as a powerful tool, several different communities have laid claim to the subject: Statistics Artificial Intelligence (AI) where it is called â€Å"Machine Learning† Researchers in clustering algorithms Visualisation researchers Databases: When data is large and the computations is very complex, in this context, data mining can be thought of as algorithms for executing very complex queries on non-main-memory data. 2.2.3 Stages of data mining process The following are the different stages of data mining process, sometimes called as a life cycle of data mining as shown in 2.1: Data gathering: Data warehousing, web crawling. Data cleansing: Eliminate errors and/or bogus data e.g. Patients fever = 125oC. 3- Feature extraction: Obtaining only the interesting attributes of the data e.g. â€Å"data acquired† is probably not useful for clustering celestial objects as in skycat. 4- Pattern extraction and discovery: This is the stage that is often thought of as â€Å"data mining† and is where we shall concentrate our efforts. 5- Visualisation of the data: 6- Evaluation of results: Not every discovered fact is useful, or even true! Judgment is necessary before following the softwares conclusions. [22] [23] [24] 2.3 Machine Learning There are five major techniques of machine learning in Artificial Intelligence (AI), which are discussed in the following sections. 2.3.1 Supervised Learning It relies on a teacher that provides the input data as well as the desired solution. The learning agent is trained by showing it examples of the problem state or attributes along with the desired output or action. The learning agent makes a prediction based on the inputs and if the output differs from the desired output, then the agent is adjusted or adapted to produce the correct output. This process is repeated over and over until the agent learns to make accurate classifications or predictions e.g. Historical data from databases, sensor logs or trace logs is often used as training or example data. The example of supervised learning algorithm is the ‘Decision Tree, where there is a pre-specified target variable. [25] [5] 2.3.2 Unsupervised Learning It depends on input data only and makes no demands on knowing the solution. It is used when learning agent needs to recognize similarities between inputs or to identify features in the input data. The data is presented to the Agent, and it adapts so that it partitions the data into groups. This process continues until the Agents place the same group on successive passes over the data. An unsupervised learning algorithm performs a type of feature detection where important common attributes in the data are extracted. The example of unsupervised learning algorithm is â€Å"the K-Means Clustering algorithm†. [25] [5] 2.3.3 Reinforcement Learning It is a kind of supervised learning, where the feedback is more general. On the other hand, there are two more techniques in the machine learning, and these are: on-line learning and off-line learning. [25] [5] 2.3.4 On-line and Off-line Learning On-line learning means that the agent is adapting while it is working. Off-line involves saving data while the agent is working and using the data later to train the agent. [25] [5] In an intelligent agent context, this means that the data will be gathered from situations that the agents have experienced. Then augment this data with information about the desired agent response to build a training data set. Once this database is ready it can be used to modify the behaviour of agents. These approaches can be combined with any two or more into one system. In order to develop Learning Intelligent Agent(LIAgent) we will combine unsupervised learning with supervised learning. We will test LIAgents on Iris dataset, Vote dataset about the polls in USA and two medical datasets namely Breast and Diabetes. [26] See Appendix A for all these four datasets. 2.4 Supervised Learning (Decision Tree ID3) Decision trees and decision rules are data mining methodologies applied in many real world applications as a powerful solution to classify the problems. The goal of supervised learning is to create a classification model, known as a classifier, which will predict, with the values of its available input attributes, the class for some entity (a given sample). In other words, classification is the process of assigning a discrete label value (class) to an unlabeled record, and a classifier is a model (a result of classification) that predicts one attribute-class of a sample-when the other attributes are given. [40] In doing so, samples are divided into pre-defined groups. For example, a simple classification might group customer billing records into two specific classes: those who pay their bills within thirty days and those who takes longer than thirty days to pay. Different classification methodologies are applied today in almost every discipline, where the task of classification, because of the large amount of data, requires automation of the process. Examples of classification methods used as a part of data-mining applications include classifying trends in financial market and identifying objects in large image databases. [40] A particularly efficient method for producing classifiers from data is to generate a decision tree. The decision-tree representation is the most widely used logic method. There is a large number of decision-tree induction algorithms described primarily in the machine-learning and applied-statistics literature. They are supervised learning methods that construct decision trees from a set of input-output samples. A typical decision-tree learning system adopts a top-down strategy that searches for a solution in a part of the search space. It guarantees that a simple, but not necessarily the simplest tree will be found. A decision tree consists of nodes, where attributes are tested. The outgoing branches of a node correspond to all the possible outcomes of the test at the node. [40] Decision trees are used in information theory to determine where to split data sets in order to build classifiers and regression trees. Decision trees perform induction on data sets, generating classifiers and prediction models. A decision tree examines the data set and uses information theory to determine which attribute contains the information on which to base a decision. This attribute is then used in a decision node to split the data set into two groups, based on the value of that attribute. At each subsequent decision node, the data set is split again. The result is a decision tree, a collection of nodes. The leaf nodes represent a final classification of the record. ID3 is an example of decision tree. It is kind of supervised learning. We used ID3 in order to print the decision rules as its output. [40] 2.4.1 Decision Tree Decision trees are powerful and popular tools for classification and prediction. The attractiveness of decision trees is due to the fact that, in contrast to neural networks, decision trees represent rules. Rules can readily be expressed so that humans can understand them or even directly used in a database access language like SQL so that records falling into a particular category may be retrieved. Decision tree is a classifier in the form of a tree structure, where each node is either: Leaf node indicates the value of the target attribute (class) of examples, or Decision node specifies some test to be carried out on a single attribute value, with one branch and sub-tree for each possible outcome of the test. Decision tree induction is a typical inductive approach to learn knowledge on classification. The key requirements to do mining with decision trees are:  · Attribute value description: Object or case must be expressible in terms of a fixed collection of properties or attributes. This means that we need to discretise continuous attributes, or this must have been provided in the algorithm.  · Predefined classes (target attribute values): The categories to which examples are to be assigned must have been established beforehand (supervised data).  · Discrete classes: A case does or does not belong to a particular class, and there must be more cases than classes. * Sufficient data: Usually hundreds or even thousands of training cases. A decision tree is constructed by looking for regularities in data. [27] [5] 2.4.2 ID3 Algorithm J. Ross Quinlan originally developed ID3 at the University of Sydney. He first presented ID3 in 1975 in a book, Machine Learning, vol. 1, no. 1. ID3 is based on the Concept Learning System (CLS) algorithm. [28] function ID3 Input: (R: a set of non-target attributes, C: the target attribute, 2.4.3 Functionality of ID3 ID3 searches through the attributes of the training instances and extracts the attribute that best separates the given examples. If the attribute perfectly classifies the training sets then ID3 stops; otherwise it recursively operates on the m (where m = number of possible values of an attribute) partitioned subsets to get their best attribute. The algorithm uses a greedy search, that is, it picks the best attribute and never looks back to reconsider earlier choices. If the dataset has no such attribute which will be used for the decision then the result will be the misclassification of data. Entropy a measure of homogeneity of the set of examples. [5] Entropy(S) = pplog2 pp pnlog2 pn (1) (2) 2.4.4 Decision Tree Representation A decision tree is an arrangement of tests that prescribes an appropriate test at every step in an analysis. It classifies instances by sorting them down the tree from the root node to some leaf node, which provides the classification of the instance. Each node in the tree specifies a test of some attribute of the instance, and each branch descending from that node corresponds to one of the possible values for this attribute. This is illustrated in 2.3. The decision rules can also be obtained from ID3 in the form of if-then-else, which can be use for the decision support systems and classification. Given m attributes, a decision tree may have a maximum height of m. [29][5] 2.4.5 Challenges in decision tree Following are the issues in learning decision trees: Determining how deeply to grow the decision tree. Handling continuous attributes. Choosing an appropriate attribute selection measure. Handling training data with missing attribute values. Handling attributes with differing costs and Improving computational efficiency. 2.4.6 Strengths and Weaknesses Following are the strengths and weaknesses in decision tree: Strengths Weaknesses It generates understandable rules. It is less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. It performs classification without requiring much computation. It is prone to errors in classification problems with many class and relatively small number of training examples. It is suitable to handle both continuous and categorical variables. It can be computationally expensive to train. The process of growing a decision tree is computationally expensive. At each node, each candidate splitting field must be sorted before its best split can be found. Pruning algorithms can also be expensive since many candidate sub-trees must be formed and compared. It provides a clear indication of which fields are most important for prediction or classification. It does not treat well non-rectangular regions. It only examines a single field at a time. This leads to rectangular classification boxes that may not correspond well with the actual distribution of records in the decision space. Table 2.1 2.4.7 Applications Decision tree is generally suited to problems with the following characteristics: a. Instances are described by a fixed set of attributes (e.g., temperature) and their values (e.g., hot). b. The easiest situation for decision tree learning occurs when each attribute takes on a small number of disjoint possible values (e.g., hot, mild, cold). c. Extensions to the basic algorithm allow handling real-valued attributes as well (e.g., a floating point temperature). d. A decision tree assigns a classification to each example. i- Simplest case exists when there are only two possible classes (Boolean classification). ii- Decision tree methods can also be easily extended to learning functions with more than two possible output values. e. A more substantial extension allows learning target functions with real-valued outputs, although the application of decision trees in this setting is less common. f. Decision tree methods can be used even when some training examples have unknown values (e.g., humidity is known for only a fraction of the examples). [30] Learned functions are either represented by a decision tree or re-represented as sets of if-then rules to improve readability. 2.5 Unsupervised Learning (K-Means Clustering) Cluster analysis is a set of methodologies for automatic classification of samples into a number of groups using a measure of association, so that the samples in one group are similar and samples belonging to different groups are not similar. The inpu

Object-oriented programming Essay

Now days, in a highly technological society, human productivity is made more efficient through the development of electronic gadgets. Now, with the advent of such modernization in education, one way to globalize the process of research is to realized that technology is advancing at an incredibly fast pace. Computers are not confined to being used for entertainment but its role in education is also vast. Library is derived from the old French â€Å"libraries† which means â€Å"a collection of book†. Reading material in a school are stored in libraries. Library is place in which books and related materials are kept for use but not for sale. It is also organized for use and maintained by a public body, an institution or a private individual. In addition, it is a place in which we get information in any format and from many sources. The librarian has to keep the room neat so that it is conducive for learning. The librarian is also the person who is liable for monitoring all the books that are borrowed and returned by the borrowers. This project is concerned with developing a Library Inventory Management System using object oriented programming. In this system the library management becomes more efficient and easier to handle with its reliable system components. Many Library System are operated manually by group of people. In such situations many people in the process of managing the library such away that to keep records regarding to students or barrowers, check the books normally, keep records on issued books etc. The proponents use the visual basic as a basic programming language in a developing the system. Macromedia Flash will also be used for the design of the system. Related literature of Computerized Library system A library computer system is the software used to catalog, track circulation (where appropriate) and inventory a library’s assets. It is intended for home, church, private enterprise or other small to medium sized collections. Larger, First World libraries will typically use an integrated library system to manage the more complex activities such as acquisitions and the reference in book. Schools set the various pedagogical changes to achieve the current level of education in other countries. Because of the growing numbers of computer users, this became an effective medium to demonstrate the knowledge and skills of the students. From the traditional searching process for the books in the libraries, the interactive usage of computers can be now addressed as part of the library system. Reason of Choice Project: The proponents create this system to develop a system that can handle and manage the activities involved in library in an efficient and reliable way. Less managing personnel and easy searching availability and user profile managing are major goals in this project. The project is to develop a system that can replace the manual library managing system and developing a database which stores user details and book details. In addition, give reliable search facility for the users. Administrator, Librarian and Users should separate. Logins and create an easy to understand user friendly environment. Must important is to have an attractive user interfaces to navigate through the system for the users. Management Information System. Information systems play a crucial role in the management of any contemporary enterprise such as a small, medium or large organization; a profit making or a social service set up; a public or a private sector undertaking and an upcoming or an established business house. The fast changing scene of liberalization, competition and globalization combined with a never before seen emphasis on quality, timeliness, innovation, customer orientation and efficiency puts a premium on accurate, superfast and timely dissemination of information across the globe. The unprecedented developments in computing and communication technologies have indeed made such demands translatable into realizable goals. In simple terms, MANAGEMENT INFORMATION SYSTEM (MIS) is a computer based system that provides flexible and speedy access to accurate data. Since management involves decision making and decisions have to be supported by accurate data, information systems should help management in decision making. Not all the data or information generated by computers is useful for management. Thus MIS is a philosophy which is far deeper and complex than the mundane processing of large amounts of corporate data by computer.