Limitations Of Text Based Image Retrieval Psychology Essay

Limitations Of Text Based Image Retrieval Psychology Essay Sometimes a relevant image might be left out owed to the absence of specific keywords. While often there might be no relevant text surrounding the images or videos, but they are relevant. In fact, there might exist images or videos where the surrounding text has nothing to do with them. In these cases, these returned results might be irrelevant and have nothing in common with the required images and videos. The other approach uses the annotation of the images and vides and is often a manual task. The text-based technique first annotates with text, and then uses text-based retrieval techniques to perform image and video retrieval. Annotation of images and videos lets the user to annotate the image with the text (metadata) that is considered relevant. The text can be time, event, location, participants or whatever the user finds relevant. 2.6.1.1. Limitations of Text based Image Retrieval: Nevertheless, there exist two major difficulties, especially when the volume of image collections is large with hundreds of thousands samples. One is the huge amount of human labor required in manual image/video annotation and is very time-consuming. Textual based retrieval cannot append the perceptual significant visual features like color, shape, texture [Bimbo. 1999]. The other difficulty comes from the rich content in the images and the subjectivity of human perception which is more essential. The annotation of the image and videos completely depends on the annotation interpretation [Enser et al 1993] i.e. different people may perceive the same image differently as shown in the figure 3 . The perception subjectivity and annotation impreciseness may cause unrecoverable mismatches in later retrieval processes. And to retrieve the required data the user constructs a query consisting of the keywords that describes the desired image and video. Although the text based retrieval system has gained benefits of traditionally successful information retrieval algorithms and techniques. Figure 3: Multiple interpretation of same images Park like Tree, Sky, Horse, People, Ridding, Sunny Day, Outdoor Critics of text-based approach dispute that for accurate image annotation it must be automated. The automatic annotation is limited due to its deficiency of extracting semantic information from the images and videos. Only automatic annotation of images and videos in integration with pure text-based image retrieval will be inadequate. The available metadata is mostly restricted to the technical information surrounding the image or video, such as time, resolution of the image or video and name of the image or video. The users may find it difficult to use text to perform a query for some portion of the content of an image or video. Text-based retrieval techniques are absolutely limited to search the metadata that is tagged to the image or video. If the text queried is not annotated with the same tag as attached with the image or video, the data is not returned. This means that if a particular piece of the image or video is interesting this must be explicit included in the metadata. If the desired object is not a main part of the image or video, sometimes it may happen that is not described in the metadata and hence cannot be a retrieve as a result from a query describing such portions of the image or video. One of the disadvantages of text-based image retrieval is that a word can have different meanings. This problem is best illustrated with an example, searching for the images or videos of jaguar or Apple. The system cant differentiate either the user is looking for the jaguar car or jaguar animal as shown in the figure 4. The two concepts have the same name but contain an entirely different semantic idea. The retrieval systems dont have reliable ways to separate the concepts. These problems are present even in systems with automatic synonym lists or thesaurus capabilities [Schank et al. 2004]. There exist several text-based image retrieval services today, Google is a large player. Google is the largest player but still faces the same problem. Figure 4: Same name different Semantics Attempts have been made to make the tags attached to the image or videos more flexible by attaching vast number of descriptive words. The thesaurus based annotation or knowledge based annotation has gained much of the researchers attention [Tring et al. 2000]. Recent development in video retrieval has focused on models that combine several modalities for joint indexing and retrieval. Consideration to the demands, researchers concluded that visual features play a crucial role in the effective retrieval of digital data. This initiates to the development of the content based image and video retrieval [Venters et al. 2000]. 2.6.2. Content based Image Retrieval: The need to manage these images and locate target images in response to user queries has become a significant problem. One way to solve this problem would be describing the image by keywords. The keyword based approach has a bottleneck of manually annotating and classifying the images and videos, which is impractical for the overwhelm corpuses. The human perception subjectivity problem may affect the performance of the retrieval system. Current commercial image and video search engines retrieve the data mainly based on their keyword annotations or by other data attach with it, such as the file-name and surrounding text. This relinquishes the actual image and video more or less ignored and has been following limitations. First, the manual annotation of images requires significant effort and thus may not be practical for large image collections. Second, as the complexity of the images increases, capturing image content by text alone becomes increasingly more difficult. In seeking to overcome these limitations, content-based retrieval (CBR) was proposed in the early 1990s [Baeza-Yates et al. 1999]. Content-based means that the technology makes direct use of content of the image and video rather than relying on human annotation of metadata with keywords. Content-based retrieval (CBR) research endeavors to devise a retrieval system that exploits digital content in the retrieval process in a manner that is eventually independent of manual work. CBR is an umbrella term for content-based multimedia retrieval (CBMR), content based visual information retrieval (CBVIR), content-based image retrieval (CBIR), content-based video retrieval (CBVR) and content-based audio retrieval (CBAR). CBR may also be termed as multimedia information retrieval (MIR). Content based retrieval extract the feature of the image or video themselves and use it for retrieval rather than the user generated meat data. CBR uses the primitive features of the image and video like the color, shape, texture, motion etc. [Sharmin et al. 2002]. Content based system index the images and videos automatically by using different techniques for their visual contents. For the computer, a video is merely a group of frames with a temporal feature, where each frame is basically an image. The computer take each image as a combination of pixels characterize by the low-level color, shape and texture. CBR represents these features in the form of vectors called the descriptors of the image or video. CBR extract these primitive features by using automated techniques and then further use it for searching and retrieval. Thus, these low-level visual features extraction from images and videos has initiated to the many research in the CBR [Veltkamp et al 2000]. A typical CBIR system should be able to interpret the content of the images in a query and a collection, compare the similarity between them, and rank the images in the collection according to their degree of relevance to the users query [Tamura at al. 1984]. The figure 5 shows the typical content based retrieval system. Retrieval deals with the problem of finding the relevant data from the collection of images or videos according to the user request. The user request may be in the form of the textual data or in the form of query by example. Its relatively easy to extract the low level features from the images and videos in the query as well as in the collection and then compare it. Figure 5: Typical Architecture of Content Based Retrieval The paramount objective of CBR is efficiency during image and video search and retrieval, thereby reducing the need for human intervention. Computer can retrieve the images and videos by using CBR techniques from the large corpus without the human assumption. These low level extracted features then represent the image or video and these features are used later on for performing the similarity comparison between the other images or videos in the corpus. These extracted features serve like a signature for images and videos. Images and videos are compared by using different similarity comparison techniques. They are compared by calculating the dissimilarity of its characteristic components to other image or video descriptors. CBR approach shows substantial results with the queries like show me the images or videos of the red color, Show me the image with blue color is above the green color etc. The available automated CBR techniques deal such a type of queries elegantly but flunk to cope with the high level semantic queries like Show me the images or videos of the people in the park, people on the beach, car on the road etc. Such type of queries cannot be tackled successfully by the CBR systems. These queries require more sophisticated techniques to extract the actual semantics abstracted inside it. Related work in CBR from the perspective of images can be found from the overview studies of [Rui et al. 1999], [Smeulders et al. 2000], [Vasconcelos et al. 2001], [Eakins 2002], [Kherfi et al. 2004], [Datta et al. 2005] , [Chen et al. 2004], [Dunckley 2003], [Santini. 2001] [ Santini et al.2001], [Lew et al. 2001], and [Bimbo et al. 1999]. CBIR has received considerable research interest in the last decade [Vasconcelos et al. 2001] and has evolved and matured into a distinct research field. The CBIR mainly comprises of two main steps feature extraction and the similarity measurement. These key technical components of the CBIR system will be introduced in the following sections. 2.6.2.1. Feature Extraction: Images are described by visual words just like text is defined by textual words In fact, an image or a video frame is merely a rectangular grid of colored pixels for a computer. And to a computer an image doesnt mean anything, unless it is told how to interpret it. Image and video descriptors are intended for the motive of image or video retrieval. Descriptors seek to apprehend the image or video characteristics in such a way that it is facile for the retrieval system to identify how similar two images or videos are according to the users interest. CBR system index images or videos by using the low-level features of the image and videos itself, such as colour [Pass et al. 1998, Smith et al. 1996a, Swain et al. 1991], texture [Manjunath et al.1996, Sheikholeslami et al. 1994, Smith et al. 1996b], shape [Safar. M et al. 2000, Shahabi et al. 1999, Tao et al. 1999] , and structure features [Pickering et al. 2003, Howarth et al. 2005]. The color, shape and texture are the principal features of the images. The visual contents of images and videos are then symbolized a s a feature vector of floating numbers. For example, the colour, texture and shape features extracted from an image form an N-dimensional feature vector, and can be written as Where is a vector of its own, and is the colour, is texture and n3 is the shape. While for the video there is an additional vector, where is the motion. In the following section, we introduce the visual features to give an impression of how images and video framesncan be converted into a representation that the retrieval system can work with. 2.6.2.1.1. Color: A very common way to see at images is by analyzing the colors they contain. Colour is the most prominent visual feature in CBIR since it is well correlated with human visual perceptions of objects in an image. A digital colour image is represented as an array of pixels, where each pixel contains three or four tuples of colour components represented in a numerical form. The abstract mathematical representation of colours that computers are able to use is known as the colour model. The similarity between the images and the videos is calculated by using the color histogram value. The histogram depicts the specific values of the pixels inside the image or video frame. The current color based retrieval techniques divides the image into regions by using color proportion. The color based technique doesnt depend on the size and orientation of an image. Since 1980s various color based retrieval algorithms have been proposed [Smith et al. 1996 c]. A most basic form of color retrieval involves specifying color values that can be further used for retrieval. Indeed, Googles image and Picasa 3.0, can also provide the facility to the user to search the images that contain homogenous color composition. The most common representation of color information is in the form of color histogram and color moment. Color anglogram [Zhou X.S. et al. 2002], correlogram [Huang J. et al 1997], color co-occurrence matrix (CCM) [Shim S. et al. 2003] are some of the other feature representations for color. Figure 6: Color based image interpretation 2.6.2.1.1.1. Color Spaces: There are many color spaces designed for different systems and standards, but most of them can be converted by a simple transformation. i. RGB (Red-Green-Blue): Digital images are normally represented in RGB color space; it is the most commonly use color space in computers. It is a device dependent color space, which used in CRT monitors. ii. CMY (Cyan-Magenta-Yellow), CMYK (CMY-Black): It is a subtractive color space for printing, it models the effect of color ink on white paper. Black component is use for enhancing the effect of black color. iii. HSB (Hue, Saturation, Brightness) or HSV (Hue, Saturation, Value): It was used to model the properties of human perception. It is an additive color model. However it is inconvenient to calculate color distance due to its discontinuity of hue at 360. iv. YIQ, YCbCr, YUV: Used in television broadcast standards. Y is the luminance component for backward compatibility to monochrome signal and other components are for chrominance. It is also used in some image compression standards (e.g. JPEG) that process luminance and chrominance separately. Figure 7: The additive colour model HSV 2.6.2.1.1.2. Color Models: A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components. When this model is associated with a precise description of how the components are to be interpreted (viewing conditions, etc.), the resulting set of colors is called color space  [1]  . A color model is a formularized system for composing different of colors from a set of primary colors. There are two types of color models, subtractive and additive. An additive color model uses light emitted directly from a source. The additive color model typically uses primary color i.e. red, green and blue light to produce the other colors. Combination of any two of these additive primary colors in equal amounts produces the additive secondary colors or primary subtractive model colors i.e. cyan, magenta, and yellow. Integration of all these three colors RGB in equal intensities constitute white as shown in the Figurea8 a. Figure 8 (a): RGB: Additive Color for light-emitting computer monitors. Each colored light add to the previous colored lights. A subtractive color model illustrates the blending of paints, dyes,   and natural colorants to produce a full series of colors, each generated by subtracting (absorbing) some wavelengths of light and reflecting the others. Colors observed in subtractive models are the due to reflected light. Different wavelength lights constitute different colors. The CMYK model (Cyan-Magenta-Yellow-blacK) model is the subtractive model. The combination of any two of these primary subtractive model color i.e.(Cyan, Magenta, Yellow) results in the primary additive model or secondary subtractive model color i.e. red, blue, green and the convergence of it constitute black color as shown in the figure 8 b. Figure 8 (b): CMYK: Subtractive colors for Printer. Each color added to the first color blocks the reflection of color, thus subtracts color. For some of the concepts the color scheme helps in achieving suitable results like forest, sky, tree, grass, sea etc. The color descriptor will help in retrieving the accurate results. But for the categories like the car, house, street etc. Color descriptors cant play a vital role. The color descriptor will fail in a situation of the same car with different colors as shown in the figure 9. For the retrieval based on the color two most frequently used representative are color histogram and color moment. These representatives are represented in the section below. Figure 9: Same Car with different color composition a. Color Histogram: A histogram provides a summary of the distribution of a set of data. A color histogram provides a comprehensive overview of the image or video frame in terms of color. A colour histogram for a coloured image describes the different intensity value distributions for colours found in the image. The histogram intent to define the number of times each color appears in an image/video frame. Statistically, it utilizes a property that images having similar contents should have a similar color distribution. One simple approach is to count the number of pixels of each color and plot into a histogram. The histogram h of an image I is represented as: H(I)= Where pi is the percentage of i-th color in the color space, N is the number of colors in the color space. To enable scaling invariant property, the histogram sum is normalized to 1. The percentage is proportional to the number of pixels in the image. Figure 10: Shows the Color Histogram Mostly commercial CBR systems like Query-By-Image-Content uses color histogram as one of the feature for the retrieval. Colors are normally grouped in bins, so that every occurrence of a color contributes to the overall score of the bin it belongs to. The bin explains the intensities of different primary color i.e. quantity of red, blue or green for a particular pixel. It doesnt define individual color of the pixels. Histograms are usually normalized, so that images of different sizes can be fairly compared. The colour histogram is the most commonly and effectively used colour feature in CBIR [Swain et al. 1991, Faloutsos et al. 1994, Stricker et al. 1995, Deselaers et al. 2008, Chakravarti et al. 2009 and Smeulders et al. 2000]. Retrieving an images based on the colors technique is widely used because it does not depend on image size or orientation. The most common method to create a colour histogram is by splitting the range of the RGB intensity values into equal-sized bins. For example, a 24-bit RGB colour space contains 224 possible (RGB) values. Since this gives us approximately 16.8 million bins, it will be too large to be dealt with efficiently. Therefore, we need to quantize the feature space to a smaller number in order to reduce memory size and processing time; as examples [Stricker et al. 1995, Swain et al. 1991] have proposed techniques for colour space quantization. After having defined the bins, the numbers of pixels from the image that fall into each bin are counted. A colour histogram can be used to define the different distributions of RGB intensity values for a whole image, known as a global colour histogram, and for specific regions of an image, known as a local colour histogram. For a local colour histogram, the image is divided into several regions and a colour histogram is created for each region. A histogram refinement strategy has been proposed by Pass for comparing the images [Pass et al.1996]. Histogram refinement splits the pixels in a given bucket into several classes, based upon some local property. Within a given bucket, only pixels in the same class are compared. They describe a split histogram called a color coherence vector (CCV), which partitions each histogram bucket based on spatial coherence. [Han et al. 2002] proposed a new color histogram representation, called fuzzy color histogram (FCH), by considering the color similarity of each pixels color associated to all the histogram bins through fuzzy-set membership function. This approach is proves very fast and is further exploited in the application of image indexing and retrieval. The paradigm of the color histogram works on the assumption that all the images or videos frames with the similar color composition are similar [Jain et al. 1995]. It will retrieve all the data whose color composition is similar to the given query. This will be true in some cases. Color composition cant be the identity of the image or object inside the image. Color Moment: Color moment approach was proposed by [Stricker et al. 1995]. It is a very compact representation of color feature. The mathematical meaning of this approach is that any color distribution can be characterized by its moments. Moreover, most of the information is concentrated on the low-order moments, only the first moment, second and third central moments (mean, variance and skewness) were extracted as the color feature representation. Color similarity can be measured by Weighted Euclidean distance. Due to the ease and sound performance of color histogram technique it is widely used in color based retrieval systems. Color is the human visual perceptual property. Human discriminate an images or objects initially on the basis of colors. Color can be extracted from the digital data easily and automated and effective functions are available for calculating the similarity between the query and the data corpus. Color feature are effectively used for indexing and searching of color images in corpus. The existing CBIR techniques can typically be categorized on the basis of the feature it used for the retrieval i.e. color, shape, texture or combination of them. Color is an extensively utilized visual attribute that plays a vital role in retrieving the similar images [Low et al. 1998]. It has been observed that even though color plays a crucial role in image retrieval, when combined with other visual attributes it would yield much better results [Hsu et al. 1995]. This is because, two images with entirely similar color compositions, may have different color composition and sometimes two images have same color composition but they are not similar as shown in the figure. Hence something that looks similar is not semantically similar. The color composition of both the images in figure 11 is same but they depict the entirely different semantic idea. By analyzing both the images using the color based retrieval techniques both the images are similar

Affirmative Action Promotes Discrimination in America Essay -- affirma

Success is something that everyone yearns for in life. Whether it is becoming happy, healthy, or wise, there are certain achievements and triumphs that deem a person prosperous. However, in order to obtain success, one must dedicate themselves to what they want out of their journey, working hard to come out on top in the competitive worlds of school and work that dominate our society. All through life, people are taught that overall hard work and moral character will allow one to achieve their goals. This sounds like the ideal situation- both fair and ethical. However, our nation has often ignored these moral teachings of justice and evenhandedness in an effort to give more "equality" to the American population. A program known as Affirmative Action was introduced in 1965 by the federal government under Lyndon B. Johnson and was thought to be a great idea in order to bring more diversity and equal opportunity to the many different races, ethical groups, and certain minorities (Affirmative Action 1980). However, the intentions affirmative action provides have only discriminated members of society further, and segregated individuals in this land of equality. Affirmative action can be described as a "program for giving preference to individual applicants for positions in educational institutions, or in employment, based on that person's membership in a previously discriminated-against racial, ethnic, or gender minority" (Affirmative Action vs. Equal Rights 2003). Even before America was a nation, our country was regarded as a place of equal opportunity for all. Thomas Jefferson once said, "We hold these truths to be self- evident, that all men are created equal...," words that have echoed through the centurie... ...n "Affirmative Action vs. Equal Rights." The Jeffersonian Perspective. 20 Oct. 2014 <http://www.geocities.com/CapitolHill/7970/jefpco60.htm>. Brown, Jeff. "Two Views of Affirmative Action." Third Place. 20 Oct. 2014. http://saugus.byu.edu/writing/contest/20001/brown.htm>. McElroy, Wendy. "What Does Affirmative Action Affirm?" Zetetics. 20 Oct. 2014. <http://www.zetetics.com/mac/affirm.htm>. "The Equality Project Learning Center." Quotes on Equality. 27 Oct. 2014. <http://equalrightslesbigay.com/quotes.htm>. "The Social Construction of Reverse Discrimination: The Impact of Affirmative Action on Whites." The Impact of Affirmative Action on Whites. 20 Oct. 2014. <http://www.adversity.net/Pro_AA/docs/Pincus_JIR.htm>.

Ben Franklin :: essays research papers

Benjamin Franklin (1706-1790) was a multi-talented person. He was a printer, and inventor, and a writer. As a writer, he wrote many publications but among this vast collection was a small piece in which Franklin states his beliefs on what may be the proper task to being morally perfect. His opinions are brief; yet complete enough to be considered descript enough to follow. His steps are insightful but just as much vague.   Ã‚  Ã‚  Ã‚  Ã‚  Temperance: Franklin’s first topic, which may be thought to have Franklin’s top choice at a most important step, is Temperance. In his instructions, he states that no once should eat to dullness, nor drink to elevation. This simply means that Franklin believes it is imperfect to eat to excess or to become intoxicated.   Ã‚  Ã‚  Ã‚  Ã‚  Silence: This topic is thought to be an important one by many as well as Benjamin because it states that you should “speak not but what may benefit others or yourself; avoid trifling conversations.'; Franklins believe that to be “morally perfect'; once must not speak unless it is necessary or of importance.   Ã‚  Ã‚  Ã‚  Ã‚  Order: Here is a step, which must be the reason in which I am doing my homework right now and concentrating on just this. Franklin believes that everything must have it’s place, and business must have it’s own time. This being my “business'; it is receiving it’s own time.   Ã‚  Ã‚  Ã‚  Ã‚  Resolution: “Decide what you need to do, and do what you say you are going to do.'; Ben says that you must figure out what it is that you are responsible to do, and do it so that you can be considered responsible.   Ã‚  Ã‚  Ã‚  Ã‚  Frugality: You should be nice to others because it is only going to cause trouble if you are not pleasant to be around. Being nice to others will not only benefit the others but yourself, as well.   Ã‚  Ã‚  Ã‚  Ã‚  Industry: You must take care of precious time because once a moment has passed, it is lost forever and can never be replayed or relived. Time is valuable so you should not waste it.   Ã‚  Ã‚  Ã‚  Ã‚  Sincerity: “Use not hurtful deceit; think innocently and justly, and, if you speak, speak accordingly.

Essay --

Negative Effects on Mobile Phones Since the old times, men have always tried to find a way to communicate with people. Ever since the invention of telephone by the infamous Alexander Graham Bell, communication technology has been evolving and progressing quickly. Nowadays, people separated hundreds miles away don’t have to wait for their letters for weeks or even months to be able to communicate. The development of mobile phone has made it easier for mankind – it allows people to communicate with each other fast and easily. In this globally developing era, it is not uncommon to see someone having one type of mobile phone on his/her hand, and another type on the other one. People from all ages and status carry mobile phones every time, everywhere they go. This device certainly help people in communicating with others, but it is less likely that people realize the device they have been using all the time may bring them bad effects – physically and mentally. Talking at phones with our friends sure is fun that often we don’t even realize how much time have passed. We don’t even feel tired from holding our mobile phone in front of our ear. But the longer we hold it like that, the more we are exposed to danger. The radiation emitted from our mobile phone may cause bad effects to us, from the slight ones such as blurry vision, headache, and neck pain, to the severe ones like brain cancer and risk of brain tumor. Long being an object of debate, the statement that mobile phone radiation links us to such extent of dangerous disease was finally announced by the World Health Organization’s International Agency on Research for Cancer (WHO’s IARC) in May 2011. The risk of being suffered from these diseases is even greater for people who take th... ... is said that teenagers prefer text messaging to face-to face conversation.[2] Mobile phone certainly makes us communicate easily, and it is useful in many ways. However, just like every good thing in this world, this sophisticated device also has bad effects. People should start to consider how to use their mobile phone without it becoming a backfire to their life. People can prevent themselves from the radiation exposure by using a hands-free when they are talking and limit their time on phone. That simple thing will lower the risk of getting the danger of the radiation. On the other side, we need to keep up with our actual social life. When we can gather with our friends, use that opportunity to talk to them instead of being busy with the digital text on our phone. The time we spend with our friends is far more precious than texting which we can do just anytime.

The Role of the Modern Woman Essay -- Women

Diana Spencer, more commonly known as Princess Diana – or even Princess Di to some – was with out a doubt one of the most influential women of our lifetime. Diana represented what the woman of the 20th Century could become. Strong willed, independent and gorgeous all at once. Not in recent history had royalty, much less that of the United Kingdom, connected so well with the people. She was the first member of the royal family to travel the globe and meet with children victim to land mines and HIV/AIDS. Diana held so much power – and was loved so much by her people – that at her funeral, some referred to her as the Queen of the People. It is said by some that because of her extraordinary influence over the English nation, she suffered an untimely death. Diana was with out question adored by the English people, as well as by foreigners. However, the role she played – which she did with extreme grace – was a relatively new one. One hundred, fifty, even twenty years ago, women were not expected to play the role Diana played. There was no such thing as divorced royalty traveling to devastated parts of the third world countries raising awareness of peoples’ plights. One hundred years ago, women played extremely different roles than they do now. Most educated people are aware of this, however, the evolution of the role women partake in society is one that is not told often. In the past hundred years, two periods really stand out as periods where women made advances in how they were seen by society. The first was in the 1920s when they were granted suffrage. The second was in the 1970s with the rise of the Modern Feminist Movement. The evolution of society’s view of women is best illustrated in the visual publ ications of the last hu... ...ositions in the government, large corporations, even in certain religions. Women have made large and permanent advances that are milestones in this country. Though much needs to be done in other parts of the world – even within the United States – progress is inevitable. Sooner of later, one way or another, change will come. Works Cited The Modern Woman: A Look at Who She Is and How She Got There http://www.ellisparkerbutler.info/epb/pic/womans_world_1913_09_a.jpg http://images-eu.amazon.com/images/P/B0000AIZ63.03.LZZZZZZZ.jpg http://www.thespiderawards.com/AwardsPass/WINNERS-NOMINEES/PRO- fashion/images/Flapper.jpg http://en.wikipedia.org/wiki/Jacqueline_Lee_Bouvier http://www.archives.gov/exhibits/powers_of_persuasion/its_a_womans_war_too/images_ html/images/we_can_do_it.jpg http://www.questia.com/PM.qst?a=o&se=gglsc&d=5002183556&er=deny

John Steinbeck’s Novella Of Mice and Men

Most people are familiar with the phrase â€Å"it's too good to be true†, dreams coming true is an example of this common misconception. In John Steinbeck's novella Of Mice and Men, he uses numerous applications of juxtaposition, symbolism, foreshadowing, and other literary devices to prove dreams are unlikely to always come true because even the best plans can fail. Steinbeck highlights numerous dream failures between different people through various applications of juxtaposition. In Chapter 5, Lennie talks about the dreams he hopes to achieve with George while Curley's wife talks about her theatrical aspirations. According to Curley's wife â€Å"I could go with that show. But my ‘old lady wouldn't let me if [I would have gone] I wouldn't be living like this, you bet† (86). Lennie replies, â€Å"We gonna have a little place-an' rabbits† (86). Lennie's and Curley's wife's dreams, lets the audience to see the similarities and foreshadows that Lennie and George dreaming of getting their place with the rabbits and Curley's Wife dreaming of becoming an actress would be done in vain. Chapter 5 highlights the struggle that Lennie experiences both emotionally and mentally he has a hard time controlling his strength throughout the novella. â€Å"I don't want to hurt you, but George will be mad if you yell. I've done a bad thing. I've done a very bad thing† (91). Lennie never intentionally tried to kill anyone, but he cannot control his strength. This leads to shattering his peace of mind, which soon can also shatter his dream of getting his own place with George. Steinbeck uses numerous applications of symbolism to represent rather than saying how and why dreams can fail. Chapter 1 reveals Lennie's dream is to gets a farm with rabbits, which helps reveal Lennie's innocence through indirect characterization. â€Å"isn't fit to lick the boots of no rabbit. You'd forget 'em and let 'em go hungry† (6). It seems that Lennie likes the rabbits, but unfortunately his strong affection will soon lead to his tragic downfall. The soft animals then symbolize innocence and its elimination in cruel world. The dead mouse in Lennie's pocket symbolizes his love and strength and foreshadows the fate of Curley's wife, Lennie's puppy, George and Lennie's dream and Lennie. â€Å"Jus' a dead mouse, George. I didn't kill it. Honest! I found it. I found it dead† (3). Mice represent a fantasy for Lennie. The title is a good hint that mice are significant in this situation, but the first mouse that we encounter is a dead one which foreshadows the future fate of George and Lennie's dream. Steinbeck uses numerous applications of foreshadowing to get the audience to visualize and predict future events pertaining to George's and Lennie's dream becoming a reality. â€Å"Just wanted to pet that girl's dress-just wanted to pet it like it was a mouse† (11). This situation reveals Lennie likes to feel soft objects, no matter what it is-doesn't realize if it's wrong or right. This foreshadows Lennie's death through examples of his innocence can lead to his unfortunate downfall because he doesn't understand the effects of his actions or learns from his mistakes. Likewise, Lennie's innocence can lead to the unfortunate downfall of his dream with George. Lennie says, â€Å"I never meant [any] harm† (32) later in the novella. Lennie never means any harm in anything he does which shows he will have trouble in the future, he did not mean to get in, the death of his pup and Curley's wife for example. John Steinbeck exemplifies the fact that even dreams planed out in advance can still fail. Dreams are always visions of what people want and to make those dreams reality one must work hard and do what it takes to accomplish what they want.

Total Project Control: a Manager’s Guide to Integrated Project Planning, Measuring, and Tracking

3/11/04 Total Project Control: A Manager's Guide to Integrated Project Planning, Measuring, and Tracking By Stephen A. Devaux, published by John Wiley & Sons, NY, 1999 (A book review by R. Max Wideman) Introduction Stephen Devaux published this book in 1999. In it, Stephen attempts to establish a common metric, quantitative data and analysis, by which the project can not only be managed, but also compared to every other project conducted by the organization. In his Preface, Stephen observes: 1 â€Å"The head of a construction company erecting a downtown skyscraper, the pharmacologist overseeing clinical trials for a new drug, the account manager supervising the development of a database for a Fortune 100 client – all three are engaged in project management. Yet chances are that the things they do are very different. . . . But out side of the work itself, all these projects actually have a great deal in common. †¢ Each has a schedule . . . †¢ Each has resources . . . †¢ Each has a budget . . . †¢ Each is going to run into unforeseen circumstances . . Most important of all, each has a scope of work to be accomplished. [But] traditional project management [methodologies] are unable to deal with work scope in an acceptable quantifiable manner. As a result, traditional project management â€Å"factors out† work scope from the management process by assuming it to be a â€Å"prerequisite† to the process . The traditional approach is: â€Å"Once you determine your work scope, we can provide you with a multitude of quantitative techniques for planning, scheduling, resource budgeting, and tracking your project. All of these techniques are based on a defined and constant work scope. †¦ However, the work itself is never quantified in a way that can support decision making. . . Other than saying that â€Å"Scope definition is important,† modern project management is silent. † As many of us have experienced, for example in software development, project scope can in fact be highly variable. Since the book was written, there has been an exponential increase in these types of projects giving rise to interest in project portfolio management. So, there is clearly a need for a common metric upon which acceptance or rejection of competing projects can be based. This is true whether the projects are contemplated or on going, and extends to decisions on changes to their respective work scopes. As Stephen observes:2 Precisely because work scope varies greatly from project to project, and even over time, within a single project, the ability to manage that changing work scope is vital: †¢ To ensure a satisfactory level of quality for acceptable cost. AEW Services, Vancouver, BC  ©2004 Email: [email  protected] ca Total Project Control Page 2 of 7 †¢ †¢ †¢ To select the best elements of scope to cut when forced to do so in order to meet schedule and/or budgetary requirements. To increase scope where the project's return on investment (ROI) can be enhanced by the additional deliverables(s) To determine which of many possible project work scopes should be undertaken as part of the multi-project portfolio. In his book, Stephen introduces a number of metrics with catchy names to support his â€Å"theories†. We'll describe some of these in our next section. Book Structure Total Project Control, referred to throughout as â€Å"TPC†, consists of eleven chapters as follows: 1. The Nature of a Project 2. An Overview of TPC Planning 3. An Overview of Planning the Work 4. Planning the Work Scope 5. Developing the Work Breakdown Structure 6. Scheduling I: The Critical Path Method (CPM) 7. Scheduling II: The Precedence Diagram method (PDM) 8. Activity-Based Resource Assignments 9. Resource Scheduling and Leveling 10. Tracking and controlling the Project 11. Conclusion Stephen just loves acronyms. His first â€Å"new metric, the â€Å"DIPP†, which he claims is fundamental to TPC3 is first mentioned in chapter 1. However, it is not explained until chapter 2, and even then only after introducing the â€Å"CLUB†, Cost of Leveling with Unresolved Bottlenecks, and â€Å"AIM FIRE† his acronym for the management cycle of Aware, Isolate, Measure, Forecast, Investigate, Review and Execute. So, what does DIPP stand for? We had to search the index to find out and guess what – it stands for Devaux's Index of Project Performance! DIPP has a formu la which is EMV (expected monetary value of the project, as of the current completion date) divided by ETC (estimated cost to complete the project. Chapter 2 also mentions Stephen's VBS (value breakdown structure)5 but it is not until chapter 5 that we learn that it is a TPC concept that brings the scope/cost/schedule triangle of value analysis down to the micro-project or activity level. 6 Chapter 5 introduces another concept, the DRAG (Devaux's Removed Activity Gauge) that is the quantification of the amount of time each activity is adding to the project. It is the opposite of total float, and like total float, since it only exists on the critical path activities, it is the amount of time an activity can be shortened before it has a DRAG of zero and another path becomes critical. A good explanation of its use is given in chapter 7. A metric for the resource elasticity of an activity, called DRED, again is mentioned in chapter 6, but is explained in chapter 7. It turns out it stand s for Doubled Resource Estimated Duration and is an estimate of how long it would take if the rate of resource usage anticipated in estimating its duration were to be AEW Services, Vancouver, BC  © 2004 Email: [email  protected] ca Total Project Control Page 3 of 7 doubled. Consequently it is an index of resource elasticity. But perhaps the high point is another acronym called RAD that appears in chapter 9. Chapter 9 is a discussion of the parameters surrounding resource scheduling, leveling and availability, both on and off the critical path, and the calculation of DRAG. Stephen explains that there are three different causes of DRAG:9 1. Delay due to the logic of the work, i. e. CPM schedule DRAG, 2. Delay due to other ancestor activities, which unavoidably push out the schedule of the successor, and 3. Delay due to the specific activity having to wait for resources, which we will call resource availability DRAG or RAD. So there you have the definition of RAD. In practice, RAD itself has mathematical constraints and the calculation is complex, requiring computer software. Stephen provides the formula and explanation, but you can skip this section if you wish. The point is, this metric is typically not calculated, so the real impact of unavailable or over stretched resources on projects as a whole is unknown to the organization and hence not accounted for when it comes to assessing project failures. What we liked This may ound like fun stuff with acronyms, but behind it all is the serious issue of â€Å"How can any investment decision be made, on a quantified basis, unless there is at least some sense of what value awaits a successful outcome? â€Å"10 Indeed, Stephen might have added â€Å"or even what constitutes a quantified successful outcome? † Later, Stephen answers his own question by observing â€Å"There are thousands of corporate organizations that depend on projects for more than 90 percent of their revenues. Yet, other than intuitively, they have no way of tying the projects they do to their profits. 11 Even under traditional project management, an absolute minimum data for each project in a portfolio should be the expected monetary value, the current completion date, and the cost estimate to complete. 12 Actually, having worked for respectable real estate development companies, we can state that these concepts are well known to them. However, having also worked with software development organizations, it appears that these metrics are not only rare but tend to be foreign to proponents of the latest forms of software development project management. Under Stephen's TPC approach, the data required is even more profound. In a portfolio of projects, it should consist of:13 †¢ Project Name †¢ Expected Monetary Value †¢ As of (i. e. Current reporting date) †¢ Current Completion Date †¢ Loss per Week Late (%) †¢ Gain per Week Early (%) †¢ New Expected Value †¢ Cost Estimate to Complete †¢ Simple DIPP Note the addition of the time value of being ahead or behind schedule, not in terms of project overhead AEW Services, Vancouver, BC  © 2004 Email: [email  protected] ca Total Project Control Page 4 of 7 costs but in terms of gain or loss in value of the product to the organization. Stephen provides many examples of his approach, although not all calculations are explicit. Stephen wades into the assembly of work breakdown structures, and CPM scheduling to illustrate his theories. On the question of how do you plan the work scope, he suggests: 14 â€Å"Each type of project is different, and each project is different. It is therefore difficult to set hard-and-fast rules for assembling scope documents. The best idea I have found is to †¢ Start with the benefits you want to achieve, †¢ Incorporate them into a business plan, †¢ Then move as rapidly as possible to a concrete image of the thing that will provide those benefits. † This is sound advice [The bullets are mine, by the way. On the matter of estimating, Stephen offers more sound advice:15 The person who is going to be responsible for the work should be the one who generates the estimates. This is probably the most important contributor to accurate estimates. The reasons for this are: 1. This person will be a subject matter expert, trained in the discipline necessary for the par ticular work. 2. This person is the only one who will know precisely how he or she plans to do the work. 3. He or she will usually have a vested interest in meeting his own commitment, and establishing the reliability of his or her own estimates. Unfortunately, the practicality in many cases is that, (a) the contributors don't know how to estimate, (b) they don't want to estimate, and (c) if they are really busy, they don't have the time to estimate. Still, it does suggest that estimating ought to be a part of production skills. Downside Under Scope/Cost/Schedule Integration, Stephen observes: 16 â€Å"Work scope is the foundation on which the whole project rests. It is the reason for doing the project – to obtain the value that will accrue from the work . . . Once we recognize this, two things come into clearer focus: 1. Quantifying scope is important. It is directly related to profitably. In a project-driven company, if you haven't quantified project scope, you cannot accurately estimate, or work to increase, profit 2. The metric used to quantify scope is the dollar. To be precise, the expected dollar that measures the value that the project is undertaken to generate. † But Stephen skates round the issue of how you arrive at this expected value by stating â€Å"Now, how one goes about estimating the value of a project is a topic of its own, beyond the scope of this book. 17 Unfortunately, that means the whole premise of his book rests on an undefined EMV parameter – which itself is changing due to external influences. Stephen's thesis, and consequent metrics, relies on a tacit assumption. This is that you have projects where the activities can all be identified, their resource requirements established and the time and cost of AEW Services, Vancouver, BC  © 2004 Email: [email  prote cted] ca Total Project Control Page 5 of 7 each reasonably accurately estimated. And further, that those resources are sufficiently flexible that schedule changes can be accommodated. On most projects, this is unreasonable, and for projects in the early part of their life span, this is patently impossible. Some of the metrics may be open to question. For example, Glen Alleman, VP, Program Management Office at CH2M HILL has commented on the DIPP formula (i. e. EMV divided by ETC), as follows:18 â€Å"There are several issues with the DIPP equation. 1. The denominator creates a â€Å"divide by zero† error as the project reaches the end and the estimate to complete approaches zero. This is poor behavior of a performance indicator not a ratio of two values drawn from the same time sample. . The indicator has nonlinear behavior over its life cycle. 3. The ETC value in the equation needs to be the sum of multiple estimates to complete, since EMV is the sum of all possible outcomes. The equation's ETC is a point value with no index i to correlate with EMV's sum across the indices of possible outcomes. The primary issue here is that DIPP does not include the sunk costs of the project. â€Å"Devaux states these are not necessary for the assessment of completion decisions. In fact the estimate to complete is based on the previous performance. The ‘performance factor for remaining work' is most often derived from the performance of the previous work. Past is a predictor of the future. The sunk costs are accruals and burden the net profit of the project. Ignoring sunk costs is not only poor financial management it is poor project management as well. The sunk costs must be paid by â€Å"someone. † The project manager must consider whom and how much is to be paid in assessing future decisions for the project. Ignoring these is like driving in the rear view mirror. It can be done, but not recommended. † We may not agree entirely with Glen's assessment, but the point is well taken. Another bone of contention is about reserves. Stephen cites the example of catching a plane under a plan based on median time estimates. Such a plan would probably mean that we would miss the plane 50% of the time. Clearly this is unacceptable so we must add contingency time. Stephen then says this is sometimes called â€Å"management reserve† and19 â€Å"There is an important difference between management reserve and padding. Management reserve is always added either at the end of the project, or immediately before a major milestone. It belongs to the project manager and the entire project. We agree with the intent but not the definitions. In our view, â€Å"Contingency† should provide for variances in durations and belongs to the project manager. â€Å"Management Reserve†, as the name implies, should belong to management for possible changes in scope (like picking up a coffee and donut at the airport), and â€Å"Padding† is a political issue and should be a no, no. Still, where workers are required to work on several projects concurrently, may be it is necessary to cover loss of productivity because as Stephen says: â€Å"Such multitasking is one of the great time wasters of corporate projects. 20 But here's a thought. If we are in DRED of missing that plane we just talked about, how much safer would we be if we doubled our resources and had two people running to catch that plane? AEW Services, Vancouver, BC  © 2004 Email: [email  protected] ca Total Project Control Page 6 of 7 Summary It is time that project management practitioners started a serious dialogue on the subject of managing scope as one of the variables, and perhaps the key variable, in project management. Ask not what is the cost of this project, or change, and can we afford it? Ask instead, what is the value to the organization of this project, or change, is it worth it and how does it stack up against our other options? Some may argue that a dollar value metric is not pertinent to their particular type of project, but whichever way you look at it, money is the only common vehicle for comparison between projects in a portfolio. Stephen sums up his position at the end of chapter 1 by observing:21 †¢ The purpose of a project is not to be short or inexpensive, but to make a profit. It should be managed in such a way as to maximize that profit. All the work, and all aspects of the project that impact its profit should be analyzed together, in an integrated way that shows the effect of the various alternatives on the project profit. †¢ Each project that is managed in a context with other projects should be analyzed in an integrated way that shows the effects of each (ostensibly internal) project decision on all other projects, and, specifically, on the multi-project profit. †¢ Insofar as projects are managed without regard to profit, bad (profit-reducing) decisions will be made, both randomly and systematically, throughout the organization. Stephen's book was first published five years ago. In our experience it takes about that long for new ideas to sink into the collective psyche of the project management populace. So, we share Stephen's view. It is time that project sponsors and the creators of the enterprise planning software they use (if any) figure out how to incorporate these variable scope and value concepts, and apply them to their projects. Then, perhaps, we will be in a better position to demonstrate that the traditional definition of project success of being â€Å"On time and within budget† is short term and very narrowly focused. We think that Stephen Devaux's book makes a valuable contribution to the discussion of project and portfolio management, planning and tracking. However, some things have changed in the last five years, or are better understood, so we sincerely hope that Stephen will consider updating and reissuing his book â€Å"Total Project Control†. If he does, we hope he will also add a glossary. R. Max Wideman Fellow, PMI 1 2 Devaux, S. A. , Total Project Control, Wiley, NY, 1999, p xvii Ibid. p xix 3 Ibid. p22 4 Ibid. p7 5 Ibid. p32 6 Ibid. p93 7 Ibid. 139 AEW Services, Vancouver, BC  © 2004 Email: [email  protected] ca Total Project Control Page 7 of 7 8 9 Ibid. p184 Ibid. p257 10 Ibid. p xix 11 Ibid. p8 12 Ibid. p9 13 Ibid. p12 14 Ibid. p63 15 Ibid. p105 16 Ibid. p30 17 Ibid. p31 18 Alleman, G. , The DIPP Formula Control Flag, An Assessment of the DIPP Indicator, Viewpoints, Project Management World Today, November-December 2003, http://www. pmforum. org/pmwt03/viewpoints03-11. htm 19 Devaux, S. A. , Total Project Control, Wiley, NY, 1999, p113 20 Ibid. p114 21 Ibid. p14 AEW Services, Vancouver, BC  © 2004 Email: [email  protected] ca