The Darwinian revolution challenged Essay Example for Free

The Darwinian revolution challenged Essay Define evolution broadly and then give a narrower definition, as discussed in the overview. Evolution: Descent with modification; the idea that living species are descendants of ancestral species that were different from the present-day ones; also defined more narrowly as the change in the genetic composition of a population from generation to generation Concept 22.1 The Darwinian revolution challenged the traditional view of a young Earth inhabited by unchanging species This section takes a look at the historical setting and influences on Darwin, and it sets the stage for our formal study of evolution. How did each of the following sources view the origin of species? Aristotle and Scala Naturae: Aristotle viewed species as fixed. Through his observations of nature, Aristotle recognized â€Å"affinities† among organisms. He concluded that life-forms could be arranged on a ladder, or scale, of increasing complexity, called the scala naturae. Each form, perfect and permanent, had its allotted rung on this ladder. The Old Testament: The Old Testament holds that species were individually designed by God and therefore perfect. Carolus Linnaeus: Linnaeus adopted a nested classification system, grouping similar species into increasingly general categories. Linnaeus, adhering to the Old Testament belief that all species were designed by God, did not ascribe the resemblances among species to evolutionary kinship, but rather to the pattern of their creation. Explain the role of fossils in rock strata as a window to life in earlier times. Many fossils are found in sedimentary rocks formed from the sand and mud that settle to the bottom of seas, lakes, swamps, and other aquatic habitats. New layers of sediment cover older ones and compress them into superimposed layers of rock called strata. The fossils in particular strata provide a glimpse of some of the organisms that populated Earth at the time that the layer formed. How would Georges Cuvier have explained the appearance of the record of life shown in the rock strata? Cuvier opposed the idea of evolution. He advocated catastrophism, the principle that events in the past occurred suddenly and were caused by mechanisms different from those operating in the present. Copyright  © 2011 Pearson Education, Inc. Cuvier speculated that each boundary between strata represented a catastrophe, such as a flood, that had destroyed many of the species living at that time. James Hutton and Charles Lyell were geologists whose ideas strongly influenced Darwin’s thinking. What were the ideas each of them contributed? James Hutton : Hutton proposed that Earth’s geologic features could be explained by gradual mechanisms still operating today, such as valley formed by rivers. Charles Lyell: Lyell incorporated Hutton’s thinking into his principle of uniformitarianism, which states that mechanisms of change are constant over time. Lyell proposed that the same geologic processes are operating today as in the past, and at the same rate. What is the importance of the principle of uniformitarianism? If geologic change results from slow, continuous actions rather than from sudden events, then Earth must be much older than the widely accepted age of a few thousand years. Jean-Baptiste de Lamarck proposed a mechanism for how life changes over time. Explain the two principles of his mechanism. use and disuse: The idea that parts of the body that are used extensively become larger and stronger, while those that are not used deteriorate. inheritance of acquired characteristics: This idea states that an organism could pass these modifications of use and disuse to its offspring. Although Lamarck’s mechanism of evolution does not explain the changes in species over time, his thinking has been influential. What is considered to be the great importance of his ideas? Lamarck recognized that the match of organisms to their environments can be explained by gradual evolutionary change rather than special creation. Concept 22.2 Descent with modification by natural selection explains the adaptations of organisms and the unity and diversity of life Charles Darwin proposed that the mechanism of evolution is natural selection and that it explains how adaptations arise. What are adaptations? Give two examples of adaptations. Adaptations are inherited characteristics of organisms that enhance their survival and reproduction in specific environments. Possible examples include the mottled coloration of a fawn that allows it to blend with its environment, or the sharp talons and beaks of birds of prey so well suited for predation. Explain the process of natural selection. In the process of natural selection, individuals that have certain inherited traits tend to survive and reproduce at higher rates than other individuals because of those traits. Let’s try to summarize Darwin’s observations that drive changes in species over time: Observation 1. Variations in traits exist. Cite an Example Variation in color and spot pattern of Asian ladybird beetles 2. These variations (traits) are heritable. Variation in closely related species of elephants; offspring resemble close relatives more than other members of a population. 3. Species overproduce. Dandelions produce thousands of seeds. 4. There is competition for resources; not all offspring survive. Not all dandelion seeds germinate or survive to maturity From these four observations, what two inferences did Darwin make? 1. Individuals whose inherited traits give them a higher probability of surviving and reproducing in a given environment tend to leave more offspring than other individuals. 2. The unequal ability of individuals to survive and reproduce will lead to the accumulation of favorable traits in the population over generations. It is important to remember that differences in heritable traits can lead to differential reproductive success. This means that the individuals who have the necessary traits to promote survival in the current environment will leave the most offspring. How can this differential reproductive success affect the match between organisms and their environment? When such advantages increase the number of offspring that survive and reproduce, the traits that are favored will likely appear at a greater frequency in the next generation. To demonstrate your understanding of this section, complete the following sentences: Individuals do not evolve. Populations evolve. Now, take out your highlighter and mark the information in the box above. Hold these ideas firmly in your brain! Finally, if you are ever asked to explain Darwin’s theory of evolution by natural selection (a common AP essay question), do not pull out the phrase â€Å"survival of the fittest.† Instead, cite the points made in question 11 and explain the inferences that are drawn from them. Copyright  © 2011 Pearson Education, Inc. Concept 22.3 Evolution is supported by an overwhelming amount of scientific evidence 15. Use Figure 22.13 in your text to explain how research with soapberry bugs demonstrated observable evolutionary change. Museum specimens showed that the average beak length of soapberry bugs was comparable to that of soapberry bugs feeding on native species in southern Florida. However, contemporary data suggest that a change in the size of the soapberry bug’s food source, as seen with the introduction of the goldenrain tree, can result in evolution by natural selection for matching beak size. MRSA is in the news today because it is becoming increasingly more common. What is it? MRSA is methicillin-resistant Staphylococcus aureus, a flesh-eating strain How did it become so dangerous? Explain the evolution of MRSA’s resistance to methicillin. MRSA became dangerous because, over time, doctors used a variety of antibiotics, such as penicillin, to combat MRSA. Each time a new antibiotic was used to fight the disease, some S. aureus populations would develop resistance to the new drug. In 1959, doctors used the powerful antibiotic methicillin. Members of the S. aureus population that were resistant to methicillin reproduced at higher rates, leading to the spread of methicillin-resistant S. aureus (MRSA). Do antibiotics cause bacteria to become resistant? Explain your response. No. A drug does not create resistant pathogens; it selects for resistant individuals that are already present in the population. Let’s make a list of the four evidences for evolution that are described in this concept. Give an example of each. Evidence for Evolution Example Direct observations of evolutionary change Homology Possible examples include the evolution of MRSA or the change in beak size in soapberry bugs. Possible examples include the similarities between mammalian forelimbs. Possible examples include fossils that show ancestors of cetaceans had hind limbs. Possible examples include the creation of the evolutionary tree of horses, based on fossil locations. Fossil record Biogeography How does the fossil record give evidence for evolution? The fossil record documents the pattern of evolution, showing that past organisms differed from present-day organisms and that many species have become extinct. Copyright  © 2011 Pearson Education, Inc. What is meant by each of the following terms? Give an example of each. Term Homologous structures Vestigial structures Analogous structures (see p. 465) Explanation/Example Structures in different species that are similar because of common ancestry. For example, mammalian forelimbs. A feature of an organism that is a historical remnant of a structure that served a function in the organism’s ancestors. For example, skeletons of some snakes retain vestiges of the pelvis and leg bones. Having characteristics that are similar because of convergent evolution, not homology. For example, the wing of a butterfly and wing of a bat both make flight possible. How do homologous structures give evidence for evolution? Homologous structures represent variations on a structural theme that was present in the common ancestors of a species. What is summarized in an evolutionary tree? An evolutionary tree reflects evolutionary relationships among groups of organisms. Figure 22.17 in your text shows an evolutionary tree. What is indicated by each branch point in the following figure? Mark each branch point. Each branch point represents the common ancestors of the lineage beginning there and to the right of it. Refer to Figure 22.17 on page 464. What is indicated by the hatch marks in Figure 22.17? A hatch mark represents a homologous characteristic shared by all the groups to the right of the mark. Use the tree in question 24 to answer this question: Are crocodiles more closely related to lizards or to birds? Explain your response. Based on this evolutionary tree, crocodiles are more closely related to birds than to lizards because they share a more common ancestor with the birds than with lizards. On the evolutionary tree, label the vertical lines to the right, and annotate the key feature that marks each group. See page 464 of your text for the labeled figure. Organisms that are only distantly related can resemble each other. Explain convergent evolution, and describe how analogous structures can arise. Convergent evolution is the independent evolution of similar features in different lineages. In such examples as the marsupials of Australia, in which species share features because of convergent evolution, the resemblance is said to be analogous. Analogous features share similar function, but not common ancestry. Copyright  © 2011 Pearson Education, Inc. Convergent evolution might be summarized like this: Similar problem, similar solution. Can you give two examples of convergent evolution? Answers will vary but may include the sugar glider and the flying squirrel, and the evolution of wings in birds and bats. Study Tip Homologous structures show evidence of relatedness (whale fin, bat wing). Analogous structures are similar solutions to similar problems but do not indicate close relatedness (bird wing, butterfly wing). What is biogeography? How is it affected by continental drift and the presence of endemic species? Biogeography is the geographic distribution of species. The geographic distribution of organisms is influenced by many factors, including continental drift, the slow movement of Earth’s movement over time, and the presence of endemic species, species that are found nowhere else in the world. Let’s wrap up all of these ideas with a final summary. ORGANIZE YOUR THOUGHTS Evolution is change in species over time. Heritable variations exist within a population. These variations can result in differential reproductive success. Over generations, this can result in changes in the genetic composition of the population. And remember: Individuals do not evolve! Populations evolve. Test Your Understanding Answers Now you should be ready to test your knowledge. Place your answers here: 1. b Copyright  © 2011 Pearson Education, Inc.

Point Shaving :: essays research papers

I think that it’s a tragedy to see that many collegiate athletes are involved in gambling situations. It’s hard on the athletes too, because they’re not getting paid to play the game so it’s hard to resist thousands of dollars to only win by a few, in cases on point shaving. When I was watching that movie â€Å"Blue Chips†, which is all about illegal college betting and buying athletes to come to their school, there was a scene involving the coach and the point guard regarding a point shaving incident three years ago. After the student was harassed by the coach he finally confessed saying, â€Å"We won the game, we just didn’t beat the spread. That’s only for those gamblers anyway†. He has a point with what he said but that’s not the point. A lot of young athletes do see point shaving as partaking in an illegal and unlawful event or don’t believe that much in it, morally. So people say that if the college players were getting paid none of this would happen. I don’t know if giving the players a stipend is going end this because unless you’re paying the athletes thousands of dollars a week or a game, the stipend won’t be more than what they’re being offered. I do think that it might help but I’m not sure if that help is going to make a big enough difference. On the same note, if you eliminate spreads, can they be sure that it will make a difference when it comes to gambling. Don’t get me wrong, eliminating the spreads would decrease the amounts of point shaving incidents, but it won’t necessarily stop them. Bookies can make up their own spreads and ask players to shave points regardless of the fact that the spread is not publicized. The bookie has an idea by about how much one team may beat another. I understand that they will probably never â€Å"eliminate collegiate gambling† but they’re t rying to limit and reduce it and I’m all for it too. Unfortunately there is the big issue of the politicians and their campaigning for funds and it’s going to be hard to control that too. I can understand why Congress isn’t closing the Nevada loophole or at least delaying it, but I still don’t agree with it.

Web Mining Homework

A Recommender System Based On Web Data Mining for Personalized E-learning Jinhua Sun Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [email  protected] edu. cn Yanqi Xie Department of Computer Science and Technology Xiamen University of Technology, XMUT Xiamen, China [email  protected] edu. cn Abstract—In this paper, we introduce a web data mining olution to e-learning system to discover hidden patterns strategies from their learners and web data, describe a personalized recommender system that uses web mining techniques for recommending a student which (next) links to visit within an adaptable e-learning system, propose a new framework based on data mining technology for building a Web-page recommender system, and demonstrate how data mining technology can be effectively applied in an e-learning environment.Keywords–Data mining; web log,;e-learning; recommender readily interpreted by the analyst. A virtual e-learnin g framework is proposed, and how to enhance e-learning through web data mining is discussed. II. RELATED WORK I. INTRODUCTION With the rapid development of the World Wide Web, Web data mining has been extensively used in the past for analyzing huge collections of data, and is currently being applied to a variety of domains [1]. In the recent years, e-learning is becoming common practice and widespread in China.With the development of e-Learning, massive amounts of learning courses are available on the e-Learning system. When entering e-Learning System, the learners are unable to know where to begin to learn with various courses. Therefore, learners waste a lot of time on e-Learning system, but don’t get the effective learning result. It is very difficult and time consuming for educators to thoroughly track and assess all the activities performed by all learners.In order to overcome such a problem, the recommender learning system is required. Recommender systems are used on ma ny web sites to help users find interesting items [2], them predict a user's preference and suggest items by analyzing the past preference information of users, e-learning system is applied on the basis of the method. The user’s learning route is given and then provides the relevant learners useful messages through dynamically searching for the appropriate learning profile.This paper recommends learners the studying activities or learning profile through the technology of Web Mining with the purpose of helping they adopt a proper learning profile, we describe a framework that aims at solution to e-learning to discover the hidden insight of learning profile and web data. We demonstrate how data mining technology can be effectively applied in an e-learning environment. The framework we propose takes the results of the data mining process as input, and converts these results into actionable knowledge, by enriching them with information that can beThe route where the learner brow ses through the web pages will be noted down in Web log, carries on the technology of Web mining through Learning Profile and Web log, and analyzes from the materials related to association rule. It can be found the best learning profile from this information. These learning profiles combine with the Agent and put them on the learning website. Furthermore, the Agent recommends the function of learning profiles on learning website. Therefore, the learner will acquire a better learning profile.This chapter briefly illustrates the relevant contents including: e-Learning, Learning Profile, Agent, Web Data mining and Association rule. A. E-learning E-learning is the online delivery of information for purposes of education, training, or knowledge management. In the Information age skills and knowledge need to be continually updated and refreshed to keep up with today’s fastpaced study environment. E-learning is also growing as a delivery method for information in the education fiel d and is becoming a major learning activity. It is a Web-enabled system that makes knowledge accessible to those who need it.They can learn anytime and anywhere. E-learning can be useful both as an environment for facilitating learning at schools and as an environment for efficient and effective corporate training [3]. B. A Glance at Web Data Web usage mining performs mining on web data, particularly data stored in logs managed by the web servers. All accesses to a web site or a web-based application are tracked by the web server in a log containing chronologically ordered transactions indicating that a given URL was requested at a given time from a given machine using a given web client (i. e. browser).As shown in table 1, Web log contains the website â€Å"hit† information, such as visitor’s IP address, date and time, required pages, and status code indicating. The web log raw 978-1-4244-4994-1/09/$25. 00  ©2009 IEEE data is required to be converted into database f ormat, so that data mining algorithms can be applied to it. TABLE I. WEB LOG EXAMPLES Web logs 172. 158. 133. 121 – – [01/Nov/2006:23:46:00 -0800] â€Å"GET /work /assignmnts/midterm-solutions. pdf HTTP/1. 1†³206 29803 2006-12-14 00:23:56 209. 247. 40. 108 – 168. 144. 44. 231 GET /robots. txt – 200 600 119 125 HTTP/1. 0 www. a0598. com ia_archiver – – sefulness and certainty of a rule respectively [5]. Support, as usefulness of a rule, describes the proportion of transactions that contain both items A and B, and confidence, as validity of a rule, describes the proportion of transactions containing item B among the transactions containing item A. The association rules that satisfy user specified minimum support threshold (minSup) and minimum confidence threshold (minCon) are called strong association rules. D. Web Mining for E-learning Learning profile help learner to keep a record of their current knowledge and understanding of e-learn ing and elearning activities.Web mining is the application of data mining techniques to discover meaningful patterns, profiles, and trends from both the content and usage of Web sites. Web usage mining performs mining on web data, particularly data stored in logs managed by the web servers. The web log provides a raw trace of the learners’ navigation and activities on the site. In order to process these log entries and extract valuable patterns that could be used to enhance the learning system or help in the learning evaluation, a significant cleaning and transformation phase needs to take place so as to prepare the information for data mining algorithms [6].Web server log files of current common web servers contain insufficient data upon which to base thorough analysis. The data we use to construct our recommended system is based on association rules. E. Recommendation Using Association Rules One of the best-known examples of data mining in recommender systems is the discove ry of association rules, or item-to-item correlations [7]. Association rules have been used for many years in merchandising, both to analyze patterns of preference across products, and to recommend products to consumers based on other products they have selected.Recommendation using association rules is to predict preference for item k when the user preferred item i and j, by adding confidence of the association rules that have k in the result part and i or j in the condition part [4]. An association rule expresses the relationship that one product is often purchased along with other products. The number of possible association rules grows exponentially with the number of products in a rule, but constraints on confidence and support, combined with algorithms that build association rules with item sets of n items from rules with n-1 item sets, reduce the effective search space.Association rules can form a very compact representation of preference data that may improve efficiency of s torage as well as performance. In its simplest implementation, item-to-item correlation can be used to identify â€Å"matching items† for a single item, such as other clothing items that are commonly purchased with a pair of pants. More powerful systems match an entire set of items, such as those in a customer's shopping cart, to identify appropriate items to recommend. The web data is massive since the visitor’s every click in the website will leave several records in the tables.This also allows the website owner to track visitors’ behavior details and discover valuable patterns. C. Data Mining Techniques The term data mining refers to a broad spectrum of mathematical modeling techniques and software tools that are used to find patterns in data and user these to build models. In this context of recommender applications, the term data mining is used to describe the collection of analysis techniques used to infer recommendation rules or build recommendation model s from large data sets.Recommender systems that incorporate data mining techniques make their recommendations using knowledge learned from the actions and attributes of users. Classical data mining techniques include classification of users, finding associations between different product items or customer behavior, and clustering of users [4]. 1) Clustering Clustering techniques work by identifying groups of consumers who appear to have similar preferences. Once the clusters are created, averaging the opinions of the other consumers in her cluster can be used to make predictions for an individual.Some clustering techniques represent each user with partial participation in several clusters. The prediction is then an average across the clusters, weighted by degree of participation. 2) Classification Classifiers are general computational models for assigning a category to an input. The inputs may be vectors of features for the items being classified or data about relationships among th e items. The category is a domain-specific classification such as malignant/benign for tumor classification, approve/reject for credit requests, or intruder/authorized for security checks.One way to build a recommender system using a classifier is to use information about a product and a customer as the input, and to have the output category represent how strongly to recommend the product to the customer. 3) Association Rules Mining Association rule mining is to search for interesting relationships between items by finding items frequently appeared together in the transaction database. If item B appeared frequently when item A appeared, then an association rule is denoted as A B (if A, then B).The support and confidence are two measures of rule interestingness that reflect III. WEB DATA MINING FRAMEWORK FOR E-COMMERCE RECOMMENDER SYSTEMS A. A Visual Web Log Mining Architecture for Personalized E-learning Recommender System In this section, we present A Visual Web Log Mining Architec ture for e-learning recommender to enable personalized, named V-WebLogMiner, which relies on mining and on visualization of Web Services log data captured in elearning environment. The V-WebLogMiner is such a odel: with the mining technology and analysis of web logs or other records, the system could find learners’ interests and habits. While an old learner is visiting the website, the system will automatically match with the active session and recommend the most relevant hyperlinks what the learner interests. As shown in Figure1, V-WebLogMiner is a multi-layered architecture capable to deal with both Web learner profiles and traditional Web server logs as input data. It maintains three main components: data preprocessing module, Web mining module and recommendation module. ) Web Mining Module The Web mining module discovers valuable knowledge assets from the data repository containing learners' personal data by executes the mining algorithms, tracked data of learners' perfor mance and behavior, automatically identify each learner’s frequently sequential pages and store them to recommend database. When the learner visit the site next time, hyperlinks of those pages will be added so that the learner could directly link to his individual pages being remembered.The major component of Web mining module is Web data mining which acts as a conductor controlling and synchronizing every component within the module. The Web data mining module is also responsible for interfacing with the storage. The learning profile evaluation component provide profiling tool to collect personal data of learner and tracking tool to observe learners' actions including like and dislike information. For personalization applications, we apply rule discovery methods individually to every learner’s data.To discover rules that describe the behavior of individual learner, we use various data mining algorithms, such as Apriori [8] for association rules and CART (Classificatio n and Regression Tress) [9] for classification. 3) Recommendation Module The recommendation module is a recommendations engine; it is in charge of bulk loading data from course database, executing SQL commands against it and provides the list of recommended links to visualization tools.For the recommendation module, recommendations engine is responsible for the synchronizing process indexing and mapping, is a component for storing and searching recommend assets to be used in the learning process. The recommendation engine considers the active learners in conjunction with the recommended database to provide personalized recommendations, it directly related to the personalization on the website and the development of elearning system. The task of the recommendation engine is to determine the type of the learner online and compute recommendations based on the recent actions of that learner.The decision is based on the knowledge attained from the recommended database. The recommender en gine is activated each time that the learner visits a web page. First, if there are clusters in the recommended database, then the engine has to classify the current learner to determine the most likely cluster. We have to communicate with the engine to know the current number of pages visited and average knowledge of the learner. Then, we use the centroid minimum distance method [10] for assigning the learner to the cluster whose centroid is closest to that learner.Finally, we make the recommendation according to the rules in the cluster. So, only the rules of the corresponding cluster are used to match the current web page in order to obtain the current list of recommended links [11]. 4) The Visualization tools Visualization tools should be used to present implicit and useful knowledge from recommendations engine, Web services usage and composition. Data can be viewed at different levels Figure 1. A visual web mining architecture for Personalized E-learning Recommender System ) Da ta Preprocessing Module The data preprocessing module is set of programs used to prepare data for further processing. For instance: extraction, cleaning, transformation and loading. This module uses Web log files and learner profile files to feed the data repository. The data preparation component is used to parse and transform plain ASCII files produced by a Web server to a standard database format. This component is important to make the architecture independent from the Web server supplier. of granularity and abstractions as patrolled coordinate’s graphs [12, 13].This visual model easily shows the interrelationships and dependencies between different components. Interactively, the model can be used to discover sensitivities and to do approximate optimization, etc. B. The Procedure of the Data is Explained As show in figure 1, the beginning learner, that is to say the earliest one, will study in the e-Learning teaching platform. The course materials of Web studying system c ome from the course database. The data of learner’s learning profiles may be recorded in the learner profile files and Web log files.Then next step is to find out the best learning profile from the proceeded data of Web log through web mining to proceed with Association rule and others data mining algorithm. These learning profiles need to be classified—every field has relevant courses and better learning profiles. The recommender engine will offer the list of recommended links when learners study the courses. With the above information and learning profiles, when the future learners study in Web, recommender engine offers related link lists according to recommend database. However, these link lists may not be suitable for all learners.Therefore, after finishing recommendation every time, there are systems of assessing. The learner (n +1) evaluates the learning profiles that are recommended. Because the profiles analyzed by system may not be perfect, if there are adjus tments of evaluation would make the recommendation conform to learners’ asks more. These suggestions can help learners navigate better relevant resources and fast recommend the on-line materials, which help learners to select pertinent learning activities to improve their performance based on on-line behavior of successful learners.IV. CONCLUSION AND FUTURE WORK There are some possible extensions to this work. Research for analyzing learners’ past studying pattern will enable to detect an appropriate. Furthermore, it will be an interesting research area to effectively judge session boundaries and to improve the efficiency of algorithms for web data mining. ACKNOWLEDGMENT The authors gratefully acknowledge the financial subsidy provided by the Xiamen Science and Technology Bureau under 3502Z20077023, 3502Z20077021 and YKJ07013R project. REFERENCES [1] [2] D. J. H and, H. Mannila, and P. Smyth.Principles of Data Mining. MIT Press, 2000. J. B. Schafer, J. A. Konstan, and J. Riedl. Recommender systems in ecommerce. In ACM Conference on Electronic Commerce, pages 158166, 1999. Liaw, S. & Hung ,H. How Web Technology Can facilitate Learning. Information Systems Management, 2002. Choonho Kim and Juntae Kim, A Recommendation Algorithm Using Multi-Level Association Rules, Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence, p. 524, October 13-17, 2003. J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaurmann Publishers, 2000 Za ane, O.R. & Luo, J. Towards evaluating learners’ behaviour in a web-based distance learning environment. In Proc. of IEEE International Conference on Advanced Learning Technologies (ICALT01), p. 357– 360, 2001. Sarwar, B. , Karypis, G. , Konstan, J. A. , & Reidl, J. Item-based Collaborative Filtering Recommendation Algorithms. Proceedings of the Tenth International Conference on World Wide Web, pp. 285 – 295, 2001. R. Agrawal et al. , Fast Discovery of Association Rul es, Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, Calif. , 1996, chap. 12. L. Breiman et al. Classification and Regression Trees, Wadsworth, Belmont, Calif. , 1984. MacQueen, J. B. Some Methods for classification and Analysis of Multivariate Observations. In Proceedings of of 5-th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp. 281297. Cristobal Romero, Sebastian Ventura and Jose A. Delgado et al. , Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems, Creating New Learning Experiences on a Global Scale,2007, pp. 292-306. Inselberg, A. Multidimensionl detective, In IEEE Symposium on Information Visualization, 1997, vol. 00, p. 00-110 . Ware, C. Information Visualization: Perception for Design,Morgan Kaufmann, New York, 2000. [3] [4] [5] [6] [7] [8] [9] [10] Recommender systems have emerged as powerful tools for helping users find and evaluate items of interest. The research work presente d in this paper makes several contributions to the recommender systems for personalized e-learning. First of all, we propose a new framework based on web data mining technology for building a Web-page recommender system. Additionally, we demonstrate how web data mining technology can be effectively applied in an e-learning environment. [11] [12] [13]

Field Marshal Gerd von Rundstedt in World War II

Field Marshal Gerd von Rundstedt was prominent German commander during World War II. After commanding Army Group South during the invasion of Poland, he played a central role in the defeat of France in 1940. Over the next five years, Rundstedt held a series of senior commands on both the Eastern and Western Fronts. Though he was removed as the commander-in-chief in the West following the Allied landings in Normandy, he returned to the post in September 1944 and remained in that role until the final weeks of the war. Early Career Born December 12, 1875 at Aschersleben, Germany, Gerd von Rundstedt was a member of an aristocratic Prussian family. Entering the German Army at age sixteen, he began learning his trade before being accepted into the German Armys officer training school in 1902. Graduating, von Rundstedt was promoted to captain in 1909. A skilled staff officer, he served in this capacity at the beginning of World War I in August 1914. Elevated to major that November, von Rundstedt continued to serve as a staff officer and by the end of the war in 1918 was chief of staff for his division. With the conclusion of the war, he elected to remain in the postwar Reichswehr. Interwar Years In the 1920s, von Rundstedt rapidly advanced through the ranks of the Reichswehr and received promotions to lieutenant colonel (1920), colonel (1923), major general (1927), and lieutenant general (1929). Given command of the 3rd Infantry Division in February 1932, he supported Reich Chancellor Franz von Papens Prussian coup that July. Promoted to general of the infantry that October, he remained in that rank until being made a colonel general in March 1938. In the wake of the Munich Agreement, von Rundstedt led the 2nd Army which occupied the Sudetenland in October 1938. Despite this success, he promptly retired later in the month in protest of the Gestapos framing of Colonel General Werner von Fritsch during the Blomberg–Fritsch Affair. Leaving the army, he was given the honorary post of colonel of the 18th Infantry Regiment. Field Marshal Gerd von Rundstedt Rank: Field MarshalService: Imperial German Army ,Reichswehr, WehrmachtBorn: December 12, 1875 in Aschersleben, GermanyDied: February 24, 1953 in Hanover, GermanyParents: Gerd Arnold Konrad von Rundstedt and Adelheid FischerSpouse: Luise â€Å"Bila† von GoetzChildren: Hans Gerd von RundstedtConflicts: World War I, World War II World War II Begins His retirement proved brief as he was recalled by Adolf Hitler the following year to lead Army Group South during the invasion of Poland in September 1939. Opening World War II, the campaign saw von Rundstedts troops mount the main attack of the invasion as they struck east from Silesia and Moravia. Winning the Battle of Bzura, his troops steadily drove back the Poles. With the successful completion of the conquest of Poland, von Rundstedt was given command of Army Group A in preparation for operations in the West. As planning moved forward, he supported his chief of staffs, Lieutenant General Erich von Manstein, call for a swift armored strike toward the English Channel which he believed could lead to the strategic collapse of the enemy. Attacking on May 10, von Rundstedts forces made swift gains and opened a large gap in the Allied front. Led by General of Cavalry Heinz Guderians XIX Corps, German troops reached the English Channel on May 20. Having cut off the British Expeditionary Force from France, von Rundstedts troops turned north to capture the Channel ports and prevent its escape to Britain. Field Marshal Gerd von Rundstedt. Bundesarchiv, Bild 183-L08129 / CC-BY-SA 3.0 Traveling to Army Group As headquarters at Charleville on May 24, Hitler urged its von Rundstedt, to press the attack. Assessing the situation, he advocated holding his armor west and south of Dunkirk, while utilizing the infantry of Army Group B to finish off the BEF. Though this allowed von Rundstedt to preserve his armor for the final campaign in France, it allowed the British to successfully conduct the Dunkirk Evacuation. On the Eastern Front With the end of fighting in France, von Rundstedt received a promotion to field marshal on July 19. As the Battle of Britain began, he assisted in the development of Operation Sea Lion which called for the invasion of southern Britain. With the Luftwaffes failure to defeat the Royal Air Force, the invasion was called off and von Rundstedt was instructed to oversee the occupation forces in Western Europe. As Hitler began planning Operation Barbarossa, von Rundstedt was ordered east to assume command of Army Group South. On June 22, 1941, his command took part in the invasion of the Soviet Union. Driving through Ukraine, von Rundstedts forces played a key role in the encirclement of Kiev and capture of over 452,000 Soviet troops in late September. Pushing on, von Rundstedts forces succeeded in capturing Kharkov in late October and Rostov in late November. Suffering a heart attack during the advance on Rostov, he refused to leave the front and continued to direct operations. With the Russian winter setting in, von Rundstedt advocating halting the advance as his forces were becoming overextended and hampered by the severe weather. This request was vetoed by Hitler. On November 27, Soviet forces counterattacked and forced the Germans to abandon Rostov. Unwilling to surrender ground, Hitler countermanded von Rundstedts orders to fall back. Refusing to obey, von Rundstedt was sacked in favor of Field Marshal Walther von Reichenau. Return to the West Briefly out of favor, von Rundstedt was recalled in March 1942 and given command of Oberbefehlshaber West (German Army Command in the West - OB West). Charged with defending Western Europe from the Allies, he was tasked with erecting fortifications along the coast. Largely inactive in this new role, little work occurred in 1942 or 1943. Field Marshals Gerd von Rundstedt and Erwin Rommel.   Bundesarchiv, Bild 101I-718-0149-18A / Jesse / CC-BY-SA 3.0 In November 1943, Field Marshal Erwin Rommel was assigned to OB West as commander of Army Group B. Under his direction, work finally began on fortifying the coastline. Over the coming months, von Rundstedt and Rommel clashed over the disposition of OB Wests reserve panzer divisions with the former believing they should located in the rear and the latter wanting them near the coast. Following the Allied landings in Normandy on June 6, 1944, von Rundstedt and Rommel worked to contain the enemy beachhead. When it became obvious to von Rundstedt that the Allies could not be pushed back into the sea, he began advocating for peace. With the failure of a counterattack near Caen on July 1, he was asked by Field Marshal Wilhelm Keitel, head of the German armed forces, what should be done. To this he brusquely replied, Make peace you fools! What else can you do? For this, he was removed from command the next day and replaced with Field Marshal Gunther von Kluge. Final Campaigns In the wake of the July 20 Plot against Hitler, von Rundstedt agreed to serve on a Court of Honor to assess officers suspected of being opposed to the fà ¼hrer. Removing several hundred officers from the Wehrmacht, the court turned them over to Roland Freislers Volksgerichtshof (Peoples Court) for trial. Implicated in the July 20 Plot, von Kluge committed suicide on August 17 and was briefly replaced by Field Marshal Walter Model. Eighteen days later, on September 3, von Rundstedt returned to lead OB West. Later in the month, he was able to contain Allied gains made during Operation Market-Garden. Forced to give ground through the fall, von Rundstedt opposed the Ardennes offensive which was launched in December believing that insufficient troops were available for it to succeed. The campaign, which resulted in the Battle of the Bulge, represented the last major German offensive in the West. Field Marshal Gerd von Rundstedt (center) after his capture in 1945. Bundesarchiv, Bild 146-2007-0220 / CC-BY-SA Continuing to fight a defensive campaign in early 1945, von Rundstedt was removed from command on March 11 after again arguing that Germany should make peace rather than fight a war it could not win. On May 1, von Rundstedt was captured by troops from the US 36th Infantry Division. During the course of his interrogation, he suffered another heart attack. Last Days Taken to Britain, von Rundstedt moved between camps in southern Wales and Suffolk. After the war, he was charged by the British for war crimes during the invasion of the Soviet Union. These charges were largely based on his support of von Reichenaus Severity Order which led to mass murders in occupied Soviet territory. Due to his age and failing health, von Rundstedt was never tried and he was released in July 1948. Retiring to Schloss Oppershausen, near Celle in Lower Saxony, he continued to be plagued by heart problems until his death on February 24, 1953.