I am a Naked Woman Professor Ramos Blog

I am a Naked Woman When I was about five years old I played my first video game, it was called Quake 2. This game was played on the PC and I sucked. I kept playing my little heart out as often as I could. I would play for hours in hope that one day I could beat the game, this determination led me to the life long hobby of playing video games. This hobby taught me that no matter how many times you fail, keep trying and never give up. Every mistake is an opportunity to learn. When I was young I remember watching my dad and older brother Dustin play video games on the computer all the time. The two most memorable games they played were named Quake 2 and Diablo. They were two totally different games in terms of objects, but both intrigued me. Quake 2 was what my dad called a â€Å"shoot em’ up game† and Diablo was a RPG game where you battled the forces of evil, eventually fighting the devil himself. I would always bug them to play, but to no avail. They would simply say â€Å"No, you are to young. You wouldn’t even understand how to play† Fortunately I knew how to get my way. I simply went to the one who wore the pants in the family, my mother. I would just have to say, â€Å"Mom!! I want to play, and they won’t let me!† My mom would go to yell at them and tell them I should be allowed to play to. After about a million times of snitching out my dad and brother. I was finally allowed to play. I sat my little butt do wn and instantly lost. Dustin was usually there watching me, and as soon as I lost he would say â€Å"See!!! You can’t even play† I tried harder and harder until I was eventually able to play the game without dying every 5 seconds. My favorite game to play at the time was called Quake 2, it is a First-Person Shooter game and the objective is to complete the level and beat the occasional boss (surprising right?) I quickly discovered that I could change the way my character looked. I decided to do what any normal six-year-old boy would do, I played as the naked woman! A few days later my dad sat down to play some quake and shortly after I heard â€Å"What the Fuck, Daniel were you playing as the naked lady!† I unfortunately was no longer allowed to play my favorite video game. By the time I could play video games again, I had a few new choices of games. We even had a new system. It was called the Super Nintendo. The Super Nintendo seemed like it was a god send, playing video games was no longer limited to only one person at a time! This console had two controllers which allowed me and my brother Dustin to play together. Instead of him always teasing me he helped me get better at video games, probably because it benefitted him. There were three games we frequently played. The first two were called Sonic the Hedgehog and Super Mario Brothers. Those games were two player games, or at least we would take turns and work together. The third game we played often was called Mortal Combat, this game was not a team game and it was two players. This was a fighting game where the two players would have to fight against each other. My brother and I loved to play Mortal Combat. Yeah, you guessed it, most of the time he would win. Those few times I managed to beat my brother felt magical, but every loss I learned from. After sinking many hours of my young life into this I got good at it, I was able to play on single player and beat the entire game with no assistance. Dustin wasn’t even able to beat the game. I started to really like playing against him, because the tables were now turned in my favor. I was finally able to win most of the games we played. I was now the one saying â€Å"HA! You suck I win.† I got to be the one doing the finishing moves in Mortal Kombat. These were special moves you can do when you won the round. My two favorite moves were to violently rip off the head of the opposing character or to kick them into a spike pit below a bridge we fought on. I was able to get that sweet moment of taunting Dustin, rubbing it in his face that I was victorious. It felt great to be able to tease my brother who is five years older than me. This is when I learned I was able to get pretty good at video games. As I got older we acquired more systems and a very large variety of video games. I made it my personal goal to beat every video game I could, no matter the type or how difficult they are. By having this goal, I excelled at playing video games. Soon enough my gaming skills were leagues ahead of my brother and dad. My skill had grown so great I was basically able to play any video game with ease and had the ability to beat many on hard difficulties. When I was 11 years old, my dad bought me a computer. This was a huge game changer for me, because when I was younger most of my video game experience was on consoles. Consoles used controllers and the computer uses a mouse and keyboard. The play style was drastically different and suddenly, I was like a vacuum and sucked. When I first got my computer my brother Dustin already had a computer and a few games. He had the newer games in the series I used to watch him play as a child. Some of these games were Quake 3: Arena, Diablo 2 and even a new game called Starcraft. I watched him play a few times, but I never asked to play them. I decided to ask my brother if I could play his games. He loaned me two games, Diablo 2 and Starcraft. Diablo 2 was a revamped version of the Diablo game I used to watch him play and Starcraft was a strategy-based game. I instantly fell in love with these two games. I sunk most of my play time into Starcraft. Starcraft was an extremely hard game if you wanted to be good at it and you could play against other people online. You could play with just the mouse, but you don’t really play all that well. If you wanted to be truly great at a strategy game on the PC, you need to learn how to use the keyboard very well. These types of game are full of â€Å"hotkeys† which are keys on the keyboard that make something happen in game, its much faster then clicking. Starcraft has about 30 different â€Å"hotkeys† that were part of the game by default. It sounds easy to learn the â€Å"hotkeys† but it’s not like you just press a key and you’re done. To use them to their max potential you had to have precision timing, it’s almost like a repeated cycle of the correct pattern. After a lot of perseverance, I was skilled at using the keyboard and mouse simultaneously while still making split second decisions. I was like a well-oiled machine, constantly making precision movements. To this day I still play video games and I still think about all those experiences I had as a child from being a naked woman to online gaming. Every so often I still play some of the old school games, I love the nostalgia from it. I would be a different person than who I am today if it wasn’t for video games. They influenced my decision to get my bachelor’s degree in Computer Science, because I love the way they enthrall the user. Video games give the player an endless challenge, constantly teaching them how to be better. It is extremely interesting to see how a game goes from the drawing board to a fully immersive experience.   I plan to develop video games when I am out of college. I want to work for Blizzard Entertainment, my favorite game studio. None of this would have happened if it wasn’t for my love of video games and the valuable lessons they thought me. I learned patience and perseverance from all those times I lost and had to wait for loading screens or repeated the same level, until finally winning. I also learned that no matter how impossible the level or objective seemed. You always need to keep trying, learn from your mistakes and never ever give up.

Plastics in Childrens Toys

Plastics in Children's Toys Neither you nor your child can escape the touch of plastics, and for the most part, you dont need to worry about it. Most plastics are perfectly safe for even very small children. Plastics in their pure form typically have low solubility in water and have a low level of toxicity. However, some plastics found in toys contain a variety of additives that have been found to be toxic. Although the relative risk of injury from plastic-based toxins is low, its prudent to select your childs toys carefully. Bisphenol-A Bisphenol-A usually called BPA was long used in toys, baby bottles, dental sealants, and even thermal receipt tape. More than 100 studies have linked BPA to problems including obesity, depression and breast cancer.   PVC Avoid plastics that are marked with a 3 or PVC because polyvinyl chloride plastics often contain additives that can make plastics more harmful than they need to be for children. The volume and type of those additives will vary by the object and may differ significantly from toy to toy. The manufacture of PVC creates dioxin, a serious carcinogen. Although the dioxin shouldnt be in the plastic, its a byproduct of the manufacturing process, so buying less PVC may be an environmentally smart decision. Polystyrene Polystyrene is a rigid, brittle, inexpensive plastic commonly used to make plastic model kits and other toys. The material is also a base of  EPS foam. In the late 1950s, high-impact polystyrene was introduced, which was not brittle; it is commonly used today to make toy figurines and similar novelties. Plasticizers Plasticizers such as adipates and phthalates had long been added to brittle plastics such as polyvinyl chloride to make them pliable enough for toys. Traces of these compounds can possibly leak out of the product. The European Union placed a permanent ban on the use of phthalates in toys. Furthermore, in 2009 the United States banned certain types of phthalates commonly used in plastics. Lead According to the U.S. Centers for Disease Control and Prevention, plastic toys may contain lead, which is added to the plastic to soften it. If the toy is exposed to high heat, the lead may leach out in the form of dust, which may then be inhaled or ingested by a child or pet. A Little Bit of Vigilance Almost all plastic childrens toys are safe. A vast majority of toys are now made with polybutylene terephthalate plastic: You can tell these toys apart by sight, as they are the brightly colored,  shiny, very impact-resistant objects littering toy boxes across the country. Regardless of the type of plastic you encounter, its always wise to discard or recycle any plastic object that shows obvious signs of wear or degradation.   So although theres no need to panic about toxic toys, a little bit of vigilance especially with antique toys, or very inexpensive mass-produced toys may protect your children from unnecessary exposure.

Conraceptive use Essay Example | Topics and Well Written Essays - 500 words

Conraceptive use - Essay Example Over a long period these studies have turned to be more thoroughgoing and have enhanced in analytical sophistication and size. This topic has been highly debated and researched and still being studied to find out any association between the mortality and the prolonged use of oral contraceptives. However, according to the study conducted by Colditz (1994), the prolonged utilization of oral contraceptives is found to be unassociated with increased risk of mortality of women. This research results seem to be valid and there is no linkage between the use of oral contraceptives and mortality among females as the study has been conducted for a period of twelve years. Through the design selected, the researcher gathered data for 12 years to follow the impact of oral contraceptives on the health of women. The instrument utilized was questionnaire and the responses collected then were further categorized into groups. The number of participants was 166,755 females between the ages of 30 to 55 years and the data was initially collected in the year 1976 that was followed till 1988. The design chosen seems valid for conducting a study over several years but this design does have certain drawbacks. The first and foremost drawback is that it lacks randomization thus causing imbalances in characteristics of patients. Moreover the recognition of cohorts could be difficult because of the confounding variables. Since a particular sample is selected, the masking or blinding is complicated as in the given case all the respondents were registered nurses of ages between 30 to 55 years. Moreover it is always a time consuming option to perform a cohort research as in the given research it took 12 years to get the research results. However apart from the drawbacks the design provides a chance to match the subjects involved in the cohorts thus limiting the impact of confounding factors. Moreover the outcomes of such study can be standardized and

Marketing database Essay Example | Topics and Well Written Essays - 250 words

Marketing database - Essay Example This is because profitability levels of different client differs, and thus being able to detect the most rewarding customers is essential. Based on the number of consumers under investigation, LTV can be carried out on the whole customer base or through segmentation (Hughes, 2014). Although the latter is more demanding, its more effective in analyzing clients. For royalty program, acquisition cost is considered as ‘sunk cost’, and thus not used in the calculation, as the customers are part of the current customer base. To effectively analyze the customers, analysis need to be executed at a group level using the test versus control situation approach. In this case, the tests are used to realize the loyalty program, an aspect not addressed by control. In this regard, for success to be realized, the lifetime value should be higher than the investment costs. Therefore, LPV is an efficient tool in evaluating the worthiness of a future royalty program (Rodgers & Peppers,

Implementation Of Clustering Algorithm K Mean K Medoid Computer Science Essay

Implementation Of Clustering Algorithm K Mean K Medoid Computer Science Essay Data Mining is a fairly recent and contemporary topic in computing. However, Data Mining applies many older computational techniques from statistics, machine learning and pattern recognition. This paper explores two most popular clustering techniques are the k-means k-medoids clustering algorithm. However, k-means algorithm is cluster or to group your objects based on attributes into K number of group and  k-medoids  is a  related to the  K-means  algorithm. These algorithms are based on the k partition algorithms and both attempt to minimize  squared error. In contrast to the K-means algorithm K-medoids chooses data points as centres. The algorithms have been developed in Java, for integration with Weka Machine Learning Software. The algorithms have been run with two dataset Facial palsy and Stemming. It is having been shown that the algorithm is generally faster and more accurate than other clustering algorithms. Data Mining derives its name from the similarities between searching for valuable business information in a large database (for example, finding linked products in gigabytes of store scanner data) and mining a mountain for a vein of valuable ore.[1] Both process requires either sifting through an immense amount of material. Or intelligently probing it to find exactly where the value resides. Data Mining Data mining is also known as knowledge mining. Before it was named DATA MINING, it was called data collection, data warehousing or data access. Data mining tools predicts the behaviours of the models that are loaded in the data mining tools (like Weka) for analysis, allowing making predicted analysis, of the model. Data mining provides hands-on and practical information. Data mining is the most powerful tool available now. Data mining can be used for modelling in fields such as artificial intelligence, and neural network. What does it do? Data mining take the data which exists in unrelated patterns and designs, and uses this data to predict information which can be compared in terms of statistical and graphical results. Data mining distil / filters the information from the data that is inputted and final model is generated. Clustering What is cluster analysis? Unlike classification and prediction, which analyse class-labeled data objects, clustering analyses data objects without consulting a known class label. A 2-D plot of customer data with respect to customer locations in a city, showing three data clusters. Each cluster center is marked with a +.[6] Clustering is the technique by which like objects are grouped together. The objects are clustered or grouped based on the principle of maximizing the intra class similarity and minimizing the interclass similarity. i.e. clusters of the objects are made so that the clusters have resemblance in comparison to one another, but are very divergent to objects in other clusters. Each cluster that is made can be viewed as a class of objects, from which rules can be derived. [6] Problem overview The problem at hand is able to correctly cluster a facial palsy dataset which is given by our lecturer. This section will provide an overview of dataset being analysed, and description about dataset that we use in this implementation. Data Set 1.3.1.1 Facial_Palsy_svmlight_format Facial Palsy data is for binary classification. +1 severe facial palsy faces -1 Non-severe or normal faces 66 Principal components generated from 5050 Hamming distance images 1.3.1.2 A6_df2_stemming__svm: Attributes: 100 A6_df2_stemming__svm_100.dat +1 Open question -1 Closed question Section 2 Methodology This section will firstly discuss the methodology behind K-means k-medoids algorithm. It is than followed by steps to implement k-means and k medoids algorithms. How many input, output and what are the steps to perform k-means and k-medoids. 2.1 K-mean K-means clustering starts with a single cluster in the centre, as the mean of the data. Here after the cluster is split into 2 clusters and the mean of the new cluster are iteratively trained. Again these clusters are split and the process goes on until the specified numbers of the cluster are obtained. If the specified number of cluster is not a power of two, then the nearest power of two above the number specified is selected and then the least important clusters are removed and the remaining clusters are again iteratively trained to get the final clusters. If the user specifies the random start, random cluster is generated by the algorithm, and it goes ahead by fitting the data points into these clusters. This process is repeated many times in loops, for as many random numbers the user chooses or specifies and the best value is found at the end. The output values are displayed. The drawbacks of the clustering method are that, the measurement of the errors or the uncertainty is ignored associated with the data. Algorithm: The k-means algorithm for partitioning, where each clusters centre is represented by the mean value of the objects in the cluster. Input: k: the number of clusters, D: a data set containing n objects. Output: A set of k clusters. Method: (1) Arbitrarily choose k objects from D as the initial cluster centers; (2) Repeat (3) reassign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster; (4) Update the cluster means, i.e., calculate the mean value of the objects for each cluster; (5) Until no change; Where E is the sum of the square error for all objects in the data set; p is the point in space representing a given object; and mi is the mean of cluster Ci (both p and mi are multidimensional). In other words, for each object in each cluster, the distance from the object to its cluster center is squared, and the distances are summed. This criterion tries to make the resulting k clusters as compact and as separate as possible.[2] Clustering of a set of objects based on the k-means method. (The mean of each cluster is marked by a +.) 2.2 K- Medoids This report recommends a new algorithm for K-medoids, which runs like the K-means algorithm. The algorithm proposed scans and calculates distance matrix, and use it for finding new medoids at every constant and repetitive step. The evaluation is based on real and artificial data and is compared with the results of the other algorithms. Here we are discussing the approach on k- medoids clustering, using the k-medoids algorithm. The algorithm is to be implemented on the dataset which consist of uncertain data. K-medoids are implemented because they to represent the centrally located objects called medoids in a cluster. Here the k-medoids algorithm is used to find the representative objects called the  medoids  in the dataset. Algorithm: k-medoids. PAM, a k-medoids algorithm for partitioning based on medoids or central objects. Input: k: the number of clusters, D: a data set containing n objects. Output: A set of k clusters. Method: (1) Arbitrarily choose k objects in D as the initial representative objects or seeds; (2) Repeat (3) Assign each remaining object to the cluster with the nearest representative object; (4) Randomly select a no representative object, o random; (5) Compute the total cost, S, of swapping representative object, oj, with o random; (6) If S (7) Until no change; Where E is the sum of the absolute error for all objects in the data set; p is the point in space representing a given object in cluster Cj; and oj is the representative object of Cj. In general, the algorithm iterates until, eventually, each representative object is actually the medoids, or most centrally located object, of its cluster. This is the basis of the k-medoids method for grouping n objects into k clusters.[6] 2.3 Distance Matrix An important step in most clustering is to select a  distance measure, which will determine how the  similarity  of two elements is calculated. Common distance metrics: Euclidean Manhattan Minkowski Hamming etcà ¢Ã¢â€š ¬Ã‚ ¦ Here in our implementation we choose two distance matrix that you can see below with description. 2.3.1 Euclidean Distance Metric The  Euclidean distance  between points  p  and  q  is the length of the  line segment. In  Cartesian coordinates, if  p  =  (p1,  p2  pn) and q  =  (q1,  q2  qn) are two points in  Euclidean  n-space, then the distance from  p  to  q  is given by: 2.3.2 Manhattan Distance Metric The Manhattan (or taxicab) distance,  d1, between two vectors  in an n-dimensional  real  vector space  with fixed  Cartesian coordinate system, is the sum of the lengths of the projections of the  line segment  between the points onto the  coordinate axes. Section 3 Discussion In this section we are discussing about how Weka Machine learning work and how we implemented both k-means and k medoids algorithm. To implement these two algorithms we use Java and we are explaining how we implemented in java which function we use in order to implement these two algorithms. 3.1 Weka Machine Learning Weka is a machine learning software made using Java and many other languages. Weka has a collection of tools that are used to analyse the data that the user inputs in the form of dataset files. Weka supports more than four different input data formats. Weka uses an interactive GUI interface, which is easy for the user to use.  Ã‚  Weka provides the functionality for testing and visual aid options that can be used by the user to compare and sort the results. 3.2 Implementation In this section, we discuss about implementation of 2 clustering algorithms: K-Means and K-Medoids. Here, we use Object Oriented Programming to implement these 2 algorithms. The structure of program as below: There are 3 packages: K-Mean, K-Medoid, main. Files in K-Mean package: Centroid.java Cluster.java KMean_Algorithm.java KMean_Test.java KMean_UnitTest.java Files in K-Medoid package: KMedoid_Algorithm.java KMedoid_UnitTest.java Files in main package: Attribute.java DataPoint.java DistanceCalculation.java FileFilter.java MainFrame.java Utilities.jav There are some main functions implemented for clustering activity as below: 3.2.1 read_SVMLightFile_fill_up_missing_attribute() This function is about reading the SVM Light data file (.dat) and fill up all the missing attributes/values in data file before returning a Vector of data-points for clustering activity. 3.2.2 calculate_distance() This function is providing calculation according to the distance metric input in order to calculate distance between data objects for clustering activity. Overall, this function provides calculation for 3 different distance metrics as: Euclidean, Manhattan and Minkowski. 3.2.3 startClustering() This function is about running a particular clustering algorithm and returns a Vector of Clusters with their own data-points inside. All the steps of a particular clustering algorithm is implemented, here we implement K_Means and K_Medoids clustering algorithms. 3.2.4 calculateSumOfSquareError() This function is about calculating the total/sum square error for all the output clusters. By calling the function calculateSquareError() inside every cluster and sum up, the sum of Square Error will be calculated as long as the clustering activity finished. 3.2.5 calculateSumOfAbsoluteError() This function is about calculating the total/sum absolute error for all the output clusters. By calling the function calculateAbsoluteError() inside every cluster and sum up, the sum of Absolute Error will be calculated as long as the clustering activity finished. 3.2.6 toString() and main() The toString() function will return a string which represents the clustering output, including: total objects of every cluster, percent of object in every cluster, the error (such as: sum of square error or sum of absolute error), the centroid of every cluster and all the data-points clustered in the clusters. The main() function inside MainFrame.java class will allow to execute the GUI of the program, so users can interact with system by GUI instead of console or command-line. In this GUI, users can choose type of distance metric (such as Euclidean and Manhattan), Clustering algorithm (such as K-Means and K-Medoids) and enter input parameters such as number of clusters and number of iterations for clustering activity. Besides, users also can open any data file to view or modify and save before running clustering as well as export the original data file with missing attributes/values to new processed data file with all missing values filled up by zero (0). Section 4 Analysis In order to access the performance of the K-means k-medoids clusters, two dataset of analyses was carried out. The aim of this set to tests was provide an indicator as to how well the clusters performed using the k-means and k-medoids function. The tests were involved comparing the cluster to other cluster of various types provided within Weka cluster suite. The results are summarised throughout the remainder of this section. 4.1 Experiment (Facial Palsy dataset) results vs. Weka Here In this section how we did a comparison with our application algorithm vs. Weka you can see below. In this pattern we give iterations when we run a dataset with our application and Weka. Iterations: 10 >> 30 >> 50 >> 100 >> 200 >> 300 >> 400 >> 500 In this pattern we give a cluster when we run a dataset with our application and Weka. Clusters: 2 >> 3 >> 4 >> 5 After we run dataset with this format than each and every run we get result we combine that result, compare with Weka, we make a total of each and every column and come with average and we are displaying in table that you can see in below table. This Symbol is object. To see a result please click on this object it will show you result. We put as object because result is too big in size so we are not able to put in this A4 page. 4.2 Experiment (Stemming Question dataset) results vs. Weka Here In this section how we did a comparison with our application algorithm vs. Weka you can see below. In this pattern we give iterations when we run a dataset with our application and Weka. Iterations: 10 >> 30 >> 50 >> 100 >> 200 >> 300 >> 400 >> 500 In this pattern we give a cluster when we run a dataset with our application and Weka. Clusters: 2 >> 3 >> 4 >> 5 After we run dataset with this format than each and every run we get result we combine that result, compare with Weka, we make a total of each and every column and come with average and we are displaying in table that you can see in below table. This Symbol is object. To see a result please click on this object it will show you result. We put as object because result is too big in size so we are not able to put in this A4 page. Section 5 Conclusion In evaluating the performance of data mining techniques, in addition to predicative accuracy, some researchers have been done the importance of the explanatory nature of models and the need to reveal patterns that are valid, novel, useful and may be most importantly understandable and explainable. The K-means and k-medoids clusters achieved this by successfully clustering with facial palsy dataset. Which method is more robust-k-means or k-medoids? The k-medoids method is more robust than k-means in the presence of noise and outliers, because a medoids is less influenced by outliers or other extreme values than a mean. However, its processing is more costly than the k-means method. Both methods require the user to specify k, the number of clusters. Aside from using the mean or the medoids as a measure of cluster center, other alternative measures are also commonly used in partitioning clustering methods. The median can be used, resulting in the k-median method, where the median or middle value is taken for each ordered attribute. Alternatively, in the k-modes method, the most frequent value for each attribute is used. 5.1 Future Work The K-means algorithm can create some in efficiency as; it scans the dataset leaving some noise and outliners. These small flaws can be considered major to some of the users, but this doesnt means that the implementation can be prevented. It is always possible that sometimes the dataset is more efficient to follow other algorithms more efficiently, and the result distribution can be equal or acceptable. It is always advisable to make the dataset more efficient by removing unwanted attributes and more meaning full by pre-processing the nominal values to the numeric values. 5.2 Summery Throughout this report the k-mean and the k-medoids algorithms are implemented, which find the best result by scanning the dataset and creating clusters. The algorithm was developed using Java API and more Java classes.  

English grammar Essay

Read the passage given below: and answer the questions a, b, c, and d that follow: [25] A list of queries on email asks recipients to name the world’s richest man in 2008; the winner of the world’s golf title in 2007; the designer of the first rockets; the manufacturer of the first bicycle and so on? building up a roll call of achievers who left their mark on mankind. It ended with, â€Å"Who was the teacher who helped you to enjoy school and whom you remember most vividly? † I don’t know a single recipient of the email who answered a single one of the questions except the last. In other words, everyone’s most unforgettable person was a supportiveand encouraging teacher whose wealth, fame and social standing mattered not at all. What the grateful student received was beyond evaluation because what the teacher gave most freely was the precious gift of the self. It was not just knowledge to pass an examination that they gave their students but an understanding of the value of knowledge itself and a love of it. Great teachers seek to form, not merely inform their students. Today, when teaching (especially teaching young children who have not learned to write) is no longer a coveted profession, I wish we could all pay a silent tribute to the many obscure men and women who shaped our lives and asked for so little in return. Surely, a good teacher deserves to be called a deva because the real meaning of the word is â€Å"the shining one†. Every year, choosing a day when it is not functioning, my cousin visits his old school. The building and compound are much the same as they were when he was a student, so the sense of stepping back into the past is powerful. He moves from classroom to classroom following the exact progression his student-graph had taken him more than half a century ago, and pays a silent tribute to each of the teachers, intensely recalling those impoverished gentlemen whose wardrobes had hardly held more than two shirts. What still moves him is the memory of the care they had taken in the lives and progress of every student, encouraging each of them and guiding every child to do his best and then some. A drop in concentration or performance led to the â€Å"master† calling on the child’s parents to enquire if there was something wrong at home which the child found disturbing or was unable to cope with; very few homes had phones in those days and even if they did, a school-teacher would certainly not have been able to afford a phone call. So, umbrella held high, he would walk those extra miles. My first teacher was a smiling and very gentle woman named Mrs. Delamose, whose name my brother and I repeated over and over again to get it right so that we might greet her correctly: â€Å"delamosedelamosedelamose†. I still recall her dark eyes, and charming crooked smile with faint streaks of lipstick out of place, as she led us through Songs the Letters Sing. She was a true teacher who believed that every single child is unique and that there is no such thing as an unintelligent child I owe my profession to her. a) Give the meaning of the following words as used in the passage: [3] 1. Supportive

Machiavelli And Humanism - 930 Words

While the moral codes of Humanism and Catholicism consider being deceitful to be wrong, from an amoral perspective, betrayal is unavoidable and comes with its own set of benefits. This is Machiavelli’s view in his 1513 work The Prince, in which he details the complexities of gaining and maintaining political power to his Italian statesman Lorenzo de’ Medici, from whom he hopes to regain a political position after shifts in the unstable Florentine republic. Machiavelli argues that people, particularly political leaders, should have flexible moral codes so that they can bend them when necessary. For him, betraying allies or betraying one’s own beliefs is acceptable, even beneficial, because it aids in making tough political decisions to†¦show more content†¦Despite Machiavelli’s tongue-in-cheek perspective, his intentions in writing The Prince exemplify the two-faced nature of mankind. During political upset in Florence, the ruling family betrays Ma chiavelli himself by temporarily jailing him for treason, stripping him of his political position, and banishing him. Yet unlike Dante, who is more personally affected by his exile, Machiavelli sees Medici family’s return to power as an opportunity to regain his own position, which is why he hopes to impress Lorenzo with The Prince. For him, betrayal is more expected under the circumstances, and so he hopes to play them at their own game. By feigning loyalty and kissing up to Lorenzo, Machiavelli improves his chances of obtaining power, even though he likely doesn’t believe everything he’s saying. For him, his true intentions or beliefs don’t matter—like with religion and betrayal, his deceitfulness is a useful political tool in a world of deceitful men. With the sixteenth-century Florentine power struggle and religious tension, it was difficult for anyone to find stability. While Machiavelli experienced treachery firsthand through being banned fro m Florence, many Florentines could relate to a general lack of loyalty. During a time of political turmoil, aspiring leaders would have to hurt people in the process of gainingShow MoreRelatedThe Rise Of The Renaissance Essay1525 Words   |  7 Pagesarose and was called Humanism, and what humanism is, is an ideology where humans can lead themselves to create a prosperous life and instead of leaning on supernatural forces for answers. Humanists believe that humans have enough logic to create conclusions themselves. This brand-new way of thinking caused a lot of controversy because it did question God’s ability and the church’s power, but the ideas behind it are still heavily used today, even in the 21st century. 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