Data mining crime of data The WritePass Journal

Data mining crime of data 2  LITERATURE REVIEW Data mining crime of data 1  Ã‚  INTRODUCTION1.1   Ã‚  MOTIVATION AND BACKGROUND 1.2 PROBLEM DEFINITION1.3 PROJECT GOAL1.4 GENERAL APPROACH1.5   ORGANISATION OF DOCUMENT2  LITERATURE REVIEW2.1 INTRODUCTIO2.2  Ã‚  What is Data mining?2.3 Data mining definitions2.4 Data mining structure 2.5 Data mining methods and Techniques 2.6 Data mining modellingREFERENCERelated 1  Ã‚  INTRODUCTION 1.1   Ã‚  MOTIVATION AND BACKGROUND In the society crime issue is very important. It is common knowledge within the society that crime induces vast psychological, human and economical damages to individuals, environment and the economy of a particular society itself. It is very important that a society through its government, judiciary and legislative endeavours to control crime within their environment. Data mining is a brawny technique with expectant potency to help criminal investigators concentrate on the most important information in their crime data [1]. The cognition discovered from existing data goes to reveal a value added of its information. Successful data mining methodology depends intemperately on the peculiar selection of techniques employed by analyst. Their pragmatic applications are, for example, the criminal detectives, market sale forecasting and playing behaviour analysis in sport games. However the more the data and more composite question being treated and maintained, the more potent the system is required. This includes the potentiality of the system to analyze large quantity of data and composite information from various sources. In crime control of the law enforcement, there are many storage data and formats have to be revealed. When its amount has risen, it is hard to analyze and explore some new knowledge from them. Therefore, the data mining is employed to crime control and criminal curtailment by using frequency happening and length method under which these presumptions can be achieved. All these techniques give outcome to benefit detectives in searching behavioural practices of professional criminals. [1] The application in the law enforcement from data analysis can be categorised into two vital component, crime check and criminal curtailment. Crime check tends to use knowledge from the analysed data to control and prevent the happening of crime, while the criminal curtailment tries to arrest criminal by using his/her account records in data mining. Brown [2] has bui lt a software model for mining data in order to arrest professional criminals. They suggested an information system that can be used to apprehend criminals in their own area or regions.   The software can be employed to turn data into useful information with two technologies, data fusion and data mining. Data fusion deals fuses and translates information from multiple sources, and it overcomes confusion from conflict reports and cluttered or noisy backgrounds. Data mining is concerned with the automatic discovery of patterns and relationships in large databases. His software is called ReCAP(Regional Crime Analysis Program), which was built to provide crime analysis with both technologies (). When the terrorism was burst by 9/11 attacks, fears about national security has risen significantly and the world has varied forever. The strategy against a terrorist must be more advanced in order to prevent suicide attacks from their stratagem [5]. In the congressional conference, Robert S. Mueller – The Director of investigative department of FBI, suggested that they excessively emphasize to arrest the criminals with slightly attention for crime checks is the main problem of the law enforcement in the world [4].   It is more interesting now in data collection for criminal control plan. Abraham et. al. [1] suggested a method to use computer log register as account data search, some relationship by employing the frequency happening of incidents. Then, they analyze the outcome of produced profiles. The profiles could be employed to comprehend the behaviour of criminal. It should be observe that the types of crime could be exchanged over time determined by the variation of globalization and technology. Therefore, if we want to prevent crime efficiently, the behaviour must be employed with another kind of knowledge. We need to know the crime causes. de Bruin et. al. [3] introduced a new distance standard for comparing all individuals established on their profiles and then clustering them accordingly. This method concedes a visual clustering of criminal career and changes the identification of categories of criminals. They present the applicability of the data mining in the area of criminal career analysis. Four important elements play a role in the analysis of criminal career: crime nature, frequency, duration and severity. They also develop a particular distance standard to combine this profile difference with crime frequency and vary of criminal behaviour over time. The matrix was made that describe the number of variation in criminal careers between all couples of culprits. The data analysis can be employed to determine the trends of criminal careers. Nath[6] suggested a method for data division in order to use them present in the pattern of geographic map. We could decide the data division to be the veer of offend across many fields. 1.2 PROBLEM DEFINITION The report of the headline findings represents the 2006 Offending, crime and justice survey (OCJS). This gives description of style and degrees in youth offending anti-social behaviour (ASB) and victimisation amongst youth between the ages of 10-25 residing in a private household in England and Wales. Couple of years now data are obtained from respondent representatives’ interview 4,952 including 4,152 panel members and 799 fresh samples on the frequency consequences and characteristics of offender’s victimization in England and Wales. This survey enables the Offending, crime and justice survey (OCJS) to forecast the probability of specified outcome of victimization by assault, rape, theft, robbery, burglary, sexual assault, vehicle related theft, drug selling, for the population as a whole. The OCJS provides the largest forum in England and Wales for victims and offenders to describe the impact of crime and characteristics of offenders. 1.3 PROJECT GOAL This project aim to identify which of the data mining technique best suit the OCJS data. Identify underlying classes of offenders. 1.4 GENERAL APPROACH Data mining analysis has the tendency to work from the data up and the best techniques are those developed with a preference for large amount of data, making use of as much of the gathered data as potential to arrive at a reliable decision and conclusion. The analysis procedure starts with a set of data, employs a methodology to develop an optimal delegacy of the structure of the data during which time knowledge is gained. Once knowledge has been gained this can be broadened to large sets of data working on the effrontery that the larger the data set has a structure similar to the sample data.   Again this is analogous to a mining process where large numbers of low grade materials are sieved through in order to find something of value. Target Data Figure 1.1 Stages/Procedures identified in data mining adapted from [4]       1.5   ORGANISATION OF DOCUMENT The remainder of this report is as follow: Chapter 2 reviews the approach to data mining and also described the mining techniques. Chapter 3 introduced the basic theory for the algorithm. Chapter 4 described the adopted method. Chapter 5 presented the application and a discussion of the result. 2  LITERATURE REVIEW 2.1 INTRODUCTIO The major reason that data mining has pulled a big deal of attention in information industry in recent years is due to the broad accessibility of vast amount of data and the impending need for turning such data into useful information and knowledge. The information and knowledge acquired can be employed for application ranging from business management, production control, and market analysis, to engineering design and science exploration. [4] Having focused so much attention on the collection of data the problem was what to do with this valuable resource? It was distinguished that information is at the centre of business operations and that decision makers could make use of the data stored to acquire valuable insight into business. Database management systems gave access to the data stored but this was only a small part of what could be acquired from the data. Traditional online transaction processing systems, OLTPs, are good at putting data into database quickly, safely and efficien tly but are not good at delivering meaningful analysis in return. Analyzing data can provide further knowledge about a business by going beyond the data explicitly stored to derive knowledge about the business. This is where data mining or knowledge discovery in database (KDD) has obvious benefit for any enterprise. [7]                                                                                    2.2  Ã‚  What is Data mining? Data mining is concerned with extracting or â€Å"mining† knowledge from large amount of data. The term is really misnomer. Recall that the mining of gold from rocks and sand is concerned with gold mining rather than sand and rocks mining. Thus â€Å"data mining† should have been more suitably named â€Å"knowledge mining from data†, which is unfortunately so long. â€Å"Knowledge mining† a shorter term might not show the emphasis on mining from large amount of data.[4,6] Nevertheless, mining is a bright term characterising the procedure that discovers a small set of treasured pearl from a large conduct of raw materials (figure 1). Thus, such Fig.2.1 data mining-searching for knowledge (interesting patterns) in your data. [4] Misnomer which contains both â€Å"data† and â€Å"mining† became a big choice. There are many other terms containing a similar or slightly dissimilar meaning to data mining, such as data archaeology, knowledge extraction, data/ pattern analysis, and data dredging knowledge mining from database. Lots of people treat data mining as an equivalent word for another popular used condition, â€Å"knowledge discovery in database† or KDD. Alternatively, others regard data mining as just an essential step in the procedure of knowledge discovery in database. [2, 4] Knowledge discovery as a process is described in fig.2 below and comprises of an iterative sequence of the following steps: Data cleaning (removal of noise or irrelevant data) Data integration (where product data source may be mixed) Data selection (where data applicable to the analysis task are recovered from the database) Data transformation (where data are translated or fused into forms appropriate for mining by doing summary or collection operations, for instance) Data mining (an essential procedure where intelligent methods are used in other to extract data forms or patterns), Pattern evaluation (to discover the truly concerning forms or patterns representing knowledge based on interest measure) Knowledge representation (where visualization and knowledge representation proficiencies are used to deliver the mined knowledge to the user) Fig.2.2 Data mining as a process of knowledge discovery adapted from [4, 7] The data mining steps may interact with the user or a knowledge base. The interesting patterns are presented to the user, and may be stored as new knowledge in the knowledge base. It is very important to note that according to this view, data mining is only one step in the entire process, albeit an essential one since it uncovers the hidden patterns for evaluation. Adopting a broad view of data mining functionality, data mining is the process of discovery interesting knowledge from large amount of data stored either in database, data warehouse, or other information repositories. Based on this view, the architecture of a typical data mining system may have the following major components: (1)   Data warehouse, database, or other information repository. This is one or a set of database, data warehouse, spread sheets, or other kind f information repositories. Data cleaning and data integration techniques may be performed on the data. (2)   Database or data warehouse server. The database or data warehouse server is responsible for fetching the relevant data, base on the user’s data mining request. (3)   Knowledge base. This is the domain knowledge that is used to guide the search, or evaluate the interestingness of resulting patterns. Such knowledge can include concept hierarchies, used to organize attributes or attributes values into different level of abstraction. Knowledge such as user beliefs, which can be used to assess a pattern’s interestingness based on its unexpectedness, may also be included. Other examples of domain knowledge are additional interestingness constraints or thresholds, and metadata (e.g., describing data from multiple heterogeneous sources). (4    Data mining engine. This is essential to data mining system and ideally consists of a set of functional module for tasks such as characterisation, association analysis, classification, evaluation and deviation analysis. (5)    Pattern evaluation module. This component typically employs interestingness measure and interacts with the data mining modules so as to focus the search towards interesting patterns. It may access interestingness threshold stored in the knowledge base. Alternatively the pattern evaluation module may be integrated with the mining module, depending on the implementation of the data mining method used. For efficient data mining, it is highly recommended to push evaluation of patterns interestingness as deep as possible into the mining process so as to confine the search to only the interesting patterns. (6)    Graphical user interface. This module communicate between the user and the data mining system, allowing the user to interact with the system by specifying a data mining query or task, providing information to help focus the search, and   performing exploratory data mining based on the intermediate data mining results. In evaluate mined patterns, and visualize the pattern in different forms.[4, 6, 7] The quantity of data continues to increase at a tremendous rate even though the data stores are already huge. The common problem is how to make the database a competitive job advantage by changing apparently meaningless data into useful information. How this challenge is satisfied is vital because institutions are increasingly banking on efficient analysis of the data simply to remain competitive. A variety of new techniques and technology is rising to assist sort through the information and discover useful competitive data. By knowledge discovery in databases, interesting knowledge, regularities, or high-ranking information can be elicited from the applicable set of information in databases and be looked-into from different angles; large databases thereby assist as ample and dependable source for knowledge generation and confirmations. Mining information and knowledge from large database has been recognized by many research workers as a key research subject in database systems and m achine learning. Institutions in many industries also take knowledge discovery as an important area with a chance of major revenue. [8] The discovered knowledge can be applied to information management, query processing, decision making, process control, and many other applications. From data warehouse view, data mining can be considered as an advance stage of on-line analytical processing (OLAP). However, data mining extends far beyond the constrict measure summarization mode analytical processing of data warehousing systems by integrating more advance techniques for information understanding [6, 8]. Many individuals treat data mining as an equivalent word for another popularly applied condition, Knowledge Discovery in Databases, or KDD. Alternatively, others view data mining as simply an indispensable measure in the process of knowledge discovery in databases. For example, the KDD process as follow: Learning the application domain Creating a dataset Data cleaning and pre-processing Data reduction and projection Choosing the function of data mining Choosing the data mining algorithm(s) Data mining Interpretation Using the discovery knowledge As the KDD process shows, data mining is the fundamental of knowledge discovering, it needs elaborated data training works. Data cleaning and pre-processing: includes basic operations such as removing noise or outliers, gathering the necessary data to model or account for noise, resolving on strategies for dealing with missing data fields, and accounting for time sequence data and recognised changes, as well as settling DBMS issues, such as mapping of missing and unknown values, information type, and outline. Useful data are selected from the arranged data to increase the potency and focus on the job. After data preparation, selecting the purpose of data mining determine the aim of the model gained by data mining algorithm (e.g. clustering, classification and summarization). Selecting the data mining algorithm includes choosing method(s) to be used for researching for patterns in the data, such as determining which models and parameters many are captured and corresponding to a particular data mining method with the overall standards of the KDD process. Data mining explores for patterns concern in a particular realistic form set of such representations; including classification rules, or clustering, regression, sequence modelling, trees, dependency and line analysis. The mining outcomes which correspond to the demands will be translated and mobilised, to be taken into process or be introduced to concerned companies in the last step. For the importance of data mining in KDD process, the term data mining is turning more popular than the longer term of knowledge discovery [3, 8]. Individuals gradually conform a broad opinion of data mining functionality to be the equivalent word of KDD. The concept of data mining holds all actions and techniques using the gathered data to get inexplicit information and studying historical records to acquire valuable knowledge. 2.3 Data mining definitions Larose [9] stipulated, data mining refers to the process of discovering meaningful new correlations, patterns and trends by sifting through large amount of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques. Hand et. al.[10] stated Data mining the analysis of (often large ) observational data sets to find unexpected relationship and to summarize the data in novel way that are both understandable and useful to the data owner. Peter et.al.[11] stipulated Data mining is an interdisciplinary field bringing together techniques from machine learning, pattern recognition, statistics, databases, and visualization to address the issues of information extraction from large data bases. The SAS institute (2000) defines data mining as the â€Å"process of selecting, researching and simulating huge amount of data set to reveal antecedent strange data form for business advantage. Data mining refers to as knowledge discovery in dat abases, meaning a process of little extraction of implicit, previously obscure and potentially useful information (such as knowledge rules, regularities, constraints) from data in databases.[12] From the business view, several data mining techniques are used to better realize user conduct, to improve the service provided, and to enhance business opportunities. Whatever the definition, data mining process differs, from statistical analysis of data. First predictive data is controlled by the need to reveal, in a well timed style, rising courses whereas statistical analysis is associated to historical information and established on observed information. Secondly statistical analysis concentrates on findings and explaining the major origin of variation in the data, in contrast, data mining strives to discover, not the apparent sources of variation, but rather the significant, although presently neglected, information. Therefore statistical analysis and data mining are complementary. Sta tistical analysis explains and gets rid of the major part of data variation before data mining is employed. This explains why the data warehousing tool not only stores data but also comprises and performs some statistical analysis programs. As to on-line analysis processing (OLAP) its relationship with data mining can be considered as disassociation.[9,12] OLAP is a data summarization/collection tool that assist modify data analysis, while data mining allows the automated discovery of implicit form and interesting knowledge concealed in large amount of data. OLAP tools are directed toward backing and changing interactive data analysis; while data mining tools is to automate as much of the analysis process as possible. Data mining goes one step beyond OLAP. As noted in the former section, data mining is almost equal to KDD and they have like process. Below are the data mining processes: Human resource identification Problem specification Data prospecting Domain knowledge elicitation Methodology identification Data pre-processing Pattern discovery Knowledge post-processing In stage 1 of data mining process, human resource identification, and the human resource should be required in the plan and their various purpose are identified. In most data mining job the human resources involved are the field experts, the data experts, and the data mining expert. In stage 2 concerned jobs are analyzed and defined. Next, data searching requires in analyzing the available data and selecting the predicting subset of data to mine. The aim of stage 4, field knowledge induction, is to extract the useful knowledge already known about the job from field experts. In stage 5, methodology identification, the most reserve mining prototypes are chosen. In stage 6, data pre-processing is depicted to transform data into the state fit for mining. Pattern discovery stage which includes the computation and knowledge discovery is talked about in section 7. The patterns found in the former stage are filtered to attract the best pattern in the last stage. [8] Fayed et al. (1996), on the other hand suggested the following steps: Recovering the data from large database. Choosing the applicable subset to work with. Resolving on reserve sampling system, cleaning the data and coping with missing domain records. Employing applicable transformations, dimensionality simplification and projections. Equipping models to pre-processed data. The processes of data mining are elaborate and complicated. Many requirements should be observed on the follow of data mining, so challenges of growing data mining application are one of the crucial matters in this field. Below are the listed of challenges growth: Dealing with different types of data. Efficiency and measurability of data mining algorithm. Usefulness, certainty, and quality of data mining results Formula of several forms of data mining results. Synergistic mining knowledge at product abstraction stages. Mining data from different sources of information. Security of privacy and data protection. 2.4 Data mining structure The architecture of a distinctive data mining system may have the following major elements: Database, data warehouse, or other data deposit Database or data warehouse server Knowledge base Data mining engine Pattern rating module Graphical user interface The information sources of a data mining system can be divergent information deposits like database, data warehouse, or other deposits. The database or data warehouse server is responsible for getting the applicable data to accomplish the data mining postulation. Data mining engine is the heart of data mining system. The operational module of data mining algorithms and patterns are maintained in the engine. Knowledge base stores the field knowledge that is used to lead the data mining process, and provides the data that rules evaluation module motives to formalise the results of data mining. If the mining results has passed the establishment step then they will get to user via the graphical user interface, user can interact with the system by the interface. [4, 8, 11] 2.5 Data mining methods and Techniques Various techniques have been suggested for resolving a problem of extracting knowledge from volatile data, each of which follow different algorithm. One of the fields where information plays an important part is that of law enforcement. Obviously, the raising amount of criminal data gives rise to various problem including data storage, data warehousing and data analysis. [11] Data mining methods relate to the function cases that data mining tools provides. The abstract definition of each data mining method and the classification basis always disagree for the ease of explanation, the condition of present situation, or researcher’s scope. Association, classification, prediction, clustering are usually the common methods in different works, while the term description, summarization, sequential rules etc. Might not always be used and named in the first place. If some methods are not named it does not refer these methods are not created because the researcher may allot special term to methods to indicate certain significant characters. Progressive specification and jobs sectors can also be a good ground to consider the terminology. For example â€Å"REGRESSION† is often used to substitute â€Å"PREDICTION† because the major and conventional techniques for prediction are statistical regression. â€Å"Link analysis† can be discussed severally outlying â€Å"association† in telecommunication industry. Table 1.1 shows the method recognised by scholars: Data mining methods comprise techniques which develop from artificial intelligence, statistics, machine learning, OLAP and so on. These most often observed methods are classed into five categories according to their work types in business applications, and the five types of data mining methods are clustering, classification, association, prediction and profiling. Table 1 Data mining classification literatures Sources: This research   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Author   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   Data Mining Classification Barry (1997) Prediction, Classification, Estimation, Clustering, Description, Affinity grouping. Han, et al. (1996) Clustering, Association, Classification, Generalization, Similarity search, Path traversal pattern. Fayyed, et (1996) Clustering, Regression, Classification, Summarization, Dependency modelling, Link analysis, Sequence analysis. Association: reveals relationship or dependence between multiple things, such as Link analysis, market basket analysis, and variable dependency. Association is in two levels: quantitative and structured. The structural level of the method assigns (often in graphical form) which things are associated; the quantitative level assigns the strength of the relationship using some numerical scale. Market basket analysis is a well recognized association application; it can be executed on a retail data of customer deal to find out what item are often purchased together (also known as item sets). Apriori is a basic algorithm for finding frequent item sets. The denotation of apriori can further deal with multi-level, multi-dimensional and more composite data structure. [7] Classification: function (or classifies) a data detail into one of several set of categorical classes. Neural network, Decision tree, and some probability advances are often used to execute this function. There are two steps to carry out classification work. In the first step, classification model is form describing a predetermined set up of classes or concepts. Second step the model is used for categorization. For example, the classification learned in the first step from the analysis of data from existing customers can be used to predict the credit evaluation of new or future customers. Prediction: admits regression and part of time series analysis. Prediction can be regarded as the structure and use of a model to evaluate the value or value ranges of a property that a given sample is probably to have. This method functions a data item to a real-value prediction variable, and the goal of time series analysis is to model the state of the process generating the sequence or to extract and study deviation and style over time. In our opinion, the major deviation between prediction and classification is that prediction processes with continuous values while classification centres on judgements. Clustering: functions a data item into one of various categorical classes (or clusters) in which the categories must be determined from the data different assortments in which the classes are determine. Clusters are defined by determinations of natural grouping of data detail based on similarities metrics or probability density models, and the procedure to form these groups is named as unsupervised learning to distinguish from supervised learning of classification. Data mining techniques and methods render capable extraction of concealed predictive data from huge datasets or databases. It is a very powerful new technology with great potency to assist institutions concentrate on the important information in their database and data warehouse. Data mining instruments forecast future behaviours and trends, allowing businesses to make active, knowledge aimed decision. The automated, potential analyses proposed by data mining go beyond the analyses of previous issues provided by retrospective instruments typically for decision support systems. [2, 4, 12] Data mining instruments can respond to business question that traditionally were times consuming to conclude. They examine databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectation. Most institutions already collect and refine large quantities of data. Data mining methods and techniques can be carried out quickly on existing hardware and software program to raise the scope of the existing information resources, and can be merged with new systems and products as they are brought on-line. When enforced on high performance client/server or parallel processing computers, data mining instrument can analyze huge databases to present answers to questions such as, which clients are most likely to answer my next promotional mailing, and why?[10, 12] Recent progress in data collection, storage and manipulation instruments such as extraordinary storage and computational capability, use of the internet, modern surveillance equipments etc, have widen the range and limit for the same. Moreover, the increasing dependence on high technology equipment for common man has facilitated the process of data collection. [13] The data might be in the direct form or may not be in the direct form and might need some interpretation based on former knowledge, experience and most importantly is determined by purpose of data analyses. This job is further augmented by sheer intensity, texture of data and lack of human capabilities to deduce it in ways it is supposed to be. For this reason many computational instruments are used and are broadly named as Data mining tools. [10] Data mining tools constitutes of basic statistics and Regression methods, Decision trees, ANOVA and rule based techniques and more importantly advanced algorithm that uses neural networks and Artificial Intelligence techniques. The applications of data mining tools are limitless and basically aimed by cost, time constraints, and current demand of the community, business and the government. [14] 2.6 Data mining modelling Data mining modelling is the critical part in developing business applications. Business application, such as â€Å"cross selling†, will be turn into one or more of business problems, and the goal of modelling is to formulate these business problems as a data mining task. For example, in cross selling application, the association in the product area is determine, and then customers will be classified into several clusters to see which product mix can be matched to what customers. To know which data mining task is most suitable for current problem, the analysis and understanding of data mining task’s characters and steps is needed. Data mining algorithm consists largely of some specific mix of three components. The model: There are two relevant factors: the function of the model (e.g., clustering and classification) and the representational form of the model (e.g., a linear function of multiple variables and a Gaussian probability density function). A model contains parameters that are to be determined from the data. The preference criterion: A basis preference of one model or set of parameters over another, depending on given data. The criterion is usually some form of goodness-of-fit function of the model to the data, perhaps tempered by a smoothing term to avoid over fitting, or generating a model with too many degrees of freedom to be constrained by the given data. The search algorithm: The specification of an algorithm for finding particular models and parameters, given data, a model (or family of models), and a preference criterion. The choice of what data mining techniques to apply at a given point in the knowledge discovery processes depends on the particular data mining task to be accomplished and on the data available for analysis. The requirement of the task dedicate to the function f data mining, and the detailed characteristics of the tasks influence the feasibility between mining methods and business problems. The so called detail characteristic includes data types, parameter varieties, hybrid approaches and so on.   Slightly difference in the model will cause enormous performance change, so modelling stage effects the quality of data mining tools heavily. REFERENCE [1] T. Abraham and O. de Vel, Investigative profiling with computer forensic log data and association rules, in Proceedings of the IEEE International Conference on Data Mining (ICDM02), pp. 11 – 18,2006. [2] D.E. Brown, The regional crime analysis program (RECAP): A frame work for mining data to catch criminals, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics,Vol. 3, pp. 2848-2853, 1998. [3] J.S. de Bruin, T.K. Cocx, W.A. Kosters, J. Laros and J.N. Kok, â€Å"Data mining approaches to criminal career analysis,† in Proceedings of the Sixth International Conference on Data Mining (ICDM’06), pp.171-177, 2006. [4] J. Han and M. Kamber, â€Å"Data Mining: Concepts and Techniques,†Morgan Kaufmann publications, pp. 1-39, 2006. [5] J. Mena, â€Å"Investigative Data Mining for Security and Criminal Detection†, Butterworth Heinemann Press, pp. 15-16, 2003. [6] S.V. Nath, â€Å"Crime pattern detection using data mining,† in Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 41-44,2006. [7] S. Nagabhushana â€Å"Data warehousing OLAP and Data mining†, published by new age international,pp251-350, 2006 [8]Takao Terano, Huan Liu, Arbee L.P. Chen (Eds.) 2000 â€Å"knowledge discovery and data mining current issues and application† [9]Larose, Daniel T. 2005 Discovering Knowledge in data mining. An introduction to data mining (pg.3) [10]Hand, D. J.Heikki Mannile, padhraic Smyth, 2001 Principle of data mining (pg. 1) [11] Peter cabena, Pablo Hadjinian, Rolf stadler, Jaap verhees, and alessandro zanasi, 1998 [12]Eric D. Kolaczyk 2009 Statistical analysis of network data, method and models Discovering data mining: from concept to implementation† (pg. 2) [13]Trevor Hastic, Robert   Tibshirani, Jerome freedman 2001 The Element of Statistical learning, data mining, inference, and Prediction [14] An Introduction to Data Mining: thearling.com/text/dmwhite/dmwhite.htm (Internet site accessed on 27th April 2011.) [15] Padhye, Manoday Dhananjay â€Å"Use of Data Mining for Investigation of Crime Patterns†, [16]Graham J. williams, simeon J. simmoff (edu).2006 Data mining: Theory, methodology, Techniques, and applications [17]Hill Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani and Vipin Kumar 2009 Next generation of data mining [18]Robert G. Cowell, A. Philip David, Steffen L. Lauritzen, David J. Spieglhalter 1999 Statistics for Engineering and Information science [19]Deepayan Sarkar, 2008 Multivariate data Visualization with R. [20]Luis Torgo2011 data mining with R. learning with case studies [21] Everitt, Brian and Graham Dunn 2001 â€Å"Applied multivariate data analysis† Masters Thesis,West VirginiaUniversity. 2006. P. Thongtae and S. Srisuk An Analysis of Data Mining Applications in Crime Domain Computer and Information Technology Workshops, 2008. CIT Workshops 2008. IEEE 8th International Conference on Tabachnick, Barbara G., 1936- Using multivariate statistics / Barbara G. Tabachnick, Linda S. Fidell . 5th ed. . -Boston,Mass.;London: Pearson : Allyn and Bacon, 2007 . 0205465250

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Summarize chapter 16 - Essay Example The wheels are made in two halves which need to be matched by manufacturer and part number. d. Air craft Tire Construction. Air craft tires are tubeless tires designed to with stand high speed along with static and dynamic loading. Unlike automobile aircraft tires fail under continuous operation. e. Tire Specification The specification details are mentioned on the side walls of tires and available in manufacturer’s data sheets. These include ply rating, load rating, speed rating, retread history etc. m. Coefficient of Friction. The stopping of aircraft on runway after application of brakes is the function of friction of tire and runway. The value used in all deceleration calculation is coefficient of friction which is the ratio between the force needed between the two surfaces to slide against each other and force holding them. The coefficient is measured from MU meter and diagonally brake

A research paper on the burial and ceremonial traditions of egypt

A on the burial and ceremonial traditions of egypt compared to the romans and how it influenced america today - Research Paper Example Study of their beliefs and practices show that there existed a communication or interaction between the cultures of these two different continents, but any solid proof is still lacking. Later on, as believed, it is the influence of Christianity and that of Pilgrim Fathers led to the development of significant differences in the beliefs and practices of American cultures. In the case of Egypt, there was significant influence from Roman and Greek cultures. Thus, as time passed, there arose considerable differences among the cultures. A look into historical evidence proves that the practices related to the dead vary among various civilizations. However, it seems that all the civilizations share certain degree of similarity too. To illustrate, the Romans, Egyptians, and the ancient American civilizations like Mayan and Inca held the belief that the dead deserve some sort of rituals and planning. In addition, history proves that in societies the practices and beliefs undergo changes as ti me passes, and as civilizations decline. This work intends to look into the burial practices that existed among the ancient Egyptians and Romans, and it analyses if they had any influence on the American cultures. ... Consequently, their treatment of the dead varied accordingly. Ancient Egyptians buried their wealthy in expensive tombs which were filled with artifacts and wall paintings depicting families during their everyday lives. However, one can see that as time progresses, the positive outlook of the Egyptians about the afterlife changes significantly; the afterlife began to be considered as a place of fear; filled with evil spirits. As a result, they began to bury their dead with ‘The Book of Dead’ that contained spells that help the dead navigate in the afterlife. According to Davies (29), it is wrong to use the term ‘resurrection’ when one talks about the Egyptian belief because the Egyptians believed in the four stages of life starting from birth, life, death, and rebirth where death is an opportunity for fulfillment, not a matter of negation. Consequently, for them, death rituals were meant to ensure easy transcending of the dead to afterlife. In fact, the beli efs of the Egyptians regarding death and after life were based on the cult of Isis that had its birth around fourth millennium BCE. According to the belief, death is followed by a long journey through a number of tests and trials. The ones who pass the tests are sure to enjoy eternal life. In fact, the Egyptians were optimists about afterlife. Now, a look into the Roman burial customs proves that though they shared certain similarities in burial practices, their basic ideology was entirely different from that of Egyptians in the beginning. The first point of difference is the fact that while Egyptians believed in an exquisite afterlife, there was no such doctrine among Romans that taught there is an afterlife for which the present body is to be preserved. So, for them, the dead

Diversity Issues Essay Example for Free

Diversity Issues Essay It does not create a mouth-opening effect on us when we get to know any issue related to sexual harassment that is happening somewhere in the country, whether it is any organization, community, educational institution, or village. It is because there is no such country in the world that has totally eliminated this issue, even if they have, then other diversity issues such as, discrimination, hatred, and glass-ceiling occur. The paper would discuss an article about sexual harassment, analyze the legal concerns, and highlight the thoughts regarding the issue. Introduction Sexual harassment is a term that is used to define any act of harassment where a person or employee â€Å"makes continued, unwelcome sexual advances, requests for sexual favors, and other verbal or physical conduct of a sexual nature, to another employee, against his or her wishes† (Heathfield, 2010). Examples of Sexual Harassment There is not any specific way of harassing the opposite gender in the workplace; in fact, there are variety of ways that can be used depending on the circumstances. People pass on jokes, non-verbal gestures, or other offensive comments on the physique, dresses, or way of walking or talking of the other party. Touching and making some bodily connection also comes under sexual harassment, where patting someone’s back, holding him or her from waste, and scratching is done. Moreover, requests to the other party are made to have date even that party has refused before. And finally, sending emails or texts that contain some lewd text or pictures and playing sexy music are also part of sexual harassment. Legal Concerns Sexual harassment has increased to so much extent that now it is considered to be a common thing, which would not have any negative impact towards the organization or its performance. But in reality, it does have several legal concerns over the confidence and self-esteem of the harassed one, performance of the organization, violation of the ethical and moral principles, and also the corporate culture. Starting from the moral and ethical issues, one does not have any right to sexually harass anyone else in the workplace even if the other party does not entertain the offer or gets reluctant to it. It violates the ethical principles because one does not feel the sense of freedom where he or she is working, and since it is the workplace so protest or condemnation cannot be done openly. Second of all, it diverts the mind of both the harasser and harassee from their work since they continue to disturb and get disturbed from each other’s reactions. Therefore, the time is wasted and focus is not made on the everyday tasks, hence resulting in the decline in productivity of both parties. As far as the self-esteem and confidence of harassee is concerned, it gets hurt seriously because the harassee cannot openly raise voice in front of others to either protect him/her or complaint about the harasser. Moreover, there is a great chance of other people perceiving the harassing situation as the consensual relationship or flirting of both the parties, which might degrade one’s image. Not only this, once the rumors about sexual harassment being happening in an organization spread, it exacerbates company’s image and so many of the women or minorities would hesitate to join it in the future. Thoughts and Beliefs This growing issue in the workplaces have tarnished the image of many organizations and as well as the countries where minorities are harassed either sexually or for some other purpose. Although this is a silent issue that has not been considered to deal at the upper level either by the companies or governments; nevertheless, employees are the only one who can help themselves and get out if this nuisance. Sexual harassment can be prevented by the organizations if they develop and introduce certain stiff rules about the issue, which ensure that whoever is found to be harassing others would either be fired or suspended. Of course, the employer would not want this issue to prevail in the company so the harassee must complain secretly to the immediate boss or manager who could take the issue seriously in dealing with it. Also, harassee must have proper proofs and records of harassment been done by the harasser, so that he or she could take legal actions against the harasser and let him or her face the music (Heathfield, 2010). This would not be it; in fact the role of government and law-making bodies is very decisive here that can introduce certain laws especially for the workplaces where sexual harassment usually takes place. Once the rules are implemented, the issue would certainly take a rain check and minimize. Conclusion Therefore, there are many diversity issues prevailing in the organizations, one of them is sexual harassment that involves the passing of certain remarks, words, jokes, or some physical contact with the other party. Its outcomes are quite gloomy where the performance level of employees goes down, image is tarnished and corporate and ethical principles are violated. The issue can be managed by taking action on-the-spot by the harassee and the employer, and also by implementing stiff rules and regulations. References Heathfield. S. M. (2010). Address an Employee Sexual Harassment Complaint. Retrieved on August 4, 2010. From http://humanresources. about. com/cs/workrelationships/ht/sexualharass. htm Heathfield. S. M. (2010). Sexual Harassment. Retrieved on August 4, 2010. From http://humanresources. about. com/od/glossarys/a/sexualharassdef. htm

Free College Admissions Essays: Summer Camp Entrepreneur :: College Admissions Essays

Summer Camp Entrepreneur The first wedding that I planned was in no way a traditional wedding. Ten eager little girls decorated the printed invitations with sequins, buttons, and markers. The same energetic hands prepared the wedding feast, consisting of bagged lunches, blintz soufflé, and of course a layer cake. On the big day I looked around with excitement. Again, I noticed something odd about this wedding. All the participants and guests appeared about four feet high. The "groom" had long hair pinned up with brown lines on her face (was that supposed to be a beard?) The wedding location, a back yard with a swing set and a wading pool, seemed far from romantic. This wedding however was not supposed to be one of those types of weddings. As I pressed the "PLAY" button on the tape recorder I knew that ten 4-6-year-old girls cared deeply about this wedding. Despite the absence of a reason for celebration, I pulled all the girls into the circle and we started dancing and clapping to the music. The energ y that went into the preparation on previous days could finally be appreciated. My campers and I not only celebrated the accomplishment of the mock wedding, we celebrated the fun and excitement we experienced for the first three weeks in Camp Glitter Girls. I had begun preparing for Camp Glitter Girls over four months before by budgeting, sending out fliers, confirming registration and finally making sure that every camper would have the time of her life. As I danced, I celebrated the times I almost lost my patience but didn't, the times that I planned activities late into the night because I knew that only an organized schedule would ensure the success of my camp. The lessons I had learned from previous summer camps contributed greatly to this camp's success. At the age of thirteen, I first ran a camp for eight children. The next year a friend and I co-managed a camp for twenty children at a small school campus. Finally at the age of fifteen I created my most challenging summer camp with thirty-five children. In just three years the size of my camp tripled and so did the life lessons. I not only carried the responsibility for my own "bunk," but with my co-manager I hired other counselors, arranged busing to and from field trips, managed a $15,000 budget, and ensured that thirty-five children had a fun summer.

Importance Of A Teaching Philosophy Education Essay

Alhamdulillah, my learning experience began when I was still a pupil in secondary school. The deferral and interruptions were the occasions for me to affect myself in the instruction procedure. This continued at place whereby I used to acquire phone calls from my friends to explicate about a peculiar subject from topics like mathematics, natural philosophies and chemical science. After graduating from college, I worked for six months as instructor in a private secondary school in my vicinity before prosecuting myself into third surveies. Hence, I could state that working as a instructor after my graduation from university is something I would see as normal. Actually, I am in my 5th twelvemonth as an pedagogue and Alhamdulillah, I had the experience of working in three different schools with pupils, both male childs and misss, from different socio-cultural backgrounds. In the undermentioned chapters, I would wish to portion my instruction doctrine. But first, the importance of developing one ‘s learning doctrine will be discussed.Chapter Two – Importance of a Teaching DoctrineA instruction doctrine statement is â€Å" a systematic and critical principle that focuses on the of import constituents specifying effectual instruction and acquisition in a peculiar subject and/or institutional context † ( Schonwetter et all, 2002, p.84 ) . Harmonizing to Campos ( 2010, p.2 ) , it states â€Å" our acquisition experiences, ends, beliefs, degree of professionalism and self-development among many other elements † . As for Natasha Kenny ( 2008, p.8 ) , an educational developer at the University of Guelph, a learning doctri ne statement â€Å" clearly and logically communicates what your cardinal values and beliefs are about learning and acquisition, why you hold these values and beliefs, and how you translate these values and beliefs into your mundane instruction and acquisition experiences. † Sing the importance of developing a instruction doctrine, Coppola ( 2002, p.450 ) states that â€Å" the instruction statement gives you a get downing point for analyzing your instruction patterns, allows you to portion your thoughts with others, and allows you to supervise the advancement of your ain development as a instructor † . Brookfield ( 1990 ) identified four different intents in developing a instruction doctrine. These intents are: Personal intent ; it is good that your learning doctrine reflects why you are making what you are making. Political intent ; your learning doctrine demands to demo that your place is grounded in a well-developed and carefully doctrine of pattern. This helps you to derive regard, which is of import to turn yourself in the political country. Professional intent ; your learning doctrine demands to demo your professional ends to develop an individuality that will assist you to better the development of professional strengths among other instructors. Pedagogical intent ; instruction is about turning and happening new ways to develop your lessons harmonizing to the scholars ‘ demands, so your learning doctrine should demo what consequence you are holding on pupils and on their acquisition?Chapter Three – My Teaching Doctrine3.1 Teaching in Generalâ€Å" Teaching should be such that what is offered is perceived as a valuable gift instead than a difficult responsibility † – Albert Einstein Alhamdulillah, since I began learning, I had the juncture to learn different topics, besides Mathematicss, like English Literature, Computer Studies and Integrated Science to pupils with a wide scope of demands, cognition, and cultural backgrounds. Apart from working as pedagogue in colleges, I besides try to assist my friends and others outside school. Furthermore, I used to give a particular class on â€Å" Tajweed † to my brothers of the Islamic religion in the mosque. I one time had the chance to learn a friend who was set abouting his undergraduate grade in Mathematicss and he was older than me. Several of import general rules that I try to utilize in my instruction pattern can be formulated as follows: No affair how experienced a instructor is, every new category and every new pupil pose a challenge. For excellence in instruction, there is ever room for betterment. A instructor has to appeal to different acquisition manners, to offer a assortment of instructional experiences, and to maintain an unfastened head to new learning techniques to give every pupil the chance to take part to the full and actively in the acquisition procedure. Bing a successful instructor depends on making a acquisition environment with the unfastened geographic expedition of thoughts, a relationship in which pupils feel respected every bit good as challenged. Students should be encouraged to stretch themselves a bit beyond their degree of comfort and be given an chance to go forth every category feeling that they have overcome a new challenge successfully. Teaching should be an built-in constituent of the creative activity of new cognition, an initial measure toward go oning instruction, and foremost of all, self-cultivation. Though student satisfaction is of import for better acquisition, learning – particularly in mathematics – should non go a popularity competition, or a show. A instructor is responsible to the society in general, and should defy the force per unit area of take downing academic criterions in instruction.3.2 Teaching of Mathematicssâ€Å" We could utilize up two Eternities in larning all that is to be learned about our ain universe and the 1000s of states that have arisen and flourished and vanished from it.A Mathematics entirely would busy me eight million old ages † – Mark Twain â€Å" Mathematics is like checkerss [ draughtss ] in being suited for the immature, non excessively hard, amusive, and without hazard to the province † – Plato Bing a mathematics instructor every bit good as a lover of the topic, there are some characteristics of mathematics that makes it a really particular subject. To call a few: Mathematicss is an first-class rational game where all the participants win. Mathematicss is besides a theoretical account that can be used for developing independent and critical thought. Mathematicss can be seen as a linguistic communication that allows us to pass on thoughts exactly between ourselves. Last, mathematics is a tool used in natural scientific disciplines and, therefore, a needed subject for many pupils. The above statements entirely actuate me to portion my mathematical cognition, among others, to my pupils. However, my ends for learning mathematics are many. First, communicate to my pupils that mathematics is a merriment and better their assurance approximately mathematics every bit good as cut downing their anxiousness about mathematics. Other ends are: Detect and make full spreads in pupils ‘ anterior math cognition Develop a mathematical sense about measures, geometry, and symbols Develop a assortment of problem-solving schemes and basic computational accomplishments Enable and empower pupils Enable pupils to do judgements based on quantitative information, to turn out basic consequences, to read mathematics efficaciously and to understand their ain idea procedures Promote a broader involvement in mathematics Ensure that pupils truly understand constructs Establish constructive pupil attitudes about mathematics Facilitate acquisition of life-long acquisition accomplishments Foster a desire to inquire mathematics inquiries, critical thought, pupil find of mathematics and surrogate apprehension of the cogent evidence of cardinal theorems Teach the beauty of mathematics Aid pupils learn the cardinal theorems and their applications Improve pupils ‘ apprehension of engineering Improve pupils ‘ authorship abilities Increase the figure of mathematics big leagues every bit good as the pupils ‘ mathematical cognition Model expert problem-solving Motivate pupils to do an attempt to larn mathematics Open the doors to other chances for pupils Prepare future mathematicians, pupils for proficient callings and pupils to be knowing grownups Thatch applications to other Fieldss ; particularly for abstract topics like vectors whereby pupils frequently ask the inquiry: â€Å" where will vectors used us in our life? † Teach reckoner and computing machine accomplishments, cardinal constructs, generalizing, logical logical thinking, mathematical authorship, foretelling, problem-solving and proof-reading of mathematics Thatch pupils how to interpret back and Forth between words and mathematics Thatch pupils to work collaboratively It is, unluckily, really hard to seek to demo the existent beauty of mathematics in the model of most bing course of study as they are designed to feed pupils with frequently more facts than anyone could get down. Just covering the needed stuff normally takes a bit more clip than is available. Another job is that mathematics as a linguistic communication can be rather proficient, and to explicate something meaningful, one has to pass old ages larning regulations of the mathematical grammar foremost. I consider mathematics to be a disputing topic to learn, which makes it particularly attractive for me. I try to utilize every possibility to expose pupils to the exciting universe of mathematics, frequently implicitly, so they may non even detect it at first. I think that pupils ‘ engagement is important for larning mathematics.3.3 Teaching Manners and MethodsMy mathematics schoolroom features multiple of methods of direction that depends on: the capable affair of the lesson, that is, the subject to be taught and my experience with how single pupils and categories best learn mathematics. On some occasions, the student-centred attack or whole-class treatments are undertaken while on other occasions, small-group probes are carried out and still others see persons working one-on-one with another pupil or with me. This schoolroom interaction helps construct relationships between my pupils and I and at the same time, among the pupils themselves. These assorted schoolroom patterns help turn to the varied acquisition manners found in the mathematics schoolroom. Furthermore, I prepare my categories meticulously, but I like to improvize whenever possible. I dislike being interrupted during my account but I love it when the pupils ask inquiries afterwards ; and I congratulate them for any good inquiries and remarks. I open each session with a brief reminder of the old session ‘s stuff and an lineation of the twenty-four hours ‘s subject, and I typically conclude with a sum-up of cardinal points. There is a particular session for reappraisal before every major trial. I normally speak clearly, aloud, and easy, but enthusiastically. Students are encouraged to larn from each other as their classs are non curved, therefore, pupils are non viing with their schoolmates. In some categories, particularly my Incorporate Science categories, I give group undertakings and prep, depending on the subject being covered. When possible, I distribute solutions to homework jobs, quizzes, and trials. To cut down pupils ‘ anxiousness about trials, I usually give my pupils alteration exercisings before any major trial and I encourage them to work excess jobs on their ain and come to see me for any jobs. I do non see memorisation to be the most of import in mathematics categories ; this is why I make and promote my pupils a batch to pattern maximal figure of jobs. If the course of study would hold allowed it, I would do all my trials open-book/notes. Assignments and undertakings are of course integrated into my classs. I spend clip explicating the assignments and work outing similar jobs in category for farther treatment.3.4 Use of TechnologyI remember that one time I was in a meeting with my above 70 twelvemonth old curate and he told me: â€Å" In my clip, when we had to make a mathematical computation, we used the logarithm tabular array † and he asked me whether I knew how to utilize the logarithm tabular array! Alhamdulillah, today we have the electronic reckoner. This small piece of engineering is the lone electro nic device that our pupils can utilize in their surveies as the different course of study do non provide for the usage of other electronic devices in the scrutiny room. I would hold loved to see a alteration in these course of study which would enable more the usage of engineering in the acquisition of mathematics. Personally, I am a great fan of the newest educational package that may be used in the instruction and acquisition procedure. At present, to utilize mathematical package at college would look unrealistic but I hope to happen a alteration shortly in the course of study which will do it existent. However, pupils foremost need to understand that engineering is a tool, much like a compass or a protractor, and that engineering must be used merely as a tool. Central to my usage of engineering in the schoolroom is the thought that pupils must understand what they are making mathematically even when they use engineering as an assistance. As mentioned earlier, the most omnipresent piece of engineering in and out of the schoolroom is the electronic reckoner. While the reckoner can be a utile tool to decrease the plodding of computations, it is of import for pupils to be able to work without it. This does non intend that reckoners should non be used, but instead that pupils should hold some intuition about whether an reply is sensible. The usage of reckoners and other engineering can better a pupil ‘s intuition of mathematics and mathematical logical thinking. While most exercisings in a statistics category have comparatively little informations sets, utilizing premade informations sets and computing machine plans allows pupils to derive existent experience with professional tools and helps them visualise tendencies. The usage of computing machine package with prepared illustrations is really utile for in-class visual images in different classs, such as differential equations, concretion, and even pre-calculus. Wh ile making the assignment for the module Instructional Technology for my Bed Course, I came across package called Yenka which provides simulations. I truly appreciated the portion on chance where while explicating the different results when fliping two coins, there a simulation really fliping the two coins. I hope, insha Allah, to utilize it when I explicating this subject t my pupils in the close hereafter. With the coming of tablet PCs, I am hopeful, insha Allah, that it is merely a affair of clip before the usage of mathematical package and on-line scrutiny at secondary degree will be implemented. But, at the same clip, I strongly oppose the maltreatment of engineering. There are classs in mathematics, where pupils need to larn how to utilize their encephalon instead than calculators/computers. In my short experience in instruction, I came across an HSc ( Higher School Certificate ) pupil making the simple arithmetic job utilizing a reckoner! ! !3.5 My Best Personal Achievementâ€Å" Facilitate things to people and do non do it difficult for themaˆÂ ¦ † – Prophet Muhammad ( Peace and Blessing of God Almighty be upon Him ) I unfeignedly believe that the best academic public presentation of a pupil is non to see the high-flyer pupil being ranked foremost at the terminal of the twelvemonth but instead to see the low-achiever pupil able to reply about all inquiries of the same exam paper right at the terminal of the twelvemonth and have a really good consequence, irrespective of his/her overall rank. At my school, the construct of cyclosis is applied to divide the best pupils, frequently labeled as the â€Å" high-flyers † , and those less able pupils, frequently labeled as low-achiever pupils ( some advocates of the Marxist position will differ wholly with this construct ) . Last twelvemonth, I had the chance to work with the less able pupils of Form I and Form II, learning Integrated Science. Alhamdulillah, I was motivated but I could that a batch of these pupils lack motive. Furthermore, some of them came from hapless socio-background and some came from broken households. There was even a kid who repeated Form I! So, at first it seems really hard to anticipate that half of them would understand the different subjects even more so that they would go through their concluding twelvemonth test. After all, we are speaking about Science which purportedly is a topic for the intelligent pupils. I figured out that I should utilize a different attack with them. Finally, I opted for the â€Å" Learning by Fun † construct. It required much more energy from me but Alhamdulillah, I do non repent for one second all the energy used that twelvemonth when working with these pupils as the ground is obvious ; about all students non merely passed the concluding twelvemonth scrutiny but they really obtained really good consequences Masha Allah. The frost on the bar was when the caput of the scientific discipline section informed me that the curate was really pleased with the consequence.3.6 Reasoning NotesI believe that each kid is a alone person who needs a secure, lovingness, and exciting ambiance in which to turn and maturate emotionally, intellectually, physically, and socially. It is my desire as an pedagogue to assist pupils run into their fullest potency in these countries by supplying an environment that is safe, supports risk-taking, and invites a sharing of thoughts. The re are three indispensable elements that I believe are contributing to set uping such an environment, the instructor moving as a usher, leting the kid ‘s natural wonder to direct his/her acquisition, and advancing regard for all things and all people. When the instructor ‘s function is to steer, supplying entree to information instead than moving as the primary beginning of information, the pupils ‘ hunt for cognition is met as they learn to happen replies to their inquiries. For pupils to build cognition, they need the chance to detect for themselves and pattern accomplishments in reliable state of affairss. Supplying pupils entree to hands-on activities and leting equal clip and infinite to utilize stuffs that reinforce the lesson being studied creates an chance for single find and building of cognition to happen. Equally of import to self-discovery is holding the chance to analyze things that are meaningful and relevant to one ‘s life and involvements. Developing a course of study around pupil involvements Fosters intrinsic motive and stimulates the passion to larn. Helping pupils to develop a deep love and regard for themselves, others, and their environment occurs through an unfastened sharing of thoughts and a wise attack to train. When the voice of each pupil is heard, and environment evolves where pupils feel free to show themselves. Class meetings are one manner to promote such duologue. I believe kids have greater regard for their instructors, their equals, and the lessons presented when they feel safe and certain of what is expected of them. In puting just and consistent regulations ab initio and saying the importance of every activity, pupils are shown regard for their presence and clip. In bend they learn to esteem themselves, others, and their environment. For me, learning provides an chance for continual acquisition and growing. One of my hopes as an pedagogue is to transfuse a love of larning in my pupils, as I portion my ain passion for larning with them. I feel there is a demand for compassionate, strong, and dedicated persons who are excited about working with kids. In our competitory society it is of import for pupils to non merely have a solid instruction, but to work with person who is cognizant of and sensitive to their single demands. Alhamdulillah, I am such a individual and will ever endeavor to be the best pedagogue that I can be.

Messiah of Masses - 1942 Words

In the book Messiah of the Masses: Huey P. Long and the Great Depression, it describes and outlines the life of Huey Pierce Long. In chapter one, the author (Glen Jeansonne) begins to talk about Longs childhood days. Long was born on August 30, 1893 in Winnfield, Louisiana. Winnfield was a part of Winn Parish in the northeastern section on Louisiana. He was one of ten children born to his parents, Hugh and Caledonia (Callie) Long. Hugh Long was a man of great intelligence and charm. The family moved around a lot, and in 1907 Hugh built one of the largest homes in Winnfield, equipped with 16 rooms and all the commodities that were essential to living a successful life. Many of Hueys siblings went on to school and earned a†¦show more content†¦Huey Long stands second to nobody.# Long acquired the nickname kingfish, given to him by an associate, soon after being appointed governor. With obedience and schooling, Longs mind could have made him a success in many differen t areas. However, Huey was never disciplined or educated in the correct way, therefore causing him to borrow others ideas. Much to the peoples surprise, by 1930 Long had changed state politics drastically. Chapter four begins to tell what occurs in Longs adventurous life next. At age 34, Huey Long was the second youngest governor in the history of Louisiana. Because of his youthfulness, Long was inexperienced in certain areas of politics. Making some strategic errors and miscalculating legislators attitudes along the way, Long learned from his mistakes. Long possessed the qualities of understanding the will of the people and that his power was basically dependent on approval of the people. Although it seemed like Long was serving his office well, Cecil Morgan decided to lead a movement to impeach Long. Huey was outraged, and began to use his power to his advantage by threatening the job of Morgans father. 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He was born into a musical family and received an early instruction in music from his father, Eisenach, in Germany (Price 189). Bach wrote an immense amount of material that included six motets, a magnificent, five masses, three hundred cantatas, and two complete passions (Palisca 201). The most prominent works were Bach’s cantatas. His cantatas were unique in that they included a wide variety of difficulty levels with some performable by choirs with limited experienceRead MoreChristmas Symbols in the Philippines1165 Words   |  5 Pagesother than the Philippines have this kind of tradition. The practice of dawn masses began sometime in the 1700’s when a Spanish friar gathered his flock of farmers, at the time of harvest ( which coincides with Christmas). He told them that it was a sign of thanksgiving to God will continue to bless them with more bountiful harvest. As of today, there is still a belief that whoever completes the nine-consecutive dawn masses will be granted for his/her wishes. Today, it is the Misa de Gallo, withRead MoreA Comparison of Myself to Adolf Hitler Essay1214 Words   |  5 Pageshas positive and negative, love or hate, right or wrong, truth or lie but never half this way and half that way.† (Hitler 182) He approached his crowd under the assumption that they shared the same type of thinking and with this strategy, swayed the masses. I would even be keen to say he was just as meticulous with his moustache as he was with his political agenda. Although Hitler and I share in our quest for perfection, I prefer to keep my standards interpersonal. Hitler and I share a commonplaceRead MoreJesus Is The Teacher - What Is He Teaching You Today?1093 Words   |  5 PagesTeaching You Today? When we open the New Testament, we find four books called gospels - Matthew, Mark, Luke and John. All four present compelling accounts of the life, ministry, death and resurrection of Jesus of Nazareth, the long-awaited Jewish Messiah (the Christ) and Son of God. Mark is the shortest of the gospels, with 16 chapters. Matthew has 28 chapters; Luke has 24 and John 21. The main reason Mark s gospel is shorter than the others is because he devotes the least amount of space to the