English Language Essay Example | Topics and Well Written Essays - 1250 words

English Language - Essay Example The work has a great implication on the lives of many people. The narration of the death of Mandela was accompanied with numerous statins of his achievement and his requirement to all people. Every person or institution wanting to live the legacy left by Mandela would be moved by the text since it reminds each and every person of the importance of being willing to sacrifice for the sake of the world. The author realises this fact and puts into perspective the most influential events in the life of Nelson Mandela. The main purpose of the text was creating an editorial that would reflect the actual life and times of Nelson Mandela. Additionally, the author seeks to enlighten the reader of the steps undertaken by Mandela to become one the global recognised leaders for their efforts to bring peace and save humanity. The text has no limitation on the suitable audience since its representation and content are appropriate for very person interested in understanding why Nelson Mandela was held with high regard. However, in highlighting his objective, the author uses the responses of leaders and other people in the society on Nelson Mandela. The article starts by the author explaining the mood that was expected at the funeral. They then mention the significance of the burial of Nelson Mandela3. The text assesses the audience present at the funeral, the mood of the actual day of the funeraland the days before the funeral. The author is quick to confirm that international leaders led by the US President, Barack Obama, were presented at the send-off ceremony and delivered moving speeches on how exceptional Mandela was4. The text also analyses the general feeling of the people of South Africa on their perception Mandela. The text then diverts from the funeral to the life and times of the Nelson Mandela. Significant events such as the

Workable methodology Research Proposal Example | Topics and Well Written Essays - 500 words

Workable methodology - Research Proposal Example After data collection, editing, coding of similar themes, classification and tabulation of the data will be essential for efficient analysis. Descriptive and inferential analysis will be employed to measure and determine the relationship that exists among the variables. The researcher will also observe on research ethics by ensuring that the anonymity of respondents will be observed (Gregory, 2003). Cross-sectional survey design is concerned with relationships that exist, options that are held, and the processes to be taken. In this regard, the current phenomena will be well articulated upon by this design. Arguably, the current situation of the trend of women offenders is alarming and needs to be looked at with caution. For example, the study will try to focus on variables that contribute to women offenders. Equally, the design will also hypothesis on the possibility of reducing recidivism for women offenders by adopting prelease and reentry programs. The cross section of respondents will be selected to represent women offenders and service providers from several correction centers under the Alabama Department of Corrections. The study will use random sampling method in a bid to select correction centers under the Alabama Department of Corrections and purposive sampling will be used to select appropriate respondents from both the current women offenders and former incarcerated women. In collecting data, the study will utilize questionnaires in obtaining information from officers in the correction centers. This is crucial in that it will help to obtain wide range of information from the correction staff, and within the shortest time (Bryman & Bell, 2003). On the side of obtaining information from current and former women offenders, the study will use interview guide to obtain information about their experiences during their time in prison. Validity and reliability of the

Software development practice Research Paper Example | Topics and Well Written Essays - 2000 words

Software development practice - Research Paper Example An estimate of timeline completion of the project XII. Significant risks that may impact the project XIII. Conclusion and justification of the project XIV. References I. Introduction Computer forensics deals with the identification of any legal evidence using the computers, as well as other forms of digital storage devices. After collecting, the evidences are put through a thorough forensics examination to obtain and recover the information the investigators need. The computer forensic investigation chiefly involves the investigation of the computer crimes, the cyber crimes, and also during a court hearing. Subsequently, there is utilization of the later function of computer forensic investigation by the courts of the United States and also of the European Union as evidence of digital crime cases (Volonino & Anzaldua, 2009). II. The Proposed Project of Computer Forensics The proposed project is based on a software program that can easily retrieve any specific data from the hard disk drive of any computer. This is because the hard disk acts as a form of storage device for the computer. Thus, there is storage of most data and also files in this hardware device. Moreover, for any forensic investigator to obtain any sort of digital evidence, they must first look for the details from the computer hard disk. Therefore, the hard disk is one of the chief vital components of the computer because it contains most of the computers’ files, data and also important documents. Consequently, there is a great significant in the development of a software program for easy and quick retrieval of data contained in the hard disk. Moreover this project of computer forensic works in conjunction with the software of data recovery program. The data recovery program works by recovering any form of lost data by the computer (Volonino & Anzaldua, 2009). III. The Objectives and what the Project Intends to Deliver The main reason for the development of this computer forensic project i s to assist in the quick and effective investigation procedures required during a forensic investigation. For instance, the main part of the computer forensic is chiefly the utilization of the computer hard disk to access any information. This is because hard disks are capable of storing large amount of information about anything. Therefore, because the computer hard disk is capable of storing millions of data on anything, it becomes very tedious for the forensic investigator to search for the exact data that is needed for the investigation (Volonino & Anzaldua, 2009). Moreover, this action needs a lot of time and thus consumes time. Therefore, for the investigator to be competent and to easily access all the details from the hard disk, the forensic investigator needs to implement this computer forensic project. Thus, the chief reason for the development of this computer forensic project is to assist the investigators in easing the procedures of the investigation. Therefore, the com puter project is about how the forensic investigator is capable of obtaining and easily retrieves data from any hard disk of a computer system to be utilized in the investigation procedures. The investigator will find this project extremely helpful to him and also to the court when handling any form of forensic cases. Thus the project focuses majorly o the improvement of the hard disk devices. Additionally, this project will focus on the fundamental process of retrieving information from a given hard disk

Cyber Security A Homeland Security Dilemma Research Paper

Cyber Security A Homeland Security Dilemma - Research Paper Example The worldwide web is an information system that involves a planet-wide scope and grid. The interconnectedness of computers allows more vulnerabilities on the part of those connected to the grid and creates a problem in the case of an attack. There should be more cooperation in the international context by sharing information about criminals and their clandestine activities. There goes the question whether the job of Homeland Security Department is a dilemma or the department itself is a dilemma. Secretary Janet Napolitano has confessed that she herself doesn’t use email because email is a collector of all sorts of viruses. If people want to be secured, they have to shun email. What good is technology then? If the secretary of Homeland Security says that the best way to be secured is to shun email instead of accepting that her department cannot provide security to cyberspace, then it is better to conclude that Homeland Security is itself a dilemma. The method to be used will be collecting data and information from various databases of private and government institutions. Qualitative and quantitative data will be collected from a sample population composed of the staff from the Centre for Strategic and International Studies. The questionnaires will be emailed to the participants and their responses will be inputted into a database. The responses will be compared with the findings of the literature review and the research conducted in the past by authors on the subject of cyber security and the complex tasks of the Department of Homeland Security.

How are schools agencies of empowerment and disempowerment Essay

How are schools agencies of empowerment and disempowerment - Essay Example A disempowered state arises when there is subjugation, discrimination and domination based on race, sex, social class, poverty etc. As vehicles of empowerment, schools play a vital role in the "development of a properly trained and socialized citizenry" (Dornbusch, Kristan & Lin, 1996). Schools have two most important purposes in the society. Firstly they teach "cognitive skills" which is an integral part of technical education. Secondly, they help in sorting individuals in different job categories depending on their skills (Dornbusch et al., 1996). Various reforms have been made by the American government for improving the quality of education and increasing the student's achievement level. "In March 1994 the U. S. Congress passed the eight National Education Goals for the year 2000", under which some important rules were passed: Schools should be attended by all children, all the children should be literate and "every school would promote partnerships that would increase parental involvement and participation in promoting the social, emotional, and academic growth of children" (Hanushek & Jorgenson, 1996, p. 3). The legislation, No Child Left Behind (NCLB), passed in 2001 re-authorized the Elementary and Secondary Education Act (ESEA) of 1965 (Hess & Finn, 2004, p. 3). NCLB's major tenets were to think over the problems faced by schools, to research on the current means of improving schools, "suggest new ways of thinking ", and "help prioritize the challenges and make recommendations when possible (Juvonen, Le, Kaganoff, Augu stine, & Constant, 2004, p. xv). Research shows that parents become less involved with children's school homework and other activities as they become older (p. xix) "Several studies show that, when parents do not monitor and stay involved, their young teens are at elevated risk for compromised school achievement and conduct problems "(p. 86).The school reforms objectives was to "offer interventions for the lowest -performing children" (p. xix). These reforms are important for empowerment which in turn helps the students to acquire knowledge "outside their immediate experience in order to broaden their understanding of themselves, the world, and the possibilities for transforming the taken-for-granted assumptions about the way we live" (Irwin, 1996, p. 5). It helps them to get critical thinking skills that make them question the prevalent social setup and not accept it on face value (p. 5). Empowered teachers would be the ones who "understand systems of domination and work to transform oppressive practices in society" while respecting the "dignity of others" (Irwin, 1996, p. 13). Schools as disempowering agencies perpetuate social discrimination .They sustain a "system of structured inequality" (Dornbusch, et al., 1996). Student's class, race, ethnicity can generate a different set of treatment by schools. "The students with the greatest needs are often given the worst education" (Irwin, 1996, p. 64). Even though there is much hype about "all persons being equal in our democratic documents, we have not yet internalized this concept as a society" (p. 65). Even though, meritorious students get advantages in "status attainment" and students with lower social order can ride over their shortcomings through education, research shows that students with higher "social origins" get the benefit of status attainment (Dornbusch, et al

Leaders are made not born Essay Example for Free

Leaders are made not born Essay Good morning honorable judges, fellow debaters and audience My name is .. and I am here with my team members .. to address the issue â€Å"are leaders born or made and we are in favor of leaders are made. We will discuss eight points to support our argument. There’s a common misconception that some people are born as natural leaders and others simply don’t have it. Although it’s true that people are born with different talents, different abilities and different potential, it’s also true that we as human beings are incredibly versatile. There are skills and lessons that we all can learn to continue growing and developing both as leaders and as people Thats because leadership is an apprentice trade. Leaders learn about 80 percent of their craft on the job. They learn from watching other leaders. And choose role models. Leaders improve by getting feedback and using it. The best leaders seek feedback from their boss, their peers and their subordinates. Then they modify their behavior so that they get better results. They learn from trial and error and from experience so when something fails, a true leader learns from experience and puts it behind him. The only failure they recognize is the failure to learn from experience. If leaders are made then why we have business schools and universities, and why we study business degrees? Through different process of learning, we learn to take decision, control the situation sensibly, being responsible Rajiv Gandhi of India was made leader who was a pilot ; did not like politics. Even during 1989 elections, he said publicly that he was zero in politics.

Internet of Things Paradigm

Internet of Things Paradigm Introduction According to 2016 statistical forecast, there are almost 4.77 billion number of mobile phone users in globally and it is expected to pass the five billion by 2019. [1] The main attribute of this significant increasing trend is due to increasing popularity of smartphones. In 2012, about a quarter of all mobile users were smartphone users and this will be doubled by 2018 which mean there are be more than 2.6 million smartphone users. Of these smartphone users more than quarter are using Samsung and Apple smartphone. Until 2016, there are 2.2 million and 2 million of apps in google app store and apple store respectively. Such explosive growth of apps gives potential benefit to developer and also companies. There are about $88.3 billion revenue for mobile application market. Prominent exponents of the IT industry estimated that the IoT paradigm will generate $1.7 trillion in value added to the global economy in 2019. By 2020 the Internet of Things device will more than double the size of the smartphone, PC, tablet, connected car, and the wearable market combined. Technologies and services belonging to the Internet of Things have generated global revenues in $4.8 trillion in 2012 and will reach $8.9 trillion by 2020, growing at a compound annual rate (CAGR) of 7.9%. From this impressive market growth, malicious attacks also have been increased dramatically. According to Kaspersky Security Network(KSN) data report, there has been more than 171,895,830 malicious attacks from online resources among word wide. In second quarter of 2016, they have detected 3,626,458 malicious installation packages which is 1.7 times more than first quarter of 2016. Type of these attacks are broad such as RiskTool, AdWare, Trojan-SMS, Trojan-Dropper, Trojan, Trojan-Ransom,Trojan-Spy,Trojan-Banker,Trojan-Downloader,Backdoor, etc.. http://resources.infosecinstitute.com/internet-things-much-exposed-cyber-threats/#gref Unfortunately, the rapid diffusion of the Internet of Things paradigm is not accompanied by a rapid improvement of efficient security solutions for those smart objects, while the criminal ecosystem is exploring the technology as new attack vectors. Technological solutions belonging to the Internet of Things are forcefully entering our daily life. Lets think, for example, of wearable devices or the SmartTV. The greatest problem for the development of the paradigm is the low perception of the cyber threats and the possible impact on privacy. Cybercrime is aware of the difficulties faced by the IT community to define a shared strategy to mitigate cyber threats, and for this reason, it is plausible that the number of cyber attacks against smart devices will rapidly increase. As long there is money to be made criminals will continue to take advantage of opportunities to pick our pockets. While the battle with cybercriminals can seem daunting, its a fight we can win. We only need to break one link in their chain to stop them dead in their tracks. Some tips to success: Deploy patches quickly Eliminate unnecessary applications Run as a non-privileged user Increase employee awareness Recognize our weak points Reducing the threat surface Currently, both major app store companies, Google and Apple, takes different position to approach spam app detection. One takes an active and the other with passive approach. There is strong request of malware detection from global Background (Previous Study) The paper Early Detection of Spam Mobile Apps was published by dr. Surangs. S with his colleagues at the 2015 International World Wide Web conferences. In this conference, he has been emphasised importance of early detection of malware and also introduced a unique idea of how to detect spam apps. Every market operates with their policies to deleted application from their store and this is done thru continuous human intervention. They want to find reason and pattern from the apps deleted and identified spam apps. The diagram simply illustrates how they approach the early spam detection using manual labelling. Data Preparation New dataset was prepared from previous study [53]. The 94,782 apps of initial seed were curated from the list of apps obtained from more than 10,000 smartphone users. Around 5 months, researcher has been collected metadata from Goole Play Store about application name, application description, and application category for all the apps and discarded non-English description app from the metadata. Sampling and Labelling Process One of important process of their research was manual labelling which was the first methodology proposed and this allows to identify the reason behind their removal. Manual labelling was proceeded around 1.5 month with 3 reviewers at NICTA. Each reviewer labelled by heuristic checkpoint points and majority reason of voting were denoted as following Graph3. They identified 9 key reasons with heuristic checkpoints. These full list checkpoints can be find out from their technical report. (http://qurinet.ucdavis.edu/pubs/conf/www15.pdf)[] In this report, we only list checkpoints of the reason as spam. Graph3. Labelled spam data with checkpoint reason. Checkpoint S1-Does the app description describe the app function clearly and concisely? 100 word bigrams and trigrams were manually conducted from previous studies which describe app functionality. There is high probability of spam apps not having clear description. Therefore, 100 words of bigrams and trigrams were compared with each description and counted frequency of occurrence. Checkpoint S2-Does the app description contain too much details, incoherent text, or unrelated text? literary style, known as Stylometry, was used to map checkpoint2. In study, 16 features were listed in table 2. Table 2. Features associated with Checkpoint 2 Feature 1 Total number of characters in the description 2 Total number of words in the description 3 Total number of sentences in the description 4 Average word length 5 Average sentence length 6 Percentage of upper case characters 7 Percentage of punctuations 8 Percentage of numeric characters 9 Percentage of common English words 10 Percentage of personal pronouns 11 Percentage of emotional words 12 Percentage of misspelled word 13 Percentage of words with alphabet and numeric characters 14 Automatic readability index(AR) 15 Flesch readability score(FR) For the characterization, feature selection of greedy method [ ] was used with max depth 10 of decision tree classification. The performance was optimized by asymmetric F-Measure [55] They found that Feature number 2, 3, 8, 9, and 10 were most discriminativeand spam apps tend to have less wordy app description compare to non-spam apps. About 30% spam app had less than 100 words description. Checkpoint SÂ ­3 Does the app description contain a noticeable repetition of words or key words? They used vocabulary richness to deduce spam apps. Vocabulary Richness(VR) = Researcher expected low VR for spam apps according to repetition of keywords. However, result was opposite to expectation. Surprisingly VR close to 1 was likely to be spam apps and none of non-spam app had high VR result. [ ] This might be due to terse style of app description among spam apps. Checkpoint S4 Does the app description contain unrelated keywords or references? Common spamming technique is adding unrelated keyword to increase search result of app that topic of keyword can vary significantly. New strategy was proposed for these limitations which is counting the mentioning of popular applications name from apps description. In previous research name of top-100 apps were used for counting number of mentioning. Only 20% spam apps have mentioned the popular apps more than once in their description. Whereas, 40 to 60 % of non-spam had mention more than once. They found that many of top-apps have social media interface and fan pages to keep connection with users. Therefore, theses can be one of identifier to discriminate spam of non-spam apps. Checkpoint S5 Does the app description contain excessive references to other applications from the same developer? Number of times a developers other app names appear. Only 10 spam apps were considered as this checkpoint because the description contained links to the application rather than the app names. Checkpoint S6 Does the developer have multiple apps with approximately the same description? For this checkpoint, 3 features were considered: The total number of other apps developed by same developer. The total number of apps that written in English description to measure description similarity. Have description Cosine similarity(s) of over 60%, 70%, 80%, and 90% from the same developer. Pre-process was required to calculate the cosine similarity: [ ] Firstly, converting the words in lower case and removing punctuation symbols. Then calibrate each document with word frequency vector. Cosine similarity equation: http://blog.christianperone.com/2013/09/machine-learning-cosine-similarity-for-vector-space-models-part-iii/ They observed that the most discriminative of the similarity between app descriptions. Only 10% 15% of the non-spam had 60% of description similarity between 5 other apps that developed by same developer. On the other hand, more than 27% of the spam apps had 60% of description similarity result. This evidence indicates the tendency of the spam apps multiple cone with similar app descriptions. Checkpoint S7 Does the app identifier (applied) make sense and have some relevance to the functionality of the application or does it appear to be auto generated? Application identifier(appid) is unique identifier in Google Play Store, name followed by the Java package naming convention. Example, for the facebook , appid is com.facebook.katana. For 10% of the spam apps the average word length is higher than 10 and it was so only for 2%-3% of the non-spam apps. None of the non-spam apps had more than 20% of non-letter bigram appear in the appid, whereas 5% of spam apps had. Training and Result From 1500 of random sampling data 551 apps (36.73%) were suspicious as spam. [ ] Methods Automation We used Checkpoint S1 and S2 for data management due to its comparability and highest number of agreement from reviewers. Due to limitation of accessibility for collect description reason only 100 sample was used for the testing. We have automated checkpoint S1 and S2 according to following algorithm. Collected data were used log transformation to modify. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics. To make a code most time consuming part was description collection which takes more than two weeks to find and store. The raw data directed the description link for appID. However, many of them where not founded due to old version or no more available. So we searched all this info manually from the web and founded description was saved as a file which named as appID. (Diagram.) This allowed us to recall the description more efficiently in automation code. S1 was automated by identified 100 word-bigrams and word-trigrams that are describing a functionality of applications. Because there is high probability of spam app doesnt have these words in their description, we have counted number of occurrence in each application. Full list of these bigrams and trigrams found in Table 1. Table 1. Bigrams and trigrams using the description of top apps play games are available is the game app for android you can get notified to find learn how get your is used to your phone to search way to core functionality a simple match your is a smartphone available for app for to play key features stay in touch this app is available that allows to enjoy take care of you have to you to can you beat buy your is effortless its easy to use try to allows you keeps you action game take advantage tap the take a picture save your makes it easy follow what is the free is a global brings together choose from is a free discover more play as on the go more information learn more turns on is an app face the challenges game from in your pocket your device on your phone make your life with android it helps delivers the offers essential is a tool full of features for android lets you is a simple it gives support for need your help enables your game of how to play at your fingertips to discover brings you to learn this game play with it brings navigation app makes mobile is a fun your answer drives you strategy game is an easy game on your way app which on android application which train your game which helps you make your S2 was second highest number of agreement from three reviewers in previous study. Among 551 identified spam apps, 144 apps were confirmed by S2, 63 from 3 reviewers and 81 from 2 reviewer agreed. We knew that from pre-research result, total number of words in the description, Percentages of numeric characters, Percentage of non-alphabet characters, and Percentage of common English words will give most distinctive feature. Therefore, we automated total number of words in the description and Percentage of common English words using C++. Algorithm 1. Counting the total number of bi/tri-grams in the description From literature [], they used 16 features of to find the information from checkpointS2. This characterization was done with wrapper method using decision tree classifier and they have found 30% of spam apps were have less than 100 words in their description and only 15% of most popular apps have less than 100 words. We extracted simple but key point from their result which was number of words in description and the percentage of common English words. This was developed in C++ as followed. Algorithm 2. Counting the total number of words in the description int count_Words(std::string input_text){ int number_of_words =1; for(int i =0; i if(input_text[i] == ) number_of_words++; return number_of_words; } } Percentage of common English words has not done properly due to difficulty of standard selection. However, here is code that we will develop in future study. Algorithm 3. Calculate the Percentage of common English words(CEW) in the description Int count_CEW(std::string input_text){ Int number_of_words=1; For(int i while(!CEW.eof(){ if(strcmp(input_text[i],CEW){ number_of_words++; } else{ getline(readFile, CEW); } } return number_of_words; } Int percentage(int c_words, int words){ return (c_words/words)*100 } Normalizaton We had variables between [ min, max] for S1 and S2. Because of high skewness of database, normalization was strongly required. Database normalization is the process of organizing data into tables in such a way that the results of using the database are always unambiguous and as intended. Such normalization is intrinsic to relational database theory. Using Excel, we had normalized data as following diagram. Thru normalization, we could have result of transformed data between 0 and 1. The range of 0 and 1 was important for later process in LVQ. Diagram. Excel spread sheet of automated data(left) and normalized data (right) After transformation we wanted to test data to show how LVQ algorithm works with modified attributes. Therefore, we sampled only 100 data from modified data set. Even the result was not significant, it was important to test. Because, after this step, we can add more attributes in future study and possible to adjust the calibration. We have randomly sampled 50 entities from each top rank 100 and from pre-identified spam data. Top 100 ranked apps was assumed and high likely identify as non-spam apps. Diagram. Initial Results We used the statistical package python to perform Learning Vector Quantification. LVQ is prototype-bases supervised classification algorithm which belongs to the field of Artificial Neural Networks. It can have implemented for multi-class classification problem and algorithm can modify during training process. The information processing objective of the algorithm is to prepare a set of codebook (or prototype) vectors in the domain of the observed input data samples and to use these vectors to classify unseen examples. An initially random pool of vectors was prepared which are then exposed to training samples. A winner-take-all strategy was employed where one or more of the most similar vectors to a given input pattern are selected and adjusted to be closer to the input vector, and in some cases, further away from the winner for runners up. The repetition of this process results in the distribution of codebook vectors in the input space which approximate the underlying distribution of samples from the test dataset Our experiments are done using only the for the manufactured products due to data size. We performed 10-fold cross validation on the data. It gives us the average value of 56%, which was quite high compare to previous study considering that only two attributes are used to distribute spam, non-spam. LVQ program was done by 3 steps; [ ] Euclidean Distance Best Matching Unit Training Codebook Vectors 1. Euclidean Distance. Distance between two rows in a dataset was required which generate multi-dimensions for the dataset. The formula for calculating the distance between dataset Where the difference between two datasets was taken, and squared, and summed for p variables def euclidean_distance(row1, row2): distance = 0.0 for i in range(len(row1)-1): distance += (row1[i] row2[i])**2 return sqrt(distance) 2. Best Matching Unit Once all the data was converted using Euclidean Distance, these new piece of data should sorted by their distance. def get_best_matching_unit(codebooks, test_row): distances = list() for codebook in codebooks: dist = euclidean_distance(codebook, test_row) distances.append((codebook, dist)) distances.sort(key=lambda tup: tup[1]) return distances [0][0] 3. Training Codebook Vectors Patterns were constructed from random feature in the training dataset def random_codebook(train): n_records = len(train) n_features = len(train [0]) codebook = [train[randrange(n_records)][i] for i in range(n_features)] return codebook Future work During writing process, I found that data collection from Google Play Store can be automated using Java client. This will induce number of dataset and possible to improve accuracy with high time saving. Because number of attributes and number of random sampling, result of the research is appropriate to call as significant result. However, basic framework was developed to improve accuracy. Acknowledgement In the last summer, I did some research reading work under the supervision of Associate Professor Julian Jang-Jaccard. Ive got really great support from Julian and INMS. Thanks to the financial support I received from INMS that I can fully focused on my academic research and benefited a great deal from this amazing opportunity. The following is a general report of my summer research: In the beginning of summer, I studied the paper A Detailed Analysis of the KDD CUP 99 Data Set by M. Trvallaee et. al. This gave basic idea of how to handle machine learning techniques. Approach of KNN and LVQ Main project was followed from a paper Why My App Got Deleted Detection of Spam Mobile Apps by Suranga Senevirane et. al. I have tried my best to keep report simple yet technically correct. I hope I succeed in my attempt. Reference Appendix Modified Data Number of Words in thousands bigram/tr-gram Identified as spam(b)/not(g) 0.084 0 b 0.18 0 b 0.121 0 b 0.009 1 b 0.241 0 b 0.452 0 b 0.105 1 b 0.198 0 b 0.692 1 b 0.258 1 b 0.256 1 b 0.225 0 b 0.052 0 b 0.052 0 b 0.021 0 b 0.188 1 b 0.188 1 b 0.092 1 b 0.098 0 b 0.188 1 b 0.161 1 b 0.107 0 b 0.375 0 b 0.195 0 b 0.112 0 b 0.11 1 g 0.149 1 g 0.368 1 g 0.22 1 g 0.121 1 g 0.163 1 g 0.072 1 g 0.098 1 g 0.312 1 g 0.282 1 g 0.229 1 g 0.256 1 g 0.298 0 g 0.092 0 g 0.189 0 g 0.134 1 g 0.157 1 g 0.253 1 g 0.12 1 g 0.34 1 g 0.57 1 g 0.34 1 g 0.346 1 g 0.126 1 g 0.241 1 g 0.162 1 g 0.084 0 g 0.159 0 g 0.253 1 g 0.231 1 g