write a C program . Create a structure named Person with two components: name and age. Create two Person variables and write the records in a text file, say “records.txt”. Then read the data from file and display the average age.

Enter your name and the letters in your name are selected in alphabetical order and each letter is printed on the screen every 5 seconds. Eg enter john display hjno

This project is divided into several parts, each with a deliverable. The first four parts are drafts. These documents should resemble business reports in that they are organized by headings, include source citations (if any), be readable, and be free from typos and grammatical errors. However, they are not final, polished reports.Please see the attached requirements.

Project Part 3: Risk Mitigation Plan

Senior management at Health Network allocated funds to support a risk mitigation plan. They have requested that the risk manager and team create a plan in response to the deliverables produced within earlier phases of the project.The risk mitigation plan should address the identified threats described in the scenario for this project, as well as any new threats that may have been discovered during the risk assessment. You have been assigned to develop a draft of this new plan.

Submission Requirements

• Format: Microsoft Word (or compatible)
• Font: Arial, size 12, double-space
• Citation style: Your school’s preferred style guide

4 pages

Note its based one week 1 you did

Week 2

Create a flowchart for a simplified version of your program. Create a working version of it based on your flowchart. This should be a working program that accepts user input, does some calculation, and shows the results. It does not have to use conditional expressions, loops, and the like.

Examples: For the programming tutorial, you can describe and demonstrate to the user using screen output, how to declare variables, display information on the screen, get information from the user, and do calculations. You can ask the user for their name and include it in the tutorial output.

For a loan payment calculator, it asks for interest rate, length of loan, amount of loan, and it displays monthly payment. For a DJ manager, it asks for the average song length and the number of songs on a playlist, and it displays the length of the playlist.

 INTRODUCTION TO THE TASK!   
 This Capstone Project is about a basic form of Natural Language Processing (NLP) called Sentiment Analysis. For this task, you are required to use two different neural networks in Keras to try and classify a book review as either positive or negative, and report on which network type worked better. For example, consider the review, “This book is not very good.” This text ends with the words “very good” which indicates a very positive sentiment, but it is negated because it is preceded by the word “not”, so the text should be classified as having a negative sentiment. We need to teach our neural network to recognise this distinction and be able to classify the review correctly. This problem can be broken down into the following steps:   
 Get the dataset 2. Preprocessing the Data   3. Build the Model   4. Train the model   5. Test the Model   6. Predict Something    
 We will be working with real-world data in this task.   
 GET THE DATASET   
 For this task, we will be using a small portion of the Multi-Domain Sentiment Dataset, which contains product reviews from Amazon. The full dataset contains reviews for products under the categories: kitchen, books, DVDs, and electronics, but we will only be looking at reviews for the book category.   
 We have two files, positive.txt and negative.txt, containing the reviews. Each review is associated with a number of fields. The only field we are interested in is the “title” field, which contains the title of the review. We are going to use this title to predict the sentiment of the review. We could have used the review text itself, however, as this is a lot longer than the title, this is a much harder task.   
 For example, a review title might be “Horrible book”, whilst the review text might be “This book was horrible. If it was possible to rate it lower than one star I would have.”   
 Both the review title and the review text have the same sentiment — the title is just much more concise, which makes this task easier.   
 While some sentiments are easy to classify, like “don’t buy this horrible book”, others are less straightforward, like “’run don’t walk to buy this book”. The latter is hard to classify because it contains the word “don’t”, which might be seen as an indication that this is a negative review, whereas actually, it is a positive one (the reviewer is suggesting that you should go as fast as you can to get the book). Thus, sentiment analysis is not always straightforward — some samples will be easy to classify, while others will not.   
 Some code is already included in the notebook associated with this task to start you off, which loads the relevant part of the data (the review heading) and performs some preliminary preprocessing to remove strange characters.   
 It also is necessary to create a vocabulary (called a text corpus) — words which our neural network will know and to “tokenise” the input. If we have a review, such as “a good book”, it is necessary to turn this into a form that a computer can understand. First, each word in the dataset is mapped to a unique number in the vocabulary. A word tokeniser will then take a sentence like this and convert it to a sequence of numbers, which map to the relevant words in the vocabulary.   
 Eg: “a good book” becomes an array of numbers: [1, 12, 3]. This mapping means that “a” is the first word in the vocabulary, “good” is the twelfth and “book” is the third. This mapping depends on the dataset supplied to the tokeniser.   
 The code for tokenisation is already included, but it is important that you understand what it does.
 PREPROCESSING THE DATA   
 n order to feed this data into our network, all input reviews must have the same length. Since the reviews differ heavily in terms of lengths, we either need to trim or pad the reviews so that they are the same length. For this task, we will set the length of reviews to the mean length, which is around 4 words. If reviews are shorter than 4 words we will need to pad them with zeros, if they are longer than 4 words we will trim them to this length by cutting off any words after this. Keras offers a set of preprocessing routines that can do this for us. In order to pad our reviews, we will need to use the pad_sequences function   
 BUILD THE MODEL   
 In the task today you will need to build a recurrent neural network to classify sentiment. The network will need to start with a special layer which will assist with text classification through a process called embedding.   
 Word embedding is a class of approaches for representing words and documents using dense vectors where a vector represents the projection of the word into a continuous vector space (Brownlee, 2017).   
 The position of a word within the vector space is learned from the text and is based on the words that surround the word when it is used. The position of a word in the learned vector space is referred to as its embedding.   
 Keras offers an embedding layer, used for neural networks on text data, and requires that the input data be integer encoded, so each word is represented by a unique integer (Brownlee, 2017). We have already achieved this format through tokenisation.   
 The embedding layer is trained as a part of the neural network and will learn to map words with similar semantic meanings to similar embedding-vectors. It is initialised with random weights and will learn an embedding for all of the words in the training dataset (Brownlee, 2017).   
 The Embedding layer is defined as the first hidden layer of a network. It must have three arguments (Keras Team, 2020):   
 ● input_dim: This is the size of the vocabulary in the text data. For example, if your data is integer encoded to values between 0-5000, then the size of the vocabulary would be 5001 words.   
 ● output_dim: This is the size of the vector space in which words will be embedded. It defines the size of the output vectors from this layer for each word. For example, it could be 32 or 100 or even larger. This is a hyper-parameter that needs to be tuned — test different values for your problem.   
 ● input_length: This is the length of input sequences, as you would define for any input layer of a Keras model. For example, if all of your input documents are comprised of 4 words, this would be 4. This is the length which we padded/trimmed the inputs to during pre-processing.
  Build a neural network, as outlined below.   
 ● A recurrent neural network. This type of network is commonly used in NLP. This network should       have the following architecture:   
 Embedding layer   SpatialDropout1D(0.2)   BatchNormalization()   LSTM(32)   Dense(2, activation=’softmax’)
 TRAIN AND TUNE THE MODEL   
 You are now ready to train your model. Remember to compile your model by specifying the loss function and optimizer we want to use while training, as well as any evaluation metrics we’d like to measure — set the optimizer to ‘adam’ and the loss function to ‘binary_crossentropy’.   
 Once compiled, you can start the training process. Note that there are two important training parameters that we have to specify, namely batch size and the number of training epochs. Together with our model architecture, these parameters determine the total training time.   
 For the network:   ● Set the number of epochs to train for to 5 and batch size to 10.   ● Tune the output_dim hyper-parameter of the embedding layer. Try values: 10, 25, 50 and 100. Report on the performance metrics for each value.   ● Select the output_dim which gives the best performance on the test set and plot a graph of both the accuracy and loss of the model while training. Use these graphs to determine the point at which the model starts to overfit or if it has not yet converged. Identify a more optimal number of epochs to train for.   ● You can also try tuning other metrics — such as batch size — to get the best possible performance.   ● Report on the performance metrics of the final model.   
 MAKING PREDICTIONS   
 Finally, we would like to be able to use our model to predict something. To do this, we need to translate the sentence into the relevant integers and pad as necessary. This will allow us to put it into our model and see whether it predicts if we will like or dislike the book. A small selection of samples has been provided to get you going — you are welcome to add to this.   
 
 
 
 
 
 Compulsory Task   
 A notebook is associated with this task, which contains some useful functions/code to get you started.   
 Follow these steps:   
 ● Use the provided files positive.txt and negative.txt and follow the above steps to train a recurrent neural network. Your goal is to classify the sentiment of book reviews as positive or negative.   
 ● Note: Some blocks of code are labelled do not modify — make sure that the code in these blocks is left alone, or else you will encounter issues. Do read through it and see if you can follow what it does.   
 ● When complete, create basic point form summary of file file in which you describe your project in detail.   

i have about 4 extra credit homework problems that need to be done in C#. Ill pay $60 if done within an hour on Monday

Chapter 9 – Review the section on Establishing a Security Culture.  Review the methods to reduce the chances of a cyber threat noted in the textbook.  Research other peer-reviewed source and note additional methods to reduce cyber-attacks within an organization. (Information Technology and Organizational Learning)

The above submission should be one-page in length and adhere to APA formatting standards.

**Remember the page length does not include the APA cover page or any references**

 The honor code at SSSAS is an important part of school life. Every year at the Upper School, students attend an Honor Code assembly which culminates in all students signing a pledge to uphold the Honor Code. The honor code states, “I will not lie, cheat or steal, nor tolerate those who do”. In 250-500 words, please respond to the following scenario: During a recent test, you are aware that a student cheated. You know for a fact that they have cheated and the student’s performance has impacted the curve. You received a D on the test. How would you respond to this situation? Describe your rationale for the decision you make?   

Need in 2 days. Need a business case report based on IT sector. All the details such as what contents needed are in the docx file.

Week 3 Written Assignment

This week’s journal article focuses on attribution theory and how it influences the implementation of innovation technologies.  Two types of employee attributions are noted in the article (intentionality and deceptive intentionality), please review these concepts and answer the following questions:

1. Provide a high-level overview/ summary of the case study
2. Note how constructive intentionality impacts innovation implementations
3. Find another article that adds to the overall findings of the case and note how attribution-based perspective enhances successful innovation implementations.  Please be explicit and detailed in answering this question.

Be sure to use the UC Library for scholarly research. Google Scholar is also a great source for research.  Please be sure that journal articles are peer-reviewed and are published within the last five years.The paper should meet the following requirements:

• 3-5 pages in length (not including title page or references)
• APA guidelines must be followed.  The paper must include a cover page, an introduction, a body with fully developed content, and a conclusion.
• A minimum of five peer-reviewed journal articles.

The writing should be clear and concise.  Headings should be used to transition thoughts.  Don’t forget that the grade also includes the quality of writing.