40 skills found · Page 2 of 2
liuweiyu / RandomHostMutationBasedonOpenFlowStatic configurations, especially IP addresses, give the adversaries great opportunities to discover network targets and then launch attacks. Frequently changing hosts’ IP addresses to hide their real ones is a novel method to solve this problem. A recent paper OpenFlow Random Host Mutation: Transparent Moving Target Defense using Software Defined Networking specified this new proactive moving target defense. In this assignment, I implement a simple model to replicate the result of this paper based on Mininet network emulation environment, OpenFlow architecture and protocol, and SDN technique. This report includes three major parts: the design of the controller, the implementation of my method, and discussion about the advantages and disadvantages of my approach.
mineaktas / Weapon Target Assignment Problem As A Multi Objective Optimization ProblemThe codes in this repository are my own works that I used in my thesis. It is a python code that prints flight times, set-up times and probabilities of survive for the Weapon Target Assignment Problem
Nada-Nasser / Weapon Target Assignment Problem WTAWTA is a well-known optimization problem. Given a set of weapons of various types and a set of targets, we want to find the optimal assignment of the weapons to the targets in order to minimize the total expected threat coming from the targets.
wahidpanda / Introduction To Data Signal And Image Analysis With MATLAB Coursera AnswersIntroduction to Data, Signal, and Image Analysis with MATLAB Welcome to Introduction to Data, Signal, and Image Analysis with MATLAB! MATLAB is an extremely versatile programming language for data, signal, and image analysis tasks, including hundreds if not thousands of functions. With such a comprehensive tool set, knowing where to start can be overwhelming. My goal is to help you learn the basics, with video lessons and assignments that introduce you to the most fundamental functions, show you how to write new code, and demonstrate how to learn how to use functions you have not used before. This course is designed to introduce data, signal, and image processing and analysis to students who have little or no experience with data and signals but have basic programming experience in the MATLAB programming language, for example, those who have completed the Introduction to Programming with Matlab course. The level is targeted at first-year college students and high school seniors, but really this course is suitable for anybody who wants to learn about data and signal analysis and has experience with linear algebra. The length of the course is five weeks. As shown below, that includes four weeks of video lectures plus an extra week for a final project submission. Schedule Week 1: Introduction Week 2: Data analysis in Matlab Week 3: Signal analysis in Matlab Week 4: Image analysis in Matlab Week 5: Course Project Course objectives After completing this course, a learner will be able to use MATLAB to… Understand how signals, images, and data are represented Load and save datasets Visualize high dimensional data Apply machine learning methods for data classification Perform signal frequency analysis Design signal and image filters Process and analyze image content
nirav1997 / Graph Network PropertiesNetwork Properties in Spark GraphFrames In this project, you will implement various network properties using pySpark and GraphFrames. You may need to look into the documentation for GraphFrames and networkx to complete this project. In addition to implementing these network metrics, you will be required to answer some questions when applying these metrics on real world and synthetic networks. Submission Requirements Please create a zip file containing ONLY the following for this project : 0. Do NOT include the data or any extra files. 1. degree.py 2. centrality.py 3. articulations.py 4. A pdf document containing answers to the questions asked in the project description. [Power law questions for Stanford and the 4 random graphs provided in degree.py, answers for the two questions on centrality, answer for the closeness question). Name this as <your_unity_id>.pdf 5. [OPTIONAL] Script for checking power law (if you have written something). Instructions to run in the pdf document which contains the answers to the questions asked in project description. 6. Output for centrality.py saved in a csv file called centrality_out.csv 7. Output for articulations.py saved in a csv file called articulations_out.csv For articulations problem, you may just run it with networkx approach (5 mins run time) and save the output in the file articulations_out.csv. We will be evaluating the project using a script. So please make sure you follow the instructions, or your assignment might not get graded. Degree Distribution The degree distribution is a measure of the frequency of nodes that have a certain degree. Implement a function degreedist, which takes a GraphFrame object as input and computes the degree distribution of the graph. The function should return a DataFrame with two columns: degree and count. For the graphs provided to you, test and report which graphs are scalefree, namely whose degree distribution follows a power law, at least asymptotically. That is, the fraction P(k) of nodes in the network having k connections to other nodes goes for large values of k as P(k) ~ k−γ where γ is a parameter whose value is typically in the range 2 < γ < 3, although occasionally it may lie outside these bounds. Answer the following questions: 1. Generate a few random graphs. You can do this using networkx’s random graph generators. Do the random graphs you tested appear to be scale free? (Include degree distribution with your answer). 2. Do the Stanford graphs provided to you appear to be scale free? Note that GraphFrames represents all graphs as directed, so every edge in the undirected graphs provided or generated must be added twice, once for each direction. When finding if the degrees follow a power law you can then use either the indegree or outdegree, as they should be equal. Centrality Centrality measures are a way to determine nodes that are important based on the structure of the graph. Closeness centrality measures the distance of a node to all other nodes. We will define the closeness centrality as CC(v) =1/ ∑ d(u,v) u∈V where d(u, v) is the shortestpath distance between u and v. Implement the function closeness, which takes a GraphFrame object as input and computes the closeness centrality of every node in the graph. The function should return a DataFrame with two columns: id and closeness. Consider a small network of 10 computers, illustrated below, with nodes representing computers and edges representing direct connections of two machines. If two computers are not connected directly, then the information must flow through other connected machines. Answer the following questions about the graph: 1. Rank the nodes from highest to lowest closeness centrality. 2. Suppose we had some centralized data that would sit on one machine but would be shared with all computers on the network. Which two machines would be the best candidates to hold this data based on other machines having few hops to access this data? Articulation Points Articulation points, or cut vertices, are vertices in the graph that, when removed, create more components than there were originally. For example, in the simple chain 1-2-3, there is a single component. However, if vertex 2 were removed, there would be 2 components. Thus, vertex 2 is an articulation point. Implement the function articulations, which takes a GraphFrame object as input and finds all the articulation points of a graph. The function should return a DataFrame with two columns, id and articulation, where articulation is a 1 if the node is an articulation point, otherwise a 0. Suppose we had the terrorist communication network given in the file 9_11_edgelist.txt . If our goal was to disrupt the flow of communication between different groups, isolating the articulation points would be a good way to do this. Answer the following questions: 1. In this example, which members should have been targeted to best disrupt communication in the organization?
monsidian / WeaponTargetAssignmentNo description available
mtsznowak / WeaponTargetAssignmentAlgorithm for the Weapon Target Assignment problem
Kerem-Demir / WTAA New Optimization Model for Solving Weapon-Target Assignment Problems
smartspot2 / Canvas Grade CalculatorA simple calculator for Canvas Grades. Gives the user the ability to add and edit assignments, scores, and weights. Also has the ability to calculate required points to achieve a target grade.
GeoffRiley / TmaRepository for Open University Tutor Marked Assignment style file `ou-tma.sty` and a base target for the CTAN implementation.
TachikakaMin / ITA CBS OldFor paper "Solving Multi-Agent Target Assignment and Path Finding with a Single CBS Tree" Based on CBS-TA code
AnkurNapa / Lead Score Case StudyProblem Statement An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses. The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%. Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone. A typical lead conversion process can be represented using the following funnel: Lead Conversion Process - Demonstrated as a funnel Lead Conversion Process - Demonstrated as a funnel As you can see, there are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating etc. ) in order to get a higher lead conversion. X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%. Data You have been provided with a leads dataset from the past with around 9000 data points. This dataset consists of various attributes such as Lead Source, Total Time Spent on Website, Total Visits, Last Activity, etc. which may or may not be useful in ultimately deciding whether a lead will be converted or not. The target variable, in this case, is the column ‘Converted’ which tells whether a past lead was converted or not wherein 1 means it was converted and 0 means it wasn’t converted. You can learn more about the dataset from the data dictionary provided in the zip folder at the end of the page. Another thing that you also need to check out for are the levels present in the categorical variables. Many of the categorical variables have a level called 'Select' which needs to be handled because it is as good as a null value (think why?). Goals of the Case Study There are quite a few goals for this case study. Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted. There are some more problems presented by the company which your model should be able to adjust to if the company's requirement changes in the future so you will need to handle these as well. These problems are provided in a separate doc file. Please fill it based on the logistic regression model you got in the first step. Also, make sure you include this in your final PPT where you'll make recommendations. Results Expected A well-commented Jupyter note with at least the logistic regression model, the conversion predictions and evaluation metrics. The word document filled with solutions to all the problems. The overall approach of the analysis in a presentation Mention the problem statement and the analysis approach briefly Explain the results in business terms Include visualisations and summarise the most important results in the presentation A brief summary report in 500 words explaining how you proceeded with the assignment and the learnings that you gathered. You need to submit the following four components: Python commented file: Should include detailed comments and should not contain unnecessary pieces of code. Word File: Answer all the questions asked by the company in the word document provided. Presentation: Make a presentation to present your analysis to the chief data scientist of your company (and thus you should include both technical and business aspects). The presentation should be concise, clear, and to the point. Submit the presentation after converting it into PDF format. PDF File: Write the summary report in a word file and submit it as a PDF.
sehes2solve / Weapon Target Assignment Using Genetic AlgorithmNo description available
andreaskami / Multi Agent GPS Planning ToolA planning tool for multi-agent path planning and target assignment, based on GPS data from Google Earth.
thehamzza / Artistic Pattern Formation Using Multi Robot SystemThis project describes the work on multi-robot pattern formation on any kind of arbitrary target patterns with the help of optimal robot deployment, using a method that is independent of the number of robots with having utilized the least possible resources necessary to achieve the desired goal of non-intersecting pattern or design formation. Generation of uniform sets of goal positions, allocation of the multi-robots on the respective goal assignments having optimal path and fast convergence into consideration and finally smooth deployment of robots to avoid collisions using distributed collision avoidance methods to consequently provide a representative set of desired patterns. In this method, the trajectories are visually appealing in the sense of being smooth, oscillation free, and showing fast convergence having used the reliable positioning and assignment algorithms to generate both visually and convincing final pattern formation by optimizing the robots’ goal positions as well as simple and smooth robot motions at the transitions of patterns
BenJ-cell / Differentiable Ranks And Sorting Using Optimal TransportSorting is a necessary tool for machine learning, to create algorithms (k-NN) or test-time metrics like top-k classification accuracy or losses based on the rank. Nevertheless it seems to be a difficult task for automatically differentiable pipelines in DL. Sorting gives us two vectors, this application is not differentiable as we are working with integer-valued permutation. In the paper they aim to implement a differentiable proxy of the basic approach. The article conceive this proxy by thinking of an optimal assignment problem. We sort n values by matching them to a probability measure supported on any increasing family of n target values. Therefore we are considering Optimal Transport (OT) as a relaxation of the basic problem allowing us to extend rank and sort operators using probability measures. The auxiliary measure will be supported on m increasing values with m $\ne$ n. Introducing regularization with an entropic penalty and applying Sinkhorn iterations will allow to gain back differentiable operators. The smooth approximation of rank and sort allow to use the 0/1 loss and the quantile regression loss.
Abhik35 / Assignment Decision Tree Company DataAssignment About the data: Let’s consider a Company dataset with around 10 variables and 400 records. The attributes are as follows: Sales -- Unit sales (in thousands) at each location Competitor Price -- Price charged by competitor at each location Income -- Community income level (in thousands of dollars) Advertising -- Local advertising budget for company at each location (in thousands of dollars) Population -- Population size in region (in thousands) Price -- Price company charges for car seats at each site Shelf Location at stores -- A factor with levels Bad, Good and Medium indicating the quality of the shelving location for the car seats at each site Age -- Average age of the local population Education -- Education level at each location Urban -- A factor with levels No and Yes to indicate whether the store is in an urban or rural location US -- A factor with levels No and Yes to indicate whether the store is in the US or not The company dataset looks like this: Problem Statement: A cloth manufacturing company is interested to know about the segment or attributes causes high sale. Approach - A decision tree can be built with target variable Sale (we will first convert it in categorical variable) & all other variable will be independent in the analysis.
ShahadShaikh / Bike Sharing ProjectProblem Statement This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same. Problem Statement A bike-sharing system is a service in which bikes are made available for shared use to individuals on a short term basis for a price or free. Many bike share systems allow people to borrow a bike from a "dock" which is usually computer-controlled wherein the user enters the payment information, and the system unlocks it. This bike can then be returned to another dock belonging to the same system. A US bike-sharing provider BoomBikes has recently suffered considerable dips in their revenues due to the ongoing Corona pandemic. The company is finding it very difficult to sustain in the current market scenario. So, it has decided to come up with a mindful business plan to be able to accelerate its revenue as soon as the ongoing lockdown comes to an end, and the economy restores to a healthy state. In such an attempt, BoomBikes aspires to understand the demand for shared bikes among the people after this ongoing quarantine situation ends across the nation due to Covid-19. They have planned this to prepare themselves to cater to the people's needs once the situation gets better all around and stand out from other service providers and make huge profits. They have contracted a consulting company to understand the factors on which the demand for these shared bikes depends. Specifically, they want to understand the factors affecting the demand for these shared bikes in the American market. The company wants to know: Which variables are significant in predicting the demand for shared bikes. How well those variables describe the bike demands Based on various meteorological surveys and people's styles, the service provider firm has gathered a large dataset on daily bike demands across the American market based on some factors. Business Goal: You are required to model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations. Further, the model will be a good way for management to understand the demand dynamics of a new market. Data Preparation: You can observe in the dataset that some of the variables like 'weathersit' and 'season' have values as 1, 2, 3, 4 which have specific labels associated with them (as can be seen in the data dictionary). These numeric values associated with the labels may indicate that there is some order to them - which is actually not the case (Check the data dictionary and think why). So, it is advisable to convert such feature values into categorical string values before proceeding with model building. Please refer the data dictionary to get a better understanding of all the independent variables. You might notice the column 'yr' with two values 0 and 1 indicating the years 2018 and 2019 respectively. At the first instinct, you might think it is a good idea to drop this column as it only has two values so it might not be a value-add to the model. But in reality, since these bike-sharing systems are slowly gaining popularity, the demand for these bikes is increasing every year proving that the column 'yr' might be a good variable for prediction. So think twice before dropping it. Model Building In the dataset provided, you will notice that there are three columns named 'casual', 'registered', and 'cnt'. The variable 'casual' indicates the number casual users who have made a rental. The variable 'registered' on the other hand shows the total number of registered users who have made a booking on a given day. Finally, the 'cnt' variable indicates the total number of bike rentals, including both casual and registered. The model should be built taking this 'cnt' as the target variable. Model Evaluation: When you're done with model building and residual analysis and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set. from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set. Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen. Downloads: You can download the dataset file from the link given below: Bike Sharing Dataset Download Assignment - Data Dictionary Download Submissions Expected: Python Notebook: One Python notebook with the whole linear model, predictions, and evaluation. Subjective Questions PDF: Apart from the Python notebook, you also need to answer some subjective questions related to linear regression which can be downloaded from the file below. Answer these questions and submit it as a PDF. Note: There are some questions in the subjective questions doc that you might not be familiar with. So you're expected to research these questions and give an appropriate answer in order to expand your learnings of this topic.
ameyrocks01 / FOR MVC DEVLEOPERS THE FEAUTURES OF ASP.NET CORE FOR MVC DEVLEOPERS THE FEAUTURES OF ASP.NET CORE ASP.NET Core 1.0 gives a patched up Web development system adapted towards the prerequisites of present day Web applications. The new structure, as of now in RC1, obliges you to learn numerous new ideas not found in ASP.NET MVC 5. To that end, this article identifies a couple of essential components that ASP.NET MVC 5 designers ought to know as they get ready to take in this new structure. 1. ASP.NET Core on Numerous Runways ASP.NET Core is a piece of .NET Core—another measured structure that backings numerous stages.ASP.NET and the .NET framework are focused on towards the Windows stage. Then again, ASP.NET Core is created to bolster various stages including Windows, Mac, and Linux. This additionally implies, dissimilar to ASP.NET web applications, basically, keep running under IIS, the ASP.NET Core applications can keep running under non-IIS Web servers. Figure 1 demonstrates the part of the .NET Core and ASP.NET Core. The part of the .NET Core and ASP.NET Core - A Web application worked with ASP.NET Core can target ASP.NET Framework 4.6 or the ASP.NET Core. The Web applications focusing on ASP.NET Framework 4.6 run just on the Windows stage. The Web applications focusing on the ASP.NET Core can keep running on Windows and non-Windows stages. Obviously, as on this composition, ASP.NET Core doesn't offer the same rich usefulness offered by ASP.NET Framework 4.6. 2. Part of Project.json ASP.NET Core utilizes an exceptional document—Project.json for putting away all the undertaking level configuration data. Project.config can store numerous design settings, for example, references to NuGet bundles utilized as a part of the task and target structures. "dependencies": { "Microsoft.AspNet.IISPlatformHandler": "1.0.0-rc1-final", "Microsoft.AspNet.Server.Kestrel": "1.0.0-rc1-final", "Microsoft.AspNet.Mvc": "6.0.0-rc1-final", "Microsoft.AspNet.Mvc.TagHelpers": "6.0.0-rc1-final", "Microsoft.AspNet.StaticFiles": "1.0.0-rc1-final", "Microsoft.AspNet.Tooling.Razor": "1.0.0-rc1-final", "Microsoft.Extensions.Configuration.Abstractions": "1.0.0-rc1-final", "Microsoft.Extensions.Configuration.Json": "1.0.0-rc1-final", "EntityFramework.MicrosoftSqlServer": "7.0.0-rc1-final", "EntityFramework.Commands": "7.0.0-rc1-final", "Microsoft.AspNet.Session": "1.0.0-rc1-final", "Newtonsoft.Json": "8.0.3" The Project.json record stores configuration data in JSON position. The above markup demonstrates a conditions segment that contains a rundown of NuGet bundles required by the application. For instance, the Web application under thought requires the 6.0.0-rc1-last form of Microsoft.AspNet.Mvc get together, etc 3. Part of AppSettings.json ASP.NET stores application configuration settings in Web.config. For instance, engineers utilize the <appSettings> area to store custom application settings, the <connectionStrings> segment to store database association strings, etc. ASP.NET Core utilizes AppSettings.json to store such bits of data. Consider the accompanying configuration: { "AppSettings": { "Title": "My ASP.NET Core Application" }, "Data": { "DefaultConnection": { "ConnectionString": "data source=.; initial catalog=Northwind;integrated security=true" } } } The previous JSON markup comprises of two properties or keys, to be specific AppSettings and Data. The AppSettings property holds a sub-key named Title. The Title sub-key has a string estimation of "My ASP.NET Core Application". Also, the Data key has a DefaultConnection sub-key. The DefaultConnection thusly has a ConnectionString sub-key. 4. Application set-up In ASP.NET, Global.asax goes about as the passage point for your application. You can wire different events handlers for occasions, for example, Application_Start and Session_Start, in the Global.asax record. In ASP.NET Core, the application startup happens in an unexpected way—it happens through a Startup class. one such Startup class - public class Startup { public Startup(IHostingEnvironment env, IApplicationEnvironment app) { ConfigurationBuilder builder = new ConfigurationBuilder(); builder.SetBasePath(app.ApplicationBasePath); builder.AddJsonFile("appsettings.json"); IConfigurationRoot config = builder.Build(); string str = config.Get<string> ("Data:DefaultConnection:ConnectionString"); // do something with str } public void ConfigureServices(IServiceCollection services) { services.AddMvc(); services.AddEntityFramework() .AddSqlServer(); } public void Configure(IApplicationBuilder app) { app.UseStaticFiles(); app.UseMvc(routes => { routes.MapRoute( name: "default", template: "{controller=Home}/ {action=Index}/{id?}"); }); } public static void Main(string[] args) => WebApplication.Run<Startup>(args); } The Startup class appeared above starts with a constructor. The constructor stacks the AppSettings.json record utilizing ConfigurationBuilder class. The Get() strategy then is utilized to peruse the database association string put away in the AppSettings.json document. The ConfigureServices() technique includes the administrations required by the application. For instance, here you add MVC and Entity Framework to the administrations gathering. The Configure() technique determines and arranges the administrations included before for application's utilization. For instance, the MVC directing is designed in the code appeared previously. 5. Tag Helpers In ASP.NET MVC 5, you utilized HTML assistants, for example, BeginForm(), LabelFor(), and TextBoxFor() to render structures and frame fields. You can keep on using HTML partners in ASP.NET Core, too. However, there is a superior option: Tag Helpers. Label aides take the type of standard HTML labels with certain extraordinary asp-* credits added to them. Consider the accompanying markup that renders a structure: <form asp-controller="Home" asp-action="Save" method="post"> <table border="1" cellpadding="10"> <tr> <td><label asp-for="FirstName">First Name :</label></td> <td><input type="text" asp-for="FirstName" /></td> </tr> <tr> <td><label asp-for="LastName">Last Name :</label></td> <td><input type="text" asp-for="LastName" /></td> </tr> <tr> <td><label asp-for="Email">Email :</label></td> <td><input type="text" asp-for="Email" /></td> </tr> <tr> <td><label asp-for="Phone">Phone :</label></td> <td><input type="text" asp-for="Phone" /></td> </tr> <tr> <td colspan="2"> <input type="submit" value="Submit" /> </td> </tr> </table> </form> Observe clearly, the properties that start with asp-. They are characterized by the label aides. For instance, the structure label aide utilizes asp-controller ascribe to indicate the objective controller name and asp-activity credit to determine the objective activity technique name. Correspondingly, asp-for traits utilized with name and info label partners tie a name or a text box to a model property. Label partners are more advantageous to use than HTML assistants in light of the fact that their linguistic structure nearly takes after the HTML markup. 6. View Components In MVC 5, you utilized halfway perspectives as a way to reuse markup and code. ASP.NET Core presents View Components, the more intense and adaptable option. A perspective part comprises of a class normally acquired from ViewComponent base class and a perspective record containing the required markup. This programming model is entirely like the one utilized by controllers and perspectives. It permits you to separate code and markup from each other—code in the perspective segment class and markup in a perspective. Once made, you can utilize a perspective segment on a perspective by utilizing the @Component.Invoke() technique. 7. Dependency Injection ASP.NET Core gives an inbuilt reliance infusion system. The DI system of ASP.NET Core offers four-lifetime modes for a sort being infused: Singleton: An object of an administration (the sort to be infused) is made and supplied to all the requests to that administration. Along these lines, fundamentally all requests get the same article to work with. Scoped: An object of an administration is made for every single request. In this way, every request gets another event of an administration to work with. Transient: An object of an administration is made each time an article is asked. Instance: For this situation, you are in charge of making an object of an administration. The DI system then uses that case in singleton mode said prior public void ConfigureServices(IServiceCollection services) { services.AddMvc(); services.AddSingleton<IMyService,MyService>(); } Here, MyService is the sort to be enlisted with the DI structure and actualizes IMyService. The AddSingleton() technique enlists this type for Singleton mode portrayed previously. Once a sort is enrolled with the DI system, you can infuse it in a controller like this: public class HomeController : Controller { private IMyService obj; public HomeController(IMyService obj) { this.obj = obj; } .... .... } 8. Gulp, Grunt, and Bower Support Gulp and Grunt are JavaScript assignment runners. They help you computerize generally required undertakings, for example, packaging JavaScript and CSS records, minifying JavaScript and CSS documents, and arranging Less and Sass records (and some more). They are introduced utilizing npm (Node Package Manager). The ASP.NET Core venture made utilizing Visual Studio 2015 permits you to include Grunt and Gulp arrangement documents furthermore gives Task Runner Explorer to screen the errands. Bower is a bundle administrator basically for front-end bundles. Front-end bundles are the bundles that you use in your Web pages, for example, JavaScript libraries/systems and CSS records. For instance, you may introduce jQuery in your ASP.NET Core venture by utilizing Bower. An ASP.NET Core venture made utilizing Visual Studio 2015 permits you to include a Bower setup document. You likewise can work with the bundles utilizing the Manage Bower Packages menu choice. 9. Single Programming for Web API Model for and MVC In MVC 5, controllers acquire from the System.Web.Mvc.Controller base class. What's more, Web API 2 controllers acquire from System.Web.Http.ApiController. In ASP.NET Core, both of these structures are converged into a solitary system. Therefore, under ASP.NET Core, an MVC controller and Web API controller both acquire from Microsoft.AspNet.Mvc.Controller base class. You then can design viewpoints, for example, HTTP verb mapping and the directing of the controllers as coveted. 10. Static Files and the wwwroot Folder In ASP.NET, there is no settled area for putting away static documents, for example, picture records, JavaScript documents, and CSS records (engineers regularly utilized a Content envelope to store such documents). In ASP.NET Core, all the static records are kept under the wwwroot envelope (default). You likewise can change the name of this envelope by utilizing the Project.json document. Refer the figure following down - After arrangement, the wwwroot turns into the Web application's root. Every one of the URLs to static records are determined as for this envelope. Along these lines,/pictures/logo.png anticipates that logo.png will be available under the wwwroot/pictures envelope. Conclusion - ASP.NET Core 1.0 is a redone system outfitted towards present day cloud based, measured Web applications. Despite the fact that the new structure safeguards the key ideas of MVC 5, ASP.NET engineers will discover numerous contrasts between MVC 5 and ASP.NET Core 1.0. This article specified the imperative new components/ideas that you have to comprehend to start your voyage with ASP.NET Core 1.0.
ankit9571 / Communication SkillsCommunication Skills Have you ever attempted to convey the desired information? Maybe you took a stab at pitching an earth shattering venture, you were tasked with running a departmental meeting, or you expected to convey a convincing presentation. You did as well as can possibly be expected, however all you got were vague looks and ungainly quiets. Odds are, there wasn't much amiss with what you were attempting to say; it's simply that you expected to chip away at how you said it. The best communicators rouse and spur individuals, help them to make successful move, and expel obstructions to their comprehension. Lamentably, numerous businessmen think that its difficult to impart viably — we've all gotten tremendous, language ridden messages, and we've encountered the misconception, dissatisfaction and missed open doors that accompany them. Be that as it may, with the right aptitudes, anybody can turn into an outstanding communicator Add to My Personal Learning Plan. Perused on, and you can take in more than 130 intense relational abilities, gathered into the accompanying areas: Appreciate finding out about them, and appreciate turning into an expert communicator! Understanding the Fundamentals From starting aim, through structure and conveyance, to elucidation and input, your message will take after a procedure called the Communication Cycle Add to My Personal Learning Plan. Your message needs to succeed at every phase of this cycle, so structure it precisely to address your gathering of people's needs and desires, and to be fitting to the channel that you're utilizing. Be that as it may, correspondence is a two-way process, and you'll just realize that you've succeeded by paying consideration on input. Things being what they are, before imparting up close and personal, invest some energy asking you, "How great are my listening aptitudes? Add to My Personal Learning Plan" Demonstrate the general population you're conversing with that you truly are accepting their messages by listening empathically Add to My Personal Learning Plan and effectively Add to My Personal Learning Plan. Figure out how to be careful Add to My Personal Learning Plan, create sympathy Add to My Personal Learning Plan, and attempt to get a handle on other individuals' perspectives Add to My Personal Learning Plan with the goal that you go to a mutual comprehension Add to My Personal Learning Plan. This, thus, will permit you to make fantastic associations Add to My Personal Learning Plan with them. Taken together, these aptitudes can make your work environment more satisfied and more beneficial . In any case, to accomplish this, you have to begin with an arrangement. 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You've most likely seen a colleague who commands, innovation that comes up short at the pivotal minute, or a pioneer who doesn't comprehend the meeting's motivation. It's rankling, yet it doesn't need to be that path - by taking in a scope of clear aptitudes, you can deal with any meeting adroitly and proficiently. The essential principles for running viable gatherings Add to My Personal Learning Plan are to build up your goal and stick to it, keep the meeting as short as could reasonably be expected, and ensure that every one of the members are content with the procedure. In any case, to be a top-class facilitator Add to My Personal Learning Plan, you additionally should have the capacity to: Outline and plan a meeting. Get individuals required from the begin with appropriate icebreakers Add to My Personal Learning Plan. Guide and control the gathering amid the meeting. Record the key focuses Add to My Personal Learning Plan successfully. 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Whatever your circumstance and the kind of arrangement, it's critical to approach it both decisively and cooperatively, regardless of the fact that you may need to say "No." Add to My Personal Learning Plan As a component of this; build up what everyone included truly needs, with the goal that you can be certain about your position. Influence is the craft of inspiring individuals to concur with you as it's firmly identified with transaction. Effective influence Add to My Personal Learning Plan depends on four things: setting up your believability Add to My Personal Learning Plan, discovering shared belief with the individual that you're convincing, giving proof that you're correct, and making a passionate association. You can at present get your direction regardless of the possibility that exclusive a couple people Add to My Personal Learning Plan share your perspective. 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