A node.js utility library for creating a service that generates unique IDs at scale. Think of it as containing the common logic of Twitter's Snowflake and Boundary's Flake.

BUSINESS CONTEXT: With the enormous growth of computer networks usage and the huge increase in the number of applications running on top of it, network security is becoming increasingly more important. All the computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in the network, is becoming more important. The research in the intrusion detection field has been mostly focused on anomaly-based and misusebased detection techniques for a long time. While misuse-based detection is generally favored in commercial products due to its predictability and high accuracy, in academic research anomaly detection is typically conceived as a more powerful method due to its theoretical potential for addressing novel attacks. Conducting a thorough analysis of the recent research trend in anomaly detection, one will encounter several machine learning methods reported to have a very high detection rate of 98% while keeping the false alarm rate at 1%. However, when we look at the state of the art IDS solutions and commercial tools, there is no evidence of using anomaly detection approaches, and practitioners still think that it is an immature technology. To find the reason of this contrast, lots of research was done done in anomaly detection and considered various aspects such as learning and detection approaches, training data sets, testing data sets, and evaluation methods. BUSINESS PROBLEM: Your task to build network intrusion detection system to detect anamolies and attacks in the network. There are two problems. 1. Binomial Classification: Activity is normal or attack 2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R

Mongo db console commands //showing the existing dbs.. show dbs //use test switching to db test, (only creating it when actually adding new data) //prompts the name of the working db now db //the fllw would prompt the count(), in the link2 collection, in the current db... >db.links2.count() //inserting a record in links2 db.links2.insert({title:"unn titulo", url:"", comment:"", tags:["un primer tag", "un segundo tag"], saved_on: new Date}) //working with an object the javascript way... data = {} | data.title = "un titulo" | data.tags = ["un tag", "otro"] | data.meta = {} | data.meta.OS = "win7" | db.links2.insert(data) //printing the result of the find, in the structured json format. db.links2.find().forEach(printjson) //--> in this case we pass to forEach the printjson function... //retriving only the first of the results of the find method. db.links2.find()[0] db.links2.find()[0]._id //getting the timestamp present in the _id variable (is made of (also) the time it was created) db.links2.find()[0]._id.getTimestamp() /*the following function creates, when called, a new collection inside the same working db, that tracks the last id number we are in. This allows having the same behavieur than in relational DBs.*/ //apparently, u have to declare this function... function counter(name) { var ret = db.counter.findAndModify({query:{_id:name}, update:{$inc:{next:1}}, "new":true, upsert:true}); return ret.next; } //so u can do something like db.products.insert({_id:counter("products"), nombre:"primer nombre"}) //the result is something like: { "_id": 1, "name": "un producto" } { "_id": 2, "name": "otro producto" } /*referencing in MongoDB*/ db.users.insert({name:"Richard"}) var a = db.users.findOne({name:"Richard"}) db.links2.insert({title:"primer titulo", author:a._id}) //reference to other collection throught the _id key... //quering db.users.findOne({ _id:link.author }) //a way to make manual inner joins... within the user db, we search for a coincidence of our _ids on the links2 db, author field. ---note--- embedding is much more efficient when we have significantly more read than writes. Otherwise, consider using the normalized way. These depends on every case. /**/ #importing data from a .js in json format. With mongod running or in a services: > ../../../mongodb/bin/mongo 127.0.0.1/bookmarks bookmarks.js //the first part is the location to the mongo exe in the mongo usual location //the second part is the server and db in which we will be importing in //the third part is the file with all the mongo commands... --this bookmarks file is in C:\Tuto\mongo\trying -- https://raw.github.com/tuts-premium/learning-mongodb/master/08%20-%20bookmarks.js /*bookmarks.js extract*/ var u1 = db.users.findOne({ 'name.first': 'John' }), u2 = db.users.findOne({ 'name.first': 'Jane' }), u3 = db.users.findOne({ 'name.first': 'Bob' }); db.links.insert({ title: 'Nettuts+', url: 'http://net.tutsplus.com', comment: 'Great site for web dev tutorials', tags: ['tutorials', 'dev', 'code'], favourites: 100, userId: u1._id }); /**/ //connecting directly to db bookmarks > ../../../mongodb/bin/mongo bookmarks //searching in the collection all docs that have inside the tags array the "code" element. //this can be done because we are dealing with an array --> array advantages... db.users.find({tags:"code"}).forEach(printjson) //with findOne u can do (not with find) findOne().name db.links.find({favourites:100}, title:true, url:1) //selecting only some fields... db.links.find({favourites:100}, tags:0) //selecting all but the tag field... //selecting inside an object... db.users.findOne({"name.first": "John"}) db.users.findOne({"name.first": "John"}, "name.last":1) var john = db.users.findOne({"name.first": "John"}) db.links.find({userId:john._id}, {title:1, _id: 0}) /*queries directives*/ //greater than 150 db.links.find({favourites:{$gt:150}}, {_id:0, favourites:1, title:1}).forEach(printjson) db.links.find({favourites:{$gt:150}}, {_id:0, favourites:1, title:1}).count() //less than db.links.find({favourites:{$lt:150}}, {_id:0, favourites:1, title:1}).forEach(printjson) //$lte, $gte -- and iqual //using in db.users.find({"name.first":{$in:["John", "Jane"]}}) //the opposite is $nin db.users.find({"name.first":{$nin:["John", "Jane"]}}) //$all -- only the records with all the specifications in "tags" field. db.links.find({tags: {$all:["code", "marketplace"]}}, {title:1, tags:1, _id:0}) //$ne -- not equal //the $or flag search for the fullfillment of at least one of the elements in the array passed... db.users.find({$or: [{"name.first": "John"}, {"name.last": "Wilson"}]}) //the opposite: $nor //inclusive: $and //$exists db.users.find({email: {$exists: true}}) //$mod db.links.find({favourites: {$mod: [5, 0]}}, {_id:0, title:1, favourites:1}) db.links.find({favourites: {$not: {$mod: [5, 0]}}}, {_id:0, title:1, favourites:1}) //elemMatch -- inside logins, search for an element match that has minutes = 20, and return the complete record db.users.find({logins: {$elemMatch: {minutes: 20}}}) //searching for an 'at' prior to 2012/03/30.. and returning the whole record... db.users.find({logins: {$elemMatch: {at: { $lt: new Date(2012, 3, 30)}}}}) //using where -- c) is equivalent to a) a) db.users.find({ $where: 'this.name.first === "John"'}) b) db.users.find({ $where: 'this.name.first === "John"', age:30}) c) db.users.find( 'this.name.first === "John"') //injecting functions in mongodb -- as this example returns trueéfalse, its going to return values randomly var frand = function() {return Math.random() > 0.5} db.users.find(frand) // var f = function() { return this.name.first === "John"} db.users.find(f) //or db.users.find( {$where: f} ) //other queries //distinct -- returns a list of diff results db.links.distinct('favourites') --> [100, 32, 21, 78, ...] db.links.distinct("url") db.links.group({ key:{userId : true}, initial:{favCount: 0}, reduce: function (doc, o) {o.favCount += doc.favourites}, finalize: function(o) {o.name = db.users.findOne({ _id: o.userId}).name } }); *** //the final part is not working... db.links.group({ key:{userId : true}, initial:{favCount: 0}, reduce: function (doc, o) {o.favCount += doc.favourites} }); db.links.group({ key:{userId : true}, initial:{favCount: 0}, reduce: function (doc, o) {o.favCount += doc.favourites}, finalize: function(o) {o.name = "richard"}} ); //regex db.links.find({ title: /tuts\+$/}) db.links.find({ title: {regex: /tuts\+$/}}, {title:1}) //counting db.users.count({'name.first': 'John'}) db.users.count(); //all users in the collection //sorting, limit db.links.find({}, {title:1}).sort({title:1}).limit(1) //1: asc -1: desc //sorting, skipping and limiting... normal behavieur in the pagination rutine... db.links.find({}, {title:1, _id:0}).sort({title:1}).skip(3).limit(3) /*updating*/ //by replacement or by modification... ---general form /* db.collection.update( <query>, <update>, { upsert: <Boolean>, //if not found insert multi: <Boolean>, //change in all the condition <query> is fullfilled } ) */ // more info in http://docs.mongodb.org/manual/reference/method/db.collection.update/ db.users.update({-the query object-}, {-the update object-}, -upsert boolean-); var n = {title:"Nettuts+"} db.links.find(n, {title:1}) db.links.update(n, {$inc: {favourites: 5}}) var q = {"name.last": "Doe"} db.users.find(q, {name:1}) //we can use set to update a field or add a completly new one... db.users.update(q, {$set: {"name.last": "Doetix"}}) //modifying an existing field.. db.users.update(q, {$set: {"email": "doetix81@gmail.com"}}) //inserting a new one... //to remove a field w use unset db.users.update(q, {$unset: {job: "Web developper"}}) db.users.update({"name.first":"John"}, {$set: {job:"Web developer"}}, false, true) //modifying and then inserting an object var bob = db.users.findOne({"name.first":"Bob"}) >bob { "_id" : ObjectId("525f06242df9763abe646b62"), "name" : { "first" : "Bob", "last" : "Smith" }, "age" : 31, "email" : "bob.smith@gmail.com", "passwordHash" : "last_password_hash" } > bob.job = "Thick Brush Painter" > db.users.save(bob) //find and modify -- findAndModify {{}} /* The findAndModify command atomically modifies and returns a single document. By default, the returned document does not include the modifications made on the update. To return the document with the modifications made on the update, use the new option. { findAndModify: <string>, query: <document>, sort: <document>, remove: <boolean>, //one of | update: <document>, //this two | new: <boolean>, //if the new object must be shown or the old one.. fields: <document>, //fields to show in the result upsert: <boolean> } */ > db.links.findAndModify({ query:{favourites: {$gt:150}}, sort:{title:1}, update:{favourites: 333}, new: true, fields: {_id:0} }); //pulling into arrays db.links.update(n, { $push: {tags: "jobs"}}) > db.links.findOne(n).tags //several... db.links.update(n, {$pushAll:{tags: ['blogs','press','contests']}}) //on pull into the array if the new element is not present.. db.links.update(n, {$addToSet:{tags: "dev"}}) //doing the same with an array... db.links.update(n, {$addToSet:{ tags:{$each: ["dev", "interviews"]} }}) //pulling out content from the array... db.links.update(n, {$pull: {tags:'interviews'}}) //pulling several... db.links.update(n, {$pullAll: {tags: ['blogs','dev', 'contests']}}) //poping out from the beginning or the end.. db.links.update(n, {$pop: {tags: 1}}) //--from the end (-1 -- from the beginning) //positional operator... only the subobject gets updated... db.users.update({'logins.minutes': 20} , {$inc:{ 'logins.$.minutes': 10}}, false, true) db.users.update({'logins.minutes': 20} , {$set:{ 'logins.$.location': 10}}, false, true) db.users.update({'logins.minutes': 30}, {$set: {random: true}}, false, true) //renaming the fields name... db.links.update({url: {$exists: true}}, {$rename:{"url": "camino"}}, false, true); //more info on the positional operator in: http://docs.mongodb.org/manual/reference/operator/update/positional/ //taken from there: /* The positional $ operator facilitates updates to arrays that contain embedded documents. Use the positional $ operator to access the fields in the embedded documents with the dot notation on the $ operator. db.collection.update( { <query selector> }, { <update operator>: { "array.$.field" : value } } ) */ /***EXAMPLE Consider the following document in the students collection whose grades field value is an array of embedded documents: { "_id" : 4, "grades" : [ { grade: 80, mean: 75, std: 8 }, { grade: 85, mean: 90, std: 5 }, { grade: 90, mean: 85, std: 3 } ] } Use the positional $ operator to update the value of the std field in the embedded document with the grade of 85: db.students.update( { _id: 4, "grades.grade": 85 }, { $set: { "grades.$.std" : 6 } } ) ***/ //removing db.users.remove({'name.first': "John"}) //all the collections in the selected db... show collections //dropping completly a collection... db.acoll.drop() //indexes... db.links.find().explain db.links.ensureIndex({ title: 1}) //in ascending order.. in mainly important in cpompund indexes.. //a reflect of this index can be found in that db indexes collection db.system.indexes.find(); //u cound put an index to a canging value, but every time u change that value the index must be updated. keep in mind. //usually is a good idea to set the indexes at the beginning when no data is present in the collections. However, u could use the following formula to treat duplicates and unique data //keeping only the first one, deleting the others.. db.links.ensureIndex({ title: 1}, { unique: true, dropDups: true}) //when considering the case of some of the documents without the idexed field, to save mongo from storing space for this index if the field itself has not been inserted: db.links.ensureIndex({ title: 1}, {sparse: true}) //its important to think of the compund index as a nested one, an index of an index. Its related to each problem-case. Like in the case of the recepies: indexing first the ingredient and the the recepie, makes more sense than indexing in reverse. Its all related on how u are going to search. db.links.ensureIndex({ title: 1, url: 1}) //this one means that u can search on title; or on title and url... db.links.ensureIndex({ a: 1, b: 1, c: 1}) //searches are possible on a; a, b; a, b, c //deleting indexes db.links.dropIndex("title_1"); //the same way that appears in system.index collection... /*concepts to follow*/ //Sharding and Replica Set... http://www.slideshare.net/Dataversity/common-mongodb-use-cases-13695677 http://docs.mongodb.org/ecosystem/use-cases/product-catalog/ db.collection.update({"grades.grade":80}, { $set: {"grades.$.std": 18}})

Is a chrome extension. Find all ids in a web page and on hover detect a element on webpage with change class. I think this extension for google tag manager. Other functions?? Why not write me to add.

A modern CSS-first UI framework built on semantic HTML, custom elements, and data attributes. Uses :has(), container queries, and density scaling. No classes, no build step. Includes dashboard shell, forms, tabs, and a visual node editor for JSON API pipelines.

A mnemonic generation tool to create and decode memorable IDs, hashes, numbers, and anything else you can think of!

Its my personal hack for Zelda Ocarina of Time MQ Debug. I mostly try to add 32 permanent switch flags per scene. There could be less in smaller scenes though. And the text addon ones are the finished ones with npcs having more text. I'm unsure if they have problems with other text ids to crash or something. Haven't tested the whole game yet. I had to remove Bongo out of the deku tree sprout tale cutscene since his fist attack would crash the game. I also fixed the tower of rupees in the Fire Temple and Hyrule Field. The final ones currently are ones that say, "Escape Ganon Castle 3 fix Market Doors fix no garbage data Cucco in LH silo fix." I fixed the 3 Shadow Temple torches. The Normal Link patch version is Link with his prerendered model but that is a older patch. I believe the one's that don't say, "Link's hat" are the normal link ones. I think theres a duplicate of the normal link patch I could not delete since I renamed it without "normal link" as the patch's name was too long. The version before that ends with, "bombs no garbage data Cucco in LH silo fix" is the version with the normal title screen actors with the trees. I should maybe rename it though.

scripts to create a local bd of the encode project. this only have data associated like cell lines, IDs and conditions. this project was not thinked to have a local copy of all files

This app will be a mock inventory system for a store. Users (employees) can log in and enter items into inventory. Employees can then search for items in the inventory and also "scan" (basically mock the IDs that would be on a scanned barcode) to get information about an item or also mark it as sold. Also employees can create reports about items sold on a specific day, how much has sold in a certain timespan, and other stuff when I think of more features.

Every user will register using AADHAR ID and data will be fetched using AADHAR API as it will provide hassle free Sign Up . Working employees will feed their company email id for Validation and other users will feed their email ids. By using company Email ids for account validation, it will be easier to verify if a person is an employee or not, therefore increasing the Credibility of the Data available in our application related to each User . It will be much easier to club the employees according to their companies. The employee can remain anonymous by not revealing his name to others and his profile will include details related to work he/she do and respective working Hours. Each employee can give rating and his/her feedback on different topics related to company and also the work culture . Ratings given by employee will take into consideration, his/her designation and experience as well, so that a better experienced employee's feedback will have more significance, like an employee working for 10 years will have more effect than one working for 2 years. Rest of the feedback will include recreational activities, work burden, infrastructure and much more features like these. Being anonymous will help more and more employee's to write feedbacks without fearing about any harm. For the Users who are not employee of any company like students and other job seeking persons , it will be the best place to get the accurate information about the company as they will get the answers from the employee's of the company itself. Interactive graphs will be provided so that user can see company ratings on basis of important points as well overall satisfaction rating given by Employees of that company . If user asks some question about particular company by tagging that company, then employees of that company will get notification about that and can answers the question. It will be an elegant solution as there is no platform where user can ask and simutaneously see Details of the company and we think it will open a new Realm of Company-User interaction.

BUSINESS CONTEXT: With the enormous growth of computer networks usage and the huge increase in the number of applications running on top of it, network security is becoming increasingly more important. All the computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), as special-purpose devices to detect anomalies and attacks in the network, is becoming more important. The research in the intrusion detection field has been mostly focused on anomaly-based and misusebased detection techniques for a long time. While misuse-based detection is generally favored in commercial products due to its predictability and high accuracy, in academic research anomaly detection is typically conceived as a more powerful method due to its theoretical potential for addressing novel attacks. Conducting a thorough analysis of the recent research trend in anomaly detection, one will encounter several machine learning methods reported to have a very high detection rate of 98% while keeping the false alarm rate at 1%. However, when we look at the state of the art IDS solutions and commercial tools, there is no evidence of using anomaly detection approaches, and practitioners still think that it is an immature technology. To find the reason of this contrast, lots of research was done done in anomaly detection and considered various aspects such as learning and detection approaches, training data sets, testing data sets, and evaluation methods. BUSINESS PROBLEM: Your task to build network intrusion detection system to detect anamolies and attacks in the network. There are two problems. 1. Binomial Classification: Activity is normal or attack 2. Multinomial classification: Activity is normal or DOS or PROBE or R2L or U2R Please note that, currently the dependent variable (target variable) is not definied explicitly. However, you can use attack variable to define the target variable as required.