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The Center for Cyberspace Research (CCR) has conducted extensive research in Industrial Control System (ICS) security. ICS architectures utilize a variety of proprietary hardware and software configurations to control and monitor industrial processes and safety systems. Defenders must be familiar with the functionality, requirements and limitations of control systems in order to successfully defend them from cyber-attacks. Hands-on training and experience is crucial for defenders who must be able to interact safely with a given control system. Without this experience, defenders could mistakenly take actions that cause more harm to the system than a basic cyber-attack.
Given the physical nature of ICS architectures, training platforms can be difficult and costly to develop. To address this, the CCR has developed HILICS - a unique hardware-in-the-loop ICS platform designed to support training, education and research. HILICS utilizes a MicroLogix 1100 Programmable Logic Controller (PLC) to introduce students to PLC operating basics and the associated programming languages. This PLC is a low-cost commercial product that provides a representative set of features to give students exposure to control system functionality. The HILICS platform is a custom hardware-in-the-loop system that enables trainers to incorporate multiple physical process simulations and expose students to a range of control system applications. The platform is designed to be flexible and scalable, allowing for varying class sizes and supporting various control system applications.
the course discusses linear regression model training for economic income and consumption using sklearn and data visualization with matplotlib.
Functions in the code are:
def evaluateModel(self, model, test_data, features, labels) # evaluate model def visualizeModel(self, model, data, feature_names, label_names, error, score) # visualize model def trainModel(self, train_data, feature_names, label_names) # train model def linearModel(self, data, feature_names, label_names, split_ratio) # entry def readData(self, path) # read data from csv file
Techopedia explains Algorithm: An algorithm is a detailed series of instructions for carrying out an operation or solving a problem. In a non-technical approach, we use algorithms in everyday tasks, such as a recipe to bake a cake or a do-it-yourself handbook. Technically, computers use algorithms to list the detailed instructions for carrying out an operation. For example, to compute an employees paycheck, the computer uses an algorithm. To accomplish this task, appropriate data must be entered into the system. I develop myself to learn simple but important algorithms, I write with java.
Git and GitHub Training Kit
Sourced by Active Specialized Support Group (ASS-G)
Version control helps developers track and manage changes to a software projects code. As a software project grows, version control becomes essential. Take WordPress At this point, WordPress is a pretty big project. If a core developer wanted to work on one specific part of the WordPress codebase, it wouldnt be safe or efficient to have them directly edit the official source code. Instead, version control lets developers safely work through branching and merging. With branching, a developer duplicates part of the source code (called the repository). The developer can then safely make changes to that part of the code without affecting the rest of the project. Then, once the developer gets his or her part of the code working properly, he or she can merge that code back into the main source code to make it official. All of these changes are then tracked and can be reverted if need be.
Git is a specific open-source version control system created by Linus Torvalds in 2005. Specifically, Git is a distributed version control system, which means that the entire codebase and history is available on every developers computer, which allows for easy branching and merging. According to a Stack Overflow developer survey, over 87% of developers use Git.
The task at hand is a multi-class classification problem, for which both a training and a test set are provided as csv files.
Allows to design deep-learnings models for various problems, training them in the browser and sharing them with others. This project is still work in progress. That means some important features are still missing:
A large database of handwritten digits that is commonly used for training various image processing systems.
This project aims to develop a machine learned method for
cross-identifying radio objects and their host galaxies, using
crowdsourced labels from the
Radio Galaxy Zoo.
|PyPI| |Travis-CI| |Documentation Status| |DOI|
For setup details, see the documentation on
Read the Docs.
For a brief description of each notebook, see the documentation
The cross-identification dataset is available
Modern applications use web API to communicate together. Web APIs point out a way to communicate through a protocol over a socket (http, websocket, protobuf) synchronously or asynchronously. IT industry uses many standards or protocols : SOAP/XML, XML-RPC, RESTfull/JSON, REST-RPC/JSON, ... The last one is the most used nowadays but there also emerging tehcnologies : Facebook GraphQL, Netflix Falcor or Google grpc.io are gaining adepts since few years During this training we will learn to create a REST/Json API with Node.js, serialiaze data in a database, secure your API and deploy it on a PaaS provider.
Our project is to build a simple blog backend.
Application of Big Data Technology and 3D Visualization,
San Diego Supercomputer Center, UC San Diego, June 26-28, 2017
This 3-day hands-on workshop introduces participants to the development of fast and scalable structural bioinformatics methods using state-of-the-art Big Data technologies and Web-GL 3D visualization (). The first two days of the workshop combine lectures, hands-on applications, and programming sessions. On the third day participants apply the new technologies to their own projects.
This workshop is held at the University of California, San Diego and hosted by the Structural Bioinformatics Laboratory at SDSC in collaboration with the RCSB Protein Data Bank.
Workshop registration page:
(registration is closed) A second workshop will be held early 2018. For question about this workshop or to preregister for the second workshop:
I implemented a simple ANN on the Churn Modelling Dataset and then after training tested on a single data to predict values The idea of ANNs is based on the belief that working of human brain by making the right connections, can be imitated using silicon and wires as living neurons and dendrites. The human brain is composed of 86 billion nerve cells called neurons. They are connected to other thousand cells by Axons. Stimuli from external environment or inputs from sensory organs are accepted by dendrites. These inputs create electric impulses, which quickly travel through the neural network. A neuron can then send the message to other neuron to handle the issue or does not send it forward. Structure of Neuron ANNs are composed of multiple nodes, which imitate biological neurons of human brain. The neurons are connected by links and they interact with each other. The nodes can take input data and perform simple operations on the data. The result of these operations is passed to other neurons. The output at each node is called its activation or node value. Each link is associated with weight. ANNs are capable of learning, which takes place by altering weight values. The following illustration shows a simple ANN A Typical ANN Types of Artificial Neural Networks There are two Artificial Neural Network topologies FeedForward and Feedback. FeedForward ANN The information flow is unidirectional. A unit sends information to other unit from which it does not receive any information. There are no feedback loops. They are used in pattern generation/recognition/classification. They have fixed inputs and outputs. FeedForward ANN FeedBack ANN Here, feedback loops are allowed. They are used in content addressable memories. FeedBack ANN Working of ANNs In the topology diagrams shown, each arrow represents a connection between two neurons and indicates the pathway for the flow of information. Each connection has a weight, an integer number that controls the signal between the two neurons. If the network generates a good or desired output, there is no need to adjust the weights. However, if the network generates a poor or undesired output or an error, then the system alters the weights in order to improve subsequent results. Machine Learning in ANNs ANNs are capable of learning and they need to be trained. There are several learning strategies Supervised Learning It involves a teacher that is scholar than the ANN itself. For example, the teacher feeds some example data about which the teacher already knows the answers. For example, pattern recognizing. The ANN comes up with guesses while recognizing. Then the teacher provides the ANN with the answers. The network then compares it guesses with the teachers correct answers and makes adjustments according to errors. Unsupervised Learning It is required when there is no example data set with known answers. For example, searching for a hidden pattern. In this case, clustering i.e. dividing a set of elements into groups according to some unknown pattern is carried out based on the existing data sets present. Reinforcement Learning This strategy built on observation. The ANN makes a decision by observing its environment. If the observation is negative, the network adjusts its weights to be able to make a different required decision the next time. Back Propagation Algorithm It is the training or learning algorithm. It learns by example. If you submit to the algorithm the example of what you want the network to do, it changes the networks weights so that it can produce desired output for a particular input on finishing the training. Back Propagation networks are ideal for simple Pattern Recognition and Mapping Tasks.
A mobile app for long-term memory training using sets of flash cards.
Create decks based on subject
Combine & shuffle multiple decks together for the student to train long-term memory
Use text and images on one side of the card to convey an idea to the student, and have the answer waiting for them on the back of the card in plain text
Shuffle cards on demand
Set up a per-card timer to force the student to answer quickly
Track training history
avg. card display time
card display time as a graph for the entire deck
individual card display count
$ npm i numnormalize --save The need for the normalization of data samples is conditioned by the very nature of the variables used in neural network models. Being different in physical sense, they can often differ greatly in absolute values. So, for example, the sample can contain both concentration, measured in tenths or hundredths of percent, and pressure in hundreds of thousands of pascals. Normalization of data allows you to bring all used numerical values of variables to the same area of their change, which makes it possible to bring them together in one neural network model. In order to normalize the data, you need to know exactly the limits of the changes in the values of the corresponding variables (minimum and maximum theoretically possible values). Then the limits of the normalization interval will correspond to them. When it is impossible to set the limits of variable changes precisely, they are set taking into account the minimum and maximum values in the available data sample.
Based on examples provided by the Jest project, this project will teach you about how to use snapshot testing and how we can automate our assertions using it.
It is important for runners to get adequate training before taking on long distance running events. Finding a training plan to match your goal distance is a good first step. However, even after youve found a good training plan, it is sometimes difficult to get motivated to run some of the long runs. That is when it can be helpful to find local races or running groups to run with. I am creating an app that will help users find training plans to match their goals. In addition, this app will help users find races and running groups that fit into their training schedule. Each user will have a personal calendar populated with their training runs as well as any race or running group options available. This idea stems from how I build my training schedule for ultramarathons. I have run distances of 50 and 100 miles using this strategy.
Person detection program learns to identify a individual by learning on the training data provided. It then is able to predict the person correctly when a new image of the person is provided as a test data.
There are folders:
Pyst consists of a set of interfaces and libraries to allow programming of Asterisk from python. The library currently supports AGI, AMI, and the parsing of Asterisk configuration files. The library also includes debugging facilities for AGI.
HCEnc Provider is a program that interacts with the free mpeg2 cutting software Cuttermaran to make it support HCEnc (free mpeg2 encoder) for encoding the cut files.
Gspoof is a GTK+ program written in C language which makes easier and accurate the building and the sending of TCP packet with a data-payload or not. It's possible to modify TCP/IP fields also Ethernet header working to Link Level.
In the field of Assembly Programming learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Assembly Programming is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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