sign into network что это

А как работает wi-fi фича «Sign-in to Wi-Fi network» физически?

При коннекте к большинству нынешних Open сетей wi-fi во всяких макдональдсах, через пару секунд возникает chrome и пытается открыть google.com. Но у меня он стабильно обламывается на ошибке ERR_QUIC_PROTOCOL_ERROR, возможно потому что у меня samsung galaxy note 4 DEMO UNIT, который хрен знает с какой прошивкой и возможно что-то там в сетевых подсистемах слегка протухло, но не в этом суть.

Но если, пока chrome пытается открыть https://google.com подменить ему адрес на что-нибудь попроще, типа там какой-нибудь нешифрованный быдлосайт my_forum.ru, то происходит заветный редирект на некий внутренний сервак в той сети, к которой ты коннектишься. Открывается некая страничка, вроде «привет от нашего ресторана, теперь можете юзать wi-fi». Иногда на этой страничке надо втащить ещё номер телефона, иногда не надо.

В общем, интересует как эта фича вообще называется. Вот этот вот sign-in с открытием браузера и редиректами. Как оно работает внутри на низком уровне, что за редиректы, откуда приходит браузеру команда что надо открыться. Факультативно, если кто сможет ответить: по какой причине у меня может не открываться гугль, но через подстановку адреса какого-нибудь быдлосайта всё получается. Почему возникает именно google.com, а не mail.ru допустим. Это (адрес) зависит от тех, кто настраивает эту wi-fi сеть, которая делает этот sign-in? Зачем оно кидается в гугль, если всё равно потом не открывая его собирается показать страничку своего ресторана и оно работает даже без гугля? Т.е. гугль тут не принципиален я так понимаю.

Фича зовется «Captive portal»
Ответы можно и в вики найти, на странице об этой фиче

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Android Keeps Asking “Sign-In To WiFi Network”: 9 Fixes

Android smartphones are extremely famous because of all the right reasons. The android smartphones are pretty easy to use and are convenient to buy. On the contrary, some people have been complaining about Android keeps asking, “sign-in to Wi-Fi network.” For this purpose, we have created this article to fix this issue. Let’s check out the solutions then!

Android Keeps Asking “Sign-In To WiFi Network”

1) Router Issue

For the most part, the issue lies with your router. This is because when the Android phone is successful connected to the Wi-Fi network, it will test if the network can access the internet signals or not. We are saying this because, in the majority of cases, the Wi-Fi networks tend to redirect the requests to the login page. For the most part, “sign-in to Wi-Fi network” is because the Android smartphone is detecting that the Wi-Fi network is redirecting the request (it’s not for network authentication).

2) Change The Settings

In case you don’t think there is a router issue that’s causing the pop-up, you could try changing the settings. For fixing this pop-up, you have to open the advanced settings on the Android smartphone and move to the Wi-Fi tab. From the Wi-Fi tab, scroll down to “sign-in to Wi-Fi network” and disable this option. When it’s disabled, the pop-up won’t appear on your Android smartphone.

3) Software Update

Whenever you struggle with the network and connection issues, there are high chances that the Android software update is available, but you haven’t installed it on the smartphone yet. The Android software update is responsible for streamlining the functionality of your Android smartphone, and network connectivity are one of them. The software updates are designed to fix the bugs that might be causing connectivity errors. To check for the Android software update, check the steps mentioned below;

4) Block The Notifications

If you have installed the software update on your smartphone, but the pop-up is still popping up, you can block the notification to get rid of it. Whenever this pop-up occurs, we suggest that you pull down the notification bar of your smartphone and long-press on this notification or the alert. When you long-press this alert, there will be multiple options, and you need to click on the “block” option. As a result, the notification or alert will be blocked, and it won’t bug you again.

5) Reboot

In this heading, we are going to talk about two different things. Firstly, if you changed the Wi-Fi and network settings, you have to reboot the smartphone after saving these settings. This is because you have to reboot the smartphone after changing the Wi-Fi settings as it helps set in the settings. So, if you changed the Wi-Fi settings but didn’t reboot, it can result in such errors. So, just tweak the Wi-Fi settings again and reboot the phone.

Secondly, if you haven’t changed the Wi-Fi settings at all, we suggest that you reboot the smartphone even then. This is because congested smartphones can lead to network errors. In this case, just long-press the power button of your smartphone or just switch off the smartphone. Once the smartphone is switched off, take about five minutes before you switch on your smartphone again. When it’s switched on, connect to the Wi-Fi, and we are certain this pop-up won’t occur.

6) Connection Optimizer

In case you are still struggling with the sign-in pop-up or error, we suggest that you use the connection optimizer. Connection optimizers are basically the apps, and you can download them from the Google Play Store of your smartphone. The connection optimizer will help streamline the Wi-Fi connection or network. Even more, a connection optimizer will “optimize” the network connection, and it will also help save battery life.

7) Protection Standards

When it comes down to the Wi-Fi networks with “sign-in to Wi-Fi network,” they use the captive portal to increase the protection standards. In addition, it uses IP-based filters blocking traffic or allowing traffic. So, when you log into the connection through your credentials, it will create the session for a smartphone. These sessions usually range from one hour to twenty-four hours.

So, when the session is completed, or you disconnect from the network, you have to tweak the protection standards of the Wi-Fi network. In such a case, you have to call the internet service provider and ask them to switch off the IP-based filters.

8) DoS Attacks

In some cases, this pop-up can occur when someone is trying to plant the DoS attack on the wireless access point. So, if you are thinking that a DoS attack is being planted, it’s best to switch off your Android smartphone for a few minutes and switch on the antivirus after switching it on. This is because the antivirus will protect the smartphone. In addition to this, make sure that you have chosen the WPA2 security standard for your network connection.

9) Reset

If nothing seems to fix the pop-up, we suggest that you reset the network because it will delete the network settings that are causing this issue. That being said, you have to reset the router. The router can be reset by pressing the reset button on the router through the paperclip. When you press the reset button, the router will also reboot and will delete the network settings. After the reset, you can enter the network settings again and use the Wi-Fi network again.

Secondly, you have to reset the network settings again. For resetting the network settings, you have to open the system from the settings and open the advanced tab. From the advanced tab, click on the reset options and tap on “reset Wi-Fi.” It will ask for confirmation, so confirm that you want to reset the Wi-Fi or network settings. As a result, you have to enter the Wi-Fi settings again.

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Всплывает на Андроиде Sign in to network Помогите

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Есть ли программа, которая блокирует сайты, соцсети и приложения на определенное время?

Да, такие есть.
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Также можно поставить «термоядерный режим», когда блокируются абсолютно все сайты, кроме разрешённых.
Фокус мной особенно любим.

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RescueTime (в дополнение)
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Sign into network что это

I’ve got a Moto G (first gen), Android 5.0.2, and a Public Mobile plan without data. Ever since upgrading to Android Lollipop, I routinely get a notification saying «Sign into network», particularly when I turn off or disconnect from wifi. If I dismiss the notification, it will just show up again later.

Obviously this doesn’t have a significant effect on my phone use, but it is annoying. I’ve tried a couple of things, including changing the APN (Access Point Names) to the settings recommended by Public. Does anyone have any ideas about this? Thanks in advance.

@smallflightless To me this looks like a phone issue, it can be some app installed which is pushing the notification to your phone. You mentioned it started after the software update to lollipop did you install any app after the update?

Almost all people who own a Moto G have problem with their phone after they updated to Lollipop.

Good thing to do if you do not have DATA per month.

Motorola may have introduced some bug in Lollipops.

If I visit the link in Chrome I get : You need a data Add-on to access the web. Visit Self Serve to add one.

I have tried changing the Prefered network type from LTE to 3G butI still got the notification.

Here is my APN configuration:

Name: Public Mobile
APN: sp.mb.com
Proxy: Not set
Port: Not set
Username: Not set
Password: Not set
Server: Not set
MMSC: http://aliasredirect.net/proxy/mb/mmsc
MMSC proxy: 74.49.0.18
MMS port: 80
MCC: 302
MNC: 220
Authentication type: Not set
APN type: Not set

@smallflightless Unfortunately, I believe you will not be able to send/receive picture messages with data «off».

I think I might have a solution for you. When we were with our previous carrier, my wife did not have data on her plan and would get charged when data was on and she sent MMS messages (background processes on android). So, when we modified the APN settings it disabled all data except MMS protocols and worked like a charm. She is still with Virgin mobile on a Nexus 4 and lollipop, so should work for you too.

Go to your APN settings as shown in the attached image then under APN type, edit it and remove everything except MMS. I can’t remember if default was also left, but try that out and see if it makes a difference.,

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How To Build a Neural Network to Translate Sign Language into English

Last Validated on May 12, 2020 Originally Published on May 12, 2020

The author selected Code Org to receive a donation as part of the Write for DOnations program.

Introduction

Computer vision is a subfield of computer science that aims to extract a higher-order understanding from images and videos. This powers technologies such as fun video chat filters, your mobile device’s face authenticator, and self-driving cars.

By the end of this tutorial, you’ll have both an American Sign Language translator and foundational deep learning know-how. You can also access the complete source code for this project.

Prerequisites

To complete this tutorial, you will need the following:

Step 1 — Creating the Project and Installing Dependencies

Let’s create a workspace for this project and install the dependencies we’ll need.

On Linux distributions, start by preparing your system package manager and install the Python3 virtualenv package. Use:

We’ll call our workspace SignLanguage :

Navigate to the SignLanguage directory:

Then create a new virtual environment for the project:

Activate your environment:

Then install PyTorch, a deep-learning framework for Python that we’ll use in this tutorial.

On macOS, install Pytorch with the following command:

On Linux and Windows, use the following commands for a CPU-only build:

On Linux distributions, you will need to install libSM.so :

With the dependencies installed, let’s build the first version of our sign language translator: a sign language classifier.

Step 2 — Preparing the Sign Language Classification Dataset

In these next three sections, you’ll build a sign language classifier using a neural network. Your goal is to produce a model that accepts a picture of a hand as input and outputs a letter.

The following three steps are required to build a machine learning classification model:

In this section of the tutorial, you will accomplish step 1 of 3. You will download the data, create a Dataset object to iterate over your data, and finally apply data augmentation. At the end of this step, you will have a programmatic way of accessing images and labels in your dataset to feed to your model.

First, download the dataset to your current working directory:

Unzip the zip file, which contains a data/ directory:

Create a new file, named step_2_dataset.py :

As before, import the necessary utilities and create the class that will hold your data. For data processing here, you will create the train and test datasets. You’ll implement PyTorch’s Dataset interface, allowing you to load and use PyTorch’s built-in data pipeline for your sign language classification dataset:

Delete the pass placeholder in the SignLanguageMNIST class. In its place, add a method to generate a label mapping:

Labels range from 0 to 25. However, letters J (9) and Z (25) are excluded. This means there are only 24 valid label values. So that the set of all label values starting from 0 is contiguous, we map all labels to [0, 23]. This mapping from dataset labels [0, 23] to letter indices [0, 25] is provided by this get_label_mapping method.

Next, add a method to extract labels and samples from a CSV file. The following assumes that each line starts with the label and is then followed by 784 pixel values. These 784 pixel values represent a 28×28 image:

For an explanation of how these 784 values represent an image, see Build an Emotion-Based Dog Filter, Step 4.

This function starts by loading the samples and labels. Then it wraps the data in NumPy arrays. The mean and standard deviation information will be explained shortly, in the __getitem__ section following.

Directly after the __init__ function, add a __len__ function. The Dataset requires this method to determine when to stop iterating over data:

Finally, add a __getitem__ method, which returns a dictionary containing the sample and the label:

Your completed SignLanguageMNIST class will look like the following:

As before, you will now verify our dataset utility functions by loading the SignLanguageMNIST dataset. Add the following code to the end of your file after the SignLanguageMNIST class:

Now you’ll verify that the dataset utilities are functioning. Create a sample dataset loader using DataLoader and print the first element of that loader. Add the following to the end of your file:

You can check that your file matches the step_2_dataset file in this (repository). Exit your editor and run the script with the following:

This outputs the following pair of tensors. Our data pipeline outputs two samples and two labels. This indicates that our data pipeline is up and ready to go:

You’ve now verified that your data pipeline works. This concludes the first step—preprocessing your data—which now includes data augmentation for increased model robustness. Next you will define the neural network and optimizer.

Step 3 — Building and Training the Sign Language Classifier Using Deep Learning

With a functioning data pipeline, you will now define a model and train it on the data. In particular, you will build a neural network with six layers, define a loss, an optimizer, and finally, optimize the loss function for your neural network predictions. At the end of this step, you will have a working sign language classifier.

Create a new file called step_3_train.py :

Import the necessary utilities:

Define a PyTorch neural network that includes three convolutional layers, followed by three fully connected layers. Add this to the end of your existing script:

Now initialize the neural network, define a loss function, and define optimization hyperparameters by adding the following code to the end of the script:

Finally, you’ll train for two epochs:

Add the following code to the end of your script to extract image and label from the dataset loader and then wrap each in a PyTorch Variable :

This code will also run the forward pass and then backpropagate through the loss and neural network.

At the end of your file, add the following to invoke the main function:

Double-check that your file matches the following:

Save and exit. Then, launch our proof-of-concept training by running:

You’ll see output akin to the following as the neural network trains:

To obtain lower loss, you could increase the number of epochs to 5, 10, or even 20. However, after a certain period of training time, the network loss will cease to decrease with increased training time. To sidestep this issue, as training time increases, you will introduce a learning rate schedule, which decreases learning rate over time. To understand why this works, see Distill’s visualization at “Why Momentum Really Works”.

Check that your file matches the step 3 file in this repository. Training will run for around 5 minutes. Your output will resemble the following:

Step 4 — Evaluating the Sign Language Classifier

You will now evaluate your sign language classifier by computing its accuracy on the validation set, a set of images the model did not see during training. This will provide a better sense of model performance than the final loss value did. Furthermore, you will add utilities to save our trained model at the end of training and load our pre-trained model when performing inference.

Import the necessary utilities:

Next, define a utility to evaluate the neural network’s performance. The following function compares the neural network’s predicted letter to the true letter, for a single image:

Since both Y and Yhat are now classes, you can compare them. Yhat == Y checks if the predicted class matches the label class, and np.sum(. ) is a trick that computes the number of truth-y values. In other words, np.sum will output the number of samples that were classified correctly.

Finally, add the following script to leverage the preceding utilities:

This loads a pretrained neural network and evaluates its performance on the provided sign language dataset. Specifically, the script here outputs accuracy on the images you used for training and a separate set of images you put aside for testing purposes, called the validation set.

You will next export the PyTorch model to an ONNX binary. This binary file can then be used in production to run inference with your model. Most importantly, the code running this binary does not need a copy of the original network definition. At the end of the validate function, add the following:

This exports the ONNX model, checks the exported model, and then runs inference with the exported model. Double-check that your file matches the step 4 file in this repository:

To use and evaluate the checkpoint from the last step, run the following:

This will yield output similar to the following, affirming that your exported model not only works, but also agrees with your original PyTorch model:

Your neural network attains a train accuracy of 99.9% and a 97.4% validation accuracy. This gap between train and validation accuracy indicates your model is overfitting. This means that instead of learning generalizable patterns, your model has memorized the training data. To understand the implications and causes of overfitting, see Understanding Bias-Variance Tradeoffs.

At this point, we have completed a sign language classifier. In essence, our model can correctly disambiguate between signs correctly almost all the time. This is a reasonably good model, so we move on to the final stage of our application. We will use this sign language classifier in a real-time webcam application.

Step 5 — Linking the Camera Feed

Your next objective is to link the computer’s camera to your sign language classifier. You will collect camera input, classify the displayed sign language, and then report the classified sign back to the user.

Now create a Python script for the face detector. Create the file step_6_camera.py using nano or your favorite text editor:

Add the following code into the file:

This code imports OpenCV, which contains your image utilities, and the ONNX runtime, which is all you need to run inference with your model. The rest of the code is typical Python program boilerplate.

Now replace pass in the main function with the following code, which initializes a sign language classifier using the parameters you trained previously. Additionally add a mapping from indices to letters and image statistics:

You will use elements of this test script from the official OpenCV documentation. Specifically, you will update the body of the main function. Start by initializing a VideoCapture object that is set to capture live feed from your computer’s camera. Place this at the end of the main function:

Then add a while loop, which reads from the camera at every timestep:

Write a utility function that takes the center crop for the camera frame. Place this function before main :

Still within the while loop, run inference with the ONNX runtime. Convert the outputs to a class index, then to a letter:

Display the predicted letter inside the frame, and display the frame back to the user:

At the end of the while loop, add this code to check if the user hits the q character and, if so, quit the application. This line halts the program for 1 millisecond. Add the following:

Finally, release the capture and close all windows. Place this outside of the while loop to end the main function.

Double-check your file matches the following or this repository:

Exit your file and run the script.

Once the script is run, a window will pop up with your live webcam feed. The predicted sign language letter will be shown in the top left. Hold up your hand and make your favorite sign to see your classifier in action. Here are some sample results showing the letter L and D.

While testing, note that the background needs to be fairly clear for this translator to work. This is an unfortunate consequence of the dataset’s cleanliness. Had the dataset included images of hand signs with miscellaneous backgrounds, the network would be robust to noisy backgrounds. However, the dataset features blank backgrounds and nicely centered hands. As a result, this webcam translator works best when your hand is likewise centered and placed against a blank background.

This concludes the sign language translator application.

Conclusion

In this tutorial, you built an American Sign Language translator using computer vision and a machine learning model. In particular, you saw new aspects of training a machine learning model—specifically, data augmentation for model robustness, learning rate schedules for lower loss, and exporting AI models using ONNX for production use. This then culminated in a real-time computer vision application, which translates sign language into letters using a pipeline you built. It’s worth noting that combatting the brittleness of the final classifier can be tackled with any or all of the following methods. For further exploration try the following topics to in improve your application:

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