Create Your Password Protected Folder

Before you get started you need to create a folder that will house your password protected folder, this is just an ordinary folder and can be located anywhere and named anything.

Navigate into your newly created folder and create a new Text Document. This can easily be done from the context menu.

Open the document, now paste the following code into the contents of the document:


cls
@ECHO OFF
title Folder Private
if EXIST “HTG Locker” goto UNLOCK
if NOT EXIST Private goto MDLOCKER
:CONFIRM
echo Are you sure you want to lock the folder(Y/N)
set/p “cho=>”
if %cho%==Y goto LOCK
if %cho%==y goto LOCK
if %cho%==n goto END
if %cho%==N goto END
echo Invalid choice.
goto CONFIRM
:LOCK
ren Private “HTG Locker”
attrib +h +s “HTG Locker”
echo Folder locked
goto End
:UNLOCK
echo Enter password to unlock folder
set/p “pass=>”
if NOT %pass%== PASSWORD_GOES_HERE goto FAIL
attrib -h -s “HTG Locker”
ren “HTG Locker” Private
echo Folder Unlocked successfully
goto End
:FAIL
echo Invalid password
goto end
:MDLOCKER
md Private
echo Private created successfully
goto End
:End


Change the PASSWORD_GOES_HERE text to the password you want to set. Now go ahead and save the file as locker.bat.

Once the file is saved as a batch file you can delete the original text file.

Now run your batch file by double clicking on it–the first time you run it, it will create a folder called Private. This is where you can store all your secret things. When you have finished adding all your stuff to the Private folder, run locker.bat again.

This time you will be asked if you sure that you want to lock the folder, press the “Y” key and hit enter to lock your folder.

You will see that your Private folder quickly disappears.

If you run the script yet again, you will prompted for a password.

If you enter the same password as you set in the script the Private folder will reappear if you enter the incorrect password the script will just Terminate.

How to See the Files Again
Most people don’t know how to show system files, but anybody who has some experience will probably be able to quickly figure it out in the Folder Options. To see the files again, all you have to do is uncheck the boxes for “Show hidden files, folders, and drives” and uncheck the box for “Hide protected operating system files”.

But any ordinary user who unchecked the box will most probably be scared off by the warning message that appears.

The second way someone could comprise the folder is to open the batch file and read your password. It’s definitely not a really secure way to hide your files, but it is fun.

Attribution note: We found this script on dozens of different web sites going back many years. We have no idea who first created the script, so we’ve omitted any sort of attribution link. If you do have proof that you first created the script, contact us and we’ll adjust the article accordingly.

Google machine learning API

google-machine-learning-api

This article gives readers an overview of Google machine learning APIs. It contains code samples to get started with APIs, bringing the power of machine learning to everyday developers.

For the past two decades, Google has been at the forefront of collecting and mining data. It has built multiple machine learning models through which it now provides some of the most powerful machine learning APIs. These machine learning models are the magic behind some of the applications like Google Photos, Inbox Reply (Gmail), Google Translate and more.
In the last year, Google has opened up its machine learning models by making its APIs accessible to the public. The Google Cloud Machine Learning Platform provides easy-to-use REST APIs that have powerful capabilities like computer vision, NLP, translation and more.

Google Cloud Machine Learning Platform
One of the main issues facing developers is figuring out how much of theoretical knowledge they need before using machine learning algorithms in their applications. The team at Google has understood this and identified a spectrum across which different products from Google can be used from the Machine Learning Platform.
At one end of the spectrum is TensorFlow, which is an open source library for machine intelligence. TensorFlow does require that you are up to speed on various machine learning concepts and can use the API to build machine models, using them effectively in your applications. This might not be possible for all developers, especially those who are looking for REST APIs to use in their applications. To cater to this need, Google has identified multiple capabilities in its Machine Learning Platform and has exposed those capabilities in the form of easy-to-call REST APIs. There is also a free quota per month for these APIs.
Over the last few months, Google has released multiple REST APIs in the Machine Learning Platform. The most important ones are listed below.

figure-1-new-project

Figure 1: New Project dialogue box

Google Cloud Vision API: The Cloud Vision API provides a REST API to understand and extract information from an image. It uses powerful machine learning models to classify images into thousands of categories, detect faces, identify adult content, emotions, OCR support and more. If you are looking to classify and search through images for your application, this is a good library to consider.

Google Natural Language API: The Natural Language API is used to identify parts of speech and to detect multiple types of entities like persons, monuments, etc. It can also perform sentiment analysis. It currently supports three languages: English, Spanish and Japanese.

Google Speech API: The Speech API is used to translate audio files into text. It is able to identify over 80 languages and their variants, and can work with most audio files.
There are multiple other APIs in this family like the Translate API, but we will limit our sample code to the above three APIs.

figure-2-api-manager

Figure 2: API Manager

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Getting started
To get started with using the machine learning APIs listed above, you need to sign up for the Google Cloud Platform and enable your billing. The platform provides a trial period of two months for US$300, which is sufficient to try out the APIs.
Once you have signed up for the platform, you need to create a project. Go to the Cloud Console and click on New Project. In the New Project dialogue box, provide a name for your project, as shown in Figure 1, before clicking on Create.
Once the project is created, you need to enable the above three APIs for your project. To do that, go to the Main Menu on the top left in the Cloud Console and click on API Manager, as shown in Figure 2.
In the search bar, enter ‘Cloud Vision’ and it will display the Google Cloud Vision API in the list. Select it and then click on Enable. This will enable the API. Perform the same operation for the other two APIs also, i.e., the natural language processing (NLP) API and the Speech API. Ensure that you have enabled the above three APIs for your project before moving ahead.
Since we are going to be writing an external program that invokes the REST APIs, our calling client application needs to identify itself to the Google Cloud platform. We do that through the concept of a service account, which will be used by our client application to invoke the APIs.
To do that, we need to download the service account keys for our project. From the API Manager menu on the left, click on Credentials and then on Create Credentials. Select Service Account Key from the options. This will bring up a dialogue box as shown in Figure 3. Select New Service Account, give a name to the service account and in Role, select Project Owner.  
Click on Create. This will download a JSON file for your service account. Ensure that you save this file in a safe place, since we will use it when we run our client application. For the purpose of this article, the service account JSON file downloaded will be referred to as the mlapi-serviceaccount.json file.

Vision API
Let’s first test the vision API. As mentioned earlier, this has multiple features and, in this specific example, we are going to test its label detection feature. This feature allows you to provide an image, and it will identify various labels for this image, which can help in its classification.

 

We are going to provide it an image of a cricket match and see what it can figure out about it. The image that we are going to test it against is shown in Figure 4.
Before you run this code and the other examples, you will need to set an environment variable named GOOGLE_APPLICATION_CREDENTIALS and it will need to point to the service account JSON file that we downloaded earlier. For example:

export GOOGLE_APPLICATION_CREDENTIALS=mlapi-serviceaccount.json

The sample code in Python is shown below. This is a modification of the code that is available at its API page.

from googleapiclient import discovery
import argparse
import base64
import json
from oauth2client.client import GoogleCredentials
 
DISCOVERY_URL = (‘https://{api}.googleapis.com/’
                ‘$discovery/rest?version={apiVersion}’)
 
def main(photo_file): 
 credentials = GoogleCredentials.get_application_default() 
 service = discovery.build(‘vision’, ‘v1’,credentials=credentials) 
 with open(photo_file, ‘rb’) as image:
        image_content = base64.b64encode(image.read())
        service_request = service.images().annotate(body={
            ‘requests’: [{
                ‘image’: {
                    ‘content’: image_content.decode(‘UTF-8’)
                },
                ‘features’: [{
                    type’: ‘LABEL_DETECTION’,
                    ‘maxResults’: 5
                }]
            }]
        })
        response = service_request.execute()
        print(json.dumps(response[‘responses’],indent=4))
 
if __name__ == ‘__main__’:
    parser = argparse.ArgumentParser()
    parser.add_argument(‘image_file’, help=’The image you\’d like to label.’)
    args = parser.parse_args()
    main(args.image_file)

Let us understand the code first. In the main block, we read the file name that we provided. The first step in the main function is to establish the credentials; we use the standard application credentials, which were set via the environment variable. We then read the file bytes and invoke the REST API endpoint for the cloud vision API. Notice that in the REST API call, we provide not just the bytes for the image but also the feature that we are interested in, i.e., LABEL_DETECTION, and command it to return us a maximum of five such label results. We finally invoke the API and print out the results.
The output can be downloaded from http://opensourceforu.com/article_source_code/dec16/gml.zip. Notice that each of the labels that it detected has a confidence score out of 10. It is generally considered a positive response if it is about 70-80 per cent, but it is entirely up to your application to accept these results. Note that it has also detected erroneously that there is a baseball player —it is important to keep these points in mind.

figure-3-service-account-key-creation

Figure 3: Service Account Key creation

Natural Language API
The Natural Language API helps us to identify parts of speech in a text, entities (persons, monuments, organisations, etc) and also do sentiment analysis. In the specific example that we will execute here, we are going to test how the API extracts entities. This will help us to identify the different entities that were present in the piece of text that we provided to it.
The text that we will provide to the REST API is as follows:
The search for a new chairman of Tata Sons following the ouster of Cyrus Mistry has thrown up quite a few names. The high-profile names include Indra Nooyi, the head of PepsiCo Inc, Arun Sarin, the former head of Vodafone Group, and Noel Tata, chairman of the Tata retail unit, Trent. 
The code is shown below and it follows a pattern similar to the vision API that we saw earlier.

from googleapiclient import discovery
import httplib2
import json
from oauth2client.client import GoogleCredentials
DISCOVERY_URL = ('https://{api}.googleapis.com/'
'$discovery/rest?version={apiVersion}')
def main():
http = httplib2.Http()
credentials = GoogleCredentials.get_application_default().create_scoped(
['https://www.googleapis.com/auth/cloud-platform'])
http=httplib2.Http()
credentials.authorize(http)
service = discovery.build('language', 'v1beta1',
http=http, discoveryServiceUrl=DISCOVERY_URL)
service_request = service.documents().analyzeEntities(
body={
'document': {
'type': 'PLAIN_TEXT',
'content': "The search for a new chairman of Tata Sons following the ouster of Cyrus Mistry has thrown up quite a few names. The high-profile names include Indra Nooyi, the head of PepsiCo Inc, Arun Sarin, the former head of Vodafone Group, and Noel Tata, chairman of the Tata retail unit Trent."
}
})
response = service_request.execute()
entities = response['entities']
print(json.dumps(entities,indent=4))
return 0
if __name__ == '__main__':
main()

Once the credentials are established, we are simply going to invoke the REST API and, specifically, the analyzeEntities method. The output results can be obtained from http://opensourceforu.com/article_source_code/dec16/gml.zip.
It is interesting to see that the Natural Language API has been able to correctly identify the entities (organisations and individuals) and, in some cases, also provided information on their Wikipedia entries.

figure-4-cricket-image

Figure 4: Cricket image

Speech API
The Speech API is used to convert a RAW audio file to its equivalent text format. The API is powerful enough to work with disturbances in the audio and has multiple optimisations inbuilt to detect the text.
The code shown below is taken from the official documentation, and the parameter that we pass to it at runtime is the audio file in RAW format. The code simply reads the audio file bytes, provides information in the request about the kind of audio and then, in a synchronous fashion, gets back the results of the audio converted to text.

import argparse
import base64
import json 
from googleapiclient import discovery
import httplib2
from oauth2client.client import GoogleCredentials 
# [START authenticating]
DISCOVERY_URL = (‘https://{api}.googleapis.com/$discovery/rest?’
                 ‘version={apiVersion}’) 
def get_speech_service():
    credentials = GoogleCredentials.get_application_default().create_scoped(
        [‘https://www.googleapis.com/auth/cloud-platform’])
    http = httplib2.Http()
    credentials.authorize(http)
 
    return discovery.build(
        ‘speech’, ‘v1beta1’, http=http, discoveryServiceUrl=DISCOVERY_URL) 
def main(speech_file):
    with open(speech_file, ‘rb’) as speech:
        speech_content = base64.b64encode(speech.read()) 
    service = get_speech_service()
    service_request = service.speech().syncrecognize(
        body={
            ‘config’: {
                ‘encoding’: ‘LINEAR16’, 
                ‘sampleRate’: 16000, 
                ‘languageCode’: ‘en-US’, 
            },
            ‘audio’: {
                ‘content’: speech_content.decode(‘UTF-8’)
                }
            })
    response = service_request.execute()
    print(json.dumps(response)) 
if __name__ == ‘__main__’:
    parser = argparse.ArgumentParser()
    parser.add_argument(
        ‘speech_file’, help=’Full path of audio file to be recognized’)
    args = parser.parse_args()
    main(args.speech_file)

The output is shown below, and is the actual audio content that was present in the file.

{“results”: [{“alternatives”: [{“confidence”: 0.98267895, “transcript”: “how old is the Brooklyn Bridge”}]}]}

Google Machine Learning APIs provide a wide range of APIs that developers can use today to incorporate powerful machine learning algorithms in the domains of computer vision, language analysis, sentiment analysis, audio conversion and translation. The key factors that make this a compelling list of APIs is Google’s track record in machine learning—the complex machine learning models that it has built over the last decade—and the ease and accuracy of the APIs.

How can I earn money online by working from home?

Yes, there are many ways through which you can earn a decent income. And yes, like you have mentioned above there are a lot of scams. Please do not pay any upfront fee for work at home jobs, no matter how much proofs they offer. Genuine work at home jobs are always free. I am earning an easy 10-13K INR consistently for the past few months, that too by working less than 2 hours daily.

Leapforce, is a US based company that provides work at home opportunities across the globe. They mainly provides jobs that can be categorized under “Social Media Ad Rater” or ” Search engine result evaluator”. They pay $13/ hour for US citizens and $7/ hour for India. However, you are allowed to work only for an hour a day (per project). It can vary from project to project. You need to upload your resume and a scanned copy of your government issued identity proof. Based on the details you have submitted, Leapforce team will contact you back. Upon successful completion, you will be asked to provide your bank details.

You can now login to the website and apply for various projects. You will have to clear a qualification test for each projects you are applying. The more projects you enroll, the more you can earn. Exams are bit tough though. I could only clear one exam out of three I have attempted. Study materials are lengthy and I recommend spending some quality time with the materials before attempting the exam.

Current openings in leapforce can be found here

PS: They prefer graduates.

If you are still not convinced, you can read what other people have to say about Leapforce here: Leapforce Reviews

For me, this serves as a passive income. Where else can you earn 10-13K monthly just by spending 1-2 hours per day? Payments are made in the form of direct bank transfers through Western Union by 20-30th of each month.

मी कशाला जगतोय.?

बागेत जा, एखाद्या गुलाबाच्या झाडाला, एखाद्या मोगर्‍याच्या रोपट्याला विचारा,
‘कंटाळा कसा येत नाही तुला, सारखी तीच ती फुलं. त्याच त्या रंगाची, त्याच त्या वासाची. वर्षांनुर्वे तेच. उपयोग काय त्याच त्या गोष्टीचा.’
ते झाड हसेल. म्हणेल, ‘अरे, कंटाळा कसा येईल? ही फुलं फुलवणं. सुगंध वाटणं, सुंदर दिसणं आणि आपण सुंदर आहोत, हेच आपलं काम हे मान्य करणं, हेच माझं जगणं म्हणून स्वीकारणं, यात कंटाळा यायचं काय कारण आहे.’
आपण गप्पं बसावं आणि सरळ एखाद्या नदीकाठी जावं. तिचा तोच खळखळाट, तोच प्रवाह. आपण विचारावं तिला, ‘कशाला इतकी उधाणतेस? उपयोग काय तुझ्या उधाणाचा, या खळखळून वाहण्याचा, या खळखळाटाचा? आणि एवढं करून करतेस काय तर ठरलेल्या वाटेनं वाहत शेवटी सम्रुद्राच्या खार्‍या पाण्यात हरवूनच जातेस ना.?’
नदी खळाळून वाहत म्हणेल, ‘मी ‘उपयोगी’ बनण्याचा आणि तसं वागण्याचा प्रयत्न करतेय असं कोण म्हणालं, मी नदी आहे आणि मी फक्त नदीसारखं वागण्याचा प्रयत्न करतेय इतकंच.’
आणि मग संतापून देवालाच विचारावं, कशाला रे हा एवढा घोळ घालून ठेवलास, उपयोग काय या सार्‍याचा.
तो म्हणेल ‘ उपयोग नाही कसं? माझ्यादृष्टीनं तर झाडावरून पडणारं प्रत्येक पानही महत्त्वाचं आणि उपयोगीच आहे. तुझ्या डोक्यावरचा केस आणि हवेतला डोळ्यांनी न दिसणारा जीवाणूही त्याचं त्याचं कामच करतोय. ’
मग आपण स्वत:लाच विचारावं, ‘पण माझं काय, माझा आहे काही उपयोग? मी कशाला जगतोय.?’
खरं सांगू जे नदीनं, जे फुलझाडानं, जे देवानं सांगितलं ते लक्षात ठेवलं तर हा प्रश्नच पडणार नाही.
‘आपण ‘युजफूल‘-उपयुक्त आणि उपयोगी व्हायचा प्रयत्न करणं सोडून, आपण ‘आपण’ म्हणून सहज जगायल लागलं पाहिजे, त्या जगण्याचा होईलच कुणाला न कुणाला उपयोग. म्हणून मग आपण जसे आहोत तसे जगावं, आपण कशाला गुलाब व्हायचं, नदी व्हायचं नी देव व्हायचं, आपण जे आहोत ते जगलो. की, जगलोच आपण आपल्या आनंदासाठी!
-सुप्रसिद्ध लेखक पाऊलो कोईलो
यांच्या ब्लॉगवरची एक नोंद