UltraShipTMS drives exceptional value in supply chain management by making logistics IT solutions easy to implement, integrate, scale and maintain. One United Bank is a well developed online banking PHP script, developed with php and mysql, it is web based Banking management system that can be accessible for anyone on the web this online banking has Login notification, Credit funds Notification, Debit Funds Notification, Account Registration notification and activation notification email and sms api installed. OpenUnderwriter is an open source software house specialising in the development of IT solutions for the insurance market.
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The OpenUnderwriter platform has been collaboratively developed by some of the best technical brains in the Insurance Industry and offers a viable alternative to traditional solutions. To date, OpenUnderwriter has been Customer registration acknowledgement goes to email and sms with client account number 3.
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Login notification by email exposing users IP 4. Functional Account statement 5. Change PIN and Account password email alert 6. Credit card Request form 7.
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Loan Request form 8. Internal Messaging applications 9. Local Funds Transfer International Funds Transfer Debit sms alert and email alert for transfers It uses Branch and Bound method for solving. An input is a number of cities and a matrix of city-to-city travel prices.
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Free update and patch auto notification via RSS Feed. Receive sms with python online script. Python virtual mobile number sms api. You can use this code for SMS verification facebook, telegram, google, whatsapp or second fake number on your PC. Install Flask and Ngrok. Download receivesms. LambdaTest wordpress plugin can be used to take full page screenshots of post and pages across different desktop and mobile browsers right from wordpress admin panels. These screenshots will help in fastracking cross browser compatibility testing of the web pages and websites hosted on WordPress.
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The recent Meltdown and Spectre vulnerabilities that exploit design flaws in computer processors, are also side-channel attacks. Mobile devices are perfect targets for this sort of attack from an unexpected direction. They are stuffed with sensors , usually including at least one accelerometer, a gyroscope, a magnetometer, a barometer, up to four microphones, one or two cameras, a thermometer, a pedometer, a light sensor and a humidity sensor.
Apps can access most of these sensors without asking for permission from the user. When a user taps on the screen in different locations, the phone itself rotates slightly in ways that can be measured by the three-axis micromechanical gyroscopes found in most current phones. A tap close to the center of the screen will not move the phone much, will reach both microphones at the same time, and will sound roughly the same to all the microphones. However, a tap at the bottom left edge of the screen will rotate the phone left and down; it will reach the left microphone faster; and it will sound louder to microphones near the bottom of the screen and quieter to microphones elsewhere on the device.
Processing the movement and sound data together let us determine what key a user pressed, and we were right over 90 percent of the time.
This sort of function could be added secretly to any app and could run unnoticed by a user. The route taken by a driver, for instance, can be simplified into a series of turns, each in a certain direction and with a certain angle. And the accelerometer showed whether a user was stopped, or moving. By measuring a sequence of turns, and stringing them together as a person travels, we could make a map of their movements. In our work, we knew which city we were tracking people through, but a similar approach could be used to figure out what city a person was in.
Imagine we observe a person in Boston heading southwest , turning degrees to the right, making a sharp U-turn to the left to head southeast, turning slightly to the right, continuing straight, then following a shallow curve to the left, a quick jog to the right, bumping up and down more than usual on a road, turning 55 degrees right, and turning 97 degrees left and then making a slight curve right before stopping.
We developed an algorithm to match those movements up against a digitized map of the streets of the city the user was in, and determined which were the most likely routes a person might take. We were even able to refine our algorithm to incorporate information about curves in roads and speed limits to help narrow options. We produced our results as a list of possible paths ranked by how likely the algorithm thought they were to match the actual route.
About half the time, in most cities we tried, the real path a user followed was in the top 10 items on the list. Further refining the map data, sensor readings and the matching algorithm could substantially improve our accuracy. Again, this type of capability could be added to any app by a malicious developer, letting innocent-appearing apps snoop on their users.
Our research group is continuing to investigate how side-channel attacks can be used to reveal a variety of private information.