New Computer Algorithm Detects Repeat Offenders – Could It Change the Justice System?

A new study might change the results of the criminal justice system. Researchers from Stanford University and the University of California, Berkley, created algorithms that are more accurate than people in determining which of the accused will commit a crime again. In the new work, the researchers note that even untrained people can fairly accurately predict the risk of reoffending by a person: to do this, they only need to know a few simple variables. But the criminal justice system, which does not have the right to make mistakes, works with a large number of parameters: in this case, it is much more difficult for even a professional to draw conclusions. Recently, it was found that algorithms are able to predict a possible relapse much better than people.

Making the Invisible Visible: Astonishing Tech that Can See Through Walls!

The new work by MIT researchers “Making the Invisible Visible: Action Recognition Through Walls and Occlusions” will be presented at the International Conference on Computer Vision (ICCV), an annual research conference sponsored by the Institute of Electrical and Electronics Engineers. It is considered, together with CVPR, the top level conference on computer vision. The conference is usually spread over four to five days. It will take place in Seoul, South Korea, October 27 – November 2, 2019.

Human action recognition is a core task in computer vision. It has broad applications in video games, surveillance, gesture recognition, behavior analysis. The work uses a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeleton as an intermediate representation, and recognizes actions and interactions of multiple people over time.  

The paper introduces, RF-Action, an end-to-end deep neural network that recognizes human actions from wireless signals. End to end deep learning is an idea of outputting complex data types from raw features, for example, audio transcripts, image captures.

A deep neural network is a neural network with a certain level of complexity, a neural network with more than two layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways.

Usually, computer technologies use human poses on video. Algorithms are then used to identify the behavior parameters of the multiple people. The engineers from MIT developed an algorithm that combines multiple parameters: raw camera data transmitted to the neural network, while also creating a skeletal model with the body. Next algorithmic analysis models and chooses appropriate actions. It is also able to identify body movements, such as a hand shake between two people.

To achieve the visual data using a system from multiple cameras, an Alpha Pose algorithm was used, which takes 2D skeletal models and converts them to 3D. Alpha Pose is an accurate multi-person pose estimator, which is the first real-time open-source system that matches poses that correspond to the same person across frames, they also provide an efficient online pose tracker called Pose Flow.

Consequently, to achieve RF scanning through the walls and other obstacles, the engineers designed a transceiver.  A transceiver is a device comprising both a transmitter and a receiver that are combined and share common circuitry or a single housing. The transceiver has two sets of antennas oriented vertically and horizontally. Hence, the signals are formed in 2D and the neural network receives multiple images.

Recently, University of California researchers created a method to identify a person through a wall using video and Wi-FI signals. The video-WiFi cross-modal gait-based person identification system XModal-ID has a variety of applications, including surveillance and security. The approach makes it possible to determine if the person behind the wall is the same as the one in a video footage, using only a pair of off-the-shelf WiFi transceivers outside.

The full paper can be read here.

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Russia Announces Revolutionary Quantum Computer Working Prototype – There are Defense Applications

Russia has announced the first functioning prototype of the Quantum Computer. The Quantum computing idea began in the 1980s when physicist Paul Benioff proposed a quantum mechanical model of the Turing machine. The field of quantum computing is closely related to quantum information science, which includes quantum cryptography and quantum communication.

The Soul of the Internet

Internet is One Brain communication connecting the whole world.  The computer has become a source of all knowledge and even has the ability to fill the gap between the knowledge, the knower and the known.  In the internet is a potential memory base to give knowledge to the knower.  It makes it easier to seek knowledge allowing the knowledge which has been given by man to be at his fingertips through asking the search engine for this knowledge. Internet has a soul which its contributors give it for good and bad.