Number Recognition

Number Recognition

From an image predict the number from it

Artificial Intelligence

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Number prediction

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Andre C. created project MagicMirror RealSense Module

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MagicMirror RealSense Module

The idea is very simple: add a small board and a monitor behind a two-way mirror and code some software to provide useful information to the person in front of it.

I’ve seen on the Internet some implementations of this, but I wanted to provide personalized information to the person in front of it using a camera and facial recognition algorithms. Something like providing commuting time and next appointments for me and change the information when my wife would stand in front of it.

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Srinivasan S. updated status

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Srinivasan Saripalli

Design and Development of a Android App for Autistic Children. This App would be simultaneously Developed in both English and Kannada languages and would serve as a great helper App for such physically challenged children. The App subsequently would also have the feature to interact with the child, understand the child behaviour and automagically respond to his/ her requests of any kind.

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Nilesh created project Demand prediction on graphs

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Demand prediction on graphs

Using localized spectral filtering and deep learning to predict demand between two nodes as in a graph based on current research which showed promising results for classification problems on a network basis. The next step is to build a node level classifier & that's what we have in mind.

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Minlan Y. updated status

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Minlan Yu

Trumpet: Timely and Precise Triggers in Data Centers:

As data centers grow larger and strive to provide tight performance and availability SLAs, their monitoring infrastructure must move from passive systems that provide aggregated inputs to human operators, to active systems that enable programmed control. In this paper, we propose Trumpet, an event monitoring system that leverages CPU resources and end-host programmability, to monitor every packet and report events at millisecond timescales. Trumpet users can express many network-wide events, and the system efficiently detects these events using triggers at end-hosts. Using careful design, Trumpet can evaluate triggers by inspecting every packet at full line rate even on future generations of NICs, scale to thousands of triggers per end-host while bounding packet processing delay to a few microseconds, and report events to a controller within 10 milliseconds, even in the presence of attacks. We demonstrate these properties using an implementation of Trumpet, and also show that it allows operators to describe new network events such as detecting correlated bursts and loss, identifying the root cause of transient congestion, and detecting short-term anomalies at the scale of a data center tenant.

http://www.cs.yale.edu/homes/yu-minlan/writeup/sigcomm16.pdf

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