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    The Magical Math behind Modern Cryptography Computer Science

    July 14, 2020

    The Magical Math behind Modern Cryptography Computer Science

    The Magical Math behind Modern Cryptography

    Posted: 13 Jul 2020 05:22 PM PDT

    This presentation: https://youtu.be/mSMQ-xowqAg

    Introduces the magical math that secures our digital lives. It is presented graphically so complex ideas can be appreciated by the expert and layperson alike.

    Presentation topics include:

    How to achieve privacy when someone is always listening. (encryption)

    How to decide on a secret when everyone is watching. (key agreement)

    How to turn one random number into unlimited random numbers. (PRNGs)

    How to speak in a way that's impossible to imitate. (digital signatures)

    How to help protect data without possessing it. (secret sharing)

    How to check work you can't see. (zero knowledge proofs)

    How to process data you don't have access to. (homomorphic encryption)

    submitted by /u/gcnaccount
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    Open Source Guide: Software Engineering for Self Driving Cars

    Posted: 13 Jul 2020 06:52 PM PDT

    original post (last week): https://www.reddit.com/r/csMajors/comments/hmih9f/an_open_source_autonomous_vehicle_swe_guide_for/

    ​

    Recently, I created an open source guide to compile and put together a guide for computer science students to learn about software engineering in self-driving cars.

    GitHub Project: https://github.com/tabaddor/av-swe-guide

    ​

    I've been adding content, and will definitely continue to work on it, however, contributors are always welcome. Which is why I'm reaching out! I don't nearly have the time and knowledge and to do it all alone and would love to build a large community around this.

    ​

    This guide will contain structured content, research papers, tutorials working with av technologies, av company open job positions, and hopefully much more. There's also some open issues anyone can start tackling.

    Keeping this short and sweet. Check out the project and give it a star! Open to suggestions and answering any questions (feel free to PM me)

    Yurroff

    submitted by /u/Yurroff
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    A Simple Guide to Cloud Computing

    Posted: 14 Jul 2020 04:15 AM PDT

    [N] ICML 2020 Announces Outstanding Paper Awards

    Posted: 13 Jul 2020 03:52 PM PDT

    Organizers of the 37th International Conference on Machine Learning (ICML) have announced their Outstanding Paper awards, recognizing papers from the current conference that are "strong representatives of solid theoretical and empirical work in our field."

    A total of 1,088 papers out of 4,990 submissions made it to the prestigious machine learning conference. The acceptance rate of 21.8 percent is slightly lower than 2019's 22.6 percent (774 accepted papers from 3,424 submissions), and it seems likely the drastic increase in submissions helped contribute to this.

    Here is a quick read: ICML 2020 Announces Outstanding Paper Awards

    submitted by /u/Yuqing7
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    Types of SSL Certificates

    Posted: 14 Jul 2020 03:09 AM PDT

    How a web Attack Works: The Five Stages

    Posted: 14 Jul 2020 03:49 AM PDT

    Does anyone know mxGraph or React Diagrams

    Posted: 13 Jul 2020 11:43 AM PDT

    [R] DeepMind Explores Generalization and Efficiency in Algorithm Design

    Posted: 13 Jul 2020 11:01 AM PDT

    UK-based AI company DeepMind recently introduced a new approach designed to improve the generalizability (correctness beyond the training distribution) and efficiency of algorithms represented by neural networks. The researchers propose that properly setting up the input and output interface of a neural network and making good use of supervised learning should be central to tackling generalization and efficiency challenges. Their research applies a neural program induction paradigm to learn neural networks to represent algorithms in solving tasks.

    Here is a quick read: DeepMind Explores Generalization and Efficiency in Algorithm Design

    The paper Strong Generalization and Efficiency in Neural Programs is on arXiv.

    submitted by /u/Yuqing7
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    [R] New Google Research Incorporates Societal Context in ML Systems

    Posted: 13 Jul 2020 09:20 AM PDT

    In the recent paper Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context, a team of researchers from Google, System Stars and DeepMind argue that "machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs. Such oversimplified mathematical models abstract away the underlying societal context where ML models are conceived, developed, and ultimately deployed." The paper's first author Donald Martin, Jr., Technical Program Manager at Google, tweeted, "understanding societal systems is more important than ever."

    Here is a quick read: New Google Research Incorporates Societal Context in ML Systems

    The paper Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context is available on arXiv, and is also the foundation work for the paper Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics.

    submitted by /u/Yuqing7
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    Grammar for a PDA, Help required in understanding theorem and example

    Posted: 13 Jul 2020 07:18 AM PDT

    Does my project qualify for a research paper ?

    Posted: 13 Jul 2020 08:08 AM PDT

    I have finished a prototype of a web app that includes existing machine learning algorithms and neural network architectures (SVM and LSTM), databases, other simple algorithms.

    It's a web app to aid senile patients. As far as I have seen, I haven't found any similar app like this.

    I m still an undergraduate student. Would this qualify for a reserch paper in a small conference? It would look good on my resume.

    submitted by /u/Civil_Piano
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    Programmers are NOT in huge demand

    Posted: 13 Jul 2020 06:21 AM PDT

    I want to dispel the notion that computer science / engineering is a golden ticket to a high paying job that employers are scrambling to find. This was true maybe 10-20 years ago, when people were impressed you knew how to use Excel, but these days kids are learning to code in C# from about the age of 13 and have about a decade of exposure before they even begin their entry level job. And to remain competitive, you'll have to put in long hours in your free time to learn new technologies and build things. No matter how much you enjoy it, a situation like that only leads to burnout.

    Once upon a time you could get a pretty good web development job without a degree or if you barely even knew PHP, because managers weren't tech savvy. They still aren't, but they have phones and at least know that their kids and new hires aren't intimidated by this stuff so the bar has been raised. In the 2000s Java/C++(basic level) and SQL carried you far. Today I think the average web dev knows more technology than most systems developers did back in the day.

    The entry level market is flooded, it is so bad that you have to be very good to even interview at a mid-sized company. That means they want you to have internships, a degree, and an ability to solve algorithmic puzzles. Some of these companies will keep a vacant seat and not hire at all waiting for the talent to show up - that's what they mean by shortage. And all the truly junior jobs are easily outsourced because you can do a lot of work remotely.

    None of this is a problem if you are the top 5% in your field, have amazing projects, and can solve leetcode hards on the fly. You can find work at big tech companies at will and be compensated handsomely. But that is true of any field you are in the top 5% of. If you have that kind of ability, you can go into medicine and make as much or more with greater security. This does not apply to the remaining 95% though. Any market is hot for high talent.

    Lastly I want to dispel two more myths:

    1. A lot of mid/senior devs here think they are hot stuff because recruiters are always trying to pouch them on LinkedIn. Wrong. Typically they aren't being offered jobs at big tech companies at all. What is more typical is that they are being invited by an automaton recruiter an opportunity to attempt their highly difficult assessment which very few people can even clear the warmup rounds to. Getting offers to interview is not the same as getting a job offer.
    2. It is often stated that computer science is at the forefront of technology and buzzwords like AI and big data will lead to healthy growth. The problem is these positions are reserved for people with PhDs from top schools and by the time they become applied enough to be common in the work force you'll be competing with fresh grads who are willing to work more for less with their school's new focused area of study in that particular subject.
    submitted by /u/throwaway892121312
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