CompSci Weekend SuperThread (October 11, 2019) Computer Science |
- CompSci Weekend SuperThread (October 11, 2019)
- CS50 IDE — Online C/C++ IDE
- How was Grace Hopper Conference for you?
- Python OOPs��, Deep Dive Into Inheritance��️ And Their Types - Part Two
- All You Need to Know About Big O Notation [Python Examples]
- Is NVIDIA Doubling Down On RISC-V?
- What to take after MIT 6.00.1x?
- University of Illinois Springfield (UIS) and where does it rank amoung CS University's.
- Which university computer science/IT curriculum is better?
- On Classifications
- Open Source Hardware Trends, Arm Takes a Different Tack
- Facebook Debuts PyTorch 1.3 With PyTorch Mobile, Quantization, TPU Support and More
- 11th International Conference on Network and Communications Security
- Lear Problem Solving through Pattern Programming Examples
| CompSci Weekend SuperThread (October 11, 2019) Posted: 10 Oct 2019 06:04 PM PDT /r/compsci strives to be the best online community for computer scientists. We moderate posts to keep things on topic. This Weekend SuperThread provides a discussion area for posts that might be off-topic normally. Anything Goes: post your questions, ideas, requests for help, musings, or whatever comes to mind as comments in this thread. Pointers
Caveats
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| Posted: 10 Oct 2019 09:15 AM PDT |
| How was Grace Hopper Conference for you? Posted: 10 Oct 2019 09:12 PM PDT Curious if anyone went to any interesting talks, or came across a new company that interested them? Met any cool people? I met a group of people from Toronto who were super nice and shared their food with me even though I had just met them/we were all starving :---) [link] [comments]  |
| Python OOPs��, Deep Dive Into Inheritance��️ And Their Types - Part Two Posted: 11 Oct 2019 05:43 AM PDT |
| All You Need to Know About Big O Notation [Python Examples] Posted: 10 Oct 2019 08:51 AM PDT |
| Is NVIDIA Doubling Down On RISC-V? Posted: 10 Oct 2019 08:06 AM PDT |
| What to take after MIT 6.00.1x? Posted: 10 Oct 2019 11:38 PM PDT MIT's edX introductory computer science course is about to end in 3 weeks, and I'm unsure of where to progress next. My current options are MIT 6.00.2x and Harvard's CS50 course. 6.00.2x seems like a logical progression after 6.00.1x but I've read that it can be very math-intensive; I don't have a significant background in math, but I'm willing to give it a try if it meant developing a deeper understanding of computation. I've heard that CS50 is an amazing course that introduces a variety of programming languages and gives problem sets on different implementations, but I don't want to be repeating content I'm already familiar with. Or maybe there's an entirely different course altogether that would be a better choice than these two? Well, let that be a discussion in case I'm breaking the first rule with the title of my post. [link] [comments]  |
| University of Illinois Springfield (UIS) and where does it rank amoung CS University's. Posted: 10 Oct 2019 07:27 PM PDT I'm in my second semester in CS at a Community College, and I decided to transfer to UIS in the near future for their online CS degree. Where does it stack up with other top Universities for a CS degree? [link] [comments]  |
| Which university computer science/IT curriculum is better? Posted: 10 Oct 2019 08:54 PM PDT Please let me know if this is the wrong sub to ask. Struggling to carefully pick a university for next year. Option 1: https://study.unisa.edu.au/degrees/bachelor-of-information-technology-software-development Option 2: https://students.flinders.edu.au/my-course/course-rules/undergrad/bcsc#program-of-study Any help will be much appreciated! [link] [comments]  |
| Posted: 10 Oct 2019 02:01 PM PDT I've been doing some thinking about the mathematics and theory of classifications, as opposed to actually implementing particular classifications, and I've come to a few conclusions that I thought I'd share, in advance of a formal article. The initial observation comes from the nearest neighbor technique that I've been making use of lately: given a dataset, and an input vector, the nearest neighbor algorithm uses the class of the vector that is most similar to the input vector as predictive of the class of the input vector. In short, if you want to predict what the class of a given input vector is, you use the class of the vector to which the input vector is most similar. This is generally measured using the Euclidean norm operator, but you could substitute other operators as well depending upon the context. This method works astonishingly well, and can be implemented very efficiently using vectorized languages like MATLAB and Octave. The assumption that underlies this model is that the space in which the data exists is locally consistent. That is, there is some sufficiently small neighborhood around any given point, within which all points have the same classifier. The size of this neighborhood is arguably the central question answered by my initial paper on Artificial Intelligence, if we interpret the value of \delta as a radius. Because this method works so well, it must be the case that this assumption is in fact true for a lot of real world data. Common sense suggests that this is a reasonable assumption, since, for example, the color of an object is generally locally consistent; income levels exhibit local consistency by neighborhood; and temperature is generally locally consistent in every day objects. These examples provide some insight into why the nearest neighbor method works so well for a typical dataset. https://derivativedribble.wordpress.com/2019/10/10/on-classifications/ [link] [comments]  |
| Open Source Hardware Trends, Arm Takes a Different Tack Posted: 10 Oct 2019 10:02 AM PDT |
| Facebook Debuts PyTorch 1.3 With PyTorch Mobile, Quantization, TPU Support and More Posted: 10 Oct 2019 01:32 PM PDT |
| 11th International Conference on Network and Communications Security Posted: 10 Oct 2019 05:07 AM PDT 11th International Conference on Network and Communications SecurityNovember 23 ~ 24, 2019, zurich, Switzerland https://cseit2019.org/ncs/index.html Scope The purpose of this conference is to publish latest & high-quality research works on Network and Communications Security in theoretical and practical aspects. This conference aims to promote state-of-the-art research in the area of Network and Communications Security. Topics of interest include, but are not limited to, the following · Access Control, Anonymity, Audit and Audit Reduction & Authentication and Authorization · Applied Cryptography, Cryptanalysis, Digital Signatures · Biometric Security · Boundary Control Devices · Certification and Accreditation · Cross-Layer Design for Security · Security & Network Management · Data and System Integrity, Database Security · Defensive Information Warfare · Game and Software Engineering · Denial of Service Protection, Intrusion Detection, Anti-Malware · Distributed Systems Security · Electronic Commerce · E-mail Security, Spam, Phishing, E-mail Fraud, Virus, Worms, Trojon Protection · Grid Security · Information Hiding and Watermarking & Information Survivability · Insider Threat Protection, Integrity · Intellectual Property Protection · Internet/Intranet Security · Key Management and Key Recovery · Language-Based Security · Mobile and Wireless Security · Mobile, Ad Hoc and Sensor Network Security · Monitoring and Surveillance · Multimedia Security ,Operating System Security, Peer-to-Peer Security · Performance Evaluations of Protocols & Security Application · Privacy and Data Protection · Product Evaluation Criteria and Compliance · Risk Evaluation and Security Certification · Risk/Vulnerability Assessment · Security & Network Management · Security Models & protocols · Security Threats & Countermeasures (DDoS, MiM, Session Hijacking,Replay attack etc,) · Trusted Computing · Ubiquitous Computing Security · Product Evaluation Criteria and Compliance Paper submission Authors are invited to submit papers through the conference Submission system by October 12, 2019. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this conference. The proceedings of the conference will be published by Computer Science Conference Proceedingsin Computer Science & Information Technology (CS & IT) series (Confirmed). Selected papers from NCS 2019, after further revisions, will be published in the special issue of the following journal. · International Journal of Computer Networks & Communications (IJCNC) – Scopus, ERA Indexed · International Journal of Wireless & Mobile Networks (IJWMN)– ERA Indexed · International Journal of Network Security & Its Applications (IJNSA) – ERA Indexed, UGC Listed · International Journal of Security, Privacy and Trust Management (IJSPTM) · International Journal On Cryptography And Information Security (IJCIS) Important dates · Submission Deadline: October 12, 2019 · Authors Notification: November 12, 2019 · Registration & camera – Ready Paper Due: November 15, 2019 Contact us Here's where you can reach us : [ncs@cseit2019.org ](mailto:ncs@cseit2019.org) [link] [comments]  |
| Lear Problem Solving through Pattern Programming Examples Posted: 10 Oct 2019 07:41 AM PDT |
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