In the source code to perform el.classList = v.classes the author uses:
for (const name of v.classes)
if (!el.classList.contains(name)) el.classList.add(name)
for (const name of el.classList)
if (!v.classes.includes(name)) el.classList.remove(name)
Why the standard decided to make classList read-only?
Could ele.className=(v.classes).join(" ") be a valid and performant solution?, perhaps is to avoid the string to token traslation for performance reasons?, then why don't they include a classList.set method?
I wonder if a two step process could work better than this, first use a variational autoencoder or simple an autoencoder then use it to train the labeled sampled.
In (1) there is a full example of using the two step strategy but using more labeled data to obtain 92% of accuracy. Someone can try changing the second part to use only ten labels for the classifying part and share results?
Edited: I found a deep analysis in (2), in short for CIFAR 10 the VAE semi-supervised learning approach provides poor results, but the author has not used augmentation!
Thanks for all the info. I think that mediasoup is a very good SFU. I wish you the best and I hope mediasoup SFU to become a crucial tool for rtc.
Edited: In a recent article (1) it seems that multicast is better than SFU in webrtc. In the PhD. Thesis (2) a hybrid model is used: Hybrid multicast-unicast video streaming over heterogenous cellular networks.
Each receiver sending feedback does not prevent the server for using datagrams with multiple destinations. I can see that each peer use a different resolution and bitrate but that is another layer, is like sending information at several resolutions and each peer selecting the best one.
Edited: I must learn something about multicast in IPv6, the idea seems interesting.
In order to send a datagram to multiple IP the first, and naive idea, one can think of is to change the datagram protocol to allow for multiple destination. Today, 2020, is the right time. I am thinking about platforms that have hundred or thousand of simultaneous receiving ends, so that the branching point occurs near the destination. Again, googling this proposal is not new (1) RFC 1770, category informational.
Edited: It seem that RtcDataChannel can be used with SFU, example LiveSwitch in 2018, but they don't use multiple destination datagrams (3)
More on similar proposal (2).
(1) IPv4 Option for Sender Directed Multi-Destination Delivery. The Selective Directed Broadcast Mode (SDBM) is an integral part of the U.S. Army standard for tactical data communication networks as defined in MIL-STD-188-220().
To understand the terms: webrtc, stun, turn, mesh, sfu, mcu, ice and trickle ice, there is (1). 15 minutes to understand what is all this about. What about IPv6 stun and turn?, it seems other people asked the same idea I thought: (2)
bout all of this, one is the answer is: As IPv6 takes over the complexity of new networks, STUN and ICE will become irrelevant. I think that with the surge in video conferences and rtc, ipv6 with take off.
In my very humble opinion, I would suggest to reserve some address space in IPv6 for rtc, so that a peer is able to adopt a new special ip reserved for rtc. Nothing new under the sun, in 2014 someone comment along this line of thought (2) and (3).
(3) 2014, AshleysBrain,
https://news.ycombinator.com/item?id=7496986
I think the solution is IPv6. Once every device on the Internet is uniquely addressable again, we can do away with these NAT hacks and two endpoints should be able to reliably connect to each other again, no matter where they are. Of course, that's assuming we don't get more short-sighted engineering that breaks things again...
You are right. Also the Universal Approximation theorem (1) for neural networks guarantees that neural networks can approximate continuous function on compact subsets of R^n, in this case max(x,y).
argmax([x,y]) = (sign(x[0]-x[1])+1)/2
Going beyond continuous functions, can deep learning be used for primality test?
it seems that neural networks have problem for computing the maximum function, and a human can compute the maximum easily, so it seems that the three heuristic rules don't work in this case.
IANAL, but if the purpose of taking screenshot every 30 minutes is to control the work of the employee you must know that in the EU you have the right to be informed about any measure taken to control you.
If you can convince the judge that taking the screenshot has other purpose then GDPR doesn't apply.
From (2): The WP29 outlines that a DPIA is likely to
be required if «a company systematically monitor(s) its employees’ activities,
including the monitoring of the employees’ work station, internet activity»
since it implies a «systematic monitoring and data concerning vulnerable data
subjects» (23), form GDPR and Personal Data Protection
in the Employment Context CLAUDIA OGRISEG
In (1) at point 8: the employeer has to inform the employee about: (i) whether and when monitoring is applied.
(ii) the purpose of data processing,
(iii) the means used for data processing.
Some more info from (1):
The new Netwalker phishing campaign is using an attachment named "CORONAVIRUS_COVID-19.vbs".
Related:
Also from (2):
APT36 uses two lure formats in this campaign: Excel documents with embedded malicious macros and RTF documents files designed to exploit the CVE-2017-0199 Microsoft Office/WordPad remote code execution vulnerability.
from (3) .Ransomware Gangs to Stop Attacking Health Orgs During Pandemic
TP + FN means true positives plus false negatives, that is the number of deseased people. P should be the number of people that gives positive in the test, since TP means true positive in that sense.
Also TP + FN = Prevalence of the desease, but using P for prevalence and for positive creates a lot of confusion in a wikipedia page to explain a concept.
My plea is that I don't want people to get confused. On top of it there is a warning on that page of wikipedia: This article may be too technical for most readers to understand. I couldn't understand why they used that notation. So I ask to delete that page because is going to confuse people.
I can't understand why one would use P in such a way that P = TP + FN, this is going to be a real mess.
Afortunately there is the following page in which they only use TP,FP, TN, FN that is true/false and positive/negative. So replace that page with https://en.wikipedia.org/wiki/Precision_and_recall or change notation. Please.
P = TP + FN is something that I would never put on paper.
I think that assigning credit to individual features doesn't work if there is a strong interaction among features. I would like to see a method of constructing a map that describes sets of features with strong interaction, that is new features that are not reducible to individual features in order to explain how the model work.
Edited, (1) is a good first step at shapely value theory and applications:
Clearly, if your generator for equations is limited the system can learn the dictionary
lhs (integral) => rhs (derivative) to go rhs => lhs. (integrate)
(1) https://stackoverflow.com/questions/171251/how-can-i-merge-p...