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Mountain climbing. Increases social skills, teamwork and trust.
Building a house. Increases spatial visualization and planning skills.
Electronics. Increases mathematical and problem solving skills.
Chemistry. Increases precision and analytical reasoning skills.
Psychedelic drugs. Increases imagination, inspiration and abstraction skills.24 -
---WiFi Vision: X-Ray Vision using ambient WiFi signals now possible---
“X-Ray Vision” using WiFi signals isn’t new, though previous methods required knowledge of specific WiFi transmitter placements and connection to the network in question. These limitations made WiFi vision an unlikely security breach, until now.
Cybersecurity researchers at the University of California and University of Chicago have succeeded in detecting the presence and movement of human targets using only ambient WiFi signals and a smartphone.
The researchers designed and implemented a 2-step attack: the 1st step uses statistical data mining from standard off-the-shelf smartphone WiFi detection to “sniff” out WiFi transmitter placements. The 2nd step involves placement of a WiFi sniffer to continuously monitor WiFi transmissions.
Three proposed defenses to the WiFi vision attack are Geofencing, WiFi rate limiting, and signal obfuscation.
Geofencing, or reducing the spatial range of WiFi devices, is a great defense against the attack. For its advantages, however, geofencing is impractical and unlikely to be adopted by most, as the simplest geofencing tactic would also heavily degrade WiFi connectivity.
WiFi rate limiting is effective against the 2nd step attack, but not against the 1st step attack. This is a simple defense to implement, but because of the ubiquity of IoT devices, it is unlikely to be widely adopted as it would reduce the usability of such devices.
Signal obfuscation adds noise to WiFi signals, effectively neutralizing the attack. This is the most user-friendly of all proposed defenses, with minimal impact to user WiFi devices. The biggest drawback to this tactic is the increased bandwidth of WiFi consumption, though compared to the downsides of the other mentioned defenses, signal obfuscation remains the most likely to be widely adopted and optimized for this kind of attack.
For more info, please see journal article linked below.
https://arxiv.org/pdf/...9 -
Note that the example tags for rants includes Xcode... There is a reason.
In 1984 Apple introduced Macintosh and we got a computer that could barely handle multiple windows on the screen. But by 1986 multiple windows all over the place.
In 1985 Microsoft introduced Window 1.0 Yes boys and girls, no "s" because it hand only one Window. 7 years later in 1992 Microsoft got their head out of their ass and produced "MDI" or Multiple Document Interface and woohoo, had multiple windows on the screen... big boy pants 8 years after Mac.
Today we have Xcode from Apple. The old Window 1.0 engineers are apparently alive and well as Xcode is a One Window Trick Pony and if you fight with it, it starts randomly jamming your separate Windows into tabs into one window, completely fucking up your spatial organization of your working source code.
It is like the bastards going into a blind man's house and re-arranging his furniture for fun.
Xcode - it's only free if your time is worth absolutely nothing.5 -
-- This is my first rant so sorry if it's bad--
We have a nice project that I am working on that needs to store and interact with location data. It is a .NET Core API using Entity Framework Core to interact with the database. All good and well. Until today when I started working on the implementation of storing location data we retrieve from mobile devices.
SQL has a nice data type named: "Geography" which can store a location and do calculations on it with queries. Such as proximity and distance which is what we need.
But then it turns out that EntityFramework Core does not have support for the Spatial data types. even though version 6 did have Spatial support.
Then i found the following issue on GitHub: https://github.com/aspnet/...
Turns out this feature has been requested since 2014 and is even on the "High-priority" list and is still not implemented to this day. Even though in the issue many people are asking to have this implemented.
WHY IS THIS TAKING SO LONG MICROSOFT!!
So now i have to figure out how to work around this. But that is an issue for tomorrow.1 -
More from my big black book of ai and neuroscience:
I think if trace theory is true to any degree it would go some distance in explaining phenomenal consciousness, assuming I haven't misunderstood anything.
In fuzzy trace theory (FTT) it is posited that people form two types of mental representations about a past event:
*verbatim traces: detailed representations of a past event.
*gist traces: fuzzy representations of a past event.
People can reason with verbatim *and* gist traces but prefer gists.
*vision was suggested to work similarly in 1999. With human vision, two processes could be used: one that aggregates local receptive fields and one that parses the local receptive spatial field. It was suggested that people used prior experience, gists, to decide which dominates a perceptual decision.
Gist processes form representations of events, semantic details, where verbatim reinstates the context found in the surface details of an event.
__notes__
Parallel storage: asserts encoding/storage of verbatim/gist traces operate in *parallel*, not in serial.
I like to think of verbatim traces as databases, and gists as queries constructed by recognition.
Several studies have found that the meaning (gist) of an item is encoded even *before* the surface details (verbatim).
This might be important as a survival mechanism but should not be taken to mean strictly that gists are formed wholly *without* details or important and recognizable features of the item in question. It may well be for high level el processing and classification efficiency this may be an important reprocessing step, in the same way that many functions of the brain are duplicated throughout.5 -
Just finished a little proof of concept of a reprojected multisample antialiasing technique and daaaaamn it's looking good. First time ever a rendering technique I invented isn't complete shit so that's an improvement
It still has some (pretty big) issues with both spatial and temporal ghosting but I have some ideas in the pipeline
I wish I could show you guys comparison images but as it turns out most anti aliasing techniques look pretty good on still frames but only the good ones stand up under motion and I don't know of a good way right now to capture pixel perfect clips like that5
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