User Profile

Ok, I can do that but meanwhile I just need my app to work. Do you know a reliable way to retrieve the dns name of a device on a local network, given its ip address?

I guess I can assume this is a bug in iOS 13, since I have not received a response.

And to add insult to injury, it works fine on the simulator, iPad Pro, iOS 13.3

Since adding the flag kDNSServiceFlagsTimeout, the callback is called with an error indicating that it timed out. Before adding that flag, the callback would never be called. (on my iPad Pro running iOS 13.3)Of course when executing it on my iPad Pro running 12.3.1 the callback is called either way.Furthermore, and more importantly, when the callback is called on the iPad Pro running 12.3.1 the machine name of the arp address that was provided when I called DNSServiceQueryRecord, is provided via the callback through the parameter called rdata in the callback example below:static void QueryRecordCallback ( DNSServiceRef dnsServiceReference, DNSServiceFlags flags, uint32_t interfaceIndex, DNSServiceErrorType errorCode, const char * fullname, uint16_t rrtype, uint16_t rrclass, uint16_t rdlen, const void * rdata, uint32_t ttl, void * context) { DNSServiceRefDeallocate(dnsServiceReference); char result[1024] = {0}; dn_expand(rdata, rdata + rdlen, rdata, result, 1024); if (strlen(result) > 0) { NSString *machineName = [[NSString stringWithUTF8String: result] stringByDeletingPathExtension]; }}

it could easily work similar to image classification, such that I could only tell it about the thing I'm interested in... say for example I put a box around a dog in all the images in a given folder, that is not to say that there isn't a cat in the image, I am only trying to teach it dog. Then if I add a folder for cat, I could box only the cats in those images... that is not to say there are no dogs on those images. So it could train on dog, then on cat independently of each other, then merge what it learns together so that I have one model that knows how to identify a cat, and a dog. Alternatively, a human has to go through and relabel everything, each time a new item needs to be added.

Thanks Stuart, That does explain some things. It is kind of frustrating that it works that way because the real world is significantly different than I want to label a "cat" or "dog. Especially... say, hypothetically you wanted to label fruit in their "natural" environment. So you go off and start labelling bananas, and apples, etc. You do so in 1000s of images so that your model performs well. Everything goes great, you have 1000s of images labeled, and you're set. Next thing you know, you get a requirement to label vegetables. So now you have to go back through the 1000s of images and add labels for the vegetables that may or may not be in the images that you labeled the fruit in, along with labeling all the fruit that might occur in your new vegetable images? That is both painful and frustrating and does not scale very well. I'm open to any advice. 🙂