networking – Network printer with cups, do not keep clients running

I want to use cupsd to make two USB printers network wide usable.
Is there a way to do this, so I can print from a device and power it down while printing? The problem is, when I print large jobs (100 pages or so), it would be good if I can shut the client down, because that printing will take forever. Can cups “store” the print jobs completely, and send them to the printer one at a time without needing the client, which initiated the printing to stay online?
Regards
TNT2k

networking – Network interface dropping in ubuntu 18.04

I have a robot with the follow setup and the ethernet interface (enp4s0) of (Computer A) drops randomly.

The interface does not drop when the computers are not doing much. However when (Computer B) starts streaming a compressed Image stream to (My Computer) via (Computer A), the ethernet interface drops.

Hence it seems like it is a configuration problem where I am possibly flooding the network unintentionally and I would like to get some advice regarding this matter.

Output from dmesg wasn’t really helpful

(Mon Apr 12 13:43:33 2021) igb 0000:04:00.0 enp4s0: igb: enp4s0 NIC Link is Down
(Mon Apr 12 13:43:33 2021) br0: port 1(enp4s0) entered disabled state
(Mon Apr 12 13:43:35 2021) igb 0000:04:00.0 enp4s0: igb: enp4s0 NIC Link is Up 1000 Mbps Full Duplex, Flow Control: RX/TX
(Mon Apr 12 13:43:35 2021) br0: port 1(enp4s0) entered blocking state
(Mon Apr 12 13:43:35 2021) br0: port 1(enp4s0) entered listening state
(Mon Apr 12 13:43:39 2021) br0: port 1(enp4s0) entered learning state
(Mon Apr 12 13:43:44 2021) br0: port 1(enp4s0) entered forwarding state
(Mon Apr 12 13:43:44 2021) br0: topology change detected, propagating
(Mon Apr 12 14:05:50 2021) igb 0000:04:00.0 enp4s0: igb: enp4s0 NIC Link is Down
(Mon Apr 12 14:05:50 2021) br0: port 1(enp4s0) entered disabled state
(Mon Apr 12 14:05:52 2021) igb 0000:04:00.0 enp4s0: igb: enp4s0 NIC Link is Up 1000 Mbps Full Duplex, Flow Control: RX/TX 

Things I have already done:

  1. Checked and replaced all the ethernet cables with new ones.
  2. Swap out the all the computers and sensors with spares
  3. Enable STP on the network bridge with forward delay of 4

Unfortunately I am unable to connect everything to the switch due to the size constrain within the robot.

Any help is greatly appreciated thanks!

Setup

Sorry I do not have sufficient reputation to post an image =(

View post on imgur.com

All the computers are running ubuntu 18.04 and ROS melodic.

I have setup a network bridge using netplan as shown below

# Netplan on Computer A
# Let NetworkManager manage all devices on this system
network:
  version: 2
  renderer: NetworkManager
  ethernets:
    #Computer B
    enp1s0:
      dhcp4: no
    #Sensor 
    enp2s0:
      dhcp4: no
    #SBC to switch
    enp4s0:
      dhcp4: no
      

  #Setup a bridge and link eno1 to it
  bridges:
    br0:
      interfaces: (enp1s0, enp2s0, enp4s0)
      addresses: (192.168.1.1/24)
      gateway4: 192.168.1.253
      nameservers:
        addresses: (8.8.8.8)
      dhcp4: no

Other things I have tried

Enable Port forwarding & add a route from (Computer B) to (Computer A) & setting up the nics on different subnet

/etc/sysctl.conf

net.ipv4.ip_forward = 1

Computer B

route add -net 192.168.2.0 netmask 255.255.255.0 gw 192.168.1.1


# Netplan on Computer A
# Let NetworkManager manage all devices on this system
network:
  version: 2
  renderer: NetworkManager
  ethernets:
    #Computer B
    enp1s0:
      addresses:
        - 192.168.2.1/24
      dhcp4: no
    #Sensor 
    enp2s0:
      addresses:
        - 192.168.3.1/24
      dhcp4: no
    #SBC to switch
    enp4s0:
      addresses:
        - 192.168.1.1/24
      dhcp4: no
      gateway4: 192.168.1.253      
      nameservers:
        addresses: (8.8.8.8, 8.8.4.4)

apparmor – HOW to customize docker container profile to implement fine-grained network access control

1.materials

apparmor policy reference https://gitlab.com/apparmor/apparmor/-/wikis/AppArmor_Core_Policy_Reference#AppArmor_globbing_syntax

2.my profile

#include <tunables/global>profile docker-test flags=(attach_disconnected,mediate_deleted) {

#include <abstractions/base>
deny /data/** rwl,

deny /usr/bin/top mrwklx,

deny /usr/bin/hello mrwklx,

deny network,

file,

capability,

deny network inet tcp,

deny network bind inet tcp src 192.168.1.1:80 dst 170.1.1.0:80,
}

3.my error

syntax error, unexpected TOK_ID, expecting TOK_END_OF_RULE

the error comes from the last line which contains specific ip_addr, I test it on ubuntu18.04 and my kernel version is 5.4.0-42-generic, apparmor version is 3.0.1 which I compiled from source.

network – Macbook 2007 (A1181) Wi-Fi issues in Windows

I have:

  • Macbook A1181
  • Windows 8.1 in Bootcamp with the latest patches
  • All bootcamp drivers installed
  • Keenetic Viva router with 2.4/5 GHz dual-band Wi-Fi, located in ~1m from the Macbook

The issue is that download/upload speed via Wi-Fi is very low in any app (~60kbps) and often interrupts. On the other hand, ethernet connection works great.

And the same Macbook works great with Wi-Fi when I boot macOS, but, unfortunately, I need Windows.

Other devices, like M1 Macbook Pro (late 2020), iPhones and lots of Android phones (even 2.4 GHz) work fine too, so it seems to be a driver issue.

Any ideas on how to fix it?

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– Sales Department – For all sales inquiries or non technical questions.
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network – How to make a peer to peer system like Bitcoin

network – How to make a peer to peer system like Bitcoin – Bitcoin Stack Exchange

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– 24/7/ 365 Dedicated Support
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– 24/7 Hardware Replacement
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– Sales Department – For all sales inquiries or non technical questions.
– Technical Support Department – Technical Support available 24 hours a day, 7 days a week.
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TensorFlow 2 Python 3 Fully Connected Neural Network

I would really appreciate constructive feedback and suggestions for this fully-connected neural network I have written with TensorFlow 2, Python 3. It estimates the period and amplitude of a sine curve given 100 sample y-points on the curve.

I am mainly interested in how this can be optimised for speed, improving the styling & readability, if I have made any strange programming choices, if there’s a powerful DL technique I am missing which might be useful (etc). Snippets of this script will be used in a pedagogical document to introduce DNN concepts alongside the TensorFlow methods so improvements given this context would be very beneficial.

One immediate flaw I will can address later is that currently the model size is far too big so it will not actually generalise outside the range of parameters in the training examples. That’s fine; for now I just wanted to get everything working and will tune hyperparameters and do regularisation later.

Any advice would be deeply appreciated. Additionally, I am yet to implement a normalisation layer (I would like this to normalise (in the context of them being curves however) using the whole training data and automatically inputs to the model once trained. I am also yet to vectorise the make_curve function. Suggestions for either of these next steps would be fantastic also.

This is of course a toy problem and I will adapt the network to a different problem in which I will be interested in the efficiency and high-dimensional inputs. I have access to both cluster CPU and GPU cores, as well as my fairly laptop with GeForce GTX 1050 Ti Max-Q GPU so I would be interested in optimising this for taking advantage of the parallel computing availability.

The 3d plot is just for fun and shows how the squared error of a prediction blows up for degenerate cases such as zero period or amplitude. Would I be right to assume that a network which has generalised well would have better error specifically at the boundaries?

With the current settings, this take 2.5 mins to run on my 2018 Dell XPS laptop (‘s CPU?) with the following output:

Average test loss:  8.497130045554286
Average val loss:  7.136056077585638
Time taken:  146.38214015960693

Here is the code:

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
import tensorflow.keras.backend as kb
from tensorflow import math as tm

import math, time
import numpy as np
from datetime import datetime

#import warnings
#warnings.filterwarnings("ignore")
kb.set_floatx('float64')

start = time.time()

num_sample_pts = 100
train_size     = (5*10**2)**2  # Funky format so it is a square and easily configurable during development
train_sqrt     = int(train_size**0.5)
epoch_nums     = 2**3
minibatch      = 2**6          # Remember to have minibatch << train_size*epochs
callbacks      = False         # TensorBoard logging is much slower than the learning itself 

learning_rate  = 0.00063
num_layers     = 28
reg1_rate      = 0.001
reg2_rate      = 0.001
act_func       = 'elu'
dropout        = 0.2
units          = 37

def make_curve(period, amp, aug=False):
    if False:
        a = 2
        # Make this vectorised so randoms are a vector of randoms.
    return ( amp * np.sin(( np.linspace(-4,4, num_sample_pts) + aug*np.random.rand() ) * (2*np.pi/period))
            + aug*np.random.rand() ).reshape(num_sample_pts)

def data(sample_interval = (-4, 4), amp_interval = (0,30), aug=False, gridsize=train_sqrt):
    sample = np.linspace(*sample_interval, num_sample_pts)
    mesh   = np.meshgrid(0.05 + 10*np.pi*np.random.rand(gridsize),          # Periods
                         amp_interval(0) + (amp_interval(1) - amp_interval(0)) *
                         np.random.rand(gridsize))                          # Amplitudes
    pairs  = np.array(mesh).T.reshape(-1, 2)
    curves = np.array((make_curve(w,a,aug) for w,a in pairs))               # Change when make_curve is vectorised
    glob_centre, glob_max = np.mean(curves), max(amp_interval)              # Globally centre all curves within pm 1
    curves = (curves - glob_centre) / glob_max                              # - Replace with a normalisation layer
    return (curves, pairs)
# Returns list of 2 arrays (x,y):
# - x is an array of each curve sample list
# - y is an array of the period-amplitude pairs corresponding to curve samples in x

df = data(aug=True)

# Not used. Another possibility could be rmse against a sample from predicted curve
def custom_mean_percentage_loss(y_true, y_pred): #Minimax on the percentage error
    diff = y_pred - y_true
    non_zero = y_true + 10**-8
    res = tm.reduce_mean(tm.abs(tm.divide(diff,non_zero)))
    return res

if callbacks:
    logdir = "logs\scalars\" + datetime.now().strftime("%Y%m%d-%H%M-%S")
    tensorboard_callback = (keras.callbacks.TensorBoard(log_dir=logdir, update_freq='epoch'))
else:
    tensorboard_callback = ()
kb.set_floatx('float64')


def model_builder():
    initializer = keras.initializers.TruncatedNormal(mean=0., stddev=0.5)
    model = keras.Sequential()
    model.add( Dense( units = num_sample_pts,                           # Number of input nodes equals input dimension
                                   kernel_initializer = initializer,    # Initialize weights
                                   activation = act_func,
                                   kernel_regularizer = keras.regularizers.l1_l2(reg1_rate, reg2_rate),
                                   dtype='float64'))
    BatchNormalization()

    for layer in range(num_layers):
        Dropout(dropout)
        model.add( Dense( units = units,                                # Number of nodes to make number of inputs
                         activation = act_func,
                         kernel_regularizer = keras.regularizers.l1_l2(reg1_rate, reg2_rate),
                         dtype='float64'))
        BatchNormalization()
        
    model.add( Dense(units = 2, activation = 'linear', dtype='float64'))
    # Outputting amplitude-period pair requires 2 nodes in the output layer.
    
    model.compile(
        optimizer = keras.optimizers.Adam(learning_rate = learning_rate),
        loss = 'mse',
        metrics = ('mse') ) # Measures train&test performance
    return model

model = model_builder()
training_history = model.fit(*df,
                             batch_size = minibatch,                               # Number of data per gradient step
                             epochs = epoch_nums,
                             verbose = 0,
                             validation_split = min(0.2, (train_sqrt**2)/5000),    # Fraction of data used for validation set
                             callbacks=tensorboard_callback)
                             
print("Average test loss: ", np.average(training_history.history('loss')(:10)))
print("Average val loss: ", np.average(training_history.history('val_loss')(:10)))
print('Time taken: ', time.time()-start)
print(' ')
    
import winsound
for i in range(2):
    winsound.Beep(1000, 250)

This is the first neural network I have written. Thank you very much for your thoughts, improvements and contributions.

mobile network – Able to text and use internet, but no phone calls

It is a refurbished Yureka Note.

I added a newly activated sim in it. It’s sending and receiving text messages. After saving the gprs/mms settings sent by mobile operator, I am able to use internet (which is pretty fast) as well.

However, despite trying variety of things, I am unable to make or receive calls on it. When I tried calling it from another number it says: outside network coverage.

Given that signal is full, my guess is that this has something to do with no VoLTE support in this phone.

I tried many options from ##4636## also. If I select WCDMA/GSM only then there is no signal. When I use LTE only, there is signal but calling to it says Line Busy.

Is there any way to make calling work on this device with this sim?

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