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منشورات القناة
| 2 | +1 Linux_System_Engineer:_Web_Servers_and_DNS_Using_Apache,_NGINX,.zip | 0 |
| 3 | https://github.com/cpldcpu/MisguidedAttention | 0 |
| 4 | https://github.com/cpldcpu/MisguidedAttention | 0 |
| 5 | https://bbycroft.net/llm | 0 |
| 6 | https://bbycroft.net/llm | 0 |
| 7 | ⭐️ Learn Python 3 From Scratch: Python Basics And Fundamentals | 0 |
| 8 | ⭐️ Learn Python 3 From Scratch: Python Basics And Fundamentals | 0 |
| 9 | CVE-2024-43468: ConfigMgr/SCCM 2403 Unauth SQLi to RCE
PATCHED: Oct 8, 2024
Exploit: https://github.com/synacktiv/CVE-2024-43468
Blog: https://www.synacktiv.com/advisories/microsoft-configuration-manager-configmgr-2403-unauthenticated-sql-injections
#git #exploit #ad #rce #sccm #pentest #redteam | 0 |
| 10 | 🔔 Linux Kernel 6.12.9 is here with some impressive updates! 🚀 From Real-Time Support and QR Codes on panic screens, to new security modules, it's a game-changer!
https://lwn.net/Articles/1004549/ | 0 |
| 11 | 🔔 Linux Kernel 6.12.9 is here with some impressive updates! 🚀 From Real-Time Support and QR Codes on panic screens, to new security modules, it's a game-changer!
https://lwn.net/Articles/1004549/ | 0 |
| 12 | Replicas and Consistency in Distributed Systems
Replication is the process of creating copies of data between nodes in a distributed system to improve fault tolerance, scalability, and availability.
System administrators often face the task of setting up and managing replicas, especially in scalable systems such as Cassandra, Kafka, or Redis.
Replication Models
1. Synchronous Replication
⏺Data is confirmed only after being written to all nodes.
⏺Pros: Strong consistency.
⏺Cons: High latency, availability issues during failures.
⏺Example: Financial systems where data accuracy is important.
2. Asynchronous Replication
⏺Data is written to one node and then synchronized with the others.
⏺Pros: High availability, fast query processing.
⏺Cons: Temporary data inconsistencies are possible.
⏺Example: geographically distributed systems, CDN.
Setting up replication in MongoDB
1️⃣ Create a replica set:
rs.initiate(
{
_id: "myReplicaSet",
members: [
{ _id: 0, host: "node1:27017" },
{ _id: 1, host: "node2:27017" },
{ _id: 2, host: "node3:27017" }
]
}
)
2️⃣ Check the replication status:
rs.status()
Setting up consistency in Kafka
1️⃣ Set the minimum number of replicas that must acknowledge a write:
min.insync.replicas=2
2️⃣ Ensure consistency for writes:
acks=all | 0 |
| 13 | Replicas and Consistency in Distributed Systems
Replication is the process of creating copies of data between nodes in a distributed system to improve fault tolerance, scalability, and availability.
System administrators often face the task of setting up and managing replicas, especially in scalable systems such as Cassandra, Kafka, or Redis.
Replication Models
1. Synchronous Replication
⏺Data is confirmed only after being written to all nodes.
⏺Pros: Strong consistency.
⏺Cons: High latency, availability issues during failures.
⏺Example: Financial systems where data accuracy is important.
2. Asynchronous Replication
⏺Data is written to one node and then synchronized with the others.
⏺Pros: High availability, fast query processing.
⏺Cons: Temporary data inconsistencies are possible.
⏺Example: geographically distributed systems, CDN.
Setting up replication in MongoDB
1️⃣ Create a replica set:
rs.initiate(
{
_id: "myReplicaSet",
members: [
{ _id: 0, host: "node1:27017" },
{ _id: 1, host: "node2:27017" },
{ _id: 2, host: "node3:27017" }
]
}
)
2️⃣ Check the replication status:
rs.status()
Setting up consistency in Kafka
1️⃣ Set the minimum number of replicas that must acknowledge a write:
min.insync.replicas=2
2️⃣ Ensure consistency for writes:
acks=all | 0 |
| 14 | p = gcd(x + 1, N)
q = gcd(x - 1, N)
if p * q != N:
return ShorsResult(
factors=(1, N),
quantum_circuit=circuit,
measurement_results=counts,
period=period,
success=False,
error_message="Invalid factorization"
)
return ShorsResult(
factors=(p, q),
quantum_circuit=circuit,
measurement_results=counts,
period=period,
success=True
)
# Example usage
def main():
# Example factorization
N = 15
shor = ImprovedShorsAlgorithm(shots=1000)
try:
result = shor.factor(N)
if result.success:
logger.info(f"Successfully factored {N} = {result.factors[0]} × {result.factors[1]}")
logger.info(f"Found period: {result.period}")
logger.info(f"Circuit depth: {result.quantum_circuit.depth()}")
logger.info(f"Number of qubits: {result.quantum_circuit.num_qubits}")
else:
logger.error(f"Factorization failed: {result.error_message}")
except Exception as e:
logger.error(f"An error occurred: {str(e)}")
if __name__ == "__main__":
main() | 0 |
| 15 | """
Process measurement results to find the period
Args:
counts: Measurement results
N: Number to factor
Returns:
Tuple[int, bool]: (period, success flag)
"""
max_result = max(counts, key=counts.get)
measured_phase = int(max_result, 2) / (2 ** len(max_result))
# Use continued fractions to find r
fraction = self._continued_fraction_expansion(measured_phase)
candidates = self._find_closest_fractions(fraction, N)
for denominator in candidates:
if denominator % 2 == 0:
return denominator, True
return 0, False
def _continued_fraction_expansion(self, x: float, depth: int = 10) -> List[int]:
"""
Compute continued fraction expansion
Args:
x: Input float
depth: Maximum depth of expansion
Returns:
List[int]: Continued fraction coefficients
"""
fractions = []
for _ in range(depth):
integer_part = int(x)
fractions.append(integer_part)
fractional_part = x - integer_part
if abs(fractional_part) < 1e-10:
break
x = 1 / fractional_part
return fractions
def _find_closest_fractions(self, fractions: List[int], N: int) -> List[int]:
"""
Find closest fractions from continued fraction expansion
Args:
fractions: Continued fraction coefficients
N: Number to factor
Returns:
List[int]: List of candidate denominators
"""
convergents = [(1, 0), (fractions[0], 1)]
candidates = []
for i in range(1, len(fractions)):
num = fractions[i] * convergents[-1][0] + convergents[-2][0]
den = fractions[i] * convergents[-1][1] + convergents[-2][1]
if den < N:
convergents.append((num, den))
candidates.append(den)
else:
break
return candidates
def factor(self, N: int) -> ShorsResult:
"""
Main method to factor a number using Shor's algorithm
Args:
N: Number to factor
Returns:
ShorsResult: Result object containing factors and metadata
"""
try:
self._validate_input(N)
except ValueError as e:
return ShorsResult(
factors=(1, N),
quantum_circuit=None,
measurement_results={},
period=0,
success=False,
error_message=str(e)
)
# Choose random number coprime to N
a = 2 # Start with smallest value for simplicity
while gcd(a, N) != 1:
a += 1
# Create and execute quantum circuit
circuit = self._quantum_phase_estimation(a, N)
transpiled_circuit = transpile(circuit, self.simulator,
optimization_level=self.optimize_level)
job = self.simulator.run(transpiled_circuit, shots=self.shots)
counts = job.result().get_counts()
# Find period from measurements
period, success = self._process_measurement(counts, N)
if not success:
return ShorsResult(
factors=(1, N),
quantum_circuit=circuit,
measurement_results=counts,
period=period,
success=False,
error_message="Failed to find valid period"
)
# Classical post-processing
x = pow(a, period // 2, N)
if x == 1 or x == N - 1:
return self.factor(N) # Try again | 0 |
| 16 | Implementation of Shor's Quantum Algorithm with Qiskit ⚙
import numpy as np
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, transpile, Aer, assemble
from qiskit.visualization import plot_histogram
from qiskit.circuit.library import QFT
from qiskit.providers.aer import AerSimulator
from typing import Tuple, List, Optional
import logging
from math import gcd, ceil, log2
from dataclasses import dataclass
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class ShorsResult:
"""Data class to store Shor's algorithm results"""
factors: Tuple[int, int]
quantum_circuit: QuantumCircuit
measurement_results: dict
period: int
success: bool
error_message: Optional[str] = None
class ImprovedShorsAlgorithm:
def __init__(self, shots: int = 1000, optimize_level: int = 3):
"""
Initialize Shor's Algorithm with configurable parameters
Args:
shots: Number of shots for quantum measurement
optimize_level: Optimization level for circuit transpilation
"""
self.shots = shots
self.optimize_level = optimize_level
self.simulator = AerSimulator()
def _validate_input(self, N: int) -> bool:
"""
Validate the input number N
Args:
N: Number to be factored
Returns:
bool: True if input is valid
"""
if N < 3:
raise ValueError("Number must be greater than 2")
if N % 2 == 0:
raise ValueError("Number must be odd")
if ceil(log2(N)) != floor(log2(N)):
return True
return False
def _create_controlled_unitary(self, a: int, N: int, n_count: int) -> QuantumCircuit:
"""
Create the controlled unitary operation for QPE
Args:
a: Random coprime number
N: Number to factor
n_count: Number of counting qubits
Returns:
QuantumCircuit: Controlled unitary circuit
"""
qr = QuantumRegister(n_count + ceil(log2(N)), 'q')
circuit = QuantumCircuit(qr)
# Implement modular exponentiation
for i in range(n_count):
power = pow(a, 2**i, N)
for j in range(ceil(log2(N))):
if (power >> j) & 1:
circuit.cx(qr[i], qr[n_count + j])
return circuit
def _quantum_phase_estimation(self, a: int, N: int) -> QuantumCircuit:
"""
Improved Quantum Phase Estimation implementation
Args:
a: Random coprime number
N: Number to factor
Returns:
QuantumCircuit: Complete QPE circuit
"""
n_count = 2 * ceil(log2(N)) # Increased precision
n_register = ceil(log2(N))
# Create registers
qr_count = QuantumRegister(n_count, 'count')
qr_register = QuantumRegister(n_register, 'register')
cr = ClassicalRegister(n_count, 'c')
circuit = QuantumCircuit(qr_count, qr_register, cr)
# Initialize register
circuit.x(qr_register[0]) # Initialize |1⟩ state
# Apply Hadamard gates
for i in range(n_count):
circuit.h(qr_count[i])
# Apply controlled unitary operations
controlled_unitary = self._create_controlled_unitary(a, N, n_count)
circuit.compose(controlled_unitary, inplace=True)
# Apply inverse QFT
inverse_qft = QFT(n_count).inverse()
circuit.compose(inverse_qft, qubits=range(n_count), inplace=True)
# Measure counting qubits
circuit.measure(qr_count, cr)
return circuit
def _process_measurement(self, counts: dict, N: int) -> Tuple[int, bool]: | 0 |
| 17 | Setting up VLAN using ip and bridge in Linux
VLAN (Virtual Local Area Network) is a way to divide a single physical network into multiple logically isolated networks.
It is useful for traffic management, improving security, and reducing broadcast load.
Example task
We have an eth0 interface connected to a switch that supports VLAN. We want to set up two VLANs:
• VLAN 10: with IP address 192.168.10.1/24.
• VLAN 20: with IP address 192.168.20.1/24.
1️⃣Installing the required packages
In modern distributions, the ip and bridge utilities are already included in the iproute2 package.
Check that it is installed:
sudo apt install iproute2 # For Debian/Ubuntu
sudo yum install iproute # For CentOS/RHEL
2️⃣ VLAN setup
Create VLAN interfaces
Using the ip link command, create virtual interfaces for VLAN 10 and VLAN 20:
sudo ip link add link eth0 name eth0.10 type vlan id 10
sudo ip link add link eth0 name eth0.20 type vlan id 20
Assign IP addresses to the VLAN interfaces
sudo ip addr add 192.168.10.1/24 dev eth0.10
sudo ip addr add 192.168.20.1/24 dev eth0.20
Enable the interfaces
sudo ip link set eth0.10 up
sudo ip link set eth0.20 up
3️⃣ Verify the configuration
Check that the interfaces are created and enabled:
ip -d link show
You should see interfaces eth0.10 and eth0.20 linked to eth0.
4️⃣ (Optional) Configuring VLAN bridges
If you want to connect multiple VLANs to a bridge (e.g. for virtualization):
Create a bridge:
sudo ip link add name br-vlan type bridge
Add a VLAN to the bridge:
sudo ip link set eth0.10 master br-vlan
sudo ip link set eth0.20 master br-vlan
Assign an IP address to the bridge:
sudo ip addr add 192.168.30.1/24 dev br-vlan
sudo ip link set br-vlan up
5️⃣ Delete a VLAN
To delete a VLAN interface:
sudo ip link delete eth0.10
sudo ip link delete eth0.20
Your server now supports two isolated VLANs. This is useful for separating traffic between services or users.
And use configuration files (such as in /etc/netplan or /etc/sysconfig/network-scripts) to make settings persistent across reboots. | 0 |
| 18 |  🔅 Tweaking Custom Environment Rewards - Reinforcement Learning with Stable Baselines 3 (P.4)
Helping our reinforcement learning algorithm to learn better by tweaking the environment rewards. | 0 |
| 19 |  🔅 Custom Environments - Reinforcement Learning with Stable Baselines 3 (P.3)
How to incorporate custom environments with stable baselines 3 | 0 |
| 20 |  🔅 Saving and Loading Models - Stable Baselines 3 Tutorial (P.2)
How to save and load models in Stable Baselines 3 | 0 |
