Computer Tips - Programming, Cybersecurity and Tech Tutorials

HELR is a software project for performing a logistic regression training on encrypted data (Secure Logistic Regression based on Homomorphic Encryption: Design and Evaluation ( https://medinform.jmir.org/2018/2/e19/ )). Step 1: brew install GMP Step 2: brew install NTL Step 3: git clone  https://github.com/K-miran/HELR.git Step 4: cd HELR

Step 1/3: If you have opened a PDF from Google Chrome, go to the Print dialogue box and click on the link "Print using system dialog.." Step 2/3: Click on the dropdown menu that says "Media & Quality" and change it to "Finishing Options" Step 3/3: Click on Staple dropdown options and select the number of staples and the orientation where you want the pages to be stapled.

I have been trying to import the basemap module in my python script using " from mpl_toolkits.basemap import Basemap " but with no luck! I ran into issues while installing basemap with pip. Excerpt from the error:    Installing build dependencies ... error    error :  subprocess-exited-with-error       ×   pip subprocess to install build dependencies  did not run successfully.    │  exit code:  1    ╰─>   [552 lines of output]         Ignoring numpy: markers 'python_version >= "3.10"' don't match your environment         Ignoring numpy: markers 'python_version == "2.6" or (python_version >= "3.2" and python_version <= "3.3")' don't match your environment         Ignoring cython: markers 'python_version == "3.2"' don't match your environment ... ...             note: This error ...

 This is a sample program to run bidirectional LSTM in Python using Tensorflow. The complete code repository is available at  https://github.com/devharsh/LSTM-Demo . Code: #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jun 1 13:34:00 2022. @author: devharsh """ import matplotlib.pyplot as plt from pandas import read_csv from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder from tensorflow import keras from tensorflow.keras import layers inputs = keras.Input(shape=(None,), dtype="float64") xem = layers.Embedding(101, 128)(inputs) xl1 = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(xem) xl2 = layers.Bidirectional(layers.LSTM(64))(xl1) outputs = layers.Dense(1, activation="sigmoid")(xl2) model = keras.Model(inputs, outputs) model.summary()