Double-check the exact name of the encoding, as to not misspell it: https://www.iana.org/assignments/character-sets/character-sets.xhtml The run $ unzip -O <encoding> <filename> -d <target_dir> or $ unzip -I <encoding> <filename> -d <target_dir> choosing between -O or -I according to instructions here: $ unzip -h UnZip 6.00 of 20 April 2009, by Debian. Original by Info-ZIP. ... -O CHARSET specify a character encoding for DOS, Windows and OS/2 archives -I CHARSET specify a character encoding for UNIX and other archives .

A bit background 🔗Lenovo did’t release the official driver and libfprint could not support it issue #296 . However, luckily, there is a nice GitHub repo that provides a viable solution. Let’s go 🔗Here I used Ubuntu, but other distros should be more or less the same while the package manager can be different, the naming of the deps might vary a little. Clone the project 🔗git clone https://github.com/Popax21/synaTudor.git Install dependencies 🔗# the tools if you don't already have $ sudo apt-get install -y meson ninja cmake # build deps $ sudo apt-get install -y libgusb-dev libcap-dev libseccomp-dev libfprint-2-tod-dev libjson-glib-dev innoextract # fprint $ sudo apt-get install -y fprintd libpam-fprintd Build and install 🔗cd synaTudor meson build cd build ninja sudo ninja install Voilà, you should have the fingerprint option enabled in your desktop environment at this moment, or you can use fprint to manage manually:

Concepts & Terms 🔗WAL 🔗Write-Ahead Log (WAL) is a record of the changes made to the data. It ensures that when there is a crash in the system or loss of connection, the database can be recovered. WAL Level 🔗WAL level determines how much information is written to the WAL, it corresponds to the wal_level option in the postgresql.conf file. The default value is replica, which writes enough data to support WAL archiving and replication, including running read-only queries on a standby server.

There are several ways of logging exception information in Python: import sys, logging l = logging.getLogger('test') l.addHandler(logging.StreamHandler(sys.stdout)) def foo(): try: raise KeyError("haha, an error") except Exception as e: l.error("%ssep %s", "hello", e, exc_info=True) print("--------") l.error("%ssep %s", "hello", e) print("--------") l.exception(e) print("--------") l.exception("", exc_info=e) print("--------") l.exception(e, exc_info=1) print("--------") print(str(e)) print("--------") print(repr(e)) The output would be: hello sep 'haha, an error' Traceback (most recent call last): File "<ipython-input-1-3354851c0b12>", line 7, in foo raise KeyError("haha, an error") KeyError: 'haha, an error' -------- hello sep 'haha, an error' -------- 'haha, an error' Traceback (most recent call last): File "<ipython-input-1-3354851c0b12>", line 7, in foo raise KeyError("haha, an error") KeyError: 'haha, an error' -------- Traceback (most recent call last): File "<ipython-input-1-3354851c0b12>", line 7, in foo raise KeyError("haha, an error") KeyError: 'haha, an error' -------- 'haha, an error' Traceback (most recent call last): File "<ipython-input-1-3354851c0b12>", line 7, in foo raise KeyError("haha, an error") KeyError: 'haha, an error' -------- 'haha, an error' -------- KeyError('haha, an error') logger.

stemming and lemmatization 🔗 lemmatization: like stemming but gives human-understandable word bag-of-words 🔗 purpose: ML accept only vectors of same length after stemming/lemmatization ignoring case etc, build vectors for each sentence, of which each variable corresponds to the score of a word in the vacabulary words hashing: use a hash function to map words to a hash space(vector) TF(Term Frequency)-IDF 🔗 TF = repetition of a word in a document / number of words in a document IDF = log(number of all document / number of document containing the word) The final result is a table where for each word, its score is given by the product of the score in IF and the socre in IDF Word2Vec 🔗 word embedding represent a word by a vector of features uses a neural network model to learn word associations from a large corpus of text and produce word embeddings

Like 2 years ago I firstly learned about the python3 MRO(method resolution order), I though I pretty much got the hang of it, until I met this: class X:pass class Y: pass class Z:pass class A(X,Y):pass class B(Y,Z):pass class M(B,A,Z):pass Apprently the search order should be M B Y Z A X Y Z thus the MRO is supposed to be M B Y Z A X

Typology of three types of problem 🔗1. combinatoire 🔗definition trouver dans un ensemble X donne les elements x satisfaisant un ensemble de contraintes donnees K(x) 2. changement 🔗3. décompositon en sou-problèmes 🔗General problem solver 🔗The idea is to finda a way to reduce the difference between the initial state and the desired state. transformation Build a list of differences between object A and object B Reduction

Classification de Flynn 🔗 SISD mono-core PC SIMD some multi-dimensional data, image/matrix etc. MISD not exist MIMD mulitple cores, paralle calculation shared memory distributed memory Matrice to measure perfomance 🔗Definition 🔗T1: The time for one core sequential calculation. Tn: The time for parallel calculation. N signifies the number of processors. speed_up = T1/Tn O: The overhead, the cost of communication between processors, synchronization, etc.. Fixed-sized model (Loi d’Amdahl) 🔗The proportion f part is fixed, which means it is independent from n.