Status published Summary:

Neural networks have become a standard for classifying images. However, by their very nature, their internal data representation remains opaque. To solve this dilemma, attention mechanisms have recently been introduced. They help to highlight regions in input data that have been used for a network’s classification decision. This article presents two attention architectures for the classification of medical images. Firstly, we are explaining a simple architecture which creates one attention map that is used for all classes. Secondly, we introduce an architecture that creates an attention map for each class. This is done by creating two U-nets - one for attention and one for classification - and then multiplying these two maps together. We show that our architectures well meet the baseline of standard convolutional classifications while at the same time increasing their explainability.

BibTex | DOI: 10.1007/978-3-030-39343-4_33

I am running my own mail server for a while now. Since the beginning I was thinking about how to store the mails encrypted, so that no one can read the mails with access to the server. The solution I came up with is relative easy to setup and is based upon OpenPGP/GnuPGP.

The basic idea is to take incoming mail before it is stored and encrypt it. I'm running postfix, which has the option to filter queued mails with external content filters. A content filter gets a mail via stdin, does whatever it needs to do and either rejects a mail or put it back into the mail queue.

I wrote a relativ simple Python script that takes a mail from stdin, processes it and then writes it back to stdout. The script can either decrypt, encrypt, sign or sign and encrypt a mail. It also tries to protect the mail headers following the memoryhole specs and supports Thunderbirds/Enigmails encrypted subject feature. The drawback is that Enigmail only supports the encrypted header from the memoryhole specs and other mail clients don't support them at all. For the content_filter in postfix I wrote a Bash script, that will resend the encrypted mail to put it back into the mail queue. The scripts can be found on GitHub.

Setup

1. Install gpgmail

2. Add a new user:

   adduser --shell /bin/false --home /home/gpgmail --disabled-password --disabled-login --gecos "" gpgmail
   

3. Create .gnupg folder and change permissions:

   mkdir /home/gpgmail/.gnupg
   chown gpgmail:gpgmail /home/gpgmail/.gnupg/
   chmod 700 /home/gpgmail/.gnupg/
   

4. If mails should not just get encrypted but also signed, create a new key pair:

   sudo -u gpgmail /usr/bin/gpg --homedir=/home/gpgmail/.gnupg --expert --full-gen-key
   

5. Import public keys and chnage trust:

   sudo -u gpgmail /usr/bin/gpg --homedir=/home/gpgmail/.gnupg --import /home/gpgmail/pubkey.asc
   sudo -u gpgmail /usr/bin/gpg --homedir=/home/gpgmail/.gnupg --edit-key <KEY> trust save
   sudo -u gpgmail /usr/bin/gpg --homedir=/home/gpgmail/.gnupg --edit-key <KEY> trust quit
   

6. Edit /etc/postfix/master.cf

   smtp          inet  n       -       y       -       -       smtpd -o content_filter=gpgmail-pipe
   smtps         inet  n       -       y       -       -       smtpd -o content_filter=gpgmail-pipe
   submission    inet  n       -       y       -       -       smtpd -o content_filter=gpgmail-pipe
   gpgmail-pipe  unix  -       n       n       -       -       pipe
     flags=Rq user=gpgmail argv=/usr/bin/gpgmail-postfix sign-encrypt gnupghome=/home/gpgmail/.gnupg key=<KEY_ID> passphrase=<PASSPHRASE> encrypt-subject -oi -f ${sender} ${recipient}
   

7. Restart postfix.

Sources

• https://www.grepular.com/Automatically_Encrypting_all_Incoming_Email
• http://kacangbawang.com/encrypting-stored-email-with-postfix/
• https://blog.jak-linux.org/2019/06/13/encrypted-email-storage/

We evaluate different densely connected fully convolutional neural network architectures to find and extract text from maps. This is a necessary preprocessing step before OCR can be performed. In order to locate the text, we train a neural network to classify whether a given input is text or not. Our main focus is on the output level, either classifying text or no text for the whole input or predicting the text position pixel wise by outputting a mask. Acquiring enough training data especially for pixel wise prediction is quite a time consuming task, so we investigate a method to generate artificial training data. We compare three training scenarios. First training with images from historical maps, which is quite a small dataset, second adding artificially generated images and third training just with the artificially generated data.

Full Abstract | Poster

Zum Beispiel könnte man alle nationalen Pässe durch einen Europäischen Pass ersetzen. Ein Pass der Europäischen Union, in dem der Geburtsort vermerkt ist, aber nicht die Nationalität. Ich glaube, dass allein dies etwas im Bewusstsein der Generation bewirken würde, die mit einem solchen Pass aufwächst. Und das würde nicht einmal etwas kosten. […] Aber das ist nicht genug, setzte er fort.

Die Hauptstadt von Robert Menasse, Seite 392

Im Rahmen unterschiedlicher Projekte und Qualifizierungsarbeiten entstanden und entstehen eine Reihe neuronaler Netze. Diese Netze dienen entweder der Klassifizierung von Bildern oder dem Auffinden von Objekten in Bildern. Um diese neuronalen Netze besser miteinander hinsichtlich unterschiedlicher Architekturen, unterschiedlicher Hyperparameter oder unterschiedlicher Trainingsdaten zu vergleichen und sie einem breiteren Personenkreis verfügbar zu machen, wurde eine Webseite entwickelt, auf der man trainierte neuronale Netze und Bilder hochladen, eine Objekterkennung starten und die Ergebnisse einheitlich visualisieren kann. Die Webseite soll sowohl für Entwickler neuronaler Netze sein, die Ihre Netze vergleichen wollen, als auch für Forscher, die auf ihren Bildern eine Objekterkennung durchführen wollen.

Poster https://fdhl.info/dhdl-2018-materialien/