Autarchy of the Private Cave

If you try starting Outlook 2010 and get an error like this:
“Outlook 2010 cannot open your default e-mail folders. An unexpected error has occurred. MAPI was unable to load the information service gwmsp1.dll”

you can easily fix this problem by going to Control Panel, clicking on Mail, then Show Profiles button.
Remove everything that’s there. Now start outlook again.

Note: removing all the mail profiles will disable your Novell Groupwise client.
If you still want to use non-Outlook email profiles, then the better solution is to manually create a new mail profile for Outlook.

Source: CNET forums.

All sane people agree that spam is a blight of the internet, be it email spam or comments spam or forum spam or any other form of unsolicited, blatant, shameless, out-of-context advertising. Multiple spam-fighting and spam-stopping systems are being developed.

With automated spam, automated spam-fighting systems might be the only choice. Sending rightfully angry emails to ISPs to notify about their customers violating service agreements is probably a waste of effort (something tells me most of these complaints end up in the trash folder, or even in the… spam folder). However, I get a feeling that some spam is not automated – it appears to have been actually prepared and sent by a human. (Alternatively, spammers behind those spams simply have better software.) Anyway, some spams seem to contain valid contact data of the advertized entity – like an email.

The resulting idea is very simple and was probably already implemented somewhere by someone: simply publish online contact emails of the entities which, apparently, had chosen spam as the primary means of advertising. These emails will be sooner or later harvested by spammers, added to spam databases, and will start getting progressively more spam.

There are a few drawbacks to this approach:

• knowing spam-collection points enables “black PR”-like mass-mailings in the name of one’s competitor, double-hurting the innocents; I do not see a clear method of preventing this, other than by concealing spam collection methods;
• human intelligence is required to identify if the contained email truly belongs to the advertised entity; this is fairly time-consuming, especially when scaled up; a possible solution (with its own problems) would be to build an online gateway for submitting curated spam samples, thus distributing the workload to all the participating volunteers;
• the next logical step is actually harvesting and then publishing all the emails from the advertised website;
• the biggest drawback, however, is low efficiency of this approach; increasing spam percentage will only be a mild nuisance, which isn’t likely to propagate high enough to affect spam-deciders; also, indirectly spamming someone’s mailbox will result in the loss of time, which could have been otherwise used for facebook and other important activities

What do you think? Should such a method be used?

Below I provide a few sample records from real spam comments, which had true-looking emails. I’m including some extra meta-data. Ideally, this should be stored in some kind of a database.

Submitted on 2013/11/13 at 15:23 GMT
Author : Виктор (IP: 95.134.110.37 , 37-110-134-95.pool.ukrtel.net)
E-mail : aionind@yandex.ru
E-mail : sale@aion-industry.ru
E-mail : info@aion-industry.ru
Submitted on 2013/11/26 at 8:53 GMT
Author : Виктор (IP: 95.134.146.235 , 235-146-134-95.pool.ukrtel.net)
E-mail : kvazargr@yandex.ru
E-mail : info@kvazar-gr.ru
Submitted on 2013/11/28 at 7:24 GMT
Author : Виктор (IP: 95.134.117.155 , 155-117-134-95.pool.ukrtel.net)
E-mail : relevater@yandex.ru
E-mail : info@relevate.ru
E-mail : support@relevate.ru
E-mail : billing@relevate.ru

There’s definitely a need for a public database, API keys, and quorum algorithms…

Author : casinoworka (IP: 91.207.4.201 , 201.4.207.91.unknown.SteepHost.Net)
E-mail : pharmacywork7777777@gmail.com
E-mail : info@prowessmedical.com

Right now, when I see that I have to often repeat/retype some sets and sequences of commands, I’m trying to wrap them up into some kind of a script, every time choosing the most appropriate language – shell when I need to start lots of existing command-line tools, Python when there’s some data handling and processing involved, and R when I’m invoking commands from R packages. So far I have been avoiding the fairly popular makefile-based approach to automating pipelines and workflows which rely heavily on existing tools. However, being curious, I’ve compiled a short list of modern make-like alternatives, to possibly explore… sometime later…

• First comes make itself – the oldest and the most widely used software build tool. Stable and powerful. Still, even people who got used to using make, have some gripes about it. The most detailed list of gripes is probably here.
• SCons is a build tool written in Python. I guess I like that “configuration files are Python scripts” – maybe knowing Python is enough to use SCons, which makes SCons a better choice than make for me. SCons seems to have gained some support (scroll down for comments/discussion). There were some doubts about SCons performance (1, 2, and 3); not sure where SCons is at right now in that regard.
• waf, a Python-based framework for configuring, compiling and installing applications.
• pyDoIt is a Python automation tool. It seems to use Python syntax. It aims at bringing the power of build-tools to execute any kind of task, where a task describes some computation to be done (actions), and contains some extra meta-data. Based on the description alone, I’m quite intrigued! I wonder if anyone had already worked with pyDoIt and can share experiences?…
• Rake – Ruby make – is a simple build program with capabilities similar to those of make. Had seen a lot of positive feedback about this one – mostly regarding simplicity of use. Still [py]DoIt so far looks more attractive to me personally.
• Ruffus is a lightweight python module for running computational pipelines. Sounds like some good competition to [py]DoIt!
• Anduril is an open source component-based workflow framework for scientific data analysis. Sounds promising, though the latest downloadable version is over 400 MBs… It probably already contains a bunch of binaries and maybe even data and complete workflows for data analysis. Probably worth a look, but may turn out a little overweight for simple pipelining.
• snakemake is a scalable bioinformatics workflow engine. I get the feeling that Python is truly dominating the pipelines/workflows world: snakemake, as even the name suggests, is in Python, too. The front-page example is so simple and clear, that snakemake immediately pushes DoIt down from the 1st place! Awesome.
• Paver is a yet-another Python-based software project scripting tool along the lines of Make or Rake, designed to help out with repetitive tasks with the convenience of Python’s syntax. Sounds similar to DoIt. Have no idea how they actually compare to each other.

That is it for now.

What were your experiences with automating repetitive tasks and building simple pipelines?

The usual, or even classical way is to create the list of installed packages with sudo dpkg --get-selections > package_list, and then restore when/if necessary with cat package_list | xargs sudo apt-get -y install.

As VihangD points out in his serverfault answer, the same can be achieved with aptitude, while also excluding dependent, automatically installed packages (which are included by the classical method). To create the list of packages, run aptitude search -F '%p' '~i!~M' > package_list. Here, -F '%p' asks aptitude to only print package names (instead of the default output, which also contains package state and description); search term ‘~i!~M’ asks for all non-automatically installed packages.

To install packages using the created list, run xargs aptitude --schedule-only install < package_list; aptitude install. The first of these two commands instructs aptitude to mark all the packages from the list as scheduled for installation. The second command actually performs the installation.

Hamish Downer suggests an alternative way of getting the initial package_list: using the deborphan utility, deborphan -a --no-show-section > package_list. This command asks deborphan to show a list of packages, which have no dependencies on them. Sounds very similar to what we did with aptitude above, but using deborphan will most likely result in a much shorter list of packages (on my system, deborphan printed 291 package names, aptitude printed 847, and dpkg printed 3650 package names). One more potentially important difference between aptitude- and deborphan-produced package lists is that aptitude only specifies package architecture when it is different from native (e.g. 'googleearth:i386' on a 64-bit system), while deborphan specifies architectures for all the packages (resulting in e.g. 'google-talkplugin:amd64' and 'googleearth-package:all' on a 64-bit system).

I’ve tried [briefly] Cantor (which also supports Octave and KAlgebra as backends), rkward, deducer/JGR, R Commander, and RStudio.

My personal choice was RStudio: it is good-looking, intuitive, easy-to-use, while powerful.

Next step would be using some R-equivalent of the excellent ipython’s Mathematica-like Notebook webinterface…

In one of the previous posts I’ve mentioned that BitBucket is über-cool

Redmine is also really cool, and is actually more feature-reach than what BitBucket has to offer, but maintaining it needs just a tiny bit more time and attention than I’m willing to spend these days. So, migration it is!

Redmine has issue 3647 titled “Data import/export system”; it is not resolved, but has a number of links to other resources. Like the redmine exporter at hostedredmine.com, which provides free hosted redmine service. Redmine itself has REST API, though I have no idea if it allows exporting all the data I may need. There’s also an XLS export plugin, but it has to be installed first, and I’m too lazy There’s also TaskAdapter, but they do not support BitBucket (yet?).

For the complete backup, I think of using the pure-ruby redmine project data export script. To migrate issues only, I’ll consider the redmine2bitbucket script.

P.S. Not implying anything (yet?), but my previous migration was from Trac to Redmine… At that time, Trac seemed to have less features than I wanted. And now I’m migrating back to “less features”, but with a benefit of no support required from me.

Note: this is a draft post back from 2010. As it is still useful to me, I’ve decided to publish it as is.

I had already mused on the powers of rsync before.

This time, a reminder to self on how to resume copying broken scp/mc/fish transfers, using rsync.

First, an assortment of example commands.
export RSYNC_RSH=ssh
rsync --partial file_to_transfer user@remotehost:/path/remote_file
rsync -av --partial --progress --inplace SRC DST
rsync --partial --progress --rsh=ssh host:/work/source.tar.bz2 .
rsync --partial --progress --rsh=ssh -r me@host.com:/datafiles/ ./

One could also try the --append option of rsync to base the transfer resumption on the sizes of the two files rather than verifying that their contents match.

Now a single command line explained in a little more details:
rsync -vrPtz -e ssh host:/remote_path/* /local_path/
Explained:
-e ssh rsync will use ssh client instead of rsh, which makes data exchange encrypted
-z compress file transfer
-t preserve time (other attributes, such as owner and permissions are also possible)
-P resume incomplete file transfer
-r recurse into subdirectories
-v verbose

To specify a port when using ssh you must add it to the ssh command.
Example: rsync --partial --progress --rsh="ssh -p 16703" user@host:path