この記事はopenSUSE Advent Calendar 2019の13日目です。

今回は、openSUSEでパッケージをインストールする３つのメジャーな方法を紹介させて頂きます。

• YaSTの「ソフトウエア管理」からインストール
• コンソールからzypperコマンドでインストール
• Webサイトから1-clickインストール

YaSTの「ソフトウエア管理」からインストール

簡単設定ツールYaSTの「ソフトウェア管理」で、パッケージをインストールしたりアップグレードしたりできます。YaSTは、左下のアプリケーションランチャーから、「アプリケーション > 設定 > 管理者設定（YaST）」で起動できます。

起動したら、「ソフトウエア管理」を選びましょう。リポジトリ情報などを取得した後、管理画面が開きます。

左上の場所にパッケージ名を入力して、「検索」をクリックすれば、右側に一覧が表示されます。インストールしたいパッケージを選択して、右下の「了解」をクリックしましょう。

コンソールからzypperコマンドでインストール

コマンド「zypper 」で、パッケージ管理ができます。パッケージを検索したい時は以下のコマンドです。

$ zypper se [検索したいパッケージ名]

seはsearchのseです。searchとフルでタイプしても大丈夫です。該当するパッケージがあれば、一覧が表示されます。

リポジトリのデータを読み込んでいます…
インストール済みのパッケージを読み込んでいます…

S  | 名前                   | 概要                                                                  | 種類             
—+————————+———————————————————————–+—————–
  | podman                 | Daemon-less container engine for managing containers, pods and images | ソースパッケージ
i+ | podman                 | Daemon-less container engine for managing containers, pods and images | パッケージ       
i  | podman-cni-config      | Basic CNI configuration for podman                                    | パッケージ       
  | python-podman          | A library to interact with a Podman server                            | ソースパッケージ
  | python-podman-compose  | A script to run docker-compose using podman                           | ソースパッケージ
  | python2-podman         | A library to interact with a Podman server                            | パッケージ       
  | python3-podman         | A library to interact with a Podman server                            | パッケージ       
  | python3-podman-compose | A script to run docker-compose using podman                           | パッケージ  

上記は「podman」で検索してみた例です。（折り返してしまっているので見にくくてすみません。）

ちなみに、左端の「i」がインストールされているという意味で、「i+」は、ユーザーが選んでインストールしたパッケージ、という意味です。

目当てのパッケージが見つかったら、インストールは以下のコマンドです。

$ zypper in [インストールしたいパッケージ]

inはinstallの略です。フルでinstallとタイプしても大丈夫です。

そうすると、同時にインストールすることが推奨されるパッケージや利用予定ディスク容量などが表示され、インストールするかを聞かれますので、Y（Yes）で答えればインストールです。なお、root権限が必要ですので、一般ユーザーの場合はsudoで実行するなどしましょう。

Webサイトから1-clickインストール

openSUSEにはソフトウェアサイトがあり、コミュニティ作成のパッケージや、実験的なパッケージをインストールすることもできます。（もちろん、自己責任となりますのでご注意ください。）

左側でディストリビューションを選べますので、希望のものを選びます。今ですと、Leap15.1になるでしょう。それからパッケージ名を検索します。そうすると、各パッケージのページに行くことができます。

ここから「直接インストール」を選択します。

あるいは、下部に利用可能な各ディストリビューションのコミュニティ作成パッケージや実験的パッケージが表示されますので、希望のものをえらび「1-クリックインストール」をクリックします。

追加のリポジトリが必要な場合は、それを設定するか確認されます。「ちゃんと、変更内容わかってる？」と、かなり念を押してくれます。

まとめ

どの方法でも、zypperで操作できるパッケージ管理の配下に入っていきます。ですので、WebサイトからインストールしたものをYaSTで確認、などもできます。気になるパッケージもこれで手に入りますね。なお、Webサイトからですと実験的なものなども見れますので、他の方法で見つからない場合は覗いてみるのもアリだと思います。（くどいようですが、自己責任ですので・・）

• リポジトリとパッケージの関係をとりまとめてブログに書きたい
• libzyppとzypperとYaSTの関係図を書けるようになりたい
• なんなら、zypperのソースを読んでみたい

As in the past 3 years, I joined the r-b summit where many people interested in reproducible builds met.

There were several participants from companies, including Microsoft, Huawei and Google.
Also some researchers from universities that work on tools like DetTrace, tuf and in-toto.
But the majority still came from various open-source projects – with Fedora/RedHat being notably absent.

We had many good discussion rounds, one of which spawned my writeup on the goal of reproducible builds

Another session was about our wish to design a nice interface, where people can easily find the reproducibility status of a package in various distributions. I might code a Proof-of-Concept of that in the next weeks (when I have time).
I also got some help with java patches in openSUSE and made several nice upstream reproducibility fixes – showing some others how easy that can be.

This whole event also was good team-building, getting to know each other better. This will allow us to better collaborate in the Future.

Later there will be a larger report compiled by others.

Communiqué de Presse Quand des ‘‘White Hats’’ (NUI) multiplient les solutions de déploiement de LA solution de Cybersécurité sur des équipements déjà en la possession des utilisateurs de la Free Delta (et des autres FAI, si les VM sont généralisés sur leur box respectifs par ces derniers). NUI combine des agents d’avant-garde en Cybersécurité et […]

The post #Rouen #Cybersécurité Communiqué de Presse du 4 Décembre #lujam #FOSS appeared first on Network Users Institute - Cybersécurité, Intégration de Linux & Logiciels Libres à Rouen, Normandie..

In this post, I’m going to share the results of some benchmark testing I have done on multi_type_vector, which is included in the mdds library. The benchmark was done to measure the impact of the change I made recently to improve the performance on block searches, which will affect a major part of its functionality.

Background

One of the data structures included in mdds, called multi_type_vector, stores values of different types in a single logical vector. LibreOffice Calc is one primary user of this. Calc uses this structure as its cell value store, and each instance of this value store represents a single column instance.

Internally, multi_type_vector creates multiple element blocks which are in turn stored in its parent array (primary array) as block structures. This primary array maps a logical position of a value to the actual block structure that stores it. Up to version 1.5.0, this mapping process involved a linear search that always starts from the first block of the primary array. This was because each block structure, though it stores the size of the element block, does not store its logical position. So the only way to find the right element block that intersects the logical position of a value is to scan from the first block and keep accumulating the sizes of the encountered blocks. The following diagram depicts the structure of multi_type_vector’s internal store as of 1.5.0:

The reason for not storing the logical positions of the blocks was to avoid having to update them after shifting the blocks after value insertion, which is quite common when editing spreadsheet documents.

Of course, sometimes one has to perform repeated searches to access a number of element values across a number of element blocks, in which case, always starting the search from the first block, or block 0, in every single search can be prohibitively expensive, especially when the vector is heavily fragmented.

To alleviate this, multi_type_vector provides the concept of position hints, which allows the caller to start the search from block N where N > 0. Most of multi_type_vector’s methods return a position hint which can be used for the next search operation. A position hint object stores the last position of the block that was either accessed or modified by the call. This allows the caller to chain all necessary search operations in such a way to scan the primary array no more than once for the entire sequence of search operations. It was largely inspired by std::map’s insert method which provides a very similar mechanism. The only prerequisite is that access to the elements occur in perfect ascending order. For the most part, this approach worked quite well.

The downside of this is that there are times you need to access multiple element positions and you cannot always arrange your access pattern to take advantage of the position hints. This is the case especially during multi-threaded formula cell execution routine, which Calc introduced some versions ago. This has motivated us to switch to an alternative lookup algorithm, and binary search was the obvious replacement.

Binary search

Binary search is an algorithm well suited to find a target value in an array where the values are stored in sorted order. Compared to linear search, binary search performs much faster except for very small arrays. People often confuse this with binary search tree, but binary search as an algorithm does not limit its applicability to just tree structure; it can be used on arrays as well, as long as the stored values are sorted.

While it’s not very hard to implement binary search manually, the C++ standard library already provides several binary search implementations such as std::lower_bound and std::upper_bound.

Switch from linear search to binary search

The challenge for switching from linear search to binary search was to refactor multi_type_vector’s implementation to store the logical positions of the element blocks and update them real-time, as the vector gets modified. The good news is that, as of this writing, all necessary changes have been done, and the current master branch fully implements binary-search-based block position lookup in all of its operations.

Benchmarks

To get a better idea on how this change will affect the performance profile of multi_type_vector, I ran some benchmarks, using both mdds version 1.5.0 – the latest stable release that still uses linear search, and mdds version 1.5.99 – the current development branch which will eventually become the stable 1.6.0 release. The benchmark tested the following three scenarios:

1. set() that modifies the block layout of the primary array. This test sets a new value to an empty vector at positions that monotonically increase by 2, until it reaches the end of the vector.
2. set() that updates the value of the last logical element of the vector. The update happens without modifying the block layout of the primary array. Like the first test, this one also measures the performance of the block position lookup, but since the block count does not change, it is expected that the block position lookup comprises the bulk of its operation.
3. insert() that inserts a new element block at the logical mid-point of the vector and shifts all the elements that occur below the point of insertion. The primary array of the vector is made to be already heavily fragmented prior to the insertion. This test involves both block position lookup as well as shifting of the element blocks. Since the new multi_type_vector implementation will update the positions of element blocks whose logical positions have changed, this test is designed to measure the cost of this extra operation that was previously not performed as in 1.5.0.

In each of these scenarios, the code executed the target method N number of times where N was specified to be 10,000, 50,000, or 100,000. Each test was run twice, once with position hints and once without them. Each individual run was then repeated five times and the average duration was computed. In this post, I will only include the results for N = 100,000 in the interest of space.

All binaries used in this benchmark were built with a release configuration i.e. on Linux, gcc with -O3 -DNDEBUG flags was used to build the binaries, and on Windows, MSVC (Visual Studio 2017) with /MD /O2 /Ob2 /DNDEBUG flags was used.

All of the source code used in this benchmark is available in the mdds perf-test repository hosted on GitLab.

The benchmarks were performed on machines running either Linux (Ubuntu LTS 1804) or Windows with a variety of CPU’s with varying number of native threads. The following table summarizes all test environments used in this benchmark:

It is very important to note that, because of the disparity in OS environments, compilers and compiler flags, one should NOT compare the absolute values of the timing data to draw any conclusions about CPU’s relative performance with each other.

Results

Scenario 1: set value at monotonically increasing positions

This scenario tests a set of operations that consists of first seeking the position of a block that intersects with the logical position, then setting a new value to that block which causes that block to split and a new value block inserted at the point of split. The test repeats this process 100,000 times, and in each iteration the block search distance progressively increases as the total number of blocks increases. In Calc’s context, scenarios like this are very common especially during file load.

Without further ado, here are the results:

You can easily see that the binary search (1.5.99) achieves nearly the same performance as the linear search with position hints in 1.5.0. Although not very visible in these figures due to the scale of the y-axes, position hints are still beneficial and do provide small but consistent timing reduction in 1.5.99.

Scenario 2: set at last position

The nature of what this scenario tests is very similar to that of the previous scenario, but the cost of the block position lookup is much more emphasized while the cost of the block creation is eliminated. Although the average durations in 1.5.0 without position hints are consistently higher than their equivalent values from the previous scenario across all environments, the overall trends do remain similar.

Scenario 3: insert and shift

This last scenario was included primarily to test the cost of updating the stored block positions after the blocks get shifted, as well as to quantify how much increase this overhead would cause relative to 1.5.0. In terms of Calc use case, this operation roughly corresponds with inserting new rows and shifting of existing non-empty rows downward after the insertion.

Without further ado, here are the results:

These results do indicate that, when compared to the average performance of 1.5.0 with position hints, the same operation can be 4 to 6 times more expensive in 1.5.99. Without position hints, the new implementation is more expensive to a much lesser degree. Since the scenario tested herein is largely bottlenecked by the block position updates, use of position hints seems to only provide marginal benefit.

Adding parallelism

Faced with this dilemma of increased overhead, I did some research to see if there is a way to reduce the overhead. The suspect code in question is in fact a very simple loop, and all its does is to add a constant value to a known number of blocks:

template
void multi_type_vector<_CellBlockFunc, _EventFunc>::adjust_block_positions(size_type block_index, size_type delta)
{
    size_type n = m_blocks.size();

    if (block_index >= n)
        return;

    for (; block_index < n; ++block_index)
        m_blocks[block_index].m_position += delta;
}

Since the individual block positions can be updated entirely independent of each other, I decided it would be worthwhile to experiment with the following two types of parallelization techniques. One is loop unrolling, the other is OpenMP. I found these two techniques attractive for this particular case, for they both require very minimal code change.

Adding support for OpenMP was rather easy, since all one has to do is to add a #pragma line immediately above the loop you intend to parallelize, and add an appropriate OpenMP flag to the compiler when building the code.

Adding support for loop unrolling took a little fiddling around, but eventually I was able to make the necessary change without breaking any existing unit test cases. After some quick experimentation, I settled with updating 8 elements per iteration.

After these changes were done, the above original code turned into this:

template
void multi_type_vector<_CellBlockFunc, _EventFunc>::adjust_block_positions(int64_t start_block_index, size_type delta)
{
    int64_t n = m_blocks.size();

    if (start_block_index >= n)
        return;

#ifdef MDDS_LOOP_UNROLLING
    // Ensure that the section length is divisible by 8.
    int64_t len = n - start_block_index;
    int64_t rem = len % 8;
    len -= rem;
    len += start_block_index;
    #pragma omp parallel for
    for (int64_t i = start_block_index; i < len; i += 8)
    {
        m_blocks[i].m_position += delta;
        m_blocks[i+1].m_position += delta;
        m_blocks[i+2].m_position += delta;
        m_blocks[i+3].m_position += delta;
        m_blocks[i+4].m_position += delta;
        m_blocks[i+5].m_position += delta;
        m_blocks[i+6].m_position += delta;
        m_blocks[i+7].m_position += delta;
    }

    rem += len;
    for (int64_t i = len; i < rem; ++i)
        m_blocks[i].m_position += delta;
#else
    #pragma omp parallel for
    for (int64_t i = start_block_index; i < n; ++i)
        m_blocks[i].m_position += delta;
#endif
}

I have made the loop-unrolling variant of this method a compile-time option and kept the original method intact to allow on-going comparison. The OpenMP part didn't need any special pre-processing since it can be turned on and off via compiler flag with no impact to the code itself. I needed to switch the loop counter from the original size_type (which is a typedef to size_t) to int64_t so that the code can be built with OpenMP enabled on Windows, using MSVC. Apparently the Microsoft Visual C++ compiler requires the loop counter to be a signed integer for the code to even build with OpenMP enabled.

With these changes in, I wrote a separate test code just to benchmark the insert-and-shift scenario with all permutations of loop-unrolling and OpenMP. The number of threads to use for OpenMP was not specified during the test, which would cause OpenMP to automatically use all available native threads.

With all of this out of the way, let's look at the results:

Here, LU and OMP stand for loop unrolling and OpenMP, respectively. The results from each machine consist of four groups each having two timing values, one with 1.5.0 and one with 1.5.99. Since 1.5.0 does not use neither loop unrolling nor OpenMP, its results show no variance between the groups, which is expected. The numbers for 1.5.99 are generally much higher than those of 1.5.0, but the use of OpenMP brings the numbers down considerably. Although how much OpenMP reduced the average duration varies from machine to machine, the number of available native threads likely plays some role. The reduction by OpenMP on Core i5 6300U (which comes with 4 native threads) is approximately 30%, the number on Ryzen 7 1700X (with 16 native threads) is about 70%, and the number on Core i7 4790 (with 8 native threads) is about 50%. The relationship between the native thread count and the rate of reduction somewhat follows a linear trend, though the numbers on Xeon E5-2697 v4, which comes with 32 native threads, deviate from this trend.

The effect of loop unrolling, on the other hand, is visible only to a much lesser degree; in all but two cases it has resulted in a reduction of 1 to 7 percent. The only exceptions are the Ryzen 7 without OpenMP which denoted an increase of nearly 16%, and the Xeon E5630 with OpenMP which denoted a slight increase of 0.1%.

The 16% increase with the Ryzen 7 environment may well be an outlier, since the other test in the same environment (with OpenMP enabled) did result in a reduction of 7% - the highest of all tested groups.

Interpreting the results

Hopefully the results presented in this post are interesting and provide insight into the nature of the change in multi_type_vector in the upcoming 1.6.0 release. But what does this all mean, especially in the context of LibreOffice Calc? These are my personal thoughts.

• From my own observation of having seen numerous bug reports and/or performance issues from various users of Calc, I can confidently say that the vast majority of cases involve reading and updating cell values without shifting of cells, either during file load, or during executions of features that involve massive amounts of cell I/O's. Since those cases are primarily bottlenecked by block position search, the new implementation will bring a massive win especially in places where use of position hints was not practical. That being said, the performance of block search will likely see no noticeable improvements even after switching to the new implementation when the code already uses position hints with the old implementation.
• While the increased overhead in block shifting, which is associated with insertion or deletion of rows in Calc, is a certainly a concern, it may not be a huge issue in day-to-day usage of Calc. It is worth pointing out that that what the benchmark measures is repeated insertions and shifting of highly fragmented blocks, which translates to repeated insertions or deletions of rows in Calc document where the column values consist of uniformly altering types. In normal Calc usage, it is more likely that the user would insert or delete rows as one discrete operation, rather than a series of thousands of repeated row insertions or deletions. I am highly optimistic that Calc can absorb this extra overhead without its users noticing.
• Even if Calc encounters a very unlikely situation where this increased overhead becomes visible at the UI level, enabling OpenMP, assuming that's practical, would help lessen the impact of this overhead. The benefit of OpenMP becomes more elevated as the number of native CPU threads becomes higher.

What's next?

I may invest some time looking into potential use of GPU offloading to see if that would further speed up the block position update operations. The benefit of loop unrolling was not as great as I had hoped, but this may be highly CPU and compiler dependent. I will likely continue to dig deeper into this and keep on experimenting.

Il y a eu 20 instantanés openSUSE Tumbleweed publiés au mois de novembre et les instantanés ont apporté un grand nombre de paquets mis à jour pour inclure KDE Applications 19.08.3, Frameworks 5.64.0 Plasma 5.17.2 et 5.17.3, Linux 5.3.9 et 5.3.11, Mozilla Firefox 70, libvirt 5.9.0 et plus. Java 12 OpenJDK a été supprimé de la distribution.

Un point important de l'actualité de Tumbleweed en novembre est que les 10 derniers instantanés du mois ont tous produit un niveau constant de note stable et les sept derniers avaient une note de 99 sur 100, selon le Tumbleweed snapshot reviewer.

À la fin du mois, deux instantanés avec des mises à niveau de version et un instantané (20191127) ont apporté quelques modifications mineures à Advanced Linux Sound Architecture (ALSA).

Le premier instantané Tumbleweed de décembre est arrivé avec l'instantané 20191202. Des mises à jour ont également été apportées à ALSA avec la mise à jour des versions 1.2.1.1 des plugins alsa, alsa-utils et asla, qui ont supprimé 25 bogues et corrigé des régressions pour l'analyseur UCM. GNOME avait plusieurs mises à jour de paquets pour gedit, [Evolution](https: //wiki.gnome .org / Apps / Evolution) et plus encore. La version 3.34.2 de gnome-software a corrigé un plantage de thread potentiel lors de l'utilisation de flatpak et avait un correctif amont pour fwupd. Une version mise à jour de ModemManager 1.12.0, un démon activé par DBus qui contrôle les appareils et les connexions haut débit mobiles, a apporté une grande quantité d'améliorations et de changements pour inclure l'ajout de la prise en charge de Mobile Station Based Assisted-GPS. mercurial 5.2 a apporté quelques modifications de compatibilité descendante et ajouté de nouvelles extensions de fonctionnalités avec sa version trimestrielle. La mise à jour de perl 5.30.1 a déclenché un problème enregistré sur le snapshot reviewer car la version la plus récente et le correctif fourni sont problématiques pour l'utilisation Perl intégrée. Plusieurs autres paquets ont été mis à jour dans l'instantané dont qemu 4.1.93, re2 20191101, xen et xorg-x11-server. Le seul changement de version majeur dans l'instantané était une mise à jour du multiplexeur de terminaux tmux 3.0a; la version majeure qui permet à ses utilisateurs de basculer facilement entre plusieurs programmes dans un seul terminal offre de nouvelles fonctionnalités telles que la prise en charge supplémentaire de la séquence d'échappement SD (défilement vers le bas) et des couleurs de soulignement.

Il y avait plusieurs paquets RubyGems mis à jour dans l'instantané 20191128, mais l'application de peinture numérique Krita avait le plus de correctifs. Krita 4.2.8.2 a supprimé le filtre d'exportation CSV qui ne fonctionnait pas depuis longtemps et a corrigé l'outil de recadrage qui perd un rapport constant lorsque les poignées touchent le bord du canevas. tuned, un démon pour la surveillance et le réglage adaptatif des périphériques système, a été mis à jour en version 2.20 et a corrigé un bogue qui utilise l'argument profile _dir auto-défini au lieu...

この記事はopenSUSE Advent Calendar 2019の11日目です。

さて、今回は豆知識です。

この間 Cross Distro Developers Campの秋の合宿に参加したのですが（レポートは17日に書きます）、この時に以下の豆知識を教わりました。

Noto Sans CJK JP

C(Chinese)、J(Japanese）、K(Korean)の全てが入っているフォントで、その中でJapaneseを優先的に表示するようになっているのだとか。

Noto Sans JP

JaPaneseのみのフォントが入っている。容量はもちろんCJKよりも小さい。

まとめ

もちろん、アルファベットは両方に入っています。openSUSEではデフォルトでNoto Sans JPが使われているようですね。

合宿では、フォントの形？をグリフと呼ぶ、とか、表示されるフォントの優先順位を決めているfontconfigというユーティリティがあるが、この使い方が難しい、だとか、色々とお話を聴かせて頂きました。また、どちらを使うかがディストリビューションによって違ったりするので、名前が一致しなくて、文書がきちんと表示できない、なんてこともあるとか。

このあたりのお話や情報も、まとめて、正しい知識にしたいと思っています。

なお、22日はftakeさんのフォントに関するお話です。（この記事にミス等あれば、ご指摘して頂けますでしょう）

明日は最近DNSの申し子になっているemaxserさんの記事です。どうぞお楽しみに！

You might know that Piwik was renamed into Matomo more than a year ago. While everything is still compatible and even the scripts and other (internal) data is still named piwik, the rename is affecting more and more areas. Upstream is working hard to finalize their rename - while trying not to break too much on the other side. But even the file names will be renamed in some future version.

Time - for us - to do some maintenance and start following upstream with the rename. Luckily, our famous distribution already has matomo packages in the main repository (which currently still miss Apparmor profiles, but hey: we can and will help here). So the main thing left (to do) is a database migration and the adjustments of all the small bits and bytes here and there, where we still use the old name.

While the database migration silently happened already, the other, "small" adjustments will take some time - especially as we need to find all the places that need to get adjusted and also need to identify the contact persons, who can do the final change. But we are on it - way before Matomo upstream will do the final switch. :-)

Vim is a powerful text editor, appreciated for a large number of plugins, syntax highlighter and the small memory requirements, it can also run in console or virtual terminal flawless. Once configured, customized and get accustomed with keyboard’s combinations, it reveals great features for every programmer. One of them is the advanced use of search & replace command.

Dear Tumbleweed users and hackers,

Once again I’m spanning two weeks; besides the normal work on getting you openSUSE Tumbleweed updated and timely delivered, the release team has been working together with the build service team to implement/stabilize the OBS-internal staging workflow. There is (should) not be any real noticeable difference for the contributors – except the new used URLs. The Factory Staging dashboard can now be found at https://build.opensuse.org/staging_workflows/1

During the last two weeks, we have pushed out 10 Tumbleweed Snapshots (1121, 1122, 1123, 1124, 1126, 1127, 1128, 1202, 1203 and 1204) containing those changes:

• openSSL 1.1.1d
• YaST updates
• Linux kernel 5.3.12
• Mesa 19.2.6
• firewalld 0.7.2
• bind 9.14.8
• alsa 1.2.1
• fprintd 0.9.0
• Perl 5.30.1
• QEmu 4.2.0-rc3

Currently, all stagings are full and a lot of things are happening – and OBS does not like me for this it seems. Stagings seem to progress a bit slower at the moment than they used to. But this could be just a timing issue as all stagings were rebased at the same time after the workflow switch. Hopefully, things will settle soon there. The notable changes being staged at the moment are:

• RPM 4.15.0
• Python 3.8
• Rust 1.39.0: break Firefox and Thunderbird so far
• libxml 2.9.10
• Linux kernel 5.4.1
• libvirt 5.10.0
• KDE Frameworks 5.17.4
• KDE Applications 19.12.0 (currently rc2 in testing)
• Qt 5.14.0 (currently release candidate in testing)

                                   Même le kawa est offert ! Description Le samedi 21 Décembre, 2019, nous organisons notre Journée Mensuelle du Logiciel Libre et de la Cybersécurité à la Maison St Sever à Rouen. (Rez de Chaussée, Centre Commercial St Sever, 10-12 rue Saint-Julien 76100 Rouen) de 14h00 à […]

The post #Rouen Journée de la #CyberSécurité et du #Logiciel Libre samedi 25 janvier, 2020 appeared first on Network Users Institute - Cybersécurité, Intégration de Linux & Logiciels Libres à Rouen, Normandie..