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• 10 Customer Segments Examples and Their Benefits

  9 mai 2024, par Erin

  Now that companies can segment buyers, the days of mass marketing are behind us. Customer segmentation offers various benefits for marketing, content creation, sales, analytics teams and more. Without customer segmentation, your personalised marketing efforts may fall flat. 

  According to the Twilio 2023 state of personalisation report, 69% of business leaders have increased their investment in personalisation. There’s a key reason for this — customer retention and loyalty directly benefit from personalisation. In fact, 62% of businesses have cited improved customer retention due to personalisation efforts. The numbers don’t lie. 

  Keep reading to learn how customer segments can help you fine-tune your personalised marketing campaigns. This article will give you a better understanding of customer segmentation and real-world customer segment examples. You’ll leave with the knowledge to empower your marketing strategies with effective customer segmentation. 

  What are customer segments ?

  Customer segments are distinct groups of people or organisations with similar characteristics, needs and behaviours. Like different species of plants in a garden, each customer segment has specific needs and care requirements. Customer segments are useful for tailoring personalised marketing campaigns for specific groups.

  Personalised marketing has been shown to have significant benefits — with 56% of consumers saying that a personalised experience would make them become repeat buyers. 

  Successful marketing teams typically focus on these types of customer segmentation :

  

  1. Geographic segmentation : groups buyers based on their physical location — country, city, region or climate — and language.
  2. Purchase history segmentation : categorises buyers based on their purchasing habits — how often they make purchases — and allows brands to distinguish between frequent, occasional and one-time buyers. 
  3. Product-based segmentation : groups buyers according to the products they prefer or end up purchasing. 
  4. Customer lifecycle segmentation : segments buyers based on where they are in the customer journey. Examples include new, repeat and lapsed buyers. This segmentation category is also useful for understanding the behaviour of loyal buyers and those at risk of churning. 
  5. Technographic segmentation : focuses on buyers’ technology preferences, including device type, browser type, and operating system. 
  6. Channel preference segmentation : helps us understand why buyers prefer to purchase via specific channels — whether online channels, physical stores or a combination of both. 
  7. Value-based segmentation : categorises buyers based on their average purchase value and sensitivity to pricing, for example. This type of segmentation can provide insights into the behaviours of price-conscious buyers and those willing to pay premium prices. 

  Customer segmentation vs. market segmentation

  Customer segmentation and market segmentation are related concepts, but they refer to different aspects of the segmentation process in marketing. 

  Market segmentation is the broader process of dividing the overall market into homogeneous groups. Market segmentation helps marketers identify different groups based on their characteristics or needs. These market segments make it easier for businesses to connect with new buyers by offering relevant products or new features. 

  On the other hand, customer segmentation is used to help you dig deep into the behaviour and preferences of your current customer base. Marketers use customer segmentation insights to create buyer personas. Buyer personas are essential for ensuring your personalised marketing efforts are relevant to the target audience. 

  10 customer segments examples

  Now that you better understand different customer segmentation categories, we’ll provide real-world examples of how customer segmentation can be applied. You’ll be able to draw a direct connection between the segmentation category or categories each example falls under.

  One thing to note is that you’ll want to consider privacy and compliance when you are considering collecting and analysing types of data such as gender, age, income level, profession or personal interests. Instead, you can focus on these privacy-friendly, ethical customer segmentation types :

  1. Geographic location (category : geographic segmentation)

  The North Face is an outdoor apparel and equipment company that relies on geographic segmentation to tailor its products toward buyers in specific regions and climates. 

  For instance, they’ll send targeted advertisements for insulated jackets and snow gear to buyers in colder climates. For folks in seasonal climates, The North Face may send personalised ads for snow gear in winter and ads for hiking or swimming gear in summer. 

  The North Face could also use geographic segmentation to determine buyers’ needs based on location. They can use this information to send targeted ads to specific customer segments during peak ski months to maximise profits.

  2. Preferred language (category : geographic segmentation)

  Your marketing approach will likely differ based on where your customers are and the language they speak. So, with that in mind, language may be another crucial variable you can introduce when identifying your target customers. 

  Language-based segmentation becomes even more important when one of your main business objectives is to expand into new markets and target international customers — especially now that global reach is made possible through digital channels. 

  Coca-Cola’s “Share a Coke” is a multi-national campaign with personalised cans and bottles featuring popular names from countries around the globe. It’s just one example of targeting customers based on language.

  3. Repeat users and loyal customers (category : customer lifecycle segmentation)

  Sephora, a large beauty supply company, is well-known for its Beauty Insider loyalty program. 

  It segments customers based on their purchase history and preferences and rewards their loyalty with gifts, discounts, exclusive offers and free samples. And since customers receive personalised product recommendations and other perks, it incentivises them to remain members of the Beauty Insider program — adding a boost to customer loyalty.

  By creating a memorable customer experience for this segment of their customer base, staying on top of beauty trends and listening to feedback, Sephora is able to keep buyers coming back.

  

  4. New customers (category : customer lifecycle segmentation)

  Subscription services use customer lifecycle segmentation to offer special promotions and trials for new customers. 

  HBO Max is a great example of a real company that excels at this strategy : 

  They offer 40% savings on an annual ad-free plan, which targets new customers who may be apprehensive about the added monthly cost of a recurring subscription.

  This marketing strategy prioritises fostering long-term customer relationships with new buyers to avoid high churn rates. 

  5. Cart abandonment (category : purchase history segmentation)

  With a rate of 85% among US-based mobile users, cart abandonment is a huge issue for ecommerce businesses. One way to deal with this is to segment inactive customers and cart abandoners — those who showed interest by adding products to their cart but haven’t converted yet — and send targeted emails to remind them about their abandoned carts.

  E-commerce companies like Ipsy, for example, track users who have added items to their cart but haven’t followed through on the purchase. The company’s messaging often contains incentives — like free shipping or a limited-time discount — to encourage passive users to return to their carts. 

  Research has found that cart abandonment emails with a coupon code have a high 44.37% average open rate. 

  6. Website activity (category : technographic segmentation)

  It’s also possible to segment customers based on website activity. Now, keep in mind that this is a relatively broad approach ; it covers every interaction that may occur while the customer is browsing your website. As such, it leaves room for many different types of segmentation. 

  For instance, you can segment your audience based on the pages they visited, the elements they interacted with — like CTAs and forms — how long they stayed on each page and whether they added products to their cart. 

  Matomo’s Event Tracking can provide additional context to each website visit and tell you more about the specific interactions that occur, making it particularly useful for segmenting customers based on how they spend their time on your website. 

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  Amazon segments its customers based on browsing behaviour — recently viewed products and categories, among other things — which, in turn, allows them to improve the customer’s experience and drive sales.

  7. Traffic source (category : channel segmentation) 

  You can also segment your audience based on traffic sources. For example, you can determine if your website visitors arrived through Google and other search engines, email newsletters, social media platforms or referrals. 

  In other words, you’ll create specific audience segments based on the original source. Matomo’s Acquisition feature can provide insights into five different types of traffic sources — search engines, social media, external websites, direct traffic and campaigns — to help you understand how users enter your website.

  You may find that most visitors arrive at your website through social media ads or predominantly discover your brand through search engines. Either way, by learning where they’re coming from, you’ll be able to determine which conversion paths you should prioritise and optimise further. 

  8. Device type (category : technographic segmentation)

  Device type is customer segmentation based on the devices that potential customers may use to access your website and view your content. 

  It’s worth noting that, on a global level, most people (96%) use mobile devices — primarily smartphones — for internet access. So, there’s a high chance that most of your website visitors are coming from mobile devices, too. 

  However, it’s best not to assume anything. Matomo can detect the operating system and the type of device — desktop, mobile device, tablet, console or TV, for example. 

  By introducing the device type variable into your customer segmentation efforts, you’ll be able to determine if there’s a preference for mobile or desktop devices. In return, you’ll have a better idea of how to optimise your website — and whether you should consider developing an app to meet the needs of mobile users.

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  Get the web insights you need, while respecting user privacy.

  Start for free

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  9. Browser type (category : technographic segmentation)

  Besides devices, another type of segmentation that belongs to the technographic category and can provide valuable insights is browser-related. In this case, you’re tracking the internet browser your customers use. 

  Many browser types are available — including Google Chrome, Microsoft Edge, Safari, Firefox and Brave — and each may display your website and other content differently. 

  So, keeping track of your customers’ preferred choices is important. Otherwise, you won’t be able to fully understand their online experience — or ensure that these browsers are displaying your content properly. 

  

  10. Ecommerce activity (category : purchase history, value based, channel or product based segmentation) 

  Similar to website activity, looking at ecommerce activity can tell your sales teams more about which pages the customer has seen and how they have interacted with them. 

  With Matomo’s Ecommerce Tracking, you’ll be able to keep an eye on customers’ on-site behaviours, conversion rates, cart abandonment, purchased products and transaction data — including total revenue and average order value.

  Considering that the focus is on sales channels — such as your online store — this approach to customer segmentation can help you improve the sales experience and increase profitability. 

  Start implementing these customer segments examples

  With ever-evolving demographics and rapid technological advancements, customer segmentation is increasingly complex. The tips and real-world examples in this article break down and simplify customer segmentation so that you can adapt to your customer base. 

  Customer segmentation lays the groundwork for your personalised marketing campaigns to take off. By understanding your users better, you can effectively tailor each campaign to different segments. 

  If you’re ready to see how Matomo can elevate your personalised marketing campaigns, try it for free for 21 days. No credit card required.

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• Developing A Shader-Based Video Codec

  22 juin 2013, par Multimedia Mike — Outlandish Brainstorms

  Early last month, this thing called ORBX.js was in the news. It ostensibly has something to do with streaming video and codec technology, which naturally catches my interest. The hype was kicked off by Mozilla honcho Brendan Eich when he posted an article asserting that HD video decoding could be entirely performed in JavaScript. We’ve seen this kind of thing before using Broadway– an H.264 decoder implemented entirely in JS. But that exposes some very obvious limitations (notably CPU usage).

  But this new video codec promises 1080p HD playback directly in JavaScript which is a lofty claim. How could it possibly do this ? I got the impression that performance was achieved using WebGL, an extension which allows JavaScript access to accelerated 3D graphics hardware. Browsing through the conversations surrounding the ORBX.js announcement, I found this confirmation from Eich himself :

  You’re right that WebGL does heavy lifting.

  As of this writing, ORBX.js remains some kind of private tech demo. If there were a public demo available, it would necessarily be easy to reverse engineer the downloadable JavaScript decoder.

  But the announcement was enough to make me wonder how it could be possible to create a video codec which effectively leverages 3D hardware.

  Prior Art
  In theorizing about this, it continually occurs to me that I can’t possibly be the first person to attempt to do this (or the ORBX.js people, for that matter). In googling on the matter, I found various forums and Q&A posts where people asked if it were possible to, e.g., accelerate JPEG decoding and presentation using 3D hardware, with no answers. I also found a blog post which describes a plan to use 3D hardware to accelerate VP8 video decoding. It was a project done under the banner of Google’s Summer of Code in 2011, though I’m not sure which open source group mentored the effort. The project did not end up producing the shader-based VP8 codec originally chartered but mentions that “The ‘client side’ of the VP8 VDPAU implementation is working and is currently being reviewed by the libvdpau maintainers.” I’m not sure what that means. Perhaps it includes modifications to the public API that supports VP8, but is waiting for the underlying hardware to actually implement VP8 decoding blocks in hardware.

  What’s So Hard About This ?
  Video decoding is a computationally intensive task. GPUs are known to be really awesome at chewing through computationally intensive tasks. So why aren’t GPUs a natural fit for decoding video codecs ?

  Generally, it boils down to parallelism, or lack of opportunities thereof. GPUs are really good at doing the exact same operations over lots of data at once. The problem is that decoding compressed video usually requires multiple phases that cannot be parallelized, and the individual phases often cannot be parallelized. In strictly mathematical terms, a compressed data stream will need to be decoded by applying a function f(x) over each data element, x₀ .. x_n. However, the function relies on having applied the function to the previous data element, i.e. :

  f(xn) = f(f(xn-1))
  

  What happens when you try to parallelize such an algorithm ? Temporal rifts in the space/time continuum, if you’re in a Star Trek episode. If you’re in the real world, you’ll get incorrect, unusuable data as the parallel computation is seeded with a bunch of invalid data at multiple points (which is illustrated in some of the pictures in the aforementioned blog post about accelerated VP8).

  Example : JPEG
  Let’s take a very general look at the various stages involved in decoding the ubiquitous JPEG format :

  
  
  

  What are the opportunities to parallelize these various phases ?
  

  • Huffman decoding (run length decoding and zig-zag reordering is assumed to be rolled into this phase) : not many opportunities for parallelizing the various Huffman formats out there, including this one. Decoding most Huffman streams is necessarily a sequential operation. I once hypothesized that it would be possible to engineer a codec to achieve some parallelism during the entropy decoding phase, and later found that On2′s VP8 codec employs the scheme. However, such a scheme is unlikely to break down to such a fine level that WebGL would require.
  • Reverse DC prediction : JPEG — and many other codecs — doesn’t store full DC coefficients. It stores differences in successive DC coefficients. Reversing this process can’t be parallelized. See the discussion in the previous section.
  • Dequantize coefficients : This could be very parallelized. It should be noted that software decoders often don’t dequantize all coefficients. Many coefficients are 0 and it’s a waste of a multiplication operation to dequantize. Thus, this phase is sometimes rolled into the Huffman decoding phase.
  • Invert discrete cosine transform : This seems like it could be highly parallelizable. I will be exploring this further in this post.
  • Convert YUV -> RGB for final display : This is a well-established use case for 3D acceleration.

  Crash Course in 3D Shaders and Humility
  So I wanted to see if I could accelerate some parts of JPEG decoding using something called shaders. I made an effort to understand 3D programming and its associated math throughout the 1990s but 3D technology left me behind a very long time ago while I got mixed up in this multimedia stuff. So I plowed through a few books concerning WebGL (thanks to my new Safari Books Online subscription). After I learned enough about WebGL/JS to be dangerous and just enough about shader programming to be absolutely lethal, I set out to try my hand at optimizing IDCT using shaders.

  Here’s my extremely high level (and probably hopelessly naive) view of the modern GPU shader programming model :

  
  
  

  The WebGL program written in JavaScript drives the show. It sends a set of vertices into the WebGL system and each vertex is processed through a vertex shader. Then, each pixel that falls within a set of vertices is sent through a fragment shader to compute the final pixel attributes (R, G, B, and alpha value). Another consideration is textures : This is data that the program uploads to GPU memory which can be accessed programmatically by the shaders).

  These shaders (vertex and fragment) are key to the GPU’s programmability. How are they programmed ? Using a special C-like shading language. Thought I : “C-like language ? I know C ! I should be able to master this in short order !” So I charged forward with my assumptions and proceeded to get smacked down repeatedly by the overall programming paradigm. I came to recognize this as a variation of the scientific method : Develop a hypothesis– in my case, a mental model of how the system works ; develop an experiment (short program) to prove or disprove the model ; realize something fundamental that I was overlooking ; formulate new hypothesis and repeat.

  First Approach : Vertex Workhorse
  My first pitch goes like this :

  • Upload DCT coefficients to GPU memory in the form of textures
  • Program a vertex mesh that encapsulates 16×16 macroblocks
  • Distribute the IDCT effort among multiple vertex shaders
  • Pass transformed Y, U, and V blocks to fragment shader which will convert the samples to RGB

  So the idea is that decoding of 16×16 macroblocks is parallelized. A macroblock embodies 6 blocks :

  
  
  

  It would be nice to process one of these 6 blocks in each vertex. But that means drawing a square with 6 vertices. How do you do that ? I eventually realized that drawing a square with 6 vertices is the recommended method for drawing a square on 3D hardware. Using 2 triangles, each with 3 vertices (0, 1, 2 ; 3, 4, 5) :

  
  
  

  A vertex shader knows which (x, y) coordinates it has been assigned, so it could figure out which sections of coefficients it needs to access within the textures. But how would a vertex shader know which of the 6 blocks it should process ? Solution : Misappropriate the vertex’s z coordinate. It’s not used for anything else in this case.

  So I set all of that up. Then I hit a new roadblock : How to get the reconstructed Y, U, and V samples transported to the fragment shader ? I have found that communicating between shaders is quite difficult. Texture memory ? WebGL doesn’t allow shaders to write back to texture memory ; shaders can only read it. The standard way to communicate data from a vertex shader to a fragment shader is to declare variables as “varying”. Up until this point, I knew about varying variables but there was something I didn’t quite understand about them and it nagged at me : If 3 different executions of a vertex shader set 3 different values to a varying variable, what value is passed to the fragment shader ?

  It turns out that the varying variable varies, which means that the GPU passes interpolated values to each fragment shader invocation. This completely destroys this idea.

  Second Idea : Vertex Workhorse, Take 2
  The revised pitch is to work around the interpolation issue by just having each vertex shader invocation performs all 6 block transforms. That seems like a lot of redundant. However, I figured out that I can draw a square with only 4 vertices by arranging them in an ‘N’ pattern and asking WebGL to draw a TRIANGLE_STRIP instead of TRIANGLES. Now it’s only doing the 4x the extra work, and not 6x. GPUs are supposed to be great at this type of work, so it shouldn’t matter, right ?

  I wired up an experiment and then ran into a new problem : While I was able to transform a block (or at least pretend to), and load up a varying array (that wouldn’t vary since all vertex shaders wrote the same values) to transmit to the fragment shader, the fragment shader can’t access specific values within the varying block. To clarify, a WebGL shader can use a constant value — or a value that can be evaluated as a constant at compile time — to index into arrays ; a WebGL shader can not compute an index into an array. Per my reading, this is a WebGL security consideration and the limitation may not be present in other OpenGL(-ES) implementations.

  Not Giving Up Yet : Choking The Fragment Shader
  You might want to be sitting down for this pitch :

  • Vertex shader only interpolates texture coordinates to transmit to fragment shader
  • Fragment shader performs IDCT for a single Y sample, U sample, and V sample
  • Fragment shader converts YUV -> RGB

  Seems straightforward enough. However, that step concerning IDCT for Y, U, and V entails a gargantuan number of operations. When computing the IDCT for an entire block of samples, it’s possible to leverage a lot of redundancy in the math which equates to far fewer overall operations. If you absolutely have to compute each sample individually, for an 8×8 block, that requires 64 multiplication/accumulation (MAC) operations per sample. For 3 color planes, and including a few extra multiplications involved in the RGB conversion, that tallies up to about 200 MACs per pixel. Then there’s the fact that this approach means a 4x redundant operations on the color planes.

  It’s crazy, but I just want to see if it can be done. My approach is to pre-compute a pile of IDCT constants in the JavaScript and transmit them to the fragment shader via uniform variables. For a first order optimization, the IDCT constants are formatted as 4-element vectors. This allows computing 16 dot products rather than 64 individual multiplication/addition operations. Ideally, GPU hardware executes the dot products faster (and there is also the possibility of lining these calculations up as matrices).

  I can report that I actually got a sample correctly transformed using this approach. Just one sample, through. Then I ran into some new problems :

  Problem #1 : Computing sample #1 vs. sample #0 requires a different table of 64 IDCT constants. Okay, so create a long table of 64 * 64 IDCT constants. However, this suffers from the same problem as seen in the previous approach : I can’t dynamically compute the index into this array. What’s the alternative ? Maintain 64 separate named arrays and implement 64 branches, when branching of any kind is ill-advised in shader programming to begin with ? I started to go down this path until I ran into…

  Problem #2 : Shaders can only be so large. 64 * 64 floats (4 bytes each) requires 16 kbytes of data and this well exceeds the amount of shader storage that I can assume is allowed. That brings this path of exploration to a screeching halt.

  Further Brainstorming
  I suppose I could forgo pre-computing the constants and directly compute the IDCT for each sample which would entail lots more multiplications as well as 128 cosine calculations per sample (384 considering all 3 color planes). I’m a little stuck with the transform idea right now. Maybe there are some other transforms I could try.

  Another idea would be vector quantization. What little ORBX.js literature is available indicates that there is a method to allow real-time streaming but that it requires GPU assistance to yield enough horsepower to make it feasible. When I think of such severe asymmetry between compression and decompression, my mind drifts towards VQ algorithms. As I come to understand the benefits and limitations of GPU acceleration, I think I can envision a way that something similar to SVQ1, with its copious, hierarchical vector tables stored as textures, could be implemented using shaders.

  So far, this all pertains to intra-coded video frames. What about opportunities for inter-coded frames ? The only approach that I can envision here is to use WebGL’s readPixels() function to fetch the rasterized frame out of the GPU, and then upload it again as a new texture which a new frame processing pipeline could reference. Whether this idea is plausible would require some profiling.

  Using interframes in such a manner seems to imply that the entire codec would need to operate in RGB space and not YUV.

  Conclusions
  The people behind ORBX.js have apparently figured out a way to create a shader-based video codec. I have yet to even begin to reason out a plausible approach. However, I’m glad I did this exercise since I have finally broken through my ignorance regarding modern GPU shader programming. It’s nice to have a topic like multimedia that allows me a jumping-off point to explore other areas.

• C# on linux : FFmpeg (FFMediaToolkit) MediaOutput..Video.AddFrame(FrameToImageData(ImageData)) causes program to exit with code 139

  19 mai 2021, par Jan Černý

  In my C# program I have instance of MediaOutput from FFMediaToolkit. It is initialized like this :

  


  MediaOutput buffer = MediaBuilder.CreateContainer(videoPath).WithVideo(new VideoEncoderSettings(width: width,
                height: height, framerate: frameRate,
                codec: VideoCodec.H264)
            ).Create();


  


  When I want to add frame to buffer I use this code :

  


  private static ImageData FrameToImageData(Bitmap bitmap) {
    Rectangle rect = new Rectangle(System.Drawing.Point.Empty, bitmap.Size);
    BitmapData bitLock = bitmap.LockBits(rect, ImageLockMode.ReadOnly, PixelFormat.Format24bppRgb);
    ImageData bitmapImageData = ImageData.FromPointer(bitLock.Scan0, ImagePixelFormat.Bgr24, bitmap.Size);
    bitmap.UnlockBits(bitLock);
    return bitmapImageData;
}

public void AddFrame(Bitmap frame) {
    buffer.Video.AddFrame(FrameToImageData(frame));
}


  


  But when code reaches buffer.Video.AddFrame(); it exits with code 139 without throwing any exception.

  


  I have two test files and only one is causing it. One is .png file 100x100 and it works fine. The other is .png file 1000x1000 and it makes program exit as soon as it reaches this method.

  


  What exit code 139 means in C# ?
  
How can I diagnose this problem when it is not throwing any exceptions ?
  
How can I fix it ?

  


  Thank you for help. If you need any more information, leave a comment and I will add it soon as possible.

  


  Edit1 :
  
This is my instel drivers :

  


  john@arch-thinkpad ~> yay -Qs intel
local/intel-gmmlib 21.1.1-1
    Intel Graphics Memory Management Library
local/intel-media-driver 21.1.3-1
    Intel Media Driver for VAAPI — Broadwell+ iGPUs
local/intel-media-sdk 21.1.3-1
    API to access hardware-accelerated video on Intel Gen graphics hardware platforms
local/intel-mkl 2020.4.304-1
    Intel Math Kernel Library
local/intel-ucode 20210216-1
    Microcode update files for Intel CPUs
local/intellij-idea-ultimate-edition 2021.1.1-1
    An intelligent IDE for Java, Groovy and other programming languages with advanced refactoring features intensely focused on developer productivity.
local/libmfx 21.1.3-1
    Intel Media SDK dispatcher library
local/libva-intel-driver 2.4.1-1
    VA-API implementation for Intel G45 and HD Graphics family
local/onednn 2.2.2-1
    oneAPI Deep Neural Network Library (oneDNN)
local/tbb 2020.3-1
    High level abstract threading library
local/xf86-video-intel 1:2.99.917+916+g31486f40-1 (xorg-drivers)
    X.org Intel i810/i830/i915/945G/G965+ video drivers


  


  EDIT2 :

  


  [17091.524781] Slimulator[20962]: segfault at 7fd84003a011 ip 00007fd8400cd348 sp 00007ffddd9d7fb8 error 4 in libswscale.so.5.9.100[7fd840062000+75000]
[17091.524791] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17091.524829] audit: type=1701 audit(1621440058.690:188): auid=1000 uid=1000 gid=1000 ses=1 pid=20962 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17091.546116] audit: type=1334 audit(1621440058.713:189): prog-id=61 op=LOAD
[17091.546209] audit: type=1334 audit(1621440058.713:190): prog-id=62 op=LOAD
[17091.546262] audit: type=1334 audit(1621440058.713:191): prog-id=63 op=LOAD
[17091.547395] audit: type=1130 audit(1621440058.713:192): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@6-20996-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17094.458151] audit: type=1131 audit(1621440061.623:193): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@6-20996-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17094.542823] audit: type=1334 audit(1621440061.710:194): prog-id=63 op=UNLOAD
[17094.542832] audit: type=1334 audit(1621440061.710:195): prog-id=62 op=UNLOAD
[17094.542836] audit: type=1334 audit(1621440061.710:196): prog-id=61 op=UNLOAD
[17295.099124] Slimulator[21147]: segfault at 7f555b1de011 ip 00007f555b271348 sp 00007fff48239f48 error 4 in libswscale.so.5.9.100[7f555b206000+75000]
[17295.099132] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17295.099197] audit: type=1701 audit(1621440262.267:197): auid=1000 uid=1000 gid=1000 ses=1 pid=21147 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17295.108536] audit: type=1334 audit(1621440262.277:198): prog-id=64 op=LOAD
[17295.108679] audit: type=1334 audit(1621440262.277:199): prog-id=65 op=LOAD
[17295.108752] audit: type=1334 audit(1621440262.277:200): prog-id=66 op=LOAD
[17295.109589] audit: type=1130 audit(1621440262.277:201): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@7-21181-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17297.322989] audit: type=1131 audit(1621440264.487:202): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@7-21181-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17297.401409] audit: type=1334 audit(1621440264.571:203): prog-id=66 op=UNLOAD
[17297.401421] audit: type=1334 audit(1621440264.571:204): prog-id=65 op=UNLOAD
[17297.401426] audit: type=1334 audit(1621440264.571:205): prog-id=64 op=UNLOAD
[17353.331142] Slimulator[21281]: segfault at 7f35f1fd3011 ip 00007f35f2066348 sp 00007ffe7d1288e8 error 4 in libswscale.so.5.9.100[7f35f1ffb000+75000]
[17353.331160] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17353.331214] audit: type=1701 audit(1621440320.498:206): auid=1000 uid=1000 gid=1000 ses=1 pid=21281 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17353.344382] audit: type=1334 audit(1621440320.511:207): prog-id=67 op=LOAD
[17353.344518] audit: type=1334 audit(1621440320.511:208): prog-id=68 op=LOAD
[17353.344566] audit: type=1334 audit(1621440320.511:209): prog-id=69 op=LOAD
[17353.345651] audit: type=1130 audit(1621440320.511:210): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@8-21378-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17356.180885] audit: type=1131 audit(1621440323.345:211): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@8-21378-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17356.261051] audit: type=1334 audit(1621440323.428:212): prog-id=69 op=UNLOAD
[17356.261055] audit: type=1334 audit(1621440323.428:213): prog-id=68 op=UNLOAD
[17356.261057] audit: type=1334 audit(1621440323.428:214): prog-id=67 op=UNLOAD
[17379.499165] Slimulator[21454]: segfault at 7f68418a1011 ip 00007f6841934348 sp 00007ffea9f22eb8 error 4 in libswscale.so.5.9.100[7f68418c9000+75000]
[17379.499174] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17379.499245] audit: type=1701 audit(1621440346.665:215): auid=1000 uid=1000 gid=1000 ses=1 pid=21454 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17379.509368] audit: type=1334 audit(1621440346.675:216): prog-id=70 op=LOAD
[17379.509448] audit: type=1334 audit(1621440346.675:217): prog-id=71 op=LOAD
[17379.509481] audit: type=1334 audit(1621440346.675:218): prog-id=72 op=LOAD
[17379.510098] audit: type=1130 audit(1621440346.675:219): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@9-21492-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17381.661151] audit: type=1131 audit(1621440348.828:220): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@9-21492-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17381.740919] audit: type=1334 audit(1621440348.908:221): prog-id=72 op=UNLOAD
[17381.740924] audit: type=1334 audit(1621440348.908:222): prog-id=71 op=UNLOAD
[17381.740926] audit: type=1334 audit(1621440348.908:223): prog-id=70 op=UNLOAD
[17389.743524] Slimulator[21565]: segfault at 7f95075a4011 ip 00007f9507637348 sp 00007ffccfab3f18 error 4 in libswscale.so.5.9.100[7f95075cc000+75000]
[17389.743535] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17389.743613] audit: type=1701 audit(1621440356.908:224): auid=1000 uid=1000 gid=1000 ses=1 pid=21565 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17389.753604] audit: type=1334 audit(1621440356.918:225): prog-id=73 op=LOAD
[17389.753783] audit: type=1334 audit(1621440356.918:226): prog-id=74 op=LOAD
[17389.753847] audit: type=1334 audit(1621440356.918:227): prog-id=75 op=LOAD
[17389.755847] audit: type=1130 audit(1621440356.921:228): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@10-21600-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17392.121917] audit: type=1131 audit(1621440359.288:229): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@10-21600-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17392.204160] audit: type=1334 audit(1621440359.371:230): prog-id=75 op=UNLOAD
[17392.204167] audit: type=1334 audit(1621440359.371:231): prog-id=74 op=UNLOAD
[17392.204169] audit: type=1334 audit(1621440359.371:232): prog-id=73 op=UNLOAD
[17409.596374] Slimulator[21674]: segfault at 7fddab4c5011 ip 00007fddab558348 sp 00007ffe55e75e28 error 4 in libswscale.so.5.9.100[7fddab4ed000+75000]
[17409.596383] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17409.596441] audit: type=1701 audit(1621440376.762:233): auid=1000 uid=1000 gid=1000 ses=1 pid=21674 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17409.606014] audit: type=1334 audit(1621440376.772:234): prog-id=76 op=LOAD
[17409.606096] audit: type=1334 audit(1621440376.772:235): prog-id=77 op=LOAD
[17409.606139] audit: type=1334 audit(1621440376.772:236): prog-id=78 op=LOAD
[17409.606845] audit: type=1130 audit(1621440376.772:237): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@11-21706-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17411.977651] audit: type=1131 audit(1621440379.145:238): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@11-21706-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17412.074091] audit: type=1334 audit(1621440379.242:239): prog-id=78 op=UNLOAD
[17412.074098] audit: type=1334 audit(1621440379.242:240): prog-id=77 op=UNLOAD
[17412.074101] audit: type=1334 audit(1621440379.242:241): prog-id=76 op=UNLOAD
[17431.213606] Slimulator[21785]: segfault at 7f218cdca011 ip 00007f218ce5d348 sp 00007ffffd122a98 error 4 in libswscale.so.5.9.100[7f218cdf2000+75000]
[17431.213616] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17431.213648] audit: type=1701 audit(1621440398.378:242): auid=1000 uid=1000 gid=1000 ses=1 pid=21785 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17431.223086] audit: type=1334 audit(1621440398.388:243): prog-id=79 op=LOAD
[17431.223210] audit: type=1334 audit(1621440398.388:244): prog-id=80 op=LOAD
[17431.223272] audit: type=1334 audit(1621440398.388:245): prog-id=81 op=LOAD
[17431.224003] audit: type=1130 audit(1621440398.392:246): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@12-21817-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17433.560362] audit: type=1131 audit(1621440400.725:247): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@12-21817-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17433.620924] audit: type=1334 audit(1621440400.788:248): prog-id=81 op=UNLOAD
[17433.620929] audit: type=1334 audit(1621440400.788:249): prog-id=80 op=UNLOAD
[17433.620931] audit: type=1334 audit(1621440400.788:250): prog-id=79 op=UNLOAD
[17636.068527] audit: type=1701 audit(1621440603.236:251): auid=1000 uid=1000 gid=1000 ses=1 pid=22189 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=6 res=1
[17636.075124] audit: type=1334 audit(1621440603.243:252): prog-id=82 op=LOAD
[17636.075299] audit: type=1334 audit(1621440603.243:253): prog-id=83 op=LOAD
[17636.075334] audit: type=1334 audit(1621440603.243:254): prog-id=84 op=LOAD
[17636.075947] audit: type=1130 audit(1621440603.246:255): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@13-22213-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17636.859400] audit: type=1131 audit(1621440604.030:256): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@13-22213-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17636.952582] audit: type=1334 audit(1621440604.123:257): prog-id=84 op=UNLOAD
[17636.952586] audit: type=1334 audit(1621440604.123:258): prog-id=83 op=UNLOAD
[17636.952587] audit: type=1334 audit(1621440604.123:259): prog-id=82 op=UNLOAD
[17683.442450] Slimulator[22349]: segfault at 7fce7b840011 ip 00007fce7b8d3348 sp 00007ffdf12fde88 error 4 in libswscale.so.5.9.100[7fce7b868000+75000]
[17683.442461] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17683.442489] audit: type=1701 audit(1621440650.613:260): auid=1000 uid=1000 gid=1000 ses=1 pid=22349 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17683.451485] audit: type=1334 audit(1621440650.620:261): prog-id=85 op=LOAD
[17683.451530] audit: type=1334 audit(1621440650.620:262): prog-id=86 op=LOAD
[17683.451561] audit: type=1334 audit(1621440650.620:263): prog-id=87 op=LOAD
[17683.452200] audit: type=1130 audit(1621440650.620:264): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@14-22400-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17685.702716] audit: type=1131 audit(1621440652.873:265): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@14-22400-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17685.789205] audit: type=1334 audit(1621440652.960:266): prog-id=87 op=UNLOAD
[17685.789209] audit: type=1334 audit(1621440652.960:267): prog-id=86 op=UNLOAD
[17685.789211] audit: type=1334 audit(1621440652.960:268): prog-id=85 op=UNLOAD
[17741.587367] audit: type=1701 audit(1621440708.757:269): auid=1000 uid=1000 gid=1000 ses=1 pid=22506 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=6 res=1
[17741.597924] audit: type=1334 audit(1621440708.767:270): prog-id=88 op=LOAD
[17741.597991] audit: type=1334 audit(1621440708.767:271): prog-id=89 op=LOAD
[17741.598017] audit: type=1334 audit(1621440708.767:272): prog-id=90 op=LOAD
[17741.598635] audit: type=1130 audit(1621440708.770:273): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@15-22533-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17743.536566] audit: type=1131 audit(1621440710.707:274): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@15-22533-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17743.645445] audit: type=1334 audit(1621440710.817:275): prog-id=90 op=UNLOAD
[17743.645456] audit: type=1334 audit(1621440710.817:276): prog-id=89 op=UNLOAD
[17743.645460] audit: type=1334 audit(1621440710.817:277): prog-id=88 op=UNLOAD
[17826.501073] Slimulator[22630]: segfault at 7efff17b2011 ip 00007efff1845348 sp 00007ffe58353908 error 4 in libswscale.so.5.9.100[7efff17da000+75000]
[17826.501081] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[17826.501144] audit: type=1701 audit(1621440793.671:278): auid=1000 uid=1000 gid=1000 ses=1 pid=22630 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[17826.508176] audit: type=1334 audit(1621440793.681:279): prog-id=91 op=LOAD
[17826.508254] audit: type=1334 audit(1621440793.681:280): prog-id=92 op=LOAD
[17826.508285] audit: type=1334 audit(1621440793.681:281): prog-id=93 op=LOAD
[17826.508907] audit: type=1130 audit(1621440793.681:282): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@16-22667-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17828.821665] audit: type=1131 audit(1621440795.994:283): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@16-22667-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[17828.911512] audit: type=1334 audit(1621440796.084:284): prog-id=93 op=UNLOAD
[17828.911523] audit: type=1334 audit(1621440796.084:285): prog-id=92 op=UNLOAD
[17828.911528] audit: type=1334 audit(1621440796.084:286): prog-id=91 op=UNLOAD
[18255.197203] Slimulator[23128]: segfault at 7f44e8746011 ip 00007f44e87d9348 sp 00007ffdfc25d318 error 4 in libswscale.so.5.9.100[7f44e876e000+75000]
[18255.197213] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[18255.197237] audit: type=1701 audit(1621441222.370:287): auid=1000 uid=1000 gid=1000 ses=1 pid=23128 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[18255.205057] audit: type=1334 audit(1621441222.376:288): prog-id=94 op=LOAD
[18255.205138] audit: type=1334 audit(1621441222.376:289): prog-id=95 op=LOAD
[18255.205165] audit: type=1334 audit(1621441222.376:290): prog-id=96 op=LOAD
[18255.206164] audit: type=1130 audit(1621441222.380:291): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@17-23164-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18257.792719] audit: type=1131 audit(1621441224.967:292): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@17-23164-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18257.862155] audit: type=1334 audit(1621441225.037:293): prog-id=96 op=UNLOAD
[18257.862159] audit: type=1334 audit(1621441225.037:294): prog-id=95 op=UNLOAD
[18257.862161] audit: type=1334 audit(1621441225.037:295): prog-id=94 op=UNLOAD
[18312.879423] audit: type=1334 audit(1621441280.053:296): prog-id=97 op=LOAD
[18312.879566] audit: type=1334 audit(1621441280.053:297): prog-id=98 op=LOAD
[18312.879634] audit: type=1334 audit(1621441280.053:298): prog-id=99 op=LOAD
[18312.918107] audit: type=1130 audit(1621441280.090:299): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18342.984220] audit: type=1131 audit(1621441310.157:300): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18343.084948] audit: type=1334 audit(1621441310.260:301): prog-id=99 op=UNLOAD
[18343.084955] audit: type=1334 audit(1621441310.260:302): prog-id=98 op=UNLOAD
[18343.084959] audit: type=1334 audit(1621441310.260:303): prog-id=97 op=UNLOAD
[18527.198876] Slimulator[23504]: segfault at 7fd59543e011 ip 00007fd5954d1348 sp 00007ffcc5d59e78 error 4 in libswscale.so.5.9.100[7fd595466000+75000]
[18527.198885] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[18527.198911] audit: type=1701 audit(1621441494.371:304): auid=1000 uid=1000 gid=1000 ses=1 pid=23504 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[18527.207861] audit: type=1334 audit(1621441494.381:305): prog-id=100 op=LOAD
[18527.207959] audit: type=1334 audit(1621441494.381:306): prog-id=101 op=LOAD
[18527.207996] audit: type=1334 audit(1621441494.381:307): prog-id=102 op=LOAD
[18527.208682] audit: type=1130 audit(1621441494.381:308): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@18-23538-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18530.192509] audit: type=1131 audit(1621441497.364:309): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@18-23538-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18530.274214] audit: type=1334 audit(1621441497.448:310): prog-id=102 op=UNLOAD
[18530.274221] audit: type=1334 audit(1621441497.448:311): prog-id=101 op=UNLOAD
[18530.274224] audit: type=1334 audit(1621441497.448:312): prog-id=100 op=UNLOAD
[18788.989941] i915 0000:00:02.0: [drm] *ERROR* Atomic update failure on pipe B (start=1126979 end=1126980) time 153 us, min 1073, max 1079, scanline start 1072, end 1082
[18793.024933] audit: type=1334 audit(1621441760.199:313): prog-id=103 op=LOAD
[18793.025042] audit: type=1334 audit(1621441760.199:314): prog-id=104 op=LOAD
[18793.025082] audit: type=1334 audit(1621441760.199:315): prog-id=105 op=LOAD
[18793.061481] audit: type=1130 audit(1621441760.235:316): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18823.124309] audit: type=1131 audit(1621441790.299:317): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-hostnamed comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[18823.193828] audit: type=1334 audit(1621441790.369:318): prog-id=105 op=UNLOAD
[18823.193847] audit: type=1334 audit(1621441790.369:319): prog-id=104 op=UNLOAD
[18823.193856] audit: type=1334 audit(1621441790.369:320): prog-id=103 op=UNLOAD
[19615.817516] Slimulator[24574]: segfault at 7fb94cbbd011 ip 00007fb94cc50348 sp 00007fffefd935c8 error 4 in libswscale.so.5.9.100[7fb94cbe5000+75000]
[19615.817524] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[19615.817544] audit: type=1701 audit(1621442582.996:321): auid=1000 uid=1000 gid=1000 ses=1 pid=24574 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[19615.825508] audit: type=1334 audit(1621442583.002:322): prog-id=106 op=LOAD
[19615.825620] audit: type=1334 audit(1621442583.002:323): prog-id=107 op=LOAD
[19615.825652] audit: type=1334 audit(1621442583.002:324): prog-id=108 op=LOAD
[19615.826425] audit: type=1130 audit(1621442583.006:325): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@19-24609-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[19617.863304] audit: type=1131 audit(1621442585.043:326): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@19-24609-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[19617.939124] audit: type=1334 audit(1621442585.119:327): prog-id=108 op=UNLOAD
[19617.939129] audit: type=1334 audit(1621442585.119:328): prog-id=107 op=UNLOAD
[19617.939132] audit: type=1334 audit(1621442585.119:329): prog-id=106 op=UNLOAD
[20633.629288] Slimulator[25687]: segfault at 7fed842b8011 ip 00007fed8434b348 sp 00007ffcbf0f45d8 error 4 in libswscale.so.5.9.100[7fed842e0000+75000]
[20633.629298] Code: 45 85 c0 0f 8e 58 01 00 00 41 8d 40 ff 49 89 cc 48 89 d5 48 89 f9 48 89 44 24 f8 44 89 c0 31 f6 48 89 44 24 e0 0f 1f 44 00 00 <0f> b6 41 01 44 0f b6 41 02 48 83 c1 06 44 8b 7c 24 d4 8b 54 24 d8
[20633.629328] audit: type=1701 audit(1621443600.811:330): auid=1000 uid=1000 gid=1000 ses=1 pid=25687 comm="Slimulator" exe="/home/john/Projects/Slimulator/bin/Debug/net5.0/Slimulator" sig=11 res=1
[20633.640886] audit: type=1334 audit(1621443600.824:331): prog-id=109 op=LOAD
[20633.640985] audit: type=1334 audit(1621443600.824:332): prog-id=110 op=LOAD
[20633.641017] audit: type=1334 audit(1621443600.824:333): prog-id=111 op=LOAD
[20633.641934] audit: type=1130 audit(1621443600.824:334): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@20-25722-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[20636.303870] audit: type=1131 audit(1621443603.488:335): pid=1 uid=0 auid=4294967295 ses=4294967295 msg='unit=systemd-coredump@20-25722-0 comm="systemd" exe="/usr/lib/systemd/systemd" hostname=? addr=? terminal=? res=success'
[20636.410990] audit: type=1334 audit(1621443603.594:336): prog-id=111 op=UNLOAD
[20636.411006] audit: type=1334 audit(1621443603.594:337): prog-id=110 op=UNLOAD
[20636.411014] audit: type=1334 audit(1621443603.594:338): prog-id=109 op=UNLOAD