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• Playing With Emscripten and ASM.js

  1er mars 2014, par Multimedia Mike — General

  The last 5 years or so have provided a tremendous amount of hype about the capabilities of JavaScript. I think it really kicked off when Google announced their Chrome web browser in September, 2008 along with its V8 JS engine. This seemed to spark an arms race in JS engine performance along with much hyperbole that eventually all software could, would, and/or should be written in straight JavaScript for maximum portability and future-proofing, perhaps aided by Emscripten, a tool which magically transforms C and C++ code into JS. The latest round of rhetoric comes courtesy of something called asm.js which purports to narrow the gap between JS and native code performance.

  I haven’t been a believer, to express it charitably. But I wanted to be certain, so I set out to devise my own experiment to test modern JS performance.

  Up Front Summary
  I was extremely surprised that my experiment demonstrated JS performance FAR beyond my expectations. There might be something to these claims of magnficent JS speed in numerical applications. Basically, here were my thoughts during the process :

  • There’s no way that JavaScript can come anywhere close to C performance for a numerically intensive operation ; a simple experiment should demonstrate this.
  • Here’s a straightforward C program to perform a simple yet numerically intensive operation.
  • Let’s compile the C program on gcc and get some baseline performance numbers.
  • Let’s use Emscripten to convert the C program to JavaScript and run it under Chrome.
  • Ha ! Pitiful JS performance, just as I expected !
  • Try the same program under Firefox, since Firefox is supposed to have some crazy optimization for asm.js code, allegedly emitted by Emscripten.
  • LOL ! Firefox performs even worse than Chrome !
  • Wait a minute… the Emscripten documentation mentioned using optimization levels for generating higher performance JS, so try ‘-O1′.
  • Umm… wow : Chrome’s performance increased dramatically ! What about Firefox ? Not only is Firefox faster than Chrome, it’s faster than the gcc-generated code !
  • As my faith in C is suddenly shaken to its core, I remembered to compile the gcc version with an explicit optimization level. The native C version pulled ahead of Firefox again, but the Firefox code is still close.
  • Aha ! This is just desktop– but what about mobile ? One of the leading arguments for converting everything to pure JavaScript is that such programs will magically run perfectly in mobile browsers. So I wager that this is where the experiment will fall over.
  • I proceed to try the same converted program on a variety of mobile platforms.
  • The mobile platforms perform rather admirably as well.
  • I am surprised.

  The Experiment
  I wanted to run a simple yet numerically-intensive and relevant benchmark, and something I am familiar with. I settled on JPEG image decoding. Again, I wanted to keep this simple, ideally in a single file because I didn’t know how hard it might be to deal with Emscripten. I found NanoJPEG, which is a straightforward JPEG decoder contained in a single C file.
  

  I altered nanojpeg.c (to a new file called nanojpeg-static.c) such that the main() program would always load a 1920×1080 (a.k.a. 1080p) JPEG file (“bbb-1080p-title.jpg”, the Big Buck Bunny title), rather than requiring a command line argument. Then I used gettimeofday() to profile the core decoding function (njDecode()).

  Compiling with gcc and profiling execution :

  gcc -Wall nanojpeg-static.c -o nanojpeg-static
  ./nanojpeg-static
  

  Optimization levels such as -O0, -O3, or -Os can be applied to the compilation command.

  For JavaScript conversion, I installed Emscripten and converted using :

  /path/to/emscripten/emcc nanojpeg-static.c -o nanojpeg.html \
    —preload-file bbb-1080p-title.jpg -s TOTAL_MEMORY=32000000
  

  The ‘–preload-file’ option makes the file available to the program via standard C-style file I/O functions. The ‘-s TOTAL_MEMORY’ was necessary because the default of 16 MB wasn’t enough. Again, the -O optimization levels can be sent in.

  For running, the .html file is loaded (via webserver) in a web browser.

  Want To Try It Yourself ?
  I put the files here : http://multimedia.cx/emscripten/. The .c file, the JPEG file, and the Emscripten-converted files using -O0, -O1, -O2, -O3, -Os, and no optimization switch.

  Results and Charts
  Here is the spreadsheet with the raw results.

  I ran this experiment using Ubuntu Linux 12.04 on an Intel Atom N450-based netbook. For this part, I was able to compare the Chrome and Firefox browser results against the C results :

  
  
  

  These are the results for a 2nd generation Android Nexus 7 using both Chrome and Firefox :

  
  
  

  Here is the result for an iPad 2 running iOS 7 and Safari– there is no Firefox for iOS and while there is a version of Chrome for iOS, it apparently isn’t able to leverage an optimized JS engine. Chrome takes so long to complete this experiment that there’s no reason to muddy the graph with the results :
  

  
  
  

  Interesting that -O1 tends to provide better optimization than levels 2 or 3, and that -Os (optimize for size) seems to be a good all-around choice.

  Don’t Get Too Smug
  JavaScript can indeed get amazing performance in this day and age. Please be advised, however, that this isn’t the best that a C decoder implementation can possibly do. This version doesn’t leverage any SIMD extensions. According to profiling (using gprof against the C code), sample saturation in color conversion dominates followed by inverse DCT functions, common cases for SIMD ASM or intrinsics. Allegedly, there will be some support for JS SIMD optimizations some day. We’ll see.

  Implications For Development
  I’m still not especially motivated to try porting the entire Native Client game music player codebase to JavaScript. I’m still wondering about the recommended development flow. How are you supposed to develop for Emscripten and asm.js ? From what I can tell, Emscripten is not designed as a simple aide for porting C/C++ code to JS. No, it reduces the code into JS code you can’t possibly maintain. This seems to imply that the C/C++ code needs to be developed and debugged in its entirety and then converted to JS, which seems arduous.

• How HSBC and ING are transforming banking with AI

  9 novembre 2024, par Daniel Crough — Banking and Financial Services, Featured Banking Content

  We recently partnered with FinTech Futures to produce an exciting webinar discussing how analytics leaders from two global banks are using AI to protect customers, streamline operations, and support environmental goals.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

  Δ

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&lt;/script&gt;

  Meet the expert panel

  Roshini Johri heads ESG Analytics at HSBC, where she leads AI and remote sensing applications supporting the bank’s net zero goals. Her expertise spans climate tech and financial services, with a focus on scalable analytics solutions.

   

  Marco Li Mandri leads Advanced Analytics Strategy at ING, where he focuses on delivering high-impact solutions and strengthening analytics foundations. His background combines analytics, KYC operations, and AI strategy.

   

  Carmen Soini Tourres works as a Web Analyst Consultant at Matomo, helping financial organisations optimise their digital presence whilst maintaining privacy compliance.

   

  Key findings from the webinar

  The discussion highlighted four essential elements for advancing analytics capabilities :

  1. Strong data foundations matter most

  “It doesn’t matter how good the AI model is. It is garbage in, garbage out,”

  Johri explained. Banks need robust data governance that works across different regulatory environments.

  2. Transform rather than tweak

  Li Mandri emphasised the need to reconsider entire processes :

  “We try to look at the banking domain and processes and try to re-imagine how they should be done with AI.”

  3. Bridge technical and business understanding

  Both leaders stressed the value of analytics translators who understand both technology and business needs.

  “We’re investing in this layer we call product leads,”

  Li Mandri explained. These roles combine technical knowledge with business acumen – a rare but vital skill set.

  4. Consider production costs early

  Moving from proof-of-concept to production requires careful planning. As Johri noted :

  “The scale of doing things in production is quite massive and often doesn’t get accounted for in the cost.”

  This includes :

  • Ongoing monitoring requirements
  • Maintenance needs
  • Regulatory compliance checks
  • Regular model updates

  Real-world applications

  ING’s approach demonstrates how banks can transform their operations through thoughtful AI implementation. Li Mandri shared several areas where the bank has successfully deployed analytics solutions, each benefiting both the bank and its customers.

  Customer experience enhancement

  The bank’s implementation of AI-powered instant loan processing shows how analytics can transform traditional banking.

  “We know AI can make loans instant for the customer, that’s great. Clicking one button and adding a loan, that really changes things,”

  Li Mandri explained. This goes beyond automation – it represents a fundamental shift in how banks serve their customers.

  The system analyses customer data to make rapid lending decisions while maintaining strong risk assessment standards. For customers, this means no more lengthy waiting periods or complex applications. For the bank, it means more efficient resource use and better risk management.

  The bank also uses AI to personalise customer communications.

  “We’re using that to make certain campaigns more personalised, having a certain tone of voice,”

  noted Li Mandri. This particularly resonates with younger customers who expect relevant, personalised interactions from their bank.

  Operational efficiency transformation

  ING’s approach to Know Your Customer (KYC) processes shows how AI can transform resource-heavy operations.

  “KYC is a big area of cost for the bank. So we see massive value there, a lot of scale,”

  Li Mandri explained. The bank developed an AI-powered system that :

  • Automates document verification
  • Flags potential compliance issues for human review
  • Maintains consistent standards across jurisdictions
  • Reduces processing time while improving accuracy

  This implementation required careful consideration of regulations across different markets. The bank developed monitoring systems to ensure their AI models maintain high accuracy while meeting compliance standards.

  In the back office, ING uses AI to extract and process data from various documents, significantly reducing manual work. This automation lets staff focus on complex tasks requiring human judgment.

  Sustainable finance initiatives

  ING’s commitment to sustainable banking has driven innovative uses of AI in environmental assessment.

  “We have this ambition to be a sustainable bank. If you want to be a sustainable finance customer, that requires a lot of work to understand who the company is, always comparing against its peers.”

  The bank developed AI models that :

  • Analyse company sustainability metrics
  • Compare environmental performance against industry benchmarks
  • Assess transition plans for high-emission industries
  • Monitor ongoing compliance with sustainability commitments

  This system helps staff evaluate the environmental impact of potential deals quickly and accurately.

  “We are using AI there to help our frontline process customers to see how green that deal might be and then use that as a decision point,”

  Li Mandri noted.

  HSBC’s innovative approach

  Under Johri’s leadership, HSBC has developed several groundbreaking uses of AI and analytics, particularly in environmental monitoring and operational efficiency. Their work shows how banks can use advanced technology to address complex global challenges while meeting regulatory requirements.

  Environmental monitoring through advanced technology

  HSBC uses computer vision and satellite imagery analysis to measure environmental impact with new precision.

  “This is another big research area where we look at satellite images and we do what is called remote sensing, which is the study of a remote area,”

  Johri explained.

  The system provides several key capabilities :

  • Analysis of forest coverage and deforestation rates
  • Assessment of biodiversity impact in specific regions
  • Monitoring of environmental changes over time
  • Measurement of environmental risk in lending portfolios

  “We can look at distant images of forest areas and understand how much percentage deforestation is being caused in that area, and we can then measure our biodiversity impact more accurately,”

  Johri noted. This technology enables HSBC to :

  • Make informed lending decisions
  • Monitor environmental commitments of borrowers
  • Support sustainability-linked lending programmes
  • Provide accurate environmental impact reporting

  Transforming document analysis

  HSBC is tackling one of banking’s most time-consuming challenges : processing vast amounts of documentation.

  “Can we reduce the onus of human having to go and read 200 pages of sustainability reports each time to extract answers ?”

  Johri asked. Their solution combines several AI technologies to make this process more efficient while maintaining accuracy.

  The bank’s approach includes :

  • Natural language processing to understand complex documents
  • Machine learning models to extract relevant information
  • Validation systems to ensure accuracy
  • Integration with existing compliance frameworks

  “We’re exploring solutions to improve our reporting, but we need to do it in a safe, robust and transparent way.”

  This careful balance between efficiency and accuracy exemplifies HSBC’s approach to AI.

  Building future-ready analytics capabilities

  Both banks emphasise that successful analytics requires a comprehensive, long-term approach. Their experiences highlight several critical considerations for financial institutions looking to advance their analytics capabilities.

  Developing clear governance frameworks

  “Understanding your AI risk appetite is crucial because banking is a highly regulated environment,”

  Johri emphasised. Banks need to establish governance structures that :

  • Define acceptable uses for AI
  • Establish monitoring and control mechanisms
  • Ensure compliance with evolving regulations
  • Maintain transparency in AI decision-making

  Creating solutions that scale

  Li Mandri stressed the importance of building systems that grow with the organisation :

  “When you try to prototype a model, you have to take care about the data safety, ethical consideration, you have to identify a way to monitor that model. You need model standard governance.”

  Successful scaling requires :

  • Standard approaches to model development
  • Clear evaluation frameworks
  • Simple processes for model updates
  • Strong monitoring systems
  • Regular performance reviews

  Investing in people and skills

  Both leaders highlighted how important skilled people are to analytics success.

  “Having a good hiring strategy as well as creating that data literacy is really important,”

  Johri noted. Banks need to :

  • Develop comprehensive training programmes
  • Create clear career paths for analytics professionals
  • Foster collaboration between technical and business teams
  • Build internal expertise in emerging technologies

  Planning for the future

  Looking ahead, both banks are preparing for increased regulation and growing demands for transparency. Key focus areas include :

  • Adapting to new privacy regulations
  • Making AI decisions more explainable
  • Improving data quality and governance
  • Strengthening cybersecurity measures

  Practical steps for financial institutions

  The experiences shared by HSBC and ING provide valuable insights for financial institutions at any stage of their analytics journey. Their successes and challenges outline a clear path forward.

  Key steps for success

  Financial institutions looking to enhance their analytics capabilities should :

  1. Start with strong foundations
     • Invest in clear data governance frameworks
     • Set data quality standards
     • Build thorough documentation processes
     • Create transparent data tracking
  2. Think strategically about AI implementation
     • Focus on transformative rather than small changes
     • Consider the full costs of AI projects
     • Build solutions that can grow
     • Balance innovation with risk management
  3. Invest in people and processes
     • Develop internal analytics expertise
     • Create clear paths for career growth
     • Foster collaboration between technical and business teams
     • Build a culture of data literacy
  4. Plan for scale
     • Establish monitoring systems
     • Create governance frameworks
     • Develop standard approaches to model development
     • Stay flexible for future regulatory changes

  Learn more

  Want to hear more insights from these industry leaders ? Watch the complete webinar recording on demand. You’ll learn :

  • Detailed technical insights from both banks
  • Extended Q&A with the speakers
  • Additional case studies and examples
  • Practical implementation advice

   

   

  Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

  Watch the on-demand webinar : Advancing analytics maturity.

  Email

  By providing your email and clicking “submit”, you agree to receive direct marketing materials relating to Matomo products and services, surveys, information about events, publications and promotions. You can unsubscribe at any time by clicking the opt-out link provided in each communication. We will process your personal information in accordance with our Privacy Policy.

  
  
  
  
  

  Δ

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• Adding text to movie using ffmpeg

  23 août 2014, par microspace

  I use git to track *.ass subtitle files.
  Here is example of *.ass file :

  [Script Info]
  
  ; Script generated by Aegisub 3.1.2
  
  ; http://www.aegisub.org/
  
  Title: Default Aegisub file
  
  ScriptType: v4.00+
  
  
  
  [V4+ Styles]
  
  Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour,    BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
  
  Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,2,2,2,10,10,10,1
  
  Style: titr,DejaVu     
  
  
  
  Sans,20,&H007DDBFA,&H000000FF,&H00000000,&HFF000000,0,0,0,0,100,100,0,0,1,2,2,1,10,10,10,1
  
  
  
  [Events]
  
  Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
  
  Dialogue: 0,0:00:00.46,0:00:11.22,Default,,0,0,0,,Если это можно было бы
  
  Dialogue: 0,0:00:03.44,0:00:08.96,titr,,0,0,0,,{\pos(20,240)\fad(600,600)}бывший министр

  After commit I burn subtitles into video :

  ffmpeg -i video.avi -vf "ass=subtitle.ass" out.avi

  My goal is to show commit date for 10 second at the start of movie. This should be done automatically.

  1) It can be easily done with by modifying subtitle.ass itself, but I can’t do it after commit and there are other reasons.

  2) It can be done by ffmpeg from command line : How to use ffmpeg to add a text to avi video ?

  Problem is that in this case text will be shown for the whole lenght of movie.

  3) I can copy *.ass file to temporary directory, insert date, render and delete *.ass file.

  Is there a simpler way ?