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• vc2enc : do not allocate packet until exact frame size is known

  2 mars 2016, par Rostislav Pehlivanov

  vc2enc : do not allocate packet until exact frame size is known
  This commit solves most of the crashes and issues with the encoder and
  
  the bitrate setting. Now the encoder will always allocate the absolute
  
  lowest amount of memory regardless of what the bitrate has been set to.
  
  Therefore if a user inputs a very low bitrate the encoder will use the
  
  maximum possible quantization (basically zero out all coefficients),
  
  allocate a packet and encode it. There is no coupling between the
  
  bitrate and the allocation size and so no crashes because the buffer
  
  isn’t large enough.
  The maximum quantizer was raised to the size of the table now to both
  
  keep the overshoot at ridiculous bitrates low and to improve quality
  
  with higher bit depths (since the coefficients grow larger per transform
  
  quantizing them to the same relative level requires larger quantization
  
  indices).
  Since the quantization index start follows the previous quantization
  
  index for that slice, the quantization step was reduced to a static 1
  
  to improve performance. Previously with quant/5 the step was usually
  
  set to 0 upon start (and was later clipped to 1), that isn’t a big change.
  
  As the step size increases so does the amount of bits leftover and so
  
  the redistribution algorithm has to iterate more and thus waste more
  
  time.
  Signed-off-by : Rostislav Pehlivanov <atomnuker@gmail.com>
  

  • [DH] libavcodec/vc2enc.c

• Four Trends Shaping the Future of Analytics in Banking

  27 novembre 2024, par Daniel Crough — Banking and Financial Services

  While retail banking revenues have been growing in recent years, trends like rising financial crimes and capital required for generative AI and ML tech pose significant risks and increase operating costs across the financial industry, according to McKinsey’s State of Retail Banking report.

   

  Today’s financial institutions are focused on harnessing AI and advanced analytics to make their data work for them. To be up to the task, analytics solutions must allow banks to give consumers the convenient, personalised experiences they want while respecting their privacy.

   

  In this article, we’ll explore some of the big trends shaping the future of analytics in banking and finance. We’ll also look at how banks use data and technology to cut costs and personalise customer experiences.
  
  So, let’s get into it.

  

  This doesn’t just represent a security risk, it also impacts the usability for both customers and employees. Does any of the following sound familiar ?

  • Only specific senior employees know how to navigate the software to generate custom reports or use its more advanced features.
  • Customer complaints about your site’s usability or online banking experience are routine.
  • Onboarding employees takes much longer than necessary because of convoluted systems.
  • Teams and departments experience ‘data siloing,’ meaning that not everyone can access the data they need.

  These are warning signs that IT systems are ready for a review. Anyone thinking, “If it’s not broken, why fix it ?” should consider that legacy systems can also present data security risks. As more countries introduce regulations to protect customer privacy, staying ahead of the curve is increasingly important to avoid penalties and litigation.

  And regulations aren’t the only trends impacting the future of financial institutions’ IT and analytics.

  4 trends shaping the future of analytics in banking

  New regulations and new technology have changed the landscape of analytics in banking.

  New privacy regulations impact banks globally

  The first major international example was the advent of GDPR, which went into effect in the EU in 2018. But a lot has happened since. New privacy regulations and restrictions around AI continue to roll out.

  • The European Artificial Intelligence Act (EU AI Act), which was held up as the world’s first comprehensive legislation on AI, took effect on 31 July 2024.
  • In Europe’s federated data initiative, Gaia-X’s planned cloud infrastructure will provide for more secure, transparent, and trustworthy data storage and processing.
  • The revised Payment Services Directive (PSD2) makes payments more secure and strengthens protections for European businesses and consumers, aiming to create a more integrated and efficient payments market.

  But even businesses that don’t have customers in Europe aren’t safe. Consumer privacy is a hot-button issue globally.

  For example, the California Consumer Privacy Act (CCPA), which took effect in January, impacts the financial services industry more than any other. Case in point, 34% of CCPA-related cases filed in 2022 were related to the financial sector.

  California’s privacy regulations were the first in the US, but other states are following closely behind. On 1 July 2024, new privacy laws went into effect in Florida, Oregon, and Texas, giving people more control over their data.

  

  One typical issue for companies in the banking industry is that their privacy measures regarding user data collected from their website are much less lax than those in their online banking system.

  It’s better to proactively invest in a privacy-centric analytics platform before you get tangled up in a lawsuit and have to pay a fine (and are forced to change your system anyway). 

  And regulatory compliance isn’t the only bonus of an ethical analytics solution. The right alternative can unlock key customer insights that can help you improve the user experience.

  The demand for personalised banking services

  At the same time, consumers are expecting a more and more streamlined personal experience from financial institutions. 86% of bank employees say personalisation is a clear priority for the company. But 63% described resources as limited or only available after demonstrating clear business cases.

  McKinsey’s The data and analytics edge in corporate and commercial banking points out how advanced analytics are empowering frontline bank employees to give customers more personalised experiences at every stage :

  • Pre-meeting/meeting prep : Using advanced analytics to assess customer potential, recommend products, and identify prospects who are most likely to convert
  • Meetings/negotiation : Applying advanced models to support price negotiations, what-if scenarios and price multiple products simultaneously
  • Post-meeting/tracking : Using advanced models to identify behaviours that lead to high performance and improve forecast accuracy and sales execution

  Today’s banks must deliver the personalisation that drives customer satisfaction and engagement to outperform their competitors.

  The rise of AI and its role in banking

  With AI and machine learning technologies becoming more powerful and accessible, financial institutions around the world are already reaping the rewards.

  McKinsey estimates that AI in banking could add $200 to 340 billion annually across the global banking sector through productivity gains.

  • Credit card fraud prevention : Algorithms analyse usage to flag and block fraudulent transactions.
  • More accurate forecasting : AI-based tools can analyse a broader spectrum of data points and forecast more accurately.
  • Better risk assessment and modelling : More advanced analytics and predictive models help avoid extending credit to high-risk customers.
  • Predictive analytics : Help spot clients most likely to churn 
  • Gen-AI assistants : Instantly analyse customer profiles and apply predictive models to suggest the next best actions.

  Considering these market trends, let’s discuss how you can move your bank into the future.

  Using analytics to minimise risk and establish a competitive edge 

  With the right approach, you can leverage analytics and AI to help future-proof your bank against changing customer expectations, increased fraud, and new regulations.

  Use machine learning to prevent fraud

  Every year, more consumers are victims of credit and debit card fraud. Debit card skimming cases nearly doubled in the US in 2023. The last thing you want as a bank is to put your customer in a situation where a criminal has spent their money.

  This not only leads to a horrible customer experience but also creates a lot of internal work and additional costs.Thankfully, machine learning can help identify suspicious activity and stop transactions before they go through. For example, Mastercard’s fraud prevention model has improved fraud detection rates by 20–300%.

  

  Implementing a solution like this (or partnering with credit card companies who use it) may be a way to reduce risk and improve customer trust.

  Foresee and avoid future issues with AI-powered risk management

  Regardless of what type of financial products organisations offer, AI can be an enormous tool. Here are just a few ways in which it can mitigate financial risk in the future :

  • Predictive analytics can evaluate risk exposure and allow for more informed decisions about whether to approve commercial loan applications.
  • With better credit risk modelling, banks can avoid extending personal loans to customers most likely to default.
  • Investment banks (or individual traders or financial analysts) can use AI- and ML-based systems to monitor market and trading activity more effectively.

  Those are just a few examples that barely scratch the surface. Many other AI-based applications and analytics use cases exist across all industries and market segments.

  Protect customer privacy while still getting detailed analytics

  New regulations and increasing consumer privacy concerns don’t mean banks and financial institutions should forego website analytics altogether. Its insights into performance and customer behaviour are simply too valuable. And without customer interaction data, you’ll only know something’s wrong if someone complains.

  Fortunately, it doesn’t have to be one or the other. The right financial analytics solution can give you the data and insights needed without compromising privacy while complying with regulations like GDPR and CCPA.

  That way, you can track usage patterns and improve site performance and content quality based on accurate data — without compromising privacy. Reliable, precise analytics are crucial for any bank that’s serious about user experience.

  Use A/B testing and other tools to improve digital customer experiences

  Personalised digital experiences can be key differentiators in banking and finance when done well. But there’s stiff competition. In 2023, 40% of bank customers rated their bank’s online and mobile experience as excellent. 

  Improving digital experiences for users while respecting their privacy means going above and beyond a basic web analytics tool like Google Analytics. Invest in a platform with features like A/B tests and user session analysis for deeper insights into user behaviour.

  

  Behavioural analytics are crucial to understanding customer interactions. By identifying points of friction and drop-off points, you can make digital experiences smoother and more engaging.

  Matomo offers all this and is a great GDPR-compliant alternative to Google Analytics for banks and financial institutions. 

  Of course, this can be challenging. This is why taking an ethical and privacy-centric approach to analytics can be a key competitive edge for banks. Prioritising data security and privacy will attract other like-minded, ethically conscious consumers and boost customer loyalty.

  Get privacy-friendly web analytics suitable for banking & finance with Matomo

  Improving digital experiences for today’s customers requires a solid web analytics platform that prioritises data privacy and accurate analytics. And choosing the wrong one could even mean ending up in legal trouble or scrambling to reconstruct your entire analytics setup.

  Matomo provides privacy-friendly analytics with 100% data accuracy (no sampling), advanced privacy controls and the ability to run A/B tests and user session analysis within the same platform (limiting risk and minimising costs). 

  It’s easy to get started with Matomo. Users can access clear, easy-to-understand metrics and plenty of pre-made reports that deliver valuable insights from day one. Form usage reports can help banks and fintechs identify potential issues with broken links or technical glitches and reveal clues on improving UX in the short term.

  Over one million websites, including some of the world’s top banks and financial institutions, use Matomo for their analytics.

  Start your 21-day free trial to see why, or book a demo with one of our analytics experts.

• GC and onTouch cause Fatal signal 11 (SIGSEGV) error in app using ffmpeg through ndk

  30 janvier 2015, par grzebyk

  I am getting a nasty but well known error while working with FFmpeg and NDK :

  A/libc(9845): Fatal signal 11 (SIGSEGV), code 1, fault addr 0xa0a9f000 in tid 9921 (AsyncTask #4)

  UPDATE

  After couple hours i found out that there might be two sources of the problem. One was related to multithreading. I checked it and I fixed it. Now the app crashes ONLY when the video playback (ndk) is on.

  I put a "counter" in touch event

    surfaceSterowanieKamera.setOnTouchListener(new View.OnTouchListener() {
  
              int counter = 0;
  
              @Override
  
              public boolean onTouch(View v, MotionEvent event) {             
  
                  if ((event.getAction() == MotionEvent.ACTION_MOVE)){
  
                      Log.i(TAG, "counter = " + counter);
  
                      //cameraMover.setPanTilt(some parameters);
  
                      counter++;
  
                   }

  And I started disabling other app functionalities one by one, but no video. I found out, that with every single functionality less, it takes app longer to crush - counter reaches higher values. After turning off everything besides video playback and touch interface (cameraMover.setPanTilt() commented out) the app crushes usually when counter is between 1600 - 1700.

  In such case logcat shows the above error and GC related info. For me it seems like GC is messing up with the ndk.

  01-23 12:27:13.163: I/Display Activity(20633): n = 1649
  
  01-23 12:27:13.178: I/art(20633): Background sticky concurrent mark sweep GC freed 158376(6MB) AllocSpace objects, 1(3MB) LOS objects, 17% free, 36MB/44MB, paused 689us total 140.284ms
  
  01-23 12:27:13.169: A/libc(20633): Fatal signal 11 (SIGSEGV), code 1, fault addr 0x9bd6ec0c in tid 20734 (AsyncTask #3)

  Why is GC causing problem with ndk part of application ?

  ---

  ORIGINAL PROBLEM

  What am I doing ?

  I am developing an application that streams live video feed from a webcam and enables user to pan and tilt the remote camera. I am using FFmpeg library built with NDK to achieve smooth playback with little delay.

  I am using FFMpeg library to connect to the video stream. Then the ndk part creates bitmap, does the image processing and render frames on the SurfaceView videoSurfaceView object which is located in the android activity (java part).

  To move the webcam I created a separate class - public class CameraMover implements Runnable{/**/}. This class is a separate thread that connects through sockets with the remote camera and manages tasks connected ONLY with pan-tilt movement.

  Next in the main activity i created a touch listener

  videoSurfaceView.setOnTouchListener(new View.OnTouchListener() {/**/
  
  cameraMover.setPanTilt(some parameters);
  
  /**/}

  which reads user’s finger movement and sends commands to the camera.

  All tasks - moving camera around, touch interface and video playback are working perfectly when the one of the others is disabled, i.e. when I disable possibility to move camera, I can watch video streaming and register touch events till the end of time (or battery at least). The problem occurs only when task are configured to work simultaneously.

  I am unable to find steps to reproduce the problem. It just happens, but only after user touches the screen to move camera. It can be 15 seconds after first interaction, but sometimes it takes app 10 or more minutes to crash. Usually it is something around a minute.

  What have I done to fix it ?

  • I tried to display millions of logs in logcat to find an error but
    the last log was always different.
  • I created a transparent surface, that I put over the videoSurfaceView and assigned touch listener to it. It all ended in the same error.
  • As I mentioned before, I turned off some functionalities to find which one produces the error, but it appears that error occurs only when everything is working simultaneously.

  Types of the error

  Almost every time the error looks like this :

  A/libc(11528): Fatal signal 11 (SIGSEGV), code 1, fault addr 0x9aa9f00c in tid 11637 (AsyncTask #4)

  the difference between two errors is the number right after libc, addr number and tid number. Rarely the AsyncTask number varies - i received #1 couple times but I was unable to reproduce it.

  Question

  How can i avoid this error ? What can be the source of it ?