New York, NY 16 Oct 2019:The global encryption software market size is anticipated to reach USD 20.44 billion by 2026 according to a new research published by Polaris Market Research. The report “Encryption Software Market Share, Size, Trends, Industry Analysis Report By Deployment Model (On-Premise, Cloud-Based); By Application (File Encryption, Disk Encryption, Database Encryption, Cloud Encryption, Communication Encryption, Others); By Organization Size (Large Enterprises, Small and Medium Businesses); By End-User (BFSI, Healthcare, Aerospace and Defense, Government and Public Utilities, Retail, Others); By Regions, Segments & Forecast, 2019 – 2026” provides strong market indices and taps on future growth parameters.

In 2018, the BFSI segment dominated the global market in terms of revenue. North America was the leading contributor to global revenue in 2018. An urgency to protect critical data and growing number of data lapses has boosted the adoption of encryption software. The widespread growth of mobile devices and increasing trend of BYOD further support the growth of this market. The rising spread of virtualization, cloud and big data analytics has supported market growth over the years. Growing investments in technological advancements by vendors, coupled with growing demand for cloud-based encryption software would accelerate the growth of encryption software market during forecast period. However high costs related to advanced encryption solutions and an awareness shortage among small and medium enterprises hinder growth. Growing demand from developing economies and technological advancements are expected to provide several growth opportunities in the future.

North America generated highest revenue for market in 2018 and is expected to lead the global market throughout forecast period. The increase in number of cyber-attacks and growing number of data breaches drive the market growth. A growing trend of BYOD, IoT, big data analytics and virtualization evinces the need of encryption software for data protection and data loss. A rising penetration of mobile devices and technological advancements bolster growth in the region. A greater spending on data protection in BFSI and defense sectors in the region promotes growth in the region.

A rushing request from emerging economies, expanding adoption of the software by BFSI sector and flooding demand for cloud-based encryption solutions are factors boosting growth of product during forecast period.

The sample for the study can be requested using the following link: https://www.polarismarketresearch.com/industry-analysis/encryption-software-market/request-for-sample

Enormous walks in strong innovation, data loss among enterprises has made encryption software very crucial for safe data transmissions. Furthermore, as undertakings are pushing forward with distributed computing, the product has become all the more important to prevent data slips by safeguarding touchy information.

Asia Pacific is expected to display highest CAGR during forecast period owing to urging need for data integrity at all levels in the industries in developing countries of the region.

The companies include Microsoft Corporation, Symantec Corporation, IBM Corporation, EMC Corporation, CISCO Systems Inc., Intel Security, Check Point Software Technologies Ltd., Oracle Corporation, Trend Micro, Inc., and Sophos Group Plc. among others.

Polaris Market research has segmented the encryption software market report on the basis of deployment, application, organization size, end-use and region.

• Encryption Software Deployment Model Outlook (Revenue USD Millions 2015 – 2026)
  • On-Premise
  • Cloud- Based
• Encryption Software Application Outlook (Revenue USD Millions 2015 – 2026)
  • File Encryption
  • Disk Encryption
  • Database Encryption
  • Cloud Encryption
  • Communication Encryption
  • Others
• Encryption Software Organization Size Outlook (Revenue USD Millions 2015 – 2026)
  • Large Enterprises
  • Small Enterprises
  • Medium Enterprises
• Encryption Software End-user Outlook (Revenue USD Millions 2015 – 2026)
  • BFSI
  • Healthcare
  • Aerospace and Defense
  • Government and Public Utilities
  • Retail
  • Others
• Encryption Software Regional Outlook (Revenue USD Millions 2015 – 2026)
  • North America
    • US.
    • Canada
  • Europe
    • UK
    • France
    • Germany
    • Italy
  • Asia Pacific
    • India
    • Japan
    • China
  • Latin America
    • Brazil
    • Mexico
  • Middle East & Africa

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I am looking to buy a laptop that can handle basic ML/DL stuff for fastai and andrew ng’s deep learning and tensorflow course.

I’ve come across two laptops. One with i5 9th Gen + GTX 1650 4GB and another with Ryzen 5 3550H + 1650 4GB. Except the processor all the specs are same but Intel laptop costs 150$ higher than the Ryzen one.

I’ve looked around but haven’t found much. Some say ryzen is good and some say intel is way better because of the ‘Intel MKL’. Is ‘Intel MKL’ worth the extra 150$? What would you suggest?

This is a little video clip I made of a project of mine where I’ve used the 2D bounding box data from PJReddie’s YOLOv3 to guide the joint positions of a pan tilt servo.

https://m.youtube.com/watch?v=_iQWCRToUkA

The simplified version of how this works, is that I created an ROS node that utilizes OpenCV to syncronize the depth frame with the RGB camera frame from the Kinect 360. In the callback function, it then takes the centermost rgb pixel matrix coordinates of the bounding box of a detected object, and retrieves the depth data from the syncronized depth image (uv,xyz), and converts that to a pose-stamped message that is sent to a modified “head_tracker.py” module from the original rbx2 code (sourced below).

Some of the prep work and background on this project:

I used Google’s OpenImages V4 to train YOLO and create the pretrained weights for “Human Head” detection that I’m using here. (V5 is out now?.. mmmm juicy!) Here’s my tutorial I made for this process: https://github.com/WyattAutomation/Train-YOLOv3-with-OpenImagesV4

The version of ROS used here is Melodic, running on Xubuntu 18.04. You have to build the freenect dependencies and ROS package for the 360 Kinect from source (doesn’t appear to be an apt install option for melodic?)

I also used leggedrobotic’s darknet_ros ROS module for YOLO: https://github.com/leggedrobotics/darknet_ros

The robotics hardware I used here is a PhantomX pan/tilt turret from Trossen robotics with 2 Dynamixel ax18-a’s: https://www.trossenrobotics.com/p/phantomX-robot-turret.aspx

And probably the most important to mention out of all, I rewrote the “nearest_pointcloud” ROS node and other code from “Robotics by Example, Vol2”, retrofitting it for the purpose of tracking objects in 3D space from the 2D bounding box pixel coordinates published by the YOLOv3 ROS package: https://github.com/pirobot/rbx2

I’ll be willing to answer any questions on how I got this working, so feel free to ask!

I have a version of this that uses an Astra Orrbec Pro and I am nearly finished setting it up to run on a Raspberry Pi 4, with Darknet/YOLO running remotely and publishing rostopics over WAN or LAN from my desktop (using a GTX 1060 for YOLO).

I will post progress as it is made, as well as comprehensive documentation and source code on my GitHub account on this build, for whoever it may help. If you like it, feel free to visit my Patreon!

Hi everyone,

My current project requires the speech recognition model to output custom domain-specific vocabulary. To be honest, the domain specific named entities are the most important to output correctly. In my literature review, I was unable to find any paper that had inclusion of vocabulary that the model was not trained on. Any paper/article recommendations are welcome!

Does anyone know where to obtain the “3D Cars dataset” used in the disentanglement literature? e.g. this recent paper: https://arxiv.org/abs/1905.12614 (landing page), Figure 1.

I’ve basically traced the citation breadcrumb to this original paper: 1 (warning:pdf) with associated download 2 (WARNING: large .tar file) which only provides the basic 3D CAD models but not the more advanced latent factor variations shown in the linked Deepmind paper above. Would be nice for reproducibility of results.

I know it’s easier to learn robotics when I’ve decided on a robot to build. In the same way, I bet it’s easier to learn ML when you have a specific goal in mind. Maybe even something novel. So.

My challenge to you is to train a ML artist. this would be a network that inputs a bitmap picture and outputs a set of vectors that could be drawn by a plotter robot. I have lots of non-ML image->vector converters and I’m a big fan of turtletoy.net. I would like to see your network run on turtetoy given a source image. They could be any style you want. You don’t have to share your training data, just the final resulting weights and the NN to run in javascript on their site. Your result would be public for everyone to try and enjoy, while your trade secret training stays all yours.

I suspect I’m going to get a lot of “that’s dumb I won’t do that”, and I feel embarrassed making this post. But you miss 100% of the shots you don’t take, right?

Extrapolation on math is hard for NNs. We propose a new set of benchmarks and find current methods are fragile https://arxiv.org/abs/1910.01888.

So how should we build NNs that can learn the logic behind math? Inductive bias? More data does not seem to solve this problem!