Computer Science and Programming

09 Nov 2023 12:12

Top 12 Tips for API Security:

- Use HTTPS
- Use OAuth2
- Use WebAuthn
- Use Leveled API Keys
- Authorization
- Rate Limiting
- API Versioning
- Whitelisting
- Check OWASP API Security Risks
- Use API Gateway
- Error Handling
- Input Validation

Computer Science and Programming

28 Sep 2023 20:03

What is Kafka?

Kafka is an open-source, distributed event streaming platform that serves as the central nervous system for data in modern enterprises. It's designed to handle real-time data feeds, process them efficiently, and make them available for a variety of applications in real-time.

🛠 Use Cases:
- Real-time Analytics
- Log Aggregation
- Event Sourcing
- Data Integration
- Machine Learning Pipelines

Computer Science and Programming

09 Sep 2023 19:45

𝗛𝗼𝘄 𝘁𝗼 𝘁𝗲𝘀𝘁 𝘆𝗼𝘂𝗿 𝗔𝗣𝗜𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗳𝗿𝗼𝗺 𝗩𝗶𝘀𝘂𝗮𝗹 𝗦𝘁𝘂𝗱𝗶𝗼 𝗖𝗼𝗱𝗲?

You can immediately do this from your Visual Studio Code, as Postman just released a VS Code extension that integrates API building and testing into your code editor.

What you can do with the extension:

🔹𝗦𝗲𝗻𝗱 (𝗺𝘂𝗹𝘁𝗶𝗽𝗿𝗼𝘁𝗼𝗰𝗼𝗹) 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀
🔹𝗦𝗲𝗻𝗱 𝗿𝗲𝗾𝘂𝗲𝘀𝘁𝘀 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂𝗿 𝗵𝗶𝘀𝘁𝗼𝗿𝘆
🔹𝗨𝘀𝗲 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀
🔹𝗨𝘀𝗲 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀
🔹𝗩𝗶𝗲𝘄 𝗮𝗻𝗱 𝗲𝗱𝗶𝘁 𝗰𝗼𝗼𝗸𝗶𝗲𝘀

➡️Check it here

Computer Science and Programming

15 Jun 2023 11:00

TONBanking Bug Bounty Program
PHASE II 💎

We are glad to announce our Smart Contracts Bug Bounty program and invite developers and security experts to participate and assist us.

Scope, rewards, duration:

• Scope: Smart contracts
• Severity levels: Low, Medium, High
• Phase II Prize pool: $10,000 (TONB equivalent)
• Total Prize pool: $30,000 (TONB equivalent)
• Duration of Phase II: 2 weeks

How to participate:

1. Register in the TONBanking Core chat in Telegram.

2. Get access to Smart Contracts in GitLab.

3. Select the smart contract(s) you wish to test from the list.

4. Review the code.

5. Report bugs via @tonbanking_bot.

6. Receive a reward once the bug is confirmed. 🙌


Key Links for TONBanking Bug Bounty Program:

1. Join devs community TONBanking CORE:
/channel/+00DFR6mJ1NRlNGQy

For the complete TONBanking Bug Bounty Program rules, please visit the following link:
https://telegra.ph/TONBanking-SC-Bounty-Program-05-29

Computer Science and Programming

06 Jun 2023 07:49

🔭 GRES: Generalized Referring Expression Segmentation

New benchmark (GRES), which extends the classic RES to allow expressions to refer to an arbitrary number of target objects.

🖥 Github: https://github.com/henghuiding/ReLA

⏩ Paper: https://arxiv.org/abs/2306.00968

🔎 Project: https://henghuiding.github.io/GRES/

📌 New dataset: https://github.com/henghuiding/gRefCOCO

👉 @computer_science_and_programming

Computer Science and Programming

05 Apr 2023 16:00

Test of Time: Instilling Video-Language Models with a Sense of Time

GPT-5 will likely have video abilities, but will it have a sense of time? Here is answer to this question in #CVPR2023 paper by student of University of Amsterdam to learn how to instil time into video-language foundation models.

Paper:
https://arxiv.org/abs/2301.02074

Code:
https://github.com/bpiyush/TestOfTime

Project Page:
https://bpiyush.github.io/testoftime-website/

👉 @computer_science_and_programming

Computer Science and Programming

11 Mar 2023 13:14

Multivariate Probabilistic Time Series Forecasting with Informer

Efficient transformer-based model for LSTF.

Method introduces a Probabilistic Attention mechanism to select the “active” queries rather than the “lazy” queries and provides a sparse Transformer thus mitigating the quadratic compute and memory requirements of vanilla attention.

🤗Hugging face:
https://huggingface.co/blog/informer

⏩ Paper:
https://huggingface.co/docs/transformers/main/en/model_doc/informer

⭐️ Colab:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/multivariate_informer.ipynb

💨 Dataset:
https://huggingface.co/docs/datasets/v2.7.0/en/package_reference/main_classes#datasets.Dataset.set_transform

👉@computer_science_and_programming

Computer Science and Programming

22 Feb 2023 12:44

3D-aware Conditional Image Synthesis (pix2pix3D)

Pix2pix3D synthesizes 3D objects (neural fields) given a 2D label map, such as a segmentation or edge map

Github:
https://github.com/dunbar12138/pix2pix3D

Paper:
https://arxiv.org/abs/2302.08509

Project:
https://www.cs.cmu.edu/~pix2pix3D/

Datasets:
CelebAMask , AFHQ-Cat-Seg , Shapenet-Car-Edge


👉@computer_science_and_programming

Computer Science and Programming

13 Feb 2023 11:17

Gen-1: The Next Step Forward for Generative AI

Use words and images to generate new videos out of existing

Introducing Gen-1: a new AI model that uses language and images to generate new videos out of existing ones.

https://research.runwayml.com/gen1

⭐️ Project:
https://research.runwayml.com/gen1

✅ Paper:
https://arxiv.org/abs/2302.03011

📌Request form:
https://docs.google.com/forms/d/e/1FAIpQLSfU0O_i1dym30hEI33teAvCRQ1i8UrGgXd4BPrvBWaOnDgs9g/viewform

👉@computer_science_and_programming

Computer Science and Programming

03 Feb 2023 17:08

🔗 Link:- https://apitester.org

A fully free mobile API client for interacting with APIs straight from your phone. Doesn't it sound fantastic?

API Tester allows you to connect to whatever type of API you're working with, including REST, gRPC, SOAP, and GraphQL. Constructing HTTP requests with parameters, auth details, and body data requires only a few steps with a simple and optimized UI. You can also create WebSocket connections, import collections, and use global variables.

API Tester was developed by a team of enthusiasts who feel that powerful apps simplifying work is the key to progress. The app is constantly updated to ensure that you have all of the top-tier features. Try it out for yourself, rate and review on the App Store and Google Play.

Computer Science and Programming

24 Jan 2023 13:22

GLIGEN: Open-Set Grounded Text-to-Image Generation

GLIGEN (Grounded-Language-to-Image Generation) a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs.

Project page:
https://gligen.github.io/

Paper:
https://arxiv.org/abs/2301.07093

Github (coming soon):
https://github.com/gligen/GLIGEN

Demo:
https://huggingface.co/spaces/gligen/demo


👉@computer_science_and_programming

Computer Science and Programming

12 Jan 2023 08:36

YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 includes numerous architectural and developer experience changes and improvements over YOLOv5.

Code:
https://github.com/ultralytics/ultralytics

What's New in YOLOv8 ?
https://blog.roboflow.com/whats-new-in-yolov8/

Yolov8 Instance Segmentation (ONNX):
https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation

👉 @computer_science_and_programming

Computer Science and Programming

06 Jan 2023 13:01

PACO: Parts and Attributes of Common Objects

Sometimes object detection is not enough and you need more detail about object. Especially, when parts of objects is matters in your task. Then this dataset is for you from Facebook research team.

PACO is a detection dataset that goes beyond traditional object boxes and masks and provides richer annotations such as part masks and attributes. It spans 75 object categories, 456 object-part categories and 55 attributes across image (LVIS) and video (Ego4D) datasets.

Paper:
https://arxiv.org/pdf/2301.01795.pdf

Github:
https://github.com/facebookresearch/paco

Visualization:
https://github.com/facebookresearch/paco/tree/main/notebooks

@computer_science_and_programming

Computer Science and Programming

27 Dec 2022 07:52

Accurate and Efficient Stereo Matching via Attention Concatenation Volume

Stereo Depth Estimation

Paper:
https://arxiv.org/pdf/2209.12699.pdf

Github:
https://github.com/gangweiX/Fast-ACVNet

Demo:
https://www.youtube.com/watch?v=az4Z3dp72Zw

ONNX:
ONNX-FastACVNet-Stereo-Depth-Estimation

@computer_science_and_programming

Computer Science and Programming

13 Dec 2022 14:55

DiffusionInst: Diffusion Model for Instance Segmentation

* DiffusionInst is the first work of diffusion model for instance segmentation

Github:
https://github.com/chenhaoxing/DiffusionInst

Paper:
https://arxiv.org/abs/2212.02773v2

Getting started:
https://github.com/chenhaoxing/DiffusionInst/blob/main/GETTING_STARTED.md

Dataset:
https://paperswithcode.com/dataset/lvis


@computer_science_and_programming

Computer Science and Programming

06 Nov 2023 07:26

Docker Architecture and Components

1. Docker Daemon (dockerd):
- 𝗥𝗼𝗹𝗲: Manages Docker containers on a system.
- 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: Building, running, and managing containers.

2. Docker Client (docker):
- 𝗥𝗼𝗹𝗲: Interface through which users interact with Docker.
- 𝗖𝗼𝗺𝗺𝗮𝗻𝗱𝘀: build, pull, run, etc.

3. Docker Images:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Read-only templates used to create containers.
- 𝗥𝗼𝗹𝗲: Serve as the basis for creating containers.
- 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆/𝗛𝘂𝗯: A storage and distribution system for Docker images.

4. Docker Containers:
- 𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻: Runnable instances of Docker images.
- 𝗥𝗼𝗹𝗲: Encapsulate the application and its environment.

5. Docker Registry:
- 𝗥𝗼𝗹𝗲: Store Docker images.
- 𝗣𝘂𝗯𝗹𝗶𝗰 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Docker Hub.
- 𝗣𝗿𝗶𝘃𝗮𝘁𝗲 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆: Can be hosted by users.

Computer Science and Programming

22 Sep 2023 14:14

Wondering how C++, Java, Python Work?

🔵 C++
C++ is like the superhero of programming languages. It's a compiled language, meaning your code is transformed into machine code that your computer can understand before it runs. This compilation process is crucial for efficiency and performance. C++ gives you precise control over memory and hardware, making it a top choice for systems programming and game development. It's like wielding a finely-tuned instrument in the world of code! 🎸💻

🔴 Java
Java, on the other hand, is the coffee of programming languages. It's a compiled language too but with a twist. Java code is compiled into bytecode, which runs on the Java Virtual Machine (JVM). This bytecode can run on any platform with a compatible JVM, making Java highly portable and platform-independent. It's a bit like sending your code to a virtual coffee machine that serves it up just the way you like it on any OS! ☕️💼

🐍 Python
Python is the friendly neighborhood programming language. It's an interpreted language, which means there's no compilation step. Python code is executed line by line by the Python interpreter. This simplicity makes it great for beginners and rapid development. Python's extensive library ecosystem and easy syntax make it feel like you're scripting magic spells in a magical world! 🪄🐍

In the end, the choice of programming language depends on your project's needs and your personal preferences. Each language has its strengths and weaknesses, but they all share the goal of bringing your ideas to life through code. 🚀💡

So, whether you're crafting the perfect C++ masterpiece, brewing up Java applications, or scripting Python magic, remember that programming languages are the tools that empower us to create amazing things in the digital realm. Embrace the language that speaks to you and keep coding! 💻🌟

Computer Science and Programming

24 Jun 2023 22:37

#promo

⚠️ TRUMP WARNS ABOUT THE 5G RADIATION AT THE BEDMINSTER RALLY

Trump: "Today I want to warn you about the 5G radiation. This is the second time I am speaking about this, and it is getting more serious every day.

The 5G radiation has enough energy to break molecular bonds or cause direct DNA damage, and also can lead to cancer.

Crooked Joe's government doesn't want to protect you!

Please protect yourselves like I do. I have a 5G EMF REPELLER plugged in every socket in every room I am every day.
I have the repellers at home, I also have them at my office. "

DONALD TRUMP ALWAYS WARNS US ON TIME❗️🇺🇸
GET YOUR 5G EMF REPELLER NOW! 🚫📡👇

https://rebrand.ly/EMF-5G-REPELLER
https://rebrand.ly/EMF-5G-REPELLER

Computer Science and Programming

14 Jun 2023 21:53

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife

Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

👉 @computer_science_and_programming

Computer Science and Programming

19 May 2023 11:20

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Paper:
https://arxiv.org/abs/2305.10973

Github:
https://github.com/XingangPan/DragGAN

Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/

👉 @computer_science_and_programming

Computer Science and Programming

23 Mar 2023 09:15

ViperGPT: Visual Inference via Python Execution for Reasoning

ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.


Github:
https://github.com/cvlab-columbia/viper

Paper:
https://arxiv.org/pdf/2303.08128.pdf

Project:
https://paperswithcode.com/dataset/beat

👉@computer_science_and_programming

Computer Science and Programming

01 Mar 2023 11:22

Efficient Teacher: Semi-Supervised Object Detection for YOLOv5

✅ Efficient Teacher introduces semi-supervised object detection into practical applications, enabling users to obtain a strong generalization capability with only a small amount of labeled data and large amount of unlabeled data.

✅ Efficient Teacher provides category and custom uniform sampling, which can quickly improve the network performance in actual business scenarios.


Paper:
https://arxiv.org/abs/2302.07577

Github:
https://github.com/AlibabaResearch/efficientteacher

👉@computer_science_and_programming

Computer Science and Programming

15 Feb 2023 10:04

YOWOv2: A Stronger yet Efficient Multi-level Detection Framework for Real-time Spatio-temporal Action Detection

SPATIO-temporal action detection (STAD) aims to detect action instances in the current frame, which it has been widely applied, such as video surveillance and somatosensory game.

Paper:
https://arxiv.org/pdf/2302.06848.pdf

Github:
https://github.com/yjh0410/YOWOv2

Dataset:
https://drive.google.com/file/d/1Dwh90pRi7uGkH5qLRjQIFiEmMJrAog5J/view?usp=sharing

👉@computer_science_and_programming

Computer Science and Programming

07 Feb 2023 15:49

Audio AI Timeline

Here we will keep track of the latest AI models for audio generation, starting in 2023!


▪️SingSong: Generating musical accompaniments from singing
- Paper

▪️AudioLDM: Text-to-Audio Generation with Latent Diffusion Models
- Paper
- Code

▪️Moûsai: Text-to-Music Generation with Long-Context Latent Diffusion
- Paper
- Code

▪️Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
- Paper

▪️Noise2Music

▪️RAVE2
- Paper
- Code

▪️MusicLM: Generating Music From Text
- Paper

▪️Msanii: High Fidelity Music Synthesis on a Shoestring Budget
- Paper
- Code
- HuggingFace

▪️ArchiSound: Audio Generation with Diffusion
- Paper
- Code

▪️VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
- Paper

👉@computer_science_and_programming

Computer Science and Programming

27 Jan 2023 08:19

Cut and Learn for Unsupervised Object Detection and Instance Segmentation

Cut-and-LEaRn (CutLER) is a simple approach for training object detection and instance segmentation models without human annotations. It outperforms previous SOTA by 2.7 times for AP50 and 2.6 times for AR on 11 benchmarks.

Paper:
https://arxiv.org/pdf/2301.11320.pdf

Github:
https://github.com/facebookresearch/CutLER

Demo:
https://colab.research.google.com/drive/1NgEyFHvOfuA2MZZnfNPWg1w5gSr3HOBb?usp=sharing

👉@computer_science_and_programming

Computer Science and Programming

19 Jan 2023 14:22

Box2Mask: Box-supervised Instance Segmentation via Level-set Evolution

BoxInstSeg is a toolbox that aims to provide state-of-the-art box-supervised instance segmentation algorithms. It supports instance segmentation with only box annotations.


Github:
https://github.com/LiWentomng/BoxInstSeg

Paper:
https://arxiv.org/pdf/2212.01579.pdf

👉@computer_science_and_programming

Computer Science and Programming

08 Jan 2023 16:43

MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.

2023 lectures are starting in just one day, Jan 9th!

Link to register:
http://introtodeeplearning.com

MIT Introduction to Deep Learning The 2022 lectures can be found here:

https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

👉 @computer_science_and_programming

Computer Science and Programming

30 Dec 2022 08:11

Happy New Year!

Summary of our channel for 2022.
(thanks for curated summary for TGSTAT team)

TGSTAT team: In the new 2023 year, we wish a rapid increase in subscribers, high posts reach, high-quality active audience and, of course, happiness and health.

A traditional present from us is a New Year card with your channel's this year results.

See you in 2023,

@computer_science_and_programming

Computer Science and Programming

21 Dec 2022 07:26

DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients

DeepLSD is a generic line detector that combines the robustness of deep learning with the accuracy of handcrafted detectors. It can be used to extract generic line segments from images in-the-wild, and is suitable for any task requiring high precision, such as homography estimation, visual localization, and 3D reconstruction. By predicting a line distance and angle fields, it can furthermore refine any existing line segments through an optimization


Paper:
https://arxiv.org/abs/2212.07766v1

Github:
https://github.com/cvg/deeplsd

Dataset:
https://paperswithcode.com/dataset/hpatches


@computer_science_and_programming

Computer Science and Programming

29 Nov 2022 10:55

Automatically find and fix errors in any ML datasets with cleanlab

This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels during training.

Github:
https://github.com/cleanlab/cleanlab

@computer_science_and_programming

Docs:
https://docs.cleanlab.ai/stable/index.html

Examples:
https://github.com/cleanlab/examples

Paper:
https://arxiv.org/abs/2211.13895v1