Big Data and Cognitive Computing MDPI’s Post

Groups are out🔴and DDNS keys are in🟢, you heard it here first. Still using No-IP Groups? Discover why it’s time to make the switch to No-IP’s newest feature, DDNS keys. https://lnkd.in/gKGn_HDN

Easily one of my favorite boxes as of late. The priv esc is pretty breezy, but I feel like the initial access was perfect for my CBBH prep and I would imagine also helpful in prepping for the PJWT, PWPT, etc. Without giving away any spoilers it's perfect practice with web enumeration and chaining web attacks for initial access. https://lnkd.in/eUN_KN2y

JEP-461 is ready as a preview in JDK 22. Looks like an interesting one. A good number of intermediate operations for Stream API are on the way!

Users or implementors of #SHACL_Compact (#SHACLC), post here https://lnkd.in/ddjVZPt8. This is an important feature competitive to #SHEX

🔥 CTR Manipulation Secrets Revealed! 💥 Learn what works, what doesn't, and why in this mind-blowing video! 🤯 Let's dive right in and discover the power of multiple PCs for guaranteed success! 💪 #CTRmanipulation #SecretsRevealed #MindBlowingVideo #MultiplePCs #GuaranteedSuccess

One of the most critical aspects of an investigation is maintaining focus. Enabling focus was one reason why I didn't want users to have to change computer programs or tabs. We wanted users to be able to easily switch between collection, verification, and a right-clicking-to-monitor function that allows you to create your own monitoring feed. The smallest shift can disrupt the value of focus, misdirecting you away from asking one of the best questions in a case. Monitor easily in any language for the things that matter to you with ShadowDragon. https://lnkd.in/efArbbc2 #OSINT #PAI #BrandMonitoring

Private Proof Markets using Multiparty Computation (MPC) In our latest blog post we would like to show you how to combine #ZK and #MPC and share our plans for enhancing the privacy of ZK proof markets, starting with =nil; Foundation's proof market: https://lnkd.in/drQvh2DP

In tackling a real-life problem related to spam SMS detection, I developed a model that classifies messages as either spam or not. By thoroughly analyzing the confusion matrix, I was able to identify areas for improvement, leading me to optimize the model's performance. Ultimately, I found that the Random Forest Ensemble Technique provided the best results, significantly enhancing the model's accuracy and reliability. #MachineLearning#Spam Classifier

Spoiler alert - Operation OShINT — Shake the Cobwebs here is the walkthrough https://lnkd.in/gbqcDeyJ if you want to try it before or with the walkthrough, here is the Operation link: https://lnkd.in/g_HqbFNs

The best way to package for MSIX.