Dynatrace’s Post

❄ Snow depth (SD) datasets are scarce, making stochastic generation crucial. Here, we've advanced the CoSMoS tool to accurately model SD characteristics, allowing for reliable SD time series generation at multiple time scales. ☃ Simon Michael Papalexiou Full paper (open access): https://lnkd.in/gcmMMFqs

[Causal Inference 🤓 ] After some (long) discussions with the great David Masip Bonet, I've just merged a new capability into the cluster-experiments package. Now, you can also use synthetic control as a type of analysis, where you see the power from using a treatment cluster and keeping the rest of clusters as control. This is useful because we often see discussions on the effectiveness of this method, but this simulation approach is the closest you can get to understand how useful it is for your problem, rather than some generic example. https://lnkd.in/dFF8NDZ4 Some credit goes to Matheus Facure, as I took some code snippets from his book Causal Inference for The Brave and True :)

Wake Walk Water An easy and awesome morning protocol for you. Here’s the quick science to explain why this is helpful.

7 months ago, we began systematizing knowledge on foundation models. Here's a summary of our findings: - key concepts - techniques - resources 👇🏼 https://lnkd.in/eHSnFJW2

Deep fake and AI when combined can cause a real threat to what is true and what is fake. At this pace, after some time we’ll have a huge issue in identifying what’s real from fake when the technology out smarts reverse engineering.

✅ Great to hear how the 'week in the life of' analysis from Dixons Academies Trust has helped unlock capacity. ℹ This approach works across all types of businesses and sectors, from education to financial services, and everything in between! Would you be interested in finding out how we can unlock your team's capacity and focus? Drop us a message 👍 Duncan DeLaMare Liam Kenchenten Kevin Baldwin

Ofcom Deepfake defences discussion paper (2024) Share and follow Threat Intelligence Lab!

**1. Overview of Deepfakes** - Definition: AI-generated or manipulated audio-visual content misrepresenting someone or something. - Public Concern: Increased due to incidents like fake videos and audio involving public figures. - Key Statistics: 43% of adults and 50% of teens reported encountering deepfakes recently. **2. Deepfakes and Harm** - **Demeaning**: Non-consensual intimate images to humiliate; e.g., sexual deepfakes targeting women. - **Defrauding**: Used in scams like fake advertisements and fraudulent communications. - **Disinforming**: Spreads misinformation on political and social issues; e.g., false political statements. **3. Tackling Deepfakes** - **Prevention**: Use of prompt/output filters and data curation to limit creation. - **Embedding**: Watermarks, metadata, and content labeling to indicate authenticity. - **Detection**: Forensic techniques, hashing, and user reporting for identifying deepfakes. - **Enforcement**: Clear rules and penalties for violating content policies on platforms.

Let's dive into the world of collection framework with a snapshot of essential characteristics

I was honored to give the February webinar to the BIRD ICRDE consortium members along with my advisor, Professor 🐪 Yossi Oren. Our talk covered the theoretical background behind the power and the electromagnetic side channels, as well as a practical approach to mitigate control flow hijacking attacks with modern machine learning algorithms. Link to the consortium website is in the first comment.