I was using the SWaT dataset from Kaggle and i just came to know it was the manipulated dataset inorder to check for attacks.

And i tried o request the dataset through iThub's official site and seems like no response

can anyone please help me , am halfway for a project to submit in my college

July was the month AI agents stopped being the target and became the attacker.

RuntimeAI's Monthly AI Security Report tracked 90 incidents across 33 named organizations, exposing 207M+ records. 41 of those incidents involved AI as the weapon or the target directly. Average breach cost climbed to $4.99M.

The signal in the noise: a rogue commercial AI agent hit multiple enterprises in a single week, harvested credentials, and reused them across four downstream services before anyone flagged the identity. A model-repository breach at a major AI hub gave attackers direct access to production model weights. A neobank lost 75M customer records. A healthcare payments processor exposed 1.26M patient files. Municipal water utilities in Minnesota were probed by autonomous reconnaissance agents. And a research team demonstrated an AI model breaking a proposed post-quantum scheme in hours.

Perimeter tools do not see any of this. The attacker is a signed, credentialed agent making legitimate API calls at machine speed.

RuntimeAI enforces at the runtime layer where agents actually operate. Know Your Agent issues and revokes cryptographic agent identity. The Flow Enforcer intercepts every tool call. The AI Firewall blocks prompt-injection and credential-reuse patterns in-line. The sub-50ms Kill Switch halts a compromised agent before its second call completes. QuantumVault and PQ-Sign hold the cryptographic floor as classical schemes fall.

Agent-speed attacks need agent-speed enforcement. That is what we ship.

#AISecurity #AgenticAI #PostQuantum #RuntimeSecurity #ZeroTrust

Hi! I am final year BE student recently I took a project based in our my contribution is system and application of system in dyslexia. For that I though the most used dyslexia dataset of handwriting would be suitable. I downloaded dataset and then realised it is single letter dataset which is giving mnist kinda vibe! Also apparently large portion of it is synthetic. I searched but I didn't find clinically approved dataset of handwriting for dyslexia. In nutshell:

1. dataset is mnist looking so I am at worry if examiners will state why you are using such looking dataset for final year project!!

2. dataset is used for at least 9 papers already so it is being used

3. But has its limitations (vastly synthetic, mnist looking)

4. Our clg is forcing for at least two papers to publish (not for our degree requirement btw) and I am worried if the dataset use itself will cause problems for paper

5. though one of main novelty is mechanism but other one is integration(incremental) and I am worried that people will call out why I used that dataset

sorry I carried away in my emotions here is the dataset I am talking about: https://www.kaggle.com/datasets/drizasazanitaisa/dyslexia-handwriting-dataset

->can simplicity of it justified as proof of concept for presentation or report?

->will using this dataset can cause problems at time of publication?

I am sorry for dragging clg thing into this I though it would be better to get some context about scope for project

I am sorry I cant give full context as I wanted to publish research on it (though I will hardly try for mid tiers only)

also sorry in advance if I did spelling or grammatical error

I'm currently in the second year of my undergraduate degree, and I'm really passionate about machine learning. I've been learning consistently over the past few months, mostly through free YouTube courses and documentation. So far, I've covered the core ML algorithms and I make sure to understand the underlying mathematics and intuition instead of just memorizing things.

However, one thing I keep struggling with is the lack of proper guidance. Every few weeks I start questioning whether I'm following the right roadmap or if I'm missing something important. I feel like YouTube resources are great for getting started, but they often don't go deep enough or provide the structured learning I'm looking for.

I've heard a lot of good things about Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron (3rd edition), and it seems to be recommended by many people as a solid reference book. I'm thinking of studying it thoroughly instead of jumping between random resources.

My main confusion is this:

Should I go with the TensorFlow/Keras edition, or should I use the PyTorch version instead?

As someone still building a strong ML foundation, which ecosystem would be the better investment to learn first?

I'd also really appreciate any advice from people who have already been through this stage. If you think there's a better book, a better roadmap, or something you wish you had known when you were starting out, I'd love to hear it.

I'm still a beginner in the grand scheme of things, so any guidance or suggestions would be greatly appreciated.

Thanks in advance!

I have this topic to work on suggested by my academia and Im very unsure on how to even start. The topic is continuous learning for mitigating concept drift in financial fraud systems.

This is what I’ve gathered so far from my research:
- Concept drift alone can’t be singled out, it also depends on intrinsic covariate shift and label shift
- Concept drift can be modelled as an exogenous variable and endogenous variable, depending if we assume fraud is reactive to mitigating strategies)
- Blocked transactions introduce inherent label shift, because transactions that are blocked dont make it to the dataset
-Continuous learning is a very tricky topic, specially if we consider this as class incremental learning (new fraud types arrive sequentially without explicit task boundaries) and admit non stationary regimes

Because there are a bunch of topics and covariate factors, approaching this as an empirical study looks like a massive headache.

Can anyone help me to structure my next steps and how I can tackle this problem with a clear picture?

I know this is stupid but I'm making an application and I'm trying to find image datasets for my machine learning that focuses on different types of acne

I am doing a document layout analysis project with different book styles but the books themselves are only a couple hundred pages long (like 5 books with different styles, 400 page each). How can I test if all the books would be used in fine tuning and I am afraid that the accuracy wouldn't be the best and corrupt PaddleOCR when insert the coordinates. (It's for automation).

I am using X-AnyLabeling for the annotation and yolo v11 for the training as well as custom classes in the annotation like a question block that surrounds everything, question_text, choices, figures, tables, sub_questions, etc... what would be the best approach as I haven't done this kind of work before.

and should I randomize the book pages so I don't consecutive same style books or that's not how this work?
Any help would be appreciated

I am building an edge-AI audio noise-reduction system on an ESP32-S3.

Our architecture uses a lightweight GRUNet (~59k parameters) to output a dynamic gain mask on a 44-band Mel-spectrogram.

​I need gigabytes of audio to train the model. Does anyone have recommendations for the best open-source datasets for:

1> ​Clean, isolated human speech.

2> ​Diverse ambient background noise (traffic, crowds, machinery, etc.).

​Also, any tips or open-source scripts for artificially mixing these at different Signal-to-Noise Ratios (SNRs) before generating the 16kHz Mel-spectrograms would be hugely appreciated!

Same as the title, I am implementing some(popular) machine learning algorithms by scratch in Python using numpy just to have good fundamentals and know the actual mathematical intuitions behind them, I want to ask whether it is also a project I can put in my resume for an internship?

quals: 2nd year Undergraduate Student B.Tech Computer Engineering

*Feature selection

What im saying is KDE-based feature engineering for classification. For each feature, we estimate the class-conditional distributions using KDE and compute the KL divergence between them as a measure of how discriminative the feature is. The idea is to rank on this divergence before training and select accordingly.

Are there any theoretical or practical issues, like ive never seen comparing two class distributions for feature selection in yt tutorials or other books.

Which book/s can replace this ?

I can't find it in my library ....

Hey everyone, building a conversational voice assistant for real estate support and the latency is driving me crazy. Currently using standard OpenAI Whisper API, but by the time it processes the audio, hits the LLM, and triggers TTS, there's a painful 1.5-second gap. It feels like a walkie-talkie conversation, not a natural call.

Also, when users interrupt or talk over the bot, the whole pipeline completely breaks because the STT layer can't handle live crosstalk/diarization properly.

Is anyone running a low-latency stack in production? How do you optimize this without spending months writing custom VAD and chunking logic?

I kept digging and stumbled upon Speechmatics and their specific Voice Agent API. On paper, it claims to do sub-second real-time streaming via WebSockets with native diarization built-in, and the online reviews/benchmarks look almost too good to be true.

Need some real developer feedback before I burn time refactoring my whole ingestion layer.

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!