Constructing Secure Encryption Schemes

📅 January 2026 ⏱️ ~18 min read

This article presents a principled construction of symmetric encryption schemes from pseudorandom generators. Starting from the analogy with the one-time pad, it explains how pseudorandomness enables indistinguishable encryptions in the presence of an eavesdropper. The discussion then extends to variable-length messages, stream ciphers, and shows why deterministic encryption and naive keystream reuse fail under multiple-message security.

Cryptography Pseudorandomness Encryption Security Models Stream Ciphers
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RNN in Time Series Forecasting: RNN, GRU, and LSTM Explained

📅 January 2026 ⏱️ ~15 min read

Recurrent networks introduce a key idea: a hidden state updated at every timestep. This post explains why vanilla RNNs struggle with long sequences (vanishing gradients), then shows how GRUs and LSTMs use gating mechanisms to control forgetting and memory. The focus is forecasting intuition, practical model choice, and a compact math view.

Time Series Deep Learning RNN GRU LSTM
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Perfect Secrecy, One-Time Pad, and Shannon’s Theorem

📅 January 2026 ⏱️ 6 min read

Perfect secrecy formalizes an extreme (and elegant) security guarantee: the ciphertext should reveal absolutely nothing about the message. This article introduces the information-theoretic definition, the indistinguishability experiment, the one-time pad, and Shannon’s theorem explaining why perfect secrecy requires keys at least as large as the message space.

Cryptography Perfect Secrecy One-Time Pad Shannon
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Time Series Forecasting Through the Lens of Supervised Learning

📅 January 2026 ⏱️ 8 min read

Time series forecasting is often presented as a specialized discipline, yet it naturally fits within the supervised learning framework. This article explains how forecasting problems are structured, how multi-step predictions are constructed, and why classical statistical models should always be compared with machine learning approaches in practice.

Time Series Machine Learning Forecasting Supervised Learning
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Kerckhoffs’ Principle in Cryptography

📅 December 2025 ⏱️ 5 min read

Kerckhoffs’ principle is a foundational concept in modern cryptography. It states that a cryptographic system must remain secure even if the encryption algorithm is fully known to the adversary, as long as the secret key remains unknown.

Cryptography Security Mathematics Foundations
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Kasiski’s Method: Breaking Vigenère (and the Birth of Modern Cryptography)

📅 December 2025 ⏱️ 4 min read

Vigenère weakens simple frequency attacks but remains vulnerable. This article explains Kasiski’s insight—repeated patterns reveal the key length—and connects classical cryptanalysis to the emergence of modern cryptographic definitions and security proofs.

Cryptography Vigenère Kasiski Security
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OpenSpot (FindSpot): Parking Space Detection

📅 November 2025 ⏱️ 2 min read

Presentation of OpenSpot (FindSpot), a computer vision project for detecting and classifying parking spaces from images. The article compares several CNN architectures (MobileNetV3, EfficientNet, ResNet) with respect to accuracy, inference speed, and real-world deployment constraints.

Computer Vision Deep Learning CNN Smart Cities
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Poetry

Symmetry

In the darkness looming over me,
I see nothing but shadows.
Despair and melancholy overwhelm me.
Who will save me from the abyss?
What is this light?
What is this faint glimmer?
Is it an escape or a cruel snare?
Symmetry, my savior.
You, master of structures and forms.
You, devoted steward of Nature.
You, who tame fields and time.
You, said to be local or global.
How better to show you my gratitude
Than by offering myself as a faithful disciple,
By submitting to your divine critique?
From now on, your laws shall be my shelter,
Your principles, a source of hope.
Welcome me to your lofty realms.
I will study your geometry,
I will confront your nemeses: asymmetries.
          

A poem inspired by the TRUTH , structure, and order.