February 2026

Document authentication remains a pressing challenge in various domains, including financial services, academic credentialing, healthcare, and supply chain management. Existing centralized verification systems are vulnerable to manipulation, inefficiency, and limited transparency. Blockchain technology, with its immutability and tamper-resistant capabilities, offers a strong decentralized alternative; however, many current implementations lack structured, issuer-bound relationships for documents. This paper proposes a blockchain-based model that leverages a hierarchical token structure to authenticate and trace the provenance of high-value digital documents, with a […]

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Federated Learning (FL) enables collaborative model training without sharing raw data. However, shared local model updates remain vulnerable to inference and poisoning attacks. Secure aggregation schemes have been proposed to mitigate these attacks. In this work, we aim to understand how these techniques are implemented in quantum-assisted FL. Quantum Secure Aggregation (QSA) has been proposed, offering information-theoretic privacy by encoding client updates into the global phase of multipartite entangled states. Existing QSA protocols, however, rely on a single […]

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Early detection of battery degradation is essential for ensuring the safety and reliability of electric vehicle (EV) systems under real-world operating variability. This paper proposes a physics-guided multi-sensor learning framework, termed SensorFusion-Former (SFF), for early warning of short-term EV battery performance degradation. The proposed approach integrates a physics-based baseline model for operational normalization, a multi-sensor fusion attention mechanism to model cross-modality interactions, and a lightweight transformer architecture for efficient temporal representation learning. Weak supervision is derived from physics-consistent […]

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Clinical risk prediction models often underperform in real-world settings due to poor calibration, limited transportability, and subgroup disparities. These challenges are amplified in high-dimensional multimodal cancer datasets characterized by complex feature interactions and a p >> n structure. We present a fully reproducible multimodal machine learning framework for 5-year overall survival prediction in breast cancer, integrating clinical variables with high-dimensional transcriptomic and copy-number alteration (CNA) features from the METABRIC cohort. After variance- and sparsity-based filtering and dimensionality reduction, […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Microsoft’s AutoDev: The AI That Builds, Tests, and Fixes Code on Its Own By @microsoft [ 27 Min read ] Microsoft’s AutoDev uses AI agents to write, test, and fix code autonomously, hitting 91.5% on HumanEval in Docker. Read More. How CMOs Win CFO […]

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Mobile edge computing (MEC) enables resource-constrained mobile devices to execute delay-sensitive and compute-intensive applications by offloading tasks to nearby edge servers. However, task orchestration in MEC is challenged by the highly dynamic system conditions, unreliable networks, and the distributed edge environments. Moreover, as the number of users, tasks, and resources increases, the offloading decision-making problem becomes increasingly complex due to the exponential growth of the search space. To address these challenges, this paper proposes a Multi-Criteria Hierarchical Clustering-based […]

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This work presents a method for implementing dynamic difficulty adjustment in the arcade game of Air Hockey using reinforcement learning. The resulting AI-controlled opponent is capable of adapting its skill level to the player’s performance, with the goal of maintaining engagement and providing a balanced gameplay experience throughout a match. The approach relies on generating several AI agents through progressively longer training durations, producing distinct and smoothly transitioning difficulty levels that can be switched dynamically. In addition, the […]

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Digital signatures serves as a crucial cryptographic primitive in an e-governance system for the authentication of citizen-government interactions. Traditional methods (DSA, ECDSA) pose computational overheads at resource-limited endpoints and centralized verification servers. While complex-number cryptography provides theoretical efficiency through the Complex Discrete Logarithm Problem (CDLP), prior works often fail to meet the requirements for real-world applications. This paper advances the knowledge in lightweight cryptography by introducing LDSEGoV, a lightweight digital signature scheme for e-governance infrastructure. The proposed method […]

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