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2,975 papers
An optimised hybrid ensemble learning strategy integrating threshold-based detection, Isolation Forest, Random Forest, Support Vector Machine (SVM), and Logistic Regression (LR) classifiers is proposed to improve fault detection accuracy in IoT-enabled 3D printers.
It is suggested that edge based controllability captures circuit level properties relevant to treatment response and may inform personalized neuromodulation strategies.
This work shows that production‐grade, privacy‐preserving intrusion detection is feasible in IoT networks at the edge, and addresses the concerns of data privacy and non‐IID data distribution inherent to distributed IoT networks.
MineSSM is a efficient design that routes each band to the operator it needs: a linear-complexity state-space model (Mamba) that homogenizes the global illumination and a lightweight convolutional branch that performs dust suppression on the high-frequency bands, so that no heavy operator ever runs at full resolution.
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An open-source Learning Management System architecture supporting hybrid pre-laboratory instruction in a resource-constrained university setting is reported, together with a quantified account of its capacity envelope and its limits.
Across the corpus, blockchain is used most often for device authentication, data integrity, access control, traceability, secure data exchange, and distributed trust management.
This paper proposes a three-layer security-by-design architecture that reaches 94.3% accuracy, the highest of the models tested, while cutting attack success rate from over 60–84% down to 38.9% and raising the query cost for model extraction more than five times over the weakest baseline.
This work proposes a multiagent belief state computation method termed mutual awareness belief (MAB), which extends belief modeling to decentralized partially observable settings by incorporating agents' behavioral intentions and collaborative relationships and proves effective in the nonmonotonic Pursuit environment.
The proposed framework provides an interpretable geometry-based approach for extending conventional obstacle detection toward railway risk-aware perception, showing potential for intelligent railway inspection, early warning, and safety-oriented operation and maintenance.
TripleBound is proposed, a hybrid framework for automated monolith-to-microservices decomposition that augments a heterogeneous graph neural network with weakly supervised triplet constraints derived from parser-inferred service groups based on package structure, naming conventions, and code location.
A deep learning-based forensic framework for real-time detection of suspicious human activity in CCTV videos, trained without relying on any external sensors is proposed, and incorporates anonymization of personal identities and local edge-based processing to prevent raw data exposure.
We implement a second-order accurate constrained transport (CT) magnetohydrodynamics (MHD) module with first-order flux correction in the open-source, adaptive mesh refinement (AMR), GPU-accelerated code quokka, supporting both ideal and resistive (constant Ohmic) regimes. For computing and averaging the electromotive...
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
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For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
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