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.
Si-Han Zhou, Tian-Tian He, Yi-Nan Guo et al.· IEEE Transactions on Neural...· 0 citations
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.
Shan-Ping Ning, Hang Li, Yi-Dan Chen et al.· Infrastructures· 0 citations
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.
M. Weerasinghe, Himindu Kularathne, Methmini Madhushika et al.· 0 citations
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.
Qazi Mazhar Ul Haq, Muhammad Imran, M. Waqas et al.· Arab Journal of Forensic Sci...· 0 citations
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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...
N. Kriel, Elizabeth Cole-Kodikara, B. Wibking et al.· 0 citations
We compute the energy gained by accretion onto a compact star due to matter falling from the inner edge of a thin accretion disk. We employ a controlled slow-rotation expansion based on the Hartle-Thorne metric and find that in neutron stars in general only leading order rotational corrections to the metric, describing...
Digital function-oriented communication is presented, in which finite-alphabet symbol representations and receiver decisions are jointly designed so that multiple-access superposition encodes the desired function without recovering individual inputs.
The first prototype of a 5G-connected mobile edge computing (MEC)-enabled semi-autonomous prosthetic hand is presented, which streams RGB-D images to an edge server for real-time grasp planning and establishes 5G edge-offloading as a practical path to deploying compute-intensive prosthesis control.
Ozan Karaali, Hossam M. Farag, S. Došen et al.· 0 citations
This paper synthesizes reported evidence on ZTA across three research questions: effectiveness relative to perimeter models, the implementation challenges enterprises face, and the security contribution of individual components.
Md Shahnawaj, Hamim Islam Hellol, Debabrata Biswas et al.· Journal of Computers, Mechan...· 0 citations
Split federated learning (SFL) is renowned for its low computational overhead, extremely suitable for resource-constrained edge computing scenarios while inheriting privacy-preserving characteristics from federated learning (FL). In this framework, clients employ lightweight models to process private data locally and t...
Jia-Xiong Tang, Zhengchunmin Dai, Liantao Wu et al.· IEEE Internet of Things Jour...· 0 citations
The proposed IR-AGN model exhibits a higher mean Average Precision (mAP) of 95.19% on the Revisited Paris (RParis) dataset, when compared with the existing Multi-Layer Orientation Histogram (MLOH) model.
Prabhuraj Metipatil, M. Mrutyunjaya, S. P. Kaulgud et al.· Cybernetics and Information...· 0 citations
The results show that a sub-6 GHz anchor with selective high-band duplication provides a good trade-off between latency, reliability and resource consumption.
R. Mahmood· International Innovations Jo...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
