The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
Layered and Collecting NDFS with Subsumption for Parametric Timed Automata
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A Maude-with-SMT framework for sound and complete formal analysis and parameter synthesis of parametric time Petri nets, including a folding approach that terminates on finite parametric state-class graphs and support for LTL model checking.
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.
citing papers explorer
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Routine Computing: A Systematic Review of Sensing Daily Life Dimensions Towards Human-Centered Goals
The first systematic review of routine computing synthesizes literature into a taxonomy of temporal, behavioral, cognitive, and variability aspects, outlining applications in health, accessibility, and adaptive support along with persistent challenges.
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A rewriting-logic-with-SMT-based formal analysis and parameter synthesis framework for parametric time Petri nets
A Maude-with-SMT framework for sound and complete formal analysis and parameter synthesis of parametric time Petri nets, including a folding approach that terminates on finite parametric state-class graphs and support for LTL model checking.
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Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Introduces Generative Privacy Funnel (GenPF) and deep variational PF (DVPF) models that extend the privacy funnel to generative settings and provide a controllable privacy-utility trade-off with reduced sensitive attribute leakage in face recognition.
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Optimizing OpenFaaS on Kubernetes: Comparative Analysis of Language Runtimes and Cluster Distributions
Go outperforms Python and Node.js in throughput and CPU efficiency on OpenFaaS; K3s and Kubeadm deliver higher throughput and lower latency than MicroK8s or K0s under concurrent load.