Results 131 to 140 of about 3,121 (282)

EVMK-SSE: Efficient and Verifiable Multi-Keyword SSE from client-independent relaxed OPRF for outsourced database in cloud

open access: yesJournal of King Saud University: Computer and Information Sciences
Symmetric searchable encryption (SSE) is a foundational technology that enables privacy-preserving queries of encrypted data stored on untrusted cloud servers.
Huihui Zhu   +5 more
doaj   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

open access: yesAdvanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He   +2 more
wiley   +1 more source

Enabling Stochastic Dynamic Games for Robotic Swarms

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper scales stochastic dynamic games to large swarms of robots through selective agent modeling and variable partial belief space planning. We formulate these games using a belief space variant of iterative Linear Quadratic Gaussian (iLQG). We scale to teams of 50 agents through selective modeling based on the estimated influence of agents ...
Kamran Vakil, Alyssa Pierson
wiley   +1 more source

Searchable Symmetric Encryption and its applications

open access: yes, 2022
In the age of personalized advertisement and online identity profiles, people’s personal information is worth more to corporations than ever. Storing data in the cloud is increasing in popularity due to bigger file sizes and people just storing more information digitally.
openaire   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

open access: yesAdvanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy   +2 more
wiley   +1 more source

Quantifying Security in Volume-Hiding Searchable Symmetric Encryption Schemes With a Novel Scoring Metric

open access: yesIEEE Access
Size pattern leakage remains a critical issue in oblivious RAM (ORAM)-based Searchable Symmetric Encryption (SSE) schemes. Despite efforts to define security notions against size pattern leakage, existing studies either overly restrict analysis by ...
Kangmo Ahn   +4 more
doaj   +1 more source

Optimizing 3D Bin Packing of Heterogeneous Objects Using Continuous Transformations in SE(3)

open access: yesAdvanced Intelligent Systems, EarlyView.
This article presents a method for solving the three‐dimensional bin packing problem for heterogeneous objects using continuous rigid‐body transformations in SE(3). A heuristic optimization framework combines signed‐distance functions, neural network approximations, point‐cloud bin modeling, and physics simulation to ensure feasibility and stability ...
Michele Angelini, Marco Carricato
wiley   +1 more source

MusicSwarm: Biologically Inspired Intelligence for Music Composition

open access: yesAdvanced Intelligent Systems, EarlyView.
Biologically inspired swarms of frozen foundation models self‐organize to compose complex music without fine‐tuning. By coordinating through stigmergic signals, decentralized agents dynamically evolve specialized roles and adapt to solve complex tasks.
Markus J. Buehler
wiley   +1 more source

A Hybrid Semi‐Inverse Variational and Machine Learning Approach for the Schrödinger Equation

open access: yesAdvanced Physics Research, EarlyView.
A hybrid semi‐inverse variational and machine‐learning framework is presented for solving the Schrödinger equation with complex quantum potentials. Physics‐based variational solutions generate high‐quality training data, enabling Random Forest and Neural Network models to deliver near‐perfect energy predictions.
Khalid Reggab   +5 more
wiley   +1 more source

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