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Pairing-free blind signatures from standard assumptions in the ROM
Get PDFBlind Signatures are a useful primitive for privacy preserving applications such as electronic payments, e-voting, anonymous credentials, and more. However, existing practical blind signature schemes based on standard assumptions require either pairings or lattices. We present the first practical construction of a round-optimal blind signature in the random oracle model based on standard assumptions without resorting to pairings or lattices. In particular, our construction is secure under the strong RSA assumption and DDH (in pairing-free groups). For our construction, we provide a NIZK-friendly signature based on strong RSA, and efficiently instantiate a variant of Fischlin’s generic framework (CRYPTO’06). Our Blind Signature scheme has signatures of size 4.28 KB and communication cost 10.98 KB. On the way, we develop techniques that might be of independent interest. In particular, we provide efficient relaxed range-proofs for large ranges with subversion zero-knowledge and compact commitments to elements of arbitrary groups
Exploring the Search Space of Neural Network Combinations obtained with Efficient Model Stitching - Source Code
No full textBayesian learning for the robust verification of autonomous robots
Get PDFAutonomous robots used in infrastructure inspection, space exploration and other critical missions operate in highly dynamic environments. As such, they must continually verify their ability to complete the tasks associated with these missions safely and effectively. Here we present a Bayesian learning framework that enables this runtime verification of autonomous robots. The framework uses prior knowledge and observations of the verified robot to learn expected ranges for the occurrence rates of regular and singular (e.g., catastrophic failure) events. Interval continuous-time Markov models defined using these ranges are then analysed to obtain expected intervals of variation for system properties such as mission duration and success probability. We apply the framework to an autonomous robotic mission for underwater infrastructure inspection and repair. The formal proofs and experiments presented in the paper show that our framework produces results that reflect the uncertainty intrinsic to many real-world systems, enabling the robust verification of their quantitative properties under parametric uncertainty
Preference-based allocation of patients to nursing homes
Get PDFIn many countries, the rapid aging of the population leads to an additional burden on already stretched long-term care systems. This often manifests itself in excessive waiting times for long-term care centers, and in abandonments (i.e., patients passing away while they are waiting). Interestingly, in practice, long waiting times are not caused by a lack of available total capacity in the system, but by systematic inefficiencies in the allocation of patients, each with their personal preferences and (in)flexibility, to geographically distributed care centers. Motivated by this, we propose a new and easy-to-implement method for the optimal allocation of patients-in-need to nursing homes, balancing the trade-off between the waiting time performance and the individual patients’ preferences and levels of flexibility. The optimal placement policy found by solving a Markov Decision Process demonstrates that for small instances, the mean optimality gap of the allocation model is equal to 1. 3%. We validate a simulation model for a real-life use case of allocating somatic patients to nursing homes in the Amsterdam area. The results show that if more patient replacements are approved, the allocation model can reduce the abandonment fraction under the current policy from 32.2% to 7.4% and waiting times at the same time. Moreover, with the allocation model individual preferences can be served better, which thus provides a powerful means to face the increasing need for patient-centered and sustainable long-term care solutions
Optimal decision rules for marked point process models
Get PDFWe study a Markov decision problem in which the state space is the set of finite marked point patterns in the plane, the actions represent thinnings, the reward is proportional to the mark sum which is discounted over time, and the transitions are governed by a birth-death-growth process. We show that thinning points with large marks maximises the discounted total expected reward when births follow a Poisson process and marks grow logistically. Explicit values for the thinning threshold and the discounted total expected reward over finite and infinite horizons are also provided. When the points are required to respect a hard core distance, upper and lower bounds on the discounted total expected reward are derived
Studenten ontcijferen inhoud van tweeduizend jaar oude papyrusrollen - De Ingenieur - 13-02-2024
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