The random number generation capabilities of the GNU/Linux operating system are subject to certain limitations. As of Linux version 5.6, /dev/random operates in a non-blocking manner and, as such, no longer satisfies the criteria for a True Random Number Generator (TRNG). While dedicated quantum random number generator (QRNG) hardware is the preferred source of unpredictable entropy, it is often expensive and difficult to deploy in virtualized/cloud environments and Internet of Things (IoT) devices. Furthermore, hardware RNG integration typically requires cryptographic applications to adhere to vendor-specific APIs. This article proposes a user-space integration approach for a shared , potentially remote QRNG device. We develop a QRNG service on top of D-Bus, a ubiquitous inter-process communication framework. It serves as an interface for applications to retrieve true random numbers. Communication with the remote QRNG device occurs over mutually authenticated TLS 1.3 channels, protected by post-quantum cryptography (PQC) algorithms. We show, as a proof-of-concept, how the proposed D-Bus service can be integrated into the OpenSSL 3 cryptographic library, demonstrating the use of TRNG in a wide range of Linux applications. Our approach is resistant to entropy starvation attacks, supports sharing a QRNG across host and virtualized environments, requires no kernel-level or system-wide modifications, supports mixing multiple sources of entropy, and configuration of post-processing. It provides applications with a TRNG interface suitable for information-theoretically secure (ITS) use cases.

Modern neural networks obtain information about the problem and calculate the output solely from the input values. We argue that it is not always optimal, and the network's performance can be significantly improved by augmenting it with a query mechanism that allows the network at run time to make several solution trials and get feedback on the loss value on each trial. To demonstrate the capabilities of the query mechanism, we formulate an unsupervised (not depending on labels) loss function for Boolean Satisfiability Problem (SAT) and theoretically show that it allows the network to extract rich information about the problem. We then propose a neural SAT solver with a query mechanism called QuerySAT and show that it outperforms the neural baseline on a wide range of SAT tasks.

Web applications require not only more sophisticated infrastructure than traditional single-PC applications, but also a different way of thinking, where network-specific aspects have to be considered. In this paper, we introduce the web computer concept, which factors out network-related issues and provides an illusion of a single computer with directly attached CPUs, memory, and I/O devices. By assuming the web computer and its open operating system (webAppOS) as a target platform for web applications, developers can preserve the same level of thinking as when developing classical desktop applications. With this approach, which corresponds to the physiology of the human brain, web applications can be created faster. Besides, the proposed web computer specification can be viewed as a standardized environment (a Java Virtual Machine analog) for web applications.