With the rapid advancement of quantum computing, research on quantum machine learning (QML) algorithms has grown significantly. Among these, the Quantum Neural Network (QNN) stands out as one of the promising algorithms that integrates the principles of quantum computing with artificial neural networks to process data. Inspired by applications of QNN across fields, we investigate their use in software testing for the Cancer Registry of Norway (CRN), part of the Norwegian Institute of Public Health (NIPH), responsible for cancer statistics among the Norwegian population. CRN develops a complex socio-technical software system, Cancer Registration Support System ( \(\mathtt{CaReSS}\) ), interacting with many entities (e.g., hospitals, medical laboratories, and other patient registries) to achieve its task. For cost-effective testing of \(\mathtt{CaReSS}\) , CRN has employed \(\mathtt{EvoMaster}\) , an AI-based REST API testing tool combined with an integrated classical machine learning model \(\mathtt{EvoClass}\) . Within this context, we propose \(\mathtt{EvoQlass}\) to investigate the feasibility of using, inside \(\mathtt{EvoMaster}\) , a QNN classifier, instead of the existing classical machine learning model. Results indicate that \(\mathtt{EvoQlass}\) can achieve performance comparable to that of \(\mathtt{EvoClass}\) . We further explore the effects of various QNN configurations on performance and offer recommendations for optimal QNN settings for future QNN developers.

In this work, we review the architecture design of existing federated General Adversarial Networks (GAN) solutions and highlight the security and trust-related weaknesses in the existing designs. We then describe how these weaknesses make existing designs unsuitable for the requirements needed for a consortium of health registries working towards generating synthetic data sets for research purposes. Moreover, we propose how these weaknesses can be addressed with our novel architecture solution. Our architecture solution combines several building blocks to generate synthetic data in a decentralised setting. Federated GANs, Consortium blockchains, and Shamir Secret Sharing algorithm are the core building blocks of our proposed architecture solution. Finally, we discuss our proposed solution’s advantages, disadvantages and future research directions.

Increasing affordability of genome sequencing and, as a consequence, widespread availability of genomic data opens up new opportunities for the field of medicine, as also evident from the emergence of popular cloud-based offerings in this area, such as Google Genomics [1]. To utilize this data more efficiently, it is crucial that different entities share their data with each other. However, such data sharing is risky mainly due to privacy concerns. In this article, we attempt to provide a privacy-preserving and efficient solution for the “similar patient search” problem among several parties (e.g., hospitals) by addressing the shortcomings of previous attempts. We consider a scenario in which each hospital has its own genomic dataset and the goal of a physician (or researcher) is to search for a patient similar to a given one (based on a genomic makeup) among all the hospitals in the system. To enable this search, we propose a hierarchical index structure to index each hospital’s dataset with low memory requirement. Furthermore, we develop a novel privacy-preserving index merging mechanism that generates a common search index from individual indices of each hospital to significantly improve the search efficiency. We also consider the storage of medical information associated with genomic data of a patient (e.g., diagnosis and treatment). We allow access to this information via a fine-grained access control policy that we develop through the combination of standard symmetric encryption and ciphertext policy attribute-based encryption. Using this mechanism, a physician can search for similar patients and obtain medical information about the matching records if the access policy holds. We conduct experiments on large-scale genomic data and show the high efficiency of the proposed scheme.

The architectures presented in the literature, and current practices and solutions for computing statistics on data from health registries distributed across the world are manual and suffers from security and privacy problems. In this paper, we suggest a solution design with a infrastructure architecture providing improved security, automation and privacy guarantees compared to the related works. Our solution builds on top of the key research accomplishments from several areas such as distributed computing, blockchain, cryptography, and medical informatics rather than completely re-inventing the wheel from scratch for the healthcare domain. The proposed architecture is currently being prototyped in the Cancer Registry of Norway.

With the advent of recent technological advances, more demanding tele-immersive applications have started to emerge. In the World Opera application, artists from different opera houses across the globe can participate in a single united performance, and interact almost as if they were co-located. One of the main design challenges in this application domain is to assess to what extent the inevitable failures of some of the numerous and complex hardware, software, and network components affect the quality of experience for the user. This challenge cannot be addressed by traditional system-centric methods for dependability evaluation, which do not take personalized user perspective into account when considering meaningful and acceptable degradation of services. In this paper, we propose a novel method to assess the quality of experience in presence of failures, based on a new metric called perceived reliability. The method takes the human perspective into account and allows considering factors such as human perception of video and audio, characteristics of the audience, as well as performance elements and artistic content. This method can help system designers and engineers compare architectural variants and determine the dependability budget. We show the feasibility of our method by applying it to a World Opera performance. To this end, we construct a SAN-based model and run simulations in the Möbius framework. The obtained results provide useful guidelines for system engineers towards improving the quality of experience of World Opera performances despite the presence of failures.

State transfer mechanisms are an essential building block in the design of many distribution applications that replicate the state, such as partially replicated databases or view-synchronous group communication. When a reconfiguration occurs, a need arises to ship a subset of objects that constitute the application state to a subset of nodes in the system. The most commonly employed solution is to elect a leader that collects state objects and transmits them to the nodes that need to receive them. In this paper, we present the problem of communication-balanced state transfer wherein the goal is to distribute the load of communication due to state transfer evenly across the participating nodes. We propose an algorithm that achieves the optimal balance, analyze it, and describe how it can be used in a variety of applications. We evaluate the algorithm on a typical setup of partially replicated databases and show that it attains a significant improvement compared with existing approaches.