Reviewerly/exHarmony huggingface dataset:
A quantum-enhanced support vector machine for galaxy classification

Abstract Galaxy morphology, a key tracer of the evolution of a galaxy’s physical structure, has motivated extensive research on machine learning techniques for efficient and accurate galaxy classification. The emergence of quantum computers has generated optimism about the potential for significantly improving the accuracy of such classifications by leveraging the large dimensionality of quantum Hilbert space. This paper presents a quantum-enhanced support vector machine (SVM) algorithm for classifying galaxies based on their morphology. The algorithm requires the computation of a kernel matrix, a task that is performed on a simulated quantum computer using a quantum circuit conjectured to be intractable on classical computers. The result shows similar performance between classical and quantum-enhanced SVM algorithms. For a training size of 40k, the receiver operating characteristic curve for differentiating ellipticals and spirals has an under-curve area of 0.946 ± 0.005 for both classical and quantum-enhanced algorithms. Additionally, we demonstrate for a small data set that the performance of a noise-mitigated quantum SVM algorithm on a quantum device is in agreement with simulation. Finally, a necessary condition for achieving a potential quantum advantage is presented. This investigation is among the very first applications of quantum machine learning in astronomy and highlights their potential for further application in this field.
Bayesian design with sampling windows for complex spatial processes

Abstract Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like those typically exhibited in natural ecosystems. Coordinate exchange algorithms are commonly used to find optimal design points. However, collecting data at specific points is often infeasible in practice. Currently, there is no provision to allow for flexibility in the choice of design. Accordingly, we also propose an approach to find Bayesian sampling windows, rather than points, via Gaussian process emulation to identify regions of high design efficiency across a multi-dimensional space. These developments are motivated by two ecological case studies: monitoring water temperature in a river network system in the northwestern United States and monitoring submerged coral reefs off the north-west coast of Australia.
Operationalising Representation in Natural Language Processing

Neural models achieve high performance on a variety of natural language processing (NLP) benchmark tasks.How models perform these tasks, though, is notoriously poorly understood.As models are deployed more widely in real-world settings, there has been increased focus on explaining model behaviour.Since the task the model performs is linguistic, it seems intuitive that models perform tasks by representing linguistic properties of inputs.Despite its centrality in the philosophy of cognitive science, there has been little prior philosophical work engaging with 'representation' in contemporary NLP practice.This paper attempts to fill that lacuna: drawing on ideas from the philosophy of cognitive science, I introduce a framework for evaluating the representational claims made about components of neural NLP models, proposing three criteria with which to evaluate whether a component of a model represents a property and operationalising these criteria using 'probing classifiers', a popular analysis technique in NLP (and deep learning more broadly).The project of operationalising a philosophically-informed notion of representation should be of interest to both philosophers of science and NLP practitioners.It provides philosophers insight into the connections between empirical model interpretability work and foundational questions in cognitive science, assisting them in engaging with downstream philosophical issues in the development of AI.It helps NLPers organise the large literature on probing experiments, suggesting novel avenues for empirical research.
FedSSC: Joint client selection and resource management for communication-efficient federated vehicular networks

As a promising distributed technology, federated learning (FL) has been widely used in vehicular networks involving large amounts of IoT-enabled sensor data, which derives federated vehicular networks (FVNs). However, the efficiency of FVN is generally limited by vehicle selection policy and communication conditions, which leads to high communication costs and latency. The original FVN transmits model parameters from a random subset of vehicles to the roadside unit (RSU) and ignores the diversity of learning quality among vehicles. In addition, a few vehicles with poor wireless channel conditions may prolong communication latency. To address these two issues, we propose a communication-efficient federated learning approach, composed of Vehicle Selection, Student–Project Allocation (SPA) matching model, and Convex Optimization, called FedSSC, to improve the communication efficiency in FVN. The parameter variations for the same vehicle in two consecutive rounds are used to quantify the quality of the learning results by cosine distance and Affinity Propagation (AP) clustering. Moreover, a subchannel allocation algorithm based on the SPA matching model, as well as a convex optimal power allocation solution are integrated to minimize the communication latency of each training round. According to extensive experiments, the proposed FedSSC reduces the communication overhead by 26.32% on average compared with the benchmarks, whereas the communication latency decreases by 21.84%.
Generating Efficient Sensor Paths for Ground Target Search: A Performance Parameter and GIS-Driven Approach

In this work, we present a novel process to plan dynamically occurring search tasks for single, ground-based targets from an airborne platform equipped with a zoomable imaging sensor. To describe the underlying motivation, we first outline our use case in aerial multi-target tracking before presenting our approach to derive search plans. We achieve the goal of reducing the time to search by first generating a prior distribution of the target’s likely location based on background knowledge about the search area. This focuses search effort on parts within the area that have a higher likelihood of containing the target. To derive a mission and cell-specific detection function for the discretized search area, we further use sensor and platform specific parameters as well as the relative positions of target and platform. Additionally, we outline how perception performance models previously developed at our institute can be used to improve the results. We consecutively construct an optimal search problem from the prior distribution and detection function. The problem can be solved with well-established methods to find optimal search plans. Finally, we introduce how the resulting search density can be used to calculate a sensor path and show the process in an example application. We round out the paper with our conclusions and ideas for further improvement of the algorithm.
Co-simulation Digital Twin Framework for Testing Future Advanced Air Mobility Concepts: A Study with BlueSky and AirSim

The UK Future Flight Vision and Roadmap outlines the anticipated development of aviation in the UK by 2030.As part of the Future Flight demonstration segment, project HADO (High-intensity Autonomous Drone Operations) will develop, test, and deploy fully automated unmanned aircraft system (UAS) operations at London Heathrow Airport.Cranfield University is leading the synthetic test environment development within the HADO project, and a digital twin (DT) prototype was developed to enable mixed-reality tests for autonomous UAS operations.This paper enhances the existing DT by introducing new co-simulation capacities.Specifically, a co-simulation DT framework for autonomous UAS operations is proposed and tested through a demonstrative use case based on BlueSky and AirSim.This prototype integrates the traffic simulation capabilities of BlueSky with the 3D simulation capabilities of Airsim, to efficiently enhance the simulation capacities of the DT.Notably, the co-simulation framework can leverage the 3D visualization modules, UAS dynamics, and sensor models within external simulation tools to support a more realistic and high-fidelity simulation environment.Overall, the proposed co-simulation method can interface several simulation tools within a DT, thereby incorporating different communication protocols and realistic visualization capabilities.This creates unprecedented opportunities to combine different software applications and leverage the benefits of each tool.
Runway Sign Classifier: A DAL C Certifiable Machine Learning System

In recent years, the remarkable progress of Machine Learning (ML) technologies within the domain of Artificial Intelligence (AI) systems has presented unprecedented opportunities for the aviation industry, paving the way for further advancements in automation, including the potential for single pilot or fully autonomous operation of large commercial airplanes. However, ML technology faces major incompatibilities with existing airborne certification standards, such as ML model traceability and explainability issues or the inadequacy of traditional coverage metrics. Certification of ML-based airborne systems using current standards is problematic due to these challenges. This paper presents a case study of an airborne system utilizing a Deep Neural Network (DNN) for airport sign detection and classification. Building upon our previous work, which demonstrates compliance with Design Assurance Level (DAL) "D", we upgrade the system to meet the more stringent requirements of Design Assurance Level "C". To achieve DAL C, we employ an established architectural mitigation technique involving two redundant and dissimilar Deep Neural Networks. The application of novel ML-specific data management techniques further enhances this approach. This work is intended to illustrate how the certification challenges of ML-based systems can be addressed for medium criticality airborne applications.
<b>OPUPO</b>: Defending Against Membership Inference Attacks With <b>O</b>rder-<b>P</b>reserving and <b>U</b>tility-<b>P</b>reserving <b>O</b>bfuscation

In this work, we present OPUPO to protect machine learning classifiers against black-box membership inference attacks by alleviating the prediction difference between training and non-training samples. Specifically, we apply order-preserving and utility-preserving obfuscation to prediction vectors. The order-preserving constraint strictly maintains the order of confidence scores in the prediction vectors, guaranteeing that the model's classification accuracy is not affected. The utility-preserving constraint, on the other hand, enables adaptive distortions to the prediction vectors in order to protect their utility. Moreover, OPUPO is proved to be adversary resistant that even well-informed defense-aware adversaries cannot restore the original prediction vectors to bypass the defense. We evaluate OPUPO on machine learning and deep learning classifiers trained with four popular datasets. Experiments verify that OPUPO can effectively defend against state-of-the-art attack techniques with negligible computation overhead. In specific, the inference accuracy could be reduced from as high as 87.66% to around 50%, i.e., random guess, and the prediction time will increase by only 0.44% on average. The experiments also show that OPUPO could achieve better privacy-utility trade-off than existing defenses.
Challenges and Solutions in Designing a Network Architecture for Red and Blue Cybersecurity Competitions

With the increasing importance of cybersecurity in our digital age, cybersecurity competitions have become a popular way to test the incident response abilities of participants. This paper proposes a network architecture to test the vulnerabilities that may appear in a Red and Blue cybersecurity competition. The network architecture is designed to simulate a real-world cyber-attack scenario and includes a router, a core system, and multiple subnets representing different teams. Each subnet contains vulnerable systems that must be protected by the teams, who must also launch attacks on the other teams to identify flags. The architecture presents several challenges, including the large number of rules required for router configuration, which prohibit direct access to opposing teams’ virtual machines (VMs) and restrict/block access to specific phases of the competition. The VMs used in the competition include various vulnerabilities related to cryptocurrency wallet operation, medical clinics, chat services, X-ray clinics, SCADA communication protocols, and industrial power plants. Overall, the network architecture and VMs used in this competition provide a challenging and realistic scenario for participants to test their skills in identifying and mitigating cybersecurity threats.
Topological Neural Networks: theory and applications

This short review paper describes the theory of Restricted Radial Basis Function (rRBF) Network and its various applications. The rRBF is a hierarchical neural network with a two-dimensional topological layer that organizes low-dimensional representations of high-dimensional inputs while executing supervised learning. The applications of the rRBF exploit the low-dimensional characteristics of these representations. This paper explains some applications that include dimensionality reductions for visualizations, visual explainability, visualization for dynamics matching, and new ideas for infusing humans’ common sense into neural networks.
Resolving Fock states near the Kerr-free point of a superconducting resonator

Abstract We have designed a tunable nonlinear resonator terminated by a SNAIL (Superconducting Nonlinear Asymmetric Inductive eLement). Such a device possesses a Kerr-free point in which the external magnetic flux allows to suppress the Kerr interaction. We have excited photons near this Kerr-free point and characterized the device using a transmon qubit. The excitation spectrum of the qubit allows to observe photon-number-dependent frequency shifts about nine times larger than the qubit linewidth. Our study demonstrates a compact integrated platform for continuous-variable quantum processing that combines large couplings, considerable relaxation times and excellent control over the photon mode structure in the microwave domain.
Provenance blockchain for ensuring IT security in cloud manufacturing

Provenance blockchain is an evolving concept for protection of production, logistics, and supply chain networks from rogue Industrial Internet of Things (IIoT) devices. Such rogue IIoT devices are a recognized threat in the cloud manufacturing networks. In extreme cases, they can be used to cause industrial accidents. A brief version of provenance is about end-to-end tracking and tracing of data and the nodes involved in creating, modifying, transmitting, storing, and deleting it at specific times and locations. It provides an end-to-end verifiable and controlled computation for ensuring trustworthiness, quality, reliability, and validity of data. Provenance has existed in computing using logging software systems. This research is focused on threats to food supply chains between two countries. A scenario for protecting food supply chain from India to UAE has been modeled. This research recognized the threat of harmful food items getting mixed with flow of genuine products in a supply chain. The IIoT devices used to control the flow can be authenticated using the evolving provenance blockchain technology. With the help of recent design recommendations in the literature, a model design has been created and simulated in this research. Observations from the simulation revealed that TCP congestions and unpredictable turnaround time for assigning cryptographic keys to IIoT device sessions may have to be explored in future. A collaborative design between the two nations has been proposed. All IIoT devices not supporting cryptography will be eliminated from the cloud manufacturing and supply chain networks. Currently, this design may be used for one time registration only. Future studies may provide improved versions in which, repeated authentication and keys replacements may be implemented.
Web Application Security Assessment

Web Application Security Assessment (WASA) is vital for identifying and rectifying vulnerabilities in web-based software. It addresses issues like authentication vulnerabilities, injection attacks, and data exposure. WASA employs methods like penetration testing, code review, and vulnerability scanning to bolster web app security, safeguarding data and user privacy. This paper delves into WASA methodologies and challenges, aiming to create a safer online environment.
Why do people buy virtual clothes?

Abstract Virtual clothes consumption is part of the digital virtual consumption practices of the global NFT phenomenon and the global digital fashion trend. While cutting‐edge technologies shape customer experience and behavior, research examining how consumers desire, own, and experience these technologies in general, and virtual objects in particular, remains scarce. Adopting the uses and gratifications (U&amp;Gs) theory and building on research related to brand roles and category, the present research aims to unveil the psychological determinants of purchasing NFTs‐virtual clothes. A mixed‐method, two‐stage approach was adopted. First, a qualitative study and a pilot study aimed to capture and understand what motivates people to buy NFTs‐virtual clothes and identify contextualized U&amp;Gs. Second, two original confirmatory studies verified the U&amp;Gs of purchasing virtual clothes and investigated how they differ based on brand category (luxury vs. non‐luxury). The results show that enjoyment, social interactivity, and curiosity are the main predictors of NFTs‐virtual clothes purchases. Consumers mainly seek hedonic, social, and technological gratifications as part of the digital fashion consumption trend. Brand managers must recognize virtual clothing gratifications for effective virtual world positioning. Products should encourage networking, community, and user interaction. Demographics and virtual experience should be considered to understand NFT‐virtual clothes motivations.
Integration - Growth relationship: A literature review and future research agenda using a TCCM approach

Recognizing the ongoing debate on the relationship between integration and growth, this study aims to present a systematic review of academic literature that discusses the impact of regional integration on countries’ economic growth. By analyzing 82 papers, this study demonstrates the evolution of the integration-growth discourse and identifies the most recurring topics within the regional integration literature. Building on prior research, the study conceptualizes the key channels of integration impact on growth. In addition, the study offers guidance for future research using a Theory-Context-Characteristics-Methodology (TCCM) framework.
Frequency-guidance Collaborative Triple-branch Network for single image dehazing

Image dehazing is a crucial pre-processing step in many high-level vision applications, e.g., crowd surveillance, automatic driving, and remote sensing. Recently, learning-based methods have achieved promising performance by designing various convolutional neural networks (CNNs). However, most existing CNNs are based on a single feature branch and cannot fully utilize the low-frequency and high-frequency features of haze, which inevitably affects the quality of the restored image. To this end, we develop an effective Frequency-guidance Collaborative Triple-branch Network (FCT-Net) for image dehazing. It casts dehazing as a dual-frequency component restoration task and deals with pertinent features based on a triple-branch-like architecture. Specifically, our FCT-Net can be decomposed into four components: low-frequency branch (LFB), high-frequency branch (HFB), progressive fusion branch (PFB), and image reconstruction tail (IRT). The LFB aims to mine more contextual information with dual-path feature modulation block (DFMB), while the HFB aims to recover the missing structural detail via mixed attention residual block (MARB). PFB is a fusion branch built between LFB and HFB, where different feature streams (low- and high-frequency information) can be integrated together through continuous selective progressive fusion (SPF) modules. Finally, our IRT exploits all these derived features to generate the clear haze-free image. Furthermore, we utilize a multi-strategy loss to optimize our FCT-Net from the perspectives of pixel domain and feature domain, which can achieve better image recovery quality. Extensive experiments on different benchmark datasets show that our FCT-Net is able to get comparable results with other state-of-the-art methods.
Revisiting one-dimensional discrete-time quantum walks with general coin

Quantum walk (QW) is the quantum analog of the random walk. QW is an integral part of the development of numerous quantum algorithms. Hence, an in-depth understanding of QW helps us to grasp the quantum algorithms. We revisit the one-dimensional discrete-time QW and discuss basic steps in detail by incorporating the most general coin operator, constant in both space and time, and a localized initial state using numerical modeling. We investigate the impact of each parameter of the general coin operator on the probability distribution of the quantum walker. We show that by tuning the parameters of the general coin, one can regulate the probability distribution of the walker. We provide an algorithm for the one-dimensional quantum walk driven by the general coin operator. The study on general coin operators also includes the popular coins – Hadamard, Grover, and Fourier.
Towards Formal Verification of a TPM Software Stack

The Trusted Platform Module (TPM) is a cryptoprocessor designed to protect integrity and security of modern computers. Communications with the TPM go through the TPM Software Stack (TSS), a popular implementation of which is the open-source library tpm2-tss. Vulnerabilities in its code could allow attackers to recover sensitive information and take control of the system. This paper describes a case study on formal verification of tpm2-tss using the Frama-C verification platform. Heavily based on linked lists and complex data structures, the library code appears to be highly challenging for the verification tool. We present several issues and limitations we faced, illustrate them with examples and present solutions that allowed us to verify functional properties and the absence of runtime errors for a representative subset of functions. We describe verification results and desired tool improvements necessary to achieve a full formal verification of the target code.