Reviewerly/exHarmony huggingface dataset:
Hierarchical Distribution-aware Testing of Deep Learning

With its growing use in safety/security-critical applications, Deep Learning (DL) has raised increasing concerns regarding its dependability. In particular, DL has a notorious problem of lacking robustness. Input added with adversarial perturbations, i.e., Adversarial Examples (AEs) , are easily mispredicted by the DL model. Despite recent efforts made in detecting AEs via state-of-the-art attack and testing methods, they are normally input distribution–agnostic and/or disregard the perceptual quality of adversarial perturbations. Consequently, the detected AEs are irrelevant inputs in the application context or noticeably unrealistic to humans. This may lead to a limited effect on improving the DL model’s dependability, as the testing budget is likely to be wasted on detecting AEs that are encountered very rarely in its real-life operations. In this article, we propose a new robustness testing approach for detecting AEs that considers both the feature-level distribution and the pixel-level distribution, capturing the perceptual quality of adversarial perturbations. The two considerations are encoded by a novel hierarchical mechanism. First, we select test seeds based on the density of feature-level distribution and the vulnerability of adversarial robustness. The vulnerability of test seeds is indicated by the auxiliary information, which are highly correlated with local robustness. Given a test seed, we then develop a novel genetic algorithm–based local test case generation method, in which two fitness functions work alternatively to control the perceptual quality of detected AEs. Finally, extensive experiments confirm that our holistic approach considering hierarchical distributions is superior to the state-of-the-arts that either disregard any input distribution or only consider a single (non-hierarchical) distribution, in terms of not only detecting imperceptible AEs but also improving the overall robustness of the DL model under testing.
CryptoAudit: Nature, requirements and challenges of Blockchain transactions audit

CryptoAudit, the process of auditing cryptocurrencies and blockchain-based transactions, is essential in ensuring the accuracy, security, and compliance of activities within the emerging digital asset landscape. This paper explores the unique challenges faced in CryptoAudit, including the absence of a comprehensive regulatory framework, the complexities of blockchain technology, security concerns, valuation difficulties, and the global nature of cryptocurrencies. It also highlights the differences between CryptoAudit and traditional financial audit, emphasizing the need for specialized expertise and adaptability in this evolving field. The study employs a literature review approach and AI-based data analysis to provide insights into existing research and publications related to CryptoAudit. Key findings reveal the importance of staying updated on regulatory developments, collaborating with experts, and developing specialized audit procedures to address these challenges effectively. While CryptoAudit presents significant obstacles, it also offers opportunities for enhancing transparency, efficiency, and trust in the digital financial ecosystem. Auditors play a critical role in ensuring the reliability of cryptocurrency audits, fostering confidence, and facilitating the integration of blockchain technology into accounting and auditing practices. As the cryptocurrency industry continues to evolve, auditors must remain proactive and adaptable to navigate this complex landscape successfully.
Classification of RBA monetary policy announcements using ChatGPT

ChatGPT is a tool that has gained much attention and has accelerated the adoption of Artificial Intelligence across many applications. Using monetary policy decisions made by the Reserve Bank of Australia (RBA), we test whether ChatGPT's classifications of monetary policy announcements are consistent with market-observed characteristics, whether this has changed with the update from GPT-3.5 to GPT-4, and whether this provides additional informativeness for changes in the yield implied by interest rate futures. Our results indicate that, regardless of statement readability, ChatGPT provides a classification of hawkish / dovish tone that is consistent with "Target Surprises" and "Path Surprises" and although it seems to improve in the latest version has more difficulty with "sentiment" classification. However, this information does not appear to help to explain changes in interest rate markets on the day of the policy announcement.
GuaranTEE: Introducing Control-Flow Attestation for Trusted Execution Environments

Many cloud providers offer Trusted Execution Environments (TEEs) to protect critical data and processes from high privileged adversaries. Unfortunately, TEEs can only be attested at launch. To also enable attestation during run-time, we present GuaranTEE. GuaranTEE uses control-flow attestation to ensure the integrity of a service running within a TEE. To protect the attesting code from a potentially compromised service, we place it in a separate TEE. Additionally, the TEEs guard both the service and the attestation from malicious cloud providers. To reduce the overhead resulting from the use of two TEEs, we securely cache collected information and perform the attestation in parallel to executing the service. The detailed performance evaluation of our prototype based on Intel SGX in Microsoft Azure demonstrates that GuaranTEE provides a practical solution for cloud users focused on protecting the integrity of their data and processes at run-time.
FedGen: Generalizable Federated Learning for Sequential Data

Existing federated learning models that follow the standard risk minimization paradigm of machine learning often fail to generalize in the presence of spurious correlations in the training data. In many real-world distributed settings, spurious correlations exist due to biases and data sampling issues on distributed devices or clients that can erroneously influence models. Current generalization approaches are designed for centralized training and attempt to identify features that have an invariant causal relationship with the target, thereby reducing the effect of spurious features. However, such invariant risk minimization approaches rely on apriori knowledge of training data distributions which is hard to obtain in many applications. In this work, we present a generalizable federated learning framework called FedGen, which allows clients to identify and distinguish between spurious and invariant features in a collaborative manner without prior knowledge of training distributions. We evaluate our approach on real-world datasets from different domains and show that FedGen results in models that achieve significantly better generalization and can outperform the accuracy of current federated learning approaches by over 24%.
Workload Failure Prediction for Data Centers

Failed workloads that consumed significant computational resources in time and space affect the efficiency of HPC data centers significantly and thus limit the amount of scientific work that can be achieved. While the computational power has increased significantly over the years, detection and prediction of workload failures have lagged far behind and will become increasingly critical as the system scale and complexity further increase. In this study, we analyze workload traces collected from a production cluster and train machine learning models on a large amount of data sets to predict workload failures. Our prediction models consist of a queue-time model that estimates the probability of workload failures before execution and a runtime model that predicts failures at runtime. Evaluation results show that the queue-time model and runtime model can predict workload failures with a maximum precision score of 90.61% and 97.75%, respectively. By integrating the runtime model with the job scheduler, it helps reduce CPU time, and memory usage by up to 16.7% and 14.53%, respectively.
Increasing Features in MAP-Elites Using an Age-Layered Population Structure

The multi-dimensional archive of phenotypic elites (MAP-Elites) algorithm is a popular evolutionary algorithm which returns a highly diverse set of elite solutions. The population is separated into a grid-like feature space defined by user-specified feature dimensions where each cell of the grid corresponds to a unique behaviour combination. The algorithm is conceptually simple and effective at producing high-quality, diverse solutions, but it comes with a major limitation on its exploratory capabilities. With every added feature, the set of solutions grows exponentially, making high-dimensional feature spaces infeasible. This work proposes a way of increasing features with the novel Age-Layered MAP-Elites (ALME) algorithm where the population is separated into age-layers and each layer has its own feature space. By using different features in the layers, the population migrates up through the layers experiencing selective pressure towards different features. This algorithm is applied to a simulated intelligent agent environment where agents are controlled by genetic programming (GP) trees to observe interesting emergent behaviours in underexplored regions of the feature space. It is observed that ALME is capable of producing a high-quality and diverse set of solutions that is competitive with traditional MAP-Elites without the combinatorial explosion in the resulting number of solutions.
Object as a Service (OaaS): Enabling Object Abstraction in Serverless Clouds

Function as a Service (FaaS) paradigm is becoming widespread and is envisioned as the next generation of cloud computing systems that mitigate the burden for programmers and cloud solution architects. However, the FaaS abstraction only makes the cloud resource management aspects transparent but does not deal with the application data aspects. As such, developers have to intervene and undergo the burden of managing the application data, often via separate cloud services (e.g., AWS S3). Similarly, the FaaS abstraction does not natively support function workflow, hence, the developers often have to work with workflow orchestration services (e.g., AWS Step Functions) to build workflows. Moreover, they have to explicitly navigate the data throughout the workflow. To overcome these inherent problems of FaaS, our hypothesis is to design a higher-level cloud programming abstraction that can hide the complexities and mitigate the burden of developing cloud-native application development. Accordingly, in this research, we borrow the notion of object from object-oriented programming and propose a new abstraction level atop the function abstraction, known as Object as a Service (OaaS). OaaS encapsulates the application data and function into the object abstraction and relieves the developers from resource and data management burdens. It also unlocks opportunities for built-in optimization features, such as software reusability, data locality, and caching. OaaS natively supports dataflow programming such that developers define a workflow of functions transparently without getting involved in data navigation, synchronization, and parallelism aspects. We implemented a prototype of the OaaS platform and evaluated it under real-world settings against state-of-the-art platforms regarding the imposed overhead, scalability, and ease of use. The results demonstrate that OaaS streamlines cloud programming and offers scalability with an insignificant overhead to the underlying cloud system.
GreenKube: Towards Greener Container Orchestration using Artificial Intelligence

This paper introduces the GreenKube framework, which aims to reduce energy consumption while meeting Quality of Service (QoS) requirements through the use of various AI methodologies such as deep learning time-series forecasting, deep reinforcement learning, and graph neural networks. The paper also explores the limitations of contemporary container orchestration frameworks, including Kubernetes, and describes how GreenKube aims to progress beyond them. Additionally, the paper presents a prototype of the GreenKube framework that was evaluated in an extensive simulation using CloudSim Plus, and compares its performance to Kubernetes' Horizontal Pod Autoscaler. The results demonstrate that GreenKube outperforms Kubernetes in terms of latency and task execution time while requiring fewer computational resources.
A randomized measurement toolbox for an interacting Rydberg-atom quantum simulator

Abstract We present a toolbox to probe quantum many-body states implemented on Rydberg-atoms quantum hardware via randomized measurements. We illustrate the efficacy of this measurement toolbox in the context of probing entanglement, via the estimation of the purity, and of verifying a ground-state preparation using measurements of the Hamiltonian variance. To achieve this goal, we develop and discuss in detail a protocol to realize independent, local unitary rotations. We benchmark the protocol by investigating the ground state of the one-dimensional Su–Schrieffer–Heeger model, recently realized on a chain of Rydberg atom, and the state resulting after a sudden quench in a staggered XY chain. We probe the robustness of our toolbox by taking into account experimental imperfections, such as pulse fluctuations and measurement errors.
Blockchain Methods and Data-Driven Decision Making With Autonomous Transportation

Blockchain encourages artificial intelligence towards intelligence while also increasing its autonomy and credibility. In this chapter, the authors examine the relationship between blockchain technology and artificial intelligence from a more thorough and three-dimensional standpoint. One of the greatest problems with blockchain implementations in IoV is that they cannot meet the computational and energy needs of conventional blockchain systems since IoV nodes are limited in their ability to use resources. A marketplace that enables stakeholders (CSPs, asset suppliers, service providers, regulators, etc.) to interact and exchange value with confidence based on smart provenance and governance may be developed using blockchain and distributed ledger technologies (DLT). These innovations offer a decentralised audit architecture that is safe. Such transactions (who uses what) can be kept on a distributed ledger marketplace in an immutable setting. A decentralised consensus process that does not need mining or incentivization in a permissionless architecture ensures data integrity.
An Adaptive DeepLabv3+ for Semantic Segmentation of Aerial Images Using Improved Golden Eagle Optimization Algorithm

Semantic segmentation is a significant task in the field of Remote Sensing and Computer Vision. DeepLabV3+ is a convolutional neural network architecture that excels in the task of semantic segmentation, which involves assigning a class label to each pixel in an input image. This Paper proposes an Adaptive Deeplabv3+ model for semantic segmentation of Aerial Images, which combines Deeplabv3+ with the Improved Golden Eagle Optimization Algorithm (IGEO), to solve imprecise target segmentation and poor border segmentation accuracy. To enhance the quality of segmentation, Adaptive DeepLabV3+ employs atrous spatial pyramid pooling (ASPP) with multiple dilation rates in the encoder and allows the model to capture multi-scale context information efficiently, enabling it to distinguish between objects with varying scales. The proposed model effectively segmented the aerial images by optimizing the hyper-parameters such as hidden neuron count and learning rate. The suggested model achieved 98.46% accuracy, 96.32% correlation, 96.48% precision and 98.36% dice coefficient within the computation time of 136.8912 and 147.2684 seconds for dataset 1 and 2 respectively. Therefore, the evolutional outcomes of the proposed model show significantly improved than the state-of-the-art techniques.
Hungarian Qubit Assignment for Optimized Mapping of Quantum Circuits on Multi-Core Architectures

Modular quantum computing architectures offer a promising alternative to monolithic designs for overcoming the scaling limitations of current quantum computers. To achieve scalability beyond small prototypes, quantum architectures are expected to adopt a modular approach, featuring clusters of tightly connected quantum bits with sparser connections between these clusters. Efficiently distributing qubits across multiple processing cores is critical for improving quantum computing systems' performance and scalability. To address this challenge, we propose the Hungarian Qubit Assignment (HQA) algorithm, which leverages the Hungarian algorithm to improve qubit-to-core assignment. The HQA algorithm considers the interactions between qubits over the entire circuit, enabling fine-grained partitioning and enhanced qubit utilization. We compare the HQA algorithm with state-of-the-art alternatives through comprehensive experiments using both real-world quantum algorithms and random quantum circuits. The results demonstrate the superiority of our proposed approach, outperforming existing methods, with an average improvement of 1.28×.
Analysis of arbitrary superconducting quantum circuits accompanied by a Python package: SQcircuit

Superconducting quantum circuits are a promising hardware platform for realizing a fault-tolerant quantum computer. Accelerating progress in this field of research demands general approaches and computational tools to analyze and design more complex superconducting circuits. We develop a framework to systematically construct a superconducting quantum circuit's quantized Hamiltonian from its physical description. As is often the case with quantum descriptions of multicoordinate systems, the complexity rises rapidly with the number of variables. Therefore, we introduce a set of coordinate transformations with which we can find bases to diagonalize the Hamiltonian efficiently. Furthermore, we broaden our framework's scope to calculate the circuit's key properties required for optimizing and discovering novel qubits. We implement the methods described in this work in an open-source Python package <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="monospace">S</mml:mi><mml:mi mathvariant="monospace">Q</mml:mi><mml:mi mathvariant="monospace">c</mml:mi><mml:mi mathvariant="monospace">i</mml:mi><mml:mi mathvariant="monospace">r</mml:mi><mml:mi mathvariant="monospace">c</mml:mi><mml:mi mathvariant="monospace">u</mml:mi><mml:mi mathvariant="monospace">i</mml:mi><mml:mi mathvariant="monospace">t</mml:mi></mml:mrow></mml:math>. In this manuscript, we introduce the reader to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="monospace">S</mml:mi><mml:mi mathvariant="monospace">Q</mml:mi><mml:mi mathvariant="monospace">c</mml:mi><mml:mi mathvariant="monospace">i</mml:mi><mml:mi mathvariant="monospace">r</mml:mi><mml:mi mathvariant="monospace">c</mml:mi><mml:mi mathvariant="monospace">u</mml:mi><mml:mi mathvariant="monospace">i</mml:mi><mml:mi mathvariant="monospace">t</mml:mi></mml:mrow></mml:math> environment and functionality. We show through a series of examples how to analyze a number of interesting quantum circuits and obtain features such as the spectrum, coherence times, transition matrix elements, coupling operators, and the phase coordinate representation of eigenfunctions.
A Robust Privacy-Preserving ECC-Based Three-Factor Authentication Scheme for Metaverse Environment

Metaverse is revolutionizing the future iteration of technology with the potential to ameliorate efficiency and innovation drastically. It fashions engaging spaces for user interaction that simulate the real world by integrating augmented and virtual reality. Metaverse furnishes immersive interactive experiences, unrestricted time and space, visualizations, minimal learning costs, and endorses communication. A metaverse offers various services, such as virtual environments and avatars for telecommuting, education, and gaming. Yet, to access services, users must register with the server, where the registration requires user's identity, password, and personal information. Additionally, the user interaction in metaverse is mediated through avatars over public channels, fabricating opportunities for security attacks such as replay and impersonation attacks. Consequently, we propose a secure mutual authentication scheme for safer user-server and avatar–avatar interactions utilizing Elliptic Curve Cryptography (ECC) and fuzzy extractor. The security of protocol is examined with Burrows–Abadi–Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Validation of Internet Security Protocols Applications (AVISPA). We also conduct a comparative analysis of the computational and communication costs and the security features of proposed scheme with pre-existing works. Hence, the suggested protocol offers magnificent security and efficiency, making it suitable for the metaverse environment.
The negative effect of service robots’ affective human-likeness on consumer satisfaction in frontline service encounters

Although companies have widely deployed service robots with affective human-likeness, scant attention has been paid to their negative effects on consumer satisfaction. We adopt a consumer–robot relationship perspective to investigate the mechanism and boundary conditions of the negative influence of affective human-likeness on consumer satisfaction. We suggest that service robots with affective human-likeness may violate consumer–robot relationship norms, making consumers feel social discomfort, thereby negatively affecting their satisfaction. Study 1 shows that a high degree of affective human-likeness negatively affects consumer satisfaction by causing social discomfort. Study 2 finds that consumer power which affects the perception of social distance, can be the boundary condition for previous negative effects. This research makes contributes to the literature on service robot anthropomorphism, consumer–robot relationships, and consumer power in artificial intelligence marketing and can guide service robots’ anthropomorphic use.
Scaling of errors in digitized counterdiabatic driving

Abstract We study errors caused by digitization of shortcuts to adiabaticity by counterdiabatic driving. We find possibility of error scaling <?CDATA $\mathcal{O}(M^{-2})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi class="MJX-tex-calligraphic">O</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>M</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> with the number of time slices M , whereas worse error scaling <?CDATA $\mathcal{O}(M^{-1})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi class="MJX-tex-calligraphic">O</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>M</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> is predicted in the conventional theory of the first-order Suzuki-Trotter decomposition. We point out this possibility by considering a state-dependent error bound and confirm emergence of this error scaling <?CDATA $\mathcal{O}(M^{-2})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi class="MJX-tex-calligraphic">O</mml:mi> </mml:mrow> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mi>M</mml:mi> <mml:mrow> <mml:mo>−</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> by numerical simulation. Moreover, we numerically show that intermediate error scaling can be observed in digitization of approximate counterdiabatic driving. These results reveal usefulness of digitized counterdiabatic driving from the viewpoints of both cost and performance.