Reviewerly/exHarmony huggingface dataset:
Blockchain’s role in e-commerce sellers’ decision-making on information disclosure under competition

In e-commerce, sellers can disclose product information (such as quality, size information, function, and so on) to make consumers understand the products. However, in the process of information disclosure, consumers often fall into information distortion or information loss. Because of its immutability and traceability, blockchain can help e-commerce sellers improve information disclosure and ensure the efficiency of information transmission. We study a duopoly competitive e-commerce market in which two e-commerce sellers compete in information disclosure. According to whether to apply blockchain, we divide the sellers' decision-making into four research scenarios (NN, BN, NB, BB). Based on the above four scenarios, we get the market demand of different products depending on the consumer utility, and further establish the game model in the competitive environment. This paper explores the impact of blockchain on information disclosure and consumer surplus, and achieves the Nash equilibrium of blockchain application for both sides. In the expansion model, we study e-commerce sellers' risk aversion and capital constraints, and further explore their impact on blockchain in practice. Finally, combining with blockchain's characteristics, we also analyze the impact of the application of blockchain at other aspects on the supply chain. We find that when consumers' trust in information is low or the cost of blockchain applications is low, all e-commerce sellers in competition will adopt blockchain. In addition, when consumers have low trust in information, it will be difficult to achieve complete equilibrium in the application of blockchain as their risk aversion increases. For capital constrained sellers, when the cost of blockchain application is low, it will be difficult to achieve full equilibrium for blockchain applicants as the bank financing rate increases.
Enabling Hand Gesture Customization on Wrist-Worn Devices

We present a framework for gesture customization requiring minimal examples from users, all without degrading the performance of existing gesture sets. To achieve this, we first deployed a large-scale study (N=500+) to collect data and train an accelerometer-gyroscope recognition model with a cross-user accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on everyday non-gesture data. Next, we design a few-shot learning framework which derives a lightweight model from our pre-trained model, enabling knowledge transfer without performance degradation. We validate our approach through a user study (N=20) examining on-device customization from 12 new gestures, resulting in an average accuracy of 55.3%, 83.1%, and 87.2% on using one, three, or five shots when adding a new gesture, while maintaining the same recognition accuracy and false-positive rate from the pre-existing gesture set. We further evaluate the usability of our real-time implementation with a user experience study (N=20). Our results highlight the effectiveness, learnability, and usability of our customization framework. Our approach paves the way for a future where users are no longer bound to pre-existing gestures, freeing them to creatively introduce new gestures tailored to their preferences and abilities.
Global correlation and local information flows in controllable non-Markovian open quantum dynamics

Abstract In a fully-controllable experiment platform for studying non-Markovian open quantum dynamics, we show that the non-Markovianity could be investigated from the global and local aspects. By mixing random unitary dynamics, we demonstrate non-Markovian and Markovian open quantum dynamics. From the global point of view, by tuning the base frequency we demonstrate the transition from the Markovianity to the non-Markovianity as measured by the quantum mutual information (QMI). In a Markovian open quantum process, the QMI decays monotonically, while it may rise temporarily in a non-Markovian process. However, under some circumstances, it is not sufficient to globally investigate the non-Markovianity of the open quantum dynamics. As an essential supplement, we further utilize the quantum Fisher information (QFI) flow to locally characterize the non-Markovianity in different channels. We demonstrate that the QMI in combination with the QFI flow are capable of measuring the non-Markovianity for a multi-channel open quantum dynamics.
Fast Readout and Reset of a Superconducting Qubit Coupled to a Resonator with an Intrinsic Purcell Filter

Quantum information processing requires qubits that can retain their energy for a long time, and one also needs to be able to rapidly manipulate and measure the qubits. The authors utilize the spatial extent of a microwave resonator to meet both of these requirements. They protect a superconducting qubit from energy decay by simply shifting the position of the coupling port in a conventional device. This study demonstrates a simple technique that enables fast readout and reset of a superconducting qubit without compromising its lifetime. The technique could immediately be incorporated into the design of a superconducting quantum computer.
Many-Body Quantum Magic

Magic (nonstabilizerness) is a necessary but “expensive” kind of “fuel” to drive universal fault-tolerant quantum computation. To properly study and characterize the origin of quantum “complexity” in computation as well as physics, it is crucial to develop a rigorous understanding of the quantification of magic. Previous studies of magic mostly focused on small systems and largely relied on the discrete Wigner formalism (which is only well behaved in odd prime power dimensions). Here we present an initiatory study of the magic of genuinely many-body quantum states that may be strongly entangled, with focus on the important case of many qubits, at a quantitative level. We first address the basic question of how “magical” a many-body state can be, and show that the maximum magic of an n-qubit state is essentially n, simultaneously for a range of “good” magic measures. As a corollary, the resource theory of magic has asymptotic golden currency states. We then show that, in fact, almost all n-qubit pure states have magic of nearly n. In the quest for explicit, scalable cases of highly entangled states whose magic can be understood, we connect the magic of hypergraph states with the second-order nonlinearity of their underlying Boolean functions. Next, we go on and investigate many-body magic in practical and physical contexts. We first consider a variant of measurement-based quantum computation where the client is restricted to Pauli measurements, in which magic is a necessary feature of the initial “resource” state. We show that n-qubit states with nearly n magic, or indeed almost all states, cannot supply nontrivial speedups over classical computers. We then present an example of analyzing the magic of “natural” condensed matter systems of physical interest. We apply the Boolean function techniques to derive explicit bounds on the magic of certain representative two-dimensional symmetry-protected topological states, and comment on possible further connections between magic and the quantum complexity of phases of matter.Received 21 November 2020Revised 28 February 2022Accepted 11 April 2022DOI:https://doi.org/10.1103/PRXQuantum.3.020333Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingPhase transitionsQuantum computationQuantum entanglementQuantum error correctionResource theoriesSymmetry protected topological statesTopological phases of matterQuantum InformationCondensed Matter, Materials & Applied Physics
Electric Vehicle Charging: A Survey on the Security Issues and Challenges of the Open Charge Point Protocol (OCPP)

The increased use of smart Electric Vehicles (EVs) and Plug-in Electric Vehicles (PEV) opened a new area of research and development. The number of EV charging sites has considerably increased in residential as well as in public areas. Within these EV charging sites, various entities need to communicate in a secure and efficient way. The Open Charge Point Protocol (OCPP) offers a way to coordinate this communication and is already being used in many implementations. However, only the latest OCPP 2.0 version of the protocol includes certain security features. In this article, we present the entities that take part in an OCPP-based smart charging scenario, we identify security issues and threats and present solutions that have been proposed by scholars. We identify open security issues for OCPP and propose future research directions for the security enhancement of the protocol.
Edge-of-chaos learning achieved by ion-electron–coupled dynamics in an ion-gating reservoir

Physical reservoir computing has recently been attracting attention for its ability to substantially reduce the computational resources required to process time series data. However, the physical reservoirs that have been reported to date have had insufficient computational capacity, and most of them have a large volume, which makes their practical application difficult. Here, we describe the development of a Li+ electrolyte-based ion-gating reservoir (IGR), with ion-electron-coupled dynamics, for use in high-performance physical reservoir computing. A variety of synaptic responses were obtained in response to past experience, which were stored as transient charge density patterns in an electric double layer, at the Li+ electrolyte/diamond interface. Performance for a second-order nonlinear dynamical equation task is one order of magnitude higher than memristor-based reservoirs. The edge-of-chaos state of the IGR enabled the best computational capacity. The IGR described here opens the way for high-performance and integrated neural network devices.
Random vector functional link network: Recent developments, applications, and future directions

Neural networks have been successfully employed in various domains such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, direct links, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the first comprehensive review of the evolution of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFLs, ensemble RVFLs, deep RVFLs and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we present potential future research directions/opportunities that can inspire the researchers to improve the RVFL’s architecture and learning algorithm further.
Practical and Robust Federated Learning With Highly Scalable Regression Training

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.
Towards Network-Aware Service Composition in the Cloud

Composing several API-defined services into one composite service per user requirements has become an important service creation approach in the cloud-enabled API economy. Various service selection approaches in support of service composition on demand have been proposed. They usually assume that networking resources are over-provisioned and their usage needs not be considered when making quality-aware service composition decisions. In practice, these approaches often lead to wasteful network resource consumption and impractical end-to-end QoS optimality for cloud-based services. This paper proposes a network-aware cloud service composition approach, named NetMIP, with comparative experimental evaluations for the clouds that adopt the widely deployed fat-tree network topology. By formalizing the service composition goal as a multi-objective constraint optimization problem, we have validated the proposed approach can be used to effectively reduce network resource consumption and deliver QoS optimality while satisfying the end-to-end QoS constraints for the candidate composite services in the cloud. The comparative experimental evaluations are done via a credible cloud infrastructure simulation system, named WebCloudSim. Extensive evaluation results show that NetMIP outperforms several representative cloud service composition approaches in terms of network resource consumption, QoS optimality, and computation time under various service selection workloads and fat-tree network topology settings.
Link-Aware Virtual Machine Placement for Cloud Services based on Service-Oriented Architecture

Data center benefits cloud applications in providing high scalability and ensuring service availability. However, virtual machine (VM) placement in data center poses new challenges for service provisioning. For many cloud services such as storage and video streaming, present placement approaches are unable to support network-demanding services due to overwhelming communication traffic and time. Therefore VM placement concerning link capacity is vital to cloud data centers. In this paper, we define the network-aware VM placement optimization (NAVMPO) problem based on integer linear programming. The objective function of NAVMPO problem aims to minimize communication time for VMs of the same service type. Then we propose the service-oriented physical machine (PM) selection (SOPMS) algorithm and link-aware VM placement (LAVMP) algorithm. The SOPMS algorithm selects the most appropriate PM based on service-oriented architecture, and then the LAVMP algorithm deploys the most suitable VM to target PM regarding to the link capacity between them. Simulation results show that the proposed placement approach significantly decreases communication time compared to existing non-service-oriented and service-oriented VM placement algorithms, and also improves the average utility rate of PMs with lower power consumption.
Quantitative Modeling and Analytical Calculation of Elasticity in Cloud Computing

Elasticity is a fundamental feature of cloud computing and can be considered as a great advantage and a key benefit of cloud computing. One key challenge in cloud elasticity is lack of consensus on a quantifiable, measurable, observable, and calculable definition of elasticity and systematic approaches to modeling, quantifying, analyzing, and predicting elasticity. Another key challenge in cloud computing is lack of effective ways for prediction and optimization of performance and cost in an elastic cloud platform. The present paper makes the following significant contributions. First, we present a new, quantitative, and formal definition of elasticity in cloud computing, i.e., the probability that the computing resources provided by a cloud platform match the current workload. Our definition is applicable to any cloud platform and can be easily measured and monitored. Furthermore, we develop an analytical model to study elasticity by treating a cloud platform as a queueing system, and use a continuous-time Markov chain (CTMC) model to precisely calculate the elasticity value of a cloud platform by using an analytical and numerical method based on just a few parameters, namely, the task arrival rate, the service rate, the virtual machine start-up and shut-down rates. In addition, we formally define auto-scaling schemes and point out that our model and method can be easily extended to handle arbitrarily sophisticated scaling schemes. Second, we apply our model and method to predict many other important properties of an elastic cloud computing system, such as average task response time, throughput, quality of service, average number of VMs, average number of busy VMs, utilization, cost, cost-performance ratio, productivity, and scalability. In fact, from a cloud consumer's point of view, these performance and cost metrics are even more important than the elasticity metric. Our study in this paper has two significance. On one hand, a cloud service provider can predict its performance and cost guarantee using the results developed in this paper. On the other hand, a cloud service provider can optimize its elastic scaling scheme to deliver the best cost-performance ratio. To the best of our knowledge, this is the first paper that analytically and comprehensively studies elasticity, performance, and cost in cloud computing. Our model and method significantly contribute to the understanding of cloud elasticity and management of elastic cloud computing systems.
Partial coherence and quantum correlation with fidelity and affinity distances

A fundamental task in any physical theory is to quantify a certain physical quantity in a meaningful way. In this paper, we show that both fidelity distance and affinity distance satisfy the strong contractibility, and the corresponding resource quantifiers can be used to characterize a large class of resource theories. Under two assumptions, namely, convexity of ``free states'' and closure of free states under ``selective free operations,'' our general framework of resource theory includes quantum resource theories of entanglement, coherence, partial coherence, and superposition. In partial coherence theory, we show that fidelity of partial coherence of a bipartite state is equal to the minimal error probability of a mixed quantum state discrimination task and vice versa, which complements the main result in Xiong and Wu [J. Phys. A: Math. Theor. 51, 414005 (2018)]. We also compute the analytic expression of fidelity of partial coherence for $(2,n)$ bipartite $X$ states. At last, we study the correlated coherence in the framework of partial coherence theory. We show that partial coherence of a bipartite state, with respect to the eigenbasis of a subsystem, is actually a measure of discord-type quantum correlation.
Device-Independent Detection of Genuine Multipartite Entanglement for All Pure States

We show that genuine multipartite entanglement of all multipartite pure states in arbitrary finite dimension can be detected in a device-independent way by employing bipartite Bell inequalities on states that are deterministically generated from the initial state via local operations. This leads to an efficient scheme for large classes of multipartite states that are relevant in quantum computation or condensed-matter physics, including cluster states and the ground state of the Affleck-Kennedy-Lieb-Tasaki (AKLT) model. For cluster states the detection of genuine multipartite entanglement involves only measurements on a constant number of systems with an overhead that scales linear with the system size, while for the AKLT model the overhead is polynomial. In all cases our approach shows robustness against experimental imperfections.
Ultrastrong Jaynes-Cummings model

We propose a reliable scheme to realize the ultrastrong Jaynes-Cummings (JC) model by simultaneously modulating the resonance frequencies of the two-level system and the bosonic mode in the ultrastrong quantum Rabi model. We find that in both the high- and low-frequency modulation regimes, the counter-rotating terms can be completely suppressed without reducing the coupling strength of the rotating-wave terms, and hence the ultrastrong JC Hamiltonian is achieved. The ultrastrong JC interaction can not only be used to implement ultrafast quantum operations, but also will open up a new route to the demonstration of quantum phase transition associated with the JC Hamiltonian across the deep-strong coupling point. Some discussions on the experimental implementation of this scheme with circuit-QED systems are presented.
Benchmarking unsupervised near-duplicate image detection

Unsupervised near-duplicate detection has many practical applications ranging from social media analysis and web-scale retrieval, to digital image forensics. It entails running a threshold-limited query on a set of descriptors extracted from the images, with the goal of identifying all possible near-duplicates, while limiting the false positives due to visually similar images. Since the rate of false alarms grows with the dataset size, a very high specificity is thus required, up to 1–10−9 for realistic use cases; this important requirement, however, is often overlooked in literature. In recent years, descriptors based on deep convolutional neural networks have matched or surpassed traditional feature extraction methods in content-based image retrieval tasks. To the best of our knowledge, ours is the first attempt to establish the performance range of deep learning-based descriptors for unsupervised near-duplicate detection on a range of datasets, encompassing a broad spectrum of near-duplicate definitions. We leverage both established and new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a known ground truth is provided for all possible pairs over a general, large scale image collection. To compare the specificity of different descriptors, we reduce the problem of unsupervised detection to that of binary classification of near-duplicate vs. not-near-duplicate images. The latter can be conveniently characterized using Receiver Operating Curve (ROC). Our findings in general favor the choice of fine-tuning deep convolutional networks, as opposed to using off-the-shelf features, but differences at high specificity settings depend on the dataset and are often small. The best performance was observed on the MFND benchmark, achieving 96% sensitivity at a false positive rate of 1.43×10−6.