Reviewerly/exHarmony huggingface dataset:
Credible Decentralized Exchange Design via Verifiable Sequencing Rules

Trading on decentralized exchanges has been one of the primary use cases for permissionless blockchains with daily trading volume exceeding billions of U.S. ‍dollars. In the status quo, users broadcast transactions they wish to execute in the exchange and miners are responsible for composing a block of transactions and picking an execution ordering — the order in which transactions execute in the exchange. Due to the lack of a regulatory framework, it is common to observe miners exploiting their privileged position by front-running transactions and obtaining risk-fee profits. Indeed, the Flashbots service institutionalizes this exploit, with miners auctioning the right to front-run transactions. In this work, we propose to modify the interaction between miners and users and initiate the study of verifiable sequencing rules. As in the status quo, miners can determine the content of a block; however, they commit to respecting a sequencing rule that constrains the execution ordering and is verifiable (there is a polynomial time algorithm that can verify if the execution ordering satisfies such constraints). Thus in the event a miner deviates from the sequencing rule, anyone can generate a proof of non-compliance.
Oracles Integration in Blockchain-Based Platform for Smart Crop Production Data Exchange

Blockchain oracles are an intermediary designed to connect external non-deterministic information and real-world data to the blockchain digital infrastructure. The variety of proposed solutions and purposes are of great variety and suggest that it is necessary to take into account different features of the process and specifically define the required functionalities. The purpose of this paper is to present the integration of oracles into an EOSIO blockchain-based platform for smart crop production data exchange by smart contracts. The functions of two oracles are presented. Their integration is described at the design level and at the implementation of the smart contracts. The design level is illustrated by workflow diagrams of internal processes between oracle applications and the blockchain smart contract and by external processes in the oracles’ smart contracts. The implementation level is illustrated by oracle application configuration files and elements of C++ smart contracts, such as constant and variable declarations, multi-index tables, internal contract functions, and actions called by other contracts and external programs. As results of the oracles’ operation, a report on the detected emergency failures and an estimate of the cost of ram resource are presented.
Gamification and neuromarketing: A unified approach for improving user experience

Abstract Neuroscience and its offshoot as neuromarketing have helped marketers understand neuroscientific aspects of customers while gamification has helped them understand the psyche of consumers. Despite neuromarketing's many potential benefits to businesses, little academic work has been done on the field so far. Most studies have examined consumer shifts during and after pandemics without analyzing them from two critical perspectives: neuroscientific theories and psychology theories. As the two streams of knowledge lean on each other, their interdependence in the field of e‐engagement needs exploration. The purpose of this study is to answer an important question – “How do marketers use gamification and neuromarketing to understand online engagement of consumers?”. We used a quantitative empirical research approach to assess the inter‐relationship between neuromarketing and gamification. The study collected data from digital marketing strategists of retail firms to propose a theoretical framework for self‐determination theory in successfully implementing new age technologies by plugging the cues of gamification and neuromarketing. The framework would be useful for retail firms to design digital marketing strategies for capturing the attention of consumers across different geographies. Findings indicated that, marketers are interested in neuromarketing for two main reasons: first, they think it can help them save money and improve their marketing plans, and second, they think that cutting‐edge research techniques such as brain imaging can help them get more accurate findings.
Immersive and desktop virtual reality in virtual fashion stores: a comparison between shopping experiences

With the high growth and prosperity of e-commerce, the retail industry needs to explore new technologies that improve digital shopping experiences. In the current technological scenario, Virtual Reality (VR) emerges as a tool and an opportunity for enhancing shopping activities, especially for the fashion industry. This study explores whether using Immersive Virtual Reality (IVR) technologies enhances the shopping experience in the fashion industry compared to Desktop Virtual Reality (DVR). A within-subject experiment was carried out involving a sample of 60 participants who completed a simulated shopping experience. In the first mode (DVR), a desktop computer setup was used to test the shopping experience using a mouse and keyboard for navigation. The second mode (IVR) exploited a Head-Mounted Display (HMD), and controllers, that allowed navigation while seated on a workstation to avoid sickness. Participants had to find a bag in the virtual shop and explore its features until they were ready to purchase it. Post-hoc measures of time duration of the shopping experience, hedonic and utilitarian values, user experience, and cognitive load were compared. Results showed that participants experienced higher hedonism and utilitarianism in the IVR shop compared to DVR. The cognitive load was comparable in both modes, while user experience was higher in IVR. In addition, the time duration of the shopping experience was higher in IVR, where users stayed immersed and enjoyed it for longer. This study has implications for fashion industry research, as the use of IVR can potentially lead to novel shopping patterns by enhancing the shopping experience.The online version contains supplementary material available at 10.1007/s10055-023-00806-y.
Squeezed state generation using cryogenic InP HEMT nonlinearity

Abstract This study focuses on generating and manipulating squeezed states with two external oscillators coupled by an InP HEMT operating at cryogenic temperatures. First, the small-signal nonlinear model of the transistor at high frequency at 5 K is analyzed using quantum theory, and the related Lagrangian is theoretically derived. Subsequently, the total quantum Hamiltonian of the system is derived using Legendre transformation. The Hamiltonian of the system includes linear and nonlinear terms by which the effects on the time evolution of the states are studied. The main result shows that the squeezed state can be generated owing to the transistor’s nonlinearity; more importantly, it can be manipulated by some specific terms introduced in the nonlinear Hamiltonian. In fact, the nonlinearity of the transistors induces some effects, such as capacitance, inductance, and second-order transconductance, by which the properties of the external oscillators are changed. These changes may lead to squeezing or manipulating the parameters related to squeezing in the oscillators. In addition, it is theoretically derived that the circuit can generate two-mode squeezing. Finally, second-order correlation (photon counting statistics) is studied, and the results demonstrate that the designed circuit exhibits antibunching, where the quadrature operator shows squeezing behavior.
Applying Smart Glasses in Situated Exploration for Learning English in a National Science Museum

Research on the use of augmented reality technology in museums is mostly limited to scientific knowledge. The use of wearable device technology learning materials to benefit students in the process of English learning has been somewhat explored, along with differences in students’ learning styles and their influence on students’ learning motivation, but there remains much to be done in this field. This study explored the learning effect of smart glasses integration into the English language dinosaur exhibition at the Museum of Natural Science and investigated their impact on motivation. The experimental group used smart glasses learning strategy to facilitate their learning experience, whereas the control group used a tablet learning strategy. The research results show that: 1) Learning effectiveness and motivation were significantly superior when using smart glasses learning strategy, as compared to using a tablet learning strategy when learning through the English language situated exploration game. 2) The interaction between different learning strategies and learning styles had a significant impact on learning effectiveness. In the smart glasses learning strategy group and the tablet learning strategy group, the learning effectiveness of the kinesthetic students was significantly higher than those of the auditory students. The learning effectiveness of the visual students using the tablet learning strategy was higher than auditory students. 3) The interaction between different learning strategies and learning styles had no significant effect on learning motivation. In terms of learning motivation, visual students were more affected than auditory and kinesthetic students.
Blockchain and digital twin empowered trustworthy self-healing for edge-AI enabled industrial Internet of things

The public has regarded Edge-AI enabled Industrial Internet of Things (IIoT) as the crucial foundation in the intelligent digital factories in Industry 4.0. It can fully catch the massive production data derived from the complex production process, and provide efficient, intelligent services. However, the deployment of edge AI aggravates the complexity and security risks caused by the massive heterogeneous resource-constrained and vulnerable edge IIoT devices. Effective fault prevention is crucial to ensure the security and robustness of the IIoT with numerous vulnerable edge devices. Most existing solutions are based on the history log, which can hardly defend against attacks and is easy to cause excessive maintenance. To address this issue, we propose a trustworthy self-healing scheme based on the combination of distributed digital twin (DT) and blockchain, to ensure the security and robustness of the industrial system network. We first propose an implementation architecture of the distributed DT based self-healing IIoT to apply the distributed DT simulation capability fully. In addition, we provide a DT simulation operating mechanism for the controlled industrial devices, considering the requirement of users and constrained resources of edge servers. Moreover, this work proposes a blockchain-based decentralized trust management mechanism to ensure the reliability of self-healing. The security analysis and performance evaluation show the security and efficiency of our proposal.
Effects of compressed speckle image on digital image correlation for vibration measurement

Digital image correlation (DIC) can accurately measure subpixel displacement field based on speckle images. However, in vibration measurement, the extensive high-resolution images captured by DIC systems impose significant data transmission and storage requirements on hardware. A framework combining image compression techniques with DIC methods is proposed. The framework effectively reduces the memory footprint of images while preserving the primary speckle features with subpixel errors. The DIC Challenge 2.0 image sets are employed in simulation to study the effects of compressing images on data storage and 2D DIC measurements, and the effectiveness are compared between JPEG and JPEG 2000 image compression techniques. A cantilever-beam is adopted to experimentally investigate the impacts of the compressed image sets on 3D surface deformation and vibration response measurement. The proposed framework is a valuable option for DIC vibration measurement with high efficiency, the JPEG lossy compression and JPEG 2000 lossless compression each has its own characteristics in performance of DIC based vibration measurement.
Swap Test-based characterization of decoherence in universal quantum computers

Unreliable quantum processes stemming from noisy operations and interactions with the environment represent one of the greatest challenges to be overcome in the development of scalable, fault-tolerant universal quantum computers. Generic characterizations of such quantum processes, however, can be very resource-intensive, and many tools have been developed for identifying particular aspects of a quantum computer. In this article, we verify whether a tool called Swap Test can be used in order to identify decoherence in an implementation-agnostic way. Simulations and experimental results show that a modified version of a Toffoli Swap Test can be employed as an alternative to a full quantum process tomography, consuming considerably less computational resources as a consequence. Moreover, it is also shown to be able to simulate in a generic quantum computer a quantum optical communication link that employs two-photon interference.
Solving larger maximum clique problems using parallel quantum annealing

Quantum annealing has the potential to find low energy solutions of NP-hard problems that can be expressed as quadratic unconstrained binary optimization problems. However, the hardware of the quantum annealer manufactured by D-Wave Systems, which we consider in this work, is sparsely connected and moderately sized (on the order of thousands of qubits), thus necessitating a minor-embedding of a logical problem onto the physical qubit hardware. The combination of relatively small hardware sizes and the necessity of a minor-embedding can mean that solving large optimization problems is not possible on current quantum annealers. In this research, we show that a hybrid approach combining parallel quantum annealing with graph decomposition allows one to solve larger optimization problem accurately. We apply the approach on the Maximum Clique problem on graphs with up to 120 nodes and 6395 edges.
Development of a high-performance seismic phase picker using deep learning in the Hakone volcanic area

Abstract In volcanic regions, active earthquake swarms often occur in association with volcanic activity, and their rapid detection and analysis are crucial for volcano disaster prevention. Currently, these processes are ultimately left to human judgment and require significant time and money, making detailed real-time verification impossible. To overcome this issue, we attempted to apply machine learning, which has been successfully applied to various seismological fields to date. For seismic phase pick, several models have already been trained using a large amount of training data (mainly crustal earthquakes). Although there are some cases in which these models can be applied without any problems, regional dependence on pre-trained models has been reported. Since this study targets earthquakes in a volcanic region, applying existing pre-trained models may be difficult. Therefore, in this study, we compared three models; the publicly available trained model (model 0), a model which was trained with approximately 220,000 P- and S-wave onset reading data recorded at the Hakone volcano from 1999 to 2020 with initialized parameters (model 1) using the same architecture, and a model fine-tuned with the aforementioned Hakone data using the parameters of model 0 as initial values (model 2), and evaluated their phase identification performance for the Hakone data. As a result, the seismic phase detection rates of models 1 and 2 were much higher than those of model 0. However, small-amplitude signals are often missed when multiple seismic events occur within a detection time window. Therefore, we created training data with two earthquakes in the same time window, retrained the model using the data, and successfully detected events that previously would have been missed. In addition, it was found that more events were detected by setting the threshold to a low probability value for detection, increasing the number of seismic phase detections, and filtering by phase association and hypocenter location. Graphical Abstract
Adding virtual plants leads to higher cognitive performance and psychological well-being in virtual reality

Previous research has shown the positive effects of exposure to real and virtual nature. To investigate how such benefits might generalize to ever-more-prevalent virtual workplaces, we examined the effects of the absence or presence of virtual plants in an office environment in Virtual Reality (VR) on users' cognitive performance and psychological well-being. The results of our user study with 39 participants show that in the presence of virtual plants, participants performed significantly better in both short-term memory and creativity tasks. Furthermore, they reported higher psychological well-being scores, including positive affect and attentive coping, whilst reporting lower feelings of anger and aggression after exposure to virtual plants in VR. The virtual office with plants was also perceived as more restorative and induced a higher sense of presence. Overall, these results highlight how the presence of virtual plants in VR can have positive influences on users, and therefore, constitute important design considerations when developing future working and learning spaces.
Privacy-Preserving Federated Learning of Remote Sensing Image Classification With Dishonest Majority

The classification of remote sensing images can give valuable data for various practical applications for smart cities, including urban planning, construction, and water resource management. The federated learning (FL) solution is often adopted to resolve the problems of limited resources and the confidentiality of data in remote sensing image classification. Privacy-preserving federated learning (PPFL) is a state-of-art FL scheme tailored for the privacy-constrained situation. It is required to address safeguarding data privacy and optimizing model accuracy effectively. However, existing PPFL methods usually suffer from model poisoning attacks, especially in the case of dishonest-majority scenarios. To address this challenge, in this work, we propose a blockchain-empowered PPFL for remote sensing image classification framework with the poisonous dishonest majority, which is able to defend against encrypted model poisoning attacks without compromising users' privacy. Specifically, we first propose the method of proof of accuracy (PoA) aiming to evaluate the encrypted models in an authentic way. Then, we design the secure aggregation framework using PoA, which can achieve robustness in a majority proportion of adversary settings. The experimental results show that our scheme can reach 92.5%, 90.61%, 87.48%, and 81.84% accuracy when the attacker accounts for 20%, 40%, 60%, and 80%, respectively. This is consistent with the FedAvg accuracy when only benign clients own the corresponding proportion of data. The experiment results demonstrate the proposed scheme's superiority in defending against model poisoning attacks.
Supply chain traceability and counterfeit detection of COVID-19 vaccines using novel blockchain-based Vacledger system

We propose a novel framework, Vacledger, for supply chain traceability and counterfeit detection of COVID-19 vaccines using a blockchain network. It includes four smart contracts on a private-permissioned blockchain network for supply chain traceability and counterfeit detection of COVID-19 vaccine, more specifically to (i) handle the rules and regulations of vaccine importing countries and provide authorization for cross the borders (regulatory compliance and border authorization smart contract), (ii) register new and imported vaccines in the Vacledger system (vaccine registration smart contract), (iii) find the number of stocks that have arrived in the Vacledger system (stock accumulation smart contract), and (iv) identify the exact location of the stock (location tracing update smart contract). Our results show that the proposed system keeps track of all activities, events, transactions, and all other past transactions, permanently stored in an immutable Vacledger connected to decentralized peer-to-peer file systems. We observe no algorithm complexity differences between the proposed Vacledger system and existing supply chain frameworks based on different blockchain types. In addition, based on four use cases, we estimate our model's overall gasoline cost (transaction or gas price). The Vacledger system empowers distribution companies to manage their supply chain operations effectively and securely using an in-network, permissioned distributed network. This study employs the COVID-19 vaccine supply chain (the healthcare industry) to demonstrate how the proposed Vacledger system operates. Despite this, our proposed approach might be implemented in other supply chain industries, such as the food industry, energy trading, and commodity transactions.
Unifying Dual-Attention and Siamese Transformer Network for Full-Reference Image Quality Assessment

Image Quality Assessment (IQA) is a critical task of computer vision. Most Full-Reference (FR) IQA methods have limitation in the accurate prediction of perceptual qualities of the traditional distorted images and the Generative Adversarial Networks (GANs) based distorted images. To address this issue, we propose a novel method by Unifying Dual-Attention and Siamese Transformer Network (UniDASTN) for FR-IQA. An important contribution is the spatial attention module composed of a Siamese Transformer Network and a feature fusion block. It can focus on significant regions and effectively maps the perceptual differences between the reference and distorted images to a latent distance for distortion evaluation. Another contribution is the dual-attention strategy that exploits channel attention and spatial attention to aggregate features for enhancing distortion sensitivity. In addition, a novel loss function is designed by jointly exploiting Mean Square Error (MSE), bidirectional Kullback–Leibler divergence, and rank order of quality scores. The designed loss function can offer stable training and thus enables the proposed UniDASTN to effectively learn visual perceptual image quality. Extensive experiments on standard IQA databases are conducted to validate the effectiveness of the proposed UniDASTN. The IQA results demonstrate that the proposed UniDASTN outperforms some state-of-the-art FR-IQA methods on the LIVE, CSIQ, TID2013, and PIPAL databases.