Reviewerly/exHarmony huggingface dataset:
An improved PBFT consensus mechanism with trust value evaluation application in the agricultural product trusted traceability system

Blockchain has the advantages of tamper proof, high reliability and traceability, so it can better support the agricultural product traceability system. However, the agricultural product supply chain has the characteristics of long chain and decentralized production, which makes the application of the combination of blockchain technology and agricultural product traceability have many nodes, large system overhead and other problems. In order to solve the above problems, this paper proposes a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism based on trust value evaluation, and uses this optimization algorithm to propose an architecture model to meet the demand for trusted traceability of agricultural product supply chain, so as to realize the distributed storage and security protection of supply chain data. In order to ensure the effective operation of the blockchain system, this paper studies the consensus mechanism in the blockchain network, designs a trust value evaluation model to calculate the trust value of nodes, and selects trusted nodes to join the consensus group based on the trust value, which ensures the credibility of the consensus nodes. At the same time, the consistency protocol is optimized to reduce the communication overhead in the consensus process. The experimental results show that the consensus mechanism improved in this paper has certain advantages in communication overhead, transaction delay and throughput. Compared with the existing storage model, the model described in this paper has higher security and throughput efficiency, effectively ensures the reliable traceability of data, and can be better applied to the traceability scenario of agricultural product supply chain.
Quantum-Enhanced Pattern Recognition

This work demonstrates experimentally that the advantage gained in sensing using quantum photonic resources can be sustained, and even amplified, through complex classical post-processing aimed at extracting relevant features. Despite the very different architectures of the classical algorithms tested here, the quantum advantage in classification performance appears to be robust and qualitatively very consistent. Thus the results argue for widespread use of quantum sensing technologies, in any field that deals with pattern recognition in large datasets.
A Low-Cost Detail-Aware Neural Network Framework and Its Application in Mask Wearing Monitoring

The use of deep learning techniques in real-time monitoring can save a lot of manpower in various scenarios. For example, mask-wearing is an effective measure to prevent COVID-19 and other respiratory diseases, especially for vulnerable populations such as children, the elderly, and people with underlying health problems. Currently, many public places such as hospitals, nursing homes, social service facilities, and schools experiencing outbreaks require mandatory mask-wearing. However, most of the terminal devices currently available have very limited GPU capability to run large neural networks. This means that we have to keep the parameter size of a neural network modest while maintaining its performance. In this paper, we propose a framework that applies deep learning techniques to real-time monitoring and uses it for the real-time monitoring of mask-wearing status. The main contributions are as follows: First, a feature fusion technique called skip layer pooling fusion (SLPF) is proposed for image classification tasks. It fully utilizes both deep and shallow features of a convolutional neural network while minimizing the growth in model parameters caused by feature fusion. On average, this technique improves the accuracy of various neural network models by 4.78% and 5.21% on CIFAR100 and Tiny-ImageNet, respectively. Second, layer attention (LA), an attention mechanism tailor-made for feature fusion, is proposed. Since different layers of convolutional neural networks make different impacts on the final prediction results, LA learns a set of weights to better enhance the contribution of important convolutional layer features. On average, it improves the accuracy of various neural network models by 2.10% and 2.63% on CIFAR100 and Tiny-ImageNet, respectively. Third, a MobileNetv2-based lightweight mask-wearing status classification model is trained, which is suitable for deployment on mobile devices and achieves an accuracy of 95.49%. Additionally, a ResNet mask-wearing status classification model is trained, which has a larger model size but achieves high accuracy of 98.14%. By applying the proposed methods to the ResNet mask-wearing status classification model, the accuracy is improved by 1.58%. Fourth, a mask-wearing status detection model is enhanced based on YOLOv5 with a spatial-frequency fusion module resulting in a mAP improvement of 2.20%. Overall, this paper presents various techniques to improve the performance of neural networks and apply them to mask-wearing status monitoring, which can help stop pandemics.
Automatic Facial Aesthetic Prediction Based on Deep Learning with Loss Ensembles

Deep data-driven methodologies have significantly enhanced the automatic facial beauty prediction (FBP), particularly convolutional neural networks (CNNs). However, despite its wide utilization in classification-based applications, the adoption of CNN in regression research is still constrained. In addition, biases in beauty scores assigned to facial images, such as preferences for specific, ethnicities, or age groups, present challenges to the effective generalization of models, which may not be appropriately addressed within conventional individual loss functions. Furthermore, regression problems commonly employ L2 loss to measure error rate, and this function is sensitive to outliers, making it difficult to generalize depending on the number of outliers in the training phase. Meanwhile, L1 loss is another regression-loss function that penalizes errors linearly and is less sensitive to outliers. The Log-cosh loss function is a flexible and robust loss function for regression problems. It provides a good compromise between the L1 and L2 loss functions. The Ensemble of multiple loss functions has been proven to improve the performance of deep-learning models in various tasks. In this work, we proposed to ensemble three regression-loss functions, namely L1, L2, and Log-cosh, and subsequently averaging them to create a new composite cost function. This strategy capitalizes on the unique traits of each loss function, constructing a unified framework that harmonizes outlier tolerance, precision, and adaptability. The proposed loss function’s effectiveness was demonstrated by incorporating it with three pretrained CNNs (AlexNet, VGG16-Net, and FIAC-Net) and evaluating it based on three FBP benchmarks (SCUT-FBP, SCUT-FBP5500, and MEBeauty). Integrating FIAC-Net with the proposed loss function yields remarkable outcomes across datasets due to its pretrained task of facial-attractiveness classification. The efficacy is evident in managing uncertain noise distributions, resulting in a strong correlation between machine- and human-rated aesthetic scores, along with low error rates.
On Kahan's automatic step-size control and an anadromic gradient descent iteration

AbstractAccelerating the basic gradient iteration is a critical issue in numerical optimization and deep-learning. Recently, William Kahan [Kahan, Automatic Step-Size Control for Minimization Iterations, Technical report, University of California, Berkeley CA, USA, 2019] proposed automatic step-size strategies for the gradient descent iteration without explicitly using any prior information of the Hessian; moreover, a new momentum-based gradient acceleration method, namely, an Anadromic Gradient Descent (AGD) iteration, was proposed. Besides the capability of accelerating the basic gradient descent iteration, AGD enables the iteration to return to the past iterates by just reversing steps in sign and order in the same updating formulation. Numerical performances of the automatic step-size strategies and AGD are claimed to be favourable. In this paper, for the quadratic model, through a revisit of some classical momentum-based gradient methods, we perform new analysis on the convergence and their optimal hyper-parameters. We also investigate the convergence behaviour of AGD with the optimal hyper-parameters and connect one Kahan's automatic step-size scheme with the long Barzilai–Borwein step-size. Numerical results are presented to reflect the theoretical convergence behaviours and demonstrate the practical performances of various momentum-based gradient methods.Keywords: Accelerated first-order methodsmomentumconvergence analysisanadromic methodsBarzilai–Borwein step-sizeAMS Subject Classifications: 65L2065B9990C25 AcknowledgmentsThe authors would like to thank the associate editor and the anonymous referees for their useful comments and suggestions. The last author also wishes to thank Dr. Ren-Cang Li, the University of Texas at Arlington, for sharing the re-typeset latex form of [Citation16] and also helpful discussions on AGD iterations.Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 As xx is close to a minimizer in Regime 1, a convex quadratic surrogate model can be used to investigate the asymptotic convergence of the iteration.2 In [Citation16,Citation17], it is named as Nemurov's version of GDM.3 The fact 1≤ωi≤2 follows from (Equation24(24) ωk+1=2Tk(1/ρ)ρTk+1(1/ρ)=1+Tk−1(1/ρ)Tk+1(1/ρ)=:1+ψk(24) ), ρ<1 and the recurrence (Equation25(25) ωi+1=44−ρ2ωi∈(1,2),i≥2, with ω1=2,ω2=22−ρ2.(25) ).4 For the quadratic model (Equation21(21) minxx∈RnbbTxx−12xxTHxx.(21) ), we have exactly ∇f(xxˆk)=∇f(xx∗)+H(xxˆk−xx∗).5 We choose a unit-norm vector bb is to ensure the relative residual ‖Hxxk−bb‖2‖bb‖2=‖Hxxk−bb‖2 with the initial point xx0=0.Additional informationFundingThis work is supported in part by the National Natural Science Foundation of China NSFC-12071332.
General Nonlinear Function-on-Function Regression via Functional Universal Approximation

AbstractVarious linear or nonlinear function-on-function (FOF) regression models have been proposed to study the relationship between functional variables, where certain forms are assumed for the relationship. However, because functional variables take values in infinite-dimensional spaces, the relationships between them can be much more complicated than those between scalar variables. The forms in existing FOF models may not be enough to cover a wide variety of relationships between functional variables, and hence the applicability of these models can be limited. We consider a general nonlinear FOF regression model without any specific assumption on the model form. To fit the model, inspired by the universal approximation theorem for the neural networks with “arbitrary width,” we develop a functional universal approximation theorem which asserts that a wide range of general maps between functional variables can be approximated with arbitrary accuracy by members in our proposed family of maps. This family is “fully” functional in that the complexity of the maps within the family is completely determined by the smoothness of the component functions in the map. With this functional universal approximation theorem, we develop a novel method to fit the general nonlinear FOF regression model, which includes all existing FOF models as special cases. The complexity of the fitted model is controlled by smoothness regularization, without the necessity to choose the number of hidden neurons. Supplemental materials for code and additional information are available online.KEYWORDS: Function-on-function regressionFunctional dataFunctional universal approximation theoremNeural networkNonlinear functional regressionUniversal approximation theorem Disclosure StatementNo potential conflict of interest was reported by the author(s).
Improving Climate Change Awareness through Immersive Virtual Reality Communication: A Case Study

The gradual pace of climate change means that its awareness plays a particularly important role in encouraging support for its amelioration or adopting adaptive behavior. This case study involves an action research project that engages twelve urban planning-related professionals in the experience of immersive virtual reality (IVR) as a tool to improve awareness of the effects of climate change. Mobile LiDAR technology was used to digitally recreate urban models in which the participants could navigate a simulated inundated urban environment and interact with the virtual objects involved. Feedback from the participants indicated the IVR technology to be a potentially useful educational tool for both professionals and the community, offering unparalleled immersion and interaction for climate change awareness which, based on its unique attributes, could offer insights and understanding of the necessity for building resiliency into our living environments.
Policy-driven Knowledge Selection and Response Generation for Document-grounded Dialogue

Document-grounded dialogue (DGD) uses documents as external knowledge for dialogue generation. Correctly understanding the dialogue context is crucial for selecting knowledge from the document and generating proper responses. In this article, we propose using a dialogue policy to help the dialogue understanding in DGD. Our dialogue policy consists of two kinds of guiding signals: utterance function and topic transfer intent. The utterance function reflects the purpose and style of an utterance, and the topic transfer intent reflects the topic and content of an utterance. We propose a novel framework exploiting our dialogue policy for two core tasks in DGD, namely, knowledge selection (KS) and response generation (RG). The framework consists of two modules: the policy planner leverages policy-aware dialogue representation to select knowledge and predict the policy of the response; the generator uses policy/knowledge-aware dialogue representation for response generation. Our policy-driven model gets state-of-the-art performance on three public benchmarks, and we provide a detailed analysis of the experimental results. Our code/data will be released on GitHub.
Online Destinations Map using Google Maps API Based on the Private Database

Online mapping applications have undergone a radical change as a result of the widespread use of the Internet,and an interested in the developing of the online mapping services due to the use of the Google Maps Application Programming Interface (API), Yahoo Maps API, and ESRI ArcGIS API, as well as the support for maps by operating systems on tablets and smartphones. One of the most helpful technologies for identifying locations and providing information about roads is online mapping. Although these applications are simple to use, they have a limited amount of the data that may be inaccurate, and the mapping technology does not have sophisticated or detailed characteristics of the required places. As a result, developers and programmers use maps as a platform to create features that are tailored to the needs of the user by using various programming languages. The goal of this paper is to develop the Google API by connecting it to a local, private database that houses data that is regularly gathered and developed. It also aims to create opportunities that make it easier for clients to access information stored on particular servers that can provide a variety of locations and details in order to meet their needs.
Prospects for the Incorporation of the Digital Ruble into Russia’s Monetary Turnover: Attributes and Principles for Developing a Trusted Digital Environment

The rapid nature of the modernization of monetary turnover, accompanied by geopolitical risks against the background of post-pandemic economic recovery and the regionalization process, no longer just arouses the discursive interest of society, but becomes an indispensable condition of the new reality. The process of money turnover transformation by introducing digital currencies into circulation in the wave of digitalization among world powers lagging behind the evolving environment of the cryptocurrency industry is developing into a process of formalization of metaverses and penetrates deeper into the socio-economic reality. The problem of the Russian practice of developing the payment environment consists in its catching-up character, caused by the spontaneous formation of the digital society, resulting in the expansion of the scope of alternative finance outside the legal field. The purpose of the paper is to determine the inherent attributes of the digital trusted environment necessary to ensure digital ruble turnover, based on an empirical study of society’s perceptions about the prospects for using the digital form of the national monetary unit by representatives of various generations. We used systematization, grouping, comparative and content analysis, surveys, and the quota method to achieve the paper’s purpose. The survey involved 35,327 residents from different regions. As a result, the authors revealed the low level of readiness of society for the introduction of the digital ruble as a substitute for cash and cryptocurrency. The paper focuses on the need for an integrated approach to the disclosure of the digital ruble’s value and benefits, which contributes to its successful launch and promotion in the market. The results of the research highlight the importance of correspondence in the digital ruble category “digital currencies”, and also the impossibility of making incorrect decisions in the transformation of money turnover, which leads to the growth of risks of digital inequality, the clash of interests of certain groups of the population in the prevailing behavioral patterns in the form of a cautious attitude to digital finance with insufficient financial literacy.
Alleviating Video-length Effect for Micro-video Recommendation

Micro-video platforms such as TikTok are extremely popular nowadays. One important feature is that users no longer select interested videos from a set; instead, they either watch the recommended video or skip to the next one. As a result, the time length of users’ watching behavior becomes the most important signal for identifying preferences. However, our empirical data analysis has shown a video-length effect that long videos can more easily receive a higher value of average view time, and thus adopting such view-time labels for measuring user preferences can easily induce a biased model that favors the longer videos. In this article, we propose a V ideo L ength D ebiasing Rec ommendation (VLDRec) method to alleviate such an effect for micro-video recommendation. VLDRec designs the data labeling approach and the sample generation module that better capture user preferences in a view-time-oriented manner. It further leverages the multi-task learning technique to jointly optimize the above samples with the original biased ones. Extensive experiments show that VLDRec can improve users’ view time by 1.81% and 11.32% on two real-world datasets, given a recommendation list of a fixed overall video length, compared with the best baseline method. Moreover, VLDRec is also more effective in matching users’ interests in terms of the video content.
Image Defogging Based on Regional Gradient Constrained Prior

Foggy days limit the functionality of outdoor surveillance systems. However, it is still a challenge for existing methods to maintain the uniformity of defogging between image regions with a similar depth of field and large differences in appearance. To address above problem, this article proposes a regional gradient constrained prior (RGCP) for defogging that uses the piecewise smoothing characteristic of the scene structure to achieve accurate estimation and reliable constraint of the transmission. RGCP first derives that when adjacent similar pixels in the fog image are aggregated and spatially divided into regions, clusters of region pixels in RGB space conform to a chi-square distribution. The offset of the confidence boundary of the clusters can be regarded as the initial transmission of each region. RGCP further uses a gradient distribution to distinguish different regional appearances and formulate an interregional constraint function to constrain the overestimation of the transmission in the flat region, thereby maintaining the consistency between the estimated transmission map and the depth map. The experimental results demonstrate that the proposed method can achieve natural defogging performance in terms of various foggy conditions.
Face image deblurring with feature correction and fusion

The key to image deblurring is to extract and screen valid information from blurred images and use this information to restore sharp images. Given this, we carefully design a face deblurring method. We adopt a simple feature extraction technique that extracts multilevel face features using a pretrained feature extraction subnetwork (FEN), e.g., VGGNet. To ensure adequate and accurate face information, we use all but the lowest-level features, which may contain much blurring information, for restoration. The selected features are preprocessed with self-attention modules (SAMs), correcting the features by synthesizing the context, and then injected into a feature fusion network (FFN) to restore clear images. The FFN stem is intentionally designed as a mirror of the FEN. Therefore, the corrected features are directly added to the corresponding (same-sized) features in the FFN naturally without manual compression/expansion. In the FFN, the features are layerwise fused and corrected again in the last layers with the SAMs; the deblurring results are finally output. Thus, the resulting images are firmly controlled by the extracted face features. This is advantageous for restoring true faces. To pursue results with both high sharpness and fidelity, the networks are trained with discriminator and fidelity constraints. Experimental results obtained on abundant challenging datasets show that our method achieves competitive results relative to state-of-the-art method outputs. Specifically, the proposed method does not need to align faces or perform iterations; thus, it is simple and practical.
Fast and perfect state transfer in superconducting circuit with tunable coupler

In quantum computation and quantum information processing, the manipulation and engineering of quantum systems to suit certain purposes are an ongoing task. One such example is quantum state transfer (QST), an essential requirement for both quantum communication and large-scale quantum computation. Here we engineer a chain of four superconducting qubits with tunable couplers to realize the perfect state transfer (PST) protocol originally proposed in quantum spin networks and successfully demonstrate the efficient transfer of an arbitrary single-qubit state from one end of the chain to the other, achieving a high fidelity of 0.986 in just 25 ns. This demonstrated QST is readily to extend to larger chain and multi-node configurations, thus serving as a desirable tool for scalable quantum information processing.