Reviewerly/exHarmony huggingface dataset:
Federated blockchain system (FBS) for the healthcare industry

Blockchain is a distributed technology that introduced the well known Bitcoin cryptocurrency into action. Blockchain has been considered for research by many countries and industries. It is being applied in many fields such as the healthcare domain. Many companies started using Blockchain to increase the security and privacy of the Electronic Healthcare Records for their patients. The work in this paper discusses some existing healthcare problems and challenges. In addition, the paper reviews some related work models and provides a comparison that shows their objectives and limitations. Also, a proposed Federated Blockchain System (FBS) is introduced to provide solutions for these healthcare problems and elaborates the technical details of the system architecture. Moreover, the effectiveness of the system has been validated which showed an average of 68-100 ms for performing query operations and average of 0.944-19.041 s for performing writing operations on the system. Finally, a discussion of the system validation and future work are presented.
A Fuzzy-Based Fast Feature Selection Using Divide and Conquer Technique in Huge Dimension Dataset

Feature selection is commonly employed for identifying the top n features that significantly contribute to the desired prediction, for example, to find the top 50 or 100 genes responsible for lung or kidney cancer out of 50,000 genes. Thus, it is a huge time- and resource-consuming practice. In this work, we propose a divide-and-conquer technique with fuzzy backward feature elimination (FBFE) that helps to find the important features quickly and accurately. To show the robustness of the proposed method, it is applied to eight different datasets taken from the NCBI database. We compare the proposed method with seven state-of-the-art feature selection methods and find that the proposed method can obtain fast and better classification accuracy. The proposed method will work for qualitative, quantitative, continuous, and discrete datasets. A web service is developed for researchers and academicians to select top n features.
Comparison of quantum advantage experiments using random circuit sampling

Random circuit sampling, the task of sampling bit strings from a random unitary operator, has been implemented to demonstrate quantum advantage on the Sycamore quantum processor with 53 qubits and on the Zuchongzhi quantum processor with 56 and 61 qubits. Recently, it was claimed that classical computers using tensor network simulation could catch on to current noisy quantum processors for random circuit sampling. While the linear cross-entropy benchmark fidelity was used to certify all these claims, it may not capture statistical properties of outputs in detail. Here, we compare the bit strings sampled from classical computers using tensor network simulation by Pan et al. [F. Pan, K. Chen, and P. Zhang, Phys. Rev. Lett. 129, 090502 (2022)] and by Kalachev et al. [G. Kalachev, P. Panteleev, P. Zhou, and M.-H. Yung, arXiv:2112.15083] with the bit strings from the Sycamore quantum processor. It is shown that all of Kalachev et al.'s samples passed the NIST random number tests. The heat maps of bit strings show that Pan et al.'s and Kalachev et al.'s samples are quite different from the Sycamore or Zuchongzhi samples. The analysis with the Marchenko-Pastur distribution and the Wasssertein distances demonstrates that Kalachev et al.'s samples are statistically closer to the Sycamore samples than Pan et al.'s while the three datasets have similar values for the linear cross-entropy fidelity. Our finding implies that further study is needed to certify or beat the claims of quantum advantage using random circuit sampling.
Traceability and quality assessment of Norway spruce (Picea abies (L.) H.Karst.) logs: the TreeTrace_spruce database

Abstract Key message The TreeTrace_spruce database contains images and measurements of 100 Norway spruce ( Picea abies (L.) H.Karst.) logs from Northeastern France, each about 4.5 m long. The image database includes RGB images of large and small ends of the logs and hyperspectral and computed tomography (CT) images of wood discs sampled at both log ends. The 100 logs were also fully X-ray scanned with a CT device for roundwoods and their top surface was scanned with a terrestrial LiDAR device. The measurements performed on discs include wood local density, growth ring widths and pith location. This database is complementary to another one (TreeTrace_Douglas) resulting from the same ANR project TreeTrace, but if the objectives are similar, the protocols and conditions of acquisition are not the same for these two databases. TreeTrace_spruce dataset is available at https://doi.org/10.57745/WKLTJI and associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/cffee2f1-18e1-4b53-9f5b-6cc4c66f1cb8 .
Calibrated multi-task subspace learning via binary group structure constraint

Multi-task learning (MTL) is a joint learning paradigm to improve the generalization performance of the tasks. At present, most of MTL methods are all based on one hypothesis that all learning tasks are related and approximate for joint learning. However, this hypothesis may not be held in some scenarios, which may further lead to the problem of negative transfer. Therefore, in this paper, we aim to deal with the negative transfer problem and simultaneously improve the generalization performance in the joint learning. Combining with the subspace learning, we proposed a calibrated multi-task subspace learning method (CMTSL) under the binary group constraint. With the low-rank constraint on subspaces and the binary group indicator, our model can identify “with whom” one task should share and perform the multi-task inference on the high-dimensional parameter space in the meantime. To better approximate the low-rank constraint, we introduce a capped rank function as the tight relaxation term. Last, an iteration based re-weighted algorithm is proposed to solve our model and the convergence analysis is also proved in theory. Experimental results on benchmark datasets demonstrate the superiority of our model.
Cycle-object consistency for image-to-image domain adaptation

Recent advances in generative adversarial networks (GANs) have been proven effective in performing domain adaptation for object detectors through data augmentation. While GANs are exceptionally successful, those methods that can preserve objects well in the image-to-image translation task usually require an auxiliary task, such as semantic segmentation to prevent the image content from being too distorted. However, pixel-level annotations are difficult to obtain in practice. Alternatively, instance-aware image-translation model treats object instances and background separately. Yet, it requires object detectors at test time, assuming that off-the-shelf detectors work well in both domains. In this work, we present AugGAN-Det, which introduces Cycle-object Consistency (CoCo) loss to generate instance-aware translated images across complex domains. The object detector of the target domain is directly leveraged in generator training and guides the preserved objects in the translated images to carry target-domain appearances. Compared to previous models, which e.g., require pixel-level semantic segmentation to force the latent distribution to be object-preserving, this work only needs bounding box annotations which are significantly easier to acquire. Next, as to the instance-aware GAN models, our model, AugGAN-Det, internalizes global and object style-transfer without explicitly aligning the instance features. Most importantly, a detector is not required at test time. Experimental results demonstrate that our model outperforms recent object-preserving and instance-level models and achieves state-of-the-art detection accuracy and visual perceptual quality.
Geometric algebra-based multiview interaction networks for 3D human motion prediction

3D skeleton-based human motion prediction is an essential and challenging task for human-machine interactions, which aims to forecasts future poses given a history of their previous motions. Recent works based on Graph Neural Networks (GCNs) show promising performance for motion prediction due to the powerful ability of feature aggregation of GCNs. However, with the deep and multi-stage GCN model deployment, its feature extraction mechanism tends to result in feature similarity over all joints, and degrade the prediction performance. In addition, such a graph structure in recent works was still insufficient to process the high dimensional structural data in Euclidean space when inference through multi-layer networks. To solve the problem, we propose a novel Geometric Algebra-based Multi-view Interaction network (GA-MIN), which captures and aggregates motion features from two interactions: 1) global-interaction, which refactors various spectrum dependencies using geometric algebra-based structure, and 2) self-interaction, which leverage self-attention mechanism to capture compact representations. Extensive experiments are conducted on three public datasets: Human3.6M, CMU Mocap, and 3DPW, which prove that the proposed GA-MIN outperforms state-of-the-art methods on 3D Mean Per Joint Position Error (MPJPE) and Mean Angle Error (MAE) on average.
What Blockchain Can and Can't Do: Applications to Marketing and Privacy

Marketing has enjoyed unparalleled transformation in terms of its effectiveness and ability to delivery measurable return on investment due to the digital revolution. The spread of blockchain technology has led to suggestions that it may have significant marketing applications. Though blockchain is indeed an amazing technology with significant applications, this paper argues that its usefulness in many traditional marketing applications may be limited. The reasons why throw important light on the interactions between blockchain technologies, data and privacy. Blockchain, by its nature, may lead to substantial privacy harms when used indiscriminately for marketing purposes.
Games of GANs: game-theoretical models for generative adversarial networks

Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium profile. Despite the improvement accomplished in GANs in the last few years, several issues remain to be solved. This paper reviews the literature on the game-theoretic aspects of GANs and addresses how game theory models can address specific challenges of generative models and improve the GAN’s performance. We first present some preliminaries, including the basic GAN model and some game theory background. We then present a taxonomy to classify state-of-the-art solutions into three main categories: modified game models, modified architectures, and modified learning methods. The classification is based on modifications made to the basic GAN model by proposed game-theoretic approaches in the literature. We then explore the objectives of each category and discuss recent works in each class. Finally, we discuss the remaining challenges in this field and present future research directions.
A Robust Constellation Diagram Representation for Communication Signal and Automatic Modulation Classification

Automatic modulation recognition is a necessary part of cooperative and noncooperative communication systems and plays an important role in military and civilian fields. Although the constellation diagram (CD) is an essential feature for different digital modulations, it is hard to be extracted under noncooperative complex communication environment. Frequency offset, especially the nonlinear frequency offset is a vital problem of complex communication environment, which greatly affects the extraction of traditional CD and the performance of modulation recognition methods. In the current paper, we propose an antifrequency offset constellation diagram (AFO-CD) extraction method, which combines the constellation diagram with a convolutional neural network (CNN). The proposed method indicates the change of the CD with time and enables us to suppress the influence of frequency offset efficiently. Additionally, a residual units-based classifier is designed for multiscale feature extraction and modulation classification. The experimental results demonstrate that the proposed method can effectively improve the recognition accuracy and has a good application prospect in the complex electromagnetic environment.
Software Package to Support Students’ Research Activities in the Hybrid Intellectual Environment of Mathematics Teaching

The urgent need of society for creative and critical thinkers dictates the need to manage and develop the project-based and research activities of schoolchildren. Neural networks, as effective tools for solving complex, multi-component, multifunctional problems, have created an opportunity to develop tools for the content and quality assessment of project-based, research activities and personal achievements of each student with a dynamic and stratified sampling of complex knowledge, to identify individual educational paths, tools and instructions for research and creative activity reflected on the layers of an artificial neural network. The purpose of this study is to define and develop the concept of supporting a hybrid intelligent system of project-based and research activities in a comprehensive information and educational environment to create an applied intellectual technology that supports project-based and research activities and classifies the growth of schoolchildren’s scientific potential. The paper considers the development of technologies for the pedagogical, algorithmic and information organization of ontological engineering and support models for project-based and research activities, as well as the growth of scientific student training based on the construction of an artificial neural network with a teacher and an array of training samples using expert systems and decision theory. It also defines the selection criteria, hierarchies and content of the generalized constructs of complex knowledge (modern achievements in science) by modeling creative activities while further predicting the growth of a student’s scientific potential based on the layers of the neural network. The original technology of training samples used for constructing hybrid neural networks was determined using expert systems, the psychological method of parallel slices and clustering of the personal characteristics of schoolchildren. An innovative intellectual environment is being introduced into the practice of mathematical education in high schools in Russia. The study made it possible to create applied intelligent technology to support and display the dynamic profiles of the project-based and research activities of schoolchildren and act as a growth classifier of their scientific potential. In terms of concept implementation, the phased growth technology to analyze students’ scientific potential was developed during the study of the generalized construct of complex knowledge. Individual educational workshops for school students to master project-based and research activities and display their dynamic profiles during the implementation of a hybrid intellectual environment for support and decision-making will allow school students to develop their personal potential, increase their educational motivation and allow researchers and educators to realize the potential of adapting modern achievements in science to school mathematics and create conditions for the modernization of educational programs in a developing digital environment.
Pricing Decision of Three-Level Agricultural Supply Chain Based on Blockchain Traceability and Altruistic Preference

The traceability of blockchain is crucial to the quality and safety of agricultural products, primarily when bacterial contamination occurs in the agricultural supply chain. To gain a deeper understanding of the impacts on the quality and safety of agricultural products, we built a three-level agricultural supply chain model comprising one retailer, one manufacturer, and one supplier. We aimed to explore the impact of blockchain traceability and retailers’ altruistic preference on pricing decisions and channel member profit, and to then examine the motivation of enterprises to participate in blockchain technology. Our study showed that: (1) blockchain technology has the potential to improve the prices and profits of supply chain members without considering the cost of blockchain; and (2) blockchain technology has the potential to improve the retailer’s price when the unit variable cost of blockchain is higher than a certain threshold. It can also reduce the manufacturer’s price and the supplier’s output. However, the excessive cost of blockchain can also aggravate the double marginalization effect of the supply chain. When both the unit variable cost and the fixed input cost of the blockchain are low, each enterprise has the incentive to participate in the blockchain. (3) In the scenario of the altruistically-preferred retailer, when the blockchain variable cost is lower than a certain threshold, the retailer’s altruistic preferences can improve the manufacturer’s and supplier’s profit, but it will reduce the retailer’s profit.
Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine

This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs the probability of each fault mode occurring. First, multiple two-class sub-classifiers based on SBELM are trained by using single-fault samples to extract the correlation between various pairs of single-fault, and the sub-classifiers are integrated with the proposed hierarchical multi-label classification (HMLC) strategy to form the diagnostic model based on HMLC-SBELM. Then, samples of single faults and simultaneous faults are used to generate the optimal discriminative thresholds by using optimization algorithms. Finally, the probabilistic output generated by the HMLC-SBELM-based model is transformed into the final fault modes by using the optimal discriminative threshold. The model performance is evaluated by using actual vibration signals of the main reducer and is compared with several classical models. The contrastive results indicate that the proposed model is more accurate, efficient, and stable.
Reversible Image Processing for Color Images with Flexible Control

In this paper, we propose an image processing method for color images to reversibly achieve flexible functions. Most previous research has focused on reversible contrast enhancement (CE) for grayscale images. When we directly apply these methods to color images, hue distortion is caused. Several previous methods have been proposed for color images. These methods, however, only have a CE function. We previously proposed a reversible method for color images that enhances the brightness contrast and improves the saturation. Without losing the advantages of our previous method, we propose a new method to expand the ability of image processing. The proposed method reversibly achieves not only CE and saturation improvement but also sharpening or smoothing and brightness increases or decreases. It ensures full reversibility and thus perfectly reconstructs raw images in any case. The experimental results demonstrate the effectiveness of the proposed method in terms of image quality and reversibility.
Multi-Layer QoE Learning System Implemented by Fiberhost

Modern telecommunications networks, despite their ever increasing capacity, mostly attributed to optical fiber technologies, still fail to provide ideal channels for transmitting information. Disruptions in ensuring data throughput or the continuous flow of data required by applications remain as major unresolved problems. Most network mechanisms, protocols and applications feature adaptations that allow them to change the parameters of the transmission channel and try to minimize the negative impact of the network on the perceived quality, for example by temporarily changing the modulation scheme, or coding scheme, or by re-transmitting lost packets, or buffering to compensate for the interruptions in transmission. To respond appropriately, network operators are interested in knowing how well these adaptations are performing in order to assess the ultimate quality of their networks from the user perspective, i.e., Quality of Experience (QoE). Due to the huge amount of data associated with the collection of various parameters of the telecommunications network, machine learning methods are often needed to discover the relationships between various parameters and to identify the root cause of the observed network quality. In this paper, we present a Multi-layer QoE learning system implemented by Fiberhost for QoE analysis with a multi-layer approach based on machine learning tools.
Adaptations of final-year project tutoring during COVID-19: teachers’ perceptions and learnings ( <i>Adaptaciones en la tutorización de trabajos fin de estudios durante el COVID-19: percepciones y aprendizajes del profesorado</i> )

This work aimed to analyse the adaptations made to the design, supervision and teacher-tutor assessment of final-year projects (FYP) in a physical education teacher education programme to identify their perceptions of the changes made to non-face-to-face teaching derived from the COVID-19 epidemic. For this, an open-ended questionnaire was administered to 17 teachers familiar with formative assessment at the end of the 2019–20 academic year. A qualitative content analysis was also carried out that gave rise to different categories. The teachers did not perceive major changes in FYP tutoring, although the absence of face-to-face contact did cause them difficulties as they had to offer methodological solutions based on new technologies and give greater attention to the students. We highlight the need to continue investigating the perspectives of both teachers and students when implementing strategies to help students to self-regulate when working on their FYPs.