Reviewerly/exHarmony huggingface dataset:
The target selection and quantitative evaluation for deep geothermal resource zoning of typical geothermal fields in central Hebei of North China plain

With the rapid economic development in North China, the demand for geothermal energy is increasing. It is urgent to find favorable deep geothermal resource targets in North China. Although geothermal resources in the North China Plain are widely distributed, in order to develop deep geothermal resources in North China safely, stably and efficiently, it is essential to carry out the target selection and evaluation of geothermal resource zoning. This article takes the typical geothermal fields in the central Hebei region as the research object, and through the comprehensive collation of regional geothermal geological data, constructs an optimization evaluation indicator system for geothermal resource target areas from three aspects: resource conditions, mining potential, and heating demand. On this basis, it establishes a linear relationship between the attribute values and scores of each indicator, and uses the analytic hierarchy process to assign weights to each indicator and calculate the comprehensive weight. Then, the comprehensive evaluation value is obtained by weighted calculation of the scores and comprehensive weights of each block in the grid segmentation of the evaluation area. Finally, through the spatial analysis function of GIS, the comprehensive evaluation values of all blocks in the evaluation area were analyzed using kriging difference analysis, and a comprehensive evaluation map, the geothermal resource prospective target area map, was finally obtained. Using quantitative zoning evaluation methods, the target areas for exploration and development of deep geothermal resources in central Hebei Province have been delineated within a large region. The evaluation results indicate that the Cambrian-Ordovician reservoir target area is relatively large, and there are many favorable target areas with good reservoir conditions in the Middle-Upper Proterozoic. The excellent prospective target area of the Mesoproterozoic geothermal reservoir accounts for 56%, mainly located in the Xingji uplift and Gaoyang low uplift. Xiong’an New Area, Cangzhou, and the eastern part of Hengshui are excellent prospective target areas for the evaluation of the Mesoproterozoic geothermal reservoir in a single area. This evaluation method can provide a reference for the optimization of resource exploration and development target areas in key regions.
Comparative analysis of foreign and Russian software for libraries

The author accomplishes comparative analysis of Russian and foreign software for the library sector. The need for such analysis is determined by the strategy of import substitution stimulated by the Western sanctions. Software and databases recommended for purchase and application on the territory of the Russian Federation are included into the Russian Software Register and Eurasian Software Register. Every library uses its own software package, however, the essential software includes operating system, web browser, office suite, automated library information system (ALIS). The author compares the core functionality of each software type: OS (Microsoft Windows, Astra Linux, Alt Linux, RedOS), web browsers (Google Chrome, Mozilla Firefox, Yandex Browse), and office software suite (Microsoft Office, LibreOffice, MyOffice, R7-Office). The comparison between foreign and Russian ALIS is inappropriate because, unlike other software categories, Russian ALISes were initially developed by Russian companies in Russian reality, in accordance with Russian laws and Russian traditions of librarianship. The author suggests that the main functionality of foreign and national software beyond the accomplished analysis (e. g. video editors, drawing programs, etc.) is also similar. Based on the comparative analysis of foreign and Russian software, the author concludes that their core functionality are similar which means that import substitution would not impact the organizational information processes significantly. The paper is prepared under the Government Order to RNPLS&amp;T for 2023, project No. 720000F.99.1.BN60AA03000, theme No. 1021062311369-1- 1.2.1;5.8.2,5.8.3.
Two-Dimensional Analysis of Lower Extremities to Predict Best Initial Condition on Marching Movement

Himpunan Mahasiswa Mesin ITB has a specific type of marching that follows a march song’s rhythm called “derap”. In practice, derap was a hard move to maintain and unify in a group. This difficulty was observed by noting the differences that occur between the subjects' initial and middle march motion conditions. The most noticeable difference was the height of the group. This study intends to observe and find how the differences vary in this motion. The differences that occur corresponded to the relative knee and ankle angle. This study utilized five active markers to reconstruct the lower extremities' activity during motion, one action camera at 60 fps, and a workstation. This study was also constrained by using three cycles of march song at 115 bpm. Direct Linear Transformation was used to obtain the intrinsic factor of the camera to reconstruct the motion. Evaluating the angular kinematic parameters of relative knee and ankle angle, the authors found the relative knee angle has increased from 110° to around 130°, which happened at the second song cycle and kept stable at the rest of the song. This result brought the conclusion that more extended derap motion would tend to a steady condition of relative knee angle and suggested that the subject should begin with 130° of relative knee angle to give less effort on marching.
The information culture of teenagers in the context of modern education paradigm

The growing information streams and expanding information space have made information easy accessible while the need for its filtration, critical reevaluation, and clear-cut assessment criteria emerged. The younger generation has to master skills of retrieving, analyzing and using reliable information to support their lives, career and social success. Being skillful Internet users, the teenagers yet demonstrate the low level of information culture. However, the requirement to educate information culture is formulated in The Federal State Educational Standard of Comprehensive Secondary Education (FGOS) in the disciplines “Mathematics”, “Informatics” and “Literature”. The results of the regional stage of the All-Russian Olympiad of School Students – the republican Olympiad in the personal information culture (Republic of Buryatia) – have demonstrated non-conformity of teenagers’ performance with the FGOS standard. No similar Olympiads are held in other RF regions, which makes the study particularly valuable. The authors emphasize that the Olympiad in this subject area has to be implemented in other regions. They applied several research methods to assess information literacy, namely empirical (observation, comparison) and theoretical (analysis, generalization). The analysis is based on students’ answers. The newness of the study lies in characterizing information culture of Buryat schoolchildren and specifying major challenges, substantiating and extending obtained data, and suggesting new vectors of information culture regional studies.
Increasing the equitability of data citation in paleontology: capacity building for the big data future

Abstract Data compilations expand the scope of research; however, data citation practice lags behind advances in data use. It remains uncommon for data users to credit data producers in professionally meaningful ways. In paleontology, databases like the Paleobiology Database (PBDB) enable assessment of patterns and processes spanning millions of years, up to global scale. The status quo for data citation creates an imbalance wherein publications drawing data from the PBDB receive significantly more citations (median: 4.3 ± 3.5 citations/year) than the publications producing the data (1.4 ± 1.3 citations/year). By accounting for data reuse where citations were neglected, the projected citation rate for data-provisioning publications approached parity (4.2 ± 2.2 citations/year) and the impact factor of paleontological journals ( n = 55) increased by an average of 13.4% (maximum increase = 57.8%) in 2019. Without rebalancing the distribution of scientific credit, emerging “big data” research in paleontology—and science in general—is at risk of undercutting itself through a systematic devaluation of the work that is foundational to the discipline.
Research on performance optimization of virtual data space across WAN

For the high-performance computing in a WAN environment, the geographical locations of national supercomputing centers are scattered and the network topology is complex, so it is difficult to form a unified view of resources. To aggregate the widely dispersed storage resources of national supercomputing centers in China, we have previously proposed a global virtual data space named GVDS in the project of “High Performance Computing Virtual Data Space”, a part of the National Key Research and Development Program of China. The GVDS enables large-scale applications of the high-performance computing to run efficiently across WAN. However, the applications running on the GVDS are often data-intensive, requiring large amounts of data from multiple supercomputing centers across WANs. In this regard, the GVDS suffers from performance bottlenecks in data migration and access across WANs. To solve the above-mentioned problem, this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation strategy. In a WAN environment, the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources, guaranteeing lower average migration latency when multiple data migration tasks are running in parallel. In addition, it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests (the IO proxy is a module of GVDS), so as to improve the application’s performance across WANs. The experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.
DrawGAN: Multi-view Generative Model Inspired by the Artist’s Drawing Method

We present a novel approach for modeling artists’ drawing processes using an architecture that combines an unconditional generative adversarial network (GAN) with a multi-view generator and multi-discriminator. Our method excels in synthesizing various types of picture drawing, including line drawing, shading, and color drawing, achieving high quality and robustness. Notably, our approach surpasses the existing state-of-the-art unconditional GANs. The key novelty of our approach lies in its architecture design, which closely resembles the typical sequence of an artist’s drawing process, leading to significantly enhanced image quality. Through experimental results on few-shot datasets, we demonstrate the potential of leveraging a multi-view generative model to enhance feature knowledge and modulate image generation processes. Our proposed method holds great promise for advancing AI in the visual arts field and opens up new avenues for research and creative practices.
Integrating IOTA’s Tangle with the Internet of Things for Sustainable Agriculture: A Proof-of-Concept Study on Rice Cultivation

Addressing the critical challenges of resource inefficiency and environmental impact in the agrifood sector, this study explores the integration of Internet of Things (IoT) technologies with IOTA’s Tangle, a Distributed Ledger Technology (DLT). This integration aims to enhance sustainable agricultural practices, using rice cultivation as a case study of high relevance and reapplicability given its importance in the food chain and the high irrigation requirement of its cultivation. The approach employs sensor-based intelligent irrigation systems to optimize water efficiency. These systems enable real-time monitoring of agricultural parameters through IoT sensors. Data management is facilitated by IOTA’s Tangle, providing secure and efficient data handling, and integrated with MongoDB, a Database Management System (DBMS), for effective data storage and retrieval. The collaboration between IoT and IOTA led to significant reductions in resource consumption. Implementing sustainable agricultural practices resulted in a 50% reduction in water usage, 25% decrease in nitrogen consumption, and a 50% to 70% reduction in methane emissions. Additionally, the system contributed to lower electricity consumption for irrigation pumps and generated comprehensive historical water depth records, aiding future resource management decisions. This study concludes that the integration of IoT with IOTA’s Tangle presents a highly promising solution for advancing sustainable agriculture. This approach significantly contributes to environmental conservation and food security. Furthermore, it establishes that DLTs like IOTA are not only viable but also effective for real-time monitoring and implementation of sustainable agricultural practices.
PCCNet: A Few-Shot Patch-Wise Contrastive Colorization Network

Few-shot colorization aims to learn a model to colorize images with little training data. Yet, existing models often fail to keep color consistency due to ignored patch correlations of the images. In this paper, we propose PCCNet, a novel Patch-wise Contrastive Colorization Network to learn color synthesis by measuring the similarities and variations of image patches in two different aspects: inter-image and intra-image. Specifically, for inter-image, we investigate a patch-wise contrastive learning mechanism with positive and negative samples constraint to distinguish color features between patches across images. For intra-image, we explore a new intra-image correlation loss function to measure the similarity distribution which reveals structural relations between patches within an image. Furthermore, we propose a novel color memory loss that improves the accuracy of the memory module to store and retrieve data. Experiments show that our method allows the correctly saturated color to spread naturally over objects and also achieves higher scores in quantitative comparisons with related methods.
Enhancing Continual Noisy Label Learning with Uncertainty-Based Sample Selection and Feature Enhancement

The task of continual learning is to design algorithms that can address the problem of catastrophic forgetting. However, in the real world, there are noisy labels due to inaccurate human annotations and other factors, which seem to exacerbate catastrophic forgetting. To tackle both catastrophic forgetting and noise issues, we propose an innovative framework. Our framework leverages sample uncertainty to purify the data stream and selects representative samples for replay, effectively alleviating catastrophic forgetting. Additionally, we adopt a semi-supervised approach for fine-tuning to ensure the involvement of all available samples. Simultaneously, we incorporate contrastive learning and entropy minimization to mitigate noise memorization in the model. We validate the effectiveness of our proposed method through extensive experiments on two benchmark datasets, CIFAR-10 and CIFAR-100. For CIFAR-10, we achieve a performance gain of 2% under 20% noise conditions.
How virtual tourism influences travel intention: a study combined with eye movement and scenario experiment

To explore the impact of virtual tourism on travel intention, this study developed the model incorporating virtual tourism content and form, visual appeal, presence, virtual attachment, and travel intention. Eye movement experiments, scenario experiments, and questionnaire surveys were integrated. Analysis of 48 eye movement datasets and 210 survey responses indicated that virtual tourism affected travel intention mostly through visual appeal: (1) Regarding visual appeal, videos of real scenes were stronger than videos of virtual scenes, and videos of virtual scenes were stronger than pictures of virtual scenes. (2) Visual appeal had a direct positive effect on travel intention.
METRO-X: Combining Vertex and Parameter Regressions for Recovering 3D Human Meshes with Full Motions

It is well known that regressing the parametric representation of a human body from a single image suffers low accuracy due to sparse information use and error accumulation. Although being able to achieve higher accuracy by avoiding these issues, directly regressing vertices may result in vertex outliers. We present METRO-X, a novel method for reconstructing full-body human meshes with body pose, facial expression and hand gesture from a single image, which combines the advantages from the two disciplines so as to achieve higher accuracy than parameter regression while bear denser vertices and generate smoother shape than vertices regression. It first detects and extracts hands, head and the whole body parts from a given image, then regresses the vertices of three parts separately using METRO, and finally fits SMPL-X to the reconstructed meshes to obtain the complete parametric representation. Experimental results show that METRO-X outperforms the ExPose method, with a significant 23% improvement in body accuracy and a 35% improvement in gesture accuracy. These results demonstrate the potential of our approach in enabling various applications.
Few-Shot Classification with Semantic Augmented Activators

AbstractMetric-based methods predict class labels by measuring the distance between a few given samples, often failing to preserve more useful semantic details in their vectorial representations. In this paper, we propose Semantic Augmented Activators (SAA), which are generated based on the variance of the intra-set samples in an unsupervised manner, to enhance the discriminability of feature vectors with more class-related semantic information. This generation process does not rely on any learnable parameters. Meanwhile, to align the SAA preferred to operate in the intra-set and sufficiently leverage the finite samples, we treat the Self-Cross loss as an auxiliary loss, which bi-directionally complements the limitations of the traditional loss function. Additionally, we introduce Map-To-Cluster, a transductive module to map the SAA-enhanced features to a lower-dimensional embedding space. This encourages proximity among similar samples and separation among dissimilar samples. The resulting methods are lightweight and computationally efficient. Our methods demonstrate competitive performance on the mini-ImageNet and tiered-ImageNet benchmarks, and achieve outstanding results in Cross-Domain Few-Shot classification.KeywordsFew-shot learningMetric-based learningTransductive inference
An Image Extraction Method for Traditional Dress Pattern Line Drawings Based on Improved CycleGAN

To address the problem of missing details in the general dress pattern line extraction method, we propose a traditional dress pattern line extraction method based on the improved CycleGAN. First, we input the traditional dress pattern image and extract the outline edge image by using a bi-directional cascade network. Afterwards, we construct an improved CycleGAN network model, input the traditional dress pattern image and its outline edge image into the generator model for line drawing extraction, use the discriminator model to discriminate between the generated image and the real image, and output the binary classification matrix. Finally, we construct the adversarial loss, cycle consistency loss and contour consistency loss functions to constrain the network model, output a detail rich line drawing image. Experiments show that the proposed method achieves the extraction of traditional dress pattern line images with perfect details, and the generated traditional dress pattern line images have more realistic and natural lines compared with other dress pattern line extraction methods. The method can accurately extract traditional costume pattern line images and contribute to the preservation and transmission of Chinese traditional costume culture.
Arbitrary Style Transfer with Style Enhancement and Structure Retention

Arbitrary style transfer is to transfer the style of any reference image to another image by a trained neural network, while preserving its content as much as possible. So far, a lot of work focused on reducing training costs without achieving sufficient style transfer, while some other work has neglected image frequency domain alignment. Balancing style presentation and content retention is no doubt challenging, we therefore propose a style transfer method that introduces frequency domain alignment and style secondary embedding, which is mainly embodied in two parts: style enhancement module (SEM) and content retention module (SRM). SEM aligns the stylistic image and stylized image statistics in the feature space. SRM reduces the loss of content by mapping the original and stylized images into the frequency domain and airspace for synchronous alignment. This new approach works well in terms of both style transfer and content retention. Experimental and questionnaire results show that this method can generate satisfactory stylized images without loss of content information.
SADD: Generative Adversarial Networks via Self-attention and Dual Discriminator in Unsupervised Domain Adaptation

Image classification is an active research in the field of computer vision. There are still significant challenges in the classification accuracy of cross-domain images due to the privacy, human, and material cost issues involved in labeled data collection, and the distribution differences among the collected images of the same category. To address the above problems, unsupervised domain adaptation (UDA) methods emerge, which transfer prior knowledge from the labeled source domain to the unlabeled target domain. In this work, we propose a new UDA architecture, SADD, which performs feature-level and pixel-level discrimination in a self-attention generative adversarial network. Specifically, we use the self-attention mechanism in extracting features to obtain globally dependent embeddings. In addition, we apply pixel-level distribution consistency loss on the embedding-generated images to mitigate the pixel-level distribution shifts due to unstable image style shifts. Further, we use discriminators for embedding reconstruction to assist the feature extractor in aligning features and enhancing the classification ability of the classifier. We evaluate our approach on the DIGITS classification dataset and the OFFICE-31 recognition dataset, and the results demonstrate the robustness and superiority of our approach.