Reviewerly/exHarmony huggingface dataset:
Risk Assessment Method and Application of Urban River Ecological Governance Project Based on Cloud Model Improvement

This study classifies risk factors and establishes the risk-evaluation index system for an urban river ecological management project. That system includes construction personnel risk, construction technology risk, construction management factor risk, construction duration factor risk, and other risks. A total of 23 indicators determine the level and standard of each indicator. This study proposes a risk index system that uses the cloud model evaluation method and explains that model’s process. To demonstrate the risk evaluation methodology, it was applied to the ecological management project of Jinghe River in Jinghe New City, China. The results showed that: (1) the urban river ecological management project was subject to “a medium level of personnel risk, management risk, and construction period risk and a relatively high level of technical risk and other risks.” According to the evaluation results, the overall risk level of the project was medium, which is consistent with the risk level of the Jinghe River ecological management project at the current stage. (2) The limitation of the subjective qualitative concept caused by the traditional fuzzy comprehensive evaluation was fully considered in the improved method, and the application of the cloud-model based improved method in risk evaluation of the urban river ecological management project significantly improved the reliability and visualization of the evaluation results. Finally, the urban river ecological management project in China was adopted as an example to prove that the model boasts high stability, and some corresponding countermeasures were also proposed. The research results are expected to provide valuable references and scientific criteria for implementing urban river ecological management projects.
Refined Division Features Based on Transformer for Semantic Image Segmentation

Transformer can build global relationships between pixels and enhance pixel representation. The existing methods only establish the context relationship from the whole image but will reduce the representation between the category areas. In addition, the existing methods based on the transformer self-attention do not combine the advantages of convolution and transformer, resulting in more calculation parameters of the model. In order to solve these two problems, this paper proposes to enhance the segmentation accuracy and performance by enhancing the relationship between image-level regions and the relationship between semantic level pixels. First, we design a refined division feature (RDF) module to enhance the channel representation and thus the same locale representation. Second, we design a transformer based on convolution (CTrans), which first computes the relationship between similar pixels and enhances the pixel representation. Then, the feature map is compressed and enriched to reduce the computational load of CTrans, and finally the relationship between pixels is established from a global perspective. We design a refined division feature module based on transformer for semantic image segmentation (RFT) model combining RDF and CTrans module. The experimental results show that the mIoU result of our method in Cityscapes test data set is 81.9%, and the model parameter is 64.6M, which is superior to other methods in terms of data. In addition, we conducted visualization experiments with Cityscapes and Pascal voc 2012 datasets with other methods, and the results showed that our method was superior to other methods.
Designing Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs

Abstract One of the most critical infrastructures during the COVID-19 pandemic are intensive care units (ICU). ICU’s crucial task is to preserve the lives of patients and mitigate the pandemic’s impact on the population. However, most ICUs plan only one day ahead. This short-term planning becomes an obstacle during disaster situations since physicians need to decide efficiently and ensure the timely treatment of high-risk patients. Integrating machine learning (ML) systems for clinical decision support could improve this process by predicting the mortality risk of critically ill patients. Several ML approaches tackling this problem have already shown promising results. However, these systems mostly neglect the integration of explicit domain knowledge, which is crucial to ensure prediction quality and adaptability. Otherwise, black-box systems might base their decision on confounding variables and improper relationships. Following design science research, we utilize a unique dataset of patients diagnosed with SARS-CoV-2 in ICU care to design a clinical decision support system by combining ML and expert knowledge in the form of a severity score. We show that by augmenting the system with expert knowledge, its overall performance improves compared to the baseline approach.
A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification

Quantification of the cardiac function is vital for diagnosing and curing the cardiovascular diseases. Left ventricular function measurement is the most commonly used measure to evaluate the function of cardiac in clinical practice, how to improve the accuracy of left ventricular quantitative assessment results has always been the subject of research by medical researchers. Although considerable efforts have been put forward to measure the left ventricle (LV) automatically using deep learning methods, the accurate quantification is yet a challenge work as a result of the changeable anatomy structure of heart in the systolic diastolic cycle. Besides, most methods used direct regression method which lacks of visual based analysis. In this work, a deep learning segmentation and regression task-unified network with transformer and spatial-temporal convolution is proposed to segment and quantify the LV simultaneously. The segmentation module leverages a U-Net like 3D Transformer model to predict the contour of three anatomy structures, while the regression module learns spatial-temporal representations from the original images and the reconstruct feature map from segmentation path to estimate the finally desired quantification metrics. Furthermore, we employ a joint task loss function to train the two module networks. Our framework is evaluated on the MICCAI 2017 Left Ventricle Full Quantification Challenge dataset. The results of experiments demonstrate the effectiveness of our framework, which achieves competitive cardiac quantification metric results and at the same time produces visualized segmentation results that are conducive to later analysis.
Machine learning algorithm-based spam detection in social networks

Many social media (SM) platforms have emerged as a result of the online social network’s (OSN) rapid expansion. SM has become important in day-to-day life, and spammers have turned their attention to SM. Spam detection (SD) is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique’s accuracy depends on expert knowledge, and it takes huge time to detect the spams. Thus, ML-based spam detection is preferred in OSN. Spam identification on social networks is a difficult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives flexibility to spammers for corrupting our devices. SD based on ML algorithms like logistic regression (LR), K-nearest neighbor (KNN), decision trees (DT), random forest (RF), support vector machine (SVM) and eXtreme gradient boosting (XGB), voting classifier (VC) and extra tree classifier (ETC) are used to design the address balance and to attain high assessment accuracy in an imbalanced datasets. ETC method minimizes the bias through the original sampling process. For reducing processing complexity, the ETC method uses a smaller size constant factor instead of a larger one. Thus, the ETC technique produces better data splitting than DT and RF techniques. Text is vectorized by vectorizers, and all the relative results are stored in it. The VC is an ensemble method that integrates predictions form several methods to forecast an output class depending on which predictions have the highest probability. The multi-class results are aggregated and forecast for the majority voted class. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB and DT, the proposed VC provides a higher classification accuracy rate of 97.96%, 97.56% of precision, 89.95% of recall and 91.96% of F1-measures. Similarly, ETC provides 97.77% accuracy, 98.31% of precision, 84.78% of recall and 91.05% of F1-measures. Compared to conventional ML algorithms, VC and ETC provide higher accuracy, precision, recall and F1-measures. Thus, ETC and VC are preferable for spam detection. The website has been designed to detect messages as spam or not.
Current problems of creation of research infrastructure for synthesis of new materials in the framework of the digital transformation of society

The article discusses the main aspects of the significance of the development of a high-performance environment for scientific research in the conditions of digital transformation, used in solving the problems of synthesis of new materials with desired properties. Some historical facts are given that testify to the enormous role played by Soviet scientists in the creation of computer technology for scientific research. The analysis of the experience of the Federal Research Centre “Information and Control”, RAS in the creation of a modern high-performance platform and its use for scientific research is given. The necessity of its application for solving applied problems related to the selection of new materials in the field of microelectronics is substantiated.
System interface of scientific services of a digital platform for multiscale modeling

The paper deals with the formation of system interfaces of a digital platform for managing the process of providing scientific services, including the integration of scientific services for solving a complex research problem, for example, multiscale modeling of the properties of new materials or conducting interdisciplinary research. On the basis of the functional structure of the integration of scientific services of a digital platform (in relation to multiscale modeling) presented in the work, requirements for the system interface are formulated, and the architecture of the system interface for the integration of scientific services of a digital platform is proposed. The proposed model of a system interface for information interaction with services and datasets of a digital platform in management processes for the provision of scientific services is based on modern solutions for managing virtual infrastructure, based on container processing technologies and microservices, as well as container orchestration and communication (service mesh), flexible technologies (agile) integration. Taking into account that the main function of the digital platform is to provide processes for the preparation and conduct of research by formalizing the interaction scenarios of researchers, suppliers (sources) of initial data, consumers of the results, along with tools for supporting system interfaces to the catalogs of the digital platform, the project offers means for organizing the interaction of services, registered on the platform in order to ensure the execution of scientific research. Synchronization of the processes of providing services, ensuring the transfer of data between services and obtaining the final result are also ensured by implementing the control processes of the digital platform, which is based on the proposed system interface. The developed model of system interfaces is new in the work. The proposed interaction interface allows you to effectively consolidate high-performance computing resources and mathematical models based on digital platform technologies. This is especially important for organizing the solution of multiscale modeling problems as a complex of models, each of which operates on the same space-time scale.
Smooth Transitions Between Parallel Coordinates and Scatter Plots via Polycurve Star Plots

Abstract This paper presents new techniques for seamlessly transitioning between parallel coordinate plots, star plots, and scatter plots. The star plot serves as a mediator visualization between parallel coordinate plots and scatter plots since it uses lines to represent data items as parallel coordinates do and can arrange axes orthogonally as used for scatter plots. The design of the transitions also motivated a new variant of the star plot, the polycurve star plot, that uses curved lines instead of straight ones and has advantages both in terms of space utilization and the detection of clusters. Furthermore, we developed a geometrically motivated method to embed scatter points from a scatter plot into star plots and parallel coordinate plots to track the transition of structural information such as clusters and correlations between the different plot types. The integration of our techniques into an interactive analysis tool for exploring multivariate data demonstrates the advantages and utility of our approach over a multi‐view approach for scatter plots and parallel coordinate plots, which we confirmed in a user study and concrete usage scenarios.
Disentangling Content Information by Combining ASR and TTS Bottleneck Features for Voice Conversion

With the development of deep learning, nonparallel voice conversion (VC) has achieved a significant progress recently. Automatic speech recognition (ASR) and text-to-speech (TTS) for leveraging knowledge are the two mainstream methods in VC research. In this paper, we demonstrate that the two bottleneck features (BNFs) in the above methods are complementary. ASR-BNFs are more robust especially in any-to-many tasks, but suffer from leakage of source speaker’s timbre information; TTS-BNFs are less likely to reveal speaker’s timbre information, but lack robustness. Therefore, a nonparallel any-to-many voice conversion model is proposed by combining ASR-BNFs and TTS-BNFs. The whole modules in the proposed model can be trained jointly without any pre-trained models. Experiments are conducted on a private multi-speaker TTS dataset. It is demonstrated that the proposed model achieves the best balance in speech quality, timbre similarity and robustness compared to baseline models.
Quantum Correlation Generation Capability of Experimental Processes

Abstract Einstein–Podolsky–Rosen (EPR) steering and Bell nonlocality illustrate two different kinds of correlations predicted by quantum mechanics. They not only motivate the exploration of the foundation of quantum mechanics, but also serve as important resources for quantum‐information processing in the presence of untrusted measurement apparatuses. Herein, a method for characterizing the creation of EPR steering and Bell nonlocality is introduced for dynamical processes in experiments. It shows that the capability of an experimental process to create quantum correlations can be quantified and identified simply by preparing separable states as test inputs of the process and then performing local measurements on single qubits of the corresponding outputs. This finding enables the construction of objective benchmarks for the two‐qubit controlled operations used to perform universal quantum computation. It demonstrates this utility by examining the experimental capability of creating quantum correlations with the controlled‐phase operations on the IBM Quantum Experience and Amazon Braket Rigetti superconducting quantum computers. The results show that the method provides a useful diagnostic tool for evaluating the primitive operations of nonclassical correlation creation in noisy intermediate scale quantum devices.
The Role of Mobile Money Innovations in the Effect of Inequality on Poverty and Severity of Poverty in Sub-Saharan Africa

Abstract This study investigates the role of mobile money innovations in the incidence of income inequality on poverty and severity of poverty in 42 sub-Saharan African countries over the period 1980 to 2019. Mobile money innovations are understood as the mobile used to send money and the mobile used to pay bills online while income inequality is measured with the Gini index. Poverty is measured as the poverty headcount ratio while the severity of poverty is generated as the squared of the poverty gap index. The empirical evidence is based on interactive Quantile regressions. The following main findings are established. (i) Income inequality unconditionally reduces poverty and the severity of poverty though the significance is not throughout the conditional distributions of poverty and the severity of poverty. (ii) Mobile money innovations significantly moderate the positive incidence of income inequality on poverty and the severity of poverty in some quantiles. (iii) Positive net effects are apparent exclusively in the poverty regressions. (iv) Given the negative conditional effects, policy thresholds or minimum mobile money innovation levels needed to completely nullify the positive incidence of income inequality on poverty are provided: 27.666 (% age 15 +) and 24.000 (% age 15 +) of the mobile used to send money in the 50th and 75th quantiles, respectively and 16.272 (% age 15 +) and 13.666 (% age 15 +) of the mobile used to pay bills online in the 10th and 50th quantiles, respectively. Policy implications are discussed with respect of SDG1 on poverty reduction and SDG10 on inequality mitigation.
An efficient blockchain-based anonymous authentication and supervision system

The fast expansion of the Internet, as well as people’s concern for personal privacy and security, have raised the expectations for the identity authentication process. Although current controlled anonymous authentication techniques may provide anonymous authentication and supervision, they are inefficient. The one issue is the high processing cost of presenting and verifying the certificate. Another issue is that a single certification authority (CA) cannot reply timely when there are various requests for certificates and tracing fraudulent users. This article presents an efficient blockchain-based anonymous authentication and supervision system (EAAS) to overcome these issues. In comparison to previous solutions, our EAAS system adopts a double-layer CA architecture to address the issue that a single CA cannot react to a large number of requests in a short period of time. Additionally, it reduces the computational cost, making certificate presentation and verification more effective. Security analysis indicates that the proposed scheme enjoys anonymity, traceability, and unlinkability, and can resist forgery attacks. The theoretical and experimental comparison demonstrates its practicality in terms of presenting and verifying the certificate.
A Characterization of Interactive Visual Data Stories With a Spatio‐Temporal Context

Abstract Large‐scale issues with a spatial and temporal context such as the COVID‐19 pandemic, the war against Ukraine, and climate change have given visual storytelling with data a lot of attention in online journalism, confirming its high effectiveness and relevance for conveying stories. Thus, new ways have emerged that expand the space of visual storytelling techniques. However, interactive visual data stories with a spatio‐temporal context have not been extensively studied yet. Particularly quantitative information about the used layout and media, the visual storytelling techniques, and the visual encoding of space‐time is relevant to get a deeper understanding of how such stories are commonly built to convey complex information in a comprehensible way. Covering these three aspects, we propose a design space derived by merging and adjusting existing approaches, which we used to categorize 130 collected web‐based visual data stories with a spatio‐temporal context from between 2018 and 2022. An analyzis of the collected data reveals the power of large‐scale issues to shape the landscape of storytelling techniques and a trend towards a simplified consumability of stories. Taken together, our findings can serve story authors as inspiration regarding which storytelling techniques to include in their own spatio‐temporal data stories.
Long‐term desertification process monitoring and driving factors analysis in rare earth mining area

With the exploitation of rare earth elements (REE) in southern China, desertification has become a serious ecological problem in this region. To accurately understand the desertification process in mining areas and formulate targeted ecological restoration programs, taking the Lingbei mining area in Ganzhou City, Jiangxi Province as the study area, we explored desertification information extraction methods, desertification dynamics monitoring, and desertification drivers in REE mining areas using multisource data from 1986 to 2021. The results show that from the method of extracting desertification information, Random Forest is the prominent (83.33%), followed by Albedo–NDVI (Normalized Difference Vegetation Index) space (74.44%), and thirdly Linear Spectral Mixture Model (65.56%). From 1986 to 2021, the desertification land area in Lingbei mining areas first increased and then decreased, showing a reverse trend, but it has not recovered to the pre‐mining level. The government's ecological reclamation measures are the key factors for the restoration of desertification land. There are still 17.81 km 2 areas that have not been restored, of which 2.16 km 2 are at the level of severe desertification. Moderate desertification and light desertification have not been completely curbed, which needs continuous attention. Vegetation coverage has the highest explanatory power to the distribution of land desertification in mining areas, followed by land use classification, indicating that human activities have a great influence on the spatial differentiation of land desertification in mining areas, and it is necessary to pay attention to regional balanced and sustainable development in the future.
Learning scalable and efficient communication policies for multi-robot collision avoidance

Abstract Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions to avoid collisions. However, the risk of collision between robots varies as they move and communication may not always be needed. This paper presents an efficient communication method that addresses the problem of “when” and “with whom” to communicate in multi-robot collision avoidance scenarios. In this approach, each robot learns to reason about other robots’ states and considers the risk of future collisions before asking for the trajectory plans of other robots. We introduce a new neural architecture for the learned communication policy which allows our method to be scalable. We evaluate and verify the proposed communication strategy in simulation with up to twelve quadrotors, and present results on the zero-shot generalization/robustness capabilities of the policy in different scenarios. We demonstrate that our policy (learned in a simulated environment) can be successfully transferred to real robots.