Reviewerly/exHarmony huggingface dataset:
Construction of Resource Element Portrait Analysis Model Based on Data Center

The rapid development of digitalisation, information technology, and other technologies has led to the development of various enterprises in the direction of modernisation. The data centre can reflect the data related to the resource elements of the enterprise. For the development of enterprises, it is crucial to analyse the input and output of resource elements, so this paper proposes a study on constructing a model for analysing resource element portraits based on the data centre. This paper uses the data centre as a basis and deep learning models to classify and process resource elements. The features of resource elements are extracted based on a two-way long and short-term memory network and expressed as vectors. The “ODAS” open analysis conceptual model is applied to develop a process for constructing and drawing resource element portraits. From various perspectives, such as the optimal allocation of enterprise resources, professional synergy and index improvement, the input-output analysis model of resource elements is built, and the application of resource elements is comprehensively evaluated to realise the portrait analysis of resource elements. The experimental data shows that the maximum value of completeness of extracting features of resource elements obtained by applying the constructed model is 95%, which fully confirms the better performance of resource element portrait analysis.
Research and Design of landslide monitoring alarm system Platform

Geological disasters in China are very severe, landslides have caused billions of losses and thousands of casualties. Landslides pose a great threat to people’s lives and property, Considering the threat of landslide to people’s lives, in this paper, ZigBee wireless sensor network technology is used to analyse the landslide generation process, and use MPU6050 inclination sensor to collect the slope Angle of the mountain. This paper designs and realizes a landslide warning and monitoring platform of Android App and Web front page, realizes the landslide warning system, and sends the collected data and information and displays the terminal nodes in real time.
Evaluation of Data Visualizations for an Electronic Patient Preferences Tool for Older Adults Diagnosed with Hematologic Malignancies

Patients diagnosed with hematologic malignancies account for 10% of cancer related deaths. The growth of treatment options for hematologic malignancies has led to increased focus on treatment decision-making. However, little research has been done integrating patient-generated data and shared decision making to facilitate patient-clinician collaboration and understand patient preferences in cancer care. Our study aims to develop and evaluate data visualizations to support an electronic healthcare tool (EHT) to facilitate patient understanding of treatment outcomes using human-centered design methods. Data visualizations were developed and updated based on feedback from healthy volunteers, older adults with hematologic malignancies (patients), caregivers, and clinicians. We conducted a content analysis on the qualitative data gathered from participants. Our findings showed that users preferred easy to understand visualizations with simple, explanatory text compared to visualizations that were not immediately intuitive. Users also preferred visualizations that were more reflective of the individual's cancer treatment rather than a comparison to the patient population. Iterative improvements were made to the visualizations to reflect user feedback and will be used to inform the next iteration of visualizations for user testing in the clinic. This paper demonstrates the benefit of human- and user-centered design to iterate on data visualizations used to support a patient preference tool.
Applying Visual Analytics to Develop a Clinical Workflow Analysis Tool (CWAT) to Explore Time and Motion Data in Healthcare

Understanding clinical workflow is a crucial first step to improve the quality, safety, and efficiency of patient care delivery. It enables quality improvement processes and provides a basis to compare and quantify workflow improvements. A common source of data for studying clinical workflow is through time and motion studies, which generates multi-dimensional datasets that are challenging to analyze. Visual analytics can be an effective technique to show patterns and bottlenecks in the time and motion data. Moreover, workflow analysis often involves mixed-method design. The triangulation between the quantitative and qualitative data would require the support of a powerful data exploration tool. To address these challenges, we applied visual analytics to develop a clinical workflow analysis tool (CWAT) that allows for easy identification of significant workflow patterns. In this system demonstration paper, we describe the visualization design choices and validation through case studies.
The Implications of Using Digital Technologies in the Management of COVID-19: Comparative Study of Japan and South Korea

Technology can assist in providing effective infectious disease management, but it can also become a source of social injustice and inequality. To control the rapidly increasing SARS-CoV-2 infections and promote effective vaccine administration, both South Korea and Japan have been using several technology-based systems and mobile apps. However, their different approaches to technology use have yielded contrasting social implications.Through comparative studies of the use of digital technologies for pandemic management and its social implications in Japan and South Korea, this study aimed to discuss whether the active and optimal use of technology for pandemic management can occur without subverting or compromising important social values, such as privacy and equality.This study compared the social implications of Japan's and South Korea's contrasting approaches to technology implementation for COVID-19 pandemic management in early 2022.Digital technologies have been actively and comprehensively used in South Korea, enabling effective COVID-19 management, but have raised serious concerns about privacy and social equality. In Japan, technologies have been more carefully implemented, thereby not causing similar social concerns, but their effectiveness in supporting COVID-19 regulations has been criticized.Potential social implications such as equality concerns, the balance between public interest and individual rights, and legal implications must be carefully assessed in conjunction with effective and optimal infectious disease control to achieve sustainable use of digital health technologies for infectious disease management in the future.
Leadership in chilean early childhood education during the pandemic: experiences and meanings

Abstract The COVID-19 health emergency that began in 2019 continues, to this day, to affect a large percentage of the world population. In order to deter its progress, countries have generated various health protocol strategies, including the readjustment of their educational systems. Thus, face-to-face learning was replaced with online classes, and teaching staff were tasked with generating the adjustments necessary to provide continuity to learning processes — at least, to the extent to which their contexts allowed them to. The complexity of this task has called for leaders whose practices, in tandem with all members of the educational community, may invigorate the pedagogical, management and communicational processes involved, as this ever-evolving context demands. This article presents the construction of meanings and leadership practices of 5 leaders in early child educational centers in Chile during the years 2020 and 2021. We worked under a comprehensive-interpretive paradigm, a qualitative approach, and a grounded theory situated design. Our data collection instrument were narrative interviews. We studied the data through content analysis and using the software ATLAS.ti 9.0. The findings here presented highlight the importance of leadership practices in educational work and show some characteristics of early childhood education, a level of education at which leadership processes are located and tend towards care, the distribution of tasks, and trust.
Development Strategies of Intelligent Automotive Industry Under the Background of Increasing Demand for Computational Capacity

The demand for computational capacity in intelligent vehicles is increasing rapidly, which drives the automotive industry to accelerate the integration with the information and communication technology industry. This study evaluates the demand for computational capacity from three levels: vehicle, driving environment and industrial chain. Based on deductive reasoning method, it is revealed that to realize the increase of computational capacity, the technical architecture of vehicles needs to be reshaped, the driving environment needs intelligent upgrade, and the industrial chain needs ecological development. Then, according to the development needs of relevant technologies and industries, brand-new market competition strategies, business relationships and ecological evolution paths are identified. Finally, some suggestions are put forward to original equipment manufacturers, chip enterprises and information and communication technology enterprises, which are related to computational capacity of intelligent vehicles.
The Study on Re Test Judgement Based on Bayes Rule

In the electromagnetic compatibility test, the result of RE (radiation emission) is uncertain. When the result is close to the threshold, it is difficult to make an accurate judgment. CNAS-GL07 considers the influence of uncertainty on the decision and prescribes a decision method based on a single test. In this paper, Bayes rule is used to analyze the results of single RE test, which proves that there is a high possibility of misjudgment, and suggests that the number of tests should be increased to improve the reliability of the final decision. The method is improved and perfected on the basis of CNAS-GL07, and the result is good through experiment. It can provide some guidance for the determination of RE test results.
Online Perception Performance Estimation for Autonomous Driving Vehicles

Accurate estimation on the perception performance is an essential prerequisite for autonomous driving safety. State-of-the-art perception models get excellent performance on existing well-labelled dataset, while suffering the lack of reliable confidence in varying scenarios. This paper is dedicated to investigate an online perception performance estimation, in particular, to calculate the precision rate of each detected object adaptively without manually labelled annotations. A parameterized relationship is constructed between the confidence output by perception models and the precision performance. And then the relationship can be online updated according to a robust multiple hypothesis tracking (MHT) system iteratively. The proposed method is applied on widely studied Mask-RCNN on the KITTI dataset, to get precision estimations accordant to the ground-truth in different scenarios, respectively.
A Trade-off Design Approach for Integrating Cybersecurity, Safety, and Other Aspects of Intelligent Connected Vehicles

With the increasing connectivity of the modern automotive, cybersecurity is becoming increasingly essential for an auto. Security by design is a recommended way to protect automotive systems rather than after-sale solutions. However, other than cybersecurity, an auto should also be safe and comfortable to transport people and cargo. How to achieve a trade-off design considering various system requirements is a challenge. In this paper, a trade-off design approach for integrating various aspects, like cybersecurity, safety, and user experience, is proposed. The affecting map and the affecting score are proposed as tools to help the designer to select proper security countermeasures qualitatively or quantitatively. The proposed methods are compatible with the international standard ISO/SAE 21434. A case study of a Diagnostic on Internet Protocol (DoIP) system is demonstrated with details to show how to use the proposed methods, and also verify the feasibility and effectiveness of the approach.
Design of an E-Administration Platform and Its Cryptography-Based Security Model

E-administration is performing administrative works via computer and its associated technologies such as the Internet.It is administrative efforts that center on the exchange of information and providing services to people and the business sector at high speed and low cost through computers and networks with the assurance of maintaining information security.It is based on the positive investment in information technology and communication in administrative practices.This paper presents the design of the e-administration platform that adopts the concept of cryptography for identity management.The architectural framework of the platform comprises subcomponents for service and forms identification, business process redesign, service architecture, amalgamation, and deployment.The cryptography model for securing the platform was designed based on the combination of authentication criteria presented in the Rijndael-Advanced Encryption Standard (AES), Latticebased cryptography (LBC), and Secure Hash Algorithm (SHA512).It is required that a record be encrypted prior to its commitment to the database via a double encryption method.The AES algorithm-based encryption's output will form the input to the LBC algorithm to obtain the final output.
The collaborative strategy of data governance of state-owned enterprises in China

The new transformation spawned by digitization brings a new change in the logic of data governance. In recent years, the state-owned enterprises (SOEs) of China has been strengthening the construction of data governance system, but there are still a series of key issues that need to be solved. It is urgent to carry out research on issues related to data governance, to play the data elements of the amplification, superposition and multiplier effect. This study analyzes the new requirements of data governance in the new situation and the reasons for the non-synergy of data governance of SOEs. Then this research proposes the collaborative optimization strategy of data governance in the SOEs. The main findings of this research are as followed: On the one hand, the reasons for the lack of synergy in the SOEs’ data governance are comprehensively analyzed. On the other hand, some key strategies for the optimization of the company’s data governance synergy are proposed. For example, this stuty proposes to build multilevel data governance internal market, design internal market transaction elements, create internal market operation management mechanism, establish internal market performance evaluation and assessment mechanism, and improve the internal market-oriented and efficient operation supporting capacity.
A Survey Report on the Characteristics of Cognitive Ability of English Prefabricated Chunks for Higher Vocational College Students

This paper uses a questionnaire to find out that the cognitive ability of higher vocational college students in English prefabricated chunks is weak, and there are many problems such as insufficient knowledge reserve of prefabricated chunks and weak awareness of their use. The aim is to develop students’ learning habits of memorizing, understanding, applying and transferring English language with prefabricated chunks as the smallest unit.
Domestic Large Model Technology and Medical Applications Analysis

I n recent years, the continuous development of AI technology has led to the emergence of many new ultrasound technologies based on artificial intelligence, greatly promoting the development of ultrasound medicine.Advanced technologies such as ChatGPT and 5G have ushered in a new era of innovation in the field of ultrasound medicine, offering new possibilities for digital intelligence.The potential applications of ChatGPT in ultrasound have been discussed and hotly debated by Chinese scholars.This discussion has also been of great interest to our Editor-in-Chief, Ji-Bin Liu, who suggested a special issue on ultrasound AI and ChatGPT-related ultrasound applications, leading to the creation of this issue.Undoubtedly, ultrasound medicine will undergo significant upgrades and improvements, providing doctors with even more advanced tools and methods to aid in the diagnosis and treatment of diseases.As ChatGPT comes online, the Advanced Ultrasound in Diagnosis and Therapy (AUDT) has collaborated with leading experts in the field of ultrasound medicine and artificial intelligence to release a special issue in both print and online formats.This special issue will focus on the application of AI, ChatGPT and 5G technologies in ultrasound medicine, and will feature review articles on the current status and trends in ultrasound medicine technology.We hope to provide leading-edge knowledge and useful information for both clinical researchers and basic scientists.The Advanced Ultrasound in Diagnosis and Therapy (AUDT) is an open access journal in English with both online and printed publications which was created by the China-American Ultrasound Scholar Alliance.The journal covers various topics related to ultrasound technology, including advanced ultrasound imaging techniques, ultrasound-guided interventions, and the development of new ultrasound technologies.This special issue will introduce the application progress and prospects of AI in advanced ultrasound technologies, such as modality conversion, 3D/4D reconstruction, semi-supervised ultrasound video object segmentation (SUVOS), automated breast volume scanning (ABVS) and cloud handheld ultrasound system.Due to the large number of articles received in this special issue, some of them will be published in the upcoming issue.
Maximally Machine-Learnable Portfolios

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AN EFFICIENT PRIMARY POPULATION INITIALIZATION METHOD FOR METAHEURISTIC ALGORITHMS

It is widely recognized that convergence capabilities of metaheuristic optimization algorithms can be enhanced by properly chosen initial population. Most of the state-of-theart initialization techniques are suffering from numerous shortcomings, that ultimately make them non-viable or not efficient enough. To overcome this, this paper proposes a new algorithm for population initialization, which adopts the approach of dividing the search space into nested cubes and picking edge-points for sampling. Based on the data obtained after testing the method on 8 complex benchmark functions against other popular initialization strategies in the scope of WOA algorithm, the proposed approach outperformed all of the candidates in finding the global optimum.
FORMING THE REQUIREMENTS FOR SIGN LANGUAGE DETECTION

Sign language is a way of communicating through body movements. Body language recognition has been one of the most challenging research problems in the last few years. Their recognition plays an increasingly important role due to the huge spread of digital technologies. With the development of the fields of deep learning and computer vision, researchers have developed various automatic motion language recognition methods that can understand body movement. The idea of this study is to examine the existing sign language recognition systems in the world. These works are mainly divided into sensor-based systems and vision-based systems. Studies have shown that sensor-based tracking is more resource-intensive and difficult to implement than traditional image-based research, and there are also combinations of these two methods. As a result of this study, it can be seen that there are many sign languages in the world, most of which do not have their own databases available, and also dynamic gesture recognition systems still need new research to improve the results. During the work, several points have been formed that can help improve the quality of the work by adding studies and databases for unknown languages, using them also to obtain an acceptable accuracy of dynamic gesture detection, as well as ensuring the system work in real-time and use of few resources.
Parallel Extremely Randomized Decision Forests on Graphics Processors for Text Classification

The amount of readily available on-line text has grown exponentially, requiring efficient methods to automatically manage and sort data. Automatic text classification provides means to organize this data by associating documents with classes. However, the use of more data and sophisticated machine learning algorithms has demanded an increasingly computing power. In this work we accelerate a novel Random Forest-based classifier that has been shown to outperform state-of-art classifiers for textual data. The classifier is obtained by applying the boosting technique in bags of extremely randomized trees (forests) that are built in parallel to improve performance. Experimental results using standard textual datasets show that the GPU-based implementation is able to reduce the execution time by up to 20 times compared to an equivalent sequential implementation.
Hyperparameter Tuning Based Machine Learning Classifier for Breast Cancer Prediction

Currently, the second most devastating form of cancer in people, particularly in women, is Breast Cancer (BC).In the healthcare industry, Machine Learning (ML) is commonly employed in fatal disease prediction.Due to breast cancer's favourable prognosis at an early stage, a model is created to utilize the Dataset on Wisconsin Diagnostic Breast Cancer (WDBC).Conversely, this model's overarching axiom is to compare the effectiveness of five well-known ML classifiers, including Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Naive Bayes (NB) with the conventional method.To counterbalance the effect with conventional methods, the overarching tactic we utilized was hyperparameter tuning utilizing the grid search method, which improved accuracy, secondary precision, third recall, F1 score and finally the AUC & ROC curve.In this study of hyperparameter tuning model, the rate of accuracy increased from 94.15% to 98.83% whereas the accuracy of the conventional method increased from 93.56% to 97.08%.According to this investigation, KNN outperformed all other classifiers in terms of accuracy, achieving a score of 98.83%.In conclusion, our study shows that KNN works well with the hyper-tuning method.These analyses show that this study prediction approach is useful in prognosticating women with breast cancer with a viable performance and more accurate findings when compared to the conventional approach.
Deep Anomaly Analytics: Advancing the Frontier of Anomaly Detection

Deep anomaly analytics is a rapidly evolving field that leverages the power of deep learning to identify anomalies in various datasets. The use of deep anomaly analytics has increased significantly in recent years due to the growing need to detect anomalies in complex data that traditional methods struggle to handle. Deep anomaly analytics has the potential to transform various industries, including, e.g., healthcare, finance, and cybersecurity, by providing valuable insights and helping to diagnose diseases, prevent fraud, and detect cyber threats. However, there are also many challenges associated with deep anomaly analytics. This editorial provides an overview of the field of deep anomaly analytics, and highlights a few key challenges facing this field, i.e., time series anomaly detection, graph anomaly detection, efficiency (of models), and solving real-world problems. Additionally, it serves as an introduction to this special issue that delves further into these topics.