Reviewerly/exHarmony huggingface dataset:
Trends and hotspots in research on medical images with deep learning: a bibliometric analysis from 2013 to 2023

With the rapid development of the internet, the improvement of computer capabilities, and the continuous advancement of algorithms, deep learning has developed rapidly in recent years and has been widely applied in many fields. Previous studies have shown that deep learning has an excellent performance in image processing, and deep learning-based medical image processing may help solve the difficulties faced by traditional medical image processing. This technology has attracted the attention of many scholars in the fields of computer science and medicine. This study mainly summarizes the knowledge structure of deep learning-based medical image processing research through bibliometric analysis and explores the research hotspots and possible development trends in this field.Retrieve the Web of Science Core Collection database using the search terms "deep learning," "medical image processing," and their synonyms. Use CiteSpace for visual analysis of authors, institutions, countries, keywords, co-cited references, co-cited authors, and co-cited journals.The analysis was conducted on 562 highly cited papers retrieved from the database. The trend chart of the annual publication volume shows an upward trend. Pheng-Ann Heng, Hao Chen, and Klaus Hermann Maier-Hein are among the active authors in this field. Chinese Academy of Sciences has the highest number of publications, while the institution with the highest centrality is Stanford University. The United States has the highest number of publications, followed by China. The most frequent keyword is "Deep Learning," and the highest centrality keyword is "Algorithm." The most cited author is Kaiming He, and the author with the highest centrality is Yoshua Bengio.The application of deep learning in medical image processing is becoming increasingly common, and there are many active authors, institutions, and countries in this field. Current research in medical image processing mainly focuses on deep learning, convolutional neural networks, classification, diagnosis, segmentation, image, algorithm, and artificial intelligence. The research focus and trends are gradually shifting toward more complex and systematic directions, and deep learning technology will continue to play an important role.
AI Powered Sign Language Translator

This work proposes an AI-based sign language translator that aims to establish equal surroundings and enable people with hearing handicaps to communicate in their native language. In order to translate sign language into text, we used a transformer neural network, which can analyse more than 500 data points from a person's face and motions. Our machine learning pipeline allows the translator to grow, create new datasets, and develop models for sign language recognition. We created a sign language interpreter with over 200 phrases for an emergency call as a proof of concept. Encouraging those with hearing impairments to engage in social, political, economic, and cultural aspects of life is the overarching objective. Key Words: Sign Language, Inclusion, Social Development, Artificial Intelligence, Machine Learning.
Safeguarding the unseen: a study on data privacy in DeFi protocols

The financial sector’s adoption of technology-driven data analysis has enhanced operational efficiency and revenue generation by leveraging personal sensitive data. However, the inherent characteristics of blockchain hinder decentralized finance (DeFi) from accessing necessary sensitive user data. To address this challenge, we introduce a protocol that both safeguards user privacy and ensures data availability through the incorporation of homomorphic encryption and zero-knowledge-proof techniques in blockchain technology. This novel protocol helps mitigate privacy risks caused by sensitive data leaks while improving the capital efficiency of the DeFi market. Furthermore, we explore the applicability of these privacy-preserving methods in on-chain ecosystems and cross-border financial applications. Our solution contributes to secure, user-centric solutions for DeFi while upholding principles of decentralization and privacy protection.
A hybrid online and offline teaching model of professional English based on empirical modal decomposition method empowered by digital technology

Abstract With the rapid development of artificial intelligence digital technology, its related techniques have penetrated and been applied to the education industry and field. In this paper, we address the limitations of CEEMD in processing student performance prediction data and combine the hybrid gray wolf optimization algorithm with the minimum gating unit recurrent neural network to establish an English performance prediction model by simulating the leadership, hunting, and ranking mechanisms of the gray wolf population, as a way to complete the optimization of EMD limitations. Then, using the empirical modal decomposition method and other auxiliary methods, we conducted an experimental study on the online and offline “hybrid” teaching model for two classes of English majors in the School of Foreign Languages of Huanghe Science and Technology University in 2021 and passed equal-group pre-test and t-test. The results showed that the significance level α of English performance in class B was 0.10, and since the probability value P was less than 0.10, it was considered that there was a significant difference between the two overall means, i.e., there was a significant difference between the overall means of the improvement in the performance of class B and class A. The overall performance of class B students was significantly higher than that of class A. This study combines online and offline teaching modes through digital technology to promote the use of online and offline “hybrid” teaching modes in higher education institutions, which is very important.
DataVisage: A Card-Based Design Workshop to Support Design Ideation on Data Physicalization

As a rapidly booming research field, data physicalization has received significant explorations in recent decades. While previous studies have considered various aspects of creating physical artifacts, there is still a lack of structured method for guiding designers to generate design ideations. In this paper, we propose a card-based design workshop that takes proactive health as a starting point to design physicalizations, aiming at promoting users’ self-reflection. Building upon prior researches and our thoughts, we propose design strategies and develop cards and toolkits to bridge designers’ thinking processes and workshop process, thereby maximizing the effectiveness. The workshop was implemented and validated within a graduate-level design course. Qualitative and quantitative results demonstrated the effectiveness of the workshop in optimizing group work and structuring the design process. The workshop also contributes to the creation of quick, aesthetic, and practically applicable physical prototypes.
Tools for Analysis of Treatment Dynamics and Outcomes for Personalized Medicine in Asthma

This paper introduces a methodology for tracking the dynamic changes in parameters related to asthma treatment. Leveraging extensive data gathered from clinical trials, blood analysis, and patient records, this study has developed sophisticated tools for visualizing and evaluating the treatment dynamics among afflicted patients, as well as appraising the effectiveness of the treatments administered. To manage and process the copious amounts of data, advanced software tools were devised, thereby enabling precise and insightful analyses. Utilizing state-of-the-art statistical models, discernible patterns and trends associated with asthma have been identified, contributing to a deeper comprehension of the dynamics of treatment response and efficacy assessment. Through a thorough examination of longitudinal patient data, intricate insights into treatment patterns, disease progression, and long-term outcomes have been uncovered. In summary, this paper conducts a comprehensive analysis of patient parameters, employing a multidimensional strategy that encompasses clinical trials, blood parameter evaluations, and patient-year data assessments. The innovative software tools, models, and data analysis techniques employed in this study facilitate a meticulous evaluation of treatment dynamics and eventual outcomes. The implications of these findings are invaluable for enhancing clinical practices and ultimately enhancing the quality of life for individuals grappling with asthma.
Instance-Dependent Inaccurate Label Distribution Learning

Label distribution learning (LDL) is a novel learning paradigm that assigns each instance with a label distribution. Although many specialized LDL algorithms have been proposed, few of them have noticed that the obtained label distributions are generally inaccurate with noise due to the difficulty of annotation. Besides, existing LDL algorithms overlooked that the noise in the inaccurate label distributions generally depends on instances. In this article, we identify the instance-dependent inaccurate LDL (IDI-LDL) problem and propose a novel algorithm called low-rank and sparse LDL (LRS-LDL). First, we assume that the inaccurate label distribution consists of the ground-truth label distribution and instance-dependent noise. Then, we learn a low-rank linear mapping from instances to the ground-truth label distributions and a sparse mapping from instances to the instance-dependent noise. In the theoretical analysis, we establish a generalization bound for LRS-LDL. Finally, in the experiments, we demonstrate that LRS-LDL can effectively address the IDI-LDL problem and outperform existing LDL methods.
The Routledge companion to digital media and childrenThe Routledge companion to digital media and children, Edited By Lelia Green, Donell Holloway, Kylie Stevenson, Tama Leaver, Leslie Haddon, New York, Routledge, 2022, 630pp., £43.99 (Paperback), ISBN: 9780367559069

"The Routledge companion to digital media and children." Education 3-13, ahead-of-print(ahead-of-print), pp. 1–2
INFLUENCE OF THE STATE ON THE DEVELOPMENT OF INFORMATION SOFTWARE IN THE EDUCATION SYSTEM OF UKRAINE

The value of information in modern society is difficult to overestimate. In the period of the war with russia on the territory of Ukraine, the informatization of society is very fast, and in the field of education, the flow of a wide variety of information has increased, the means and methods of its transmission, processing and storage are being improved. Due to the development of processes of informatization of the education system of Ukraine, educators are involved in a whole system of information relations, and their activities without obtaining and using relevant information are practically impossible. Information provision of education, as the professional activity of a teacher, is an urgent need and is becoming more and more relevant. Education reform is impossible without improving the procedure for its completion, as well as information support. The rapid expansion of the scientific space of the Internet contributed to the growing popularity and importance of scientific Internet publications. Observations have shown that most scientists search for information on the Internet by keywords. This method uses too many sources, most of which do not meet the needs of the teacher. Placing electronic versions of scientific publications, textbooks, manuals, dissertations, and encyclopedic materials on the Internet created opportunities for wide use of these resources in educational activities. The analysis of the topics of publications in scientific-metric databases allows us to identify the fastest-growing industries, the distant prospects of technological breakthroughs, which are very relevant in this difficult time for Ukraine. Improving the teacher’s ability to use information and communication tools in the work should take place in a specially organized activity, in which each of the components should find development: personal, scientific-theoretical, practical.
An Efficient Enhanced-YOLOv5 Algorithm for Multi-scale Ship Detection

Ship detection has gained considerable attentions from industry and academia. However, due to the diverse range of ship types and complex marine environments, multi-scale ship detection suffers from great challenges such as low detection accuracy and so on. To solve the above issues, we propose an efficient enhanced-YOLOv5 algorithm for multi-scale ship detection. Specifically, to dynamically extract two-dimensional features, we design a MetaAconC-inspired adaptive spatial-channel attention module for reducing the impact of complex marine environments on large-scale ships. In addition, we construct a gradient-refined bounding box regression module to enhance the sensitivity of loss function gradient and strengthen the feature learning ability, which can relieve the issue of uneven horizontal and vertical features in small-scale ships. Finally, a Taylor expansion-based classification module is established which increases the feedback contribution of gradient by adjusting the first polynomial coefficient vertically, and improves the detection performance of the model on few sample ship objects. Extensive experimental results confirm the effectiveness of the proposed method.
Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates

The Visual Mesh is an input transform for deep learning that allows depth independent object detection at very high frame rates. The present study introduces a Visual Mesh based stereo vision method for sparse stereo semantic segmentation. A dataset of simulated 3D scenes was generated and used for training to show that the method is capable of processing high resolution stereo inputs to generate both left and right sparse semantic maps. The new stereo method demonstrated better classification accuracy than the corresponding monocular approach. The high frame rates and high accuracy may make the proposed approach attractive to fast-paced on-board robot or IoT applications.
A SaaS Concept-Based Shopping Center Fire Risk Assessment Model for the Safety Management Applications

This paper proposes a solution to the problem of software absence for the quantitative fire safety analysis of complex objects, which is suitable not only for highly qualified scientists and experts but also for fire safety engineers and inspectors and is compatible with mobile devices. Existing software solutions for quantitative fire safety analysis are desktop-based, and all require a high level of competence of users. We propose a solution by combining the formalism of typical probabilistic structural–logical (TPSL) models, the Software-as-a-Service (SaaS) concept, and end-to-end encryption. Using TPSL models allows the development of cloud-based applications with a simple request-based application programming interface and intuitively understandable user interface to assess the level of safety of complex objects. As a result, we could use such mobile device-ready applications both for developing mobile device applications and developing mobile-device-friendly website-based services to carry out safety control inspections or level of safety management.
Research progress of ion trap quantum computing

Ion trap is one of the leading physical platforms to implement quantum computation. Currently, high-fidelity elementary quantum operations above the fault-tolerant threshold, including state preparation, measurement and universal gates, have been demonstrated for tens of ionic qubits. One important future research direction is to further enlarge the qubit number to the scale required for solving practical problems while maintaining the high performance of individual qubits. This paper introduces the current mainstream schemes for scalable ion trap quantum computation like quantum charge-coupled device (QCCD) and ion-photon quantum network, and describes the main limiting factors in current research. Then we further explore new schemes to scale up the qubit number like two-dimensional ion crystals and dual-type qubit, and discuss the future research directions.
An ultra-short-term PV power forecasting method based on groundbased cloud images

Movement of clouds will directly change solar irradiance, which is the most important factor for instability of PV power. In this thesis, cloud information is obtained through cloud images collected by ground-based sky imager, and an ultra-short-term PV power forecasting method based on cloud images is proposed. Firstly, cloud image pre-processing solve the distortion problem caused by fish-eye lens of the sky imager, restore realistic distribution of clouds and prepare for extraction of feature points of two consecutive frames. Then, cloud motion speed is obtained by matching cloud cluster pixels of two frames of cloud images before and after predicted time. In order to improve the accuracy of speed, a calculation method is proposed based on cloud pattern classification and particle swarm optimization weights for cloud speed prediction. Finally, a model from cloud images to PV power is established. The proposed method is verified with public cloud image data collected by National Autonomous University of Mexico (NAUM), and the outcome is compared with results of two common persistence models. Results show that the proposed method significantly improves forecasting performance in most prediction time ranges.
“Blood Analysis Using Image Processing”

Abstract: The human blood is a health indicator; it delivers necessary substances such as oxygen and substance that provides nourishment is necessary. Hence, segmentation of blood cells and identification of blood type is very important. The human blood consists of the RBCs, WBCs, Platelets and Plasma. Presently, lab technician tests blood groups manually and they use a device called Hemacytometer and microscope to count blood cells. But this method is extremely time consuming, monotonous and leads to the inaccurate results due to human errors. To overcome the problems regarding time, accuracy and cost, a method is proposed based on processing of images acquired from laboratory. The image processing techniques such as Segmentation, Morphological operations and Circular Hough Transform will test with some samples. In healthcare, blood testing is observed to be as one of the most significant medical examination tests. In pathology labs, different types of blood cells are counted to diagnose the diseases patient.
STUDENTS’ ANXIETY AND SPEAKING ABILITY: CORRELATIONAL STUDY OF EFL CLASSROOM

The aim of this study was to find out the relationship between a student's anxiety and their ability to speak. The research design used in this study was quantitative. The population of this study were the second grades students of SMK Pawyatan Daha 1 Kediri. A total of 80 students were chosen as the sample. The data was collected using two instruments: the first was questionare to assess students' anxiety in learning English as a foreign language by using the Foreign Language Classroom Anxiety Scale (FLCAS) developed by Howirtz, and the second was a speaking test to assess students' speaking ability. The researcher used Pearson product moment to analyze the data. The basis for decision making, if the significance value (sig.) is less than 0.05 then there is a relationship between the two variables. From the data result it can be seen that the significance value indicates 0.777 (0,777 &gt; 0,05). The value of R count is 0,032. This is obviously lower than the r-table (level of significance (5% or 0.05, n= 80) so the r table is 0,220. It automatically indicates that H0 is accepted. It can be concluded that there is no a significant relationship between the two variables.
A Thematic Analysis to Determine the Future Design of Mobile Cryptocurrency Wallet

The growth of cryptocurrency has increased the usage of mobile cryptocurrency wallet applications. Even though the cryptocurrency wallet is an essential tool in managing access to crypto assets, the application’s usability still needs to improve in many aspects. This research aims to determine the essential factors for future mobile cryptocurrency wallet design. The user reviews of several popular crypto wallets are collected from several application stores. A machine learning approach is used to classify the review category based on their relevancy to user experience. The result shows numerous problems still exist even with the most adopted mobile crypto wallet. Hence, we propose a recommendation of features to solve the problem and help preserve the credibility of the mobile wallet.
A Feature Model of Consensus Algorithms in Distributed Ledger Technology

A distributed ledger is a database distributed across multiple systems, with each system holding a synchronized copy of the data. Distributed ledger technology has applications in various healthcare, finance, and cybersecurity domains. However, the intricacies of the features of consensus algorithms, which ensure consistency across different ledgers, remain challenging, as the relevant knowledge is scattered across a wide range of literature or in the form of tacit knowledge of software practitioners. This study presents a systematic data collection comprising an extensive literature review and a set of expert interviews to provide insights into designing and evaluating of consensus algorithms for web3 applications. The usability and usefulness of the extracted knowledge were evaluated by seven experienced practitioners in web3 development companies, resulting in an overview of 13 consensus algorithms, their features, and their impacts on quality models. With this comprehensive knowledge, web3 developers can expedite evaluating, selecting, and implementing consensus algorithms for distributed ledgers.
FeDis: Federated Learning Framework Supported by Distributed Ledger

Despite the several advantages that distributed ledgers provide to end-users and the system, it also gives access to all data in the chain to every user with a participating ledger node, even if encrypted. While modifying data in the blockchain is a difficult and complex process, the sole existence of data in this way raises some security concerns. To address this issue, this article proposes FeDis, a framework that combines federated learning with a distributed ledger. In the proposed FeDis framework, federated learning is used to train a global model by aggregating local gradient models trained by the participants in the learning process. One of the key features of the framework is the possibility of handling heterogeneous data and devices from different participants. By combining federated learning with a distributed ledger, local model weights are saved in the ledger after encryption and then fetched by the server to be aggregated and to create a global model. The use of a distributed ledger in FeDis increases user trust by providing an additional layer of security and traceability. In addition, the framework is tested using the NASA Bearing dataset showing that the extra layer of security does not compromise the accuracy of the local models.
Blockchain-Based Decentralized Autonomous Organizations (DAOs) for Democratization in the Environmental, Social, and Governance (ESG) Rating Process

The Environmental, Social, and Governance (ESG) initiative has recently become an emerging research area. ESG rating is a critical activity in ESG initiatives that still holds some issues. The ESG rating process needs significant transparency, democratization, and standardization improvement. This research aims to propose the technical architecture of a blockchain-based ESG rating system that applies the concept of DAO as the democratization enabler. The Design Science Research (DSR) method is used for observation and theory building of the technical architecture. This research’s outcome illustrates how the proposed technical architecture can fit into the typical ESG rating process. The technical architecture can also become the guideline for future experiments and implementing blockchain-based ESG rating systems.