Examining the Effectiveness of Teaching Strategies for Alleviating EFL Students’ Writing Anxiety: A Mixed-Method Study

Abstract: At higher education, students at the end of the study are required to write a final project. Writing English papers is challenging and might create writing anxiety. Therefore, it is necessary to look at what strategies are used by the teachers and their effect on anxiety levels and performance. Furthermore, it is also crucial to accommodate students’ perceptions of the strategies so that students’ voices provide valid information on how the classroom should be. This research uses a mixed-method methodology to analyze lecturers' anxiety-reduction strategies and students' perceptions of them. Three instruments were used to collect data, namely a Second Language Writing Anxiety Inventory (SLWAI), open-ended questions about the perception, and students’ writing scores. A paired sample t-test was used to compare students' writing anxiety and performance before and after treatment. Content analysis was used to assess student opinions of the strategies. The statistics showed that students' writing anxiety fell from high (71.35) to moderate (66.47). Meanwhile, students' writing grade rises from 72.00 to 79.03. SPSS analysis showed that anxiety-reducing strategies significantly affect writing anxiety and student performance (p = 0.000). Consequently, this study's findings should be beneficial in writing classes.


A COMPARATIVE ANALYSIS OF INFORMATION SECURITY INCIDENTS IN PUBLIC ADMINISTRATION IN SELECTED EUROPEAN UNION COUNTRIES

Abstract: The article presents the issue of information security incident management in public administration. The goal of security incident management is to minimize the negative impact of incidents and ensure the continuity of the organization's operations. It is critical to know what the threats are, including the number and types of incidents reported. The article outlines key incident management and legal issues related to the topic of security incidents and the work of computer incident response teams. Examples of incidents that took place recently in public administration units in Poland were also presented. This was followed by an analysis of statistical data on reported incidents in Poland in the years 2020-2022, and the results were compared with the number of incidents reported in selected European Union countries. The results of the study show that the dominant type of incidents is fraud (mainly phishing), and public administration is one of the main targets of cybercriminals' attacks. Difficulties in conducting such comparative analysis have also been demonstrated.


Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

Abstract: The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.


A novel anomaly detection model for the industrial Internet of Things using machine learning techniques

Abstract: In recent decades, the pervasive integration of the Internet of Things (IoT) technologies has revolutionized various sectors, including industry 4.0, telecommunications, cloud computing, and healthcare systems. Industry 4.0 applications, characterized by real-time data exchange, increased reliance on automation, and limited computational resources at the edge, have reshaped global business dynamics, aiming to innovate business models through enhanced automation technologies. However, ensuring security in these environments remains a critical challenge, with real-time data streams introducing vulnerabilities to zero-day attacks and limited resources at the edge demanding efficient intrusion detection solutions. This study addresses this pressing need by proposing a novel intrusion detection model (IDS) specifically designed for Industry 4.0 environments. The proposed IDS leverages a Random Forest classifier with Principal Component Analysis (PCA) for feature selection. This approach addresses the challenges of real-time data processing and resource limitations while offering high accuracy. Based on the Bot-IoT dataset, the model achieves a competitive accuracy of 98.9% and a detection rate of 97.8%, outperforming conventional methods. This study demonstrates the effectiveness of the proposed IDS for securing Industry 4.0 ecosystems, offering valuable contributions to the field of cybersecurity.


An Evaluation of the Quality Educational Services in D3 Computerized Accounting at Politeknik Negeri Banjarmasin with the Matrix Method Importance Performance Analysis

Abstract: Evaluation of the quality of educational services is an important process to assess the extent to which an educational institution is able to meet the needs of students and provide quality services. Therefore, there is a need for consistent evaluation of continuous improvements to the quality of educational services to achieve customer needs and satisfaction, especially for the D3 Computerized Accounting Study Program, Accounting Department, Politeknik Negeri Banjarmasin (POLIBAN). By improving the satisfaction of all stakeholders involved in academic services, it is hoped that it can increase the number of prospective new students and the competitiveness of graduates so that they can be accepted into the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN. This research aims to evaluate the quality of educational services in the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN using the Importance Performance Analysis Matrix method. The research results found that there was a significant difference between expectations and perceptions regarding educational services in the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN. This research provides recommendations to the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN, to make continuous improvements so as to increase the number of students entering the D3 Computerized Accounting Study Program, Accounting Department, POLIBAN.


Komparasi dan Implementasi Algoritma Regresi Machine Learning untuk Prediksi Indeks Harga Saham Gabungan

Abstract: Indeks Harga Saham Gabungan (IHSG) or Indonesia Composite Index (ICI) is part of the macro indicators of a country that describes the economic condition of a country. ICI is an interesting study to research since its existence will be able to show market sentiment regarding an event that occurred in a country. This research tries to predict the ICI in the future based on historical data. The dataset used in this research is publicly available in Yahoo Finance. The experiment is conducted by implementing some regression machine learning algorithms, such as Decision Tree, Random Forest, k-Nearest Neighbor (kNN), and Linear Regression. As a result, Decision Tree has the lowest MSE value compared to other methods: 1268.242. In this research, a website-based application prototype was also developed that can be used to view IHSG graphs and make future predictions, using the 4 (four) tested algorithms.


Rancang Bangun Aplikasi Keuangan Untuk Mengatur Jumlah Pengeluaran Pribadi Berbasis Android

Abstract: Not everyone is able to manage their finances well. Consumptive lifestyles and lack of awareness about the importance of managing finances make a person unable to escape financial problems. In an effort to reduce the impact of existing problems, everyone should have the awareness and effort to manage their finances by recording their financial expenses regularly. In modern times and the rapid development of technology like today, almost everyone has a mobile device at least one Android device. By utilizing existing technology, various things can be done using only their Android devices such as shopping, communicating with someone even though it is quite far away or even to record financial activities. By identifying and collecting data and interviews from related problems, this research aims to be a solution for someone in managing their finances by recording every financial activity in an existing application and making the use of stationery and books to record and recap every expense can be abandoned. With this application, a person can easily record finances anytime and anywhere without fear of losing the notes that have been made. As a result, it is hoped that eating will become wiser and know how much and where the money is spent so that the record can be used as a picture of a person in controlling their money.


Pengumpulan Informasi pada Situs Web Dengan Menyusun Kerangka Kerja Keamanan Siber NIST

Abstract: In the current era, the rapid development of websites has made them one of the most significant modern information media. Website creation is not only focused on the design and information presented, but also focuses on security aspects. The presence of security on a website is very important, considering the need to protect the data and information contained therein. Information Gathering is one method used to test a website's security. This information gathering is the earliest stage to obtain ownership and other sensitive information. This research aims to conduct security testing of the oase.poltektegal.ac.id website using tools in the form of penetration testing software; then, the testing results are entered into the cybersecurity framework issued by N.I.S.T. The test results obtained and adjusted to N.I.S.T. Cybersecurity are that the oase.poltektegal.ac.id website has vulnerabilities in the form of CVE-2003-1418 (apache webserver vulnerability), CVE-2005-3299 (PHP vulnerability), CVE-2010-4344 ( Buffer Overflow Vulnerability), CVE-2007-6750 (XSS). The solution to this vulnerability is updating the software and closing unused ports. These results will be used as a benchmark in creating or improving similar websites to increase awareness and vigilance in achieving cyber resilience


Evaluation of Different Stemming Techniques on Arabic Customer Reviews

Abstract: Customer opinion and reviews play a vital role in marketing expansion. Big companies all over the world assign a lot of their efforts to analyzing customers’ feedback to keep track of their needs. Natural Language Processing (NLP) is widely used to analyze such review texts. Arabic customer analysis and classification also began to gain researchers’ attention due to the wide range of Arabic language speakers. Working with Arabic Language is a very challenging task because of the orthographic nature of Arabic. Also, customers often write their reviews in their dialectical style, which often diverts from standard Arabic. This study presents a method to classify Arabic customer reviews using four classifiers (K-nearest Neighbor (KNN), Support Vector Machine (SVM), Logistic Regression (RL), and Naïve Bayes (NB)). The classification is implemented with three stemming techniques (Snowball, Khoja, and Tashaphyne). The HARD dataset is adopted to perform the experiments. The results stated that the stemming methods can enhance classification performance despite the complexity of Arabic scripts and dialects. This work sheds light on utilizing and investigating more machine learning (ML) techniques and evaluating the results.


Permainan bahasa dalam penggunaan bahasa Inggris oleh pedagang asongan di pantai Kuta Lombok

Abstract: The playfulness of English is the result of language interference, which can be a source of creating new English variations. The purpose of this study is to describe the form of English playfulness by hawkers at Kuta Beach, Lombok, and the factors of English playfulness. The method is descriptive qualitative. The data collection by ‘simak libat cakap’ or involved conversation observation technique, recording, and taking notes technique. The data analysis method is reduction, presentation, and conclusion. Data analysis shows that the playfulness of English by hawkers comes from loan translation in the form of reduplication of Indonesian and Sasak words into English. There are two forms of loan translation: a change in the meaning of English in accordance with Sasak and Indonesian, and not experiencing a change in meaning but experiencing the addition of English meaning in accordance with Sasak and Indonesian. The factors causing the English playfulness are internal factors such as the difference in vocabulary structure from Sasak and Indonesian to English and the influence of Indonesian and Sasak as the dominant language. Factors external the language are influential in English playfulness, such as level of education, economic status, occupation, regional origin, and the situation of speech.


Storybook berbasis augmented reality (AR) meningkatkan hasil belajar bahasa Inggris siswa

Abstract: This study focuses on the combination of digital media and students' literacy, utilizing an Augmented Reality (AR)-based storybook as a tool for English language subject. The aim of this research is to assess how the use of AR-based storybooks can influence and enhance students' learning outcomes in English language subject. Employing an experimental research design, two groups were exposed to different stimuli. The experimental group's test results showed a significant difference with a two-tailed significance value of 0.032, indicating an impact attributed to the utilization of AR-based storybooks. Additionally, the N-gain percentage results revealed a 60% improvement, categorizing the AR-based storybook as moderately effective in enhancing students' learning outcomes compared to conventional storybook media. The findings of this study indicate that the implementation of AR-based storybooks significantly and effectively enhances students' learning outcomes in English language subject.


Dampak strategi quick write berbantuan media animasi terhadap keterampilan menulis teks persuasi siswa

Abstract: This research is motivated by the need for innovation in the learning process in the Industrial Revolution 4.0 era. This research aims to determine the impact of the quick write (QW) strategy assisted by animation media on the ability to write persuasive texts for class VIII students. This research implemented a pretest-posttest control group design involving students from SMP Negeri 5 Pontianak. Students were divided into two groups consisting of 64 students selected through cluster random sampling. The instrument consists of essay questions in the form of a pre-test and post-test. The data were analyzed using the t-test and the results showed that 1) students who were treated with the QW strategy assisted by animation media showed a significant increase in their ability to write persuasive texts, and 2) the QW strategy assisted by animation media contributed to student independence in learning to write, students were active and able to highlight the argument process quickly and responsively related to actual problems with the linguistic skills they have. These findings show that students who use the QW strategy assisted by animation media have a better improvement in their ability to write persuasive texts than students who use direct learning.


Library Book Recommendation System Using Content-Based Filtering

Abstract: In today's digital era, libraries are developed to adapt to student needs. Students can easily search for books on digital library services. However, the large number of books sometimes makes it difficult for students to find the books they want. Overcoming this problem can be done by using a recommendation system. A recommendation system is a system used to provide suggestions to users. This research aims to develop a library book recommendation system at Darul Mustofa Bangkalan Vocational School using content-based filtering. The content-based filtering method offers recommendations based on user preferences according to the item description. The algorithms used are Term Frequency Inverse Document Frequency (TF-IDF) and Cosine similarity. The method used in this research is research and development with a waterfall model. The researcher's testing stage used black box testing. Black box testing results were obtained from validation by system experts, website experts, and user tests with the qualification "Very Eligible."


Conceding Sentiment Prognosis on Twitter Data

Abstract: Twitter is the biggest micro-blogging website that gives people a platform to share their opinions about any new happenings around the world. The size of tweets is generally short which makes it very suitable for opinion mining. The key focus of the paper is to analyze the feelings and ideas. In this paper, analysis is done on the classification of tweets on a particular keyword. The tweets related to the given keyword are collected, analyzed, and the result is generated in the form of percentage of positive, neutral and negative sentiments, which gives us a sense of overall sentiment of the keyword. Further, Classification is done using supervised learning algorithms and the best among these will be found by calculating the accuracy of each.


1. THE MOST POPULAR ARTIFICIAL INTELLIGENCE APPLICATION USED BY AEROSPACE ENGINEERING STUDENTS’ TO LEARN MATHEMATICS

Abstract: Artificial intelligence (AI) develops over time. Education is concurrently the AI evolution,sometimes in front, sometimes behind. Education underlies AI, and later on, AI is used in education. Therefore, the utilization of AI in education cannot be denied. There are many advantages, but also some limitations and ethical issues that arise from the use of AI in education. There are many applications of AI in mathematics scattered throughout society, but not all of them are useful enough. Thus, the purpose of this research is to gain the famous application AI in mathematics (app AI math) that is mostly used and recommended by the students’ experience in doing and learning mathematics. T-test independent samples were used to test the score of two groups, using the AI math app or not, in order to know whether it was affected or not. The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway. But still, many students did not use this app. Relating to their score, the students who have used the AI math app to learn math before are not different from the students who do not use the AI math app.


Digital Literacy Activities

Abstract: In an increasingly technological-focused environment, it is paramount that library instructors infuse digital literacy skills into their teaching. Librarians as information professionals are in a prime position to impart digital literacy skills into patrons. This not only creates a more exciting lesson plan but also has the potential to teach students necessary digital literacy skills that are transferable beyond their schoolwork. In this chapter, the author examines the position of digital literacy in instruction and as a way to build employable skills. Examples of digital literacy activities' that can be incorporated into information literacy instruction sessions no matter the length of the session are also offered. These activities include digital footprint tracking, games, and website building. The chapter also discusses the assessment of these activities which evaluated the students perceived value of the information and skills learned from the digital literacy activities.


Intersecting Minds and Machines

Abstract: The integration of technology in the medical sector, particularly for data acquisition and analysis via the internet of health things (IoHT), offers significant potential for enhancing community health management by monitoring individual lifestyle patterns. A systematic literature review focusing on articles published between 2019 and 2021 explored the behavioral intention toward using IoHT-based healthcare systems. Analyzing 20 selected articles revealed a preference for theories such as the technology acceptance model (TAM), the unified theory of acceptance and use of technology (UTAUT), and the theory of planned behaviour (TPB) to facilitate behavioral change. Key factors influencing behavioral intention toward these systems include performance expectancy, perceived usefulness, effort expectancy, social influence, and resistance to change. The findings highlight the importance of user-centric design in developing IoHT-based healthcare systems to effectively meet community health needs, emphasizing that healthcare solutions should be tailored to individual health behaviors and preferences.


Narrative Threads and Cinematic Connections Using Intelligent Systems to Enhance Movie Recommendations with Market Basket Analysis and Advanced Algorithms

Abstract: Movie streaming services are businesses driven by data and strategies to predict future viewing patterns based on historical data. Without unsupervised learning techniques, industries like movie streaming services might face laborious tasks and issues in anticipating customer preferences and forecasting changes in customer behavior. In this chapter, market basket analysis (MBA) and recommender systems were implemented on MovieLens Data. In MBA, movie watching patterns were identified using two types of rule-generating algorithms, namely the apriori algorithm and the FP-growth algorithm. Three visualization idioms were generated to understand the association rules extracted in the MBA. Secondly, five types of recommender systems, namely memory-based collaborative filtering, model-based collaborative filtering, content filtering, context filtering, and the hybrid method, were implemented to suggest relevant movies to customers. Each recommender system was experimented with three different TopN configurations, and the results were evaluated using information retrieval metrics.


Use Of Social Media For Marketing Of Library And Information Services In Academic Libraries

Abstract: Academic libraries play a crucial role in facilitating the acquisition of knowledge and information for students in higher education.This study examines the utilization of social media platforms as a means of promoting academic libraries and enhancing their marketing effectiveness.Through the collection of primary quantitative data via a survey administered to 165 participants, the study explores the relationship between social media usage and library marketing strategies.Utilizing statistical analysis tools such as SPSS, the study interprets data pertaining to key variables including social media engagement and marketing approaches.The findings underscore the significance of leveraging social media platforms to amplify the visibility and utility of academic libraries, particularly in higher education settings.This research contributes to a deeper understanding of the evolving role of social media in modern library marketing practices and highlights opportunities for enhancing engagement and outreach within the academic community.


LEGAL-NORMAL BASIS OF FORMING SOCIAL-PEDAGOGICAL COMPETENCE OF A TEACHER IN INCLUSIVE EDUCATIONAL ENVIRONMENT

Abstract: The article covers the issue of the development of socio-pedagogical competence of educators in the environment of inclusive education. Also, the state has analyzed the conditions created by students with disabilities in the educational environment and the implementation of developed laws and decisions.


ACTIVATION OF PEDAGOGICAL AND PSYCHOLOGICAL OPPORTUNITIES IN THE DEVELOPMENT OF THE INDEPENDENT EDUCATIONAL ACTIVITY OF THE FUTURE MUSIC TEACHERS

Abstract: This article presents ideas on the pedagogical importance of developing the independent learning activity of future music teachers, the technologies and didactic games used in the development of independent learning activity of future music teachers.


THE ESSENCE OF CREATIVE COMPETENCE IN PEDAGOGY AND MODERN APPROACHES

Abstract: This article talks about creative competence in pedagogy, its place and essence in the educational process, competence approaches to it, specific features of formation and development of creative competence in students.


3D orientation field transform

Abstract: Abstract Vascular structure enhancement is very useful in image processing and computer vision. The enhancement of the presence of the structures like tubular networks in given images can improve image-dependent diagnostics and can also facilitate tasks like segmentation. The two-dimensional (2D) orientation field transform has been proved to be effective at enhancing 2D contours and curves in images by means of top-down processing. It, however, has no counterpart in 3D images due to the extremely complicated orientation in 3D against 2D. Given the rising demand and interest in handling 3D images, we experiment with modularising the concept and generalise the algorithm to 3D curves. In this work, we propose a 3D orientation field transform. It is a vascular structure enhancement algorithm that can cleanly enhance images having very low signal-to-noise ratio, and push the limits of 3D image quality that can be enhanced computationally. This work also utilises the benefits of modularity and offers several combinative options that each yield moderately better enhancement results in different scenarios. In principle, the proposed 3D orientation field transform can naturally tackle any number of dimensions. As a special case, it is also ideal for 2D images, owning a simpler methodology compared to the previous 2D orientation field transform. The concise structure of the proposed 3D orientation field transform also allows it to be mixed with other enhancement algorithms, and as a preliminary filter to other tasks like segmentation and detection. The effectiveness of the proposed method is demonstrated with synthetic 3D images and real-world transmission electron microscopy tomograms ranging from 2D curve enhancement to, the more important and interesting, 3D ones. Extensive experiments and comparisons with existing related methods also demonstrate the excellent performance of the proposed 3D orientation field transform.


SPACE-TIME INTEGRATION APPROACH TO TRACKING WEAK TARGETS USING SPATIALLY DISTRIBUTED SENSORS

Abstract: This paper investigates the performance and robustness of the space-time integration technique by applying it to two different scenarios; a target moving in a straight line through a sonobuoy field; and a target undertaking a major course change. Although most techniques will track a target moving in a straight line quite successfully many have difficulty Packing the target as it manoeuvres through a slow rum. The performance of this approach is compared with the more traditional extended Kalman Filter algorithm where the advantages and dis-advantages of each technique is highlighted. Results are presented for scenarios using both simulated and sea trial data.


A novel multi-scale loss function for classification problems in machine learning

Abstract: We introduce two-scale loss functions for use in various gradient descent algorithms applied to classification problems via deep neural networks. This new method is generic in the sense that it can be applied to a wide range of machine learning architectures, from deep neural networks to support vector machines for example. These two-scale loss functions allow to focus the training onto objects in the training set which are not well classified. This leads to an increase in several measures of performance for appropriately-defined two-scale loss functions with respect to the more classical cross-entropy when tested on traditional deep neural networks on the MNIST, CIFAR10, and CIFAR100 data-sets.


Egocentric Image Captioning for Privacy-Preserved Passive Dietary Intake Monitoring

Abstract: Camera-based passive dietary intake monitoring is able to continuously capture the eating episodes of a subject, recording rich visual information, such as the type and volume of food being consumed, as well as the eating behaviors of the subject. However, there currently is no method that is able to incorporate these visual clues and provide a comprehensive context of dietary intake from passive recording (e.g., is the subject sharing food with others, what food the subject is eating, and how much food is left in the bowl). On the other hand, privacy is a major concern while egocentric wearable cameras are used for capturing. In this article, we propose a privacy-preserved secure solution (i.e., egocentric image captioning) for dietary assessment with passive monitoring, which unifies food recognition, volume estimation, and scene understanding. By converting images into rich text descriptions, nutritionists can assess individual dietary intake based on the captions instead of the original images, reducing the risk of privacy leakage from images. To this end, an egocentric dietary image captioning dataset has been built, which consists of in-the-wild images captured by head-worn and chest-worn cameras in field studies in Ghana. A novel transformer-based architecture is designed to caption egocentric dietary images. Comprehensive experiments have been conducted to evaluate the effectiveness and to justify the design of the proposed architecture for egocentric dietary image captioning. To the best of our knowledge, this is the first work that applies image captioning for dietary intake assessment in real-life settings.


Temporal segment dropout for human action video recognition

Abstract: Temporal information is important for human action video recognition. With the widely used spatio-temporal neural networks, researchers have found that the learned high-level features preserve overfitted spatial information and limited temporal information, leading to inferior performance. This is because existing networks lack efficient regularization for the temporal structure. To learn more robust temporal features, we propose a temporal regularization method named Temporal Segment Dropout (TSD). TSD drops the most salient spatial features in order to enhance the temporal features in a clip of temporal segments. Without learning from complex examples, TSD can be easily deployed in existing networks. In the experiment, TSD is extensively evaluated on benchmark action recognition datasets, which brings consistent improvements over the baselines, especially for the action-centric classes.


Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images

Abstract: Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical level are no longer suitable. New statistical approaches to model tumor shapes are in urgent need. In this article, we consider the problem of modeling a tumor boundary as a closed polygonal chain. A Bayesian landmark-based shape analysis model is proposed. The model partitions the polygonal chain into mutually exclusive segments, accounting for boundary roughness. Our Bayesian inference framework provides uncertainty estimations on both the number and locations of landmarks, while outputting metrics that can be used to quantify boundary roughness. The performance of our model is comparable with that of a recently developed landmark detection model for planar elastic curves. In a case study of 143 consecutive patients with stage I to IV lung cancer, we demonstrated the heterogeneity of tumor boundary roughness derived from our model effectively predicted patient prognosis (p-value <0.001). Supplementary materials for this article are available online.


An unfolding method based on conditional invertible neural networks (cINN) using iterative training

Abstract: The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the pp \to Z \gamma \gamma <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>p</mml:mi><mml:mi>p</mml:mi><mml:mo>→</mml:mo><mml:mi>Z</mml:mi><mml:mi>γ</mml:mi><mml:mi>γ</mml:mi></mml:mrow></mml:math> process.


Joint Multi-View Unsupervised Feature Selection and Graph Learning

Abstract: Despite significant progress, previous multi-view unsupervised feature selection methods mostly suffer from two limitations. First, they generally utilize <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either cluster structure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or similarity structure to guide the feature selection, which neglect the possibility of a joint formulation with mutual benefits. Second, they often learn the similarity structure by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">either global structure learning <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">or local structure learning, which lack the capability of graph learning with both global and local structural awareness. In light of this, this article presents a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j oint <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m ulti- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">v iew unsupervised <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f eature selection and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g raph learning (JMVFG) approach. Particularly, we formulate the multi-view feature selection with orthogonal decomposition, where each target matrix is decomposed into a view-specific basis matrix and a view-consistent cluster indicator. The cross-space locality preservation is incorporated to bridge the cluster structure learning in the projected space and the similarity learning (i.e., graph learning) in the original space. Further, a unified objective function is presented to enable the simultaneous learning of the cluster structure, the global and local similarity structures, and the multi-view consistency and inconsistency, upon which an alternating optimization algorithm is developed with theoretically proved convergence. Extensive experiments on a variety of real-world multi-view datasets demonstrate the superiority of our approach for both the multi-view feature selection and graph learning tasks. The code is available at https://github.com/huangdonghere/JMVFG.


CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting

Abstract: Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge’s improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.


Reducing redundancy in the bottleneck representation of autoencoders

Abstract: Autoencoders (AEs) are a type of unsupervised neural networks, which can be used to solve various tasks, e.g., dimensionality reduction, image compression, and image denoising. An AE has two goals: (i) compress the original input to a low-dimensional space at the bottleneck of the network topology using an encoder, (ii) reconstruct the input from the representation at the bottleneck using a decoder. Both encoder and decoder are optimized jointly by minimizing a distortion-based loss which implicitly forces the model to keep only the information in input data required to reconstruct them and to reduce redundancies. In this paper, we propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. To this end, we propose an additional loss term, based on the pairwise covariances of the network units, which complements the data reconstruction loss forcing the encoder to learn a more diverse and richer representation of the input. We tested our approach across different tasks, namely dimensionality reduction, image compression, and image denoising. Experimental results show that the proposed loss leads consistently to superior performance compared to using the standard AE loss.


The formation of innovative behavior values in new type high schools - national research universities

Abstract: Abstract An important task of the education system in modern market conditions is the preparation and retraining of personnel for innovative activity. Current methods of training specialists do not fully provide the solution of the problem, most of the management training given to the theoretical knowledge. It is necessary to organize context educational process with the participation of managers, owners of existing enterprises, startups and enterprises on the way of modernization of various sectors of the economy that will also help to practice the innovative and entrepreneurial skills. Strategic partnership of Kazan national research technological University and all-Russian public organization "Support of Russia" in the training of personnel for innovative activity in Russia creates a new resource - innovative knowledge, achievements and technologies of the future, creating conditions for the implementation of the national development strategy of the Russian Federation.


Fostering Interest and Knowledge in the Information Security Industry for K-12 Students Using Virtual Machines

Abstract: Abstract The field of information security is growing rapidly as malicious attacks to information systems become more frequent and detrimental to individuals and organizations who have become increasingly dependent upon these systems. With such a dramatic growth in demand in a short timeframe, there exists a shortage in the supply of professionals entering the field. In order to assist in the remediation of this gap, the Penn State Berks Information Sciences and Technology (IST) and Security Risk Analysis (SRA) programs are hosting an annual NSA and NSF sponsored summer camp as part of the GenCyber program. This camp is focused on critical thinking, problem-solving, and igniting interest in information security and other STEM fields through the application of hands-on activities in the security of a virtual machine environment. By implementing this program, Penn State Berks aims to show students the value of information security as early as reasonably possible, share the knowledge of safe practices and principles for using the Internet safely, and instill an interest in cybersecurity, as well as the STEM field as a whole. The evaluation of the program shows that the participants make significant progress towards achieving the learning outcomes of the summer camps.


Findings of the Pilot Offering of an Application Oriented Course (ENGR101)

Abstract: Students' inability to easily apply concepts of mathematics to engineering problems and applications is detrimental to their success in pursuing an engineering degree.It has a direct impact on the retention and graduation numbers in engineering colleges.In addition, high failure rate in first year mathematics courses is also hurting students' chances to make satisfactory progress towards their degree and ultimately graduate within even six years.In order to address these serious issues, in Fall of 2016, New Jersey Institute of Technology offered ENGR 101 -an application oriented course based on Wright State University model to engineering students placed in pre-calculus courses.Although only one year worth of data has been collected thus far and there is much to be studied and analyzed on the effectiveness of the course, we can already see that this experiment has produced encouraging results and students taking this course performed better in their pre-calculus courses compared to students who did not take ENGR 101.This paper will present the results of our analysis, including performance in the mathematics course while taking ENGR 101 simultaneously, and performance in the mathematics course in the subsequent semester.With the lessons learned from last year, a few additions and changes will be made for Fall 2017.


SO(2)-Equivariant Downwash Models for Close Proximity Flight

Abstract: Multirotors flying in close proximity induce aerodynamic wake effects on each other through propeller downwash. Conventional methods have fallen short of providing adequate 3D force-based models that can be incorporated into robust control paradigms for deploying dense formations. Thus, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning</i> a model for these downwash patterns presents an attractive solution. In this paper, we present a novel learning-based approach for modelling the downwash forces that exploits the latent geometries (i.e. symmetries) present in the problem. We demonstrate that when trained with only 5 minutes of real-world flight data, our geometry-aware model outperforms state-of-theart baseline models trained with more than 15 minutes of data. In dense real-world flights with two vehicles, deploying our model online improves 3D trajectory tracking by nearly 36% on average (and vertical tracking by 56%).


A small object detection algorithm based on feature interaction and guided learning

Abstract: At present, for the diverse target sizes, different angles, and complex environment in aerial images, how to detect small objects effectively in aerial images is still a challenge task. In order to further improve the detection accuracy and reduce the missing rate, in this paper, we proposes a small target detection algorithm based on feature interaction and guided learning. Firstly, the feature guided alignment module is designed to deal with the problem of information loss and overlap. Secondly, the misallocation of small object samples is optimized by introducing gaussian sample allocation strategy. Then the interactive parallel detection header is designed to solve the detection task conflict problem. Finally, the new regression loss function is designed to enhance the localization ability of the target. The experimental results on the Tinyperson dataset and Visdrone dataset show that this model can improve the detection accuracy and reduce the missing rate of small targets effectively.


Predicting Hypersonic Glide Vehicle Behavior With Stochastic Grammars

Abstract: Hypersonic glide vehicles are a new class of vehicles that fly at hypersonic speeds and have high maneuverability. These fast-moving targets exhibit different flight characteristics compared to conventional vehicles, so traditional tracking and defense systems require new methods to contend with them. In this paper, we propose a machine learning method for predicting the behavior of hypersonic glide vehicles. Our method is based on a stochastic grammar, which is a mathematical framework that describes the possible transition patterns of sequences. We use the stochastic grammar to predict the transition patterns in hypersonic glide vehicle trajectories. Given a partial trajectory, our method uses the grammar to predict the hypersonic glide vehicle's future kinematics, such as its altitude, velocity, and acceleration. We evaluate our method on two datasets of simulated hypersonic glide vehicle trajectories and show that it can successfully predict hypersonic glide vehicle behavior, even in the presence of noise. We also show that our method can predict several minutes into the future and can accurately predict future hypersonic glide vehicle behavior based on shorter observation times. Our results suggest that our method has the potential to be a valuable tool for predicting behavior of hypersonic glide vehicles.


Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Abstract: Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets, which is costly and time-consuming. In this paper, we demonstrate that the abundance of unlabeled images that can be extracted from Pap smear test whole slide images presents a fertile ground for self-supervised learning methods, yielding performance improvements compared to off-the-shelf pre-trained models for various downstream tasks. In particular, we propose Cervical Cell Copy-Pasting (C3P) as an effective augmentation method, which enables knowledge transfer from public and labeled single-cell datasets to unlabeled tiles. Not only does C3P outperforms naive transfer from single-cell images, but we also demonstrate its advantageous integration into multiple instance learning methods. Importantly, all our experiments are conducted on our introduced in-house dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our long-term objective of deep learning-assisted telecytology for diagnosis in low-resource settings.


Exploring the Learning Difficulty of Data: Theory and Measure

Abstract: ‘‘Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples refers to how easy/hard a sample is during a learning procedure. An increasing need of measuring learning difficulty demonstrates its importance in machine learning (e.g., difficulty-based weighting learning strategies). Previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, influential factors for learning difficulty are summarized. Under various situations conducted by summarized influential factors, correlations between learning difficulty and two vital criteria of machine learning, namely, generalization error and model complexity, are revealed. Second, a theoretical definition of learning difficulty is proposed on the basis of these two criteria. A practical measure of learning difficulty is proposed under the direction of the theoretical definition by importing the bias-variance trade-off theory. Subsequently, the rationality of theoretical definition and the practical measure are demonstrated, respectively, by analysis of several classical weighting methods and abundant experiments realized under all situations conducted by summarized influential factors. The mentioned weighting methods can be reasonably explained under the proposed theoretical definition and concerned propositions. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.


An Adaptive Variational Outlier-Robust Filter for Multisensor Distributed Fusion

Abstract: The fusion accuracy of distributed fusion algorithms may degrade in multi-sensor measurements with unknown noise and outliers. To tackle the problem, an improved adaptive feedback covariance intersection fusion algorithm based on the modified slide window variational outlier-robust adaptive Kalman filter is proposed. To estimate unknown noise covariance and eliminate outliers, a modified slide window variational outlier-robust adaptive Kalman filter is proposed. The algorithm devises a simplified slide-window variational adaptive filter according to the Student’s t distribution, which treats the Student’s t distribution as the approximation of the posterior distribution to eliminate the effect of outliers. The adaptive factor is introduced into this algorithm to realize the tradeoff between measurement and prediction. Moreover, an improved adaptive feedback covariance intersection fusion algorithm is developed with respect to multisensor information fusion with uncertain noise. The simulations verify that the improved fusion algorithm outperforms other existing filtering and fusion algorithms.


A study on a recommendation algorithm based on spectral clustering and GRU

Abstract: With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.


Contrastive image adaptation for acquisition shift reduction in medical imaging

Abstract: The domain shift, or acquisition shift in medical imaging, is responsible for potentially harmful differences between development and deployment conditions of medical image analysis techniques. There is a growing need in the community for advanced methods that could mitigate this issue better than conventional approaches. In this paper, we consider configurations in which we can expose a learning-based pixel level adaptor to a large variability of unlabeled images during its training, i.e. sufficient to span the acquisition shift expected during the training or testing of a downstream task model. We leverage the ability of convolutional architectures to efficiently learn domain-agnostic features and train a many-to-one unsupervised mapping between a source collection of heterogeneous images from multiple unknown domains subjected to the acquisition shift and a homogeneous subset of this source set of lower cardinality, potentially constituted of a single image. To this end, we propose a new cycle-free image-to-image architecture based on a combination of three loss functions : a contrastive PatchNCE loss, an adversarial loss and an edge preserving loss allowing for rich domain adaptation to the target image even under strong domain imbalance and low data regimes. Experiments support the interest of the proposed contrastive image adaptation approach for the regularization of downstream deep supervised segmentation and cross-modality synthesis models.


An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success

Abstract: In the planning of a software development project, managers must estimate the amount of effort needed for distinct phases of activity. A number of rules of thumb exist in the literature to help the program manager in this task. However, very little work has been done to validate these rules of thumb. Applying least square models and Hotelling’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T 2 test, we evaluate these rules of thumb against a large database of Department of Defense projects. We determine that variability limits the simple application of any such rule. However, there are some worthy of closer attention, and we recommend adjustments for improved application. We also determine that projects which give extra attention to early phases experience less schedule growth. These findings were robust across developmental process type, military service, and project size.


Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Abstract: Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.


Acknowledgment of Reviewers 2023

Abstract: Big DataAhead of Print Free AccessAcknowledgment of Reviewers 2023Published Online:19 Dec 2023https://doi.org/10.1089/big.2023.29063.ackAboutSectionsPDF/EPUB Permissions & CitationsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookXLinked InRedditEmail Critical evaluations of articles by expert reviewers make a vital contribution to ensuring the high quality of a journal's content. The editorial leadership of Big Data is very grateful for the support of the highly qualified peer reviewers who have dedicated their time and effort to reviewing our articles. We would like to show our appreciation by thanking the following individuals for their assistance with the review of articles for the journal in 2023.**Data as of November 30, 2023.GabrielAmeen Abdel HaiAwder Omar AbdulqadirShewa AbidAntonio AbreuMohamed Nafrees A.C.Mohamad AfendeeAmin AhtarAli AkbariOmid AkbarzadehAreen Al-BashirShahzad AliAyman AL-KhatibUdayakumar AllimuthuSaman AlmuftiLaila Al-QaisiMutaz AlshafeeyMohmmed AlshehriOmar A. AlzubiTari AnianTahira Anwar LashariShafique Ahmed AwanMohammad Ayoub KhanAbdullah Ayub KhanPadmavathy B.Santhosh Kumar B.Mohammed BalfaqihMohammadhossein BarkhordariPablo Basanta-ValElisavet BatziouKamal BerahmandZhaori BiSivaparthipan C.B.Vinoth Kumar C.N.SNelio CachoMassimo CafaroHaipeng CaiYao CaiJeffrey D. CammRichardo ChalmetaJyotir Moy ChatterjeeKalpana ChauhanJiahui ChenMin ChenMu-Yen ChenQianjing ChenNaganna ChettyDaban Kadhim Omar DabbaghWei DaiNripendra Narayan DasLuca DavoliAryan DeshwalVikram DhimanAlibeigi drHayder Abbas DrebeePaulo DuarteRameshwar DubeyFezzeh EbrahimiAdeniyi Abidemi EmmanuelMuhammad FaheemBawar Mohammed FarajMnara FarajDi FengRolyana FeriniaShibly FhaSimon FongYamila García MartínezR. GayathriStefan Cristian GherghinaJia GuoLigang HeShiyue HouSulaiman Mohammed IbrahimKonstantinos IoannidisMuhamad Deni JohansyahShahzad JumaniAwais Khan JumaniMaheswari K.Pradeeban KathiraveluFarshid KeyniaSara KhanBui Thanh KhoaSuraj KothawadeVeerandra Kumar RJing-Ming KuoJuan LaraZhouping LiChih-Chin LiangJerry Chun-Wei LinHuizhen LongAnand M.Golsa MahdaviYassine MalehOthmane MarfoqGiacomo MarziAristote MatondoFotios S. MilienosMazin MohammedJosé Moleiro MartinsMohammad Javad MoradiKamalakanta MuduliSarhad Faiq NamiqHelena NavasPhuong Mai NguyenMuhammad Nur FaizVaggelis OikonomouPunitha P.Beslin PajilaHao PanSachin PandeyAnastasios Papazoglou ChalikiasElnaz PashaeiMartin PavlovskiAjax PersaudJohan Hendri PrasetyoDia Zeglat PrinceShanjita Akter PromeGuanqiu QiYiyue QianLakshmana Kumar RMervin RSabitha RRenas Rajab AsaadHaroon RasheedAiswarya R.S.Asma S.Kirubakaran S.Stewart S.Siti Rohani S. RaihanZubaida SaidameenRamzi SaifanShoffan SaifullahYuta SaitoMuzafer SaracevicAsma SbeihFrancesco ScotognellaYuichi SeiMohamed Adel SerhaniMuhammad Khuram ShahzadXuefeng ShaoBassam ShargabiSunil Kumar SharmaSahil SidheekhNitin SinghHongwen SongChristos StergiouSukono SukonoTran Huu TamYanchao TanMingyue TangAbiy TasissaAdel ThaljaouiYijun TianChibueze Tobias OrjiHsin-Hsi TsaiArban UkaSaba UsnanPraveena V.Sakthivel V.Savitha V.Sathishkumar V.E.Renjith V. RaviWouter VerbekeChandru VigneshGabriel Villalobos-CamargoDing WangLi WangWenshuo WangMuhammad WaqasK. WatermanInes WilmsZhaojun YangKuo-Hui YehSun YiAndré ZanellaChunhui ZhangWeijia ZhangYufeng ZhangYiming ZhangLu-Tao ZhaoLeven J. ZhengBing ZhuFiguresReferencesRelatedDetails Volume 0Issue 0 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Acknowledgment of Reviewers 2023.Big Data.ahead of printhttp://doi.org/10.1089/big.2023.29063.ackOnline Ahead of Print:December 19, 2023PDF download


Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Abstract: Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging.However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis.To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm.To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans.For the real-world task, PPPML-HMI achieved ∼5% higher Dice score on average compared to conventional FL under the heterogeneous scenario.Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI.By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.


Examining the role of self-reported somatosensory sensations in body (dis)ownership: A scoping review and empirical study of patients with a disturbed sense of limb ownership

Abstract: Patients with a disturbed sense of limb ownership (DSO) offer a unique window of insight into the multisensory processes contributing to the sense of body ownership. A limited amount of past research has examined the role of sensory deficits in DSO, and even less is known regarding the role of patient self-reported somatosensory sensations in the pathogenesis of DSO. To address this lack of knowledge we first conducted a systematic scoping review following PRISMA-SR guidelines, examining current research into somatosensory deficits and patient self-reported somatosensory sensations in patients with DSO. Eighty studies, including 277 DSO patients, were identified. The assessment of sensory deficits was generally limited in scope and quality, and deficits in tactile sensitivity and proprioception were most frequently found. The reporting of somatosensory sensations was even less frequent, with instances of paraesthesia (pins-and-needles), stiffness/rigidity, numbness and warmth, coldness and heaviness amongst the deficits recorded. In a second part of the study, we sought to directly address the lack of evidence concerning the impact of patient self-reported somatosensory sensations in DSO by measuring DSO and self-reported somatosensory sensations in a large (n = 121) sample of right-hemisphere stroke patients including N = 65 with DSO and N = 56 hemiplegic controls. Results show that feelings of coldness and stiffness modulate DSO symptoms. Sense of heaviness and numbness are more frequent in patients with DSO but do not have a clear impact on disownership symptomology. Although preliminary, these results suggest a role of subjective sensations about the felt body in the sense of limb ownership.


Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning

Abstract: Although existing deep reinforcement learning-based approaches have achieved some success in image augmentation tasks, their effectiveness and adequacy for data augmentation in intelligent medical image analysis are still unsatisfactory. Therefore, we propose a novel Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for Automatic Data Augmentation (AutoAug) in intelligent medical image analysis. The improvements of ASDRL-AutoAug are two-fold: (i) To remedy the problem of some augmented images being invalid, we construct a more accurate reward function based on different variations of the augmentation trajectories. This reward function assesses the validity of each augmentation transformation more accurately by introducing different information about the validity of the augmented images. (ii) Then, to alleviate the problem of insufficient augmentation, we further propose a more intelligent automatic stopping mechanism (ASM). ASM feeds a stop signal to the agent automatically by judging the adequacy of image augmentation. This ensures that each transformation before stopping the augmentation can smoothly improve the model performance. Extensive experimental results on three medical image segmentation datasets show that (i) ASDRL-AutoAug greatly outperforms the state-of-the-art data augmentation methods in medical image segmentation tasks, (ii) the proposed improvements are both effective and essential for ASDRL-AutoAug to achieve superior performance, and the new reward evaluates the transformations more accurately than existing reward functions, and (iii) we also demonstrate that ASDRL-AutoAug is adaptive for different images in terms of sequence length, as well as generalizable across different segmentation models.


Enhancing patient education in cancer care: Intelligent cancer patient education model for effective communication

Abstract: The incidence of cancer is on the rise annually, whereas there exists a significant deficit of healthcare personnel. Inadequate communication between healthcare providers and patients may result in adverse emotional outcomes for the latter and interfere with their treatment progress. A viable solution to alleviate patient distress involves utilizing text generation models as an efficacious tool for delivering patient education.In this study, we proposed an intelligent cancer patient education model (ICPEM) based on the pre-trained T5 model. Meanwhile, we presented a new method for optimizing the model's comprehension of the patient's intent through simulating the inquiries that the patient may ask. The datasets used include a doctor and patient dialogue dataset and a cancer patient education scenario dataset. After prompt-tuning, the model is capable of educating patients through four major aspects including medical examination, health care, radiotherapy, chemotherapy.We conducted a comprehensive evaluation of the model by employing both automated and manual metrics. Our findings indicate that the responses generated by the model effectively catered to the requirements of patient education. Furthermore, our visualization analysis demonstrated the model's proficiency in processing sentences that are prone to confusion while maintaining a robust comprehension of human intent. Finally, several shortcomings of the model are presented, such as the inadequate amount of knowledge and the limited range of responses.We proposed a method for training and deploying medical language models in a low-resource environment. The proposed model facilitated the comprehension of cancer patients' intentions, resulting in suitable responses, which promotes effective patient-provider communication.


Low-power laser images enhancement via deep feature recovery for HDR 3D measurement

Abstract: Laser 3D measurement has gained widespread applications in industrial metrology . Still, it is usually limited by surfaces with high dynamic range (HDR) or the colorful surface texture of measured surfaces, such as metal and black industrial parts. Currently, conventional methods generally work with relatively strong-power laser intensities, which could potentially damage the sample or induce eye-safety concerns. For deep-learning-based methods, due to the different reflectivity of the measured surfaces, the HDR problem may require cumbersome adjustment of laser intensity in order to acquire enough training data. Even so, the problem of inaccurate ground truth may occur. To address these issues, this paper proposes the deep feature recovery (DFR) strategy to enhance low-light laser stripe images for achieving HDR 3D reconstruction with low cost, high robustness, and eye safety. To the best of our knowledge, this is the first attempt to tackle the challenge of high measurement costs associated with measuring HDR surfaces in laser 3D measurement. In learning the features of low-power laser images, the proposed strategy has a superior generalization ability and is insensitive to different low laser powers and variant surface reflectivity. To verify this point, we specially design the experiments by training the network merely using the diffusely reflective masks (DRM951) and testing the performance using diffusely reflective masks, metal surfaces, black industrial parts (contained in the constructed datasets DRO690, MO191, and BO107) and their hybrid scenes. Experimental results demonstrate that the proposed DFR strategy has good performances on robustness by testing different measurement scenes. For variously reflective surfaces, such as diffusely reflective surfaces, metal surfaces, and black parts surfaces, the reconstructed 3D shapes all have a similar quality to the reference method.


Weakly supervised image segmentation beyond tight bounding box annotations

Abstract: Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance greatly when supervised by loose bounding boxes. However, compared with loose bounding box, it is much more difficult to acquire tight bounding box due to its strict requirements on the precise locations of the four sides of the box. To resolve this issue, this study investigates whether it is possible to maintain good segmentation performance when loose bounding boxes are used as supervision. For this purpose, this work extends our previous parallel transformation based multiple instance learning (MIL) for tight bounding box supervision by integrating an MIL strategy based on polar transformation to assist image segmentation. The proposed polar transformation based MIL formulation works for both tight and loose bounding boxes, in which a positive bag is defined as pixels in a polar line of a bounding box with one endpoint located inside the object enclosed by the box and the other endpoint located at one of the four sides of the box. Moreover, a weighted smooth maximum approximation is introduced to incorporate the observation that pixels closer to the origin of the polar transformation are more likely to belong to the object in the box. The proposed approach was evaluated on two public datasets using dice coefficient when bounding boxes at different precision levels were considered in the experiments. The results demonstrate that the proposed approach achieves state-of-the-art performance for bounding boxes at all precision levels and is robust to mild and moderate errors in the loose bounding box annotations. The codes are available at https://github.com/wangjuan313/wsis-beyond-tightBB.


A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution

Abstract: Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on Malay-English, known as Manglish. Manglish speakers are primarily located in Malaysia, Indonesia, Brunei, and Singapore. As global adoption of second languages and social media usage increases, language code-switching, such as Spanglish and Chinglish, becomes more prevalent. In the case of Malay-English, this phenomenon is termed Manglish. To enhance the status of the Malay language and its transition out of the low-resource category, this unique text corpus, with binary annotations for biological gender and anonymized author identities is presented. This bi-annotated dataset offers valuable applications for various fields, including the investigation of cyberbullying, combating gender bias, and providing targeted recommendations for gender-specific products. This corpus can be used with either of the annotations or their composite. The dataset comprises of posts from 50 Malaysian public figures, equally split between biological males and females. The dataset contains a total of 709,012 raw X posts (formerly Twitter), with a relatively balanced distribution of 53.72% from biological female authors and 46.28% from biological male authors. Twitter API was used to scrape the posts. After pre-processing, the total posts reduced to 650,409 posts, widening the gap between the genders with the 56.88% for biological female and 43.12% for biological male. This dataset is a valuable resource for researchers in the field of Malay-English code-switching Natural Language Processing (NLP) and can be used to train or enhance existing and future Manglish language transformers.


Unsupervised topological analysis of polarized light microscopy: application to quantitative birefringence imaging

Abstract: The determination of birefringence (magnitude and axis orientation) of optical materials is of significant interest in various fields. In the case of composite samples, this task becomes complicated and time-consuming; therefore, a partially automated procedure for reconstructing birefringence spatial distribution becomes valuable. Herein, we propose a procedure to reconstruct the spatial distributions of the retardance and optical axis orientation in a geological thin section from sparse quantitative birefringence measurements, using automatic boundary detection on cross-polarized light microscopy images. We examine two particular areas on the selected geological thin section: one that presents a uniaxial crystal with a circular cross-section of its refractive index ellipsoid and the other with grains of varying orientations. The measurement gives the orientation of the grain’s optical axis both in and out of the plane of the thin section, which explains the qualitative observations with the cross-polarized light microscope. Future work will connect the measured orientation of the rock thin section with its 3D geological orientation in the field.


Focal plane coincidence method for a multi-view telecentric 3D imaging system

Abstract: Multi-view microscopic fringe projection systems, which use high-resolution telecentric lenses and the Scheimpflug condition, face challenges in coinciding focal planes accurately, resulting in inconsistent measurements between views. In this Letter, we developed a sharpness evaluation function based on the total power of the line-spread function, which was subsequently used to generate a full-field sharpness distribution map. Then we employed the correlation between the sharpness map and orientation of the focal plane to precisely coincide the focal planes. Experimental results validate the proposed method and demonstrate its improved consistency in 3D reconstruction.


Generative adversarial networks with attentional multimodal for human face synthesis

Abstract: Face synthesis and editing has increased cumulative consideration by the improvement of generative adversarial networks (GANs). The proposed attentional GAN-deep attentional multimodal similarity modal (AttnGAN-DAMSM) model focus on generating high-resolution images by removing discriminator components and generating realistic images from textual description. The attention model creates the attention map on the image and automatically retrieves the features to produce various sub-areas of the image. The DAMSM delivers fine-grained image-text identical loss to generative networks. This study, first describe text phrases and the model will generate a photorealistic high-resolution image composed of features with high accuracy. Next, model will fine-tune the selected features of face images and it will be left to the control of the user. The result shows that the proposed AttnGAN-DAMSM model delivers the performance metrics like structural similarity index measure (SSIM), feature similarity index measure (FSIM) and frechet inception distance (FID) using CelebA and CUHK face sketch (CUFS) dataset. For CelebFaces attribute (CelebA) dataset, the SSIM achieves 78.82% and for CUFS dataset, the SSIM achieves 81.45% which ensures accurate face synthesis and editing compared with existing methods such as GAN, SuperstarGAN and identity-sensitive GAN (IsGAN) models.


Robust Federated Learning for Heterogeneous Model and Data

Abstract: Data privacy and security is an essential challenge in medical clinical settings, where individual hospital has its own sensitive patients data. Due to recent advances in decentralized machine learning in Federated Learning (FL), each hospital has its own private data and learning models to collaborate with other trusted participating hospitals. Heterogeneous data and models among different hospitals raise major challenges in robust FL, such as gradient leakage, where participants can exploit model weights to infer data. Here, we proposed a robust FL method to efficiently tackle data and model heterogeneity, where we train our model using knowledge distillation and a novel weighted client confidence score on hematological cytomorphology data in clinical settings. In the knowledge distillation, each participant learns from other participants by a weighted confidence score so that knowledge from clean models is distributed other than the noisy clients possessing noisy data. Moreover, we use symmetric loss to reduce the negative impact of data heterogeneity and label diversity by reducing overfitting the model to noisy labels. In comparison to the current approaches, our proposed method performs the best, and this is the first demonstration of addressing both data and model heterogeneity in end-to-end FL that lays the foundation for robust FL in laboratories and clinical applications.


Strategies for secondary use of real-world clinical and administrative data for outcome ascertainment in pragmatic clinical trials

Abstract: Pragmatic trials are gaining popularity as a cost-effective way to examine treatment effectiveness and generate timely comparative evidence. Incorporating supplementary real-world data is recommended for robust outcome monitoring. However, detailed operational guidelines are needed to inform effective use and integration of heterogeneous databases. Lessons learned from the Veterans Affairs (VA) Diuretic Comparison Project (DCP) are reviewed, providing adaptable recommendations to capture clinical outcomes from real-world data. Non-cancer deaths and major cardiovascular (CV) outcomes were determined using VA, Medicare, and National Death Index (NDI) data. Multiple ascertainment strategies were applied, including claims-based algorithms, natural language processing, and systematic chart review. During a mean follow-up of 2.4 (SD = 1.4) years, 907 CV events were identified within the VA healthcare system. Slight delays (∼1 year) were expected in obtaining Medicare data. An additional 298 patients were found having a CV event outside of the VA in 2016 - 2021, increasing the CV event rate from 3.5 % to 5.7 % (770 of 13,523 randomized). NDI data required ∼2 years waiting period. Such inclusion did not increase the number of deaths identified (all 894 deaths were captured by VA data) but enhanced the accuracy in determining cause of death. Our experience supports the recommendation of integrating multiple data sources to improve clinical outcome ascertainment. While this approach is promising, hierarchical data aggregation is required when facing different acquisition timelines, information availability/completeness, coding practice, and system configurations. It may not be feasible to implement comparable applications and solutions to studies conducted under different constraints and practice. The recommendations provide guidance and possible action plans for researchers who are interested in applying cross-source data to ascertain all study outcomes.


Design methodology for catadioptric zoom panoramic optical systems based on image plane bending matched correction

Abstract: Zoom panoramic optical systems incorporate a panoramic peripheral vision zoom imaging function, enabling swift wide-area coarse and localized precise detection. These systems find applicability in examining pipes and other internal structures. This study centers on the catadioptric zoom panoramic optical system and its associated optical design method. A mathematical model of the front mirror was formulated based on the imaging relationship. Subsequently, the bending of the image plane of the front mirror was computed and simplified through curve fitting. The object surface bending of the zoom subsystem was established correspondingly, leveraging the solution for the mirror bending curve matched correction, culminating in deriving the initial structure of the zoom. Integrating the front mirror and zoom subsystems facilitated the comprehensive design realization of the catadioptric zoom panoramic optical system. A catadioptric zoom panoramic optical system was designed to validate the proposed design method. This study introduces a novel, to the best of our knowledge, conceptual approach to crafting catadioptric zoom panoramic optical systems.


Attention gate and dilation U-shaped network (GDUNet): an efficient breast ultrasound image segmentation network with multiscale information extraction

Abstract: In recent years, computer-aided diagnosis (CAD) systems have played an important role in breast cancer screening and diagnosis. The image segmentation task is the key step in a CAD system for the rapid identification of lesions. Therefore, an efficient breast image segmentation network is necessary for improving the diagnostic accuracy in breast cancer screening. However, due to the characteristics of blurred boundaries, low contrast, and speckle noise in breast ultrasound images, breast lesion segmentation is challenging. In addition, many of the proposed breast tumor segmentation networks are too complex to be applied in practice.We developed the attention gate and dilation U-shaped network (GDUNet), a lightweight, breast lesion segmentation model. This model improves the inverted bottleneck, integrating it with tokenized multilayer perceptron (MLP) to construct the encoder. Additionally, we introduce the lightweight attention gate (AG) within the skip connection, which effectively filters noise in low-level semantic information across spatial and channel dimensions, thus attenuating irrelevant features. To further improve performance, we innovated the AG dilation (AGDT) block and embedded it between the encoder and decoder in order to capture critical multiscale contextual information.We conducted experiments on two breast cancer datasets. The experiment's results show that compared to UNet, GDUNet could reduce the number of parameters by 10 times and the computational complexity by 58 times while providing a double of the inference speed. Moreover, the GDUNet achieved a better segmentation performance than did the state-of-the-art medical image segmentation architecture.Our proposed GDUNet method can achieve advanced segmentation performance on different breast ultrasound image datasets with high efficiency.


Phase measuring deflectometry for convex aspheric surface measurement

Abstract: This paper introduces what we believe to be a novel approach to accurately measure the shape of convex aspherical surfaces with large slope gradients. This approach employs a pre-distortion system to enhance the visibility of the structured light pattern that is captured by camera. The data processing involves iterative methods to obtain surface shape data. The initial step in the experimental calibration involves establishing a reference plane, which serves as the starting point for the iterative process. The calculation for slope is subsequently utilized to determine the initial slope of the surface under test, and the height of the tested element is derived by integrating these slopes. Through multiple iterations and continuous updating of the surface height, the precise and authentic true surface height is ultimately achieved. The method’s accuracy is assessed through the measurement of a highly steep convex aspherical area with a diameter of 5.2 mm and a radius of curvature of approximately 7.7 mm. The proposed method demonstrates root mean square accuracy that can reach half a wavelength when compared to the measurement results obtained from high-precision profilers.


Author identification of literary works based on text analysis and deep learning

Abstract:
Abstract
 With the development of science, speech, picture, and other analysis, problems have been gradually better solved, but the study of Chinese text has been a complex problem to overcome. Chinese text analysis requires not only statistics but also semantic comprehension analysis. Different text types need other language style feature modeling to obtain good recognition results. In this study, we use the deep learning method to construct an automatic text feature extraction model and classify it with the author as a classification label. This study presents a literature author recognition model based on deep learning, which is mainly divided into three phases: text preprocessing, feature extraction, and classification. Each part consists of several small modules or steps. First, we input the corpus to Word2Vec to generate the new word vector. Then, the improved text feature extractor based on CNN and Attention extracts the text features and uses them as the input of the CNN convolution layer. After convolution, the text is combined with bits to get Window Feature Sequence. It is the text feature vector. Next, based on LSTM and Softmax classification output, Window Feature Sequence is used as the input of LSTM to obtain two one-dimensional vectors spliced by concatenate layer. Finally, the result is classified through the fully connected layer, Batch Normalization layer, and Softmax. The performance of the proposed model in recognizing authors of Chinese literature was evaluated using two datasets. In the research process, the data we collected included works of different forms, such as prose and fiction. The research results show that the proposed model can effectively identify author identity. The classification accuracy of our proposed algorithm is significantly better than that of the benchmark model.


Optimization of track and field training methods based on SSA-BP and its effect on athletes' explosive power

Abstract: Digitalization and informationization are important trends in the development of the sports industry. The study first introduced the sparrow search algorithm to improve the generalization ability of traditional neural networks, optimizing the assignment of initial weights and thresholds of neural networks; Secondly, the chicken swarm algorithm is introduced to optimize the training combination, period, and intensity of athletes based on the evaluation results, improving the subjective limitations of traditional training methods. The results of model performance analysis show that the sparrow search algorithm is better than other intelligent optimization algorithms in finding fitted parameters, and the solution error is less than 0.50. The evaluation model performs well in terms of accuracy, recall, average relative error, and R2 evaluation indicators. The model has high repeatability and is suitable for evaluating track and field training methods. The accuracy and computational speed of the chicken swarm algorithm are relatively good; Compared with other optimization models, the chicken swarm algorithm has better optimization ability and accuracy. Friedman test found significant differences in the chicken swarm algorithm, and the optimized training method has a significant positive impact on the explosive power of athletes, and the training period and intensity arrangement are reasonable and more helpful to the improvement of athletic performance. This study improves the scientific rationality of the development of track and field training methods, which is conducive to optimizing the training effect of track and field sports, and facilitates the risk management and personalized training of athletes. At the same time, it greatly promotes the integration and development of sports and computer disciplines.


Two-particle Hadamard walk on dynamically percolated line and circle

Abstract: Abstract Asymptotic dynamics of a Hadamard walk of two non-interacting quantum particles on a dynamically percolated finite line or a circle is investigated. We construct a basis of the attractor space of the corresponding random-unitary dynamics and prove the completeness of our solution. In comparison to the one-particle case, the structure of the attractor space is much more complex, resulting in intriguing asymptotic dynamics. General results are illustrated on two examples. First, for circles of length not divisible by 4 the boundary conditions reduces the number of attractors considerably, allowing for fully analytic solution. Second, we investigate line of length 4 and determine the asymptotic cycle of reduced coin states and position distributions, focusing on the correlations between the two particles. Our results show that a random unitary evolution, which is a combination of quantum dynamics and a classical stochasticity, leads to correlations between initially uncorrelated particles. This is not possible for purely unitary evolution of non-interacting quantum particles. The shared dynamically percolated graph can thus be considered as a weak form of interaction.


PPML-Omics: A privacy-preserving federated machine learning method protects patients’ privacy in omic data

Abstract: Modern machine learning models toward various tasks with omic data analysis give rise to threats of privacy leakage of patients involved in those datasets. Here, we proposed a secure and privacy-preserving machine learning method (PPML-Omics) by designing a decentralized differential private federated learning algorithm. We applied PPML-Omics to analyze data from three sequencing technologies and addressed the privacy concern in three major tasks of omic data under three representative deep learning models. We examined privacy breaches in depth through privacy attack experiments and demonstrated that PPML-Omics could protect patients' privacy. In each of these applications, PPML-Omics was able to outperform methods of comparison under the same level of privacy guarantee, demonstrating the versatility of the method in simultaneously balancing the privacy-preserving capability and utility in omic data analysis. Furthermore, we gave the theoretical proof of the privacy-preserving capability of PPML-Omics, suggesting the first mathematically guaranteed method with robust and generalizable empirical performance in protecting patients' privacy in omic data.


Digital teaching: Developing a structured digital teaching competency framework for medical teachers

Abstract: To explore the core competencies needed on part of the medical and dental teachers to carry out effective digital teaching for their students.It was a qualitative study which employed phenomenological approach. The data were collected from 12 teachers who were purposively selected for semi-structured interviews. They were all expert digital teachers. The data were transcribed verbatim, coded and analyzed thematically. Textural and structural description of the themes helped to develop a new competency framework.The data yielded 47 selective codes with 15 sub-themes and five themes. The emergent themes included general digital competencies, specific digital teaching competencies, mastery of the subject matter, mastery of pedagogical strategies and proficiency in using innovative digital technologies for teaching. The themes 1, 2 and 5 relate to digital competencies only whereas the themes 3 and 4 are generic competencies which apply to both digital and non-digital teaching. These generic competencies form the basis of all kinds of teaching, hence equally important for digital teaching.Medical teachers should possess diverse digital competencies. The competency framework that emerged in the current research encompasses the essential attributes that should be included in any future training program aiming at the digital capacity building of the teachers. This will keep them primed for effective digital teaching. Given its crucial importance, the digital teaching competency should be considered as a cross-cutting competency that applies to almost all of the famous eight roles of medical teacher.


Dilemma and coping strategies of news communication based on artificial intelligence and big data

Abstract: News dissemination is an important way for people to obtain information. With the development of new technologies, traditional news dissemination models have been impacted. It has problems with information filtering and bias, and has certain limitations in news quality, dissemination efficiency, etc., which makes it difficult to effectively meet people's information needs. In order to improve the quality and efficiency of news dissemination, promote the positive impact of news dissemination on society, this article combined artificial intelligence and big data technology to conduct in-depth research on the difficulties and coping strategies of news dissemination. This article first analyzed the characteristics and functions and influencing factors of news dissemination, then provided an overview of the difficulties and coping strategies in news dissemination. Finally, using association rule algorithms, personalized recommendations for news dissemination are achieved. To verify the effectiveness of artificial intelligence and big data in coping with the dilemma of news dissemination, this article conducted experimental analysis from the perspectives of news content quality, dissemination efficiency, objectivity, and dissemination cost. The experimental results show that under the application of news dissemination strategies based on artificial intelligence and big data, the quality of news content and dissemination efficiency have been improved by 4.76 % and 3.63 %, respectively. The conclusion indicates that artificial intelligence and big data can help improve the quality and dissemination efficiency of news content, and meet the diverse needs of the public for information.


Performance of machine translators in translating French medical research abstracts to English: A comparative study of DeepL, Google Translate, and CUBBITT

Abstract: Background Non-English speaking researchers may find it difficult to write articles in English and may be tempted to use machine translators (MTs) to facilitate their task. We compared the performance of DeepL , Google Translate , and CUBBITT for the translation of abstracts from French to English. Methods We selected ten abstracts published in 2021 in two high-impact bilingual medical journals ( CMAJ and Canadian Family Physician ) and used nine metrics of Recall-Oriented Understudy for Gisting Evaluation ( ROUGE-1 recall/precision/F1-score , ROUGE-2 recall/precision/F1-score , and ROUGE-L recall/precision/F1-score ) to evaluate the accuracy of the translation (scores ranging from zero to one [= maximum]). We also used the fluency score assigned by ten raters to evaluate the stylistic quality of the translation (ranging from ten [= incomprehensible] to fifty [= flawless English]). We used Kruskal-Wallis tests to compare the medians between the three MTs. For the human evaluation, we also examined the original English text. Results Differences in medians were not statistically significant for the nine metrics of ROUGE (medians: min-max = 0.5246–0.7392 for DeepL , 0.4634–0.7200 for Google Translate , 0.4815–0.7316 for CUBBITT , all p-values > 0.10). For the human evaluation, CUBBITT tended to score higher than DeepL , Google Translate , and the original English text (median = 43 for CUBBITT , vs. 39, 38, and 40, respectively, p-value = 0.003). Conclusion The three MTs performed similarly when tested with ROUGE , but CUBBITT was slightly better than the other two using human evaluation. Although we only included abstracts and did not evaluate the time required for post-editing, we believe that French-speaking researchers could use DeepL , Google Translate , or CUBBITT when writing articles in English.


Visualization Method of Pathophysiology Pathway in Nursing Diagnosis Learning

Abstract: Setiawan, Henri MSiMed, RN; Hidayat, Nur MM; Richard, Selvia David MN, RN Author Information