Reviewerly/exHarmony huggingface dataset:
Long-Tail Entity Alignment

Abstract Most entity alignment solutions currently rely on structural information, specifically KG embedding, to align entities. However, in real-life KGs, the majority of entities have a sparse neighborhood structure, while only a few entities are densely connected to others. These less-connected entities are referred to as long-tail entities , and this phenomenon limits the effectiveness of using structural information for entity alignment. To address this issue, we propose an approach that incorporates entity name information, which is often overlooked but readily available. We amplify the weak structural information of long-tail entities with concatenated power mean word embeddings of their names during pre-alignment. To align entities, we introduce a novel complementary framework that combines both structural and name signals. It uses the entity’s degree as a guide to fuse the two sources of information effectively and proposes a degree-aware co-attention network that dynamically adjusts the significance of features in a degree-aware manner. Finally, we propose using confident entity alignment results as anchors to complement original KGs with facts from their counterparts via iterative training during post-alignment. Experimental evaluations show the effectiveness of the proposed techniques.
Weakly Supervised Entity Alignment

Abstract The majority of state-of-the-art entity alignment solutions heavily rely on the labeled data, which are difficult to obtain in practice. Therefore, it calls for the study of EA with scarce supervision . To resolve this issue, we put forward a reinforced active entity alignment framework to select the entities to be manually labeled with the aim of enhancing alignment performance with minimal labeling efforts. Under this framework, we further devise an unsupervised contrastive loss to contrast different views of entity representations and augment the limited supervision signals by exploiting the vast unlabeled data. We empirically evaluate our proposal on eight popular KG pairs, and the results demonstrate that our proposed model and its components consistently boost the alignment performance under scarce supervision.
Processo de Construção e Elaboração de Vídeos Educativos sobre Meditação Adaptada da Mente Plena (Mindfulness) para a Comunidade como Estratégia para o Enfrentamento da Pandemia da COVID-19

For two years, we experienced traumatic moments due to the COVID-19 pandemic, and several psycho-emotional symptoms, such as anxiety, stress and depression, intensified during this period, especially due to the social isolation established by the higher authorities for control and disease prevention. However, seeking forms of intervention to help in the decline of these cases was a major challenge for the Unified Health System (SUS), and the Integrative and Complementary Practices in Health (PICS) were an excellent health promotion and education strategy to alleviate these symptoms. Therefore, this article aimed to describe all the operational planning for the elaboration of educational videos on the adapted mindfulness meditation as an educational practice to improve the quality of life and health of community leaders (CL), partners of the Plataforma project of Knowledge (PPS), and their peers, during the period of the new coronavirus pandemic.
State-of-the-Art Approaches

Abstract This chapter performs a thorough assessment and meticulous examination of the most advanced EA techniques. Initially, we introduce a broad EA framework that covers all current methods and classify these methods into three main groups. Then, we carefully appraise these solutions on various scenarios, taking into account their efficacy, efficiency, and scalability. Lastly, we create a novel EA dataset that reflects the actual difficulties encountered in alignment, which prior literature mostly ignored. This chapter aims to offer a comprehensive understanding of the advantages and drawbacks of current EA methods, in order to encourage further high-quality research.
The effects of information sharing and logistics practices on supply chain performances

This study aimed to investigate the effect of information sharing and logistics practices on the supply chain performances of firms. To achieve the objective, the study targeted the supply chain practices of some companies operating in Ethiopia. Data were collected from 452 respondents including employees, suppliers, and distributors of the companies under study. The specified objective and proposed hypotheses in this study were tested by structural equation modelling (SEM). The result shows that the higher information sharing the better logistic management, which in turn leads to a greater supply chain performance of firms. The study concludes that information sharing has both direct and indirect effects on the supply chain performances, whereas logistics practices have a direct effect on the supply chain performance. Generally, the results theoretically and practically allow the companies and industries to recognise the significant effects of information sharing and logistics practices.
Computer modeling - a new mechanism of discoveries in natural science

The paper notes that, on the one hand, the scientific methodology has been enriched with new, previously unknown tools - a personal computer, and on the other hand - all the previous processes of calculations, numerical simulation have been significantly accelerated by orders of magnitude. And, in addition, it was found that due to the permanent interactive communication with a personal computer connected to the world wide web, researchers have new opportunities to search for new effects and phenomena. A new reality of scientific research has emerged - “man-computer-network”. Below are new phenomena discovered exclusively through computer modeling.
Image Composition Method Based on a Spatial Position Analysis Network

Realistic image composition aims to composite new images by fusing a source object into a target image. It is a challenging problem due to the complex multi-task framework, including sensible object placement, appearance consistency, shadow generation, etc. Most existing researchers attempt to address one of the issues. Especially before compositing, there is no matching assignment between the source object and target image, which often leads to unreasonable results. To address the issues above, we consider image composition as an image generation problem and propose a deep adversarial learning network via spatial position analysis. We target the analysis network segment and classify the objects in target images. A spatial alignment network matches the segmented objects with the source objects, and predicts a sensible placement position, and an adversarial network generates a realistic composite image with the shadow and reflection of the source object. Furthermore, we use the classification information of target objects to filter out unreasonable image compositing. Moreover, we introduce a new test set to evaluate the network generalization for our multi-task image composition dataset. Extensive experimental results of the SHU (Shanghai University) dataset demonstrate that our deep spatial position analysis network remarkably enhances the compositing performance in realistic, shadow, and reflection generations.
Exploring the relationship between digital competence proficiency and student learning performance

Abstract The increased use of digital technology in higher education requires university students to be digitally proficient. The main objective of this study is to examine the extent to which university students' proficiency in various digital competences could be helpful towards their learning performance. This study adopted DigComp 2.2 to design a data collection instrument for an online questionnaire survey and conducted data analysis on the valid 314 responses using partial least squares structural equation modelling (PLS‐SEM). The findings indicate that, with the exception of safety, the digital competence areas of information and data literacy, communication and collaboration, digital content creation, and problem solving were significant to student learning performance. The findings also revealed that students' current semester had no significant moderating effect on the relationships between their proficiency in digital competence areas and learning performance, with the exception of a relationship between information and data literacy and learning performance, as well as a relationship between problem solving and learning performance, between respondents in semesters 3 and 4, and between respondents in semesters 5 and 6, respectively. The findings contribute to a better understanding of DigComp 2.2's applicability in higher education and can be used by educational institutions and educators to design learning activities and formative assessments that can help students improve their digital competence proficiency.
Design and application of secondary operation and maintenance supervision system based on AR modeling and indoor positioning

In order to facilitate the observation in the process of secondary equipment operation and maintenance supervision and the detection and tracking of operation and maintenance personnel, a secondary operation and maintenance supervision system based on AR modeling and indoor positioning is designed. The whole system is divided into seven levels and a unified information base, in which the basic level contains all kinds of secondary equipment; AR modeling layer uses augmented reality technology to create models for each secondary equipment in the basic layer, and determines the equipment position information based on ranging positioning technology; The data acquisition layer collects all kinds of original management data based on the constructed secondary equipment model; The data analysis layer reads and analyzes the information of the data acquisition layer through the data bus; The process support layer provides task scheduling support for the integrated management application based on the data analysis results; The integrated application layer uniformly monitors the secondary equipment based on the task scheduling results; The presentation layer is responsible for the interface presentation of all operation and maintenance and security management information of the system, and the unified information base provides data support for the whole system. The experimental results show that the secondary equipment model in the designed system has high definition, can obtain more image details, can realize the 3D display and real-time interaction of the secondary equipment operation and maintenance supervision results, and accurately mark the target and track for the staff.
Violence against women on Twitter in India: Testing a taxonomy for online misogyny and measuring its prevalence during COVID-19

Online misogyny is a violation of women's digital rights. Empirical studies on this topic are however lacking, particularly in low- and middle- income countries. The current study aimed to estimate whether prevalence of online misogyny on Twitter in India changed since the pandemic.Based on prior theoretical work, we defined online misogyny as consisting of six overlapping forms: sexist abuses, sexual objectification, threatening to physically or sexually harm women, asserting women's inferiority, justifying violence against women, and dismissing feminist efforts. Qualitative analysis of a small subset of tweets posted from India (40,672 tweets) substantiated this definition and taxonomy for online misogyny. Supervised machine learning models were used to predict the status of misogyny across a corpus of 30 million tweets posted from India between 2018 and 2021. Next, interrupted time series analysis examined changes in online misogyny prevalence, before and during COVID-19.Qualitative assessment showed that online misogyny in India existed most in the form of sexual objectification and sexist abusive content, which demeans women and shames them for their presumed sexual activity. Around 2% of overall tweets posted from India between 2018 and 2021 included some form of misogynistic content. The absolute volume as well as proportion of misogynistic tweets showed significant increasing trends after the onset of COVID-19, relative to trends prior to the pandemic.Findings highlight increasing gender inequalities on Twitter since the pandemic. Aggressive and hateful tweets that target women attempt to reinforce traditional gender norms, especially those relating to idealized sexual behavior and framing of women as sexual beings. There is an urgent need for future research and development of interventions to make digital spaces gender equitable and welcoming to women.
How are exclusively data journals indexed in major scholarly databases? An examination of four databases

The data paper is becoming a popular way for researchers to publish their research data. The growing numbers of data papers and journals hosting them have made them an important data source for understanding how research data is published and reused. One barrier to this research agenda is a lack of knowledge as to how data journals and their publications are indexed in the scholarly databases used for quantitative analysis. To address this gap, this study examines how a list of 18 exclusively data journals (i.e., journals that primarily accept data papers) are indexed in four popular scholarly databases: the Web of Science, Scopus, Dimensions, and OpenAlex. We investigate how comprehensively these databases cover the selected data journals and, in particular, how they present the document type information of data papers. We find that the coverage of data papers, as well as their document type information, is highly inconsistent across databases, which creates major challenges for future efforts to study them quantitatively, which should be addressed in the future.
A machine learning approach to graduate admissions and the role of letters of recommendation

The graduate admissions process is time-consuming, subjective, and complicated by the need to combine information from diverse data sources. Letters of recommendation (LORs) are particularly difficult to evaluate and it is unclear how much impact they have on admissions decisions. This study addresses these concerns by building machine learning models to predict admissions decisions for two STEM graduate programs, with a focus on examining the contribution of LORs in the decision-making process. We train our predictive models leveraging information extracted from structured application forms (e.g., undergraduate GPA, standardized test scores, etc.), applicants' resumes, and LORs. A particular challenge in our study is the different modalities of application data (i.e., text vs. structured forms). To address this issue, we converted the textual LORs into features using a commercial natural language processing product and a manual rating process that we developed. By analyzing the predictive performance of the models using different subsets of features, we show that LORs alone provide only modest, but useful, predictive signals to admission decisions; the best model for predicting admissions decisions utilized both LOR and non-LOR data and achieved 89% accuracy. Our experiments demonstrate promising results in the utility of automated systems for assisting with graduate admission decisions. The findings confirm the value of LORs and the effectiveness of our feature engineering methods from LOR text. This study also assesses the significance of individual features using the SHAP method, thereby providing insight into key factors affecting graduate admission decisions.
Performance enhancement of high order Hahn polynomials using multithreading

Orthogonal polynomials and their moments have significant role in image processing and computer vision field. One of the polynomials is discrete Hahn polynomials (DHaPs), which are used for compression, and feature extraction. However, when the moment order becomes high, they suffer from numerical instability. This paper proposes a fast approach for computing the high orders DHaPs. This work takes advantage of the multithread for the calculation of Hahn polynomials coefficients. To take advantage of the available processing capabilities, independent calculations are divided among threads. The research provides a distribution method to achieve a more balanced processing burden among the threads. The proposed methods are tested for various values of DHaPs parameters, sizes, and different values of threads. In comparison to the unthreaded situation, the results demonstrate an improvement in the processing time which increases as the polynomial size increases, reaching its maximum of 5.8 in the case of polynomial size and order of 8000 × 8000 (matrix size). Furthermore, the trend of continuously raising the number of threads to enhance performance is inconsistent and becomes invalid at some point when the performance improvement falls below the maximum. The number of threads that achieve the highest improvement differs according to the size, being in the range of 8 to 16 threads in 1000 × 1000 matrix size, whereas at 8000 × 8000 case it ranges from 32 to 160 threads.
Features of digital signal processing algorithms using Galois fields GF(2n+1)

An alternating representation of integers in binary form is proposed, in which the numbers -1 and +1 are used instead of zeros and ones. It is shown that such a representation creates considerable convenience for multiplication numbers modulo p = 2n+1. For such numbers, it is possible to implement a multiplication algorithm modulo p, similar to the multiplication algorithm modulo the Mersenne number. It is shown that for such numbers a simple algorithm for digital logarithm calculations may be proposed. This algorithm allows, among other things, to reduce the multiplication operation modulo a prime number p = 2n+1 to an addition operation.
A Transformer-based Approach for Dynamic Referee Assistance

Deep learning-based algorithms have shown great potential in video analysis tasks such as action recognition and event detection. The detection of football events in videos is a growing field that has many benefits, including enhancing the viewing experience for fans and enabling teams to analyze player behavior. In this paper, we present a novel deep learning-based algorithm for accurately and efficiently identifying football events in match videos. Our approach utilizes a combination of NetVLAD++ pooling, early feature-level fused transformer features, and a dynamic hybrid loss function. To evaluate the proposed framework, we create a dataset of 200 football games covering 17 events for training and testing. Through empirical evaluation, we demonstrate the effectiveness of our approach in spotting football events. Our proposed framework has achieved an average mAP of 14.16%, outperforming baseline methods by 11.26%. This breakthrough has significant implications for the ongoing Action Spotting 2023 Challenge and has the potential to revolutionize how football events are detected, analyzed, and acted upon, providing valuable insights to coaches and players.
Real Time Attendance Capturing through Face Recognition

This application proposes the implementation of face recognition technology to enhance classroom attendance tracking. It involves two main steps: First is Face detection and the other is Face recognition. In face detection, the system identifies the locations of faces within classroom images and extracts face sub-images. The faces detected are then processed with an existing database of student images for recognition, leading to the recording of attendance. Unlike conventional methods, the model utilizes real-time video for attendance tracking, making it more efficient and accurate. The main objective of this approach is to speed up the process of taking attendance even when used in a crowded area in real-time unlike other existing systems. The Deep Learning Model employs machine learning tools like OpenCV, Dlib, and CNN to achieve its goals. This real-time, technology-driven approach aims to modernize attendance recording in educational settings.
Deep Learning with Improved Gravitational Search Optimization for Remote Sensing Image Classification

In earth observation technology using remote sensing (RS) data, RS image classification plays a significant role and has been extensively used in many domains. However, RS image classification presents substantial scientific and practical challenges because of the properties of RS data, such as high dimensionality and a need for more readily available labeled samples. Deep learning (DL) methods for RS image classification have advanced significantly in recent years as new DL approaches appear, opening up new opportunities for the study and growth of RS image classification for agricultural applications. The classification of RS images using an Improved Gravitational Search Optimization (IGSO) algorithm is discussed in this study. The field of organic carrots was collected in 60 top-down images by an autonomous vehicle and expertly labeled as part of the dataset for this study. Also provided is a comparison of the effectiveness of common DL-based RS approaches. Using 60 training images, IGSO outperforms existing methods by recognizing weeds with an accuracy of 98.6%. The challenges and prospective investigation paths are finally presented.
Application of CG Rendering Algorithm in Automatic Generation Framework of Bamboo and Wood Furniture Images

Image rendering mainly replaces the three-dimensional geometric objects in the image with a flat signboard and at the same time pastes the real-time image of the object onto the signboard as a texture. This algorithm and principle can quickly render and generate a surrogate for a real-time image with very little computational effort. But when the viewing angle of the real-time image changes, if the substitute cannot change in time according to the change in viewing angle, an obvious parallax effect will occur. So to get better rendering, the proxy needs to be updated as the viewing angle changes. In this study, the novel CG rendering algorithm in automatic generation framework of the bamboo and wood furniture images is proposed. The highlights are: 1. efficient image feature extraction algorithms are reviewed and the proper method is selected for the target scheme of CG rendering algorithm. 2. novel image generation framework is proposed. 3. novel CG rendering algorithm is proposed and the bamboo and the wood furniture images are selected as the experimental examples to validate the proposed model.
Hybrid U-Net and ADAM Algorithm for 3DCT Liver Segmentation

Segmentation of images is a computer procedure that involves dividing an image into separate and distinct regions of pixels. This method is often accomplished by using filters or labeling. The U-net architecture, which is based on convolutional neural networks (CNNs), is widely used in the domain of segmentation of images. Nevertheless, a notable limitation of the U-net’s design is in its diminished potential for learning in the more complex aspects of the model. Instead of using the stochastic gradient descent (SGD) method, researchers have proposed an alternative optimization technique called Adam to instruct deep learning models. Adam’s method successfully utilizes the benefits provided by the Momentum and RMS Prop algorithms, allowing it to efficiently manage gradients with low density in situations with high levels of noise. The U-net design may be enhanced with the integration of the Adam algorithm.