Reviewerly/exHarmony huggingface dataset:
Rapid calibration method for head-mounted eye-tracker

Authors:
raw_author_name: Pengxiang XUE
raw_affiliation_string: Xi’an Technological University (China)
raw_author_name: Changyuan Wang
raw_affiliation_string: Xi’an Technological University (China)
raw_author_name: Guangyi Jiang
raw_affiliation_string: Xi’an Aeronautical Institute (China)
raw_author_name: Gongpu Wu
raw_affiliation_string: Xi’an Technological University (China)

Abstract: Head-mounted eye-tracking instruments are important devices for sports and human factors research, and their calibration is a more tedious task due to the varied morphology of the human head as well as wear differences. In order to improve the calibration efficiency, we innovatively proposed a Z-shaped moving target instead of a fixed target in our self-research for the calibration of the wearable eye-tracking device, which improved the time needed for calibration from 100 seconds to less than 30 seconds. In addition, in order to reduce the calibration error caused by the user's head movement during the calibration, the world camera image is feature-matched, and the virtual optic target is computed instead of the recognized optic target, thus eliminating the effect of head movement. In the calibration experiments of nine graduate students, the calibration accuracy was less than 20 pixels, and the average calibration time was about 24 seconds.
A Novel Algorithm for Multi-Criteria Ontology Merging through Iterative Update of RDF Graph

Authors:
raw_author_name: Mohammed Rudwan
raw_affiliation_string: School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa
raw_author_name: Jean Fonou-Dombeu
raw_affiliation_string: School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa

Abstract: Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies remains challenging. Consequently, existing ontology-merging solutions produce new ontologies that do not include all the relevant semantic features from the candidate ontologies. To address these limitations, this paper proposes a novel algorithm for multi-criteria ontology merging that automatically builds a new ontology from candidate ontologies by iteratively updating an RDF graph in the memory. The proposed algorithm leverages state-of-the-art Natural Language Processing tools as well as a Machine Learning-based framework to assess the similarities and merge various criteria into the resulting output ontology. The key contribution of the proposed algorithm lies in its ability to merge relevant features from the candidate ontologies to build a more accurate, integrated and cohesive output ontology. The proposed algorithm is tested with five ontologies of different computing domains and evaluated in terms of its asymptotic behavior, quality and computational performance. The experimental results indicate that the proposed algorithm produces output ontologies that meet the integrity, accuracy and cohesion quality criteria better than related studies. This performance demonstrates the effectiveness and superior capabilities of the proposed algorithm. Furthermore, the proposed algorithm enables iterative in-memory update and building of the RDF graph of the resulting output ontology, which enhances the processing speed and improves the computational efficiency, making it an ideal solution for big data applications.
Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net

Authors:
raw_author_name: Qing-Dao-Er-Ji Ren
raw_affiliation_string: School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
raw_author_name: Zerui Ma
raw_affiliation_string: School of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, China
raw_author_name: Saheya Barintag
raw_affiliation_string: School of Mathematics Science College, Inner Mongolia Normal University, Hohhot 010028, China

Abstract: Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved algorithm with horizontal wave transformation (HWT) to augment the dataset for training the Mongolian handwriting recognition. Second, the classical CRNN handwriting recognition model was improved. The structure of the encoder and decoder was adjusted according to the characteristics of the Mongolian script, and the attention mechanism was introduced in the feature extraction and decoding stages of the model. An improved handwriting recognition model, named the EGA model, suitable for the features of Mongolian handwriting was suggested. Finally, the effectiveness of the EGA model was verified by a large number of data tests. Experimental results demonstrated that the proposed EGA model improves the recognition accuracy of Mongolian handwriting, and the structural modification of the encoder and coder effectively balances the recognition accuracy and complexity of the model.
Continuing Professional Development for Physical Education Teachers in China: Basis for Teaching Enhancement Program

Authors:
raw_author_name: Yutao Wang

Abstract: In pursuit of life-long learning, the teaching profession requires continuing education for institutional promotion, salary increase, and administrative position. Supportive academic institutions increase their teacher’s professional learning in the 21st century to increase their students’ further education and work as well. China consistently invested in their teachers’ professional development but empirical evidence on these programs’ effectivity is limited. Specifically, there is a demand for more quality professional development (PD) opportunities for PE teachers focused on the following reasons: (a) the education standards movement, (b) professional organizations, and (c) a call for research on teaching. Apparently, in China, most of them have not recognized their leading role and undervalued their educational career. Thus, this study revisited the impact of the PD programs in China focusing focus its lens on the continuing professional development (CPD) of physical education teachers to address the urgent call to improve the PE-CPD. Guided by the global Continuing Professional Development (CPD) framework, this study investigated the continuing professional development provided to physical education teachers in China and the results of this investigation would be the basis for their teaching enhancement program. The findings revealed that most of the PE teachers in China were male, middle aged with relative bachelor’s degree and permanent position, and developing teachers with 1-10 years teaching experience. They partly considered their involvement in skill development opportunities and interaction and collaboration, but highly considered their involvement in personal growth and reflection in their continuing professional development. Their level of involvement in continuing professional development were significantly different considering their age, sex, educational attainment, and years of teaching. Moreover, the found professional development about classroom management very effective and impactful but teaching practices about ICT skills has minimal effectiveness. However, there is a significant difference in the evaluation of PE teachers on the effectivity of the professional development when they are grouped according to their age, educational attainment, and years of teaching. Nevertheless, the significant relationship found between the their involvement in professional development (PD) and evaluation on the effectivity of the professional development became the basis for the strategic factors to be integrated in the teaching enhancement program for the CPD of PE teachers in the new normal.
How reliable is the scholarly literature?

Authors:
raw_author_name: Björn Brembs

Abstract: A few years ago, I came across a cartoon that seemed to capture a particular aspect of scholarly journal publishing quite well: The academic journal publishing system sure feels all too often a bit like a sinking boat.
Deep-learning-based head pose estimation from a single RGB image and its application to medical CROM measurement

Authors:
raw_author_name: Panrasee Ritthipravat
raw_author_name: Wen-Nung Lie
raw_author_name: Worapan Kusakunniran

Abstract: Abstract For human beings, neck movement will be degraded due to aging, trauma, musculoskeletal disorders, or degenerative diseases. Cervical range of motion (CROM) measurement is one of the popular quantitative neck examinations. Despite radiography is considered as the gold standard, it suffers from invasiveness, radiation exposure, and expensiveness. Recently, vision-based methods have been applied for CROM measurement but achieve large errors and require depth camera. On the other hand, deep neural networks provide good performances on head pose estimation (HPE) from a single image, thus promising for medical CROM measurement. We propose to use CNN networks to extract pyramidal or multi-level image features, which are passed to cross-level attention modules for feature fusion and then to a modified ASPP module and a multi-bin classification/regression module for spatial-channel attention and Euler angle conversion/prediction, respectively. The proposed technique was evaluated on public datasets, such as 300W_LP, AFLW2000, and BIWI, to verify its superior performances (with mean MAE = 3.50°, 3.40°, and 2.31° for different experimental protocols) than state-of-the-art methods. Our pre-trained model was also evaluated with our own collected dataset from hospital for CROM measurement. It also achieved the lowest MAE of 4.58° among other methods and conformed with a medical standard of 5 degrees except the pitch angle (which has a MAE of 5.70°, larger than the standard and the yaw (MAE = 3.60°) and roll angles (MAE = 4.44°)). In general, HPE technique is feasible for CROM measurement and shows its advantages of speed, non-invasiveness, free of anatomical landmark and low cost of operation.
An Effective Randomized Algorithm for Hyperspectral Image Feature Extraction

Authors:
raw_author_name: Jinhong Feng
raw_author_name: Rui Yan
raw_author_name: Gaohang Yu
raw_author_name: Zhongming Chen

Abstract: Analyzing the spectral and spatial characteristics of Hyperspectral Imaging (HSI) in a three-dimensional space is a challenging task. Recently, there have been developments in 3D feature extraction methods based on tensor decomposition, which allow for the effective utilization of both global and local information in HSI. These methods also explore the inherent low-rank properties of HSI through tensor decomposition. In this paper, we propose a new approach called variable randomized T-product decomposition (Vrt-SVD), which is a variation of Tensor Singular Spectral Analysis. The goal of this approach is to improve the efficiency of tensor methods for feature extraction and reduce artifacts of image processing. By using a randomized algorithm based on the variable t-SVD, we are able to capture both global and local spatial and spectral information in HSI efficiently, which enables us to explore its low-rank characteristics. To evaluate the effectiveness of the extracted features, we use a Support Vector Machine (SVM) classifier to assess the accuracy of image classification. By conducting numerous numerical experiments, we provide strong evidence to show that the proposed method outperforms several advanced feature extraction techniques.
Comparative Evaluation of Imbalanced Data Management Techniques for Solving Classification Problems on Imbalanced Datasets

Authors:
raw_author_name: Khamron Sunat
raw_author_name: Nipotepat Muangkote

Abstract: Dealing with imbalanced data is crucial and challenging when developing effective machine-learning models for data classification purposes. It significantly impacts the classification model's performance without proper data management, leading to suboptimal results. Many methods for managing imbalanced data have been studied and developed to improve data balance. In this paper, we conduct a comparative study to assess the influence of a ranking technique on the evaluation of the effectiveness of 66 traditional methods for addressing imbalanced data. The three classification models, i.e., Decision Tree, Random Forest, and XGBoost, act as classification models. The experimental settings have been divided into two segments. The first part evaluates the performance of various imbalanced dataset handling methods, while the second part compares the performance of the top 4 oversampling methods. The study encompasses 50 separate datasets: 20 retrieved from the UCI repository and 30 sourced from the OpenML repository. The evaluation is based on F-Measure and statistical methods, including the Kruskal-Wallis test and Borda Count, to rank the data imbalance handling capabilities of the 66 methods. The SMOTE technique is the benchmark for comparison due to its popularity in handling imbalanced data. Based on the experimental results, the MCT, Polynom-fit-SMOTE, and CBSO methods were identified as the top three performers, demonstrating superior effectiveness in managing imbalanced datasets. This research could be beneficial and serve as a practical guide for practitioners to apply suitable techniques for data management.
Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems

Authors:
raw_author_name: Fargana Abdullayeva
raw_author_name: Suleyman Suleymanzade

Abstract: In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation, mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the transfer adaptation technique impacts stable loss decreasing and learning speed during the training process.&#x0D; &#x0D;
Game Analysis of Dual-channel Supply Chain Based on Different Rights Structure

Authors:
raw_author_name: Jingwen Li

Abstract: Based on members' different bargaining power in a dual channels system, this paper discusses the impact ofthree games on channel pricing, demand and members' profits by building Manufacturer Stackelberg, Retailer Stackelberg and Vertical Nash game model.The study finds that:(1) When the coefficient of cross-price elasticity of demand is 0, the effects of the three game strategies on the competitive equilibrium solution of the two-channel supply chain are undifferentiated. (2) When the coefficient of cross-price elasticity of demand is not 0, under the Stackelberg game dominated by the retailer (manufacturer), the traditional channel (direct channel) has the highest pricing, and the demand of the traditional channel (direct channel) is the smallest, while the demand of the traditional channel (demand of the direct channel) is the largest in the Stackelberg game dominated by the manufacturer (retailer); both manufacturers and retailers are willing to choose to give up their power and do the Stackelberg game. Both manufacturers and retailers are willing to give up power in favor of being a follower of the Stackelberg game, and the Nash game is always a strict downside for the game participants. Finally, the validity of the conclusions is further tested by case studies.
Virtual Reality Technology Transitions and Reshape the Aesthetic Education Paradigm

Authors:
raw_author_name: Qingliang Zeng

Abstract: With the rapid development of technology, virtual reality technology has gradually penetrated into the field of education. The traditional aesthetic education model is limited by the lack of time and space and interaction. The virtual reality technology is based on its immersive and interactive characteristics. Provides new possibilities. Elapse the aesthetic education from three aspects: immersive experience, interactive teaching and time and space reshaping. Through the comparative analysis of the case, the actual application and innovation teaching model of virtual reality technology in aesthetic education is discussed, and the virtual reality technology and aesthetic education are better integrated to provide more effective and innovative ways to cultivate students' aesthetic ability.
Factors Influencing Investment Decisions among College Students: A Comparative Analysis Between India &amp; Nigeria

Authors:
raw_author_name: Anjali Anjali
raw_author_name: Rudresh Pandey

Abstract: Investment is done for the purpose to gain profitable returns. Investing in a well-diversified portfolio offers a multitude of benefits, including capital appreciation to grow wealth over time. As a student, managing finance can be challenging, but exploring opportunities for passive income can make a significant difference in financial stability and independence. This study endeavors to comparatively analyze the factors influencing investment decisions among college students in India and Nigeria. Demographics play a pivotal role in shaping investment choices, and understanding the differences in the attitude of these countries is vital for effective financial education initiatives. The study reveals that there is a positive relationship between different factors that students consider when making these decisions. Factors such as financial security, return on investment (ROI), and risk minimization are found to have a significant impact on their choices.
Integrate between information systems engineering and software engineering theories for successful quality engineering measurement of software: Valid instrument pre-results

Authors:
raw_author_name: Ibrahim Eskandar Ibrahim Fadhel

Abstract: An extensive number of instruments and systems assessment tools are weak and not good enough in the appraisal of systems’ quality engineer success measurement. Thus, the comprehension of systems’ success is very serious. One of the purposes of this research topic is to develop a successful, novel, validated instrument for measuring system quality success based on the integration between theories of information systems (Seddon and DeLone &amp; McLean) and software engineering theory (ISO 25010). To ensure the quality of the instrument before use, eight academic experts have validated. The reason for expanding the number of experts to eight is to accurately build and evaluate the instrument because this instrument is the first one erring. After expert validation done successfully, researchers started the process of instrument pre-test. Pre-test verification and validation results done by test the instrument of 74 users. The results of the statistical testing were perfect. The Composite reliability proposed value is 0.7, The average variance extracted value is 0.5. Cronbach’s alpha is higher than 0.7. The value of Spearman’s reliable rhea is &gt;0.6. Results approved that this instrument is strong and perfect to be used as a valid tool for system success measurement.
The relationship between perceived learning, academic performance and academic engagement in virtual education for university students

Authors:
raw_author_name: Vanessa Vega
raw_author_name: Hugo Bayona
raw_author_name: Victor Bernal

Abstract: This study aimed to determine whether the three dimensions of academic engagement (cognitive, emotional and behavioral) were positively associated with perceived learning and academic performance. The participants were 301 university students from Lima. Structural equation models were used to test the proposed theoretical relationship between the variables. The results indicated that the model showed satisfactory fit indices (CFI = 0.956, TLI = 0.949, RMSEA = 0.043, SRMR = 0.062). Perceived learning was found to be predicted by cognitive engagement (β = 0.447, p &lt; 0.01) and emotional engagement (β = 0.230, p &lt; 0.05). However, there was no statistically significant relationship between behavioral engagement and perceived learning (β = 0.035, p = 0.840). On the other hand, academic performance was predicted by behavioral engagement (β = 0.393, p &lt; 0.05) but not by cognitive (β = -0.164, p = 0.301) or emotional (β = 0.001, p = 0.991) engagement. The study highlights the importance of fostering academic engagement in university students to enhance both their academic performance and perceived learning.
Pemanfaatan Media Audio Visual pada Kegiatan Bercerita dalam Meningkatkan Perkembangan Bahasa Anak di TK Khalifah 23 Palembang

Authors:
raw_author_name: Windi Dwi Andika

Abstract: Language is an important component in early childhood development. Thus, a stimulus is needed to increase this development with various activities, namely storytelling activities using audio-visual media. This research aims to be one way to improve children's abilities in language development through storytelling activities through audio-visual media. This research will be carried out in 2 cycles, using classroom action research methods, improvement plans, objects, and reflection. The place where this research was carried out was TK Khalifah 23 Palembang. Based on the results of research activities that have been carried out, the results obtained are cycle I children's language abilities from a total of 15 children, 8 children (53%) have developed very well, 4 children (27%) have developed as expected. 3 children (20%) have started to develop. In the improvement cycle II, the results obtained were that 13 children (87%) had received very good grades, 2 children (13%) had obtained good grades and were developing according to expectations. So we can conclude that storytelling activities using audio-visual media carried out at TK Khalifah 23 Palembang have been able to improve children's language skills. What can be seen from each cycle is that there is a very significant increase.