Reviewerly/exHarmony huggingface dataset:
3D Human Mesh Recovery with Sequentially Global Rotation Estimation

Model-based 3D human mesh recovery aims to reconstruct a 3D human body mesh by estimating its parameters from monocular RGB images. Most of recent works adopt the Skinned Multi-Person Linear (SMPL) model to regress relative rotations for each body joint along the kinematics chain. This pipeline needs to transform each relative rotation matrix into a global rotation matrix to articulate the canonical mesh, and suffers from accumulated errors along the kinematics chain. This paper proposes to directly estimate the global rotation of each joint to avoid error accumulation and pursue better accuracy. The proposed Sequentially Global Rotation Estimation (SGRE) directly predicts the global rotation matrix of each joint on the kinematics chain. SGRE features a residual learning module to leverage complementary features and previously predicted rotations of parent joints to guide the estimation of subsequent child joints. Thanks to this global estimation pipeline and residual learning module, SGRE alleviates error accumulation and produces more accurate 3D human mesh. It can be flexibly integrated into existing regression-based methods and achieves superior performance on various benchmarks. For example, it improves the latest method 3DCrowdNet by 3.3 mm MPJPE and 5.0 mm PVE on 3DPW dataset and 3.0 AP on COCO dataset, respectively <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> .
Multi-dimensional Quantum Secret Sharing Scheme with Noisy Environment

Quantum secret sharing (QSS) is a cryptography technique relying on the transmission and manipulation of quantum states to distribute secret information across multiple participants securely. However, quantum systems are susceptible to various types of noise that can compromise their security and reliability. Therefore, it is essential to analyze the influence of noise on QSS to ensure their effectiveness and practicality in real-world quantum communication. This paper studies the impact of various noisy environments on multi-dimensional QSS. Using quantum fidelity, we examine the influence of four noise models: d-phase-flip(dpf), dit-flip(df), amplitude damping(ad), and depolarizing(d). It has been discovered that the fidelity declines with an increase in the noise parameter. Furthermore, the results demonstrate that the efficiency of the QSS protocol differs significantly across distinct noise models.
An Improved Vulnerable Pedestrian Detection Algorithm Based on YOLOv7

With the spread of autonomous vehicles, vulnerable pedestrians such as the elderly and the disabled are at greater risk than ordinary pedestrians in traffic. However, due to the weak representation of vulnerable pedestrians, the existing studies paying insufficient attention to them generally received low precision and low recall. This study focuses on three deficiencies of YOLOv7, including the information loss, the weak correlation between high-level and low-level features, and the feature generalization, and proposes an improved version that can better adapt to the characteristics of vulnerable pedestrians. Firstly, by adopting the average-pooling, the enhanced spatial pyramid average-and-max pooling cross stage partial (SPAMPCSP) module can capture the global crucial information of vulnerable pedestrians, which ensures the completeness of features. Secondly, squeeze-and-excitation connection is added to realize the guidance of high-level features to low-level features and reorganize low-level features, which is conducive for classification. Thirdly, a spatial and channel efficient layer aggregation network (SCELAN) based on attention mechanism is designed to help the model focus on important features. The experiments conducted on the BG Vulnerable Pedestrian data set show that the improved YOLOv7 model reaches mAP@0.5 = 83.08%, which is 4.7 higher than the original YOLOv7 model.
SO(3)-based Continuity Representation to Intensify Head Pose Estimation

In this paper, We proposed an improved succinct structure for head pose estimation from a single image while maintaining continuous representation. which transforms the Head Pose Estimation (HPE) problem into a problem of directly predicting continuous 6D rotation matrix parameters belongs 3D Special Orthogonal Group (SO(3)). The model uses the lighter RepVGG-A2 as the backbone, and the improved Root Mean Square Error of Geodesic Distance(RSME-GD) as the loss function. Experiments show that compared with the original SOTA method, our model parameters are reduced by about 40%, the training speed is 6X faster and the results are even better.
Synaptic Sampling of Neural Networks

Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; however, the development of techniques that quantify uncertainty by well-understood methods such as Monte Carlo sampling has been limited by the high costs of stochastic sampling on deterministic computing hardware. Emerging computing systems that are amenable to hardware-level probabilistic computing, such as those that leverage stochastic devices, may make probabilistic neural networks more feasible in the not-too-distant future. This paper describes the scANN technique—sampling (by coinflips) artificial neural networks—which enables neural networks to be sampled directly by treating the weights as Bernoulli coin flips. This method is natively well suited for probabilistic computing techniques that focus on tunable stochastic devices, nearly matches fully deterministic performance while also describing the uncertainty of correct and incorrect neural network outputs.
Joint face super-resolution and deblurring using multi-task feature fusion network

Face image is an essential category of images. However, due to many factors, face images are often accompanied by one or more degradations, such as low-resolution and motion blurring. To solve these two problems simultaneously, we propose a new multi-task feature fusion network based on double branches, which can directly obtain high-resolution and clear face images from low-resolution and blurred face images. The accumulation of errors is effectively avoided by connecting the super-resolution feature extraction branch and the deblurring feature extraction branch in parallel. Adding the hybrid attention mechanism enhances feature selection at both channel and spatial levels. In addition, we propose a new multi-scale feature fusion module, which effectively integrates the features of the two tasks. In the loss part of the network, we also propose a local loss to guide the network to restore clearer facial details. Experiments show that our method can well increase image resolution and remove blurring. The restored image performs well in objective image evaluation indicators and advanced face visual tasks.
Research on the current situation and problems of informatization teaching in higher vocational English

This article analyzes the current situation of English informatization teaching in higher vocational education, identifies the problems, and proposes practical and feasible solutions. Through a detailed analysis of the application of information based teaching methods, the integration of information technology with classroom teaching, the evaluation of information based teaching effectiveness, and the challenges faced in information based English teaching in vocational colleges have been revealed. The main problems include insufficient informatization teaching ability of teachers, low level of development and utilization of network resources, and incomplete teaching management and evaluation. To address these issues, measures have been proposed to strengthen teacher information based teaching ability training, improve the development and utilization of network resources, and improve the teaching management and evaluation mechanisms.
Research and practice of credit-based cross-school study models for Japanese language majors

The National Standards for Teaching Quality in Foreign Languages and Literature states that foreign language majors are an essential part of China's higher education in the humanities and social sciences. Each university needs to formulate reasonable training objectives based on its own educational practices and talent development goals. Additionally, the Guidelines for Teaching in Foreign Languages and Literature Majors issued in recent years put forth various competencies that Japanese language majors should possess, including Japanese language proficiency, information technology application skills, self-directed learning abilities, and practical skills. In light of these guidelines and the policy of improving the credit recognition system for online open courses, promoting the mutual recognition of credits, and advancing the construction and sharing of high-quality resources, Liaoning Province has been developing high-quality teaching resources and collaborative commissioned projects, continually deepening the reform of higher education teaching. This paper takes cross-school credit-based study in Japanese language majors as an example, summarizing the current credit teaching model, analyzing the issues that have arisen during practical implementation, and providing recommendations for the reform of teaching in Japanese language majors' high-quality resource construction and sharing projects.
Discussion on the construction of vocational undergraduate mathematics curriculum system

The country has established a pilot project for undergraduate vocational education, proposing new requirements for mathematics courses. Taking the curriculum system positioning and construction planning as the benchmark, this paper starts from the early stage of preparation, reconstructs the curriculum, and establishes a brand-new mathematics curriculum system. The final framework consists of a core curriculum of general education that strengthens the foundation and a disciplinary foundation platform curriculum that highlights the cultivation of applied abilities. Different courses are designed for different majors to adapt to the new challenges of vocational undergraduate education.
Strategies, methods and policy support to improve students' academic achievement and career development across academic periods

This study explores the strategies, methods and impacts of personalized education implemented at different school stages (from kindergarten to high school). Aiming to solve the problem that the traditional education model ignores individual differences among students and based on empirical analysis, this study explores the effect of personalized education on students' academic achievement, career planning and personal development through cross-stage long-term tracking methods. Using cross-stage long-term tracking methods and experimental grouping of the entire K12 school, combined with artificial intelligence and big data tools, quantitative and qualitative data are analyzed to develop new assessment tools and scales. Data analysis will cover descriptive statistics, multivariate analysis, content analysis, and case studies to evaluate the impact of personalized education on students and test multiple hypotheses. The study also highlights the importance of ethical standards to ensure informed consent and privacy protection of participants.
The Influence of work Engagement on Employee Performance: a Scope Review

Previous literature reviews have explored the relationship of Work Engagement with Employee Performance. The purpose of this scoping review is to map the existing studies that assess the influence of Work Engagement on Employee Performance. The main stages of the scoping review are: (1) identifying research questions; (2) identifying relevant studies; (3) study selection; (4) data charting, and (5) compiling, summarizing, and reporting results. Spanning 10 years (2013-2023), the scoping review results show that most studies recognize that work engagement affects employee performance. However, as claimed by Rahmadalena &amp; Asmanita (2020) that employee engagement has no effect on employee performance.
INTEGRATION OF LOCAL AND GLOBAL SCHEDULER INTO A MOBILE ROBOT CONTROL SYSTEM

This paper investigates the problem of integrating local and global motion planning methodsin a robot control system. The current level of technological development allows mobile robotsnot only to follow predetermined coordinates, but also to make real-time decisions independentlyof the operator, reacting to changes in the environment. However, the dynamic nature of the environment and the constraints on planning time, as well as the high speeds of mobile robots, complicatethe problems solved by planning algorithms. In this paper, some motion planning methodsbased on cellular decomposition (such as A*, D* and Wavefront) and random search procedureson graphs (such as fast growing random RRT trees and probabilistic roadmaps PRM) integratedwith a motion trajectory prediction algorithm (DWA) are reviewed. A study of the performancecharacteristics of each of the above algorithms has been conducted, as well as a series of numericaland in-situ experiments to analyze the effect of map topology on the execution time andmemory usage of the algorithms. The effect of the speed of local and global planning under differentconfigurations of the external environment was investigated. To confirm the effectiveness of theinvestigated algorithms in real conditions, software for a mobile robot based on a wheeled chassishas been created. The paper presents structural and functional schemes of interaction between theimplemented modules of planning and motion control of the mobile robot and the environment.It also presents a mathematical model of a wheeled platform, for which, based on the consideredmethods, motion planning algorithms are developed. In this paper, quantitative measures includingthe computation time of the motion planning algorithm and the amount of memory used by thealgorithms under different environment maps are evaluated. Both environments with randomlyplaced obstacles and different types of mazes are considered. The implementation of the developedalgorithms in the ROS-2 environment is also described. It is shown that the implemented systemprovides real-time control and motion planning of the mobile robot.
Improvement of Point Spread Function Estimation Method for Motion Blurred Image and Image Restoration

The estimation of point spread function (PSF) of motion-blurred images is the key to image restoration, which is mainly affected by two physical quantities: blur angle and blur length. The traditional point spread function estimation method is easy to be disturbed by the bright cross in the centre of the blurred image spectrum when estimating the fuzzy angle, which affects the quality of image restoration. In addition, when using the traditional Radon transform to detect fuzzy angles, the area of bright spots in the picture is large, which will cause large fluctuations in the detection results. Therefore, this paper proposes a cepstrum analysis method for motion blurred images. Firstly, the gradient cepstrum estimation of the blurred image is given, and the cepstrum is binarized and further refined by setting the gray threshold of the cepstrum. Finally, the line fitting is performed on the refined binary image, and the slope of the fitting line is calculated, so that the point spread function of the blurred image can be accurately and directly estimated, and better image restoration effect can be obtained.
Sentiment Analysis on Satusehat Application Using Support Vector Machine Method

Sentiment analysis is important in language processing and machine learning. SVM is proven to classify positive and negative sentiments with high accuracy effectively. SatuSehat application provides users with various health services and medical information, previously known as the PeduliLindungi Application. Once, this application was used to handle vaccination history used in the new normal era. Along the way, many problems arose due to the immaturity of the application after it was launched, which resulted in many user reviews being given through the Google Play Store application. Therefore, this study aims to determine SVM's performance in classifying user reviews of the SatuSehat application into positive and negative sentiments and to show visualization to find out the most frequent words from user reviews. Based on the research results, 25,000 data were divided into 18,359 negative class data and 6,641 positive class data. At the SVM classification stage, it produces a negative sentiment of 73.4% and a positive sentiment of 26.6%. In addition, the results of the SVM accuracy test obtained a result of 91% with a positive sentiment, namely having a precision test of 92%, a recall of 71%, and an f1-score of 80%, while for negative sentiment, namely having a precision test of 90%, a recall of 98% and f1-score of 94%. The visualization results found that the topics often appearing in positive reviews are good and sometimes great. In contrast, the negative reviews are update, difficult, strange, login, and bug.
Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.
Developing Life Skills with Digital Comic Books to Improve Students' Critical Thinking Abilities in Science Subject

This research aims to develop a life skills-based science material digital comic book teaching material product based on life skills that is feasible, practical and effective to use. This research uses development research (R&amp;D) with a 4D model (Define, Design, Development, and Disseminate) with research subjects of 24 class IV students at SDN 068006 Medan. Data collection techniques use observation, tests, questionnaires and documentation. The results of the research produced feasibility with an average percentage of 91% in the "Very Feasible" category, the practicality test percentage was 79% in the "Practical" category. Next, test effectiveness through student pretest and posttest scores with an N-Gain of 0.51 for the "Effective" criterion. So, it can be concluded that digital comic book teaching materials based on life skills can be used in the learning process.
The Formation and Changing Role of the Japanese Military Gendarmerie in Korea in the 1910~20s

This article examines the organization and changing role of the Japanese military gendarmerie stationed on the Korean Peninsula before and after the 1919 March 1 Movement. The gendarmerie played a key role in Japanese imperialism’s colonization of Korea. In particular, the gendarmerie was primarily responsible for policing colonial Korea until the “gendarmerie-police system” was abolished after the 1919 March 1 Movement. However, existing studies have focused on analyzing the police during the colonial period, and the changes in the gendarmerie after 1919 have not been well covered. This paper focuses on the transformation of the Japanese military gendarmerie in the Korean Peninsula after 1919. From 1919 to 1923, the number of gendarmes was reduced in two stages. In 1919, a total of 14 regional gendarmerie units were reduced to eight, and their number was reduced from more than 8,000 to 1,200. During this period, Japanese gendarmes stationed in Korea were mainly deployed along the northern border of the Korean Peninsula. In 1922 and 1923, the number of gendarmes was further reduced to around 500, as the gendarmes were no longer required to defend the border. From then on, the Japanese military gendarmerie in the Korean Peninsula changed from the role of military police to that of inspectors and spies.
Secondary Level Teachers’ Professional Development through Research and Development Project

This study explores the lived experiences of Nepali public secondary level school teachers in taking initiatives for their professional development, and the support they received in research and development projects. The data were collected through conducting in-depth interviews with five teachers teaching in public schools run under the education trust of Pokhara and Kathmandu in Nepal. Findings revealed that the Research and Development Project introduced in these selected schools turned out to be supportive of teachers ‘professional development in several ways. Their involvement in the Research and Development Project offered them opportunities to form a new identity and developed skills in self-reflection. This opportunity has contributed to creating a positive impact on improving classroom practices and enhancing teachers’ professional development. Future research might explore modes of effective interventions for helping teachers to grow in their professional practices.
Media Literacy: A Survey among Headteachers of Community Schools in Kathmandu Valley

This study has aimed to measure the level of media literacy among headteachers of community schools in Nepal and examine the relationship between their communication behavior and their awareness of media literacy. The study focuses on the level of media literacy among headteachers and the contribution of headteachers' media literacy to their communication behavior. The study considers demographic variables such as gender, ethnicity, age, marital status, academic qualification, teaching experience, and training to achieve the expected outcomes. The population of this study includes 295 secondary-level headteachers of community schools in Kathmandu Valley, and a sample of 170 headteachers were randomly selected from three districts. The collected data were analyzed using inferential statistics, and reliability, validity, and ethics were considered at each stage of the research. The findings indicate that headteachers are not sufficiently aware of the influence of media on their jobs in general, but there is a positive relationship between media literacy and communication behavior among them. Female headteachers felt recognized and had good relationships with colleagues, while older headteachers were less literate about media. Headteachers with higher qualifications and training were more literate with media and more communicative with subordinates and students. The study suggests that headteachers should constructively engage in building their schools, develop a social network, and maintain autonomy in their decision-making process to support the quality improvement of community schools. Therefore, the study recommends that stakeholders of the school consider the knowledge development, autonomy, and media literacy of headteachers to improve communication, collaboration, and quality of education in community schools.