Reviewerly/exHarmony huggingface dataset:
A Deformation Field-Based Approach for Expression Processing in Head Motion Correction

It is a routine procedure to use Iterative Closest Point (ICP) algorithm to correct motion in medical imaging of the head. However, the basic assumption of ICP is the rigid body motion, and it is not strictly valid when there is non-rigid motion (facial expression). In this study, we propose a novel method to reduce the adverse effects of facial expressions on head motion estimation. First, we design a network named DFG-Net to generate a deformation field on the RGB domain and compute a confidence map. We then extend the original ICP by embedding the confidence map representing each point’s degree of non-rigid motion. Compared to other techniques, our proposed method eliminates the negative effects of facial expression while making the most of pose information. The experiments demonstrate that our approach reduces the position error by 19.23% and orientation error by 36.37% compared to the original ICP algorithm. They also show that our method is robust to variations of expressions.
PENGARUH MEDIA PERMAINAN ULAR TANGGA TERHADAP KEMAMPUAN MEMBACA PADA ANAK KELOMPOK B DI TK FATIMA GELURAN

This research was motivate by the lack of reading abilitys in group B children in TK Fatima Geluran. The low reading ability of children as a result of the learning method used by the teacher does not provide maximum results. The use of snake and lader game media researchers chose as one of the solutions, because snake and ladder game media has a positive impact on increasing children's reading skills. The formula of the problems in this study, is there any influences of snake and ladder game media on reading ability in group B children in Fatima Geluran. The purposes of this research is to finds out whethers theres is an effect of snake and ladder games media on reading ability in group B children in Fatima Geluran. The types of research that the author used is quantitatives with the types of one groups pretest-posttest design. The data collections technique used is observations and processed by Pair Sample T Test. The result show that there was a significant increased in the snake and ladder game media on children's reading ability, with df or db 11, if we look at the t-value table with a significant level of 5%, the "t" value is greater than tt, namely: 0.000&lt;0,05. Then by comparing the results of t arithmetic (tₒ) with t table, it can be analyzed that t0 is greater than tt (t0 &gt; tt), so H0 is reject and Ha is accepted. &#x0D;
Research on the Construction Method of System Off-nominal States Model Based on Ontology Model

In view of the current lack of ontology model construction methods for system anomalies, and the inconsistent information expression of each analysis model and the difficulty of integrating correlation analysis, this paper combines ontology theory to construct failure analysis ontology models and anomaly analysis ontology models respectively, and then synthesizes them to form anomaly and failure comprehensive analysis ontology model. Through the comprehensive analysis model, the coordination between system design and system off-normal state analysis is realized, which lays a foundation for the follow-up application model to carry out reliability and security analysis and design.
PRESUPPOSITION IN JOE BIDEN’S INAUGURATION SPEECH

This study aims to find the types of presupposition used by Joe Biden in his inauguration speech. The meanings of the utterances are based on the context of the speech used theory of context proposed by Huang (2014). This study used a qualitative descriptive method because the researcher describes presupposition and meaning in speech. The data is taken from BBC News. The results of this study found 32 utterances that indicated presupposition. These utterances were classified into nine types of presupposition were used by Joe Biden in his inauguration speech. They are thirteen utterances of definite description, three utterances of factive predicates, two utterances of aspectual of state predicates, three utterances of iteratives, one utterance of implicative predicates, three utterances of quantifiers, one utterance of temporal clause, three utterances of cleft sentences, and three utterances of counterfactual conditional presupposition. The study did the analysis based on two types of context they are linguistic context and non-linguistic context. Besides, the study found that the utterances of Joe Biden in his inauguration speech have implied meanings that were not conveyed directly.
Peningkatan Kreativitas Video Editing dengan Camtasia bagi Karang Taruna RW. 07 Perwira, Bekasi Utara, Jawa Barat

IMPROVING VIDEO EDITING CREATIVITY WITH CAMTASIA FOR YOUTH ORGANIZATIONS RW 07 OFFICERS, NORTH BEKASI, WEST JAVA. Utilization of technology has become a mandatory requirement for users who want to develop their knowledge. The application of technology is not only on hardware but software is also growing rapidly which is balanced with the varied needs of users. Video editing is a form of development of software technology that plays a role in managing and producing works in the form of videos. Many video works are used in providing material delivery in the form of education, distance learning, and the development of new knowledge that is manifested in practical applications. Processing Video editing can use the Camtasia application or other applications. The editing application with Camtasia can be applied to the training of young people groups in the RW 07 Perwira officer village with the aim that training participants are able to make video editing with good editing standards, as well as create professionals who are able to create editing works that are full of creativity and understanding. in using animations, transitions, and effects to bring scine video to life. This training activity is supported by all authorized parties, both from the RW 07 Perwira office and from the activity committee in the form of community service. The results obtained from the Camtasia application training activities are in the form of mastery of material from all menu elements in the Camtasia application and creating professional young people groups in RW 07 Perwira with a short time in the form of community service.
Pelatihan Implementasi Penelitian Tindakan Kelas Bagi Guru melalui Webinar

CLASSROOM ACTİON RESEARCH IMPLEMENTATİON TRAİNİNG FOR TEACHERS VİA WEBİNAR. PROFESSİONAL TEACHERS ARE the hope for every person who dedicates himself to being an educator at the Kindergarten to High School level. One of the demands in the continuous professional development of teachers is the implementation of scientific publications or innovative works. This community service aims to train teachers on Classroom Action Research and Article Writing in National Journals through webinars. Ninety-four participants participated in the training. Based on the results of the training, it was obtained that the participant's assessment of the quality of the training included: 1) the quality of the audio (voice) was very clear and clear at 93.83%; 2) visual quality (text, images) is very clear and clear by 92.34%; 3) the suitability of the material presented by the resource persons according to the title of the webinar by 94, 89%; 4) the quality of the material delivered by the resource persons is very good and easy to understand by 93.83%; 5) the question and answer session/discussion on the webinar was effective and was able to answer my questions by 90.43%. In addition, the participants wanted the PkM Team to provide further training in the form of 1) assistance in writing scientific articles; 2) development of interactive learning multimedia using Ms Power point; 3) handwriting learning video development; 4) development of LKPD; and, 5) development of TPACK-oriented RPP.
Driver's Gaze Estimation Algorithm Based on Deep Learning

In traffic safety accidents, 80% of accidents are caused by driver distraction,there is an important mapping relationship between the direction of human sight and the focus of attention,studying the driver's gaze estimation algorithm can reduce the incidence of vehicle traffic accidents.In this paper, a driver's gaze estimation system based on deep learning technology is designed.For human eye feature localization, a multi-level localization method is adopted,firstly, the detection network is used to locate the face position, and then the key points of 68 faces are located based on the face image.In the aspect of the expression of the human eye's line of sight, a method of the expression of the human eye's line of sight based on the central point of the pupil and the direction vector of the human eye's line of sight is adopted,a convolution neural network is used to return the line of sight direction.On the MPIIGaze public data set and self-made data set, the experimental results show that the method can accurately estimate the human eye line of sight.
Applied Steganographic System for Hiding Textual Information on Audio Files

The article presents an applied steganographic system of hiding textual information in audio files on the basic of an advanced approach that combines the methods of steganography and cryptography. The method of the least significant bit (LSB) was used as a steganographic transformation algorithm. The low level of reliability of the method can be improved by introducing a cryptographic layer, the feasibility of which is justified in the article. The improvement is to add one of the modern symmetric encryption algorithms – the AES algorithm in CBC mode. The applied steganographic system for hiding textual information in audio files, taking into account the improvement of the LSB method, is implemented in the Microsoft Visual Studio 2019 environment using the cryptographic libraries that are present in it. The application is developed in the C++ programming language. NIST tests were used to assess the stability of the implemented system to steganalysis. The proposed approach is considered in order to anticipate its role in emerging networks.
Estimation of Global Solar Radiation at Mid Hill Region, Okhaldhunga

Global solar radiation knowledge can be valuable in fulfilling energy demands and exploring numerous applications of solar technology. Since only a few meteorological stations are available and the installation of additional stations is complicated because the cost is very high, a comparative study of various models is essential for accurate global solar radiation prediction rather than measurement. In the present study, four models based on sunshine hour, temperature, and relative humidity for estimating daily global solar radiation were compared using the regression technique for Okhaldhunga (Lat.27.30810 N, Lon.86.50420 E, Alt.1720m.a.s.l.). These models were evaluated and compared based on four statistical error tests. The basis of analysis, model M4, which uses sunshine hour, maximum temperature, and relative humidity, was used for estimating daily global solar radiation with acceptable accuracy. The empirical constants obtained for M4 models were a=0.62, b=0.47, c= -0.003, and d= -0.003. The site's annual average global solar radiation was 4.16 kWh/m2 /day. The results of this study help estimate global solar radiation for similar geographical locations in Nepal.
Social Media and Purchase Habits of Consumers

This research focused on social media’s impact on purchasing decisions in the Lalitpur district. Through the convenience sample, two-hundred responses were obtained through five-point Likert-type questionnaires. Based on descriptive statistics, correlation analysis, and regression analysis, Consumers’ actions were shown to be significantly and positively correlated with the degree to which they trusted content shared on social media platforms. However, perceived risk was found to have a negative and significant relationship with consumer behavior. In addition, the perceived value was found to have a significant positive association with consumer behavior. The regression analysis showed that trust in social media and perceived value significantly affect consumer behavior. At the same time, perceived risk was found to have a significant adverse effect on consumer behavior. This study concluded that the impact of social media has a significant effect on consumer behavior in Lalitpur District.
A Semi-Supervised Self-Training Ensemble Method Based on Clustering

In the process of training classifier with self-training algorithm, this paper proposes a self-training method based on clustering to reduce the problem of low classification accuracy due to the classification error of unlabelled samples. The method calculates the centroids of the labelled samples by KNN method and selects the samples that are close to the centroids of the class from the unlabelled samples as the samples with higher confidence. At the same time, a semi-supervised self-training ensemble method based on clustering is proposed by taking advantage of the differences among classifiers and the idea of ensemble. The results show that the comparison experiment of a semi-supervised self-training ensemble method based on clustering on public data sets and bird song data sets fully verifies the effectiveness of the proposed method.
Decoupled Teacher for Semi-Supervised Drone Detection

Aerial drones come in handy in a variety of science and research applications, sometimes even cause privacy harms during aerial surveillance. In many anti-drone situations, detection, tracking, and classification of drones are of great significance to secure the airspace. Drones are small in size, different in appearance, and have different flight attitudes for various flight environments, which makes the drone data sets too expensive to annotate and the foreground-background imbalance occurs in drone detection. Previous works on drone detection have focused on supervised learning, which depends on large labeled data set. To alleviate the problem of scarce labeled data sets, Semi-Supervised Learning can be employed to leverage unlabeled samples. In this paper, we propose a Semi-Supervised Object Detection method Decoupled Teacher to use unlabeled data and address the foreground-background imbalance issue. Specifically, Decoupled Teacher decouples the Exponential Moving Average mechanism in the general SSOD paradigm, and applies a fusion method of weak/strong data augmentation. We have bench-marked our method and the state-of-the-art SSOD methods using two publicly available drone data sets. The experiment results demonstrate the superior performance of our approach compared with the state-of-the-art methods.
FedTLBOHB: Efficient Hyperband with Transfer Learning for Vertical Federated Learning

The issue of data privacy is receiving increasing attention nowadays. Federated learning, which is a distributed machine learning setting where many clients (e.g. mobile devices or organizations) train a model collaboratively while keeping the training data local, was born as a result. And it has been applied to an increasing number of business scenarios. Like traditional machine learning, federated learning methods are also very sensitive to hyperparameters. However, data privacy will be protected using cryptographic techniques such as homomorphic encryption and secret sharing in federated learning, resulting in significant data amounts transferred among clients. Given this, we should find a more efficient method to optimize hyperparameters, which means utilizing fewer budgets (time, iterations, etc.). In this paper, we proposed a new efficient method FedTLBOHB, which combines both Hyperband and knowledge transfer for model-based optimization, to optimize hyperparameters of vertical federated learning. FedTLBOHB achieve high security level by avoiding a meta-feature based transfer learning paradigm, which will bring about security concerns in federated learning. Instead, a method similar to the model ensemble in Adaboost is designed for knowledge transfer. Meanwhile, according to our analysis, FedTLBOHB outperforms BOHB. The proposed method has been tested extensively on OpenML datasets and has been shown to be more effective than conventional methods at finding better hyperparameter configurations.
Towards Development of a Novel DevOps Platform for Ultra-Large-Scale Commercial Applications: Part 1- System Design

DevOps (development and operations) methodology, as the integration of agile software development method and lean concept, has aroused great interest and concern in the software industry in recent years. The application value of DevOps has been widely recognized by the whole industry due to its massive advantages, such as the significantly decreased time to production, in-creased reliability and stability. However, currently very limited application cases have been reported while there is almost no large-scale application of DevOps software platform. In this study, we have proposed and built an end-to-end and innovative DevOps platform of the whole process, namely the YunDao platform. The system design of the YunDao platform, mainly including the overall architecture, technical architecture, development architecture and functional architecture, was systematically presented and discussed. This work can be considered as a breakthrough in the field of commercial application of large-scale DevOps platform.
Correlation Filter Based on Training-Set Distillation and Distortion Suppression

Correlated filters (CF) have recently shown good performance in visual object tracking. This online learning method depends heavily on the quality of the training set, but complex scenarios such as occlusion or out-of-sight distortions diminish the credibility of the tracker. Therefore, a correlation filter algorithm (TDDS) based on training-set distillation and distortion suppression is proposed. First, a dynamic high-quality training-set is established, the historical samples are adaptively scored, and the frame of the high-confidence tracking result is used as the keyframe to divide the tracking process into multiple time slots. When a new frame of image arrives, if the score is too low, the rate of change of the response map of the reframed image is suppressed, so as to achieve the purpose of suppressing distortion. In order to reduce the sample size in the training set, the previous samples are weighted and fused to produce a key sample when a new time slot is created, and when the maximum number of frames allowed by the time slot is reached, the lowest-scoring sample will be discarded to make the training set more efficient and reliable.
End-to-End Speech Recognition Technology Based on Multi-Stream CNN

At a time when end-to-end speech recognition technology is becoming more and more popular, we conduct research on various end-to-end speech technologies, and use the Transformer-based speech framework to study and find that its multi-head attention is not effective in local feature acquisition. And in the face of noise problems in real scenes, the training convergence speed is too slow. In order to solve the problems caused by Transformer, a new speech recognition framework based on MCNN-Transformer-CTC speech recognition method is proposed. Through MCNN (multi-stream convolutional neural network) in the pre-acoustic unit through multiple parallel channels Local feature extraction is carried out in terms of time width and spectral capability, which makes up for the lack of self-attention mechanism in local feature extraction, and the multitask learning method is used to add CTC structure to make up for the problem of slow training convergence. The training effect of this model on the Aishell1 dataset has reached a CER of 6.23%, which is a further improvement compared to the Transformer model.
Effective Work Environment: The Foremost Arbitrator to Persuade Job Satisfaction Using NIPM-WE Model

The dynamic nature of working environment leads to many confronts in the organizations. The major confront that organizations want to focus is the satisfaction of their employees as well as to cope up with changing environment. In order to reach the efficiency and effectiveness, organization should provide very good working environment which in turn emphasis the employees to work towards their goal. The research paper reveals the impact of work environment towards job satisfaction of employees working in manufacturing industry. A valid sample of 239 respondents was selected from different categories of job profile from the manufacturing industry and the collected data were analyzed by using the SPSS through correlation and regression analysis. The findings revealed that there is a close association as well as impact of Job Satisfaction with Work Environment. The manufacturing industry necessitates its focus on the reshuffle of roles and the responsibility of individual employee’s which in turn helps to attain the competitive advantage without forfeiting the basic objective.
Bots and their Uses in Software Development: A Systematic Mapping Study

Tools that help software developers in their activities have continually been created for tasks in the Software Development Life Cycle. Over the last few years, there has been an increasing interest in the research and development of bots designed to boost the software development process. Consequently, there has been a recent surge in published studies regarding bots in the software development process. Our aim is to present the state of the art of bots for software development. We detail uses, benefits, challenges, and intelligence levels of bots involved in software development reported in academic papers. In this work, we conducted a Systematic Mapping Study (SMS). From the primary studies, we have identified nine activities related to the software development phases in which bots are used, seven benefits, and seven challenges in using bots for software development. The most popular activities for bot applications in software development, in descending order, are project management, code analysis, debugging, and project onboarding. The main benefits are generating bug fixes, providing helpful information, and automating tasks. Alongside the benefits, the primary drawbacks were low effectiveness of fixes, poor bot performance in a task, and a dependency on third-party technologies. We seek to help developers learn about bots for their use in future projects, and investigators identify future research topics through this SMS.
Assessing Wellbeing and Soft Skills of Software Developers and Students in the Context of Working from Home during the COVID-19 era

Derived from COVID-19 pandemic remote work is now a trend, involving new skills and variations on developers' wellbeing. Current students are going to live this new era in the labor market; it is needed to know if they are getting ready for this scenario. This paper presents an exploratory study based on a survey to assess how developers in industry and students in the academy experienced their wellbeing expressed in emotions during the pandemic, as well as how they practiced the soft skills involved in software development. The results of developers are considered as basis to assess how students are prepared for this context. The descriptive study has shown that developers experienced more positive emotions, while students had a mix of positive and negative emotions. In terms of soft skills, developers and students showed closer levels of practice, however students should reinforce some skills.