Reviewerly/exHarmony huggingface dataset:
PRIORITY ASPECTS OF APPLICATION OF THE INSTITUTE OF MEDIATION IN RESOLVING DISPUTES: NATIONAL AND FOREIGN EXPERIENCE

The article provides recommendations on the results of the analysis of the problems of promoting the Institute of Mediation, enshrined in national legislation (substantive and procedural law) of the Republic of Uzbekistan, among the population; the development of favorable mechanisms for applying health practice; the process of training mediators; the practice and benefits of mediation in resolving disputes. The suggestions and recommendations were made on the basis of the study of domestic and foreign scientific and practical experience. The research results can be used in preparation the amendments and additions to the Law of the Republic of Uzbekistan «On Mediation», the Civil Procedure Code of the Republic of Uzbekistan, the Law of the Republic of Uzbekistan «On the Enforcement of Decisions of Courts and Other Bodies», conducting new scientific researches on specialization Civil Procedural Law; Economic Procedural Law; Arbitration and Mediation.
SPSTT: Second-Order Propagation Spatial Temporal Transformer Network for Space-Time Video Super-Resolution

In this paper, we explored the space-time video super-resolution task, which aims to generate high frame rate (HFR) and high resolution (HR) videos from low frame rate (LFR) and low resolution (LR) videos. Most of the existing space-time video super-resolution methods simply combine two sub-tasks of video interpolation (VFI) and video super-resolution (VSR). And these methods usually use recursive propagation structures, but their structures are complex, very time-consuming, and do not make full use of feature information. To address these problems, we proposed a single-stage space-time super-resolution architecture that is based on the Swin Transformer and second-order network propagation. The Swin Transformer allows a natural combination of two subtasks into a single task, and then the second-order network propagation enhances information propagation and efficiently utilizes the information of all the input video frames. We also introduced a dataset pre-cleaning module, which can not only alleviate the image degradation before being propagated, but also suppress the artifacts in the model output, and improve the reconstruction performance of the proposed model. The experimental results show that compared with the related two-stage network, our proposed model is lighter and the reasoning speed is faster with competitive performance.
A Study on Online Readiness for Learning for Applied Undergraduate Students Based on Rooting Theory

It has been three years since the outbreak of the Covid-19, and until the epidemic is completely over, online learning will continue, based on a full understanding of the advantages of online classes, and how to readiness for online learning in order to better meet the learning needs of students and thus further improve the efficiency of online classes.In this context, it is of great importance to conduct an in-depth analysis of students' readiness for online learning.Using structured interviews, 10 students from an applied undergraduate school across the region were interviewed to explore the factors that influence students' readiness for online learning: 1) The differences in online learning readiness among students of different genders are greater.2) The availability of technology and hardware facilities is the main factor in the readiness required for online learning.3) Emergencies affect online learning readiness to some extent.4) The impact of vision and students' perceptions are external factors that affect readiness for learning, and learning confidence and motivation are internal factors that affect readiness for learning.Accordingly, the following suggestions are made for the online learning readiness of university students: using gender differences to improve online learning readiness, improving the proficiency of teachers and students in using equipment (technology), improving the ability to deal with online learning emergencies, and clarifying learning motivation and correcting learning attitudes.
Design of Convolutional Neural Network Accelerator Based on RISC-V

Nowadays, it is more and more difficult for general-purpose CPUs to meet the large-scale computing of neural networks, and research on domain-specific neural network accelerators is gradually emerging. Limited by the high cost of customization and lack of flexibility due to patents and licenses of x86 and ARM commercial architecture CPUs, this paper designs a RISC-V based convolutional neural network accelerators for volume and neural network hardware acceleration platforms to improve the arithmetic power of processors. Finally, verification is performed by writing test beach files under the vivado platform. The results show that for convolutional computation, the operation speed is effectively improved with the accelerators architecture compared to using only the basic instruction set.
DEEPFAKE CLI: Accelerated Deepfake Detection Using FPGAs

Because of the availability of larger datasets and recent improvements in the generative model, more realistic Deepfake videos are being produced each day. People consume around one billion hours of video on social media platforms every day, and that’s why it is very important to stop the spread of fake videos as they can be damaging, dangerous, and malicious. There has been a significant improvement in the field of deepfake classification, but deepfake detection and inference have remained a difficult task. To solve this problem in this paper, we propose a novel DEEPFAKE C-L-I (Classification - Localization - Inference) in which we have explored the idea of accelerating Quantized Deepfake Detection Models using FPGAs due to their ability of maximum parallelism and energy efficiency compared to generalized GPUs. In this paper, we have used light MesoNet with EFF-YNet structure and accelerated it on VCK5000 FPGA, powered by state-of-the-art VC1902 Versal Architecture which uses AI, DSP, and Adaptable Engines for acceleration. We have benchmarked our inference speed with other state-of-the-art inference nodes, got 316.8 FPS on VCK5000 while maintaining 93% Accuracy.
Intelligent Speech Continuous Recognition System Based on NOSE Algorithm

With the development of speech recognition technology, speech has become more and more popular as an important means of human interaction. People hope to use it to communicate between humans and machines, make the communication between the two more convenient, and improve the human-computer interaction experience. Nowadays, speech recognition technology is relatively mature and developed, and many algorithms and applications have been widely used. However, the processing power of computer technology is limited, and it is difficult to ensure real-time and accuracy in low-capacity systems. Therefore, the implementation of speech recognition technology research work has practical significance and broad application prospects. This paper studies the intelligent speech continuous recognition system based on the NOSE algorithm, and understands the relevant knowledge and theories of intelligent speech continuous recognition on the basis of literature. Then the intelligent speech continuous recognition system based on the NOSE algorithm is designed, and the system is tested. The test result shows that the correct rate of the system in this paper is 85.7%.
Application of Data Mining Technology in Business Administration Data

In order to play the role of data mining technology to do a good job in business management, this paper will carry out relevant research. This paper firstly introduces the basic concept of data mining technology, then analyzes the current situation of business administration, and puts forward the importance and difficulties of data application in management. Finally, the paper constructs the data application system of business administration centering on data mining technology. Through research, business management data application system based on data mining technology has good practical value, with the help of the system can play a technical role, so that business management in the support of data, can improve the quality of management.
Analyzing I/O Performance of a Hierarchical HPC Storage System for Distributed Deep Learning

Deep learning is a vital technology in our lives today. Both the size of training datasets and neural networks are growing to tackle more challenging problems with deep learning. Distributed deep neural network (DDNN) training technique is necessary to train a model with large datasets and networks. For large-scale DDNN training, HPC clusters are excellent computation environments. I/O performance is critical in large-scale DDNN on HPC clusters because it is becoming a bottleneck. Most flagship-class HPC clusters have hierarchical storage systems. It is necessary to quantify the performance improvement effect of the hierarchical storage system on the workloads to design future HPC storage systems. This study demonstrates the quantitative performance analysis of the hierarchical storage system for DDNN workload in a flagship-class supercomputer. Our analysis shows how much performance improvement and storage volume increment will be required to achieve the performance goal.
Intelligent recommendation of educational resources combining Neu-MF and T-S fuzzy control

The research uses Takagi-Sugeno (T-S) fuzzy control combined with neural matrix factorization (Neu MF) model to study the intelligent recommendation of educational resources. The recommendation performance of TS-Neu MF model is compared with other similar recommendation algorithm models under two test sets of E's dx and C er. The results of the experiments show that the TS-Neu MF model outperforms Deep FM by 56.6% in root mean square error (RMSE) metrics and 71.5% in mean absolute error (MAE) metrics, and outperforms the Neu MF model by 33.1% in RMSE metrics and 22.5% in MAE metrics under the E dx dataset. The training loss is about 0.04 lower than the Deep FM model, about 0.006 lower than the BPNN model, and about 0.02 lower than the Neu MF model.
Big Data Automatic Classification Processing System Based on Cloud Computing

How to quickly, accurately and fully obtain the information we need from social media, and how to effectively organize and manage this information are major challenges facing the current information technology space. Considering the needs of experimental research and practical application, this paper makes a detailed modular design of the automatic text classification processing system based on semantic Chinese. In terms of the impact of keyword weights on specific categories, the original algorithm is improved by introducing two parameters: the ratio of keywords to all keywords contained in the current category, and the average number of samples in this category compared to the current category. Number of samples. According to the principle of combining category concepts, the weights between keywords and categories are combined and calculated, and the highest weighted sorting number is obtained as the optimal sorting number of the text to be sorted. The final test results show that the improved algorithm has certain advantages compared with other algorithms in terms of recall rate, precision rate, F1 value, macro average and micro average. The macro-average precision, recall and FI of the improved algorithm are 4–5 percentage points higher than those of the other two algorithms.
Investigating the Relationship of Change Leadership, Knowledge Acquisition, and Firm Performance in Digital Transformation Context

The purpose of this research is (1) to confirm the impact of change leadership (CL) and Knowledge Acquisition (KA) on firm performance (FP) of large manufacturing companies implementing digital transformation projects; (2) to investigate the mediation effect of KA on the relationship between CL and FP.The relationships between CL-FP and KA-FP have been studied separately.However, empirical research studying these relationships in a digital transformation context is still scarce.In addition, previous studies investigating the relationship between CL and KA can hardly be found.To the best of our knowledge, this is the first integrated study of CL, KA, and FP relationships in the context of digital transformation among large Indonesian manufacturing companies.A quantitative research method was used in this study.Quasi Snowball sampling method was used to distribute the questionnaires.From the 148 returned questionnaires, only 122 samples are usable for further processing.The respondents are scattered throughout 10 provinces of Indonesia.Their products are diverse in 19 categories.A Structural Equation Model was developed for this study and analyzed using AMOS.The findings support previous research suggesting that change leadership is a prerequisite for firm performance, specifically in this study in the digital transformation context.The study also contributes to the body of knowledge by confirming that KA mediates the effect of CL on FP.Based on the findings in this study, the authors develop a framework to guide digital transformation leaders in their digital transformation journey.It is called the 6Cs of Digital Transformation Leadership.
FRONT MATTER, 6(1), Maret 2021

Front Matter Volume 6 Nomor 1 Maret 2021
ACKERMANN STEERING BASED FORWARD COLLISION DETECTION

This paper is focusing on making a simple and accurate model for the estimation of vehicle trajectory based on the steering angle.This paper will helpful to the ADAS and safety engineers in automobile field to develop safer and more reliable car.We take Ackermann Steering based vehicle for estimation of trajectory of the car and also checked for objects in the world environment for any possible collision.
Global Off-Road Path Planning of Unmanned Ground Vehicles Based on the Raw Remote Sensing Map

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded. The map elevation information is assigned based on the digital elevation model (DEM) and the terrain is classified and labeled in the grid map based on the back propagation neural network (BPNN). Based on the reconstructed off-road grid map, a modified A* algorithm considering the safety and efficiency of UGV passage is designed for global path planning on off-road environment. Simulation results based on real off-road environment show that the proposed global planning algorithm can avoid impassable areas and make UGVs drive on high traffic efficiency roads as much as possible.&lt;/div&gt;&lt;/div&gt;
Intelligent Information Technology for Inductive Modeling of Complex Processes on the Basis of Recurrent-and-Parallel Computations

The paper develops a novel intelligent information technology for inductive modeling of complex processes by experimental data, the high level of productivity of which is achieved by applying a new concept of combining the efficiency of recurrent and parallel computations. The implementation of such technology in modern intelligent information-and-analytical systems provides a significant increase in the efficiency and validity of making managerial decisions in the tasks of operational management of complex processes. An example is done of using the developed technology for evaluation and forecast of the investment activity in Ukraine.
A Pose Estimation Method Combining Instance Segmentation and Point Pair Features

For the traditional pose estimation method based on point pair features, both the preprocessing of scene point cloud and the construction of point pair features of scene point cloud have serious time-consuming problems, which cannot meet the needs of actual industry. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. Therefore, this paper proposes a pose estimation method that combines instance segmentation and point pair features. First, use the Mask R-CNN-based instance segmentation network to obtain the location of the target object in the two-dimensional image of the scene; then, obtain the local point cloud data of the space where the target object is located from this position and the depth information of the scene; finally, the local the point cloud data is used as the scene point cloud based on point pair feature pose estimation to perform feature matching with the point cloud of the target object.
Public Administration of Police Accountability and Transparency

Police accountability and transparency have been long-standing concerns in public administration, as the actions of law enforcement have a significant impact on the lives of citizens. This research focuses on exploring the ways in which police accountability and transparency can be improved, particularly through the use of technology and community engagement. The study begins by examining the current landscape of police accountability and transparency, highlighting the challenges and limitations of traditional methods of oversight. This includes a discussion of the issues related to police misconduct, use of force, and biased policing. The study then turns to exploring potential solutions, including the use of body-worn cameras, data analytics, and community policing strategies. One of the key findings of the research is the importance of community engagement in improving police accountability and transparency. This involves fostering trust between law enforcement and the communities they serve, as well as providing opportunities for citizens to voice their concerns and provide feedback. The study also emphasizes the role of technology in facilitating transparency and accountability, particularly through the use of body-worn cameras and data analytics. Overall, this research contributes to the growing body of literature on police accountability and transparency in public administration. It underscores the need for innovative solutions that leverage technology and community engagement to promote greater trust and transparency between law enforcement and the public.
EXPLORING CLASSROOM LANGUAGE IMPLEMENTED BY NOVICE ENGLISH TEACHERS AT JUNIOR HIGH SCHOOLS

Using English as a classroom language is widely acknowledged as the most effective means to encourage students to get used to English. The more often they are exposed to English, the more proficient they will be. However, teachers' awareness of the role of classroom language as a means to maximize student proficiency remains vague. Studies reveal that EFL teachers tend to use their own language in the classroom rather than the target language, English. Furthermore, teachers are susceptible to code-switching, which is the use of two or more languages in a single sentence or phrase. Despite the growing attention of studies exploring classroom language used by EFL teachers, studies focusing on novice EFL teachers with less than five years of teaching experience are considered limited. Accordingly, this descriptive qualitative study aims to investigate the implementation of classroom language used by two EFL novice teachers based on Sallaberri's framework from the English Classrooms Handbook: Classroom Language (1995). The data were gathered through a questionnaire and semi-structured interviews. The study's findings revealed that two EFL novice teachers use Bahasa Indonesia as their primary classroom language, including code-switching. Besides, using English as a classroom language became teachers’ dilemma since the students' proficiency levels and their experience in teaching are strongly affected their actions. Some recommendations and potential future practices and research are also discussed thoroughly.
Assessing the feasibility of machine learning-based modelling and prediction of credit fraud outcomes using hyperparameter tuning

Both the actual theft of a credit card and the deletion of private credit card data are considered forms of credit card fraud.For detection, there are numerous machine learning algorithms accessible.So, several algorithms that can be used to categorize transactions as fraudulent or lawful are illustrated in this study.In this experiment, the credit card fraud prediction dataset was utilized.The dataset is extremely skewed, hence undersampling is used rather than oversampling.The dataset is separated into test and training data portions, and feature selection is made.The experiment uses the methods of Logistic Regression, Random Forest, SVM, ADABoost, XGBoost, and LightGBM.Moreover, the SMOTE and Optuna's hyperparameter tweaking ways provide model customization.The findings suggest that specific algorithms may be capable of accurately recognizing credit card fraud.
The effectiveness of metacognition training on motivational beliefs and self-regulated learning strategies of students with learning disorders

The effectiveness of metacognition training on motivational beliefs and self-regulated learning strategies of students with learning disorders
Correlation Filter Detection and Tracking Model Based on Dynamic Spatial Feature Selection

With the development of science and technology and the improvement of people’s pursuit of quality of life, the importance of computer vision technology in daily life is increasing day by day. As an important branch in the field of computer vision, visual object tracking can be applied to intelligent monitoring, human–computer interaction and autonomous driving, etc., so it has received extensive attention from academia and industry. The main purpose of this paper is to study the tracking model of correlation filter detection based on dynamic spatial feature selection, mainly for dynamic spatial feature selection, spatial feature selection and channel feature selection fusion, and the analysis and research of accelerated correlation filtering detection and tracking model optimized by the improved alternating direction multiplier method. Experiments show that LADC/LADCF* provides 3.1%/1.8% improvement in AUC index and 2.9%/2.4% improvement in DP index, which proves the effectiveness of the proposed timing consistency constraint component.
Research on the Development of American Education Informatization in the Post-Epidemic Era

Education informatization has entered the era of rapid development, and the reform of education will be the new normal of education.Taking American colleges and universities as an example, they reviewed the development of information teaching after the outbreak of the epidemic, analyzed the advantages and disadvantages of information teaching, and put forward constructive suggestions on this basis, which expects to provide reference for the development of China's education.