Reviewerly/exHarmony huggingface dataset:
Memory-based Stochastic Trajectory Optimization for Manipulator Obstacle Avoiding Motion Planning

Obstacle avoidance planning for manipulator is always a challenging research problem especially for manipulator with high degree-of-freedom. The Stochastic Trajectory Optimization for Motion Planning (STOMP) can find solutions efficiently by generating noise trajectories to optimize. However, STOMP’s optimization process starts with an initial trajectory linearly interpolating between the start and the goal configuration, which maybe far away from the optimal trajectory. In addition, STOMP cannot use past knowledge to accelerate the solution of new planning tasks, resulting in more iterations to reach a satisfactory solution. In this paper, We propose a Memory-based STOMP (M-STOMP) algorithm, which stores past trajectories in Memory so that it can generalize the experience obtained from past trajectory optimization to new and similar tasks. Before optimization, a trajectory most relevant to the current situation is matched and that trajectory must be modified in order to reduce the difference between the matched trajectory and the current task. In the optimization process, a more efficient way to generate noise trajectory is used to speed up the convergence process. After optimization, the highly divergent trajectories are stored in memory to maintain diversity and limit memory size. In the simulation experiment, we set up several new planning tasks with different obstacles and goal configurations to verify the effectiveness of the algorithm. The simulation results show that the proposed method can obtain collision-free trajectory with shorter iterations, and the algorithm has more advantages in the new similar planning tasks.
Research on hydrogeological conditions based on ArcGIS-taking Yongding River's ecological water supplement as an example

With the progress of digitization and the continuous development of GIS, it is convenient for us to better understand, manage and coordinate the geographical information display in engineering application from all levels. This paper introduces the extraction and conversion of geological survey, deep well data and long-term groundwater level monitoring data with ArcGIS mapping technology in the study of hydrogeological conditions in the western part of Beijing, and the standardized drawing of the extracted and transformed data and graphic information. The display and characterization of regional geographic information, analysis of groundwater influencing factors and local fine research are realized, which provides support for research work. By comparing with Sufer and AutoCAD, advantages of ArcGIS software are as follows: 1) It supports multiple data formats and has strong linkage with other software. 2) Rich drawing commands. 3) It has powerful data editing function.
Multi-MelGAN Voice Conversion for the Creation of Under-Resourced Child Speech Synthesis

Voice conversion (VC) is an important technique for the development of text-to-speech voices in the use case of lacking speech resources. VC can convert an audio signal from a source speaker to a specific target speaker whilst maintaining the linguistic information. The benefit of VC is that you only require a small amount of target data which therefore makes it possible to build high quality text-to-speech voices using only a limited amount of speech data. In this work, we implement VC using a mel-spectrogram Generative Adversarial Network called MelGAN-VC. This technique does not require parallel data and has been proven successful on as little as 1 hour of target speech data. The aim of this work was to build child voices by modifying the original one-to-one MelGAN-VC model to a many-to-many model and determine if there is any gain in using such a model. We found that using a many-to-many model performs better than the baseline one-to-one model in terms of speaker similarity and the naturalness of the output speech when using only 24 minutes of speech data.
Image-Based Text Recognition &amp; Translation Using Machine Learning

Text recognition in images is a research area that aims to create a computer system that can automatically read text from images. There is high demand these days for storing information available in paper document format on a computer storage disc and then reusing this information through the searching process. One simple method for transferring information from paper documents to a computer system is to scan the documents and then save them as images. However, it is extremely difficult to reuse this information by reading the individual contents and searching the contents of these documents line-by-line and word-by-word. The difficulties involved in this are the font characteristics of the characters in paper documents and the image quality. Because of These difficulties, the computer is unable to recognize the characters while reading them. As a result, character recognition mechanisms are required to perform In this paper, we will look at a method for recognizing text from images. The goal of this paper is to recognize text from images for better reader comprehension by employing a specific sequence of different processing modules. Many people in today's world face the problem of language translators. A technology known as machine translation can help to solve this type of problem. This paper proposed machine translation using a recurrent neural network with an attention mechanism, where a recurrent neural network (RNN) is a type of neural network designed to capture information from sequence and time-series data. As human language is one big complex pattern or a complicated pattern, RNN is useful for learning patterns in a given set of data. In machine translation, two recurrent neural networks collaborate to transform one sequence into the other. An encoder network modifies an input sequence in a vector, whereas a decoder network modifies the vector in a new sequence. To improve the previously stated RNN model, we will employ the attention mechanism, which aids in focusing on a specific range of input sequences.
Recursive Joint Cramér-Rao Lower Bound for Nonlinear Parametric Systems with Colored Noise

The performance evaluation for joint state and parameter estimation (JSPE) is of great significance. Joint Cramér-Rao lower bound (JCRLB) has been widely studied for JSPE of nonlinear parametric systems with white noise. However, in practice, the noise is often colored due to high measurement frequency and bandlimited signal channels. In this paper, a recursive JCRLB is developed for JSPE of nonlinear parametric systems with colored noise, characterized by auto-regressive (AR) models. First, we propose a unified recursive JCRLB for JSPE of general nonlinear parametric systems with higher-order autocorrelated process noises and autocorrelated measurement noise simultaneously. Then its relationship with the posterior Cramér-Rao lower bound (PCRLB) for filtering of nonlinear systems with colored noise and the hybrid Cramér-Rao lower bound (HCRLB) for JSPE of regular parametric systems with white noise are provided. Illustrative examples in radar target tracking verify the effectiveness of the proposed JCRLB for the performance evaluation for JSPE of nonlinear parametric systems with colored noise.
Deductive Proofs of Predicate Logic Formulas

Proofs by deduction in Predicate Logic. Axiom schemas and their instances. Implementation of “Schema” and “Proof” objects for Predicate Logic in Python. Use of tautologies in proofs. The Soundness Theorem for predicate logic.
MEDIA QUESTION CARD TOWARDS KNOWLEDGE OF DENTAL AND ORAL HEALTH IN ELEMENTARY SCHOOL AGE MENTALLY RETARDED CHILDREN

Background: Dental and oral health is something that must be considered for everyone, including mentally retarded children. Children with mental retardation are children who have intellectual abilities below the average normal child in general. Children with mental retardation have poor oral and dental hygiene by 62.6% and have a low level of knowledge about dental and oral health. One of the efforts to increase knowledge is counseling using question card media. Objective: To analyze the effect of counseling using question card media on dental and oral health knowledge in mentally retarded children of elementary school age at the Yayasan Bahagia Special School, Tasikmalaya City. Methods: The type of research used is a quasi-experimental design with a one-group pre-test and post-test design. sampling using total sampling with a sample of 35 children. The measuring instrument of the study was a questionnaire to measure the knowledge of the oral health of children with mental retardation. Data analysis used the Wilcoxon test on the SPSS program. Results: Before being given counseling using question card media, the most knowledge criteria were 28 children (80%), less criteria, while after being given counseling using question card media it increased to 11 children (31%) good criteria and 24 adequate criteria. children (69%), so that the average value of knowledge of mentally retarded children has increased from 47% to 73%. Wilcoxon test results obtained p-value 0.000 &lt;0.05, it can be concluded that Ha is accepted, it means that there is an effect of dental and oral health education using question card media. Conclusion: There is an effect of dental and oral health counseling using question card media on the dental and oral health knowledge of children with mental retardation of elementary school age at the Yayasan Bahagia Special School, Tasikmalaya City.
Multi-Label Image Classification with Weak Correlation Prior

Image classification is vital and basic in many data analysis domains. Since real-world images generally contain multiple diverse semantic labels, it amounts to a typical multi-label classification problem. Traditional multi-label image classification relies on a large amount of training data with plenty of labels, which requires a lot of human and financial costs. By contrast, one can easily obtain a correlation matrix of concerned categories in current scene based on the historical image data in other application scenarios. How to perform image classification with only label correlation priors, without specific and costly annotated labels, is an important but rarely studied problem. In this paper, we propose a model to classify images with this kind of weak correlation prior. We use label correlation to recapitulate the sample similarity, employ the prior information to decompose the projection matrix when regressing the label indication matrix, and introduce the <inline-formula id="M1"> <math id="mathml_M1" display="inline" overflow="scroll"><msub><mi>ℓ</mi><mrow class="MJX-TeXAtom-ORD"><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></math></inline-formula> norm to select features for each image. Finally, experimental results on several image datasets demonstrate that the proposed model has distinct advantages over current state-of-the-art multi-label classification methods.
THOR 50<sup>th</sup> Dummy Neck Calibration Analysis Based on Bi-LSTM Neural Network

In this paper, a neural network model is established based on the neck calibration data of the Thor50th crash test dummy using a bi-directional long and short-term memory (Bi-LSTM) neural network algorithm. The model input is the factors affecting the neck calibration test, and the output is the maximum value of My in the neck calibration test, and the accuracy of the model is calibrated by comparing it with the actual calibration data. The accuracy and suitability of the Bi-LSTM neural network model is further verified by comparing with the radial basis (RBF) neural network algorithm.
Configuring Virtual Hosts

Ah, the wonders of modern technology. You’re likely to be working with more than just one domain, especially if you are using Apache for a local development server. This chapter explains Virtual Hosting in Apache, and guides you through configuring your web server to support multiple domains.
Automatic balance principle and system implementation in large drilling-rig

Levelness of drilling platform is an important index to evaluate its performance. Based on angle sensor as measuring unit and single chip microcomputer as the control core, a four-legged drilling platform is constructed. A hydraulic automatic balancing system is designed by using the synchronous characteristics of hydraulic transmission and the way of unidirectional balancing and linkage of two legs. The practical application of engineering drilling-rig shows that the balancing system has good reliability, especially suitable for large tonnage drilling-rig.
Privately Owned But Government "Tele-Guided"

The third wave of democratisation that swept across sub-Saharan African countries has led to the privatisation of media broadcast as early as in the 1980s. In Cameroon, such privatisation was seriously delayed as it actually saw the light of the day in the year 2000, leading to the emergence of a plurality of private broadcasters, many of which chose, from their inception, to adopt adversarial postures vis-à-vis government. In a bid to manage and tele-guide these new sources of political opposition and pressure, the Cameroonian government has adopted various forms of undemocratic policies and paradigms ranging from political intimidation to subtle blackmail. Using critical observations and secondary sources, this chapter argues that one of the subtle undemocratic strategies adopted by government has been the culture of not granting permanent licenses to private broadcast media, rendering them technically illegal and exposed to arbitrary and unpredictable closure as well as to many other severe sanctions by the government. Another undemocratic strategy has been to offer economic assistance (subventions) only to media organs which appear to be politically innocuous or loyal to government. These unorthodox ways of controlling private media output have had a serious negative impact on political pluralism and the democratisation process in Cameroon.
iKS2 An improved Kernel and Set Similarity Measure for Fuzzy Clustering of Seqential Data

No Abstract.
Crowd E-Home—A Crowd-Based Collaborative Decoration Platform

With the rapid development of Chinese urbanization and industrialization level, the Chinese real estate transaction area continues to grow, and the country's decoration industry is expanding. Due to the rapid expansion, the decoration industry has failed to establish a good industry order, and the current decoration industry has many problems, such as opaque information and low industrial efficiency. Traditional and Internet decoration companies have failed to solve these problems effectively. The purpose of this study is to build a new collaborative decoration platform to solve the shortcomings of the decoration industry and promote its healthy development. A crowd-based collaborative decoration platform called Crowd E-home is proposed. Based on the Building Information Modeling (BIM) of houses, the platform constructs collaborative intelligent digital models, which have the characteristics of evolution, coordination, multifaceted, and initiative. These models act as intermediate coordinators to complete the collaborative division of labor. This platform plays a role in matching multiparty transactions among consumers, designers, construction teams, material suppliers, and supervisors to solve the problem of the huge coordination costs of traditional decoration companies. It also breaks the space-time limit of consumer selection during the decoration process, improves the decision-making power of consumers, and meets consumer needs for personalized services. The collaborative concept of the platform and the application of BIM optimize the existing decoration industry chain, which transforms the traditional chain model into a star-shaped decoration closed-loop ecosystem.
Analysis of Gender Differences in the Effect of Remote Teaching Types on Mathematical Achievement of High School Students

Objectives The purpose of this study is to investigate whether there is a significant gender difference in the effect of variables that influenced high school students' math achievement during non-face-to-face remote classes due to the COVID-19 pandemic. Methods For the purpose, I analyzed the math scores of 10,523 high school students in the national level academic achievement test provided by the Korea Institute of Curriculum and Evaluation in 2020. For data analysis, a multi-group structural equation model and a hierarchical multiple regression model were applied, and the structural equation model analysis used parental influence, remote class form, and class activity as exogenous potential variables, mathematical value recognition and learning effort as parameters, and mathematical scores as endogenous variables. Hierarchical multiple regression analysis reviewed the increase in explanatory power by using sub-variables related to parental influence, remote classes, and class activities as independent variables. In addition, a bootstrapping method was applied to verify the mediating effect of mathematical value recognition and mathematical learning efforts. Results As a result of the multi-group structural equation model analysis, there was a significant gender difference in the relationship with parents, the type of distance class, and the influence of teachers' teaching activities on high school students' math value recognition and math learning efforts. Second, there was a significant gender difference in the effect of mathematical value recognition and mathematical learning efforts on mathematical grades. Third, there was a significant gender difference in the mediating effect of mathematical value recognition and mathematical learning efforts between exogenous variables and mathematical scores. Fourth, there was a significant gender difference in the effect of the subtype of remote class and the subtype of teacher's class activities on mathematical grades. Conclusions These results show that in order to improve high school students' math achievement in remote class situations due to the pandemic, a class design considering gender differences is necessary.&#x0D;
The use of digital tools in interdisciplinary projects of students’ personal and professional self-development

The article reveals the problem of using digital tools in the context of blended and distance learning. The purpose of the study - to prove the effectiveness of using digital tools in interdisciplinary projects of students` personal and professional self-development. Different approaches in modern scientific discourse of above mentioned problem are highlighted. Groups of digital tools that should be used at each stage of the interdisciplinary project is specified and characterized. The article contains author's practical developments and examples on using digital tools to fulfill the tasks of educational disciplines, which are integrated in interdisciplinary projects, implemented in the process of researching the scientific topic "Psychological and pedagogical support of personal and professional development of future teachers in the implementation of new educational standards". The results of the empirical study of the effectiveness of using digital tools in interdisciplinary projects are summarized according to certain indicators: value-motivated, cognitive, self-learning, procedural, self-movement. The purpose has been achieved by comparing quantitative and qualitative assessments of the dynamics of indicators of high level students’ personal and professional self-development in control and experimental groups. The statistical significance of the study results has been verified by applying Student t-criterion. It has been proved that as a result of using digital tools in the interdisciplinary project the students of the experimental group significantly increased the level of personal and professional self-development.
Survey on Clustering Techniques for Image Categorization Dataset

Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image.It could relieve a user from the laborious and timeconsuming metadata assigning for an image while working on massive image collection.For an image, user's definition or description is subjective where it could belong to different categories as defined by different users.Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques.This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation.There are 5 sections arranged in this paper; CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study.Outcome of this paper will be benefiting CBIR developer for various applications.
THE SIGNIFICANCE OF PHYSICS FOR THE FORMATION OF COMPETENCES OF THE FORESTRY UNIVERSITY GRADUATES

The competence-based approach in higher education is aimed at improving the quality of professional training of graduates. The paper shows the importance of mastering the academic discipline "Physics" for the formation of competencies among graduates of a forestry engineering university. It is concluded that the development of technical thinking among students should begin with the study of physics, which contributes to the development of modeling skills, teaches a systematic approach to a competent goal setting, justification for choosing a solution method, ways to assess the influence of external factors and analysis of practically realizable results.
A Smart Wearable Device for Abnormal Event Handling of Multiple Machines in Modern Factories

This study proposes an interconnected smart wearable device and enables the state of the machine to be quickly fed back to the operator. First, the internal parameters are obtained through the communication protocol provided by the CNC machine tool controller manufacturer. In addition, the interconnection function of the personal smart wearable device is set according to the work rights and responsibilities. Then, the error codes are defined according to the manual. The error code generated by the abnormality of the machine is sent to the Raspberry Pi through the industrial personal computer. The database records information about abnormal events, including machine numbers, timestamps, and error codes. However, each operator's smart wearable device will receive different messages. The experimental results show that the connection and information sharing between the machine and the smart wearable device is successful. The interconnection function and proxy mechanism can reduce the processing time of machine exceptions. In future research work, more complete experiments and analyses will be carried out, and more data will be used to highlight the value and contribution of the intelligent system.
The Cost-Effective Video Stabilization Method for Wearable Camera

This paper presents a cost-effective video stabilization method for fast and large-shaking frames. To achieve real-time and high-quality video stabilization for fast and large-shaking frames, the main strategy of the proposed method is to find the optimal feature-point match to generate the better transformation matrix. Besides, the image pre-processing is exploited to down-sample the picture and then set the ROI area for substantially increasing the speed of the subsequent processing without affecting the detection of feature points. The proposed method is more cost-effective in stabilization than other approaches, especially for fast and large-shaking frames (e.g., video-shooting while running) and can be applied to the wearable cameras, sports cameras, and vehicle cameras.