Reviewerly/exHarmony huggingface dataset:
Machine learning for inverse problems (Conference Presentation)

Machine learning is emerging as an essential tool in many science and engineering domains, fueled by extraordinarily powerful computers as well as advanced instruments capable of collecting high-resolution and high-dimensional experimental data. However, using off-the-shelf machine learning methods for analyzing scientific and engineering data fails to leverage our vast, collective (albeit partial) understanding of the underlying physical phenomenon or models of sensor systems. Reconstructing physical phenomena from indirect scientific observations is at the heart of scientific measurement and discovery, and so a pervasive challenge is to develop new methodologies capable of combining such physical models with training data to yield more rapid, accurate inferences. We will explore these ideas in the context of inverse problems and data assimilation; examples include climate forecasting, uncovering material structure and properties, and medical image reconstruction. Classical approaches to such inverse problems and data assimilation approaches have relied upon insights from optimization, signal processing, and the careful exploitation of forward models. In this talk, we will see how these insights and tools can be integrated into machine learning systems to yield novel methods with significant accuracy and computational advantages over naïve applications of machine learning.
All-optical nonlinear activation function based on Germanium Silicon composite waveguide

Nonlinear activation functions, a part of artificial neuron network, is responsible for nonlinearly mapping the input of the neuron to the output. The optical realization of nonlinear activation functions has become a research hotspot, which is optical activation function. Here, we propose and experimentally demonstrate an all-optical implementation of optical activation function based on germanium silicon composite waveguide, which takes advantage of the intrinsic absorption and the plasma dispersion effect of germanium in the communication band. It realizes two different nonlinear activation functions. This proves that our scheme has a great potential for optical activation functions.
DeFVIS: Visual Analysis of Microscopic Parameters and Macroscopic Properties of Fluoropolymers

For the preparation of fluoropolymers, both process flow analysis and data analysis are needed. Meanwhile, the data sources for the study of fluoropolymer preparation are various, the production and characterization metrics are highly dimensional, and the parameters are unknown. Manual line-byline data analysis drastically restricts the effectiveness of analysts. This paper proposes DeFVIS, a visual analysis system for decoding fluorine materials, an interactive visual analysis system that proposes a multi-source data import and centralized storage technique; an adapted visualization model that encodes abstract preparation process data and high-dimensional metric data into intuitive graphics, thereby establishing an intuitive link from formulation to process to micro-polymer and macro-properties. Two case studies and user interviews demonstrate the efficiency and usability of DeFVIS.
More Data and Execution Structures

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
The biopsychosocial impacts caused by Covid-19: A literature review

Introduction: The pandemic of COVID-19 shook the foundations of modern societies, bringing numerous significant biopsychosocial consequences, which should be highlighted and analyzed in order to structure viable ways to overcome them. Objective: The present study aimed to evaluate the biopsychosocial impacts caused by the pandemic of COVID-19. Materials and Methods: This is an integrative literature review about the biopsychosocial consequences caused by the COVID-19 pandemic. The PICO strategy was used to develop the guiding question. Moreover, the descriptors "COVID-19"; "Psychosocial Impact"; "Health" were cross-referenced with the National Library of Medicine (PubMed MEDLINE), Scientific Electronic Library Online (SCIELO), Ebscohost, Google Scholar, and Virtual Health Library (VHL) databases. Results and Discussion: A significant portion of the articles indicated a correlation between the pandemic of COVID-19 and impacts on the biopsychosocial sphere, such as the manifestation of negative feelings and psychic suffering. In addition, other articles approached the importance of the practice of physical exercises to face the coronavirus, and another part of the articles indicated the direct involvement of health care workers. Conclusion: It was possible to realize that the pandemic scenario compromised the most diverse areas of the biopsychosocial context, reinforcing the need to give new meaning to the health-disease process, based on a more humanistic and social approach.
Analysis of Eye Tracking Characteristics of Latent Groups Based on Reading Ability of Elementary Students with Learning Difficulties

This study analyzes the eye tracking characteristics associated with the reading ability of elementary school students with learning difficulties by using the LPA(latent profile analysis). A total of 32 elementary school students in S city were classified based on the results of the reading test. Eye tracking characteristics of each latent group were compared and analyzed by measuring the movement of the gaze in the reading fluency and silent word reading test. As a result, LPA identified five subgroups with patterns of strengths and weakness in the reading tests. There was a significant difference in the reading test scores for each latent group. Regarding the eye tracking variables for the latent groups, there was a statistically significant difference between the groups in both the total fixation time and regression in the reading fluency test. In addition, it was confirmed that the number of fixed gazes, the number of regressions, and the number of saccades were significant variables in the classification of the latent group. Based on these results, Implications of these results and suggestions for follow-up studies were discussed.
Co-occurrence visualization of Japanese emotion vocabulary by network analysis: Focusing on two-letter kanji nouns

This paper introduces one methodology of Japanese research based on data and aims to explore the co-occurrence relationship of Japanese emotion vocabulary using “network analysis”. The subject of the analysis is “100 books of Shincho Bunko”. Specifically, it collates texts of 65 Japanese novels in “100 of Shincho Bunko”, which completed the morphological comprehensive analysis, to “Japanese sentiment expression dictionary (JIWC-Dictionary)” which was used as a criterion for categorizing emotions. Targeting two-character kanji noun vocabulary, it then uses the network analysis method and visualizes the co-occurrence relationship of the various vocabulary that belong to the emotion categories of “sadness”, “anxiety”, “anger”, “disgust”, “trust”, “surprise”, and “excited”. The two-character kanji noun vocabulary to be analyzed was 33,207 out of 134,370 total tokens, of which 211 were output as “TRUE” from the seven emotion categories. “Anxiety” shows the highest number with 45, followed by “trust” 36, “disgust” 30, “sadness” 26, “surprise” and “excited” each 25, and 24 from “anger”. With these results, we observed the aspect of the vocabulary by emotion category, and at the same time, listed the co-occurrence relationship for the “earthquake” of “anxiety”, which contained the most vocabulary in the emotion category.
Debris Flow Infrasound Denoising Based on Improved Wavelet Threshold Algorithm

Aiming at the problem of signal distortion caused by improper threshold in the processing of debris flow infrasound signals by the traditional wavelet threshold algorithm, an improved adaptive threshold algorithm is proposed. Meanwhile, the improved threshold function is constructed to integrate the advantages of soft and hard threshold functions, and its feasibility is proved on the principle of wavelet threshold denoising. Simulation experiments are carried out on MATLAB software: the improved signal denoising method can get higher SNR and lower mean square error than the traditional method. The improved method is applied to the measured debris flow infrasound signal, and it is found that the improved method can effectively remove the noise and retain the effective signal, which proves that it has certain feasibility in the application of debris flow infrasound signal denoising.
LP Relaxation-Based Approximation Algorithms for Maximizing Entangled Quantum Routing Rate

There will be a fast-paced shift from conventional network systems to novel quantum networks that are supported by the quantum entanglement and teleportation, key technologies of the quantum era, to enable secured data transmissions in the next-generation of the Internet. Despite this prospect, migration to quantum networks cannot be done at once, especially on the aspect of quantum routing. In this paper, we study the maximizing entangled routing rate (MERR) problem. In particular, given a set of demands, we try to determine entangled routing paths for the maximum number of demands in the quantum network while meeting the network&#x2019;s fidelity. We first formulate the MERR problem using an integer linear programming (ILP) model to capture the traffic patent for all demands in the network. We then leverage the theory of relaxation of ILP to devise two efficient algorithms including HBRA and RRA with provable approximation ratios for the objective function. To deal with the challenge of the combinatorial optimization problem in big scale networks, we also propose the path-length-based approach (PLBA) to solve the MERR problem. Using both simulations and an open quantum network simulator platform to conduct experiments with real-world topologies and traffic matrices, we evaluate the performance of our algorithms and show up the success of maximizing entangled routing rate.
Quantum amplitude amplification operators: exact quantum search

Quantum search algorithms show quadratic speedups over their classical counterparts, and the speedup also turns out to be optimal. They share the common structure that an iteration contains two inversions, one with respect to a target state and the other to an initial state, and such iterations are applied O( √ N) times for an unsorted database of N items. In this work, we present the characterization to an iteration that leads to exact quantum search with a quadratic speedup. We show that the fixed-point quantum search algorithm with a quadratic speedup contains iterations that are not QAAOs whereas exact quantum algorithms are sequences of QAAOs. We also demonstrate 3-qubit QAAOs in cloud-based quantum computing services, IBMQ and IonQ.
FAKE REVIEW IDENTIFICATION USING VARIOUS KINDS OF NEURAL NETWORKS – A SYSTEMATIC REVIEW

In modern times, the foremost factors influencing the purchase decision of a customer are the reviews of other customers. People make their purchase decisions based on the reviews given by other people, thereby making these reviews a valuable source of information for any companies that wish to sell products online, and customers that wish to buy these products. The reliance of people on these customer reviews in turn may lead to a concern that people may misuse this platform and write fake reviews to untruthfully promote or devalue a certain product or commodity. These are known as Spam Reviews, or fake reviews, wherein people change, manipulate, and poison reviews for their own benefit. This review paper will focus on the latest techniques that have been developed or are in the research phase in order to deal with this research challenge. The methods previously used for this particular application are Data Mining, Natural Language Processing, Machine learning based on supervised learning architecture, etc. These methods are better developed, but have certain limitations. To provide a clearer picture on the latest methods, this paper shall also discuss methods like Bi-directional LSTM’s, Generative Adversarial Networks, and other related methods based on deep learning.
Analysis of the Moderating Effect of Ego-resilience on the Structural Relationships among Satisfaction of Major, Job Performance Efficacy, and Career Maturity of Pre-service Early Childhood Teachers

Objectives The purpose of this study is to analyze the structural relationships among pre-service early childhood teachers' satisfaction of major, job performance efficacy, and career maturity, and to analyze the moderating effect of ego-resilience.&#x0D; Methods This study analyzed the correlation between the variables based on the results of a survey of 191 students enrolled in the department of early childhood education in the 3-year university in the Gyeonggi region, and a structural equation model was applied to grasp the relationship characteristics. For moderating effect analysis, multi-group structural equation modeling was applied to verify the difference by non-metric variables.&#x0D; Results First, there was a significant correlation between satisfaction of major, job performance efficacy, career maturity, and ego-resilience. Second, satisfaction of major was found to have a direct effect on job performance efficacy and career maturity, respectively, and job performance efficacy was found to have a direct effect on career maturity. In addition, satisfaction of major was found to have an indirect effect on career maturity through job performance efficacy as a medium. Third, for groups with high ego-resilience, satisfaction of major had a positive effect on career maturity, but did not affect job performance efficacy. Job performance efficacy was found to have a positive effect on career maturity. On the other hand, for groups with low ego-resilience, satisfaction of major was found to have a positive effect on both career maturity and job performance efficacy, and have a positive effect on career maturity through job performance efficacy as a medium. That is, in the results of analyzing the moderating effect of ego-resilience, it was confirmed that only the effect of satisfaction of major on career maturity had a moderating effect.&#x0D; Conclusions Career education for pre-service early childhood teachers in teacher training institutions should be conducted with content and methods that can substantially increase satisfaction of major and job performance efficacy, and in addition, it means that psychological diagnosis and counseling support for improving ego-resilience are also important.
Specifics of student youth’s social perception about readiness, competence and self-efficacy

Introduction. Global transformations require an individual to be ready for change, the ability to make decisions in a situation of uncertainty. Theoretical analysis. A comparative analysis allows us to conclude that, depending on the context, readiness is understood differently. Psychological readiness is associated with the presence of knowledge, skills, and is considered as a certain level of activity of the individual, a special emotional-volitional state, the ability to mobilize before the start of activity. There is some overlap between the meanings of the concepts “readiness”, “competence” and “self-efficacy”. Empirical analysis. The study revealed that the concepts of “readiness” and “competence” contains such components as “confidence” and “preparedness”, and readiness and self-efficacy have common components “confidence”, “preparedness”, “goal”. Conclusion. The study made it possible to expand the concept of readiness as a special state, determined by decisiveness, determination, confidence, firmness, concentration and calmness, including strong-willed qualities and the ability to mobilize internal resources to solve a problem or achieve a goal.
Data Loading and Pre-Processing

This chapter provides details about importing some predefined Python libraries, various data pre-processing techniques, and visualization techniques using various modules in python.
Leveraging Block Chain Concept: Service Delivery Acceleration in Post-pandemic Times

The service delivery mechanism in our country is at different stages of maturity across 7000 urban centres where majority of services operate in manual mode or in standalone automation mode. This makes a personal visit to office a must-have requirement for individual to avail the service he desires. In recent pandemic times, it has become challenging to physically visit any public place, thereby making service provision even more difficult. However, with the Digital India initiative automation has taken place in almost all services offerings across all provider departments. The pandemic situation is an opportunity to leverage the work done in service automation space to overcome the need of in person visits. This paper explores avenue towards meeting this objective through proposing information linkage solution which can be scaled up to Block chain initiative over a period of time.
Implementing AI-Based Comprehensive Web Framework for Tourism

In current societies, the movement of all business activities is for effective participation among the market contenders, in addition not just withstanding their physical presence but also challenging their virtual presence. The smart tour recommender is a web-based framework for facilitating tourists through tour planning. Unlike other similar web-based systems our system is unique as it streamlines all the processes required for travel planning making it easy and convenient to use. It provides a plethora of features like providing information about tourist attractions, recommending tours based on the user’s interests, searching hotels or restaurants based on the user’s budget, booking accommodation, and providing users with a personalized itinerary. It focuses on making e-tourism easier and convenient as more and more people use such travel websites to plan their trips.
Architecting the User Interface

With Streamlit, the developer is able to focus on implementing their backend logic while leaving the frontend implementation largely to the framework itself. In addition, using Streamlit you can render responsive interfaces for PC, tablet, and mobile platforms at your fingertips with no added overhead. However, should one need to develop more bespoke and tailored applications, Streamlit affords a considerable degree of customization of its frontend design to the developer without soliciting any knowledge of HTML, CSS, or JavaScript. One may configure their application using a multitude of color schemes, fonts, and appearances both graphically and programmatically.
Managing Files, Directories, and Programs

Python is one of the most popular programming languages, widely used for data analysis and modelling, and is fast becoming the leading choice for scientists and engineers. Unlike other textbooks introducing Python, typically organised by language syntax, this book uses many examples from across Biology, Chemistry, Physics, Earth science, and Engineering to teach and motivate students in science and engineering. The text is organised by the tasks and workflows students undertake day-to-day, helping them see the connections between programming tools and their disciplines. The pace of study is carefully developed for complete beginners, and a spiral pedagogy is used so concepts are introduced across multiple chapters, allowing readers to engage with topics more than once. “Try This!” exercises and online Jupyter notebooks encourage students to test their new knowledge, and further develop their programming skills. Online solutions are available for instructors, alongside discipline-specific homework problems across the sciences and engineering.
Forward and inverse kinematics modeling of 3-DoF AX-12A robotic manipulator

The presence of robots that can assist humans with heavy or dangerous work makes the need for robots more pressing at the moment. One type of robot needed is a robot arm, which is widely used in the manufacturing industry, such as in the assembly process and pick and place. The types of robotic arms used vary both in terms of configuration and the number of degrees of freedom. However, with different types of robotic arms, different models of movement are used. Therefore, research related to the modeling of the robotic arm continues to be carried out to obtain the appropriate movement of the robotic arm. One of the methods used as a first step in designing a robotic arm movement model is kinematics analysis. Kinematics analysis aims to analyze the movement of the robot arm without knowing what force causes the movement. This paper aims to produce an ideal movement model for the AX-12A 3-DoF robotic arm using forward kinematic and inverse kinematic analysis using two methods, the Denavit-Haterberg method and the geometric approach method. The difference from other papers is that this paper makes the kinematics model using Robotic, Vision, and Control (RVC) tools based on the Peter I. Corke model on MATLAB software first before implementing it on hardware. The results show that the error percentage for the forward kinematic model is 1.04% and the inverse kinematic is 0.76%, which means the two models achieved the target that the model’s error maximum must be less than 2%.
Superando las barreras del imaginario social creado sobre la educación a distancia

In this paper, the authors will talk about the development of technological platforms and tools that will support the creation and implementation of techno-pedagogical paradigms to tech the XXI Century generations. By proving how the innovative model of the open and distance learning universities (UNAD, for its Spanish acronym) is not run out and can be renewed, taking as an example the COVID-19 pandemic and analyzing the carriers that provide change to a new education, where the need of innovation in higher education in open and distance modalities is an urgent matter. Undoubtedly, learning is one of the human capacities that has most influenced their development and evolution. Thus, the e-education concept is presented as an effort to develop talents and human capacities using the Internet and new information and communication technologies (ICT). This will allow introducing a generalized remote mode, which will create a different culture adjusting the school curriculum to an online strategy, and not a rigid one, that involves technological improvements, so general and specialized knowledge are combined to the needs of scientific, technological, and social development. Teachers work with students differently and provide help through more knowledge and experience. Recent studies are showing that there are more open and online options for teacher professional development and teachers access the Internet to expand their development opportunities through social media platforms. In this sense, the non-attendance process of education has improved its relevance globally. Although, the COVID-19 pandemic caused an accelerator effect on the digital transformation of the educational institutions.