Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis using Machine Learning

With the rise of e-commerce, consumers are becoming accustomed to shopping online and leaving reviews of their experiences on retailer and review websites. These op-ed pieces provide future customers with useful information for making decisions, and they also help businesses improve their goods and/or services. Yet, when the number of reviews increases quickly, users are forced to deal with a serious information overload issue. Several opinion mining techniques, such as opinion summarization, opinion polling, and comparative analysis, have been suggested to address this issue. How to correctly estimate the sentiment direction of review sentences is the main challenge. Sentiment analysis is a quickly developing subfield of the study of Natural Language Processing (NLP).In recent years, it has drawn a lot of interest. Sentiment analysis is used to assess or investigate user comments in order to make judgements about their opinions. Sentiment analysis is a machine learning (ML) technique in which computers classify and investigate human attitudes, feelings, and viewpoints towards the things that are communicated through text, star ratings, thumbs up and down, and other textual or graphical representations. The data for this study came from online product reviews acquired from the model website we created. Adjectives and adverbs can convey the opposite feeling with the use of negative prefixes. Such words are located using a negative phrase identification method. By using evaluation measures, the performance is assessed. Finally, we provide insight. Finally, we also provide a preview of our upcoming sentiment analysis research.
QGA–QGCNN: a model of quantum gate circuit neural network optimized by quantum genetic algorithm

Using a global optimization algorithm to optimize the initial weights and thresholds of traditional neural network models can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slow convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA–QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA–QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA–QGCNN model. Experimental results show that the proposed intrusion detection model has a lower false alarm rate and significant accuracy compared to conventional attack detection models. And QGCNN optimized by QGA improves the convergence performance of the model.
Rapid earthquake magnitude estimation combining a neural network and transfer learning in China: Application to the 2022 Lushan M6.1 earthquake

Introduction: China is one of the most seismically active countries in the world. It is an important task for a Chinese earthquake early warning system to quickly obtain robust magnitude estimation. However, within the first few seconds after P-wave arrival, there is considerable scatter in magnitude estimation for traditional methods based on a single early warning parameter. Methods: To explore the feasibility of using a convolutional neural network for magnitude estimation in China, establish a magnitude estimation model suitable for China and provide more robust magnitude estimation based on strong-motion data from China, we propose a new approach combining a convolutional neural network and transfer learning (TL) to construct a magnitude estimation model (TLDCNN-M) in this study. Results and Discussion: Our results show that for the same test dataset, in terms of the mean absolute error and standard deviation of magnitude estimation errors, the TLDCNN-M model has better performance than traditional methods and convolutional neural network models without using TL. Meanwhile, we apply the method to the 2022 Lushan M6.1 earthquake occurred in Sichuan province, China. At 3 s after the earliest P phase, the magnitude estimation error is less than 0.5. With the increase in time after the earliest P phase, the magnitude estimation is close to the catalog magnitude; at 10 s after the earliest P phase, the magnitude estimation error is less than 0.2.
Augmentative and Alternative Communication/ Hearing Impairments

Data-driven technologies aid in effective communication for deaf people. Research on sign language recognition, sign language generation and tools based on them is going at a fast pace. With the easy availability of depth sensors, specialized data sets, efficient machine learning algorithms, and computational capabilities provided by specialized hardware, the development of efficient data science-based solutions for deaf users and people with difficulty in hearing is possible now. This chapter focuses on recent research on Automatic Sign Language Recognition (ASLR), Sign Language Production (SLP) and tools based on them. A major focus of this chapter is research and tools using Sign Languages since they are the most commonly used communication medium by deaf people. Research on sign languages from different parts of the world as well as the effectiveness of Machine Learning techniques for ASLR and SLP, are discussed in detail.&lt;br&gt;
A novel computerized adaptive testing framework with decoupled learning selector

Abstract Computerized adaptive testing (CAT) targets to accurately assess the student’s proficiency in the required subject/area. The key issue is how to design a question selector that adaptively selects the best-suited questions for each student based on previous performance step by step. Most existing question selectors execute via greedy metric functions (e.g., question information and uncertainty), which can not effectively capture data characteristics. There also exist learning-based question selectors that redefine the CAT problem as a bilevel optimization problem, where the parameter learning of the question selector and the student proficiency estimation model are coupled, which is not flexible enough. To this end, in this paper, we propose a novel CAT framework with Decoupled Learning selector (DL-CAT). Specifically, we first use the currently estimated student ability and question characteristics as input and design a deep learning-based question selector to predict question selection scores. Then, to address the issue that there is no ground truth to measure the quality of the selected question, an approximate ground-truth and a pairwise rank loss function are specially designed to update the parameters of the question selector independently. Extensive experiments on two real datasets demonstrate that our proposed DL-CAT has certain advantages in effectiveness and significant advantages in efficiency.
Mathematical Modelling and Analysis of the Simplest Fuzzy Two-Input Two-Output Two-Term Controller of Takagi−Sugeno Type

Mathematical modelling of the simplest Takagi-Sugeno fuzzy Two-Input Two-Output (TITO) Proportional-Integral/Proportional-Derivative (PI/PD) controller is presented in this paper.Mathematical model of fuzzy PI/PD controller is proposed using Algebraic Product (AP) t-norm, Bounded Sum (BS) t-co-norm and Center of Gravity (CoG) defuzzifier.The inputs are fuzzified by fuzzy sets having L and type membership functions.The rule base consists of five rules with different linear models in the consequent parts.Both static and dynamic couplings are taken into account while deriving the models.The model of the fuzzy PI/PD controller reveals that it is a variable gain/structure controller.Also, each output of the TITO fuzzy controller is the sum of two nonlinear PI or PD controllers with variable gains.The properties and the gain variations of the controllers are investigated.
Geospatial model of composition of water service lines in Flint,<scp>Michigan</scp>: Validation using excavation data and a new compositional geostatistical approach

Abstract Following the Flint drinking water crisis, a service line (SL) replacement program was implemented to replace lead SLs and galvanized SLs connecting residences to Flint's water system, leading to the excavation and inspection over a 5‐year period (2016–2020) of a total of 26,750 lines, representing close to 50% of all tax parcels in the City of Flint. These recent data were used to validate an earlier geospatial model created by residual indicator kriging (IK) to predict the probability that a home has a lead, galvanized, or copper private‐side SL based on neighboring house inspections (i.e., 3254 homes visited in 2017) and secondary information (i.e., built year and city records on SL composition). Receiver operating characteristic curves indicated an average frequency of detection (i.e., area under the curve [AUC]) of 0.9 for copper and galvanized material and 0.6 for lead service lines. Predicting the composition of SL at unmonitored residences by IK, however, can result in negative probabilities of occurrence and probabilities that do not sum to 1. These limitations were overcome by adopting simplicial IK, whereby data undergo a logratio transform before the geospatial analysis. This first application of a compositional approach to SL data improved the detection of lead SLs (AUC = 0.8 vs. 0.6) while providing coherent predictions. Incorporating secondary information, in particular using standardized cokriging and a new rescaled cross‐semivariogram estimator introduced to correct for geographical clustering of house inspections, increased the accuracy of the prediction.
Methodological Aspects of the Process of Digitization of the Education System in the XXI Century

The author discusses methodological aspects of digitalization of the education system in Russia. The author's research team focuses on the philosophical-anthropological and socio-pedagogical dimensions of the studied phenomenon of the XXI century. It is shown that in the field of education, the first steps towards digital literacy of all participants in educational relations were made back in the late 1990s. During this period, this process was called "informatization" of the educational environment of educational institutions. The transition to the introduction of a wide range of information and computer technologies into the pedagogical process took place during the implementation of the first national project "Education" (2005-2010). In the 2010s, big data analytics, the all-encompassing use of artificial intelligence and the Internet of Things, testing the possibilities of virtual and augmented reality, the use of 3-D printing in teaching – these and other new technologies allowed us to move to a new stage in the development of the Russian education sector. The formation of the educational environment in the context of digital transformation encourages understanding of the prospects and significant risks that the digitization process brings to the consumer society. The use of philosophical and anthropological analysis allowed the authors to identify the following risks: the risk of the formation of an individual with a "one-dimensional consciousness" in the educational environment, the tendency of the destruction of the ideological matrix of personality under the influence of digitisation, the risk of destructive transformation of the role of the teacher with the subsequent replacement of the latter with "gadgets" and software shells with "artificial intelligence", the risk of dictate from the "technocrats-developers" of educational content, the risk of worsening the health of students due to the adverse effects of digital technologies.
Plugging System of Rf Connector Based on the Fusion of Monocular and Binocular Vision

Abstract Aiming at the problems of poor consistency and low efficiency in the manual plugging test of 5G RF(Radio Frequency) device, an intelligent plugging system based on robotic manipulation and machine vision is proposed. The mathematical model of the RF device is firstly established. The pixel size calibration and hand-in-eye calibration algorithms are then designed. A positioning algorithm is proposed, consisted of monocular vision and binocular vision. The monocular vision is employed to calculate the position information of the 5G RF device, and consequently, the binocular vision facilitates more accurate positioning, which reduces the positioning error between the actual model and the theoretical model and improves the positioning accuracy. In the binocular ranging, this algorithm improves the traditional Census transform by adopting a double cross window and utilizing the median value of the pixels in the window as the reference value, effectively reducing the impact of noise and meanwhile preserving detailed information. The experimental results show that the average absolute error of the coordinate axis X direction is 0.33mm, the average absolute error of the coordinate axis Y direction is 0.46mm, the average absolute error of the coordinate axis Z direction is 0.27mm, and the plugging success rate is 100%.
Fast Two-position Initial Alignment of SINS Based on CKF

Abstract In practical vehicle applications, the initial alignment environment of SINS is more complicated, and the coarse alignment accuracy is not high due to carrier sloshing, which leads to the filtering problem of nonlinear system in fine alignment. The traditional two-position alignment method based on EKF is no longer applicable. In this paper, a fast two-position alignment method of SINS based on CKF is proposed. Use two CKFs for state estimation at two locations respectively, then the error parameters are solved through the spatial relationship between the two positions. The simulation shows that using CKF improves accuracy compared to EKF. Verified by experiment, the two-position alignment method based on CKF can better adapt to the nonlinear system, which can be applied in practical engineering.
The Misadjustment Error of a New Cylindrical Surface Testing Method

Abstract A cylindrical combination testing method is proposed in this paper. Three cylindrical mirrors are combined in pairs. By constructing a confocal optical system for interference testing to obtain combined wavefront aberration. The cylindrical surface errors are calculated by the combined wavefront aberration. The source of misalignment error is analyzed in detail and the mathematical model combined with the Chebyshev polynomial coefficients is established. That the misalignment error can be effectively removed by polynomial fitting is proved by simulation.
Artefact Indicator for Flow‐based Temporal Interpolation of Power Doppler Ultrasound

Abstract Power Doppler ultrasound is a sonography technique to detect and visualise blood circulation. Its acquired image sequences suffer from poor temporal resolution and artefacts caused by aliasing, noise, large displacements, shearings or out‐of‐plane movements. These pitfalls distort flow fields and have unforeseen consequences on flow‐based temporal interpolations. We propose a method to detect artefacts caused by such distorted flow fields and to improve the visual quality by replacing regions with detected artefacts by locally better results. This is demonstrated on real data.
Light Source Optimization of 3D Inner Contour Measurement System Based on Ring Beam Method

Abstract Ring beam method is a simple and efficient method for measuring three-dimensional internal contour. The whole three-dimensional inner contour is stitched from the contour lines of cross-sectional images collected during scanning. The contour image with uniform and moderate light intensity and fine ring band is beneficial to the calculation of contour line extraction algorithm. In practice, due to the quality of the ring beam and the modulation effect of the sample surface microstructure, the light band of the contour image has local widening, overexposure and speckle phenomena, which directly affect the contour extraction effect. The ring beam generation system is optimized here, which makes the whole system have better size adaptability and convenient adjustment. In addition, experiments results show that the measurement repeatability of the cross-sectional profile obtained after the light source optimization has been effectively improved.
Face Value

As their use in consumer electronics becomes more widespread, facial recognition technologies must be painstakingly tested to ensure their safety and effectiveness, says Radiant Vision Systems.
Crossover Service Infrastructure: Service Network

Although enterprises in the crossover service ecosystem open their own internal services, they also need to invoke open services from the other service participants to collaborate and complete the complex BP. The collaboration of services between crossover businesses requires microservices to invoke each other. The myriad microservices, which are distributed across multiple organizations, form a heterogeneous, complex, and distributed service-invocation and -operation network, which we call a service network. Crossover service collaboration must shield this heterogeneous environment and provide a virtual transparent and virtual unified convergence environment for service governance.
Crossover Service: A Brief Overview

With the development of crossover in the modern service industry (MSI), various traditional boundaries have been broken, and the cross-integration of services has led to the need for crossover services. A crossover service is the deep convergence and model innovation of services that cross the boundaries of different industries, organizations, and value chains, aiming to provide users with high quality and high value services. The key to achieving crossover service is convergence, which includes the deep multidimensional convergence of patterns, requirement design, runtime environment, quality, and value.
On the Results in Physics Obtained in 2020‒2021

This article is based on the annual reports presented by the Head of the Physical Sciences Division of the Russian Academy of Sciences at the General Meetings of the Division in April 2021 and April 2022. It describes some scientific results in physics obtained in 2020–2021 at the research institutions (institutes of the Russian Academy of Sciences) under the individual powers of the Russian Academy of Sciences stipulated by the regulations of the Government of the Russian Federation no. 521 dated June 5, 2014, and no. 1652 dated December 24, 2018. The information about the research in different fields of physics carried out at these institutions is summarized briefly and illustrated by examples of some bright scientific breakthroughs.
Reflections and Prospects; Problem-based Learning and Co-op Education in Undergraduate Engineering Programs focusing on Incongruity between Student’s Major and Company’s Field of Activity

While some failures or inadequate operations have been pointed out for a final research project done by senior students of undergraduate engineering education, the possibility of PBL (problem-based learning) programs in the curricula set in HEI has attracted prospects to nurture problem-solving skill and attitude among future engineers. The authors have been in charge of directing college students to tackle PBL projects as a regular and obligatory subject in the newly implemented program. At the opening of the curriculum, it was considered that differences between the technological or scientific fields of collaborating firms or organizations and the students’ majors or interests could be of the biggest barrier to successful PBL, however, we observed some cases the students rather acquired problem-solving skill based on the interdisciplinary point of view so effectively even with these gaps as a result. This paper compares PBL cases with the “well-matched goal” and the “ill-defined problem” of the project presented to the student, and shows the hopeful perspective of PBL in undergraduate engineering programs reflecting on problem-set research projects that might not raise students’ independency and/or interdisciplinarity.
Effects of Field-of-View Expansion Using a Wide-field HMD on Active Linear Motion

Visual information is the most important information that humans use to perceive their own movements. In this study, we investigated how the perception of our own movement changes when we walk with a more expanded field-of-view (FOV). To expand the visual field, we constructed an FOV expansion system using a wide-field head-mounted display (HMD), two omnidirectional cameras, and a control PC. Using this system, the human FOV could be freely expanded from 160 to 360° in real-time. The results of the experiment indicated that the greater the expansion of the FOV, the faster the subject perceived their own walking speed. In addition, there was one case wherein the faster the perceived walking speed was, the shorter the perceived walking distance was. These results suggested that FOV expansion could create the illusion of increased athletic performance.
Distance Learning Assignments for Mechanical Design and Drafting by Onshape

The selection criteria and the selection results of a distance learning assignments on "mechanical design and drafting" considering the Corona Disaster are studied, which were examined through the actual confirmation work by the students in the author's laboratory. From the actual confirmation works, it was found that Onshape was effective for distance lectures on mechanical design and drafting, and for providing an environment for conducting collaborative assignments. However, it also became clear that there are technical issues to consider when using Onshape to perform the drafting of a classic manual winch. The actual lecture management was conducted in collaboration with the university's technical staff. The effectiveness of the collaborative lecture by the teaching staff and technical staff is described.
Investigation of simultaneous estimation of state and unknown parameters using a modified Kalman filter incorporating with moment equations in the prediction step

The modified Kalman filter, incorporating stochastic dynamic analysis in the prediction step, was introduced in a previous paper to systematically deal with nonlinear and parameter uncertainty excitation systems. In addition, the KF-SDA filter was extended to parameter estimation problems. However, the problem of simultaneous estimation of state and unknown parameters has not been developed in previous research. The problem of simultaneous estimation of state and unknown parameters is of great engineering significance as a method of state estimation and system identification under the limitation of the number and types of sensors. This paper deals with a method to apply KF-SDA to the problem of simultaneous estimation of state and unknown parameters. As a basic verification of the proposed method, numerical simulations are performed for a single-degree-of-freedom system which is subject to random noise excitation of parameters. Furthermore, the cases that the estimated value and the true value agree and the cases that they do not agree are considered using the time required for convergence.