Reviewerly/exHarmony huggingface dataset:
Life cycles of free reed instruments: The accordion-concertina family and the reed organ-harmonium family as case studies

In 1800, the free reed instruments of European origin did not exist. Beginning in the 1820s, there was very rapid invention and innovation in the development and production of instruments like the reed organ and accordion so that within a few decades these instruments had reached something approaching their final form. By 1900 instruments with hand-driven bellows (accordions and concertinas) and instruments with foot-driven air supplies (reed organs and harmoniums) of a wide variety of types were being manufactured and played worldwide in large numbers. From around 1900 through the 1920s, there was a sharp drop in the number of new reed organs and harmoniums manufactured and sold. This number decreased to near zero by mid-century and has remained there. Meanwhile, the manufacture of the “squeeze box” instruments has remained robust to the present day. Factors involved in the discrepancy between these two families of instruments of almost identical acoustical design will be discussed. These include changes in musical styles and taste, socio-economic factors, and competition from other musical instruments.
Reporting behaviors of familiar identity theft victims: an empirical test of Black’s theory of law

Purpose Familiar identity theft, which occurs when an individual known to a victim steals his/her identity, is a common problem in the USA. The purpose of this study was to understand familiar identity theft victims’ reporting behaviors using Black's (1976) theory of law as a conceptual framework. Design/methodology/approach Data were obtained from the 2012, 2014, 2016 and 2018 administrations of the National Crime Victimization Survey – Identity Theft Supplement. A series of cross-tabulations were conducted to examine the relationship between reporting behaviors and age, minority, gender, income, marital status and education. Fisher’s exact test was used to interpret the findings. Findings Key findings include older familiar identity theft victims who personally lost greater sums of money because of identity theft victimization were more likely to report to law enforcement than younger familiar identity theft victims who personally lost less money. Married familiar identity theft victims were less likely to report to law enforcement than those who were not married. Originality/value This study extends the work of Golladay (2017) to explore the reporting behaviors of a population of identity theft victims that have been largely overlooked in empirical literature.
2D Local identification of the elasticity parameters of heterogeneous materials by the finite element model updating method

Abstract This work is subjected to an identification of elasticity properties which are the Young’s modulus E and the Poisson’s ratio v of a heterogeneous material. We have created the experimental displacements by solving the direct problem. A re-calibration of the experimental and the simulated displacements is done based on the principle of the finite element model updating (FEMU). The minimization of the cost function obtained by the FEMU method in the inverse problem is done by Newton type algorithms. A complete study has been done by studying the sensitivity of the identified values with respect to the refinement of the mesh and with respect to the level of disturbance.
CRYPTOCURRENCY SECURITY

Security and privacy are two conditions that are closely linked to current trends in cryptocurrency, and this study offers a similar comprehensive review. Cryptocurrency adds security to transactions and controls the formation of additional currency units. Great growth for cryptocurrency market testing leads to the misuse of failures to benefit enemies. In this study, the review was designed to focus on safety and security standards of cryptocurrencies especially in Bitcoin. This study explains cryptocurrency agreements, their benefits, and communications within the framework
Interaction with Paintings by Augmented Reality and High Resolution Visualization. A Real Case Exhibition

In this paper, an interactive software system for the enjoyment of the artworks in an art exhibition environment is presented. By using Augmented Reality technology, mobile application and High Resolution visualization we provide the users with a visual augmentation of the paintings and a touch interaction technique to display digital contents for art promotion, allowing exhibition visitors to interact with digital contents in an intuitive and exciting manner. The exhibition here presented is the result of previous research over the use of new technologies (e.g. Augmented Reality) for artwork promotion. Descriptions of the hardware system component and software development details are presented, with particular focus over the application implementation. Furthermore, we outline a possible Multi- media AR Installation connected with a semantic network.
A proof of Cauchy theorem by action of group

Group theory is one of the most important aspects in modern science, mathematics, statistics, and computer technology. It was discovered in the nineteenth century in the context of delivering algebraic expression solutions. The group was defined precisely as the set of all permutations of an algebraic statement's roots that satisfy the condition that any two of these permutations belong to the set. This paper defines finite group actions as well as infinite group actions and provides examples. We reviewed some essential features of group action in this study. A comprehensive demonstration of the Cauchy theorem was also provided as an application of group action theory. Cauchy's theorem is a theoretic statement that claims that elements of all conceivable prime orders exist in a finite group.
Linearization of nonlinear frequency modulated continuous wave generation using model-based reinforcement learning

The prevalence of machine learning (ML) opens up new directions for plenty of scientific fields. The development of optics technologies also benefits from it. However, due to the complex properties of nonlinear and dynamic optical systems, optical system control with ML is still in its infancy. In this manuscript, to demonstrate the feasibility of optical system control using reinforcement learning (RL), i.e., a branch of ML, we solve the linearization problem in the frequency modulated continuous wave (FMCW) generation with the model-based RL method. The experiment results indicate an excellent improvement in the linearity of the generated FMCW, showing a sharp peak in the frequency spectrum. We confirm that the RL method learns the implicit physical characteristics very well and accomplishes the goal of the linear FMCW generation effectively, indicating that the marriage of ML and optics systems could have the potential to open a new era for the development of optical system control.
Keynote: Designing Serverless Platforms to Support Emerging Applications

Serverless computing offerings from cloud providers have gained significant traction in recent years due to the advantages that these platforms bring with their flexible pricing models, built-in scalability, and minimal operational requirements. In a recent survey of serverless use cases, we found a wide variety of applications that depend on these services, including implementing the core functionality at the backend of mobile applications, automating the DevOps tasks of complex distributed applications, real-time processing of IoT streaming data, and scientific applications. To properly support these applications, the platforms should be fast, self-managing, and provide support for diverse QoS requirements. As a result, novel improvements to serverless platforms are rapidly being proposed and adopted. Evaluating these solutions necessitates application-based, workload-aware benchmarking tools that the community can rely on. This talk addresses these challenges and our research efforts on tackling them, presenting a performance engineering perspective about the current state and future challenges of serverless computing research. I will describe our solutions in autonomic resource management for serverless platforms, focusing on solutions that improve performance or reduce costs via scheduling, caching, and right-sizing of resources, along with our ongoing efforts in developing an application-driven serverless benchmark.
Locally connected interrelated network: A forward propagation primitive

End-to-end learning for planning is a promising approach for finding good robot strategies in situations where the state transition, observation, and reward functions are initially unknown. Many neural network architectures for this approach have shown positive results. Across these networks, seemingly small components have been used repeatedly in different architectures, which means improving the efficiency of these components has great potential to improve the overall performance of the network. This paper aims to improve one such component: The forward propagation module. In particular, we propose Locally Connected Interrelated Network (LCI-Net) – a novel type of locally connected layer with unshared but interrelated weights – to improve the efficiency of learning stochastic transition models for planning and propagating information via the learned transition models. LCI-Net is a small differentiable neural network module that can be plugged into various existing architectures. For evaluation purposes, we apply LCI-Net to VIN and QMDP-Net. VIN is an end-to-end neural network for solving Markov Decision Processes (MDPs) whose transition and reward functions are initially unknown, while QMDP-Net is its counterpart for the Partially Observable Markov Decision Process (POMDP) whose transition, observation, and reward functions are initially unknown. Simulation tests on benchmark problems involving 2D and 3D navigation and grasping indicate promising results: Changing only the forward propagation module alone with LCI-Net improves VIN’s and QMDP-Net generalisation capability by more than 3× and 10×, respectively.
IAG Membership Information

Geostandards and Geoanalytical ResearchVolume 46, Issue 2 p. 379-379 Membership InformationFree Access IAG Membership Information First published: 19 May 2022 https://doi.org/10.1111/ggr.12389AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume46, Issue2June 2022Pages 379-379 RelatedInformation
Illusions in reasoning with quantifiers

The mental model theory postulates that reasoners build models of the situations described in premises, and that these models normally make explicit only what is true. A computer program revealed an unexpected consequence of the theory: it predicts that certain inferences should have compelling but erroneous conclusions. Two experiments corroborated the existence of such illusions in inferences about what is possible given quantified assertions, such as ‘At least some of the plastic beads are not red.’ Experiment 1 showed that, as predicted, participants erroneously inferred that impossible assertions were possible, and that possible situations were impossible, but they performed well with control problems. Experiment 2 demonstrated the existence of similar illusions in inferences from dyadic assertions, e.g. ‘All the boys played with the girls’.
Implementasi Virtual Reality pada Game Edukasi Protokol Kesehatan

In the year of 2020 until now has been a tough year for almost all industries and the general public due to the coronavirus pandemic. However, the increase in cases per day illustrates that the community has not complied with health protocols optimally. The impact of the pandemic has increased gamers in Asia by more than 75%. With a positive impact on society, the role of games can be used as a medium for education. Virtual Reality will be implemented in the health protocol educational game to see the good role of virtual reality in educational game interactions. The implementation of Virtual Reality in the health protocol educational game is carried out using the GDLC (Game Development Life Cycle) approach with stages starting from initiation, pre-production, production, testing, and release. The results of the recapitulation of the questionnaire on the acceptance and satisfaction of the health protocol educational game application showed the results of 44,9.% strongly agree, 44,9% agree, 9,15% are neutral and 1% are not agree that the application has functioned well and can be selected as one of the learning media especially about health protocol to reduce the growth rate of people infected with the coronavirus.
Spontaneous parametric down-conversion: Revisiting the parameters of transverse entanglement outside the near zone

The reduced density matrix ${\ensuremath{\rho}}_{r}(x,{x}^{\ensuremath{'}})$ of the transverse biphoton state beyond the near zone is found to be real on and only on diagonals in the plane of the photon's transverse coordinates $(x,{x}^{\ensuremath{'}})$. This remarkable feature is found to occur at any distances of photon propagation with diffraction spreading completely taken into account. The functions of the reduced density matrix at two orthogonal diagonals, ${\ensuremath{\rho}}_{r}(x,x)$ and ${\ensuremath{\rho}}_{r}(x,\ensuremath{-}x)$, are considered as the only two complementary single-particle distributions generated by the reduced density matrix. The ratio of widths of these two distributions is interpreted as the new entanglement parameter ${R}_{\mathrm{diag}}$ which is found to be very close to and completely compatible with the Schmidt entanglement parameter $K$.
Investigating the effect of drumstick diameter on drum sound level

Acoustic drum sets typically generate high sound levels when played. Over decades, players have developed numerous techniques and manufacturers have created various products to reduce sound levels. Many of these approaches and products not only lower sound levels but also alter the drum sound, which can impact sound quality for the drummer and listener. For example, multi-rods, a collection of wooden rods wrapped together, have been created as a quieter "alternative" to traditional drumsticks. Perceptively, multi-rods do not replicate the sound of a drumstick, however, creating their own unique sound and vibe. The same can be said for brushes, a collection of retractable metal bristles held together by a hollow case. Reducing the diameter of a traditional drumstick design may generate lower sound levels while maintaining a traditional drum sound. This study investigates the effect on sound level due to changes in drumstick diameter.
Proof of payment is required for conclude the Ministry of University-Industry Collaborative Education Program

Starting from the 2021 first phase of Ministry of Education University-Industry Collaborative Education Program (UICEP) promulgated in 2021, a proof of payment must be submitted and the principal must list the program outcomes in the UICEP platform (http://cxhz.hep.com.cn) in in order to conclude the program.
Two Level Authentication for E-Voting System Using IoT Technology

Voting is a citizen's most fundamental right and one of his or her most significant obligations. The votes we cast determine the country's destiny, and we must vote honestly and without succumbing to any pressure, since, as the saying goes, "with power comes tremendous responsibility." We must guarantee that our votes are not tampered with after they have been cast. As a result, the existing voting method (EVM) is simple to manipulate, i.e., there is a lot of human interaction, which might jeo pardise election outcomes. As a result, in this project, we will attempt to address this vulnerability by implementing a two-step verification system that will help avoid the middle man attack, namely, only when the voter is physically present can the voter's fingerprint be registered, and only then will the OTP be sent to the user's mobile number.
Improving the indicator framework for general education evaluation

Subject. This article discusses the Russian Federation national policy in the field of education. Objectives. The article aims to improve the tools for evaluating the general education quality. Methods. For the study, we used comparative, systems, quantitative, and qualitative analyses, and the information categorization technique. Results. Based on the identified shortcomings of the system for determining the quality of general education, the article offers additional indicators that can be assessed in the process of monitoring the quality of general education. Conclusions. It is necessary to amend the laws and statutory instruments regulating the evaluation of the general education quality.
Application of cloud-based data analysis in applied mathematics

The application field of big data technology is very wide. In any industry, data and information can be analyzed through big data, and the results of these analysis are of high practical value. However, with the advent of the Internet era today, various fields are constantly developing, including the education circle. Using network education to carry out a series of teaching activities, can comprehensively improve the effectiveness of education. In the current COVID-19 epidemic, many schools and many industries have adopted computer technology and big data technologies, such as web conferencing, online classroom, remote conferencing, remote video and other teaching and production activities. This paper focuses on the role and value of computer data analysis in applied mathematics. The research shows that cloud data analysis has super high application value in applied mathematics.
A New Design of Occlusion-Invariant Face Recognition Using Optimal Pattern Extraction and CNN with GRU-Based Architecture

Face detection is a computer technology being used in a variety of applications that identify human faces in digital images. In many face recognition challenges, Convolutional Neural Networks (CNNs) are regarded as a problem solver. Occlusion is determined as the most common challenge of face recognition in realistic applications. Several studies are undergoing to obtain face recognition without any challenges. However, the occurrence of noise and occlusion in the image reduces the achievement of face recognition. Hence, various researches and studies are carried out to solve the challenges involved with the occurrence of occlusion and noise in the image, and more clarification is needed to acquire high accuracy. Hence, a deep learning model is intended to be developed in this paper using the meta-heuristic approach. The proposed model covers four main steps: (a) data acquisition, (b) pre-processing, (c) pattern extraction and (d) classification. The benchmark datasets regarding the face image with occlusion are gathered from a public source. Further, the pre-processing of the images is performed by contrast enhancement and Gabor filtering. With these pre-processed images, pattern extraction is done by the optimal local mesh ternary pattern. Here, the hybrid Whale–Galactic Swarm Optimization (WGSO) algorithm is used for developing the optimal local mesh ternary pattern extraction. By inputting the pattern-extracted image, the new deep learning model namely “CNN with Gated Recurrent Unit (GRU)” network performs the recognition process to maximize the accuracy, and also it is used to enhance the face recognition model. From the results, in terms of accuracy, the proposed WGSO-[Formula: see text] model is better by 4.02%, 3.76% and 2.17% than the CNN, SVM and SRC, respectively. The experimental results are presented by performing their comparative analysis on a standard dataset, and they assure the efficiency of the proposed model. However, many challenging problems related to face recognition still exist, which offer excellent opportunities to face recognition researchers in the future.
Managing Learners’ Memory Strength in a POMDP-Based Learning Path Recommender System

This paper views the learning path recommendation task as a sequential decision problem and considers Partially Observable Markov Decision Process (POMDP) as an adequate approach. This work proposes M-POMDP, a POMDP-based recommendation model that manages learners’ memory strength, while limiting the increase in complexity and data required. M-POMDP has been evaluated on two real datasets.
Reform and Practice of Public English Examination Mode in Colleges and Universities Using Big Data Analysis and Speech Recognition

In terms of the CPE (College Public English) test mode, it suffers from inefficiency and uniqueness. The test must be comprehensive and objective in order to serve as a standard for evaluating English proficiency. Promoting English examination reform and practice is extremely important for English teachers. In light of the current situation, the CPE test system is being researched and designed using BD (big data) analysis. To establish the index system and parameters for evaluating test questions, the test database maintenance module uses a method based on problem-solving theory. For pronunciation recognition, error judgment, and scoring of spoken English, speech recognition and HMM (hidden Markov model) scores are used, and the final spoken English score is obtained through percentile transformation. In intelligent scoring, the AdaBoost CT (adaptive enhancement classification) algorithm is introduced, and various algorithms are compared through extensive literature reading. The results show that when the parameter is set to 50, the AdaBoost CT algorithm has the highest accuracy, at 88.39%. The platform improves students’ learning efficiency and quality by diagnosing their learning problems and intervening in their learning in real time. The results are significantly better than those of other students.
Analysis of Korean-English Patent Terminology Translation Errors in Neural Machine Translation — A Case Study Based on Google Translation Results.

Terminology translation plays a key role in technical machine translation. This paper conducts quantitative and qualitative evaluation and presents a comprehensive error analysis on terminology translation from Korean to English. For this study, 637 terms are selected from a patent domain corpus. Among them, 425 terms are translated correctly while other 212 terms have one or more errors. Those identified errors are classified into seven error categories which include addition, omission, lexical errors, order errors, morphological errors, partial errors, and miscellanies. Among them, partial errors and morphological errors are the two top error categories, and lexical errors and omission cases are also high. The study also presents n-gram wise error rates. The calculation of n-gram wise terminology translation error rates shows that the curve for the Google Translation results goes upwards with the increasing size of n. This shows that neural machine translation can be vulnerable in translating a higher-order of n-gram terms. This study has implications for Korean-English machine translation research, in that it classifies error cases spotted in machine translation of terminology which is critical in securing accuracy of translations.