Reviewerly/exHarmony huggingface dataset:
Measurements and simulations of sung multiphonics on a trombone played by an artificial mouth

Playing a sung multiphonic on a brass instrument is a specialised technique in which the performer sounds one pitch through lip vibration while simultaneously singing a different pitch. The result is typically a complex sound in which several different pitches are audible. A full acoustical description of the sound generation process would include the mutual interactions of the two nonlinear flow valves (vocal folds and lips), the player’s windway resonances, and the resonances of the instrument. Velut et al. (JASA 2016) reported measurements of mouth pressure, pressure inside the mouthpiece, and radiated sound while a trombone player performed various sung multiphonics. A simplified time domain simulation model replacing vocal folds and windway by a sinusoidal source was partially validated. In the present work the human player has been replaced by an artificial mouth, allowing the “sung” mouth pressure to be provided either by a sinusoidally excited loudspeaker or a human singer. Results are broadly in agreement with those of Velut et al., and with time domain simulations using sinusoidal forcing in the mouth cavity.
Developing SLH Theory for Use with Microwave Circuits

Circuit modeling and simulation techniques are necessary to the development of advanced microwave architectures. In particular, microwave circuits used in the quantum regime can produce a variety of single-photon phenomena related to qubits such as unitary gate operations, quantum illumination, and quantum interferometry. However, classical microwave formalism is incomplete to sufficiently describe these systems at this level. Noisy environments, for instance, destroy internal quantum information at the single-photon level, which makes it difficult to reliably control a quantum state. A theoretical infrastructure to account for this problem has been well-established in quantum optics as quantum input-output network (QION) theory. In this work, we consider an extension to QION theory, called SLH theory, to expand quantum stochastic differential equations to multi-port networks. A mathematical object composed of a network’s scattering parameters (S), coupling vector (L), and internal energy (H), known as an SLH triple, can fully describe its dynamical evolution at the quantum level. We show how microwave components can be converted into an SLH framework that retains the mathematical formalism of classical S-parameter techniques at the quantum limit. This makes SLH theory applicable to many interdisciplinary subfields within physics and engineering. We describe how a dynamical circuit model with universal quantum multi-port networks can be devised within SLH formalism. This can be applied to microwave circuit designs in the quantum limit.
FORMATION OF PROFESSIONAL SKILLS OF FUTURE MATHEMATICS TEACHERS TO WORK IN THE SYSTEM OF ADDITIONAL MATHEMATICAL EDUCATION

The article is devoted to the peculiarities and prospects of practical training of future mathematics teachers to carry out cultural and educational activities in mathematics in educational institutions, including in the system of additional mathematical education. The experience of organizing educational and industrial practices of students of the directions 44.03.01 Pedagogical education, profile «Mathematics» and 44.03.05 Pedagogical education, profile «Mathematics and Computer Science», studying at the Institute of Mathematics, Mechanics and Computer Science named after I.I. is given. Vorovich of the Southern Federal University of Rostov-on-Don.&#x0D; The article describes the main activities of students during academic and industrial practices: the study of the specific content of additional mathematical education, methods and means of organizing training; the development of didactic materials for the implementation of classes in the field of additional mathematical education; the implementation of career guidance in schools of Rostov-on-Don; conducting extracurricular activities in mathematics in educational institutions.
On the issue of using a training ground “Square” for training the patrol-guard service officers

The research object is the process of teaching police officers the basic elements of patrol-guard service. The research subject is the systematization of using a training ground &amp;ldquo;Square&amp;rdquo; as one of effective ways of reinforcing skills required for patrol-guard service. The training ground &amp;ldquo;Square&amp;rdquo; is considered as a separate type and means of teaching. The author attempts at solving the problem of using the training ground &amp;ldquo;Square&amp;rdquo; for training police officers in new conditions connected with the application of interactive technologies and the combination of various elements of the modern process of police officers training.&amp;nbsp; The author substantiates the role of training grounds in the formation of professional competence of the patrol-guard service officers. The author formulates the main requirements to the module configuration and technical equipment of the training ground &amp;ldquo;Square&amp;rdquo;. The research reveals the peculiarities of conducting classes on a training ground, the author analyzes the use of general pedagogical and special teaching method, and designs a logically structured system of teaching with the help of a training ground. The research can be used for further improvement of the process of patrol-guard officers training. The scientific novelty of the research consists in the fact that it proves the good perspectives of using the training ground &amp;ldquo;Square&amp;rdquo; as a means of teaching. &amp;nbsp;
Unmanned Vehicle Task Scheduling Method Based on Iterative Cognitive Interaction

At present, in the field of autonomous driving, the application of unmanned vehicles has made considerable progress. However, there are still a large number of engineering problems to be solved in practical application. For example, the ability of multi-vehicle interactive autonomous operation in high-dynamic and time-sensitive environment is not sufficient for large-scale application. These kinds of problems restrict the effective integration of unmanned intelligent transportation system and current traffic system. Facing the problems described above, this paper focuses on introducing knowledge-driven and experiential memory decision-making process for agents, which is based on the research on the process characteristics of human brain cognitive reasoning under the condition of dynamic changes in the observation results of multi-agent clusters. Thus, the method of unmanned vehicles tasks scheduling method based on iterative cognitive interaction is proposed in this paper. In view of the behavior and decision-making process of multi-vehicle in aspects of cognition, interaction, and association, the analysis and research on the time scale will provide a research route for solving the continuous autonomous operation of multi-vehicle system in a high dynamic environment. Contribution of this paper can provide theoretical basis and practical value for multi-application fields.
Shadow Modeling and Detection

Shadows are the common phenomena in most outdoor scenes, which bring many problems in image processing and computer vision. In this chapter, we present two novel methods focusing on modeling and extracting shadows from a single outdoor image, i.e., tricolor attenuation model and tricolor linear model. The tricolor attenuation model (TAM) describes the attenuation relationship between shadow and its non-shadow background, and it is derived based on image formation theory. The parameters of the TAM are fixed by using the spectral power distribution (SPD) of daylight and skylight, which are estimated according to Planck’s blackbody irradiance law. Based on the TAM, a multi-step shadow detection algorithm is proposed to extract shadows. The shadow linearity model is a linear ratio model which is derived from the perspective of physical imaging. The parameters of the linearity model are also fixed by the SPD of daylight and skylight, which are estimated according to our SPD calculation method proposed in Chap. 2 , and they do not depend on the reflection curve of an object and the model can be applied to different scenes. Besides, we find additional new physical properties of shadows based on the tricolor linear model and take these shadow properties as features to design an effective shadow detection method. Finally, we conduct an extensive evaluation of eight widely adopted shadow features for shadow detection. Through sets of experiments, several useful and interesting conclusions are obtained.
Dynamic Chaotic Look-Up Table for MRI Medical Image Encryption

Nowadays, a variety of cryptosystem based on the chaos theory have been proposed. In this paper, we propose a new scheme encryption for Magnetic Resonance Imaging (MRI); medical images, using the chaos theory to define a dynamic chaotic Look-Up Table (LUT). Theoretic analyses and simulation results show that our scheme is secure and efficient. Also, the proposed cryptosystem is resistant to the known plaintext attack.
Research on high-reliability intelligent-sensing health service support platform and key technologies based on Biometrics and blockchain security technology

A new type of high-reliability intelligent-sensing health service support platform and its key technologies are introduced in this article. By the technologies of automatic data collection, perceptual data removal, abnormal data detection, perceptual heterogeneous data identification, it is realizable to collect, analyze and process the health data of users. In order to solve the security problem in the process of data transmission, biometric identification and blockchain are used to realize the high-reliability transmission. At the end of the paper, a high-reliability intelligent-sensing health service support platform is built. And the implementation and service support process are expounded, indicating that the platform has high practical value.
Super-resolution algorithm combining SAE dictionary learning and anchored neighborhood regression

To improve the efficiency of the super resolution algorithm based on dictionary learning, a super-resolution algorithm combining sparse autoencoder dictionary learning and anchored neighborhood regression is proposed. The sparse autoencoder with outstanding learning ability is used to learn a dictionary witch has better feature expression ability in the stage of dictionary learning. For the improvement of autoencoder, the mean absolute error principle is taken as the reconstruction error term to improve the accuracy of model error measurement. In the stage of data preprocessing, the whitening technology is used to construct the low redundancy input data to improve the generalization ability of sparse autoencoder dictionary learning model. In the stage of image reconstruction, the dictionary obtained is applied to the super-resolution algorithm based on anchored neighborhood regression to achieve fast real-time reconstruction by reducing the computation of sparse coding. In this study, the proposed super-resolution algorithm combines the advantages of sparse autoencoder model and anchored neighborhood regression, which can not only improve the quality of image reconstruction, but also guarantee the reconstruction speed at the same time. So it has high reconstruction efficiency.
Waring’s Problem for Cubes

The analytic Hardy–Littlewood circle method was originally developed to treat additive problems in number theory, typically concerning the representation of large positive integers as a sum of integers drawn from a particular set, such as the set of primes, squares or cubes. All of the examples in this book concern cubic polynomials and the goal of this chapter is to illustrate the genesis of the circle method through one of the most famous additive problems.
MLResNet: An Efficient Method for Automatic Modulation Classification Based on Residual Neural Network

In the face of a complex electromagnetic environment, the modulation mode of communication signals is becoming increasingly complicated. Existing modulation mode recognition methods of communication signals cannot accurately and quickly identify the modulation mode of communication signals. In this letter, we propose an efficient architecture for automatic modulation classification (AMC) based on residual neural network (ResNet). We combine the improved residual neural network with long short-term memory network (LSTM) to obtain a new network structure (MLResNet), which solves the problems of gradient disappearance and too many parameters. In the experiments, MLResNet reaches the overall 24-modulation classification rate of 96.60% at 18 dB SNR on the well-known DeepSig dataset.
HDR Image Generation Based on Polarization Image

In camera imaging, a popular flaw that depresses the image quality is under- or over-exposure, which is typically caused by specular reflection. In this paper, we propose a high dynamic range (HDR) image generation method based on polarization image. We use four polarized images with different polarization angles as the input, and adopt L1-norm and Structural Similarity Index (SSIM) as the hybrid loss function in ExpandNet for training. In addition, for the construction of the polarization HDR dataset, we employ the multi-exposure fusion method to generate the ground truth HDR images. Experimental results show that the proposed HDR image generation method is better than the original ExpandNet in terms of PSNR, SSIM and FSIM. The proposed method is able to effectively suppress scene glare and restore the original image details of the scene.
Role of machine learning in text classification – An extensive review

Cyberspace has elevated business insights and created a virtual space to store all forms of information online. Due to the rapid development in the online world, the usage of digital documents has increased because it is comfortable for the users to share, update or keep track of the records in one place without losing data. However, maintaining massive data does not suit optimal decision-making and is extremely expensive for storage, processing, and collection. There is a gigantic possibility that human annotators make errors while classifying data because of distraction, monotony, fatigue, and failure to meet the requirements. Once the text classification method uses machine learning approaches, the process will execute with fewer mistakes and more accuracy. The main goal of this review paper is to highlight and explain the role of different machine learning methodologies in text classification. Concurrently, this paper describes the challenges faced by other machine learning techniques and text representation. Furthermore, this review paper will provide an extensive survey on how various machine learning techniques such as Neural Networks, Naive Bayes, Logistic Regression, Random Forest, Decision Trees, and Support Vector Machine (SVM) - are implemented in Text classification.
Design of High-Precision Indoor 3D Space Positioning System Based on Ray Tracing

Conventional indoor space positioning systems have a low positioning success rate and large errors between positioning coordinates and actual coordinates. To solve this problem, a high-precision indoor three-dimensional space positioning system based on ray tracing is designed. In terms of hardware, three parts: tags, readers, and computer communication equipment are used to form the overall structure of the system, and the auxiliary power circuit of radio frequency readers and RFID tag communication filter circuits are optimized; in terms of software, the ray receiving field is calculated according to the principle of indoor ray tracking and positioning. Strong, obtain the positioning parameter information of each ray of the mobile terminal to be located, including field strength, time delay, angle of arrival, etc., build a ray tracing positioning parameter sequence library, and locate the three-dimensional coordinates of indoor space objects. An indoor house is selected as the experimental area, 15 terminals to be positioned are set up, and comparative experiments are set up. The results show that the design system improves the positioning success rate, reduces the error of the indoor space measured coordinates and the positioning coordinates, and fully ensures the positioning accuracy.
English Translation of Hot Words in the Internet plus Era

With the rapid development of Internet media, the hot words on the Internet have become a problem in translation and cross-cultural communication. A large number of Internet hot words are emerging, which not only bring people a lot of joy, but also have a great impact on foreign cultural exchanges. Based on the analysis of the phenomenon of Internet hot words, this paper puts forward some strategies and methods for the translation of new words according to the translation theory. This paper uses Nida's functional equivalence theory to explore the English translation of Chinese Internet buzzwords. Based on this, three methods of translating such words are put forward, namely literal translation, transliteration and creative translation. Based on the analysis of the causes and characteristics of the popularity of Chinese Internet hot words, this paper puts forward specific translation strategies and techniques according to the guiding principles of the translation of hot words based on the theory of "use and satisfaction". The experimental results show that the proposed method can improve the accuracy of translation.
Automated Support to Capture Environment Assertions for Requirements-Based Testing

In order to mitigate the ever-increasing trend in software failures with far reaching consequences, research has suggested close coordination of requirements engineering (RE) and testing. The literature also advocates the notion of requirements-based testing (RBT) focusing on checking both the quality attributes and implementation of requirements. As requirements reside in the environment comprised of certain problem domain phenomena, the environment assertions connecting some of these phenomena in the indicative mood play a critical part in determining the correctness of a software solution. For formulating environment assertions, current literature provides manual techniques that are extremely time consuming and highly dependent on an individual's domain knowledge. In addition, developers often struggle to formulate good assertions from scratch. To address this issue, our work develops a boilerplate with certain placeholders that can be replaced with relevant attributes to formulate individual environment assertions. Leveraging this boilerplate, we further present a framework to capture environment assertions in an automated manner. An empirical study, involving 45 developers and 56 different software systems from 13 application domains, suggests that our framework captures useful environment assertions that are also relevant to software providing similar features in other application domains. Our work, to that end, opens new avenues for further coordination between RE and testing.
A Browser Extension to Facilitate Language Acquisition

This work implements multiple language acquisition hypotheses in the form of software. The Input Hypotheses by Krashen [19] are applied in the form of a Google Chrome browser extension. The language acquisition hypotheses are explored and used to extract functional requirements to create a new language learning tool. The resulting tool presents such features as personalized learning, translating phrases in context in-place inside web pages, and supporting more than 100 languages.
A Bibliometric Analysis on Cybercrime in Nigeria

The development and improvement of the internet with new distinctive features and abilities has widened the access to computer technology and created new opportunities for work and business activities. It has also posed threats to people using it either for business or leisure. According to Brenner (), the emergence of technology and electronic communication has brought a far-reaching increase in the incidence of criminal activities. As technology evolves, so does the nature of the crimes committed through the internet. There are numerous crimes of this nature committed daily on the internet. Criminal activity on the Internet is wide-ranging and can include an assortment of offenses such as illegal interception, copyright violation, stalking, money laundering, extortion, fraud, and resource theft involving the illegal use of computers (Broadhurst and Choo 2011). Internet-based cybercrime has become a huge menace threatening the socio-economic and technological advancement of Nigeria. Since the advent of the internet, cybercrime has become a recurring decimal in Nigeria. The main objective of this study is to create more awareness of cybercrime in Nigeria through a bibliometric analysis while enlightening the public on the existence of cybercrimes. The analysis was carried out through software like Harzing, VOSviewer, maxQDA, bibExcel, Mendeley, and Citespace. The software is used to establish rising scope in the field of cybersecurity.
Recognizing the Physical Activity of Hospitalized Older People From Wearable Sensors Data Using IoT

The IoT is a new concept that provides a world where smart, connected, embedded systems operate, giving rise to the amount of data from different sources that are considered to have highly useful and valuable information. Data mining would play a critical role in creating smarter IoT. Traditional care of an elderly person is a difficult and complex task. The need to have a caregiver with the elderly person almost all the time drains the human and financial resources of the health care system. The emergence of Artificial intelligence has allowed the conception of technical assistance where it helps and reduces the time spent by the caregiver with the elderly person. This work aims to focus on analyzing techniques that are used for prediction purposes of falls in the elderly. We examine the applicability of three classification algorithms for IoT data. These algorithms are analyzed and a comparative study is undertaken to find the classifier that performs the best analysis on the dataset using a set of predefined performance metrics to compare the results of each classifier.
WND-Identifier: Automated and Efficient Identification of Wireless Network Devices

The widespread application of wireless communication technology brings great convenience to people, but security and privacy problems also arise. To assess and guarantee the security of wireless networks and user devices, discovering and identifying wireless devices become a foremost task. Currently, effective device identification is still a challenging issue, as device fingerprinting requires huge training datasets and is difficult to expand, and rule-based identification is not accurate and reliable enough. In this paper, we propose WND-Identifier, a universal and extensible framework for the identification of wireless devices, which can generate high-precision device labels (vendor, type, and product model) efficiently without user interaction. We first introduce the concept of device-info-related network protocols. WND-Identifier makes full use of the natural language features in such protocol messages and combines with the device description in the welcome page, thereby utilizing extraction rules to generate concrete device labels. Considering that the device information in the protocol messages may be incomplete or forged, we further take advantage of the application logic independence and stability of the device-info-related protocol, so as to build a multiprotocol text classification model, which maps the device to a known label. We conduct experiments in homes and public networks and present three application scenarios to verify the effectiveness of WND-Identifier.