Reviewerly/exHarmony huggingface dataset:
Contribution of the “Mighty Bunch” Composers to the Development of Russian Music Pedagogy of the XIX Century

The article considers the pedagogical component of the activity of the composers of the “Mighty bunch” — M. A. Balakirev and N. A. Rimsky-Korsakov, who had a significant influence on the formation and development of Russian music education. Developing the musical and aesthetic ideas put forward by the creative work of the kuchkists, both representatives of the “New Russian music school” justified progressive pedagogical attitudes and principles that are in great demand in the modern practice of music education. Realizing their pedagogical views in the process of leadership and teaching in such Russian musical and educational institutions of the 19th century as the free music school, the Court singing Capella, the St. Petersburg Conservatory, etc., the above-mentioned representatives of the “Mighty bunch” moved to solve the problem they faced from alternative positions: if Balakirev was characterized by an empirical approach to solving creative, including pedagogical tasks, then Rimsky-Korsakov was able to justify the conceptual foundations and basic provisions of Russian music pedagogy. This article is devoted to the consideration of these provisions.
Ultra-fast detail enhancement for a short-wave infrared image

Image detail enhancement is critical to the performance of short-wave infrared (SWIR) imaging systems, especially to the long-range systems. However, the existing high-performance infrared (IR) image enhancement methods typically have difficulty in meeting the requirements of the imaging system with high resolution and high frame rate. In this paper, we propose an ultra-fast and simple SWIR image detail enhancement method based on the difference of Gaussian (DoG) filter and plateau equalization. Our method consists of efficient edge detail information extraction and histogram equalization. The experimental results demonstrated that the proposed method achieves outstanding enhancement performance with a frame rate around 50 fps for 1280×1024 SWIR images.
Social Image Annotation Based on Image Captioning

With the popularity of new social media, automatic image annotation (AIA) has been an active research topic due to its great importance in image retrieval, understanding, and management. Despite their relative success, most of annotation models suffer from the low-level visual representation and semantic gap. To address the above shortcomings, we propose a novel annotation method utilizing textual feature generated by image captioning, in contrast to all previous methods that use visual feature as image feature. In our method, each image is regarded as a label-vector of k userprovided textual tags rather than a visual vector. We summarize our method as follows. First, the image visual features are extracted by combining the deep residual network and the object detection model, which are encoded and decoded by the mesh-connected Transformer network model. Then, the textual modal feature vector of the image is constructed by removing stop-words and retaining high-frequency tags. Finally, the textual feature vector of the image is applied to the propagation annotation model to generate a high-quality image annotation labels. Experimental results conducted on standard MS-COCO datasets demonstrate that the proposed method significantly outperforms existing classical models, mainly benefiting from the proposed textual feature generated by image captioning technology.
Acceptance and Use of Smartphones: AR-enhanced EFL Reading Practices

The increasing technological practices in educational settings have boosted up a wide variety of mobile tools use. One of the most recent tools is Augmented Reality (AR). As this newest technology whets many educators’ appetite in various fields, EFL learning has taken its place among the recent research related with AR enhanced practices. Although AR is a novel and promising tool for educational objectives, little is known about EFL learners' perceptions towards AR-enhanced reading practices and the effect of these practices on EFL learners’ smartphone acceptance levels in EFL learning. Herewith, the aim of this study is to investigate EFL learners’ perceptions regarding AR-enhanced reading practices and these practices' effect on EFL learners’ smartphone acceptance levels in EFL learning. A total of 32 second year vocational school students studying in the department of culinary at a state university participated in this study. This present study adopted a quasi-experimental mixed methods research design. The participants were introduced reading passages in the target language enhanced with AR technologies to increase the comprehension of these texts. Survey of Acceptance and Use of Smartphone Applications for English Language Learning was used to gather quantitative data and a semi-structured focus-group interview was conducted to understand their perceptions on using AR and acceptance of smartphones in EFL learning. The results of this study revealed that these EFL learners have positive attitudes towards using smartphones in their English reading practices and have moderate levels of smartphone acceptance in EFL learning. They found these practices motivating and helpful for understanding the reading passages. Taken together, this study will contribute to the EFL education and technology-enhanced language learning research field.
The Iron Combat Hatchet with an Anthropomorphic Image from the Novotroitsk Necropolis (Materials for the Course “Archaeological heritage of Altai”)

Purpose . The article presents the results of the development of a problem task for the academic discipline “ Archaeological heritage of Altai ”, studied by second-year students of the Institute of History , Social Communications and Law of the Altai State Pedagogical University, studying in the direction of training “ Tourism ”. As part of the educational project , students are invited to establish the reliability of the interpretation of a well-known archaeological find in the region – an Iron Combat Hatchet with an anthropomorphic image from the Novotroitskoye-2 necropolis . Results . During the implementation of the educational project , the study of the history of the discovery of the Combat Hatchet , the search and analysis of its multi-temporal photographs , the comparison of the original photographs and research reconstruction are carried out . In the course of these operations , it is established that the anthropomorphic image on the Hatchet was not recorded at the time of its discovery . During the processing of the Hatchet , fragments of corrosion were removed from it , due to which it acquired human-like shapes . There are no analogies to such an image on the sites of the south of Western Siberia . Thus , the fact of the presence of an anthropomorphic image on the Hatchet from Novotroitskoye-2 is questioned . Conclusion . Problem tasks based on the consideration of particular debatable topics that involve variability of judgments have a clear promise in the modern educational practice of higher educational institutions . Their involvement in the educational process contributes to the formation of students ' critical thinking .
A 181µW Real-Time 3-D Hand Gesture Recognition System based on Bi-directional Convolution and Computing-Efficient Feature Clustering

Vision-based hand gesture recognition (HGR) system, as an intuitive and portable approach for human-computer interaction (HCI), has been widely deployed on smart edge devices. While the prior endeavors remain different limitations to achieve a balance between power consumption and stability of the system. The HGR processors based on deep neural networks [1]–[3] achieved high recognition accuracy at the cost of significant power consumption. In contrast, the emerging energy-efficient HGR systems [4]–[5] based on ultra-compact customized algorithms suffer from performance degradation as the disturbing factors in the background increase.
Snake Game Using Hand Recognition System

Abstract: This paper presents engineering and execution nuances of a hand's movement furthermore motion acknowledgment framework which has been created at Meerut Institute of Engineering and Technology. The framework utilizes live 15-piece shading video from an arranged camera, runs in genuine time (On a DEC Alpha, 25 edges per second), and adapts well to foundation mess. Following is accomplished utilizing the 2D deformable Active Shape Models (savvy snakes), what's more a genetic calculation is utilized to play out an underlying worldwide picture search. The Point Distribution Model, which was used for both the snakes and the hereditary calculations, is a nonexclusive and extensible model that can be used to follow any 2D deformable object. Keywords: OpenCv, Feature tracking, Saavy Snakes, Genetic Algorithm, Lightning issues.
An Interpretive Study of Six Major Models towards Translation Quality Assessments

Juliane House, Katherina Reiss, Malcolm Williams, Xinxiang Wu, Hong’an Li, Xianzhu Si and Sanning He have been recognized as the most renowned scholars who’ve done profound and systematic researches on translation quality assessment. Through the interpretive study of these six evaluation models, we can grasp the advancement and effectiveness of the researches of translation quality evaluation at home and abroad, and bring some inspiration and suggestions to the future human-centered as well as computer-centered translation quality evaluation researches on the basis of the apprehension of the limitations of the researches in existence.
Quantum coherence protection by noise

Abstract In this paper, we propose a scheme to protect quantum coherence by adding another noise. We consider an example of a Jaynes–Cummings model coupled to an external non-Markovian bosonic bath. We solve this model by using the dressed state method in the presence of a stochastic coupling and obtain the density matrix by numerically averaging many stochastic trajectories. We show that the noisy atom-cavity coupling can effectively suppress both the relaxation and dephasing effects caused by the leakage of the cavity. Besides, we further illustrate the impacts of the standard deviation of the noisy coupling and the non-Markovian memory effect on the coherence protection. Then, the mechanism of the protection is analyzed. It is our hope that our research may open a new path to consider the role of noise in quantum coherence preservation.
A Research on Library Space Layout and Intelligent Optimization Oriented to Readers’ Needs

With the rapid development of the Internet age, all walks of life have been affected to a certain extent. The layout of a traditional library is an extremely important part of the library. A good layout can make full use of the space in the library to maximize the use of space and give readers a better sense of experience. It is better to study or work in the library. At the same time, with the rapid development of the Internet, all walks of life have been affected to a certain extent. For the library, the intelligent library based on GA-RFID technology can improve both the readers and the staff in the library. Reading books can improve the efficiency of study or work. In this article, some comparisons are made between the intelligent library based on GA-RFID technology and various types of libraries, and the technology is compared from many aspects, thus reflecting that this technology is beneficial to readers.
FR-GAN: A self-supervised learning method for super-resolution reconstruction of optical remote sensing images

Aiming at the problem of object blur in optical remote sensing images caused by atmospheric disturbance and cloud disturbance, a remote sensing image super-resolution reconstruction network, namely feature-enhanced reconstruction generative adversarial network (FR-GAN), based on a generative confrontation structure is proposed. The network adopts a self-supervised learning mode to achieve high-quality image reconstruction by learning the characteristics of the reconstructed image. First, to simulate the blurring effect of remote sensing images affected by interference factors, Gaussian noise is introduced into the image degradation model of self-supervised learning. Second, a background information enhancement module is designed in the FR-GAN image generator, which solves the problem of blurred objects in the reconstructed image by extracting the background contour information of the image and enhancing its expressive ability. Finally, the relative loss and perceptual loss are used to construct the loss function of FR-GAN. On the one hand, it improves the stability of the training process, and on the other hand, it improves the quality of image detail texture reconstruction. The experimental results show that, compared with the existing super-resolution reconstruction networks, the image reconstructed by FR-GAN has a clear texture, rich details, and better subjective visual effect.
The 3D Deburring Processing Trajectory Recognition Method and Its Application Base on Random Sample Consensus

As of 2022, most automatic deburring trajectories are still generated using offline programming methods. The trajectories generated using these methods are often suboptimal, which limits the precision of the robotic arms used to perform automatic deburring and, in turn, results in workpiece dimensional errors. Therefore, despite advances in automated deburring trajectory generation, deburring is still mostly performed manually. However, manual deburring is a time-consuming, labor-intensive, and expensive process that results in small profit margins for organizational equipment manufacturers (OEMs). To address these problems and the obstacles to the implementation of automated deburring in the robotics industry, the present study developed an online automated deburring trajectory generation method that uses 2D contouring information obtained from linear contour scanning sensors, a CAD model, and curve fitting to detect burrs and generate appropriate trajectories. The method overcomes many of the limitations of common deburring methods, especially by enabling real-time trajectory tracking. When the method was tested using bicycle forks, work that originally took three to four people 8–12-h to complete was completed by one person in 30 min, and the production cost was reduced by 70%.
Blockchain and Edge computing for Industrial Internet of Things (IIoT) Applications

Today IIoT is widely used in a wide range of Internet applications for items outside the consumer space and the iot business market, such as the umbrella name for applications and case applications in many industrial sectors.The goal of this paper is to highlight the ease of application of iiot(Industrial Internet Of Things) using Blockchain and Edge computing it also highlight various challenges of blockchain for iiot and with respect to that their approachable solutions.It also gives a detailed study of Deployment of edge computing in iiot sector.
AN IMPROVED ENERGY SEGMENTATION BASED STEREO MATCHING ALGORITHM

Abstract. The stereo matching algorithm is commonly used in dealing with disparity discontinuous areas. However, the algorithm may have higher computational complexity and lack of well-aligned input images. To improve that, this paper presents an improved energy segmentation based stereo matching algorithm. The proposed algorithm incorporates gradient information of each segmented region into the energy function according to the facts that gradients inside the same segmentation should be closer than those from different segmentation. The invalid matching pixels are interpolated with vertical-horizontal nearest neighboring pixels to keep the consistency of stereo image pairs in the initial disparity map. Thus, the proposed method can reduce the deviations and the number of executions of the Random Sample Consensus (RANSAC) which arises from misalignment of input image pairs. Experiments results demonstrate that the proposed algorithm has a better disparity result while the running time is decreased by about 20%.
High-Speed Reservoir Computing Based on Circular-Side Hexagonal Resonator Microlaser with Optical Feedback

In the current environment of the explosive growth in the amount of information, the demand for efficient information-processing methods has become increasingly urgent. We propose and numerically investigate a delay-based high-speed reservoir computing (RC) using a circular-side hexagonal resonator (CSHR) microlaser with optical feedback and injection. In this RC system, a smaller time interval can be obtained between virtual nodes, and a higher information processing rate (Rinf) can also be achieved, due to the ultra-short photon lifetime and wide bandwidth of the CSHR microlaser. The performance of the RC system was tested with three benchmark tasks (Santa-Fe chaotic time series prediction task, the 10th order Nonlinear Auto Regressive Moving Average task and Nonlinear channel equalization task). The results show that the system achieves high-accuracy prediction, even with a small number of virtual nodes (25), and is more feasible, with lower requirements for arbitrary waveform generators at the same rate. Significantly, at the high rate of 10 Gbps, low error predictions can be achieved over a large parameter space (e.g., frequency detuning in the interval 80 GHz, injected strength in the range of 0.9 variation and 2% range for feedback strength). Interestingly, it has the potential to achieve Rinf of 25 Gbps under technical advancements. Additionally, its shorter external cavity length and cubic micron scale size make it an excellent choice for large-scale photonic integration reservoir computing.
Ranking Micro-Influencers: A Multimedia Framework with Multi-Task and Interpretable Architectures

With the rise in use of social media to promote branded products, the demand for effective influencer marketing has increased. Brands are looking for improved ways to identify valuable influencers among a vast catalogue; this is even more challenging with micro-influencers, which are more affordable than mainstream ones but difficult to discover. In this paper, we propose a novel multi-task learning framework to improve the state of the art in micro-influencer ranking based on multimedia content. Moreover, since the visual congruence between a brand and influencer has been shown to be a good measure of compatibility, we provide an effective visual method for interpreting our model’s decisions, which can also be used to inform brands’ media strategies. We compare with the current state of the art on a recently constructed public dataset and we show significant improvement both in terms of accuracy and model complexity. We also introduce a methodology for tuning the image and text contribution to the final ranking score. The techniques for ranking and interpretation presented in this work can be generalized to arbitrary multimedia ranking tasks that have datasets with a similar structure.
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning

Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Recent studies on CD-FSL generally focus on transfer learning based approaches, where a neural network is pre-trained on popular labeled source domain datasets and then transferred to target domain data. Although the labeled datasets may provide suitable initial parameters for the target data, the domain difference between the source and target might hinder fine-tuning on the target domain. This paper proposes a simple yet powerful method that re-randomizes the parameters fitted on the source domain before adapting to the target data. The re-randomization resets source-specific parameters of the source pre-trained model and thus facilitates fine-tuning on the target domain, improving few-shot performance.
Analysis and Research on the Characteristics of Modern English Classroom Learners’ Concentration Based on Deep Learning

There are some problems in modern English education, such as difficulties in classroom teaching quality evaluation, lack of objective evaluation basis in teaching process management, and quality monitoring. The development of artificial intelligence technology provides a new idea for classroom teaching evaluation, but the existing classroom evaluation scheme based on artificial intelligence technology has a series of problems such as high system cost, low evaluation accuracy, and incomplete evaluation. In view of the above problems, this paper proposes a solution of English classroom concentration evaluation system based on deep learning. The program studies the evaluation methods of students’ class concentration, class activity, and enrichment degree of teaching links, and constructs an information evaluation system of students’ learning process and class teaching quality. Based on the edge computing system architecture, a hardware platform with cloud platform AI+ embedded visual edge computing devices managed by an FPGA deep learning accelerated server was built. The design, debugging, and testing of classroom evaluation and student behavior statistics-related functions were completed. This scheme uses edge computing hardware architecture to solve the problem of high system cost. Deep learning technology is used to solve the problem of low accuracy of classroom evaluation. It mainly evaluates the classroom objectively by extracting indicators such as the students' attention in the classroom, and solves the problems of the students’ inattentiveness in the classroom. After the test, the classroom evaluation system designed by the paper runs stably and all functions run normally. The test results show that the system can basically meet the requirements of classroom teaching evaluation application.
Local is Best: Efficient Reductions to Modal Logic K

Abstract We present novel reductions of extensions of the basic modal logic $${\textsf {K} }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>K</mml:mi> </mml:math> with axioms $$\textsf {B} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>B</mml:mi> </mml:math> , $$\textsf {D} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>D</mml:mi> </mml:math> , $$\textsf {T} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>T</mml:mi> </mml:math> , $$\textsf {4} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>4</mml:mn> </mml:math> and $$\textsf {5} $$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>5</mml:mn> </mml:math> to Separated Normal Form with Sets of Modal Levels $$\textsf {SNF} _{sml}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>SNF</mml:mi> <mml:mrow> <mml:mi>sml</mml:mi> </mml:mrow> </mml:msub> </mml:math> . The reductions typically result in smaller formulae than the reductions by Kracht. The reductions to $$\textsf {SNF} _{sml}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>SNF</mml:mi> <mml:mrow> <mml:mi>sml</mml:mi> </mml:mrow> </mml:msub> </mml:math> combined with a reduction to $$\textsf {SNF} _{ml}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>SNF</mml:mi> <mml:mrow> <mml:mi>ml</mml:mi> </mml:mrow> </mml:msub> </mml:math> allow us to use the local reasoning of the prover $${\text {K}_{\text {S}}}{\text {P}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mtext>K</mml:mtext> <mml:mtext>S</mml:mtext> </mml:msub> <mml:mtext>P</mml:mtext> </mml:mrow> </mml:math> to determine the satisfiability of modal formulae in the considered logics. We show experimentally that the combination of our reductions with the prover $${\text {K}_{\text {S}}}{\text {P}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msub> <mml:mtext>K</mml:mtext> <mml:mtext>S</mml:mtext> </mml:msub> <mml:mtext>P</mml:mtext> </mml:mrow> </mml:math> performs well when compared with a specialised resolution calculus for these logics, the built-in reductions of the first-order prover SPASS, and the higher-order logic prover LEO-III.