Reviewerly/exHarmony huggingface dataset:
THE FORMATION OF MOTIVATION OF MUSICAL ACTIVITY DURING THE PROCESS OF INSTRUMENTAL-PERFORMING PREPARATION

The purpose of this study is to present theoretical bases of the formation of future music teacher’s motivation of musical activity during the process of instrumental-performing preparation. The author uses the scientific-bibliographic method that applies the analysis of psychological, pedagogical, and musicological literature to classify the theoretical achievements of the research problem, pedagogical observation with future musical art teachers, and generalization of the conditions of successful motivation for musical activity formation during the process of instrumental-performing preparation. The results indicate that students with artistic-cognitive and procedural-substantive priorities are enthusiastically attracted in creative activities, take an active part in concerts and competitions, learn musical works of the individual program with a great pleasure, are passionate about the search for a convincing creation of the artistic image of a musical work, strive to develop their own instrumental-performing skills. The scientific relevance of this study lies in substantiating of the basics of formation of the musical activity motivation, the conditions which, according to the author, will promote success of this process in the course of instrumental and executive educational activity are outlined. An attempt is made to determine the way of formation of future music teacher’s motivation of musical activity during the process of instrumental- performing preparation. The author suggests the general way of formation of motivation of musical activity which consists in optimal pedagogical interaction in directing the individual to the internal awareness of the purpose of activity as personally significant. This leads to the predominance of higher meaning-making motives like self-improvement and self-actualization in a modern society.
"Relativistic Ring" Simulation New Approach Resolves Apparent Paradoxes

Recent analysis of the Relativistic Ring problem revealed that its angular momentum features a paradox maximum at circumferential velocity $\approx 0,24$ c declining to near zero with increasing velocity. This apparent paradox can be resolved by a new approach based on simulated high external forces up to the Weak Energy Limit in a low velocity regime $0<v<<c$. Simulations comprise a Relativitic Rod in uniform translational motion subjected to a pair of mutually opposed external forces and a pressurized Relativistic Ring. If the external forces simulate centrifugal force on a Relativitic Rod its canonical momentum features a maximum at velocity $\hat{v} = \sqrt{\frac{2}{3}}$ c being analogue to the maximum canonical angular momentum of a Relativistic Ring. Remarkably, rotational velocity of a pressurized Relativistic Ring can be modulated by variation of pressure - at constant canonical angular momentum.
Nodes, Geometries, Materials, and Anchors

In this chapter, we will look at the building blocks that go together to create everything we can see and interact with in our Augmented Reality experiences. Let’s start adding things to our AR scenes.
PENGEMBANGAN E-LABORATORY INSTRUCTION MODEL GUIDED INQUIRY BERBASIS BLENDED LEARNING PADA MATERI TITRASI ASAM BASA

Learning practicum material has not been carried out optimally. This is because students do not understand how to use practical tools and processes, so students' skills in implementing them are still not good. Blended learning is a way to minimize these problems. This study aims to see the feasibility, effectiveness and practicality of the e-laboratory guided inquiry learning model based on Blended Learning on acid-base titration material. Model development is carried out using the 4D model. This research through the 3D stage is by defining (Define), Design (Design) and Development (Develop). Data collection techniques through interviews, documentation, questionnairs and observation. The result shows that the validation of media experts obtained an average score of two validators of 75.5 and the results of the validation of material experts obtained an average score of two validators of 53 which means that the e-laboratory instruction is very feasible to use. The results of observations of student skills during large-scale trials showed that overall e-laboratory instruction was declared Very Effective with a percentage of 88.3%. Based on the results of the questionnaire responses of students showed that e-laboratory instruction stated Practical was used with a percentage of 82.4%.
Deep Convolutional Neural Networks with Unitary Weights.

While normalizations aim to fix the exploding and vanishing gradient problem in deep neural networks, they have drawbacks in speed or accuracy because of their dependency on the data set statistics. This work is a comprehensive study of a novel method based on unitary synaptic weights derived from Lie Group to construct intrinsically stable neural systems. Here we show that unitary convolutional neural networks deliver up to 32% faster inference speeds while maintaining competitive prediction accuracy. Unlike prior arts restricted to square synaptic weights, we expand the unitary networks to weights of any size and dimension.
Analyzing Curriculum Learning for Sentiment Analysis along Task Difficulty, Pacing and Visualization Axes

While Curriculum Learning (CL) has recently gained traction in Natural language Processing Tasks, it is still not adequately analyzed. Previous works only show their effectiveness but fail short to explain and interpret the internal workings fully. In this paper, we analyze curriculum learning in sentiment analysis along multiple axes. Some of these axes have been proposed by earlier works that need more in-depth study. Such analysis requires understanding where curriculum learning works and where it does not. Our axes of analysis include Task difficulty on CL, comparing CL pacing techniques, and qualitative analysis by visualizing the movement of attention scores in the model as curriculum phases progress. We find that curriculum learning works best for difficult tasks and may even lead to a decrement in performance for tasks with higher performance without curriculum learning. We see that One-Pass curriculum strategies suffer from catastrophic forgetting and attention movement visualization within curriculum pacing. This shows that curriculum learning breaks down the challenging main task into easier sub-tasks solved sequentially.
PERBEDAAN PRESTASI MAHASISWA LULUSAN SMA IPA DENGAN LULUSAN SMA IPS PADA MAHASISWA TINGKAT I DAN II D-III KEBIDANAN UNIVERSITAS BATAM TAHUN 2017

Midwifery education merupkan education that learn about sians, education is expected midwives accept students graduates SPR or SMA IPA. The fact that NON IPA high school graduate students are accepted in midwifery affects their learning achievement. This study aims to, note the difference in student achievement of high school graduates IPA with IPS in students of grade I and II D-III Midwifery University of Batam Year 2017. Research design, Comparative study. Sample 46 respondents with technique of sampling purposive sample, data analysis with T-test, research time may-August 2017. the result of the research is 26 graduate students (56,5%) have average higher learning achievement that is 3,37, student of IPS graduates as much as 20 people (43,5%) have average achievement of lower learning that is 3 , 13. based on the T-test test obtained ρ-value 0.003 <0.05, it can be concluded there is a significant difference in learning achievement between high school graduate students IPA and IPS in students of grade I and II D-III midwifery Universitan Batam Year 2017. It is expected that the University is more selective to accept new students, and study program more attention to learning that is easy to understand student.
Adaptive Image Watermarking based on K-NN Clustering

The key challenge faced by researchers is the rise in the use of social media communication to prove ownership rights  to  multimedia  material  such  as  video,  audio,  text,  graphics,  etc.  Watermarking  is  the  method  of multimedia concealment of digital content that can be used later to prove ownership credentials. The researchers in this field contribute a lot of work, but there is still a need for more robust methods. In this paper, we use the KNN clustering method to find the features in the image, which are then used to embed the content of the watermark.  Later,  the  KNN  clustering  approach  is  again  used  for  watermark  extraction  to  classify  the characteristics where the watermark is embedded and extraction is performed from those characteristics.
KESALAHAN BERBAHASA PADA SURAT DINAS SMA NEGERI 9 KOTA JAMBI

This research aims at describing language errors in writing letter numbers, mailing letter, opening paragraph, and closing paragraph of official letters of SMA Negeri 9 Jambi City year of 2019. This research is a qualitative descriptive. The technique used in collecting data are observation and documentation. The results of the analysis show that the errors in writing letter numbers are the use of colons and slashes which are preceded and followed by a space. Errors in writing mailing letter is indicated in the use of kepada which indicates language redundancy; the use of a colon (:) which should be replaced by a period (.); the use of punctuation marks (.) at the end of the mailing address; and the use of bold letters in the mailing address. Written errors in the opening paragraph of the letter are the writing of the opening greeting Dengan hormat in the beginning of the paragraph that should be included in the first sentence of the letter opening. Written errors in the closing paragraphs of letters are the incomplete closing sentences of the letter which should have been mentioned earlier, written behind the word demikianlah; and errors in the use of preposition di.
Semi-Parametric Independence Testing in Count Data and the Role of the Support

This article considers testing for independence in a time series of small counts within an Integer Autoregressive (INAR) model, taking a semiparametric approach that avoids any distributional assumption on the arrivals process of the model. The nature of the testing problem is shown to differ depending on whether or not the support of the arrivals distribution is the full set of natural numbers (as would be the case for Poisson or Negative Binomial distributions for example) or some strict subset of the natural numbers (such as for a Binomial or Uniform distribution). The theory for these two cases is studied separately. For the case where the arrivals have support on the natural numbers, a new asymptotically efficient semiparametric test, the effective score (Neyman-Rao) test, is derived. The semiparametric Likelihood-Ratio, Wald and score tests are shown to be asymptotically equivalent to the effective score test, and hence also asymptotically efficient. Asymptotic relative efficiency calculations demonstrate that the semiparametric effective score test can provide substantial power advantages over the first order autocorrelation coefficient, which is most commonly applied in practice. For the case where the arrivals have support that is a strict subset of the natural numbers, the theory is considerably altered because the support of the observations becomes different under the null and alternative hypotheses. The semiparametric Likelihood-Ratio, Wald and score tests become asymptotically degenerate in this case, while the effective score test remains valid. Remarkably, in this case the effective score test is also found to have power against local alternatives that shrink to the null at the rate T−1. In rare cases where the arrival support is partly or totally known, additional tests exploiting this information are considered. Finite sample properties of the tests in these various cases demonstrate the semiparametric effective score test can provide substantial power advantages over the first order autocorrelation test implied by a parametric Poisson specification. The simulations also reveal situations in which the first order autocorrelation is preferable in finite samples, so a hybrid of the effective score and autocorrelation tests is proposed to capture most of the benefits of each test.
One Hundred Probability and Statistics Inequalities

Herein we present one hundred inequalities culled from various corners of the probability, statistics, and combinatorics literature. We welcome new suggestions.
Design of Monitoring System for Uneven Settlement of Soft Soil Foundation based on Web GIS

Due to the limitation of the monitoring range of the existing soft soil foundation uneven settlement monitoring system, there is a defect of poor real-time monitoring and early warning, so a Web-GIS-based soft soil foundation uneven settlement monitoring system is designed. System hardware unit includes data acquisition unit, serial communication unit, A/D conversion unit and LCD liquid crystal display unit; software module includes data acquisition module, coordinator middleware module, soft soil foundation uneven immersion prediction module, dynamic early warning module and database Module. Through the design of hardware unit and software module, the monitoring system of uneven settlement of soft soil foundation based on Web GIS is realized. The experimental results show that: compared with the average value of the existing system, the designed system has better real-time monitoring performance and early warning real-time performance, which fully indicates that the designed system has better monitoring performance and is suitable for vigorously promoting and using.
Design of Shooting Attitude Control System for Soccer Robot based on Vision Tracking Technology

In the actual work of traditional robot shooting attitude control system, there are conversion errors in attitude conversion angle, which results in excessive redundant interference data, which leads to the small load capacity of the control system. Aiming at this problem, a soccer robot shooting attitude control system based on visual tracking technology is designed. In the hardware part, attitude angle sensor is selected and connected with hardware interface circuit. In the software part, visual tracking technology is used to track the soccer movement posture, and the equation of football shooting attitude transformation is set. The control parameters are introduced to control the attitude conversion angle error, and the system design is completed. The experimental instruments are prepared and the experimental environment is set up. Two traditional control systems are compared with the control system designed in this paper. The results show that the load capacity of the control system designed in this paper is the strongest.
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high performance deep learning training. We present the design for a convolutional neural network, BPLight-CNN, which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models including LeNet and VGG-Net. The proposed design achieves (i) at least 34x speedup, 34x improvement in computational efficiency, and 38.5x energy savings, during training; and (ii) 29x speedup, 31x improvement in computational efficiency, and 38.7x improvement in energy savings, during inference compared to the state-of-the-art designs. All these comparisons are done at a 16-bit resolution; and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared to the state-of-the-art.
무용음악의 가치 및 가능성 연구

Dance is an arts combining music and movement and expresses human life in a sophisticated manner. Over the years, it has boldly joined with music and expanded its spectrum of the collaboration and communication. As such, musicians have been developing a great interest in dance music and producing varieties of the experimental outcomes accordingly. As dance considers every sound, dance music features comprehensiveness and originality. On the outside, it is a convergence arts combined with dance, and on the inside, it is an arts encompassing a total sound containing works’ texture, image, energy, and story.This study aims to establish a new concept for dance music at the present time by discussing the distinctive and comprehensive values and potential of dance music. For this, the study examines the areas where dancers can participate in creating music, including silence sound, language, noise, space sound, and computer music. This study further explores the potentials of dance music - a collaboration of sound and movement, expansion into music pedagogy, provision of ideas for innovative compositions - to break new ground.
Design a contactless authentication system using hand gestures technique in covid-19 panic situation

Spread of Covid 2019 pandemic has shattered the globe, putting everybody in panic stage. In view of its long-term impact on daily life, the need to wear face masks and maintain social distances necessitates. In this current satiation everyone need a contactless biometric communication system for all future authentication schemes. One of the alternatives is the use of Contactless Authentication System biometrics because of its non-contact alike normal biometric finger prints and is capable of recognising even people wearing face masks. In this work, a novel hand gesture-based sign digits scheme is proposed. This work presents the design and implementation of a deep learning system that can be verified without contact by the user using 'authentication code.' The 'authentication code' is a 'n' numeric code, and the digits are hand movements of the sign language digits. We propose a memory-efficient deep learning convolution neural network model to identify and classifying the hand movements of the sign language digits and also to extract the function by combing the two BEMD and SIFT algorithm techniques. The model is deployed in the Raspberry pi 4 Model B edge computing system to act as an edge device for user verification. The model achieves a classification accuracy of 98.47 percent for the publicly accessible sign language digits dataset.
Serial-parallel Multi-Scale Feature Fusion for Anatomy-Oriented Hand Joint Detection

Accurate hand joints detection from images is a fundamental topic which is essential for many applications in computer vision and human computer interaction. This paper presents a two stage network for hand joints detection from single unmarked image by using serial-parallel multi-scale feature fusion. In stage I, the hand regions are located by a pre-trained network, and the features of each detected hand region are extracted by a shallow spatial hand features representation module. The extracted hand features are then fed into stage II, which consists of serially connected feature extraction modules with similar structures, called "multi-scale feature fusion" (MSFF). A MSFF contains parallel multi-scale feature extraction branches, which generate initial hand joint heatmaps. The initial heatmaps are then mutually reinforced by the anatomic relationship between hand joints. The experimental results on five hand joints datasets show that the proposed network overperforms the state-of-the-art methods.
Law’s Fruitful Fictions

Can law keep up with runaway technology? In an era of runaway corporate surveillance, artificial intelligence, deep fakes, election hacking, social media disinformation, do-it-yourself biology, genetic modification, automation, and three-dimensional printed medical implants, law has seemed to take a back seat to rampant technological change. To listen to Silicon Valley barons, there is nothing any of us can do about it.CAN LAW KEEP UP? calls their bluff. The book provides a fresh look at law, at what it actually is, how it works, and how we can create the kind of laws that help humans survive and thrive in the face of technological change. It shows that law can keep up with technology because law is a kind of technology – social technology built by humans out of cooperative fictions like firms, nations, or money. To survive the challenges of the future, we need a new kind of law, and a new way of understanding how humans use language to cooperate, to build an ever-growing and ever-changing system of understandings about how we can secure the benefits of changing technology to all.
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance

Self-supervised depth estimation has made a great success in learning depth from unlabeled image sequences. While the mappings between image and pixel-wise depth are well-studied in current methods, the correlation between image, depth and scene semantics, however, is less considered. This hinders the network to better understand the real geometry of the scene, since the contextual clues, contribute not only the latent representations of scene depth, but also the straight constraints for depth map. In this paper, we leverage the two benefits by proposing the implicit and explicit semantic guidance for accurate self-supervised depth estimation. We propose a Semantic-aware Spatial Feature Alignment (SSFA) scheme to effectively align implicit semantic features with depth features for scene-aware depth estimation. We also propose a semantic-guided ranking loss to explicitly constrain the estimated depth maps to be consistent with real scene contextual properties. Both semantic label noise and prediction uncertainty is considered to yield reliable depth supervisions. Extensive experimental results show that our method produces high quality depth maps which are consistently superior either on complex scenes or diverse semantic categories, and outperforms the state-of-the-art methods by a significant margin.
Game Theory Application Resources Management and Distribution in Blockchain Network

The paper illustrated a basic blockchain system, applying game theory to simulate resource management in blockchain transactions. By the method of illustration, simulation, our team has demonstrated the effect of game theory transactions, transactions with specific value can demonstrate the benefits of game theory in co-life. time can be used to manage resources in blockchain. Based on the proposed algorithm model, we have built a test system with the maximum number of virtual machines to demonstrate the effectiveness in applying game theory in managing and distributing resources for transactions in the blockchain network.