Reviewerly/exHarmony huggingface dataset:
Voice Isolation Using Artificial Neural Network

Abstract: The paper reflects the use of Artificial Neural Networks with the help of various machine learning algorithms for voice isolation. In particular, we consider the case of a voice sample recognition by analyzing the speech signals with the help of machine learning algorithms such as artificial neural networks, independent component analysis, activation function. The strategies by which our central nervous network decodes the network stimuli same as artificial neural network will analyze the given speech sample. After first step, a set of machine learning algorithms will be used like independent component analysis algorithm and gradient function algorithm for processing. After the processing, a decision statement will be applied to generate the desired output. Keywords: Artificial Neural Network, Voice Isolation, Fast Independent Component Analysis, Gradient Descent
Military Target Detection in Remote Sensing Imagery Based on YOLOv4-Faster

The continuing development of remote sensing has resulted in a rapidly increasing number of remote sensing applications.High-resolution remote sensing images are used in various fields in the military.We propose methods for object detection based on remote sensing images.We develop a signal processing method for normalizing remote sensing images to eliminate noise such as fog, haze, and poor lighting.This method further improves detection accuracy and reduces error rates.We develop YOLOv4-faster, an accelerated neural network model based on the YOLO (You-Only-Look-Once) object detection method.YOLOv4-faster outperforms existing networks in terms of execution time and detection performance.We conduct a series of experiments on two public datasets (TGRS-HRRSD and NWPU VHR-10) as well as a dataset containing six military target classes provided by IMINT & Analysis and collected from Google Earth.YOLOv4-faster improves efficiency by utilizing multi-scale operations for the accurate detection of objects of various sizes, especially small objects.The experimental results show improved mAP (mean average precision) performance of the proposed method for object detection in remote sensing images.We thus propose a novel system for automatic object detection for high-resolution remote sensing images.
Automatically search an optimal face detector for a specific deployment environment

Abstract Face detection plays an important role in many artificial intelligence applications, such as identity recognition, facial expression recognition, and gender/age recognition. Recently, the development of deep learning techniques has greatly improved face detection’s performance. However, it is still ineffective and time-consuming to manually design hyperparameters of face detectors for different deployment environments with diverse distributions. Besides, due to the limited computation capability, many previous networks are hard to meet the latency requirements in deployment environments, and the improved resolution of current cameras further increases the computation burden. Motivated by the above problems, we propose a searching framework aiming to automatically search a real-time face detector architecture with a fixed complexity constraint, to adapt a specific deployment environment. We model the whole searching space into two parts, including the hyperparameters of the network and the detector. Instead of only searching the network structure, the proposed method considers the whole model’s hyperparameters space which contains the preprocessing and postprocessing parameters. The evolutionary algorithm is employed to find the optimal solution, and new evolutionary operations are proposed to explore architecture space. During the whole searching procedure, we guarantee the computation cost is under the restrictions. The advantages of the proposed framework are that it considers a hard computation cost constraint and the preprocessing and postprocessing hyperparameters, leading to a fully automatic design style and global optimization. Finally, we evaluate the proposed model on the most popular Widerface and FDDB datasets. The proposed detector significantly surpasses the existing lightweight face detectors in the comprehensive performances, and the average latency is twice as shorter as the best competitor.
THE PROBLEM OF INDIVIDUALIZATION OF LEARNING AS MEANS OF CREATIVE PERSONALITY BUILDING HISTORIES OF FOREIGN AND DOMESTIC PEDAGOGY

The article are considered glances of foreign and domestic teachers to the problem of personalization of educating as facility of condition in forming of creative personality of teacher.
Separator-Transducer-Segmenter: Streaming Recognition and Segmentation of Multi-party Speech

Streaming recognition and segmentation of multi-party conversations with overlapping speech is crucial for the next generation of voice assistant applications. In this work we address its challenges discovered in the previous work on multi-turn recurrent neural network transducer (MT-RNN-T) with a novel approach, separator-transducer-segmenter (STS), that enables tighter integration of speech separation, recognition and segmentation in a single model. First, we propose a new segmentation modeling strategy through start-of-turn and end-of-turn tokens that improves segmentation without recognition accuracy degradation. Second, we further improve both speech recognition and segmentation accuracy through an emission regularization method, FastEmit, and multi-task training with speech activity information as an additional training signal. Third, we experiment with end-of-turn emission latency penalty to improve end-point detection for each speaker turn. Finally, we establish a novel framework for segmentation analysis of multi-party conversations through emission latency metrics. With our best model, we report 4.6% abs. turn counting accuracy improvement and 17% rel. word error rate (WER) improvement on LibriCSS dataset compared to the previously published work.
ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts

Systems that can automatically define unfamiliar terms hold the promise of improving the accessibility of scientific texts, especially for readers who may lack prerequisite background knowledge. However, current systems assume a single "best" description per concept, which fails to account for the many potentially useful ways a concept can be described. We present ACCoRD, an end-to-end system tackling the novel task of generating sets of descriptions of scientific concepts. Our system takes advantage of the myriad ways a concept is mentioned across the scientific literature to produce distinct, diverse descriptions of target scientific concepts in terms of different reference concepts. To support research on the task, we release an expert-annotated resource, the ACCoRD corpus, which includes 1,275 labeled contexts and 1,787 hand-authored concept descriptions. We conduct a user study demonstrating that (1) users prefer descriptions produced by our end-to-end system, and (2) users prefer multiple descriptions to a single "best" description.
Study on Integration and Application of Artificial Intelligence and Wireless Network in Piano Music Teaching

Until 2019, most people had never faced the situation that would be their life-changing moment. Most universities are conducting classes for their students with the help of virtual classrooms indicating massive technological growth. However, this development does not take enough time to reach the students and the teaching person. Within five to six months of successful projects, most application producers have launched their official sites to conduct online classes and test ways for students. The introduction of virtual classes is not the only example of technological advancement; cloud computing, artificial intelligence, and deep learning have collaborated to produce appropriate, fine, and less error-prone results in all such fields of teaching. These technological advancements have given way to design models created with the wireless networks that are being made, particularly for music-related courses. The Quality-Learning (Q-Learning) Algorithm (QLA) is a pillar study for improving the implementation of artificial intelligence in music teaching in this research. The proposed algorithm aids in improving the accuracy of music, its frequency, and its wavelength when it passes. The proposed QLA is compared with the existing K-Nearest Neighbour (KNN) algorithm, and the results show that QLA has achieved 99.23% accuracy in intelligent piano music teaching through wireless network mode.
Technological Traditions in Pottery of the Kulay Cultural-Historical Community on Barsova Gora (Based on the Materials of the Sites Barsov Gorodok I/5 and I/7)

Purpose. Barsova Gora is a unique archaeological and landscape site located in the Tyumen Region of the Russian Federation. The archaeological sites Barsov gorodok I/5 and I/7 are located on the edge of the bank of Utoplaya River. The aim of the study is to reconstruct the content of the production steps in pottery of the surgut variant of the kulai culture in the sites Barsov gorodok I/5 and I/7. Results. A technical and technological analysis was carried out for 33 vessels from the site Barsov gorodok I/5 and for 5 vessels from the site Barsov gorodok I/7. Nine recipes of clay paste have been found at the site Barsov gorodok I/5. The main ones are unmixed recipes Clay + Broken stone, Clay + Chamotte. Recipes with the addition of sand, introduced in a ratio of 1 : 1, stand out. Three recipes have been found on the site Barsov gorodok I/7. Vessels were molded with the help of flaps. Cases of using tapes with an overlay have been recorded. Various combinations of tools for surface treatment. 22 variants have been found on the site Barsov gorodok I/5. On the site Barsov gorodok I/5, 4 variants were found for 5 vessels. On both sites, a specific method of smoothing down with a serrated tool was recorded on the inside of the transition point from the shoulder to the body. Ware could be fired in a reducing and reducingoxidizing environment. Conclusion. The pottery skills of the population of both settlements are similar to the general pottery traditions of the kulay culture on Barsova Gora. This is manifested in the similarity of the clays used, a close range of artificial impurities and recipes for clay paste, variability in surface treatment, and the presence of specific technological skills. The difference is manifested in the skills of constructing a hollow body and in the use of sand as an artificial impurity. The use of patchwork molding along with the band molding reflects the coexistence of two groups of potters with different skills in vessel construction at these sites. This may be due to the interaction within these settlements of populations with different technological traditions or to the influence of earlier pottery traditions of previous cultures. The addition of sand may be related to the interaction of this population with the more eastern groups of the kulay culture, for which this admixture is dominant, or be the result of mixing with an earlier pottery tradition.
Design creativity of professional career planning app for college students

Nowadays, many college students are lack of proper self-awareness, and unclear about their future, which reflects the conflicts between traditional college careers guidance and careers guidance needs of the present-day college students. With the popularization of mobile Internet technology, the campus career planning guidance should also gradually transform to informationization. Based on the needs of professional career planning guidance of college students, this paper discusses the design creativity of a professional career planning APP, sorts out the design ideas of this APP, hoping to help college students establish overall professional career planning system of four years in the college, and help them to strengthen their professional abilities and comprehensive capabilities. Through the research of this article, it is expected to provide new design ideas and reference direction for the design of professional career planning APP for college students.
Design of an anti-lost tracking system for the elderly with dementia based on Beidou navigation and OneNET cloud platform

This paper designs a tracking system based on Beidou navigation and positioning. The system uses the Beidou positioning module to obtain the position information of the wearer in real time, which can be uploaded to the OneNET cloud platform through NB-IoT wireless communication module. You can also customize the Web on the PC side to view the wearer's current position information and movement trajectory. In this way, the guardian can grasp the location of the elderly in real time, and can find the elderly in time when they are lost, so as to avoid accidents of the elderly. The system is stable in function, simple in operation, convenient in use and accurate in positioning, and can effectively solve the problem of lost elderly people with dementia.
Readle: A Formal Framework for Designing AI-based Edge Systems

With the wide spread use of AI-driven systems in the edge (a.k.a edge intelligence systems), such as autonomous driving vehicles, wearable biotech devices, intelligent manufacturing, etc., such systems are becoming very critical for our day-to-day lives. A challenge in designing edge intelligence systems is that we have to deal with a large number of constraints in two design spaces that form the basis of such systems: the edge design space and the deep learning design space. Thus in this work, a new systematic, extendable, manual approach, READLE, is proposed for creating representations of specifications in edge intelligent systems, capturing constraints in the edge system design space (e.g. timing constraints and other performance constraints) and constraints in the deep learning space (e.g. model training duration, required level of accuracy) in a coherent fashion. In particular, READLE leverages benefits of real-time logic and binary decision diagrams to generate unified specifications. Several insights learned in building READLE are also discussed, which should help in future research in the domain of formal specifications for edge intelligent systems.
Exploring the Adjugate Matrix Approach to Quaternion Pose Extraction

Quaternions are important for a wide variety of rotation-related problems in computer graphics, machine vision, and robotics. We study the nontrivial geometry of the relationship between quaternions and rotation matrices by exploiting the adjugate matrix of the characteristic equation of a related eigenvalue problem to obtain the manifold of the space of a quaternion eigenvector. We argue that quaternions parameterized by their corresponding rotation matrices cannot be expressed, for example, in machine learning tasks, as single-valued functions: the quaternion solution must instead be treated as a manifold, with different algebraic solutions for each of several single-valued sectors represented by the adjugate matrix. We conclude with novel constructions exploiting the quaternion adjugate variables to revisit several classic pose estimation applications: 2D point-cloud matching, 2D point-cloud-to-projection matching, 3D point-cloud matching, 3D orthographic point-cloud-to-projection matching, and 3D perspective point-cloud-to-projection matching. We find an exact solution to the 3D orthographic least squares pose extraction problem, and apply it successfully also to the perspective pose extraction problem with results that improve on existing methods.
Construction and Practice of “Internet +” Era Blended Teaching Mode in Database Principle and Application Course

The blended teaching in the “Internet +” era defines the new connotation of students’ learning. Students should not only acquire common standardized knowledge, but also pay attention to the independent construction of personalized knowledge and creative knowledge application. By analyzing the characteristics of students majoring in computer science and technology, this research constructs a blended teaching model based on the learning Platform. To this end, this study takes the needs analysis and conceptual structure design in database design as examples, and expounds the specific implementation process of teaching preparation, knowledge construction, knowledge transfer and teaching reflection. Finally, through quasi-experimental research to verify the teaching effect of the database principle and the application of the blended teaching model, the research results show that the blended teaching model can significantly improve students’ academic performance.
Missing data reconstruction method based on Kmeans and GBDT combined model

Complete and available data is of great significance for improving the theoretical line loss calculation in the low-voltage transformer area. However, with the upgrading of equipment, the electrical data presents the characteristics of small granularity and high complexity, resulting in insufficient line loss calculation accuracy. This paper proposes a missing data reconstruction method based on Kmeans and GBDT combined model. Since it is difficult to unify the reconstruction models of different types of data, the data set is clustered and divided by Kmeans, and GBDT is used for training respectively. During the test, the corresponding GBDT model is used for reconstruction according to the sample category. The simulation results show that the proposed method is suitable for actual data and has higher reconstruction accuracy compared with the traditional mean filling, KNN and decision tree methods. The proposed method can reconstruct the missing of multiple datasets and has good generalization.
A 600 MHz Balanced Homodyne Detector for Quantum Information Processing

Balanced homodyne detector (BHD) has been widely used in quantum information processing. In this paper, we design a wide bandwidth BHD and implement an optical layout to test the performance of our BHD. The test results show that the bandwidth is about 600 MHz, the quantum to classical noise ratio (QCNR) is 15.2 dB and the common mode rejection ratio (CMRR) is 41.16 dB. The performance of BHD shows that it can meet the requirements of high-speed applications in quantum information processing.
Research on the Application of Computer Artificial Intelligence Technology in English Teaching

The article designs a scheme of an English online teaching system based on computer artificial intelligence technology, which focuses on the overall structure of the English online teaching system program, the design of the core modules, key technologies and specific implementation. The application results show that the application of this system could not only provide teachers and students with a favorable user interface for online communication, but also realize the functions of user registration, course information viewing, online exams, homework upload and download, and data management. Having applied this system, the realization of college English online teaching could be effectively promoted, and students' self-directed learning ability improved. Moreover, Effective combination of English teaching and computer artificial intelligence technology make it possible that the process of natural language computer processing in corpus linguistics is an important part.
Research on Computer Big Data Model Based on Deep Volume Learning Algorithm

With the rapid development of information and communication technology, the amount of global data is increasing explosively. How to effectively analyze massive and complex data, mine and realize its potential value and make rational use of it is one of the important topics at present. An incomplete big data filling algorithm based on deep learning is proposed. Firstly, the filling automatic coder is established based on the automatic coder. On this basis, the depth filling network model is constructed, the depth characteristics of incomplete big data are analyzed, and the network parameters are calculated according to the layer by layer training idea and back propagation algorithm The target detection algorithm based on deep learning is far ahead of the traditional target detection algorithm in detection accuracy. Relying on big data to automatically learn and extract features, the effect is far better than that of manually designed features. Although big data has brought great potential to many fields such as industry, education and health care, it is a very arduous task to obtain valuable knowledge from big data. Learning the characteristics of big data and mining the information hidden in big data requires both advanced technology and interdisciplinary cooperation. Based on the deep convolution NN model of deep learning algorithm, the proposed field of computer vision has made remarkable achievements in recognition ability. This paper mainly discusses the application of deep convolution NN in computer vision.
MODERN APPROACHES TO THE DEFINITION OF THE CONCEPT, CITIES AND STRUCTURES OF ECOLOGICAL CULTURE FUTURE TEACHER

The different approaches for the definition of the essence, of the notion of ecological culture, are analyzed. Great attention isfocused on the structural components of the ecological culture of future teachers.
Idiosyncratic Audio Feedback Networks for Music Creation

This article presents practical and artistic contributions to the field of computer music systems based on audio feedback networks. The ideas that oriented the conception of two digital music instruments and examples of their use in the author's music practice are discussed. The article begins with a general conceptualization of feedback systems as well as a brief historical review of its use in experimental music. Later, a more specific contextualization of its application in recent computer music research and artwork is made, and two contributions are presented: the first is a network of cross-modulated sinusoidal oscillators (by frequency modulation), and the second is a network of algorithms for playing and transforming pre-recorded sound samples. In conclusion, examples of the artistic usage of the described systems to the creation of electroacoustic music are discussed.
PERSONAL-DEVELOPMENTAL APPROACH IN TEACHING POLITICAL SCIENCE FOR STUDENTS AGROTECHNICAL INSTITUTE

No abstract.
METHODOLOGY FOR SELECTION OF TEST SUBJECTS TASKS IN THE IMPLEMENTATION OF THE PRINCIPLE PEDAGOGICAL COOPERATION

No abstract.
A Collaborative Filtering Algorithm based on Entropy and Trust Optimized Neighbor Selection

The Collaborative filtering (CF) is an emerging technology that deals with overload problems by providing recommendations for products that users may be interested in. Neighbor selection is a key part of the recommendation process. The traditional collaborative filtering algorithm not only does not consider the recommendation contribution ability of neighbor users to the target user, but also does not consider the impact of the user’s rate difference, which leads to untrusted similarity and increases the proportion of pseudo-nearest neighbors in the neighbor set. Aiming at these problems, a collaborative filtering recommendation algorithm based on entropy and trust optimization of neighbors is proposed. The proposed method uses user entropy to calculate user rating distribution characteristics, and measures the recommendation contribution ability between users through the difference in rating distribution. Analyze the difference in rating differences between users through the method of rapid prediction, and tap the trust among users. Finally, the dual criteria are used to jointly calculate the recommended weights and construct a set of nearest neighbors. Empirical outcomes substantiate that the proposed method accurately selects the set of nearest neighbors and significantly improves recommendation accuracy of traditional collaborative filtering algorithms.
CRITICAL THINKING. ACTIVITY APPROACH

The author defines different approaches to the notion of critical thinking and ways of forming if. On the basis of adialogue.