Reviewerly/exHarmony huggingface dataset:
Motivation of Future Musicians-Teacher to the Choice of Distance Learning and Evaluation of Its Current State

The author reflects on the specifics of distance musical and pedagogical education, which is perceived by many in a negative connotation – a reduced number of academic disciplines, first of all, individual lessons. And also – the important factor that distance education is often paid and the initial level of students is rather weak. However, there are no clear criteria for the comparative quality of full-time and part-time diplomas. Moreover, in the music profession, where individual training actualizes not only professional knowledge and skills, but also the formation of personal qualities of the future specialist. at the same time, it is necessary to separate two concepts – “correspondence” and “distance” learning. There is practically no research that would deal with the problems of distance musical and pedagogical education. As a rule, those few works that exist on correspondence training of pedagogical specialties do not take into account the specifics of the profession of music teacher, for example, in terms of the creative component of the educational process or the development of performing skills. to date, the most noticeable research gaps are, first of all, the actualization of the contextual approach and the study of the motivational component in distance learning. The author analyzes the data developed by him a detailed questionnaire for part-time students with information about the real and intended place of work of future graduates, their motivation for distance learning, the degree of satisfaction with such training, its advantages and disadvantages, the level of musical training of future teachers, musicians, their specific professional problems, etc. The students’ answers showed that there is a powerful trend in the musical and pedagogical profession associated with the public expression of musical creativity, the actualization of not only education and upbringing, but also the enlightenment component of the educational process. In addition to the performance qualities of a music teacher, those skills that have only recently seemed to be insignificant, such as organizational, directorial, lecturers, and managerial ones, have become extremely popular. According to the author, in the correspondence musical and pedagogical education it is much easier to develop the abilities of teachers in this regard, since, on the one hand, there is a knowledge base, and on the other – a specific professional situation, familiar and close to the correspondence student and teacher in one person. In this contextual paradigm laid a powerful lever of motivation of part-time students to maximize the “whole” of the educational process and, accordingly, the optimal combination and complementarity of study and professional activities.
Subconscious Requirements: The Fright of Every Tester

Subconscious requirements are requirements that stakeholders do not explicitly ask for, but still expect to be present in a system. Such requirements are problematic to testers as they will easily be overlooked and not be included in the specifications and therefore may not be tested at all. Over time, less notable (non-) functional requirements tend to end up as subconscious. The development of ever more complex IT solutions adds to the occurrence of subconscious requirements. They do need to be tested, as ignoring them will lead to expensive rework or rejection. Due to the lack of specifications, tests can only rely on experience-based techniques. To acquire the necessary experience, testers can turn to proven techniques from the requirements engineering discipline.
Comparison of texture synthesis methods by sample

This article provides a comparative analysis of existing texture synthesis methods. The advantages and disadvantages of the methods are revealed. In our work, we were guided by the results of extensive expert testing, which was attended by more than 25 people on 20 fundamentally different textures. This allowed us to draw conclusions about what types of textures which methods should be used. Our study showed that recently popular neural network and statistical methods are neither the best in terms of synthesis quality nor speed. However, they also generate more diverse textures. On the other hand, the easiest and fastest method for rearranging patches showed the best quality and speed. Thus, pattern synthesis of textures is one of the promising areas of research in which different approaches have different advantages.
Multi-region processing method for single image deraining

Outdoor imaging often suffers from the negative effects of the rain streaks, which would lead to the distortion of image contents. Most of the existing deraining approaches propose to build prior models to formulate the appearance of rain streaks and image features. However, these works deal with the rain streaks removal on the whole image regions. This may lead to the image regions without rain streaks distorted. In this paper, we proposed a new method for simultaneously investigating the candidates of rain streaks and image restoration. We use the L0 sparsity regularization for rain region estimation , and apply the framelet with L0 norm to protect the sharpen images. Our method only processes the regions with rain streaks and leaves other regions free. Experimental results demonstrate the effectiveness of our approach on rain streak removal and image restoration.
System Modelling Using Neural Networks with External Recurrence and Exponential Quadratic Cost Function

A novel cost function to improve the performance of neural networks in system modelling is proposed in this paper, the exponential quadratic cost function. This cost function combines the strength of penalizing errors exponentially and the feasibility of quadratic function in finding global minima. By applying the exponential quadratic cost function, the learning algorithm is expected to have an improved ability to minimize the difference between system output and model output. Simulations by using a test system with strong nonlinear dynamics were conducted to prove the effectiveness of the proposed cost function. The results show that the network trained using the exponential quadratic cost function performs better in emulating the test system in transient region.
Study on the Construction Value and Development Strategy of Ecological Sports Park under the Background of Sponge City

Abstract In recent years, with the acceleration of urbanization construction, the “seeing the sea” of the city that is raining and flooding has been solved in the “sponge city” strategy of “breathing”. Guizhou Province actively develops the strategic arrangement of sponge city, Put forward taking the harmonious symbiosis of the public fitness and ecological environment as the carrier. Integrating sports competition and tourism, the “sponge type” ecological sports park is planned and built with “sports” to promote “tourism”. This paper uses literature, field research and other research methods, combined with the topographical characteristics of “mountain, water, forest” in Guizhou Province, taking ecological sports park as the research object, in order to provide advice and suggestions for the scientific and rational construction of green ecological sports park.
A Denotational Semantics for Dynamic Architectures

With the emergence of mobile and adaptive computing, dynamic architectures have become increasingly important. In such architectures, components can appear and disappear, and connections between them can change over time. Verification of such architectures is performed over the composition of its components, which is usually defined in an operational style. Sometimes however, a denotational style might be more convenient for verification. Thus, in the following paper, we propose a denotational semantics for composition in dynamic architectures based on fixed points in lattices. We show that it is well-defined by proving that fixed points are guaranteed to exist. Finally, we use our definition to derive a logical characterization of composition, which forms the basis of a framework for the interactive verification of dynamic architectures.
Unsupervised Person Re-identification Based on Clustering and Domain-Invariant Network

Person re-identification (Re-ID) is a task which aims to determine whether a pedestrian in a camera has emerged in other cameras. Earlier works stress importance of the supervised learning methods, however, creating labels by hand is too slow and expensive. Hence, supervised methods are always limited in real-world applications. To address the problem, we propose a novel domain adaptation framework for unsupervised person Re-ID. First, target data are clustered and selected to add relative reliable supervised information for target domain. Second, a novel domain adaptive network is designed to decompose the representations to person-related and domain-related part. The former aims at learning domain-invariant and discriminative representation by a adversarial loss and a Re-ID loss with the label smoothing regularization. And the latter further improve a model’s ability of extracting domain-invariant features by separating the domain unique features. What’s more, during learning representation for target domain, a labeled source data not only is utilized to initialize the model but also participate in the training as a beneficial supervision information to generalize the Re-ID model. Experimental results on Market-1501 and DukeMTMC-reID evidence the superior performance of the proposed model over state-of-the-art methods.
APAC-Net: Unsupervised Learning of Depth and Ego-Motion from Monocular Video

We propose an unsupervised novel method, Attention-Pixel and Attention-Channel Network (APAC-Net), for unsupervised monocular learning of estimating scene depth and ego-motion. Our model only utilizes monocular image sequences and does not need additional sensor information, such as IMU and GPS, for supervising. The attention mechanism is employed in APAC-Net to improve the networks’ efficiency. Specifically, three attention modules are proposed to adjust feature weights when training. Moreover, to minimum the effect of noise, which is produced in the reconstruction processing, the Image-reconstruction loss based on PSNR $$L_{PSNR}$$ is used to evaluation the reconstruction quality. In addition, due to the fail depth estimation of the objects closed to camera, the Temporal-consistency loss $$L_{Temp}$$ between adjacent frames and the Scale-based loss $$L_{Scale}$$ among different scales are proposed. Experimental results showed APAC-Net can perform well in both the depth and ego-motion tasks, and it even behaved better in several items on KITTI and Cityscapes.
Target Marketing Strategies Using Computer Based analysis in Profiling Potential School

Abstract The purpose of this research is to obtain information about schools that have good prospects for becoming university marketing targets. So schools can work directly with universities that target them. University can directly target high-achieving students from schools that have good reputations too. This study used data mining clustering methods to analyze the data of prospective new students and the data of graduating students. Data input for the data mining process used segmentation that considers retention, frequency, and average grade point average (GPA), this was needed to determine school characteristics. The results of this study are school profiles to identify a list of potential schools that will be targeted by university marketing.
Multi-Component Spatiotemporal Attention and its Application to Object Detection in Surveillance Videos

This paper describes multi-component spatiotemporal attention mechanisms in application to object detection in videos. The detection of objects of interest relies on the analysis of feature-point areas (FPAs), which correspond to the object-relevant focus-of-attention (FoA) points extracted by the proposed spatiotemporal mechanisms of attention focusing. The attention mechanisms give detection priority to object-relevant FPAs with spatial saliency, spatiotemporal coherence, and area temporal change including motion. The preliminary test results of the proposed attention focusing mechanisms for object detection and tracking have confirmed its advantage in terms of robustness over existing visual attention-based detectors with comparable run-times.
Segmentation Algorithm Based on Square Blocks Propagation

This research is devoted to the segmentation of heart and brain anatomical structures. In the study, we present a segmentation algorithm based on the square blocks (superpixels) propagation. The square blocks propagation algorithm checks two criteria. For the first criteria, the current intensity of the pixel is compared to the average intensity of the segmented region. For the second criterion, the intensity difference of the pixels lying on the superpixel sides is compared to the threshold. Once these criteria are successfully checked, the algorithm merges homogeneous superpixels into one region. Then the following superpixels are attached to the final superpixel set. The last step of the proposed method is the spline generation. The spline delineates the borders of the region of interest. The main parameter of the algorithm is the size of a square block. The cardiac MRI dataset of the University of York and the brain tumor dataset of Southern Medical University were used to estimate the segmentation accuracy and processing time. The highest Dice similarity coefficients obtained by the presented algorithm for the left ventricle and the brain tumor are 0.93±0.03 and 0.89±0.07 respectively. One of the most important features of the border detection step is its scalability. It allows implementing different one-dimensional methods for border detection.
Application of Improved Non-local Mean Filtering Algorithm in Detection of Oil and Gas Wells

Abstract As the oil and gas exploration in most parts of China has entered into the middle and late stages, the aging and wear problems of mining equipment have become increasingly prominent. In the process of visual image acquisition of oil and gas well casing damage, due to the influence of the complex environment of light and dust, the image data source noise is serious, and the image recognition model has low accuracy. This paper improves the neighbourhood filtering algorithm block search process with the ant colony pheromone correlation theory, optimizes the block matching process with the Pearson algorithm, and eventually applies the optimized non-local mean filtering algorithm to the oil and gas well casing damage visual detection process. The experimental results show that the improved non-local mean filtering algorithm has a significant impact on the adaptability and effectiveness of the noise reduction effect.
A robust target recognition and tracking panoramic surveillance system based on deep learning

A panoramic monitoring system is designed to achieve continuous monitoring of the surrounding environment. The image acquisition module of the system is composed of five fixed-focal-length cameras and a variable-focal-length camera, which realizes 360 degree environmental monitoring. Usually, the background of continuous photography changes due to fluctuations of ambient light, humidity and wind. Therefore, a dynamic adaptive threshold is used to dynamically update the background template in order to better accommodate various weather changes. Further, a motion-aware algorithm based on background updates is applied to effectively detect whether an intruding target exists and determine the direction of the target. Once an intrusive target is found, the deep convolution neural network Yolo is employed to recognize the target quickly. It shows the advantages of less computation and preferable detection accuracy. In addition, according to the preset warning level, when the intrusion target needs to be alarmed, the target orientation is transmitted to the platform through the central control processing unit, so that the variable-focal-length camera can take real-time snapshots. we propose an end-to-end lightweight siamese convolution neural network to achieve fast and robust target tracking. The network structure replaces the hand-crafted features by the multi-layers deep convolution features of the target, so that higher precision can be achieved. The experiment result shows panoramic surveillance system can effectively and robustly perform security tasks such as panoramic imaging, target recognition and fast target tracking.
Some issues in modelling comprehensive situation awareness

This paper discusses some issues confronted in the design process of a system creating comprehensive situation awareness for a small country. We strive to merge the experience obtained in modelling and analyzing cyber-physical-social systems and the results published on situation awareness. For that, we develop a reasonably simple and efficient monitoring framework that enables to capture and analyze heterogeneous dynamic phenomena in a country. The final goal is to build a model for comprehensive situation awareness, which merges information from a feasible set of interoperable models, describing operation of country’s major institutions; and provides situational information for decision-makers at all required levels.
Foundations of Real‐World Economics: What Every Student Needs to Know, 2nd edn, by JohnKomlos (Routledge, New York, 2019), pp. 306.

Economic RecordVolume 95, Issue 311 p. 514-516 Review Foundations of Real-World Economics: What Every Student Needs to Know, 2nd edn, by John Komlos ( Routledge, New York, 2019), pp. 306. John Foster, John Foster School of Economics, University of Queensland, Brisbane, Qld, AustraliaSearch for more papers by this author John Foster, John Foster School of Economics, University of Queensland, Brisbane, Qld, AustraliaSearch for more papers by this author First published: 29 November 2019 https://doi.org/10.1111/1475-4932.12514Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume95, Issue311December 2019Pages 514-516 RelatedInformation
How Effectively Train Large-Scale Machine Learning Models?

The stochastic gradient method (SGM)Stochastic gradient method (SGM) is widely used as an optimization tool in many machine learning applications including support vector machines (SVM)s, Support vector machines (SVM) logistic regression, graphical models and deep learning. SGM computes the estimates of the gradient from a single randomly chosen sample in each iteration. Therefore, applying a stochastic gradient method for large-scale machine learning problems can be computationally efficient. In this work, we focus on generating generalization bounds for a randomized algorithm Algorithm such as Random Fourier features learned with stochastic gradient descent algorithm. Our findings are based on a mutual relationship between the generalization error of an algorithm and its stability Stability properties. The stability of an algorithm is measured by the generalization error Generalization error regarding the absolute difference between the testing and the training error. Training error Overall, an algorithm is called stable if by changing any single training data Training data point the training error varies slightly. In this work, we measured the stability of stochastic gradient method (SGM) for learning an approximated Fourier primal support vector machine. In particular, under certain regularity assumptions, we showed that a randomized algorithm such as Random Fourier feature where is trained by a stochastic gradient method (SGM) with few iterations has vanishing generalization error. Therefore, the iterative optimization algorithm can stop long before its convergence to reduce computational cost. We empirically verified the theoretical findings for different parameters using several data sets.
Detect Attributes of Medical Concepts via Sequence Labeling

In this study, we present a new method for detecting attributes of medical concepts, which uses a sequence labeling approach to recognize attribute entities and classify relations between concepts and attributes simultaneously within one step. A neural architecture combining bidirectional Long Short-Term Memory networks and Conditional Random fields (Bi-LSTMs-CRF) was adopted to detect disorder-modifier pairs in clinical text. Evaluations on the ShARe corpus show that the proposed method achieved higher accuracy and F1 scores than the traditional two-step approaches, indicating its potential to accelerate practical clinical NLP applications.
Systematic Evaluation of a Conversational Voice User Interface for Decision-Making

When making routine and critical purchase decisions, consumers often have a need to process a surplus of information to make the right choice. Today’s technology must be able to assist them in this process. Although conversational voice user interfaces have the potential to help consumers in their decision-making, extensive testing is required to ensure that they are up to par with the expectations and the needs of users and contexts. Therefore, we focus on evaluating the ability of a multi-strategy conversational mobile decision-aid (MODA) (Alikhademi et al., in press) in correctly classifying the decision-making strategies used by consumers and recognizing attributes, brands, and criteria voiced in an air filter purchase context. Our system evaluation results revealed that MODA performed with high levels of accuracy with classifying the user’s decision-making strategy (over 80%) and recognizing decision parameters (over 75%). The main contribution of MODA is that it can support users in many domains and disciplines by recognizing voiced decision parameters.
Anti-jamming technology of electronic communication based on improved intelligent algorithm

Intelligent interference communication technology is an important direction for the development of a new generation of anti-jamming communication system through the recognition of complex electromagnetic interference environment and the use of learning and intelligent decision-making methods to ach ieve efficient and reliable information transmission. A channel prediction algorithm based on intelligent interference communication technology is proposed, and only a small-scale fading channel model is considered. Under the background of rapid development of information technology, the level of anti-interference of electronic communication is further improved, and the reliability of information transmission is effectively guaranteed. Under the background of fully expounding the physical layer security technology, the anti-interference scheme in large-scale multi-antenna system in electronic communication is designed. Based on the physical layer security communication model in the full-duplex network, a channel prediction scheme is innovatively proposed to reduce the impact of imperfect CSI. Through this measure to improve network security performance, the proposed anti-interference scheme was tested. The test results show that because Massive MIMO can direct the transmit beam and energy to the user direction, in the physical layer security scenario, for malicious eavesdroppers in the network, it cannot steal information and is difficult to interfere with electronic communication.