Reviewerly/exHarmony huggingface dataset:
Construction and Application of Teaching Resource Database for Musicology (Normal) Major Based on Information Processing Technology

Online teaching resources are the prerequisite and foundation for the development of online education. With the gradual expansion of online education, online teaching resources are becoming more and more abundant. Effective management of teaching resources has become the key to developing online education, providing efficient storage for various learning content objects management. The network teaching resource database is a resource management platform integrating resource distributed storage, resource management, resource evaluation, and knowledge management. Based on the information processing technology, this paper analyzes the needs of the teaching resource database, and designs the overall structure consisting of the infrastructure layer, the support platform layer, the software service layer and the user access layer, and the curriculum construction, the construction of the teaching staff and the construction of teaching materials of the musicology (normal college) major were studied in order to give full play to the role of the educational resource database.
LOROD: Fully Convolutional Network for Realtime Multi-scale Object Detection Algorithm

Although great progress has been made in object detection, especially face detection, in recent years, one of the remaining difficult problems is maintaining excellent performance while detecting in real-time. To address this problem, we propose an effective single-stage object detector named Look Once Rapid Object Detector (LOROD), which uses fully convolutional layers. In particular, our framework is consists of two major modules. One is the Residual Convolutional Layer (RCL) module, which uses ResBlocks to achieve rapid convergence. The other module, namely the Multiple Scale & Dense Connection Convolutional Layers (MS-DCCL) module, is designed to enrich the feature maps and adapt to different scales. In addition, we propose some date augment strategies to increase the model’s robustness. When running on GTX1080Ti, given images with a resolution of 352×352, the average time consumed for inference is merely 15.1 ms and the accuracy can reach 0.946 on the FDDB dataset. By comparing with the advanced algorithms, our method’s effectiveness is proved.
Joint Pedestrian Detection and Attribute Recognition Feature Learning

In recent years, with the increase of video surveillance equipment, the amount of video data and complexity are growing. How to obtain structured data has become a problem. In surveillance videos, people extremely want to obtain valuable information about the position and characteristics of pedestrians, such as the pedestrian’s gender, age, appearance. Therefore, as a basic computer vision task, pedestrian attribute recognition plays an important role in the fields of image retrieval, pedestrian re-identification and video data structuring. However, the current method for obtaining pedestrian attribute information from images is to cascade the pedestrian detection algorithm and the pedestrian attribute recognition algorithm. This method requires a lot of time and the effect of the attribute recognition is affected by the detection results. To solve these problems, we design an integrated network that includes the pedestrian detection and the attribute recognition. Based on the integrated network, we improve the accuracy of detection and recognition by resetting anchor size, pre-training model and using GIOU loss. Experimental results show that after improving the integrated network for pedestrian detection and attribute recognition, the inference time of a single picture is 18ms under the condition of Nvidia Titan RTX graphics, which is equivalent to YOLOv3. Tested on the WIDER Attribute dataset, mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> reaches 65.25% and mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</inf> reaches 34.13%.
Super-resolution restoration technique for ground-based imaging of space objects

Space objects images taken through large ground-based telescopes usually suffer from a degradation due to atmospheric turbulence, In order to reduce the cost of Charge-coupled Device (CCD) and improve the image signal-tonoise ratio, ground-based telescopes are usually designed with down-sampling, the observed image is blurry and aliased. We present a Super-Resolution (SR) algorithm to restore under-sampled image sequences with randomly varying blur, the algorithm significantly improves the quality and resolution of space object images degraded by atmospheric turbulence, it is a unifying framework that simultaneously performs Multi-frame Blind Deconvolution (MFBD) and SR in a maximum a posteriori (MAP) framework. The object and the Point Spread Function (PSF) are estimated by minimizing a cost function coming from the MAP criteria, the Total Variation (TV) regularization is imposed on the object estimation, TV regularization is remarkably effective to suppress the noise and to preserve the sharp edges in the image. We use the conjugate gradient method for the minimization for its fast convergence. Encouraging simulation results demonstrate that the restored image produced by this algorithm often have better quality than MFBD.
Energy consumption forecasting as a service

The analysis and forecasting of time series in relation to the problem of forecasting energy consumption is considered. A scheme for constructing a forecast model, a forecasting method and algorithm, taking into account the features of the analyzed time series, is proposed. The developed machine learning pipeline works in automated mode and it allows to make forecasts of energy consumption. The implementation of the developed method of forecasting as a service is considered.
Absolute phase measurement method with few-patterns

To unwrap the wrapping phase from phase measuring profilometry in 3D surface measurement, many spatial and temporal phase retrieval methods have been presented. However, spatial phase retrieval method often leads to errors because of discontinuous morphology, noise and fringe undersampling. Temporal phase retrieval method can solve this problem. But the method needs multiple frames of fringe images which would take much time. We proposed a novel absolute phase measurement method with few-patterns to overcome this problem. The proposed method combines an object reflectivity correction and a half-period gray-coded phase unwrapping algorithm. One group of precoded three-step phase-shifting fringe patterns are used to determine the wrapped phase, divide the region, and correct the surface reflectivity of the tested object, a frame of half-period gray-coded pattern is used to determine the fringe order. The proposed method can obtain a large number of codewords for fringe order without decreasing the intensity level for each stair. The result shows that our half-period gray-level coding (HGC) method is robust and efficient.
A Stacking-Based Ensemble Movie Recommender System

Recommender systems are information filtering systems that influence customer behavior in the digital world by suggesting relevant items to users. In this paper, we have proposed a stacking-based ensemble movie recommender system. The proposed recommender system has been tested on the MovieLens dataset obtained from the Grouplens project. We have experimented with different combinations of base learners and meta-learners to find the best base learners and meta-learner combination for the proposed recommender system. Different regression models like Linear Regressor and K-Nearest Neighbors regressor and ensemble models like Gradient Boost, AdaBoost, Random Forest and XGBoost (eXtreme Gradient Boosting) have all been tested for this purpose. The experiments conducted show that stacked models outperform stand-alone models. It is also observed that XGBoost gives excellent results when used as a base learner and also as  a meta-learner in  the  proposed  stacked  model..
Multitype Learning via Multimodal Data Embedding

This paper creates a multimodal retrieval system for image and text data in a multi-type learning approach that enables text-to-image, image-to-text, text-to-text, and image-to-image retrievals. As a practical solution, a mobile application is developed in which the users can upload their images to search a description sentence for the images. The user system is created on the application, which is done with React Native, and crucial features like e-mail authentication and reset password options are added to the application. An essential database system is designed with PostgreSQL to store user information and search for the user. The multimodal embedding study is worked, and the model that recognizes multitype retrievals is formed. The image-to-text retrieval model, which is our application’s idea, is applied to the mobile application.
Artificial Intelligence and Machine Learning in Crystallography Editorial for Crystallography Reviews, Issue 2 of Volume27, 2021

“Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never ...
Aesthetic education in the aspect of the ecology of culture

The object of this research is the ecological-cultural concept, which is based on the theory of the noosphere. Special emphasis is placed on the ethical ecology. The author explores the essence and specificity of the noospheric reality, as well as the role of the new scientific direction &amp;ndash; ecology of culture in development of the ideas of environmental safety in modern world. The subject of this research is the aesthetic education in the context of the ecology of culture, the concept formulated by the Academician D. S. Likhachev. The author believes that aesthetic education in the context of the ecology of culture is an irreplaceable instrument for the formation of environmental competencies of an individual. The concept of aesthetic education developed by B. T. Likhachev, as well as the latest approaches in art therapy are used as the examples. Analysis is conducted on the theoretical and practical literature that allows revealing the essence and specificity of aesthetic education in light of the ecological-cultural concept. The acquired results can be used in the development of academic programs and methodologies in the field of aesthetic education and art therapy for dealing with different population categories. The novelty consists in the fact that this article is&amp;nbsp; first to examine the essence and specificity of aesthetic education in the context of ecological-cultural concept based on the representations of the noosphere. The author's contribution consists in comparison of different, at first glance, concepts (the theory of noosphere, ecological-cultural concept, concept of aesthetic education) and obtain the results that are relevant for both, theoretical research and practical use. The main conclusions are as follows: 1. The development of ecological-cultural representations is fully implemented only in the context of the theory of noosphere. 2. The aesthetic education in the context of the ecology of culture is currently a powerful instrument for the formation of environmental competencies of an individual.
Two methods of conjoint summands of generating bivariate and trivariate normal pseudo-random numbers

The primary aim of the paper is to put forward methods of generating two dimensional (2D) and three dimensional (3D) normal pseudo-random numbers (NPRNs). Both proposed methods are presented in two versions. The first version is based on uniform (0,1) random variables, while the second version is based on normal random variables. Joint summands in these methods make resulting normal numbers correlated. The secondary aim of the paper is to compare the existing methods of generating 2D and 3D NPRNs with new proposals using goodness-of-fit tests (GoFTs), generation time and specially defined measures. The obtained results indicate that the methods based on uniform (0,1) random variables are faster than the others (have the best computational complexity) and also stand out from the other measures and GoFTs used.
Performance Evaluations of Hand Number Gesture Recognition by Convolution-Based Deep Neural Networks

With rapid developments of artificial intelligence (AI) techniques, biometrical recognition will be further extended to the field of hand gesture recognition. It’s expected that common hand gesture actions in the daily life will be able to be smartly recognized by an AI system to further promote communication between persons and persons (or machines). This paper presents a hand number gesture recognition system where the popular deep learning model technique, the convolution neural network (CNN), is employed for classifications of hand number gesture images. In this work, recognition performance evaluations are done by different system settings including mainly CNN input images, CNN structures and fully connected layer (FC) parameters of CNN. In hand number gesture experiments with various system settings, the setting that the input hand number gesture image with segmentations of region of interest (ROI) combined with the specific CNN model containing two FC layers can have the most satisfactory performance, achieving the validation accuracy of 92.3% and the test accuracy of 81.3%.
Depth Recovery from Single Monocular Image in 3D Reconstruction and Visual Precise Positioning

The depth information of the image is essential for accurate positioning. However, for the scale of the model, stricter standards are inevitable. This paper proposed Lower Layer Efficient Neural Network (LL-ENet) on the problems of requirements with light model, mostly using on the mobile devices. Firstly, The model reconstruct encoder by changing the convolution for less calculation, then classify the depth for efficient predictive ability. Finally, the result by experience shows that LL-EN et has a better effect on depth recovery with single monocular image in the outdoor scenes. Moreover the further experience shows that the proposed LL-ENet has better performance than other algorithms.
Implementasi Naïve bayes Clasifier dalam Klasifikasi Jenis Berita

Sometimes the classification of news categories is still an obstacle. Classification can be wrong because it is still subjective. As a result, the selected category does not match the uploaded news description. Based on these problems, the authors feel the need to make Classification of News Types with the Naïve Bayes Classifier Algorithm. The importance of this system is to be able to classify news and help news seekers to get the news they want.&#x0D; Based on the test results, the Naïve Bayes Classifier algorithm has a good performance for the classification of news types. This is evidenced in testing using news data taken from www.kompasiana.com, then news is classified into four categories namely politics, economics, sports, and entertainment. The classification results using 16 test news obtained an accuracy of 87.5%.
Green Development Level Evaluation of Urban Agglomeration Based on Improved Cloud Model

Urban agglomeration is the product of the economic and urban development of a country at a given time. In this study, an index system for evaluating the green development level of the SC region was initially constructed based on four dimensions. Then, using the cloud model theory, we scored the green development level of the SC region, Yangtze River Delta, and BTH region and generated a cloud model image.
Fusion-Based Feature Extraction Approach for Recognition of Handwritten Devanagari Numerals

Exploring fresh methods for handwritten digit recognition receiving close attention from respective research communities due to its wide application fields ranging from bank and postal services automation to archaeological surveys. Handwritten digit recognition is a challenging task due to unconditional variation in shapes and sizes of handwritten digits written by different individuals. The task becomes more demanding for the scripts like Devanagari due to the richness of curvatures in digit structure. The proposed scheme implemented a transfer learning approach for feature extraction from handwritten digits. For this purpose, pre-trained deep convolutional models VGG-16 and VGG-19 were deployed due to their splendid capability of minute features extraction from the given pattern. The features collected from both the models were fused into a single feature map by reducing their dimensionality with the help of the effective principal component analysis (PCA) method. The model managed to attain a recognition accuracy of 97% with a fusion-based approach.
Strategies for Improving the Quality of Talent Cultivation by Applying Informatization and Computer Aided Teaching

With the continuous expansion of colleges and universities in China, the number of students has increased dramatically. When universities and colleges construct teaching buildings and dormitories to accommodate a larger number of students, more attention should be paid to the quality of talent cultivation. Only by improving the overall quality of students in teaching process can we complete the mission of higher education and deliver high-quality talents for the society. Under the background of Education Informatization, colleges and universities have begun to use information technology. The application of various multimedia teaching equipment has greatly changed the traditional teaching model. The information-based teaching model will play an increasingly important role in the cultivation of talent students.
Research on Improving Empathy Based on the Campus Barrier-Free Virtual Experience Game

As the world’s population aging and chronic disease grow, the disability rate is also growing. Therefore, let more people pay attention to the disabled, and establishing the right accessibility concept is critical. However, barrier-free science popularization is not common at present. Most studies have shown that virtual reality can be moderately enhanced. Therefore, this research aims to explore the mechanism of empathy in the immersive virtual environment, and then form a set of design methods to be applied to empathy design.According to three levels of emotional design, this research construct a design model includes the atmosphere layer, the interaction layer and the reflection layer. In this study, we tested the importance of visual, hearing, game form and game perspective on empathy of 34 participants. This experiment uses a method of combining qualitative and quantitative, and records the subject’s eye data, ECG data, and subjective evaluation data. The results found that visual and game forms have a significant impact on empathy. Hearing is the strongest in people’s subjective feelings, so it also has a certain influence on emotional arousal. The perspective of the game has the weakest influence on emotional arousal. On the whole, in a barrier-free virtual game environment in the form of low-saturated colors, intense music, and customs clearance form, the empathy for the disabled can be most stimulated. Therefore, the construction of subsequent barrier-free science games is recommended to adopt this principle for design, and it also provides a reference for the design of future empathy games.
Prospects for Generative - Adversarial Networks in Network Traffic Classification Tasks

Abstract The paper presents an approach that allows increasing the training sample and reducing class imbalance for traffic classification problems. The basic principles and architecture of generative adversarial networks are considered. The mathematical model of network traffic classification is described. The training sample taken to solve the problem has been analyzed. The data proprocessing is carried out and justified. An architecture of the generative-adversarial network is constructed and an algorithm for generating new features is developed. Machine learning models for traffic classification problem were considered and built: Logistic regression, k Nearest Neighbors, Decision tree, Random forest. A comparative analysis of the results of machine learning models without and with the generation of new features is conducted. The obtained results can be applied both in the tasks of network traffic classification, and in general cases of multiclass classification and exclusion of unbalanced features.
The Role of the Students’ Independent Work in the Learning Process

The paper discusses the importance of students’ independent work in the field of higher education and gives the reasons of the need to form a new model for its organization. The role of the teacher in the mentioned process is multi-functional, he/she is an advisor, a source of information and a consultant who is ready to help the students in the case of necessity. We believe that the modernization of a particular discipline within the framework of an independent work requires constant updating and development. With the use of the appropriate methods, student gradually acquires the skill of analysis and evaluation, which later encourages student’s self-organized work in the learning process. As a result, students will be able to adapt effectively to higher education conditions. Lots of experimental studies test the efficiency of the model. Thus, claiming that diverse independent activities have a positive impact on the development of students’ creative independence. The paper emphasizes the necessity of certain discipline or the need of certain component in the learning course that enables students to independently work on certain modules in reading and listening.
WordPress and Kanban Use in Software Engineering Education Evaluated by Natural Language Processing and Neural Network

One of the challenges in software engineering education is to present students a real-world scenario and train them in project management techniques in combination with technological literacy and intercultural communication to collaborate effectively. Especially in undergraduate education, students benefit from project-based learning. Standardized tools help both the learning and teaching side in team and class management. For this paper, 28 undergraduate students from an international engineering course in Japan were surveyed on their assessment of WordPress and Kanban usefulness, engagement, distraction, and preference for other methods. In addition, four open-ended questions were asked to students about their project’s success, what they liked and disliked, and general comments. The text was pre-processed by natural language processing and a language model was trained using the skip-gram method of Word2Vec. The results from the language model were interpreted in combination with students’ perception of WordPress and Kanban use.
On the Refinement of Compensation-Based Semantics for Weighted Argumentation Frameworks

Acceptability semantics for the frameworks of weighted argumentation can satisfy up to one of the principles of (Quality Precedence), (Cardinality Precedence) or (Compensation), which are pairwise incompatible. In this paper we define two new principles: (Quality Compensation) and (Cardinality Compensation), which are weakened versions of (Quality Precedence) and (Cardinality Precedence), respectively. We show that these new principles are compatible with (Compensation) and propose two new semantics: a t-conorm-based, which can satisfy (Quality Compensation) and a cumulative sum-based semantics, which satisfies (Cardinality Compensation).