Reviewerly/exHarmony huggingface dataset:
Problems of the development of artistic consciousness in teaching the piano to students of the music and pedagogical faculties of the universities of art and culture

The work deals with the problems of the development of artistic consciousness among students of higher educational institutions, music and pedagogical faculties, studying to play the piano. Methodic recommendations for the development of ear for music, musical rhythm and musical memory are given, taking into account the specifics of teaching a piano class. The work is based on the concept of “play”, described in the book by J. Huizinga “Homo Ludens. A Study of the Play-Element in Culture”. A description of the belonging of music to the concept of “play” in culture is created, and also the relationship between playing a musical instrument and playing forms of culture are shown. The work is the first to apply the analogy between the philosophical concept of “playing” and directly playing the piano. Playing as a cultural phenomenon, playing a musical instrument, concert activities that bring pleasure to listeners are the main parameters that allow researchers to combine not only philosophical concepts, but also to touch upon the pedagogical problems facing a piano teacher at a university. On the basis of the concept “Taxonomy of pedagogical goals”, a classification of areas of pedagogical activity for the development of artistic consciousness in teaching students to play the piano is created. When analyzing pedagogical tasks, we use the experience of leading performers – F. Busoni, G. Neuhaus, S. Savshinsky, I. Hoffmann.
A Method for Suppressing the Fluctuation of Aircraft Taxiing Posture

Illumination variation in the scene, random noise in imaging, video blurring caused by encoding and decoding, which cause a certain degree of random fluctuations between the taxiing posture of aircraft detected from video and the actual taxiing posture in the scene. This paper proposes a method for suppressing the fluctuation of aircraft taxiing posture. The method firstly uses the background subtraction (Vibe) to extract the region of target, and obtain the initial position of the taxiing posture, then the Kalman filter is used to predict the taxiing trajectory of the initial position, the random fluctuations are suppressed to obtain a smooth estimate of the position in the taxiing posture, finally, obtain the speed and direction of the taxiing posture according to the smooth estimated position, output the taxiing posture based on the image plan. the actual test at the airport shows that the method in this paper has a center ability to restrain the fluctuation of taxiing aircraft posture.
Data-driven Embedding with Pixel Repetition for High Capacity Reversible Data Hiding

This paper introduces a new data-driven paradigm of Pixel-Repetition (PR) based embedding for high capacity Reversible Data Hiding (RDH). A simplified up-sampling with PR is applied to an input image that repeats each input pixel to create an image-block of size $2\times 2$ in the up-sampled image. Each up-sampled pixel is then proposed to get embedded in their least significant bits (LSBs) leaving the least possible most significant bits (MSBs). This consideration significantly maximises the embedding capacity, but it also increases the distance between the original and embedded up-sampled pixels. To minimise these high variations in the embedded pixels, we also propose to determine the closer version of an embedded pixel from its original and (2’s) complement versions. A flag-bit is reserved at the LSB of each up-sampled pixel to track which version is used during embedding for extraction of the embedded data-bits. For the pixel values of 2-bits or lesser, however, direct LSB embedding is used without any flag bit. The proposed data-driven embedding ensures better utilization of the redundancy as observed with an impressive embedding rate-distortion performance having more than 8% improvements in both the embedding rate and embedded image quality over the prominent PR-based schemes. Index Terms–Embedding capacity, data-driven embedding, data hiding, pixel repetition, imperceptibility, reversibility.
A New Method to Compress Neural Networks

Neural networks have become one of the most important research areas in the past few years. However, most excellent successes have a huge number of trainable parameters, which restricts the application to edge devices, such as cellphones. This study mainly focuses on how to compress neural network models with acceptable accuracy reduction. Different from other approaches, the proposed algorithm is from the perspective of system identification, and uses an additive model for compression. With the forward-regression orthogonal estimator, our algorithm not only provides a compressed model but also a compression number that is suitable for retraining. The extreme learning machine is used to verify our algorithm. Although experimental results show that our method is not suitable for the extreme learning machine, for the regression task, the compression number acquired from our algorithm makes the testing accuracy after retraining even better. Moreover, the reason why the extreme learning machine is not suitable for our algorithm is also analyzed.
Co-op Training: a Semi-supervised Learning Method for Data Streams

Applying Machine learning algorithms to data streams is a challenging task because traditional strategies suppose datasets to be labeled, finite and stationary. In the context of data streams, where the data is generated in real-time and the labels may be missing due to the high cost of the labeling process, the proposal of semi-supervised learning (SSL) strategies to learn from labeled and unlabeled data at the same time seems to be a viable solution, despite also being challenging. In this paper, we present a novel approach to handle missing labels for classification learning in data streams, named co-op training, which is based on self-training incremental and co-training. In a controlled experiment, we execute the proposed algorithm, along with most well-known semi-supervised learning strategies, in 11 artificial and real-world datasets, and compare the results. We found our strategy to be more accurate than the other SSL algorithms in most datasets, also presenting better run-times when accuracies were similar. These methods are implemented in the Massive Online Analysis (MOA) open-source software as an internal benchmark component, to help researchers to run experimental comparisons on semi-supervised learning on data streams easily.
Using a parallel corpus to adapt the Flesch Reading Ease formula to Czech

Abstract Text readability metrics assess how much effort a reader must put into comprehending a given text. They are, e.g., used to choose appropriate readings for different student proficiency levels, or to make sure that crucial information is efficiently conveyed (e.g., in an emergency). Flesch Reading Ease is such a globally used formula that it is even integrated into the MS Word Processor. However, its constants are language-dependent. The original formula was created for English. So far it has been adapted to several European languages, Bangla, and Hindi. This paper describes the Czech adaptation, with the language-dependent constants optimized by a machine-learning algorithm working on parallel corpora of Czech and English, Russian, Italian, and French, respectively.
Sharpest Hill Climbing Algorithm for the Row Puzzle Board in Scout Trail Game

Scout is one of the extracurricular activities in Indonesia. Currently, extra-curricular activities have decreased the number of members due to students' interest in other activities at their school. After COVID-19 pandemic, many students were no longer actively participating in Scout extracurriculars. Even though there are many benefits that can be taken in scouting activities, one of which is applying skill material such as learning codes, learning a form of cheating, general public knowledge, and even the ability to survive in protecting something. Scouting activities are the key to the practice of nation building in Indonesia. One way to increase student interest is by utilizing information technology. Scouting activities can be combined into a game with a scouting theme as is commonly played, which is like a educational puzzle to sharpen critical thinking. This research is more about the application of the SAHC algorithm for puzzle cases based on the scout trail game. The application of this method is to find a solution point from a predetermined character position. Interesting games can be used as promotions for students, so students can be interested in scouting activities.
OVAE: Out-of-Distribution Detection with Multi-label-enhanced Variational Autoencoders

Existing out-of-distribution (OOD) detection methods are mainly established on the use of output probability of softmax classifiers. However, the classifier-based approach only leverages the relationship between the input data and their associated labels, without making use of the information hidden in the abundant input data at all. Moreover, the inherent normalization characteristic in the softmax function is prone to make the output prediction probability large even for an OOD input. To address these issues, a generative-model-based approach is proposed. Specifically, to make full use of the input data and associated label, we propose to employ variational auto-encoder (VAE) to model the input data and their associated labels simultaneously. Moreover, to alleviate the issue of false large output probability, we transform the one-hot label into multi-label forms and propose to model the label with a multi-label branch in the VAE. Through experimental comparisons, it is verified that the model can effectively improve the OOD detection performance.
Attention-based Dual-Branches Localization Network for Weakly Supervised Object Localization

Weakly supervised object localization exploits the last convolutional feature maps of classification model and the weights of Fully-Connected (FC) layer to achieves localization. However, high-level feature maps for localization lack edge features. Additionally, the weights are specific to classification task, causing only discriminative regions to be discovered. In order to fuse edge features and adjust the attention distribution for feature map channels, we propose an efficient method called Attention-based Dual-Branches Localization (ADBL) Network, in which dual-branches structure and attention mechanism are adopted to mine edge features and non-discriminative features for locating more target areas. Specifically, dual-branches structure cascades low-level feature maps to mine target object edge regions. Additionally, during inference stage, attention mechanism assigns appropriate attention for different features to preserve non-discriminative areas. Extensive experiments on both ILSVRC and CUB-200-2011 datasets show that the ADBL method achieves substantial performance improvements.
High Utility Co-location Patterns

In this chapter, we incorporate utility into spatial pattern mining through the concept of pattern utility, and a general framework for spatial high utility co-location pattern mining is defined. Furthermore, we define the concepts of extended pattern utility ratio and partial extended pattern utility ratio, and present the Extended Pruning Algorithm (EPA) and Partial Pruning Algorithm (PPA) to prune down the number of candidates and obtain the complete set of spatial high utility co-location patterns. EPA and PPA improve the mining performance and accelerate the spatial high utility co-location pattern generation under different parameter environments.
IMAGE SHAPE COMPARISON IN MOSAIC DIFFUSION MORPHOLOGIES

In this paper, developing the early proposed unified approach to the representation of morphological models, we show that since morphological models in the attribute representation have the same mathematical form as images, the morphological models themselves can be the subject of morphological analysis and can be directly compared in form with using morphological operators. In this case, the previously introduced formalism of mosaic diffusion morphology is used.In the framework of mosaic diffusion morphology, two alternative descriptions of the projection of the image on the form are considered, which are based on a clear model and a fuzzy model of the form respectively. It is shown that the projection operator in the second case is a one-sorted diffuse operator that makes direct comparision of model to model instead of image to model. In this case, a fuzzy mosaic model appears in this scheme as a projection of a clear mosaic model onto another clear mosaic model. Based on this shape-to-shape projection idea, we propose the new version of Pytiev morphology tools for shape comparison: the morphological shape difference map, the morphological quasi-distance between shapes, as well as the Morphological Shape Correlation Coefficient (MSCC). We show that MSCC from the resource parameters of the reciprocal model has exactly the same formula as the standard effective morphological correlation coefficient proposed earlier based on statistical averaging of projected images.
Methods to Increase the Contrast of the Image with Preserving the Visual Quality

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.
Comparative Analysis of Pre-Trained Deep Neural Networks for Vision-Based Security Systems on a Novel Dataset

The era of neural networks has changed the world of automation by introducing smarter and more capable machines that can learn complex features from data. Transfer learning refers to using knowledge gained by a model and utilizing it for another machine learning task. In this research, the authors perform comparative analysis for feature extraction of several state-of-the-art neural networks. The authors also introduce a self-curated dataset and test the performance of Xception, VGG16, VGG19, ResNet50, InceptionV3, InceptionResNetV2, MobileNet and DenseNet121 pre-trained models as feature extractors on the dataset. On these extracted features, the researchers train a convolutional neural network and compare their performance and computational complexities. DenseNet121 and InceptionResNetV2 achieve the best performance but take longer time to train compared to other models. VGG16 and VGG19 architectures are computationally expensive due to their large number of parameters. We propose our system as a security application for public places where we need a bifurcation between the permissibility of toddlers and pets. Future work is centered around data security by employing federated learning algorithms and introducing new categories and expanding the data for a wider application.
Improving OCR Accuracy on Images with Motion Blur via GAN Derivatives

This research aims to remove motion blur from images to increase conversion accuracy of OCR (optical character recognition) based programs, by utilizing generative adversarial network derivatives. Utilizing a self-created dataset, images were taken with and without motion blur to be used in the training of the network. The final images were fed into OCR programs to compare their accuracy.
Exploiting the Medical Data Storage Implementation and Privacy Protection with Consortium Blockchain and IPFS

Due to the limitations of technologies, the traditional electronic medical record system has the risk of data being tampered with and attacked, which makes personal privacy easy to be leaked. Combined with the consortium blockchain development platform – FISCO BCOS, a new electronic medical record storage model is proposed. The model controls the access of medical records through blockchain and smart contract, strengthens the privacy protection of patients, improves the convenience of medical data sharing, and provides reliable data support for medical research. Finally, the proposed electronic medical record storage model is fully realized, and its effectiveness and practicability are proved.
Efficient learning of discrete graphical models*

Abstract Graphical models are useful tools for describing structured high-dimensional probability distributions. Development of efficient algorithms for learning graphical models with least amount of data remains an active research topic. Reconstruction of graphical models that describe the statistics of discrete variables is a particularly challenging problem, for which the maximum likelihood approach is intractable. In this work, we provide the first sample-efficient method based on the interaction screening framework that allows one to provably learn fully general discrete factor models with node-specific discrete alphabets and multi-body interactions, specified in an arbitrary basis. We identify a single condition related to model parametrization that leads to rigorous guarantees on the recovery of model structure and parameters in any error norm, and is readily verifiable for a large class of models. Importantly, our bounds make explicit distinction between parameters that are proper to the model and priors used as an input to the algorithm. Finally, we show that the interaction screening framework includes all models previously considered in the literature as special cases, and for which our analysis shows a systematic improvement in sample complexity.
ExOpSum: An Extractive Opinion Summarization methodology based on aspect-sentence-review ranking

With the evolving web technology, anything you need can be purchased online. With these purchases, the feel of the purchase is also recorded in the web portals in the form of user generated comments and feedbacks. The unlimited pour of opinions on the web has now paved way for automated sentiment analysis of opinionated text. Furthermore, in this fast-paced world, humans don’t find time to stand and stare, hence an automated opinion summary is the need of the hour. Summary should include highly ranked aspects, highly ranked sentences and highly ranked reviews. Also, the emotional touch of customers can be deliberately seen only through extractive summarization, which is the core objective of our work. The mathematical computational model designed for our work has proven to work better than the existing work on extractive summarization.
Generation of a Large-Scale Line Image Dataset with Ground Truth Texts from Page-Level Autograph Documents

Recently, Deep Learning techniques help to recognize Japanese historical cursive with high accuracy. However, most of the known cursive dataset have been gathered from printed documents which are written for the general public and easy to read. Our research aims to improve the recognition of autograph documents, which are more difficult to recognize than printed documents because they are often private and written in various writing styles. To create a useful autograph document dataset, this paper devises a technique to generate many line images accompanied by the corresponding ground truth (GT) texts, given an autograph document whose GT transcription is available only at the page-level.
User Reviews Based Rating Prediction in Recommender System

The user feedback information based Collaborative Filtering algorithm is often used to discover the hidden preferences of users and potential features of items in Recommender System for the past few years. Nevertheless, the potential relationship between different users is often ignored in the current Collaborative Filtering algorithm which contained in the user feedback information. To enhance the precision of Rating Prediction task in the Recommender System, this paper structures a new Rating Prediction method named the Review-based Rating Prediction (RRP) to explore the potential relationship among the different users. Through analyzing the reviews text, the method can derive the users’ potential relationship. Moreover, The resulting potential user relationship data was fused into the rating prediction task. The results of our experiments on open data sets suggest that our proposed model has a performance ascension over the traditional rating prediction methods.
The Application of Artificial Intelligence Technology in Online Teaching System in University Under the Background of Epidemic Prevention and Control

The current teaching mode has been greatly changed, through the combination of “online + offline” way, the integration of traditional teaching and network teaching advantages, and has become an important direction of education information era teaching development. Under the background of epidemic prevention and control, students unified online courses at home, and Internet teaching has become the focus of today's teaching method. The development and application of artificial intelligence technologies such as intelligent agent, natural language processing, pattern recognition and data mining have also provided new means for Internet teaching. The purpose of this paper is to study the application of artificial intelligence technology in college online teaching system and provide better technical suggestions for college online teaching system. Based on the existing teaching system, platform and the corresponding modern programming method and database technology as the foundation, combined with the disadvantages of the present stage in the aspect of teaching in colleges and universities, the author puts forward the concept of online remote network teaching, realizes the flexibility of teaching, explores the remote teaching internal potential concept and mechanism and meets the thirst for flexible time between teachers and students, and the results show that in two months, network teaching pattern to the hall rate is as high as 98%, and in part by collecting university students to online teaching evaluation and to provide reasonable suggestions for online teaching.