Reviewerly/exHarmony huggingface dataset:
Actual problems and trends in the development of research activities in educational institutions of the MIA of Russia (review of materials of the intradepartmental scientific and practical conference)

On June 17, 2022, an intradepartmental scientific and practical conference was held at the Saint Petersburg University of the MIA of Russia, during which the following issues were discussed: problems of legal regulation of scientific activities of educational organizations of the system of the MIA of Russia; issues of optimizing the system of dissertation councils operating at educational organizations of the MIA of Russia; prospects for creating an automated information and analytical system for recording the results of scientific activities of educational organizations of the MIA of Russia; the procedure for registration and criteria for evaluating the activities of scientific schools of the system of the MIA of Russia; problems of assessing the publication activity of scientific and pedagogical workers of educational organizations of the MIA of Russia in scientometric databases; methodology for preparing and conducting scientific and representative events in educational organizations of the MIA of Russia; actual problems of publishing activities of educational and scientific organizations of the system of the MIA of Russia; the work of printing production in educational organizations of the system of the MIA of Russia; the current state and prospects for the release of electronic publications in educational institutions of the system of the MIA of Russia; problems and trends in the development of the publication of departmental peer-reviewed journals; the system «Antiplagiat» as a tool to improve the quality of educational and scientific publications; problems of reviewing educational and scientific publications in educational institutions of the system of the MIA of Russia; modern problems of copyright in educational book publishing; issues of preparation of literature and its submission for publication with the stamp of the MIA of Russia by educational organizations of the MIA of Russia; interaction of a departmental university with publishing houses and libraries.&#x0D; More than 80 people took part in the conference.&#x0D; On the eve of the scientific forum, the competition «Development and improvement of departmental publishing: a look into the future» was held. By the beginning of the conference, an electronic collection of abstracts of the speeches of its participants was published [1].
Dynamic stabilization-based trajectory planning for drones

This work presents several approaches that deal with the control of a fixed wing UAV (Unmanned Aerial Vehicle) with a constant flying altitude which provide a target supervision and/or track a moving target. In this paper, the drone is considered to join a pre-designed admissible circular trajectory around a fixed target. We propose two different UAV target-tracking algorithms: The open loop basic tangent method and the tangent based Lyapunov-Lasalle method. The proposed methods were compared at the level of their performance and stability.
An Unsupervised 3D Image Registration Network for Brain MRI Deformable Registration

In recent years, deep learning has made successful applications and remarkable achievements in the field of medical image registration, and the method of medical image registration based on deep learning has become the current research hotspot. However, the performance of convolutional neural networks may not be fully exploited due to neglect of spatial relationships between distant locations in the image and incomplete updates of network parameters. To avoid this phenomenon, MHNet, a multiscale hierarchical deformable registration network for 3D brain MR images, was proposed in this paper. This network was an unsupervised end-to-end convolutional neural network. After training, the dense displacement vector field can be predicted almost in real-time for the unseen input image pairs, which saves a lot of time compared with the traditional algorithms of independent iterative optimization for each pair of images. On the basis of the encoder-decoder structure, this network introduced the improved Inception module for multiscale feature extraction and expanding the receptive field and the hierarchical forecast structure to promote the update of the parameters of the middle layers, which achieved the best performance on the augmented public dataset compared with the existing four excellent registration methods.
Evaluation of College Students’ Classroom Learning Effect Based on the Neural Network Algorithm

With the advent of the digital society, the amount of information we face will increase exponentially, which will challenge our level of educational knowledge, so we begin to pay attention to the effect of education and teaching in the context of digitalization. The purpose of this paper is to study the evaluation of students’ classroom learning effect based on the neural network algorithm and scientific objectivity. Assessment and other principles are to create an assessment system for students’ learning outcomes in the classroom. The system includes three first-level indicators, including first-level indicators and their weights. By selecting a university to test the system, the results show that the system can quantitatively evaluate the learning outcomes, and the corresponding scores and grades can be obtained through the formula. After adjusting the parameters of the hidden layer nodes, the BP elastic gradient algorithm is used to complete the evaluation model generation. The training error results show that the target curve and the output curve almost coincide, and the error curve is also between −0.2 and 0.5. Therefore, the learning outcome score obtained by the BP neural network based on the principal component factor data is basically consistent with the given learning outcome score.
Comparison of Optical Handheld 3D Scanners Suitable for Prosthetic and Orthotic Applications

3D scanning technology is globally frequently used in the field of prosthetics and orthotics.Techniques and methods using 3D scanners make it easier and faster for CPOs (Certified prosthetic orthotist) to obtain important data for prosthetic and orthotic device design.Nevertheless, in low-income countries, this is still a new approach, and some CPOs might find it hard to trust.Also, the prices of some of these 3D scanning devices and data processing software are too high.Because of these issues, modern methods are being neglected and the whole prosthetic device designing process isn´t advancing.The aim of this study is to compare optical handheld 3D scanners of different brands, designs, and price ranges.A polystyrene model of the human torso with predetermined geometric forms will be scanned with multiple handheld optical 3D scanning devices.For the precision evaluation of the 3D scanners and hand measurement it is necessary to position the reference forms in a way that it will allow measurement by both methods.Selected dimensions must include all three axes of the cartesian coordinate system, so it will be possible to scanners precision in all individual axes.Scanning will be performed by one person in a room with stable lighting and room conditions.The torso model will be scanned by individual scanners and measured by hand 3 times and a mean value and standard deviation will be calculated.The geometric forms will be also used for the alignment of obtained 3D models in VGStudio Max software, which will be used for the analysis process.Distances between predetermined forms on the obtained 3D models will be evaluated.These nominal values will be compared with the actual values from the torso model, which were measured by hand.The quality and surface precision of actual obtained 3D models will be compared to determine the minimal or ideal requirements of optical handheld 3D scanners for the use in the field of prosthetics and orthotics.
Characterizing Apparel and Fit for Virtual and Physical Worlds with Logic Statements

This paper conceptualizes clothing fit and represents fit attributes in detail using Logic statements with either common logic symbols or uniquely defined symbols to indicate thought process and then expand to the implemented level of Fit.These Logic statements explain the relationships between body and fit in 3D and 2D along with 1D and 0D features and measures.This work introduces four assets: Human, Humanoid, Cover, and Coveroid; and the fit attributes depict the relationships of those assets.Subsequently,16 transforms are combined to broadly define technology offerings involving physical and virtual fit.Importantly, the Logic statements are shown to depict proposed relationships that determine Intended Fit and Expected Fit.Fit statements were expressed for both known and unknown parameters; therefore, it can simulate a Ready-to-Wear Use Case, and Bespoke to Made-to-Measure Use Cases.In addition, 2D and 3D fit landmarks are described in the form of geometric details, including body geometries and measurement locations for known and unknown Humans and Humanoids.This work has characterized the essential parameters of Design Realm, Production process impact, and underneath cover(s).The Design Realm has incorporated: Universal style tag requirements, Emotive Design, and Human shape-dependent parameters.Since the fit is impacted by time, 4D concepts can be incorporated into a virtual avatar, in which Planned Fit is described by Rigging.This work translates all the fit principles used in the apparel industry into logical statements for both mathematicians and digital tool developers.Consequently, this will augment software intended to automate Cover design and development.
Biopolitical Aspect of Contemporary Urban Media Architecture

This article is devoted to the study of problems that may occur within the framework of biopolitical approach implementation in the context of contemporary urban management. Considerable emphasis is made upon the study of the media phenomenon development, formation of media architecture infrastructure as an approach of the biopolitics of emotions. The aim of the study is to form a consistent comprehensive understanding of the current trends in the practical implementation of the biopolitical approach methodology while analyzing the specifics of the mediatized space in contemporary city.&#x0D; The article analyzes particularities of nowaday communication in a virtual environment, its impact on the psyche of a moden person, and also raises the problems of contemporary media which has not only acquired the character of abstract information transmitted in distribution, but is embodied in the certain structural specifics of buildings, that has given rise to the development of such a phenomenon as media architecture. The result of the study is an analysis of the problems and prospects of the tendencies of the development of a modern city from the point of a biopolitical approach to urban management in the context of the (trans)formation of the media architectural infrastructure. The main problems, as the study shows, are caused by the lack of proper regulation of urban planning policy implementations from the standpoint of providing a comfortable environment for the life of the urban population.&#x0D; This article is intended for specialists in political science, as well as for researchers whose studies are devoted to the digital biopolitical aspect of modern urban governance.
Digital Transformation of Education for Sustainability of the Caspian Region

In the modern times of the fourth industrial revolution, digital technologies have spread throughout all spheres of life. In the context of rapid technological changes, it is necessary to maintain social sustainability, implying a balance between the development of economic, environmental and social – including educational – spheres. Theoretical analysis of WoS scientific papers published within the period from 2017 to 2021 has identified specific features of the dependence of sustainable development on the digital transformation of education. Analysis of these features in relation to Astrakhan State University allowed to identify areas of digitalization in education that are being actively developed (training and retraining of HR for the digital economy, improving digital literacy of the population, developing competencies that are professionally relevant for the digital economy) and areas of activities that require effort mobilization (upgrading educational technologies, methods, content and learning outcomes, developing a digital learning environment, increasing the digital and ethical competence of teachers, stimulating environmental education). The results obtained are of applied relevance for the training of highly skilled professionals capable of addressing professional tasks with the use of modern digital tools, both for sustainable development of Astrakhan Region and other Caspian bordering regions of Russia, as well as the Caspian countries.
Contextual Path Retrieval: A Contextual Entity Relation Embedding-based Approach

Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based Contextual Path Retrieval (ECPR) framework. ECPR is based on a three-component structure that includes a context encoder and path encoder that encode query context and path, respectively, and a path ranker that assigns a ranking score to each candidate path to determine the one that should be the contextual path. For context encoding, we propose two novel context encoding methods, i.e., context-fused entity embeddings and contextualized embeddings. For path encoding, we propose PathVAE, an inductive embedding approach to generate path representations. Finally, we explore two path-ranking approaches. In our evaluation, we construct a synthetic dataset from Wikipedia and two real datasets of Wikinews articles constructed through crowdsourcing. Our experiments show that methods based on ECPR framework outperform baseline methods, and that our two proposed context encoders yield significantly better performance than baselines. We also analyze a few case studies to show the distinct features of ECPR-based methods.
Learning Strategies in Lifelong Learning

The individual's interest in reading, thinking and analyzing to gain information for personal or professional reasons can be called lifelong learning. Learning for an individual begins in the family, develops in school, and continues throughout life. That is why learning is sustainable. To succeed in life, it is essential to be educated. To obtain new information, the individual must have a learning style and develop himself with strategies. In this article, the writer emphasizes the teachers' mission on teaching strategies and why learners need learning strategies in lifelong learning.
RFNet: Fast and efficient neural network for modulation classification of radio frequency signals

Automatic Modulation Classification (AMC) is a well-known problem in the Radio Frequency (RF) domain. Solving this problem requires determining the modulation of an RF signal. Once the modulation is determined, the signal could be demodulated making it possible to analyse the signal for various purposes. Deep Neural Networks (DNNs) have recently proven to be successful in solving this problem efficiently. However, since deep networks consist of several layers resulting in a high number of trainable parameters, the hardware implementations of these solutions are resource-demanding. In order to address this challenge, we propose an efficient deep neural network referred to as RFNet to tackle the AMC problem efficiently. This network introduces the novel Multiscale Convolutional (MSC) layer to extract robust features in different resolutions. In addition, the network takes advantage of several Separable Convolution Blocks (SCB). These blocks employ pointwise and depth-wise convolutions to reduce network complexity. We further introduce RFNet+ and RFNet++ as extensions of RFNet with fewer number of parameters. These variants include fewer floating-point operations and hence a lower hardware implementation cost. Experimental results using the challenging RadioML 2018 dataset show that RFNet-32++ achieves an average classification accuracy of 56.09% over all Signal-to-Noise Ratios (SNRs) and an accuracy of 92.21% in+20dB SNR using only 3.1K parameters. The small number of parameters makes the RFNet family a promising solution for future AMC systems.
A Data-efficiency Training Framework for Deep Reinforcement Learning

Sparse reward long horizon task is a major challenge for deep reinforcement learning algorithm. One of the key barriers is data-inefficiency. Even in the simulation environment, it usually takes weeks to training the agent. In this study, a data-efficiency training framework is proposed, where a curriculum learning is design for the agent in the simulation scenario. Different distributions of the initial state are set for the agent to get more informative reward during the whole training process. A fine-tuning of the parameters in the output layer of the neural network for value function is conduct to bridge the gap between sim-to-real. An experiment of UAV maneuver control is conducted in the proposed training framework to verify the method more efficient. We demonstrate that data-efficiency is different for the same data in different training stages.
Comparison of LSTM, SVM, and naive bayes for classifying sexual harassment tweets

Twitter is now a very open and extensive social media; anyone can freely express their opinion on any topic on social media. The content or discussion on Twitter is also quite diverse and unlimited. However, because it is unlimited, many misuse it for negative things. One of them is verbal sexual harassment through Twitter. This research aims to identify sexual harassment in an Indonesian tweet using sentiment analysis using the LSTM, SVM, and naive bayes methods with text normalization. In this study, 2990 tweets in the Indonesian language were tested from 4th to 6th in May 2022. The Twitter data shows that tweets included in sexual harassment are more than those not included in sexual harassment, totaling 2026 data. From the results of the evaluation of tweet data classification using text normalization with LSTM, the accuracy is 84.62%, SVM is 86.54%, and naive bayes is 85.45%. Using the SVM algorithm with text normalization gets the highest accuracy compared to LSTM and naive bayes in classifying Indonesian sexual harassment tweets.
Optimization of breast cancer classification using feature selection on neural network

Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.
Levy Enhanced Cross Entropy-based Optimized Training of Feedforward Neural Networks

An Artificial Neural Network (ANN) is one of the most powerful tools to predict the behavior of a system with unforeseen data. The feedforward neural network is the simplest, yet most efficient topology that is widely used in computer industries. Training of feedforward ANNs is an integral part of an ANN-based system. Typically an ANN system has inherent non-linearity with multiple parameters like weights and biases that must be optimized simultaneously. To solve such a complex optimization problem, this paper proposes the Levy Enhanced Cross Entropy (LE-CE) method. It is a population-based meta-heuristic method. In each iteration, this method produces a "distribution" of prospective solutions and updates it by updating the parameters of the distribution to obtain the optimal solutions, unlike traditional meta-heuristic methods. As a result, it reduces the chances of getting trapped into local minima, which is the typical drawback of any AI method. To further improve the global exploration capability of the CE method, it is subjected to the Levy flight which consists of a large step length during intermediate iterations. The performance of the LE-CE method is compared with state-of-the-art optimization methods. The result shows the superiority of LE-CE. The statistical ANOVA test confirms that the proposed LE-CE is statistically superior to other algorithms.
Truncated Diffusion Probabilistic Models and Diffusion-based Adversarial Auto-Encoders

Employing a forward diffusion chain to gradually map the data to a noise distribution, diffusion-based generative models learn how to generate the data by inferring a reverse diffusion chain. However, this approach is slow and costly because it needs many forward and reverse steps. We propose a faster and cheaper approach that adds noise not until the data become pure random noise, but until they reach a hidden noisy data distribution that we can confidently learn. Then, we use fewer reverse steps to generate data by starting from this hidden distribution that is made similar to the noisy data. We reveal that the proposed model can be cast as an adversarial auto-encoder empowered by both the diffusion process and a learnable implicit prior. Experimental results show even with a significantly smaller number of reverse diffusion steps, the proposed truncated diffusion probabilistic models can provide consistent improvements over the non-truncated ones in terms of performance in both unconditional and text-guided image generations.
Minimalistic Unsupervised Learning with the Sparse Manifold Transform

We describe a minimalistic and interpretable method for unsupervised learning, without resorting to data augmentation, hyperparameter tuning, or other engineering designs, that achieves performance close to the SOTA SSL methods. Our approach leverages the sparse manifold transform, which unifies sparse coding, manifold learning, and slow feature analysis. With a one-layer deterministic sparse manifold transform, one can achieve 99.3% KNN top-1 accuracy on MNIST, 81.1% KNN top-1 accuracy on CIFAR-10 and 53.2% on CIFAR-100. With a simple gray-scale augmentation, the model gets 83.2% KNN top-1 accuracy on CIFAR-10 and 57% on CIFAR-100. These results significantly close the gap between simplistic "white-box" methods and the SOTA methods. Additionally, we provide visualization to explain how an unsupervised representation transform is formed. The proposed method is closely connected to latent-embedding self-supervised methods and can be treated as the simplest form of VICReg. Though there remains a small performance gap between our simple constructive model and SOTA methods, the evidence points to this as a promising direction for achieving a principled and white-box approach to unsupervised learning.