Reviewerly/exHarmony huggingface dataset:
FAMLINN: Representation for Storing Neural Network Architecture

To date, there are several ways to store neural networks, however, each of them has drawbacks. Sometimes it becomes necessary to store not only the weights of a particular model but also its architecture. The ONNX and TorchScript representations manage this task but have a problem that after saving they do not allow changing the network architecture. We propose a more flexible neural network saving method that solves this problem by allowing automated architecture generation and the network architecture manual tuning. For the convenience of working with existing formats, the ability to convert our format to the PyTorch format was implemented. According to the comparison results on several well-known architectures, the new format surpasses existing solutions. The method implementation is publicly available at https://github.com/IvanMaslov/famlinn.
Convolutional Neural Network Graph-based Embedding for Neural Architecture Search

Recently, network architecture search is gaining popularity. The neural network representation as a directed acyclic graph is considered for subsequent architecture optimization. Currently, most of the existing encoders rely only on the model layer properties and do not take into account the attributes of layers. This work proposes an algorithm for mapping a CNN network to a vector space considering the layer attributes, such as different dimensions of a particular layer. The proposed algorithm was compared with D-VAE and DVAE-EMB and showed less information loss caused by the mapping of a network to a vector space. As the results show, the performance of the model was shown after direct conversion to embedding and reverse conversion to architecture. The method allows more accurate neural network architecture mapping into a vector form, which will improve the search for the best architecture. The method implementation is publicly available at https://github.com/Turukmokto/GraphEmbedding-dev.
Proactive selection of machine learning models for small sample sizes in cerebral stroke detection based on PAC-learning theory

The article presents a developed method of proactive selection of machine learning models, in which the PAC-learning theory and a parameterizied Pareto frontier are used to describe the hyperparameter ratio. The theoretical substantiation of the proposed method is given. Based on the developed method, a proactive comparison of ML models for detecting cerebral stroke was made. The theoretical results were compared with the results of an experimental evaluation of the effectiveness of the same models, as well as baseline model, and the results of the comparison show the validity of the developed approach. Besides, the developed theoretical approach made it possible to substantiate the choice of the XAI tool for solving a specific problem of detecting cerebral stroke. The proposed method can be used in various subject areas for which ML models with an identical structure of cores are being developed. In addition, according to the authors, it is of interest to study the possibilities of the method in relation to models with a different core structure, for example, to models of the Active Contour group, which, in particular, are used to assess the affected area in stroke. This will allow developers at the design stage to expand the range of compared models and thereby speed up and reduce the cost of the development process.
Generating of synthetic datasets using diffusion models for solving computer vision tasks in urban applications

The paper illustrates the process of generating synthetic data using diffusion neural networks for solving popular computer vision tasks in urban applications: traffic vehicles detection and classification. The result of this study demonstrated new approach for synthetic datasets creation using modern generative neural networks. To elaborate this approach, experiments were performed to generate synthetic dataset to solve the above basic computer vision problem in urban applications using the most advanced diffusion neural network models (Kandinsky 2.2). Experiments were performed on these datasets to train deep neural networks to solve the object detection problem (YOLOv5). The results of testing the trained detectors on a specially selected validation dataset showed the potential viability of a synthetic dataset generation approach using diffusion neural network models. However, full-fledged use of this approach to generate synthetic datasets that can be used in training deep neural networks of computer vision in production is accompanied by some difficulties, namely high difference between domains of real data and generated data, high labor costs of tuning trained diffusion models to achieve the highest quality of generation, etc.
An Approach to Automate the Scientific Paper's Evaluation Based on NLP Technologies: the Experience in the Russian Segment of Financial Technologies Field

This paper reflects the research to design and application the complex method to evaluate the quality of the paper written in the IMRaD format. As a field we choose Russian segment of the financial technologies area since we had interest to use multilingual language model. The method covers paper's relevance to the chosen field and internal topics, the actuality, relation between parts, text borrowing, and experiment part. The clustering and classifying algorithms, and language machine learning model were used. The experimental part based on corpus of Russian papers shows the deviation of method's recommendations from expert opinion not more than 20%. The results may be interested not only reviewers but student as authors of futures papers to avoid common mistakes during paper's writing.
Development of domain-specific automatic speech recognition models based on open-source data

Currently, deep neural network architectures are used in the development of automatic speech recognition models for specialized subject areas, which require a large volume of training data. However, these models often prove to be poorly suitable for use in specific information systems due to the inadequate recognition of specialized subject vocabularies. Further training of models to improve their quality in a specific context of recognition encounters difficulties in obtaining sufficient data and the laboriousness of their labeling. Given this, a pertinent task is the creation of methods that allow for a reduction in the effort required to build applied speech recognition models and improve their quality when used in various subject areas. The study used language model-based text topic modeling techniques to adapt open-source data. A deep neural network was used as the pre-trained speech recognition model. For training, data sets from open sources were utilized. A method has been developed for creating automatic speech recognition models for specialized subject areas. This method involves an intermediate step of learning the lexicon of the subject area from data obtained from open sources, selected using thematic sampling. Based on this method, automatic speech recognition models for the energy and healthcare sectors were created and researched, demonstrating higher recognition results than models developed through traditional methods. The validation of the proposed method confirmed its effectiveness. The applied neural network models, developed based on this method, demonstrated their ability to operate in information systems of energy and healthcare facilities in Russian, English, and Italian languages without additional training on closed data.
Predicting dataset size for neural network fine-tuning with a given quality in object detection task

Currently, object detection is an important task in many areas of both industrial and research work. In practice, object detection is commonly done using a publicly available model pretrained on some dataset, for example, ImageNet. The model is then further fine-tuned on a custom dataset specific to the task. One of the most important parameters of fine-tuning is the training set size. According to the Neural Scaling Laws, the larger the training dataset is, the higher is the model quality. However, collecting and labeling a dataset is frequently a costly process, so the smallest dataset size that provides the desired model performance is preferred. This article proposes two models for predicting the minimal dataset size for the pretrained Fast R-CNN and YOLOv8 models, which is necessary to achieve the target accuracy by mean Average Precision (mAP) score in the object detection task. The described approach allows automated prediction of a correspondence between the characteristics of a typical image of a given object class and the target dataset size. We compared accuracy with Neural Scaling Laws (NSL). According to the results of the study, our algorithm surpassed the NSL. Open source code is available at https://github.com/phoenix-1202/Predicting-dataset-size.
Feedback2Event: Public attention event extraction from spontaneous data for urban management

With the widespread use of social networks, citizens actively use this channel to discuss problems and events arising during life in the city. Thus, representatives of the authorities and other stakeholders can monitor the process of interaction between people and urban infrastructure and services in real time. However, monitoring is complicated by the noisy and unstructured nature of spontaneous data. Informed decision-making based on such data requires extracting the cause of the messages, formulated as an event, to infer them from the unknown and characterize them. The method proposed in the work allows to extract from the flow of spontaneous data events and their spatial and temporal characteristics, as well as to form connections between them. Results of the experiment shows that this approach can represent processes with semantic similarity in the context of urban spatial model with different levels of localization and variants of distribution. Such events can be used for the effective management of urban processes based on spontaneous data, which acquires for maximum permissible level of spatial detail.
Saudi Arabia’s Post-Oil Economic Prospects: Economic Diversification, Entrepreneurship and Women’s Empowerment

This study aimed to provide a development model in Saudi Arabia that contributes to getting out of the current oil crisis, the current study provides a model that depends on exploiting Saudi Arabia’s capabilities through four basic pillars: The trend towards economic diversification, entrepreneurship, and women’s economic empowerment are all these changes in light of public sector reform and governance. The proposed study model was theoretically tested through a comprehensive review of the relevant economic and social literature, and then the study applied this model in Saudi Arabia, using a time series that extends since (2001-2020) and constitutes a total of (20) years. Based on econometric methods in collecting and analyzing data and ensuring its validity, and building representative models of the relationship between the study model with its four pillars and economic growth: The study found a number of results indicating a positive impact of economic diversification, entrepreneurship, and public corporate governance on the economic growth of Saudi Arabia, with mixed results on the impact of women’s empowerment on economic growth, and based on those results, a set of recommendations were built related to getting out of the oil crisis, encouraging economic diversification and entrepreneurship, and women’s economic empowerment.
Adaptive Fusion and Transfer Learning for Enhanced E –Commerce Recommendations

Adaptive Fusion and Transfer Learning for Enhanced E-commerce Recommendations (AFETLER) is a holistic combination of some state-of-the-art recommendation frameworks. Most of the existing frameworks, be it Matrix Factorisation model, Deep learning-based model, Factorisation Machines and Two tower recommendation models, used in real world recommendation engines lack diversity and serendipity. Also, cold start problem is one that downgrade the recommendation algorithm. Defining the optimisation goal and closed feedback improvement loop is also something that is not given primary weightage. To overcome the above stated challenges and provide a novel approach to overcome those, we propose the AFETLER model. AFETLER incorporates an adaptive fusion technique that dynamically combines recommendations from several sources such as new arrivals forecast, Click-Through Rate (CTR) prediction, and popularity estimation. This fusion technique improves accuracy by giving varying weights to each source based on the user's context, history preferences, and current behaviour, resulting in highly relevant and tailored buying recommendations. To address the cold-start problem and improve recommendations for new items, AFETLER employs a transfer learning mechanism inspired by the Adversarial Two-tower Neural Network (ATNN) framework, utilizing knowledge from existing items' interactions to extract valuable features from new items' profiles. Furthermore, AFETLER leverages hierarchical attention methods to effectively capture user-item interaction patterns, boosting feature extraction and overall recommendation quality through dynamic weighing of interaction relevance. Proposed AFETLER model outperforms some of the existing recommendation models by an average 2%-5% on metrics such a precision, ROC-AUC, and Top-K recommendation accuracy. Further, we also see point reduction in Mean absolute error and mean squared error.
Methodology of event extraction from unstructured medical texts on the example of the Russian language

Processing and structuring medical texts today are still difficult tasks due to specific terminology and abbreviations, a large number of errors and a lack of labeled data for high-quality model training. One of the important tasks is event extraction from unstructured texts. The most efficient event extraction methods to date are deep learning methods, however, they require labeled data to train them. Manual marking is labor-intensive, and expert knowledge is required for high-quality marking. A good alternative is automated pre-labeling. In the public domain, it is difficult to find works on preliminary data markup for the task of extracting events. In this paper, we propose a methodology for rude event extraction based on rules that evaluate the elements of a syntax tree. This methodology can also be used for pre-labeling data for training machine learning and deep learning models.
Semi-supervised method for improving general-purpose and domain-specific textual corpora labels

The study is devoted to the problem of label quality assurance and labeling cost minimization. It is important to have high quality labels in order to build efficient supervised machine learning pipelines. The aforementioned labels have proved to be costly to acquire; however, one can work with weaker instances of supervision that may be helpful to create a reliably set of ground truth labels for classifiers. The authors present a semi-supervised approach using pretrained language models to increase supervision information quality from general-purpose as well domain-specific document sets where weak labels and unlabeled data is present.
Automated Generation of Ensemble Pipelines using Policy-Based Reinforcement Learning method

At the moment, there are a considerable number of different automated machine learning frameworks. They are often use predefined pipelines and choose the best one among them. However, searching for optimal pipelines can be improved by using methods that generate pipelines step by step. The paper introduces an approach to generate ensemble pipelines using policy-based reinforcement learning. Approach consists of pipeline, environment, state, action and reward representations. This approach was successfully integrated into automatic machine learning framework. The generated pipelines were tested by comparing a baseline model using OpenML datasets, and the proposed approach demonstrated high efficiency, even surpassing the metrics for some datasets. This research has the potential to enhance the existing pipeline generation methods.
The Use of AR in Secondary Education: Educational Augmented Reality Material to Enhance Students’ Digital and Social Skills

The aim of this study was the design, the development and the evaluation of an innovative educational augmented reality (AR) material, in order to investigate the contribution of AR in increasing motivation and cooperation among secondary school students. At the same time, it was studied the possibility of enhancing the social and digital skills of the students in the context of an educational mobile learning scenario based on situated learning. The methodology that was used is Design-Based Research (DBR). Among other things, the usefulness and the ease of use of the material, its advantages, as well as the factors that could prevent its implementation in teaching practice were studied. Postgraduate students with excellent knowledge of ICT in education, teachers and secondary school students participated in its evaluation through interviews and questionnaires based on the Technology Acceptance Model (TAM). The major findings showed that the material achieves its purpose, develops skills, is easy to use and has been generally accepted by teachers and students; however, it requires improvements regarding its design and solutions for the technical access problems that arose during its practical implementation.
mHAR: a novel convolutional recurrent model for recognizing motion-based human activity

With sensors built into smartphones and wearable devices, Human Activity Recognition (HAR) makes it possible to identify regular activities. One of the challenges is to access the motion information from activity data and extract the features from this information. A general model trained without motion features may perform poorly while recognizing the contextual patterns in human activities. In this paper, we proposed the mHAR (motion HAR) model for recognizing static, gradual, and dynamic motion activity to address this challenge. We examine the impact of the proposed model against the general non-convolutional and convolutional recurrent networks in terms of accuracy rates and F1 score. As an experimental dataset, the publicly accessible WISDM was employed. Additionally, the findings of the experiments demonstrate that, in contrast to the unbalanced class problem, motion features play a significant role in activity recognition by achieving accuracy rates of 92.29% and an average F1 score of 83.33%.
Generative approach to Aspect Based Sentiment Analysis with GPT Language Models

Aspect Sentiment Triplet Extraction (ASTE) is a modern and effective form of sentiment analysis that enables the extraction of highly representative features of source textual data. Recent solutions rely on models built upon Bidirectional Encoder Representations from Transformers (BERT) embeddings and large manually-tagged datasets. This implies that usage of such methods requires large amounts of gold-tagged domain-specific data and is vulnerable to data drifts, while not being able to recognize segmented and summarize more complex terms. We propose an open-domain generative method for ASTE based on Generative pre-trained transformer (GPT) with few-shot and fine-tuning strategies. This method has shown to be applicable for the task, with the models being capable of consistent structuring of the output triplet, simplification of the terms without losing meaningful information, as well as successful analysis of data from unknown domains. Resulting models was tested on mixed domain Russian-language automatically tagged data with thorough manual editing by means of a large language model (LLM) with a few-shot approach and English data, which was only automatically tagged. The developed models have shown to take advantage of the ability to perform learning in a few-shot way, allowing knowledge distillation from larger to cardinally smaller ones. Models have also been tested on summarizing of large amounts of reviews and have shown results comparable to enterprise grade solutions.
An Overview of Ten Years of Augmented Reality Smart Glasses in Education

Augmented Reality Smart Glasses are an emerging technology that gains interest more and more among the research community. Their unique affordances such as the hands-free access and the transmition of first person view, differentiate them from other mobile and wearable technologies. The present study presents an overview of Augmented Reality Smart Glasses by emphasizing on the field of education. It includes the presentation of the affordances of AR and ARSG and the research activity on the effect of ARSG on education. Also, the factors that can influence their acceptance as well as students’ views on viewing augmented books through glasses are presented. Finally, suggestions for future research in the field are made.
The Metaverse and the Role of Accounting Culture: Reporting of Digital Assets According to International Standards

The research paper aims to identify the accounting guide for what might be done when the eco-nomic unit conducts business deals of a new digital nature, setting up a guide for management that can be used for choosing the appropriate accounting policies for digital transactions. So, this guide is based on international bodies represented by the International Accounting Standards Board (IASB), these bodes and companies seek to put on the steps to accounting recognition initial of digital assets and, then reporting based on some international standards. For example, international accounting standard (IAS 2), international accounting standard (IAS 38) standard, or (IFRS 1), through the use of a digital bill from a company (BVNK) then using the steps by the accounting guide to making (accounting recognition and disclosure) of that digital data. The re-search concluded the possibility of creating a new accounting culture that keeps pace with digital developments by this guide and, also found out that the metaverse and its applications whether the blockchain, cloud computing and others, will have a role in reducing the costs, and saving the time to processing digital invoices, which helps to prepare and present financial statements for users in actual time, to meets their needs in getting high quality information and on-demand, become aware of the financial situation which enables them to make the correct economic decisions about continue or increase investment. This of course will be reflected in the continuity of the unit by keep their resources, as well as correcting management decisions regarding the appropriate use of resources.