Reviewerly/exHarmony huggingface dataset:
Mining the CORD-19: Review of Previous Work and Design of Topic Modeling Pipeline

The Covid-19 Open Research Dataset (CORD-19) is a dataset conceived in 2020 with the purpose of gathering scientific papers addressing COVID-19 and related coronavirus research. The dataset aims to allow the machine learning community to exploit the knowledge expressed by biomedical experts through scientific papers in a natural language format. This paper addresses the problem of extracting knowledge from CORD-19 through different text mining tasks. These tasks vary from simple NLP operations, papers clustering, question answering, text summarization, topic modeling and classification, to knowledge graph extraction. For this purpose, we present a review of different works carried out on CORD-19 and we introduce the design of a new pipeline of different NLP tasks for papers clustering and topic detection preformed on this dataset.
Industrial Accident Prevention Based on Reinforcement Learning

On both the environment and human health, industrial accidents can have devastating consequences. Machine learning models are trained using reinforcement learning to create a set of judgments. Establishing a mechanism to stop accidents in an industrial setting is the goal of this article. By avoiding big accidents from occurring in these situations and lessening the effects of a large accident, it is concerned with protecting workers, the general public, and the environment. In order to implement our strategy, we started by building a simulation environment that will imitate an industrial setting with moving objects and a deep reinforcement learning agent that will take steps to avoid any barriers.
Part of Speech Tagging of Amazigh Language as a Very Low-Resourced Language: Particularities and Challenges

The objective of this paper is to present the particularities and the challenges of part of speech (hereby referred to as POS) tagging task. Indeed, POS tagging is a crucial step before every natural language processing application (syn tactic analysis, machine translation, autocorrection….) because the performance of each application depends, inter alia, on the performance of the used POS tagging. Thus, in order to realize an efficient POS tagger, we should care about improving the quality of these three phases: the phase of segmentation, the organization of the lexical units and the phase of disambiguation.
Comparative Study Between Double Vector Quantization Using SOM and GMM for Prediction Time Series Problem

Machine learning algorithms are built using training data, when new data is received, they can make predictions and decisions based on past data. The prediction of time series has become a major problem in different fields. Double Vector Quantization is a modeling method that has been dedicated specifically for the prediction of long-term trends. It’s based on the double use of the clustering method. In this paper, we illustrate an application that allows us to make a comparison between the two prediction methods: double quantification using SOM and GMM.
Vehicle Image Classification Method Using Vision Transformer

Vehicle detection and classification has become a critical task of the Advanced Driver Assistance System (ADAS) and Intelligent Transportation System (ITS), with the goal of increasing road safety and saving lives. Advances in image processing, pattern recognition, and deep learning techniques have overcome many obstacles to achieve this goal and still do, especially convolutional neural network. However, most object detection models cannot achieve satisfactory performance under some conditions as low-quality images, nighttime, and other insufficient illumination conditions. In this paper, we propose to increase the effectiveness of ADAS using the Vision Transformer Pre-Trained Model, the latest model for computer vision tasks, for vehicle classification problem. The dataset we used is a small dataset of tiny, low-resolution vehicle images. The proposed model is based on the attention mechanism and pre-training on ImageNet-21k dataset. The proposed model achieves good performance compared with state-of-the-art methods.
A Relaxed Variant of Distributed Q-Learning Algorithm for Cooperative Matrix Games

The Distributed Q-learning algorithm is known to converge in the case of deterministic multiagent systems. However, the algorithm fails to converge in the presence of the stochasticity problem due to the over-estimation of action values. In this article, we present a new relaxation of the Distributed Q-learning by introducing a new update rule for the Q-function of each agent. We discuss the existing literature, then compare our algorithm with different algorithms found in the literature: Decentralized Q-learning algorithm, Distributed Q-Learning algorithm, Hysteretic Q-Learning algorithm, and Lenient Q-Learning algorithm. Experiments in popular matrix games demonstrate that our algorithm is very effective in terms of convergence.
A Comprehensive Analysis of Consumers Sentiments Using an Ensemble Based Approach for Effective Marketing Decision-Making

Every day, millions worldwide communicate thanks to the Internet and social media. Consequently, there are now more channels available for sharing knowledge with others. The field of sentiment analysis (SA) has come into the spotlight over the past decade. However, As a result of their online interaction, people generate vast amounts of dialogue every second. During a conversation, it is possible to gather valuable information from the participants’ emotions. Using Sentiment Analysis for Natural Language Processing (NLP), any text may be analyzed and summarized. Other languages, such as English, have well-developed libraries and tools for natural language processing, but studies have yet to be undertaken on the Moroccan context utilizing French social media commentary. Consequently, it opens up several study opportunities. Consequently, Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), Adaboost classifier (AC), and Logistic Regression (LR) were employed. Positive and negative labels were assigned to data collected from the dataset. In addition, a model for ensemble-based sentiment analysis was developed; in contrast to other machine learning classifiers cited before, we achieved better performance in terms of accuracy, kappa, precision, recall, and F1-score.
Remote Sensing Image Super-Resolution Using Deep Convolutional Neural Networks and Autoencoder

Deep convolutional neural networks have become the most used architectures in learning-based super-resolution methods. They can extract the mapping relationship between low-resolution images and high-resolution ones. This paper explores deep convolutional auto-encoder-based super-resolution, which includes one auto-encoder unit with several convolutional blocks in both the encoding and the decoding sub-units. In this study, we used RGB slices composed of the red, green, and blue bands of Sentinel 2B MSI images with a spatial resolution of 10 m. After degrading their resolution to restore their initial resolution, we introduced them into the model. The model’s performance is encouraging (PSNR: 31.94, SSIM: 0.906, MSE: 0.00637training accuracy: 0.9055, validation accuracy: 0.90, and test accuracy: 0.89). Compared to other advanced methods such as SRCNN and VDSR for the same data, our proposed method achieves satisfactory results.
An Improved YOLOv5 Based on Attention Model for Infrared Human Detection

Human detection plays an important role in surveillance by ensuring security and maintaining public order. It is still considered a complex task in the deep learning field due to the highly varying illumination conditions under which humans should be detected. This paper proposes a new approach based on the enhanced YOLOv5 to detect humans in thermal images. It consists of integrating the Convolutional Block Attention Module (CBAM) into the backbone network to enhance the model’s ability to extract features. To measure the effectiveness of our method, we evaluated its performance on two benchmark thermal image datasets: the Ohio State University thermal pedestrian dataset, and the autonomous system lab thermal infrared dataset. Both datasets represent various challenges and images are collected in different humidity and weather conditions. From the obtained results, our approach performs human detection with 96% mean average precision and 91,8% recall, outperforming state-of-the-art CNN-based techniques like YOLOv5, RCNN, and Cascade RCNN.
Genetic Algorithm for CNN Architecture Optimization

Convolutional Neural Network (CNN) is a famous type of deep feed-forward network that has proved a significant success in the area of computer vision. Despite this success, the choice of the optimal architecture for a given problem remains a challenging issue. The performance of CNN is significantly impacted by a few of its hyper-parameters. In this paper, the Genetic Algorithm (GA) is used to explore the architecture design space of convolutional neural networks. Most of the existing studies focus only on CNN hyperparameters namely filter number and filter size. The proposed algorithm extends existing research in this field by including the type of pooling. The MNIST dataset was used to evaluate our proposed method, and the simulations show the ability of GA to select the optimal CNN hyper-parameters and generate an optimal CNN architecture.
Learning Sparse Fully Connected Layers in Convolutional Neural Networks

Convolutional neural networks (CNNs) are very powerful learning method in the deep learning framework. However, they contain a very large number of parameters, which restricts their utilization in platform-limited devices. Searching for an appropriate and simple convolutional neural network architecture with an optimal number of parameters is still a challenging problem. In fact, in many well-known convolutional neural networks like LeNet, AlexNet, and VGGNet, the percentage of weight in their fully connected layers exceeded 86% of the total weights in the network. Based on this remark, we propose a sparse regularization term based on smoothing the $$L_0$$ and $$L_2$$ regularizers to minimize the unnecessary neurons in the fully connected layers. This ensures neuron sparsity and reduces effectively the complexity of convolutional neural networks as shown in many experiments.
A Method for Bengali Author Detection Using State of the Arts Supervised Machine Learning Classifiers

Text classification is an important topic of study in the area of natural language Processing. To identify the authorship of the provided Bangla text, we create a model using the State of Arts Supervised method. Because our work is a multi-class categorization, we may use it to determine who wrote articles, news, emails, or messages. It can also use to find ghostwriters, identify anonymous authors, and detect plagiarism. This article focuses on the categorization of five Bengali authors. They are well-known writers in Bengali literature and poetry. Humayun Ahmed, Rabindranath Tagore, Muhammad Zafar Iqbal, Kazi Nazrul Islam, and Sarat Chandra Chattopadhyay are the five writers. Data were manually collected from various sources in the novels or books of these five writers, and we contained over 4500 paragraphs. A completely new dataset is created for the experimental evaluation. We preprocess Bengali text for training reasons. Logistic regression, naive Bayes, decision trees, support vector machines, random forests, XG-Boost, and K-nearest neighbor are among the seven classification methods employed. In our experiment, the Support Vector Machine produces the best experimental classification report. Support vector machine gives 82% model accuracy.
Strategic Content Generation and Monetization in Financial Social Media

Financial social media platforms, which rely on social media analysts (SMAs) to contribute content to investors, have emerged as a crucial channel for investors to gain access to financial information and for SMAs to monetize their content. However, we still have a limited understanding of the factors that affect how content is generated and monetized in financial social media platforms. This study focuses on the novel role of investors’ preferences for free/paid content and its sentiment, and investigates the extent to which SMAs exhibit strategic content generation and monetization behaviors by catering to and trading off the investors’ preferences. We also evaluate the underlying mechanisms and implications of such strategic behaviors. Utilizing a dataset from a financial social media platform based in China, we propose a Bayesian empirical model to jointly analyze the investor’s demand and SMAs’ strategic supply of financial social media content. The model estimation results show that SMAs cater to investors’, especially paid subscribers’, preferences in their content generation, such that their strategic behaviors account for 46.20% (24.50%) of the variation in SMAs’ generation decision for free (paid) content sentiment. In addition, a SMA is more likely to produce paid content when the expected free readership increases and is less likely to do so when the expected paid subscriptions increase – evidence that SMAs do balance the preferences of different investors when monetizing content. We find that SMAs are strategic in acquiring readers via their content monetization decisions and retaining subscribers via their content generation decisions. Importantly, we uncover that the orientation of a SMA’s strategic catering behavior is driven by the audience composition effect. Our study provides new empirical evidence, associated theoretical explanations for the results, and a practical illustration of an approach to reduce the potential confirmation bias of investors who may favor information from some SMAs that are prone to strategic catering behaviors.
A Graph-Based Soft-Decision Decoding Scheme for Reed-Solomon Codes

This paper presents a soft decoding scheme based on the binary representations transferred from the parity-check matrices (PCMs) for Reed-Solomon (RS) codes. Referring to the modified binary PCM that has a systematic part and a high-density part corresponding to the least reliable variable nodes (LRVNs) and the most reliable variable nodes (MRVNs), respectively, an informed dynamic scheduling method, called Nested-Polling Residual Belief Propagation (NP-RBP), is applied to the corresponding Tanner graph. As with the popular adaptive BP (ABP) decoding approach, adaptation in a binary PCM based on the reliability of variable nodes is also conducted in the proposed NP-RBP decoding. The NP-RBP enables the LRVNs to receive significant updates and limits the correlation accumulation from the short cycles in the MRVNs. In order to enhance the error-rate performance for long codes, a bit-flipping (BF) technique is conducted in order to correct a selection of the errors in the MRVNs such that the propagation of these errors in the subsequent NP-RBP process can be avoided. The resultant decoder is termed NP-RBP-BF. For short codes such as the (31, 25) and (63, 55) RS codes, NP-RBP is able to provide an error-rate performance close to the maximum-likelihood (ML) bound. A more significant improvement can be observed for long codes. For instance, when the proposed NP-RBP-BF decoding is applied to the (255, 239) RS code, it can provide a gain of about 0.4 dB compared to the ABP decoding and the performance gap to the ML bound can be narrowed to about 0.25 dB at a frame error rate of 2×10-3.
The possibilities of individualization of the educational trajectory for persons with secondary vocational education while continuing their studies at the university

The reform of the modern education system involves a paradigm shift in education, where the focus is on the development of personal creative abilities. Special attention is given to the search for individual self-determination of students, creating conditions for their full development and self-realization, and organizing the learning process according to their needs and abilities. In this context, educational organizations need to change the organization of the educational process and implement innovative personal-oriented technologies and tools for managing the educational process. The goal of this article is to examine approaches to managing the personalization of the educational process through the development and implementation of individual educational trajectories. The authors are present arguments in favor of the necessity of personalization in the educational process, as well as its essence and characteristics. Personalization is considered as a way of designing an educational environment that meets the interests, values, motivations, and needs of each learner and is based on their personal resources and individual characteristic, as well as ensuring the optimal choice of forms and methods of learning. The article is discussing methods of individualizing the educational trajectory of a graduate of vocational education, identifi es and describes a set of organizational and substantive measures aimed at managing the personalization of the educational process through the creation of individual educational trajectories in mixed groups. From the information presented, it can be concluded that the management of the personalization of the educational process is a sequential process, which results in a change in the personality of the student. The personalization is contributing to the eff ectiveness of educational activities and requires special complex and systematic actions on the part of the educational organization and all participants in educational relations.
Possibilities of individualization of teaching students of higher education

At present, the organization of the educational process involves the creation of conditions for learning corresponding to the individual characteristics of students; development of individuality, independence and creative potential of the individual. The freedom to choose the elements of the educational process is provided. The main goal of this is to organically combine the system of higher education with the training of highly qualifi ed mid-level personnel.
Thinking about the language models and what we can do with them

Abstract Massive developments in technology, ICT, and artificial intelligence have been witnessed in recent years, with various projects emerging showing the apparent superiority of artificial intelligence over human intelligence, such as Deep Blue, AlphaGo, OpenAI Five, and GPT 3. In connection with them and with the favoured method of machine learning with deep learning, the term language model has appeared, which has the ambition to become the basis of general artificial intelligence (AGI). Critical responses, however, have been claiming that this is a dead end. An alternative view to these critical responses is attempted to be shown in this article, with consideration given as to whether the language model is just what critics consider it to be or whether something more can be sought behind it.
Organization of competitions and olympiads among students and teachers of secondary vocational education

A signifi cant place in the national project “Education” is occupied by the federal project “Social elevators for everyone”, aimed at creating opportunities for professional and career growth for citizens through the formation and development of a system of professional competitions. Olympiads and competitions organized by the founder play an important role in developing the creative potential of students and teaching staff of educational organizations subordinate to the Federal Agency for Railway Transport. The Samara State University of Railway Transport has formed a mechanism and traditions of holding Olympiads and competitions, which allows motivating all participants of these events, stimulates the systematization and improvement of pedagogical and methodological support of the educational process, contributing to the professional and personal growth of students and teachers. Particularly important in the work of this mechanism is the participation of employees of higher educational institutions and the most authoritative representatives of branches implementing educational programs of secondary vocational education in the composition of the competition commissions, which makes it possible to make the process of evaluating competitive works as open and objective as possible. The mobility and effi ciency of the work of the commission members at all stages allows us to ensure the on-line mode of its work. There can be no losers in the self-realization of both students and teachers — participants in competitions. Therefore, during the regional stages of competitions, almost all participants are awarded with diplomas of winners of the regional stage and letters of thanks from the rector.
Actual problems of teaching mathematics in educational institutions of secondary vocational education

Topical issues of teaching mathematics in institutions of secondary vocational education are considered. Some problems and methods for their solution are analyzed. Diff erent levels of students’ mathematical knowledge base presuppose the use of learning diff erentiation. A diff erentiated approach is necessary not only to improve the performance of weak students, but also to develop the abilities of strong ones. A connection between learning and practice is required, since a motivated interest and desire to achieve any success arises in students when they see the possibility of practical application of the material being studied. One of the principles of developmental education is the development of independent thinking of the student, the ability for self-education and self-development. In addition to professional skills and competencies, it is important for a modern specialist to have such qualities as a responsible attitude to his actions, the ability to act consciously in any conditions, and make unconventional decisions. A differentiated approach, teaching the practical application of mathematical knowledge in solving applied problems, independent work of students, distance learning is just some of the forms used in the educational process. Any methods and approaches in teaching mathematics must have a specifi c goal, a system of actions, means and techniques.
Toward High-Resolution Face Image Generation From Coded Aperture Camera

Coded aperture cameras can be manufactured cheaply, have a very thin form-factor, and may be transparent and flexible, thus providing easy-to-integrate and compact image sensors. However, limitations in reconstructed image quality and resolution have impeded the growth of their applications. We propose a method to generate high-resolution face images from low-resolution coded aperture sensor snapshots. Using the point spread function of the coded aperture camera, we generate a set of training images to train an image enhancement network. We then apply a face recognition model to extract facial templates and project them into the intermediate latent space of a face generator network to generate high-resolution (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1024\times 1024$</tex-math></inline-formula> ) face images. Our experimental results show significant retention of the subject's identity in the generated high-resolution face images. Our cross-dataset evaluation shows the generalization of our method on other datasets for generating high-resolution face images. To our knowledge, this is the first work for generating high-resolution face images from coded aperture imaging. The source code of our experiments is publicly available to facilitate the reproducibility of our work.