Reviewerly/exHarmony huggingface dataset:
Impact of Partial Solar Eclipse on Solar Radiation Intensity

United electrical power system operation is planned considering changes of power generation of solar power plants. These changes are predominantly random. However, such natural phenomena as solar eclipses could be predicted with high accuracy. It allows planning operation mode of power system taking into the account the corresponding alterations of PV plant’s power generation due to solar radiation fluctuations. The analysis of impact of the partial solar eclipse that occurred on 25th of October 2022 on solar radiation is provided in this paper, numerical estimations of value and rate of the solar radiation intensity change are given. The obtained data helps to optimize process of planning operation mode of power systems with PV plants.
Review of Ultra-Short-Term Solar Radiation Prediction Methods

Compared to other types of energy, solar energy is one of the most environmentally friendly type. In modern realities, the introduction, usage, and integration of renewable energy sources into energy systems is growing every year. However, the output from PV systems is unstable due to changes in solar radiation, which results from the rapid change of clouds in the sky. Therefore, short-term forecasting becomes the most important and integral part of such systems, in order to comply with the efficiency and safety of operation of power systems. This article presents not only the future project of our team on the topic of solar radiation forecasting, but also the currently available methods of short-term forecasting, as well as a more complete overview of the introduction of modern ultra-short-term ground-based forecasting.
Computer Technologies for Modeling the Competencies of Employees of Industrial Enterprises as Part of a Digital Twin of a Person

The article discusses one of the most active areas of digitalization: the technology of creating a digital twin of a person and the tasks that can be solved with its application. In the article, the principles and procedure for designing a digital twin of a person are analyzed step by step. Each step is illustrated with examples of the equipment used and the results achieved. Thus, the authors of the article present research in the field of forming a model of the necessary competencies of employees of industrial enterprises based on the proposed technology. A brief description of the software solutions adopted in the concept of Digital twins is presented. Using concrete examples, the conditions and problems of the introduction of Digital twins in the world, in particular in Russia, are considered. The analysis of publications of domestic and foreign authors in the field of the development of a digital twin of a person is carried out.
Modeling of Complex Processes and Systems Based on Fuzzy Cognitive Temporal Graphs

This article discusses the main types of models of complex processes and systems, which are represented by structures based on graphs - graphs, fuzzy cognitive maps, fuzzy temporal graphs. Particular attention is paid to the analysis of methods for constructing, modelling, and using various types of graphs that generalize the properties of existing varieties of cognitive maps and fuzzy temporal graphs. The analysis of differences in the formalization of the task is presented in the form of graphs and matrices, which reflect the methods for determining the dependence of the factors (vertices) of the considered transport and logistics task. It is shown that with the help of fuzzy cognitive temporal graphs it is possible to reveal both direct dependence of factors (objects) of the system and hidden (mediated) ones. This circumstance plays an important role in identifying patterns inherent in complex systems under conditions of uncertainty and variations of parameters depending on time characteristics.
A Framework for Scalable Object Storage and Retrieval Considering Privacy Concerns: A Case Study on the Signature Detection

Nowadays, we are faced with a huge amount of private data generated in different ecosystems, including the Internet of Things, social networks, peer-to-peer networks, and e-commerce, to mention a few. The performance of traditional object storage and retrieval systems concerning response time is going to be degraded by a drastic increase in generated data. This problem becomes more challenging when a specific private object should be found. To address the mentioned problem, this paper suggests scalable object storage and retrieval. To do this, three well-known methods, content-addressable networks from peer-to-peer systems, vector space model from information retrieval systems, and siamese neural networks from neural networks theory, are involved in a framework to bring a scalable system to solve the mentioned problems. The suggested framework is unique to the best of our knowledge because there is no fusion among mentioned fields reported so far. A case study on signature detection is also conducted to evaluate the proposed framework. The results show that, compared to a centralized system, the proposed framework significantly decreases the response time for detecting a signature while maintaining the same accuracy. In addition, the proposed mechanism does not require the private objects to be sent to a central entity, which helps to alleviate privacy concerns.
A Semi-Automated Labeled Data Generation Approach Based on Deep Learning to Improve Sentiment Analysis in the Persian Language

Opinions play an essential role in human life. With the ease of sharing opinions, ideas, and feelings on various topics through the web and social networks, the analysis of opinions and emotions has become increasingly important. As social networks continue to expand, the importance of sentiment analysis will only grow. While much research has been conducted on sentiment analysis in the Persian language, its accuracy still falls short compared to available English methods, and it faces several challenges. One of the most significant challenges is the lack of labeled datasets. To improve sentiment analysis, numerous datasets have been collected during various research projects. Despite these efforts, the volume of labeled data remains insignificant because labeling unlabeled data is a costly and time-consuming process due to its manual and human nature. This research presents a semi-automatic method for generating labeled datasets. The proposed method combines pre-trained deep learning models with a human agent, allowing more labeled data to be obtained while spending less money, time, and manpower and using the power of deep learning models. Some unlabeled data were labeled based on this method and added to the basic dataset to create a new dataset called the "proposed dataset". To evaluate the effectiveness of the proposed method, both the basic and proposed datasets were tested on the ParsBERT language model using the same test dataset. The results showed a 4% improvement in ParsBERT 's F1 score on the proposed dataset compared to the basic dataset. Notably, fine-tuning ParsBERT with the new dataset also made it more general and removed one of its weaknesses, i.e., overfitting.
A Novel Fuzzy Logic-based Method for Modeling and Recognizing Yoga Pose

The estimation of the human pose is an interesting computer vision problem. The principles of fuzzy logic can be used to distinguish the human poses due to the existing uncertainty. In this paper, we suggest fuzzy logic-based modeling for identifying human actions. Fuzzy membership functions that highlight the discriminative pose connected to each action are considered for feature extraction. Additionally, to identify human activity, a multilayer perceptron classifier is applied. For a more accurate classification of yoga poses, various methods of estimating poses and identifying key points were discussed in detail. Evaluations of the proposed method on the benchmark datasets indicate the performance of the proposed method.
A Context-based Image Retrieval Method for Persian Databases

Descriptive summarization of documents in databases results in better indexing and management of information. Images in documents usually contain valuable information and retrieving them provides tools for document summarization. In context-based image retrieval systems, descriptive tags for the images are extracted from auxiliary information sources available to them. The search engine uses these tags to retrieve images. Here we suggest an automated image tagging method that exclusively relies on information mined from the document’s text associated with the image. Because of complications in the Persian language, lack of resources, and studies on this language, it has received little attention in the literature. The suggested method is built based on Persian documents. Two groups of tags are suggested. Specific tags are extracted from the caption of the image and the nearby text. General tags are obtained from the keywords of the document. Suggested methods are evaluated on the test data from the Iran scientific information database (GANJ), the largest database of Persian scientific documents. The F-measure of our suggested method is 43% for the specific tags. As for general tags, 89% are descriptive and the false positive rate is 0.002. Although suggested method has been tested on scientific documents it can be generalized for any type of Persian.
A Blockchain-Based Approach for Data Storage in Drug Supply Chain

Traceability and auditability are essential features in supply chain management and construction. However, from the customers' perspective, trust is the most critical aspect of these systems. Additionally, relying on third parties for trade in centralized systems is indispensable. Blockchain-based drug traceability provides a potential solution to create a platform for an immutable, trustworthy, accountable, and transparent system in the pharmaceutical supply chain. Furthermore, we present a model for storing pharmaceutical supply chain data using blockchain, which leverages the key advantages of blockchain and smart contracts. Our proposed solution is based on hyperledger Besu and is complete and cost-effective in terms of the privacy and confidentiality of the blockchain network. Additionally, our proposed model addresses the challenges of storing large data in the blockchain, which we intend to solve by using the IPFS storage system. In such a system, instead of storing big data, we store their abstracts in the blockchain to reduce the load pressure of the chain and realize efficient information queries.
IoMT-based Automated Leukemia Classification using CNN and Higher Order Singular Value Decomposition

The Internet of Things (IoT) is a concept by which objects find identity and can communicate with each other in a network. One of the applications of the IoT is in the field of medicine, which is called the Internet of Medical Things (IoMT). Acute Lymphocytic Leukemia (ALL) is a type of cancer categorized as a hematic disease. It usually begins in the bone marrow due to the overproduction of immature White Blood Cells (WBCs or leukocytes). Since it has a high rate of spread to other body organs, it is a fatal disease if not diagnosed and treated early. Therefore, for identifying cancerous (ALL) cells in medical diagnostic laboratories, blood, as well as bone marrow smears, are taken by pathologists. However, manual examinations face limitations due to human error risk and time-consuming procedures. So, to tackle the mentioned issues, methods based on Artificial Intelligence (AI), capable of identifying cancer from non-cancer tissue, seem vital. Deep Neural Networks (DNNs) are the most efficient machine learning (ML) methods. These techniques employ multiple layers to extract higher-level features from the raw input. In this paper, a Convolutional Neural Network (CNN) is applied along with a new type of classifier, Higher Order Singular Value Decomposition (HOSVD), to categorize ALL and normal (healthy) cells from microscopic blood images. We employed the model on IoMT structure to identify leukemia quickly and safely. With the help of this new leukemia classification framework, patients and clinicians can have realtime communication. The model was implemented on the Acute Lymphoblastic Leukemia Image Database (ALL-IDB2) and achieved an average accuracy of %98.88 in the test step.
Product Recommender System Using Hypergraph Neural Networks

Improving the performance of recommender systems in the field of product purchase has been one of the important challenges of e-commerce in recent years. The correct recognition of the user's preference and the accurate recommendation of items that meet his needs will certainly lead to progress in the development of online shopping and selling of items. Since the meaning of each item is different for users and the purpose of buying that item changes over time, it is difficult to understand what the user's thinking is now and what kind of product they are looking for. In this work, an approach based on hypergraph neural networks is presented to create a powerful representation for items and users, which can be used to create a correct representation for the user's intent and finally, according to his dynamic intent, offer him/her the right product. The idea of this approach is to use the short-term correlation of items with each other to give them meaning, which is very similar to the logic of buying items by people in the real world. To evaluate the approach, the results of the implementation of the model presented in this article and similar models in recent years are compared on a dataset called Amazon review, and the improvement of the results of the evaluation metrics compared to those models is observed.
Persian Ezafeh Recognition using Transformer-Based Models

In Persian, the grammatical particle ezafe connects two words. Ezafe is one of the salient factors in Persian phonology and morphology to understand the meaning of a sentence completely and truly, whereas it is not usually written in sentences, resulting in mistakes in reading complex sentences and errors in natural language processing tasks. Therefore, recognizing words that need Ezafe at the end of themselves, is a major factor to improve the performance of a variety of NLP-based systems such as a Text TTSsystem. Because in Persian TTS systems without an Ezafe recognition module cannot make Ezafe constructions to read the text correctly and does not recognize the relations between the words. As Transformer-based methods shows state-of-the-art results in lots of NLP tasks, in this paper, we experiment ParsBERT in the task of ezafe recognition. The latter earning 2.68% better F1-score than the prior state-of-the-art, we obtain the most advantageous outcomes.
MFS: A Serverless FaaS Simulator

Serverless computing is one of the concepts that has become a trend these days. Serverless computing facilitates the deployment of software on the cloud, fog, edge, and IoT infrastructures. Serverless computing helps developers implement their codebase without worrying about server management hassles. These are the reasons why the concept of serverless computing is presented in the cloud, the edge, the fog, and the Internet of things. Research on serverless computing, on the other hand, requires extensive hardware equipments. It can also be time consuming. Simulation is needed to overcome these problems. MFS is a simulator based on the Apache Open Whisk architecture that seeks to facilitate the evaluation process of serverless computing researches. Compared to similar researches, this simulator supports more features including containers, heterogeneous processing resources (e.g., CPU, GPU, and TPU), different runtimes, and Apache OpenWhisk architecture. It also calculates and reports metrics such as average response time, utilization of different computing resources, rejection ratio, missed ratio, number of containers used, and costs. The mentioned metrics can be calculated in overall and individually for each physical machine. The status of all functions is also reported at the end of the simulation (e.g., completed, missed, and rejected).
Improving English to Persian Machine Translation with GPT Language Model and Autoencoders

Machine translation is a technology that reduces costs and speeds up the translation for users by mechanizing translation from one language to another. Machine translation is essentially a step towards globalization for every culture, science, industry, and system. This technology has made great strides in cognitive understanding of natural language since 2013 with the advent of deep learning models. But deep models also always need a lot of data for training. Therefore, the lack of a lot of data and parallel corpus in machine translation is one of the most important problems in this area. Machine translation from English to Persian always suffers from the problem of a lack of resources and data. This article tries to study the deep learning models in machine translation from English to Persian and their strengths and weaknesses, In this article, to solve the problem of lack of English- to-Persian data, the Transformer-based model has been integrated and improved with the Persian language model that has GPT architecture, in addition, the CNN model has been integrated and improved with Autoencoder to improve feature selection and reduce dimensions.
Evaluating Security Anomalies by Classifying Traffic Using Deep Learning

Network traffic identification is an essential function for network domain systems, which facilitates accurate management through the classification of network traffic flows. In this research we used traffic separation using deep learning approach to detect security anomalies. The method proposed has several steps. Since many features are usually used to detect network security anomalies, in the first stage, feature selection was an optional step to select some of the most important features associated with the problem of detecting security anomalies in the network. Then the SMOTE balancing method was used to balance the data when the evaluated data set was unbalanced in class distribution. The results of balanced data and imbalanced data were obtained. Ultimately, the convolutional neural network was used to train the proposed model. The proposed model was tested and evaluated after training the model. The evaluation results indicated that in the mode of feature reduction and data balancing, the proposed CNN classifier showed the accuracy of 96.88% and 98.18% in feature reduction and data imbalance mode, when using no feature reduction and data balancing mode we reached the accuracy of 97.35% and 98.57% accuracy in feature reduction and non-balancing data.
Aspect-base Sentiment Analysis with Dual Contrastive Learning

Aspect-based sentiment analysis (ABSA) is a type of sentiment analysis that aims to identify the polarity of sentiment for aspects in a sentence. Also according to the studies, it is an important research area that plays an important role in business intelligence, marketing and psychology. To solve this problem different methods based on dictionary, machine learning and deep learning have been used. Research shows that among the methods based on deep learning, Transformers has been able to achieve good results and help to understand the language better. In this paper we use induced trees from Fine-tuning pre-trained models (FT-PTMs). We also use dual contrastive learning and different pre-trained models such as BERT, RoBERTa and XLNet in our proposed model. The results obtained from the implementation of the model in SemEval2014 benchmarks confirm the performance of our model.
L-Atur, a Generative Design L-Systems Based Web Application with a Human-Machine Collaboration Approach

L-Atur is a web application start-up that produces highly customizable generative designs for consumers at affordable prices. It uses L-Systems as its vital core to generate a wide range of designs. L-Systems can provide numerous visual responses to the creative exploration of our users. Consumers would be able to design their desired products without any coding or professional design skills. The human and machine collaborative nature of L-Atur helps them to explore creative opportunities while we consider technical limitations to physically produce their designs. The final results are based on the countless visual possibilities of L-Systems, so we can make one-of-a-kind designs accessible to a wide range of people.
Real-Time Bitcoin Price Prediction Using Hybrid 2D-CNN LSTM Model

Due to the growing importance of the cryptocurrency market, as well as the diversity and expansion of online trading platforms, cryptocurrency technology has piqued the curiosity of a wide range of people, from market traders to researchers and analysts. Reliable price prediction is a necessity since investors face multiple challenges including market volatility, risk management, and market complexity. Therefore, numerous studies have been done using deep learning and machine learning algorithms to demonstrate their functionality and efficiency in this area. In this paper, we employed Bitcoin historical data to make predictions for the next day's closing price using a new hybrid 2D-CNNLSTM model with OPTUNA hyperparameter tuning. The dataset used to train the model was gathered using an automated web scraping technique. With the proposed model, the R2 error achieved 0.98166 and the MAPE was 0.034. Our proposed model is compared with three different models: CNN, LSTM, and GRU. The predicted results show that the proposed hybrid model is efficient for accurately predicting bitcoin prices and reliable for supporting investors to make their informed investment decisions. Additionally, the proposed model has outperformed other commonly used algorithms, namely CNN, LSTM, and GRU in terms of R2, and MAPE. This model is also capable of performing real-time forecasting.
Stream Data Classification with Hoeffding Tree : An Ensemble Learning Approach

Scientists around the world study data mining extensively, but many methods are limited to analyzing small databases. Technological advances have led to the emergence of Incremental Machine Learning and Stream Data Classification to handle large amounts of diverse data. The challenge is to quickly extract information from incoming sequences of data, but the high speed and complexity of the input data limit the application of previously proposed methods. The Hoeffding tree algorithm is crucial for Stream Data Classification and employs the Hoeffding bound to select a splitting feature. In this paper, we propose a method that combines an Incremental Decision Tree called the Hoeffding tree with Ensemble machine learning using bagging to enhance accuracy. Our implementation and analysis show that our proposed method improves accuracy compared to the simple Hoeffding tree. We also analyze the algorithm with different numbers of base models and examine graph diagrams to illustrate the improvement in accuracy.
A Persian Wake Word Detection System Based on the Fine Tuning of A Universal Phone Decoder and Levenshtein Distance

This paper deals with the preparation of a universal phone decoder and using it to build a speech-based Persian wake word detection system. First, two sets of spoken data in Persian are used to adjust the symbols and fine-tune the parameters of the phone decoder, which is named Allosaurus, until a wake word detection system with high accuracy is obtained. During this process, a slightly modified version of the Levenshtein Distance algorithm is used to calculate a confidence score for the system output decision. After the initial wake word detector is ready, the values used for calculating the Levenshtein Distance and their weights are optimized in order to achieve the highest possible accuracy. In the end, this work focuses on also maximizing the accuracy of noisy speech signal inputs, which is something that hasn’t been done in previous works.