Reviewerly/exHarmony huggingface dataset:
Features of Knowledge Representation of Automatic Control Systems Design Methods

Complication of controlled plants, expansion of their functionality, variability of operating conditions and also changes of goal of control in the course of system's functioning, caused need of transition to more flexible intellectual control systems. As a result, the need for the systems of support of acceptance of design solutions of designers of intellectual control systems amplified. The complexity of a problem of creation and use of systems of support of acceptance of design solutions of designers of intellectual control systems is caused not only need of providing of the develop intellectual systems by knowledge, but also first of all need intellectualization, actually, of the system of support of acceptance of design solutions. At the same time not only knowledge of area of functioning of system, but also of the methods making basic properties of intellectual control systems is a part of the specified knowledge. However, development of knowledge models of data domains extremely hard work which is carried out by experts. Attempts of automation of creation of models of knowledge on the basis of use of the ontology approach providing difference in the description of declarative and procedural knowledge show practical impossibility to exclude availability of errors in the constructed models of knowledge unacceptable in design of technical systems. The offered approach to a solution is based on use of the multi-level representation of knowledge created on the basis of clear both for the expert, and for the designer of a single-level domain model. The structure of a problem solver on the basis of multi-level model of knowledge is provided. The generalized structure of interrelations of components of model of knowledge and examples of screen forms of the researcher-expert's software environment used for development of models of knowledge are provided.
Approach to Building an Intelligent Repository of Software Projects

Projects of modern software systems comprise a wide number of various design artifacts. There is an extensive set of approaches and tools for creating design artifacts. It is very important to use good practices to improve the quality of design artifacts and a development process of creating software systems. Modern software project repositories contain many software systems. However, there are no effective methods for information retrieval and analyzing existing projects to gain access to best practices. Thus, it is necessary to develop a model of a software system project, considering various approaches and tools for a design artifacts creation and a development process organization. Such a model will make it possible to search for software projects, considering their domain features and properties of design artifacts and development process. Various data mining and systems analysis methods can apply to a found set of projects to extract a set of best practices. We described the approach to building the information retrieval module for the intelligent design repository in this paper. Also, we presented the model of a software system project. We considered the algorithm for indexing a software system project to build the information retrieval module index. There is a measure of a distance between a search query and an index element of the information retrieval module. We proposed the algorithm for calculating the relevance of an index element to a search query. The article also provides examples of the work of the information retrieval module for the intelligent design repository.
Extracting Entity Relationship from Arabic Texts

Information Extraction (IE) systems extract relations from Arabic texts, an unstructured dataset, by identifying the entities and relations in a sentence, which is very useful in many applications like Information Retrieval (IR), and Text Mining. In the Arabic language, we can find a considerable amount of unstructured data, meaning we need a more potent instrument to analyze and retrieve texts. This work uses Deep Learning techniques, specifically the Long Short Term Memory (LSTM), to build the Named Entity Recognition (NER) model using the Trax library, a new deep learning library from Google. The model was trained on an Arabic NER dataset containing 37,000 labeled words. Moreover, the IE model uses an Arabic relation dataset containing 2700 relations. The model was built on the Google-Colab Platform. The information extraction model’s ability from the Arabic texts was checked using a dataset containing 137 sentences. The accuracy according to the F-measure reached 72.56%. The results of this work could play an essential role in Arabic text mining, analyzing Arabic text into more understandable text, or in information retrieval from Arabic texts corpus.
A Probabilistic-Statistical Approach to Detection of Semantic Relations Between Indexing Terms

An approach to dynamic structurization of information space based on the analysis of semantic relations between indexing terms is proposed. Mathematical methods of pattern recognition are adapted to solve the problem of semantic relations analysis. Justification and verification of hypothesis about statistical distinctiveness of semantic relation classes in a subject-oriented database are presented. The main types (classes) of semantic relations between indexing terms in a problem-oriented database are defined. The statistical characteristics of the selected classes of semantic relations are given. The experiment revealed the following property: in the classes of hierarchical relations, the distribution of probabilities of occurrence of pairs of terms with relative frequency fij obeys the lognormal law. Analytical expressions for lognormal equalizing distribution curves were derived. Based on these expressions, a probabilistic-statistical model of semantic relations and an algorithm for their detection were developed. Experimental study has shown the effectiveness of the proposed approach to identify semantic relations of hierarchical type.
Topic Modeling Algorithm Using Quantum Probability Theory

LDA and pLSA are classical topic modeling methods based on conditional and full probability models, respectively. In addition, the context of a document implies taking the distance between terms in the corpus into account. Information retrieval, topic modeling, and context search can all benefit from the use of probability and distances in quantum mathematics. Through quantum formalism, the projection matrix shows the influence of different parts of the system on each other, taking into account the influence of vector superpositions on each other and normalization conditions. It is therefore possible to display links, contexts in Hilbert space by defining a subspace using basis vectors. The purpose of this paper is to examine the applicability of quantum mathematics methods to context search, information retrieval, and topic modeling. An analysis of existing context extraction and topic modeling methods is presented. Based on quantum information theory, the author proposes a way to solve the problem by weighing advantages and disadvantages. This algorithm uses iterative calculation of hidden parameters based on the assumption of superposition by normalizing conditions that take probability into account when representing documents and words.
Rule-Based Model for Describing the Processes of Rise and Transfer of Anthropogenic Emissions in the Atmosphere

The article develops a rule-based model that reduces the time complexity of algorithms for describing the processes of rise and transfer of anthropogenic emissions in the atmosphere. A general view of the rule-based model has been developed, 7 rule-based models have been built, covering the most common types of transport of atmospheric emissions from industrial enterprises and vehicles. The reliability of the developed software was confirmed with computational experiments, the results of which are presented in the form of graphs. It is indicated that the developed rule-based models can be used in modifying the software of systems for monitoring and managing anthropogenic atmospheric emissions.
Method for Detecting Person in a Frame of Video Stream and Classifying his Position Using an Artificial Neural Network

The article presents a method for detecting a person in a video frame and classifying his position using artificial neural networks technology. A YoloV4 network is used to detect a person in a frame, and a separate convolutional network based on ResNet is built for classification. The presented method is implemented in the form of a software package suitable for implementation in video surveillance systems in order to quickly respond to situations dangerous to human health, such as sudden cardiac arrest, fainting, and traffic accidents. The results of numerical testing are presented, confirming the high accuracy of the proposed classifier.
Analytics of Indian English News Articles Through Deep Learning

This paper uses natural language processing to study the bias prevalent in the content published by various media sources. Deep learning models for natural language processing have proved effective at incorporating the complicated nuances of language and therefore predicting the correct sentiment. Iyer et al. (2014) have used recursive neural networks (RNNs) for the sentential level of political ideology detection. Inspired by their work, this paper deals with recursive neural network models to develop two classifiers: technology determinism vs. skepticism and pro-versus- anti-policy attitudes. In this paper, deep-learning-powered stance detection classifiers have been built for which in-house datasets have been created and annotated
Service-Oriented Architecture for Block-Base Construction Building

In the paper, the authors considered a concept for creating a distributed WEB-server based on the language of mathematical logic of Horn’s clauses (Prolog). The built WEB-server allows you to perform logical processing of 3D products created by the designer. The presented concept consists of seven types of servers, four of which are independent of the designed product and three servers that depend on it. Each of the servers implements logic based on the facts and rules of the Prolog language. The purpose of this work is to create a distributed WEB-server for automatic verification and modification of the algorithm for assembling 3D products based on the proposed concept. All interaction between servers occurs through the main server, which makes it possible to ensure the scalability of the system. The general principle of operation: when receiving the algorithm from the client, the main server sends the data to the verification server. Based on the results of its work, either it is transferred back to the client (if verification was successful or modification is impossible), or it is transferred to the modification server. Also, the verification server uses a database server of available assembly parts, which makes it possible to determine the possibility of modification. The modification server transforms the assembly algorithm in accordance with the rules received from the verification module. The result of the modification module is sent to the client. This approach allows parallel processing of several clients at the same time, identifying them by the header in the packets that are transmitted between servers. An example of the implementation of a self-modification module for transforming the sequence of operations when assembling a 3D structure is presented. Interserver interaction is carried out via the HTTP protocol in JSON format. An example of converting between JSON format and Prolog terms is shown.
Is Microethnography an Ethnographic Case Study? and/or a mini-ethnographic case study? An analysis of the literature

Selecting the research approach that addresses the research question is often challenging for novice researchers. However, getting a better understanding of the research approaches available in the field, is likely to help novice researchers identify and choose the research approach that fits their situation. In this paper, we discuss microethnography, ethnographic case study, and mini-ethnography case study in order to show that these approaches may have similarities but are different. The author hopes that this discussion will help researchers get a better understanding of these approaches and dissipate the confusion that may exist.
A Blockchain-Based Microgrid Measurement Data Storage and Tampering Detection Framework

With the rapid development of distributed power generation and microgrids, secure storage of microgrid measurement data becomes an imminent issue. In order to defend against cybersecurity threats, a microgrid measurement data storage framework should not only ensure the integrity and traceability of the stored microgrid measurement data, but also be able to detect any malicious data tampering. In this paper, a microgrid measurement data storage framework based on Hyperledger Fabric, a blockchain technology, is proposed and implemented, on top of which a data tampering detection method based on the uniqueness of the transaction number (TxId) and the Strong Collision-Resistant of hash theory is developed. Case studies show that the proposed framework can well meet the requirement of data storage traceability, transparent sharing, and tampering resistance, and the requirement of automatic data tampering detection, which provides a new research idea and direction for the future study in microgrid measurement data storage.
The Establishment of a New Electric Marketing Audit Pattern Based on Time Series

The role of electric marketing audit is to protect marketing affairs away from the possible risks. By using power big data technology, the marketing information system becomes more and more intelligent. Especially the new generation of energy internet marketing service system in short name as marketing 2.0 system breaks the barriers between different businesses through data sharing under dual-center platform architecture. The time series-based rules of marketing affairs have been deployed into the marketing 2.0 system to do very important and reinforced supervisions before the event, during the event and after the event, which provides a strict inspection and control of the overall process of the marketing business to prevent various possible risks. As a result, a new pattern of electric marketing audit is established.
Markov Chain Monte Carlo Cubature Particle Filter for Unbalanced Distribution System State Estimation

In order to overcome the inaccurate proposal distribution along with the particle degeneracy problem of particle filter (PF), this paper proposed a state estimator based on cubature particle filter (CPF) and Markov chain Monte Carlo (MCMC) sampling, and applied it on unbalanced distribution system. The proposal distribution of PF is constructed by cubature Kalman filter (CKF). To preserve particle diversity, MCMC sampling is used to resample particles in CPF. Markov chain Monte Carlo cubature particle filter (MCMC-CPF) has both advantages of CKF and PF. MCMC-CPF is no longer limited by the types of systems and noises, and can effectively abate particle degeneracy, meanwhile, preserve particle diversity. Simulations results on the IEEE 33-node unbalanced distribution system proved the accuracy superiority of the MCMC-CPF compared with the CPF.
Research on Automatic Identification Technology of Transmission Line Images Based on Deep Learning

With the development of UAV aerial photography technology and the detection and identification technology of targets in images, the intelligent detection of defects through aerial photography has become the focus and difficulty of the research of smart grid inspection, and is also the main direction of the development of the current electric power automatic inspection system. In the practical engineering application of power intelligent inspection, there are difficulties of inaccurate recognition caused by the target object being obscured, the complex environment in which it is located, the change of light and darkness, and the difficulty of recognizing multiple similar targets. In this paper, we propose an improved YOLOv2-based model for detecting components such as pole tower nuts and pins. The YOLO network combined with dictionary learning is used to extract and recognize the image blocks of pole tower nuts and pins and classify their obstacle types. Finally, the experiments are compared with other models and the method of this paper is better.
Research on Fault Location Expert System of Secondary System of Intelligent Substation Based on Fuzzy Fault Tree Theory

The secondary system of the intelligent substation has a complex logic structure and a variety of failure modes, making it difficult and time-consuming to locate the fault. Aiming at the shortcomings of traditional fault location methods, a fault tree-based positioning method is proposed. Fault tree analysis method can effectively react to fault alarms and the causal relationship between fault locations that cause them. The fault tree analysis method is applied to the expert system, which solves the problems of low efficiency, lack of knowledge and inaccurate reasoning results of the traditional rule-based expert system, and the logical relationship between knowledge is not reflected. Aiming at the difficulty of obtaining the probability of the event at the bottom of the fault tree, the fuzzy number method is proposed to improve the traditional fault tree method, and the triangular fuzzy number is used to express the fuzzy probability of the bottom events, thereby improving the efficiency of the fault maintenance and positioning of the maintenance personnel.
Sentence coherence evaluation based on neural network and textual features for official documents

&lt;abstract&gt; &lt;p&gt;Sentence coherence is an essential foundation for discourse coherence in natural language processing, as it plays a vital role in enhancing language expression, text readability, and improving the quality of written documents. With the development of e-government, automatic generation of official documents can significantly reduce the writing burden of government agencies. To ensure that the automatically generated official documents are coherent, we propose a sentence coherence evaluation model integrating repetitive words features, which introduces repetitive words features with neural network-based approach for the first time. Experiments were conducted on official documents dataset and THUCNews public dataset, our method has achieved an averaged 3.8% improvement in accuracy indicator compared to past research, reaching a 96.2% accuracy rate. This result is significantly better than the previous best method, proving the superiority of our approach in solving this problem.&lt;/p&gt; &lt;/abstract&gt;
Human Activity Recognition Using a Hybrid Dilated CNN and GRU

Human activity recognition is believed to be experiencing rapid growth in the area of computer vision. With advancements in video capturing technologies, more data is being analysed. Study of various activities performed, makes it simple to comprehend the context of the video files, and as a result, various actions may be taken. In this paper, we intend to recognise various human activities by using a hybrid model of dilated convolution neural network (CNN) and recurrent neural network (RNN). We validate its performance on a publically available dataset—HMDB51—and compare its performance with that of the existing models. The model proposed gives an accuracy of 84%.
Diversified Recommendation Generation Using Graph Convolution Neural Network

Many methods have been proposed for recommendation generation using graph neural networks (GNNs). The advantage of using GNN in a recommendation system is learning the structural information of the user and item and their interaction more efficiently than traditional learning techniques. Most of the proposed models for recommendation generation are concerned only about their accuracy enhancement. Besides accuracy, novelty, diversity, and serendipity in the recommendation are often desirable for a better user experience in a real-world application. Earlier diversity in the recommendation system is achieved using the re-ranking algorithms. These approaches often compromise with accuracy to include diversity in the recommendation. Here, we proposed a methodology for diversity inclusion in the recommendation system using the GNN. We proposed a method based on Cluster-GCN for diversification of the recommendation. In our proposed method, we cluster users’ nodes based on their dissimilarity, and further, their subgraph is used for their neighborhood-based representation learning using graph convolution neural network (GCN). The novelty of the work is the clustering for the user’s pre-trained diversity enhancement in the recommendation generation. The proposed diversified cluster graph convolution neural network (Div-ClusGCN) model is trained for diversified recommendation generation. We achieved around 7% more diverse recommendations from the other state-of-the-art models.
Real-time fast learning hardware implementation

Machine learning algorithms are widely used in many intelligent applications and cloud services. Currently, the hottest topic in this field is Deep Learning represented often by neural network structures. Deep learning is fully known as deep neural network, and artificial neural network is a typical machine learning method and an important way of deep learning. With the massive growth of data, deep learning research has made significant achievements and is widely used in natural language processing (NLP), image recognition, and autonomous driving. However, there are still many breakthroughs needed in the training time and energy consumption of deep learning. Based on our previous research on fast learning architecture for neural network, in this paper, a solution to minimize the learning time of a fully connected neural network is analysed theoretically. Therefore, we propose a new parallel algorithm structure and a training method with over-tuned parameters. This strategy finally leads to an adaptation delay and the impact of this delay on the learning performance is analyzed using a simple benchmark case study. It is shown that a reduction of the adaptation step size could be proposed to compensate errors due to the delayed adaptation, then the gain in processing time for the learning phase is analysed as a function of the network parameters chosen in this study. Finally, to realize the real-time learning, this solution is implemented with a FPGA due to the parallelism architecture and flexibility, this integration shows a good performance and low power consumption.
Deep Learning-Based Similar Languages’ POS Tagging: Experiments on Bhojpuri, Maithili, and Magahi

Monolingual corpora and similar language resources are vastly available for a few languages. These resources stimulate the exploration and building of potential NLP tools for new languages or dialects. This paper deals with the part-of-speech (POS) tagging for the Indo-Aryan languages, i.e., Magahi, Maithili, and Bhojpuri, a dialect of Hindi. The POS model is trained by BiLSTM-CRF and explores the effectiveness of Word2Vec, GloVe as word and FastText, and BPE as subword-level embeddings, trained on the raw corpus of these languages. All these languages are dialects of Hindi; hence, multilingual embedding at the BPE level has been evaluated. Better results are obtained than with monolingual BPE embedding. However, the best results have been obtained from word embeddings, i.e., GloVe on Maithili and Magahi, with 81.23% and 82.24%, respectively.