Dependency Graph Research Articles

Data storage format for analytical systems based on metadata and dependency graphs between CSV and JSON

In the modern information society, the volume of data is constantly growing, and its effective processing is becoming key for enterprises. The transmission and storage of this data also plays a critical role. Big data used in analytics systems is most often transmitted in one of two popular formats: CSV for structured data and JSON for unstructured data. However, existing file formats may not be effective or flexible enough for certain data analysis tasks. For example, they may not support complex data structures or provide sufficient control over metadata. Alternatively, analytical tasks may require additional information about the data, such as metadata, data schema, etc. Based on the above, the subject of this study is a data format based on the combined use of CSV and JSON for processing and analyzing large amounts of information. The option of sharing the designated data types for the implementation of a new data format is proposed. For this purpose, designations have been introduced for the data structure, which includes CSV files, JSON files, metadata and a dependency graph. Various types of functions are described, such as aggregating, transforming, filtering, etc. Examples of the application of these functions to data are given. The proposed approach is a technique that can significantly facilitate the processes of information analysis and processing. It is based on a formalized approach that allows you to establish clear rules and procedures for working with data, which contributes to their more efficient processing. Another aspect of the proposed approach is to determine the criteria for choosing the most appropriate data storage format. This criterion is based on the mathematical principles of information theory and entropy. The introduction of a criterion for choosing a data format based on entropy makes it possible to evaluate the information content and compactness of the data. This approach is based on the calculation of entropy for selected formats and weights reflecting the importance of each data value. By comparing entropies, you can determine the required data transmission format. This approach takes into account not only the compactness of the data, but also the context of their use, as well as the possibility of including additional meta-information in the files themselves and supporting data ready for analysis.

Certified Hop Independence: Properties and Connections with other Variants of Independence

Let G be a graph. Then B ⊆ V (G) is called a certified hop independent set of G if for every a, b ∈ B, dG(a, b) ̸= 2 and for every x ∈ B has either zero or at least two neighbors in V (G) \ B. The maximum cardinality among all certified hop independent sets in G, denoted by αch(G), is called the certified hop independence number of G. In this paper, we initiate the study of certified hop independence in graphs and we establish some of its properties. We give realization results involving hop independence and certified hop independence parameters, and we show that the difference between these two parameters can be made arbitrarily large. We characterize certified hop independent sets in some graphs and we use these results to obtain the exact values or bounds of the parameter. Moreover, we show that the certified hop independence and independence parameters are incomparable.

ВИЗНАЧЕННЯ ТА АНАЛІЗ РОБОЧИХ ХАРАКТЕРИСТИК ВИСОКОВОЛЬТНИХ ЕЛЕКТРОТЕХНІЧНИХ СИСТЕМ НА ОСНОВІ ПОСЛІДОВНОГО ВИСОКОДОБРОТНОГО РЕЗОНАНСНОГО LC-КОНТУРУ

The paper analyzes the performance characteristics of the electrоtechnical system (ETS), built on the basis of a series high-Q resonant LC circuit and intended for diagnosing the isolation of high-voltage equipment. The frequency dependences of the parameters L and C elements of the resonant circuit were experimentally studied and it was shown that for both elements there is an optimal frequency at which the value of the element’s Q- factor reaches its maximum value. It was established that the Q-factor of the investigated circuit is determined mainly by the Q-factor of its inductance and reaches the maximum value QLC,max = 280 at the frequency f= 40 kHz. The Simulink model was developed for the analysis of the performance characteristics of the ETS in steady state. Graphical dependences are given, which allow to determine the level of voltage on the load for a given active resistance of this load, as well as the energy characteristics and the value of the efficiency of the ETS at the stage of its design. A Simulink model has also been developed to analyze the dynamic characteristics of the ETS when a breakdown of the diagnosed insulation occurs. It is shown that the breakdown of the insulation in the ETS, which has a series high-Q resonant circuit, will cause a rapid decrease in current by Q times, and not an increase in current to emergency values, as in an ETS based on high-voltage transformers. In this way, a fast-acting parametric stabilization of the output current is implemented in the ETS even in case of electrical breakdowns of the high-voltage insulation of the power equipment under diagnosis. Thus, the ETS implements high-speed parametric stabilization of the output current, which prevents the destruction of both the ETS and the diagnosed high-voltage insulation of power equipment even during its electrical breakdown. References 11, figures 9.

Time evolution and the Schrödinger equation on time dependent quantum graphs * *Dedicated to Michael Berry on his forthcoming anniversary.

The purpose of the present paper is to discuss the time dependent Schrödinger equation on a metric graph with time-dependent edge lengths, and the proper way to pose the problem so that the corresponding time evolution is unitary. We show that the well posedness of the Schrödinger equation can be guaranteed by replacing the standard Kirchhoff Laplacian with a magnetic Schrödinger operator with a harmonic potential. We then generalize the result to time dependent families of vertex conditions. We also apply the theory to show the existence of a geometric phase associated with a slowly changing quantum graph.

Analysis of the oil extraction process and prospects of automation of screw press operation

Over the last few years, vegetable oils have become an integral food part of the vast majority of the planet's population. In order to preserve all valuable substances in vegetable oils, it is necessary to correctly choose the rational parameters of the oil extraction process. The main purpose of this research is to analyze the influence of various factors on the quantitative and force parameters of the oil extraction process and substantiate the prospective directions of automation of the screw press operation. The research methodology is focused on mathematical modeling of the pressing process and analyzing the stress-strain characteristics of the pressed material (sunflower seeds and cake) at different operational conditions and design parameters of the screw. The obtained results are presented in the form of analytical dependencies of the press output (productivity), extraction pressure, and consumed power on the screw rotational frequency and screw flight pitch. The corresponding graphical dependencies are plotted for visualization of the obtained results with reference to the existing household press LiangTai LTP200. The conclusions on the prospective directions of automation of the screw press operation are drawn. Particularly, the necessity of regulating the temperature of the pressing chamber and extracted oil by means of applying additional heating and cooling devices is substantiated. As well, the sensors that monitor the mass of the extracted oil and the consumed current of the electric motor are proposed to be installed. The rotational frequency of the screw is suggested to be adjustable by applying the frequency converter controlled by the additional electronic system based on the data obtained from the sensors. The paper may be used by scientists and engineers working on designing screw presses and improvement of their performance characteristics. Further investigations on the subject of this research may be focused on experimental verification of the qualitative and quantitative parameters of the extracted oil depending on the type of the seeds or kernels to be pressed, and operational parameters of the press (screw rotation frequency, temperature of the pressing chamber, etc.).

Evaluation of the Informativeness of Features in Datasets for Continuous Verification

Continuous verification eliminates the flaws of existing static authentication, e.g. identifiers can be lost or forgotten, and the user logs in the system only once, which may be dangerous not only for areas requiring a high level of security but also for a regular office. Checking the user dynamically during the whole session of work can improve the security of the system, since while working with the system, the user may be exposed to an attacker (to be assaulted for example) or intentionally transfer rights to him. In this case, the machine will not be operated by the user who performed the initial login. Classifying users continuously will limit access to sensitive data that can be obtained by an attacker. During the study, the methods and datasets used for continuous verification were checked, then some datasets were chosen, which were used in further research: smartphone and smart watch movement data (WISDM) and mouse activity (Chao Shen’s, DFL, Balabit). In order to improve the performance of models in the classification task it is necessary to perform a preliminary selection of features, to evaluate their informativeness. Reducing the number of features makes it possible to reduce the requirements for devices that will be used for their processing, and to increase the volume of enumeration of classifier parameter values at the same time, thereby potentially increasing the proportion of correct answers during classification due to a more complete enumeration of value parameters. For the informativeness evaluation, the Shannon method was used, as well as the algorithms built into programs for data analysis and machine learning (WEKA: Machine Learning Software and RapidMiner). In the course of the study, the informativeness of each feature in the selected datasets was evaluated, and then users were classified with RapidMiner. The used in classifying features selection was decreased gradually with a 20% step. As a result, a table was formed with recommended sets of features for each dataset, as well as dependency graphs of the accuracy and operating time of various models.

Flexibly utilizing syntactic knowledge in aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) refers to ascertaining the propensity of sentiment expressed in a text towards a particular aspect. While previous models have utilized dependency graphs and GNNs to facilitate information exchange, they face challenges such as smoothing of aspect representation and a gap between word-based dependency graphs and subword-based BERT. Taking into account the above deficiencies, we argue for a new approach called SRE-BERT that flexibly utilizes syntax knowledge to enhance aspect representations by relying on syntax representations. First, we propose a syntax representation encoder to acquire the syntactic vector for each token. Then, we devise a syntax-guided transformer that employs syntax representation to compute multi-head attention, thereby enabling direct syntactic interaction between any two tokens. Finally, the token-level vectors derived from the syntax-guided transformer are employed to enhance the semantic representations obtained by BERT. In addition, during the aforementioned process, we introduced a Masked POS Label Prediction (MPLP) method to pre-train the syntax encoder. A wide range of studies have been undertaken on data collections covering three distinct fields, and the results indicate that our SRE-BERT outperforms the second-ranked model by 1.97%, 1.55%, and 1.20% on the Rest14, Lap14, and Twitter 3 datasets, respectively.

Modeling Structured Dependency Tree with Graph Convolutional Networks for Aspect-Level Sentiment Classification.

Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect-based sentiment analysis tasks. However, most existing models still contain a large amount of noisy nodes that cannot precisely capture the contextual relationships between specific aspects. Meanwhile, most studies do not consider the connections between nodes without direct dependency edges but play critical roles in determining the sentiment polarity of an aspect. To address the aforementioned limitations, we propose a Structured Dependency Tree-based Graph Convolutional Network (SDTGCN) model. Specifically, we explore construction of a structured syntactic dependency graph by incorporating positional information, sentiment commonsense knowledge, part-of-speech tags, syntactic dependency distances, etc., to assign arbitrary edge weights between nodes. This enhances the connections between aspect nodes and pivotal words while weakening irrelevant node links, enabling the model to sufficiently express sentiment dependencies between specific aspects and contextual information. We utilize part-of-speech tags and dependency distances to discover relationships between pivotal nodes without direct dependencies. Finally, we aggregate node information by fully considering their importance to obtain precise aspect representations. Experimental results on five publicly available datasets demonstrate the superiority of our proposed model over state-of-the-art approaches; furthermore, the accuracy and F1-score show a significant improvement on the majority of datasets, with increases of 0.74, 0.37, 0.65, and 0.79, 0.75, 1.17, respectively. This series of enhancements highlights the effective progress made by the STDGCN model in enhancing sentiment classification performance.

Technical parameters analyses of different types of impact-vibration soil compacting machines

Introduction. Impact-vibration machines, which perform soil compaction with periodic removal of the working body from the ground, are the most common for mechanization of soil compaction in road construction: self-propelled non-reversible and reversible vibratory plates, vibratory rammers, mounted excavator vibratory plates, trench vibratory rollers and soil vibratory rollers. It is necessary to analyze the main technical characteristics of impactvibration machines of different types in order to assess the possibility of developing a mathematical model of soil compaction, combining several types of impact-vibration machines at once.Materials and methods. The technical characteristics of different types of impact-vibration machines for the analysis: 342 models of non-reversible self-propelled vibratory plates; 312 models of reversible self-propelled vibratory plates; 311 models of vibratory ground rollers; 63 models of mounted vibratory plates for excavators; 24 models of vibratory rammers and 21 models of trench vibratory rollers are used. The machine mass (for vibratory plates of different types) or the mass per compaction module (for vibratory rollers of different types) was taken as a main parameter.Results. The graphical dependencies have been obtained. They illustrate that for the given types of shock-vibration machines the excitation force, relative excitation force and oscillation frequency are located on mutually contiguous and continuing parts of curves. This is a common pattern: as the mass of the machine increases, the excitation force increases, but the relative excitation force and frequency of oscillation decrease. Vibratory rammers are out of this general pattern, because at comparable weight they have significantly lower frequency of oscillation (9...12 Hz) and high amplitude (50...75 mm). Also, the mounted excavator vibratory plates stand out from the general pattern, having a smaller range of vibration frequencies for machines of similar weight, moreover, they perform soil compaction positionally, and not in the process of movement at a certain speed, as other impact-vibration machines.Discussion and conclusions. The common pattern of changes in the main technical characteristics of selfpropelled non-reversible and reversible vibratory plates, trench rollers and soil vibratory rollers at close nominal amplitude of their vibrations gives grounds for the development of a unified theoretical description of the interaction of the working bodies of these types of machines with the compacted soil and a common method of calculating the influence of technical characteristics and operating modes of these machines on the results of soil compaction.

A knowledge-guided graph attention network for emotion-cause pair extraction

Emotion-Cause Pair Extraction (ECPE) is a research objective focused on identifying and extracting all emotion-clause and cause-clause pairs from unannotated emotional text. Traditional methodologies have predominantly employed attention mechanisms or joint learning techniques for feature information interaction. However, these approaches often overlook the aggregation of features under the guidance of external knowledge. To enhance performance in addressing this ECPE challenge, we propose a novel knowledge-guided graph attention network, i.e., GAT-ECPE. This model chiefly relies on an interclause dependency graph as a guiding principle. By employing this knowledge-guided graph attention network, we can proficiently combine semantic and structural information between clauses. In addition, an interpair possibility graph, derived from the outcomes of subtasks, is integrated as an additional guiding principle. As such, we are able to aggregate features between clause pairs, thereby facilitating interaction between multiple tasks. Extensive experiments were conducted to validate our proposed model, and the obtained results demonstrate its superiority when compared to 12 considered baselines. In terms of performance metrics, our model achieves an F1 score F1 of 74.92% and a recall R of 77.52%. These values significantly outperform those achieved by state-of-the-art approaches, indicating the effectiveness and superiority of our GAT-ECPE.

Graph-based two-level indicator system construction method for smart city information security risk assessment

<p>The rapid development of urban informatization has led to a deep integration of advanced information technology into urban life. Many decision-makers are starting to alleviate the adverse effects of this informatization process through risk assessment. However, existing methods cannot effectively analyze internal and hierarchical relationships because of the excessive number of indicators. Thus, it is necessary to construct an indicator's dependency graph and conduct a comprehensive hierarchical analysis to solve this problem. In this study, we proposed a graph-based two-level indicator system construction method. First, a random forest was used to extract the indicators' dependency graph from missing data. Then, spectral clustering was used to separate the graph and form a functional subgraph. Finally, PageRank was used to calculate the prioritization for each subgraph's indicator, and the two-level indicator system was established. To verify the performance, we took China's 25 smart cities as examples. For the simulation of risk level prediction, we compared our method with some machine learning algorithms, such as ridge regression, Lasso regression, support vector regression, decision trees, and multi-layer perceptron. Results showed that the two-level indicator system is superior to the general indicator system for risk assessment.</p>

ON STYLISTIC DIFFERENTIATION IN UKRAINIAN POETIC SPEECH BASED ON THE SYNTACTIC STRUCTURE OF SENTENCES

Background. The article is devoted to the stylistic differentiation of syntactic structures of Ukrainian-language poetic texts of the Ukrainian language corpus. The goal is to study the idiostyle on models of syntactic structures, graphically presented in the form of dependency trees, with the establishment of parameters for characterizing the author's style. The properties of the linear structure of 7,752 Ukrainian-language sentences in the verse style were studied. An "averaged" dependency graph was built for each author as a generalization of the syntactic parameters of dependency trees. Methods. Applied methods of structural linguistics included quantitative methods and modeling methods, distributive analysis, direct component method, method of constructing dependency trees (DT) and direct components in linguistic analysis modules: automatic morphological (distributive) and automatic syntactic (direct component and dependency tree method). Results. As a result of the study, it was established that the architecture of the phrase is important, that is, a scheme in the form of a graph that reflects the real sequence of syntactic elements of the statement. If the author has developed his individual writing style, this will inevitably affect the originality of the graphic drawings for the structures of his typical phrases. It is in syntactic stylistics that dependence graphs as a research method will find an unlimited field of application. Eight graph parameters are proposed, the choice of which is determined by the fact that they have stylistic distinguishing power. Conclusions. To justify the applied method of statistical analysis, it can be said that in stylistics each analysis gives only what it can give; the overall research picture consists of a comprehensive review of the results of all analyses. Qualitative methods can describe what is perceived from the text through its symbolic character, but only quantitative methods can explain why the text is constructed in the way it is. Qualitative characteristics consist of precisely those quantitative characteristics that cannot be seen by the average reader, and their detection is the task of science.

Bridging the gap between requirements engineering and systems architecting: the Elephant Specification Language

Abstract This article presents a domain-specific language for writing highly structured multilevel system specifications. The language effectively bridges the gap between requirements engineering and systems architecting by enabling the direct derivation of a dependency graph from the system specifications. The dependency graph allows for the easy manipulation, visualization and analysis of the system architecture, ensuring the consistency among written system specifications and visual system architecture models. The system architecture models provide direct feedback on the completeness of the system specifications. The language and associated tooling has been made publicly available and has been applied in several industrial case studies. In this article, the fundamental concepts and way of working of the language are explained using an illustrative example.

DEVELOPMENT OF A MATHEMATICAL MODEL OF THE EFFECT OF LITHIUM ON THE WEAR RESISTANCE PROPERTIES OF ALUMINUM-LITHIUM ALLOYS

In this research paper, the authors investigated the wear resistance of one of the mechanical properties, studying the change in its wear resistance as a result of the action of lithium fluoride on aluminum alloys. Based on the results obtained, a dependence graph was developed, on the basis of which a mathematical model of the effect of lithium on the edibility property was developed.

Empirical Network Structure of Malicious Programs

A modern binary executable is a composition of various types of networks. Control flow graphs are a commonly used representation of an executable program used for classification tasks. Control flow and term frequency representations are widely adopted, but provide only a partial view of program semantics and present challenges to increases in resolution. By performing a quantitative analysis of program networks, we enable the identification of patterns within these features that are correlated to structure. This allows for increases in feature resolution and pattern recognition in classification tasks. These are necessary steps in order to obtain greater explainability in classification results. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. An increase in feature resolution allows for the structural

АНАЛИЗ СОВРЕМЕННЫХ СПОСОБОВ ПРИКЛАДНОГО ИСПОЛЬЗОВАНИЯ СВЕРТОЧНЫХ НЕЙРОННЫХ СЕТЕЙ В ГОРНОЙ ПРОМЫШЛЕННОСТИ

Currently there are a lot of image recognition tasks, that are being successfully solved by the use of different types of neural networks. In its turn, the leading type of neural networks architecture for mentioned tasks class is convolutional neural network (CNN). Convolutional neural networks consist of multiple layers with small sets of neurons. Each of the neurons represents a small part of the original image. Combining all sets of neurons in a layer gives us a complete representation of the image. Other layers repeat this algorithm, but work with the same image presented in a different way, allowing the system as a whole to «learn» what the picture ultimately consists of. For example, each of the convolution layers can be responsible for highlighting certain features in the image (contour lines, flat areas, sharp edges). Research results. In this article we present a classification of groups, that reflect main directions of neural networks usage in mining industry. Different examples of implemented real-world use-cases were presented. Some important CNN-algorithm alterations were mentioned and explained. Conclusion. As we can see, many major mining process stages are positively affected by the usage of convolutional neural network. Machine learning algorithms help to find illegal deposits, maintain security and stability while mining and mineral processing goes on. Neural networks are being successfully trained by researchers to work with satellite map pictures, mineral surface photos and plots of signals from various engineering equipment. It is worth mentioning, that many researchers work for solving similar industry problems, but every time they have to slightly (or drastically) change CNN algorithm details to complete the tasks for the certain material type or certain GIS object type. Resume. The high growth rate of popularity of neural networks in the mining industry was made possible due to many factors. For example, the accumulation of large amounts of digital data (satellite images, dependency graphs, photo and video materials from real-world objects) has made the process of selecting materials for CNN training and testing easier and more efficient.

Analysis and determination of the optimal collector array orientation characteristics in solar systems

This paper presents results analysis for the distribution of direct and diffuse solar radiation for a specific climatic zone in Bulgaria. The influence of the slope angle of the collector array over monthly solar radiation incident is studied and regression dependencies regarding incident total, direct and diffuse specific annual solar radiation are derived. Based on the obtained dependencies, the optimal value of the solar collectors slope angle, that guarantees maximum value of the annual solar radiation, is determined. The ratio of direct radiation on an inclined to that on a horizontal surface depending on the azimuth of collector arrays with different slope angle is investigated using a dynamic simulation software. Graphical dependencies, allowing the determination of the total annual solar radiation at different values of the azimuth and the slope angle of the collector array are obtained.

Research on the distribution of heat fluxes during infrared drying of grain material

Purpose. To develop mathematical models of unsteady heat and mass transfer during the infrared (IR) drying process, and to create an algorithm for calculating the variable power of IR energy input to ensure quality drying of thermolabile material. Methods. The methods of mathematical modeling used to describe the unsteady drying process of thermolabile material in a dryer are based on the formalized description in the form of nonlinear inhomogeneous differential equations, taking into account the change in the power of IR energy input during the drying process. Results. A dependency for calculating the perceivable power of the IR source by the grain, based on the given process mode parameters, has been obtained. The drying kinetics of the grain, derived from formula (10), more accurately characterizes the process from a physical point of view than the drying equation (8) with a constant coefficient kck. It was established that three characteristic periods of the drying process can be traced in the graphical dependencies: I – heating; II – constant drying rate; III – falling drying rate. The calculation of heat fluxes for downward cross-ventilation of the grain layer, where air washes over IR heaters and reflectors and heats up to 35 °C, is presented. It was found that in the implementation of the process with a downward airflow, it is necessary to supply 20–25 % less radiant energy to the material at the same drying exposures according to the graphical dependencies. Conclusions. A mathematical description of the unsteady drying process of thermolabile material in a dryer, considering the change in the power of IR energy input during drying, has been formulated. An algorithm for calculating parameters that can be used for process identification and optimization of the operating parameters of existing grain dryers has been developed. Keywords: drying, grain material, mathematical model, diffusive transfer, moisture content, temperature, speed.

Joint Optimization of Spare Part Supply and Opportunistic Condition-Based Maintenance for Onshore Wind Farms Considering Maintenance Route

Maintenance and inventory control of spare parts are of great importance for efficiently managing onshore wind farms. Considering the economic and graphical dependencies among wind turbines and the stochastic degradations of the components in the turbines, this article studies the joint optimization of opportunistic condition-based maintenance and spare parts supply for onshore wind farms to minimize the operation and maintenance cost rate. For each field maintenance, the specific turbines, with failed components, are selected for replacement using a clustering method, and the shortest route for visiting the turbines with failed components is scheduled. The maintenance team departs from a maintenance center with spare parts, maintains the selected turbines with failed or degraded components, and then returns to the center. This complicated and practical problem has never been considered as a whole in the literature. An optimization model is established, and a simulation-based approach is developed to solve it. A special case, for which a solution can be obtained by directly solving a nonlinear programming model, is provided to verify the results obtained by the simulation-based approach. Examples for various scales of wind farms are provided, and an analysis is performed for determining the length of a recommended maintenance interval.

Алгоритмы оценки основных параметров надежности низковольтного оборудования схем цеховых сетей

The article presents the classification of fuses, breakers, circuit breakers and packet switches. The laws of change of probabilistic reliability characteristics of the studied devices, as well as low-voltage cable lines are determined. Types of functions of change of basic reliability parameters are determined and corresponding graphical dependences are shown. The comparison of the obtained results of the values of probability of failure-free operation time with the requirements of GOST and passport data is carried out. The investigated probabilistic parameters of lowvoltage devices and cable lines are checked for compliance with the accepted law of distribution of random variables.