Problem Of Discrepancy Research Articles

Популяризация научного знания в медиасреде: позитивные и негативные аспекты

Modern media are integrated into many spheres of society. Scientific discourse is also influenced by the objective process of mediatization. The aim of the article is to highlight important problems of mediatization of science by means of the Internet. The study is based on the analysis of scientific works by famous foreign and domestic authors on the topic of mediatization. The article outlines the main features of social media, which play an increasingly important role in popularizing scientific knowledge and in communication between scientists. The article notes the positive aspects of mediatization of science, and also identifies the following problems: inability to select a universal method of transmitting information; mythologization of scientific knowledge; infantilization of the audience; demonization of media discourses by the academic community; formation of a pseudoscientific functional style; the problem of discrepancy between the communicator and the recipient (dilettantism); creation of false meanings alien to science. The article may be of interest to researchers of mediatization of science and higher education, as well as managers in the field of science.

Pseudo-ISP: Learning pseudo in-camera signal processing pipeline from a color image denoiser

Existing deep denoisers usually ignore the problem of noise discrepancy between training and test images caused by the different noise modeling of sensor and in-camera signal processing (ISP) pipeline, which inevitably degrades the denoising performance. In this paper, we present an unpaired learning scheme to adapt the color image denoisers for handling test images with noise discrepancy. To this end, we consider a practical training setting equipped with a pre-trained color denoiser, a set of test noisy sRGB images, and an unpaired set of clean sRGB images. Then, we propose to alternate between two modules, i.e., Pseudo-ISP training for learning noise modeling of sensor and in-camera signal processing (ISP) pipeline and denoiser adaption. Instead of modeling the complex noise in sRGB images, we assume the signal-dependent and spatially independent noise in rawRGB space, and specifically design the Pseudo-ISP to learn the pseudo ISP pipeline and pseudo rawRGB noise model jointly. Applying the learned Pseudo-ISP to the unpaired set of clean sRGB images, the corresponding realistic noisy sRGB observations can be easily obtained. Conversely, using the generated paired data for model fine-tuning, the color denoiser can be well adapted for test images. Through iterating between Pseudo-ISP training and denoiser adaption, denoising performance of deep denoisers can be further improved. Experiments show that our Pseudo-ISP not only can boost simple Gaussian blurring-based denoiser to achieve competitive performance against CBDNet, but also is effective in improving state-of-the-art deep denoisers, e.g., CBDNet and RIDNet.

Метод потоков в диагностике угроз национальной экономической безопасности России

The work identifies the problem of discrepancy between the rapidly growing importance of national economic security (NES) of Russia and the degree of its scientific elaboration according to such criteria as clarity in defining the subject of NES, methodology for studying NES, mechanisms for connecting scientific research with education and practice of NES. The article is devoted to the study of the dynamics of teacher and scientist staff. The research is carried out using a new method developed by the author – the Flow Method. The novelty of the Flow Method begins with a change in the idea of the object of study. In traditional approaches, an object is fixed, and is studied from different sides in different periods of time; the Flow Method focuses the researcher’s attention on the movement of resource flows in the economic environment. In such a movement of resources, the following are distinguished: inflows, outflows, accumulations. Normally, the movement of resources is balanced. It is appropriate to consider sanctions as the most important component of a hybrid war, aimed at unbalancing the resources of the national economy by blocking flows. The study showed that the representation of the movement of resources by the Flow Method differs from traditional representations in statistics and accounting. Approbation of the Method in diagnosing the state of personnel in scientific and educational institutions showed strategic threats to the national security of the Russian Federation. Since 2015, the number of scientists and teachers with an academic degree has been continuously decreasing. The flow method allows us to dispel the myth that if scientists leave region A, they will simply work in another region. This study refutes such a hypothesis. Scientists, in particular, can change their specialty, leave the country, etc. The noted situation is similar for all 16 cities with a population of over a million in Russia, including Moscow and St. Petersburg and 18 constituent entities of the Russian Federation. The influx of young scientists is decreasing while the workforce continues to age. If we ignore the results obtained, then in 5–10 years, for example, the Omsk region will begin to lose the ability to train specialists with higher education, not to mention candidates and doctors of science. A similar situation awaits other regions.

AsdinNorm: A Single-Source Domain Generalization Method for the Remaining Useful Life Prediction of Bearings

The remaining useful life (RUL) of bearings is vital for the manipulation and maintenance of industrial machines. The existing domain adaptive methods have achieved major achievements in predicting RUL to tackle the problem of data distribution discrepancy between training and testing sets. However, they are powerless when the target bearing data are not available or unknown for model training. To address this issue, we propose a single-source domain generalization method for RUL prediction of unknown bearings, termed as the adaptive stage division and parallel reversible instance normalization model. First, we develop the instance normalization of the vibration data from bearings to increase data distribution diversity. Then, we propose an adaptive threshold-based degradation point identification method to divide the healthy and degradation stages of the run-to-failure vibration data. Next, the data from degradation stages are selected as training sets to facilitate the RUL prediction of the model. Finally, we combine instance normalization and instance denormalization of the bearing data into a unified GRU-based RUL prediction network for the purpose of leveraging the distribution bias in instance normalization and improving the generalization performance of the model. We use two public datasets to verify the proposed method. The experimental results demonstrate that, in the IEEE PHM Challenge 2012 dataset experiments, the prediction accuracy of our model with the average RMSE value is 1.44, which is 11% superior to that of the suboptimal comparison model (Transformer model). It proves that our model trained on one-bearing data achieves state-of-the-art performance in terms of prediction accuracy on multiple bearings.

Investigating the Bubble Point Pressure Discrepancy by History Matching for Mishrif Reservoir, Southern Iraqi Oil Field

Carbonate reservoirs have been a primary focus for academics and oil and gas companies for many years. However, the complex nature of these rocks has always posed challenges. This is particularly true in the study area, which is located in the unstable Mesopotamian Basin, where multiple oil fields produce hydrocarbons from NW-SE-trending anticlines along the Zagros Fold-Thrust Belt. The primary goal of reservoir simulation is to forecast reservoir performance and apply different techniques to improve hydrocarbon recovery under different conditions. The study area faces a problem of discrepancy in the bubble pressure at the same reservoir unit, which is attributed to the faults in the Mishrif reservoir. A study was conducted to investigate the causes of this phenomenon. The proposed method for verification involves simulating history matching using the Petrel platform after building two geological models on the probability of the fault and the reef; as a result of seismic survey interpretation. Field data for gas production of the reservoir and Pressure-Volume-Temperature analyses, especially the wells (well pad F), were used to determine the causes of the discrepancy. The current study found that the calculated and observed data match more consistently in the case of the fault rather than in the case of the reef, indicating the presence of fault in the area near the well pad F. This caused compartmentalization, leading to the discrepancy in the bubble pressure. This study could significantly improve regional exploration, especially in Iraq.

Research on the method of eliminating differences between small-sample databases based on cloud computing

Abstract As computer technology continues to advance, more and more people are using databases, leading to variations in data backup and transfer between databases. This paper proposes a small sample inter-database discrepancy data elimination method based on cloud computing architecture as a way to solve the problem of data synchronization discrepancy between different databases. The semantic features of the data in the database are represented in the form of a directed graph, and the semantic Gaussian marginalized data fusion system is constructed by combining the rectangular window function of Gaussian marginalization to realize the fusion filtering processing of discrepant data. Then, the particle swarm discriminant tree algorithm is used to extract the features of the difference data between the small sample databases, and the KL transform is used to compress the difference data to improve its confidence level. The rough weighted average single dependency method is introduced to detect and identify the difference data between small sample databases and combined with the artificial intelligence algorithm to construct the principal component feature set of the difference data in the small sample databases, thus realizing the elimination of the difference data between small sample databases. When the proportion of difference data is increased from 0.05% to 1.00%, the leakage alarm rate and false alarm rate of this paper’s method for the difference data between small sample databases are 0.113% and 0.099%, respectively. When eliminating the inter-database discrepancy data, its time consumption is between 0.06μs and 0.3μs, and the average value of the removal rate of discrepancy data can reach 95.54%. Small sample databases that utilize cloud computing technology can utilize a variety of differential data elimination algorithms to ensure high-quality migration and synchronized backup of inter-database data.

Інфляція як мірило незалежності центральних банків

The aim of the article is to study the process of formation of the modern paradigm of independence of central banks, determine the role of inflation in it, outline the criteria for independence and determine the dynamics of legal indicators of autonomy of monetary institutions. It is noted that the impetus for changes in relations between governments and central banks of many industrialized countries was the episodes of high inflation in the 1970s. Awareness of the problem of the time discrepancy between the short-term perspective of political decisions and their long-term impact on the economy provoked institutional changes in the status of monetary institutions. They envisaged the prioritization of central banks’ activities to ensure a consistently low level of inflation and provide independence to achieve it. It is also noted that in countries with economies in transition, the tendency to separate central banks into an independent institution was implemented during the 1990s. The criteria for the independence of central banks are determined by legislation and their charters. These usually include the following: isolation of central bank leadership from political pressure; prohibition to give instructions to central banks in the conduct of monetary policy; a clearly defined purpose of monetary policy; restrictions on lending to government programs and budget deficits. In addition to the formally defined independence, there is also the actual independence of central banks, which is usually outlined through the index of management turnover of monetary institutions and its correlation with the inflation rate. But this does not take into account other (exogenous) factors that affect the inflation rate, which makes the studies less relevant. In addition, the construction of any indices includes an arbitrary weight value of each of them, which reflects the sensitivity of the results obtained depending on their composition. As noted in the study, instead of a static analysis of the indicators of independence of central banks, it is more expedient to determine their modification from the beginning of the formation of the modern paradigm of independence to the present day. The analysis showed that since the beginning of the 1970s, most of the countries studied have changed the rules prohibiting central banks from financing (in any form) the budget deficit. At the same time, obstacles to interference in monetary policy were established. This suggests that the change in these criteria had the greatest impact on the downward trend in the level and volatility of inflation in the period under study.

PEST-FACTORS OF SOCIAL ENTREPRENEURSHIP DEVELOPMENT IN UKRAINE: HELICOPTER VIEW

The article presents the results of the study of PEST-factors of social entrepreneurship development in Ukraine. The aim was to identify their current and projected impact in order to develop an action strategy. The systematization of the processed sources testified to the accumulation in recent years of significant information on social entrepreneurship development in Ukraine. However, the problem of discrepancy between the amount of theoretical knowledge and the needs of practice to act in conditions of permanent challenges remains relevant. The study was conducted from August 2020 to May 2021. The methodology of PEST-analysis was used: desk research, expert survey, preparation of a summary table and calculations based on survey results, development of actions for social entrepreneurship development in the upcoming years (within the factors that experts gave the most importance). The results: 1) PEST-factors of social entrepreneurship development in Ukraine are characterized by ambivalence; 2) the most illustrative ambivalent tendencies exist in political and socio-cultural groups of factors (risks of socio-economic and political instability border on the possibilities of institutional capacity of civil society organizations and decentralization); 3) the main challenges are grouped in a group of economic factors (dependence on international donors, insufficient budget funding, low competitiveness, etc.); 4) there exist an imbalance in the group of technological factors (the use of online resources received the most weight among all the factors proposed for evaluation in the format of PEST-analysis, whereas lack of necessary equipment, premises received the lowest); 5) experts predict the strengthening of the influence of the following factors in the nearest future: strengthening of the institutional capacity of civil society organizations, decentralization of power in the framework of administrative refor, use of online resources; 6) no changes are expected in terms of the impact of the following factors: socio-economic and political instability, dissemination of social problems, insufficient understanding of the concept of social entrepreneurship, legislative irregularities, dependence on international sources of funding, lack of strong teams, development strategies, financial and marketing plans, and lack of social entrepreneurship evaluation tools; 7) none of the studied factors is noted by experts as one that will not cease to exist in the future. The authors attribute the actions for the further development of social entrepreneurship mainly to the possibilities of decentralization. The obtained results can be useful for the development of SE development strategies at the regional level and the application of the PEST analysis methodology in further monitoring studies.

A meta network pruning framework for remaining useful life prediction of rocket engine bearings with temporal distribution discrepancy

Accurate remaining useful life prediction (RUL) is important for the reliability and safety of liquid rocket engines. In this paper, a meta network pruning framework with attention augmented convolutions is proposed for RUL prediction. To address the problem of distribution discrepancy in engineering data under transient working conditions, a data-driven distribution matching strategy is designed. Besides, in view of the prediction accuracy and computation complexity of the model, an iterative meta network pruning algorithm, which automatically calculates the meta-gradients of each convolutional kernel according to the chain rule, is developed to identify, and then delete the unimportant connections in the framework. The method is verified on a high-precision cryogenic rocket engine bearing experiment platform under liquid nitrogen and received better performance than benchmark algorithms.

Cross-Corpus Speech Emotion Recognition Based on Multi-Task Learning and Subdomain Adaptation.

To solve the problem of feature distribution discrepancy in cross-corpus speech emotion recognition tasks, this paper proposed an emotion recognition model based on multi-task learning and subdomain adaptation, which alleviates the impact on emotion recognition. Existing methods have shortcomings in speech feature representation and cross-corpus feature distribution alignment. The proposed model uses a deep denoising auto-encoder as a shared feature extraction network for multi-task learning, and the fully connected layer and softmax layer are added before each recognition task as task-specific layers. Subsequently, the subdomain adaptation algorithm of emotion and gender features is added to the shared network to obtain the shared emotion features and gender features of the source domain and target domain, respectively. Multi-task learning effectively enhances the representation ability of features, a subdomain adaptive algorithm promotes the migrating ability of features and effectively alleviates the impact of feature distribution differences in emotional features. The average results of six cross-corpus speech emotion recognition experiments show that, compared with other models, the weighted average recall rate is increased by 1.89~10.07%, the experimental results verify the validity of the proposed model.

From Laboratory to Field: Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild.

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization, namely, Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification (MSUN). Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial training. Specifically, MSUN comprises multirepresentation, subdomain adaptation modules and auxiliary uncertainty regularization. The multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source domain. This effectively alleviates the problem of large interdomain discrepancy. Subdomain adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass variation. Finally, the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain transfer. MSUN was experimentally validated to achieve optimal results on the PlantDoc, Plant-Pathology, Corn-Leaf-Diseases, and Tomato-Leaf-Diseases datasets, with accuracies of 56.06%, 72.31%, 96.78%, and 50.58%, respectively, surpassing other state-of-the-art domain adaptation techniques considerably.

The Motive of Discrepancy in Hryhorii Skovoroda’s Works

The article explores the discrepancy of the form and the content as a philosophical, moral and axiological problem in the works of Hryhorii Skovoroda. Using the phenomenological reading and structural analysis, the author investigates the interaction between the form and the content in treatises, soliloquies, poems, and letters of Skovoroda. The intellectual and aesthetic background of the Baroque epoch to a large extent explains why this motive of discrepancy occupies a prominent place among the writings of the Ukrainian philosopher. The article analyzes the main plots, in which the discrepancy is revealed: vocation against non-congenial work; a real friend and a flunkey; the truth and a false thing; and the heavenly and an earthly city. After considering all these aspects and other crucial issues, it is concluded what makes the problem of discrepancy an invariant motive in the works of Hryhorii Skovoroda.

Refugee Education under International NGOs: A Major Shift from National Institutions to Patron–Client Relations

What happens when a group of structurally powerless refugees exist within a nation-state’s territory but outside its regulatory institutions? An empirical study of the education of Pakistani Christian refugees in Bangkok, Thailand, identifies an entrenched gap between the education provided by INGOs and Pakistani Christian refugee expectations of the academic education of their children. We generalise from the specific problem of the entrenched educational discrepancy to a deeper structural inequality by using a ‘realist conceptual methodology’ characterised by the type of co-dependency found in the historical form of patron–client relations. The patron–client relationship is the outcome of being placed outside a nation-state’s institutions and the co-dependence that the relationship itself creates between the INGO providers and the refugees. We suggest that patron–client theory is a useful conceptual tool with which to explain the sociopolitical position of groups today who find themselves placed outside a modern nation-state’s institutions.

The Principle of the Rule of Law Is An Attribute of the Modern Legislation Reform

The author depicted the problem of discrepancy of the acting Ukrainian legislation in the sphere of the legal regulation of scientific research in one of the heading principles and standards of the EU law-making, namely the principle of the rule of law. The author has conducted an analysis of the national legislation and compared it with the international law in the afore-mentioned sphere, and has found out a number of divergences. Hence, the author has proposed the methods of solving the said legal problem, namely the author has chosen the most sufficient and actionable method of fulfilling the unification of legal terminology in the acting national legislation, and as a result, its adaptation to international law. The author finds it concordant to perform a number of changes to a chain of laws and bylaws, which are acting in Ukraine. Similar changes testify that the Ukrainian legislator fulfills the principle of the rule of law, especially one of its’ key features – namely, the legal certainty, clarity and non-ambiguity of the legal norm.

A fault diagnosis method of bearings based on deep transfer learning

In recent years, many deep transfer learning methods have been widely used in bearing fault diagnosis under varying working conditions to solve the problem of data distribution shift. However, the deep transfer learning methods used in bearing fault diagnosis have low accuracy when source domain data differ much and feature distribution misaligns, so a deep multi-source transfer learning model is proposed in this paper. To solve the problem of the large discrepancy in source domain data, we use maximum mean discrepancy (MMD) to select suitable source domain data to form a new source domain to help model training, we utilize independent domain-specific feature extractors to extract domain features to avoid poor source domains affecting feature extraction of other domains. At the same time, classifier output is aligned using Wasserstein distance to reduce the probability of misclassification of boundary samples, and the weighted decision is used to impose more weight on the better source domains. In addition, aiming at the problem of data distribution misalignment, we propose an alignment method combining MMD, local maximum mean discrepancy (LMMD), and triplet loss. Experimental results show that the proposed model can achieve more than the accuracy of 95% in the four working conditions of the Paderborn bearing fault dataset, and is suitable for bearing fault diagnosis under varying working conditions.

The Eyes of the Gods: A Survey of Unsupervised Domain Adaptation Methods Based on Remote Sensing Data

With the rapid development of the remote sensing monitoring and computer vision technology, the deep learning method has made a great progress to achieve applications such as earth observation, climate change and even space exploration. However, the model trained on existing data cannot be directly used to handle the new remote sensing data, and labeling the new data is also time-consuming and labor-intensive. Unsupervised Domain Adaptation (UDA) is one of the solutions to the aforementioned problems of labeled data defined as the source domain and unlabeled data as the target domain, i.e., its essential purpose is to obtain a well-trained model and tackle the problem of data distribution discrepancy defined as the domain shift between the source and target domain. There are a lot of reviews that have elaborated on UDA methods based on natural data, but few of these studies take into consideration thorough remote sensing applications and contributions. Thus, in this paper, in order to explore the further progress and development of UDA methods in remote sensing, based on the analysis of the causes of domain shift, a comprehensive review is provided with a fine-grained taxonomy of UDA methods applied for remote sensing data, which includes Generative training, Adversarial training, Self-training and Hybrid training methods, to better assist scholars in understanding remote sensing data and further advance the development of methods. Moreover, remote sensing applications are introduced by a thorough dataset analysis. Meanwhile, we sort out definitions and methodology introductions of partial, open-set and multi-domain UDA, which are more pertinent to real-world remote sensing applications. We can draw the conclusion that UDA methods in the field of remote sensing data are carried out later than those applied in natural images, and due to the domain gap caused by appearance differences, most of methods focus on how to use generative training (GT) methods to improve the model’s performance. Finally, we describe the potential deficiencies and further in-depth insights of UDA in the field of remote sensing.

PH Roundtable: Tailoring CTEPH Imaging for Evaluation and Postintervention Assessment—What Works and What’s New?

PH Roundtable: Tailoring CTEPH Imaging for Evaluation and Postintervention Assessment—What Works and What’s New?

Conservative approach to threat and risk assessment in the implementation of national development goals

Subject. We consider the protection of Russia's national interests, positioning of Russia in the world. Objectives. The purpose is to perform a comprehensive analysis of compliance with international and national development goals and approaches to assessment of threats and risks associated with their implementation; to develop and underpin basic requirements for identification and assessment of major threats to the national interests of Russia. Methods. The study employs methods of comparative and systems analysis, scalarization of vector indicators of achieving national development goals. Results. We considered a current problem of discrepancy between Western and national approaches to the analysis and assessment of threats to sustainable development. The study proves that the realism and degree of achieving the strategic national goals should be assessed on the basis of a new conservative approach to the analysis and assessment of threats to national interests. Conclusions. An important component of protecting the national interests is a combination of international approaches to ensuring sustainable development and national priorities. The Russian Federation has the right to formulate and implement its own development goals that correspond to the national interests of the country and society.

Multi-Weighted Partial Domain Adaptation for Sucker Rod Pump Fault Diagnosis Using Motor Power Data

Motor power curves (MPCs) have received great attention for use in diagnosing the working conditions of sucker rod pumping systems (SRPSs) because of their advantages in accessibility and real-time performance. However, existing MPC-based approaches mostly need a rigorous assumption that the MPC instances of different working conditions are sufficient, which does not hold in industrial scenarios. To this end, this paper proposes an unsupervised fault diagnosis methodology to leverage readily available dynamometer cards (DCs) to diagnose collected unlabeled MPCs. Firstly, a mathematical model of the SRPS is presented to convert actual DCs to MPCs. Secondly, a novel diagnostic methodology based on adversarial domain adaptation is proposed for the problem of data distribution discrepancy across the collected and converted MPCs. Specifically, the collected unlabeled MPCs may only cover a subset of the working conditions of the abundant DCs, which will easily cause negative transfer and lead to dramatic performance degradation. This proposed methodology employs class-level and distribution-level weighting strategies so as to guide the network to focus on the instances from shared categories and down-weight the outlier ones. Validation experiments are performed to evaluate the mathematical model and the diagnostic methodology with a set of actual MPCs collected by a self-developed device. The experimental result indicates that the accuracy of the proposed algorithm can reach 99.3% in diagnosing actual MPCs when only labeled DCs and unlabeled actual MPCs are used.

Does the Increase of the Scientific Achievements Lead to Decrease in the Quality of Education?

This research article is devoted to the problem of training personnel with higher medical education during the transition to an independent assessment of the graduates’ qualifications and new requirements for the effectiveness of scientific work of universities. The aim of this work was to study the correspondence between the quality of graduate training and the scientific productivity of medical universities. The authors analyzed the results of the accreditation examination of graduates of medical universities as an indicator of the quality of specialists’ training and scientometric indicators in the relevant specialty. As a result of the data analysis, the problem of discrepancy between high scientific productivity of universities and successful completion of accreditation by young specialists was revealed. The authors believe that a possible reason for this discrepancy is the high load on the teaching staff.Furthermore, detailed study of this problem is needed and, most likely, a revision of the approach to the work organization of medical universities’ teaching staff.