Fuzzy coordinator in control problems

In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant

How social interactions can affect Modern Code Review

Introduction: Modern Code Review (MCR) is a multistage process where developers evaluate source code written by others to enhance the software quality. Despite the numerous studies conducted on the effects of MCR on software quality, the non-technical issues in the MCR process have not been extensively studied. This study aims to investigate the social problems in the MCR process and to find possible ways to prevent them and improve the overall quality of the MCR process.Methodology: To achieve the research objectives, we applied the grounded theory research shaped by GQM approach to collect data on the attitudes of developers from different teams toward MCR. We conducted interviews with 25 software developers from 13 companies to obtain the information necessary to investigate how social interactions affect the code reviewing process.Results: Our findings show that interpersonal relationships within the team can have significant consequences on the MCR process. We also received a list of possible strategies to overcome these problems.Discussion: Our study provides a new perspective on the non-technical issues in the MCR process, which has not been extensively studied before. The findings of this study can help software development teams to address the social problems in the MCR process and improve the overall quality of their software products.Conclusion: This study provides valuable insights into the non-technical issues in the MCR process and the possible ways to prevent them. The findings of this study can help software development teams to improve the MCR process and the quality of their software products. Future research could explore the effectiveness of the identified strategies in addressing the social problems in the MCR process

A Systemic Approach to Evaluating the Organizational Agility in Large-Scale Companies

This paper presents action research to analyze an approach for assessment of the alleged agile transformation. This approach was implemented at AK Bars Digital Technologies, an IT spin-off of one of the largest banks in Russia using the Scaled Agile Framework. The approach is based on the Goal-Question-Metric approach, non-invasive measurement collection, and systemic analysis. It uses data from several different sources, including interviews, code repositories, user ratings in the play stores, and templates for agile assessment. The effectiveness of the approach is subjectively validated by the adoption of the proposed recommendations by the banks’ senior management. Details are provided on the approach, the required effort from the side of both those assessing and of the people being assessed and the results. The final part of the paper is devoted to the discussion of its generalizability and the plan for future experimentation and refinement

The Fallout of Catastrophic Technogenic Emissions of Toxic Gases Can Negatively Affect Covid-19 Clinical Course

The coronavirus D-19 (Covid-19) pandemic has shaken almost every country in the world: as we stand, 6,3 million deaths from the infection have already been recorded, 167,000 and 380,000 of which are in Italy and the Russian Federation, respectively. In the first wave of the pandemic, Italy suffered an abnormally high death toll. A detailed analysis of available epidemiological data suggests that that rate was shockingly high in the Northern regions and in Lombardy, in particular, whilst in the southern region the situation was less dire. This inexplicably high mortality rate in conditions of a very well-developed health care system such as the one in Lombardy recognized as one of the best in Italy certainly cries for a convincing explanation. In 1976, the small city of Seveso, Lombardy, experienced a release of dioxin into the atmosphere after a massive technogenic accident. The immediate effects of the industrial disaster did not become apparent until a surge in the number of tumors in the affected population in the subsequent years. In this paper, we endeavor to prove our hypothesis that the release of dioxin was a negative cofactor that contributed to a worsening of the clinical course of COVID-19 in Lombardy

Shape recognition through multi-level fusion of features and classifiers

Shape recognition is a fundamental problem and a special type of image classification, where each shape is considered as a class. Current approaches to shape recognition mainly focus on designing low-level shape descriptors, and classify them using some machine learning approaches. In order to achieve effective learning of shape features, it is essential to ensure that a comprehensive set of high quality features can be extracted from the original shape data. Thus we have been motivated to develop methods of fusion of features and classifiers for advancing the classification performance. In this paper, we propose a multi-level framework for fusion of features and classifiers in the setting of gran-ular computing. The proposed framework involves creation of diversity among classifiers, through adopting feature selection and fusion to create diverse feature sets and to train diverse classifiers using different learn-Xinming Wang algorithms. The experimental results show that the proposed multi-level framework can effectively create diversity among classifiers leading to considerable advances in the classification performance

Spikiness Assessment of Term Occurrences in Microblogs: An Approach based on Computational Stigmergy

n/

Remote sensing imagery segmentation: A hybrid approach

In remote sensing imagery, segmentation techniques fail to encounter multiple regions of interest due to challenges such as dense features, low illumination, uncertainties, and noise. Consequently, exploiting vast and redundant information makes segmentation a difficult task. Existing multilevel thresholding techniques achieve low segmentation accuracy with high temporal difficulty due to the absence of spatial information. To mitigate this issue, this paper presents a new Rényi’s entropy and modified cuckoo search-based robust automatic multi-thresholding algorithm for remote sensing image analysis. In the proposed method, the modified cuckoo search algorithm is combined with Rényi’s entropy thresholding criteria to determine optimal thresholds. In the modified cuckoo search algorithm, the Lévy flight step size was modified to improve the convergence rate. An experimental analysis was conducted to validate the proposed method, both qualitatively and quantitatively against existing metaheuristic-based thresholding methods. To do this, the performance of the proposed method was intensively examined on high-dimensional remote sensing imageries. Moreover, numerical parameter analysis is presented to compare the segmented results against the gray-level co-occurrence matrix, Otsu energy curve, minimum cross entropy, and Rényi’s entropy-based thresholding. Experiments demonstrated that the proposed approach is effective and successful in attaining accurate segmentation with low time complexity

Fuzzy rule-based systems for recognition-intensive classification in granular computing context

In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve