Reviewerly/exHarmony huggingface dataset:
A Recommender for Choosing Data Systems based on Application Profiling and Benchmarking

In our data-driven society, there are hundreds of possible data systems in the market with a wide range of configuration parameters, making it very hard for enterprises and users to choose the most suitable data systems. There is a lack of representative empirical evidence to help users make an informed decision. Using benchmark results is a widely adopted practice, but like there are several data systems, there are various benchmarks. This ongoing work presents an architecture and methods of a system that supports the recommendation of the most suitable data system for an application. We also illustrates how the recommendation would work in a fictitious scenario.
Gauss Nonlinear Self-feedback Chaotic Neural Network and Its Application

The new network model with gauss nonlinear self-feedback is constructed. The appropriate initial value of each parameter is selected, making the network reflect the chaotic dynamic characteristics. The comparison of simulation figures illustrates the feasibility of the application of the model, and the network’s sensitivity to chaotic parameters is also reflected. It is proved that the width coefficient increase or decrease in different structure-function influences the output to change differently by comparing Gaussian and the contrary multiquadric function. The new model is used in the combinatorial optimization problem. The experimental data show that the new network can effectively escape from the minimum range of chaotic characteristics and achieve a high convergence effect to a stationary point if appropriate parameters are adopted. We also study the influence of the ability of optimization belonging to main parameters in the network.
Detecting Anomalies in Power Consumption of an Internet of Things Network Using Statistical Techniques

Jose, Edwin Chemmanam, Ajai John Jose, Bijoy A. Mooppan, AsifPower consumption of household and commercial establishments is increasing every year. The vast amount of data generated by the smart energy metres cannot be monitored manually. Hence, automated power consumption monitoring and anomalous usage triggering are the need of the hour. Effective models for such processes are hindered due to the limited availability of labelled datasets. The lack of labelled datasets and high variance of data from various sources makes it difficult to develop a universal monitoring algorithm. Our study was accelerated by the acquisition of energy metre data of ATMs, through industrial collaboration. Analysis and study of real-world data from ATMs, lead to the development of custom labelled datasets. Artificial anomalies of various amplitudes were added to fixed locations on the data modelled. Custom labelled dataset generated was used to tune and evaluate the statistical anomaly detection algorithms and packages. The hyper parameters of the algorithms are tuned using hyperopt bayesian optimization. The results from the evaluations are tabulated and concluded that the hyper-tuned seasonalAD algorithm from ADTK package performed better amongst the statistical anomaly detection algorithms.
Think Like a Cheater or How to Increase the Uniqueness of the Text

The purpose of the article is to investigate and theoretically justify ways to increase the percentage of the scientific paper uniqueness, defining the basic procedure of text modification based on studying the principles sites work designed to increase the originality of the research material artificially. Research methodology. The authors have applied general scientific research methods as empirical, analysis, logic, comparison, experiment, and visualisation methods to achieve the purpose. Scientific novelty. The article first describes the working principle of the following sites ‘The First Ukrainian Service to Increase Uniqueness and Reduce Plagiarism’, ‘Pidvyschyty-antiplagiat.rf’, ‘Anti-antiplagiat.rf’, ‘Anti-plagiat killer’, the work’s mechanism of which is to increase artificially the originality of the text. Conclusions. The study has established that eye-catching site titles that promise to increase the percentage of the work uniqueness are not a guarantee that such cheat will not be noticed during verification. Identifying methods for modifying the text of the scientific paper is one of a specialist’s tasks who checks academic papers. Therefore, there is an increasing need to deepen knowledge in the field of computer technology every time, to develop the ability to think logically, to improve the level of qualifications, getting acquainted with the experience of colleagues. The presence of anti-plagiarism systems, the software of which is constantly updated, simplifies the search for matches, saves time and human resources aimed at analysing the submitted file and identifying signs of academic integrity violation.
Preface

International Conference on Statistics, Mathematics, Teaching, and Research (ICSMTR) 2021 is the 4th ICSMTR and the biennial event organized by the Department of Statistics and the Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar. The conference provides a forum for discussion on improving the quality of research and publication by promoting cutting-edge methods of Statistics, Mathematics, Science, and Teaching. The conference program includes a keynote presentation, workshops, invited speakers, and parallel presentation sessions. Due to the Covid-19 outbreak, this conference has been successfully held virtual and non-virtual (for presenters or participants from Makassar with health protocol of Covid-19) on 9-10 October 2021 at the Faculty of Mathematics and Natural Sciences, UNM, Makassar Indonesia. The theme of this conference is “Optimizing Data Science to Reach Society 5.0 through High-Quality Research” with aims to increase collaboration between researchers from various universities and countries in the fields of Statistics, Mathematics, Science, and Teaching as well as to increase the number of researcher publications in journals or proceedings indexed by indexers with international reputations such as SCOPUS and Web of Science. We have 8 invited speakers and 84 presenters both from local and international presenters. The international presenters come from 10 countries namely Nigeria, Thailand, China, Taiwan, Australia, French, Morocco, Saudi Arabia, Malaysia, and Indonesia. The local presenters come from about 46 institutions/universities. The time spent delivering the talks by each invited speaker was 20-30 minutes following by a Question Answer (Q &amp; A) Session for 30 minutes and 7- 10 minutes for each contribution talk. Around 280 participants attended the conference via zoom meeting. On behalf of the committee would like to deliver our highest appreciation to the Keynote Speaker, Rector of Universitas Negeri Makassar, Prof. Dr. H. Husain Syam, M. TP., IPU., ASEAN Eng. and all invited speakers who have been willing to spend their time to share their knowledge to all the participants of the 4th ICMSTR 2021. We would like thanks to sponsors and all the organizing committee for their hard work and effort in the accomplishment of this event. Finally, we would like also to thank the State University of Makassar, the Statistics higher education forum (FORSTAT), the Indonesian Statistics Association (ISI), and the Indonesian Mathematical Society (IndoMS) for their support. List of Steering Committee, International Scientific Committee, Committee, Executive Committee, Secretary, Treasurer, Reviewer, Administration Committee, Editing and Publication Committee, Program Committee, Logistic and Decoration Committee, Fund and Sponsorhip Committee are available in this pdf.
Geometric block-coherence and quantum state discrimination

Abstract Quantum coherence with respect to orthonormal bases has been studied extensively in the past few years. From the perspective of operational meaning, geometric coherence can be equal to the minimum error probability to discriminate a set of pure states (2018 J. Phys. A: Math. Theor. 51 414005). By regarding coherence as a physical resource, Baumgratz et al (2014 Phys. Rev. Lett. 113 140401) presented a comprehensive framework for coherence. Recently, geometric block-coherence as an effective block-coherence measure has been proposed. In this paper, we reveal an equivalence relationship between mixed quantum state discrimination (QSD) task and geometric block-coherence, which provides an operational interpretation for geometric block-coherence and generalizes the main result in coherence resource theory. Meanwhile, we show that partial coherence is a special case of block-coherence. By linking the relationship between geometric partial coherence and QSD tasks, we show that the value range of the two measures is the same. Finally, we reveal the relationship between geometric positive operator-valued measure-based coherence and QSD task.
ACTIVIDADES FORMATIVAS DE LOS ENSEMBLES DE MÚSICA CONTEMPORÁNEA GALLEGOS

In this year of 2020, the challenges faced by humanity were immense due to the Covid-19 pandemic.The cultural area was one of the sectors to stop early and will be one of the last to return.Culture sectors and research groups began to move in order to produce content, debates; online concerts, research and distance training.This text brings the author´s reflection on this situation, as well as points out some activities developed by them during this period.Furthermore, it establishes a reflection supported by studies by scholars such as CUERVO and SANTIAGO (2020);
An Ensemble Model for Tumor Type Identification and Cancer Origins Classification

Tissue biopsy can be wildly used in cancer diagnosis. However, manually classifying the cancerous status of biopsies and tissue origin of tumors for cancerous ones requires skilled specialists and sophisticated equipment. As a result, a data-based model is urgently needed. In this paper, we propose a data-based ensemble model for tumor type identification and cancer origins classification. Our model is an ensemble model that combines different models based on mRNA groups which serve distinct functions. The experiment on the TCGA dataset exhibits a promising result on both tasks - 98% on tumor type identification and 96.1% on cancer origin classification. We also test our model on external validation datasets, which prove the robustness of our model.
Preface

This February 2022 issue of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Transactions on Visualization and Computer Graphics (TVCG)</i> contains the proceedings of IEEE VIS 2021, held online on October 24-29, 2021, with General Chairs from Tulane University and Universidade de Sao Paulo. With IEEE VIS 2021, the conference series is in its 32nd year.
A Classification-Guided Segmentation Algorithm Based on Deep Learning for Epithelium Segmentation in Histopathological Images of Radicular Cysts

In histopathological analysis of radicular cysts (RCs), lesions in epithelium can provide pathologists with rich information on pathologic degree, which is helpful to determine the type of periapical lesions and make precise treatment planning. Automatic segmentation and localization of epithelium from whole slide images (WSIs) can assist pathologists to complete pathological diagnosis more quickly. However, the class imbalance problem caused by the small proportion of fragmented epithelium in RCs imposes challenge on the typical automatic one-stage segmentation method. In this paper, we proposed a classification-guided segmentation algorithm (CGSA) for accurate segmentation. Our method was a two-stage model, including a classification network for region of interest (ROI) location and a segmentation network guided by classification. The classification stage eliminated most irrelevant areas and alleviated the class imbalance problem faced by the segmentation model. The results of 5-fold cross validation demonstrated that CGSA outperformed the one-stage segmentation method which was lacking in prior epithelium localization information. The epithelium segmentation achieved an overall Dice's coefficient of 0.722, and intersection over union (IoU) of 0.593, which improved by 5.5% and 5.9% respectively compared with the one-stage segmentation method using UNet.Clinical Relevance— This work presents a framework for automatic epithelium segmentation in histopathological images of RCs. It can be applied to make up for the shortcomings of manual annotation which is labor-intensive, time-consuming and objective.
A Novel Neural Network Cell Method for Solving Nonlinear Electromagnetic Problems (Adv. Theory Simul. 12/2021)

Numerical-Intelligent Coupled Model for Nonlinear Problems In article number 2100216, Longnv Li and co-workers propose a NNCM coupling neural network (NN) and cell method (CM) for the effective calculation of nonlinear electromagnetic fields in ferromagnetic regions. The NNCM model the nonlinearities with partially connected NNs and construct the linearities through CM to guarantee both the training and the computing efficiencies.
Framework maps continuous-space inverse design problems for compression

The approach compresses optimization problems into surrogates to be optimized by a quantum annealer.
Weight Least Square Filter for Improving the Quality of Depth Map on FPGA

The techniques based on measuring the distance between objects and the camera itself have made progress in recent years. Through this technology, the system used for 3D imaging will provide a great convenience for people’s lives. In this project, we focus on optimizing depth maps with better quality for the 3D scene display system on the car. During the 3D imaging process, to calculate the distance between a certain location and one of the specific points of the scene, it will produce two depth maps computed from two images as an intermediate product for 3D imaging processing. However, there is a great number of invalid pixels that distance cannot be measured. In dealing with such drawbacks, post-filtering has been introduced as a solution. In this project, we propose implementing the Weight Least Square (WLS) filter on FPGA, which can fill the invalid pixels by using the results of neighboring pixels. Through our approach, we can improve the quality of the depth map on M-KUBOS. Besides that, the optimized memory usage can successfully suit the size of the memory on-chip, and a 69.29% acceleration compared to the ARM core.
Image Format P&amp;ID Recognition Technique Using Synthetic Data and Text-symbol Integrated Detection

A Piping and Instrumentation Diagram (P&ID) is a diagram used in the process plant industry. Digital format P&ID like intelligent P&ID can utilize DB technology, so it is easy to search and modify. Therefore, its use in the field has become common. However, there are cases in which digital P&IDs do not exist but exist only in image format because they were created before the digital P&ID was universalized or for security reasons. Thus, a technique for converting image format P&ID to digital P&ID is required. In this study, by modifying the deep learning-based symbol and text recognition structure presented in previous studies for symbol and text recognition of image format P&ID we propose a new structure that can improve performance while reducing the amount of computation required for recognition. In addition, we propose a synthetic data generation method suitable for P&ID in order to improve symbol recognition performance through data augmentation of the P&ID dataset. An experiment was performed to confirm the symbol and text recognition performance through a total of 82 P&ID drawings, and it was confirmed that the symbol and text recognition performance was improved through the method proposed in this study.
Tracking and Reporting Software Maintenance Requests Challenges in CMMS Systems

Abstract- It is almost impossible to produce a software system which do not need to be changed. Once the software system is put into use, new requirements may appear, and changes of the current features may be requested by the users. Also, parts of the software may have to be modified to avoid errors found during operation, or to improve software performance, by changing functional or non-functional characteristics. After software deployment, software usually evolve in response to requests for change or in response to market competition. Computerized maintenance management systems CMMS used to verify maintenance regulatory compliance. To control the maintenance process manually it requires a lot of effort and time. In the same time the CMMS have some challenges while covering software changes requests. This paper suggests a way and a tool for tracking and reporting software maintenance requests to cover all challenges in CMMS systems, and supporting software changes requisites more effectively.
Domain adaptation in nuclei semantic segmentation

Medical image segmentation has long been suffering from the lack of datasets since labelling pathological data is laborious work and requires specialized skills, which could only be done by professional doctors, especially when it comes to nuclei semantic segmentation. Besides, due to the fact that the domain gap inevitably exists between different datasets, which could be caused by diversified staining methods or the heterogeneous appearance of different tissues, it is almost impossible to get labelled data under all circumstances. This paper applies domain adaptation as an effective and efficient method to align two domains in latent feature space. We experiment on both IoU and Excepted Calibration Error (ECE), an indicator mostly used in biomedical segmentation to evaluate our work. In two domain adaptation tasks, i.e., TNBC and MoNuSeg, we proved that by exchanging the low frequency of two styles of the datasets, can Fourier Domain Adaptation (FDA) successfully achieve a considerable increasement of 1% and 2.29% higher than simply using source images to train with U-net in the target dataset.
Psychological features of improvement of educational organization managers' subjective well-being indicators

Introduction. Educational organization managers' subjective well-being is especially important in the context of social transformations and social tensions in Ukraine. Aim: to analyze the psychological features of improvement of the indicators of educational organization managers’ subjective well-being. Methods. G. Perrudet-Badoux Subjective Well-Being Scale (adapted by М. Sokolova). Results. It was found that more than half of the respondents had low values of the subjective well-being indicator called «satisfaction with daily activities». More than half of the respondents underestimated the importance of the social environment, while a significant part of the respondents had negative emotional experiences and were not positive about their health. It was also found that a small number of respondents had a high level of the subjective well-being index. Conclusions. A special healthy educational organization development program can promote educational organization managers' subjective well-being.
Promoção do autocuidado na pessoa com COVID-19 hospitalizada: Intervenções de enfermagem de reabilitação

Background: COVID-19 appears to have an impact on the functionality and dependence of the infected person, with health and socio-economic consequences.Objective: Highlight the impact that COVID-19 has on the functionality of the basic activities of daily living of the sample and to highlight the results that rehabilitation nursing interventions have on the restoration of the functionality.Methodology: Quantitative and retrospective cohort study through the application of a data collection form to 27 people who underwent the rehabilitation nursing program with periodic evaluation of evolution using the modified Katz index and Borg scale.Results: It was found that COVID-19 generated/aggravated dependence in 96% of the sample and had a significant impact on activity intolerance.After the rehabilitation nursing interventions, there was an improvement in functional status in 93% of the sample and for 96% there was improved activity intolerance, both resulting in an increase in capacity for self-care.Conclusion: Rehabilitation interventions have had a positive impact on the recovery of functionality, emerging as an asset today.
Aspects of linguistic ageing in literary authors across time

This work offers an investigation into linguistic changes in a corpus of literary authors hypothesised to be possibly attributable to the effects of ageing. In part, the analysis replicates an earlier study into these effects, but adds to it by explicitly analysing and modelling competing factors, specifically the influence of background language change. Our results suggest that it is likely that this underlying change in language usage is the primary force for the change observed in the linguistic variables that was previously attributed to linguistic ageing.
Socio-cultural dimension of XXI century information wars regarding to the place and role of libraries in them

The relevance. The priority of information confrontation in the postmodern era is to change the mentality of the nation and the deformation of national memory patterns, which is clearly reflected in the events associated with the attempts of the Russian Federation to restore its imperial status. Complex transformation processes in the development of the modern library system, the problem of determining its role and place in the protection of national cultural heritage, resisting attempts to purposefully instill hostile cultural values in the Ukrainian state in the context of integrated use of information warfare are given.&#x0D; The purpose of our scientific research is to reveal the essence of the socio-cultural dimension of information warfare in its memetic and network varieties and to identify the potential for library system functioning in the context of information conflict.&#x0D; The methodology. The author’s vision of the problem is based on the main principles of science: scientificity and objectivity. A systematic approach to determining the functionality of libraries enabled to establish the content and prospects of transformation processes of the modern library system in the information war and to determine its effectiveness in terms of performing tasks of effective information confrontation.&#x0D; The results. It is proved that the library, as a traditional collector and translator in the time and space of documented knowledge, the guardian of the national cultural heritage becomes the object of influence and manipulation in the terms of information warfare. At the same time, it can and should be an effective tool for information confrontation. The role and place of the library system in the types of information wars — memetic and network wars — are clarified. A number of functions of libraries for effective confrontation in the context of the specified types of information wars of the present are characterized in detail.&#x0D; The scientific topicality. For the first time, an attempt was made to comprehensively analyze the role of libraries in the information conflict. The tasks and functions of the library system as an effective tool for the protection of the national information space are specified.&#x0D; The practical significance. The results of the study can be used by librarians, as well as higher education institutions that implement educational programs in the specialty 029 “Information, Library and Archival Affairs” in the development of new disciplines (e.g. “Information Warfare and National Security”). It will provide training for library and information specialists at modern requirements level, taking into account the peculiarities of modernization processes development in the library and archives.