Reviewerly/exHarmony huggingface dataset:
Smart regulatory inspection in research reactor using balis SMILE software

Balis SMILE is a software developed by Bapeten to enhance the inspection system in Research Reactor. Balis SMILE stands for Bapeten Licensing and Inspection System – Inspection Management System and Electronic Reporting which utilizes information technology (IT) in collecting the reactor safety parameters data and the other data sent as participation of the licensee workers. The system is analyzing the data based on the safe operation limit (smart system). The result of analyzing can be used to upgrade the detailed inspection in the research reactor. Each analysis is connecting to the Bapeten safety performance indicator (IKK). Compliance with.
The Algebra of Cosmic Intelligence: Inhumanism and Cosmology in the Reflexive Neocybernetics of Vladimir Lefebvre

This article reconstructs the theory of the Soviet-American psychologist Vladimir Lefebvre as part of the neocybernetic movement. In particular, I propose to explore such elements of his research of the 1970s—1990s as systemic vision; reflexive analysis; a search for holistic configuration and Janus cosmology; and the realization of neocybernetics. An interest in the reflexive structures of cognition and action led Lefebvre to an understanding of the limited nature of the world’s scientific picture. The conflicting objects he studied proved too complex for behavioral modeling and forecasting. Lefebvre therefore expanded the boundaries of the application of his formal theory of intelligence, relying on functionalist positions, which led him to the science-fiction narrative of “The Big Correction.” I contend that Lefebvre’s works not only propose an original Soviet version of the neocybernetics the 1970s but develop their own model of inhumanism long before it appeared on the scene of modern philosophy. Lefebvre’s formal theory, reinforced by thermodynamics and elements of Russian cosmism, builds cognitive behavior into the teleology of the Big Correction, which involves the idea of overcoming the heat death of the universe. The activity of cosmic intelligence, which Lefebvre equates with magnetic-plasma structures in the finished areas of the universe, is aimed at cooperation and prolonging the existence of life and the intellect.
Estimation of Locally Stationary Graph Processes from Incomplete Realizations

Stationarity is a well-studied concept in signal processing and the concept of stationary random processes has been extended to graph domains in several recent works. Meanwhile, in many scenarios a globally stationary process model may fail to accurately represent the correlation patterns of the data on the whole graph, e.g. when data is acquired on big graphs or when the behavior of the process varies significantly throughout the graph. In this work, we first propose a locally stationary graph process model, where the overall graph process is expressed through a combination of several local models. We then propose an algorithm that learns a locally stationary graph process model from partially observed realizations of the process. Experimental results show that the proposed locally stationary process model can provide significant gain in signal estimation performance compared to globally stationary models, even in cases where the process is highly stationary.
SOCIAL AND PEDAGOGICAL CONDITIONS OF EFFECTIVE DEVELOPMENT OF CONSCIOUS PATRIOTISM AMONG UNIVERSITY STUDENTS

The article explains the content of the concept of «conscious patriotism» at the level of both personal quality of a person and his leading feature in the form of love for the motherland. It reveals social and pedagogical conditions for the effective development of conscious patriotism among university students: the combination of the requirements of state and personal patriotism in organization of patriotic education at the universities; implementation of social and pedagogical technologies based on the active role of students in public life, mastering worldview and life-purpose attitudes; support of educational example of the personality of a university teacher. The article suggests the author’s approach to the development of conscious patriotism among university students in classes.
RESEARCH ON WORK MOTIVATION AMONG ORPHANAGE GRADUATES

The article presents theoretical and empirical research data on motivation to work among graduates of orphanages. The components of motivation of labor activity are identified: personal understanding of the purpose of work, the development of mean- ings and internal motives of an orphanage graduate, the ratio of teenager’s conscious mo- tives to real motives for work. The results of the study revealed: a low level of internal motivation for work; formed external positive and external negative motives; poorly formed ideals and standards of the purpose of work. Results obtained can serve as a basis for the development and implementation of a career guidance program for orphanages.
Proof of Karma (PoK): A Novel Consensus Mechanism for Consortium Blockchain

In blockchain-based systems, participants can be malicious. Therefore, this work first characterizes several properties expected in systems where the honest behaviour of involved parties plays significant role in the success. Considering these properties, a new consensus mechanism, Proof of Karma (PoK), is proposed based on karma (actions) of nodes. PoK incorporates a self-stabilizing leader election algorithm based on karma score to ensure consistency in the system. In PoK, both new and existing nodes get a fair chance to earn profit by becoming a leader and adding a valid block to the blockchain. PoK gives incentives and imposes penalties to encourage and discourage the nodes' honest and malicious actions. PoK is analyzed with respect to the CAP theorem. The work provides security analysis to demonstrate the resistance of PoK against various blockchain specific attacks and karma specific attacks. Several experiments are also performed to assess the performance of PoK and compare it with the baseline model. The results show the feasibility, effectiveness, usability and scalability of PoK. PoK is also compared based on the characterized properties with various existing consensus mechanisms that consider malicious actions of nodes. PoK achieves consensus finality, decentralization and fairness, outperforming existing works.
Working with Strings: stringr

The stringr package gives you functions for string manipulation. The package will be loaded when you load the tidyverse package:
Opposition Based Binary Particle Swarm Optimization Algorithm for Feature Selection

In this study, we propose a Binary Particle Swarm Optimization algorithm hybridizing with Oppositionbased Learning for solving the feature selection problem. Opposition-based Learning is used in three different ways: (1) opposition-based population initialization; (2) opposition-based generation jumping; and (3) opposition-based population initialization and generation jumping. We conduct experiments on two medicine data sets. Based on the results, the oppositionbased population initialization and generation jumping performs better. Additionally, we investigate the effect of the eight different transfer functions on the performance of the proposed approach. Among the eight transfer functions, the sigmoid function (S1(x)) yields better performance than others. To evaluate the performance of the proposed method, the Binary Particle Swarm Optimization algorithm is applied to the problem. The results reveal that our approach outperforms the other methods.
CRITERIA AND INDICATORS OF THE FUTURE TEACHER’S PSYCHOLOGICAL AND PEDAGOGICAL COMPETENCE DEVELOPMENT

The research is based on the idea that the prerequisites for the individual’s professional competencies begin to form already at the stage of mastering the profession. The necessity of identifying criteria and indicators for the future teachers’ psychological and pedagogical competence development is theoretically substantiated. The article proposes criteria and level indicators that allow assessing the features of the psychological and pedagogical competence structure development of future teachers. The structural composition of psychological and pedagogical competence has been empirically studied and its development levels among students being educated at a pedagogical university have been determined.
Signal Extraction from 3D LIM Data

In the previous chapters, we have used two-dimensional LIM information. In actual observations, spectral data is also obtained, which can provide additional information. In this chapter, we use three-dimensional CNNs to extract signals from three-dimensional intensity data cubes containing spectral and angular distribution information.
Peningkatan Skill Opinion Mahasiswa Pendidikan Ekonomi Menggunakan Model Pembelajaran Debat

Abstract: The purpose of this research is to develop the skill opinion of Economic Education students using the debate learning model. Pre-cycle observation results in students' opinion skills that are still at a low level of interpretation so that efforts are needed to improve it. The research subjects were 25 students of Economics Education who took Social Studies courses. Collecting research data using the observation method with a checklist sheet on each indicator. The research was carried out in two main cycles with 2 meetings in each cycle. The results showed that the debate model was able to improve opinion skills, both specifically and in general. Specifically, an increase in each indicator is known from the comparison of the average results of the pre-cycle, cycle 1, and cycle 2 stages on each indicator. Indicators of tolerance, courage, and substantive have a very significant increase from the low category to very high. The indicators of confidence and sentence structure increased from low to high level in cycle 2. The increase in opinion skills in general also experienced a very significant increase from the pre-cycle stage with a low level of interpretation to very high in cycle 2. In general, opinion skills of pre-cycle students included the level of interpretation. low with a success rate of 40%, increased to high in cycle 1 with a success rate of 60%, and again increased in a very high level of interpretation in cycle 2 with a success rate of 92%.
Interference-aware Workload Scheduling in Co-located Data Centers

Modern data centers typically contain thousands of servers, providing various computing and storage services for users. The strategy to provide reliable and high-performance online services is to over-allocate resources for online services, which results in a waste of cluster resources. Therefore, cloud vendors tend to co-locate online services and offline batch jobs into the same cluster to improve resource utilization. However, the co-location leads to contention on shared resources and causes mutual performance interference, which may degrade the QoS (Quality of Service) of online services. We present a performance interference model based on linear regression to predict the performance interference. Furthermore, the model can perceive the status of servers in real-time for more refined and accurate prediction. Then, we design an interference-aware workload scheduling strategy that can schedule batch jobs to the server while introducing minimal interference. The evaluation demonstrates that our scheduling strategy can at best increase the throughput of batch jobs by 48.95% and 27.09% compared with round-robin scheduling and random scheduling while guaranteeing the QoS of online services. The paper aims to provide some elements of answering the following general question: How do we design cloud systems and data centers scheduling strategies that can withstand the human pressure of global-scale use and still provide robust and secure services to end-users?
Digital learning environment: digital literacy, e-exclusion and cyber-wellness

The Covid-19 pandemic has inevitably demonstrated the importance of the digital competence of each individual, both personally and socially. Apparently, emergency remote learning has revealed the total unpreparedness of many teachers and students to interact in the online format. Hence, the development of full-scale digital competence is perceived today as a must-have for all actors in the educational process. This goal requires careful and large-scale preparation. It is necessary to understand how modern students interpret the digitalization of education, how they perceive the current level of their digital competence development, and what they see as the reasons for “digital illiteracy” demonstrated by the educational process of the “covid era”. This article presents a non-experimental study that aims to evaluate the digital competence of students in higher education and to identify the main obstacles to its efficient deepening and development. Descriptive research was designed using a mixed methodology. We worked with a sample of 431 students. A Framework for Developing and Understanding Digital Competence in Europe, Questionnaire “Problems of Students’ Involvement in Digital Socialization”, and semi-structured interview were used for data collection. The results show a heterogeneous and rather conditional level of digital knowledge and skills influenced by the region of residence, family economics, cultural backgrounds, and conflicting motivation for the development of digital literacy, as well as the students' access to digital devices and software. The results achieved should be a cause for reflection in universities, in order to implement curricular programs that strengthen this type of skill.
Towards an Asset Administration Shell Integrity Verification Scheme

Integrity as one property of trustworthiness is an important aspect for the adoption of the Asset Administration Shell (AAS) as a data exchange format and source for data driven services. Until now, the AAS offers an access control solution as a preventive integrity measure. Nevertheless, preventive methods lack in detecting integrity violations due to accidental or malicious modifications from access permitted endpoints. This work in progress paper proposes a concept to complement the access control with a detective integrity measure. By signing submodels of the AAS, business partners along the life cycle can verify the integrity of the data inside the AAS. Therefore, a Certificates Submodel and a Signature Submodel are proposed in the concept to enable a flexible and interoperable integrity verification. In future work, the concept will be implemented and evaluated.
Loudness effect on pitch and timbre discrimination; a continuing study

Fine discrimination in pitch and timbre is a requisite skill for musicians and audio professionals. While several trade organizations have standardized electronic-acoustic calibration levels for equipment testing and operations, conflicting and often unfounded perceptual theories along with a lack of psychophysical evidence make it difficult to pinpoint the optimal presentation level for best pitch and timbre discrimination. Results from our previous study (Bulla &amp; Chon, FDMC, 2021) revealed higher scores for lower listening levels in conditional 2-AFC pitch and timbre discrimination tasks (15 vs 83 dBA SPL). While outcomes there indicated a consistent negative influence of the larger signal strength on the accuracy of both pitch and timbre discrimination performance, this continuing study examined changes of signal strength in order to establish whether or not observations here may apply across a variety of listening levels. Data collection is ongoing and will provide insight through observed performance metrics and help determine if there is an optimal loudness range for engaging in listening tasks such as identifying differences of pitch, discriminating fine details in timbre, or the blending and balancing of musical instruments.
Method of Medical Image Segmentation based on Chan-Vese Model

During the imaging process, medical images will be affected by other undesirable factors such as the noise of other human tissues or imaging equipment, resulting in images showing low resolution, weak boundaries, severe noise, and uneven light distribution, which increases the difficulty of accurately segmenting the target. The model of Chen Vese is a classical geometric active contour model. It can effectively divide images with weak and intermittent borders, with strong resistance to noise, but it has poor segmentation effect for medical images with irregular gray level. Considering this, this paper utilizes top cap transformation and bottom cap transformation to do preliminary processing of medical images. The new image is a linear combination of top-hat transformation and bottom-hat transformation. Through reasonable selection of parameters, while enhancing the the image’s great difference, the gray level of the image becomes more uniform. Then the model of Chen Vese which is furthered is utilized for dividing the medical image. From the experimental results, the new method improves the curve evolution speed and can segment medical images quickly and effectively.
Chapter 2

Abstract This chapter shows how designers utilize trend sites and shopping trips to develop ideas and turn them into sketches, samples, and prototypes.
Frontline Employees’ Attitude Toward Embodied Social Robots In Customer Service: An Integrative Framework And Empirical Test

Embodied social robots-robots providing services for and in cocreation with consumers-are expected to profoundly change the way services are delivered. Yet, their integration in customer service poses a challenge: their adoption by frontline employees (FLEs). Accordingly, this study aims to examine FLEs’ attitude toward embodied social robots and to uncover its antecedents. This work presents an integrative framework which builds upon the technology acceptance model and examines the influence of potential factors on FLEs’ attitude toward embodied social robots. An online survey among 165 FLEs is used to test the integrative framework. Despite the growing knowledge regarding customers’ perceptions of (embodied social) robots, the perspective of FLEs is under-investigated while crucial to foster FLEs’ acceptance of such robots. This research concludes with several strategies service providers can implement to possibly enhance FLEs’ attitude toward embodied social robots, and thus, to support their adoption by FLEs.
Personas and their Influencers in the Low Voltage Electrical Sector in Brazil

The Brazilian low voltage electricity sector has been experiencing a natural monopoly situation for decades, framed by a lack of competition and no customer focus. This environment tends to change, as like it did in other countries, where small customers can choose their electricity supplier. It is necessary for distribution utilities to modernize and get to know their customers better. Therefore, the objective of this work is to present the main personas previously identified for residential and commercial in energy sector in Brazil, identifying their main influencers and how the influence occurs. The article discusses the importance of knowing consumer behavior in a digital reality, bringing theoretical references about personas and influencers. The work also deals with the perceived credibility of influencers and the factors that make communication more effective. Some figures related to the digital world, such as the most accessed social networks in Brazil and in the world, are also presented.
The Hidden Factory

Abstract The chapter attempts to situate labor’s place in social media. It turns to aspects of the political economy of social media which are often obscured behind the debates about fake news, privacy and data protection: the infrastructure of social media. Hidden in these infrastructures, we find workers such as content moderators who scan social media platforms for any form of illicit content or “raters” – human workers whose job is to improve search algorithms. Content moderation labor proves to be especially hard emotional labor: workers regularly confront the darker sides of web culture and the emotional aspect of such labor is very hard for many to endure. Scanning up to 6,000 pictures or 1,000 videos every day, many of which contain brutal violence or hate speech, leaves many workers with emotional scars. Exploring the labor behind code, data centers, and content moderation, Chapter IV sheds light on the different and often hidden forms of labor that are crucial to both social media and other sectors of the digital economy.
Fuzzy Partial Ordering

In the expansive paper, added a cushioned fragmentary outlines and forsakenly demand the usage of warm assessment area and content with lacking assessment locale.
DIGITAL LABORATORY COMPLEX «TRIZOLOGIKA»

We have designed a digital environment for developing the inventive abilities of primary school students through educational experiments. The content of the digital laboratory includes special didactic tools based on TRIZ models for organizing inventive experiments. The proposed method of organizing the educational environment allows to expand the children’s experimental activity in solving inventive problems, namely: to build the skills of correct formulation of hypotheses in experimental activity; to improve the quality of practical solutions, while retaining the ability to show intellectual initiative; to develop the ability to identify experimentally reliable solutions to a problem; to form a desire to use scientific terminology, to conduct a constructive dialogue in the process of joint research activities; to formulate hypotheses and conclusions during the experiment.
LabGAN: Blood Cell Detection Method Based on Automatic Label Generation for Generative Adversarial Network

The problem is tackled that the existing conditional generation adversarial network takes fixed position labels as input, generates cell samples without sufficient diversity, and the accuracy of cell detection cannot be improved to meet the actual detection needs. An approach of automatic generation of blood cells is proposed based on the generation adversarial network. The detection method generates a cell sample from random noise, and then uses the label generator to generate the cell image labels from both the generated cell sample and the real cell sample. At the same time, a multifunctional discriminator is introduced. The generated cell images, labels and real cell images are fed into the multi-function discriminator in a one-to-one correspondence for matching and authenticating the cell position. Experiments on the blood cell dataset indicate that the generation of cell samples from random noise not only increases the diversity and quality, but also increases the IOU of cell detection from 90.3% to 91.1%, and increases precision from 91.7% to 94.8% compared with the mainstream method LCGAN.
FIMF Score-CAM: Score-CAM Based Visual Explanations via Fast Integrating Multiple Features of Local Space for Deep Networks

The interpretability of deep networks is a hot issue in the field of computer vision. This paper proposes a FIMF Score-CAM model that fast integrates multiple features of local space. The model only needs to perform a forward convolution calculation on the image once to extract the feature maps, and then the feature selection template is introduced to integrate the features of different channels in local space to improve the ability of model interpretation. The FIMF Score-CAM model is superior to the existing mainstream models in interpreting the visual performance and fairness indicators of the decision-making, having more complete explanation of the target class and the advantage of fast calculation speed. Meanwhile, in some network models requiring larger convolution calculation, the operation time is reduced by more than 90% compared to Score-CAM.