Reviewerly/exHarmony huggingface dataset:
Practical aspects of organizational culture assessment

Organizational culture activates the ability to effectively solve management problems and plays a positive role in the development of the organization, provided, that managers are aware of its significance and have appropriate knowledge. The results of organizational culture study of one of the companies in the Perm territory using two methods: Organizational Culture Assessment Instrument (OCAI) and the method by S. I. Cordon, have been given in the article. Application of foreign and domestic methods gave comparable results: clan organizational culture according to OCAI correlates with the type of culture "Family", highlighted by S. I. Cordon, adhocratic culture - with the type of culture "Business". The results of organizational culture assessment of the organization under study and directions of possible managerial influences have been presented in the article.
Client Performance Predictions for Private Blockchain Networks

The recent public adaptation of cryptocurrencies sparked a great interest in alternative uses of the blockchain technology.Private or permissioned blockchain-based systems are a promising technology, initiating novel applications in several important fields, such as financing, commerce, and administration.One of the largest challenges in its application is the necessity of capacity planning.In public blockchains such as the ones powering cryptocurrenciesthe network is self-scaling and self-organizing, made up of individual nodes working for profit.In private blockchain, where capacity is provided by a few selected parties, these abilities are not inherently present as there is no financial or other motivation for clients to participate.This necessitates the introduction of efficient capacity planning and performance predictions to operate such a network efficiently.In this paper, we deal with methods for providing performance predictions of private blockchains.
ANALISIS DAN PERANCANGAN SISTEM INFORMASI PENENTUAN SHUTDOWN POINT PADA PT. LARIS SUMUT MAKMUR MEDAN

This research was conducted aiming to design a New Student Selection Decision Support System. To help the selection process for new student admissions at the Indonesian Business Polytechnic, a decision support system (SPK) was designed to help the data processing of prospective new students who register using the Weighteded Product (WP) method. This Weighteded Product method is done by using multiplication to link the attribute rating, where the rating of each attribute must be raised first with the Weighted of the attribute in question. The criteria used in this Decision Support System consist of: capacity, achievement, academic tests, age, and passing scores. From the sample of data examined, the ranking results were found to be the best value of prospective students with a value of 0.0841 and 0.0791.
Systems Based Assessment of Multiple Choice Questions (MCQS) for Quality Assurance in Testing

This study aimed to assess the properties of MCQs using various courses in a novel holistic approach employing quantitative-comparative design.The data were collected from two university campuses in Saudi Arabia offering a Bachelor of Science in Nursing (BSN) program between 2018 and 2020.The multiple-choice questions on 'item difficulty ' (34.36 ± 17.42), 'item discrimination' (0.32 ± 0.20), and 'option affinity' (0.32 ± 0.16) were reasonably good.The 'lexical density' was "Very Complex" (85.08 ± 13.37) with the Basic Adult Care Nursing final examination (91.54 ± 9.42).The 'readability index' was low (7.65 ± 3.08) in the Fundamentals of Nursing I Theory final examination (6.18 ± 3.13), but "High" in information entropy (4.23 ± 0.10) with the Fundamentals of Nursing I Theory examination (4.25 ± 0.11).Statistical differences (<.001) were noted in option affinity,' 'lexical density,' and 'readability index.Further, the multilayer perceptron (MLP) prediction model shows 'lexical density' (100%) as the most important.Findings indicate the need for quality assurance measures in the form of faculty training.
Effectiveness Of The Use Of Madras E-Learning Applications During The Covid Pandemic 19 (Case Study at Madrasah Aliyah Negeri 2 Bandung)

This study aims to identify the effectiveness of the use of madrasah e-learning applications and determine the inhibiting and supporting factors of the implementation of e-learning applications as learning media. This research is quantitative. Data collected through questionnaires, interviews, and observations, then the data were analyzed descriptively. Respondents from this study were teachers and students of Madrasah Aliyah Negeri 2 Bandung, who were directly involved with learning to utilize e-learning applications. The results showed that the affective component of the use of e-learning madrasas tended to be less effective. It was shown from the view of the students 'activeness in utilizing e-learning madrasas in learning which showed an average of 76.46 less effective, students' understanding of teaching materials after using the application that is, 35.7% never understood, 55.6 sometimes understood, and only 7.1% understood the material taught through the madrasah e-learning application. Overall it can be concluded that the implementation of madrasah e-learning learning as e-learning media in MAN 2 Bandung is less effective, due to several factors of its implementation including the lack of HR related to madrasah e-learning, network problems, and quota but MAN 2 Bandung is very effective in online learning due to several factors of its implementation including the lack of human resources related to e-learning madrassas, network problems, and quotas but MAN 2 Bandung is very effective in online learning using other applications besides madrasa e-learning applications such as google classroom, google form, zoom, Whatsapp Group, kahoot, and Quizizz.
Research of opportunities to improve the quality of educational process and infrastructure of the university

Modern marketing approach to improving the educational process and infrastructure of the university in order to enhance its competitiveness has been studied. A model of the marketing educational environment of the university has been proposed. The need for diagnostics of "bottlenecks" in the work of the university’s functional units in the transition period to the knowledge paradigm of the country’s economic development has been actualized. The experience of the Department of Marketing of the State University of Management in conducting annual marketing research to study the possibilities of improving the quality of educational process and infrastructure of the University using the method of student team consulting has been outlined. The distinctive features of this method and the specifics of its application in the spring semester of2020 with an emphasis on virtual teams in the context of the COVID-19 pandemic have been described. Debatable issues and prospects for further scientific research in this area have been formulated.
A Two-Phase Cloud Resource Provisioning Algorithm for Cost Optimization

Cloud computing is a new computing paradigm to deliver computing resources as services over the Internet. Under such a paradigm, cloud users can rent computing resources from cloud providers to provide their services. The goal of cloud users is to minimize the resource rental cost while meeting the service requirements. In reality, cloud providers often offer multiple pricing models for virtual machine (VM) instances, including on-demand and reserved pricing models. Moreover, the workload of cloud users varies with time and is not known a priori. Therefore, it is challenging for cloud users to determine the optimal cloud resource provisioning. In this paper, we propose a two-phase cloud resource provisioning algorithm. In the first phase, we formulate the resource reservation problem as a two-stage stochastic programming problem, and solve it by the sample average approximation method and the dual decomposition method. In the second phase, we propose a hybrid ARIMA-Kalman model to predict the workload, and determine the number of on-demand instances based on the predicted workload. The effectiveness of the proposed two-phase algorithm is evaluated using a real-world workload trace and Amazon EC2’s pricing models. The simulation results show that the proposed algorithm can significantly reduce the operational cost while guaranteeing the service level agreement (SLA).
StarCraft agent strategic training on a large human versus human game replay dataset

Real-time strategy games are currently very popular as a testbed for AI research and education.StarCraft: Brood War (SC:BW) is one of such games.Recently, a new large, unlabeled human versus human SC:BW game replay dataset called STARDATA was published.This paper aims to prove that the player strategy diversity requirement of the dataset is met, i.e., that the diversity of player strategies in STARDATA replays is of sufficient quality.To this end, we built a competitive SC:BW agent from scratch and trained its strategic decision making process on STARDATA.The results show that in the current state of the competitive environment the agent is capable of keeping a stable rating and a decent win rate over a longer period of time.It also performs better than our other, simple rule-based agent.Therefore, we conclude that the strategy diversity requirement of STARDATA is met.
“TWO CAN PLAY AT THAT GAME”: COMMUNICATING DISSENT AS A MICRO-CELEBRITY IN A RESTRICTED NATIONAL TWITTERSPHERE

Turkey has a notoriously restricted Twittersphere where any explicit criticism towards the government can be framed as a criminal offence (Saka, 2018). However, despite the efforts to eradicate public expression of dissent on social media, Twitter remains as an essential space for political deliberation for Turkish dissidents (Dogu and Mat, 2019). This surveillance regime creates a necessity of engaging in political talk that remains under the radar. Especially, the accounts with higher visibility such as micro-celebrities suffer most scrutiny, whilst being important actors of information dissemination (Sanjari and Khazraee, 2014). In this paper, I analyse the evasion tactics used by dissenting Twitter micro-celebrities in navigating these restrictions. In order to do this, I specifically focus on micro-celebrity accounts by ordinary users who gained fame due to the humorous commentaries they offer on the daily events in Turkey. Without a claim for expertise or any exceptional ability beyond being avid observers of the social, cultural and political realities of life in Turkey, these accounts claim follower numbers that often exceed 30-40k and some reaching millions. This paper presents how these accounts express political criticism through a narration of everyday life and daily events by utilising platform specific cultural capital in formulating a language (Scott, 1990) that is understood by subgroups who are acclimated to the platform culture. I argue that these practices signal a change in the conventions of dissident political talk that relies on a perceived gap in platform cultural knowledge between the pro- and anti-government groups.
Batch Prioritization of Data Labeling Tasks for Training Classifiers

In a data labeling process for building machine learning, the choice of labeling data instances is known to have a significant impact on the performance of classifiers. So far, the study of active learning has addressed the issue of how to choose the subset by prioritizing the data instances based on the state of the current classifier. However, the active learning approach has two drawbacks that (i) require a training loop to update the priorities of labeling tasks and (ii) require us to choose a specific active learner while we do not know the optimal classification model. In this paper, we propose a new framework of priority-aware labeling system that allows a parallel task assignment to crowd workers without assuming a particular classifier, which is based on novel methods called “batch prioritization” and “label expansion”. We conducted experiments with multiple datasets to examine the effectiveness of the approach and found that the proposed method improves the performance of the final classifiers more quickly than the active learning approach despite that the labeling tasks can be processed in a fully parallel manner.
Value Intentions in Future Art Teachers’ Professional Training

The article is devoted to the study of future art teachers’ value sphere. A wide range of applications of the theory of values in various fields of knowledge is shown: culturology, psychology, pedagogy, art education, economics; classification of values in science is given. A number of issues is actualized, concerning values in the context of cultural development and at the same time global crises; professional development, competitiveness and life views and needs; development of person’s abilities and his/her self-realization in art creativity. The artistic-communicative, professional-hermeneutic and motivational-need spheres in the future art teachers’ creative self-realization are singled out. These areas are subject to pedagogical influence, while becoming a conglomeration of value intentions of the individual. Theoretical substantiation is conducted and the essence of the phenomenon of future art teachers’ value intentions is determined, which combines the specified multi-vector values. The meaning of the terms “intentionality” and “intentions” is clarified.Emphasis is placed on the importance of value intentions of the individual in the economic projection and compliance of this socio-personal construct with the current concept of student-centered learning.The results of the study of future art teachers’ value intentions, which are formed during professional training and creative activity, are given. The results of the value intentions diagnostics and dynamics of their changes in accordance with future musical art teachers’ professional training are highlighted. Scientific approaches and technologies that effectively influence their value intentions are outlined. It is proved that definition of a conglomeration of value intentions of an individual allows a qualitative choice of methods for the formation of creative and competitive personality of future musical art teachers and teachers of art disciplines in general.
Analisis Serangan DDoS Menggunakan Metode Jaringan Saraf Tiruan

DDoS attack (Distribute Denial of Service) is one of the weapons of choice from hackers because it’s proven it has become threat on the internet worlds. The frequent of DDoS attacks creates a threat to internet users or servers, so that requires the introduction of several new methods that occur, one of which can use the IDS (Intrusion Detection System) method. This study took advantage of Neural Network ability to detect DDoS attack or normal based on traffic log processed statistically using Fixed Moving Window. The DDOS attack scheme uses a network topology that has been designed based on the needs and objectives that are found in monitoring network traffic. In each DDoS data and normal consist of 27 traffic log with total numbers of dataset as much as 54 data along with each testing data as much as 10 DDoS data and normal. Data collection was performed using LOIC, HOIC, and DoSHTTP with 300 seconds of traffic monitoring. The result of the Fixed Moving Window processing is the extraction value that will be put in the Neural Networks have 6 input values, one hidden layer with 300 neurons and 2 outputs which consist of a normal dataset and a DDoS dataset. The results of this study showed that Neural Network can detect DDoS and Normal in a good way with accuracy value as much as 95%.
Using Domain Adversarial Learning for Text Captchas Recognition

Nowadays the problem of legal regulation of automatic collection of information from sites is being actively solved. This means that interest in tools and programs for automatic data collection is growing and that's why interest in automatic programs for solving CAPTCHA («Completely Automated Public Turing test to tell Computers and Humans Apart») is increasing too. In spite of сreation of more advanced types of captcha, nowadays text captcha is quite common. For instance, such large services as Yandex, Google, Wikipedia, VK continue to use them. There are many methods of breaking text captchas in literature, however, it should be noted that most of them have a limitation to priori know the length of the text on the image, which is not always the case in the real world. Also, many methods are multi-stage, which brings additional inconvenience to their implementation and application. Moreover, some methods use a large number of labeled pictures for training, but in reality, to collect data one has to be able to solve captchas from different sites. Respectively, manually labeling dozens of thousands of examples for training for each new type of captcha is an unrealistically difficult action. In this paper we propose a one-step algorithm of attack on text captchas. This approach does not require a priori knowledge of the text's length on the image. It has been shown experimentally that the usage of this algorithm in conjunction with the adversarial learning method allows one to achieve high quality on real data, using the low number (200-500) of labeled examples for training. An experimental comparison of the developed method with modern analogs showed that using the same number of real examples for training, our algorithm shows a comparable or higher quality, while it has a higher speed of working and training.
Development of services in the field of research data management and scholarly communication at the University of Vienna

In 2008 the University of Vienna released the institutional repository PHAIDRA. With this release, several services for research data management followed soon. It is still essential to review the services and integrate new one, related to the change of the research practice. The demand for training and support is increasing, and more often, we see now the need for consulting services. That service supports the research cycle from writing a data management plan to getting DOIs for data and are integrated into the PHAIDRA portfolio. PHAIDRA is a cooperation between the Central University Computer Center and the University Library for researcher and research projects of the University of Vienna. To be alleged with the actual changes in research data management, PHAIDRA regularly participates in national and international projects, like Austrian Data Lab and Services and FAIR Data Austria nationally as well as implementing with COAR (Confederation of Open Access Repositories) a vocabulary service and engaging with researcher projects together with RDA (Research Data Alliance) internationally. Several networks such as the network for repository managers (RepManNet) or the network for sustainable software development (code4research), and the network of other PHAIDRA users at other universities in Austria, Italy and Balkan countries, enable constructive cooperation on a technical, library and scientific level on various topics. The poster intends to give an insight into these activities.
Meningkatkan Hasil Belajar Siswa SD melalui Model Teams Games Tournament dan Media Scrapbook

Learning outcomes indicate the students' success in learning. When the learning result was good, the the learning means successful. In vice versa, when the learning means unsuccessful when the outcomes were poor. The problem of poor learning outcomes was found in fourth grade of State Elementary School 2 of Tenggeles. The poor learning outcomes were caused by unsuitable learning models and media applied. This study aimed to improve students’ learning outcomes by applying the Teams Games Tournament model and Scrapbook media. This research was a classroom action research with the subject of 19 students. The instrument of data collecting methods used tests and non tests in form of observation, documentation and interviews. The data was analysed quantitatively and qualitatively. Cognitif learning outcomes in Science subject improved from the first cycle I with an average of 69 with a classical completeness percentage by 47%, to be an average of 79 with classical completeness precentage of 84% at the cycle II. While the Indonesian language content improved from an average of 72 with a classical completeness percentage of 67% to be an average of 81 with classical completeness of 84%. The improvement of skill aspects obtained an average score of 71 in the first cycle I to be 83 in the cycle II. The result indicated that the use of TGT with Scropbook media can improve the students’ learning outcome in cognitive and skill aspects in elementary school level.
Social and psychological aspects of distance learning (case study of bachelor students)

This article follows the practical analysis of the challenges of distance learning which started in 2016 based on a case study of distance learning implementation at a Bulgarian university focusing on economics (Mukova 2016). Some of the primary conclusions of the author were as follows: (1) recent changes in the country’s secondary and higher education have led to their mutual imbalance, resulting in poorer conceptual thinking skills of university students and, consequently, lower abstraction level of academic disciplines, which reduces higher education to the level of a secondary vocational school; (2) this imbalance leads to lower socio-economic benefits of higher education: the businesses do not get high-quality professionals and suffers losses, resulting in a financial loss for society as a whole; (3) universities generally introduce distance learning practices, which a more economically profitable due to their the limited financial resources, in order to simply increase the number of students disregarding all other considerations. The main subjective problem of distance learning is weak control in knowledge assessment. The purpose of writing the previous article was to warn universities that adopting this model may lead to loss in education effectiveness. This study is focused on a specific distance learning implementation model which is most common for private universities focusing on economics. The subject of this study is analysing the behaviour of participants of the distance learning process, and more specifically of the three main groups of actors: university leadership, students and teachers. The key research method used was the analysis of existing practice. Research objectives include reflecting the real state of the distance learning for bachelors and identifying goals, behaviours and psychological traits of the participants in the educational process. The paper demonstrates the current trends in the field of distance learning: the imbalance in the interests of the subjects of distance learning, the changes in their motivation and interactions with each other, lower quality of education and university performance. The negative impact of distance learning on the behaviour of various actors, does not mean that it cannot be used in university setting; but a different distance learning modality is indeed required.