Reviewerly/exHarmony huggingface dataset:
Coalgebras for Bisimulation of Weighted Automata over Semirings

Weighted automata are a generalization of nondeterministic automata that associate a weight drawn from a semiring $K$ with every transition and every state. Their behaviours can be formalized either as weighted language equivalence or weighted bisimulation. In this paper we explore the properties of weighted automata in the framework of coalgebras over (i) the category $\mathsf{SMod}$ of semimodules over a semiring $K$ and $K$-linear maps, and (ii) the category $\mathsf{Set}$ of sets and maps. We show that the behavioural equivalences defined by the corresponding final coalgebras in these two cases characterize weighted language equivalence and weighted bisimulation, respectively. These results extend earlier work by Bonchi et al. using the category $\mathsf{Vect}$ of vector spaces and linear maps as the underlying model for weighted automata with weights drawn from a field $K$. The key step in our work is generalizing the notions of linear relation and linear bisimulation of Boreale from vector spaces to semimodules using the concept of the kernel of a $K$-linear map in the sense of universal algebra. We also provide an abstract procedure for forward partition refinement for computing weighted language equivalence. Since for weighted automata defined over semirings the problem is undecidable in general, it is guaranteed to halt only in special cases. We provide sufficient conditions for the termination of our procedure. Although the results are similar to those of Bonchi et al., many of our proofs are new, especially those about the coalgebra in $\mathsf{SMod}$ characterizing weighted language equivalence.
Особливості розвитку дітей з порушеннями мовлення в ігровій діяльності

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
Accounting firms and their relationship with clients in the face of the COVID-19 crisis

Management support can be understood as the area that assists in decision-making related to management (organizational performance, financial planning, and control). It is suggested that the changes brought about by COVID-19 have impacted the way accounting – especially in terms of management – has been used by companies and accounting firms. In this context, the research aimed to identify how the crisis caused by COVID-19 has affected the management support offered by accountants. The study is characterized as a descriptive research, with a qualitative focus, whose operationalization was conducted by means of a previously defined script, with six accounting firms located in the states of Santa Catarina, Parana and Sao Paulo (Brazil). As for the data analysis, the content analysis technique was used. The results revealed that the COVID-19 crisis presented, in its first months, the potential to generate changes in the relationship between accounting firms and their clients, impacting the management support offered by accountants. Furthermore, it was observed that the consequences of the crisis led to a process of broadening the understanding of the role of accounting in the decision-making process. In this sense, the study contributes not only to the understanding of the ways in which the pandemic of COVID-19, in its initial stage, has influenced and continues to influence human relationships and, especially, professional relationships in the field of accounting, but also to show the opportunities arising from the moment experienced, so that accounting firms get closer to their clients.
APPLICATION OF GUIDED INQUIRY LEARNING MODELS TO IMPROVE STUDENTS 'SOCIAL STUDIES LEARNING OUTCOMES CLASS IVA SDN 07 JATI MULYA

Based on the description of the table above, of the 28 grade IVA students of SDN 07 Jati Mulya (18 male students and 8 female students) who did not reach the minimum completeness criteria were 21 people, namely around 45% where classically they did not meet the completeness criteria set by the school 75%. Thus there are still many students who have not reached the minimum completeness criteria. So that the teacher has to repeat the same material over and over again for several meetings in an effort to improve student learning outcomes. This makes the demands of the curriculum not achieved because the time allocation that should be used for the next material is used to discuss the same material. Seeing this condition, the researcher considers it necessary to provide a learning model that can arouse students in learning. One learning model that leads to student curiosity and improves student learning outcomes is the guided inkuri learning model. Guided inkuri learning model is a learning activity that emphasizes the process of thinking critically and analytically to find and discover for themselves the problem that is being questioned to students. Social studies learning with inquiry is able to lead students to realize what they have obtained after learning. As for the formulation of the problem in this study is "Can the application of guided inquiry learning models improve social studies learning outcomes of class IVA SDN 07 Jati Mulya students?" This study aims to improve the social studies learning outcomes of grade IVA students of SDN 07 Jati Mulya by applying the Guided Inquiry learning model. Based on the results of the research and discussion carried out, it can be concluded that the application of the guided inquiry learning model can improve the learning outcomes of grade IVA students at SD Negeri 07 Jati Mulia, Kerinci Kanan District, Siak Regency, which is proven that student learning outcomes increase from the basic score, cycle I and cycle II. In the basic score, the class average was 63.75, increasing in the first cycle with an increase of 2.35% to 65.25. In the second cycle, the class average increased again with an increase of 8.00% to 74.25
Energy Efficient and VM Consolidation Framework using Improved Spider Monkey Optimization Algorithm

Cloud Computing is rapidly being utilized to operate informational technological services by outstanding technologies for a variety of benefits, including dynamically improved resources planning and a new service delivery method. The Cloud computing process is occurred by allowing the client devices for data access through the internet from a remote server, computers, and the databases. An internet connection is linked among the front end users such as client device, network, browser, and software application with the back end that constitutes of servers, computers, and database. For satisfying the demands of the Service Level Agreement (SLA), providers of cloud service should reduce the usage of energy. Capacity reservations oriented system is available by clouds’ providers to permit users for customizing Virtual Machines (VMs) having specified age and geographic resources, reduces the amount to be paid for cloud services. To overcome the aforementioned issue, an Improved Spider Monkey Optimization (ISMO) approach is proposed for cloud center optimization. The VM consolidation architecture based on the proposed ISMO algorithm decreases energy usage while attempting to prevent Service Level Agreement breaches. The accessibility of hosts or virtual machines (VMs) for task performance is measured by fitness. If the number of tasks to be handled increases the hosts of VMs available at right state. The proposed VM consolidation architecture decreases energy usage while also attempting to prevent Service Level Agreement breaches and also provide energy-efficient computing in data centers. The proposed approach may be utilized to provide energy-efficient computing in data centers. The energy efficiency of the proposed ISMO method is achieved 28266 whereas, the existing algorithm showed an energy efficiency of 6009 and 10001.
COMPARISON OF THE ADDITION OF Manihot utilisima (PATI UBI WOOD) TO DIMENSIONAL STABILITY OF IRREVERSIBLE HYDROCOLLOID MOLD

Alginate impression material is used to print the teeth and the oral tissues; alginate molding material is from abroad and expensive. Modification to the impression material alginate with cassava starch with 1: 1 obtained detailed reproduction of the same printed material alginate without added with cassava starch. The study's objective was to get an alternative form of printed material alginate plus Manihot utilisima (cassava starch) with the nature of the physical properties following the standard. The study methods used laboratory experiments. The materials used in this study were printed material alginate (Hydrogum fast setting), starch cassava starch cap dua kelinci, water, and tools calliper inside digital. There were four groups of a control group, a group cassava starch ratio of 50: 50, groups of cassava starch ratio of 60: 40, and a comparison group of cassava starch 70: 30. The results were dimensional stability of printed materials Alginate without additions were (73.72, 48.77, 30.51), the dimensional stability of the printed material alginate with a ratio of 50: 50 were (74.24, 40.82, 30.76), the dimensional stability of the printed material alginate with a ratio of 60: 30 were ( 75.39, 49.67, 31.57), and the results of dimensional stability alginate ((irrevesible hydrocolloid ) molding material with a ratio of 70: 30 were (76.36, 50.94, 32.86). The conclusion is the addition of Manihot utilisima on standard printed material alginate (irrevesible hydrocolloid) obtained dimensional stability test results that met the standards at a ratio of 50: 50 of ANSI/ADA no.18/1992. Manihot utilisima was one of the appropriate alternatives as a mixture in impression material alginate (irrevesible hydrocolloid).
Педагогічні аспекти дозвіллєвої діяльності

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
PENINGKATAN AKTIVITAS DAN HASIL BELAJAR IPA DENGAN DISCOVERY LEARNING MELALUI AUDIO VISUAL

This study aims to increase student activity and student learning outcomes in science subject matter through the application of the Discovery Learning Learning Model through Audio Visual Media in fifth grade students of State Elementary School 3 Karangasem in the 2020/2021 academic year.The research subjects were 12 fifth grade students of SD Negeri 3 Karangasem, Cawas District, Klaten Regency, for the 2020/2021 academic year, which consisted of 4 boys and 8 girls.The subject of the action was the researcher and the class teacher as an observer.Data collection techniques through observation, documentation and tests.The data analysis technique used is descriptive qualitative which includes the stages of data collection, data reduction, and drawing conclusions.The results showed that the students' activities with a minimum category of good in the first cycle reached 41.66% and the second cycle 83.33%.While the results of student learning completeness in the first cycle reached 50%, and the second cycle 91.66%.Thus, from this study, it can be concluded that
From word embeddings to text similarities for improved semantic clustering of functional requirements (S)

Requirements engineering starts by requirements elicitation which consists in gathering software requirements from stakeholders.Then, the elicited requirements are usually manually recorded in a requirements specification document.In recent years, modern software projects are becoming more complex than projects of the past due to the increase in the number of requirements and stakeholders involved in a project.Thus, manually managing requirements becomes a tedious, time consuming and error-prone task.One historical strategy to manage this kind of complexity is "divide to conquer", meaning to categorize them into groups in order to breakdown the system into a set of smallest sub-systems at early stages.In this paper, we propose an approach to automatically cluster functional requirements based on their semantic similarity which is the usual strategy used by system architects to define sub-systems candidate to simplification of the original problem.First, we use word2vec, as a predictive word embedding model to compute the word-level similarity.Second, we derive the requirement-level similarity using a scoring function for text similarity.Third, we adopt hierarchical clustering to group the requirements.Experimental results performed on four open-access software projects show that our approach succeeded to improve the results of clusters identification compared with existing studies.
An Efficient Distributive Framework for Preserving Data Privacy through Block Chain

Deep Learning Models has gained much attention to perform various artificial intelligence tasks. The accuracy of the models relies on the availability of data. Privacy and auditability has become the major concern for data providers. First issue is the centralised server which may become malicious causing break in privacy. Second is no incentives are given for data providers and trainers. Block chain is the most emerging innovation as of late. Decentralised connectivity of block chains gives another approach to interface information without the overheads of security, trust and controls. To address the above issues we propose an algorithm where clients send the model to the block chain for training where the honest trainers are incentivized for training, sharing weights. The weights are averaged; parameters are updated by a smart contract that resides on block chain which guarantees privacy and audit ability
A Practical User Feedback Classifier for Software Quality Characteristics

It is common practice for users to provide feedback on apps through social media or app store reviews.This feedback is a rich source of requirements for these apps.However, manually analyzing vast amounts of user feedback is unfeasible, so automated user feedback classifiers are useful tools.This research work presents a user feedback classifier based on Machine Learning (ML) for the classification of reviews according to software quality characteristics complaint with the ISO25010 standard.We developed this approach by testing several ML algorithms, features, and class balancing techniques for classifying user feedback on a data set of 1500 reviews.The maximum F1 and F2 scores obtained were 60% and 73%, with recall as high as 94%.This approach does not replace human specialists, but reduces the effort required for requirements elicitation.
Universal Face Recognition Using Multiple Deep Learning Agent and Lazy Learning Algorithm

Mainstream face recognition systems have a problem regarding the disparity of recognizing faces from different races and ethnic backgrounds. This problem is caused by the imbalances in the proportion of racial representations found in mainstream datasets. Hence, the research proposes using a multi-agent system to overcome this problem. The system employs several face recognition agents according to the number of races that are necessary to make data encodings for the classification process. The first step in implementing this system is to develop a race classifier. The number of races is arbitrary or determined differently in a caseby-case scenario. The race classifier determines which face recognition agent will try to recognize the face in the query. Each face recognition agent is trained using a different dataset according to their assigned race, so they have different parts in the system. The research utilizes lazy learning algorithms as the final classifier to accommodate a system with the constant data flow of the database. The experiment divides the data into three racial groups, which are black, Asian, and white. The experiment concludes that dividing face recognition tasks based on racial groups into several face recognition models has better performance than a single model with the same dataset with the same imbalances in racial representation. The multiple agent system achieves 85% on the Face Recognition Rate (FRR), while the single pipeline model achieves only 80.83% using the same dataset.
Logical Thinking Improvement in Children Aged 5 to 6 Years Through Pop-Up Book

The study was set back by the children's low logic ability of 5-6 years age at nagari muaro, sijunjung district. One of the key factors that led to the alleged lack of interesting learning facilities /media. This study is aimed at seeing an increase in logical thinking ability in children as young as 5-6 years of age through the use of pop-up book media in the family at nagari muaro, sijunjung district. The type of research is quantitative using pseudo-experiment methods (quasi exsperiment) with pretest design and posttest. The research population is 15 people. While the sample of the six selected kids from jorong Pematang Sari Bulan, sampling retrieval techniques are used sampling samples. The data analysis USES t-test samples samples. As a result of the conclusion to the data collection and management, 1) before being given actions (pretests) the most logical thinking ability of children is seen but inconsistently, this is seen from tests given by researchers ata time when the pretesting of children is more questionable. 2) after being given a posttest of logical thinking ability, children are characterized at the most consistent level, as indicated by tests given by researchers during posttest sessions, children are more able to answer. 3) there is a sophisticated difference between pretest results and posttests after a child has used inside pop-up book media
A survey on Content Based Image Retrieval in Cloud Environment with Privacy Preservation &amp; Copy Deterrence

Abstract: Importance of images in day to day life increased tremendously. Therefore Content Based Image Retrieval studied extensively. Cloud computing offers on demand services to cloud user therefore many organizations prefer to use cloud for data storage. To protect images with sensitive or private information needs to be encrypted before being outsourced to cloud. However, this causes difficulties in image retrieval and management. The purpose of this study is to provide privacy preservation and copy deterrence Content Based Image Retrieval method using Lucene Indexing. Keywords: CBIR, Lucene Indexing, Copy Deterrence.
PENERAPAN MODEL CONTEXTUAL TEACHING AND LEARNING (CTL) UNTUK MENINGKATKAN HASIL BELAJAR MATEMATIKA TENTANG BANGUN RUANG PADA SISWA KELAS V SDN 1 WALUYOREJO TAHUN AJARAN 2019/2020

&lt;p&gt;&lt;strong&gt;&lt;em&gt;Abstract: &lt;/em&gt;&lt;/strong&gt;&lt;em&gt;The research aimed o: (1) describe the steps of Contextual Teaching and Learning (CTL) model, (2) improve mathematics learning outcomes about solid figures, (3) describe obtacle and solution in applying Contextual Teaching and Learning (CTL) model. It was collaborative classroom action research conducted in three cycles and six meetings. The subjects were teacher and 33 students of fifth grade at SDN 1 Waluyorejo. The types of data were qualitative and quantitative. Data collection techniques included observation, interviews, and tests. The data validity used triangulation of techniques and triangulation of sources. Data analysis included data reduction, presentation, and drawing conclusions. The results showed that: (1) the steps in applying the Contextual Teaching and Learning (CTL) model were: (a) the students developed their skills, (b) the students carried out discovery activities, (c) the teacher encouraged the students to ask questions, (d) the students worked in group, (e) the students presented concrete objects, (f) the students made reflection, and (g) the teacher made assessment; (2) the application of Contextual Teaching and Learning (CTL) model improved mathematics learning outcomes about solid figures to the fifth grade students of SDN 1 Waluyorejo in academic year of 2019/2020; (3) the obstacle was that the teacher was difficult to accurately evaluate the contribution of individual student within group work while the solution was that the teacher needed to focus during process of group work.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Archival Resource Keys for Collaborative Historical Ontology Publication

This case study builds on previous research transforming the 1910 Library of Congress Subject Headings (1910 LCSH) into a computation-ready dataset, compliant with linked data standards.The Archival Resource Key (ARK) system offers persistent identifiers for vocabularies and is becoming more widely adopted.We provide background and motivation for our work with the Nineteenth-Century Knowledge Project (NCKP), review our data sources, and detail the implementation of ARKs.Finally, we review the Helping Interdisciplinary Vocabulary Engineering (HIVE) system and preset the unified workflow, which involved contributions from information science and humanities scholars, as well as computer science and information systems researchers.The conclusion provides insight into planned future work.
Study of the solar power plant power generation forecasting model.

The question of determining the possible capacity of a photovoltaic power plant is very acute due to the growing demand for renewable energy, coupled with the fact that during the day we have limited time to generate energy from such a source. Thus, based on the obtained analytical data, which allows to predict weather conditions, it is possible to regulate the amount of energy supplied to the network in a certain way due to more maneuverable power plants. In previous years, electrical engineering scientists and researchers from different countries have developed and implemented methods for determining weather conditions, such as clouds, air temperature, atmospheric dust and others, as well as their impact on the energy output of a solar power plant. A photovoltaic panel is a complex nonlinear object with many variables. In addition to the structural features of the module, the output is most affected by solar radiation and panel temperature. When researching the prediction of the amount of energy produced, it is important to find sufficiently reliable and consistent data. At the forefront of these issues are US universities and research centers. For example, the University of Nevada in Las Vegas, in 2006 put into operation a set of measurements of weather conditions: the level of sunlight, ambient temperature, wind speed, humidity and others. When calculating the power generated by the panels, it is assumed that the system operates at the point of maximum power. The scheme works as follows: we set the values of temperature (Temperature) and irradiation (Irradiance); we apply voltage to the output terminals of the array by changing its value from 0 to Voc. We take current readings at each point, we find the power for each point, we find the maximum among the obtained array of points. Repeat over the entire range of input values. Thus, we obtain a graph of the output power of Figs. 4 pre-considering the losses in the inverter.
The Analysis Of Student's Response To The Development Of Media-Based Android On Subjects Nahwu

In previous studies, researchers have been developing a learning media which is used for increasing the achievement of students on the subjects nahwu with the utilization of powerpoint and ispring suite, but researchers do not yet know how the response of the students of class VIII MTsN 3 Jombang as a research subject. This study aims to determine how the response of students after the development of interactive learning media based on android. This research uses descriptive method with data collection in the form of a closed questionnaire, calculated with the scale linkert then analyzed according to the needs of researchers. The results of the questionnaire responses of students towards learning media shows the results of percentage with very good category.