Reviewerly/exHarmony huggingface dataset:
Analisis dan Perancangan Sistem Informasi Koperasi Mahasiswa Berbasis Web Pada Koperasi Mahasiswa

The student cooperative (Kopma) is a business entity managed by students that offers entrepreneurial experience to students. Kopma has many activities including purchasing, sales, consignation, maintenance scheduling, Business Profit Calculation (SHU). Problems faced by Kopma include the process of scheduling guarding, selling, purchasing, purchasing, and consignment transactions carried out manually so that when calculating the contribution of members often experience problems, because waiting for the recap of each district that resulted in the calculation of SHU for Rapat Anggota Tahunan (RAT) often experience recording errors reporting. Of the problems faced by Kopma, the solution provided is the design and analysis of Kopma information systems. Where the analysis and analysis of this information system produces a Kopma information system that can integrate reports from each Kopma operational activity. The results of the analysis and design of the Kopma information system as a reference for the implementation of the system so that the workmanship is in accordance with the analysis and design documents of the Kopma information system. The results of these documents are in accordance with user requirements based on the results of usability testing conducted to AKMS representatives where 5 testers can easily use the Kopma information system design prototype. the advice given for the next development of the Kopma information system prototype can be continued until the system implementation stage.
AN ANALYSIS OF LECTURERS’ METACOGNITIVE LEARNING STRATEGIES IN TEACHING ENGLISH FOR YOUNG LEARNER AT ENGLISH EDUCATION PROGRAM OF MUHAMMADIYAH UNIVERSITY OF BENGKULU

The purpose of this reseach was to analyze the lecturers' metacognitive learning strategies in teaching English for young learner at English Education Program of Muhammadiyah University of Bengkulu Academic year 2018/2019. The designof the research used descriptive qualitative method. The objects of this research were 60 students with open questionnaire. The instrument was check list form about metacognitive strategies skills by Anderson and Krathwohl. The researcher found that although not fully applied, with this metacognitive strategy students are more independent in learning and lecturers do not provide material repeatedly.The lecturers should consider several teaching strategies, especially metacognitive strategies.
Thoughts on strengthening the "3+2" Integrated School-running Cooperation between secondary and higher vocational Colleges

At present, there are many problems in vocational education in China, such as the lack of attraction of secondary vocational education, a serious shortage of students in higher vocational education, and poor connection between them. This paper starts from the problem of “3+2” integration of secondary and higher vocational colleges and puts forward the solution according to the actual situation of running a school.
Image Shadow Removal Based on Generative Adversarial Networks

Accurate detection of shadows and removal in the image are complicated tasks, as it is difficult to understand whether darkening or gray is the cause of the shadow. This paper proposes an image shadow removal method based on generative adversarial networks. Our approach is trained in unsupervised fashion which means it does not depend on time-consuming data collection and data labeling. This together with training in a single end-to-end framework significantly raises its practical relevance.Taking the existing method for unsupervised image transfer between different domains, we have researched its applicability to the shadow removal problem. Two networks have been used. Тhe first network is used to add shadows in images and the second network for shadow removal. ISTD dataset has been used for evaluation clarity because it has ground truth shadow free images as well as shadow masks. For shadow removal we have used root mean squared error between generated and real shadow free images in LAB color space. Evaluation is divided into region and global where the former is applied to shadow regions while the latter to the whole images. Shadow detection is evaluated with the use of Intersection over Union, also known as the Jaccard index. It is computed between the generated and ground-truth binary shadow masks by dividing the area of overlap by the union of those two. We selected random 100 images for validation purposes. During the experiments multiple hypotheses have been tested. The majority of tests we conducted were about how to use an attention module and where to localize it.Our network produces better results compared to the existing approach in the field. Analysis showed that attention maps obtained from auxiliary classifier encourage the networks to concentrate on more distinctive regions between domains. However, generative adversarial networks demand more accurate and consistent architecture to solve the problem in a more efficient way.Manuscript received 10.06.2020
Connect Web Services to Microsoft Teams with Webhooks

Webhooks and connectors are a simple way to connect your web services to channels and teams inside Microsoft Teams. Outgoing webhooks allow your users to send text messages from a channel to your web services. Connectors allow users to subscribe to receive notifications and messages from your web services. There are two types of connectors available in Microsoft Teams: incoming webhooks and Office 365 connectors. In this chapter you will learn about webhooks and connectors and how to implement them in Microsoft Teams channels.
Blockchain, black magic and event ontology Interview with Alexander Boldachev

Alexander Boldachev is a Russian philosopher, futurologist (member of the Association of Futurologists of Russia), author of books and articles on universal evolutionism, biological evolution, philosophy of artificial intelligence, temporal ontology, epistemology, and logic. System architect and analyst of blockchain applications, author of articles on the problems of trust technologies, eGovernment, web 3.0, semantic modeling of complex systems, speaker of many specialized conferences.
Design of a Near Field Communication-Enabled Points Collection Application System

Nowadays, users collect points by stickers after they purchase products. However, to issue stickers and to exchange points are inconvenient to store staffs and users. In addition, the traditional way for points collection increases the business cost because of additional cards and stickers. In this paper, we propose a novel, practical and integrated application system to resolve above issues namely NFC-enabled Point Collection Application System (NPCAS). NPCAS mainly consists of three subsystems including user-side APP, store-side APP and backend management system. The user-side APP let users to quickly complete points management, for instance, they can accumulate points by communicating with the store-side APP through Near Field Communication (NFC) technique. The store-side APP calculates the points of each transaction and updates them to the backend management system. Then, the users can inquire their current points, exchange details, exchangeable commodities and so on. Android operating system is the mainstream on smart phones so that the Android-based APPs attract a lot of attention. Accordingly, we develop both APPs based on Java, XML, Android Software Development Kit (SDK) and Android Studio. The backend management system is built by Apache server, PHP module, PHP files and MySQL database. The format that we used on information exchange between backend management system and both APPs is JavaScript Object Notation (JSON). In a word, NPCAS greatly simplifies the procedures of points collection and management.
Retracted: “Nonlinear Integrated Simulation of Dropped Container Impact With Platform Deck” [Proceedings of the ASME 2020 39th International Conference on Ocean, Offshore and Arctic Engineering, Volume 2A: Structures, Safety, and Reliability, Virtual, Online, August 3–7, 2020, Conference Sponsors: Ocean, Offshore and Arctic Engineering Division, ISBN: 978-0-7918-8432-4, Copyright © 2020 by ASME. Paper No. OMAE2020-18581, V02AT02A012; 10 pages; doi: 10.1115/OMAE2020-18581]

Abstract This paper was removed from publication at the author’s request, August 30, 2021. Copyright © 2021 by ASME
Network Pseudohealth Information Recognition Model: An Integrated Architecture of Latent Dirichlet Allocation and Data Block Update

The wanton dissemination of network pseudohealth information has brought great harm to people’s health, life, and property. It is important to detect and identify network pseudohealth information. Based on this, this paper defines the concepts of pseudohealth information, data block, and data block integration, designs an architecture that combines the latent Dirichlet allocation (LDA) algorithm and data block update integration, and proposes the combination algorithm model. In addition, crawler technology is used to crawl the pseudohealth information transmitted on the Sina Weibo platform during the “epidemic situation” from February to March 2020 for the simulation test on the experimental case dataset. The research results show that (1) the LDA model can deeply mine the semantic information of network pseudohealth information, obtain the features of document-topic distribution, and classify and train topic features as input variables; (2) the dataset partitioning method can effectively block data according to the text attributes and class labels of network pseudohealth information and can accurately classify and integrate the block data through the data block reintegration method; and (3) considering that the combination model has certain limitations on the detection of network pseudohealth information, the support vector machine (SVM) model can extract the granularity content of data blocks in pseudohealth information in real time, thus greatly improving the recognition performance of the combination model.
Simulation extractable versions of Groth’s zk-SNARK revisited

Abstract Zero-knowledge succinct non-interactive arguments of knowledge (zk-SNARKs) are the most efficient proof systems in terms of proof size and verification. Currently, Groth’s scheme from EUROCRYPT 2016, $$\textsf{Groth16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Groth</mml:mi> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> , is the state-of-the-art and is widely deployed in practice. $$\textsf{Groth16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Groth</mml:mi> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> is originally proven to achieve knowledge soundness, which does not guarantee the non-malleability of proofs. There has been considerable progress in presenting new zk-SNARKs or modifying $$\textsf{Groth16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Groth</mml:mi> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> to efficiently achieve strong Simulation extractability, which is shown to be a necessary requirement in some applications. In this paper, we revise the Random oracle based variant of $$\textsf{Groth16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Groth</mml:mi> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> proposed by Bowe and Gabizon, BG18, the most efficient one in terms of prover efficiency and CRS size among the candidates, and present a more efficient variant that saves 2 pairings in the verification and 1 group element in the proof. This supersedes our preliminary construction, presented in CANS 2020 (Baghery et al. in CANS 20, volume 12579 of LNCS, Springer, Heidelberg. pp 453-461, 2020), which saved 1 pairing in the verification, and was proven in the generic group model. Our new construction also improves on BG18 in that our proofs are in the algebraic group model with Random Oracles and reduces security to standard computational assumptions in bilinear groups (as opposed to using the full power of the generic group model (GGM)). We implement our proposed simulation extractable zk-SNARK (SE zk-SNARK) along with BG18 in the library, and compare the efficiency of our scheme with some related works. Our empirical experiences confirm that our SE zk-SNARK is more efficient than all previous simulation extractable (SE) schemes in most dimensions and it has very close efficiency to the original $$\textsf{Groth16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>Groth</mml:mi> <mml:mn>16</mml:mn> </mml:mrow> </mml:math> .
Discriminative sparsity graph embedding based on histogram of rotated princial orientation gradients

The unconstrained face images collected in the real environments include many complicated and changeable interference factors, and sparsity preserving projections (SPP) cannot well obtain the low-dimensional intrinsic structure embedded in the high-dimensional samples, which is important for subsequent sparse representation classifier (SRC). To deal with this problem, in this paper we propose a new method named discriminative sparsity graph embedding based on histogram of rotated principal orientation gradients (DSGE-HRPOG). Firstly, it extracts multi-scale and multi-directional gradient features of unconstrained face images by HRPOG feature descriptor and incorporates them into a discriminative feature dictionary of sparse representation classifier. Secondly, it seeks an optimal subspace of HRPOG feature dictionary in which the atoms in intra-classes are as compact as possible, while the atoms in inter-classes are as separable as possible by adopting the proposed DSGE dimensionality reduction method. Finally, an optimal algorithm is presented in which the low-dimensional projection and the sparse graph construction are iteratively updated, and the accuracy of unconstrained face recognition is further improved. Extensive experimental results on AR, Extended Yale B, LFW and PubFig databases demonstrate the effectiveness of our proposed method.
LEARNING INNOVATION USING THE WEB (E-LEARNING) TO IMPROVE ENGLISH FOR JUNIOR HIGH SCHOOL STUDENTS IN THE VILLAGE

This research is expected to be an innovation for several schools in villages to implement e-learning and fulfill the following objectives: 1) the role and function of the multimedia center and the added value students get from using the Web (E-Learning) through the multimedia center at school level junior high school The research method used is descriptive qualitative. The research data were obtained through recorded interviews from several informants, classroom observation, documentation, and literature study. This study used the interactive model data analysis technique of Miles and Huberman. The results of the study will show that the role of the multimedia center is as a supporting facility for the English learning syllabus in junior high schools. Its function is as a means of accessing the Web (ELearning), as a means of learning that is more attractive and interactive because it brings students closer to information and communication technology devices for learning English, and serves as a means of gathering and discussion for students. Students get added value by using the Web (E-Learning) through a multimedia center.
Market Comment Generation from Data with Noisy Alignments

End-to-end models on data-to-text learn the mapping of data and text from the aligned pairs in the dataset. However, these alignments are not always obtained reliably, especially for the time-series data, for which real time comments are given to some situation and there might be a delay in the comment delivery time compared to the actual event time. To handle this issue of possible noisy alignments in the dataset, we propose a neural network model with multi-timestep data and a copy mechanism, which allows the models to learn the correspondences between data and text from the dataset with noisier alignments. We focus on generating market comments in Japanese that are delivered each time an event occurs in the market. The core idea of our approach is to utilize multi-timestep data, which is not only the latest market price data when the comment is delivered, but also the data obtained at several timesteps earlier. On top of this, we employ a copy mechanism that is suitable for referring to the content of data records in the market price data. We confirm the superiority of our proposal by two evaluation metrics and show the accuracy improvement of the sentence generation using the time series data by our proposed method.
Teaching Simple Present Tense to the Fifth Year Students of SD Negeri 12 Palu through Jumbled Words

This research aimed at proving whether or not the use of jumbled word technique is effective in teaching simple present tense. The population of this research was the fifth year students of SD Negeri 12 Palu. The sample of this research was selected by using total sampling. The number of the sample was 23 students. This research applied a pretest – posttest one group design. It involved one group only. To get the data, the group was tested before and after the treatment by giving pretest and posttest. The data were analyzed by using statistical analysis to know the significant difference of the students in pretest and posttest. Based on the result of statistical computation, the researcher found that the value of the t-counted is 4,49. Consulting to the t-value by applying 37 degree of freedom (df) of 23-1 = 22 of 0,05 of critical value, the researcher found that the t-table value is 2,07. This shows that the hypothesis of the research is accepted. Therefore, it could be concluded that the use of jumbled words technique was effective in improving students’ ability in constructing simple present tense.
META-TECHNOLOGIES OF BUSINESS AND PROFESSIONAL CONSULTING IN EDUCATIONAL ORGANIZATIONS

The article is devoted to the analysis of the main principles of business and professional consulting as a practice of interpersonal relations and the being of its subjects. The main problems of clients connected with violations of their intrapersonal and interpersonal relations, as well as relations to their own life (being) in general, subjects of business consulting as participants in business processes and life, in general, are considered. The purpose of the study is to analyze the basic principles or meta-technologies of business and professional consulting in educational organizations. The article summarizes the basic concepts and concepts of business and professional consulting, reflecting its meaning as helping the socio-psychological practice of developing people and educational organizations. Relying on the tetrad of principles of psychological counselling, isolated in the school of C.R. Rogers, as well as the leading principles of organizational counselling in educational organizations, the authors consider their manifestations and features in the main aspects and dimensions of advisory interaction. Traditionally, such dimensions are distinguished: power/management system, competence/experience system, love/system of relations. Each of these dimensions is associated with one or another intention of consultative relations in educational organizations: transformational, dialogical, and relational. Within each of these intentions, the basic principles of business and professional consulting in educational organizations are manifested as meta-technologies or meta-technology groups. In addition, the authors consider the principles of business and professional consulting in educational organizations in relation to the life of the consultant and the client as a whole, as "meta-technologies of life".
LSC-CyFair Library Guides: ENGL 1301 Oberle: Contact the library

This guide is for students in Prof. Oberle's ENGL 1301 classes. It has resources and tips to help you with your cause/effect essay. How to contact librarians for help even while the campus is closed
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks

While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education.Quantifying uncertainty helps pave the way towards more interpretable neural networks.In classification and regression tasks, Bayesian neural networks have been effective in estimating model uncertainty.However, inference time increases linearly due to the required sampling process in Bayesian neural networks.Thus speed becomes a bottleneck in tasks with high system complexity such as question-answering or dialogue generation.In this work, we propose a hybrid neural architecture to quantify model uncertainty using Bayesian weight approximation but boosts up the inference speed by 80% relative at test time, and apply it for a clinical dialogue comprehension task.The proposed approach is also used to enable active learning so that an updated model can be trained more optimally with new incoming data by selecting samples that are not well-represented in the current training scheme.
Penggerombolan Hasil Ujian Nasional Menggunakan K-Rataan Samar

National examination scores can be a basis for the government to make a mapping of education quality in order to increase it. The mapping can be done by using fuzzy cluster analysis. The objective of this experiment is to cluster districts/cities in Indonesia based on national examination score in natural and social science in 2014/2015 until 2017/2018 school year by using the fuzzy c-means method. The evaluation criteria that will be used are the standard deviation ratio, silhouette coefficient, and Xie Beni index. The best cluster size is two clusters, A and B. The clustering result shows cluster A has a higher mean from each subject than cluster B. Therefore, cluster A will be categorized as good, whereas cluster B as bad. The proportion of districts/cities that belong to cluster A decreased each year. The final cluster result can be determined by the mean of its degree of membership from those four school years. The analysis results show that the distribution of education quality is dominated in Java Island and squatter cities. East Nusa Tenggara, West Sulawesi, Central Sulawesi, and North Kalimantan don’t have any districts/cities belong to cluster A.
Modeling in solving application-oriented tasks as the basis for the formation of professional competence of future computer science and information technology teacher

The article is devoted to the study of matters in solving applied tasks in computer science by using methods of mathematical and computer modeling. These methods allow to create an innovative approach to solving research tasks that are the basis for the development of key competencies of the future teacher of computer science. The article provides an example of building a model of “predator”-“prey”, which includes: a general description of the model, stating the problem, compilation of a mathematical model for dealing with the task, transferring a mathematical model into a computer one. Modeling in the field of dealing with applied tasks is actively used while teaching students at Lipetsk State Pedagogical University named after P.P. Semenov-Tyan-Shanskiy.