Reviewerly/exHarmony huggingface dataset:
Pengaruh Model Pembelajaran Kooperatif Tipe Talking Stick Terhadap Hasil Belajar Siswa Pada Pembelajaran Tematik Kelas V Sekolah Dasar Negeri Kota Bengkulu

This research to determine the effect of the Talking Stick Cooperative Learning Model on Student Learning Outcomes in Thematic Learning Class V of Bengkulu City Primary School. Types of research is quantitative. Research method is experimental. The design in this study is “The Matching Only Pretest-Posttest Control Group Design”. Population in this study is all students class V of elementary school Bengkulu City that applies the 2013 curriculum and accreditation A. The study sample was taken using the Random Sampling Cluster technique, so that the VA class SDN 71 Bengkulu city was obtained ad an experimental class and the VA Class SDN 103 Bengkulu city as control class. The research instrument used in the form of cognitive learning test result in the form of multiple choice provided through pretest and posttest in the experimental class and the control class. Data analysis technique in this research is quantitative analysis using descriptive statistics and infertial statistics. From the result of the study note the result of the t-test obtained tcound of Indonesian subjects at 4.37 and on natural science subjects at 4.81 with a ttable at a significant level of 5% of 2.00 where tcount is greater than ttable. From these data in appears that there are differences in learning outcomes achieved by student in the experimental and control classes. Thus it can be concluded that there is a significant effect on student learning outcomes in Thematic learning that uses the Talking Stick type of cooperative learning model.
User Identity Linkage for Different Behavioral Patterns across Domains

As customers use and benefit from multiple services, a large amount of customer data are accumulating daily. Connecting a customer's identity on a service with her identity on a different service, known as user identity linkage (UIL), enables a comprehensive understanding of users in a variety of real-world applications. The difficulties of UIL tasks in marketing applications are mainly the lack of user demographics and diverse user behavioral patterns, which differs from UIL tasks in social networking services that previous UIL methods have mainly been used to tackle. In this paper, we propose a novel method for UIL for different behavioral patterns to determine whether two given behavioral histories come from the same user without using any user demographics. Our proposed method links users by using natural language processing to efficiently characterize user intrinsic features and bridging the gap between two different behavioral patterns of the same user. We conducted experiments to evaluate our proposed method for three real-world open source datasets and observed that it successfully linked users compared to conventional UIL methods.
Analisis Clustering Pengelompokan Penjualan Paket Data Menggunakan Metode K-Means

At present with the COVID-19 pandemic situation that requires all activities based in the network, starting from work, college, school, everything is based on the network. Certain provider users will experience excessive data plan usage. This also has an effect on a counter that sells data packages, which must provide several data package services in accordance with current conditions. This research was conducted to analyze the grouping of sales of data packages that are often purchased by customers in a counter by using the K-Means method. The K-Means method is used because the K-Means algorithm is not influenced by the order of the objects used, this is proven when the writer tries to determine the initial cluster center randomly from one of the objects in the first calculation. sales of data packages at a counter. Variables used include Price, Active period, and number of data packages. The K-Means Cluster Analysis algorithm is basically applied to the problem of understanding consumer needs, identifying the types of data package products that are often purchased. The K-Means algorithm can be used to describe the characteristics of each group by summarizing a large number of objects so that it is easier.
DEFS—Data Exchange with Free Sample Protocol

Distrust between data providers and data consumers is one of the main obstacles hampering the take-off of digital-data commerce. Data providers want to get paid for what they offer, while data consumers want to know exactly what they are paying for before actually paying for it. In this article, we present a protocol that overcomes this obstacle by building trust based on two main ideas. First, a probabilistic verification protocol, where some random samples of the real dataset are shown to buyers in order to allow them to make an assessment before committing any payment; and second, a guaranteed, protected payment process enforced with smart contracts on a public blockchain that guarantees the payment of data if and only if the provided data meet the agreed terms, and that honest players are otherwise refunded.
A Framework for Exploration and Visualization of SPARQL Endpoint Information

Widely accepted standards, such as the Resource Description Framework, have provided unified ways for data provision aiming to facilitate the exchange of information between machines. This information became of interest to a wider audience due to its volume and variety but the available formats are posing significant challenges to users with limited knowledge of the Semantic Web. The SPARQL query language alleviates this barrier by facilitating the exploration of this information and many data providers have created dedicated SPARQL endpoints for their data. Many efforts have been dedicated to the development of systems that will provide access and support the exploration of these endpoints in a semantically correct and user friendly way. The main challenge of such approaches is the diversity of the information contained in the endpoints, which renders holistic or schema specific solutions obsolete. We present here an integrated platform that supports the users to the querying, exploration and visualization of information contained in SPARQL endpoints. The platform handles each query result independently based only on its characteristics, offering an endpoint and data schema agnostic solution. This is achieved through a Decision Support System, developed based on a knowledge base containing information experimentally collected from many endpoints, that allows us to provide case-specific visualization strategies for SPARQL query results based exclusively on features extracted from the result.
SecDD: Efficient and Secure Method for Remotely Training Neural Networks (Student Abstract)

We leverage what are typically considered the worst qualities of deep learning algorithms - high computational cost, requirement for large data, no explainability, high dependence on hyper-parameter choice, overfitting, and vulnerability to adversarial perturbations - in order to create a method for the secure and efficient training of remotely deployed neural networks over unsecure channels.
On Sparsifying Encoder Outputs in Sequence-to-Sequence Models

Sequence-to-sequence models usually transfer all encoder outputs to the decoder for generation.In this work, by contrast, we hypothesize that these encoder outputs can be compressed to shorten the sequence delivered for decoding.We take Transformer as the testbed and introduce a layer of stochastic gates in-between the encoder and the decoder.The gates are regularized using the expected value of the sparsityinducing L 0 penalty, resulting in completely masking-out a subset of encoder outputs.In other words, via joint training, the L 0 DROP layer forces Transformer to route information through a subset of its encoder states.We investigate the effects of this sparsification on two machine translation and two summarization tasks.Experiments show that, depending on the task, around 40-70% of source encodings can be pruned without significantly compromising quality.The decrease of the output length endows L 0 DROP with the potential of improving decoding efficiency, where it yields a speedup of up to 1.65× on document summarization and 1.20× on character-based machine translation against the standard Transformer.We analyze the L 0 DROP behaviour and observe that it exhibits systematic preferences for pruning certain word types, e.g., function words and punctuation get pruned most.Inspired by these observations, we explore the feasibility of specifying rule-based patterns that mask out encoder outputs based on information such as part-of-speech tags, word frequency and word position. 1
A Comprehensive Survey of Frequent Itemsets Mining on Transactional Database with Weighted Items

In 1993, Agrawal et al. proposed the first algorithm for mining traditional frequent itemset on binarytransactional database with unweighted items - This algorithmis essential in finding hindden relationships among items inyour data. Until 1998, with the development of various typesof transactional database - some researchers have proposed afrequent itemsets mining algorithms on transactional databasewith weighted items (the importance/meaning/value of itemsis different) - It provides more pieces of knowledge thantraditional frequent itemsets mining. In this article, the authors present a survey of frequent itemsets mining algorithmson transactional database with weighted items over the pasttwenty years. This research helps researchers to choose theright technical solution when it comes to scale up in big datamining. Finally, the authors give their recommendations anddirections for their future research.
Pengaruh Motivasi Belajar terhadap Kemampuan Menulis Pantun Bahasa Daerah

Writing ability is one of the language skills possessed by a person. One of the skills to write old poetry is pantun. Pantun develops in the people of the archipelago with a variety of regional languages. This study aimed to determine the effect of learning motivation on the ability to write local language rhymes. This study uses multiple linear regression analysis. From the research results, it can be seen that the learning motivation is in a good category and the ability to write local language pantun is in a good category. The motivation of learning showed an influence on the poetry writer's ability, which was proven that the significance value was 0.000 that smaller than the probability of 0.05. From the research results, it can be seen that the learning motivation contributed 49.4% to the ability to write regional language pantun, and other factors influenced the remaining 50.6%.
USAGE OF THE MANAGEMENT LEARNING SYSTEM МООDLE IN THE PROCESS OF DISTANCE LEARNING IN HIGHER AND APPLIED MATHEMATICS

Тhe pandemic, which created educational problems in Ukraine, led to the significant changes in the educational organization. All higher education institutions have mooved to distance learning. Thanks to the usage of distance learning systems, Moodle, which is its own website content management system, specifically designed to create quality online teaching courses, has a wide range of opportunities, and very actual. The purpose of our article is to show how the usage of the distance learning system Moodle during the study of the course «Higher and Applied Mathematics» affects the organization of independent students work, which is the basis of higher education. After all, only the knowledge that a person came to on their own way, become a really strong asset. Description of applied research methods: (i) Theoretical: analysis of pedagogical, psychological and special literature in order to justify the need to use the learning management system Moodle in the process of distance learning; generalization of domestic and foreign researches results on the specified problem. (ii) Empirical: pedagogical observations, conversations with teachers and students of higher education on the use of distance learning Moodle in the course «Higher and Applied Mathematics». The results of the research indicate that the advantages of the Moodle learning platform while studying the discipline «Higher and Applied Mathematics» allow not only to effectively organize independent work of students, but also to increase their motivation through the usage of new forms and methods of teaching. And this, in turn, allows not only to intensify the learning of students, but also lays a solid foundation for their further continuing self-education. As you can see, the integration of traditional and modern distance technologies in the organization of the educational process in higher education institutions allows to make more effective the quality of the entire educational process in a whole.
Attention Beam: An Image Captioning Approach (Student Abstract)

The aim of image captioning is to generate textual description of a given image. Though seemingly an easy task for humans, it is challenging for machines as it requires the ability to comprehend the image (computer vision) and consequently generate a human-like description for the image (natural language understanding). In recent times, encoder-decoder based architectures have achieved state-of-the-art results for image captioning. Here, we present a heuristic of beam search on top of the encoder-decoder based architecture that gives better quality captions on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.
Entity Alignment for Knowledge Graphs with Multi-order Convolutional Networks (Extended Abstract)

Knowledge graph (KG) entity alignment is the task of identifying corresponding entities across different KGs. Existing alignment techniques often require large amounts of labelled data, are unable to encode multi-modal data simultaneously, and enforce only a few consistency constraints. In this paper, we propose an end-to-end, unsupervised entity alignment framework for cross-lingual KGs using multi-order graph convolutional networks. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of accuracy, efficiency, and label saving.
Evaluasi Penerimaan Sistem Perencanaan dan Penilaian Anak Didik menggunakan Technology Acceptance Model

Most teachers have difficulty in developing Learning Implementation Plans (RPP) due to the large number of instruments in the RPP that need to be made and adapted to the standard format. Likewise, when evaluating students, teachers need to look back at the lesson plan before conducting an assessment. Teacher activities in making lesson plans and assessing according to lesson plans are very time-consuming. Therefore, the Learning Planning and Assessment System for Students (SPADIK) was developed. This new application aimed to ease teacher effort in making learning plans and assessments for students at TK Khalifah Banjarmasin II. The teacher always welcomes the existence of new technology that can help their work. However, if the teacher cannot operate this new technology, they will certainly be in vain. Therefore, evaluation of acceptance of the SPADIK system is required by using the Technology Acceptance Model (TAM). This study uses a qualitative approach by conducting interviews with six kindergarten teachers of Khalifah Banjarmasin II, where this system was implemented. Data from interviews with teachers then reduced to a short description following the variables in the TAM model. From the results of testing with the TAM model, it was concluded that SPADIK could be accepted and used well by the teachers.&#x0D; &#x0D; Index Terms—evaluation; information system; RPP; students assessment; Technology Acceptance Model (TAM)
Study the ICT-Based Tutorial Model for Upper-Class of Elementary School in the Perspective of Constructivist Approach

This research aims to reveal the syntax, factors that take effect the application process of the ICT-based tutorial model, and the strengths of the tutorial model from a constructivist approach to online learning. The research method used in this research is the library research method with a descriptive qualitative approach. The research subjects were online learning articles and tutorial models. The data analysis technique was carried out by analyzing the contents of the document, namely precious research scientific articles and relevant theories. The results in this study point out that the tutorial model can be one of the solutions and means to be utilized during the online learning process SD/MI. the tutorial model was developed by combining the software in a coherent and tested manner. Variations of tutorial models are tailored to the needs and characteristics of students. The syntax of the ICT-based tutorial model includes an introduction, presentation of information, question and responses, judging of responses, providing about responses, remediation, sequencing lesson segment, and closing. The ability of teachers and students, as well as the existence of devices, laptops, and internet networks, are supporting factors. Meanwhile, the quality of the internet network connection speed is an inhibiting factor. The tutorial model can facilitate students to be more independent when learning, evaluate self-achievement, flexible, and learner-centred. However, the tutorial model can trigger boredom, frustration due to frequent remedies, depending on teacher directions, and limited free applications. The tutorial model is in tune with the perspective of a constructivist approach. The ICT-based tutorial model can help teachers carry out online learning according to process standards, provide meaningful experiences for students, effective, and efficient experiences for students.
THE EFFECT MIND MAPPING TECHNIQUE AND VOCABULARY MASTERY ON STUDENT’S WRITING SKILL

The purposes of this research is to know the effect mind mapping technique and vocabulary mastery on students’writing skill. Also how the implementation mind mapping technique on students’writing skill. Students need vocabulary mastery to arrange the sentences for getting a good paragraph and mind mapping technique is one of the technique that use to treat students for getting writing well. This research use experiments method. There were 40 students chosen at random from fourth grade semester English Education at STKIP Kusuma Negara Jakarta.Collecting data use test writing skill. Research of instrument has tested and tried by the validity and reliability test used the SPSS (Statistic Program Social Science). Data analyzed by using kolmogorv Smirov, know data was normality.The text analysis is to test the data homogeneity by using Levenu’s test, to see if the population was derived from homogeneous population. The result of this research concluded that 1) Using Mind Mapping Technique has significant effect on student’s writing skill. Result F for mind mapping (A) Fo=290.285 and sig.= 0.000&lt;0.05, then the conclusion is that there is significant effect between mind mapping technique (X1) on student’s writing skills. 2). Vocabulary mastery gave the significant effect to the student’s writing skill. Result research F for vocabulary mastery (B) Fo=91.401 and sig.= 0.000&lt;0.05. It has shown that there is significant effect vocabulary mastery (X2) on student’s writing skills. 3) There is significant effect interaction between mind mapping and vocabulary mastery on student’s writing skill. Result of this research F from mind mapping (A) and vocabulary (B) Fo=5.274 and sig.=0.028&lt;0.05. Using mind mapping technique in learning give effect in students’writing skill. Students who have rich vocabulary mastery can get good writing English.Keywords: Podcasts, Promote, Speaking, Online Learning
IGA Using Offset-based Overlapping Domain Parameterizations

Isogeometric analysis (IGA) is a numerical method that connects computer-aided design (CAD) with finite element analysis (FEA). In CAD the computational domain is usually represented by B-spline or NURBS patches. Given a NURBS parameterization of the domain, an isogeometric discretization is defined on the domain using the same NURBS basis as for the domain parameterization. Ideally, such an isogeometric discretization allows an exact representation of the underlying CAD model. CAD models usually represent only the boundary of the object, thus, for planar domains, it is given as a collection of curves. Finding a suitable parameterization of the interior is one of the major issues in IGA, similar to the mesh generation process in FEA. The objective of this parameterization problem is to obtain a set of patches, which exactly represent the boundary of the domain and which are parameterized regularly and without self-intersections. This can be achieved by segmenting the domain into patches which are matching along interfaces, or by covering the domain with overlapping patches. In this paper we follow the second approach. To construct from a given boundary a planar parameterization suitable for IGA, we propose an offset-based domain parameterization algorithm. Given a boundary curve, we obtain an inner curve by generalized offsetting. Those two curves define a ring-shaped patch, which has a hole that can be covered by a multi-cell domain. Consequently, the domain is represented as a union of two overlapping subdomains which are both regularly parameterized. On such a configuration, one can employ the overlapping multi-patch method introduced in (Kargaran, J\"uttler, Kleiss, Mantzaflaris, Takacs; CMAME, 2019), to solve PDEs on the given domain. The performance of the proposed method is reported in several numerical examples, considering different shapes of the domain.
Adaptive Teaching of Temporal Logic Formulas to Preference-based Learners

Machine teaching is an algorithmic framework for teaching a target hypothesis via a sequence of examples or demonstrations. We investigate machine teaching for temporal logic formulas—a novel and expressive hypothesis class amenable to time-related task specifications. In the context of teaching temporal logic formulas, an exhaustive search even for a myopic solution takes exponential time (with respect to the time span of the task). We propose an efficient approach for teaching parametric linear temporal logic formulas. Concretely, we derive a necessary condition for the minimal time length of a demonstration to eliminate a set of hypotheses. Utilizing this condition, we propose an efficient myopic teaching algorithm by solving a sequence of integer programming problems. We further show that, under two notions of teaching complexity, the proposed algorithm has near-optimal performance. We evaluate our algorithm extensively under different classes of learners (i.e., learners with different preferences over hypotheses) and interaction protocols (e.g., non-adaptive and adaptive). Our results demonstrate the effectiveness of the proposed algorithm in teaching temporal logic formulas; in particular, we show that there are significant gains of teaching efficacy when the teacher adapts to feedback of the learner, or adapts to a (non-myopic) oracle.