Reviewerly/exHarmony huggingface dataset:
Meningkatkan kemampuan menghafal juz amma melalui media pembelajaran audio visual

Learning is a process of communication between learners, instructors and teaching materials. SDIT ATH-THAARIQ 2 Dumai is one of the integrated Islamic elementary schools in the city of Dumai, has a vision of realizing the Qur'anic generation, Islamic civilization, intelligent, high achievers, towards global-minded human beings. SDIT ATH-THAARIQ 2 Dumai familiarizes students with praying in congregation and dhuha prayers every day, practicing Islamic customs such as eating and drinking, going to the toilet, adab to old people, procedures for taking the right and memorizing methods juz amma. Teaching and learning activities currently still use book and blackboard media. This makes students less interested and bored during the learning process, thus giving the effect of lazy learning which results in a decrease in students' ability to absorb lessons. Learning media are designed according to the needs and attractive appearance, in the application are designed lightweight games to eliminate boredom in students and facilitate students in memorizing. The application design is made using Adobe Photoshop CS6 software, and Adobe Flash Professional CS6. This application can improve students 'ability in memorizing juz amma and understand how to take whudu correctly so that it makes it easier for teachers in the learning process. Students' abilities increase by 50% and boredom during learning can be overcome by the existence of such learning media, because the display in the application is easily understood by students.
Calibrated Multivariate Regression Networks

In this paper, we propose a new multi-layer learning architecture, the calibrated multivariate regression network (CMRN). Compared to previous multivariate models, the CMRN is able to simultaneously handle major challenges in multivariate regression including highly nonlinear input-output relationships, underlying inter-output correlations and calibration of multiple outputs within one single framework. The CMRN is comprised of a nonlinear module with cosine activations and a linear module with the low-rank expansion, which establishes a compact multivariate regression network. By seamlessly working with the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> loss, the CMRN automatically calibrates multiple outputs with distinct noise levels to achieve improved performance. Being succinctly formulated but theoretically well-founded, the CMRN offers a compact multi-layer learning architecture that can be efficiently trained to scale up with massive datasets. We conduct extensive experimental evaluation on two representative large multivariate regression tasks for both machine learning and computer vision. The proposed CMRN can produce high performance on all tasks, which is better or competitive to state-of-the-art models. Extensive ablation studies offer deep insights into the effectiveness of the proposed CMRN.
Reconstruction of Three-Dimensional Geometry of the Vessels by Computed Tomography Data

We consider algorithms of 3D reconstruction for the internal surface of cardiac vessels. The precise reconstruction of vessel geometry is necessary for the creating a hydrodynamic model of blood supply for the heart and computing various parameters of blood flow. To compute a triangulation of blood vessel walls, we use the combination of two methods. At the first stage we apply the 3D seeded region growing algorithm to reconstruct a set of voxels inside vessels. At the second stage we use the isosurface reconstruction algorithm based on the tessellation of 3D space into small tetrahedral cells. We use the tetrahedral mesh, which was proposed in the works of S. Chan, E. Purisima (1998), and V. Skala (2000). Tetrahedra in this mesh are constructed on common faces of adjacent cubes in a cubic lattice, so it fits well with the voxel model. The mesh is constructed only in the neighborhood of the border of voxel set obtained at the first stage as the result of seeded region growing algorithms.
An Empirical Study of Encoders and Decoders in Graph-Based Dependency Parsing

Graph-based dependency parsing consists of two steps: first, an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">encoder</i> produces a feature representation for each parsing substructure of the input sentence, which is then used to compute a score for the substructure; and second, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">decoder</i> finds the parse tree whose substructures have the largest total score. Over the past few years, powerful neural techniques have been introduced into the encoding step which substantially increases parsing accuracies. However, advanced decoding techniques, in particular high-order decoding, have seen a decline in usage. It is widely believed that contextualized features produced by neural encoders can help capture high-order decoding information and hence diminish the need for a high-order decoder. In this paper, we empirically evaluate the combinations of different neural and non-neural encoders with first- and second-order decoders and provide a comprehensive analysis about the effectiveness of these combinations with varied training data sizes. We find that: first, when there is large training data, a strong neural encoder with first-order decoding is sufficient to achieve high parsing accuracy and only slightly lags behind the combination of neural encoding and second-order decoding; second, with small training data, a non-neural encoder with a second-order decoder outperforms the other combinations in most cases.
Who wrote this book? A challenge for e-commerce

Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing.Of particular significance is the book category, where the author name(s) field poses a significant challenge.Indeed, books written by a given author might be listed with different authors' names due to abbreviations, spelling variants and mistakes, among others.To solve this problem at scale, we design a composite system involving open data sources for books, as well as deep learning components, such as approximate match with Siamese networks and name correction with sequence-tosequence networks.We evaluate this approach on product data from the e-commerce website Rakuten France, and find that the top proposal of the system is the normalized author name with 72% accuracy.
Mutual intelligibility of Finnish and Estonian vocabulary

This paper reports on an investigation on Finnish-Estonian mutual intelligibility carried out by means of a word translation task among 307 Finnish and 118 Estonian participants. The results confirm previous findings by Kaivapalu (2015) that both in the spoken and in the written mode Estonians understand isolated words in Finnish better than Finns understand isolated words in Estonian. Older participants performed better than younger participants. Many of the participants had at least some previous exposure to the test language and were therefore familiar with some words and sound correspondences. However, we were also interested in testing how well speakers of the two languages could understand the other language purely on the basis of the similarity of the languages. We therefore separately analyzed the data produced by a subset of participants with no or little exposure to the test language. The results were still asymmetric, but only in the spoken mode. We looked at various linguistic and non-linguistic factors that could explain why some cognates are more easily recognized than others. To put our results into perspective, we compared them to the results of the similar experiments involving 70 Germanic, Romance and Slavic language combinations.
Penerapan Algoritma K-Means Clustering untuk Pengelompokan Harga Eceran Beras di Pasar Tradisional Berdasarkan Wilayah Kota

Rice is the main food commodity of the Indonesian people, almost all residents in this country consume rice every day. This causes rice commodities to have a very strategic value, apart from being in control of the lives of many people, it can also be used as a parameter of the country's economic and social stability. This study discusses the application of K-Means Clustering Algorithm for Grouping Retail Prices of Rice in Traditional Markets. The source of this research data is collected based on documents describing the retail price group of rice produced by the National Statistics Agency. The data used in this study are data from 2011-2016 consisting of 33 cities. Data is processed by clustering in 3 clusters, namely high population level clusters, medium and low population level clusters. Centroid data for high population level clusters 10,776, Centroid data for moderate population level clusters 9,436 and Centroid data for low population clusters 8,590. To obtain an assessment based on the grouping of the average retail price of rice in traditional markets in 33 cities with high clusters (C1) of 11 cities namely Padang, Pekanbaru, Tanjung Pinang, Bandar Lampung, Jakarta, Pontianak, Palangkaraya, Banjarmasin, Ternate, Jayapura and Manokwari for medium cluster (C2) as many as 11 cities and for low cluster (C3) as many as 11 cities. From the results of this study can be used as input for the government, especially in cities, so that cities that are included in the high cluster can normalize the retail price of rice in each city area.
Visage – A Visualization and Exploration Framework for Environmental Data

Diverse airborne and ground-based environmental observations are important technologies for disaster assessment and response, as well as for the validation of environmental satellite observations and atmospheric models which can improve forecasts. The VISAGE (Visualization for Integrated Satellite, Airborne and Ground-based data Exploration) project is working to provide three-dimensional visualization and basic analytics capabilities for such datasets in an interactive user interface. The use of cloud-native, serverless technologies and analysis optimized data storage will position VISAGE for integration with other technologies into a Data Analytic Center Framework.
A PROCESS MODEL FOR THE FORMALISATION OF QUALITY ATTRIBUTES OF SERVICE-BASED SOFTWARE SYSTEMS

The benefits offered by software architectures include providing an improved understanding of high-level relationships amongst systems components and facilitating in making principled decisions between alternatives. A vast majority of architectural decisions are focused on realising functional requirements and often ignore the nonfunctional requirements (quality attributes) altogether or include them as an afterthought. This has a negative impact on the overall acceptance of the software system by its intended users. Moreover, these architectures are specified using diagrammatic notations such as boxes and lines as connectors between them. As a result, such semiformal representations lack precision in their definitions and may lead to misinterpretations of the architecture resulting in errors being introduced at later stages of development. Although several Architectural Description Languages (ADLs) exist for the formalisation of systems architectures, few are designed for formalising nonfunctional requirements. Since ADLs concentrate on component-level representations and not on the system as a whole, their support of non-functional requirements is limited. Additionally, as each ADL normally supports one quality attribute, multiple ADLs are required to cater for various quality attributes. This articles aims to address the challenges of inadequacies of specifying quality attributes in software systems in a manner that is both formal and applicable to a wider range of quality attributes. To achieve this goal, this paper present a process model that assists in formalising the non-functional requirements of a service-based software system and eliminates the need for multiple ADLs. It demonstrates the practicability of the proposed model by applying it to one quality attribute, namely â€œavailabilityâ€, using the general purpose Z specification language.
New number-theoretic cryptographic primitives

Abstract This paper introduces new p r q -based one-way functions and companion signature schemes. The new signature schemes are interesting because they do not belong to the two common design blueprints, which are the inversion of a trapdoor permutation and the Fiat–Shamir transform. In the basic signature scheme, the signer generates multiple RSA-like moduli n i = p i 2 q i and keeps their factors secret. The signature is a bounded-size prime whose Jacobi symbols with respect to the n i ’s match the message digest. The generalized signature schemes replace the Jacobi symbol with higher-power residue symbols. Given of their very unique design, the proposed signature schemes seem to be overlooked “missing species” in the corpus of known signature algorithms.
The Concordia NLG Surface Realizer at SRST 2019

This paper presents the model we developed for the shallow track of the 2019 NLG Surface Realization Shared Task.The model reconstructs sentences whose word order and word inflections were removed.We divided the problem into two sub-problems: reordering and inflecting.For the purpose of reordering, we used a pointer network integrated with a transformer model as its encoder-decoder modules.In order to generate the inflected forms of tokens, a Feed Forward Neural Network was employed.
Formation of the orientational basis of Informatics future teachers’ professional activities

In the article here is considered the problem of formation of students’ professional abilities in pedagogic higher education institutions when studying the course of a way of training and education in the field of informatics. Need is reasoned to use basic provisions of the theory of stage-by-stage formation of intellectual activities when developing practical tasks of the course. The model of the teacher’s and students’ activity, developed by authors, allows them to allocate types of tasks of the course of a way of training and education in the field of informatics, their use in the educational process promotes formation of the orientational basis of future Informatics teacher’s professional activities. In addition, the algorithm for development of tasks is described in the article, examples of tasks of each type are given.
Efficiency through Auto-Sizing:Notre Dame NLP’s Submission to the WNGT 2019 Efficiency Task

This paper describes the Notre Dame Natural Language Processing Group's (NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019).We investigated the impact of auto-sizing (Murray and Chiang, 2015;Murray et al., 2019) to the Transformer network (Vaswani et al., 2017) with the goal of substantially reducing the number of parameters in the model.Our method was able to eliminate more than 25% of the model's parameters while suffering a decrease of only 1.1 BLEU.
Application Of Canvas Model Business Analysis: Case Study Of Free Power Business

One of the technological tools that can not be separated from people's lives is cellphones, but the consequences of increasingly sophisticated technological tools are the higher power requirements. Current technology has developed into portable tools, namely tools that do not need to be connected directly to the main source of electric current but use the power stored in the battery, FREE POWER is a business that is engaged in Powerbank, where the product of the power source Free Power utilizes magnetic energy generator that can be used continuously without having to worry about running out of power. The types and sources of data used in this study are qualitative, the data needed in this study are primary data and secondary data. FREE POWER in the form of an innovative charging device on a smartphone by utilizing a magnetic generator that has the advantage of being able to charge continuously without having to recharge. With a cheaper price compared to a power bank. FREE POWER is the right solution to support the smooth use of the smartphone itself, so that the operation is not constrained due to the depletion of the smartphone battery used. And for consumers themselves are intended for teenagers to adults.
Conceptual Metaphor in Human Communication

The study is aimed at investigating the forms of conceptual metaphors using the word ‘komunikasi’ (communication)in delivering messages. The data were expressions containing the word ‘komunikasi’in communication processes, which were in metaphorical formsand collected from various written sources by applying content analysis technique.The results of the study showed that the interpersonal communicationsconveying messagesrelated tothe word ‘komunikasi’ implying conceptual metaphors can be classified into four domains such as plants, human life, construction and building, and space and time.
Pengklasteran Dokumen Tentang Dispensasi Nikah Menggunakan Algoritma K-Medoids

Marriage dispensation is the marriage of a prospective bride or groom who is underage and has not been approved to marry according to the regulations. In fact there are still many young women who are married under the age of 20 years. This study aims to determine the marriage dispensation cluster, because there is no research on clustering marriage dispensation documents using a computer method to cluster any area that often conducts marriage dispensations with high and low clusters. The research method used is Data Mining with the K-Medoids algorithm. Based on calculations using the K-Medoids algorithm, high cluster results of 22 sub-districts and low clusters were obtained in 8 sub-districts. The results obtained from this study are expected to be input to the government through socialization activities in order to reduce the number of marriage dispensation in each region.
Prototype Pengembangan Sistem Informasi Koperasi Simpan Pinjam Pada Sekolah XYZ Menggunakan Metode Throwaway Prototyping Development

Cooperatives as one of the economic activity actors in Indonesia must transform operational activities and management to keep abreast of technology in the current digital era. Operational and management activities at the XYZ School cooperative are currently limited to desktop-based systems. These activities certainly need to be developed following the era of digitalization and current technological developments. Operational time of cooperatives is a major obstacle in savings and loan cooperatives at XYZ School. Operational and management activities in the current technological era should not be limited by time and place, making it easier for cooperative staff members and members in operational activities at the XYZ School savings and loan cooperatives. Based on these conditions, the authors make a prototype research on the Development of a Savings and Loan Cooperative Information System at XYZ Schools Using the Throwaway Prototyping Development Method. Research begins with gathering needs, defining the overall objective of the software, identifying all needs, then designing information systems. The results of the development of analysis and design will be illustrated in the form of UML diagrams and the design of a savings and loan information system screen at the Web-Based XYZ School. With the development of cooperative information systems it can help cooperative staff and members to carry out operational activities without being limited by school operational hours, the information produced is more informative and can be accessed anytime and anywhere.
TSRuleGrowth: Mining Partially-Ordered Prediction Rules From a Time Series of Discrete Elements, Application to a Context of Ambient Intelligence

This paper presents TSRuleGrowth, an algorithm for mining partially-ordered rules on a time series. TSRuleGrowth takes principles from the state of the art of transactional rule mining, and applies them to time series. It proposes a new definition of the support, which overcomes the limitations of previous definitions. Experiments on two databases of real data coming from connected environments show that this algorithm extracts relevant usual situations and outperforms the state of the art.
Commonsense about Human Senses: Labeled Data Collection Processes

We consider the problem of extracting from text commonsense knowledge pertaining to human senses such as sound and smell.First, we consider the problem of recognizing mentions of human senses in text.Our contribution is a method for acquiring labeled data.Experiments show the effectiveness of our proposed data labeling approach when used with standard machine learning models on the task of sense recognition in text.Second, we propose to extract novel, common sense relationships pertaining to sense perception concepts.Our contribution is a process for generating labeled data by leveraging large corpora and crowdsourcing questionnaires.
Linguistic Classification: Dealing Jointly with Irrelevance and Inconsistency

In this paper we present new methods for language classification which put to good use both syntax and fuzzy tools, and are capable of dealing with irrelevant linguistic features (i.e.features which should not contribute to the classification) and even inconsistent features (which do not make sense for specific languages).We introduce a metric distance, based on the generalized Steinhaus transform, which allows one to deal jointly with irrelevance and inconsistency.To evaluate our methods, we test them on a syntactic data set, due to the linguist G. Longobardi and his school.We obtain phylogenetic trees which sometimes outperform the ones obtained by Atkinson and Gray (Gray and Atkinson, 2003;Bouckaert et al., 2012).
Data Dimensionality Reduction Method Combining Intra-class and Inter-class Distance

Traditional principal component analysis method has the problems of low computational efficiency and large memory consumption when faces high-dimension data. After the information entropy is introduced, although it reduces the memory and running time, but it cannot meet the requirements of the actual classification effect. In order to extract low-dimensional features with good discrimination ability, the idea of intra-class and inter-class distance is introduced, the principal component analysis data dimension reduction algorithm based on intra-class and inter-class distance is proposed. In this algorithm, firstly, the entropy of attribute information is calculated, and then the threshold value of information entropy is compared to it, which to realize the feature screening of data matrix. Then, the improved PCA algorithm based on the ideas of intra-class distance maximization and inter-class distance minimization is used to reduce the dimension of data. Finally, the data after dimension reduction is classified by KNN and SVM algorithms. Compared with the PCA, principal component analysis based on information entropy (E-PCA) and LDA algorithms, the experimental results show that the proposed algorithm can not only improve the result of dimension reduction, but also significantly improve the discrimination performance of low-dimensional data after dimension reduction.