Reviewerly/exHarmony huggingface dataset:
PENGARUH PEMBERIAN REWARD TERHADAP HASIL BELAJAR SISWA PADA PELAJARAN MATEMATIKA KELAS IV SD DI SD NEGERI 101740 TANJUNG SELAMAT TAHUN PEMBELAJARAN 20182019

This study aims to determine the effect of giving rewards to student learning outcomes. This research was conducted at the Public Elementary School 101740 Tanjung Selamat in April 2019. The study sample consisted of 60 students. The method of this research is the Quasi experimental design method which in the design of this study there are two groups, namely, the experimental group and the control class. The instrument used to collect data is the multiple choice test. Hypothesis testing is done using the "t" test.The results of statistical calculations show that the ability of students to answer post-test questions by giving rewards is included in the good category with an average value of 79.75. While the ability of students to answer post-test questions without being given treatment, giving rewards is included in the category with an average value of 65.81. Furthermore hypothesis testing shows ð‘¡ð‘ð‘œð‘¢ð‘›ð‘¡ is 2,042 then consulted with ð‘¡ð‘¡ð‘Žð‘ð‘™ð‘’ at a significant level Î± = 0.05. with df 58 = 2,042 Because ð‘¡ð‘ð‘œð‘¢ð‘›ð‘¡ obtained is greater than ð‘¡ð‘¡ð‘Žð‘ð‘™ð‘’, that is, 6,174 &gt; 2,042, it is evident that the null or (Ho) hypothesis is rejected and the alternative hypothesis (Ha) is accepted. So, it can be concluded that giving rewards has a significant effect on the learning outcomes of class IV students at SD Negeri 101740 Tanjung Selamat Learning Year 2018/2019. And Then, giving this reward is well used in the learning process, especially in fractions.
NADS: Neural Architecture Distribution Search for Uncertainty Awareness

Machine learning (ML) systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a distribution different from training data. It becomes important for ML systems in critical applications to accurately quantify its predictive uncertainty and screen out these anomalous inputs. However, existing OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples. Unlike standard learning tasks, there is currently no well established guiding principle for designing OoD detection architectures that can accurately quantify uncertainty. To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS). NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify common building blocks among all uncertainty-aware architectures. With this formulation, we are able to optimize a stochastic OoD detection objective and construct an ensemble of models to perform OoD detection. We perform multiple OoD detection experiments and observe that our NADS performs favorably, with up to 57% improvement in accuracy compared to state-of-the-art methods among 15 different testing configurations.
A metric based on saliency line feature extraction and connection for matching area selection

Selecting a reliability matching area as template is one of the key issues to vision navigation. This paper proposes a metric for matching area selection based on line feature extraction and connection. Firstly, a new line feature is introduced to approximate the reliability information about matching area, which is called saliency line feature. Then, extracting method of these line features is put forward based on monogenic phase congruency model. Secondly, a convex shape descriptor is proposed to represent the spatial distribution characteristic of the line features by connection. Finally, a measure method is defined by merging the quantity and spatial distribution characteristic of the saliency line features, which can guide to select better matching area. The experimental results show that the proposed metric is valid and effective.
ELECTRUM-G, A Tangible Coin Actualization Schema: Patents for Casting, Forensics and Trade Options

Our interdisciplinary research concluded with a new member of the ancient Electrum family of golden coins: the ELECTRUM-G coin. It is golden representing an actual value with metal abnormalities to give a universal traceable RID and a modern engravement and other forgery fighting attributes and associated electronics. Other ELECTRUM-G characteristics are ; A tangible item with the form of a coin or sphere; there is a coin engravement with a QR code pointing the Electrum Ecosystem (www.electrum.gr). Four different patents are prepared for the project needs: a unique QR code stamping; a RID code casting; a two-layer gold foundry with RID intermediate layer; and a professional electronics simple spectrometer to read and report the RID code. Two Electrum narrative examples are given for ELECTRUM-G use and forensics, a nouvelle with the happy Electrum traveler and the lyrics of a song defining the “thin line between love and hate”. Finally, as inspired scientists we reverse the ELECTRUM Universe with an old story of “Stop RFID” and the beast (665+1) endangerment. Our research and product headlines faced also two contradictory intra-co-operating design aspects: the personal interest of the Electrum-G coin owner and the Public interest of civil and national security.
Peningkatan Keterampilan ICT untuk Guru melalui Pelatihan Konten Digital Pembelajaran Berbasis Sumber Terbuka (Open Sources)

ABSTRAK Pemanfaatan dan pengembangan konten digital adalah persyaratan yang harus terpenuhi untuk menjadi guru profesional Abad 21. Hal ini yang kemudian membuat adanya kebutuhan mendesak dikalangan guru yang sudah lama berkecimpung dalam profesi pendidikan. Berdasarkan survey yang dilakukan melalui kuesioner online (Google Form), responden (n=83) dari beberapa sekolah dan beberapa mata pelajaran mengayatakan bahwa kebutuhan keterampilan pembuatan konten digital pembelajaran merupakan prioritas utama (n=42) dan diikuti kebutuhan pembuatan online classroom and mobile learning (n=38). Artikel ini bertujuan untuk menyampaikan identifikasi masalah, analisa kurikulum, implementasi solusi, dan evaluasi hasi dari kegiatan pengabdian pada masyarakat dalam bentuk pelatihan pengembangan konten digital pembelajaran untuk guru di Kota Tangerang. Kata Kunci: konten digital; pembelajaran informatika; sumber terbuka.ABSTRACT Digital content development and utilization are considered as high requirements that must be mastered by qualified teachers in this 21st century learning. The needs eventually create gaps between prior teachers’ competencies and the requirements for further qualifications. An online survey delivered through Google form was conducted to validate this hypothesis. The survey found that respondents (n=83) who were school teachers from different learning subjects expressed that they demanded new skills on digital content development and utilization. It was considered as the highest priority (n=42) among any others skills related technology enhanced learning, such as online classroom or mobile learning (n=38). This article is aimed to describe the process of problem identification, curriculum analysis, the implementation of solution, and program evaluation of a community service activities entitled ‘Training of Digital Content Development for School Teachers’. Keywords: computer science education; digital content; open sources.
Mitigating Both Data Corruption and Content Replay Attacks with Implicit Data Integrity

We study the security of the recently proposed implicit integrity methodology. Implicit integrity is a novel methodology that supports corruption detection without producing, storing or verifying mathematical summaries of the content such as MACs or ICVs, as typically done today. The main idea behind implicit integrity is that, whereas typical user data demonstrate patterns such as repeated bytes or words, decrypted data resulting from corrupted ciphertexts no longer demonstrate such patterns. Thus, by checking the entropy of decrypted ciphertexts, corruption can be possibly detected. Past contributions to the implicit integrity methodology have focused on observed patterns on client and server data that motivate the methodology, entropy definitions for arbitrarily small messages, and constructions that mitigate data corruption attacks. In this paper, we extend the known analytical results concerning implicit integrity addressing content replay attacks as well. We demonstrate that the class of cryptographic constructions known as ‘random oracles according to observer functions’, which has been proposed for mitigating data corruption attacks, is actually simultaneously secure under two different adversary models: an input perturbing adversary performing content corruption attacks, and an oracle replacing adversary performing content replay attacks.
Manajemen Ekstrakurikuler Pendidikan Anak Usia Dini (PAUD) dalam Meningkatkan Multiple Intelegensi

This research examines the management of extracurricular education early childhood. It is backed by that implementation of extracurricular activities on early childhood education face varieties of constraints in management. Therefore, extracurricular activities, early childhood education is supposed to be managed thoughtfully. The management of extracurricular activities are good and true will effect positively to the quality of early childhood education. The qualitative research methodology is library research. Method of data collection by collecting reference books the subject matter and book supporters. Data analysis techniques are content analysis. Results of the study show that the extracurricular management, early childhood education is implemented through the following four stages: (1) plan a needs analysis, types, background, goals, budget, materials, assessment tools, schedule, facilities and infrastructure, output criteria instructors, (2) organize the Division of duties, authority and responsibilities of the personnel of extracurricular activities, (3) carry out extracurricular activities appropriate planning and organizing, and (4) evaluate extracurricular activities.
The Gift Illusion

The epitome of modern scientific infrastructure and distributed knowledge systems is scientific social networks (NSS). Their number, as well as the number of their users, is constantly growing and reaches millions. They are in demand, and, therefore, perform significant social functions. It is still unclear what their own nature is, what their functions are and how they perform and, finally, what are the consequences of their integration with the social institute of science. Along with the obvious advantages, the NSS creates clear cultural dissonances and challenges that change the usual ways of communication. There is already enough evidence that the NSS not only bring about positive change, but also face rejection. Science policy, the scientist's moral code, the scientific citation and evaluation systems are all affected by the activities of the NSS and become an important subject matter of science and technology studies (STS). This text is a response to the article “Social Networks for Researchers on the Internet: A New Sociality?” by S.V. Shibarshina.
Comparative Analysis of Bayesian Methods for Estimation of Locally-Invariant Extremes

Statistical methods are often fitting solutions to prediction and classification problems. Case studies have revealed that premature death is a priority problem in India and has often been rooted to malnutrition and birth-transmitted diseases. Aggregation of such medical and mortality records data helps to reveal important correlations between multiple factors affecting and aggravating the health conditions. The ground of research on the prediction of these diseases based on their severity has helped in opening up scopes of developing robust and swifter alternatives and taking affirmative actions to prevent further deaths. Bayesian methods have been proved to be more accurate in such case of spatial and temporal data points. This research explores the application of Local-Invariant and Independent features on various prolific algorithms like Bayesian Model Averaging, MHMCMC, RJ-MCMC and Bayesian Tail Regression. Further more likelihood estimators are used to predict the parameters and also measure the sampling accuracy. The proposed approach obtains a posterior probability of 91% for the simulated dataset from various regions involving several severity parameters.
Object Pose Estimation in Monocular Image Using Modified FDCM

In this paper, a new method for object detection and pose estimation in a monocular image is proposed based on FDCM method. it can detect object with high speed running time, even if the object was under the partial occlusion or in bad illumination. In addition, It requires only single template without any training process. The Modied FDCM based on FDCM with improvments, the LSD method was used in MFDCM instead of the line tting method, besides the integral distance transform was replaced with a distance transform image, and using an angular Voronoi diagram. In addition, the search process depends on Line segments based search instead of the sliding window search in FDCM. The MFDCM was evaluated by comparing it with FDCM in dierent scenarios and with other four methods: COF, HALCON, LINE2D, and BOLD using D-textureless dataset. The comparison results show that MFDCM was at least 14 times faster than FDCM in tested scenarios. Furthermore, it has the highest correct detection rate among all tested method with small advantage from COF and BLOD methods, while it was a little slower than LINE2D which was the fasted method among compared methods. The results proves that MFDCM able to detect and pose estimation of the objects in the clear or clustered background from a monocular image with high speed running time, even if the object was under the partial occlusion which makes it robust and reliable for real-time applications.
Predictions for Blockchain in 2020

During our 2019 ConVerge2Xcelerate (ConV2X) conference in Boston, we focused on the theme "Proving Market Value with Pragmatic Innovation in Healthcare"&nbsp;(see&nbsp;https://conv2x-2019.eventcreate.com/). This year, along with BHTY editorial board members, conference speakers were invited to join with Tory Cenaj, Publisher of BHTY, to contribute their&nbsp;expertise and share insights for the near-term landscape of blockchain in healthcare.&nbsp;&nbsp
Information literacy research trend in Malawi: a guide for future research

Research on information literacy skills (IL) that people of a particular area needs or lack is important in informing quality IL programs. Moreover, creating a collective understanding on IL research is important in guiding researchers towards gaps they have to address. This study has attempted to do that by reviewing IL research that has been conducted in Malawi for a period of 10 years starting from 2008. The study has purposively selected 18 studies that have met requirements for inclusion. Following the analysis, the study has established that majority of IL research has been carried out at institutions of higher learning. The study has also revealed lack of focus on some categories of literacy. A number of IL challenges have also been reported in the studies reviewed. Based on the findings, the study finds it imperative for information science researchers in the country to conduct more IL studies so as to address the current problem of limited studies. The study also finds the need for libraries and information providers to come up with a research agenda that will take researchers' focus to IL imperative.
THE USE OF E-BOOK IN TEACHING ENGLISH FOR COMPUTER STUDENTS

This research deals with the use of E-book in teaching English for computer students. The objectives of this research is to know how English is taught in computer at Harapan University of Medan. This research conducted qualitative method by using R&D (Research and Development) approach. And the data used was observation among English lecturer and students. The use of E-book for computer students contains reading materials, grammar and language functions. The result shows that the use of E-book in teaching learning process for computer students is quite interesting for teachers and students. The teachers can interact with students and students can interact with other students without having to looked. The students can access E-book from their gadgets, it is user friendly. The use of  E-book in teaching learning English for computer students is hoped that the lecturer can share the knowledge well also the students can be motivated in learning English, especially English for computer in order to improve the students’ achievement. Keywords: E-book,  Teaching English, Computer Students
A Mathematical Model of the Cognitive Semantics of the English Preposition ON

This study aims to present a mathematical linguistic analysis in establishing the relations between TRs, LMs, potential senses, and actual senses by using the case of the preposition on found in academic texts under the framework of Trajector (TR) and Landmark (LM) configurations. Data were corpora taken from 10 bachelor’s theses written by Indonesian students. To sort the data, Ant Conc 3.4.1.0 was used to parse clauses or sentences based on the TR-LM configurations. Based on the TR-LM configurations, a mathematical model was developed to discover how these variables are quantitatively related to the number of potential senses produced by using a geometric representation of TR and LM. This study indicates that the relation between TRs and LMs, on the one hand, and the sum of potential senses, on the other, follows the integral function of , which means that the total number of potential senses of Ps equals the integral of TR with respect to LM. Meanwhile, the total number of actual senses, ∑As can be obtained by the integral function of , which equals TR.LM + C where C is -Ls representing the constant of the number of lost senses. This mathematical modeling confirms that TR-LM configurations may be used to generate senses which prove the polysemous nature of prepositions.
BL-general fuzzy automata and minimal realization: Based on the associated categories

The present paper is an attempt to study the minimal BL-general fuzzy automata which realizes the given fuzzy behavior. Of two methods applied for construction of such automaton presented here, one has been based on Myhill-Nerode's theory while the other has been based on derivatives of the given fuzzy behavior. Meanwhile, the categories of BL-general fuzzy automata and fuzzy behavior, along with a functorial relationship between them, are introduced.
Metadata in Psychology 1.0: What researchers really need - Study description of the data referring to the expert interviews conducted in the BMBF-funded project PsyCuraDat

This research was funded by the Federal Ministry of Education and Research (BMBF), funding code: 16QK08.
Deterministic Algorithms for Solving Boolean Polynomial Equations Based on Channel Coding Theory

Solving the satisfiability problems of Boolean polynomial equations is still an open challenge in the fields of mathematics and computer science. In this paper, our goal is to propose a non-algebraic method for solving maximal Boolean polynomial equations (Max-PoSSo problem). By leveraging channel coding theory and dynamic programming, we propose three deterministic and robust algorithms for solving the satisfiability problems of Boolean polynomial equations. Comparisons are made among the three proposed algorithms and Genetic and Gröbner algorithms. Simulation results show that the proposed algorithms exhibit better performance in terms of the largest number of Boolean polynomials equal to 0 compared to the benchmark schemes in the literature.
Wearables as Augmentation Means: Conceptual Definition, Pathways, and Research Framework

Wearables pervade many facets of human endeavor, thanks to their integration into everyday artifacts and activities.From fitness bands to medical patches, to augmented reality glasses, wearables have demonstrated immense potential for intelligence augmentation (IA) through human-machine symbiosis.To advance an understanding of how wearables engender IA and to provide a solid foundation for grounding IS research on wearables and IA, this study draws from Engelbart's framework for augmenting human intellect to: (1) develop a conceptual definition of wearable technology as a digitally enhanced bodyborne device that can augment a human or non-human capability by affording context sensitivity, mobility, hands-free interaction, and constancy of operation, (2) extend Engelbart's framework to the sociomaterial domain to account for the emergence of augmented capabilities that are neither wholly social nor wholly material, and (3) propose and elaborate four augmentation pathways -complementation, supplementation, mediation, and mutual constitutionto facilitate IA research.
Promoting Critical Reading Strategy in Literacy Based-Writing Instruction: Indonesian Context

Teaching writing English as a foreign language (EFL) in university setting aims to achieve both linguistic and literacy competencies. It means that the instruction not only focuses on facilitating students to build writing English proficiency, but also on leading students to become literate. Based on this context, it is important for lecturers to select appropriate and effective strategies in the writing instruction. Critical reading is regarded as meaningful and effective reading strategy which can be applied by academic writing instructors or lecturers in order to enable the students in writing an English-written paper. This article attempts to discuss why and how critical reading strategy should be utilized in literacy model of EFL writing instruction, especially in writing a research paper. By understanding the relationship between reading and writing, it is hoped that the activities in the EFL writing classroom can be more meaningful.
Neural Network for Volatile Stock Market Prediction

Stock businesses assume extremely critical and challenging part for the current society. Historical datasets of the few stock markets has been collected and used for this purpose. Then it will be trained and compared by the real data to check the deviation from the actuality of the model. From the last few years, specialists have tried to build a computationally efficient techniques and algorithms which predict and capture the nature of stock market. Maximum number of the available reviewed paper has its own kind of limits as concentrating on the particular application of financial market or explores machine learning tools and techniques which applied on the particular dataset. This paper will provide the comparative study of all tools and the techniques available in the recent machine learning frameworks and uses in the field of the financial market prediction methods. The main aim of this study is: (i) a comprehensive study of the available and recent literature in this area, (ii) Provide a generalized modeling for the stock market analysis and prediction (iii) review of the fundamental challenges of the field (iv)Realization of model by the use of fundamental Neural Network package available in R-language.