Reviewerly/exHarmony huggingface dataset:
Blockchain Network as a Platform: Conceptualising its Adapted Layered Architecture Design

Digital platform as an organising logic has prominently reshaped the innovation activities in many sectors.Previous studies have extensively investigated the digital platforms from two views: economic view (i.e. as a sided marketplace) and engineering view (i.e. as an innovation infrastructure).Blockchain -a digital artefact that connects the distributed ledgers -resembles great overlaps and specifics to digital platforms.Building on this aspect, I first demarcate the Blockchain Product as a Platform (BPaaP) informed by the economic view and the Blockchain network as a platform (BNaaP) inspired by the engineering view.Given the scant of research around BNaaP, this research aims to depict the BNaaP's architectural design by drawing on the layered design of digital technologies.Using Ping An Group as a case, this research applies the thematic analysis method in analysing online open data.As the main contribution, this research proposes the Adapted Layered Architecture of BNaaP that consists of (1) three design layers (foundation layer, interaction layer, and application layer); (2) adapted business scenarios; and (3) environmental factors.The suggested architectural design implies the BNaaP's internal synergistic collaboration among layers and the external adaptability to different business contexts.Overall, this research provides a novel angle to understand the Blockchain phenomenon and brings insightful implications to Blockchain practitioners.
Using Personas to Visualize the Need for Data Stewardship

There is a current discussion in universities regarding the need for dedicated research data stewards. This article presents a set of fictional personas for research data support based on experience and requests by experts in different areas of data management. Using a modified Delphi study, 24 participants from different stakeholder groups have contributed to the skills and backgrounds necessary to fulfill the needs for data stewardship. Inspired by user experience (UX) methodology, different data personas are developed to illustrate the range of skills required to support data management within universities. Further, as a competency hub for data stewards, the development of a research data support center is proposed.
Development of Learning Everywhere Class (LEKAS) Platform for Economic Education

The main objective of this research is to develop an Android-based mobile learning platform for economic education students so that they can study anywhere and anytime. Following the current trend, learning must be flexible in accordance with learning styles that tend to lead to online learning. Students will be more comfortable studying with their smartphones anywhere than having to carry books everywhere. This platform consists of several features, namely lecture material, evaluation results and progress of reading material in the form of PDF and video embedding. The design development in this study uses the ADDIE model, which consists of 5 stages, namely Analysis, Design, Development, Implementation, and Evaluation. This product was tested on 60 students of economic education class 2019 in introduction of macroeconomics courses. The final product of this research is an Android-based mobile learning platform with the title LEKAS (Learning Everywhere Class). The results of the evaluation of this product are that there is still a need for improvements in the ease of access to various types of android. The advantage is that students can immediately learn and find out the extent of understanding by taking tests after studying the material that has been shared. Through this platform students can study material and evaluate the material they read anywhere and anytime. The convenience provided on this platform will be able to increase the effectiveness and efficiency of learning in economic education.
Android Based English Learning Media and Quiz Using Augmented Reality

The pandemic hits the world today has made educators and parents moved to develop attractive English learning media for young learners to keep them excited while learning from home.Android smartphone devices are currently popularly used for interaction media between young learners and teachers during learning from home.From this background, researchers developed English learning media using Augmented Reality that can be easily accessed from Android smartphone for young learners.This research focuses on creating learning media and English quizzes based on Android using Augmented Reality (AR) technology.The English materials used in this research are taken from Thematic English Learning 1 book from chapter 1 to chapter 5 and focused on enriching student's vocabulary.The AR application was made using Unity with the addition tool named Vuforia SDK.It is used to detect an image target so that the AR objects can be displayed throughout the screen.Vuforia SDK detects image target by comparing captured images with those stored in the database.This research resulted 24 threedimensional AR objects, 6 two-dimensional AR objects which are ready to be used as English learning media and 54 quizzes which are feasibly to evaluate students' skills after learning English through AR objects.
Dual Phase CBIR Model using Hybrid Feature Extraction and Manhattan Distance Measure

Recently, the multimedia complexity is noticeably increased due to the growth of internet and multimedia technologies and it has become one of the prime research topics in multimedia content retrieval.In this research, a dual phase model is proposed to enhance the performance of content based image retrieval.Initially, normalization technique is employed to improve the visibility level of the images that are collected from Wang and COREL datasets.Next, color moments and Alex-Net convolutional neural network are used to extract the feature values from the normalized image.By combining low and high level features, the semantic space between the extracted feature values is less that helps in retrieving the relevant content images significantly.Finally, the Manhattan distance measure is applied to measure the distance between the query image and an image dataset to retrieve the relevant images.The experimental result showed that the proposed dual phase model achieved better performance in content based image retrieval by means of recall, precision and f-score.From the comparative analysis, the proposed dual phase model showed a maximum of 0.43 precision value and 0.06 recall value improvement in content based image retrieval compared to the existing works like Bi-layer system, spatial and frequency domain features with Euclidean distance, and local directional pattern with color histograms.
Students’ Perception on Learning Unit Conversion Through a Smart Conversion Card Tool

In the fundamental of Physics, the measurement of physical quantities is expressed in terms of units.However, most students have been having difficulties in understanding the correct method to perform unit conversion of any physical quantities.To tackle this problem, the use of flashcards namely Smart Conversion Card (SCC) tool was introduced in the teaching and learning process.This paper discusses the students' perception towards the effectiveness of using flashcards in Physics I (PHY430) course, in order to give them better understanding of the concept of unit conversion that need to be applied throughout the syllabus.The flashcards were distributed among students who enrolled in the PHY430 course during their first semester of study in the Bachelor's Degree (Hons) Computational Mathematics programme in UiTM Branch of Negeri Sembilan, Campus of Seremban.The effectiveness of using flashcards as a learning tool was demonstrated through scores obtained by the students' responses in the pre-test and post-test questions.Based on the results obtained, 84% of students managed to perform unit conversion after using the flashcards, and 70% of them improved their score in post-test questions.
AUTOMATIC ANTHROPOLOGICAL LANDMARKS RECOGNITION AND MEASUREMENTS

Abstract. Many anthropological researches require identification and measurement of craniometric and cephalometric landmarks which provide valuable information about the shape of a head. This information is necessary for morphometric analysis, face approximation, craniafacial identification etc. Traditional techniques use special anthropological tools to perform required measurements, identification of landmarks usually being made by an expert-anthropologist. Modern techniques of optical 3D measurements such as photogrammetry, computer tomography, laser 3D scanning provide new possibilities for acquiring accurate 2D and 3D data of high resolution, thus creating new conditions for anthropological data analysis. Traditional anthropological manual point measurements can be substituted by analysis of accurate textured 3D models, which allow to retrieve more information about studied object and easily to share data for independent analysis. The paper presents the deep learning technique for anthropological landmarks identification and accurate 3D measurements. Photogrammetric methods and their practical implementation in the automatic system for accurate digital 3D reconstruction of anthropological objects are described.
Book Recommendation for Library Automation Use in School Libraries by Multi Features of Support Vector Machine

This paper proposed the algorithms of book recommendation for the open source of library automation by using machine learning method of support vector machine.The algorithms consist of using multiple features (1) similarity measures for book title (2) The DDC for systematic arrangement combination of Association Rule Mining (3) similarity measures for bibliographic information of book.To evaluate, we used both qualitative and quantitative data.For qualitative, sixty four students of Banpasao Chiang Mai school reported the satisfaction questionnaire and interview.For Quantitative, we used web monitoring and precision measures to effectively use the system.The results show that books recommended by our algorithms can suggest books to students "Very interested" and "interested" by 14.5% and 22.5% and improve usage of the OPAC system's highest average of 52 per day.Therefore, these systems suitable for library automation of Thai language and small library with not much book resource.
Video mosaic watermarking using plasma key

Video watermarking is one of the most widespread techniques amongst the many watermarking techniques presently are used; this is because the extreme existences of copyright abuse and misappropriation occur for video content. In this paper, a new watermarking algorithm is proposed to embed logo in digital video for copyright protection. To make the watermarks more robust to attack, host frame and host embedding indices must be changeable. A new algorithm is proposed to determined host frames by plasma function, Host location indices in frames are also determined by another plasma function. Logo is divided using the mosaic principle, the size of mosaic blocks is determined initially according to the degree of protection, whenever the size of mosaic blocks is small, it leads to safe embedding, and vice versa. Digital watermarks are embedded easily without any degradation for video quality, In the other side, the watermarked is retrieved by applying the reverse of proposed embedding algorithm and extracted watermark is still recognizable. The experimental results confirm that watermark is robust against three types of attacks which are addition of Gaussian noise, JPEG compression, and rotation process.
Model Simplification of Deep Random Forest for Real-Time Applications of Various Sensor Data

The deep random forest (DRF) has recently gained new attention in deep learning because it has a high performance similar to that of a deep neural network (DNN) and does not rely on a backpropagation. However, it connects a large number of decision trees to multiple layers, thereby making analysis difficult. This paper proposes a new method for simplifying a black-box model of a DRF using a proposed rule elimination. For this, we consider quantifying the feature contributions and frequency of the fully trained DRF in the form of a decision rule set. The feature contributions provide a basis for determining how features affect the decision process in a rule set. Model simplification is achieved by eliminating unnecessary rules by measuring the feature contributions. Consequently, the simplified and transparent DRF has fewer parameters and rules than before. The proposed method was successfully applied to various DRF models and benchmark sensor datasets while maintaining a robust performance despite the elimination of a large number of rules. A comparison with state-of-the-art compressed DNNs also showed the proposed model simplification’s higher parameter compression and memory efficiency with a similar classification accuracy.
Application of a Deep Neural Network to Phase Retrieval in Inverse Medium Scattering Problems

We address the inverse medium scattering problem with phaseless data motivated by nondestructive testing for optical fibers. As the phase information of the data is unknown, this problem may be regarded as a standard phase retrieval problem that consists of identifying the phase from the amplitude of data and the structure of the related operator. This problem has been studied intensively due to its wide applications in physics and engineering. However, the uniqueness of the inverse problem with phaseless data is still open and the problem itself is severely ill-posed. In this work, we construct a model to approximate the solution operator in finite-dimensional spaces by a deep neural network assuming that the refractive index is radially symmetric. We are then able to recover the refractive index from the phaseless data. Numerical experiments are presented to illustrate the effectiveness of the proposed model.
The Study of Monotonic Core Functions and Their Use to Build RNS Number Comparators

A non-positional residue number system (RNS) enjoys particularly efficient implementation of addition and multiplication, but non-modular arithmetic operations in RNS-like number comparison are known to be difficult. In this paper, a new technique for designing comparators of RNS numbers represented in an arbitrary moduli set is presented. It is based on using the core function for which it was shown that it must be monotonic to allow for RNS number comparison. The conditions of the monotonicity of the core function were formulated, which also ensured the minimal range of the core function (essential to obtain the best characteristics of the comparator). The best choice is a core function in which only one coefficient corresponding to the largest modulus is set to 1 whereas all other coefficients are set to 0. It is also shown that the already known diagonal function is nothing else but the special case of the core function with all coefficients set to 1. Performance evaluation suggests that the new comparator uses less hardware and in some cases also introduces smaller delay than its counterparts based on diagonal function. The potential applications of the new comparator include some recently developed homomorphic encryption algorithms implemented using RNS.
Illicit Activities Beneath the Surface Web: Investigating Domestic Extremism on Anonymous Social Media Platforms

Abstract At the beginning of 2021, the Internet was used to spread words to incite insurrection and violence through social media, incited a pro-Trump mob riot into a U.S. Capitol building. Furthermore, due to recent acts of domestic terrorism in Texas, New Zealand, and California, police authorities have begun investigating social media presence that premeditated harmful acts. In all three instances, the shooters posted their manifesto online and had a presence on 8chan. The common thread among all shooters is their identification as a white nationalist and their social media site affiliation with Infinitechan, where they hid their radical ideas. Similarities in the shooters’ profiles include their perceived viewpoints of population groups regarding their political, ethnic, and social identities. This paper will provide insight into the forums where domestic terrorists spread their agendas. It will also set the foundation for further research towards a strategic algorithm that compiles and analyses relevant users’ profiles by using OSINT and data analytics techniques).
EFEKTIVITAS PEMBELAJARAN BERBASIS STEAM TERHADAP KEMAMPUAN BERPIKIR KRITIS (HOTS) ANAK USIA 5-6 TAHUN

This study aims to determine the effectiveness of STEAM based learning on critical thinking skills (HOTS) in children aged 5-6 years at kindergarten District Ngajum Malang Regency. This research is categorized as quantitative research which is using Quasi Experimental research design Nonequivalent Control Group Design. The research was conducted in group B at RA Diponegoro and TK Bhayangkari. The research data are collected using observation and documentation technique. Data were analyzed using statistical analysis by Anova with SPSS 21.0 for Windows. This research results show that: (1) is 361,182 with significant value 0.000 &lt; 0.05, so the hypothesis of STEAM based learning on critical thinking skills (HOTS) in children can be accepted. According this research result, it can be concluded that the effectiveness of STEAM based learning that effect of to critical thinking skills (HOTS) in children aged 5-6 years.
A novel edge detection method based on efficient gaussian binomial filter

Most basic and recent image edge detection methods are based on exploiting spatial high-frequency to localize efficiency the boundaries and image discontinuities. These approaches are strictly sensitive to noise, and their performance decrease with the increasing noise level. This research suggests a novel and robust approach based on a binomial Gaussian filter for edge detection. We propose a scheme-based Gaussian filter that employs low-pass filters to reduce noise and gradient image differentiation to perform edge recovering. The results presented illustrate that the proposed approach outperforms the basic method for edge detection. The global scheme may be implemented efficiently with high speed using the proposed novel binomial Gaussian filter.
Management Educational Information System During Pandemic Covid-19 Through Teachers’ Professionalism and Pedagogic

Management of education information systems is the management and utilization of information and communication technology (ICT) in education or e-Education. The management of the education information system has contributed to facilitating the tasks carried out by all teaching staff during the pandemic that occurred in Indonesia. This research is a quantitative study where this study aims to determine whether implementing education information system management on the professionalism and pedagogic competence of teachers in MI Muslimat NU Pucang and SD Muhammadiyah 1 Pucanganom Sidoarjo. In the process, this study conducted data collection techniques in questionnaires, interviews, and observations. Data management obtained from data collection results is managed using quantitative techniques through statistical test management using SPSS version 19. It is known from the results of the regression test from the analysis of the research results showing that the t value in the table is 1.993, for professional competence t count is 4.540 then ( 4,540&gt; 1,993), and the pedagogic competence t count is 4,030 then (4,030&gt; 1,993). So it concluded that Ho is rejected. It states that the education information system's management affects teachers' professionalism and pedagogic competence at MI Muslimat NU Pucang and SD Muhammaidyah 1 Pucanganom Sidoarjo
Readiness of Teachers and Students to Take Minimum Competency Assessments

This research aims to analyze the readiness of teachers and students through their perceptions of the Minimum Competency Assessment (AKM) questions.This research was conducted at the Junior High School and Senior High School levels, the samples took in Jakarta, Bogor, Depok, Tangerang and Bekasi.There are 60% of Junior High School teachers said that most of the AKM questions were in accordance with the learning method and process, but in this case, there are only 40% of high school teachers.Middle school students' perceptions of 54% and SMA 50% said that the AKM questions were by the learning method or process.The learning process in schools has not emphasized students to think analytically.Teachers' readiness to face AKM needs to adjust teaching methods in the learning process.There are less than 50% of Junior High School and Senior High School teachers whose assessments were by the types of AKM questions.It means that more than 50% of the teachers do not use similar assessments with the AKM.There are 50% of Junior High School and Senior High School teachers said that most of the AKM was difficult.There are 28% of Junior High School and 34% of Senior High School Students said that most of AKM is difficult.In working on AKM questions, students can obtain answers based on their opinions.For students, if they have answered, it will be worth it.A recommendation that the teacher's book: be revised with HOTS learning accompanied by examples of HOTs questions.Readability of questions, such as the use of words that are familiar to students, clarity of appearance of pictures and complexity of question discourse.
CosG: A Graph-Based Contrastive Learning Method for Fact Verification

Fact verification aims to verify the authenticity of a given claim based on the retrieved evidence from Wikipedia articles. Existing works mainly focus on enhancing the semantic representation of evidence, e.g., introducing the graph structure to model the evidence relation. However, previous methods can’t well distinguish semantic-similar claims and evidences with distinct authenticity labels. In addition, the performances of graph-based models are limited by the over-smoothing problem of graph neural networks. To this end, we propose a graph-based contrastive learning method for fact verification abbreviated as CosG, which introduces a contrastive label-supervised task to help the encoder learn the discriminative representations for different-label claim-evidence pairs, as well as an unsupervised graph-contrast task, to alleviate the unique node features loss in the graph propagation. We conduct experiments on FEVER, a large benchmark dataset for fact verification. Experimental results show the superiority of our proposal against comparable baselines, especially for the claims that need multiple-evidences to verify. In addition, CosG presents better model robustness on the low-resource scenario.
A Complete Map of Conditional Knowledge Bases in Different Normal Forms and Their Induced System P Inference Relations Over Small Signatures

Conditional knowledge bases consisting of qualitativeconditionals play a predominant role in knowledge representationand reasoning. In this paper, we develop a full map of allconsistent conditional knowledge bases over a small signature indifferent normal forms. We introduce two new normal formsthat take the induced system P inference relation into account,the system P normal form (SPNF) and the renaming SPNF(ρSPNF) considering additionally renamings of theunderlying signature. For a two-element signature, we systematicallygenerate and compare all consistent knowledge bases in ANF,RANF, SPNF, and their renaming counterparts, as well as allcomplete system P inference relations induced by conditionalknowledge bases.