Reviewerly/exHarmony huggingface dataset:
Pengembangan Sistem Pelatihan Jarak Jauh Berbasis Moodle di Balai Diklat Keagamaan Bandung

[MOODLE BASED DISTANCE TRAINING SYSTEM DEVELOPMENT IN BALAI DIKLAT KEAGAMAAN BANDUNG]. Developing online training system is essential to create equalization over training opportunities, enrich learning materials, and increase trainees participation in learning. The development is also a medium to create accessible learning environment. Online training requires learning management system. One of the most widely used applications is Moodle. It has the latest version that can be used to improve online training’s quality, namely Moodle version 3.7.1. This study aims to find out Moodle-based remote training system implementation at Bandung Religious Training Center in 2020. Research and Development waterfall model is applied. Application testing is by using blackbox techniques. The analysis uses SCORM and Haughey theory. The result showed that Moodle-based remote training is considered good based on black box tests. It can be seen from several aspects of websites, such as appearance and content, implementation, institutional readiness, as well as facilities and infrastructure. Moodle 3.7.1 based remote training websites are recommended for socialization and provided remote training manual instructions
An Application of Data Visualization in Electronic Health Records

An Application of Data Visualization in Electronic Health Records Asikur Rahman, Sherrie Komiak Abstract Recent development in Electronic Health Record (HER) systems has resulted in producing data at an unprecedented rate. The complex, growing, and high-dimensional big data available in EHRs represent great opportunities and challenges for researchers and practitioners in the healthcare section. To deal with such a challenge, we can take the advantage of advanced technology like information visualization techniques. This paper presents an outline of how we will apply different visualization techniques such as zooming, focusing, multiple coordinated views (MCVs), and details on demand etc. on the e-health data to visualize the medical and health information in a very interactive way. The proposed application can help the doctors and medical students with their decision making. Doctors or medical students will be able to visualize the information based on their needs which will improve the decision accuracy and decision time as well. Full Text: PDF DOI: 10.15640/jmise.v7n2a1
Google Translate Accuracy in Translating Specialized Language From English to Bahasa Indonesia:

This research aims to discover the accuracy level of Google Translate in English -Bahasa Indonesia translation, especially in translating specialized language in mechanical engineering terms.This study belongs to descriptive qualitative research.The primary source of the data was the output translation texts of Google Translate.The sample texts were sentences taken from the book of the mechanical engineering field.The main concerns were on word and phrase levels.Since there were only two categorizations in accuracy research, the researchers divided the number into two.A text was said to be accurate if the accurate meanings were more than 50%.Conversely, it was inaccurate if the accurate meanings were less than 50%.The results revealed that in English -Bahasa Indonesia translation, Google Translate, especially in translating specialized language in mechanical engineering terms, was accurate for word translation, but inaccurate for phrases.This statement was based on the findings of accurate occurrence percentage in Google Translate translations, which were 70.73 % for the word level and 46.37% for the phrase level.The number for words was above 50 %, but it was the opposite for phrases.The inaccuracy was indicated by four inaccuracy indicators (omission, addition, different meaning and zero meaning).Moreover, several sentences translated by Google Translate were inaccurate.This research unveiled that Google Translate translated the sentences or paragraphs based on the sentence structure from the source text and the general meaning of the words or phrases.
Interactive Qr Code Based Online Shopping System

User authentication over the Internet has long been an issue for Internet service providers and users. A good authentication protocol must provide high security and mutual authentication on both sides. In addition, it must stable in safety measures and usability, which has been publicized in the literature to be a difficult problem. The solution for this issue, we suggest a new common verification and validation authentication protocol with high security and usability. In QR code based Interactive online shopping system the waiting time of people is being converted to shopping time even travel time. Whether by Metro, Railway or by any means of public transport life has become tedious. In this system, the various QR codes will be generated for the daily used household products. These QR code are posted along with its advertisement at different locations like Railway stations, Bus stops, Cinema Halls & public places. That means system is bringing shopping malls to busy life style. People feel comfortable with this system instead of visiting a mall to shop by adjusting their time from their busy schedule. There will also be reductions in shopping mall investments. Anyone who wants to buy the product after looking in our hoardings will immediately scanning the Quick Response code by his smart phone, which is having QR camera. He will get all the details about that product along with cost and end user should place the order for that along with quantity he wants. As this proposed system will take online orders from customers, accepting their payments, providing information about order confirmation, and many more activities getting involved as a functionality of our system from order placing to order deliver to the customer.
Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction

Existing methods for incorporating symmetries into neural network architectures require prior knowledge of the symmetry group. We propose to learn the symmetries during the training of the group equivariant architectures. Our model, the Lie algebra convolutional network (L-conv), is based on infinitesimal generators of continuous groups and does not require discretization or integration over the group. We show that L-conv can approximate any group convolutional layer by composition of layers. We demonstrate how CNNs, Graph Convolutional Networks and fully-connected networks can all be expressed as an L-conv with appropriate groups. By allowing the infinitesimal generators to be learnable, L-conv can learn potential symmetries. We also show how the symmetries are related to the statistics of the dataset in linear settings. We find an analytical relationship between the symmetry group and a subgroup of an orthogonal group preserving the covariance of the input. Our experiments show that L-conv with trainable generators performs well on problems with hidden symmetries. Due to parameter sharing, L-conv also uses far fewer parameters than fully-connected layers.
Usability Testing pada Sistem Informasi Akademik IAIN Salatiga Mengunakan Metode System Usability Scale

One of the goals of implementing a smart campus is to improve public services. Whereas the Academic Information System (Siakad) is a very important type of management information system (MIS) digital service. To find out the quality of service from the viewpoint of the user of this information system, a usability test was made. Usability is a qualitative analysis in determining the quality of an information system based on the user. The purpose of this study is to conduct usability testing SIAKAD in IAIN Salatiga using the System Usability Scale (SUS) method. Based on the usability test results, the majority of respondents use and utilize this information system well, this is proven by the value of the test results of 84,75. The evaluation results show that the Academic Information System at IAIN Salatiga is categorized as excellent.
ZigBee Technology to Provide Elderly People with Well-Being at Home

Background: The number of elderly people is growing, and it becomes a challenge for the families and governments regard their health. They are facing declines in their health and missing some of the quality of life that is provided to them. They are unable to control their self. They are vulnerable to fall and get in dangerous or restricted area. Aim: This study aims at conducing an experiment using Real-Time Location System (RTLS) via ZigBee technology in order to track the elderly person at his/her home. Objectives: To track the old age people and provide them proper care and support whenever they are in challenging times. Method: RTLS is a system that offers a real-time tracking and positioning of people or objects. Results: The results showed that the ZigBee technology provides valuable insights to determine the location of an elderly person at home and sends an alert when a person gets in a restricted area. Conclusion: ZigBee have contributed to be one of the best of its kind protocol that can help to track and mange old aged people at house.
Benefits and Risks of Introducing Artificial Intelligence Into Trade and Commerce: The Case of Manufacturing Companies in West Africa

With innovations in technology, the application of artificial intelligence (A.I) in the area of commerce is rising to the top with an expected growing number of business transactions not just for entrepreneurs but for consumers as well.It advances the understanding of how A.I. can be used to enhance businesses around the world by establishing their presence online to reach customers beyond borders.This study highlights the benefits and risks of introducing A.I. into trade in terms of how the commerce industry operates and revolutionize products shopping.Significantly, the primary aim of this paper is to explore ways A.I. is integrated into commerce to help understand its impact on existing/potential customers and its efficiency in sales processes.With a sample size of 2,903 manufacturing companies in four West-African countries, the empirical results show that value-based adoption of A.I. approach outperforms the traditional/human search of customers' products delivery in both convenience, accuracy and profitability.Furthermore, A.I. approach within commerce achieved competitive advantage with several modernized customer service machine learning approach such as automated content creation, voice assistance, image search among others.Clearly, this shows that the application of A.I system into commerce introduces significant competitive advantages in terms of trust, quality, openness and security.
The 4mat Model in English Language Teaching

This paper aims at highlighting the 4MAT Model as a teaching procedure in teaching English Language for undergraduate students at colleges and Universities.Moreover, the current study tries to focus on using the 4MAT model as a framework for teaching and learning English as a Foreign Language based on the principles of learning methods and their relationship to the learning cycle.The results of the study indicate that the learning cycle is designed 4 MAT with four learning styles.Each quarter is appropriate for teaching methods, which were used depending on the dominance of the right or left hemisphere of the learner's brain in order to be suitable for all types of learners.
The manipulation of the advertising discourse: rhetoric and stylistic aspects

This type of communication is presented in the form of brand advertising messages on the eve of various holidays or events linking a product with them. Online platforms and applications commercials are getting the significant evolvement in the TV advertising discourse. Online advertising drags TV content consumers into digital communication. Eventually, it should be emphasized that advertisers exert influence on the discourse as well as develop the advertising communication and, consequently, affect the recipients’ emotions. Consumers, in turn, guided by emotions, make a choice in favor of a particular product.
Stakeholders’ Perspectives on Benefits and Challenges in Blockchain Regulatory Frameworks

Prior research has indicated that institutional stakeholders influence the elaboration of regulatory frameworks by seeking to maximize their institutional power to achieve favorable policy outcomes. In recent years, Blockchain services regulation has been issued with the aim at influencing the process of technological change and diffusion. Based on stakeholder theory and empirical data collected from key stakeholders, this chapter seeks to contribute to the literature on stakeholder involvement in formulating and enacting regulatory frameworks, understanding the perspectives and needs of different key stakeholders regarding benefits, challenges, and expected outcomes of legislatives initiatives to regulate services based on Blockchain Technology/Distributed Ledger Technology (BCT/DLT). Findings show that the various stakeholders analyzed have competing views and interests in the respective service regulation that ranges from the functioning of BCT/DLT services inside financial markets (financial regulators and government decision makers), to safety, security, and practical risk-management and operational measures for their conducting business (lobbyists and Fintech firms).
Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST)

An abundance of various segmentation techniques are available in the literature, that cater to wide range of image understanding applications.The paper proposes a unified way of systematic categorization of the research work on image segmentation called Multi-Faceted Hierarchical Image Segmentation Taxonomy (MFHIST), which consist of six facets presented in a hierarchical manner -scope, requirement, control, feature, image representation and approach specifications.Every scope is exemplified with research works from the literature for better understanding.The paper gives an illustration of populating MFHIST, to provide the reader a quick grasp of few important state-of-art image segmentation research works and their adaptations.As a case study, the illustrations display a limited version to uncover the journey of basic to modern adaptations in the areas region based segmentation approach, such as Markov Random Fields, Spectral Clustering, Active Contour Model, Mean Shift Clustering.The other segmentation approaches have not been considered here, owing to the enormous volume of works in the past four to five decades and limitation in articulating all of them using MFHIST.The performance analysis of the algorithms using quantitative metrics is not in the present scope and will be considered in future version of MFHIST.
SEQUENTIAL CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC BREAST CANCER IMAGE CLASSIFICATION USING HISTOPATHOLOGICAL IMAGES

Breast cancer is the major cause of death among the women in all over the world and detection of breast cancer at early or initial stage can increase the survival rate of the patient. Malignancy of the cells of breast tissue is detected for diagnosing the breast cancer. Now days various image processing techniques are used to analyze the histopathological images for diagnosing the cancer. Manual detection of these cells is very time consuming and the result will depend upon the experience of the pathologist. So computer aided techniques are used for fast processing and accurate result of the diagnosis. The Deep learning model uses the Convolutional neural network (CNN) that automatically extracts the features and classify the image using fully connected network. In this paper, we have trained a Sequential convolutional neural network and obtained the highest prediction accuracy for detection of breast cancer up to 99.61%.
A Frobenius Algebraic Analysis for Parasitic Gaps.

The interpretation of parasitic gaps is an ostensible case of non-linearity in natural language composition. Existing categorial analyses, both in the typelogical and in the combinatory traditions, rely on explicit forms of syntactic copying. We identify two types of parasitic gapping where the duplication of semantic content can be confined to the lexicon. Parasitic gaps in adjuncts are analysed as forms of generalized coordination with a polymorphic type schema for the head of the adjunct phrase. For parasitic gaps affecting arguments of the same predicate, the polymorphism is associated with the lexical item that introduces the primary gap. Our analysis is formulated in terms of Lambek calculus extended with structural control modalities. A compositional translation relates syntactic types and derivations to the interpreting compact closed category of finite dimensional vector spaces and linear maps with Frobenius algebras over it. When interpreted over the necessary semantic spaces, the Frobenius algebras provide the tools to model the proposed instances of lexical polymorphism.
Optimize 2-D Path Planning of Mobile Robot by using ACO with Guidance Factor

In this research article, the path planning of a mobile robot done by using ant colony optimization (ACO) with guidance factor. A two-dimensional (2-D) threat map structure design for path planning strategy, in which threat points are fixed in the path of moving robot. The two main objective of this research; to reach the mobile robot at the target position by using optimal route using ACO strategy. Secondly, by using guidance factor all the ants of ACO arrive at the fixed targeted area. Moreover, the results of the proposed algorithm compares with the classical ant system methodology. The simulated results show that the design method has short path planning and less steady state error to reach the designated path robustly.
Global Interpretation for Patient Similarity Learning

As an important family of learning problems in healthcare domain, patient similarity learning has received much attention in recent years. Patient similarity learning aims to measure the similarity between a pair of patients according to their historical clinical information, which helps to improve the clinical predictions of the patient of interest. Although patient similarity learning has achieved tremendous success in many real-world applications, the lack of transparency behind the behavior of the learned patient similarity model impedes users from trusting the predicted results, which hampers its further applications in the real world. To tackle this problem, in this paper, we investigate how to enable interpretation in patient similarity learning and propose a global interpretation method for patient similarity learning. Based on the proposed global interpretation method, we can identify a minimal sufficient subset of data features that are sufficient in themselves to justify the global predictions made by the well-trained patient similarity model. The identified minimal sufficient feature subset can help us to better understand the overall behaviors of the learned model across different subpopulations of patients. We also conduct experiments on real-world datasets to evaluate the performance of the proposed global interpretation method.
Blockchain Based Spectrum Sensing: A Game-Driven Behavior Strategy

Spectrum sensing is an important step of Dynamic Spectrum Access (DSA). However, the existing sensing technology are limited by the scarcity of sensing nodes, the narrow sensing range, and the security issues. In this paper, Crowd Sensing (CS) is used to collect sensed data with a large number of mobile devices equipped with sensors to ensure reliability and sensing range. In order to solve single point of failure caused by the vulnerability of the cluster in CS, we used the characteristics of blockchain (i.e. de-centralization and de-trust) to ensure the security and correctness of the spectrum trading process. Specifically, a Blockchain Based Spectrum Sensing (BBSS) system is proposed, in which requester recruit sensing workers to involve in spectrum sensing, sensing workers obtain benefits by participating in spectrum sensing and miner verifies and packages transactions into the blockchain. In particular, the recruitment and sensing worker's response is modeled as a Reward-Sensing-time game, and the strategy of requester is the total reward R, and the strategy of sensing worker is the sensing time t. We proved that both of them can adopt strategies to maximize their own benefits. Simulation results show that requester benefit increases with the growing number of sensing workers, but the amplification is smaller and smaller. A certain sensing worker who adopted strategy can achieve larger benefit compared with random allocation of sensing time when the number of sensing workers is smaller (less than 500) in the system.
An Efficient Search Algorithm for Large Encrypted Data by Homomorphic Encryption

The purpose of this study is to provide an efficient search function over a large amount of encrypted data, where the bit length of each item is several tens of bits. For this purpose, we have improved the existing hybrid homomorphic encryption by enabling the longer data items to be stored while using multiple encrypted databases and by suggesting an improved search method working on top of the multiple instances of the database. Further, we found the optimal number of databases to be needed when 40-bit information, such as social security number, is stored after encryption. Through experiments, we were able to check the existence of a given (Korean) social security number of 13 decimal digits in approximately 12 s from a database that has 10 million encrypted social security numbers over a typical personal computer environment. The outcome of this research can be used to build a large-scale, practical encrypted database in order to support the search operation. In addition, it is expected to be used as a method for providing both security and practicality to the industry dealing with credit information evaluation and personal data requiring privacy.
Multiple Robot motion Planning Considering shortest and safest trajectory

Extending multiple robot motion planning in unknown workspaces is a interesting scope in the Robot motion planning that in this paper is considered. It is used from radiation robots sensors in order to detect their surroundings and positions of the other robots. In this article a new approach considering two objectives is introduced. Finding safest path is as the first objective. The second objective is achieving a trajectory with minimum length. For finding the safest path, it is used from Voronoi Diagram (VD). To provide the safest path, minimizing the distance to the VS is as important criterion. Due to the VD is a geometric location with a distance from all obstacles to the workspace, therefore it is can be a suitable criteria for finding the safest path. For achieving the shortest path as the secondary objective, it is applied Euclidean distance the current position of the robot to goal position. Algorithm that applied for solving this problem is particle swarm optimization (PSO).