Reviewerly/exHarmony huggingface dataset:
Blockchain Technology in Healthcare

This article describes the implementation of Blockchain Technology in the HealthCare domain, using the Ethereum framework is an important tool.This is a proof of concept application (PoC); Since, it is Blockchain applications (Dapp) are heavily dependent upon smart-contracts, the application is required to use a solidity framework, ganache, Web3.js for Front-end development.This application shows how patient data can be retrieved and stored in the Blockchain cloud.The designed system also helps the doctors to directly connect to their patients.The opinion of experts can be made available to the patients.
RANCANG BANGUN SISTEM PERPUSTAKAAN DIGITAL (STUDI KASUS : SMK 1 BANDAR LAMPUNG)

State Vocational High School 1 Bandar Lampung several activities have used computers, but especially in the processing of library data is still done manually, every month officers have to look at the book data and calculate the number of borrowers per day to make monthly reports, it is very retang against data damage, the problem caused is when going to make monthly report officers often have difficulty if there are documents that are damaged or lost. because the officer does not have a backup of the daily report data archive. Based on these problems, the system is designed to be able to develop and implement digital libraries that can be accessed through the website so as to improve the quality of library services without limited space and time. The digital library is expected to facilitate students in the process related to the library. Because the library will be created utilizing Web-based technology. With the user's website will be easier in finding a collection of books, scientific journals, and others needed by each member of the library. In addition, the website can also be accessed anywhere, anytime, and anyone without having to come to the location of smk negeri 1 Bandar Lampung library
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer

Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling inaccuracies do not generalize to the physical world. In this paper, we present a domain-invariant representation learning (DIRL) algorithm to adapt deep models to the physical environment with a small amount of real data. Existing approaches that only mitigate the covariate shift by aligning the marginal distributions across the domains and assume the conditional distributions to be domain-invariant can lead to ambiguous transfer in real scenarios. We propose to jointly align the marginal (input domains) and the conditional (output labels) distributions to mitigate the covariate and the conditional shift across the domains with adversarial learning, and combine it with a triplet distribution loss to make the conditional distributions disjoint in the shared feature space. Experiments on digit domains yield state-of-the-art performance on challenging benchmarks, while sim-to-real transfer of object recognition for vision-based decluttering with a mobile robot improves from 26.8 % to 91.0 %, resulting in 86.5 % grasping accuracy of a wide variety of objects. Code and supplementary details are available at https://sites.google.com/view/dirl
SMAD: Text Classification of Arabic Social Media Dataset for News Sources

Due to the advances in technology, social media has become the most popular means for the propagation of news.Many news items are published on social media like Facebook, Twitter, Instagram, etc. but are not categorized into various different domains, such as politics, education, finance, art, sports, and health.Thus, text classification is needed to classify the news into different domains to reduce the huge amount of news available over social media, reduce time and effort for recognizing the category or domain, and present data to improve the searching process.Most existing datasets don't follow preprocessing and filtering processes and aren't organized based on classification standards to be ready for use.Thus, the Arabic Natural Processing Language (ANLP) phases will be used to preprocess, normalize, and categorize the news into the right domain.This paper proposes an Arabic Social Media Dataset (SMAD) for text classification purposes over the social media using ANLP steps.The SMAD dataset consists of 15,240 Arabic news items categorized over the Facebook social network.The experimental results illustrate that the SMAD corpus gives accuracy of about 98% in five domains (Art, Education, Health, Politics, and Sport).The SMAD dataset has been trained tested and is ready for use.
BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.
Errata for "teseo and the analysis of structural dynamic graphs"

In our paper [4], we experimentally evaluated our work, Teseo, together with five other systems under the LDBC Graphalytics benchmark [6]. We developed and publicly released [2] an ad-hoc driver for the purpose. Since the time the paper was published, a bug [1] in the driver has been found. Due to this bug, we discovered that the completion times for Graphalytics have been incorrectly measured by 1 second or slightly more than their actual values. This issue involves the results of Table 2, Figure 8 and Table 3 reported in our paper [4]. Still, because the bug equally affected all systems evaluated and it is only related to the measurements, most of the comparisons and the general conclusions in the paper still hold.
Deteksi Sarung Samarinda Menggunakan Metode Naive Bayes Berbasis Pengolahan Citra

Samarinda sarong is one of the cultural treasures in the form of cloth from Samarinda, East Kalimantan. It has a characteristic in the form of a square motif with a unique color combination. However, several people do not know the difference between a Samarinda sarong and a non-Samarinda sarong because the Samarinda sarongs may have a similar motif or color to a non-Samarinda sarong. This study aims to develop a Samarinda sarong detection method to distinguish between the sarong of Samarinda and non-Samarinda. The detection of the Samarinda sarong was carried out based on two features: color and texture. The feature extraction of color was applied using color moments and Gray Level Co-Occurrence Matrix (GLCM) for texture. The classification was implemented using the Naive Bayes method. The dataset used consists of 250 sarong images (150 Samarinda sarong images and 100 Non-Samarinda sarong images) divided into training and test data. It was divided using percentage split and cross-validation. The test results show the implementation of the color moments, GLCM, and Naive Bayes methods using a percentage split (70%) produce the best accuracy of 0.987 compared to using cross-validation (K=10) with an accuracy of 0.984. The difference may occur because the number of training and testing data used on percentage split and cross-validation is different. Moreover, the sarong images used on training and test data were chosen randomly. &#x0D;
An Adaptive Image Encryption Scheme Using Fractal Dynamical System and DNA Operations

The chaotic natures of a new fractal dynamical system have been investigated experimentally and used to yield chaotic sequences to design cryptosystems. An adaptive permutation-diffusion mechanism is introduced to achieve strong resistance against chosen/known plaintext attacks. Plain image related chaotic sequences are generated to shuffle the positions of pixels in the permutation stage. In the diffusion process, DNA coding, decoding operations and diffusion function are proposed to alter the pixels' gray values. The security and performance analysis, such as histogram analysis, correlation analysis, entropy analysis, differential analysis, have been done. The simulation results show that the proposed cryptosystem is secure enough for practical application.
Investment Analysis for Submarine Power Cable Project Between Belakang Padang Island and Sambu Island

PT Pertamina (Persero) Riau Islands proposed the installation of electrical power for the fuel storage tank terminal on Sambu Island with a capacity of 7 MVA. Currently, Sambu Island is still receiving electricity by operating a Diesel Power Plant (PLTD) with diesel fuel.&#x0D; Seeing that the nearest electricity supply from Sambu Island is Pulau Belakang Padang Electricity, then as a business solution to be able to meet the demand of PT Pertamina (Persero), PT PLN Batam will build a submarine cable between Sambu Island – Pulau Belakang Padang to distribute electricity from the Power Plant in Batam to Sambu Island through the Belakang Padang Island.&#x0D; PT PLN Batam must obtain a reference regarding the feasibility of investing in the construction of a submarine cable between Belakang Padang – Sambu Island. To get this reference, an investment analysis must be carried out to determine the business feasibility of this submarine cable development.&#x0D; The business feasibility parameters to be analyzed are NPV (Net Present Value), IRR (Internal Rate of Return), BCR (Benefit to Cost Ratio) and PBP (Payback Period). To further refine the business feasibility analysis, a sensitivity analysis was carried out to see the effect of changes in investment parameters.&#x0D; After conducting investment analysis, it was found that the Capital was carried out with a portion of 30% from equity and 70% loans from domestic financial institutions after comparing with foreign financial institutions, and the construction of this submarine cable was declared feasible because it met all the feasibility parameters. business, where NPV &gt; 0, BCR &gt; 1, IRR &gt; Discount Rate and PBP &lt; Loan Tenor.&#x0D; With the construction of the submarine cable, PT PLN Batam will get a potential profit of Rp. 12.358.560.473, - in the first year after the operation of the submarine cable with an electricity tariff of Rp. 2,054 /Kwh. This electricity tariff is cheaper than the electricity tariff using PLTD of Rp 3,000/Kwh.
SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL

The Text-to-SQL task, aiming to translate the natural language of the questions into SQL queries, has drawn much attention recently. One of the most challenging problems of Text-to-SQL is how to generalize the trained model to the unseen database schemas, also known as the cross-domain Text-to-SQL task. The key lies in the generalizability of (i) the encoding method to model the question and the database schema and (ii) the question-schema linking method to learn the mapping between words in the question and tables/columns in the database schema. Focusing on the above two key issues, we propose a Structure-Aware Dual Graph Aggregation Network (SADGA) for cross-domain Text-to-SQL. In SADGA, we adopt the graph structure to provide a unified encoding model for both the natural language question and database schema. Based on the proposed unified modeling, we further devise a structure-aware aggregation method to learn the mapping between the question-graph and schema-graph. The structure-aware aggregation method is featured with Global Graph Linking, Local Graph Linking, and Dual-Graph Aggregation Mechanism. We not only study the performance of our proposal empirically but also achieved 3rd place on the challenging Text-to-SQL benchmark Spider at the time of writing.
Students’ digital competence: A scoping review of measuring instruments

In today’s society, digital competence is becoming increasingly relevant, as this competence is necessary to function on both a personal and professional level. Digital competence is essential for students, since it enables them to exist in a digitalised world. Over the last few decades, the concept of digital competence has been used more frequently (Spante et al., 2018), and now it is actively discussed, particularly in terms of policy documents (European Council, 2018; European Commission, 2014; European Commission 2021). During the discussions related to policy, the following questions have been raised: 1) what kind of skills and knowledge people should possess in a knowledge society, and 2) what should be taught to young students and how it has to be done (Ilomäki et al., 2016). The purpose of the present scoping review is to provide a comprehensive overview of relevant research regarding the instruments commonly used to measure digital competence of university students. Arksey and O’Malley’s (2005) five-stage framework underpins the scoping review. Three databases were used to conduct a scoping literature review, including ERIC, ProQuest and EBSCO. The inclusion criteria were peer-reviewed publications written in English within the period from 2014 till 2020. Initially, 395 articles in total were selected; the full texts of 43 articles were assessed. Finally, only 13 out 395 articles that met the inclusion criteria were considered in the present research. This paper reports on three main categories: (1) definition of digital competence, (2) development and characteristics of an instrument measuring digital competence, and (3) key findings. The most commonly used framework found during this research was The European Digital Competence Framework for Citizens 2.0. (Vuorikari et al., 2016). A larger part of studies reports on a designed self-assessment questionnaire comprising of multiple-choice items and quantitative evaluation of the competence. The scoping review showed that the majority of the existing tests enable to assess students’ digital information searching, communication and technical skills. The findings of previous studies indicate that students tend to overestimate their digital competence and lack knowledge of basic topics, the ones related to information and data literacy. Our findings point to the necessity to use different approaches for assessing digital competence on different levels.
The Estimation of Iron Oxide Content in Soil based on Landsat 8 OLI TIRS Imagery in Wetland Areas

Wetland areas are volatile and have high iron content. In this study, through a remote sensing approach, especially using Landsat Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery, we discussed the method to estimate the presence of iron oxide in the wetlands of South Kalimantan in 2018, 2019, and 2020. Interpretation of the Landsat OLI TIRS was employed in April 2018, August 2018, February 2019, August 2019, March 2020, and August 2020. The band ratio method was used to determine the distribution of samples in this study. The results of the iron oxide index from the image were performed regression and correlation analysis with field measurement and laboratory test results to validate the oxide index values. The results showed that the iron oxide index value in the dry season was higher than in the rainy season. Iron oxide index value in open land was higher than in vegetation cover. The wetland was in dry condition during the dry season, making it easier to detect iron oxide values. Vegetation cover could reduce the iron oxide index value on the soil surface so that the iron oxide value was more easily identified in open land. The results of linear regression testing for the wet season sample obtained a coefficient of determination R² = 0.413, while the results of linear regression testing for the dry season sample obtained a coefficient of determination R² = 0.667. Thus, the Landsat image has strong enough to estimate the iron oxide index in the wetland area of Kalimantan.
Autoscaling Techniques for Web Applications in the Cloud

That cloud computing offers numerous benefits is the reason why lots of web applications are migrated to the cloud. Elasticity is one of the important features of the cloud that allows resources to be scaled dynamically based on resource demand. Resource scaling has to be efficiently implemented in order to meet Service Level Agreement (SLA) requirements as well as to keep the costs low. Besides, automatically provisioning resources a few minutes prior to actual demand helps maintain Quality of Service at the provider end. VM bootup time needs to be considered in auto scaling techniques. Various techniques are studied in the literature which facilitate elasticity in the Cloud. This article provides a comprehensive view of auto scaling techniques.
Preference and Demand for Digital Pathology and Computer-Aided Diagnosis among Korean Pathologists: A Survey Study Focused on Prostate Needle Biopsy

Digital pathology systems (DPSs) have been globally implemented, and computer-assisted diagnosis (CAD) software has been actively developed in recent years. This study aimed to investigate perceptions of digital pathology and the demand for CAD. An online survey involving members of the Korean Society of Pathologists was conducted, and a demonstration clip of the diagnostic assistant software for a prostate needle biopsy was shown to them to provide a simple experience with CAD. One hundred sixty-four Korean pathologists (13.6% of 1210 Korean pathologists) participated. The majority (77.4%) answered affirmatively regarding the necessity of implementing a DPS, and 26.8% had plans to implement or increase the use of DPSs in the following 2–3 years at their medical institutions. Pathologists felt that multidisciplinary care or conference accessibility (56.7%), remote consultation (49.4%), and big data building (32.9%) were useful parts of DPSs. Most pathologists (81.7%) responded that CAD software would assist with the diagnostic process. In a prostate needle biopsy, pathologists used the software to improve the measurement of tumor volume and/or length and core length but not to suggest a diagnostic name or Gleason grade. Korean pathologists who participated in the survey had highly positive perceptions of digital pathology and maintained a positive attitude toward the use of CAD software.
Internet of Things Adoption for Saudi Healthcare Services

Abstract Background: Recent studies in information systems have predicted that applications of the Internet of Things (IoT) innovations will revolutionise various sectors including healthcare. Besides the issues and opportunities of IoT based innovations, existing studies have shown limitations to advance the adoption of IoT-understanding and relevant interventions to benefit researchers and healthcare practitioners. Method: In this context, a systematic literature review study was conducted to re-position a qualitative, phenomenological investigation that could offer useful insights into the factors affecting IoT-adoption in a developing country’s healthcare service. In addition to it, five participants who worked in hospitals and clinics in Jazan, Saudi Arabia, took part in the semi-structured interviews developed based on the diffusion of innovation theory. Results: The study explored the relevant literature and evaluated how the outcome is used to identify the key delivers of IoT in healthcare. Conclusions: According to the findings, the capacity of the Saudi healthcare sector to accept and implement a new IT with IoT technologies is increasing and its integrations remains a debated issue.
Angstrom-Prescott Model for Predicting Global Solar Radiation in Mubi, Nigeria

Researchers In this study Angstrom-Prescott model was used to estimate the global solar radiation based on the monthly mean sunshine hour for Mubi town, Adamawa State. Several models have been proposed to estimate global solar radiation. The Angstrom constants a and b of Angstrom-type correlation used in estimating monthly average global solar radiation was estimated to be 0.27 and 0.54 respectively. The model developed has a good correlation coefficient with r = 0.87, obtained for this analysis shows the model best fits the data. The Angstrom-Prescott model developed in this study can also be applied to other cities to predict global solar radiation. The global solar radiation intensity predicted in this study can also be utilized in design, analysis and performance estimation of solar energy systems, which is gaining significant attention in Nigeria and the world at large.
Multi-scale Extracting and Second-Order Statistics for Lightweight Steganalysis

Steganography is a technology that modifies complex regions of digital images to embed secret messages for the purpose of covert communication, while steganalysis is to detect whether secret messages are hidden in a digital image or not. In recent years, it has become necessary to deploy computer vision based algorithms on devices that are mobile or have limited computational memories. However, the emergence of steganalysis prove the point that the more parameters available to the model, the better the presentation will be. In order to enable the model to achieve the extraction of steganographic noise with a tiny number of parameters, this paper proposes a lightweight steganalysis algorithm based on multi-scale feature extraction and the fusion of multi-order statistical properties. Compared with Yedroudj-Net, Zhu-Net and SRNet with S-UNIWARD embedding rate of 0.4 bpp, the numbers of parameters was decreased by 87.9%, 98.2%, 98.9% correspondingly and the accuracy of steganalysis was improved by 7.12%, 2.65%, 3.42% respectively. Our experimental results show that the model not only has a reduced number of parameters for existing steganalysis, but also can effectively boost the accuracy of steganalysis.
Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC

Abstract This paper introduces a new neural network based prior for real valued functions. Each weight and bias of the neural network has an independent Gaussian prior, with the key novelty that the variances decrease in the width of the network in such a way that the resulting function is well defined in the limit of an infinite width network. We show that the induced posterior over functions is amenable to Monte Carlo sampling using Hilbert space Markov chain Monte Carlo (MCMC) methods. This type of MCMC is stable under mesh refinement, i.e. the acceptance probability does not degenerate as more parameters of the function's prior are introduced, even ad infinitum. We demonstrate these advantages over other function space priors, for example in Bayesian Reinforcement Learning.