Reviewerly/exHarmony huggingface dataset:
Using Smart Contracts in Smart Energy Grid Applications

The evolution of the energy production and distribution towards innovative decentralized models, dictates the introduction of emerging technologies to transform the conventional energy sector into smart.In this paper, blockchain technology is introduced, in the concept of utilization and associated benefits in smart grid sector.Moreover, potential applications of smart contracts in the smart metering environment are discussed as well.Blockchain-based technology including smart contracts, is an ideal candidate for matching energy demand and production at smart grid level.As such, consumers will have the ability to become prosumers with a significant role in the continuous smart grid stabilization, since in a smart metering environment, all the contracting nodes will be able to streamline their energy consumption according to the grid demand.In addition to that, by the introduction of smart contracts within the smart grid landscape, smart meters can go one step further, allowing customers to pay as they go, rather than relying on monthly assessments.
Analyzing Learning Autonomy in Reading English Text at MTS DDI AS-Salman Sidrap

This research is aimed at investigating the implementation of autonomous learning. Autonomous learning consists of three aspects: self-planning, self-monitoring, and self-evaluating. This research is a use a mixed-method sequential explanatory design. The subject of the research is 24 students of the first grade students of junior high school. The writer observed the classroom, and also gave a set of questionnaire to the students to find out whether they are an autonomous learner or not. The result showed that in self-planning aspect, 56.83% students are self-planned; they are categorized into â€œpoorâ€. In self-monitoring aspect, 81.85% students are self-monitored; they are categorized into â€œgoodâ€. In self-evaluating aspect, 62.53% students are self-evaluated; they are categorized into â€œfairâ€. So, the students got good category in self-monitoring aspect, whereas for self-planning and self-evaluating must be improved.
Method to Capture and Analyze the Waist-Hip-Thigh Body Region of Seated-Standing 3D Scans

The purpose of this research was to explore new methods of 3D scanning, body postures, and landmarking techniques to complete in-depth analyses of skin deformation, measurement change, and shape change of the waist-hip-thigh region of the body.There is a need to develop and test new integrated measurement analyses using 1D, 2D, and 3D data to quantify how and where the body is changing in different postures.An integrated approach was taken to select the appropriate 3D scanning technology, develop a landmarking method, and position the body to analyze the waist-hip-thigh region.A convenience sample of 11 women participated in the pilot study, ranging in age from 41-73.Using a quadrant landmarking technique, the body was divided into sections to locally analyze 1D and 2D measurements, while conducting volume and curve analysis to aid our understanding of shape change.Local percent change of each circumference was significant, and the data across the various measurements captured the expansion and shrinking of the body.Additionally, the 1D, 2D, and 3D analysis of the models shows the body deforming differently based on participant size, indicating this type of data could be critical for improved size system creation.The results from the extraction of curves represents exciting frontiers in 3D shape research and in the future will enable shape to be more easily incorporated into wearable garments.This data can improve the development of materials, trims, pattern design, and sizing systems.New 3D scanning methods to quantify diverse bodies can improve a company's competitive advantage through enhanced product fit and inclusive, quality design for all.
Peranan Sistem Informasi Akuntansi dan Tantangan Profesi Akuntan di Era Revolusi Industri 4.0 (Sebuah Studi Fenomenologi)

&lt;p&gt;&lt;em&gt;In the last few years appeared a variety of breakthrough technology that generates new values in human life through a form of artificial intelligence, data, internet of things (IoT). Business circles call the third it as the core of the industrial revolution 4.0. These conditions have an impact on the role of the human being, particularly the challenges of the profession of accountants who will be replaced by the role of the robot. The purpose of this research is to provide a solution so that the role of the accountants still exist in the era of the industrial revolution 4.0. As for qualitative research methods via the fenomenalogi approach i.e. we allow qualitative researchers implement and apply subjective abilities and exploratory process in interpersonalnya. Results of the study so that the accountant still exist in the era of the industrial revolution 4.0 then the accountant to start learning programming and algorithms to be able to adapt to change, if you don't use the technology, then the role of the accountant will be replaced by robots.&lt;/em&gt;&lt;/p&gt;
Optimising the Particle Swam Optimazion Usage for Predicting Indonesia Presidential Election Result Period 2019-2024

Indonesia is a Democrat nation. A general election known as the PEMILU has become a tradition of the nation that is synonymous with political issues and leadership turnover. Social media is one place in expressing the opinions and aspirations of people including politics, Twitter is one of the social media used as a place for politicians including two couples of presidential candidate and vice president of INDONESIA in Campaign to win a vote in the elections of 17 April 2019. This research analyzes public opinion i.e. comments on Twitter accounts @jokowi, @KyaiMarufAmin, @prabowo, @sandiuno into two categories of positive and negative opinions by comparing the text classifier model Naïve Bayes and SVM, and the implementation of The PSO algorithm to obtain optimal accuracy results. The results of the study show Prabowo Sandi won the prediction of presidential candidate with the best accuracy result of 77.00% acquired model Naïve Bayes + PSO, and 86.20% acquired model SVM + PSO, with an increase in accuracy 7.5% on model SVM, And 2.1% on the model Naïve Bayes when compared before done optimization with PSO algorithm.
Low-Dose CT Image Denoising using Image Decomposition and Sparse Representation

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated.Specifically, we give new solutions for image decomposition to easily control the filter parameters, for dictionary construction to improve the effectiveness and reduce the running-time. Instead of using a large dataset of patches, only a structured small part of patches extracted from the raw data is used to form a dictionary, to be used in sparse coding. In addition, we illustrate the effectiveness of the proposed method in preserving image details which are subtle but clinically important. Experimental results conducted on both synthetic and real noise data demonstrate that the proposed method is competitive with the state-of-the-art methods.
Information Support of Educationalists as an Important Function of a Postgraduate Education System

The conducted research on the status of information support of educationalists made it possible to install that such basic functions of scientific and pedagogical information as analytical-prognostic, integrative, and a function of operational and purposeful formation of various categories of specialists at present are not sufficiently implemented. This can be accounted for by the versatility and complexity of pedagogical process itself, as well as by a low level and limited range of developing operational systems differentiated scientific and methodological services to various categories of teaching staff. The research classified the types of information required in the system of postgraduate education of teaching staff.Based on the theoretical analysis of information access in the system of postgraduate pedagogical education (PPE), and programs of information and library service of teaching staff, it has been rationalized that there is a great necessity for establishing centers of scientific and information support aiming at improving the qualification of teaching staff. With this in view, ways of improving the informational function of PPE have been thoroughly studied and revealed. The most significant of them suggests that an automated corporate information system and a corresponding Internet site must be created. This can ensure the remote search and delivery of electronic materials from the funds of the State scientific and pedagogical library, and the libraries of educational institutions; an exchange of resources with other libraries and organizations; developing information and telecommunication technologies in institutions of postgraduate education; training teachers to use computer technologies, etc.
A multi-objective optimization procedure for solving the high-order epistasis detection problem

There are multiple research works that establish a relationship between Single Nucleotide Polymorphisms (SNPs) and complex diseases. In many cases, these diseases are caused by the interaction of two or more SNPs (epistasis). Therefore, it is important to identify which SNPs lead to the emergence of diseases. However, this problem gets harder when the epistasis order and the number of SNPs in the dataset under study are increased. To tackle this problem, this work presents an implementation of a multi-objective optimization algorithm with a problem-aware offspring process. Furthermore, due to the large amount of data in the SNPs datasets, we have also parallelized this algorithm. We experimentally validate the quality of the proposal under epistasis sizes of 2, 5, and 8 loci, although the application is not limited to those values. Moreover, a thorough comparative study of biological performance with six state-of-the-art methods has been conducted, obtaining better results than all the other biological methods. Our results confirm that, using the proposed approach, large datasets with high-order epistasis sizes can be processed in reasonable time, obtaining solutions of good quality. According to the literature review performed until the acceptance of this article, there are no other authors’ works that can process epistasis sizes above 6 loci in large datasets.
Efficient Mapping of CNNs onto Tightly Coupled Processor Arrays

In this work, we show how to systematically map Convolütional Neüral Networks (CNNs) onto Tightly Coüpled Processor Arrays (TCPAs), a class of massively parallel accelerators for many compütationally intensive tasks (e.g., from the digital signal and image processing domain).Contrary to previoüs approaches and implementations, we propose techniqües for the layer-parallel execütion of CNNs on processor arrays inclüding the maximally overlapped processing of consecütive layers.This is achieved throügh layer füsion and loop ünrolling to exploit the füll pipelining potential of süch massively parallel architectüres for given CNNs.These transformations are also necessary to decrease the nümber of onchip/off-chip data transfers.For CNNs, we present a calcülüs for achievable performance and memory reqüirements on TCPAs.Based on this calcülüs, it is shown how either throüghpüt-maximal mappings can be determined for a given architectüre.Alternatively, resoürce-minimized mappings to süstain a given throüghpüt, e.g., nümber of frames per second, are systematically derived.The approach is evalüated for a CNN model for the MNIST benchmark on a processor array of size 4x4 inclüding a comparison of the performance of the layer-parallel approach over layer-by-layer processing.
Augmented Reality in Physics Education: Motion Understanding Using an Augmented Airtable

Education is a highly interesting field for Augmented Reality (AR) applications allowing for tangible experimentation and increased immersion. In this paper we present our efforts on adding an AR visualization on a physics airtable experiment used for the understanding of object motion and collisions on a nearly frictionless surface. Using AR, information such as the objects velocity, angular velocity and kinetic energy can be overlayed over the objects in real-time to give direct understanding of physics motion laws. We present the implementation of two versions of such an AR system, using an HMD and a projector respectively, and discuss the development challenges and advantages/disadvantages of each one.
Engineering of a Training Device and Skills Through the Integration of New Information and Communication Technologies in the Field of Exact Sciences

This study examines the degree of mastery and use of information and communication technologies (ICTs) in physics and chemistry by high school learners in Casablanca -Settat (Morocco), through a survey that was conducted in middle and high public schools, in order to use a training engineering consistent with the development of information processing systems (IPS).The research results made it possible to know that there are still problems in the use of digital tools and internet.In order to push learners towards successful integration, several obstacles will be mentioned in this study, which must be taken into consideration in future attempts..
Position Bias Estimation for Unbiased Learning-to-Rank in eCommerce Search

The Unbiased Learning-to-Rank framework [16] has been recently proposed as a general approach to systematically remove biases, such as position bias, from learning-to-rank models. The method takes two steps - estimating click propensities and using them to train unbiased models. Most common methods proposed in the literature for estimating propensities involve some degree of intervention in the live search engine. An alternative approach proposed recently uses an Expectation Maximization (EM) algorithm to estimate propensities by using ranking features for estimating relevances [21]. In this work we propose a novel method to directly estimate propensities which does not use any intervention in live search or rely on modeling relevance. Rather, we take advantage of the fact that the same query-document pair may naturally change ranks over time. This typically occurs for eCommerce search because of change of popularity of items over time, existence of time dependent ranking features, or addition or removal of items to the index (an item getting sold or a new item being listed). However, our method is general and can be applied to any search engine for which the rank of the same document may naturally change over time for the same query. We derive a simple likelihood function that depends on propensities only, and by maximizing the likelihood we are able to get estimates of the propensities. We apply this method to eBay search data to estimate click propensities for web and mobile search and compare these with estimates using the EM method [21]. We also use simulated data to show that the method gives reliable estimates of the "true" simulated propensities. Finally, we train an unbiased learning-to-rank model for eBay search using the estimated propensities and show that it outperforms both baselines - one without position bias correction and one with position bias correction using the EM method.
PVIDM: Privacy-preserving verifiable shape context based image denoising and matching with efficient outsourcing in the malicious setting

With the emerging techniques of wireless communication and cloud computing, large volumes of multimedia data are outsourced from resource-constrained users to the cloud with abundant resource for both delegated storage and computation. Unfortunately, there exists a risk of users' image privacy leakage in the process of outsourcing to the cloud in the malicious setting. Most of the existing work achieved privacy-preserving verifiable image feature extraction and matching by using public key (fully) homomorphic encryption (FHE), and the heavy computational overhead and communication overhead cannot adapt to resource-constrained mobile devices. Other works disabled to achieve image denoising in the encrypted domain or only focused on the scale-invariant feature transform (SIFT) descriptor that is inappropriate for position-sensitive feature extraction. To address these issues, in this paper, a privacy-preserving verifiable shape context based image denoising and matching protocol PVIDM with efficient outsourcing in the malicious setting is proposed. To achieve this end, by exploiting any one-way trapdoor permutation, an efficient privacy-preserving verifiable image denoising scheme PPVID is firstly proposed to improve the accuracy of image matching, without exploiting public key FHE. Then, based on PPVID, a lightweight shape context based privacy-preserving verifiable image feature extraction and matching protocol PPVIM is devised, where secure and efficient comparison and counting protocols in the encrypted domain are respectively proposed. Both image privacy and image matching result privacy are well protected, and the correctness of image matching result can be efficiently verified. Finally, formal security proof and extensive simulations on real-world data sets demonstrate the efficiency and practicability of our proposed PVIDM. Especially, both the computational complexity for the usage of any one-way trapdoor permutation at data owner's/user's end and the size of the authentication tag generated by the cloud are O(1), independent to both the number of database images and the number of pixels in each image.
Scrum Agile Framework in E-business Project Management: An Approach to Teaching Scrum

With the rise of agile within companies worldwide, it is crucial for information systems education to keep up with this trend to ensure curriculum and courses are up-to-date.The topic of agile in teaching and learning is critically important.This paper attempts to point out the need to understand and implement learning about the agile approach to education.The aim of the paper is to explain Scrum and its application in student education in IT project management areas.We propose a method for managing students' project using Scrum, which enables students to learn the subject matter in a project-oriented approach.The idea is that students who study IT project management learn Scrum by leading another student team making an IoT project.The implementation was provided at The Department of e-business, Faculty of Organisational Sciences.In conclusion, the results of teaching Scrum in this way are presented and show that scrum application has a positive impact on project implementation and student education.
Multilingual Dialogue Generation with Shared-Private Memory

Existing dialog systems are all monolingual, where features shared among different languages are rarely explored. In this paper, we introduce a novel multilingual dialogue system. Specifically, we augment the sequence to sequence framework with improved shared-private memory. The shared memory learns common features among different languages and facilitates a cross-lingual transfer to boost dialogue systems, while the private memory is owned by each separate language to capture its unique feature. Experiments conducted on Chinese and English conversation corpora of different scales show that our proposed architecture outperforms the individually learned model with the help of the other language, where the improvement is particularly distinct when the training data is limited.
Implementation of K-Means Methods In Clustering Students Ability Levels in English Language

Nowadays, English extremely needs to be controlled, especially students, in communicating and reading also understanding literature written in English. In achieving mastery of English, the students, in this case, the students who are not majoring in English are given a common base subject of English. In Politeknik Indonesia, especially majoring in a Bachelor's Degree in Informatics Engineering, teaching English is using the direct method, to find out the results of teaching English within three semesters. Therefore, by doing this research for classifying the level of ability of students into three categories, they are Beginner, intermediate and advanced. The objective of the grouping is to determine how many students who have the capability level is low, medium and high so that the faculty can determine the average level of students' proficiency and the lecturers can intervene to conduct teaching in developing the students' knowledge of English. The classification used the K-Means clustering algorithm, which is one algorithm that classifies the same data on specific groups and different data in the other group. The results of this study by applying the k-means clustering method is the researchers can classify the students based on students' ability levels either they are beginner, intermediate or advanced.
Pelatihan Pemanfaatan Mendeley Sebagai Reference Tool Pada Artikel Ilmiah Mahasiswa Pendidikan Akuntansi Unipma

This dedication aims to train the use of reference tools in scientific articles on Accounting Education students at Universitas PGRI Madiun (UNIPMA) in order to answer the need for publication of scientific papers and to adhere to the ethics of publication. The dedication is carried out to all UNIPMA accounting education students who will graduate in the even semester of the 2018/2019 school year by means of presentation of material, examples, practice, and evaluation of work results. The result is that all students can cite and create automatic bibliography in their respective articles.