Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies: Miner Heterogeneity, Botnets, and Proof-of-Work Efficiency

Proof-of-work cryptocurrencies are heavily criticized for the alleged inefficiency of their mining mechanism. However, critics fail to distinguish between the resources that are used to secure the blockchain and those that are wasted. In this paper, we introduce a simple mining model and use this model to analyze the consensus protocol's efficiency, while accounting for the heterogeneity of the miners involved. We categorize the resources allocated by the miners as either useful or wasteful, and then use this to introduce a new measure of efficiency. We then demonstrate how this value depends on a set of potential miners and the variation of their marginal costs. Using this model, we then consider the existence of botnets and show how one could affect the security of the network. This analysis indicates that botnets can significantly change the mining landscape and, under certain circumstances, may lead to a dissipation ratio greater than one.
Review of the state-of-the-art methods for Privacy Preserved Classification in Outsourced Environment

An outsourced environment like cloud has become a default choice for organizations to store their data because of its extensive resources. This also allows the organizations to access their data on demand. Generally, any cloud service encrypts data collected from different data owners with its own credentials. In some special cases, when the data is sensitive, the data owners encrypt their data prior to outsourcing it to ensure confidentiality. But there is no provision for processing the encrypted data within the cloud environment without ever decrypting it. So, any data mining task such as classification over this encrypted data will require the data to be decrypted at some point of time. Moreover, privacy of any user's classification query is also at stake. As a consequence, for classification, the data needs to be decrypted by the cloud at some point of time and then processed to take proper classification decisions. However, this allows the cloud to learn about the sensitive data. Data owners have no choice but to perform the same task at their end partially or fully. But, they are reluctant to perform such heavy computations locally. This creates a need for mechanism that will perform classification over the encrypted data in an outsourced environment while maintaining its privacy. The conventional encryption methods do not allow operations on the encrypted data directly and hence are not useful in such classification problems. In this paper, we discuss the current status and problem associated with the various existing methods to solve the classification over encrypted data problem for privacy preservation assuming that encrypted data and the classification process are outsourced to the cloud. We also analyze pallier homomorphic encryption method to see how privacy of sensitive data uploaded to the cloud can be preserved by leveraging the properties of paillier homomorphic encryption.
Ontology-aided Word2vec based Synonym Identification for Ontology Alignment

Synonym identification is the key factor for ontology alignment. There are several researches which proposed synonym identification methods. However, most of the researches focus on the words in general contexts, which occurs the problem in finding synonym relations in certain domains. To address this problem, we suggest ontology-aided word2vec based synonym identification method. In this paper, we find domain-specific documents based on ontology for training word2vec model. To do so, we use Kernel Density Estimation (KDE) to estimate distributions of words and we Kullback-Leibler (KL) divergence to compare the distributions. Through this, we can find the synonym relations considering domain-specific context which is hard to be identified with existing methods.
Face recognition in office environments with Google AIY Vision Kit

The following document shows the design, development, and implementation of a computer vision system for face detection through convolutional neural networks. This system was first developed on a PC and then implemented in an embedded system such as the Google AIY Vision Kit; This kit is a powerful tool for working with machine and deep learning, as well as having an affordable cost. The main application of this system is for the surveillance of places with restricted access, such as laboratories, warehouses, offices. The methodology for the development of this system was: design the network, obtain a database for training, train the network, and test the performance of the system.
Noise Improves Noise: Verification of Pre-Training Effect with Weakly Labeled Data on Social Media NER

Recently, we are living in a flood of social media data. As the cost of data-annotating is too expensive, there is not much data annotated for natural language processing tasks. The purpose of our experiment is to boost the performance with weakly labeled data without human efforts. In this paper, we present the experimental result following our two purposes. First, we verify the effectiveness of using the pre-training depending on the source domain. Second, we figure out how much improvement appeared on each embedding model. Empirically, the pre-training improves the performance of the target domain regardless of the source domain. Our best model using weakly labeled Wikipedia data outperforms previous models with 49.89% F1-score.
Path Planning for a UAV by Considering Motion Model Uncertainty

The primary purpose of path planning for unmanned aerial vehicles (UAVs), which is a necessary prerequisite toward an autonomous UAV, is to guide the robot to the predefined target while the chosen path is optimized. This paper addresses the problem of path planning for an unmanned aerial vehicle in a 2D indoor environment, considering motion uncertainty. To cope with this challenge, the problem of motion planning is formulated in three parts. A vision-based extended Kalman Filter (EKF) is used to localize the UAV in the unstructured environment. To overcome motion uncertainty, the problem is modeled as a Markov decision problem (MDP). Finally, a novel dynamic feedback linearization based switching controller is proposed for point-to-point motion. Simulation and experimental results are given to show the effectiveness of the proposed path planning method in practice.
National Projects and Improvement of Monitoring of their Implementation in the Sphere of Lifelong Vocational Training

This is a concept article proposing trends and methods to improve monitoring of national projects in terms of training personnel for professional development programs at all levels. The paper is based on analysing Russian and international methodologies and practices of education development monitoring. The problem of precise defining personnel development quantitative indicators within national projects implementation is significant both for universities as a part of management decision planning and for government structures, with an information database (correctly representing the process and results of implementing national projects) to be relied upon by the latter. The analysis of Russian federal statistic monitoring forms of personnel development has revealed a number of weaknesses which complicate regular monitoring of national projects implementation. The research has also shown examples of incorrect key indicators setting. The article mainly aims at proposing directions for personnel development methods in ongoing professional development education system according to national project purposes in this part. The article compares EU’s, OECD’s, USA’s, and Russian methodologies and basic monitoring techniques for personnel development. There are revealed their significant differences, advantages and disadvantages, including those used within cross-country comparisons. The analysis shows that foreign models of projects framework cannot be applied to operational monitoring of national projects implementation. The Russian system of federal statistical monitoring requires a certain modification, which can be implemented quite easily and can provide monitoring of national projects implementation in terms of professional development. The article proposes suggestions on improving the monitoring system of national projects implementation according to monitoring purposes. Besides, it can be a basis for future harmonization research for Russian and foreign methodologies of education monitoring. Universities may use the suggestions expressed in the paper for improving national projects implementation monitoring as a part of their management practice. It will allow them to estimate a status and efficiency of their contribution to national projects implementation. The newly proposed methods of national projects realization monitoring can be used by educational bodies and universities within personnel training and improving the system of lifelong vocational training. These methods may be of help when developing control national projects implementation monitoring and vocational training system monitoring in general or in its parts.
PENGEMBANGAN SISTEM CERDAS UNTUK PREDIKSI DAFTAR KEMBALI MAHASISWA BARU DENGAN METODE NAIVE BAYES (STUDI KASUS: UNIVERSITAS PENDIDIKAN GANESHA)

Ganesha University of Education or Undiksha is one of the state universities in Bali, precisely in the city of Singaraja. In the admission of new students, Undiksha applies 3 admissions paths, as follows the State University National Admission Selection (SNMPTN), State University Joint Entrance Test (SBMPTN), and Independent Entrance Test (SMBJM) consisting of 2 parts namely Computer Based Test (CBT) and Interests and Talents. Each year the committees are busy with the re-registration of prospective students. In determining the number of students quota for re-registration, they are still using the manual method in form of an excel file, so they want to use a system to do the process. These problems can be overcome by using “Intelligent System for Re-Registration of New Students Prediction using the Naive Bayes Method (Case Study: Ganesha University of Education)”. The Naive Bayes method is used to determine the re-register probability of the new students so that the number of students who re-register can be determining the new students quota. In developing the system, the researcher use the CRISP-DM methodology as a standard of data mining process as well as a research method. The results of this prediction system research show that the system can predict well with the average predictive system accuracy value of 75.56%.
A Hybridized Levy Flight Fruit Fly Optimization Based Kernel Extreme Learning Machine for BioMedical Data Classification

The main motive behind classification is to map an input feature space to a predefined class labels in high dimensional microarray data sets to enhance the classification accuracy and decrease the computational time. Even though numbers of different classifiers have been proposed till now, still it is a very difficult problem for the researchers to model a correct classifier for superior diagnosis of different diseases. Therefore, in this work a new classifier called as kernel extreme learning machine (KELM) is proposed to classify the biomedical data. Again to enhance the classifier performance a optimization algorithm named as levy flight based fruit fly (LVFFO) is applied to optimized the parameters of KELM. The presented model performance is evaluated by comparing its performance with others models (SVM, ELM and ELM-FFO). The performances are evaluated by calculating some performance indices (G-mean, sensitivity, F-Score, accuracy, specificity, and precision). The results validate that KELM-LVFFO giving superior result among all the models.
Making Reproducible Research Data by Utilizing Persistent ID Graph Structure

Globally, in order to share the achievements of science, we open the published papers that are the output of scientific research. In addition, research data as supplemental data of the paper, and source data that is generated as a large amount of volumes of data from various experimental equipment are disclosed and shared to all researchers. This research data is enabling to grow and improve scientific research, quickly. In Europe, as open research data are archived enough, projects (EOSC, REANA, WES-ELIXIR, and so on) are being actively conducted for research environments in which researchers analyze and reproduce research data. Major publishers are also supporting the publication of open accessed papers and their supplemental data in keeping with the flow of open science, and developing platforms to support researchers' research activities. Therefore, in this paper, we design a system that can reproduce data analysis and research environment of researchers who published existing papers. The data structure in the proposed system utilizes a PID graph that connects papers, research data, and software. We analyze the functional requirements for building the system.
Implementation of an improved moment-based visual servoing controller on an industrial robot

In this paper, robust nonlinear control of moment-based visual servoing has been proposed. Due to the nonlinear nature of system, proportional integral sliding mode controller could improve the performance and robustness of visual servoing. The proposed method not only increases domain of attraction even in extraneous image regions, but also it achieves convergence to the target point despite of uncertainties. The stability of the controller is analyzed by Lyapunov's theorem. Furthermore, Integrated control of 4 DOF camera motions in this work, drastically improves the accuracy in compare with the kernel-based sliding mode visual servoing [1]. Different experiments are done to demonstrate considerable performance improvements on 5 DOF industrial robot.
Analyzing Embedded Semantic with JSON-LD and Microdata for Educational Resources in Large Scale Web Datasets

The use of embedded markup for semantic web annotations has been fostered in the last years to produce structured information and improve visualization of search results. Its use enables major search engines to interpret and exhibit describing data from web content. This paper presents a quantitative analysis of the deployment of widely use markup formats, JSON-LD and Microdata, conducted on datasets from a large web crawling corpus of 2018. It is focusing on the use of Schema vocabulary applied to describe educational resources. The results show that Microdata largely predominates over JSON-LD encoding. This finding was not expected because Microdata is not a W3C recommendation, while, JSON-LD is such since 2014. Further, the analysis reveals a low use of Schema specific properties to describe educational resources, which could indicate a lack of interest in using markup technology in this field.
Intra Rack Traffic Communication and Peers aware Placement Algorithm for Cloud Data Centers

Clouds composed of a collection of resources but in a virtual form which can be provisioned on demand and depending on the cloud customer's needs. Cloud computing customers are in continuous increase which leads to massive resources demand. The process of resource placement efficiently has become a critical problem to be solved in the cloud data center to satisfy the cloud customers demand with low cost for the cloud provider. Over the last eight years, the placement of the virtual machines in cloud environment was a major research subject. Different algorithms have been proposed to achieve the optimal placement results. The majority of these algorithms focused on the optimization based on the CPU and memory as resources, while not giving considerable attention to the network. Few algorithms considered the network communication in the data center but most of them were mono objective. This paper represents a joint objective network communication aware VM placement algorithm, which aims to reduce the intra traffic communication in the cloud data center. This algorithm called Intra Rack Traffic Communication and Peers aware algorithm (IRTCP). The IRTCP has proved better results compared to the common algorithms and the initial placement.
Adopting Continuous Integeration and Continuous Delivery for Small Teams

Continuous Integration is the practice of merging small code changes frequently rather than merging large number changes at the end of a development cycle. In addition, after each integration, the system should be re-built and re-tested to ensure that the system is still working after each change, and it's safe to build on top of it. Continuous delivery is software practice in which the software can be deployed to the market at any given time. Developing continuous integration and continuous delivery platform will solve a lot of problems in old agile methods such as increases time to find and fix bugs as testing process late, deploying software that has not been fully tested and shared testing resources which cases delivery delays, when multiple projects are ready for testing one project get tested and others are queued waiting for the testing resource to be released. Also continuous delivery guides development organization to build valuable software and to increase the number of releases. With all this benefits there are some problem obstructing companies from moving to the new platform, some of them are human factors such as human resistant to changes and other are technical such as moving to new platform require learning new tools and training. To help overcome these problems we need to develop simple platform which is more usable and easy to learn. Also some problems happen when applying CI/CD some of them related to testing and others related to integration. Testing problem are: ambiguous test result which is test results that doesn't guided the developers to finds bugs, which increases time to find and fix it; time consuming tests which require developers to waiting large amount of time for testing reports after each small changes, this waiting cause the developers to practice large commit (merging large number of changes) which slows the integration process. Slow integration mean large number of conflict lines, now instead of doing productive process developers wasting time to solve this conflicts. To overcome these problem we introduce a new way to configuring tests. As you see solving time consumes test will solve integrations problems. To reduce testing time we need to organize the test in such way that tests are more appointed and targeted to changed code only. This method enforces a concept called micro-pipeline. A micro-pipeline is group of testing blocks, each block is responsible for specific type of test for specific unit of code, connected together in such a way that passing the block tests triggers the next test block in series. When integration happen the developer specify test block which is responsible for testing made changes only. The idea behind micro-pipeline is to test affected part only.
High Quality Training Set Collection using Generative Adversarial Network

Image classification and object detection using deep learning have evolved with continuous research. In particular, the development of big data and the improvement of computer hardware performance have contributed extremely to the development of deep learning. Deep learning technologies like Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) can train models through training data. In other words, the optimal value of the weight parameter is automatically obtained from the training data. In this way, training data is the most important in deep learning. The quality and amount of training data affects the learning performance of deep learning models. In order to obtain high quality training data, it is difficult for the user to collect it directly. And using a web crawler to collect images for search keywords is also in problem. The images collected by the crawler include many low-resolution images and irrelevant images. These image data are bad for training deep learning model. Therefore, this paper purposes to collect high quality learning data by automatically applying SRGAN to low resolution images after image collection through image crawler and converting high quality images.
Development of a Health-Monitoring Device for Activity Recognition and Fall Detection

Activity recognition plays a crucial role in health monitoring systems. Most of our vital parameters like heartbeat rate or blood pressure are dependent on the activity we are doing at the time, and without knowing that, it is hard to figure out the anomalies. Besides, activity recognition can help us to detect emergency situations like falling, or even heart stroke. Knowing the importance of detecting unusual activities (, e.g. falling) and usual activities (, e.g. walking), this research has investigated the possibility of detecting fall and Activities of Daily Living (ADLs) by the help of the three dominant frequencies of accelerations of wrist in each axis and their amplitudes. In this regard, a wearable device is designed with an IMU to detect walking, running, staying still and falling. Decision function (statistical model) is calculated using ANN. To train the function, 674 samples are gathered from almost 30 people. Results show 94.8% accuracy in detecting ongoing activity and if we only consider distinguishing fall from ADLs, the values for accuracy, sensitivity and specificity are 96%, 88% and 98%, respectively.
COMMUNICATION STRATEGIES EMPLOYED BY HIGHER EDUCATIONAL STUDENTS IN ORAL PRESENTATION OF DAYAK NGAJU LANGUAGE

In an oral presentation of a second language, higher education students commonly face some problems in communication. To overcome these problems, communication strategies are necessary to be employed. In the speaking class of the Dayak Ngaju language course, especially in oral presentation in History of Islamic Civilization Study Program at IAIN Palangka Raya, the students employed communication strategies to overcome the problems in communication. However, most of the students tended to employ certain types of communication strategies in the oral presentation of the Dayak Ngaju language. Furthermore, there were no research reports which had been published regarding the types of communication strategies employed by the students of Dayak Ngaju language as a second language. Thus this study was intended to describe the types of communication strategies employed by the fifth-semester students of History of Islamic Civilization Study Program at IAIN Palangka Raya in oral presentation of Dayak Ngaju language. A descriptive qualitative design was employed in this study. Observation sheets, video recordings, field-notes, and interview guides are the instruments employed in this study. The results of this study showed that the students employed four of five types of communication strategies based on Celce-Murcia’s taxonomy, namely achievement or compensatory strategies, stalling or time-gaining strategies, self-monitoring strategies, and interactional strategies. The most frequent type employed was an achievement or compensatory strategies. Moreover, the most frequent subtype was code-switching.