Reviewerly/exHarmony huggingface dataset:
Implementation of Fuzzy K-Nearest Neighbor Method in Decision Support System for Identification of Under-five Children Nutritional Status Based on Anthropometry Index

Nutritional status is one of the important factors in assessing the level of health and growth of infants and under-five children. But the present, there are still many problems caused by an imbalance in nutritional intake with the nutritional needs of children. K-Nearest Neighbor method in the previous studies showed the existence of prediction results with the problem. This study used a standard anthropometric index or body size to carry out the process of calculating nutritional status using the Fuzzy K-Nearest Neighbor method. Fuzzy is applied to reduce the problem in classification. Predictions produced are three categories of nutritional status, namely BB/U (weight according to age), TB/U (height according to age), and BB/TB (weight according to height). The k value taken for the classification process is k=10. Fuzzy K-Nearest Neighbor process has done by taking the closest Euclidean distance to the number k from the training data to the test data. The prediction class results of 95 out of 96 data are stated accordingly after the value of the membership is calculated. The accuracy of the test performed produces a cumulative accuracy of 98.96%. This study can be used as a reference for further research by adding training data with more complete class variations in each category of nutritional status to obtain more optimal accuracy.
Adversarial Manifold Matching via Deep Metric Learning for Generative Modeling.

We propose a manifold matching approach to generative models which includes a distribution generator (or data generator) and a metric generator. In our framework, we view the real data set as some manifold embedded in a high-dimensional Euclidean space. The distribution generator aims at generating samples that follow some distribution condensed around the real data manifold. It is achieved by matching two sets of points using their geometric shape descriptors, such as centroid and $p$-diameter, with learned distance metric; the metric generator utilizes both real data and generated samples to learn a distance metric which is close to some intrinsic geodesic distance on the real data manifold. The produced distance metric is further used for manifold matching. The two networks are learned simultaneously during the training process. We apply the approach on both unsupervised and supervised learning tasks: in unconditional image generation task, the proposed method obtains competitive results compared with existing generative models; in super-resolution task, we incorporate the framework in perception-based models and improve visual qualities by producing samples with more natural textures. Both theoretical analysis and real data experiments guarantee the feasibility and effectiveness of the proposed framework.
AI Soccer: the Most Effective Methods to Dispossess the Opponent Player

In this paper, one of the challenges that comes with defending in AI soccer is highlighted and an attempt is made in finding a solution for the problem. In soccer, defense is an important part of the game and the research question in this research is formulated as follows: what are the most effective methods to take by surprise and dispossess the attacking opponent player who carries the ball? To find the most optimal solutions for this problem, the Webots AI Soccer environment is used, which simulates a game of five versus five soccer. To be able to answer the research question, several approaches and strategies are implemented in the environment and their success is compared. This has led to the conclusion that, in this particular environment, defensive actions like slide tackling cannot be performed and thus cannot be used to defend. Therefore, other solutions are necessary, as a result, a combination of approaches is the most important way of making the dispossession strategy as successful as possible. This combination consists of predicting where the opponent is moving to with the ball and determining the optimal side to approach the opponent from.
CONFIDENTIAL E-VOTING SYSTEM USING FINGERPRINT AND FACE DETECTION

All the world Voting is one of the fundamental rights of every citizen/people of an all country. By voting the citizen to the eligible candidate to elect them for to solves problems. Now days all country used new technology to voting every citizen to the best candidate. The voting system has a various voting techniques used such as Paper Ballot, E-Voting System (Electronic Voting System), Internet Voting System, SMS and Miss Calls Voting System. In this paper we implement location free voting system to the voters who are not possible to the come at voting location (hometown). Here we propose a system that includes multiple layers of verification to ensure the reliability of the device which includes face verification and then OTP verification with validation data. Each voter can access to the system only when being recognized and checked with the given database of enlist voters. Once the corresponding face and thumb is matched with the information provided, the voter will be allowed to proceed for choosing their preferred candidate from the panel.
Writing Skills Development: A Balancing Perspective of Brain Function in Elementary Schools

Writing is a skill that involves creativity. It can be triggered by using a stimulating activity of both hemispheres of the brain. This study aims to describe the improvement of narrative writing skills by using a learning model based on the balancing of the functions of the right and left hemispheres of the brain. The research method used is classroom action research through three implementation cycles from January – December 2019 in three elementary schools in Ponorogo, Indonesia. The sample of this study was 72 students of grade 4. The data collected through using tests, observations, and interviews were then analyzed both qualitatively and quantitatively. The results showed that to produce good writing, students need to be guided and given a lighter. After balancing the functions of the cerebral hemispheres, 86% or 62 students met the minimum completeness score. The students' ability in writing narratives has increased significantly, especially in the aspects of content, content organization, vocabulary, grammar, and spelling. Keywords: narrative writing; the balancing of the functions of the cerebral hemispheres; the brain learning model. Abstrak Menulis merupakan keterampilan yang melibatkan kreativitas. Ini dapat dipicu dengan menggunakan aktivitas stimulasi kedua belahan otak. Penelitian ini bertujuan untuk mendeskripsikan peningkatan keterampilan menulis narasi dengan menggunakan model pembelajaran berbasis keseimbangan fungsi belahan otak kanan dan kiri. Metode penelitian yang digunakan adalah penelitian tindakan kelas melalui tiga siklus pelaksanaan dari Januari – Desember 2019 di tiga sekolah dasar di Ponorogo, Indonesia. Sampel penelitian ini adalah 72 siswa kelas 4. Data yang dikumpulkan melalui tes, observasi, dan wawancara kemudian dianalisis secara kualitatif dan kuantitatif. Hasil penelitian menunjukkan bahwa untuk menghasilkan tulisan yang baik, siswa perlu dibimbing dan diberi pemantik. Setelah dilakukan penyeimbangan fungsi belahan otak, 86% atau 62 siswa memenuhi nilai ketuntasan minimal. Kemampuan siswa dalam menulis narasi meningkat secara signifikan, terutama dalam aspek isi, organisasi isi, kosa kata, tata bahasa, dan ejaan Kata kunci: menulis narasi; menyeimbangkan fungsi belahan otak; model pembelajaran otak.
RETRACTED ARTICLE: The importance of light-weight encryption cipher in restricted IoT systems to make intelligent technology safer for devices

Light-weight Cryptography is a subset of cryptography that aims to create interaction with low-power enabled devices. The use of constrained devices in most of the fields has created the need of light-weight cryptography. Many modern algorithms are designed to be used without hardware optimization tools as part of a computer software framework. This reality makes it difficult for devices with minimal computing capacity, small volumes, and low power consumption to use most current cryptographic algorithms. The foundation of light-weight cryptography has become cryptographic data protection techniques in low-cost systems. Light-weight cryptography is important in the case of the providing security to wireless sensor devices. Light-weight algorithm developers are sometimes forced to choose between two algorithm criteria, often mutually specific: protection, cost, and productivity. In practice, two of the three design goals are easy to optimize: protection and expense, while it is very challenging to simultaneously optimize all three design goals, safety and efficiency or cost and performance. Several Light-weight Cryptography algorithms are implemented in this respect: software and hardware. They have several and frequently contradictory features. In this regard two types of ciphers were used depending on the algorithm: one is block cipher and the other is stream cipher. In this paper, we are going to discuss the major role of Light-weight Cryptography in IoT devices and its challenges to run constrained devices in a specific Filed. As previously stated, the limitations of the research Lightweight Block Ciphers PRESENT, CLEFIA and Stream Ciphers Grain, Rabbit will be discussed.
Content Wizard: demo of a trans-vector digital video publication tool

In order to optimise the distribution of video assets online, media organizations need tailor their offerings for specific digital channels and better understand the interests of their audiences at particular points in time, which are often influenced by contemporary new stories and trends on social media. For this purpose, the research project ReTV has developed a Web-based tool termed ’Content Wizard’ which demonstrates an end-to-end, semi-automated workflow for video content creation, adaptation and distribution across digital channels. Digital assets can be selected based on predicted future trending topics, re-purposed according to the different digital channels they will be published upon and scheduled for the optimal future publication date. The result is an innovative video publication workflow that meets the marketing needs of media organisations in this age of transient online media spread across multiple channels.
Whether Android-Based Learning Media Actually Improve Learning Outcomes?

The purposes of this research and development are: (1) Create android-based learning media use the MIT Inventor App to improve learning outcomes in general administration subjects, (2) Know the feasibility of learning media developed through validation results by material experts and media experts as well as small group trials, and (3) Knowing the differences from learning outcomes of students, who use and do not use developed learning media.This research uses borg and gall research and development models that are modified into 8 steps, namely: (1) Initial research and information collection, (2) Planning, (3) Product Development, (4) Expert validation, (5) Product Revision, (6) Small group trials, (7) Product revisions, and (8) Large group trials.The overall validation result is 90.52% on average with the criteria very feasible to use.While the results of the Independent Sample t-Test test on this research shows that there are differences in the learning outcomes of students using learning media developed with the learning results of students who do not use developed learning media, it can be concluded that android-based learning media using MIT Inventor App can improve the learning outcomes of students in general administration subjects.
Actros Tuning Pack By SCS [1.37]

Truck Tuning Is Something That Is Close To The Heart Of All Truckers. If You Have Ever Been To A Truck Show, You Would See That The Amount Of Love And ...
Grammatical Error Generation Based on Translated Fragments.

We perform neural machine translation of sentence fragments in order to create large amounts of training data for English grammatical error correction. Our method aims at simulating mistakes made by second language learners, and produces a wider range of non-native style language in comparison to state-of-the-art synthetic data creation methods. In addition to purely grammatical errors, our approach generates other types of errors, such as lexical errors. We perform grammatical error correction experiments using neural sequence-to-sequence models, and carry out quantitative and qualitative evaluation. A model trained on data created using our proposed method is shown to outperform a baseline model on test data with a high proportion of errors.
Developing students learning motivation in science experiments using mobile augmented reality

With the advent of Mobile Augmented Reality (MAR) technology, it has the potential to be widely usedin learning because of its ability to deliver the learning materials from multiple perspectives. This paper aims to propose a combination of concepts derived from the theory of animate vision, multimedia learning principles, constructivism learning theory and Attention, Relevant, Confidence, and Satisfaction (ARCS)motivation theory. This paper recommends the combination of these concepts for an exclusive mobile learning experience through the use of three-dimensional (3D) animated visualization. The literature review method is exploited to construct a state of the art theories and models. The combination of these concepts into the Mobile Augmented Reality (MAR) learning environment is expected to improve students’ learning motivation in science experiment. The results of this study are valuable for students, teachers, instructors and researchers interested in applying Mobile Augmented Reality in learning.
European Teachers’ Technological Pedagogical Content Knowledge (TPCK) and Educational Use of Web Technologies

Several studies have been conducted on technological, pedagogical content knowledge and web-based education. In this study, the Technological Pedagogical Content Knowledge and Educational Use of Web Technologies (TPCK-W) were analyzed in addition to the self-efficacy and attitudes of 33 teachers from eight different branches carrying out their duties in 19 countries of the European Union (EU). In this study, the Technological Pedagogical Content Knowledge-Web (TPCK-W) Survey developed by Lee, Tsai, and Chan was used. The data obtained statistics software was analyzed using SPSS for Windows 17.0 statistics software. As a result of the analysis, it was revealed that TPCK-W self-efficacy of teachers carrying out their duties in EU countries was high and their attitudes were positive; and age, experience, and gender did not affect their TPCK-W self-efficacy and attitudes. Moreover, participants' general web attitudes changed positively, depending on their web communication, web content, and pedagogical use of the web.
LibGuides: Middle School Library: First Chapter

Use this guide to access the library catalogue, databases and support for any research project. You won't be able to stop reading after the first chapter of these recommended books.
A Comparative Study on Signature Schemes for IoT Devices

The use of Internet of things (IoT) devices is on the rise and given their involvement with sensitive data the security for these devices is of greater importance. As these devices are connected with the internet being able to have secure communication is crucial. Some of the major aspects for secure communication are identification and message integrity. These can both be provided by signature schemes. As IoT devices are often constrained devices picking a signature scheme is not trivial and depends among other things on storage size, security level and computation costs. One should thus look at how current available solutions compare and their suitability for IoT. This paper therefore provides a comparison of some signature schemes and presented some such as SCDSA and CLS suitable for IoT. Furthermore this paper points out that current solutions may become obsolete with the development of quantum computing. This paper thus also presents some hash based quantum resistant solutions for use in IoT.
RAPID CROSS-PLOT DISCRIMINATION OF COMMERCIAL POTASH MINERALIZATION – CASE HISTORIES

Potash minerals are a source of potassium, which is used for the manufacture of gunpowder and fertilizer. Commercial potash mineralization is often discovered when petroleum wells are drilled through evaporite sequences and the Gamma Ray log “goes off scale”. This is because potassium is one of the naturally occurring radioactive elements, emitting gamma rays from the 40K isotope, in its decay to 40Ar. However, not all potash minerals may be commercial sources of potassium via underground mechanical or solution mining techniques and Potassium is not the only radioactive element. For example, the mineralogy of the McNutt “Potash” member of the Salado Formation in SE New Mexico, is extremely complex, consisting of multiple thin (i.e., less than 10 ft thick) beds of six low-grade (radioactive) potash minerals, only two of which are commercial. There are also four non-radioactive evaporite minerals, one of which interferes with potash milling chemistry, and numerous claystones and Marker Beds (shales), with GR count rates comparable to the low-grade potash. Because of this complexity, traditional wireline and Logging While Drilling Potash Assay techniques, such as Gamma Ray log-to-core assay transforms, may not be sufficient to identify potentially commercial potash mineralization, for underground mining. Crain and Anderson (1966) and Hill (2019) developed linear programming, and multi-mineral analyses, respectively, to estimate Potash mineralogy and grades. However, both of these approaches require complete sets of multiple log measurements. In SE New Mexico, petroleum wells are drilled through the McNutt “Potash” member of the Salado Formation, with air, cased and drilled out to TD in the underlying sediments, with water based mud. Complete log suites are then run from TD to the casing shoe, with only the GR and neutron logs recorded through the cased evaporite sequence for stratigraphic and structural correlation. As a result, numerous oil and gas wells, in SE New Mexico, have cased hole gamma ray and neutron logs, through the Salado Evaporite. Logs, from these wells could provide a rapid Potash screening database, if used properly. A simple screening cross-plot technique, the Potash Identification (PID) plot, utilizing only Gamma Ray and Neutron Porosity, is proposed and successfully demonstrated, as a potential screening tool. This technique can be used with both open and cased-hole petroleum well logs, as well as core hole wire-line logs, and provides discrimination of commercial potash mineralization from non-commercial (potash and non-potash) radioactive mineralization. Case histories of the use of PID cross plots in the evaporite basins of Michigan, Nova Scotia, Saskatchewan, and SE New Mexico are described. The technique may also be useful in screening potential potash deposits in China, Europe, North Africa, and South America.
LibGuides: Making Music With Apps: Storage

The New York Tech library has many items available for making music. However, since we've had to close the physical library during the COVID-19 pandemic, the librarians have compiled a list of alternative options that you can access from home.
LibGuides: How To Get Your True-Crime Fix Digitally: Where can you find titles of true-crime related programs?

Are you an amateur crime detective? Do you find yourself on Google late in the night searching for details about the latest true crime story? The search is over: here is the ultimate guide to getting your true-crime fix, whether through podcasts, videos, Whether you are looking into articles on what true crime is, where you can find different true crime resources, or up to date news stories on your local crime, here is a list of sources to get your fix.
ДОСВІД ФОРМУВАННЯ ЛІДЕРІВ В НАЦІОНАЛЬНОМУ ТЕХНІЧНОМУ УНІВЕРСИТЕТІ «ХАРКІВСЬКИЙ ПОЛІТЕХНІЧНИЙ ІНСТИТУТ»

The concepts of charismatic leadership are considered, the qualities and factors of influence of a charismatic leader on followers are analyzed, the conclusion about the possibility of developing the qualities of a charismatic leader is made, the experience of forming leaders in NTU «KhPI» is presented.
EL USO DE LAS TIC´S COMO ESTRATEGIA DE CONOCIMIENTO PARA LA EDUCACIÓN TRADICIONAL EN HONDURAS. THE USE OF ICT AS A STRATEGY OF KNOWLEDGE FOR TRADITIONAL EDUCATION IN HONDURAS

El presente trabajo tiene como proposito analizar la relacion de las TIC´S y la gestion del conocimiento; como beneficia al sistema educativo tradicional en Honduras, para sustentar la investigacion se emplea el enfoque metodologico cuantitativo, se aplico un instrumento de recoleccion de datos a estudiantes de secundaria de instituciones privadas de tres municipios de Honduras. El trabajo propone examinar los datos brindados por los estudiantes para identificar  antecedentes de habilidades tradicionales y la asimilacion a la metodologia de ensenanza virtual, considerando las TIC´s como facilitadora de conocimientos. Los resultados muestran el impacto de dicho fenomeno de transicion del sistema de educacion tradicional al sistema de educacion virtual; sus limites, oportunidades e incidencia transformadora para el proceso de ensenanza y aprendizaje en linea. ABSTRACT The purpose of this work is to analyze the relationship between ICTS and knowledge management; As it benefits the traditional system in Honduras, the quantitative methodological approach is applied to support the research, an instrument of data collection was applied to high school students from private institutions in three municipalities in Honduras. The work proposes to examine the data provided by the students to identify antecedents of traditional skills and the assimilation to the virtual teaching methodology, considering ICTs as a facilitator of knowledge. The results show the impact of the phenomenon of transition from the traditional education system to the virtual education system; its limits, opportunities and transformative impact for the online teaching and learning process.
Sublinear update time randomized algorithms for dynamic graph regression

A well-known problem in data science and machine learning is linear regression, which is recently extended to dynamic graphs. Existing exact algorithms for updating the solution of dynamic graph regression require at least a linear time (in terms of n: the size of the graph). However, this time complexity might be intractable in practice. In the current paper, we utilize subsampled randomized Hadamard transformand CountSketchto propose the first sublinear update time randomized algorithms for regression of general dynamic graphs. Suppose that we are given a n×d matrix embedding M of the graph, where d≪n and M has certain properties. Let r be the number of samples required by subsampled randomized Hadamard transform for a 1±ϵ approximation, which is a sublinear of n. Our first algorithm supports edge insertion and edge deletion and updates the approximate solution in O(rd) time. Our second algorithm is based on CountSketchand supports edge insertion, edge deletion, node insertion and node deletion. It updates the approximate solution in O(qd) time, where q=O(d2ϵ2log6(d/ϵ)).