Reviewerly/exHarmony huggingface dataset:
PECULIARITIES OF PREPARING STUDENTS OF HIGHER LEARNING INSTITUTIONS OF THE I‒II ACCREDITATION LEVELS FOR EXTERNAL INDEPENDENT TESTING IN MATHEMATICS

The article is devoted to the problem of preparing students of higher learning institutions of the I‒II accreditation levels for external independent testing in mathematics. The objective of the article is to consider the peculiarities of the preparation period for college students to participate in the external independent testing in mathematics. The objectives of the paper are to consider the technologies of preparing the college students for their testing in mathematics, and to analyze their psychological state, peculiarities of the preparation for applicants with the educational and qualification level of a junior specialist to participate in an external independent testing in mathematics. The article reveals psychological techniques to overcome stress, fear, anxiety and other students’ psychological problems before testing; Some pieces of advice are provided, the article points to the aspects one should take into account when preparing for testing in mathematics (overcoming fears of unknown tasks, training test experience, avoiding standard mistakes, focusing on topics that are missing in the program), also the paper provides effective techniques of preparing for participation in the external independent testing, in particular, on-line courses in mathematics such as «EdEra», «iLearn», «Education-Online», «Portal Academia». Such courses allow to fill the gaps in theoretical knowledge and check their memorizing and understanding, doing practical tasks at free time, in individual regime (duration of the lesson, hour a day, number of views). The methods of choosing the correct answer to the test questions that are inherent in the discipline «Mathematics» are given, which allow to reduce time. The typical mistakes the participants of the external independent testing assume are analyzed, students are given tips on the ways to avoid them.
Guide to Streaming Video Acquisitions.

"Guide to Streaming Video Acquisitions.." Journal of Electronic Resources Librarianship, 31(2), pp. 125–126
A comparative study of unsupervised image clustering systems

The purpose of clustering algorithms is to give sense and extract value from large sets of structured and unstructured data. Thus, clustering is present in all science areas that use automatic learning. Therefore, we present in this paper a comparative study and an evaluation of different clustering methods proposed in the literature such as prototype based clustering, fuzzy and probabilistic clustering, hierarchical clustering and density based clustering. We present also an analysis of advantages and disadvantages of these clustering methods based essentially on experimentation. Extensive experiments are conducted on three real-world high dimensional datasets to evaluate the potential and the effectiveness of seven well-known methods in terms of accuracy, purity and normalised mutual information.
GRADE: A Novel Gravitational Density-Based Clustering Approach in the Multi-Objective Framework

Background: Clustering analysis plays a vital role in obtaining knowledgeable data from the huge amount of data sets in knowledge discovery. Most of the traditional clustering algorithms do not work well with high dimensional data. The objective of effective clustering is to obtain well connected, compact, and separated clusters. Density-Based Clustering (DBSCAN) is one of the popular clustering algorithms that use local density information of data points to detect clusters with arbitrary shapes. The Gravitational Search Algorithm (GSA) is one of the effective approaches inspired by Newton’s law of gravitational force where every particle in the universe attracts every other particle with a force. Objectives: The primary aim of this paper is to design and develop a novel multi-objective clustering approach to produce the desired number of valid clusters. Further, these resulting clusters are to be optimized to obtain an optimal solution. Methods: In the proposed approach, a hybrid clustering algorithm based on GSA along with DBSCAN is recommended to group the data into the desired number of clusters, and in the next phase of the algorithm, Particle swarm optimization technique is applied in order to optimize the solutions using the fitness functions. Results: In the analysis of the result, we employed two objective functions namely quantization error and inter–cluster distance on four real-life data sets such as Iris, Wine, Wisconsin, and Yeast to evaluate the performance of our algorithm. Conclusion: The effectiveness of the GRADE algorithm is comprehensively demonstrated by comparing it with the well-known traditional K-mean algorithm in terms of accuracy and computational time.
A Study Survey on the Deep Cryptographic data and its Algorithms Application

Data is a facts and statistics collected together and stored digitally for reference or analysis. In contemporary era, appraisal of networking and wireless networks. To improve the security in network we need to use cryptography. It provides secrecy and integrity to our data. The main aim of this is to provide Confidentiality, Authenticity, Integrity and Non-Repudiation. There are plenty of cryptographic techniques which is used to achieve this goal and to secure the data. In this paper we propose various tools and techniques of cryptography and its uses with various encryption and decryption algorithm..
Target Detection and Tracking With Errors in Intensity and Mapping Dimensions

This paper addresses an engineering problem that measurement errors in intensity and mapping dimensions exist in the track-before-detect (TBD) architecture simultaneously. Drawing lessons from the probability data association (PDA) and the basic generalized likelihood ratio test TBD (GLRT-TBD), the joint log-likelihood ratio test TBD (JLLRT-TBD) method is proposed. Unlike conventional TBD algorithms, which consider intensities captured in the cells occupied by physically admissible transitions only, the novel method regards the cells centered at the transitions as effective ones. After that, detection probabilities are deduced from their positions, and then weighting likelihood ratios of their intensities results in metrics of different transitions. Finally, the maximum metric is compared with the designed threshold, and the estimation of the target state is obtained by retracing the maximization process. With this, the incoherent integration gain along consecutive scans is acquired, and the increment in computational complexity is avoided. Monte Carlo numerical results demonstrate that the detection and estimation performances of the JLLRT-TBD are superior significantly to those of the basic GLRT-TBD while the lower averaged execution time is spent.
Study on the Improvement of Resource Management for the Establishment of A Smart Operation System for the Future WAR

In the era of the Fourth Industrial Revolution, where Aegis destroyers, stealth fighters, robot fighters and smart cities are located, smart operating...
CAPABLE AND SECURE DATA ACCESS TO THE INTERNET SUPPORTED OBJECTS IN THE SMART SYSTEM

Under the current plan, when the user exits the user group, the manager of the audience only cancels the group's secret key, which means that the private user key associated with the attribute remains valid. Our plan is suitable for devices with limited resources. If someone within the group intentionally discovers the public's confidential response to the revoked user, they can perform the understandings through their own key. To demonstrate this attack, a specific instance is presented. We have demonstrated our safety in our plan under the assumption of Daffier-Hellman (DCDH) for the divisible account. Unfortunately, the ABE plan requires high arithmetic expenses while performing encryption and understanding of files. This defect becomes more severe for lightweight devices due to restricted computing sources. Within this system, we focused on designing the Club-ping-ABE plan with efficient user revocation of the cloud storage system. The result of our experience shows that the cost of computing for local devices is relatively low and can be fixed. We tried to design the attack model for collusion by users who were removed and who were collaborating with existing users. In addition, we built the penguin-ABE Club plan to cancel the eligible user by increasing the current plan and proving that our plan is safe for cost-per-acquisition under the selective model.
Saliency Detection Based on the Integration of Global Contrast and Superpixels

In the field of computer vision, the detection of salient object is an important step and one of the preconditions for salient object extraction. The outcome resulting from some existing detection methods for salient object is considerably different from the Ground Truth. In view of the shortcomings of existing methods, this paper proposes a saliency detection method based on the integration of global contrast and superpixels. The salience value of each pixel is measured according to the global contrast of the pixels in the image. A histogram optimization technique is used to highlight the low-contrast pixels of the salient region in the image and omit the high-contrast pixels of the background. In order to improve the image quality of the salient image, the superpixel image segmentation based on K-Means clustering algorithm is proposed, and finally, we generate a more accurate saliency map through the integration with superpixels. The experiment is performed on the public dataset MSRA10 K. The results show that the histogram optimization can help improve the contrast of the salient pixels and generate a better saliency map by integrating with superpixels. Compared with other classical algorithms, the proposed method outperforms other methods.
THE USE OF GUESSING GAME TO ENHANCE STUDENTS’ VOCABULARY MASTERY AT SEVENTH GRADERS OF SMPN 3 CIMAHI

This research is applied a classroom action research. This research aimed to find out the effectiveness students vocabulary mastery through guessing game in seventh grade of SMP Negeri 3 Cimahi in the school year of 2018/2019. The populations of research are all of the students at seventh-grade in SMP Negeri 3 Cimahi. Method of this research is Classroom Action Research (CAR) applying a qualitative approach. Data sources are informants consist of teachers, students in the seventh grade of SMP Negeri 3 Cimahi and staffs. The data collection techniques are tests, observations, and documentation techniques. The research was taken in two cycles with total meetings were seven; one pre-test, two post-tests and four meetings as treatment. Planning, acting, observation, and reflecting was the steps which carried out in each cycle. The data has been gathered and anaylzed with IBM SPSS Statistics version 22. From pre-test, only 30,5% of students reached Kriteria Ketuntasan Maksimal (KKM), after treatment and post-test 88,8% of students can reach KKM. The improvement of student’s vocabulary results from the application of Guessing Games that helps the students develop their vocabulary memorizing ability with a fun way. The conclusion of the research said: The student’s vocabulary mastery with guessing game improves either individually or classically.
The Study of Academic Activities of Limgok's Descendants

Brothers Galcheon and Cheommodang were named as scholars in the 16th century at Anui County. Limgok, the grandson of Cheommodang has accomplished a study to converge Toegye 's and Nammyong' s studies. He read many books, taught many disciples, and built many poems and sentences. So he became a very famous scholar. Later, the king invited him as a teacher to teach his son and gave him official office. Many of his descendants studied until the end of the Joseon Dynasty. However, their works were not published and were not known to others. So many people mistakenly believe that the discipline of this family is cut off, but in fact it is not. The descendants will soon have to publish the ancestral literature of their ancestors. These works are important not only for individual families, but also for the nation. Their academic tendency was a practice-oriented discipline that did not go beyond the scope of linguistics.
Warning over lottery scam

Sandwell Council's trading standards team is warning residents to be aware of a lottery scam trying to trick people into parting with their money.
MENINGKATKAN PRESTASI BELAJAR SISWA MELALUI PEMBERIAN MOTIVASI BELAJAR

This journal describes efforts to improve student learning achievement in lessons through motivation. This study uses a test method and classroom action observation. Data analysis using descriptive analysis. The research subject was a class VI student at SD Rejeki Elementary School. The success obtained during this third cycle is as follows, the activities of students in PBM are very active due to the giving of learning motivation. And the average student's daily test score (evaluation) increased from 5.79 before the action (T0) to 6,097 cycle one (T1), 7,33 Cycle two (T2), and 8,097 cycle three (T3). Thus, it has been proven that the learning approach with the provision of learning motivation can improve the PBM process and student learning achievement.
FPGA-Based Binary Labeling Signal Transmission System

We implemented and evaluated our newly developed binary labeling signal transmission system using field-programmable gate arrays. In this system, the state of “0” or “1” of labeling signals is related to modulation or non-modulation of frame signals, and the labeling signals can be multiplexed to frame signals and transmitted without changing the configuration of the original frame signal. We designed a modulation-system protocol for implementing the above system and propose a digital smoothing technique for stabilizing a phase-locked loop circuit. We verified that our digital smoothing technique prevents bit error of the modulated frame signals in the implemented transmitter. The labeling signal multiplexed to the frame signals is demodulated without bit error in the implemented transceiver.
PENGARUH PEMBELAJARAN KOMPUTERISASI DAN FASILITAS TERHADAP KEMAMPUAN PENGUASAAN APLIKASI MAHASISWA DI FAKULTAS EKONOMI UNIVERSITAS ISLAM SUMATERA UTARA

This research aims to know and explain how the influence of computerized learning and facilities to the mastery ability of application of students at the Faculty of Economics of Islamic University of North Sumatera. In this study use a descriptive method. The data collection techniques used in this study were polls, library studies and interviews to support this research.  The purpose of this study is to determine the influence of computerized learning and facilities to the mastery ability of student applications at the Faculty of Economics of Islamic University of North Sumatera. The population in this study is a student management study program that is still active in the faculty of Economics at the Islamic University of North Sumatera, which amounted to 580 people, the sample in this study is 10% of the population, namely of 58 people with a total of 30 questions. Based on analysis and evaluation, the result of calculation using multiple linear regression in the form of T test (partial) between computerized learning (X 1 ) test T (simultaneous) obtained T count 1,672 of Student Application Mastery ability (Y) while relationship or partial correlation between facility (X 2 ) on Student Application Mastery Ability (Y) has t count > T table which is 3,768 > 1,672 and test F (simultaneous) Obtained the value f count = 34,252 > F table 2,764 because F count > f table, then H 0 rejected and H a accepted then it can be concluded that Silmultan variables free learning ability and the work facility to variable binding ability Application.
Cooperative path planning for leader – follower formation of Multi UAV based on the minimum energy consumption for load transportation

Today, unmanned aerial vehicles are highly considered for both military and commercial fields due to low cost, high maneuverability, and good survival. One of the most important design challenges of multi-UAV systems is mission planning. In the broad class of unmanned aerial vehicles (UAV), Quadrotor is an important member. The capabilities of this vehicle in load transportation has attracted the attention of many research groups around the world. In this paper, path planning based on the minimum energy consumption is studied for load transportation. The purpose of the present study is to investigate the effect of proposed cost function in order to obtain optimal path for transporting loads based on reducing the energy consumption of quadrotors. The results indicate the 35.29% energy consumption of multi UAV compared to the energy consumption of an individual quadrotor. This also leads to an increase in load carrying capacity. On the other hand, the leader- follower formation is preserved until the end of the path based on the defined relationships. The simulation results illustrate the power and efficiency of the method to overcome the high nonlinearity nature of the problem such as path optimization of multi-rotor helicopters.
Mining Hypernym-Hyponym Relations from Social Tags via Tag Embedding

With the rapid development of Internet of Thing, mobile Internet, cloud computing and other technologies, network data increases dramatically and Folksonomy plays an important role in web systems. How to obtain valuable knowledge, especially hypernym-hyponym relations, becomes a popular research topic in the field of artificial intelligence. For Folksonomy, hypernym-hyponym relation identification aims to recognize the “is-a” relation between two social tags. Most existing works about identifying hypernym-hyponym relations are based on statistical and heuristic approaches, but their performance still needs to be improved. In this paper, we propose a novel supervised learning approach to identify hypernym-hyponym relations from social tags using tag embeddings. First, we use a neural network model to learn tag embeddings. This model relies on not only the hypernym and hyponym tags, but also the contextual information between them. We then apply such embeddings as features to identify hypernym-hyponym relations using a supervised learning method. Our experimental results demonstrate that the proposed approach significantly outperforms other state-of-the-art approaches over a labeled dataset. The accuracy and F1-score of our approach achieve 0.91 and 0.86 respectively.
Twitter Texts’ Quality Classification using Data Mining and Neural Networks

Purpose: This is an attempt to classify the level of noise in twitter texts which is part of social media data analytics problem.Estimations in recent machine learning & data feeding algorithms researches' assumptions consider high data quality in social media texts, while they actually lack data accuracy, completeness, and overall quality which leads to the principle of "Garbage In Garbage Out" resulting in bizarre statistical findings.The aim of this project is to predict and classify Twitter data noise levels using a labelled dataset.Methodology: After data cleaning, a clustering technique was used to find the major dimensions in the data imported, and a dimension reduction algorithm was ran using PCA Weighting and the Wight Guided Feature Selection algorithms.They resulted into 6 most significant features which were used in the implementation.An artificial neural network model was trained to predict the Tweets' quality classes using R and RStudio.The ANN used is Neural Network (NN) and Naïve Bayes (NB) for the purpose of predicting the Twitter text quality.There will be a comparison between the 2 ANN used in terms of accuracy and precision.Findings: Three different aspects of text mining were discovered in twitter data.(1) Neural network gives surprisingly good result as compared to Naive Bayes algorithm, (2) With only 3 hidden layers, a network was created which can predict good or bad class, (3) Preprocessing of the data and implementing predictive algorithms take huge data and very high computational complexity and time.Research results show that Neural Network performs well even without Dropout layer and convolutional layers.The accuracy of the Neural Network is 99%.
A Survey on Unusual Event Detection in Videos

As the usage of CCTV cameras in outdoor and indoor locations has increased significantly, one needs to design a system to detect the unusual events, at the time of its occurrence. Computer vision is used for Human Action recognition, which has been widely implemented in the systems, but unusual event detection is lately entering into the limelight. In order to detect the unusual events, supervised techniques, semi-supervised techniques and unsupervised techniques have been adopted. Social force model (SFM) and Force field are used to model the interaction among crowds. Only normal events training samples is not sufficient for detection of unusual events. Double sparse representation has been used as a solution to this, which includes normal and abnormal training data. To develop an intelligent video surveillance system, behavioural representation and behavioural modelling techniques are used. Various machine learning techniques to identify unusual events include: Graph modelling and matching, object trajectory based, object silhouettes based and pixel based approaches. Kullback–Leibler (KL) divergence, Quaternion Discrete Cosine Transformation (QDCT) analysis, hidden Markov model (HMM) and histogram of oriented contextual gradient (HOCG) descriptor are some of the models used are used for detecting unusual events. This paper briefly discusses the above mentioned strategies and pay attention on their pros and cons.
EVERYONE IS A TEACHER HERE (ETH) : A STRATEGY IN TEACHING GRAMMAR

Abstract : This article refers to a classroom action research by implementing Everyone is a Teacher Here in teaching grammar. Everyone is a Teacher Here is one strategy which provide opportunity to students to act as teacher for their friends. It provides an opportunity for the students to be actively involved in the teaching and learning process. Grammar defines as a difficult subject because its inherent “complexity” of the feature. Grammar is difficult when students should use it connected with other element; semantics and pragmatics. The subject of the study was the second semester of English department students in one university in Bojonegoro. The result shows that the implementation of Everyone is a Teacher Here (ETH) in teaching grammar can help them understanding grammar better. The grammar score showed an improvement from the first cycle up to the third cycle. Keywords : Everyone is a Teacher Here, Grammar, Strategy
LaBeouf, Rönkkö &amp; Turner, Digital Remix, and Group Authorship

Abstract I argue that sometimes a group can author a work of art without the work being either co-authored or multiply-authored. Sometimes the group, itself, is an author, rather than any of its members alone or together. I argue that when a group is an author like this, it has mental properties that no individual member of the group possesses. For example, we can consider the groups that authored digital remixes based on a film titled #INTRODUCTIONS created by the artists LaBeouf, Rönkkö &amp; Turner. YouTubers posted their digital remixes online, and the question is: Who authored those remixes? I contend that many digital remixes are authored by groups that are capable of cognition.
Identifikasi Pola Iris Menggunakan Metode Backpropagation

In recent years, there has been a lot of research related to pattern recognition is conducted to identify various forms of patterns and controlling system. Utilizing backpropagation neural network in pattern identifying is very useful to solve problems with unknown parameter and difficult to determined. And then the data of the pattern are trained and tested. The results obtained from the recognition rate indicates a backpropagation neural network, provide excellent performance, which is an average of 98%. This neural network is expected to be developed by other researchers for the advancement of knowledge in all fields.