Reviewerly/exHarmony huggingface dataset:
Video analysis using a shaking camera

To perform a reliable video analysis, it is mandatory to avoid shaking the camera while taking the video, normally using a tripod for smartphones/cameras. But there are some situations which the only chance to make the video is using your hands and it is almost impossible to avoid shaking the camera even trying your best. Imagine the following situation: a volleyball is obliquely launched, and the video is filmed by hand. Then, a point that is fixed in relation to the ground is chosen as the origin of a ground fixed reference frame. Despite being a ground fixed point, it will appear shaky in images captured by the handheld camera. Thereby, two standard video analyses are performed, one from the ground fixed point random shaking and one from the ball’s trajectory, both in relation to the default reference frame of the video analysis software. Lastly, to implement the video analysis of the relative ball movement it is enough to subtract the position coordinates of the ball’s movement by the position coordinates of the ground fixed point tremble, at each instant of time. The graphs of the position and velocity components versus time are presented, and the results are discussed.
Incidence of virtual learning environments in the development of mathematical logical thinking

espanolEl presente trabajo de investigacion muestra el desconocimiento de los maestros del uso de los entornos virtuales de aprendizaje dentro del proceso de ensenanza aprendizaje el objetivo fundamental del trabajo investigativo fue analizar la incidencia del uso de los entornos virtuales de aprendizaje en el desarrollo del pensamiento logico matematico en el desempeno academico. La metodologia a utilizar es de tipo de investigacion bibliografica, pues se ha realizado investigaciones en libros y publicaciones en internet para detallar las falencias que se esta viviendo en el establecimiento educativo. El metodo utilizado es el cientifico ya que este el metodo de estudio sistematico, de la naturaleza que incluye la tecnica de observacion reglas para el razonamiento y la prediccion, ideas sobre la experimentacion planificadas. Los Resultados experimentales y teoricos obtenidos constan en la tabla del analisis que fueron analizados e interpretados de manera cualitativamente para tener una vision mas real de los resultados. Con las conclusiones se puede evidenciar que, en la unidad educativa existe el recurso suficiente para implementar un modulo de uso de entorno virtuales de aprendizaje para el desarrollo de pensamiento logico matematico. EnglishThis research work shows the lack of knowledge of teachers about the use of virtual learning environments within the teaching and learning process. The main objective of the research work was to analyze the incidence of the use of virtual learning environments in the development of mathematical logical thinking in academic performance. The methodology to be used is bibliographic research, since research has been carried out in books and publications on the Internet to detail the shortcomings that are being experienced in the educational establishment. The method used is the scientific method, since it is the method of systematic study of nature that includes the observation technique, rules for reasoning and prediction, ideas on planned experimentation. The experimental and theoretical results obtained are shown in the analysis table, which were analyzed and interpreted qualitatively in order to have a more realistic view of the results. With the conclusions it can be evidenced that, in the educational unit there is enough resource to implement a module for the use of virtual learning environment for the development of mathematical logical thinking.
The Usage of Social Media for Improving the Customer Satisfaction: The Mediating Role of Electronic Services Quality

The study aims to identify the impact of social media usage on customer satisfaction and the mediating role of electronic service quality through its dimensions (website design, reliability, customization, responsiveness, and trust) in the Vitas Jordan Company. A quantitative approach was used to answer the study questions and to test hypotheses. About (500) questionnaires were distributed using the Simple Random Sample technique to achieve the study purposes. (392) questionnaires were valid for analysis, based on the Statistical Package for Social Sciences (SPSS. V.25) and (AMOS) software. The findings of the study revealed that there is a significantly positive impact of social media usage on customer satisfaction and a significantly indirect positive impact of social media usage on customer satisfaction through the electronic Services Quality dimension as a mediating variable. The study recommended that managers concentrate and maintain the use of social media for providing unique services and responding to customers&amp;rsquo; inquiries in a timely manner. In addition, the need to enhance the use of Twitter and Instagram in marketing champions.&amp;nbsp;
APPLICATION OF THE METHOD OF GROUP ACCOUNTING OF ARGUMENTS FOR THE ANALYSIS OF PETROPHYSICAL DATA

The paper considers a range of tasks related to the processing and analysis of petrophysical data, which are effectively solved by the method of group accounting of arguments (MGUA). MGUA is a machine learning method that is an alternative to regression analysis and neural network modeling. The method was tested when working with information from the petrophysical database of crystalline and sedimentary rocks of the Voronezh Crystal Massif (VCM). The basis of the analysis technology is the formation of complex identification equations that allow you to generalize, analyze and effectively use petrophysical data. Previously, the use of such equations for estimating the ore content of nickel-bearing intrusions of the mamonsky complex was considered. Examples of solving problems of robust estimation and identification of petrophysical data are provided. It is shown that the identification models of the MGUA allow automating the procedure for detecting outliers in the data, assess the belonging of rocks to a certain material complex. Since petrophysical information is a necessary link in the geological interpretation of geophysical observations, the experience of obtaining complex models linking the density of metamorphic and intrusive rocks of the VCM with electrical resistivity and magnetic susceptibility is interesting.
Semantic Conceptual Relational Similarity Based Web Document Clustering for Efficient Information Retrieval Using Semantic Ontology

In the modern rapid growing web era, the scope of web publication is about accessing the web resources. Due to the increased size of web, the search engines face many challenges, in indexing the web pages as well as producing result to the user query. Methodologies discussed in literatures towards clustering web documents suffer in producing higher clustering accuracy. Problem is mitigated using, the proposed scheme, Semantic Conceptual Relational Similarity (SCRS) based clustering algorithm which, considers the relationship of any document in two ways, to measure the similarity. One is with the number of semantic relations of any document class covered by the input document and the second is the number of conceptual relation the input document covers towards any document class. With a given data set Ds, the method estimates the SCRS measure for each document Di towards available class of documents. As a result, a class with maximum SCRS is identified and the document is indexed on the selected class. The SCRS measure is measured according to the semantic relevancy of input document towards each document of any class. Similarly, the input query has been measured for Query Relational Semantic Score (QRSS) towards each class of documents. Based on the value of QRSS measure, the document class is identified, retrieved and ranked based on the QRSS measure to produce final population. In both the way, the semantic measures are estimated based on the concepts available in semantic ontology. The proposed method had risen efficient result in indexing as well as search efficiency also has been improved.
Asymmetric deep hashing for person re-identifications

The person re-identification (re-ID) community has witnessed an explosion in the scale of data that it has to handle. On one hand, it is important for large-scale re-ID to provide constant or sublinear search time and dramatically reduce the storage cost for data points from the viewpoint of efficiency. On the other hand, the semantic affinity existing in the original space should be preserved because it greatly boosts the accuracy of re-ID. To this end, we use the deep hashing method, which utilizes the pairwise similarity and classification label to learn deep hash mapping functions, in order to provide discriminative representations. More importantly, considering the great advantage of asymmetric hashing over the existing symmetric one, we finally propose an asymmetric deep hashing (ADH) method for large-scale re-ID. Specifically, a two-stream asymmetric convolutional neural network is constructed to learn the similarity between image pairs. Another asymmetric pairwise loss is formulated to capture the similarity between the binary hashing codes and real-value representations derived from the deep hash mapping functions, so as to constrain the binary hash codes in the Hamming space to preserve the semantic structure existing in the original space. Then, the image labels are further explored to have a direct impact on the hash function learning through a classification loss. Furthermore, an efficient alternating algorithm is elaborately designed to jointly optimize the asymmetric deep hash functions and high-quality binary codes, by optimizing one parameter with the other parameters fixed. Experiments on the four benchmarks, i.e., DukeMTMC-reID, Market-1501, Market-1501+500k, and CUHK03 substantiate the competitive accuracy and superior efficiency of the proposed ADH over the compared state-of-the-art methods for large-scale re-ID.
TOPNMF: Topic based Document Clustering using Non-negative Matrix Factorization

Objectives: This work focuses on creating targeted content-specific topicbased clusters.They can help users to discover the topics in a set of documents information more efficiently.Methods/Statistical analysis: The Non-negative Matrix Factorization (NMF) based models learn topics by directly decomposing the term-document matrix, which is a bag-of-word matrix representation of a text corpus, into two low-rank factor matrices namely Word-Topic feature Matrix(WTOM) and Document-Topic feature Matrix(DTOM).Topic clusters and Document clusters are extracted from obtained features matrices.This method does not require any statistical distribution and probability.Experiments were carried out on a subset of BBC sport Corpus.Findings: The experimental results indicate that the accuracy of TONMF clusters was observed as 100 percent.Novelty/Applications: NMF often fails to improve the given clustering result as the number of parameters increases linearly with the size of the corpus.The computational complexity of the TOPNMF is better than exact decomposition like Singular Value Decomposition (SVD).
IMPOLITENESS STRATEGIES IN YOUTUBE COMMENT SECTION FOUND IN INDONESIAN PRESIDENTIAL DEBATE 2019

The purpose of this study was to find impoliteness strategies used by netizens to Jokowi-Maaruf and netizens to Prabowo-Sandi based on gender in YouTube comments section based on Culpeper`s theory (1996). This research was descriptive qualitative research based on impoliteness strategies phenomena in written language. The data on this research were words, phrases, and sentences in online comments that contained impoliteness strategies found in Indonesian Presidential debate 2019 in YouTube comment. The instruments of this research were Wi-Fi, laptop, writing equipment, and indicators of impoliteness strategies. The results of analysis showed that from 200 comments collected, there were 267 impoliteness strategies found. The reason why number of strategies found bigger than the data collected is because the commentators mostly performed more than one strategy in one utterance. Four by five strategies found in this research were bald on record, positive impoliteness, negative impoliteness, and sarcasm or mock politeness. The strategies that were not found ware withhold politeness because it only occurred in spoken language. The most used strategies were positive impoliteness. The highest user of positive impoliteness was male netizens to female netizens to Prabowo-Sandi (47%).
An Improved DC Motor Position Control Using Differential Evolution Based Structure Specified H∞ Robust Controller

Traditional synthesis of an Hâˆž controller usually results in a very high order of controller that is not practical for a low-cost embedded system such as a microcontroller. This paper presents a synthesis method of a low-order Hâˆž robust controller to control the position of a dc motor. The synthesis employed Differential Evolution optimization to find a controller that guarantees robust stability performance and robust stability against system perturbation. A second-order PID structure was chosen for the synthesized controller because this structure is simple and very famous. The proposed controller performance under uncertainties was compared to some other controllers. The first was compared with a conventional PID controller that had been finely tuned using the trial and error method in the nominal transfer function of the plant. Secondly, the proposed controller was compared with a full-order Hâˆž robust controller generated from a traditional synthesis method. Thirdly, the proposed controller was compared with another structure specified Hâˆž robust controller generated differently from the proposed method. All of the controllers result in a stable response. However, the proposed controller gives a better response in terms of overshoot and response time.
A Taxonomy-Driven Model for Designing Educational Games in Visualization

Visualization literacy of the broader audiences is becoming an important topic, as we are increasingly surrounded by misleading, erroneous, or confusing visualizations. How can we educate the general masses about data visualization? We propose a twofold model for designing educational games in visualization based on the concept of constructivism and learning-by-playing. We base our approach on the idea of deconstruction and construction, borrowed from the domain of mathematics. We describe the conceptual development and design of our model through two games. First, we present a deconstruction-based game that requires the inspection, identification, and categorization of visualization characteristics (data, users, tasks, visual variables, visualization vocabulary), starting from a finalized visualization. Second, we propose a construction-based game where it is possible to compose visualizations bottom-up from individual visual characteristics. The two games use the same deck of cards with a simple design based on visualization taxonomies, popular in visualization teaching.
NL-VTON: a non-local virtual try-on network with feature preserving of body and clothes

Abstract In an image based virtual try-on network, both features of the target clothes and the input human body should be preserved. However, current techniques failed to solve the problems of blurriness on complex clothes details and artifacts on human body occlusion regions at the same time. To tackle this issue, we propose a non-local virtual try-on network NL-VTON. Considering that convolution is a local operation and limited by its convolution kernel size and rectangular receptive field, which is unsuitable for large size non-rigid transformations of persons and clothes in virtual try-on, we introduce a non-local feature attention module and a grid regularization loss so as to capture detailed features of complex clothes, and design a human body segmentation prediction network to further alleviate the artifacts on occlusion regions. The quantitative and qualitative experiments based on the Zalando dataset demonstrate that our proposed method significantly improves the ability to preserve features of bodies and clothes compared with the state-of-the-art methods.
Parallel Repetition for the GHZ Game: A Simpler Proof.

We give a new proof of the fact that the parallel repetition of the (3-player) GHZ game reduces the value of the game to zero polynomially quickly. That is, we show that the value of the n-fold GHZ game is at most n^{-Ω(1)}. This was first established by Holmgren and Raz [Holmgren and Raz, 2020]. We present a new proof of this theorem that we believe to be simpler and more direct. Unlike most previous works on parallel repetition, our proof makes no use of information theory, and relies on the use of Fourier analysis.The GHZ game [Greenberger et al., 1989] has played a foundational role in the understanding of quantum information theory, due in part to the fact that quantum strategies can win the GHZ game with probability 1. It is possible that improved parallel repetition bounds may find applications in this setting. Recently, Dinur, Harsha, Venkat, and Yuen [Dinur et al., 2017] highlighted the GHZ game as a simple three-player game, which is in some sense maximally far from the class of multi-player games whose behavior under parallel repetition is well understood. Dinur et al. conjectured that parallel repetition decreases the value of the GHZ game exponentially quickly, and speculated that progress on proving this would shed light on parallel repetition for general multi-player (multi-prover) games.
Measuring the Degree of Integration into the Global Production Network by the Decomposition of Gross Output and Imports: Korea 1970-2018

The import content of exports (ICE) is defined as the amount of foreign input embodied in one unit of export, and it has been used as a measure of the degree of integration into the global production network. In this paper, we suggest an alternative measure based on the decomposition of gross output and imports into the contributions of final demand terms. This measure considers the manner in which a country manages its domestic production base (gross output) and utilizes the foreign sector (imports) simultaneously and can thus be regarded as a more comprehensive measure than ICE. Korea's input-output tables in 1970-2018 are used in this paper. These tables were rearranged according to the same 26-industry classification so that these measures can be computed with time-series continuity and so that the results can be interpreted clearly. The results obtained in this paper are based on extended time-series data and are expected to be reliable and robust. The suggested indicators were applied to these tables, and, based on the results we conclude that the overall importance of the global economy in Korea's economic strategy has risen and that the degree of Korea's integration into the global production network increased over the entire period. This paper also shows that ICE incorrectly measures the movement of the degree of integration into the global production network in some periods.
Language Model Priming for Cross-Lingual Event Extraction

We present a novel, language-agnostic approach to "priming" language models for the task of event extraction, providing particularly effective performance in low-resource and zero-shot cross-lingual settings. With priming, we augment the input to the transformer stack's language model differently depending on the question(s) being asked of the model at runtime. For instance, if the model is being asked to identify arguments for the trigger "protested", we will provide that trigger as part of the input to the language model, allowing it to produce different representations for candidate arguments than when it is asked about arguments for the trigger "arrest" elsewhere in the same sentence. We show that by enabling the language model to better compensate for the deficits of sparse and noisy training data, our approach improves both trigger and argument detection and classification significantly over the state of the art in a zero-shot cross-lingual setting.
Cloud technologies in the management of pedagogical higher education institutions

The aim of the study is to systematize relevant information on the use of cloud technologies in the management of pedagogical higher education institutions. In the process of studying, general scientific methods of knowledge, which include analysis, synthesis, comparison, were used. Also, the survey results are used to assess the effectiveness of the application of different technologies and their use to manage the university. According to the results of the study, we can conclude that before the pandemic, the situation in the field of cloud computing was quite different. Only some of the world's best universities were implementing cloud technology in their management processes. Today, almost every higher education institution in the world fully or partially implements cloud technology to provide distance education. The main technological products are offers from Google and Microsoft. They are mainly used to store educational information and organize communications within the educational institution. The main problems of using cloud technology have been identified, including the load on the IT staff, the problem of licensing and integration with other cloud services.
Detection of Phishing Websites Using Classification Algorithms

Phishing is a form of online fraudsters try to mimic genuine websites and get the user’s sensitive and personal information. Such malicious users post phishing links and advertisements which may transmit malwares and viruses. Not only individual users are affected by such fraudsters but also several organizations and corporate those depend on internet for services and sales. Such malicious users adopt social engineering skills such as sending emails or by online pop-up advertisements. A number of solutions for detecting phishing websites are mentioned such as non-technical methods, method of black listing and white listing and machine learning techniques. In this paper, data mining techniques in machine learning have been applied to detect phishing websites. One of the data mining techniques is a classification which seems to have a high potential in detecting phishing websites. Here, bagging, C4.5 (J48) and random forest classifiers are tested on the phishing dataset. The dataset is taken from UCI repository which has 1353 instances. The C4.5 classification model has the highest accuracy rate of 90.8%.