Reviewerly/exHarmony huggingface dataset:
Psychological features of studying a foreign language for professional purposes

The article considers the issue of taking into account the psychological characteristics of students in the study of a foreign language for professional purposes. It was analyzed the methodological aspects of taking into account psychological features in teaching a foreign language. Emphasis is placed on the need and importance of taking into account the psychological characteristics of students in the process of learning a foreign language for professional purposes. The main psychological factors of learning a foreign language are identified. Among the main psychological factors are: motivation and interest in language learning, temperament, memory, ability to learn languages, language barrier. Motivation is a major factor in language learning. Only the motivation to succeed can bring results in language learning. Psychological comfort in the classroom is a way to realize their importance in society, trying to get the most information for successful self-development. Under the condition of complete psychological comfort, the intensive development of mental processes and imagination, the improvement of memory, and the ability to communicate begin. Activating memorization processes also plays an important role in foreign language learning. The human brain is a complex mechanism and the main indicator of its efficiency is attention. If attention is reduced, a person can hardly concentrate. The article states that the success of learning a foreign language is determined by a combination of factors: motivational, emotional, cognitive, linguistic, psychophysiological and interactive. This means that the psychological aspects of learning a foreign language determine not only the goals and objectives of teaching, motivation, interest of students, but also the methods and approaches to learning. Emphasis is placed on the need to constantly search for new opportunities and ways to form and improve methods of learning a foreign language for professional purposes. The leading idea of the article is that taking into account the psychological, pedagogical and individual characteristics of students in the study of a foreign language for professional purposes allows to introduce new technologies into the educational process, increase learning efficiency, develop language skills and competencies, develop creative abilities, increase independent and extracurricular activities.
Improving Image Resolution on Surveillance Images Using SRGAN

The process of generating an image from a single low-resolution input to a high-resolution image is known as single image super-resolution. Image resolution finds its application in images where there involves a blurriness or high brightness in the pictures. The same can be corrected to produce a higher resolution image using generative adversarial networks (GAN), specifically super-resolution GAN (SRGAN). This method uses perceptual losses instead of the traditional peak signal-to-noise ratio (PSNR). The application of SRGAN in the field of CCTV footage is immense as the video from a CCTV camera produces low-resolution images. The low-resolution images are passed through a window where it is sliced into smaller images, overlapping, and then passed through the SRGAN. All the SRGAN produced images are then stitched together to make the complete image. Finally, the contrast limited adaptive histogram equalization (CLAHE) filter is applied to the created image with contrast equalization. The final produced images have high resolution and increase visibility through the pictures’ hazy, foggy parts.
Usability Smells: A Systematic Review

In software engineering, bad smells are symptoms that something may be wrong in the system design or code. The term bad smell usually describes a potential problem with known consequences, and also known solutions. In the case of usability, a bad smell is a hint of poor interface design, that makes it difficult for the end-user to accomplish common tasks. This paper presents the most relevant usability smells reported in the literature, as well as tools, methods and techniques that support the process of detecting usability anomalies.
Contextual defeasible reasoning framework for heterogeneous knowledge sources

Abstract Recent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi‐context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi‐agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context‐aware non‐monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system.
Framework of the internal control under remote management: An empirical study

As Covid-19 lockdown forced firms to organize work differently, for example, managing people remotely. Despite the benefits may a firm obtain in adopting new business tools like remote management, but providing assurance that internal control function in an efficient manner is important for a firm; that is because there is a growing interest in internal control issues. It is very important to determine how the remote working model, which emerged during the Covid 19 pandemic process, has changed the accounting processes of businesses. The aim of this study is to propose an internal control model under factors such as remote management, risk assessment, control activities, information and communication, and observation. The data in the research were obtained from the experts in our sample by using the survey method. Our sample consists of managers, supervisors, and academics. The data obtained were evaluated with the Smart-PLS version 3.2.9 program. Findings highlight a positive relationship between control activities and remote management.
Multispectral Image Matching Method Based on Histogram of Maximum Gradient and Edge Orientation

Motivated by the problem of nonlinear intensity changes in multispectral image matching, this letter introduces a robust and efficient image-matching method. The proposed method consists of three steps. First, control-point candidates are identified that are widely distributed in the areas of effective main structure. Then, a novel feature descriptor called the histogram of maximum gradient and edge orientation (HGEO) is proposed for the purpose of multispectral image matching. Finally, a bilateral matching process is carried out to perform the matching process and remove mismatches. The proposed method is successfully applied for matching various multispectral remote sensing images, and experiments are performed with typical datasets that are widely applied in tests of multispectral image matching. According to some popular feature descriptors, the test results demonstrate that the proposed HGEO achieves better matching performance than do many currently used methods.
Using of the Taxonomic Structures in the Process of Studying the Foreign Languages

The article discusses the didactic potential of using taxonomic structures in the process of learning foreign languages, in particular English. There are a number of topics in the English language that are difficult to learn. These include: an extensive temporal structure of verb forms, the construction of interrogative sentences, etc. The use of taxonomic structures in the study of such topics allows you to structure, formalize and visualize the educational material to be studied and thereby solve the indicated problems. Particular attention in the article is paid to the issue of visualizing the taxonomic structures of educational material for learning English using modern software, the use of which in the process of teaching foreign languages contributes to the improvement and improvement of the quality of acquired new knowledge.
Construction engineering cost estimation based on artificial intelligence technology

The determination of construction project cost is one of the important contents of construction project management, but the estimation of construction project cost generally has the disadvantages of large errors and long preparation time. With the continuous development of computer science, artificial intelligence theory is one of the hot research topics. The purpose of this article is to study the construction cost estimation based on artificial intelligence technology. Based on the theoretical basis of artificial neural network, genetic algorithm, and engineering cost, this paper proposes an optimized radial basic function (RBF) model based on genetic algorithm (GA). The search feature combines the width, center, and hidden layer weights of the RBF network with genetic algorithms to self-correct, thereby greatly improving the accuracy of the model calculation results. In this paper, according to the model’s error (actual output-expected output), the four test samples were tested separately, and the error values obtained were 0.0125, 0.1009, –0.0791, and 0.0514. This shows the accuracy of the experimental results of the model [R] higher.
Implementation of Real Time Static Hand Gestures Recognition for Sign Language

The paper propose that uses the computer vision of hand recognition. The camera records live video streams, where the image is taken with the help of the interface. The system is train for every type of hand gesture (one, two, three, four, and five) at least once. After that a test action is given and the system tries to detect it. A proposed system in which hand gestures are detected using image processing. The system detects the number of fingers. The system finds separate fingers above the palm. The system first detects skin color in an image using a filter. The image goes through various steps of image editing to give the right amount of fingers. The system detects the nearest point from the decision point [1]. The system deletes the image according to the centroid point. After that many steps are applied to adjust the image to the effective image so that the fingers are exposed. The system finally detects the number of fingers and displays the calculation to the user. Research has been done on many algorithms that can better distinguish hand gestures. It was found that the diagonal sum algorithm provided the highest accuracy measure. In the progression phase, an automated algorithm removes the background for each training action. The image is then converted into a binary image and taken statistics for all the separated elements of the image. This amount helps us to distinguish and distinguish different hand gestures.
Research on control algorithm of a automatic driving robot based on improved model predictive control

Abstract The automatic driving robot can replace the driver in the traditional test and ADAS test; Model Predictive Control (MPC) is used in the horizontal and vertical control process of high-speed driving. According to the uncertainties and complex constraints of automatic driving horizontal and vertical control, the adaptive improvement of MPC is proposed, which can better adapt to the characteristics of self driving vehicles, sensors and tyres. This paper proposes a multi-layer and multi time automatic driving control strategy based on improved MPC. Visual studio is used to write the upper computer software of the automatic driving robot, which realizes good human-computer interaction. The automatic driving robot is verified in real vehicle test. The experimental results show that the transverse and longitudinal control strategy of the improved model predictive control is stable, and the automatic driving robot can realize the high precision and high dynamic control of vehicle steering, pedal and shift.
LKTs as a means of learning English for specific purposes in a virtual modality

Learning English implies developing oral, writing, reading, and listening competencies in real contexts to avoid its isolated conceptualization for appropriate communication since the meaning and interpretation of language deal with Semantics and Pragmatics. The study focussed on these objectives: identify the learning and knowledge technologies (LKTs) used in the Semantics and Pragmatics course; know the subjects' perceptions concerning their usage and the findings obtained from their application. Methodologically, a descriptive longitudinal quantitative approach took place, getting these results: identification of LKT resources to promote writing, oral, listening, and reading competencies focused on Semantics and Pragmatics, description of students’ perception regarding LKT resources characterized as triggers motivational to learn collaboratively with a positive attitude in their use. The study corroborated that this proposal let subjects grasp the meaning and interpretation of language and achieve a higher proficiency level. In conclusion, LKTs represent a potentially ideal means of learning the language in virtual environments, understanding how they work in context, and acquiring key fundamentals to communicate successfully.
Content-Based Retrieval of Focal Liver Lesions Using Geometrical and Textural Features of Multi-Phase CT-Scan Images

The most critical step in content-based medical image retrieval systems is feature extraction. The objective of the feature-extraction phase is to discriminate between different types of lesions. Preparing useful features improves diagnosis by an image retrieval system and successful treatment. We propose a new set of geometrical features based on the description of different types of tumors and integrate them with textural elements to enhance the discrimination of five kinds of focal liver lesions. The abnormal region is divided into three sections, including central, middle, and border regions. The textural features are obtained in each part individually, while the geometrical characteristics are calculated for the whole zone of a lesion. Evaluation of the results shows an improvement of prec@6 and mAP, which is attributed to the proposed geometric characteristics. Moreover, our method increases prec@6 by 3.9% compared to recent researches.
Comparative Study on Various Clustering Algorithms Review

Data analytics and its evaluation have now become a popular practice in recent decades, with applications in a variety of fields. Data analysis is a computational statistical technique which includes a wide variety of mathematical functions. to extract information. Clustering algorithms have been made the most important algorithms which can be helpful to solve real time problems like grouping the similar type of items in a particular cluster, this can help to identify to real time scenarios to group the positive and negative words present in an article, this can help to identify the tone of a passige using the clustering segmentation. This approach identifies newly discovered data items from vast amounts of information. Data mining application has been used by the financial and insurance industries to identify fraud, provide acceptable lending or insurance solutions to consumers, and truly comprehend customer requirements. This research work tends to make sure that, various clustering algorithms explained with this article are to be presented in brief and with much explanation. The aim of this research is to find data mining classification strategies that can be used to estimate performance and other parameters. The training functions research was carried out in order to determine whichever of the three learning patterns, overfitting, perfect case, and underfitting, occurs while learning the ML techniques. The effects of Multilayer Perceptron, k-Nearest Neighbors, and Nave Bayes were selected for this analysis.
Devices of Textual Illusion: Victimization in Romance Scam E-Letters

Virtual communication transcends national boundaries and allows establishing and maintaining contacts efficiently. However, it also poses threats to integrity and ethical online conduct. In particular, dating sites and unsolicited e-messages often prey on vulnerable unsuspecting people with an intention to extort financial means. The internet is a medium where anonymity, disguise and fraud are widespread. Scammers use linguopsychological devices to lure potential victims into the scam scenario. Devices were screened and categorized from seven letter-sets (each set containing 18–23 letters) including correspondence with scammers. The sets revealed similar lexical and macrostructural patterns common in e-romance scam. Frequently employed devices appeared to signal scammers’ malicious intents often potentially detrimental to a victim. Persuasion, flattering, appeal to trust, core human drives such as bonding, greed, altruism are among the most popular scammers’ devices. The method of structural reduction allowed establishing functional stages in the macrostructure of letter-sets.
Usability analysis using webuse method in Bisma Informatika certification registration system

Bisma Informatika is one of the educational institutions with Diploma education level in Denpasar that serves certification services for people who want to take certification expertise, especially those engaged in Information and Computer Technology. In order to serve optimally, Bisma Informatika applies registration which can be done by the computerized system so that the participant's data can be stored properly. After observing the use of the system at the time of participant registration, an initial conclusion can be drawn that many data fields cannot be understood by the user so they must ask the officer on duty. The data fields in question such as the choice of certification class are not clear because it does not include the implementation time, the unaccompanied participants who come from the common line (so can not include the student identification numbers), telephone numbers that cannot be entered if they exceed the capacity of the permitted number, and inconsistent field filling instructions. This shows that the usability aspect of the registration system is not being considered properly. This study evaluates the interface design and features of the Bisma Informatika certification registration system by considering the context and user needs. In an effort to find out the context and user needs, a Website Usability Evaluation (WEBUSE) questionnaire was used. The results of the WEBUSE questionnaire showed that the four categories which have a good usability level, only content, organization and readability categories are obtained.
Application of Deep Learning in Keyword Extraction in English and American Literature

With the advent of the information age, many new science and technology have been created. Computer network has been chased by people because of its express information, high accuracy of information and no longer the expression of information. In the teaching of British and American literature, the teaching mode based on computer network can change the mode teaching mode that people can only rely on the text, so that the teaching can develop to multi-mode. Students in multimodal teaching can also change the single way to acquire knowledge, and then can learn knowledge, accumulate knowledge, use knowledge and update knowledge from a variety of methods. At the same time, this paper also discusses that the multimodal English and American Literature Teaching Based on computer network can lead to the loss of cultural loss.
Automatic Positioning of Mobile Users via GSM Signal Measurements

Today the need for mobile communication systems and the high increase in the number of users have also made the development of new generation mobile applications indispensable. Obtaining location information has been one of the most interesting and significant areas of improvement. The purpose of the services used to determine the location is generally to obtain the information of the users such as approximate location, speed and time. The GPS system is the most preferred and globally accurate positioning system among global positioning systems. However, in addition to requiring a high installation cost of this system, it is one of the biggest constraints that galactic and meteorological factors, high buildings and other physical obstacles, and especially closed areas can lead to serious signal weaknesses and losses which may cause the system to be out of service. Considering these issues, it is seen that there is an urgent need for positioning systems that will be alternative and complementary to global positioning systems. The cellular network is widely used by almost everyone and its coverage area is increasing day by day. The employed data sets were created by recording the received signal strength (RSS), location information of the GSM base station and the user measured in indoor and outdoor areas through a mobile application we have developed in the Android Studio environment for mobile phones. The network has been trained by machine learning algorithms; extreme learning machine (ELM), generalized regression neural network (GRNN) and k nearest neighborhood (kNN). In the tests conducted with indoor, outdoor and combined data sets, it has been observed that the proposed positioning system works well with distance error rates below a meter (m) at the minimum, and between 76-216 m on average.
Should Critique on Governmental Policy Regarding COVID-19 Be Tolerated on Online Platforms? An Analysis of Recent Case-Law in the Netherlands.

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Ann : Predicting of State Retail Sukuk Based on Region in Indonesia

Abstract Retail Sukuk is a product of State Sharia Securities (Sukuk) issued by the Government of the Republic of Indonesia in this case the Ministry of Finance and sold to individuals or individuals of Indonesian Citizens through Selling Agents in the domestic Primary Market. This research contributes to the government and the Bank to be able to do the maximum promotion for the next sukuk issuance. The data used is data from the Ministry of Finance. These data are sukuk sales data with series 002-010 based on regional groups. The algorithm used in this study is Artificial Neural Network with Backpropogation method. Variabel masukan yang digunakan adalah kelompok wilayah bagian barat selain Jakarta (X1), kelompok Jakarta (X2), kelompok Indonesia Bagian Tengah(X3), dan kelompok Indonesia Bagian Timur (X4) dengan model arsitektur pelatihan dan pengujian sebanyak 4 arsitektur yakni 4-2-1, 4-3-1, 4-2-3-1 dan 4-3-2-1. The best architectural model is 4-3-1 with epoch 266, MSE 0.009918 and 100% accuracy rate. From this model, predictions of retail country sukuk will be made by region.