Reviewerly/exHarmony huggingface dataset:
An English Learning: Rural Students Beliefs

This study departs from the researcher's experience as a volunteer teacher in a school where students face obstacles in learning English due to a lack of motivation and supporting facilities that impact their English performance. Therefore, the purpose of this study is to analyze beliefs in learning English. This study uses a quantitative method with a survey design using Horwitz's Beliefs About Language Learning Inventory questionnaire as a research instrument. The population in this study found 136 students. The instrument used to collect data is a questionnaire. The technique used to analyze the research data is qualitative and quantitative analysis. The findings show that students support various beliefs about learning English. The most common beliefs held by students are motivation and expectations. Students have reasonable confidence in motivation and hope (72.35%) because they have a better education, and a future career can be achieved through good English skills. It can be said that students have good motivation to learn English because they think it will be helpful for their dream job in the future.
Voice Controlled Wheelchair

The proposed system is a smart voice-controlled wheelchair with an integrating flame sensor. The wheelchair is the most ubiquitous equipment used by people with lower limb disabilities as well as aged people who are not able to move independently because of their weakness [1-4]. Though many people’s needs are satisfied with traditional, manual, or powered wheelchairs, there are some individuals with both upper and lower limb disabilities and hands and arms impairment who find traditionally existing wheelchairs difficult to operate and are pushed into a situation where they are in need of help from other people around to control the wheelchair. A voice-controlled wheelchair is operated by using the voice commands through the given input which makes access easy for those people and lets them travel independently. This system also offers automatic protection alerts [5-7].The voice recognition API is the key feature of this project that is used to set up the desired voice command and output (Figure 1). Voice recognition API here means a system for computer analysis of human voice, especially for the purposes of interpreting words and phrases or identifying an individual voice and it converts the recognized voice command into text and sends the command text to the HC-05 Bluetooth module. The Bluetooth module transmits the converted text command to Arduino (Figure 2). When Arduino receives the command it acts as per corresponding coded data stored in the memory to Arduino Microcontroller. L298N motor driver controls the locomotion accordingly along with making the movement easier. The integrated flame sensor checks the surrounding environment to ensure the safety of the user and updates the caretaker by sending a message to their mobile (Figure 3). The main objective of this project is to provide an effortless movement and low-cost wheelchair that is available to everyone in need.
Context-based Machine Translation of English-Hindi using CE-Encoder

The difficulty in obtaining accurate word alignment and determining a target word that is the best candidate for a source context in machine translation leads to different translations. In this study, we propose a method with a more accurate context model. Our Neural Machine Translation (NMT) approach focuses on the encoder to apprehend the meaning of source sentences for improved translation. The recurrent encoder works by taking into consideration the history and future information of the source context. In this study, we implement the proposed approach into three steps. Firstly, we learn the representation of future context in advance. Secondly, a context-based recurrent encoder called as CE-Encoder with two-level Gated Recurrent Unit (GRU) is used. In this, the bottom-level GRU gathers history data of a sentence and top-level GRU assembles future data information. Finally, the future learned context and the history information from the opposite direction is integrated. The distinguishing factor of the proposed framework from the existing models, specifically Bidirectional Recurrent Neural Network (BiRNN) is that, the current models have not spent substantial time and capacity in learning future context or disambiguating source and target words based on the context which is defined by source sentence. We conduct experiments on the datasets from ILCC and CFILT for the English-Hindi language pair. From the comparative evaluation, we observed that the proposed model outperforms the Bidirectional RNN encoder in terms of translation quality. The proposed model has shown the improvement of 7 Bleu points using the ILCC dataset and 9 points using the CFILT dataset over BiRNN.
PREDICTING CERVICAL CANCER USING MULTIPLE MACHINE LEARNING TECHNIQUES

Now a day, cervical cancer is the most common and prevailing gynecologic malignancies.Cervical cancer is the third type of cancer after breast and lungs cancer in women.Although its highly preventable disease provided early screening is done so as to minimize the global burden.However, due to unawareness, ignorance, lack of medical facilities and expensive procedures in developing countries, the vulnerable patient populations cannot afford to undergo examination regularly.A novel ensemble approach is presented in this paper to predict the risk of cervical cancer using machine learning approach.In this paper, we proposed a prediction model that can predict with accuracy the presence or absence of cervical cancer from as many as 35 possible risk features recorded for each woman.As per the results, the proposed approach is accurate, scalable and practical
An application of Artificial Intelligence in emotion detection by speech only

Emotions of human are very important in our daily communications and recent years have witnessed a lot of research works to develop reliable emotion recognition systems based on various types data sources such as audio and video.Since there is no apparently visual information of human faces, emotion analysis based on only audio data is a very challenging task.In this work, a novel emotion recognition is proposed based on robust features and machine learning from only audio speech.For a person independent emotion recognition system, audio data is used as input to the system from which, Mel Frequency Cepstral Coefficients (MFCC) are calculated as features.The MFCC features are then followed by critical analysis to minimize the inner-class scatterings and maintain max value of the interclass scatterings.The robust indistinguishable features are then applied with an efficient and fast deep learning approach Neural Structured Learning (NSL) for emotion training and recognition.The proposed approach of combining MFCC, discriminate analysis and NSL generated superior recognition rates compared to other traditional approaches such as MFCC-DBN, MFCC-CNN, and MFCC-RNN during the experiments on an emotion dataset of audio speeches.The system can be adopted in smart environments such as homes or clinics to provide affective healthcare.Since NSL is fast and easy to implement, it can be tried on edge devices with limited datasets collected from edge sensors.Hence, we can push the decision-making step towards where data resides rather than conventionally processing of data and making decisions from faraway of the data sources.The proposed approach can be applied in different practical applications such as understanding peoples' emotions in their daily life and stress from the voice of the pilots or air traffic controllers in air traffic management systems, and identification of leader's emotions or intentions behind their speech.
Is It Time to Rethink Our Software Development Practices?

Covid-19 has proliferated across the nations with increasing number of cases each day. Thus, IT companies are now forced to operate from remote places with limited IT resources. However, these companies across the globe are on continuous touch with their software development and maintenance teams to ensure that they are productive and are able to deliver their services on the projects on time. We study the challenges faced by the IT companies at this juncture and the need for a different software development approach to complete the projects successfully even during such crisis. In this context, when the IT industries are making attempts to complete their on-going software projects and also to attend to some critical up-gradation in their previously delivered products, the challenges faced by them due to acute shortage of IT resources and transforming the working model from physical setup to remote platform needs to be studied. This calls for studying the existing software development models and practices and defining an alternate one that would suit the present IT scenario.
Sistem Rekomendasi Tugas Akhir Mahasiswa pada AMIK Indonesia untuk Mendukung Merdeka Belajar-Kampus Merdeka Menggunakan Metode Collaborative Filtering (CF)

The purpose of this study was to develop a model of a student's final project recommendation system by applying the Collaborative Filtering (CF) method. Agile Development Methods (ASD) is used in software development where this method is widely used for software development because it can produce system requirements data that is more descriptive and easy to implement into software. After going through the process of completing this research, the results of this study; 1) In knowing the topic or research theme in the final project, students with an expert system can be seen from the themes and sub-themes and valid calculations are carried out in helping students to find out the research title for the final project quickly, 2) By applying the Collaborative Filtering (CF) method ) in detecting research topics or themes at AMIK Indonesia, it can produce valid calculations that are the same as manual calculations so that the selection process can be carried out quickly and accurately, 3) In designing this application, it consists of the main menu page, a list of research topics, FAQs and about applications with using the programming language PHP, ReachJS and MySQL as data processing in the recommendation of the student's final project, and 4) The test is carried out using Mean Absolute Error (MAE), root-mean-square error (RMSE), Recall Value, and F-Measure. The results of the test evaluation show that the Collaborative Filtering (CF) method is quite accurate with the resulting recommendations.
Improvising Balancing Methods for Classifying Imbalanced Data

Abstract: Class Imbalance problem is one of the most challenging problems faced by the machine learning community. As we refer the imbalance to various instances in class of being relatively low as compare to other data. A number of over - sampling and under-sampling approaches have been applied in an attempt to balance the classes. This study provides an overview of the issue of class imbalance and attempts to examine various balancing methods for dealing with this problem. In order to illustrate the differences, an experiment is conducted using multiple simulated data sets for comparing the performance of these oversampling methods on different classifiers based on various evaluation criteria. In addition, the effect of different parameters, such as number of features and imbalance ratio, on the classifier performance is also evaluated. Keywords: Imbalanced learning, Over-sampling methods, Under-sampling methods, Classifier performances, Evaluationmetrices
Bird Selling System at The Archipelago Bird Breeding Association (ABBS) North Sumatra Region Using Waterfall Method

The spread of the Covid-19 Virus has resulted in sluggish business development in Indonesia. This impact is also felt by the chirping bird breeders. One of the organizations or institutions that oversees the existing chirping bird breeding is the Archipelago Bird Breeding Association (ABBS). In their daily activities, the members of the ABBS carry out captive breeding, maintenance and also buying and selling results from captivity. There are many problems faced by breeders during this pandemic, including difficulties in marketing their captive products. The purpose of this research is to create a medium for chirping mania lovers to get information on selling birds. This research uses the Waterfall method. The Waterfall has a multi-step approach to system analysis and design. The results of this study include a website-based chirping bird sales application that makes it easier for bird breeders to sell their birds.
Dynamic supervisor for cross-dataset object detection

The application of cross-dataset training in object detection tasks is complicated because the inconsistency in the category range across datasets transforms fully supervised learning into semi-supervised learning. To address this problem, recent studies focus on the generation of high-quality missing annotations. In this study, we first specify that it is not enough to generate high-quality annotations using a single model, which looks only once for annotations. Through detailed experimental analyses, we further conclude that hard-label training is conducive for generating high-recall annotations, whereas soft-label training tends to obtain high-precision annotations. Inspired by the aspects mentioned above, we propose a dynamic supervisor framework that updates the annotations multiple times through multiple-updated submodels trained using hard and soft labels. In the final generated annotations, recall and precision improve significantly through the integration of hard-label training with soft-label training. Extensive experiments conducted on various dataset combination settings support our analyses and demonstrate the superior performance of the proposed dynamic supervisor.
Crowdsourcing System using Blockchain Technology

Crowdsourcing frameworks have been receiving a lot of enthusiastic acceptance these days, and there has been a rise in significant raise in concern as well. Crowdsourcing frameworks are excellent tools that can coordinate the human insights of specific instances, and organizations together globally and help comprehend and collaborate intricate chore. Notwithstanding, these central structures subject to the inadequacies of the trusted standard like standard financial foundations. For example, single motivation behind dissatisfaction, higher organizations cost, and security disclosure. An idea is to use a shared structure for crowdsourcing basis blockchain, wherein the work of the requester is directed by the swarm of workers without relying on central freely supporting systems or foretold customers that are inclined to organizations with enrolling certifiable characters. In thought, the proposed framework design would permit Users to enroll, post, or get an endeavor securely. The design would likewise furnish clients with undeniable degree of protection, and security, and the proposed system additionally has low administration costs. By extending the adaptability and adaptability of publicly supporting the reason for existing is to show the crowdsourcing logic with intelligent contract. As per this structure, a requester needs to deposit the venture money, while posting task. The stored deposited amount is escrowed with the framework, and on the concurred task fruition, the laborer will get the undertaking amount from the escrow. Because of this system, a requester won't need to pay more than what an assignment merits, as indicated by a rule unveiled when errand is posted; and every laborer will unquestionably get an installment dependent on the rules.
Fyodor Batyushkov and Ivan Bunin. An unknown autograph

In the Manuscripts and Rare Books Division of the Regional Scientific Library of Saratov State University, an early autograph of Ivan Bunin, which has not yet been introduced in scientific circulation, has been discovered. This is a signed copy of the translation of G. G. Byron’s Manfred published by the “Znanie” Partnership. It is known that Fyodor Batyushkov, holding the post of the editor-in-chief of the magazine Mir Bojiy (The World of God), wrote reviews of the works of authors nominated for the Pushkin Prize.
Terror on the Internet: Comparing the United States and European Union Social Media Regulations to Prevent Terrorism

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Learning GANs in Simultaneous Game Using Sinkhorn With Positive Features

Entropy regularized optimal transport (EOT) distance and its symmetric normalization, known as the Sinkhorn divergence, offer smooth and continuous metrized weak-convergence distance metrics. They have excellent geometric properties and are useful to compare probability distributions in some generative adversarial network (GAN) models. Computing them using the original Sinkhorn matrix scaling algorithm is still expensive. The running time is quadratic at O(n2) in the size n of the training dataset. This work investigates the problem of accelerating the GAN training when Sinkhorn divergence is used as a minimax objective. Let G be a Gaussian map from the ground space onto the positive orthant Rr + with r ≪ n. To speed up the divergence computation, we propose the use of c(x, y) = -ε log ⟨G(x), G(y)⟩ as the ground cost. This approximation, known as Sinkhorn with positive features, brings down the running time of the Sinkhorn matrix scaling algorithm to O(r n), which is linear in n. To solve the minimax optimization in GAN, we put forward a more efficient simultaneous stochastic gradient descent-ascent (SimSGDA) algorithm in place of the standard sequential gradient techniques. Empirical evidence shows that our model, trained using SimSGDA on the DCGAN neural architecture on tiny-coloured Cats and CelebA datasets, converges to stationary points. These are the local Nash equilibrium points. We carried out numerical experiments to confirm that our model is computationally stable. It generates samples of comparable quality to those produced by prior Sinkhorn and Wasserstein GANs. Further simulations, assessed on the similarity index measures (SSIM), show that our model’s empirical convergence rate is comparable to that of WGAN-GP.
Theoretical analysis of the features of professionalself-realization of practical psychologists

the personality of other people, secondly, includes at least elements of the professional image of «I» psychologist and, thirdly, at most, the ideal image corresponding to the desired results.Research of the problem of the peculiarities of self-realization of a practical psychologist is possible on the basis of studying the psychological features and professional image of the «I-concept» in professions such as «human-human».The content of professional self-realization of practical psychologists is a multilevel dynamic system that is developing.
The Effectiveness of Using Linktree and Wordwall Applications on IPS Learning Outcomes for Class V SDN Cilangkap 01

In this globalization era, the development of technology and information is increasingly fast, and it has given a major impact on the world of education. The emergence of various technology applications used to support online learning is because of the development. Linktree and Wordwall applications are new alternatives that students can use in the online learning process. The purpose of this study is to determine the effectiveness of using Linktree and Wordwall Applications on IPS learning outcomes for Class V Students. This study is quantitative research which requires the use of numbers, starting from data collection, interpretation of the data, as well as the appearance of the results and conclusions. . The method in this study is the correlational method. This method is used to reveal the relationship and the degree of relationship between two or more variables. The data is collected from questionnaires and learning outcomes tests. The results of this study indicate that there is a significant relationship between the use of Linktree and Wordwall applications on IPS learning outcomes for Class V students with a correlation coefficient between X1 and Y of 0.365. The provisions of the correlation test by comparing rCount &gt; rTable (0.365 &gt; 0.355) and p-value of 0.022 &lt;0.05. Thus, there is a significant relationship between X1 and Y. The correlation coefficient between X2 and Y is 0.477 &gt; rTable 0.355. The provisions of the correlation test by comparing rCount &gt; rTable = 0.477 and p-value of 0.003 &lt;0.05. Thus, the relationship between X2 and Y is significant
Textual Analysis Applications: Subject Review

This paper is a literature survey about applications of textual analysis.It aims to provide brief description about the common textual analysis applications.The paper talks about the dictionary which is mostly, one of the main components for textual analysis applications.The paper highlights a number of related examples that were proved in previous published papers.Common features for the related examples are illustrated.And their results are discussed.It will be shown that "morphological and syntactic analysis" is a proved approach.Also, it will be shown that text similarity based on "morphological and syntactic analysis" approach has more accurate results than text similarity based on semantic approach..
Fusing Part-of-Speech Information in Low-Resource Neural Paraphrase Generation

Paraphrase generation is an essential yet challenging task in natural language processing. Neural-network-based approaches towards paraphrase generation have achieved remarkable success in recent years. Previous neural paraphrase generation approaches ignore linguistic knowledge, such as part-of-speech information regardless of its availability. The underlying assumption is that neural nets could learn such information implicitly when given sufficient data. However, it would be difficult for neural nets to learn such information properly when data are scarce. In this work, we endeavor to probe into the efficacy of explicit part-of-speech information for the task of paraphrase generation in low-resource scenarios. To this end, we devise three mechanisms to fuse part-of-speech information under the framework of sequence-to-sequence learning. We demonstrate the utility of part-of-speech information in low-resource paraphrase generation through extensive experiments on multiple datasets of varying sizes and genres.
Research and Practice of MOOC Construction Based on Vocational Education Platform

Vocational education is the extension and supplement of college education and post training.The rise and development of MOOC brings opportunities and challenges to vocational education.From the perspective of vocational education, this paper puts forward the methods and strategies of MOOC course content design, teaching
Moving to a Virtual Literacy Practicum: Challenges and Solutions

When the university took the unprecedented move to virtual teaching at the start of the COVID-19 pandemic in March 2020, my challenge, as a teacher educator, was to plan a virtual literacy practicum for master candidates. All aspects of curriculum design were considered, from establishing relationships with schools, parents, and students, considering technology platforms, appropriate technology applications, to considering everyone’s emotional state. In this article, I share the many challenging aspects and the solutions that lead to successful experiences for all participants.