Reviewerly/exHarmony huggingface dataset:
FORMAL AND INFORMAL METHODS OF ANTI-CORRUPTION ADVOCACY IN UNIVERSITIES

Examines formal and informal methods of anti-corruption propaganda at universities in order to involve students in the implementation of state anti-corruption policies and the formation of intolerance towards corrupt practices
Model development for pneumonia detection from chest radiograph using transfer learning

Accurate interpretation of chest radiographs outcome in epidemiological studies facilitates the process of correctly identifying chest-related or respiratory diseases. Despite the fact that radiological results have been used in the past and is being continuously used for diagnosis of pneumonia and other respiratory diseases, there abounds much variability in the interpretation of chest radiographs. This variability often leads to wrong diagnosis due to the fact that chest diseases often have common symptoms. Moreover, there is no single reliable test that can identify the symptoms of pneumonia. Therefore, this paper presents a standardized approach using convolutional neural network (CNN) and transfer learning technique for identifying pneumonia from chest radiographs that ensure accurate diagnosis and assist physicians in making precise prescriptions for the treatment of pneumonia. A training set consisting of 5,232 optical coherence tomography and chest X-ray images dataset from Mendelev public database was used for this research and the performance evaluation of the model developed on the test set yielded 88.14% accuracy, 90% precision, 85% recall and F1 score of 0.87.
PROJECT CULTURE OF FUTURE DESIGNERS AS A KEY FACTOR OF PROFESSIONAL FORMATION

The article deals with design as a cultural phenomenon that shapes society in general and man in particular, and as a type of creative professional activity. It highlights several features that determine the role and position of design in the modern socio-cultural space. The phenomenon of “project culture” is considered a special kind of design thinking and a kind of design and transformation activities. Designer culture is defined as a professionally significant quality of personality, which includes a system of values, artistic and design knowledge, skills, competencies, skills, experience and individual-creative orientation to creative design and transformation activities.The article is a generalized method of professional training of future designers, which is based on the principles of forming the culture of the project and contains the following components: 1) design and cultural qualities of the individual; 2) professional design thinking; 3) ability – the ability to formulate a project idea (design concept), on this basis to make a sketch of the project (design project), which can be implemented as a result of the project (design product). The main pedagogical conditions for the implementation of this technique are 1) involvement of future designers in the value of project culture through the formation of interests in education and project activities, acquisition of knowledge, skills, methods of artistic and project activities – creative activities; 2) the focus of theory and practice of learning on cultural, personal, psychological, technological approaches in the process of professional training of future designers; 3) co-creation of teacher and student in the process of creating a design object, their intensive and productive interaction, in which the teacher acts as a moderator, who practically and psychologically skillfully directs educational and design activities.&#x0D; Keywords: design; philosophy of culture; project culture; project-transforming activity; methods; pedagogical conditions; professional training of a designer; creativity.
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs

With the representation learning capability of the deep learning models, deep embedded multi-view clustering (MVC) achieves impressive performance in many scenarios and has become increasingly popular in recent years. Although great progress has been made in this field, most existing methods merely focus on learning the latent representations and ignore that learning the latent graph of nodes also provides available information for the clustering task. To address this issue, in this paper we propose Deep Embedded Multi-view Clustering via Jointly Learning Latent Representations and Graphs (DMVCJ), which utilizes the latent graphs to promote the performance of deep embedded MVC models from two aspects. Firstly, by learning the latent graphs and feature representations jointly, the graph convolution network (GCN) technique becomes available for our model. With the capability of GCN in exploiting the information from both graphs and features, the clustering performance of our model is significantly promoted. Secondly, based on the adjacency relations of nodes shown in the latent graphs, we design a sample-weighting strategy to alleviate the noisy issue, and further improve the effectiveness and robustness of the model. Experimental results on different types of real-world multi-view datasets demonstrate the effectiveness of DMVCJ.
Correcting optical character recognition result via a novel approach

Optical character recognition (OCR) is a recognition system used to recognize the substance of a checked picture. This system gives erroneous results, which necessitates a post-treatment, for the sentence correction. In this paper, we proposed a new method for syntactic and semantic correction of sentences it is based on the frequency of two correct words in the sentence and a recursive technique. This approach starts with the frequency calculation of each two words successive in the corpora, the words that have the greatest frequency build a correction center. We found 98% using our approach when we used the noisy channel. Further, we obtained 96% using the same corpus in the same conditions.
Is the Computation of Abstract Sameness Relations Human-Like in Neural Language Models?

In recent years, deep neural language models have made strong progress in various NLP tasks. This work explores one facet of the question whether state-of-the-art NLP models exhibit elementary mechanisms known from human cognition. The exploration is focused on a relatively primitive mechanism for which there is a lot of evidence from various psycholinguistic experiments with infants. The computation of "abstract sameness relations" is assumed to play an important role in human language acquisition and processing, especially in learning more complex grammar rules. In order to investigate this mechanism in BERT and other pre-trained language models (PLMs), the experiment designs from studies with infants were taken as the starting point. On this basis, we designed experimental settings in which each element from the original studies was mapped to a component of language models. Even though the task in our experiments was relatively simple, the results suggest that the cognitive faculty of computing abstract sameness relations is stronger in infants than in all investigated PLMs.
Research on the Connotation, Current Situation and Approaches of Cultivating the Artisan Spirit of Vocational School Students in the New Era

In the new era, with the rapid growth of economy and society in our country, teaching, as the significant section of economic and social development, has been frequently focused by our society. Vocational schools, the vital base to cultivate innovative talents, have exerted great functions to the national teaching reform and development of economy and society. How to cultivate the artisan spirit has become a contemporary theme in the new stage of boosting teaching quality and reforming the work of education and teaching. Realizing the connotations, current situations of artisan spirit of vocational students is the base and premise of cultivating their artisan spirit while studying the approaches of that is the final orientation and foothold. Accordingly, this research mainly gives a comprehensive analysis on the artisan spirit to render vocational education to serve for the economic and social development in the light of its connotation, current situation and approaches of vocational school students. Keywords: vocational school; artisan spirit; connotation; current situation; approaches.
Computing with two quantum reservoirs connected via optimized two-qubit non-selective measurements

Abstract Currently, quantum reservoir computing is one of the most promising and experimentally accessible techniques for hybrid, quantum-classical machine learning. However, its applications are limited due to practical restrictions on the size of quantum systems and the influence of noise. Here we propose a novel approach to connect two quantum reservoirs in a network to overcome these issues and enhance their computing performance. To transfer information between quantum reservoirs, we perform optimized two-qubit non-selective measurements. We suggest a general heuristic optimization strategy based on tensor network language and matrix representation of two-qubit quantum channels specified for quantum reservoir computing. In addition, we introduce a single qubit purification channel and its optimization for further enhancement of quantum reservoir computing. We also demonstrate that the optimized channels applied to a seven-qubit network can efficiently transfer information between its parts with the resulting performance comparable to a network up to twenty-five qubits connected via a classical information link.
Device-Independent Quantum Key Distribution Using Random Quantum States

We Haar uniformly generate random states of various ranks and study their performance in an entanglement-based quantum key distribution (QKD) task. In particular, we analyze the efficacy of random two-qubit states in realizing device-independent (DI) QKD. We first find the normalized distribution of entanglement and Bell-nonlocality which are the key resource for DI-QKD for random states ranging from rank-1 to rank-4. The number of entangled as well as Bell-nonlocal states decreases as rank increases. We observe that decrease of the secure key rate is more pronounced in comparison to that of the quantum resource with increase in rank. We find that the pure state and Werner state provide the upper and lower bound, respectively, on the minimum secure key rate of all mixed two-qubit states possessing the same magnitude of entanglement under general as well as optimal collective attack strategies.
AUTONOMY IN ONLINE GRAMMAR LEARNING: EFL STUDENTS’ REPORTED PRACTICE AND PERCEPTIONS

Often perceived as a challenging subject among many tertiary EFL students, grammar may present more difficulties to the learners when delivered online, especially during the COVID-19 pandemic. Within such a situation, autonomous learning, which earlier literature has suggested to be crucial in helping students with grammar learning, would be even more imperative for their success. Hence, this study attempts to investigate whether EFL tertiary students in Indonesia practice autonomous learning and how they perceive learning autonomy for their online grammar class. As many as 82 Indonesian students who had taken an online intermediate grammar course in a respected private university were involved in this study. A questionnaire with close and open-ended questions was used for the data collection. The findings indicate that although students mostly had positive views towards autonomy in online grammar learning and claimed to practice it, the autonomy level tended to be low. Factors such as fatigue from online learning, distractions, overwhelming assignments, and lack of study skills, seemed to have contributed to the difficulties in practicing learner autonomy.
MECHANISMS OF FORMATION OF PROFESSIONAL RESPONSIBILITY AND ETHICS IN STUDENTS IN THE PROCESS OF PEDAGOGICAL AND QUALIFICATION PRACTICE

This article analyzes the problem of professional responsibility of students, the specific aspects of achieving the formation of professional culture and pedagogical responsibility in the training of future teachers in the process of qualification and pedagogical practice. It also reveals the importance of the process by which future teachers realize the importance of social responsibilities and develop a sense of direct responsibility for their implementation.
Heading Generator Using Machine Learning

AI is a part of computerized reasoning (AI) and software engineering which centers around the utilization of information and calculations to emulate the way that people learn, step by step working on its exactness. AI is a part of man-made reasoning (AI) and software engineering which centers around the utilization of information and calculations to mimic the way that people learn, steadily working on its exactness. Managed learning is the sort of AI wherein machines are prepared utilizing great "marked" preparing information, and on premise of that information, machines foresee the result. We will zero in on the accompanying model specifically: Heading Generator utilizing Machine Learning utilizing the YouTube moving recordings dataset and the Python programming language to prepare a model of text age language utilizing AI, which will be utilized for the assignment of Heading generator for youtube recordings or in any event, for your web journals. Heading generator is a characteristic language handling task and is a focal issue for a few AI, including text blend, discourse to message, and conversational frameworks. To fabricate a model for the undertaking of Heading generator or a text generator, the model ought to have the option to gain proficiency with the likelihood of a word happening, utilizing words that have previously showed up in the grouping as setting.
Design and Build Smart Agriculture Using Cognitive Internet of Things (C IoT)

In Indonesia, agriculture is required to be able to develop growth in the region and be able to produce agricultural products that have high competitiveness while also empowering the community. IoT, or commonly known as the Internet of Things (Internet of Things), allows us to connect to various things. For example, we can use a smart farm. This research leads to the implementation of a plant sprayer that uses the Cognitive internet of things that can be integrated into farmers and factories whose raw material uses corn. This irrigation system uses a NodeMCU microcontroller, automatic discharge using a DC water pump, a soil moisture sensor using a capacitive soil moisture v.2 sensor. The system used uses fuzzy logic algorithms as data processing. The sensor data shows the 70% soil moisture value displayed on the smartphone so that the system will water or not automatically until the soil moisture value is as needed. Soil moisture in this system is in 3 criteria, namely dry, normal, and wet. Watering produced by this system with an average time duration of 8 seconds. This farming system is designed to increase productivity and predict problems in agriculture. This analysis illustrates that the Cognitive internet of things has great potential in agricultural technology because it can increase and facilitate corn production for farmers.
Development and Evaluation of E-Learning Module in 3D Homes Designing

The primary purpose of this research is to develop an e-learning module for 3D Home Designing and assess the value of the instructional materials to the student's user. The pandemic forces our educational system to change to remote instruction, and the construction of the electronic module is crucial because there are no existing materials that focus primarily on 3D home design. Fifty (50) students enrolled in CAD courses tested the E-learning program. The mean and standard deviation were calculated using descriptive quantitative statistics in this investigation. The content of the e-learning program was evaluated using a survey questionnaire. In addition, the questionnaire requested input on the e-module. Following that, adjustments were made. The E-module is a helpful instrument in teaching 3D Homes Designing that encourages self-directed learning and direct application of knowledge.
Segmentation of tumour from mammogram images using U-SegNet: a hybrid approach

Breast cancer is the most common cancer type around the world which majorly affects women. Early detection of breast cancer helps in increasing survival rate. Segmentation helps in easy identification of abnormalities in image and draw conclusion whether the image is normal or not. So, a hybrid approach U-SegNet is proposed, which is fully convolutional neural network. It extends U-Net by integrating SegNet for improved mass detection. The proposed method is evaluated for its accuracy on publicly available Digital Database for Screening Mammography (DDSM) dataset. A comparison analysis is done on the proposed method compared with three other models such as Watershed, Fuzzy c-means and U-Net model; it is found that the proposed method gives good results.
Impact of Learning Rate on Noise Resistant Property of Deep Learning Models

The interest in analog computation has grown tremendously in recent years due to its fast computation speed and excellent energy efficiency, which is very important for edge and IoT devices in the sub-watt power envelope for deep learning inferencing. However, significant performance degradation suffered by deep learning models due to the inherent noise present in the analog computation can limit their use in mission-critical applications. Hence, there is a need to understand the impact of critical model hyperparameters choice on the resulting model noise-resistant property. This need is critical as the insight obtained can be used to design deep learning models that are robust to analog noise. In this paper, the impact of the learning rate, a critical design choice, on the noise-resistant property is investigated. The study is achieved by first training deep learning models using different learning rates. Thereafter, the models are injected with analog noise and the noise-resistant property of the resulting models is examined by measuring the performance degradation due to the analog noise. The results showed there exists a sweet spot of learning rate values that achieves a good balance between model prediction performance and model noise-resistant property. Furthermore, the theoretical justification of the observed phenomenon is provided.
Recognizing Fake Headlines Using Clustering Algorithms

The credibility of the news sources has hit a new low during the COVID-19. Hence, it is necessary to check for facts before trusting the news. Clustering is extremely important for analysing data, making predictions, and overcoming data abnormalities. So, in this work, the two most prominent clustering algorithms, K-Means and K-Medoids, are tested on a dataset, and K-Means outperforms k-Medoid. We now utilized supervised classification methods like Logistic Regression, K-Nearest Neighbours, and Support Vector classifier to train on the same news headlines we used for clustering with the 'Prediction' column, and then chosen the technique with the highest accuracy. The Support Vector Classifier had the maximum accuracy of 94.93 percent, according to the test. We have developed is a hybrid model consisting of an unsupervised K Means clustering algorithm and a supervised Support Vector classification algorithm. The K Means algorithm organizes the news headlines into clusters by capturing the usage of certain words and the support vector algorithm learns from those clusters to predict the categories into which the unseen news headlines belong to.
Variable selection of regularized stochastic gradient descent in logistic regression

In the modern big data environment, Stochastic gradient descent is an important method for training neural networks, processing largescale data sets, optimization, etc. Deeply welcomed in various fields.With regard to SGD, the existing literature considers the stopping condition of parameter iteration.In fact, some unimportant parameters do not always have values of 0 during iteration, and it is not clear whether they are important or not even if the stop condition is reached.We consider variable selection of SGD parameter iteration with L1 regular in generalized linear regression model (taking Logistic regression as an example).Monte Carlo numerical simulation and practical application examples were given to illustrate the consistency of variable selection.The results show that high accuracy can be achieved by using the selected variables to build the model.
BOOK REVIEW: ONLINE TEACHING OF ENGLISH AS A FOREIGN LANGUAGE IN INDONESIA: LESSONS FROM COVID-19 PANDEMIC

The last two years have forced educators and learners to be digitally-literate or at least adaptable to the digital technology use drastically because of COVID-19 pandemic. This globally challenging condition was then captured and written by Didi Suherdi (2021) and published by Deepublish. Online Teaching of English as a Foreign Language in Indonesia: Lessons from Covid-19 Pandemic consists of seven chapters. It aims to provide new theoretical and practical insights of English teaching through online mode in the midst COVID-19 pandemic, specifically in Indonesia. This book covers various topics related to the research studies and notions of utilizing ICT as well as optimizing educators’ roles in the context of online teaching-learning process. Additional topics are also presented to promote the use of alternative ICT-based teaching models and teacher training in the online setting. Distinctively, this book is recommended for academicians who are concerned about ICT-use and eager to start an initial step to integrate and take advantage of ICT for the resiliency of the education system in the post-pandemic era.
Meningkatkan Aktivitas Dan Hasil Belajar Matematika Siswa Kelas IV SDN 6 Baamang Tengah Dengan Metode Penugasan Dan Latihan 2021/2022

This research was motivated by students who have good achievement in learning only 12 students out of 21 students who scored above 60 at SDN 6 Baamang Tengah, Baamang District. This is caused by the lack of active students and the lack of student interest in learning which results in low student achievement results. The purpose of this research is to increase students' learning creativity in the material of Multiplication Count Operation with assignments to students. The research was carried out in three cycles, the planning stage, the implementation and observation stage, the reflection stage and the revision stage. From the observations of colleagues, it was obtained that there was an increase in data with a significant increase in the learning process in the second cycle using the Assignment and Exercise method compared to the learning process in the first cycle which only used the lecture method, namely in the initial cycle the average value of learning reached 53, 20. Meanwhile in the first cycle the average value of learning reached 65.23. In the second cycle, the learning average reached 77.38. In the third cycle, the learning average reached 92.38.