Reviewerly/exHarmony huggingface dataset:
WAYS TO IMPROVE THE COGNITIVE INDEPENDENCE OF FUTURE PRIMARY SCHOOL TEACHERS

The article highlights one of the current problems of modern pedagogical science - the problem of improving the cognitive independence of future primary school teachers. Significance and possibility of solving the problem in the theoretical aspect of this study in domestic and foreign students. Understanding that cognitive independence is an important and necessary quality of a modern teacher, which provides professional and qualification potential of efficiency-pedagogical activity, based on activity and independence, creativity is realized by acquired knowledge in solving various professional. The article emphasizes that the formation of a certain quality can be carried out in the process of studying future teachers of disciplines of the pedagogical cycle, in particular the history of pedagogy. This science provides an opportunity for students in the process of learning pedagogical truths to think logically, to delve deeply into educational problems, which contributes to the formation of mechanisms for their self-realization, self-development, the pursuit of the creative search. The article underscores the fact that in the process of teaching this discipline the activities of students should be aimed at developing the ability to creatively apply the information and interpret it, productively using the experience gained in real-life situations. The article emphasizes that the formation of a certain quality can be carried out in the process of studying future teachers of disciplines of the pedagogical cycle, in particular the history of pedagogy. This science provides an opportunity for students in the process of learning pedagogical truths to think logically, to delve deeply into educational problems, which contributes to the formation of mechanisms for their self-realization, self-development, the pursuit of the creative search. The article underscores the fact that in the process of teaching this discipline the activities of students should be aimed at developing the ability to creatively apply the information and interpret it, productively using the experience gained in real-life situations. The authors propose to use interactive teaching methods when students study the history of pedagogy to improve cognitive independence. The special significance of discussion and game methods is characterized. It is determined that these methods provide activation of students' activities by revealing the truth through the comparison of different views in the context of solving pedagogical problems based on the time challenges of society of a certain historical period. It is emphasized that appropriately organized work of students creates conditions for independent search, provides intensive work of thought, creates favorable conditions for the development of the ability of future teachers to independently and comprehensively consider the subject. Key words: cognitive independence, teacher training, interactive learning, discussion methods, role play.
A Microservice Framework for Efficient Navigation Service

Navigation services are often the fundamental services in the mobile computing, Internet of things, edge computing and so on. Nowadays, navigation services usually use a global navigation satellite system (GNSS) and inertial measurement units for navigation. However, they often adopt the monolithic architecture for easy development, which limits the efficiency of navigation services. In this work, we propose a microservice framework for efficient navigation service. By distributing the workload to different modules in our microservice framework, our method can improve the navigation efficiency with parallel computing among multiple microservices. We implement the navigation service and conduct experiments to evaluate the efficiency and the precision of the service. Preliminary results demonstrate that our microservice framework effectively improves the efficiency and precision of navigation compared with the monolithic framework.
From the Ethics of Computational Pathology to Image Recognition in Roman Britain: Support for AI Research at the Arts and Humanities Research Council

The Arts and Humanities Research Council (AHRC), part of UK Research and Innovation (UKRI) has supported independent academic research involving information processing tools since its founding in 2005.
Effectiveness of Mobile Assisted Vocabulary Learning Applications (MAVL) as a self-learning tool for ESL learners in Sri Lanka

The advancements in mobile technologies have changed how teaching and learning processes are conducted in the higher education sector. Mobile-Assisted Vocabulary Learning (MAVL) is a part of Mobile Assisted Language Learning (MALL) which is now a demarcating element in the ESL (English as a Second Language) classroom for the language improvement of the learners. MAVL applications are used widely in international language education, yet in Sri Lanka, language learners are limited to printed materials which are not enough to maximize their updated vocabulary knowledge. MAVL applications are used among Sri Lankan ESL learners hardly less than printed material to learn vocabulary and enhance it, which creates the research gap in this study. The research aims to introduce professional and practical MAVL applications to enhance vocabulary, introduce MAVL applications as a helping tool, and enhance their vocabulary and writing competence. The research has experimented with a mixed-method approach, with a sample of twenty ESL learners and five ESL teachers from the university sector. The questionnaire, interviews, and two tests were conducted for the data collection. The results revealed that students found the MAVL application practical, motivating, and helpful. Further, the participants had the potential to use the applications independently, and it made teachers conduct lessons efficiently in the classrooms. The results illustrated that MAVL applications contained positive attitudes toward making mobile-assisted vocabulary improvements. It can be concluded that using MAVL Applications as a self-learning tool would improve ESL learners' vocabulary and the teaching-learning process.
Common Misconceptions and Misunderstandings in Magic Cut-Off for Significance: P-Value

The p-value is the most commonly used statistical term to make a decision in medical statistics.It helps the researchers to decide whether the results (which are obtained from a hypothesis test) show statistical significance or not.Most researchers up today prove their decisions based in p-values.Although the p-value is used so commonly by the researchers, they are in high rate misunderstood, miss-or over-interpreted and also wrongly reported.The purpose of this study is to emphasize some of the misconceptions about p-value, and correct their misunderstanding and suggest to researchers a straight way to use p-value.Fisher, who is called as "father of statistics" were not actually the first one, who used the p-value, however he was the first to outline formally the logic behind its use.Fisher's defined for the p value as we use today: it is equal to the probability of a given experimental observation, under a null hypothesis.If this number were smaller than the acceptable threshold, researchers could "reject" the null hypothesis as unlikely to be true.The use of a threshold p value as a basis for rejection was called a "significance test."This is important to distinguish from the "hypothesis test," which will be discussed shortly.In our study we stated some of the misconceptions such as obtaining p-values from inappropriate statistical methods, p-values <0.05 shows clinical significance, using always two-sided p-values, a scientific conclusion is always based on "1" p-value, pvalue is the probability of null hypothesis is true, format for table of p-values.As a result, some of the common misconceptions are highlighted about p-value.Being aware of these misconceptions, can increase of the quality of studies.
Applications of Matrices in Modern Scenario

In the modern world, matrices are considered to be of extreme use as they can be applied in fields like Chemistry, Physics, Economics, Construction, Finance, etc. In this work, the key applications empowered by the framework hypothesis in two significant fields of interest in graphic designing, in particular video game designing. The paper centers on how matrices and their operations play a significant role in designing and manipulating the structure on a screen to make it realistic. Among the significant applications in video games, it has a significant role in Adobe Photoshop how we put our structures in suggested dimensions and move according to our comfort in it and this is possible because of matrices. The study brings up the significant commitment made by cryptography, and the security process it carries with it so that no other person in between can decode the message and the message is delivered safely to the intended person. The significance pretended by grids in addressing and handling advanced pictures is portrayed by a few illustrative applications. This paper covers many other applications in matrices of how it is used in chemistry, physics, medical science, geology, economics, and many more.
Students’ Strategies In Overcoming Translating Barrier

The process of translation is not as easy as it seems. In fact, it is much harder. The translation result must fulfill certain criteria namely accuracy, acceptability, and readability. During the process of translation, the translator surely encounters some difficulties or barrier. Therefore, this study aimed to find out the students’ strategies in overcoming the translation difficulties. The method of the research was a descriptive case-study. The subject of the research was 10 students of English Education Department at Universitas Majalengka. The data was collected from the translation test, questionnaire, and interview. Most of the data analysis of this research was conveyed in non-statistical analysis. However, the researcher provided data to see the percentage and frequencies to support the research. The results showed that the students encounter difficulties in 1) translating words that are not in dictionary; 2) translating ambiguous words; 3) translating long sentences; 4) translating phrases; 5) translating complex sentence; and 6) translating idioms. In translating idioms, 50% used literal translation, 30% used discursive creation, and 20% used established equivalence. In translating ambiguous words, 90% used modulation, and 10 % used literal translation. In translating phrases, 40% used reduction, 20% used discursive creation, 10% used established equivalence, and 30% used generalization.
Forecasting the recovery of COVID-19 patients in East Java using the Fuzzy time series Cheng method

Coronavirus 2019 (COVID-19) has significantly impacted Indonesia. Social restrictions in Indonesia's major cities and rural areas have been put in place as the coronavirus spreads. The Indonesian government is more vigilant with the spread of COVID-19, namely by issuing a lockdown policy to PSBB (Large-Scale Social Restrictions). Almost all Indonesian people have complied with the guidelines set by the government, namely carrying out all activities in a WFH manner to minimize the chain of distribution of COVID-19 in Indonesia. The author of this work forecasts the recovery rate of Covid-19 patients in the East Java region using the Cheng Fuzzy Time Series approach. After checking the simulation with real in the field, it can be seen that using 51 data starting from February 4 2021 to March 26 2021 gives results MAPE = 0.4602%, which means the forecasting is very accurate.©2021 JNSMR UIN Walisongo. All rights reserved.
Becoming a networked learner: Unpacking identity development in networked learning communities

The aim of this autoethnography is to explore my identity development across three learning communities, underpinned by networked learning to varying degrees, to understand the impacting factors and their results.Networked learning has been described as the teaching and learning paradigm of the twenty-first century, requiring the adoption of networked learning ontology and epistemology.In this paper, I argue that it is therefore imperative to prepare teachers, educated in earlier and possibly contrasting paradigms, for the identity work this shift requires.The literature presents insight from the perspectives of researchers and teachers in higher education.There is, however, a dearth of insight from primary school teachers who also participate in this paradigm.This paper contributes the insights of a primary school teacher navigating and enculturating within the networked learning paradigm, part of which is networked learning identity development.From a Bourdieusian perspective, the findings suggest that networked learning values and identity can be developed and supported to escape the habitus of older educational paradigms.This can be achieved through the proactive facilitation of critical reflexivity in combination with eight essential networked learning features.This study is of relevance to teacher educators, school leaders, local education authorities and educational practitioners interested in the adoption of networked learning.Recommendations are provided
Indoor Home Scene Recognition through Instance Segmentation Using a Combination of Neural Networks

This work presents a technique for recognizing indoor home scenes by using object detection. The object detection task is achieved through pre-trained Mask-RCNN (Regional Convolutional Neural Network), whilst the scene recognition is performed through a Convolutional Neural Network (CNN). The output of the Mask-RCNN is fed in input to the CNN, as this provides the CNN with the information of objects detected in one scene. So, the CNN recognizes the scene by looking at the combination of objects detected. The CNN is trained using the various object detection outputs of Mask-RCNN. This helps the CNN learn about the various combinations of objects that a scene can have. The CNN is trained using 500 combinations of 5 different scenes (bathroom, bedroom, kitchen, living room, and dining room) of the indoor home generated by Mask-RCNN. The trained network was tested on 24,000 indoor home scene images. The final accuracy produced by the CNN is 97.14&#x0025;.
Zero-Bit Watermarking Technique for Generation of Unique ID Using Biometric Images

With the rapid pace of technological growth, it is very important to secure user data. A robust technique which not only secures data but prevents it from various attacks is necessary. Such a technique is proposed within this article. Biometric authentication is one such practice seen today. Contrast to other forms of authentication, biometric recognition provides a strong link between a data record and an individual and it guarantees a high level of accuracy and security. But this biometric data can be used by attackers to get illegal access. In order to prevent such acts, a robust technique known as zero-bit watermarking is proposed through this paper.
Constructing Sentiment Sentence Embedding Model Using Transfer Learning

For Sentiment classification a suitable embedding to train a machine learning model is needed. A lot of research has been done on generating embeddings that are capable of storing the context of words, but they are inefficient for sentiment extraction as they cannot differentiate words used in the same situation but contrasting sentiment polarity. To address this issue, a lot of researchers have proposed different embedding models, but most of them are word embeddings where lot of semantic information is lost. We have constructed a model by fine-tuning Google's Universal Sentence Encoder using a Deep Neural Network. The embedding layer will be allowed to retrain through backpropagation on the Sentiment dataset. Once training is completed, the hidden and output layers will be discarded, leaving only the USE layer, the output of this layer will be used as our Sentiment Embedding.
Image Class Ranking using Bag of Features

Selecting a suitable approach for image ranking or classification will form the basis of any computer vision application. Bag of features also called Bag of visual words or just visual words approach is the most used method for image classification even today. Bag of visual words is used to predict the category of images in the dataset. Feature vectors are the histogram of visual occurrences for this approach. The benchmark dataset used for validation is Caltech-101 and Flower dataset. Experimentation shows three different ways of partitioning the training and validation data. Depending on the deduced results, the performance of category wise results is calculated.
Efficient Permutation and Diffusion Model based on TCMT for Image Encryption

Chaos contains unique properties that are well-suited to the cryptography area. Due to their simplicity and chaotic nature, for security-related challenges, one-dimensional chaotic maps are often used, as opposed to multidimensional chaotic maps, which can be difficult to create and evaluate. Classic one-dimensional chaotic maps, on the other hand, have a limited spectrum of chaotic behavior. We present novel one-dimensional chaotic maps in this study, including a combination Tent-Cubic map (TC). To provide a wide spectrum of chaotic behavior, we proposed a 1-dimensional chaotic map by mixing classical and basic one-dimensional chaotic maps. Afterward, an efficient image encryption scheme named (TCMT) is proposed. It consists of two modules, Tent-Cubic map and Mersenne-permutation Twister's technique are used in each module to perform permutations and diffusion. It has low correlation coefficients, a huge keyspace, and the histogram is uniformly distributed, as well as resistance to different assaults, according to the simulation results.
Forensic Acquisition and Analysis of Webpage

Due to the onset of the Covid-19 pandemic, people are compelled to maintain social distance in all spheres of life, forcing people to adopt virtual mode of activity. Usage of social media and other internet activity has shot up in this period, and consequently, cybercrimes have also increased. If cybercrimes are reported, computer forensics analysts will examine the concerned website, online forum, or social media to find meticulous details about the cybercrime. But webpage content seen on the day may not be available on the next day. The contents of the webpage, which is the subject of crime, will be deleted or withdrawn, or deactivated to destroy evidence to escape from legal proceedings. The victims usually produce a screenshot of the webpage or image or video as a piece of evidence. But there is a distinct possibility of manipulating the offensive materials and it may not be considered a valid piece of evidence before the court of law. Such a scenario requires a forensic technique that should acquire the content of the webpage before it is removed from web site to maintain the authenticity of captured data. So, we are proposing an automated system for the forensic acquisition of a website that will effectively capture all content from the live website and make it useful for forensic investigation and may be produced before the court as valid evidence of cybercrime.
Comparative Analysis of Transfer Learning CNN for Face Recognition

The practice of identifying people by looking at their faces is known as face recognition (FR). This technology is frequently used in biometric authentication, surveillance technologies, security systems, law enforcement, real-time attendance systems, smart cards, and other applications. The face recognition technology works in two stages. First, a method for picking up or extracting face characteristics is applied, followed by pattern categorization. Deep learning has lately made important contributions to face recognition technology, particularly the convolutional neural network (CNN). Deep learning employs numerous processing layers to develop data representations with varying degrees of feature extraction. Since the achievements of DeepFace and DeepID, this developing technology has altered the study landscape of facial recognition. Deep learning has dramatically improved cutting-edge performance and aided in the development of effective real-world applications. This study examines the performance of three among the most popular CNN architectures for face recognition. In the proposed work, transfer learning is used to deploy pre-trained CNN models for face recognition such as VGG16, ResNet-50, and MobileNet. Training and validation accuracy and loss were utilized as criterion to enhance the performance of the CNN algorithm. 5 Celebrity Faces Dataset from Kaggle, as well as a local dataset, were used to train and test the models. Face recognition was implemented in two ways: static and webcam-based. The models developed for this research can be used in real-time attendance and surveillance systems.
Classification and fusion algorithm of imbalanced data set based on Gaussian Mixture Model

In real life, most data sets are unbalanced data sets, while the performance of traditional sampling classification algorithms is poor. In order to improve the detection rate of minority samples, a multi classification fusion algorithm of unbalanced data sets based on Gaussian Mixture Model (GMM) is proposed. Firstly, Gaussian Mixture Model clustering is used to generate minority samples. Then, for most samples, the ENN algorithm is used to down sample to delete the noise points, and then the random down-sampling is used to further balance the data set. Finally, the fusion classification model is constructed by using the comprehensive index weighted based on the coefficient of variation. In order to verify the effectiveness of the algorithm, multiple evaluation indicators based on confusion matrix are used to verify on the KEEL data set. The running results of public data sets and actual cases show that the algorithm can achieve better results on unbalanced data sets and can be used for multi-classification models of unbalanced data sets.
Hybrid Algorithm of Classifying Candidates for Subject Area Terms

The paper describes a method for classifying candidates for the terms of the problem area using linguistic methods using neural networks. The proposed algorithm for the automated compilation of a list of terms assumes the result in the form of a text document containing a list of terms compiled on the basis of the text using a linguistic method and selected by a neural network. Based on the results of the work, an accuracy rate of 94% was achieved.
Choosing Machine Learning Methods for Image Compression

The article deals with the comparison and selection of neural network algorithms within an image compression framework based on machine learning. The image compression framework uses convolutional neural networks, autoencoders, and generative adversarial neural networks as core image processing procedures. We compare convolutional neural networks and generative adversarial neural networks when filling trivial image regions when using the compression framework. The trivial image regions do not contain critical data, so we do not save the trivial image regions to an archive file. Instead, we fill the trivial image regions with high quality fake data when decompressing. We perform computational experiments comparing the efficiency of various neural network algorithms in filling trivial regions in natural images within the image compression framework. We evaluate the quality of fake data within image trivial regions both visually and numerically. We choose the generative neural network that has an advantage and is promising for use in the image compression framework based on the machine learning.