Reviewerly/exHarmony huggingface dataset:
Technologization of human of the postmodern age as a challenge to the natural reality of the earth

Object of research: technology as a component of human social life in the context of the postmodern era.&#x0D; Investigated problem: determination of the features of the influence of technological processes on a person of the postmodern era, as a result of which the natural is replaced by an artificial one and the images of both man and the Earth change.&#x0D; Main scientific results: the study of the contradictions of the process of technologization in the postmodern era made it possible to determine the transformation processes taking place in the life of a person and society. This defines social reality, represented by various markers, among which technology is gaining in importance. The latter act as a composite, which provides new opportunities for determining the relationship between the elements of the "world - man - culture" system. It is not only about the emergence of new human figures, among which the technological man (Homo Technologicus) occupies a prominent place, but also the emergence of a new image of the Earth, where the natural is replaced by the technological.&#x0D; The scope of practical use of the research results: the research results can be used in teaching normative courses "Philosophical Anthropology", "Ethics", "Sociology" for students and undergraduates, graduate students of humanitarian and natural faculties.&#x0D; Innovative technological product: the plurality of existing technologies (economic, medical, educational, humanitarian, etc.) destroys the new European concept of culture and man, which were provided within the framework of the monoparadigm. The anthropological dimension of technology testifies that the latter are beginning to act as a condition for human expansion. This situation has a significant impact on the existentials of human existence.&#x0D; The scope of using the innovative technological product: the intensive technologization of the last decades not only provokes environmental risks and changes the state of the Earth, the natural reality of the Earth is replaced by an artificial one, which allows to conclude that the Earth is formed by a technological one.
Construction of Modern Digital Learning Platform Based on Digital Media and LTE Communication

This paper studies the construction of modern digital learning platform based on digital media and LTE communication. It provides a new and more effective interaction method for the interaction between teachers and students, students and students, and students and teaching resources through the digital learning platform for digital media technology majors, and also makes the entire education method more efficient. Combining the professional characteristics of digital media technology and the application advantages of digital learning platforms, a systematic explanation of the construction of digital learning platforms for digital media professionals is carried out. Digital media will increase the learning efficiency of digital learning platforms by 6.5%.
Utopia of space in the book graphics by Francisco Infante

The subject of this research is the book illustration by F. Infante in comparison with his graphics and easel works. The object of this research is the image of space in the book illustration by Francisco Infante. The research employs the popular science and science fiction publications illustrated by Francisco Infante, as well as his graphics and painting. The article examines the specificity and uniqueness of the image of space in the artist&amp;rsquo;s works. Attention is given to comprehensive comparison of this image in the book illustration with the more extensive context F. Infante&amp;rsquo;s works. Based on the example of book graphics and analysis of theoretical concepts of F. Infante, the article aims to reveal the distinctness of his representations of space from the perspective of the utopia of relations between man and the Universe, as well as to determine how illustrations on this topic fit into the general context of the works of the artist. The main conclusion lies in description of the unique approach of F. Infante towards the indicated topic. The artist referred to the main motifs from his graphics and easel painting; however, modified them according to the specificity of plotline of the illustrated publication. Special attention is paid to the artist's preferred shapes: spiral and dot, with allowed reflecting the idea of the infinite space. The novelty of this article consists in reference to the material that has not yet been used for comprehensive scientific work in the field of art history. The author is first to introduce into the scientific context the book illustration by Francisco Infante on the space theme.
Predictive Modelling: An Assessment Through Validation Techniques

In this investigation, various statistical models were fitted on simulated symmetric and asymmetric data. Fitting of models was carried out with the help of various libraries in R studio, and various selection criteria were also used while fitting of models. In order to evaluate different validation techniques the simulated data was divided in training and testing data set and various functions in R were developed for the purpose of validation. Coefficient summary revealed that all statistical models were statistically significant across both symmetric as well as asymmetric distributions. In preliminary analysis TFEM (Type First Exponential Model) was found out to be the best linear model across both symmetric and asymmetric distributions with lower values of RMSE, MAE, BIAS, AIC and BIC. Among non-linear models, Haung model was found out to be best model across both the distributions as it has lower values of RMSE, MAE etc. Different validation techniques were used in the present study. Lower rates of prediction error in comparison to its counter parts, 5-folded cross validation performed better across all the statistical models.
Domain Generalisation for Mitosis Detection Exploting Preprocessing Homogenizers

The detection of mitotic figures in histological tumor images plays a vital role in the decision-making of the appropriate therapy. However, tissue preparation and image acquisition methods degrade the performances of the deep learning-based approaches for mitotic figures detection. MIDOG challenge addresses the domain-shift problem of this detection task. In an endeavour to reduce this domain shift, we propose a pre-processing autoencoder that is trained adversarially to the sources of domain variations. The output of this autoencoder, exhibiting a uniform domain appearance, is finally given as input to the retina-net based mitosis detection module.
Survey on AI-based IoT and drone-equipped smart agriculture

Over the past few decades, there are rapid advancements in technologies where massive intelligence applications came into being. These developments assist in the development of intelligent capabilities that prompt these systems to communicate among themselves. Artificial intelligence (AI) and Internet of things (IoT) remain some of the widely evolving modern intelligent concepts. These technologies connect multiple things across the Internet triggering them to make decisions like humans. The application of AI and IoT continues to evolve where specific industries boast their potential implications. The article discusses how AI and IoT continue to revolutionize agriculture with the application of smart agriculture. AI and IoT’s involvement in agriculture makes it easy to make predictions like temperature, rainfall, humidity, soil statuses among others. Agriculture’s AI and IoT continue to advance farming practices by bringing profitable ventures in the end.
The influence of social media in improving supply chain intelligence using deep learning

In recent years, there has been a significant rise in the analysis of consumer reviews expressed on social media, which can be used to obtain more meaningful and relevant information via market study to predict consumer behavior. As a result, sentiment analysis plays a crucial role in evaluating customer satisfaction and identifying consumer opinions, attitudes, and behaviors toward certain topics. In this chapter, we present a system for sentimental classification of reviews posted on social media in a way that can provide an advantage for supply chain managers. More clearly, the proposed system is a hybrid method of deep learning architectures, specifically recurrent neural networks and long short-term memory, using a word embedding word2vec to embed the features in a review to predict the sentiment. Our system achieved promising results which demonstrate the effectiveness of our approach.
Design and Interaction of Scientific Research Management Online System Based on Agile Development and Crawler Mining

This paper studies the development and implementation of scientific research management information systems based on crawler mining and agile development, and summarizes the related concepts of traditional project management systems. Secondly, it introduces the characteristics of crawler mining and agile development management techniques. The crawler mining to analyze with agile development technology and apply this technology to the scientific and technological plan project management system. From system requirements analysis to specific design, as well as the specific realization ideas and flowcharts of key parts of the system, the design and realization process of a scientific research project management system based on crawler mining and agile development technology are described, and the efficiency is increased by 7.45%.
Balanced Hashing

Hashing for nearest neighbor search has attracted considerable interest recently given its efficiency in speed and storage. Many methods follow a projection-quantization framework which firstly projects original data into low-dimensional compact space and secondly quantifies each projected dimension to 1 bit by thresholding. The variance of projected dimensions, however, may vary a lot so that quantifying them equivalently degrades the searching performance. In this paper, we put forward a novel method, dubbed Balanced hashing (BH), which finds adjustment functions to reproject the data such that the variance of dimensions can be balanced by directly and explicitly maximizing the degree of balance of data, while preserving important properties. Experiments on benchmarks demonstrate that BH can outperform several state-of-the-art methods.
Intelligent Segmentation Framework and Data Hierarchy of Chinese Language and Literature Based on Semantic Recognition

Based on semantic recognition, this paper studies the intelligent segmentation framework and data layering of Chinese language and literature. First of all, in this paper, in the study of predicting Chinese semantic word-building patterns, based on binary classification Logistic regression and naive Bayes to construct Chinese sentence segmentation patterns for intelligent simulation experiments. Then the eight types of data in the annotated corpus are grouped in pairs, and each sample set is divided into data stratification, so that the two-class Logistic regression model and the naive Bayes model are first based on the training set of each group to learn semantic word formation rules., And then predict the semantic word formation mode on the test set of each group, and the result shows that the sentence segmentation efficiency is increased by 6.5%
Spam Detection and Hate Speech Identification

The utilization of the term “spam” to explain this sort of invasive blanket-messaging. These are messages sent or comments given to multiple recipients who failed to provoke them. The issues caused by spam are because of the mixture of the unsolicited and bulk aspects; the amount of unwanted messages swamps messaging systems and drowns out the messages that recipients do want. there's no legal definition of hate speech. It can be understood as any quite communication in speech, writing or behavior that attacks or discriminatory language with relevance an individual or a gaggle on the idea of who they're, in other words, supported their religion, ethnicity, nationality, race, color, descent, gender or other identity factor. During this paper, we aim to perform binary classification of spam and hate speech with the assistance of concepts regarding computing, language Processing and Machine Learning. We aim to supply the user with the flexibility to classify the message as fake or real. We have even incorporated the emoji feature further i.e., the user can enter messages with emoji and our model is ready to classify it.
Dynamic wavefront distortion in resonant scanners

Dynamic mirror deformation can substantially degrade the performance of optical instruments using resonant scanners. Here, we evaluate two scanners with resonant frequencies <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>&gt;<!-- > --></mml:mo> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>12</mml:mn> </mml:mrow> <mml:mspace width="thickmathspace" /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">k</mml:mi> <mml:mi mathvariant="normal">H</mml:mi> <mml:mi mathvariant="normal">z</mml:mi> </mml:mrow> </mml:math> with low dynamic distortion. First, we tested an existing galvanometric motor with a novel, to the best of our knowledge, mirror substrate material, silicon carbide, which resonates at 13.8 kHz. This material is stiffer than conventional optical glasses and has lower manufacturing toxicity than beryllium, the stiffest material currently used for this application. Then, we tested a biaxial microelectromechanical (MEMS) scanner with the resonant axis operating at 29.4 kHz. Dynamic deformation measurements show that wavefront aberrations in the galvanometric scanner are dominated by linear oblique astigmatism (90%), while wavefront aberrations in the MEMS scanner are dominated by horizontal coma (30%) and oblique trefoil (27%). In both scanners, distortion amplitude increases linearly with deflection angle, yielding diffraction-limited performance over half of the maximum possible deflection for wavelengths longer than 450 nm and over the full deflection range for wavelengths above 850 nm. Diffraction-limited performance for shorter wavelengths or over larger fractions of the deflection range can be achieved by reducing the beam diameter at the mirror surface. The small dynamic distortion of the MEMS scanner offers a promising alternative to galvanometric resonant scanners with desirable but currently unattainably high resonant frequencies.
Entrevista a Linda Castañeda

In the following interview, Linda Castañeda gives us some reflections on Educational Technology in times of COVID-19: her experiences, her learnings and the main challenges that this new scenario holds for us.
Pengenalan Emosi Pembicara Menggunakan Convolutional Neural Networks

Recognition of the speaker's emotions is an important but challenging component of Human-Computer Interaction (HCI). The need for the recognition of the speaker's emotions is also increasing related to the need for digitizing the company's operational processes related to the implementation of industry 4.0. The use of Deep Learning methods is currently increasing, especially for processing unstructured data such as data from voice signals. This study tries to apply the Deep Learning method to classify the speaker's emotions using an open dataset from SAVEE which contains seven classes of voice emotions in English. The dataset will be trained using the CNN model. The final accuracy of the model is 88% on the training data and 52% on the test data, which means the model is overfitting. This is due to the imbalance of emotion classes in the dataset, which makes the model tend to predict classes with more labels. In addition, the lack of heterogeneity of the dataset makes the character of the emotion class more different from the others so that it can reduce the bias in the model so as not to overfit the model. Further development of this research can be done, such as over-sampling the existing dataset by adding other data sources, then performing data augmentation to get the data character of each emotion class and setting hyperparameter values ​​to get better accuracy values.&#x0D;
Multi-Scale Approach for Document Detection Based on the Cascade mask RCNN

The previous traditional methods could not simultaneously extract every region and represent its category, and these methods needs to be preprocessed in a complex way for the document image. This paper presents a deep learning-based end to end approach to solve a problem that is sample imbalance. Specifically, the single-scale training strategy is replaced by the proposed multi-scale on Cascade mask -RCNN which consisted of three detectors trained with increasing IoU thresholds, based on this, the robustness of the detection networks can be improved .This model can detects areas of document images, including paragraphs, text, formulas, pictures, and tables. Extensive experiments show that compared with Mask-RCNN, the AP scores of Cascade mask -RCNN increased by 0.031 and 0.037 on the Publaynet and ICDAR 2017 datasets, respectively.
Analysis of Quality of Living Data of Households of Indian Districts Using Machine Learning Approach of Fuzzy C-Means Clustering

Machine learning is used to analyse the 2011 census data of physical amenities and educational levels of Indian households in order to cluster and categorize Indian districts based on living standards and educational attainment. Household-level data on amenities such as electric lighting, television, mobile, car/scooter ownership; kitchen, toilet, bathing facilities and home ownership of families; as well as educational levels are used for 640 Indian districts, each consisting of 26 parameters (i.e. attributes). This makes the data set fairly large and complex for a clustering problem, and in order to preserve data granularity, fuzzy C-means (FCM) clustering algorithm has been chosen for analysis. The features of the algorithm are briefly presented. The analysis considers 4–10 clusters for the data set. The quality of clustering with larger clusters is discussed with appropriate indices. The results of computation yield the correlation between the variables which allow us to look at the relationships between them. The results also yield the classification of districts in a scale ‘well-off’ to ‘disadvantaged’ for various levels of clustering and show how the number of districts for each variable changes with the number of clusters. It is argued that these results can be used to orient investment plans for various sectors such as education and housing and establish ease-of-living ranking indices for districts which, in turn, can establish a rational basis for coordinated development of Indian regional economies.
From “Transit” To “Transitive” University: Risks And Resources Of Transition Strategy

The term “transitive” suggested by the research team, means “being in transit”, “transformational”, devising constant and efficient new models of strategically important interactions of all regional stakeholders. The analysis of regional university development versus global, national and regional challenges resulted in identifications of most crucial developmental problems. Insufficient financial resources, outmoded infrastructure and facilities, non-compensated educational and professional migrations, outflow of young professionals and aging of highly-qualified staff, lack of perspective for professional and career growth, low regional initiative, concerted project development are recognized as the most critical issues to be debated. Building region-oriented infrastructure, aiming to develop innovative culture of local community, should be the mission of a regional university. Lack of such culture decreases the demand for high-tech products and services, their manufacturing and selling. To achieve this goal any regional university should devise the developmental programmes by taking into account an existing demand and, on top of that it should develop university enhancement strategies in the region and beyond. With the operational management major risks of transition to transitive university are connected with Human Resources Management. Management strategies of self-contained isolationism is the highest risk for the development of university eco-system.
Improved Super-resolution Network based on SRCNN

The process of recovering high-resolution images from one or more degraded low-resolution images is known as super-resolution reconstruction. The present super-resolution (SR) deep learning technique has certain limitations, because the deeper the network layer, the higher the training cost and time consumption. This paper, based on SRCNN, revamped the three-step reconstructed process of SRCNN by including VGG and residual ideas. Enhancing its network structure by utilizing subpixel convolution approach which can replace the step of image preprocessing and upscale in SRCNN algorithm. When comparing the reconstructed pictures of Set5, Set14, BSDS100, Urban100, and Manga109 data sets, the revised SRCNN structure outperforms the original SRCNN by 1.1dB in Peak Signal to Noise Ratio (PSNR) and 0.026 in structural similarity (SSIM). When compared to other popular SR network except for SRCNN, the improved reconstructed network has fewer network layers, is easier to train, and produces higher reconstructed image quality than the original SRCNN.
Android App Rating Classification on Google Play Store Using Random Forest Algorithm with SQL Server Preprocessing

The increasing number of Android applications available on the Google Play Store with the benefits the developers get has attracted the attention of many Android application developers. To benefit from developing Android apps, one way is to know the characteristics of highly rated apps on the Google Play Store. This research will investigate the features of size, installs, reviews, type (free / paid), rating, category, content rating, and price on applications on the Google Play Store to determine the characteristics of high-rated applications. This study uses the Random Forest algorithm to identify the most influential features in high ranking applications on the Google Play Store. At the preprocessing stage, this research uses data cleaning methods and data reduction using SQL Server. This study uses feature important to find out the attributes that most influence the high ranking of Android apps on the Google Play Store. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy.