Reviewerly/exHarmony huggingface dataset:
Implementation of text to speech on web-based broadcasting radio service applications

Radio broadcasting is a source of information for the public. In delivering information, the broadcaster must read the text of the news before it is transmitted through a radio transmitter. Implementing a text-to-speech conversion can help solve this problem. The purpose of this study is to develop a Broadcasting Service Application by implementing text to speech on a Web-Based Radio Broadcasting service application that can convert data/information in text form into information in the form of voice. This paper does not discuss text-to-speech conversion techniques but emphasizes how to apply text-to-speech conversion techniques in web-based broadcasting service applications, especially news and music data management. This application utilizes web hosting services and Google voice services to convert text data into voice. The method used is Analyzing the application system of Broadcasting Radio services, Application of Text to speech (TTS), which functions to convert text into sound before being transmitted to broadcasting stations and to test the response of the conversion results and data management to the quality of the design. The process carried out in this system, namely retrieval of text data from the news database, converting text data into sound, adjusting the volume of music and sound information, and updating the status of the text data that has been broadcast. Based on application testing carried out with the Black Box Testing Method, all functional applications have been running well and are now successfully operationalized.
Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemic—ADDENDUM

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Hand authentication from RGB-D video based on deep neural network

In recent years, behavioral biometrics authentication, which uses the habit of behavioral characteristics for personal authentication, has attracted attention as an authentication method with higher security since behavioral biometrics cannot mimic as fingerprint and face authentications.As the behavioral biometrics, many researches were performed on voiceprints.However, there are few authentication technologies that utilize the habits of hand and finger movements during hand gestures.Only either color images or depth images are used for hand gesture authentication in the conventional methods.In the research, therefore, we propose to find individual habits from RGB-D images of finger movements and create a personal authentication system.3D CNN, which is a deep learning-based network, is used to extract individual habits.An Fmeasure of 0.97 is achieved when rock-paper-scissors are used as the authentication operation.An F-measure of 0.97 is achieved when the disinfection operation is used.These results show the effectiveness of using RGB-D video for personal authentication.
Square transposition: an approach to the transposition process in block cipher

The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
A New Texture Segmentation Method with Energy-driven Parametric Active Contour Model Based on Jensen-Tsallis Divergence

Texture image segmentation plays an important role in various computer vision tasks. Active contour models are one of the most efficient and popular methods for identifying the purpose and segmentation of objects in the image. This paper presents a parametric active contour model (PACM) with a robust minimization framework based on image texture energy. First, the texture features of the original image are extracted using gray level co-occurrence matrix (GLCM). Subsequently, based on the GLCM texture features inside and outside the active contour, Jensen-Tsallis divergence of energies is calculated. The Jensen-Tsallis divergence is added to the parametric active contour using the balloon equation. The divergence is maximum at the boundary between the foreground and background of the image, which results in minimizing the active contour equation at the boundary of the target object. This global minimization energy function with texture feature can avoid the existence of local minima in the PACM models. Also, as opposed to previous models, the proposed model only requires the initial contour and is not dependent on the distance of the initial contour from the target object. In terms of segmentation accuracy and efficiency, experiments with synthetic and natural images demonstrate that the proposed approach obtains more satisfactory results than the previous state-of-the-art methods.
STRATEGI BELAJAR MEMBACA UNTUK ANAK TUNARUNGU

The purpose of this study is to analyze strategies that make it easier to provide reading learning for deaf children.The research method used is a qualitative approach with the type of literature reviewresearch or library method. An online literature search was conducted through threeelectronic databases, namely from SAGE, Google Scholar and Proquest. The keywordsused in the search are strategy, reading, learning and deaf children. The researcherdetermined the criteria in the strategy of learning to read for deaf children to search for literature. The journal criteria determined by the researcher, followed by abstractselection, resulted in 41 articles: 14 from SAGE, Google Scholar 2 and 25 from Proquest. This resulted in 41 articles systematically reviewed: 14 articles from SAGE, Google Scholar 2 and 27 articles from Proquest, but those that met the criteria set by the researcher only got 2 articles that met the criteria.The results of the study found that the use of learning strategies used the reading methodby telling stories that were repeated using picture books and also using videos that weredisplayed and played repeatedly.
The Role of Communication for Business Growth: A Comprehensive Review

Communication abilities are the abilities or skills to offer and accept several kinds of information’s. Through this study the author needs to propose the benefits of communication for growth in any business relations. Whereas these skills may be prevalent in your day-to-day job, the ability to communicate clearly, effectively, and efficiently is a distinctive and valued skill. The goal of this study is to highlight and explain the importance of good communication in the management of business growth. The benefits of effective communication in terms of enhanced staff efforts to develop and implement change strategies are also emphasized. The objectives and communication requirements for each step of the change process are also defined in this article. Also covered are the numerous motivations and advantages of business growth communication, Furthermore in this paper discussed about the effect of business communication, communication in business organization as well as the various types of business communication. Within a company, communication is critical to the building of trust. The future prospects of this study is, through this study peoples get more attractive and attentive on importance of communication and how it leads helps to develop any kinds of business through effective communication.
Online approximative SPARQL query processing for COUNT-DISTINCT queries with web preemption

Getting complete results when processing aggregate queries on public SPARQL endpoints is challenging, mainly due to the application of quotas. Although Web preemption supports processing of aggregate queries online, on preemptable SPARQL servers, data transfer is still very large when processing count-distinct aggregate queries. In this paper, it is shown that count-distinct aggregate queries can be approximated with low data transfer by extending the partial aggregation operator with HyperLogLog++ sketches. Experimental results demonstrate that the proposed approach outperforms existing approaches by orders of magnitude in terms of the amount of data transferred.
Fully Decentralised, Post-Quantum Secure Networks

The Fourth Industrial Revolution, Industry 4.0, promises trillions of dollars of value creation by connecting billions of sensors and actuators to analytical engines, thereby creating the Industrial Internet of Things (IIoT). This revolution will push decision making to the edge and connect the edge to the center. The revolution faces three major technical challenges: interoperability between devices, security of edge devices and legacy infrastructure. This paper describes an implementation of a fundamentally new authentication and encryption technology (“dOISP™), that allows devices to connect regardless of operating system and transport technology. With dOISP™ a network manages its own security without use of certificates or Trusted Third Parties or any centralized or externalized source of trust and without human intervention – the network itself maintains a distributed source of trust that changes continuously and always assumes that authentication must be refreshed for every session. This technology enables the acceleration of Industry 4.0 initiatives thereby increasing their net present value as well as reducing the organizational costs of network security and reducing the risk of successful attack.
A Contrastive Stylistics Analysis of Covid – 19 News in The Guardian and The Sun British Newspapers

The language of newspapers helps people learn a wide range of topics. Its language serves primarily as a means of conveying information. Besides persuading and entertaining the people, that is what makes its style special. Its vocabularies and sentence structure have to be chosen efficiently to affect all readers. Hence, this study seeks to analyze texts from British newspapers to determine how journalists use language when reporting on some sensitive subjects, such as the writing on the current topic that has occupied the global press, Covid19. Therefore, this study sets to fulfil several aims; the most important of them are identifying the stylistic features at the syntactic and lexical levels used in The Guardian and The Sun newspaper news on the coronavirus pandemic. Besides, it aims at finding out the similarities and differences at the levels mentioned above between the Guardian and the Sun newspapers news on that specific subject.
Predicting Classification Accuracy of Unlabeled Datasets Using Multiple Deep Neural Networks

In machine learning problems, we usually assume that the validation accuracy is a good estimation of prediction accuracy for datasets without ground truth.In reality, this assumption may not hold.Therefore, we propose an approach to estimate the prediction accuracy of a target model on unlabeled datasets.The proposed approach uses multiple target homogeneous models to assign each unlabeled sample a confidence value, based on the number of models agreeing on the predicted label.With the confidence values, the prediction accuracy of the target model on the datasets can be estimated.In the experiments, the target model is a convolutional neural network (CNN) model, and the homogeneous models only differ in initial weights.The experiments are conducted with datasets from a wide variety of music genres.The estimation performance of the proposed approach is compared with the reversed testing qualities (RTQ) and the ensemble average qualities (EAQ) approaches.The RTQ approach was proposed to estimate the prediction accuracy of trained models, and the EAQ approach was originally designed for estimating the predictive uncertainty of individual samples.We apply all three compared models to estimate prediction accuracy of datasets by using a linear model.The parameters of the linear model are either computed by using multiple labeled datasets or one labeled dataset.The experimental results show that when compared with the RTQ approach, the proposed approach has much lower estimation errors for some datasets.When compared with the EAQ approach, the proposed approach is more robust for datasets with large distribution shifts.Finally, we show an additional benefit of the proposed approach.In case that the estimated accuracy is unsatisfactory, we may re-train the target model with a new training set, which contains the original training samples plus new training samples with manual labeling from the unlabeled dataset.The experimental results confirm that it is more effective to select (and label) new samples from those with low confidence values than those randomly selected.Overall, the proposed approach is a promising approach for estimating prediction accuracy on unlabeled datasets.
Psychological Reasons for Homophonic Internet Buzzwords Usage by Modern Chinese People

As a dissemination mode of mass culture, homophonic Internet buzzword has a wide audience and represents the spirit of the network society to some extent, and its influence often extends to the realistic level of the society.This paper selects Bilibili, SINA Microblog and some other network social platforms as the main source of research corpus, viewing from the perspective of audience psychological analysis, using methods including functional analysis, inductive summary and so on to explore the reason why homophonic Internet buzzwords are accepted and spread, and further summarizes its enlightenment to the Circulation of traditional Chinese Culture, enrich the research material existing in the area of homophonic Internet buzzword, which may provide some reference for the circulation of endangered culture.
A Prediction Error Nonlinear Difference Expansion Reversible Watermarking for Integrity and Authenticity of DICOM Medical Images

It is paramount to ensure the integrity and authenticity of medical images in telemedicine.This paper proposes an imperceptible and reversible Medical Image Watermarking (MIW) scheme based on image segmentation, image prediction and nonlinear difference expansion for integrity and authenticity of medical images and detection of both intentional and unintentional manipulations.The metadata from the Digital Imaging and Communications in Medicine (DICOM) file constitutes the authentication watermark while the integrity watermark is computed from Secure Hash Algorithm (SHA)-256.The two watermarks are combined and compressed using the Lempel Ziv (LZ) -77 algorithm.The scheme takes advantage of the large smooth areas prevalent in medical images.It predicts the smooth regions with zero error or values close to zero error, while non-smooth areas are predicted with large error values.The binary watermark is encoded and extracted in the zeroprediction error using a nonlinear difference expansion.The binary watermark is concentrated more on the Region of noninterest (RONI) than the Region of interest (ROI) to ensure a high visual quality while maintaining a high capacity.The paper also presents a separate low degradation side information processing algorithm to handle overflow.Experimental results show that the scheme is reversible and has a remarkable imperceptibility and capacity that are comparable to current works reported in literature.
Estimating cluster validity using compactness measure and overlap measure for fuzzy clustering

Cluster analysis discovers valuable patterns in data by partitioning n data points into valid number of clusters. The cluster validity index (CVI) helps in selecting the best partitions that fits the underlying structure of data. After presenting brief review on existing CVIs, this study formulates a competent overlap-compactness validity index (OCVI). The proposed index considers Kim et al.'s (2004b) overlap measure with compactness measure. Compactness measure considers the geometrical aspects of membership matrix (U) through cluster centres with an approach to reduce its monotonic tendency. Overlap measure calculates the average value of the overlapping degree of all probable fuzzy cluster pairs. Experiments are implemented on two artificial, two real and one biological dataset. Comparison results of partition coefficient, partition entropy, modified partition coefficient, Xie-Beni and Kim indices with the suggested index (OCVI) imply that suggested index outperforms with maximum compactness and minimum overlap than other validity indices.
Manifestations of Competition and Quasi-Competition of Russian Universities in the Struggle to Attract Applicants

Globalization and the development of the knowledge economy increase competition in the higher education market. Improving the competitiveness of higher education in the world market in the second decade of the 21st century has officially become a project to be implemented by the state. In this regard, it is extremely important to study the competition among and competitiveness of universities. The purpose of this study is to substantiate the existence of quasi-competition among Russian universities and assess its impact on improving the efficiency of Russian higher education. The research hypothesis is that despite the inequality in the system of higher education, there is quasi-competition, not only within the groups of leading universities at the federal level and among regional universities, but also against everyone, and this competition increases the efficiency of the system as a whole. Particular attention is paid to such specific feature of university competition as competition for public funding, competition in a regulated market with high concentration and even monopoly. As a practical case for studying the competition of universities, the fight for applicants is considered as a vivid manifestation of competition. The information base of the study is the results of annual surveys of applicants of Ural Federal University. The main method of research is the economic and mathematical analysis of the results of surveys of graduates. It is concluded that the quasi-competition model best describes the established higher education markets. The Russian system of higher education operates under the dominance of state funding and quasi-market competition. An important aspect of the study is to identify the relationship between competition in the field of higher education and university funding. Competition between universities increases financial inequality in the higher education system. At the same time, in a number of areas, especially in terms of preparing bachelors, regional universities can compete with leading universities. The influence of competition on the higher education system contributes to the increase in the efficiency of universities through their struggle for a limited set of resources. An interesting practical finding of the study, which can be useful when planning admission campaigns, is that price competition for undergraduate applicants is inferior to competition for the quality of education in the campaign to attract students to master's programs.
Informatization as an attribute of the information society: from retrospection to modern reflection

It is argued that the process of informatization of society should include at least three complementary elements: mediatization as a process of improving the means of working with information, computerization as a process of improving the means of information processing and intellectualization as a process of improving human knowledge and abilities to generate and perceive information. The ambiguity of the social consequences of informatization is proven. It is argued that informatization in itself is not a guarantee of success and social progress, it must be organically integrated into the overall system of social activities.
Bangla text generation system by incorporating attention in sequence-to-sequence model

In this AI-driven digital era, the pervasive nature of digital data is possible due to the widespread and cheap access to the Internet. Internet is continuously flourishing with data in many forms. Among them, textual data are a great source of information where people share their expressions in written format. Social media, blogs, online newspapers, government documents are some notable textual data sources. Information extraction from this enormous amount of data by manual inspection is time-consuming, cumbersome, and sometimes impossible. Natural Language Processing (NLP) is the computational domain for addressing those limitations by solving human language-related problems. Text summarization, Named entity recognition, Question answering are some of them where a common task for a machine is to generate coherent text. In such scenarios, the input is a sequence of text, and the output is also a sequence, but they differ in length. Sequence-to-Sequence (Seq2Seq) is an algorithmic approach to address that scenario by utilizing layers of recurrent units. However, the simple Seq2Seq model fails to capture the long-term relationship on the input sequence. Research shows that the attention mechanism guides the model to concentrate on specific inputs. Existing literature shows a lack of quality research on this text generation problem in the Bangla language, whereas many languages show excellent results. This work aims to develop such a system by incorporating attention to the Seq2Seq model and justifying its applicability by comparing it with baseline models. The model perplexity shows that the system can generate human-level readable text using a preprocessed dataset.
THE INFLUENCE OF STRUCTURE AND COMPOSITION OF THE LITHOTECTONIC UNITS ON GEOCHEMICAL SPECIALIZATION OF HYDROTHERMAL MINERALIZATION OF THE KOLYMA TERRANE IN NORTHEAST RUSSIA

This study focused on the relationship between the mineralogical composition of hydrothermal mineralization and the structure and lithology of host units with specific reference to the Kolyma passive continental-margin terrane (Northeast Asia). The long geologic history of the terrane was shown to be reflected in the geochemical specialization and distribution patterns of gold-rare metal and porphyry copper mineralization. The Proterozoic host rock units of the Kolyma region influenced the geochemistry and isotopic composition of the granitoid-related gold and porphyry copper mineralization. The imbricate-thrust structure of the terrane was formed in the Precambrian and underwent several episodes of reworking in the Phanerozoic thus contributing to the repeated rejuvenation of the ores.