Reviewerly/exHarmony huggingface dataset:
DEVELOPEMENT AN ALGORITM FOR RECOGNIZING TEXT DATA IN DIGITAL GRAPHIC IMAGES

Предложен алгоритм распознавания, реализующий процедуры: обучения выбранных классификаторов и распознавания текстовых данных, учитывающие статистические характеристики распределения коэффициентов частотной области цифровых графических изображениях формата JPEG. The article presents an approach to development an algorithm for recognizing text data within JPEG format digital graphic images. Considered a hypothesis about influence text data content in JPEG digital graphic images on the distribution of values of the discrete cosine transformation coefficients in the frequency domain JPEG images of the format. Statistical classifiers models that provide a solution to the problem of recognition of text data in JPEG images based on analysis of its frequency domain have been determined. A recognition algorithm is proposed that implements the following procedures: training of selected classifiers and recognition of text data, taking into account the statistical characteristics of the distribution of frequency domain coefficients in JPEG format images.
Text Analytics: Advances and Challenges Domenica Fioredistella Iezzi, Damon Mayaffre and Michelangelo Misuraca Springer, 2020, xi + 302 pages, £95.50, eBook ISBN: 978‐3‐030‐52680‐1

International Statistical ReviewVolume 89, Issue 2 p. 429-431 Book Review Text Analytics: Advances and Challenges Domenica Fioredistella Iezzi, Damon Mayaffre and Michelangelo Misuraca Springer, 2020, xi + 302 pages, £95.50, eBook ISBN: 978-3-030-52680-1 Jordan Rodu, Corresponding Author Jordan Rodu jordan.rodu@virginia.edu Department of Statistics, Halsey Hall, University of Virginia, Charlottesville, VA, 22904 USASearch for more papers by this author Jordan Rodu, Corresponding Author Jordan Rodu jordan.rodu@virginia.edu Department of Statistics, Halsey Hall, University of Virginia, Charlottesville, VA, 22904 USASearch for more papers by this author First published: 02 June 2021 https://doi.org/10.1111/insr.12457Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume89, Issue2August 2021Pages 429-431 RelatedInformation
Appreciations for the Role of Sensors in Everyday Life Against the Aging Population

The focus of this chapter is on the sensor within an aging population. The study involves a detailed analysis of applications with sensors and the effects of their use in the basic sectors of society, such as economic, educational, medical, social security system, social, and cultural activities. After a faithful presentation of the notion of sensor, the work makes a foray into contemporary technical history starting with the appearance of sensors, selects and appreciates some characteristic and edifying parameters of daily life from the beginning of the period of using the sensors; these parameters refer to living standards, health standards, mortality rates, life expectancy, birth rate, occupations by sex and age, educational level of individuals, employment, degree of development of a professions, the interest of the population for a certain type of product, and the tendency to use modern equipment by fields of activity by geographical areas.
How to Approach Hard Drives as Cultural Heritage

Computers and mobile phones are piling up in archives, libraries, and museums. What kind of objects are they, what can they tell us, and how can we approach them? The aim of this chapter is to exemplify what an investigation of a hard drive implicates, the methods needed to conduct it, and what kind of results we can get out of it. To focus the investigation, hard drives are approached as records of everyday media use. The chapter introduces a computer forensic method used as a media ethnographic tool. Computer forensics and media ethnography are rooted in different methodological traditions, but both take an interest in people’s routines and the way they do and organize things. The chapter argues that a hard drive represents a window into the history of new media: into time specific software, formats, and media use.
Distracted Driving Behavior Detection and Identification Based on Improved Cornernet-Saccade

While driving, drivers need to be highly concentrated due to distracted driving can lead to serious accidents. In this paper, a scheme for detecting distracted driving based on an improved CornerNet-Saccade is proposed. To eliminate the risk of traffic accidents caused by distracted driving, through detecting drivers' operations when driving. The scheme includes two stages of model training and testing. First, data set is self-built, the CornerNet-Saccade model is optimized. And, then the network model is trained to detect the smoking action and eating behaviors while driving. The experimental results show that the proposed scheme can detect the improper driving of smoking action and eating behaviors in the process of driving in real time, with the accuracy of 71.1% and with a running time of 156fps. The detection accuracy is improved by 3.0% when the improved CornerNet-Saccade is guaranteed to be detected in real time.
Smart University Innovation Efficiency Improvement Model

Modern conditions are associated with the development of progressive technologies and neural networks, the emergence of smart devices, smart gadgets, and the expansion of information technologies. The innovative activity of any organization determines its competitiveness, the potential for expanding the scope of activities and the growth of efficiency. The rapid development of science and technology is fundamentally changing the needs of society for new products and services that have distinctive characteristics from the existing ones. Consequently, the organization needs to focus on the new consumer and his needs, to offer innovative products and services ahead of competitors. This vector of economic development entails the need to expand the innovative activities of a smart university and transform approaches to its management. At the same time, it becomes necessary to develop models for increasing the efficiency of innovation, which determines the relevance of a scientific article. The article reveals the concept of the specifics of innovative activity in relation to a smart university. The organizational foundations and management principles have been clarified, a model for increasing the efficiency of innovations for a smart university has been proposed. The obtained results were tested in order to increase the innovative activity of the smart-unit of the university on the example of the department.
Spatially Transformed Text-Based CAPTCHAs

In recent years, deep learning technology has achieved great achievements in the fields of text, image, and speech recognition. As long as there is enough data, deep learning techniques often achieve good results. In the field of text-based CAPTCHA recognition, facing the anti-segmentation technology, using deep learning without using character segmentation techniques to end-to-end recognize CAPTCHAs, can also achieve good accuracy. However, some recent studies have found that deep neural networks are vulnerable to adversarial examples. By adding a very small perceptible disturbance to the input samples, the input of the disturbance will cause the model to output incorrect prediction results with high confidence. In this paper, we propose an adversarial text-based CAPTCHAs based on spatial transformation. And we use four state-of-the-art CNN models to recognize such adversarial CAPTCHAs with and without preprocessing. Experiments show that this type of CAPTCHAs can effectively reduce the recognition rate of the attack models.
Optimization of TailorGAN Using Log Hyperbolic Cosine Loss Function

Designing of the fashion clothes has always been a crucial task for the fashion designers as it needs a lot of thinking and creativity. This paper will simplify this task by taking two reference images into consideration, whereas one image would be of garment, and the other image would be of target attribute (collar), further by combining these two images, the photorealistic images will be generated with the help of GAN.In this task, it will work on a new dataset called garment set dataset, which consists of collar/sleeve and garment images. The proposed work aims to train this dataset and refine the quality of the generated images.
The Role of Motivation in the Formation of Personally Significant Qualities of Future Teachers

The work raises the question of the relationship of professional motivation and professionally important qualities of the personality, which are not always realized by university teachers, who believe that high education provides the formation of professional competencies and qualities of the future professional. This view reflects only the external picture, omitting what is happening in the system of motives that generates new motives and building their hierarchy. The relationship of the prevailing motivation with professionally important qualities of teachers is the target of this study. As the research methods, there was used author's comprehensive questionnaire including questions relating to the motives that attract to the teaching profession and to the representation of professional qualities of the respondents. The revealed relationship of the dominant motives with professional qualities of the students and the future teachers was numerous and poorly differentiated. This indicates on the equal opportunity of motives to act as a foundation for the development of any professional quality. The study suggests that the role of dominant motives in the formation of the structure of professional qualities of the teacher is different. Emerging in the university dominant motives, developing begin to determine the formation of different qualities that are the basis of competencies.
User Participation as a Matter of Care. The Configuration of Older Users in the Design of Assistive Technologies

Abstract : The participation of older people in technology development is an ambivalent field: while promising a better fit between users and technology, it still is challenging for both the project and for older users. This is particularly the case when older people with cognitive or physical impairments are to be involved. The article examines the configuration of older people as users in the design of a digital memory training for older people with mild cognitive impairment (MCI). Referring to (feminist) STS, this configuration is conceptualised as a matter of care. Based on an ethnographic field study the article unfolds the spatial, affective, discursive and material practices of user configuration and describes ambivalences and asymmetries in the configuration process that determine the configuration of older people as users in user-centred design. Keywords : age; technology; configuration; user-centred design; feminist STS.
Spoken English Assessment Using Confused Phoneme Assessment Model

Under normal circumstances, in the process of evaluating the phoneme of spoken English which is easy to be confused, it is impossible to accurately assess the oral ability of different groups of people according to their oral ability. The assessment process has problems of poor robustness and low stability. We propose a spoken English assessment method based on an easily confused phoneme assessment model to address these problems. We design an English easily confused phoneme-based evaluation model in the proposed framework by adopting fuzzy logic for the assessment task. We also present HPD set for confused phonemes and introduce the easily confused phonemes in spoken English. Moreover, we derive four fuzzy measure assessment grades of E/G/NI/GR and present the assessment model for them. We continuously recognize and annotate spoken English to find the best-matched statement and complete the recognition and assessment of easily confused phoneme. Then, we also focus on spoken English assessment based on an easily confused phoneme assessment model. Empirical results demonstrate the superior performance of our proposed models over the conventional evaluation methods. Our proposed models improve the spoken English assessment method by 30% and the stability by 45%. Besides, our model is also suitable for the spoken English assessment of different groups of people.
Research and Application on the Language and Literature Based on Network Information Technology

Abstract With the rapid development of computer technology, network information technology (hereinafter referred to as NIT) has become an indispensable basic tool in people’s work, which has been applied to all walks of life in our country, including literature education. Language and literature (hereinafter referred to as LAL) are the extension of excellent traditional culture, which is the legacy of the history of all nationalities. Therefore, the study of LAL should not be stagnant, which needs to adapt to the modern technical means. Through the NIT, we can promote the progress of Chinese LAL, which will also have a significant impact on the audience. In the face of the impact of the network, the network culture has been greatly applied, which has a great impact on our traditional literature. With the popularity of Internet catchwords, some people tend to forget their words, which has gradually ignored the art and charm of traditional culture. Therefore, colleges and universities must use NIT to assist LAL research, which will strengthen the understanding of literary works. First of all, this paper analyzes the important role of NIT in LAL teaching. Then, this paper puts forward some problems. Finally, some suggestions are put forward.
Representation Matters: Assessing the Importance of Subgroup Allocations in Training Data

Collecting more diverse and representative training data is often touted as a remedy for the disparate performance of machine learning predictors across subpopulations. However, a precise framework for understanding how dataset properties like diversity affect learning outcomes is largely lacking. By casting data collection as part of the learning process, we demonstrate that diverse representation in training data is key not only to increasing subgroup performances, but also to achieving population level objectives. Our analysis and experiments describe how dataset compositions influence performance and provide constructive results for using trends in existing data, alongside domain knowledge, to help guide intentional, objective-aware dataset design.
Improving Convolutional Networks with Boosting Attention Convolutions

Convolutional neural networks (CNNs) have been widely used in a range of tasks because of its robust convolutional feature transformation ability. In this paper, we propose a novel type of convolution called Boosting Attention Convolution (BAC) to improve the basic convolutional feature transformation process of CNNs. The proposed method is designed based on two principles, boosting and attention mechanism. Specifically, we design a set of simple yet effective Boosting Attention Modules (BAM) within grouped convolution, which progressively recalibrate distribution of feature map and enable the future filters nested in a convolution layer to focus more on the feature regions that are unactivated by previous filters. Thus, it can help CNNs generate more discriminative representations by explicitly incorporating richer information. The experimental results on various datasets verify that BAC outperforms state-of-the-art methods. More importantly, the proposed BAC is a general convolution that can be deployed to various modern networks without introducing much parameters and computational complexity.
Knowledge-Guided Efficient Representation Learning for Biomedical Domain

Pre-trained concept representations are essential to many biomedical text mining and natural language processing tasks. As such, various representation learning approaches have been proposed in the literature. More recently, contextualized embedding approaches (i.e., BERT based models) that capture the implicit semantics of concepts at a granular level have significantly outperformed the conventional word embedding approaches (i.e., Word2Vec/GLoVE based models). Despite significant accuracy gains achieved, these approaches are often computationally expensive and memory inefficient. To address this issue, we propose a new representation learning approach that efficiently adapts the concept representations to the newly available data. Specifically, the proposed approach develops a knowledge-guided continual learning strategy wherein the accurate/stable context-information present in human-curated knowledge-bases is exploited to continually identify and retrain the representations of those concepts whose corpus-based context evolved coherently over time. Different from previous studies that mainly leverage the curated knowledge to improve the accuracy of embedding models, the proposed research explores the usefulness of semantic knowledge from the perspective of accelerating the training efficiency of embedding models. Comprehensive experiments under various efficiency constraints demonstrate that the proposed approach significantly improves the computational performance of biomedical word embedding models.
CONDITIONS FOR ORGANIZATION OF THE EDUCATIONAL PROCESS AT THE UNIVERSITY USING E-LEARNING TECHNOLOGIES

Анализируется проблема разработки, характеристики и реализации важного условия организации образовательного процесса в университете с применением технологий дистанционного обучения, связанного с созданием в университете качественной цифровой образовательной среды, развития технической инфраструктуры и повышения квалификации вузовских преподавателей с целью эффективного использования ими технологий дистанционного обучения в процессах цифровой трансформации высшей школы. Причем такое условие анализируется не только с позиции повышения эффективности организации исследуемого процесса, но и с позиции обеспечения деятельности вуза, отвечающей требованиям мировых стандартов, что чрезвычайно важно в формате реализуемой государственной программы Российской Федерации «Развитие образования» на 2018–2025 гг. Приводятся данные опроса студентов и преподавателей университета, освещающие результаты измерения уровня эффективности реализации проверяемого условия организации образовательного процесса в университете с применением технологий дистанционного обучения. The materials of this publication analyze the problem of development, characteristics and implementation of an important condition for the organization of the educational process at the university with the use of distance learning technologies, associated with the creation of a high-quality digital educational environment, the development of technical infrastructure and advanced training of university teachers in order to effectively use distance learning technologies in the processes of digital transformation of higher education. And a certain condition is analyzed not only from the perspective of improving the efficiency of the organization of the process under study, but the operations of the University that meets the requirements of world standards, which is extremely important in the format of the State program of the Russian Federation “Development of education” for 2018–2025. The results of study of the levels of effectiveness of the implementation of the conditions are tested and and the data of the questionnaire survey of students and teachers are provided. The article describes the terms “distant education” and “distant learning”. The synonymy of their meanings and the peculiarities of their use in the context of the characteristics of a special way of implementing the educational process at the university are noted. The features of the implementation of the selected condition that took place in 2020 are noted: flexibility in taking into account the time frame of classes and in choosing an individual pace of training; modularity; the use of various types of communication; wide coverage of the audience of students.
Analysis of Cybercrime in Nigeria

Nigeria has both the largest economy and population in Africa, and this contribute to the growth and fast expansion of ICT and the use of Internet in Nigeria. Like other technologies, Internet has been used by both good and bad actors. The use of internet and computer to commit crime is costing global economy the loss of billions of dollars. In Nigeria, the majority of the population use the Internet for good but some few are using it to commit criminal activities such as Fraud. Cybercriminals in Nigeria, widely called Yahoo Boys in the country specialize in Internet fraud that target mostly International victims. The Nigeria government is stepping efforts to bring an end the activities of these criminals as their actions tarnishes the image of the country. While the efforts of the government had yielded some positive results, the threat of Cybercrime in Nigeria is still high, as criminals continue to take advantage of flaws in the law enforcement tactical approach in addressing the crime. This paper discusses an overview of Cybercrime in Nigeria, the common types of Cybercrime that is perpetuated from the country and the reason of doing so. It also discusses the government's success and areas of strength in its fight against Cybercrime and highlight the areas of weaknesses. Recommendations and suggestions are made on how law enforcement and the government at large can improve to tackle Cybercrime better in Nigeria.