Reviewerly/exHarmony huggingface dataset:
MultiHATHI: A Complete Collection of Multilingual Prose Fiction in the HathiTrust Digital Library

This dataset provides detailed metadata on ca. 10.2 million works of fiction and non-fiction written after 1799 in 521 different languages available in the HathiTrust Digital Library. The dataset bolsters the May 2022 Hathifile by supplying missing predicted fiction tags with a bespoke BERT-based multilingual classifier. Our classifier completes the catalogue with an additional 400,000 non-English volumes predicted to be works of fiction, capturing 95% of all works presently provided by HathiTrust. We provide each work with metadata including the work’s genre at the level of fiction or non-fiction, length in pages, original language, and the year the work was published. With a total page count of ca. 1.4 billion pages, our dataset provides researchers with a substantial source of non-English modern literature. We also present insight into how multilingual classifiers can be trained with monolingual data, itself a discovery with implications for the study of lower resource languages. We hope our provisions will accelerate empirical research into non-English prose and literature.
A Multi Cluster Secure System for Out Sourced Encrypted Data Bases

Recently, database users have begun to use cloud database services to outsource theirdatabases. The reason for this is the high computation speed and the huge storage capacity thatcloud owners provide at low prices. However, despite the attractiveness of the cloud computingenvironment to database users, privacy issues remain a cause for concern for database ownerssince data access is out of their control. Encryption is the only way of assuaging users' fearssurroundingdataprivacy,butexecutingStructuredQueryLanguage (SQL)queriesoverencrypteddatais achallengingtask,especiallyif the data a re-encryptedby arandomizedencryptionalgorithm. Theexistingsystem, whichisanindexingschemetoencodetheoriginaltable’stuplesinto bit vectors (BVs) prior to the encryption. The resulting index is then usedto narrow therange of retrieved encrypted records from the cloud to a small set of records that are candidatesfor the user's query. Based on the indexing scheme, the designed system is used to execute SQLqueries over the encrypted data. The data are encrypted by a single randomized encryptionalgorithm,namely theAdvanced Encryption Standard-Cipher-Block Chaining (AES-CBC). Inthe existing scheme, theindex values (BVs) are stored atuser's side, and which is supportedmostof relational algebra operators, such as select, join,etc. In the existing system,the clouddata server is able to extract the ratio of two underlying data records, and key management servercandecryptthe ciphertextsofowners’data records,whichbreakthe privacysecurity.
Image-Based Radical Identification in Chinese Characters

The Chinese writing system, known as hanzi or Han character, is fundamentally pictographic, composed of clusters of strokes. Nowadays, there are over 85,000 individual characters, making it difficult even for a native speaker to recognize the precise meaning of everything one reads. However, specific clusters of strokes known as indexing radicals provide the semantic information of the whole character or even of an entire family of characters, are golden features in entry indexing in dictionaries and are essential in learning the Chinese language as a first or second idiom. Therefore, this work aims to identify the indexing radical of a hanzi from a picture through a convolutional neural network model with two layers and 15 classes. The model was validated for three calligraphy styles and presented an average F-score of ∼95.7% to classify 15 radicals within the known styles. For unknown fonts, the F-score varied according to the overall calligraphy size, thickness, and stroke nature and reached ∼83.0% for the best scenario. Subsequently, the model was evaluated on five ancient Chinese poems with a random set of hanzi, resulting in average F-scores of ∼86.0% and ∼61.4% disregarding and regarding the unknown indexing radicals, respectively.
Educational Paradigm Shift of Innovative Society

The relationship between Industry 4.0 and education is a common research topic these days. Its examination is usually carried out either from the economic side or from the education side. However, this relationship also affects many other areas of life, so it is worth analysing from the perspective of other sciences as well. An outsider’s point of view can draw attention to details that do not stand out from the overall picture for either industry or education researchers the purpose of this study is to draw attention to such points of view. For this purpose, authors examined the questionnaires of the pilot test of older planned research using the method of text analysis. The educational paradigm always, in any historical era, acts as a paradigmatic idea of the development of innovations and the economic development of society as a whole. The trajectory of society’s development and the saturation of the labor market with high-quality specialists will depend on the correct interaction of the subjects of the educational process, including the management of educational institutions and guarantors of educational programs, with employers. Such interaction will influence the formation of the correct system of competencies in future specialists, which must be adapted to new challenges and market needs. Properly chosen communication approaches establish the priority of knowledge, cause the need to improve the quality of this knowledge and understand the consequences of their impact on society and, in particular, on the innovative development of society. Based on the survey results, the transformation of the teacher’s role is one of the most important changes compared to traditional education. However, the renewal of the curriculum and the application of modern methods in education also play a significant role. There is no need to give students instruction manuals, but knowledge and skills that enable them to learn new knowledge. Survey results show that educational paradigm shift can be a result of developing the modern educational content and methods and lead to the qualitative development of innovative society.
Study of Cyber Crime Awareness among Female B.Ed. Trainees

The purpose of this paper is to explore the cybercrime awareness among female B.Ed trainees with respect to their type of and locality find the cybercrime awareness among female B.Ed. trainees. The increased dependence on internet has led to the rise of cybercrime. Cybercrime is a new wave of crimes using internet facilities. Problems such as lack of proper training and education, the poor awareness of cybercrime among female have also contributed to the growth in cybercrime. In the present study researcher adopted descriptive survey method to collect the data for the cybercrime awareness among adolescents. The sample comprised 200 college going female B.Ed. trainees was selected from government and private colleges. The female B.Ed. trainee belongs to rural and urban areas were taken from Sangrur and patiala district of Punjab. Samples selected using “Cybercrime awareness scale” constructed and validated by Rajasekar S. (2011). Attempts have been made to assess the impact of females and residential area upon cybercrime awareness level of female B.Ed trainee. The finding of the study shows the result of the study showed that the female B.Ed. trainees of government colleges are more aware of cybercrimes as compared to female B.Ed. trainees of private colleges. Moreover the finding of the study showed that the female B.Ed. trainees of urban areas are more aware of cybercrimes as compared to female B.Ed. trainees of rural areas.
Data mining-based recommendation system using social networks—an analytical study

In the current age, social media is commonly used and shares enormous data. However, a huge amount of data makes it difficult to deal with. It requires a lot of storage and processing time. The content produced by social media needs to be stored efficiently by using data mining methods for providing suitable recommendations. The goal of the study is to perform a systematic literature review (SLR) which finds, analyzes, and evaluates studies that relate to data mining-based recommendation systems using social networks (DRSN) from 2011 to 2021 and open up a path for scientific investigations to enhance the development of recommendation systems in a social network. The SLR follows Kitchenhem's methodology for planning, guiding, and reporting the review. A systematic study selection procedure results in 42 studies that are analyzed in this article. The selected articles are examined on the base of four research questions. The research questions focus on publication venues, and chronological, and geographical distribution in DRSN. It also deals with approaches used to formulate DRSN, along with the dataset, size of the dataset, and evaluation metrics that validate the result of the selected study. Lastly, the limitations of the 42 studies are discussed. As a result, most articles published in 2018 acquired 21% of 42 articles, Whereas, China contributes 40% in this domain by comparing to other countries. Furthermore, 61% of articles are published in IEEE. Moreover, approximately 21% (nine out of 42 studies) use collaborative filtering for providing recommendations. Furthermore, the Twitter data set is common in that 19% of all other data sets are used, and precision and recall both cover 28% of selected articles for providing recommendations in social networks. The limitations show a need for a hybrid model that concatenates different algorithms and methods for providing recommendations. The study concludes that hybrid models may help to provide suitable recommendations on social media using data mining rules.
Advanced Driver Assistance and Alerting System Using Human Detection for Collision Avoidance

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;To avoid pedestrian fatalities due to factors such as understeer of the vehicle or negligence in steering by the driver, a step further needs to be taken to improve the steering kinematics and have object detection integration for a robust solution. Here we integrate human detection concepts in computer vision with steering dynamics concepts in vehicle kinematics to give the framework that achieves the ability to avoid pedestrian fatalities due to its ability, innovativeness, and integrated units. The paper consists of a description of each component and the overall system function.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;Results of the pedestrian sensing system and steering kinematics have been included in this document in the form of program outputs, simulation results, and calculations.&lt;/div&gt;&lt;/div&gt;
Multi‐view short‐term photovoltaic power prediction combining satellite images feature learning and graph mutual information feature representation

Abstract With the introduction of national policies, photovoltaic (PV) power forecasting requirements for PV power plants are becoming increasingly stringent. It is particularly critical that PV power predictions are accurate while new energy is being consumed. It is also important to consider the satellite imagery of the location of the PV power plant and the meteorological information of the plant itself. The authors aim to explore the impact of these two elements on PV power prediction to better support PV power prediction. Therefore, this paper explores the cloud information elements of the satellite images from a multi‐view perspective and performs feature extraction and processing of the meteorological information to learn the impact of cloud cover on PV power prediction. Meanwhile, this paper introduces the mutual information mechanism for the influence of meteorological factors on PV power generation. It constructs the mutual information matrix and adopts the graph neural network for representation learning. A time‐series prediction model for short‐term PV power prediction is constructed and more accurate prediction results are obtained. The experimental results demonstrate that the proposed method is effective, has generalisation ability, and improved performance compared with the traditional model. The proposed method can also provide a novel approach and solution for short‐term PV power prediction.
Python Programming to Solve Linear Equations by Gaussian methods

The development to f numerical processes was greatly aided by Newton and Leibniz's invention of calculus, which produced precise mathematical representations of physical reality first and then in other disciplines such as engineering, healthcare, and commerce. Typically, these mathematical models cannot be solved explicitly; therefore, approximate solutions must be obtained using numerical approaches [3][4][5].In this paper, Python Programming is used to solve system of linear equations by Gauss Elimination and Gauss Jordan method.
Dynamic Decentralized Reputation System from Blockchain and Secure Multiparty Computation

In decentralized environments, such as mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs), traditional reputation management systems are not viable due to their dependence on a central authority that is both accessible and trustworthy for all participants. This is particularly challenging in light of the dynamic nature of these networks. To overcome these limitations, our proposed solution utilizes blockchain technology to maintain global reputation information while remaining fully decentralized, and to secure multiparty computation to ensure privacy. Our system is not limited to specific settings, such as buyer/seller or provider/client scenarios, where only a subset of the network are raters while the others are ratees. Instead, it allows all nodes to participate in both rating and being rated. In terms of security, the system maintains feedback privacy in the semi-honest model, even in the presence of up to n−2 dishonest parties, while requiring only O(n) messages and having an O(n) computation overhead. Furthermore, the adopted techniques enable the system to achieve unique characteristics such as accessibility, consistency, and verifiability, as supported by the security analysis provided.
Actor Network Theory, Social Networks, Marketing of Mutual Funds

Mutual funds have been preferred investment vehicles of small investors who wish to earn better risk adjusted returns. Though the mutual fund industry is making a significant progress especially during and after the pandemic, there is still a huge untapped market potential. Banks such as, Axis, Kotak, Bank of Baroda and Union Bank, among others, own mutual funds or have majority stakes in the asset management companies floated by them. This article probes as to how banks and asset management companies can use social networks to market mutual funds effectively, through the lens of Actor network theory.
Players Versus Bots: The Perception of Artificial Intelligence in League of Legends

With the continued development of Artificial Intelligence, It has begun taking over many roles in society. One of the most prominent fields in which this is happening is virtual and video entertainment, where AI can serve as opponents, story characters, and more. Due to the unique circumstances of the video game League of Legends, it remains one of the few games in which artificial intelligence and even cheats have yet to outperform the best human players. Through an online survey, a small proportion of the community was asked about their perception of AI and hackers. The results clearly showed that a significant portion of the community withholds confidence in their ability as well as their fellow players' ability to outperform cheaters and AI. Furthermore, the AI already implemented within the game serve as little more than introductory guides for new players. As such, there is a large amount of potential for development and experimentation for AI within this game due to the level of calculations, decision-making, and overall computational power needed to create a worthy adversary.
An Empirical Study on the English Classroom Teaching Evaluation Combined with the Concept of Key Competency in Chinese Senior High School

With the sweeping of the new curriculum reform, new changes and requirements in English teaching stress the development of students' key competencies and require teachers to carry out teaching activities based on key competencies. As an important part of teaching, however, the evaluation in traditional English classes in senior high school lacks diversity and innovation and ignores the main role of students. Many explorations on classroom teaching evaluation have been conducted, but most of them are theoretical research, focusing on the compulsory education stage. This paper studies the evaluation of high school English classroom teaching combined with key competencies and explores the impact of the evaluation on English teaching and learning. The author selected two classes in a senior high school as the experimental class and the control class to conduct a two-month comparative experiment using classroom observation, testing and other experimental tools. It is found that the English classroom evaluation based on the key competencies can promote teaching effect as well as learning effect. Therefore, in the classroom evaluation, it is important for teachers to implement key competencies in the classroom teaching evaluation, use diversified teaching evaluation to arouse students' enthusiasm for learning, and improve students' comprehensive English learning ability.
BESRGAN: Boundary equilibrium face super‐resolution generative adversarial networks

Abstract Existing Generative Adversarial Networks (GAN)‐based face hallucination algorithms are hard to control the face fidelity of the generated samples, and easily generate flawed faces with unfavourable artefacts and distortions. To address this problem, the authors propose a fidelity‐controllable face super‐resolution (FSR) network boundary equilibrium face super‐resolution generative adversarial networks (BESRGAN), a fidelity ratio is introduced in their network to control how much the adversarial effect the discriminator is put on the generator; therefore, the authors’ network better trades off the objective and perceptual quality. Additionally, the authors design an equilibrium perceptual discriminator to match the perception loss distributions. Under the equilibrium constraint, the discriminator pays more attention to learning fine‐grained feature statistics of ground truths, and further guides the generator to produce photo‐realistic faces, especially in terms of facial textures. Moreover, the authors propose a novel channel‐spatial attention module (CSAM) to eliminate local distortions, by further fusing richer information from the facial prior knowledge and global high‐level facial descriptions. Extensive experiments illustrate that the authors’ approach preserves high pixel‐wise accuracy while achieving superior visual performance against state‐of‐the‐art methods. Specifically, the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) of the authors’ proposed BESRGAN rise 0.64 dB and 0.02 for CelebA compared with one of the state‐of‐the‐art face super‐resolution (FSR) methods.
Research on teaching mode of applied curricular reform based on ADI

In order to overcoming the shortcomings of local applied curricular teaching mode, the reform of applied curricular system is proposed, which is based on ADI (Argument Driven Inquiry) teaching mode. The concept, theory and implementation process of applied curricular teaching mode is explored in this paper, which is based on the course of the principles and application of MCU. The teaching reform aims to strengthen students' practical application ability, and cultivating students' basic knowledge and skills, moreover to keep focus on the importance of scientific thinking. At the same time, we propose methods for improving of teachers' professional abilities in application-oriented schools, and provide reference experience for the promotion of application-oriented courses based on ADI teaching mode.
Reinforcement Learning Inspired by Psychology and Neuroscience

Decision-making is a crucial intelligence shared by animals and humans and it helps them to act in an intricate environment to seek rewards and avoid punishments. Psychologists first became interested in this ability and studied the conditional behavioral problems associated with it, then these studies have further led to the need for unified quantitative explanation models, among which Reinforcement learning is still the most convincing and data-backed model today. The model itself, in turn, facilitates research in neuroscience. In this paper, the researcher first introduces the original framework of reinforcement learning and the potential neural correlates to it. Then the paper reviews new developments in reinforcement learning algorithms that address the limitations of the original model as well as variants further inspired by neuroscience. Finally, the study highlights some new directions for future research. This study focuses on the evolution of reinforcement learning algorithms inspired by neuroscience, shows the relationship of mutual promotion and common development between reinforcement Learning and neuroscience, and clarifies some concerns for future exploration.
Opportunities and Challenges of English Language Learning in Chinese High School Entrance Examinations (Chsee) Under the New Curriculum Reform

The introduction of the English Curriculum Standards for Compulsory Education (2022 Edition) marks the beginning of a new era in the evolution of compulsory education. Under the changed circumstances, the question of how to improve the quality of teaching and learning in the classroom and improve students' application skills while meeting the key criteria of basic education cannot be neglected. This paper reviews the opportunities and challenges under the reform, which are closely linked to the student’s English learning and even their future career plans, as well as their impact on society in the future. The correct understanding of the curricular standards depends to large extent on the correct comprehension of the nature and philosophy of the curriculum as outlined in the standards. Therefore, the English learning objectives and strategies for the Chinese High School Entrance Examinations (CHSEE) under the new curriculum reform should be formulated after studying and analyzing the reform policies. The new curriculum standard has required the students to have vocabulary, comprehension standards and writing skills, and also required the instructors and teachers to get a higher level of proficiency and competence to provide the interactive pedagogy and teach in a more practical and applicable way.