Reviewerly/exHarmony huggingface dataset:
Thematic Analysis: A Corpus-Based Method for Understanding Themes/Topics of a Corpus through a Classification Process Using Long Short-Term Memory (LSTM)

Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.
Data Sharing Privacy Metrics Model Based on Information Entropy and Group Privacy Preference

With the development of the mobile internet, service providers obtain data and resources through a large number of terminal user devices. They use private data for business empowerment, which improves the user experience while causing users’ privacy disclosure. Current research ignores the impact of disclosing user non-sensitive attributes under a single scenario of data sharing and lacks consideration of users’ privacy preferences. This paper constructs a data-sharing privacy metrics model based on information entropy and group privacy preferences. Use information theory to model the correlation of the privacy metrics problem, the improved entropy weight algorithm to measure the overall privacy of the data, and the analytic hierarchy process to correct user privacy preferences. Experiments show that this privacy metrics model can better quantify data privacy than conventional methods, provide a reliable evaluation mechanism for privacy security in data sharing and publishing scenarios, and help to enhance data privacy protection.
End-to-End: A Simple Template for the Long-Tailed-Recognition of Transmission Line Clamps via a Vision-Language Model

Raw image classification datasets generally maintain a long-tailed distribution in the real world. Standard classification algorithms face a substantial issue because many labels only relate to a few categories. The model learning processes will tend toward the dominant labels under the influence of their loss functions. Existing systems typically use two stages to improve performance: pretraining on initial imbalanced datasets and fine-tuning on balanced datasets via re-sampling or logit adjustment. These have achieved promising results. However, their limited self-supervised information makes it challenging to transfer such systems to other vision tasks, such as detection and segmentation. Using large-scale contrastive visual-language pretraining, the Open AI team discovered a novel visual recognition method. We provide a simple one-stage model called the text-to-image network (TIN) for long-tailed recognition (LTR) based on the similarities between textual and visual features. The TIN has the following advantages over existing techniques: (1) Our model incorporates textual and visual semantic information. (2) This end-to-end strategy achieves good results with fewer image samples and no secondary training. (3) By using seesaw loss, we further reduce the loss gap between the head category and the tail category. These adjustments encourage large relative magnitudes between the logarithms of rare and dominant labels. TIN conducted extensive comparative experiments with a large number of advanced models on ImageNet-LT, the largest long-tailed public dataset, and achieved the state-of-the-art for a single-stage model with 72.8% at Top-1 accuracy.
Designing face resemblance technique using near set theory under varying facial features

Near sets (also called Descriptively Near Sets) classify nonempty sets of objects based on object feature values. The Near Set Theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way humans perceive the similarity of objects. This paper presents a novel approach for face recognition using Near Set Theory that takes into account variations in facial features due to varying facial expressions, and facial plastic surgery. In the proposed work, we demonstrate two-fold usage of Near set theory; firstly, Near Set Theory as a feature selector to select the plastic surgery facial features with the help of tolerance classes, and secondly, Near Set Theory as a recognizer that uses selected prominent intrinsic facial features which are automatically extracted through the deep learning model. Extensive experimentation was performed on various facial datasets such as YALE, PSD, and ASPS. Experimentation demonstrates 93% of accuracy on the YALE face dataset, 98% of accuracy on the PSD dataset, and 98% of accuracy on the ASPS dataset. A detailed comparative analysis of the proposed work of facial resemblance with other state-of-the-art algorithms is presented in this paper. The experimentation results effectively classify face resemblance using Near Set Theory, which has outperformed several state-of-the-art classification approaches.
Deep Learning Techniques for Quantiﬁcation of Tumour Necrosis in Post-neoadjuvant Chemotherapy Osteosarcoma Resection Specimens for Eﬀective Treatment Planning

Osteosarcoma is a high-grade malignant bone tumour for which neoadjuvant chemotherapy is a vital component of the treatment plan.Chemotherapy brings about the death of tumour tissues, and the rate of their death is an essential factor in deciding on further treatment.The necrosis quantification is now done manually by visualizing tissue sections through the microscope.This is a crude method that can cause significant inter-observer bias.The suggested system is an AI-based therapeutic decision-making tool that can automatically calculate the quantity of such dead tissue present in a tissue specimen.We employ U-Net++ and DeepLabv3+, pre-trained deep learning algorithms for the segmentation purpose.ResNet50 and ResNet101 are used as encoder parts of U-Net++ and DeepLabv3+, respectively.Also, we synthesize a dataset of 555 patches from 37 images captured and manually annotated by experienced pathologists.Dice loss and Intersection over Union (IoU) are used as the performance metrics.The training and testing IoU of U-Net++ are 91.78% and 82.64%, and its loss is 4.4% and 17.77%, respectively.The IoU and loss of DeepLabv3+ are 91.09%,81.50%, 4.77%, and 17.8%, respectively.The results show that both models perform almost similarly.With the help of this tool, necrosis segmentation can be done more accurately while requiring less work and time.The percentage of segmented regions can be used as the decision-making factor in the further treatment plans.
Exploring the Challenges and Opportunities of Implementing Industry 4.0 in Jordan: Public Shareholding Manufacturing Companies Perspective

Industry 4.0 (the fourth industrial revolution) involves swift and ongoing technological changes affecting industries and societies because of advanced automation, communication and data exchange, and global connectivity. While its implementation is necessary for organizational success in the face of international competition, many challenges arise with its opportunities. This research is an investigation of the challenges (employee qualification, employee acceptance, organizational, and production fit) and opportunities (strategic, operational, environmental, and social) in the implementation of Industry 4.0 in public shareholding manufacturing companies in Jordan. A questionnaire-based approach for primary data collection with analysis via Smart-PLS was taken. The results revealed that organizational and production fit challenges, strategic opportunities, operational opportunities, and environmental and social opportunities are positively associated with Industry 4.0 implementation, while employee qualification and acceptance challenges are negatively associated. The study explained that there is not enough knowledge about Industry 4.0. This study offers guidance for establishing regulations related to Industry 4.0 implementation by removing challenges and adopting opportunities. Also, this study is an approach for further research about the development of communications in Jordan and global connectivity.
Records in manuscript and printed books as a source of information on the spread of book culture in the middle Volga region of the XVII&lt;sup&gt;th&lt;/sup&gt; century

The study of books that through their origin and/or existence are connected with the territory of the Middle Volga Region of the XVIIth Century has revealed a large number of scribes, owners, deposit, gift and selling records made by representatives of different social classes. Some of them show the geography of book writing in the region, the cost of books, give additional information on local history and culture as well as the inhabitants of the region. The study is based on the information from bibliographic sources (catalogues) of regional and central libraries, museums and archives. In some cases, records in manuscript and printed books are described de visu.
Front-end Innovation and Top Income Inequality: Evidence from Emerging Markets

We contribute to the literature on income inequality, by extending existing models to examine the effect of front-end innovation (FEI) on top income inequality. We use a fixed effect panel regression, on annual country-level data for twenty-four emerging markets, over twenty-four (1995–2018) years, and find an insignificant correlation between income inequality and FEI. The instrumental variable estimates, however, show a significant association between measures of FEI and top income shares. Furthermore, we confirm that FEI is weakly related to broad measures of income inequality. Our instrumentation strategy and robustness checks suggest that this correlation partly reflects a causality, from FEI to top income inequality. Finally, we reveal that FEI is necessary for the survival of new ventures, in the crucial early years. Overall, our findings confirm that that; a) sectors that scale slowly because institutions are a substantial barrier for FEI and, b) sectors that rely solely on the most skilled front-end innovators to access credit, significantly expand top entrepreneurial income share across emerging markets.
Multi de Bruijn Sequences and the Cross-Join Method

We show a method to construct binary multi de Bruijn sequences using the cross-join method. We extend the proof given by Alhakim for ordinary de Bruijn sequences to the case of multi de Bruijn sequences. In particular, we establish that all multi de Bruijn sequences can be obtained by cross-joining an ordinary de Bruijn sequence concatenated with itself an appropriate number of times. We implemented the generation of all multi de Bruijn sequences of type C(2,2,2) and C(3,2,2). We experimentally confirm that some multi de Bruijn sequences can be generated by Galois Nonlinear Feedback Shift Registers (NLFSRs). It is supposed that all multi de Bruijn sequences can be generated using Galois NLFSRs.
Learning Designers as Expert Evaluators of Usability: Understanding Their Potential Contribution to Improving the Universality of Interface Design for Health Resources

User-based evaluation by end users is an essential step in designing useful interfaces. Inspection methods can offer an alternate approach when end-user recruitment is problematic. A Learning Designers' usability scholarship could offer usability evaluation expertise adjunct to multidisciplinary teams in academic settings. The feasibility of Learning Designers as 'expert evaluators' is assessed within this study. Two groups, healthcare professionals and Learning Designers, applied a hybrid evaluation method to generate usability feedback from a palliative care toolkit prototype. Expert data were compared to end-user errors detected from usability testing. Interface errors were categorised, meta-aggregated and severity calculated. The analysis found that reviewers detected N = 333 errors, with N = 167 uniquely occurring within the interface. Learning Designers identified errors at greater frequencies (60.66% total interface errors, mean (M) = 28.86 per expert) than other evaluator groups (healthcare professionals 23.12%, M = 19.25 and end users 16.22%, M = 9.0). Patterns in severity and error types were also observed between reviewer groups. The findings suggest that Learning Designers are skilled in detecting interface errors, which benefits developers assessing usability when access to end users is limited. Whilst not offering rich narrative feedback generated by user-based evaluations, Learning Designers complement healthcare professionals' content-specific knowledge as a 'composite expert reviewer' with the ability to generate meaningful feedback to shape digital health interfaces.
CREATIVITY AS A KEY CHARACTERISTIC OF A MODERN LEADER

In the conditions of increasing competition in international business, leaders with creative thinking, capable of generating and implementing creative ideas, are becoming more and more in demand. A modern leader who brings creative ideas to life is able to lead his team to high effective results. Hence, the purpose of the study is formulated as an analysis of understanding the role of a leader in an organization who can influence the team to generate creative ideas. In the course of solving the problem, the author carried out a theoretical analysis of literary sources, summarized foreign scientific experience. The main conclusion of the study: the effective use of promising ideas in organizational conditions requires active leadership, which includes mastering the competencies of the relevant agents of change, since the development of new ideas contributes to more effective management of the process of change. The manager must provide not only psychological support to his subordinates in terms of their implementation.
A SPECIAL LEVEL OF CULTURE OF DISTANCE LEARNING

At present, a significant increase in the number of publications devoted to distance learning can be recorded, which indicates the relevance of this topic. The relevance of the topic of distance learning has increased in the context of the epidemiological threat caused by the COVID-19 pandemic. The article discusses the concept of "culture of distance learning", notes that it is only discussed and formed in the practice of modern education. The article highlights four levels of distance learning culture. The purpose of the article is to study the technological level as a special level of distance learning culture. Its definition is given and essential characteristics and constituent elements are analyzed. The stages of provision with computer equipment for distance learning in educational organizations of the Ministry of Internal Affairs of Russia are identified and analyzed. The article considers the formation of technological skills and abilities in the context of the availability of technological skills and abilities for high-quality work with technical means and technological skills and abilities to ensure the conduct of training sessions remotely. The authors believe that the technological level of distance learning culture sets a special standard for teacher-training relations in the conditions of distance learning.
Review of McCarthy (2020): Innovations and Challenges in Grammar

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Active Noise Cancellation in Microsoft Teams Using AI &amp; NLP Powered Algorithms

The normal method for analyzing technology is formulating many search queries to extract patent datasets and filter the data physically. The purpose of filtering the collected data is to remove noise to guarantee accurate information analysis. With the advancement in technology and machine learning, the work of physical analysis of the patent can be programmed so the system can remove noise depending on the results based on the previous data. Microsoft team generates a new artificial intelligence model that provides solutions on how individuals respond to speakers. Microsoft team, workplace, Facebook, and Google collected data from many active users hence developing artificial intelligence to minimize distracting background noise, barking and typing during the call.
Behavioural Analytics in Cyber Security for Digital Forensics Application

The paper emphasizes the human aspects of cyber incidents concerning protecting information and technology assets by addressing behavioral analytics in cybersecurity for digital forensics applications. The paper demonstrates the human vulnerabilities associated with information systems technologies and components. This assessment is based on past literature assessments done in this area. This study also includes analyses of various frameworks that have led to the adoption of behavioral analysis in digital forensics. The study's findings indicate that behavioral evidence analysis should be included as part of the digital forensics examination. The provision of standardized investigation methods and the inclusion of human factors such as motives and behavioral tendencies are some of the factors attached to the use of behavioral digital forensic frameworks. However, the study also appreciates the need for a more generalizable digital forensic method.
Research on the construction path of multi-dimensional collaborative cultivation system for innovative and entrepreneurial talents in regional colleges and universities

Based on the current problems existing in the cultivation of innovative and entrepreneurial talents in regional colleges and universities, such as lagged educational concepts, lagging development of educational models, lack of entrepreneurial teaching platform, and imperfect security system, this paper tries to build the innovation and entrepreneurship talent training system from six aspects including enhancing school-enterprise cooperation, optimizing curriculum system, reforming assessment methods, strengthening platform construction, construction of teaching staff, and practical training, and puts forward measures like strengthening organizational security and financial support, establishing evaluation mechanism, and creating a good atmosphere, in order to provide reference for the innovation and entrepreneurship talent cultivation of regional colleges and universities.
Thoughts on Writing Teaching Based on the Core Quality of English Subject

The teaching quality of writing class will affect the cultivation and promotion of students 'core literacy , the implementation and completion of the writing task is students' comprehensive embodiment of reading comprehension ability, logical thinking ability and communication expression ability, but there are many problems in the current English writing class: the writing purpose is not clear, lack of teacher’s guidance on students’ writing process and the evaluation criteria is single, these cause students are short of strong writing intention and enough language support to write a coherent and logic, fluent and beautiful article. This paper aims to analyze the above problems, think about the construction of English writing teaching mode in junior high school, and put forward some relatively superficial suggestions for writing teaching.
School Principals’ Leadership Styles and Organizational Climate Toward Innovative Leadership Program

This paper assessed the relationship between principal leadership style and school climate using Multifactorial Leadership and School Climate Questionnaires. The theoretical framework of this research comes from leadership style theory. The author surveyed 400 middle managers from 20 colleges and universities in the university town of China and used structural equation modelling for empirical analysis. Based on the findings, principal leadership has an impact on four dimensions of school climate (faculty leadership, academic climate, teacher professionalism, and institutional vulnerability) It is therefore recommended to use a larger sample of administrators from multiple school districts to see whether similar findings will occur. Educational leaders must recognize the impact of the principal's leadership behavior on the school's organizational atmosphere which would help create a learning school atmosphere that is conducive to school development. Currently, all students are under increasing pressure at the national, state, and local levels to perform to higher standards. Thus, there is an increasing demand for teachers and principals to improve the school climate.
Exploration of high school English reading teaching strategies based on the Deeper Learning Cycle (DELC)

Deeper learning is an important way to implement English subject key competence and improve English teaching. The Deeper Learning Cycle (DELC) provides specific steps and feasible strategies for the achievement of deeper learning in English teaching，overcome the existing problems in senior high school. Combined with the actual teaching situation and students' needs, this paper expounds the specific strategies based on the Deeper Learning Cycle in high school English reading teaching based on the teaching framework of the Deeper Learning Cycle.
The Digital Competence of Chinese University Students: A Survey Study

This article uses the self-assessment tool of the European Union's digital competence framework for citizens，to investigate the level of digital competence of 151 university students in China, and examine the relationship between participants' digital competence and their personal factors. The results show that university students have an intermediate level of digital competence, and their competence level has significant differences in the number of digital tools used, but there is no significant difference in gender, age, region, and participation in the competition. The results of the study can be used as a reference for curriculum and teaching reform.
Perkembangan Bahasa Anak Usia Dini 5-6 Tahun Ditnjau dari Aspek Pragmatik

The development of language or communication in children is one aspect of the stages of child development which should not escape the attention of educators in general and parents in particular. The acquisition of language by children is the greatest and most amazing human achievement. During their early years, from birth to 6 years old, they never learn languages, let alone vocabulary in particular, but by the end of their early years, the average child has stored more than 14,000 words. From these two examples it is known that from a pragmatic perspective, children also have developmental differences in understanding and using sign language. This research uses a qualitative descriptive research method. The research samples were two girls of the same age, namely 4-5 years. The researchers concluded that early childhood is from 5 to 6 years old and has differences in pragmatics. Therefore, researchers suggest that teachers understand these developmental differences and encourage students according to their development to continue to develop. The purpose of this research is to describe the language learning of children aged 5-6 years in the field and pragmatic language learning of children aged 5-6 years.