Reviewerly/exHarmony huggingface dataset:
The Use of AI In Disaster Management and Predictive Modeling

Disaster management and predictive modeling are rapidly incorporating artificial intelligence (AI). Large data sets can be analyzed at an unprecedented pace, yielding insights that help anticipate and forestall catastrophes. Real-time, decision-making assistance and information access are further advantages of AI during disaster response. Preparatory and response initiatives stand to gain enormously from AI integration into the process, as it has already demonstrated. Potential disasters can be predicted and prevented thanks to AI's ability to analyze large amounts of data and detect patterns and trends. Disaster reaction teams can now make informed decisions, allocate sources efficiently, and react quickly to emergency situations with the guide of AI. Moreover, AI-based totally predictive modeling enhances the precision of predicting natural phenomena failures, enabling government to take proactive measures to decrease the impact on affected regions. The integration of AI in catastrophe control and predictive modeling has the ability to seriously beautify response techniques and shop lives.
Beyond Polarity: The Potential Applications and Impacts of Sentiment Analysis and Emotion Detection

Opinion mining and emotion detection are two important techniques in natural language processing that have gained significant attention in recent years. Opinion mining is the process of identifying and extracting subjective information from text, such as opinions, attitudes, and emotions, while emotion detection is the process of identifying and extracting emotions from text. These techniques have a wide range of applications in various domains, including social media analysis, customer feedback analysis, and product reviews. This paper provides an overview of opinion mining and emotion detection techniques in natural language processing. We discuss the various approaches and methods used in opinion mining and emotion detection, including machine learning, deep learning, and natural language processing techniques. We also explore the challenges and limitations of these techniques, including the subjectivity of language, cultural differences, and the lack of labeled data. Furthermore, we examine the current state of the art in opinion mining and emotion detection, highlighting recent research and developments in these areas. We also discuss the potential applications of these techniques in various domains, including marketing, healthcare, and social media analysis. Overall, this paper provides a comprehensive overview of opinion mining and emotion detection in natural language processing. It provides insights into the methods, challenges, and potential applications of these techniques, and highlights the importance of these techniques in understanding and analyzing subjective information in text.
Research on Image Visual Information Storage System Based on Computer Machine Learning

A storage system suitable for SIMT architecture is designed, which mainly includes storage controller, data cache and instruction cache. An automatic image annotation framework based on multi-example and multi-label learning is proposed to express the locality of image features. The image is divided into several visually discontinuous regions by a segmentation algorithm with region constraints. At the same time, feature extraction based on texture and internal structure is carried out. The design uses the integrated Verilog HDL language to realize its hardware circuit, and builds the verification platform based on FPGA to verify the function of the storage system. All aspects of verification show that the designed storage system meets the system requirements.
Research on Resource Scheduling Method of Cloud Computing Based on New Grey Wolf (NGWO) Algorithm

Resource scheduling stands as a pivotal concern within the realm of cloud computing. Aiming at the defects of low resource utilization in traditional cloud computing resource scheduling, a model derived from the scenario of online education video processing platform is established. Simultaneously, a new grey wolf algorithm (NGWO) is introduced to enhance the resource scheduling strategy for cloud computing. Addressing the issue of sluggish convergence, limited global search capabilities, and susceptibility to local optima in multiple iterations of the traditional GWO, the NGWO algorithm is enhanced through chaotic mapping, improving nonlinear convergence factor and introducing dynamic weight strategy. The experiments demonstrate that the NGWO algorithm exhibits superior convergence and enhanced optimization accuracy on unimodal and multimodal functions. Furthermore, the results underscore the NGWO algorithm's heightened optimization prowess in comparison to the GWO and GWO-S algorithms.
Power Terminal Detection System Based on Graph Neural Network

The power terminal detection system is an important part of the power system. It is of great significance to improve the intelligence of the power detection system to maintain the safety of the power system. However, the existing detection system relies more on traditional methods, which reduces the intelligence and accuracy of the detection system. Based on this, this paper proposes a power terminal detection method based on graph neural network. Firstly, the network flow data is used to model the power system as a node with no feature but sideband feature graph. Then, the graph neural network is used to iteratively learn the vector representation of each node in the communication topology according to the features of edges in the graph. Finally, nodes are classified according to the vector representation, so as to detect the vulnerabilities in the power system. When learning node representation, the proposed method considers the flow characteristics of power system and the communication topology of power system at the same time, and clearly considers the direction of edges in the graph. In addition, the proposed method is inductive, so it can adapt to the dynamic changes of power system. Experimental results on two public datasets show that the proposed method can converge quickly during training, and the Micro-F1 score of the proposed method is higher than that of the model that cannot use the power system communication topology information. Compared with other methods, the proposed method is easier to generalize to the unseen data during training.
Developing an educational environment for preschool children following the reggio emilia educational method

The educational environment always emphasizes its crucial role in the early childhood education process. Paying attention to and selecting ways to construct an educational environment for young children is essential for managers and teachers at early childhood education institutions. The Reggio Emilia approach is one of the educational methods that excel in creating an educational environment for preschool children. The following article shares core content on building an educational environment for young children following the Reggio Emilia approach, including: principles; characteristics; methods of constructing both indoor and outdoor classroom environments for preschoolers. It ensures comprehensive development based on individual capabilities in interactions with the environment and those around the child. This article employs theoretical research methods, verified through observation and the application of the Reggio Emilia approach in the research area, with the hope of spreading the advantages of this educational method in the current early childhood education system in Vietnam.
Geochemical, Mineralogical Properties and Industrial Applicability of Kaolin in Isan Ekiti, Nigeria

This study investigates geochemical and mineralogical property of kaolin in Isan Ekiti state, using X-Ray fluorescence and X-Ray diffractometer. The X – Ray Fluorescence of Samples is the major indicator, shows that the percentage Silicon oxide (SiO2) for both samples ranges from 50.35% to 49.76% with an average of 50.055%, Aluminum oxide (Al2O3) ranges from 24.02% to 27.25% with an average of 25.6%, Ferric Oxide (Fe2O3) ranges from 3.42% to 5.14%, with an average of 4.28%, Magnesium Oxide (MgO) ranges from 4.54% to 4.24% with an average of 4.38%, Calcium oxide from 0.67% to 0.63% with an average of 0.65%, and Potassium Oxide range from 3.97 to 2.47, with an average of 3.22%. The Results of X – Ray diffractometer for samples shows that Quartz has its peak at (34.65, 2.70, 2.01 and 2.75) 2Ө values for a sample while a sample was also identified at (32.57, 3.81, 2.23 and 1.65) 2Ө values. Kaolinite has its peak at (8.82, 4.13, 4.01, 2.84, 4.22, 4.52and 1.43) 2Ө values for a sample while another sample was also identified at (8.78,4.61, 4.42, 3.63, 6.29, 4.84 and 1.52) 2Ө values. The Anatase also has its at (4.76 and 2.32) 2Ө values for a sample while a sample was also identified at (3.64 and 3.75) 2Ө values. The Kaolinite in the samples ranges from 29.97% to 34.09% with an average of 32.03%, when compared with standard parameter, its suitable and can be used in coated paper, and when properly beneficiated, will be applicable in grades of ceramic production.
The Effect of Online Learning Quality (Case Study at The Faculty of Economics and Business, Bandar Lampung University)

During the unprecedented challenges posed by the Covid-19 pandemic, Universitas Bandar Lampung (UBL) swiftly transitioned to an online learning paradigm, notably within its Faculty of Economics and Business (FEB). This transition involved the utilization of various software tools, with WhatsApp serving as the primary communication platform and zoom as the virtual classroom medium. However, the initial adoption of Zoom-based online learning witnessed only a partial attendance of students, ranging from half to three-quarters of the anticipated enrollment, thereby influencing the overall educational experience. This research endeavors to systematically assess the impact of online education on the educational quality provided by the Faculty of Economics and Business at Universitas Bandar Lampung. Employing a quantitative methodology, the study conducted surveys on the 2020 FEB UBL student body, utilizing meticulously designed questionnaires and employing Simple Linear Regression for evaluation. The findings illuminate a noteworthy observation: (1) online learning demonstrates a discernibly positive influence on the quality of education, with an effect magnitude of 30.1%. This study contributes valuable insights into the dynamics of online education and its implications for the educational caliber within the context of Universitas Bandar Lampung's Faculty of Economics and Business
A framework to integrate virtual reality into international standard safety trainings

Purpose The objective of this research is to propose an immersive framework that integrates virtual reality (VR) technology with directives international safety training certification bodies to enhance construction safety training, which eventually leads to safer construction sites. Design/methodology/approach The adopted methodology combines expert insights and experimentation to maximize the effectiveness of construction safety training. The first step was identifying key considerations for VR models such as motion sickness prevention and adult learning theories. The second step was developing a game-like VR model for safety training, with multiple hazards and scenarios based on the considerations of the previous step. After that, safety experts evaluated the model and provided valuable feedback on its alignment with international safety training practices. Finally, the developed model is tested by senior students, where the testing format followed the Institution of Occupational Safety and Health (IOSH) working safely exam structure. Findings An advanced immersive VR safety training model was developed based on extensive lessons learned from the literature, previous work and psychology-informed adult learning theories. Model testing – through focus groups and hands-on experimentation – demonstrated significant benefit of VR in upgrading and complementing traditional training methods. Originality/value The findings presented in this paper make a significant contribution to the field of safety training within the construction industry and the broader context of immersive learning experiences. It also fosters further exploration into immersive learning experiences across educational and professional contexts.
JoVE Video Dataset

Salient object detection has emerged as a burgeoning area of interest within the realm of computer vision. However, prevailing algorithms exhibit diminished precision when tasked with detecting salient objects within intricate and multifaceted environments. In light of this pressing concern, this article presents an end-to-end deep neural network that aims to detect salient objects within complex environments. The study introduces an end-to-end deep neural network that aims to detect salient objects within complex environments. Comprising two interrelated components, namely a pixel-level multiscale full convolutional network and a deep encoder-decoder network, the proposed network integrates contextual semantics to produce visual contrast across multiscale feature maps while employing deep and shallow image features to improve the accuracy of object boundary identification. The integration of a fully connected conditional random field (CRF) model further enhances the spatial coherence and contour delineation of salient maps. The proposed algorithm is extensively evaluated against 10 contemporary algorithms on the SOD and ECSSD databases. The evaluation results demonstrate that the proposed algorithm outperforms other approaches in terms of precision and accuracy, thereby establishing its efficacy in salient object detection within complex environments.
JoVE Video Dataset

After oncologic resection of malignant tumors, specimens are sent to pathology for processing to determine the surgical margin status. These results are communicated in the form of a written pathology report. The current standard-of-care pathology report provides a written description of the specimen and the sites of margin sampling without any visual representation of the resected tissue. The specimen itself is typically destroyed during sectioning and analysis. This often leads to challenging communication between pathologists and surgeons when the final pathology report is confirmed. Furthermore, surgeons and pathologists are the only members of the multidisciplinary cancer care team to visualize the resected cancer specimen. We have developed a 3D scanning and specimen mapping protocol to address this unmet need. Computer-aided design (CAD) software is used to annotate the virtual specimen clearly showing sites of inking and margin sampling. This map can be utilized by various members of the multidisciplinary cancer care team.
The Role of Blockchain Technology to Solve A Problem Distribution Of Government Assistance Wages Subsidies

Many organizations, especially government sector still using centralization-based activites such in the public services. One of the activities organized by the government that requires a touch of blockchain technology is the distribution of assistance wages subsidies (BSU) from the government for Indonesian workers affected by the pandemic Covid-19. Practically , implementation of assistance wages subsidies still met many problems. This paper will overview the role of blockchain to solve a problem distribution government assistance wages subsidies. Using tools such as smart contracs, decentralized apps, interopabiity framework and Government-To-Person (G2P), the distribution of wage subsidies assistance can take place transparently and accurately to reach the beneficiaries.
Functional literacy as a pedagogical problem

The article focuses on issues related to the teaching of a new subject within the school curriculum, called functional literacy. The paper addresses the challenges and difficulties of learning functional literacy in lower grades. In the course of work, the need for knowledge of a new subject from the point of view of pedagogy is identified and analyzed. The purpose of this article is to analyze the importance of teaching the subject of “functional literacyˮ among younger students in order to increase the level of education in a rapidly changing society. Such methods as theoretical analysis of the literature on this topic, analysis method and generalization method were applied in the work. Functional literacy in modern conditions of the educational environment implies the introduction of new methods of education and education. This includes educational technologies, which make it possible to ensure that students learn the basic skills and skills, increase their motivation for learning and involvement in the educational process. All this is due to a number of serious problems in the modern educational process. In this work, such methods as theoretical analysis of the literature on this topic, the analysis method and the generalization method were applied. The result of the work was conclusions about the presence of problems in pedagogy related to functional literacy and directions to solve these difficulties.
Surveying the Landscape: An In-Depth Analysis of Blockchain Technologies

Blockchain technology has emerged as a transformative force with far-reaching implications across various industries. This survey paper endeavors to provide a comprehensive analysis of the current landscape of blockchain technologies. Delving into the intricate details of decentralized ledgers, consensus mechanisms, and smart contracts, our exploration aims to offer readers a nuanced understanding of the multifaceted world of blockchain. The survey encompasses an examination of key applications, challenges, and emerging trends, providing insights into the diverse use cases and potential future developments. Through an in-depth analysis, this paper is all about blockchain.
THE USE OF GAME TECHNOLOGY IN THE FORMATION OF THE TECHNICAL UNIVERSITY STUDENTS’ ABILITY TO PROFESSIONAL SELF-ORGANIZATION AND SELF-EDUCATION

The article analyzes scientific sources and diagnostic experiment results in order to substantiate the relevance of the problem of forming the readiness of technical university students’ for professional self-organization and self-education, and proves the expediency of interpreting such readiness as an educational result formulated in the form of competence. The relationship between the concepts of ability and competence based on their structural similarity is presented. The substantiation of the use of game technologies in teaching higher mathematics to students of a technical university in order to form their ability to professional self-organization and self-education is given. Examples of business games aimed at developing the desire to master mathematical knowledge and methods of action necessary for professional activity, independent search for information, mastering professional competencies, qualities such as flexibility, rationality, quick thinking, as well as students' awareness of the role of scientific organization of educational work in improving its effectiveness; mastering the simplest methods of scientific organization of work in the process of educational research and self-education are offered. The content possibilities of traditional technical university disciplines such as "Higher Mathematics", "Linear Algebra", "Mathematical Analysis" for the development of business and intellectual games are argued. Game scenarios are made in the concept of activity and competence-oriented scientific approaches.
Training-quest “With Ukraine in the Heart!”

The article highlights the pedagogical potential of the pedagogical council in the form of game technology – a training-quest for pedagogical workers of institutions of out-of-school education (by the example of the training quest “With Ukraine in the Heart” in the Center for Technical Creativity and Professional Orientation of School Youth in the Darnytskyi District of Kyiv). The training-quest combines two forms of interactive technology: training (as a format of active interaction focused on obtaining new information, theoretical knowledge and their practical verification) and quest (as an innovative game-based learning technology that teaches how to find the necessary information, analyze and systematize it, perform the tasks and solve the issues). The training-quest is methodological because it involves training specialists to practice the necessary skills in carrying out national-patriotic education. By the example of the event “With Ukraine in the Heart”, the process of familiarizing the teaching staff of the institution of out-of-school education with the means of national-patriotic education aimed at fostering a respectful attitude towards the culture and history of one’s nation, recognition of the spiritual unity of generations and the commonality of cultural heritage, as well as which can be implemented into practice with adolescents in institutions of out-of-school education. Participation in the event allowed the teachers to draw the following conclusions. Firstly, the proposed content of various tasks at the locations should activate the students’ cognitive interest by actualizing already existing knowledge and transforming it as a result of layering new ones in discussions, conversations, collective and group games, and tasks. Secondly, the new work format encourages the participants to expand their knowledge about Ukraine and develops their understanding of being involved in protection and development by fostering a sense of pride in the achievements of Ukrainians in various fields. Thirdly, the training-quest makes it possible to activate students and encourage them to interact collectively. Fourthly, the development of critical thinking is important, which takes place effectively due to new technologies and allows students to use new information rationally, assess its reliability and truthfulness, and avoid risks associated with misinformation.
MultiClass Text Classification Using Support Vector Machine

Support vector machine (SVM) was initially designed for binary classification .To solve multi-class problems of support vector machines (SVM) more efficiently, a novel framework, which we call class-incremental learning (CIL) CIL reuses the old models of the classifier and learns only one binary sub-classifier with an additional phase of feature selection when a new class comes. In text classification, where computers sort text documents into categories, keeping up with new information can be tricky. Traditional methods need lots of retraining to adapt. However, Incremental Learning for multi-class Support Vector Machines (SVMs) offers a solution. It lets us update the model with new data while remembering what it learned before .In this project, we'll explore how Incremental Learning makes multi-class SVMs better at handling changing data and even learning about new categories as they appear .There is a problem in addressing the challenge of integrating new classes while maintaining classification accuracy on existing and new classes .The main goal of this project is to create a method that effectively adapts the MC- SVM to evolving data distributions while minimizing the impact on previously learned classes and optimising resource utilization. Keywords— feature extraction, support vector machine, multi class incremental learning, Gaussian kernel.