Reviewerly/exHarmony huggingface dataset:
NEW SEQUENCE ALIGNMENT ALGORITHM USING AI RULES AND DYNAMIC SEEDS

DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA sequence alignment, dynamic programming was used initially. Later faster algorithms used small DNA sequence length of fixed size to find regions of similarity, and then build the final alignment using these regions. Such systems were not sensitive but were fast. To improve the sensitivity, we propose a new algorithm which is based on finding maximal matches between two sequences, find seeds between them, employ rules to find more seeds of varying length, and then employ a new stitching algorithm, and weighted seeds to solve the problem
Analysis on the Innovative Development of University Education and Teaching Management from the Perspective of Artificial Intelligence

From the perspective of artificial intelligence, this paper analyzes the innovation and development of university education and teaching management, and discusses the application of artificial intelligence in education and teaching management.It first reviews the traditional mode of college education and teaching management and points out its shortcomings.Subsequently, the advantages and application scenarios of artificial intelligence in education and teaching management are introduced, including intelligent course design, intelligent teaching evaluation and intelligent auxiliary teaching.Finally, it summarizes the innovative development of university education and teaching management from the perspective of artificial intelligence, and prospects the future development prospects.
Personal Qualities of Future Primary School Teachers: State Requirements for Their Formation and Diagnostic Results

The article considers personal qualities as a component of the competence of future teachers, confirms the lack of a unified approach in determining the list of personal qualities of a teacher necessary in his profession. The requirements of the “Core of higher pedagogical education” to competencies and the presence of personal characteristics of the ideal image of a teacher in them were analysed. The results of diagnostics of personal qualities of students, future primary school teachers are introduced. The proposals on the possibilities of using these data in the process of teacher training are presented.
Experience in Implementation of Personality-Centered Technologies (Based on Teaching Mathematics to Students of a Technical University)

The article describes the experience of using personality-centered technologies in teaching mathematics to students of a technical university. An overview of some types of innovative educational technologies, such as collaborative learning, game technologies, project method and multi-level training, is given. The description is accompanied by examples of the implementation of these innovations in teaching students of Omsk State Transport University. The result of the application of these technologies is the development of teamwork skills, the formation of personal qualities and the improvement of students’ progress.
Formation of a Teacher-Mentor in the Digital Educational Process

The authors of the article conducted a study in the field of forming the readiness of teachers of educational organizations to implement mentoring activities in a digital educational environment. Diagnostics was carried out in order to identify professional deficits in the activities of teachers of educational organizations. A model for the formation of the readiness of a teacher-mentor for the implementation of professional activities in a digital educational environment has been developed and tested.
Stress of Innovation and Psychological and Pedagogical Support for Subjects of Higher Education in an Innovative Educational Environment

The escalation of innovations implies that the processes of change occur so quickly and intensively that the subject is deprived of the opportunity to realize their meaning and consequences, which in itself is destructive for him. The purpose of the study is to understand the problems of psychological and pedagogical support for subjects of higher education in an innovative educational environment in the context of their experience of the stress of innovation. It was found that students who are critical of innovations, many teachers are more interested in supporting their internal changes than in external effects and “simulations” of changes. A modern university needs to develop a holistic approach to education as an institution to support the development of students and other subjects of education, setting goals for psychological and pedagogical support for the development of subjects of education, tracking external and internal, short-term and long-term consequences of innovations and cooperation in making decisions about their fate.
The Effect of Social Influence and Facilitating Conditions on Electronic Textbooks Adoption in Tanzanian Secondary Schools: The Moderating Role of School Type

This paper addressed the Social Influence and Facilitating Conditions as key determinants that influence the adoption of electronic textbooks for advanced-level students learning purposes in Dar es Salaam region-Tanzania.The study employed survey research designs and strategies to collect data and test research hypotheses.Simple random sampling was employed in the selection of respondents.Questionnaires were used to obtain needed data from 370 students from public and private secondary schools in Dar es Salaam region.The data were analyzed through multi-regression analysis where descriptive and inferential statistics were done with the aid of SPSS version 26.Validity was attained by consulting experts in the field of publishing and research while reliability was tested by piloting the research instruments and calculating the Cronbach Alpha.A Cronbach value of 0.77 for Social Influence and 0.85 for Facilitating Conditions was obtained and thus the instruments were considered reliable.The findings revealed that advanced-level students' intention to adopt e-textbooks in Tanzania schools requires the availability of facilitating conditions such as infrastructures, expertise and human resources.Further, the paper results posited that facilitating conditions, moderated by school type, have shown significant negative effects between private scholars and public scholars on adoption of e-textbooks.On the other hand, when moderated by school type, the paper found that Social Influence had a significant positive effect between private scholars and public scholars.In particular, this paper's results are of importance to publishers, students, teachers, application developers and school management.Finally, the paper recommends policy change on the use of mobile devices and its contents for building students' behavioural intention to e-adopt textbook in Tanzania secondary schools.
Application of Computer Multimedia-Assisted Online Virtual Interactive Platform in Smart College Education

Application of the computer multimedia-assisted online virtual interactive platform in smart college education is studied in the paper.The model we designed is based on the client-side virtual container technology.The so-called virtual container actually completely virtualizes the terminal and builds a virtual container pool on which the operating system is then deployed.In the designed model, the virtual performance is then considered.NSX network virtualization is divided into the NSX-V architecture in the vSphere environment and the NSX-MH architecture in the multi-virtual environment .Furthermore, the smart model is integrated to construct the efficient computer multimedia-assisted online virtual interactive platform in smart college education.The testing has shown the performance.
The Importance of Private Vocational Teachers' Self-development Ability

Compared with teachers in public schools, private vocational teachers play a special social role.They have different pay for the same work, and are at a disadvantage in terms of salary treatment, personal opportunities and vocational ability development.Therefore, in the absence of external advantages, the inner driving force of vocational ability development of private vocational teachers lies in self-development ability.This paper mainly discusses the importance of private higher vocational teachers' self-development ability, carries on the investigation and research of the college teachers' self-development ability, and uses SPSS software to carry on the data statistics and analysis, draws the corresponding conclusion, in order to improve the private higher vocational teachers' selfdevelopment ability awareness.
Server Deployment In College

Server arrangement may be a basic errand for college labs, because it permits them to supply understudies and staff with get to to the assets they got to learn and inquire about.Microsoft servers are a prevalent choice for college labs, as they offer a wide run of highlights and are moderately simple to oversee.This term paper centers on investigating server sending best hones in college research facility situations.College labs play a significant part in supporting different instructive exercises, investigate ventures, and hands-on learning encounters.Appropriate server arrangement is fundamental to guarantee dependable, effective, and secure computing assets for understudies, workforce, and staff.This paper explores the key contemplations and challenges related with server sending in college labs and proposes a set of best hones to optimize the setup and administration of servers.
Design of Secure Product Information Retrieval System in Cloud Computing

Cloud computing is a widely adopted technology that offers very cost-and energy-efficient computing resource management.A growing number of businesses intend to move their on-premises data management systems to the cloud, where product data will be stored and managed by cloud servers.Finding a way to preserve the ability to search commercially confidential data while simultaneously protecting its privacy poses a challenge.We propose a privacy-preserving data search scheme for secure and efficient product information outsourcing and retrieval in cloud computing.We design the proposed scheme, and its execution and results shows that the proposed system is effective in terms of security and efficiency in retrieving product information.
Prediction of Gold Price via Feed Forward Neural Network

A precise and accurate model of the price of gold must be created in order to manage assets due to its special characteristics.In this study, the artificial neural network (ANN) model and the conventional statistical model of ARIMA (autoregressive integrated moving average) were both utilized to predict the price of gold.To assess the effectiveness of various constructed models, three performance metrics-the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE)-are used.The findings demonstrate that, in terms of various performance metrics throughout the training and validation phases, the ANN model beats the ARIMA model.
A Study of Code-mixing in Netspeak: from the Perspective of the Meme Theory

According to memetics, meme is the basic transmission unit of human ideas, culture language and behavior.The Meme Theory is a new theory based on Darwin's theory of evolution and used to explain the law of cultural evolution.From the perspective of memetics this paper analyzes the phenomenon of code.Analyzing the reasons for the production and existence of memes from the perspective of pragmatics of memetics, in order to deepen the research on the formation and evolution mechanism of internet language.Memetics is a new theory based on Darwin's theory of evolution, used to explain the laws of cultural evolution.Memes are the basic units of human thought, concepts, culture, language, and behavior transmission.With the increasing frequency of economic, cultural, and political exchanges and interactions among countries, the connections between different language communities have become closer.As a natural product of social development and human civilization language is also constantly evolving and updating.The Internet not only provides a virtual community for netizens to make information exchange more convenient but also has a great impact on our language.In this way "netspeak" came into being.The so-called "network language" refers to the language used by people in web pages, e-mails online chat rooms or online forums.Due to the richness and randomness of network language we can find a large number of code-mixing in Chinese and English phenomena in network communication.
Multi-Label Classification with Deep Learning for Retail Recommendation

Selecting the right retail business for a location is crucial for the success of a business because it determines the likelihood of favourable return on investment. One common approach used in retail recommendation is multi-class classification, where retail businesses are categorized into different classes or categories based on various features or attributes. Existing research in the field of retail recommendation has extensively proposed and evaluated different algorithms, techniques, and approaches for multi-class classification in the context of retail recommendation, however, limited work has been focusing on formulating retail recommendation as a multi-label problem. This is because in retail recommendation, one location can fit multiple retail businesses so that it can provide more options to recommend the most suitable business for the location. Therefore, multi-label classification will be attempted in this study. An analytical dataset will be constructed that provides comprehensive insights into the characteristics of the business area, and subsequently employ deep learning technique for multi-label classification. The analytical dataset is constructed based on the list of sites of interest data from YellowPages, population data from Humanitarian Data Exchange (HDX) and property data sourced from brickz.my. This work will be focusing on implement deep learning technique which is 1D convolutional neural network (CNN) model. The findings showed that the proposed model achieved 61.22% in terms of accuracy.
Chaser Priori Wolf (CPW) Optimization an Improved Optimization Technique Video Content Classification and Detection

Optimizers play a crucial role in video object detection by promoting the training and improving the performance of the model. Optimizers are responsible for minimizing the loss function during training. The parameters of models are updated iteratively based on the gradients of the loss parameters. By continuously adjusting the parameters in the direction of the steepest descent, optimizers guide the model towards convergence, reducing the loss and improving the object detection performance. In the proposed paper hybrid optimizer named chaser priori wolf optimizer is proposed. The chaser priori wolf optimization is based on the hybridization of cat swarm optimization and coyote optimization. Well-known optimizers like SGD, ADAM, adagrad, adadelta and RMSprop are used as default optimizers by researchers. The proposed work introduced CPW optimizer which works for classification to improve the convergence and feature selection. The comparative result showed an increase in the performance of CNN based YOLO model. The results are compared concerning sensitivity, specificity and accuracy. Results clearly showed improvement in all performance metrics and the average improvement in comparison with state of art architecture is 10.3%.
PAC-Bayesian Domain Adaptation Bounds for Multi-view learning

This paper presents a series of new results for domain adaptation in the multi-view learning setting. The incorporation of multiple views in the domain adaptation was paid little attention in the previous studies. In this way, we propose an analysis of generalization bounds with Pac-Bayesian theory to consolidate the two paradigms, which are currently treated separately. Firstly, building on previous work by Germain et al., we adapt the distance between distribution proposed by Germain et al. for domain adaptation with the concept of multi-view learning. Thus, we introduce a novel distance that is tailored for the multi-view domain adaptation setting. Then, we give Pac-Bayesian bounds for estimating the introduced divergence. Finally, we compare the different new bounds with the previous studies.
SciBERT: Pretrained Contextualized Embeddings for Scientific Text

Obtaining large-scale annotated data for NLP tasks in the scientific domain is challenging and expensive. We release SciBERT, a pretrained language model based on BERT (Devlin et al., 2018) to address the lack of high-quality, large-scale labeled scientific data. SciBERT leverages unsupervised pretraining on a large multi-domain corpus of scientific publications to improve performance on downstream scientific NLP tasks. We evaluate on a suite of tasks including sequence tagging, sentence classification and dependency parsing, with datasets from a variety of scientific domains. We demonstrate statistically significant improvements over BERT and achieve new state-of-the-art results on several of these tasks. The code and pretrained models are available at this https URL.
Hybrid Variational Mode Decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis

The Empirical Mode Decomposition (EMD) has been applied successfully in many forecasting problems. The Variational Mode Decomposition (VMD), a more effective decomposition technique has been proposed with an aim to avoid the limitations of EMD. This study focuses on two objectives i.e. day ahead stock price prediction and daily trend prediction using Robust Kernel based Extreme Learning Machine (RKELM) integrated with VMD where the kernel function parameters optimized with Differential Evolution (DE) algorithm here named as DE-VMD-RKELM. These experiments have been conducted on BSE S&P 500 Index (BSE), Hang Seng Index (HSI) and Financial Times Stock Exchange 100 Index (FTSE), and the daily price prediction performance of the proposed VMD-RKELM model is measured in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE). On the other hand the daily trend prediction which is defined as a classification problem is measured in terms of Percentage of Correct Classification Accuracy (PCCA). The prediction performance of the VMD-RKELM is compared with the performance of robust Extreme Learning Machine (RELM), Extreme Learning Machine integrated with EMD (EMD-RELM). Robust Kernel Extreme Learning Machine integrated with EMD (EMD-RKELM) and two benchmark approaches i.e. Support Vector Regression (SVR) and Autoregressive Moving Average (ARMA). The trend prediction results are compared with Naive–Bayes classifier, ANN (artificial neural network), and SVM (support vector machine). The experimental results obtained from this study for price prediction as well as trend classification performance are promising and the prediction analysis illustrated in this work proves the superiority of the VMD-RKELM model over the other predictive methods.
The blockchain technology and the scope of its application in hospitality operations

The recently emerged concept of the blockchain technology (BCT) can disrupt the traditional realm of tourism and hospitality operations. While some research has looked into the implications of BCT for tourism management and marketing, no studies have explicitly considered the scope of its application in the context of hospitality operations management. This is arguably a major omission as available evidence points at the foremost potential of BCT to affect the nature of hospitality business. This study partially plugs this knowledge gap and, by reviewing examples of existing applications of BCT in various economic sectors and across different consumption markets, evaluates its potential for future integration into hospitality operations management. The framework of prospective use of BCT in the hospitality industry alongside the related organisational, institutional and technological challenges that need to be overcome for its wider industrial adoption are outlined.
E-Learning in the Teaching of Mathematics: An Educational Experience in Adult High School

Currently, the e-learning method, due to the period of confinement that is occurring due to COVID-19, has increased its use and application in the teaching and learning processes. The main objective of this research is to identify the effectiveness of the e-learning method in the teaching of mathematics with adults who are in high school, in contrast to the traditional expository method. The study developed is quantitative, descriptive and correlational. The research design is quasi-experimental, with a control group and an experimental group. The results show that the use of the e-learning method has a positive influence on motivation, autonomy, participation, mathematical concepts, results and grades. It can be concluded that the e-learning method leads to improvement in adult students who are studying the mathematical subject in the educational stage of high school, provided that it is compared with the expository method. Therefore, this method is considered effective for its implementation in adults.
CLIPstyler: Image Style Transfer with a Single Text Condition

Existing neural style transfer methods require reference style images to transfer texture information of style images to content images. However, in many practical situations, users may not have reference style images but still be inter-ested in transferring styles by just imagining them. In order to deal with such applications, we propose a new framework that enables a style transfer ‘without’ a style image, but only with a text description of the desired style. Using the pre-trained text-image embedding model of CLIP, we demonstrate the modulation of the style of content images only with a single text condition. Specifically, we propose a patch-wise text-image matching loss with multiview augmentations for realistic texture transfer. Extensive experimental results confirmed the successful image style transfer with realistic textures that reflect semantic query texts.
Inter‐frame forgery detection based on differential energy of residue

Inter-frame forgery marks a central type of forgery in surveillance videos, and involves three aspects – frame duplication, insertion, and deletion – under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter-frame forgery. Furthermore, studies have confirmed that previous methods did not achieve high accuracy for all forgeries types with low computational loads at the same time. In this study, the proposed method establishes a framework that can simultaneously detect all aspects of inter-frame forgeries. During the decoding process, the authors extract residue data of each frame from a video stream. Then spatial and temporal energies are exploited to illustrate data flow, and abnormal points are determined to detect forged frames. Noise ratios of forged and original frames are estimated for differentiating insertion from duplication attacks. Experimental results indicate that the proposed method achieves higher accuracy and lower computational time for detecting inter-frame forgery.