Reviewerly/exHarmony huggingface dataset:
PERCEPTION OF MAJOR STAKEHOLDERS OF SECONDARY SCHOOLS ON LIFE SKILLS EDUCATION

The present study titled, “Perception of Major Stakeholders of secondary schools on Life Skills Education” was conducted to: (i) ascertain the perception of major stakeholders of secondary schools education such as- headmasters, teachers, students and parents with reference to the dimensions of life skills education such as- psychological abilities; interpersonal skills that help people take decisions, solve problems, think critically, communicate effectively, build healthy relationship, empathize with others, and cope with the stress and strain ultimate aim of the study was, however, to explore the perception of stakeholders for improvement LSE among the students. Four different tools and techniques were used for data collection such as-questionnaire, interview schedule, focus group discussion. The data were analysed qualitatively as well as quantitatively technique like-percentage analysis was done and thick description under qualitative technique. The study examined that lack government support and ineffective teaching learning materials. Challenges faced by headmasters of each school such as, lack of funds , negative peer pressure, lack of interest among student, lack of time, lack of teaching staffs. Majority of teachers reported that LSE lessons were not taught at all in their school. Some teachers responded that the lesson of life skills education was scheduled only once in a week, might be due to adequate time and material in the schools. While only 9 (22.5%) indicated that they were taught twice in a week. Majority of parents (76%) were said that life skills education is essential to make the child feel self-reliant in his work and duties and 17(68%) were responded that life skills education was all about to make an individual to deal effectively with confronting day to day situations. Majority of students expressed that they were not aware of about the concept life skills and importance of LSE this might be possible due to the fact that most of the teachers and headmasters of schools were not concerned about the importance of LSE.
Future Sound Dream: Virtual Reality Experiences

This chapter discusses Cyberdream, a virtual reality project, which the author developed for the Oculus Gear VR and Oculus Quest devices with the Unity video game engine. The project extends many of the principles described in the earlier chapters of the book, in order to provide a psychedelic visualisation of electronic dance music. The structure of the piece assumes the form of a DJ/VJ mix, where various scenes become analogous to music tracks, which the user moves through interactively. Cyberdream also provides a series of audio-visual ‘sound toys’, which allow the user to interact and creatively ‘paint with sound’, generating psychedelic music visualisations.
Application of Higher-Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast

Abstract In order to improve the efficiency of dynamic system prediction modelling, this paper proposes a predictive model based on high-order normal differential equations to obtain an explicit model. The high-order constant differential equation model is reduced, and the numerical method is used to solve the predictive value. The results show that the method achieves the synchronisation of model establishment and parameter optimisation, in addition to greatly enhancing the modelling efficiency.
Quantum computing and classical politics

This chapter uses the advent of quantum computing as a thought experiment to test claims about technological determination in cybersecurity. If technology determines politics, then radical changes in technical infrastructure should have important political consequences. Quantum computing is interesting in this regard because it is possible in theory to perform some mathematical operations on a quantum computer that are not practically feasible with classical computers. This could potentially put public encryption at risk, which would be a disaster for global cybersecurity. However, this deterministic perspective neglects the social factors that shape secrecy and intelligence no matter what type of technology is involved. This chapter explores the political logic, organizational implementation, and strategic context of cryptology, the contest between code makers and code breakers. Quantum computing may make this contest more complex, but its political implications are sure to be more ambiguous.
A PREDICTION MODEL OF USER BUYING BEHAVIOR BASED ON LSTM AND SVM

The rise of e-commerce has accumulated a large number of online consumption behavior data of users. Analysis of users' purchase behavior provides support for users' decision-making or merchants' marketing. A method for predicting user purchasing behavior based on Long short-term memory and support vector machines is proposed by this paper. First, it uses LSTM to automatically extract and select characteristics, saving the manpower and time for extracting features. At the same time, it extracts dynamic characteristics of user behavior data as successively related sequences as the advantages of LSTM on sequence data. Then, we predict user purchase behavior by support vector machine. Finally, we use the real monthly sales data of JD for experimental verification. The experimental results show that the accuracy of the model is higher than other models. So the model has a great application prospect in the prediction of users' purchase behavior.
Automated prescreening of MCI through deep learning models based on wearable inertial sensors data

Abstract Background During mild cognitive impairment (MCI), a precursor of dementia, often the mild symptoms go unnoticed and are assumed as normal aging signs by elderly people, resulting in late diagnosis and progression to dementia. Recently, several studies investigated the potential of behavioral analysis (ADL assessment, speech analysis etc.) based biomarkers for automated early screening of MCI in home settings. In this work, different deep learning models with walking data have been examined and a 2D‐CNN‐GRU model for automatic diagnosis of MCI based on wearable inertial sensors gait data is proposed. Method Sixty subjects (30 Cognitively Normal‐CN and 30 MCI) recruited from National Research Center of Dementia Database, Gwangju, South Korea, underwent comprehensive medical assessments by Chosun hospital doctors. The gait data of each participant was recorded using Shimmer3 inertial sensors under dual‐task settings. The subjects wore 7 shimmer3 sensors at five different body locations, i.e. waist, each thigh, each shank and each foot and performed 5 trials of dual‐task walking. For each experiment trials, the subject walked straight 10m and returned 10m while naming animals, food, fruits, colors and plants. Out of 10 meters, the central 6m stable walking signals were extracted and used for post processing. A total of eight features utilized for deep learning models’ training were, angular velocity along each axis of tri‐axial gyroscope sensor, 3‐axis acceleration data, and the signal vector magnitude of both sensors. In order to learn the gait pattern and time series characteristics for each step, the proposed model was built using six Convolutional Neural Networks‐(CNNs) as input for three Gated Recurrent Units‐(GRUs). Finally, the Leave One subject Out (LOO) Cross‐Validation (CV) was used to report the model's performance. Result As compared to individual representative deep learning models, the proposed combined model 2D‐CNN‐GRU proved to be more effective in MCI detection by learning discriminating walk signal characteristics and achieved Sensitivity 83.33%, Accuracy 73.33 % and Specificity 63.34% (Refer Table below). Conclusion This research work is the pioneer to investigate deep learning approaches for MCI diagnostic research using inertial sensor data and has the potential to facilitate early detection of MCI in home settings.
Correlation Analysis between Writing Attitude and Writing Difficulty of Vocational High School Students

The purpose of this study is to examine the writing attitudes of vocational high school students and to explore the relationship between writing attitudes and writing difficulties. And through this, we tried to find the implications of writing education in vocational high schools. As for the writing attitude according to the category, the household category was the highest among the agricultural, industrial, commercial, household, and marine categories. As a result of dichotomizing the five disciplines, the writing attitude score of the industrial and marine departments, which had a large proportion of subjects that required practical skills and skills to be acquired, was low. In addition, the writing attitude score of agricultural, commercial, and housekeeping departments with relatively small proportions of handling equipment and operating machines was high. As for the writing attitude according to gender, as in the previous research results, female students showed higher results than male students. It is clear that vocational high school students have significantly fewer opportunities to establish higher thinking skills through writing than general high school students. Some educational suggestions to overcome this are presented at the end.
Continual Semi-Supervised Learning through Contrastive Interpolation  Consistency

Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting. CL settings proposed in literature assume that every incoming example is paired with ground-truth annotations. However, this clashes with many real-world applications: gathering labeled data, which is in itself tedious and expensive, becomes infeasible when data flow as a stream. This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner. We assess how current CL methods (e.g.: EWC, LwF, iCaRL, ER, GDumb, DER) perform in this novel and challenging scenario, where overfitting entangles forgetting. Subsequently, we design a novel CSSL method that exploits metric learning and consistency regularization to leverage unlabeled examples while learning. We show that our proposal exhibits higher resilience to diminishing supervision and, even more surprisingly, relying only on 25% supervision suffices to outperform SOTA methods trained under full supervision.
Development of students’ competencies in the process of implementation programs for extracurricular activities

The article reveals the problem of the development of competencies among students in the organization of extracurricular employment programs. The content of extracurricular activities is determined on the basis of the requirements of the federal state educational standard. The goal and objectives of education are outlined. Directions of personality development (spiritual, moral, social, general cultural). Particular attention is paid to the implementation of the competence-based approach in education. Particular attention is paid to the development of social competence of students outside the classroom. The groups of social competences are designated. Technological steps are presented that solve the problem of the formation of social competencies. A technology has been developed on the basis of the teacher's functions. Stages of the technological process(diagnostic, theoretical, evaluative, practical). The program is based on classes with training elements. Criteria and indicators of competencies have been determined. Age-related tasks of students have been determined. Practical exercises aimed a
Semiotics analysis of symbol (emoji) that found in the captions of @brianimanuel instagram account

The study is focused on find out the total amount of symbol (emoji) and the meaning of symbol (emoji) that found in the captions of @brianimanuel Instagram account. This study is used theory proposed by Charles Sanders Peirce about semiotic and used semiosis process. This study took the data from the captions of @brianimanuel in his Instagram Post that used emoji from 27 Sept 2019 – 15 Feb 2021. In this study, the writers used observation procedure descriptive-qualitative method for analyzing the data. In addition, for presented the data, the writer used an informal method. This study found there are 73 total amount of emoji and 73 meaning of symbol (emoji) that found in the 55 captions of @brianimanuel in his Instagram Post.
ADR Brief

Alternatives to the High Cost of LitigationVolume 39, Issue 10 p. 165-166 ADR Brief ADR Brief Russ Bleemer, Russ BleemerSearch for more papers by this author Russ Bleemer, Russ BleemerSearch for more papers by this author First published: 25 October 2021 https://doi.org/10.1002/alt.21919Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume39, Issue10November 2021Pages 165-166 RelatedInformation
Classification Cervix Image Using Machine Learning Algorithm to Detect Malignant Area

Cervical Cancer (CC), sexually transmitted diseases, and cervicovaginal microbiota. In this Sees and Surveys, we center on a few themes in connection to the uterine cervix and barrenness: early cervical cancer and richness saving surgery, cesarean scar deformity, cervical inadequacy, and cervical Mullerian peculiarities. the case of cervix woman cancer proposed in this work revelation and classification system using the modern convolutional updated neural frameworks (CNNs). The cell pictures are fed into a CNNs appear to remove deep- classic algorithm learned highlights. At that point, an extraordinary learning machine (ELM)-based classifier classifies the input pictures. CNNs appear is utilized through trade learning updated algorithm and fine calculate method tuning. Choices to the ELM, multi-layer and perceptron algorithm (MLP) and auto en-algorithm-coder (AE)-based classifiers are in addition work with investigated.
Components of the temporary competence of a modern manager

Modern conditions dictate an urgent need for a systematic and dynamic increase in the efficiency and productivity of activities; the question of increasing the level of professional dedication of managers is especially raised. At all times, a guarantee of successful activity is the formed ability to plan in the long-term and short-term, to optimally carry out the planned actions, to respond flexibly to changes, to rationally spend money and time to achieve the set goals. In the article, from the perspective of the concept of personal organization of time, the concept of temporal competence, the peculiarities of its manifestation and formation in future managers, methods of effective time management in professional activity are considered.
Energy-Efficient VM Placement Using Backpropagation Neural Network and Genetic Algorithm

VM placement is an important phase of the VM consolidation process for the improvement of resource utilization and energy efficiency inside a cloud data center. The aim of this paper is to provide energy-efficient VM placement while improving several performance parameters using multi-objective optimization. Therefore, a backpropagation neural network–genetic algorithm (BPGA) model has been proposed that makes use of non-dominated sorted genetic algorithm (NSGA) in the first pass and backpropagation neural network in the second pass for the placement of virtual machines over physical machines. In the first pass of BPGA, the VMs are mapped over physical hosts according to fitness functions of NSGA (the multi-objective optimization) and then the remaining VMs which cannot satisfy the constraints of the first pass will be forwarded to the second pass for their mapping using BPNN. Compared with the traditional algorithms of VM placement, such as bin packing heuristics and other evolutionary algorithms, our proposed model shows better energy consumption with minimum SLA violation for the overall consolidation process. Furthermore, the proposed model also aims to minimize the overall cost of VM placement.
An Efficient Method for Facial Sketches Synthesization Using Generative Adversarial Networks

The synthesis of facial sketches is an important technique in digital entertainment and law enforcement agencies. Recent advancements in deep learning have shown its possibility in generating images/sketches using attribute guided features. Facial features are important attributes because they determine human faces' detailed description and appearance during sketch generation. Traditionally, the forensic or composite artist has to sketch by interviewing witnesses manually. To automate this process of face sketch generation, a deep learning-based generative adversarial network incorporated with multiple activation functions is proposed for its efficiency improvement. The proposed model is extensively tested using different evaluation metrics such as RMSE, PSNR, SSIM, SRE, SAM, UIQ &amp; BRISQUE.
A Blockchain-Based Authentication Scheme for 5G Applications

Authentication mechanisms are important security features for mobile operators and application providers. In addition to the primary authentication between user equipment and the mobile operator network, secondary authentication between the user and Data Networks owned by the application is introduced in the 5G system. However, the implementation of secondary authentication introduces some challenges to application providers, not only the cost but also the capability, which leaves space for improving. In this paper, we designed an authentication mechanism for applications based on blockchain. With this mechanism, mobile operators and the application providers are able to share authentication related information like authentication results, users’ identification, users’ attribute information, users’ reputation and users’ blacklists, etc. securely. As a result, some application providers could execute user access control without deploying their own authentication systems any more. In addition with the shared comprehensive user information, mobile operators and application providers can make more fine and intelligent user access control.
Peculiarities of involutory processes (aging processes) on background of light desynchronization.

Peculiarities of involutory processes (aging processes) on background of light desynchronizationP.A. ZulfugarovaBaku State University, 23 Academician Zahid Khalilov Str., Baku AZ1148, Azerbaijan; For correspondence: parvin_1982@mail ru.Accepted for publication: 12 June 2020To identify the effect of
Evolution of Approximate Functions for Image Thresholding

This paper investigates the utilisation of approximate addition and multiplication for designing image thresholding functions. Cartesian Genetic Programming is applied for the evolutionary design of circuits using various implementations of the approximate operations. The results are presented for various experimental setups and compared with the case when only exact addition and multiplication is considered. It will be shown that for some range of error metrics of the approximate operations the evolution provides solutions that are better than those provided by the exact operations. Moreover, the utilisation of approximate components allows reducing the implementation area of the resulting functions.
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SYLLABUS AS A TEAMBUILDING TOOL FOR TEACHERS

The aim of this small-scale research is to explore the team-building potential of writing a syllabus that can make communication and interaction between university teachers and students more open and transparent. The results of the study support the hypothesis about the role of the syllabus in the formation of a university teacher team, illustrating it with a case of creating the syllabus “English for Academic Purposes” at St Petersburg State University.
디지털 현장연구와 민족음악학 연구의 미래 : 일본 민속음악 전통에 끼치는 코로나 바이러스의 영향 연구

How has Coronavirus been affecting Japanese folk music traditions? And what will ethnomusicological research look like in the age of the New Normal? Since the virus’s identification and a series of governmental responses beginning in 2020, our lifestyles have undergone significant transformation. While the epidemic itself causes no structural damage - unlike natural disasters - resulting economic, social and cultural landscape illustrates that people can no longer enjoy experiencing folk music traditions as they did before. Concomitantly, the pandemic has necessitated cataclysmic changes within our ethnomusicological ‘field’ and research methods. Travel restrictions and lockdowns have been forcing many to investigate far away from the locations of our research topics. However, the evaporation of direct interaction has not only encouraged locals to use the internet to promote and provide new ways of experiencing their culture, but also diversified the methods of conducting fieldwork. In this paper, I present findings from my project and some thoughts regarding online research. I begin by analysing the impact of Coronavirus on Japanese folk music traditions, putting forward findings from my online fieldwork. Next, I examine the difference between real-life and virtual ethnographic investigation, exploring the potentialities and challenges of online-oriented research based on my experiences. Lastly, I discuss the future of ethnomusicological research in the New Normal, introducing several perspectives relating to the intrinsic nature of our scholarship. Ultimately, I aim to demonstrate the potential benefits of online research, while warning of the disadvantages of over-reliance on the virtual ‘field’.
Examination of bubbles in cryptocurrency markets using advanced unit root tests

Cryptocurrencies are attracting more investors and are reaching higher prices than ever, hence it becomes important to analyse whether the new asset class is a bubble or not. Previous literature on examination of cryptocurrency bubbles has primarily focused on Bitcoin, but the newer cryptocurrencies such as Ethereum are innovating the space with smart contracts, upstaging Bitcoin on some aspects, hence it becomes important to analyse the newer cryptocurrencies apart from Bitcoin as well for rational bubbles. The methods of recursive unit root tests suggested by Phillips, Shi and Yu (2015) has been used in this study to check for the presence of bubbles and to date stamp the periods of exuberance in three major cryptocurrencies: Bitcoin, Ethereum and Ripple from 2016 to 2021. Similarity in exuberance periods of Bitcoin and Ethereum is also detected in this research.
Google slides as a tool to collaborate

Google Presentations is a convenient tool for creating presentations, editing and collaborating on them, which allows the student and those whom he chooses as co-authors to edit files in real time from their home computers, smart phones, tablets, laptops. The principle of variability suggests that the teacher himself should select those forms of teaching that motivate students to successfully learn a foreign language, to master key competencies and ensure the principle of novelty in the classroom. The distribution offunctions of each member of the subgroups, the criteria for the final assessment were negotiated and agreed upon taking into account the wishes of the students.