Reviewerly/exHarmony huggingface dataset:
The Digital Divide in Online Education: A Study of Underserved College Students

Access to technology is essential to educational success in today's digitized society, but disparities in access to technology can handicap students. This study examines to what extent this digital divide exists among underserved students in online instruction during COVID-19 and in their adoption of free TechnologyLoaner programs.Focusing on underserved students that are characterized by their generational status, minority background or low income, we predict that underserved college students will show lower levels of technology access and higher levels of free technology adoption than their counterparts.However, the quantitative analysis of survey data (n=258) collected from a U.S. minority-serving university provides mixed, surprising results.Follow-up analysis of qualitative data from 10 interviews offers us further insights and partial explanations for these unexpected results.Our study suggests that individual background should be considered in designing a policy to mitigate digital divide and enhance student learning in online education.
FORMATION OF FUNCTIONAL LITERACY OF STUDENTS IN THE CONDITIONS OF DIGITAL TRANSFORMATION OF SOCIETY

In the period of active globalization and technogenic development, the urgent problem of mod-ern education and upbringing is the formation of functional literacy of students as an indicator of the new socio-cultural development of society, its visualdigital transformation. The functional literacy of a modern schoolchild should be brought, first of all, to the ability to quickly adapt in a dynamically changing world, think creatively, master new social roles and functions, and be competitive. The social processes of recent years associated with the global pandemic have brought about great changes in school education. The school faced a challenge - an accelerated transition to distance learning, which caused controversy among teachers, students and their parents. The subject of this work was to study the attitude of schoolchildren to digital education and identify the reasons for the decline in reading culture in order to determine new aspects of the formation of students' functional literacy. Students consider the positive aspects of digital learning to be its convenience, accessibility, and the ability to choose something alternative. Of the negative aspects of digital learning, the mental consciousness of schoolchildren is dominat-ed primarily by the lack of live communication with friends, classmates, teachers, which indicates the urgency of the problem of social adaptation of schoolchildren, their communicative development, the formation of a new type of communicative literacy, its other nature - computer-mediated. Another important problem associated with the digitalization of education is the health-preserving factor. In this regard, it is necessary to reconsider the ways of forming stu-dents' literacy in matters of preserving health, taking into account the inevitable realities of life associated with digitalization. The decline in the reading culture of students is largely due to their lack of understanding of classical literature, its abstraction from the modern world. Here, only the use of non-standard, creative approaches to teaching will make it possible to activate the readers' interest of schoolchildren. Thus, the emergence of digital technologies and the socio-psychological change of the person himself requires the expansion and rethinking of the concept of “functional literacy”, a careful solution of problems associated with various aspects of its formation.
Analysis on the effect of soybean and maize cultivation tech- nology and agricultural technology extension

With the rapid development of national economy, relevant departments gradually pay attention to the cultivation technology of soybean and maize and the promotion of agricultural technology. In order to further ensure the development of soybean and corn cultivation technology, it is necessary to solve the problems of low agricultural productivity and low labor productivity according to the actual situation, deepen the deep understanding of agricultural technology, and let the social masses recognize the importance of soybean and corn cultivation technology. Therefore, this paper mainly for soybean, corn cultivation technology and agricultural technology extension effect of brief analysis, in order to provide reference.
Student Examination Portal

Online Examination Portal may be a web portal (or) we will say an internet application helps the students where they will get their college subjects material, placement preparation material and they can access the notes and test. It’s also helpful for the staff because it provides the results automatically after the examination with accuracy. This examination contains the multiple MCQ test series, this helps students to offer the exam far away from distance and which may provide security, ease, and other beneficial features to the user. Recent references [1] should be included for showing previous work done and the importance of current work. This section should be succinct, with no subheadings unless unavoidable [2, 3]. State the objectives of the work and supply an adequate background associated with your work, avoiding an in-depth literature survey or a summary of the results.
Hierarchical Sankey Diagram: Design and Evaluation

We present the hierarchical Sankey diagram that aims to augment the original Sankey diagram by enabling users to examine inflow links and levels of detail through four different variants. We provide the details of our design along with results on a student course performance dataset. Finally, the effectiveness of the four variants for the hierarchical Sankey diagram is evaluated via a user study.
A Privacy-Preserving Incentive Mechanism in Satellite-Terrestrial Crowdsensing

In crowdsensing, users are concerned about their location privacy leakage, while users in remote areas are unable to connect to base stations, which prevents them from completing crowdsensing tasks. Current privacy-preserving algorithms and assignment mechanisms cannot provide personalized privacy protection for users. Therefore, we propose a differential private long-term privacy preserving auction with Lyapunov (DP-LAL) algorithm based on a satellite-terrestrial network (STN) that not only gives an approximate optimal total payment in polynomial time, but also compensates for the poor signal in remote areas. We employ reverse combinatorial auction and Lyapunov optimization theorem to jointly optimize queue stability and total payment. Compared with the widely used algorithms, the simulation results show that our algorithm is more efficient and has better economic benefits.
An investigation on verbal bullying among Danang high school students

Bullying among students in general and high school students in particular has become a pressing issue in the world and in Vietnam today. The paper focuses on investigating verbal bullying between high school students in Da Nang city, and developing measures to reduce bullying in high school students. Methods used in the research include theoretical analysis and synthesis, questionnaire, in-depth interviews, and educational experiment. Verbal bullying is explored in terms of the level and content of bullying, the awareness of consequences of bullying on students. Mitigating measures of verbal bullying are designed to use peer-to-peer influences through behavioral club activity, social networking forums, and acting in bullying situations brochure-building activities. The interventions are designed to focus on enhancing the ability to control emotions and form appropriate behaviors, thereby reducing verbal bullying. Experimental measures are implemented through real-life activities undertaken by high school students. Research findings reveal that all the measures suggested in the research appeared effective. Peer education is an effective option to influence verbal bullying behavior in high school students.
Efecto de las interacciones de estudiantes de ciencias con laboratorios virtuales de realidad aumentada para el desarrollo de visualización espacial

The main scope of this article is to highlight the potential of student interactions in Scienceteaching, from educational resources in augmented reality, and its effect on the development of spatialvisualization skills. Therefore, it presents a contextualization about Science teaching, the use of reallaboratories, as well as its limitations, presenting as an alternative the use of educational resources inaugmented reality with the support of mobile devices. The solution was tested in activities thatcomplement the Science teaching and learning process with basic education students in Brazil. It aimedto investigate whether students' interactions with educational resources in augmented reality cancontribute to the development of spatial visualization skills. The methodological procedures wereorganized in a quasi-experimental approach, through activities foreseen in a test protocol carried outwith 208 participants divided into two groups: Control (n=96 subjects) and Experimental (n=112subjects). In general terms, the results of this research point to contributions arising from theinteractions of students with educational resources in augmented reality, both for the development ofspatial visualization skills, as well as for the observation of the phenomenon and understanding of theabstract concepts associated with it, formulation and testing of hypotheses, as well as conclusions fromthe situations experienced.
Indian COVID-19 time series prediction using Facebook's Prophet model

The entire world has been facing an unprecedented public health crisis due to Covid-19 pandemic for the last one year. Meanwhile, more than one million people across the world have already died; many more millions are under treatment. Some countries in Europe have begun to experience the second wave of the pandemic too. This has put the entire health infrastructure of countries under severe strain and has led to downward spiral in the economy. The most worrisome part is the uncertainty as to the spread or arrest of the pandemic. In such a scenario, robust forecasting methods are needed to enable health professionals and governments to make necessary preparation in accordance with the situation. Artificial Intelligence and Machine Learning techniques are useful tools not only for collection of accurate data but also for prediction. Studies show that Time Series Forecasting Techniques like Facebook's Prophet have shown promising results. In this paper, Time Series Techniques have been used to forecast the numbers of death, recovery and positive cases 60 days ahead. The experimental results obtained demonstrate that machine learning techniques can be beneficial in forecasting the behaviour of the pandemic.
Entanglement of two dipole-coupled qubits induced by a thermal field of one-mode lossless cavity with Kerr medium

Abstract We studied the dynamics of two qubits interacting with one-mode thermal quantum electromagnetic field of microwave cavity with Kerr medium. Using the exact solution for considered model we derived the qubit-qubit negativity for separa coherent initial qubits states. We showed that initial qubits coherencee interaction can greatly enhance the degree of qubits entanglement in the presence of the Kerr nonlinearity and dipole-dipole interactionyeven for high thermal field intensities.
A Scalable and Secure Consensus Scheme Based on Proof of Stake in Blockchain

As a decentralized database, the public blockchain has broad application prospects in many fields such as finance, healthcare, and supply chain, so it is getting more and more attention. The current mainstream public blockchain protocol based on Proof of Work (PoW) cannot be applied to various extendable application scenarios with performance requirements due to performance bottlenecks. Proof of Stake (PoS) circumvents the performance bottleneck of PoW by utilizing equity instead of computing power. However, ordinary PoS protocols still have security problems for they are vulnerable to nothing-at-stake attacks, and rely on third parties to support dynamic availability. In this paper, we propose a novel scalable and secure PoS consensus scheme to support the application of public blockchain in various extendable scenarios. We classify nodes in different states and perform node state transitions through different protocols. Combining stake mechanism with consensus schemes and using dynamic stake proportion table to support dynamic stake scenario, we propose a block compression method to reduce the communication consumption of consensus. We propose a chain selection rule based on Verifiable Random Function (VRF) nonce and longest chain rule, which supports dynamic availability without third parties. In addition, we prove the security of our scheme and analyze its performance at general security threats. Finally, experimental results show that our scheme have better system performance and better scalability under the same condition.
Task-Oriented Clustering for Dialogues

A reliable clustering algorithm for taskoriented dialogues can help developer analysis and define dialogue tasks efficiently.It is challenging to directly apply prior normal text clustering algorithms for task-oriented dialogues, due to the inherent differences between them, such as coreference, omission and diversity expression.In this paper, we propose a Dialogue Task Clustering Network (DTCN) model for task-oriented clustering.The proposed model combines context-aware utterance representations and cross-dialogue utterance cluster representations for task-oriented dialogues clustering.An iterative end-to-end training strategy is utilized for dialogue clustering and representation learning jointly.Experiments on three public datasets show that our model significantly outperformed strong baselines in all metrics 1 .
Channel Jamming Detection Method of Tactical Targeting Network Technology Based on Convolutional Recurrent Network

In the case of deception jamming and repeater jamming in battlefield environment, the tactical targeting network technology (TTNT) channel detection mechanism counts the traffic pulses wrongly, which causes the channel occupancy rate to continue to exceed the threshold, and the tactical information cannot be sent normally, which easily leads to the paralysis of battlefield communication system. To solve this problem, this paper migrates the traditional traffic classification in network layer and higher layer to the physical layer, proposes a channel jamming detection method for TTNT based on convolutional recurrent network (CRN). By analyzing the characteristics of the two kinds of jamming, convolutional neural network is used to extract the characteristics of a single traffic pulse, and cyclic neural network is used to extract the sequence characteristics of multiple traffic pulses. The received traffic pulses are detected and classified to avoid the impact of jamming pulses on the channel detection mechanism, Experiments show that this method can detect two kinds of jamming pulses accurately, and the accuracy is higher than other traffic classification methods based on deep learning.
POSB310 The Impact of Small Sample Size on Selecting Appropriate Distributions for Parametric Survival Extrapolations Models: A Methodological Commentary and Simulation Study

This simulation study explored the impact of sample size on parametric survival extrapolation model selection guided using statistical criteria and the potential effect of erroneous extrapolation on economic evaluations. Weibull survival data were simulated using ‘simsurv’ package in R, applying scale parameter 1.0 and consecutive shape parameters 0.5, 1.5, and 5.0. Datasets were randomly sampled with sample sizes increasing from 25 to 250 and extrapolated using seven functional forms 1000 times per sample size. The concordance between the actual and selected parametric survival distribution was evaluated at each sample size. Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to identify the best fitting distributions. Differences compared using the restricted mean survival time (RMST). The Weibull distribution was the most probable distribution selected at all sample sizes using BIC but was selected only 31.3%, 51.9%, and 35.6% during 1000 simulations at n=25, for shape parameters 0.5, 1.5, and 5.0, respectively. Weibull selection increased linearly with each n=25 increment, by 6.15% (p=0.01), 6.38% (p<0.01), and 4.49% (p=0.01), for shape parameters 0.5, 1.5, and 5.0, respectively. AIC demonstrated similar trends to BIC apart from increasing generalized gamma selection at sample sizes greater than n=125 (p>0.05). The relative differences in the RMST to the sampled data were greatest in the log-normal and log-logistic distributions. These distributions were selected more frequently when the sample size was small and the shape parameter less than 1.0 resulting in an overestimation of the RMST by 151%. Sample size influences the probability of selecting the appropriate parametric survival extrapolation distribution to model survival outcomes when informed by statistical criteria. An incorrect extrapolation of the sampled data can lead to under- and overestimation of the RMST, directly impacting the calculation of the benefits and costs in an economic evaluation.
STRATEGI PEMASARAN BALI APOLLO DIVE AND WATER SPORT UNTUK MENINGKATKAN KUNJUNGAN WISATAWAN

Bali Apollo Dive &amp; Water Sport is a company that provides various types of activities and water sports in Tanjung Benoa. Along with the development of tourism in Bali, the interest of tourists to carry out water sport activities is increasing so that many stakeholders have established a water sport tourism business which makes competition between water sport companies tight, therefore this phenomenon is the background of this research The data collection methods used in this study were observation, interviews, documentation, literature studies and the distribution of internal and external questionnaires to 150 respondents who used the Bali Apollo Dive &amp; Water Sport water sport tour package as well as to the Bali Apollo Dive &amp; Water Sport and academics. Tourism. The results of the study The results of the study using 17 internal environmental sub-indicators and 11 external environmental sub-indicators, then obtained 14 strength factors, 3 weakness factors, 8 opportunity factors and 3 threat factors. Based on the results of data processing on external internal factors/IFAS EFAS Bali Apollo Dive &amp; Water Sport, it is known that the total value of IFAS is 3.17 and the total value of EFAS is 2.31, indicating the position of the Bali Apollo Dive &amp; Water Sport water sport package is in cell VIII, namely Conglomerate Growth and Diversification Strategy. In this position, the strategy used by Bali Apollo Dive &amp; Water Sport in marketing water sport packages requires a growth strategy through strategic emphasis on financial synergies rather than product marketing synergies.
Image Captioning Using Ensemble Model

Generation of image captions is the task of producing a description of an image using natural language. Even with the advancement in technology, the mundane task of image captioning still proves to be strenuous for a machine. The paper proposes an ensemble model of LSTM and transformer encodings. The model works on an encoder–decoder architecture. The image is passed as input to the encoder which extracts features from the image which acts as input for the decoder to output a caption from the received information. The paper evaluates the results of the implementation of the ensemble model as well as the existing model. The paper also evaluates the results on different standards of images. The ensemble model provides better results than the standalone existing models. The results were evaluated on Bleu score. The proposed model achieved a bleu score of 0.6192.
Discussion on the Auxiliary Function of Computers in Mathematics Applications in College Engineering

Discussion on the auxiliary function of computers in mathematics applications in college engineering is conducted in this paper. In Von Neumann's system, the storage model achieves work at the expense of space or time, which essentially limits the improvement of information processing capabilities. With respect to this idea, this paper considered the computer system optimization on the engineering applications with the math models. The computer organization and architecture course provides the basic theory of SoC composition structure and on- chip IP core module interconnection mode for SoC design. Hence, the novel fuzzy model is applied to enhance the structure. The designed model is simulated on the different database. The core performance is tested, and prospects are discussed.
Research on the Management of Garden Engineering Con- struction Project

With the development of society, people have higher and higher requirements for the environment. The construction of modern garden engineering can improve the ecological environment to a great extent and play an important role in promoting the construction of ecological China. Based on this, starting with the characteristics of garden engineering construction, this paper analyzes the problems existing in garden engineering construction project management, and gives some countermeasures to strengthen construction project management, hoping to provide reference for garden construction project management.
ORACLE: End-to-End Model Based Reinforcement Learning

Reinforcement Learning (RL) algorithms seek to maximize some notion of reward. There are two categories of RL agents, model-based or model-free agents. In the case of model-free learning, the algorithm learns through trial and error in the target environment in contrast to model-based where the agent train in a learned or known environment instead.Model-free reinforcement learning shows promising results in simulated environments but falls short in the case of real-world environments. This is because trial and error do not fit with the reality where errors are related to an economic burden. On the other hand, Model-based reinforcement learning (MBRL) aims to exploit a known or learned dynamics model, which substantially increases sample efficiency. This paper focuses on learning a dynamics model and use the learned model to train several model-free algorithms by directly sampling the dynamics model. However, it is challenging to achieve good accuracy on dynamics models for highly complex domains due to stochasticity and compounding noise in the system. A majority of model-based RL focuses on dynamics models that derive policies from observation space. Deriving policies from observation space is problematic because it is often high dimensional with significant complexity.This paper proposes an end-to-end model-based reinforcement learning algorithm for learning model-free algorithms to act in environments without trial and error in the real environment. This method is beneficial for existing installations that employ existing decision-making systems, such as an expert system. The proposed algorithm has the same fundamental learning principles as the Dreaming Variational Autoencoder but is substantially different architecturally. We show that the algorithm is more sample efficient and performs comparably with existing model-free approaches. We also demonstrate how the algorithm is actor agnostic, enabling existing model-free algorithms to operate in a model-based context.