Reviewerly/exHarmony huggingface dataset:
Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being.
Neural network modeling for the assessment of digital competencies of employees

The relevance of the research is determined by the development of the digital economy which stimulates a significant reorganization of industrial enterprises, creates new requirements for the skills of employees, and provides the demand for jobs in the digital technology industry. Thus, companies face the problem of a reliable assessment of the digital competencies of employees that will help to develop management decisions that increase the level of digitalization. The purpose of the research is to develop a methodology for assessing the digital competencies of industrial workers using neural network modeling. The neural network modeling method means training a neural network for its future application by an enterprise. The practical significance of the proposed methodology is provided by the fact that a correct interpretation of competencies assessment in the digital economy will allow companies to develop ways to improve the quality of human capital, which will provide them with a competitive advantage.
Advanced feature extraction method for speaker identification using a classification algorithm

This paper presents an improved method for highly accurate identification of the speaker who speaks sentences in the Uzbek language from a limited set of sentences. It is proposed the use of an advanced method for identifying the speaker speaking in the Uzbek language through the use of classification method - Gaussian mixture model (GMM) for decision making. The advanced method is a combination of Gaussian mixture model and MFCC. Current scientific research is aimed at identifying opportunities to improve performance. One of the latest implementations of MFCC are Delta MFCC (DMFCC) and Delta Delta Delta MFCC (DDMFCC), which improve speaker identification. Identification accuracy was obtained from the results of the research. According to the experimental results, speaker identification by conventional MFCC combined with GMM classification algorithm (94.5%) lower than the second (DMFCC+GMM - 96.8%) and third (DDMFCC+GMM-98.1%) feature extraction methods. It is easy to see the performance improvement observed when using DDMFCC in combination with GMM and is 98.1%. first, second, and third level headings (first level heading).
CRESustain: Approach to Include Sustainability and Creativity in Requirements Engineering

Requirements Engineering is an evolving field facing new challenges.One of the central conundrums is sustainability in software.The possibility of using known creativity techniques while introducing the dimensions of sustainability to help provide unexpected, original, practical, and sustainable answers in software development is challenging and motivating.This paper proposes an approach, CRESustain, incorporating sustainability dimensions when introducing creativity techniques in the Requirements Engineering process.CRESustain uses various creativity techniques considered appropriate for the different stages of the RE process.It is inspired by the Sustainable Development Goals, creative problem-solving methods, and the Karlskrona Manifesto.The methodology applied to give materiality to the outcome of this work was Design Science Research, a research paradigm that uses knowledge to solve problems, generate new knowledge and insights, and results in an artefact.The main results indicate that the approach stimulates discussion about sustainability in technical, economic, social, human, and environmental dimensions focusing on the Sustainable Development Goals and people's needs.
Research on Architecture of Digital Bond Trading Contract System Based on Blockchain Technology

The traditional identification method of the digital bond trading contract system is single chain identification. Due to a large number of digital bond trading users, single chain identification will affect the performance of the system in the trading process. Therefore, we aim to propose a digital bond trading contract system based on blockchain technology and study the system architecture. In this paper, the architecture of the bond trading system is optimized by using the technology of blockchain, and the software architecture is developed by using the technology of blockchain. Through the performance test of the system, the advantages of the system architecture in practical application are verified. Firstly, the overall hardware architecture of the system is designed, and the internal structure of the controller in the control layer is redeployed. Combining the Mork tree and Patricia tree, the data structure based on blockchain and the block header data structure of the transaction contract system are optimized. By analyzing the operation process of the smart contract, the process of the smart contract consensus algorithm is optimized. The system performance test results show that the performance of the designed system in system transaction throughput, transaction delay, and system security is better than the traditional system, which verifies the effectiveness and reliability of the designed system.
FIREBASE EFFICIENCY IN CSV DATA EXCHANGE THROUGH PHP-BASED WEBSITES

A database is a collection of data that can be classified into two types: SQL (structure query language), which is associated with relational databases, and NoSQL (non-relational SQL), which is associated with distributed databases. As the world becomes increasingly technologically advanced and computerized, the amount of information grows exponentially, resulting in data. In the majority of studies, the NoSQL database is referred to as "NoSQL." Firebase, a NoSQL database, is used to exchange data with a CSV file on a php-based website in this study. Two CSV files of 1000 and 4997 records were used for both the importing and exporting processes to collect the experimental results. Firebase's speed was examined by exchanging csv files with a php-based website in this research.
College Students’ Mental Health Support Based on Fuzzy Clustering Algorithm

There are some problems in the active participation of college students in ideological and mental health support services in China, such as low attention, low participation, and high data redundancy. Based on this, this paper studies the active participation of college students' ideological and mental health support service based on fuzzy cluster analysis algorithm. Compared with the disadvantages of the current mainstream discrete optimization analysis models on mental health (such as high-dimensional enterprise model, Dajiaweikang model, and short-range group control model), which need to set the known data gradient interval, this paper creatively adopts the fuzzy cluster analysis algorithm, based on the characteristics of different types of college students' ideological and mental health problems. Combined with the improved star discrete analysis model, this paper constructs the active participatory evaluation strategy of college students' ideological and mental health support services. On this basis, the model can not only record and store the participatory data of ideological and mental health support for students of different grades but also match and track different types of data based on special framework conditions, so as to achieve numerical normal analysis and directional matching for the data coupling mode of college students' ideological and mental health support services. On the other hand, the Planck constant factor is used to classify different types of ideological and psychological factor data, and combined with the idea of fuzzy clustering, the hierarchical analysis and quantitative calibration of different types of data groups are realized, so as to improve the reliability and authenticity of the active participation in college students' mental health support services. The results show that this star discrete analysis model can analyze the active participation of college students' ideological and mental health support services according to the data matching degree of different levels and can effectively improve the analysis efficiency of data vectors. Compared with the traditional research methods on the active participation of college students' ideological and mental health support services, this method can realize the matching and tracking of different types of data, so as to make a numerical and normal analysis on the data coupling mode of college students' ideological and mental health support services.
Volumetry Reality Augmented Reality Application with Android Based Marker Based Tracking Method

Volumetry reality is an application that implements AR with an android-based marker based tracking method for presenting spatial learning materials. Learning to build space and mathematics is still an obstacle in its presentation, delivery and understanding, and the Covid-19 pandemic has forced students to study independently. This encourages educational staff to innovate in providing learning media. Volumetry reality is built by applying AR technology with a marker based tracking method, in this case it is presented in the form of a handbook intended for elementary and junior high school levels. It is designed attractively and easy to use and is equipped with a calculator and mini quiz. Volumetry reality is built using unity and the Vuforia SDK (Software Development Kit). This application can be used by scanning and producing 3D objects up to a distance of 50cm and can be used in low light.
Using GIS Remote Sensing Image Data for Wetland Monitoring and Environmental Simulation

Through a comprehensive theoretical basis and actual test analysis of the application system design and functional efficiency of the cloud platform, this paper puts forward an artificial intelligence environmental data monitoring and wetland environmental simulation method based on GIS remote sensing images. First, the basic storage and computing functions have been enhanced at the physical layer. Second, the middleware layer is more flexible in the use of management methods and strategies. There are many strategies and methods that can be used in combination. Finally, based on this, the application system design framework is more convenient and faster so that you can focus on business logic, and the strategic advantages of certain functions are very obvious. The method of object-oriented classification and visual interpretation using UAV image data and satellite remote sensing images from the typical recovery area and treatment area of wetland from 2016 to 2020 is given in detail together to extract wetland information and use GIS software for dynamic calculation. Using the wetland transmission matrix method, the distribution map of the characteristic types of the survey areas in the four periods and the conversion status of the characteristic types at each stage were obtained, and the effect of wetland treatment was quantitatively studied.
Information Enhancement and Recursive Learning Network in a Coarse-Refine Manner for Pancreas Segmentation

Accurate pancreas segmentation is essential for the diagnosis and treatment of pancreatic cancer. In this paper, we propose an Information Enhancement And Recursive Learning Net-work (IERL-Net) for automatic pancreas segmentation fol-lowing a coarse-refine manner. Specifically, the Information Enhancement module (IEM) is deployed in the Coarse-Net and the Refined-Net to acquire more contextual information, which can efficiently prevent the degenerate of information. Attentively, in the Refined-Net, the Skip Attention Module (SAM) is added to IEM for optimizing the sensitivity of the network segmentation area. In addition, we design a Re-cursive Learning Module (RLM) to incorporate multi-stage visual cues. Comprehensive experiments demonstrate that IERL-Net outperforms the state-of-the-art methods.
A Quantum Mechanical Proof of Insecurity of the Theoretical QKD Protocols

Cryptography is crucial to communication security.In 1984, a well-known QKD (quantum key distribution) protocol, BB84, was published by Bennett and Brassard.The BB84 Protocol was followed by the QKD protocols published by Ekert (1991) (E91) and Bennett (1992) (B92).Some authors proved security of the theoretical QKD protocols in different theoretical frameworks by defining security of QKD protocols differently.My argument is that the previous proofs of security are neither unique nor exhaustive for each theoretical QKD protocol, which means that proof of security of the theoretical QKD protocols has not been completed or achieved.The non-uniqueness and the non-exhaustiveness of the proofs will lead to more proofs.However, a coming "proof" of security of the theoretical QKD protocols is possible to be a disproof.The research by quantum mechanics in this paper disproves security of the theoretical QKD protocols, by establishing the theoretical framework of quantum mechanical proof, defining security of QKD protocols, establishing the quantum state of the final key of the theoretical protocols from their information leakages, and applying Grover's fast quantum mechanical algorithm for database search to the quantum state of the final key to result in the Insecurity Theorem.This result is opposite to those of the previous proofs where the theoretical QKD protocols were secure.It is impossible for Alice and Bob to protect their communications from information leakage by stopping or canceling the protocols.The theoretical QKD keys are conventional and basically insecure.Disproof of security of the theoretical QKD protocols is logical.
Investigating New Patterns in Symptoms of COVID-19 Patients by Association Rule Mining (ARM)

Background: COVID-19 is a major public health emergency wreaking havoc on public health, happiness, and liberty of travel, as well as the worldwide economy. Scientists from all over the world are working to develop treatments and vaccines; the WHO has given emergency approval to eight vaccines from around the world. However, it is also seen that the efficiency of vaccines is not up to the mark in different age groups. COVID-19 symptoms come in many different shapes and sizes, so it’s important to learn about them as soon as possible so that medical attention and management can be easier. Method: The GitHub Data Repository-made COVID-19 patient data is available on the internet, which is used in this investigation. We have used the association rule mining method to look for common patterns in a targeted class or segment and then look at the symptoms based on them. Result: The result is that this study involves individuals with a median age of 52 years old. Few frequent symptoms like respiratory failure (1%), septic shock (1.4%), respiratory distress syndrome (1.8%), diarrhoea (1.8%), nausea (2%), sputum (3%), headache (5%), sore throat (8%), pneumonia (8%), weakness (7%), malaise/body pain (11%), cough (37%), fever (67%) and remaining diseases like myocardial infarction, cardiac failure, and renal illness (less than 1%) were present. If a patient had chronic disease, respiratory failure, and pneumonia, there was a higher risk of death; if a patient had a combination of chronic disease, respiratory failure, and pneumonia, respiratory failure in the age range of 45 to 84 years there was a higher risk of death. Patients having chronic conditions like pneumonia or renal disease symptoms that died as a result of the corona virus had more serious indication patterns than those without chronic diseases.
Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation

Motion (change) detection is a basic preprocessing step in video processing, which has many application scenarios. One challenge is that deep learning-based methods require high computation power to improve their accuracy. In this paper, we introduce a novel semantic segmentation and lightweight-based network for motion detection, called Real-time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation (MDNet-LBPC). In the feature extraction stage, the most computationally expensive CNN block is replaced with our single linear bottleneck operator to reduce the computational cost. During the decoder stage, our pooling compensation mechanism can supplement the useful motion detection information. To our best knowledge, this is the first work to use the lightweight operator to solve the motion detection task. We show that the acceleration performance of the single linear bottleneck is 5% higher than that of the linear bottleneck, which is more suitable for improving the efficiency of model inference. On the dataset CDNet2014, MDNet-LBPC increases the frames per second (FPS) metric by 123 compared to the suboptimal method FgSegNet_v2, ranking first in inference speed. Meanwhile, our MDNet-LBPC achieves 95.74% on the accuracy metric, which is comparable to the state-of-the-art methods.
The Increase in Security Breaches through Remote Working

Background: The rise of cloud computing has led to the increasing number of organizations that rely on it for various tasks and services, such as education, healthcare, and e-commerce. Unfortunately, many security threats can be caused by the sudden use of cloud platforms. Objective: This paper aims to provide a comprehensive overview of these threats and how they can be mitigated. Many companies are moving toward cloud computing to sustain their business growth and provide their employees with the best possible work environment. Results: Due to the rise of cyber security threats and the unprecedented number of breaches of data, small and medium-sized enterprises are also starting to take a huge leap. The outbreak of COVID-19 has affected the lives of people all around the world. Conclusion: Due to the seriousness of the situation, the WHO has declared the COVID-19 pandemic a public health emergency. To minimize the spread of the virus, the entire world has started adopting social distancing
Grasp the Trend of Distance Education and Push the Development of Rural English Learning

The research is about online English education in rural areas, which constitutes a relatively new area that has emerged from online education. Due to the superiority of urban regionality over rural areas, the inequality of English education, teacher resource shortage, and the backward thinking of people living in rural areas, rural English education lags behind. However, online education is a new trend in the education industry, and it is also a new opportunity for rural English education. Intuitively, Internet connection solves difficulties for that student who lives in remote areas without roads or funds to pay for tuition to attend a school far away. Therefore, this appears a more straightforward problem is how online English education reshapes rural English education. Aiming to understand this problem, this study principally conducts a research methodology of literature review and contrastive analysis. There has been less previous evidence for online English education. So this research aims at finding a solution to online English classes for this challenging problem of backward rural English education. Finally, the main achievements, including contributions to the field, can be summarized as follows: helping rural students to receive quality English education and promoting the equality of English education in urban and rural areas. On this basis, the concept of social distance should be taken into account when bridging rural online English classes.
Epistemic Parity: Reproducibility as an Evaluation Metric for Differential Privacy

Differential privacy (DP) data synthesizers support public release of sensitive information, offering theoretical guarantees for privacy but limited evidence of utility in practical settings. Utility is typically measured as the error on representative proxy tasks, such as descriptive statistics, accuracy of trained classifiers, or performance over a query workload. The ability for these results to generalize to practitioners' experience has been questioned in a number of settings, including the U.S. Census. In this paper, we propose an evaluation methodology for synthetic data that avoids assumptions about the representativeness of proxy tasks, instead measuring the likelihood that published conclusions would change had the authors used synthetic data, a condition we call epistemic parity. Our methodology consists of reproducing empirical conclusions of peer-reviewed papers on real, publicly available data, then re-running these experiments a second time on DP synthetic data, and comparing the results. We instantiate our methodology over a benchmark of recent peer-reviewed papers that analyze public datasets in the ICPSR repository. We model quantitative claims computationally to automate the experimental workflow, and model qualitative claims by reproducing visualizations and comparing the results manually. We then generate DP synthetic datasets using multiple state-of-the-art mechanisms, and estimate the likelihood that these conclusions will hold. We find that state-of-the-art DP synthesizers are able to achieve high epistemic parity for several papers in our benchmark. However, some papers, and particularly some specific findings, are difficult to reproduce for any of the synthesizers. We advocate for a new class of mechanisms that favor stronger utility guarantees and offer privacy protection with a focus on application-specific threat models and risk-assessment.