Reviewerly/exHarmony huggingface dataset:
Improved Multiple-Model Adaptive Estimation Method for Integrated Navigation with Time-Varying Noise

The accurate noise parameter is essential for the Kalman filter to obtain optimal estimates. However, problems such as variations in the noise environment and measurement anomalies can cause degradation of estimation accuracy or even divergence. The adaptive Kalman filter can simultaneously estimate state and noise parameters, while its performance will also be degraded in complex noise. To address the problem of estimation accuracy degradation and result divergence of the integrated navigation system in a complex time-varying noise environment, an improved multiple-model adaptive estimation (MMAE) that combines the Sage-Husa adaptive unscented Kalman filter with the MMAE is proposed in this paper. The forgetting factor is included as an unknown parameter of MMAE so that the algorithm can adjust the value of the forgetting factor according to different system states. In addition, we improve the hypothesis testing algorithm of classical MMAE to deal with the competition problem of undesirable models that severely impacts the performance of variable-parameter MMAE and enhance the algorithm's parameter identification capability. Simulation results show that this method enhances the system's robustness to noises of different statistical properties and improves the estimation accuracy of the filter in time-varying noise environments.
Counterfactual inference graph network for disease prediction

Graph convolutional networks are widely used as computational models that integrate the data of image and non-image modalities in the medical diagnostic domain, especially while exploring population-based disease prediction. However, existing methods use associative combinations of non-image information while constructing population graphs, which are often interfered while capturing the relation between subjects, and the causal relationship between these variables is ignored. In this study, we present a counterfactual inference graph network for improving disease prediction accuracy and generalization by constructing a learnable population relational graph structure. Specifically, nodes can be regarded as contexts, and edge (non-)existence and node classification can be regarded as treatments and outcomes, respectively. We enhance graph learning by predicting both, the observed factual classifications and counterfactual classifications. To provide an estimate of the prediction uncertainty associated with relational graphs, a novel concept called edge uncertainty was proposed. Through extensive experiments on six publicly available datasets on four types of diseases: autism spectrum disorder (ASD), Alzheimer’s disease (AD), thoracic diseases, and ocular diseases, we demonstrate that our proposed method can significantly and consistently improve the prediction performance compared with baseline approaches.
Blockchain and Its Application to Manage the Covid‐19 Pandemic: A Literature Review

At the end of 2019, a severe respiratory syndrome named COVID-19 is started to be transmitted in the world and it has rapidly spread to a global pandemic. Every day, a series of data are collected for real-time monitoring of the development of this pandemic. The data validation and the verification are becoming very important to manage the pandemic and give recommendations to the people. Nevertheless, sometimes, it is not possible to guarantee the truthfulness of such data and some information may be lost during collection. Due to its characteristics, the Blockchain technology can become an important support to face the COVID-19 pandemic. In this regard, the aim of this research is to propose a literature review to understand how the blockchain technology has been used for health care and supply chain management to guarantee an efficient tracing, tracking, and monitoring solution, ensure a transparent and safe data transmission, and to delineate the emerging future research directions.
Unsupervised Learning Data-Driven Continuous QoE Assessment in Adaptive Streaming-Based Television System

The quality of experience (QoE) assessment of adaptive video streaming may be crucial for detecting degradations impacting customer satisfaction. In a telecommunication environment, eliminating failure points may be the highest priority. This study aims to assess the QoE level of the video played by the STB device connected to the production TV system. The evaluation has been based on the stalling effects, video quality changes, and the time related to the last decreased bitrate change occurrence. The two-phase continuous clustering approach has been studied to assess the QoE level based on the ACR scale. The number of devices with grades 1 or 2 is relatively low, but those devices generate significantly more events than adequately functioning devices. STBs try to play the highest possible bitrate, and there is no possibility of setting the intermediate bitrate level. The STB player does not have the button to set the quality level, usually available in pure over-the-top applications. Hence the bitrate fluctuations that can annoy customers appear for the lowest grades. The boundary cases can be easily assessed. The outcome should be challenged by the customers’ opinions to find the proper QoE threshold. Continuous clustering may allow telecom operators to assess customer satisfaction with their TV service.
Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.
Evaluating Trust and Reputation Models for IoT Environment

The Internet of Things (IoT) provides advanced services by interconnecting a huge number of heterogeneous smart things (virtual or physical devices) through existing interoperable information and communication technologies. Due to its tenuous nature, IoT is vulnerable to different types of attacks, which usually lead to exposure of secrets from the node to the attacker, and compromises the authenticity, integrity, and real-time delivery of data. As such, it is important to have a trust and reputation model to evaluate the trustworthiness of the different players in IoT settings. Trust-based reputation models have been developed for this purpose, but to date, no attempts have been made to compare their performance in an IoT setting. The objective of this work is to implement a multi-agent framework to simulate a smart factory supply chain using IIOT and evaluate the performance of three well-known models: ReGreT, S-IoT, and R-D-C in terms of trustworthiness and cash utility. Based on our experiments, ReGreT performed the best among the three models in terms of evaluating trustworthiness and R-D-C gained the most cash utility.
Solving String Theories Involving Regular Membership Predicates Using SAT

String solvers gained a more prominent role in the formal analysis of string-heavy programs, causing an ever-growing need for efficient and reliable solving algorithms. Regular constraints play a central role in several real-world queries. To emerge this field, we present two approaches to encode regular constraints as a Boolean satisfiability problem, one making use of the inductive structure of regular expressions and one working on nondeterministic finite automata. We implement both approaches using Woorpje, a recently developed purely SAT-based string solver, as a framework. An evaluation of our approaches shows that they are competitive to state-of-the-art string solvers and even outperform them in many cases.
Trajectory Optimization for Target Localization and Sensor Bias Calibration with Bearing-Only Information

The problem of trajectory optimization of an unmanned aerial vehicle (UAV) for static target localization with biased bearing measurements is considered. The angular bias in sensor measurements is modeled as an additive constant in the observation model and jointly estimated with the position of the target. The necessary conditions for system observability of this estimation problem is first derived analytically with geometrical interpretations provided. The trajectory of UAV is designed based on the Fisher Information Matrix (FIM) considering physical constraints to enhance the system observability. Simulation results with Monte-Carlo runs are presented to demonstrate the improvement in target localization with biased measurements by UAV trajectory optimization.
Electroencephalogram microstates and functional connectivity of cybersickness

Virtual reality (VR) is a rapidly developing technology that simulates the real world. However, for some cybersickness-susceptible people, VR still has an unanswered problem-cybersickness-which becomes the main obstacle for users and content makers. Sensory conflict theory is a widely accepted theory for cybersickness. It proposes that conflict between afferent signals and internal models can cause cybersickness. This study analyzes the brain states that determine cybersickness occurrence and related uncomfortable feelings. Furthermore, we use the electroencephalogram (EEG) microstates and functional connectivity approach based on the sensory conflict theory. The microstate approach is a time-space analysis method that allows signals to be divided into several temporarily stable states, simultaneously allowing for the exploration of short- and long-range signals. These temporal dynamics can show the disturbances in mental processes associated with neurological and psychiatric conditions of cybersickness. Furthermore, the functional connectivity approach gives us in-depth insight and relationships between the sources related to cybersickness. We recruited 40 males (24.1 ± 2.3 years), and they watched a VR video on a curved computer monitor for 10 min to experience cybersickness. We recorded the 5-min resting state EEG (baseline condition) and 10-min EEG while watching the VR video (task condition). Then, we performed a microstate analysis, focusing on two temporal parameters: mean duration and global explained variance (GEV). Finally, we obtained the functional connectivity data using eLoreta and lagged phase synchronization (LPS). We discovered five sets of microstates (A-E), including four widely reported canonical microstates (A-D), during baseline and task conditions. The average duration increased in microstates A and B, which is related to the visual and auditory networks. The GEV and duration decreased in microstate C, whereas those in microstate D increased. Microstate C is related to the default mode network (DMN) and D to the attention network. The temporal dynamics of the microstate parameters are from cybersickness disturbing the sensory, DMN, and attention networks. In the functional connectivity part, the LPS between the left and right parietal operculum (OP) significantly decreased (p < 0.05) compared with the baseline condition. Furthermore, the connectivity between the right OP and V5 significantly decreased (p < 0.05). These results also support the disturbance of the sensory network because a conflict between the visual (V5) and vestibular system (OP) causes cybersickness. Changes in the microstates and functional connectivity support the sensory conflict theory. These results may provide additional information in understanding brain dynamics during cybersickness.
A Practical Anonymous Voting Scheme Based on Blockchain for Internet of Energy

E-voting allows us to build a democratic business in most Internet of things (IoT) systems. For example, we may vote to choose a proper energy broker in a smart grid system. In this study, we focus on e-voting services in an Internet of energy (IoE) system, which is a new-style smart grid. A practical e-voting in IoE may focus on the properties of fairness, decentralization, eligibility, anonymity, compatibility, verifiability, and coercion resistance. It is difficult to fulfil all these properties simultaneously. Traditional voting schemes often use a public bulletin board or administrator in the voting process, which makes them become centralized. Services that offer e-voting via blockchain can make the voting schemes decentralized. However, many of them ignore the complexity of organizing the data of the transactions, which should be confirmed by the miners. Moreover, to the best of the authors’ knowledge, no works have tested the performance in the blockchain while considering practical use cases and constraints. Concerning all the challenges, we propose a practical anonymous voting scheme for IoE called IoEPAV. The proposed scheme fulfils all the mentioned design goals simultaneously. We tested IoEPAV both in different test networks of the Ethereum blockchain to give an overall evaluation. The practical evaluation can show that the proposed scheme is easy to be integrated into a real system like IoE. We also gave a comparison analysis with the state-of-the-art blockchain-based e-voting. All the results show that IoEPAV is decentralized, verifiable, anonymous, and highly efficient.
Spam Identification in Cloud Computing Based on Text Filtering System

With the rapid increase of spam on the Internet and the diversification of its forms, how to quickly and effectively identify a large number of spam on the Internet has become an urgent topic. Cloud computing has obvious advantages in storage and processing, so it can effectively calculate a large amount of mail data. Due to the uncertainty and life cycle of spam, feedback re-judgment is added to the anti-spam system, and a text filtering system based on active learning with four stages of training, filtering, feedback, and re-filtering is implemented. Compared with the original system, the filtering system with feedback can improve the filtering of keywords. In order to effectively reduce the misjudgment rate of ordinary mail and improve the accuracy of spam judgment, it is suggested to improve the use of weighted decision-making of email header information to implement effective auxiliary classification. For emails lacking content, the filtering method of title weighting is feasible and effective, which can improve the identification of spam with relatively little text content. Because the filtering method on the cloud is far more advanced than the traditional algorithm, the development of the Internet can effectively solve the infinite increase of spam. Therefore, this paper makes an in-depth study on spam identification in cloud computing based on text filtering system by summarizing and analyzing the current anti-spam technologies.
Developing research data management services in a regional comprehensive university: The case of Central Washington University

This study aims to analyze the needs of researchers in a regional comprehensive university for research data management services; discuss the options for developing a research data management program at the university; and then propose a phased three-year implementation plan for the university libraries. The method was to design a survey to collect information from researchers and assess and evaluate their needs for research data management services. The results show that researchers’ needs in a regional comprehensive university could be quite different from those of researchers in a research-intensive university. Also, the results verify the hypothesis that researchers in the regional comprehensive university would welcome the libraries offering managed data services for the research community. Therefore, this study suggests a phased three-year implementation plan. The significance of the study is that it can give some insights and helpful information for regional comprehensive universities that are planning to develop a research data management program.
The effect of organizational health on the employee performance in the extractive industries

The purpose of this study was to investigate the relationship between organizational health and the performance of employees in the extractive industry. The quantitative approach was employed as the research methodology by the researchers. Participants in the study are individuals who are employed by companies that are involved in the extraction of minerals and other resources. Three hundred forty-three valid questionnaires were recovered for further analysis from a sample of 360 employees who were found to be an appropriate fit for delivering the study questionnaire to them. The findings of the study indicate that organizational health, as measured by its dimensions (communication, trust, cohesiveness, targeting, and creativity), had a statistically significant impact on the employees’ performance at the extractive sectors in Jordan. The researchers provided a number of recommendations, including the necessity to build unified and fair administrative accountability mechanisms, as well as the need for extractive companies’ decision makers to collaborate in order to understand the demands and requirements of employees.
Production Scheduling Optimization of Garment Intelligent Manufacturing System Based on Big Data

In order to improve the efficiency of cloth laying and cutting integrated production process, this article proposes a method of optimal scheduling of cloth laying and cutting garment production system process based on big data and genetic algorithm. The chromosomes in the algorithm are expressed by real strings. The method of bit string crossover and mutation is used to solve the premature problem of the algorithm. The experimental results show that the actual cutting time of the plan is 736 min, and the total idle time is 113 min. The idle time occurs in processes 25, 28, 34, 35, and 31, respectively. The cutting time of the plan arranged by the genetic algorithm is 627 min, and there is no idle time. Conclusion. This method can effectively solve the optimal scheduling problem of the cloth laying and cutting production process.
Evaluation and Analysis of the Informatization Degree of College English Education Based on Big Data Technology

Since the 21st century, thanks to the continuous achievements in the computer field, the Internet technology has developed rapidly in just a few years until it has swept the world. In the torrent of the information age, big data technology came into being. The application of big data technology is extremely high, and it is widely used in many industries. Based on the excellent performance of big data technology in the education industry, this paper will discuss the application of big data technology in the evaluation and analysis of college English education informatization. Under the condition that the evaluation standard is relatively abstract, the traditional method of evaluating through the subjective perspective of human beings has a large interference surface, and it is difficult to make the evaluation work substantive, data-based, and fair, which directly leads to evaluation and analysis work being difficult to carry out. Based on the abovementioned situation that the evaluation and analysis are relatively limited, this article will introduce the role of the big data technology algorithm model in the evaluation and analysis work. Through big data technology algorithms, qualitative problems that are difficult to evaluate are converted into quantitative problems that are easier to analyze and compare and then get rid of the limitations faced by traditional evaluation algorithms. The interference caused by human subjective factors to evaluation and analysis should be reasonably avoided as much as possible. This makes the evaluation and analysis results more objective and fair. This article is mainly based on the relevant algorithms under the big data technology to mine, classify, and purify the data with strong correlation with the important relevant indicators that affect the degree of college English informatization and carry out the experimental calculation of the evaluation of the relevant indicators. The results are as follows: we provide a reliable basis for the evaluation of the degree of informatization in college English and then conduct more scientific and rigorous evaluation and analysis. In the future development of big data technology, the database will be continuously supplemented with valuable teaching data, so that the results of big data technical analysis are more and more consistent with the real situation.
Comparative Study of Blockchain-Based Voting Solutions

The election is one of the factors due to which democracy fails as a system in place. Today, countries like India, the US, and even Japan are still facing accusations like vote rigging, lack of transparency, and hacking of EVMs. The latest election held in India, even in a situation like this pandemic, has led us to question whether our government parties are not concerned about people's well-being in their fight for power or are simply turning a deaf ear to the issue. Voting is one of the core pillars of democracy. There have been continual attempts to improve the methodology and procedures for achieving a valid, open voting system. E-voting was one the solutions for solving some issues but due to security and privacy concerns, it was not widely adopted. Blockchain technology is an emerging, decentralized technology that is promising to improve various aspects of many industries. The solution to alleviate current problems in E-voting might be to extend E-voting further into blockchain technology. An Ethereum blockchain-based decentralized voting system, named MYVOTE that protects individual voter privacy and improves accessibility while ensuring fairness, protection, and cost-efficiency, is proposed in this paper. The system also preserves the confidentiality and ease of access of the individual voter while maintaining transparency, security, and efficiency in the voting process. The strong authentication mechanism is inculcated to ensure the authenticity of system.
Design of University Educational Administration Management System Based on Sensor Data and Multidimensional Information Fusion

With the rapid development of education informatization, especially the promotion and reengineering of education business processes by big data and artificial intelligence, how to participate in this major change is the main issue that needs to be considered. Therefore, under the guidance of multisource sensor data fusion technology, this paper adopts the traditional cloud computing operation method, that is, centralized data center processing, which is difficult to adapt to the rapidly expanding data scale. We also propose a new perceptual adaptive data processing method. By designing an edge node adaptive data type sensing mechanism based on counting bloom filter, we can automatically identify the ubiquitous University Internet of things data categories and corresponding fields. The simulation results show that this method improves the recognition accuracy of different data categories in university management by more than 1.3%, which can help teachers use educational technology to do well in the teaching work before, during and after class, and promote the practical application of new teaching modes such as inquiry teaching.