Reviewerly/exHarmony huggingface dataset:
Motion trajectory planning of a mobile robot flying in a fuzzy environment

Abstract.This study involved implementing a fuzzy controller approach to create a ground-based control model for an autonomous robot in a stable environment. In addition, a classical control method was developed and tested within the results. With the fuzzy controller approach, it was studied that the robot moves easily in the test environment and goes to desired targets. An autonomous mobile robot aims to minimize the energy consumption of the robot with a fuzzy controller algorithm. The energy consumption for processing time and data error between the coordinates where the robot should go and the coordinates it goes after the operations are completed are calculated separately. By utilizing a fuzzy controller algorithm, the mobile robot is capable of operating in a flexible manner. It was observed that the fuzzy approach performs 10 times faster and more accurately compared to the traditional method. In terms of being open to development and modular, the flying mobile robot fulfilled the functions envisaged in this work. In the future, the direction of a flying robot can be determined using a digital electronic compass to reach longer distances with external sensors.
Medical Named Entity Recognition Modelling Based on Remote Monitoring and Denoising

The electronic medical records (EMRs) are used in the public data set provided by Yidu Cloud to obtain remote data sets through remote supervision. For the obtained remote data set, in order to improve the reliability of the data set, the PU learning is adapted for denoising to reduce the negative impacts of mislabeled negative samples or unlabeled samples of the model. Finally, the negative samples and the pretraining models are used to extract a cancer information.
0965 Changes in sleep status and the effects of obesity indexed by sex before and after COVID-19: a 2-year follow-up cohort study

Abstract Introduction The COVID-19 pandemic has changed people’s lifestyles. This study aimed to examine the changes in sleep status before and after COVID-19 according to sex in middle-aged Koreans, and investigate the effect of sleep status on obesity indicators. Methods This prospective cohort study was the Korean Medicine Daejeon Citizen Cohort study and included participants aged 30-59. A baseline survey was conducted from 2017 to 2019 before COVID-19, and a follow-up survey from 2020 to 2021 after the outbreak of COVID-19. This study analyzed the data of 1436 people, with 422 males and 1014 females completing follow-up. Using the Pittsburgh Sleep Quality Index (PSQI) questionnaire with validity, we evaluated sleep, sleep duration, sleep quality score, and group (categorized into good sleep and poor sleep, based on a PSQI score of 5). Body mass index (BMI), waist circumference (WC), body fat mass (BFM), and percent body fat (PBF) were measured to determine obesity indices. For changes in sleep status before and after COVID-19, Fisher's exact test and Paired t-test were conducted. ANCOVA was performed to determine changes in the obesity index according to sleep quality groups in the baseline survey, which was age-adjusted. All analyses were stratified by sex. Results Sleep duration decreased significantly by 13.88 minutes for males and 11.23 minutes for females to 6.54 hours (SD=1.2) and 6.48 hours (SD=1.2), respectively. Sleep quality scores increased by 1.03 points and 0.7 points for males and females, respectively, to 5.2 points and 5.7 points, respectively. The distribution of poor sleep increased from 25.6% before COVID-19 to 38.6% after COVID-19 for males (p &amp;lt; 0.001), and from 36.5% before COVID-19 to 43.1% after COVID-19 for females (p&amp;lt; 0.001). Significant changes in the obesity index were observed according to the sleep quality group only in males. In the male poor PSQI group, WC (Δ3.4 cm), BFM (Δ1.3 kg), and PBF (Δ1.2%) increased significantly more than those in the good PSQI group indices changes. Conclusion COVID-19 has negatively impacted sleep, and such sleep changes have contributed to an increase in the obesity index for males. Support (if any) This was funded by the Korea Institution of Oriental Medicine, KSN2023120.
An Analysis of Data Sparsity Resolution Algorithms Used in Recommender Systems

In previous years, a substantial rise has been observed in the use of YouTube, Netflix, Amazon, and other similar web services that have enabled the utilization of recommender systems. Ranging from e-commerce to online advertisements, recommender systems tend to be unavoidable in routine journeys. Fundamentally, such systems are emphasized by suggesting relevant items to end-users and are of great use in enhancing the engagement rate. However, there are several problems such as Cold Start and Data Sparsity that impact the efficacy of the recommender system. A wide range of factors is responsible for such issues. In a similar context, this paper focuses on conducting a systematic literature review and analysis of different algorithms that have been proposed by researchers and practitioners in the field of recommender systems. The preliminary purpose of this paper is associated with obtaining an in-depth and succinct understanding of a wide range of solutions that have emerged from different studies. Based on the algorithms mentioned in the literature, it has been observed that Singular Value Decomposition Plus Plus offers the best possible resolution to data sparsity issues.
Trust Chain for Managing Trust in Blockchain-Associated IoT-Enabled Supply-Chains

Recognizability, as well as trust worthiness, remain important encounters aimed at today's incomprehensibly complex supply chains. Despite the fact that BC expertise may remain able in solving these issues via availing a well-constructed audit track of accumulation system events besides statistics relevant to an element's lifespan, the problematic confidence is not indicated in the actual information. Notoriety frameworks are a persuasive solution to address this problem of trust. Current frameworks, on the other hand, are unsuitable for blockchain-founded production system requests since these rely upon proceeding limited observations, need granularity and computerization, and their above has not been studied. In this paper, we present Trust-Chain, a tri-coated trust in the executive’s structure that routines an association BC in the tracking of production network member communications and gradually downgrade trust and notoriety ratings in light of such associations. The uniqueness of Trust-Chain stems from the vertical prototype, which evaluates the property of substances besides the trustworthiness of substances in light of various observations of accumulation system events, the situation support aimed at dishonor notches, which differ among an invention system associate besides substances, enabling the mission of article unambiguous dishonors for an analogous associate, as well as the use of clever promises aimed at simple, proficient, safe, besides automatic estimation of items.
Discourse markers errors performed by EFL Iraqi learners

Discourse markers are words and phrases that play role as linking words or linking phrases, they develop ideas and relate them to one another. People use most of the discourse markers in formal context when presenting complicated information in discourse. Discourse markers contribute to discourse cohesion and coherence, at the same time the using too much of them make the discourse heavy or artificial. The problem of this study is that most EFL Iraqi learners find difficulties in selecting the suitable discourse marker in order to link two ideas or sentences. As for aims, the study tries to put a basis to treat with this topic. The data is limited to (30) students who are in third year Iraqi EFL learners/ Tikrit University/ College of Education for Humanities/English Department. In order to perform the aim of the current study, it is hypothesized that students' ability to use discourse markers is limited. It is also hypothesized that the most common errors that are committed by Iraqi EFL learners in using discourse markers are related to textual class.
A Comprehensive Analysis of Image Noise Reduction using XOR-Based Filtering Technique

Image noise reduction is a critical problem in image processing, as it affects the quality and accuracy of the images.The XOR-based filtering technique has emerged as a promising approach to address this issue in recent years.This technique employs the XOR operation to remove noise from the images while preserving the important image details.This research paper comprehensively analyses the XOR-based filtering technique for image noise reduction.We evaluate the performance of this technique on various image datasets and compare it with other state-of-the-art image denoising techniques.Our experimental results show that the XOR-based filtering technique outperforms other methods in terms of both objective and subjective image quality metrics.Our experimental results demonstrate that the XOR-based filtering technique achieves superior performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean opinion score (MOS) compared to other denoising techniques.We also observe that the performance of the XOR-based filtering technique is consistent across different types of image noise, such as Gaussian, salt and pepper, and speckle noise.Furthermore, we conduct a parameter analysis of the XOR-based filtering technique to determine the optimal parameter values for different noise levels.Our results show that the XOR-based filtering technique is robust and can adapt to various noise levels.
Hashgraph: A Decentralized Security Approach Based on Blockchain with NFT Transactions

Blockchain technology is used by developers to establish computational confidence in their goods. As a result, organizations and individuals who may not know or trust one another may collaborate quickly and cheaply. You can create and exchange value, establish identity, and check with public distributed ledgers, and the blockchain server is unique in that it achieves the same result as the most widely used public blockchains (such as Bitcoin or Ethereum). These advantages are due to the underlying hash-graph consensus technique and the global enterprise governing body that now owns and manages our proposed project’s efficient blockchain nodes. We will execute NFT transactions using our hedera-optimized blockchain network. This recommended approach combines a variety of areas, including encryption, banking, and financial transactions. The introductory portion below describes the study that allows NFT transactions in the hedera-optimized network phase of the three. This is the first eminent research on the NFT Transaction on the blockchain approach that we are aware of. By adopting an efficient method between data centers and HCS technology, it is possible to assess our multichain approach to banking and financial circumstances with ease. This technique for computational confidence in banking and financial situations is both economical and effective. The incident response and prevention team can quickly enhance any difficult and complicated procedure. It falls under the capability of NFT with blockchain to act rapidly and decide in a perplexed condition. It offers the easiest, most efficient route throughout the transaction phase for interacting with potential customers, integrating test-net, main-net, and mirror-net. With the simplicity of calculation and computation on this enhanced blockchain server, it draws new customers in the future.
Garbage classification algorithm based on improved EfficientNetV2 network

The background information of garbage images is complex, and the difference within the class is large. It is difficult for traditional methods to extract significant classification features from garbage images, resulting in poor classification results. Aiming at the above problems, a garbage image classification method based on attention mechanism and SPP model is proposed. First, EfficientNetV2 is used as the basic network, and an improved SE attention module is introduced to make the network pay more attention to the important features in the feature map, suppress the background information, and reduce the impact of intra class differences; Secondly, in order to deepen the network depth and integrate multi-scale information, a feature pyramid structure is introduced; Finally, the experimental results show that on the extended Huawei dataset, the classification accuracy is improved by about 4.8%, reaching 93.5%, which proves that this method has certain advantages in garbage classification tasks.
Coherence evolution of three-state quantum walk on lattice

Coherence is crucial in quantum information processing, and quantum walk provide an excellent model for studying fundamental quantum theory and quantum algorithm design. The coherence of the initial state, system boundaries, coin operators, and evolution time are all relevant factors in the evolution of coherence systems. Using l1 norm as the coherence measure, the evolution characteristics of coherence in the three-state quantum walk with boundaries in a 2D lattice were investigated, and the effects of broken links, phase damping, amplitude damping, and unitary noise on the coherence of the three-state quantum walk were analyzed. The results show that the boundaries suppress the growth of coherence of a single-point initial state of the entire system, but does not affect the coherence of equal-weight superposition states. Small noise can slightly enhance the coherence of the entire system and position subsystems for broken links and unitary noise, while larger noise suppresses the growth of coherence.
Localization Design and Implementation Using Combined Rotary Encoder, IMU, and ROS on Delivery Service Robots in Building Area

Developing delivery service robots requires an accurate localization system to send delivery objects to the right and appropriate locations. Localization of the robot is needed to determine its position and displacement at the intended point. To perform localization, precise sensors and appropriate algorithms are required to perform localization. Using localization only determines precision using one rotary encoder sensor, it is tough to get localization precision. Therefore, it is necessary to combine sensors and get the right algorithm to get localization precision, using a combination of Rotary Encoder and IMU sensors using EKF algorithms and robot operating system makes it easier in communicating, build a robot, and configure the setup. From the result of the tests carried out when moving linearly using a Rotary Encoder sensor, the average error result is obtained as 0.89602 m, and the result when using a combined sensor, between the rotary encoder and IMU, the average error results obtained are 0.85951 m. By using a combined sensor, it has been proven that the combination of these sensors produces a smaller distance error, so that when using a combined sensor the robot's precision level when moving is very small.
Whatever happened to propaganda? Communication curricula in Spain, democracy, and the logic of depropagandization

The term “propaganda” has gradually vanished from communication studies and has been substituted with a plethora of euphemisms. This relates to the notion of “depropagandization,” that is, the disappearance of the term “propaganda” from certain contexts after World War II. These changes have impacted how communication undergraduate students learn about their discipline. To ascertain whether depropagandization is influencing the ways in which they learn about propaganda and propaganda-related issues – and, consequently, the ways in which civic education on communication is approached – a content analysis was performed on 97 communication degree programs offered at 55 public and private Spanish universities. We analyze the degrees and courses addressing propaganda-related topics, the key terms employed in the titles of the courses, and the relationship between propaganda-related curricula and power, ideology, and the historical past. Results indicate that the Spanish academic world is immersed in a process of depropagandization, thus giving rise to a potentially uncritical – and euphemism-laden – approach to propaganda. Given the relevance that the critical appraisal of propaganda has for democratic societies, depropagandization may undermine the development of a discriminating citizenry, as well as the assessment of potentially harmful messages.
A Quantum Computing-Based Accelerated Model for Image Classification Using a Parallel Pipeline Encoded Inception Module

Image classification is typically a research area that trains an algorithm for accurately identifying subjects in images that have never been seen before. Training a model to recognize images within a dataset is significant as image classification generally has several applications in medicine, face detection, image reconstruction, etc. In spite of such applications, the main difficulty in this area involves the computation in the classification process, which is vast, leading to slow speed of classification. Moreover, as conventional image classification approaches have fallen short in terms of attaining high accuracy, an optimal model is needed. To resolve this, quantum computing has been developed. Due to their parallel computing ability, quantum-based algorithms could accomplish the classification of vast amounts of image data. This has theoretically confirmed the feasibility and advantages of incorporating a quantum computing-based system with traditional image classification methodologies. Considering this, the present study quantizes the layers of the proposed parallel encoded Inception module to improvise the network performance. This study exposes the flexibility of DL (deep learning)-based quantum state computational methodologies for missing computations by creating a pipeline for denoising, state estimation, and imputation. Furthermore, controlled parameterized rotations are regarded for entanglement, a vital component in quantum perceptron structure. The proposed approach not only possesses the unique features of quantum mechanics, but it also maintains the weight sharing of the kernel. Finally, the MNIST (Modified National Institute of Standards and Technology) and Fashion MNIST image classification outcomes are attained by measuring the quantum state. Overall performance is assessed to prove its effectiveness in image classification.
Image Classification and Recognition

Image classification and recognition are critical components of computer vision, allowing machines to analyse and comprehend visual data.These capabilities have numerous applications in various fields, including healthcare, security, transportation, and entertainment.Recent advances in deep learning techniques have significantly improved the accuracy and efficiency of image classification and recognition.This paper aims to provide a comprehensive overview of the state-of-the-art in this field, reviewing the latest research and methodologies used for image classification and recognition.Our analysis reveals that deep learning models, particularly convolution neural networks (CNNs), have proven highly effective for this task, achieving impressive results on large-scale datasets.However, challenges remain in improving the robustness of these models and addressing issues such as bias and lack of diversity in training data.
A Study on the Correlation between Multiple Sense of Humour, Academic Emotions and Academic Self-hindrance among Junior Secondary School Students

To understand the relationship between factors affecting academic performance and psychological well-being, and to provide help for junior high school students' learning approaches. The Multiple Sense of Humour Scale, the Adolescent Academic Emotion Scale and the Self-Hindrance Scale questionnaires were used to investigate the humour, academic emotion and academic self-hindrance of the first and second year students in Datong Sixth Middle School, and the data collected were analysed by correlation analysis and t-test statistical methods using SPSS25.0 software. Results: (1) Multiple humour and academic mood (r=0.129 P<0.05) were significantly correlated. (2) Self-hindrance was significantly correlated with academic mood (r=0.426 p<0.01). (3) There was no significant correlation between multiple humour and academic self-hindrance (r=-0.101 p>0.05) among junior high school students. Conclusions: (1) There is a significant difference between male and female students in terms of multiple sense of humour and academic emotions, with male students having a higher sense of humour and academic emotions than female students; there is no significant difference in terms of self-hindrance. (2) There was no significant difference between junior high school students' multiple sense of humour, academic emotions and self-impediment in terms of grade level. ( 3) There was a significant correlation between multiple sense of humour and academic emotions; there was a significant correlation between academic self-impediment and academic emotions; there was no significant correlation between multiple sense of humour and academic emotions.
Construction of Middle School English Multimodal Classroom from the Perspective of Cognitive Load Theory

With the application of multimodality in the field of teaching, in today's global integration, diversification and technicalization, the student literacy training is not the only aim during teaching. The more important aim is to cultivate students' multiple reading ability. The study will introduce multimodality and cognitive load theory into middle school English teaching, so as to improve students' listening, speaking, reading, writing, international vision and other comprehensive quality. Students can gradually form "cross-cultural communication and communication consciousness and ability; learning to be objective and form rational view of the world; setting up the international vision, conservation feelings and appraisal culture self-confidence; forming the correct world outlook, the outlook on life and values; laying the foundation to adapt to the future social development for students lifelong learning", which has been proposed in "New Curriculum Standards". It is of great value to reconstruct the English teaching ecology in middle schools. At the same time, it can help teachers to effectively integrate teaching resources, create a multi-modal teaching environment, and stimulate students' learning motivation.