Reviewerly/exHarmony huggingface dataset:
NEW TECHNIQUE OF ELECTROCARDIOGRAM DENOISING BASED ON 1D DOUBLE-DENSITY COMPLEX DWT AND STATIONARY BIONIC WAVELET TRANSFORM

In this paper, we propose a new electrocardiogram (ECG) denoising technique based on the application of a 1D double-density complex discrete wavelet transform (DWT) denoising method in the stationary bionic wavelet transform (SBWT) domain. SBWT was introduced in our previous work for speech enhancement. The first step in this proposed ECG denoising technique consists of applying the SBWT to the noisy ECG signal to obtain two noisy wavelet coefficients wtb1 and wtb2. The coefficients wtb1 and wtb2 are, respectively, the details and approximation coefficients. To estimate the level, [Formula: see text], of the additive white noise corrupting the original ECG signal, we use wtb1 which is then thresholded using soft thresholding function. The noisy approximation wtb2 is denoised by using 1D double-density complex DWT denoising method. The latter requires the use of this noise level, [Formula: see text]. The results obtained from SNR computations show the performance of the ECG denoising technique proposed in this work.
THE APPLICATION OF SEMANTIC MAPPING TECHNIQUE TO ENHANCE STUDENTS’ VOCABULARY MASTERY

This study aims to investigate the potential impact of employing the Semantic Mapping technique on improving students' proficiency in vocabulary. By examining the reactions and feedback from students who have undergone instruction using this technique, valuable insights can be gained into its efficacy in bolstering vocabulary acquisition. The findings hold significance for researchers seeking innovative approaches to enhance language learning. This research is a Classroom Action Research (CAR). The research findings showed that teaching vocabulary mastery using the Semantic Mapping Technique to the eleventh-grade students of SMA Dharma Wanita Medan could improve their vocabulary mastery. It is advisable that English teachers try applying the Semantic Mapping Technique in teaching vocabulary mastery. Quantitative data analysis shows an increase in the average score of 48% in the Pre-test, with 25% of students scoring ≥ 75. In the Formative-test, 65% of students scored ≥ 75, and 79 in the Post-test, with 80% of students achieving a value of ≥ 75. Then the results of this study increased. This research was carried out in SMA Dharma Wanita Medan with the eleventh-grade students during the academic Year 2022/2023. This research was successfully conducted at SMA Dharma Wanita Medan, as evidenced by the improved student achievement from the pre-test to the formative-test to the post-test. The results of this study are expected to provide valuable information on how to enhance students' vocabulary mastery by applying the Semantic Mapping Technique. During the research, the writer discovered that in order to improve students' vocabulary mastery skills, it is essential to create an interesting technique for students, fostering their enthusiasm for learning.
Directions of ICT use in Ukrainian educational institutions in the educational process with children with special educational needs: A literature review

The organisation of the educational process with children with special educational needs requires the implementation of special approaches to their education. Therefore, it is important to consider the relevant foreign experience, explain the features of distance learning using information and communication technologies and develop recommendations for the use of such technologies in the educational process with children with special needs. The purpose of this study was to investigate the specific features of using information and communication technologies in preschool education institutions in teaching children with special educational needs and to cover the areas of their implementation. The methodological framework of this study included an interdisciplinary approach to the analysis of a scientific problem in combination with theoretical and empirical methods, among which the priority was given to induction, deduction, analysis, synthesis, generalisation, study of scientific literature and pedagogical observation. It was found that information and communication technologies are a set of methods, educational and methodological materials, technical means of teaching and software. It was found that they increase the effectiveness of the educational process and help to unite its participants. The study proved that the use of information and communication technologies involves the application of three approaches: technocentric, holistic, and combined. It was found that the combined approach creates a subject-object model of teaching children with special needs and demonstrates the greatest effectiveness. The study presented several classifications of information and communication technologies. The classification of information and communication technologies was presented. It was found that they are divided into main and auxiliary; standard and auxiliary; virtual. The advantages of using information and communication technologies in the educational process with children with special educational needs were considered in detail, which confirmed the need for their use in preschool education institutions in the context of distance learning. The practical significance of this study is that its results, specifically recommendations for the introduction of special approaches to the education and upbringing of preschool children with special educational needs, can be used in organising an effective educational process for such children.
Fairness resource allocation based on blockchain for secure communication in integrated IoT

Abstract The emerging Space-Air-Ground, Artificial intelligence, blockchain and Vehicle-to-everything technology in Integrated Internet of Things (IoT) enables vehicles to communicate with other vehicles and roadside units (RSU), which improves communication efficiency and driving safety. Due to the rich data in the Integrated IoT, it is easy for illegal persons to steal data or deceive users. While reasonably allocating resources to complete the transmission task, it is still a challenge to ensure the communication security of vehicle users in Integrated IoT. In this paper, we study the cost of vehicle users transmitting tasks by Vehicle to Infrastructure and Vehicle to Vehicle (V2V) in Integrated IoT. In order to protect the information security, we establish the identity authentication and matching model to obtain a better channel environment of Integrated IoT. Moreover, we consider dynamic pricing based on bandwidth resource occupancy to ensure user experience and server load in Integrated IoT. Under the constraints of task tolerable delay, we fix the bandwidth on the V2V side and decouple the task decomposition and bandwidth allocation in Integrated IoT. Then, we propose algorithms Blockchain-based Vehicle Identity Authentication (BVIA) and Delay Weight Fairness Bandwidth Allocation (DWFBA). In this way, vehicle users and RSUs entering the Integrated IoT system are authenticated and matched with devices with good trust value, and the fairness of users resource allocation is guaranteed. Simulation results show that BVIA algorithm can reduce communication overhead, and DWFBA algorithm can control user costs and effectively reduce the number of task failures in Integrated IoT.
A Grey Wolf Optimized XGboost-Multilayer Stacking Approach to Detect XSS Attacks

Life is easier with web applications. However, data stealing has become a serious issue and can have unpleasant effects on human life. This is when attackers try to access sensitive users’ information or acquire illegal rights. Cross-site scripting is the most common attack that steals user information. It is also known as an XSS attack and appears on OWASP’s Top 10 Attacks as well. More than 70 percent of web-based applications are susceptible to XSS attacks. This paper presents a Grey Wolf optimizer based on the XGboost multilayer stacking approach for XSS attack detection. The grey wolf optimizer is also known as the GWO as feature optimization for machine learning. This is superior to FEP, PSO, and GSA algorithms as it overcomes real-world challenges. The proposed techniques categorize XSS and non-XSS with an accuracy of 99.5%, recall rate of 1, false positive rate of 0.004, precision of 98.5%, and F-degree of 99.28%. The proposed methodology also compares with several existing machine learning algorithms and ensemble classifier techniques. It shows that the proposed approach is more accurate than earlier.
Paths of integration of digital resources for geometry by two primary-school teachers

This paper reports some results of a study on how Colombian primary-school teachers, experienced in the use of technological tools, integrate digital resources into their practice. Specifically, we investigate the processes of selection and integration (including appropriation and orchestration) of these resources in the teaching of geometry. We aim to illustrate the potential paths (along with associated criteria) that teachers follow when selecting and integrating digital resources into their practice, using the Documentational Approach to Didactics. Accordingly, we present case studies of a 1st-grade teacher and a 5th-grade teacher, which describe their paths during their respective geometry lessons. The paths followed by the teachers were influenced by their knowledge and beliefs regarding the utilisation of digital technology resources and the potential student engagement with such resources. All these aspects come into play as teachers select and orchestrate digital resources. Our empirical data analysis enabled us to infer several criteria for resource selection and levels of orchestration and appropriation manifested by the teachers as they integrated digital resources into their classes. This work can provide valuable insights for further exploration of how teachers integrate digital resources, and can contribute to the development of professional development programmes.
Political Speech: The Influence of Speaker Sex and Verbal Aggression on Message Perception

This study examined the influence of speaker sex and verbal aggression on the persuasiveness of political speech. College students (n = 106) read eight examples of political speech where the sex of the speaker and the level of aggression were manipulated. Participants then completed measures assessing message agreement, speaker credibility, and communicative appropriateness. Results indicated that male and female politicians were evaluated similarly on measures of persuasiveness. In addition, aggressive messages were evaluated as being less persuasive than nonaggressive messages. Finally, results identified a significant interaction between speaker sex and level of aggression when predicting message persuasiveness.
COVID-19 PANDEMIC AND ITS EFFECT TO THE READING PERFORMANCE OF GRADE III TO GRADE VI PUPILS OF BANAHAW ELEMENTARY SCHOOL: A BASIS FOR READING PROGRAM INTERVENTION

Teaching reading to non-readers and frustration level readers today is different from the past because of Covid 19 Pandemic. This study was conducted to determine the effect of Covid 19 Pandemic to Reading Performance of Grade III to Grade VI Pupils of Banahaw Elementary School. The respondents were 70 Grade III to Grade VI Pupils from School Year 2019-2020 and 59 Grade III to Grade VI Pupils from School Year 2020-2021. The researchers utilized the Phil- IRI Results from School Year 2019-2020 which served as basis for reading performance before pandemic and Phil-IRI Results for School Year 2020-2021 served as basis for reading performance during pandemic. The data were gathered through the computation of mean score. Before the Pandemic there are 5.80% Non-readers, 17.39% Frustration readers, 36.23% Instructional readers and 27.54% Independent readers. During Pandemic there are 3.38% Non-readers, 35.59% Frustration readers, 38.98% Instructional readers and 22.03% Independent readers. The mean scores implied that there was a difference in the reading performance of pupils before and during pandemic. Thus designing of Action Plan in Reading is required.
A Comparison between Kalman Filtering Approaches in Aircraft Flight Signal Estimation

At present, the requirements for the accuracy of aircraft on-board measurement systems are constantly increasing, while sensors contain various errors in signal measurement, primarily random. Noisy signals from onboard measurements can be smoothed or filtered out in a variety of ways. One of the most popular approaches is Kalman filtering, the effectiveness of which has been proven by many studies. This paper presents a comparative analysis of the extended Kalman filter (EKF) and unscented Kalman filter (UKF), used to estimate the pitch angle of an aircraft using bench modeling data. During the simulation, the normal measurement noises are also generated. According to the results obtained in this paper, it can be noted that UKF performs better when a priori knowledge about the process noise is certain. However, the efficiency of UKF in estimating the signal deteriorates when a priori knowledge about the process becomes uncertain while the performance of EKF remains stable. This is due to the fact that UKF uses more sophisticated assumptions and therefore is more sensitive to these assumptions violation. The obtained results also show that various variants of Kalman filtering remain relevant in comparison with the smoothing methods that have spread in recent years, based on the ideas of optimal control and evolutionary algorithms for numerical optimization.
A Study on Syntactic Distribution of Substantivizations: Focused on Occasional Substantivizations

The purpose of this study is to describe substantivization as a linguistic mechanism, and to explain the relations of substantivization and syntactic distribution. Substantivization is a productive lexical-grammatical phenomenon that occurs through complex interactions of categorical meaning, lexical meaning, and syntactic functions. The position and syntactic function within a sentence play a crucial role in this mechanism.&#x0D; Depending on the degree of substantivization, conventional and occasional substantivizations can be distinguished. Conventional substantivization involves a transformation into nouns, displaying high frequency of use and lexicalization patterns. On the other hand, occasional substantivization occurs spontaneously and can be used to express the speaker's intentions and higher expressiveness.&#x0D; Various factors, such as conventionality, semantic factors, frequency of use, influence the mechanism of substantivization. However, the most direct influence comes from the position within a sentence. The syntactic positions that trigger substantivization are argument positions within a sentence. Valency factors as adjectives, quantifiers, demonstratives, and possessive pronouns are used as modifier.&#x0D; In this way, substantivization is a phenomenon that arises from the interplay between the language's economy principle, and the communicative desire of speakers to effectively and unusually convey meaning. It serves as a mechanism, that induces new changes in the language system and reflects the dynamic aspects of language.
Cloud forensics: A centralized cloud provenance investigation system using <scp>MECC</scp>

Summary As a new development in digital forensics, cloud forensics (CF) is being utilized to combat cyber‐crimes. Nevertheless, the centralized compilation and preservation of evidence lessen the reliability of digital evidence. Also, the Cloud environment faces the challenge of securing the Provenance Information (PI) without being forged or tampered with, by either an internal or an external party. In the existing works, the researchers have made several attempts to trounce the issues of CF investigation but still, those issues have not been eliminated. Thus, by employing modified elliptic curve cryptography (MECC), an enhanced CF investigation system (CFIS) grounded on a centralized cloud provenance (CCP) system has been proposed to surpass such issues. For encrypting the data for a user and to securely upload and download the data, the MECC was proposed. For producing the group key, SHA‐512 was wielded. The proposed system's performance, when appraised, was found to exhibit enhanced performance against the prevailing methodologies.
Nonprofit Learning Lab workshop on training, engaging volunteers

Nonprofit Business AdvisorVolume 2023, Issue 411 p. 4-4 Conferences & Events Nonprofit Learning Lab workshop on training, engaging volunteers First published: 08 November 2023 https://doi.org/10.1002/nba.31593Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2023, Issue411December 2023Pages 4-4 RelatedInformation
The Impact of the Double Reduction Policy on the Education Industry

Before the double reduction policy, students were under great pressure to study. The government issued the policy of "two reductions" has reduced schoolwork and extracurricular training in schools. This paper describes the contents and restrictions of the policy, as well as the impact of the policy on the education industry after taking New Oriental as an example. The double reduction policy was proposed to protect the physical health of students, raise the burden to the level of the minor Protection Law, and return education to schools in order to reduce the burden of family education. And how the education industry should develop based on this background.
Cancer Prediction Using Feature Fusion and Taylor-TSA-Based GAN with Gene Expression Data

This research paper develops an efficient model, named Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The developed Taylor-TSA incorporates the Taylor series with Tunicate Swarm Algorithm (TSA) algorithm. The Yeo–Johnson (YJ) transformation is employed for the data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder (Deep SAE). The fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The developed model is an effective and efficient use of information with clinical data. The Taylor-TSA-based GAN is analyzed in terms of accuracy, False Positive Rate (FPR), and True Positive Rate (TPR) with the values of 0.9184, 0.1782, and 0.9246.
Vocal Visage: Crafting Lifelike 3D Talking Faces from Static Images and Sound

In the field of computer graphics and animation, the challenge of generating lifelike and expressive talking face animations has historically necessitated extensive 3D data and complex facial motion capture systems. However, this project presents an innovative approach to tackle this challenge, with the primary goal of producing realistic 3D motion coefficients for stylized talking face animations driven by a single reference image synchronized with audio input. Leveraging state-of-the-art deep learning techniques, including generative models, image-to-image translation networks, and audio processing methods, the methodology bridges the gap between static images and dynamic, emotionally rich facial animations. The ultimate aim is to synthesize talking face animations that exhibit seamless lip synchronization and natural eye blinking, thereby achieving an exceptional degree of realism and expressiveness, revolutionizing the realm of computer-generated character interactions.
Learning Entangled Interactions of Complex Causality via Self-Paced Contrastive Learning

Learning causality from large-scale text corpora is an important task with numerous applications—for example, in finance, biology, medicine, and scientific discovery. Prior studies have focused mainly on simple causality, which only includes one cause-effect pair. However, causality is notoriously difficult to understand and analyze because of multiple cause spans and their entangled interactions. To detect complex causality, we propose a self-paced contrastive learning model, namely N2NCause, to learn entangled interactions between multiple spans. Specifically, N2NCause introduces data enhancement operations to convert implicit expressions into explicit expressions with the most rational causal connectives for the synthesis of positive samples and to invert the directed connection between a cause-effect pair for the synthesis of negative samples. To learn the semantic dependency and causal direction of positive and negative samples, self-paced contrastive learning is proposed to learn the entangled interactions among spans, including the interaction direction and interaction field. We evaluated the performance of N2NCause in three cause-effect detection tasks. The experimental results show that, with the least data annotation efforts, N2NCause demonstrates competitive performance in detecting simple cause-effect relations, and it is superior to existing solutions for the detection of complex causality.
Revolutionizing Agriculture: A Case Study of IBM's AI Innovations

Background / Purpose: The development of computer systems that can carry out tasks that traditionally require human intelligence is referred to as artificial intelligence (AI). It entails the development of intelligent machines that can reason, learn, solve issues, and make judgements. A fast-developing topic, AI has enormous ramifications for many different businesses and facets of society. By leveraging advanced algorithms and data analysis techniques, AI systems can process and interpret large amounts of information in real-time, enabling them to extract valuable insights and patterns that may be difficult for humans to perceive.AI technologies have a wide range of applications across multiple domains, including healthcare, finance, transportation, manufacturing, education, entertainment, including agriculture. When referring to AI in the context of agriculture, we mean the use of advanced analytics and computational algorithms to analyse massive volumes of agricultural data, anticipate the future, and give farmers and stakeholders useful information. The main goal of using AI to agriculture is to increase efficiency, sustainability, and productivity across a range of farming operations, also to create smart and efficient systems that can monitor, analyze, and control water resources in real-time, leading to improved water management and sustainable agricultural practices, thereby addressing the challenges faced by the agricultural sector.AI offers significant potential to optimize water usage, enhance crop productivity, and mitigate environmental impact. In this paper, IBM, a significant provider of services in the sector of agriculture in recent years, is examined. Objective: In this case study, artificial intelligence is the main topic with particular emphasis on IBM's agricultural technology. Design/Methodology/Approach: Academic works published in a variety of peer-reviewed journals, conferences, and business websites provided the necessary information and specifics for this case study on IBM. Findings/Result: This study is primarily concerned with the usefulness and significance of AI in the modern world. The demand for and necessity of the numerous resources. provided by IBM, discussion topics include the company's business plan, varied results, top clientele, and numerous service types. Originality/Value: The analysis gives a concise description of IBM, the types of data collected and managed, information on artificial intelligence (AI), and the numerous AI services offered by IBM. Paper Type: Case study on the importance of storage and computing requirements for AI services offered by different service. providers, with a focus on IBM.